the importance research in agriculture

Zero Hunger pp 71–79 Cite as

Agricultural Research: Applications and Future Orientations

• Naser Valizadeh Ph.D. Student 6 &
• Masoud Bijani Assistant Professor 7  
• Reference work entry
• First Online: 01 January 2020

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Part of the book series: Encyclopedia of the UN Sustainable Development Goals ((ENUNSDG))

Agricultural research methodology

Agricultural research can be broadly defined as any research activity aimed at improving productivity and quality of crops by their genetic improvement, better plant protection, irrigation, storage methods, farm mechanization, efficient marketing, and a better management of resources (Loebenstein and Thottappilly 2007 ).

Introduction

The objective of this document is to provide a tool to understand aspects and future orientations of agricultural research. It begins with an overview of the concept and/or definition of agricultural research. It then focuses on the role of agricultural research in achieving the goals of 2030 Agenda, different types of agricultural researched, systemic research methodology in agriculture, and finally different kinds of use for agricultural research.

The Concept and Definition of Agricultural Research

Finding answers for questions about unknown phenomena in the agricultural area is the key to agricultural...

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Aboelela SW, Larson E, Bakken S, Carrasquillo O, Formicola A, Glied SA, Haas J, Gebbie KM (2007) Defining interdisciplinary research: conclusions from a critical review of the literature. Health Serv Res 42(1–1):329–346

Article   Google Scholar  

Alrøe HF, Kristensen ES (2002) Towards a systemic research methodology in agriculture: rethinking the role of values in science. Agric Hum Values 19(1):3–23

Anastasios M, Koutsouris A, Konstadinos M (2010) Information and communication technologies as agricultural extension tools: a survey among farmers in West Macedonia, Greece. J Agric Educ Ext 16(3):249–263

Bijani M, Ghazani E, Valizadeh N, Fallah Haghighi N (2017) Pro-environmental analysis of farmers’ concerns and behaviors towards soil conservation in central district of Sari County, Iran. Int Soil Water Conserv Res 5(1):43–49

Google Scholar  

Borg WR, Gall MD, Gall JP (1963) Educational research: an introduction. Longmans, New York & London p 704

Damalas CA, Georgiou EB, Theodorou MG (2006) Pesticide use and safety practices among Greek tobacco farmers: a survey. Int J Environ Health Res 16(5):339–348

Delavar A (2017) Research methods in psychology and educational sciences. Virayesh Publishing, Tehran. In Persian

Ebrahimi Sarcheshmeh E, Bijani M, Sadighi H (2018) Adoption behavior towards the use of nuclear technology in agriculture: A causal analysis. Technol Soc 54(2018):175–182

Fallah Haghighi N, Bijani M, Parhizkar M (2019) An analysis of major social obstacles affecting human resource development in Iran. J Hum Behav Soc Environ 29(3):372–388

Feder G, Just RE, Zilberman D (1985) Adoption of agricultural innovations in developing countries: a survey. Econ Dev Cult Chang 33(2):255–298

Fleischer DN, Christie CA (2009) Evaluation use: results from a survey of US American Evaluation Association members. Am J Eval 30(2):158–175

Food and Agriculture Organization of the United Nations (FAO) (2017) Food and agriculture – driving action across the 2030 agenda for sustainable development, Rome. https://www.fao.org/3/a-i7454e.pdf

Gibbons M, Limoges C, Nowotny H, Schwartzman S, Trow M (1994) The new production of knowledge. The dynamics of science and research in contemporary societies. Sage, London

Guba EG, Lincoln YS (1994) Competing paradigms in qualitative research. In NK Denzin, YS Lincoln (Eds), Handbook of qualitative research, pp 105–117. London: Sage

Habashiani R (2011) Qanat: a sustainable groundwater supply system. Master’s thesis, School of Arts and Social Science, James Cook University, Queensland

Habibpour Gatabi K, Safari Shali R (2013) Comprehensive manual for using SPSS in survey researches. Looyeh Publications, Tehran

Henry GT, Mark MM (2003) Toward an agenda for research on evaluation. N Dir Eval 97:69–80

Iman MT (2009) Paradigmatic foundations of quantitative and qualitative research methods in humanities. Research Institute of Hawzah and University, Qom. In Persian

Khoursandi-Taskouh A (2009) Typological diversity in interdisciplinary education and research. J Interdiscip Stud Humanit 1(4):57–83

Lekka-Kowalik A (2010) Why science cannot be value-free. Sci Eng Ethics 16(1):33–41

Lockheed ME, Jamison T, Lau LJ (1980) Farmer education and farm efficiency: a survey. Econ Dev Cult Chang 29(1):37–76

Loebenstein G, Thottappilly G (2007) The mission of agricultural research. In: Loebenstein G, Thottappilly G (eds) Agricultural research management. Springer, Dordrecht, pp 3–7

Chapter   Google Scholar  

Madani K (2014) Water management in Iran: what is causing the looming crisis? J Environ Stud Sci 4(4):315–328

Majidi F, Bijani M, Abbasi E (2017) Pathology of scientific articles publishing in the field of agriculture as perceived by faculty members and Ph. D. students (The case of colleges of agriculture at Public Universities, Iran). J Agric Sci Technol 19:1469–1484

Malekian A, Hayati D, Aarts N (2017) Conceptualizations of water security in the agricultural sector: perceptions, practices, and paradigms. J Hydrol 544:224–232

Mennatizadeh M, Zamani G (2016) Water ethics: theoretical analysis of moral development theories. Indian J Fundam Appl Life Sci 6:413–428

Mohammadi-Mehr S, Bijani M, Abbasi E (2018) Factors affecting the aesthetic behavior of villagers towards the natural environment: The case of Kermanshah province, Iran. J Agric Sci Technol 20(7):1353–1367

Morales FJ (2007) The mission and evolution of international agricultural research in developing countries. In: Loebenstein G, Thottappilly G (eds) Agricultural research management. Springer, Dordrecht, pp 9–36

Patton MQ (2008) Utilization focused evaluation, 4th edn. Sage, Thousand Oaks

Popa F, Guillermin M, Dedeurwaerdere T (2015) A pragmatist approach to transdisciplinarity in sustainability research: from complex systems theory to reflexive science. Futures 65:45–56

Raeisi AA, Bijani M, Chizari M (2018) The mediating role of environmental emotions in transition from knowledge to sustainable behavior toward exploit groundwater resources in Iran’s agriculture. Int Soil Water Conserv Res 6(2):143–152

Rosenthal R, Rosnow RL (1991) Essentials of behavioral research: methods and data analysis. McGraw-Hil, Boston

Schensul SL, Schensul JJ, LeCompte MD (2012) Initiating ethnographic research: a mixed methods approach, vol 2. AltaMira Press, London

Shadish WR, Cook TD, Leviton LC (1991) Foundations of program evaluation: theories of practice. Sage, Newbury Park

Shahvali M (2013) Explanation of transcendental innovation system for sustainability. In: The proceedings of the Iranian and Islamic pattern of development, pp 1245–1267

Shahvali M, Amiri Ardakani M (2011) Research methodology for agricultural indigenous knowledge. Agricultural Research, Education, and Extension Organization, Tehran

Shiri S, Bijani M, Chaharsoughi Amin H, Noori H, Soleymanifard A (2011) Effectiveness evaluation of the axial plan of wheat from expert supervisors’ view in Ilam province. World Appl Sci J 14(11):1724–1729

Valizadeh N, Bijani M, Abbasi E (2016) Pro-environmental analysis of farmers’ participatory behavior toward conservation of surface water resources in southern sector of Urmia Lake’s catchment area. Iran Agric Ext Educ J 11(2):183–201. In Persian

Valizadeh N, Bijani M, Abbasi E (2018a) Farmers’ active participation in water conservation: insights from a survey among farmers in Southern Regions of West Azerbaijan Province, Iran. J Agric Sci Technol 20(5):895–910

Valizadeh N, Bijani M, Abbasi E, Ganguli S (2018b) The role of time perspective in predicting Iranian farmers’ participatory-based water conservation attitude and behavior. J Hum Behav Soc Environ 28:992

Weiss CH, Murphy-Graham E, Birkeland S (2005) An alternate route to policy influence: how evaluations affect D.A.R.E. Am J Eval 26(1):12–30

Yazdanpanah M, Hayati D, Hochrainer-Stigler S, Zamani GHH (2014) Understanding farmers’ intention and behavior regarding water conservation in the Middle-East and North Africa: a case study in Iran. J Environ Manag 135:63–72

Zamani GHH (2016) Human liability theory: ethical approach towards agriculture and environment. Iran Agric Ext Educ J 12(1):149–163

Zanoli R, Krell R (1999) Research methodologies in organic farming. Proceedings. REU technical series. FAO, Rome

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Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran

Naser Valizadeh Ph.D. Student

Department of Agricultural Extension and Education, College of Agriculture, Tarbiat Modares University (TMU), Tehran, Iran

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Faculty of Engineering and Architecture, The University of Passo Fundo, Passo Fundo, Brazil

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Istinye University, Istanbul, Turkey

Pinar Gökçin Özuyar

International Centre for Thriving, University of Chester, Chester, UK

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Valizadeh, N., Bijani, M. (2020). Agricultural Research: Applications and Future Orientations. In: Leal Filho, W., Azul, A.M., Brandli, L., Özuyar, P.G., Wall, T. (eds) Zero Hunger. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-95675-6_5

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Research and Science

From fostering continued economic growth to adapting to the effects of climate change and addressing food security, the United States can continue to be a leader in global agriculture. Each day, the work of USDA scientists and researchers touches the lives of all Americans - from the farm field to the kitchen table and from the air we breathe to the energy that powers our country.

The challenges facing agriculture, natural resources, and conservation are immense and can be addressed through robust research enterprise and educational programs. USDA intramural and extramural science helps to protect, secure, and improve our food, agricultural and natural resources systems.

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AGARDA: A Vision for Disruptive Science to Confront Audacious Challenges

Agriculture Advanced Research and Development Authority (AGARDA) Implementation Strategy (PDF, 1.8 MB) is a framework outlining a new approach for delivering disruptive breakthrough discoveries for agriculture.

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USDA has refocused its science agencies to ensure the most effective and efficient use of its resources, while leveraging the strengths of our partners across the scientific community.

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The Agricultural Research Service (ARS) conducts research to develop and transfer solutions to agricultural problems of high national priority.

The Economic Research Service (ERS) , through science-based economic research and analysis, informs public policy and other decisions about agriculture, food, rural development, and environmental challenges.

The National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture.

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Enhancing the Productivity of American Agriculture and Ensuring the Safety of our Food Supply

USDA invests in research, development, and outreach of new varieties and technologies to mitigate animal/plant diseases and increase productivity, sustainability, and product quality. USDA research has supported America's farmers and ranchers in their work to produce a safe and abundant food supply for over 100 years. This work has helped feed the nation and sustain an agricultural trade surplus since the 1960s.

An additional focus is to establish more sustainable systems that enhance crop and animal health. Our scientists and university partners have revealed the genetic blueprints of a host of plants and animals including the genomes of apples, pigs, and turkeys, and in 2012, they furthered understanding of the tomato, bean, wheat and barley genomes -- key drivers in developing the resilience of those crops to feed growing populations.

NASS has developed animated U.S. crop progress and topsoil moisture maps , along with other resources, to help experts assess farmland data. USDA researchers also created the Maize Genome Database, an important tool to help farmers improve traits in a crop vital to the world. Meeting growing global demand for food, fiber, and biofuel requires robust investment in agricultural research and development (R&D) from both public and private sectors. USDA is a leader in remote sensing and mapping to visualize data in support of agricultural policy and business decision making as well as program operation. We ranked first worldwide among research institutions publishing on priority diseases in animal health including salmonellosis, avian influenza , mycobacterial disease, coccidiosis, campylobacterosis, mastitis and others.

USDA conducts and supports science that informs decisions and policies contributing to a safe food supply and the reduction of foodborne hazards. Our scientists found the primary site where the virus that causes foot-and-mouth disease begins infection in cattle and developed an improved vaccine against the disease. They are also working on new strategies to control mites and other major honey bee problems such as colony collapse disorder .

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USDA builds the evidence base for food-based and physical activity strategies and develops effective education activities to promote health and reduce malnutrition and obesity in children and high-risk populations. For example, ARS evaluated school characteristics associated with healthier or less healthy food preparation practices and offerings and found that the school nutrition environment could be improved by requiring food service managers to hold nutrition-related college degrees, pass a food service training program, and by participating in a school-based nutrition program such as USDA Team Nutrition .

USDA-supported science is investigating the causes of childhood obesity so that our country can address the epidemic. In these efforts, USDA supports nutrition education programs and encourages Americans to consume more nutritious foods like fruits and vegetables. Our scientists are part of an international team that has found a way to boost the nutritional value of broccoli, tomatoes and corn, and have worked to find ways to bolster the nutritional content of other staple crops like oats and rice. USDA research has supported these efforts, showing how healthy foods can often cost less than foods that are high in saturated fat, added sugar and/or sodium.

In 2013, USDA updated the national assessment of urban and rural food deserts - low-income areas with limited access to affordable and nutritious food - and provided information on the socioeconomic and demographic characteristics that distinguish food deserts from other areas, for decision-makers and stakeholders concerned about access to healthy foods.

Conserving Natural Resources and Combating Climate Change

USDA develops and delivers science-based knowledge that empowers farmers, foresters, ranchers, landowners, resource managers, policymakers, and Federal agencies to manage the risks, challenges, and opportunities of climate variability, and that informs decision-making and improves practices in environmental conservation.

Our scientists are developing rice and corn crops that are drought- and flood-resistant and helping to improve the productivity of soil, as well as production systems that require increasing smaller amounts of pesticides or none at all.

Vegetation indices contained in VegScape have proven useful for assessing crop condition and identifying the aerial extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. This tool allows users to monitor and track weather anomalies' effects on crops in near real time and compare this information to historical data on localized levels or across States.

Additionally, our researchers have examined the potential impacts of a suite of climate scenarios on U.S. crop production. Studies like these will help policymakers, farmers, industry leaders and others better understand and adapt to a changing climate on America's crop production.

Our researchers created i-Tree , urban forest management software to help cities understand the value of urban trees through carbon sequestration, erosion protection, energy conservation and water filtration, and since 2009 have continued building on the success of the tool and expanding its use. Our scientists are conducting research on uses of wood, helping companies meet green building design standards and creating jobs using forest products. We have also worked with Major League Baseball to reduce the occurrence of broken baseball bats.

USDA supports families managing through tough economic times by helping residents save energy at home and conserve water, with a program run by Cooperative Extension and our land-grant university partners. Cooperative Extension-affiliated volunteer monitoring programs have engaged citizens in water monitoring to better understand the effects of climate change and/or aquatic invasive species on local waters. Collectively, these programs interacted with hundreds of local, State, and Federal partners. The programs help citizens detect the presence of invasive species and harmful algal blooms.

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USDA recognizes the importance of recruiting, cultivating, and developing the next generation of scientists, leaders, and a highly skilled workforce for food, agriculture, natural resources, forestry, environmental systems, and life sciences.

The NIFA interagency agreement with the U.S. Fish and Wildlife Service leverages technology and innovation and involves youth in STEM outreach and exposure. Youth participants developed science process skills related to using GIS and research design, analyzing and interpreting data, and reporting findings to the community which has enabled them to become better consumers of science and citizens capable of making wise STEM policy choices.

USDA strives to provide effective research, education, and extension activities that inform public and private decision-making in support of rural and community development . NASS holds outreach events throughout the Census cycle with underserved and minority and disadvantaged farming groups to promote participation in the Census of Agriculture . With funding and support from NIFA, many Tribal Colleges are offering Reservation citizens training ranging from basic financial literacy to business start-up and marketing information so that families not only survive, but thrive.

In addition, the ERS Atlas of Rural and Small Town America brings together over 80 demographic, economic, and agricultural statistics for every county in all 50 states and assembles statistics in four broad categories -- people, jobs, agriculture, and geography.

Research and Science Centers and Databases

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Article Contents

1. introduction, 2. analytical framework, 3. literature search, 5. discussion, 6. conclusion, acknowledgement.

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Research impact assessment in agriculture—A review of approaches and impact areas

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Peter Weißhuhn, Katharina Helming, Johanna Ferretti, Research impact assessment in agriculture—A review of approaches and impact areas, Research Evaluation , Volume 27, Issue 1, January 2018, Pages 36–42, https://doi.org/10.1093/reseval/rvx034

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Research has a role to play in society’s endeavour for sustainable development. This is particularly true for agricultural research, since agriculture is at the nexus between numerous sustainable development goals. Yet, generally accepted methods for linking research outcomes to sustainability impacts are missing. We conducted a review of scientific literature to analyse how impacts of agricultural research were assessed and what types of impacts were covered. A total of 171 papers published between 2008 and 2016 were reviewed. Our analytical framework covered three categories: (1) the assessment level of research (policy, programme, organization, project, technology, or other); (2) the type of assessment method (conceptual, qualitative, or quantitative); and (3) the impact areas (economic, social, environmental, or sustainability). The analysis revealed that most papers (56%) addressed economic impacts, such as cost-effectiveness of research funding or macroeconomic effects. In total, 42% analysed social impacts, like food security or aspects of equity. Very few papers (2%) examined environmental impacts, such as climate effects or ecosystem change. Only one paper considered all three sustainability dimensions. We found a majority of papers assessing research impacts at the level of technologies, particularly for economic impacts. There was a tendency of preferring quantitative methods for economic impacts, and qualitative methods for social impacts. The most striking finding was the ‘blind eye’ towards environmental and sustainability implications in research impact assessments. Efforts have to be made to close this gap and to develop integrated research assessment approaches, such as those available for policy impact assessments.

Research has multiple impacts on society. In the light of the international discourse on grand societal challenges and sustainable development, the debate is reinforced about the role of research on economic growth, societal well-being, and environmental integrity ( 1 ). Research impact assessment (RIA) is a key instrument to exploring this role ( 2 ).

A number of countries have begun using RIA to base decisions for allocation of funding on it, and to justify the value of investments in research to taxpayers ( 3 ). The so-called scientometric assessments with a focus on bibliometric and exploitable results such as patents are the main basis for current RIA practices ( 4–6 ). However, neither academic values of science, based on the assumption of ‘knowledge as progress’, nor market values frameworks (‘profit as progress’) seem adequate for achieving and assessing broader public values ( 7 ). Those approaches do not explicitly acknowledge the contribution of research to solving societal challenges, although they are sufficient to measure scientific excellence ( 8 ) or academic impact.

RIA may however represent a vital element for designing socially responsible research processes with orientation towards responsibility for a sustainable development ( 9 , 10 ). In the past, RIAs occurred to focus on output indicators and on links between science and productivity while hardly exploring the wider societal impacts of science ( 11 ). RIA should entail the consideration of intended and non-intended, positive and negative, and long- and short-term impacts of research ( 12 ). Indeed, there has been a broadening of impact assessments to include, for example, cultural and social returns to society ( 13 ). RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

Research on RIA and its potential to cover wider societal impacts has examined assessment methods and approaches in specific fields of research, and in specific research organizations. The European Science Foundation ( 19 ) and Guthrie et al. ( 20 ) provided overviews of a range of methods usable in assessment exercises. They discuss generic methods (e.g. economic analyses, surveys, and case studies) with view to their selection for RIAs. Methods need to fit the objectives of the assessment and the characteristics of the disciplines examined. Econometric methods consider the rate of return over investment ( 21 ), indicators for ‘productive interactions’ between the stakeholders try to capture the social impact of research ( 22 ), and case study-based approaches map the ‘public values’ of research programmes ( 8 , 23 ). No approach is generally favourable over another, while challenges exist in understanding which impact areas are relevant in what contexts. Penfield et al. ( 6 ) looked at the different methods and frameworks employed in assessment approaches worldwide, with a focus on the UK Research Excellence Framework. They argue that there is a need for RIA approaches based on types of impact rather than research discipline. They point to the need for tools and systems to assist in RIAs and highlight different types of information needed along the output-outcome-impact-chain to provide for a comprehensive assessment. In the field of public health research, a minority of RIAs exhibit a wider scope on impacts, and these studies highlight the relevance of case studies ( 24 ). However, case studies often rely on principal investigator interviews and/or peer review, not taking into account the views of end users. Evaluation practices in environment-related research organizations tend to focus on research uptake and management processes, but partially show a broader scope and longer-term outcomes. Establishing attribution of environmental research to different types of impacts was identified to be a key challenge ( 25 ). Other authors tested impact frameworks or impact patterns in disciplinary public research organizations. For example, Gaunand et al. ( 26 ) analysed an internal database of the French Agricultural research organization INRA with 1,048 entries to identify seven impact areas, with five going beyond traditional types of impacts (e.g. conservation of natural resources or scientific advice). Besides, for the case of agricultural research, no systematic review of RIA methods exists in the academic literature that would allow for an overview of available approaches covering different impact areas of research.

Against this background, the objective of this study was to review in how far RIAs of agricultural research capture wider societal implications. We understand agricultural research as being a prime example for the consideration of wider research impacts. This is because agriculture is a sector which has direct and severe implications for a range of the UN Sustainable Development Goals. It has a strong practice orientation and is just beginning to develop a common understanding of innovation processes ( 27 ).

The analysis of the identified literature on agricultural RIA (for details, see next section ‘Literature search’) built on a framework from a preliminary study presented at the ImpAR Conference 2015 ( 28 ). It was based on three categories to explore the impact areas that were addressed and the design of RIA. In particular, the analytical framework consisted of: ( 1 ) the assessment level of research; ( 2 ) the type of assessment method; and ( 3 ) the impact areas covered. On the side, we additionally explored the time dimension of RIA, i.e. whether the assessment was done ex ante or ex post (see Fig. 1 ).

Analytical framework for the review of non-scientometric impact assessment literature of agricultural research.

Agricultural research and the ramifications following from that refer to different levels of assessment (or levels of evaluation, ( 29 )). We defined six assessment levels that can be the subject of a RIA: policy, programme, organization, project, technology, and other. The assessment level of the RIA is a relevant category, since it shapes the approach to the RIA (e.g. the impact chain of a research project differs to that at policy level). The assessment level was clearly stated in all of the analysed papers and in no case more than one assessment level was addressed. Articles were assigned to the policy level, if a certain public technology policy ( 30 ) or science policy, implemented by governments to directly or indirectly affect the conduct of science, was considered. Exemplary topics are research funding, transfer of research results to application, or contribution to economic development. Research programmes were understood as instruments that are adopted by government departments, or other organizational entities to implement research policies and fund research activities in a specific research field (e.g. programmes to promote research on a certain crop or cultivation technique). Articles dealing with the organizational level assess the impact of research activities of a specific research organization. The term research organization comprises public or private research institutes, associations, networks, or partnerships (e.g. the Consultative Group on International Agricultural Research (CGIAR) and its research centres). A research project is the level at which research is actually carried out, e.g. as part of a research programme. The assessment of a research project would consider the impacts of the whole project, from planning through implementation to evaluation instead of focusing on a specific project output, like a certain agricultural innovation. The technology level was considered to be complementary to the other assessment levels of research and comprises studies with a strong focus on specific agricultural machinery or other agricultural innovation such as new crops or crop rotations, fertilizer applications, pest control, or tillage practices, irrespective of the agricultural system (e.g. smallholder or high-technology farming, or organic, integrated, or conventional farming). The category ‘other’ included one article addressing RIA at the level of individual researchers (see ( 31 )).

We categorized the impact areas along the three dimensions of sustainable development by drawing upon the European Commission’s impact assessment guidelines (cf. ( 32 )). The guidelines entail a list of 7 environmental impacts, such as natural resource use, climate change, or aspects of nature conservation; 12 social impacts, such as employment and working conditions, security, education, or aspects of equity; and 10 economic impacts, including business competitiveness, increased trade, and several macroeconomic aspects. The European Commission’s impact assessment guidelines were used as a classification framework because it is one of the most advanced impact assessment frameworks established until to date ( 33 ). In addition, we opened a separate category for those articles exploring joint impacts on the three sustainability dimensions. Few articles addressed impacts in two sustainability dimensions which we assigned to the dominating impact area.

To categorize the type of RIA method, we distinguished between conceptual, qualitative, and quantitative. Conceptual analyses include the development of frameworks or concepts for measuring impacts of agricultural research (e.g. tracking of innovation pathways or the identification of barriers and supporting factors for impact generation). Qualitative and quantitative methods were identified by the use of qualitative data or quantitative data, respectively (cf. ( 34–36 )). Qualitative data can be scaled nominally or ordinally. It is generated by interviews, questionnaires, surveys or choice experiments to gauge stakeholder attitudes to new technologies, their willingness to pay, and their preference for adoption measures. The generation of quantitative data involves a numeric measurement in a standardized way. Such data are on a metric scale and are often used for modelling. The used categorization is rather simple. We assigned approaches which employed mixed-method approaches according to their dominant method. We preferred this over more sophisticated typologies to achieve a high level of abstraction and because the focus of our analysis was on impact areas rather than methods. However, to show consistencies with existing typologies of impact assessment methods ( 19 , 37 ), we provide an overview of the categorization chosen and give examples of the most relevant types of methods.

To additionally explore the approach of the assessment ( 38 ), the dimensions ex ante and ex post were identified. The two approaches are complementary: whereas ex ante impact assessments are usually conducted for strategic and planning purposes to set priorities, ex post impact assessments serve as accountability validation and control against a baseline. The studies in our sample that employed an ex ante approach to RIA usually made this explicit, while in the majority of ex post impact assessments, this was indicated rather implicitly.

This study was performed as a literature review based on Thomson Reuters Web of Science TM Core Collection, indexed in the Science Citation Index Expanded (SCI-Exp) and the Social Sciences Citation Index (SSCI). The motivation for restricting the analysis to articles from ISI-listed journals was to stay within the boundaries of internationally accepted scientific quality management and worldwide access. The advantages of a search based on Elsevier’s Scopus ® (more journals and alternative publications, and more articles from social and health science covered) would not apply for this literature review, with regard to the drawbacks of an index system based on abstracts instead of citation indexes, which is not as transparent as the Core Collection regarding the database definable by the user. We selected the years of 2008 to mid-2016 for the analysis (numbers last updated on 2 June 2016) . First, because most performance-based funding systems have been introduced since 2000, allowing sufficient time for the RIA approaches to evolve and literature to be published. Secondly, in 2008 two key publications on RIA of agricultural research triggered the topic: Kelley, et al. ( 38 ) published the lessons learned from the Standing Panel on Impact Assessment of CGIAR; Watts, et al. ( 39 ) summarized several central pitfalls of impact assessment concerning agricultural research. We took these publications as a starting point for the literature search. We searched in TOPIC and therefore, the terms had to appear in the title, abstract, author keywords, or keywords plus ® . The search query 1 filtered for agricultural research in relation to research impact. To cover similar expressions, we used science, ‘R&D’, and innovation interchangeably with research, and we searched for assessment, evaluation, criteria, benefit, adoption, or adaptation of research.

We combined the TOPIC search with a less strict search query 2 in TITLE using the same groups of terms, as these searches contained approximately two-thirds non-overlapping papers. Together they consisted of 315 papers. Of these, we reviewed 282 after excluding all document types other than articles and reviews (19 papers were not peer-reviewed journal articles) and all papers not written in English language (14 papers). After going through them, 171 proved to be topic-relevant and were included in the analysis.

Analysis matrix showing the number of reviewed articles, each categorized to an assessment level and an impact area (social, economic, environmental, or all three (sustainability)). Additionally, the type of analytical method (conceptual, quantitative, and qualitative) is itemized

In the agricultural RIA, the core assessment level of the reviewed articles was technology (39%), while the other levels were almost equally represented (with the exception of ‘other’). Generally, most papers (56%) addressed economic research impacts, closely followed by social research impacts (42%); however, only three papers (2%) addressed environmental research impacts and only 1 of 171 papers addressed all three dimensions of sustainable development. Assessments at the level of research policy slightly emphasized social impacts over economic impacts (18 papers, or 58%), whereas assessments at the level of technology clearly focused primarily on economic impacts (46 papers, or 68%).

The methods used for agricultural RIA showed no preference for one method type (see Table 1 ). Approximately 31% of the papers assessed research impacts quantitatively, whereas 37% used qualitative methods. Conceptual considerations on research impact were applied by 32% of the studies. A noticeable high number of qualitative studies were conducted to assess social impacts. At the evaluation level of research policy and research programmes, we found a focus on quantitative methods, if economic impacts were assessed.

Overview on type of methods used for agricultural RIA

a Mix of conceptual and qualitative methods.

b Mix of conceptual, qualitative, and quantitative methods.

Additionally, 37 ex ante studies, compared to 134 ex post studies, revealed that the latter clearly dominated, but no robust relation to any other investigated characteristic was found. Of the three environmental impact studies, none assessed ex ante , while the one study exploring sustainability impacts did. The share of ex ante assessments regarding social impacts was very similar to those regarding economic impacts. Within the assessment levels of research (excluding ‘others’ with only one paper), no notable difference between the shares of ex ante assessments occurred as they ranged between 13 and 28%.

The most relevant outcome of the review analysis was that only 3 of the 171 papers focus on the environmental impacts of agricultural research. This seems surprising because agriculture is dependent on an intact environment. However, this finding is supported by two recent reviews: one from Bennett, et al. ( 40 ) and one from Maredia and Raitzer ( 41 ). Both note that not only international agricultural research in general but also research on natural resource management shows a lack regarding large-scale assessments of environmental impacts. The CGIAR also recognized the necessity to deepen the understanding of the environmental impacts of its work because RIAs had largely ignored environmental benefits ( 42 ).

A few papers explicitly include environmental impacts of research in addition to their main focus. Raitzer and Maredia ( 43 ) address water depletion, greenhouse gas emissions, and landscape effects; however, their overall focus is on poverty reduction. Ajayi et al. ( 44 ) report the improvement of soil physical properties and soil biodiversity from introducing fertilizer trees but predominantly measure economic and social effects. Cavallo, et al. ( 45 ) investigate users’ attitudes towards the environmental impact of agricultural tractors (considered as technological innovation) but do not measure the environmental impact. Briones, et al. ( 46 ) configure an environmental ‘modification’ of economic surplus analysis, but they do not prioritize environmental impacts.

Of course, the environmental impacts of agricultural practices were the topic of many studies in recent decades, such as Kyllmar, et al. ( 47 ), Skinner, et al. ( 48 ), Van der Werf and Petit ( 49 ), among many others. However, we found very little evidence for the impact of agricultural research on the environment. A study on environmental management systems that examined technology adoption rates though not the environmental impacts is exemplarily for this ( 50 ). One possible explanation is based on the observation made by Morris, et al. ( 51 ) and Watts, et al. ( 39 ). They see impact assessments tending to accentuate the success stories because studies are often commissioned strategically as to demonstrate a certain outcome. This would mean to avoid carving out negative environmental impacts that conflict with, when indicated, the positive economic or societal impacts of the assessed research activity. In analogy to policy impact assessments, this points to the need of incentives to equally explore intended and unintended, expected and non-expected impacts from scratch ( 52 ). From those tasked with an RIA, this again requires an open attitude in ‘doing RIA’ and towards the findings of their RIA.

Another possible explanation was given by Bennett, et al. ( 40 ): a lack of skills in ecology or environmental economics to cope with the technically complex and data-intensive integration of environmental impacts. Although such a lack of skills or data could also apply to social and economic impacts, continuous monitoring of environmental data related to agricultural practices is particularly scarce. A third possible explanation is a conceptual oversight, as environmental impacts may be thought to be covered by the plenty of environmental impact assessments of agricultural activities itself.

The impression of a ‘blind eye’ on the environment in agricultural RIA may change when publications beyond Web of Science TM Core Collection are considered ( 53 ) or sources other than peer-reviewed journal articles are analysed (e.g. reports; conference proceedings). See, for example, Kelley, et al. ( 38 ), Maredia and Pingali ( 54 ), or FAO ( 55 ). Additionally, scientific publications of the highest quality standard (indicated by reviews and articles being listed in the Web of Science TM Core Collection) seem to not yet reflect experiences and advancements from assessment applications on research and innovation policy that usually include the environmental impact ( 56 ).

Since their beginnings, RIAs have begun to move away from narrow exercises concerned with economic impacts ( 11 ) and expanded their scope to social impacts. However, we only found one sustainability approach in our review that would cover all three impact areas of agricultural research (see ( 57 )). In contrast, progressive approaches to policy impact assessment largely attempt to cover the full range of environmental, social, and economic impacts of policy ( 33 , 58 ). RIAs may learn from them.

Additionally, the focus of agricultural research on technological innovation seems evident. Although the word innovation is sometimes still used for new technology (as in ‘diffusion of innovations’), it is increasingly used for the process of technical and institutional change at the farm level and higher levels of impact. Technology production increasingly is embedded in innovation systems ( 59 ).

The review revealed a diversity of methods (see Table 2 ) applied in impact assessments of agricultural research. In the early phases of RIA, the methods drawn from agricultural economics were considered as good standard for an impact assessment of international agricultural research ( 39 ). However, quantitative methods most often address economic impacts. In addition, the reliability of assessments based on econometric models is often disputed because of strong relationships between modelling assumptions and respective results.

Regarding environmental (or sustainability) impacts of agricultural research, the portfolio of assessment methods could be extended by learning from RIAs in other impact areas. In our literature sample, only review, framework development (e.g. key barrier typologies, environmental costing, or payments for ecosystem services), life-cycle assessment, and semi-structured interviews were used for environmental impacts of agricultural research.

In total, 42 of the 171 analysed papers assessed the impact of participatory research. A co-management of public research acknowledges the influence of the surrounding ecological, social, and political system and allows different types of stakeholder knowledge to shape innovation ( 60 ). Schut, et al. ( 36 ) conceptualize an agricultural innovation support system, which considers multi-stakeholder dynamics next to multilevel interactions within the agricultural system and multiple dimensions of the agricultural problem. Another type of participation in RIAs is the involvement of stakeholders to the evaluation process. A comparatively low number of six papers considered participatory evaluation of research impact, of them three in combination with impact assessment of participatory research.

Approximately 22% of the articles in our sample on agricultural research reported that they conducted their assessments ex ante , but most studies were ex post assessments. Watts, et al. ( 39 ) considered ex ante impact assessment to be more instructive than ex post assessment because it can directly guide the design of research towards maximizing beneficial impacts. This is particularly true when an ex ante assessment is conducted as a comparative assessment comprising a set of alternative options ( 61 ).

Many authors of the studies analysed were not explicit about the time frames considered in their ex post studies. The potential latency of impacts from research points to the need for ex post (and ex ante) studies to account for and analyse longer time periods, either considering ‘decades’ ( 62 , 63 ) or a lag distribution covering up to 50 years, with a peak approximately in the middle of the impact period ( 64 ). This finding is in line with the perspective of impact assessments as an ongoing process throughout a project’s life cycle and not as a one-off process at the end ( 51 ). Nevertheless, ex post assessments are an important component of a comprehensive evaluation package, which includes ex ante impact assessment, impact pathway analysis, programme peer reviews, performance monitoring and evaluation, and process evaluations, among others ( 38 ).

RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

However, in the cases in which a RIA is carried out, an increase in the positive impacts (or avoidance of negative impacts) of agricultural research does not follow automatically. Lilja and Dixon ( 65 ) state the following methodological reasons for the missing impact of impact studies: no accountability with internal learning, no developed scaling out, the overlap of monitoring and evaluation and impact assessment, the intrinsic nature of functional and empowering farmer participation, the persistent lack of widespread attention to gender, and the operational and political complexity of multi-stakeholder impact assessment. In contrast, a desired impact of research could be reached or boosted by specific measures without making an impact assessment at all. Kristjanson, et al. ( 66 ), for example, proposed seven framework conditions for agricultural research to bridge the gap between scientific knowledge and action towards sustainable development. RIA should develop into process-oriented evaluations, in contrast to outcome-oriented evaluation ( 67 ), for addressing the intended kind of impacts, the scope of assessment, and for choosing the appropriate assessment method ( 19 ).

This review aimed at providing an overview of impact assessment activities reported in academic agricultural literature with regard to their coverage of impact areas and type of assessment method used. We found a remarkable body of non-scientometric RIA at all evaluation levels of agricultural research but a major interest in economic impacts of new agricultural technologies. These are closely followed by an interest in social impacts at multiple assessments levels that usually focus on food security and poverty reduction and rely slightly more on qualitative assessment methods. In contrast, the assessment of the environmental impacts of agricultural research or comprehensive sustainability assessments was exceptionally limited. They may have been systematically overlooked in the past, for the reason of expected negative results, thought to be covered by other impact studies or methodological challenges. RIA could learn from user-oriented policy impact assessments that usually include environmental impacts. Frameworks for RIA should avoid narrowing the assessment focus and instead considering intended and unintended impacts in several impact areas equally. It seems fruitful to invest in assessment teams’ environmental analytic skills and to expand several of the already developed methods for economic or social impact to the environmental impacts. Only then, the complex and comprehensive contribution of agricultural research to sustainable development can be revealed.

The authors would like to thank Jana Rumler and Claus Dalchow for their support in the Web of Science analysis and Melanie Gutschker for her support in the quantitative literature analysis.

This work was supported by the project LIAISE (Linking Impact Assessment to Sustainability Expertise, www.liaisenoe.eu ), which was funded by Framework Programme 7 of the European Commission and co-funded by the Leibniz-Centre for Agricultural Landscape Research. The research was further inspired and supported by funding from the ‘Guidelines for Sustainability Management’ project for non-university research institutes in Germany (‘Leitfaden Nachhaltigkeitsmanagement’, BMBF grant 311 number 13NKE003A).

Seidl R. et al.  ( 2013 ) ‘ Science with Society in the Anthropocene ’, Ambio , 42 / 1 : 5 – 12 .

Google Scholar

OECD . ( 2010 ) ‘Performance-Based Funding for Public Research in Tertiary Education Institutions’, Workshop Proceedings ' 2010. Paris : Organisation for Economic Co-operation and Development .

Hicks D. ( 2012 ) ‘ Performance-based University Research Funding Systems ’, Research Policy , 41 / 2 : 251 – 61 .

Martin B. R. ( 1996 ) ‘ The Use of Multiple Indicators in the Assessment of Basic Research ’, Scientometrics , 36 / 3 : 343 – 62 .

Moed H. F. , Halevi G. ( 2015 ) ‘ Multidimensional Assessment of Scholarly Research Impact ’, Journal of the Association for Information Science and Technology , 66 : 1988 – 2002 .

Penfield T. et al.  ( 2014 ) ‘ Assessment, Evaluations, and Definitions of Research Impact: A Review ’, Research Evaluation , 23 / 1 : 21 – 32 .

Meyer R. ( 2011 ) ‘ The Public Values Failures of Climate Science in the US ’, Minerva , 49 / 1 : 47 – 70 .

Bozeman B. , Sarewitz D. ( 2011 ) ‘ Public Value Mapping and Science Policy Evaluation ’, Minerva , 49 / 1 : 1 – 23 .

Helming K. et al.  ( 2016 ) ‘ Forschen für nachhaltige Entwicklung. Kriterien für gesellschaftlich verantwortliche Forschungsprozesse (Research for Sustainable Development. Criteria for Socially Responsible Research Processes) ’, GAIA , 25 / 3 : 161 – 5 .

Cagnin C. , Amanatidou E. , Keenan M. ( 2012 ) ‘ Orienting European Innovation Systems Towards Grand Challenges and the Roles that FTA Can Play ’, Science and Public Policy , 39 / 2 : 140 – 52 .

Godin B. , Doré C. ( 2004 ) Measuring the Impacts of Science: Beyond the Economic Dimension . Montréal (Québec) : Centre Urbanisation Culture Société (INRS) .

Ferretti J. et al.  ( 2016 ) Reflexionsrahmen für Forschen in gesellschaftlicher Verantwortung. (Framework for Reflecting Research in Societal Responsibility) . Berlin : Federal Ministry of Education and Research (BMBF) .

Jacobsson S. , Vico E. P. , Hellsmark H. ( 2014 ) ‘ The Many Ways of Academic Researchers: How is Science Made Useful? ’, Science and Public Policy , 41 : 641 – 57 .

Levitt R. et al.  ( 2010 ) Assessing the Impact of Arts and Humanities Research at the University of Cambridge . Cambridge : University of Cambridge .

Donovan C. ( 2011 ) ‘ State of the Art in Assessing Research Impact: Introduction to a Special Issue ’, Research Evaluation , 20 / 3 : 175 – 9 .

Ekboir J. ( 2003 ) ‘ Why Impact Analysis Should not be Used for Research Evaluation and what the Alternatives Are ’, Agricultural Systems , 78 / 2 : 166 – 84 .

Morton S. ( 2015 ) ‘ Progressing Research Impact Assessment: A ‘Contributions’ Approach ’, Research Evaluation , 24 : 405 – 19 .

Reinhardt A. ( 2013 ) ‘Different Pathways to Impact? “Impact” and Research Fund Allocation in Selected European Countries’, in Dean A. , Wykes M. , Stevens H. (eds) 7 Essays on Impact. DESCRIBE Project Report for Jisc , pp. 88 – 101 . Exeter : University of Exeter .

Google Preview

European Science Foundation . ( 2012 ) The Challenges of Impact Assessment. Working Group 2: Impact Assessment . Strasbourg : European Science Foundation .

Guthrie S. et al.  ( 2013 ) Measuring Research. A Guide to Research Evaluation Frameworks and Tools . Cambridge : RAND Corporation .

Alston J. M. et al.  ( 2011 ) ‘ The Economic Returns to US Public Agricultural Research ’, American Journal of Agricultural Economics , 93 / 5 : 1257 – 77 .

Spaapen J. , Drooge L. ( 2011 ) ‘ Introducing' Productive Interactions' in Social Impact Assessment ’, Research Evaluation , 20 / 3 : 211 – 18 .

Bozeman B. ( 2003 ) Public Value Mapping of Science Outcomes: Theory and Method . Washington : Center for Science, Policy and Outcomes .

Milat A. J. , Bauman A. E. , Redman S. ( 2015 ) ‘ A Narrative Review of Research Impact Assessment Models and Methods ’, Health Research Policy and Systems , 13 / 1 : 18.

Bell S. , Shaw B. , Boaz A. ( 2011 ) ‘ Real-world Approaches to Assessing the Impact of Environmental Research on Policy ’, Research Evaluation , 20 / 3 : 227 – 37 .

Gaunand A. et al.  ( 2015 ) ‘ How Does Public Agricultural Research Impact Society? A Characterization of Various Patterns ’, Research Policy , 44 / 4 : 849 – 61 .

Bokelmann W. et al.  ( 2012 ) Sector Study on the Analysis of the Innovation of German Agriculture (Sektorstudie zur Untersuchung des Innovationssystems der deutschen Landwirtschaft) . Berlin : Federal Office for Agriculture and Food (BLE) .

Weißhuhn P. , Helming K. ( 2015 ) ‘Methods for Assessing the Non-Scientometric Impacts of Agricultural Research: A Review’. In ImpAR Conference 2015: Impacts of Agricultural Research-Towards an Approach of Societal V alues. Paris: INRA.

European Science Foundation . ( 2009 ) Evaluation in National Research Funding Agencies: Approaches, Experiences and Case Studies . Strasbourg : European Science Foundation .

Bozeman B. ( 2000 ) ‘ Technology Transfer and Public Policy: A Review of Research and Theory ’, Research Policy , 29 / 4 : 627 – 55 .

Hummer K. E. , Hancock J. F. ( 2015 ) ‘ Vavilovian Centers of Plant Diversity: Implications and Impacts ’, Hortscience , 50 / 6 : 780 – 3 .

EC . ( 2015 ) Better Regulation “Toolbox” . Brussels : European Commission .

Helming K. et al.  ( 2013 ) ‘ Mainstreaming Ecosystem Services in European Policy Impact Assessment ’, Ecosystem Services in EIA and SEA , 40 : 82 – 7 .

Thapa D. B. et al.  ( 2009 ) ‘ Identifying Superior Wheat Cultivars in Participatory Research on Resource Poor Farms ’, Field Crops Research , 112 / 2–3 : 124 – 30 .

Holdsworth M. et al.  ( 2015 ) ‘ African Stakeholders' Views of Research Options to Improve Nutritional Status in Sub-Saharan Africa ’, Health Policy and Planning , 30 / 7 : 863 – 74 .

Schut M. et al.  ( 2015 ) ‘ RAAIS: Rapid Appraisal of Agricultural Innovation Systems (Part I). A Diagnostic Tool for Integrated Analysis of Complex Problems and Innovation Capacity ’, Agricultural Systems , 132 : 1 – 11 .

Jones M. M. , Grant J. ( 2013 ) ’Making the Grade: Methodologies for assessing and evidencing research impact’. In Dean A. , Wykes M. , Stevens H. (eds) 7 Essays on Impact. DESCRIBE Project Report for Jisc , pp. 25 – 43 . Exeter : University of Exeter .

Kelley T. , Ryan J. , Gregersen H. ( 2008 ) ‘ Enhancing Ex Post Impact Assessment of Agricultural Research: The CGIAR Experience ’, Research Evaluation , 17 / 3 : 201 – 12 .

Watts J. et al.  ( 2008 ) ‘ Transforming Impact Assessment: Beginning the Quiet Revolution of Institutional Learning and Change ’, Experimental Agriculture , 44 / 1 : 21 – 35 .

Bennett J. W. , Kelley T. G. , Maredia M. K. ( 2012 ) ‘ Integration of Environmental Impacts Into Ex-post Assessments of International Agricultural Research: Conceptual Issues, Applications, and the Way Forward ’, Research Evaluation , 21 / 3 : 216 – 28 .

Maredia M. K. , Raitzer D. A. ( 2012 ) ‘ Review and Analysis of Documented Patterns of Agricultural Research Impacts in Southeast Asia ’, Agricultural Systems , 106 / 1 : 46 – 58 .

Renkow M. , Byerlee D. ( 2010 ) ‘ The Impacts of CGIAR Research: A Review of Recent Evidence ’, Food Policy , 35 / 5 : 391 – 402 .

Raitzer D. A. , Maredia M. K. ( 2012 ) ‘ Analysis of Agricultural Research Investment Priorities for Sustainable Poverty Reduction in Southeast Asia ’, Food Policy , 37 / 4 : 412 – 26 .

Ajayi O. C. et al.  ( 2011 ) ‘ Agricultural Success from Africa: The Case of Fertilizer Tree Systems in Southern Africa (Malawi, Tanzania, Mozambique, Zambia and Zimbabwe) ’, International Journal of Agricultural Sustainability , 9 / 1 : 129 – 36 .

Cavallo E. et al.  ( 2014 ) ‘ Strategic Management Implications for the Adoption of Technological Innovations in Agricultural Tractor: The Role of Scale Factors and Environmental Attitude ’, Technology Analysis and Strategic Management , 26 / 7 : 765 – 79 .

Briones R. M. et al.  ( 2008 ) ‘ Priority Setting for Research on Aquatic Resources: An Application of Modified Economic Surplus Analysis to Natural Resource Systems ’, Agricultural Economics , 39 / 2 : 231 – 43 .

Kyllmar K. et al.  ( 2014 ) ‘ Small Agricultural Monitoring Catchments in Sweden Representing Environmental Impact ’, Agriculture, Ecosystems and Environment , 198 : 25 – 35 .

Skinner J. et al.  ( 1997 ) ‘ An Overview of the Environmental Impact of Agriculture in the UK ’, Journal of Environmental Management , 50 / 2 : 111 – 28 .

Van der Werf H. M. , Petit J. ( 2002 ) ‘ Evaluation of the Environmental Impact of Agriculture at the Farm Level: A Comparison and Analysis of 12 Indicator-based Methods ’, Agriculture, Ecosystems and Environment , 93 / 1 : 131 – 45 .

Carruthers G. , Vanclay F. ( 2012 ) ‘ The Intrinsic Features of Environmental Management Systems that Facilitate Adoption and Encourage Innovation in Primary Industries ’, Journal of Environmental Management , 110 : 125 – 34 .

Morris M. et al.  ( 2003 ) ‘ Assessing the Impact of Agricultural Research: An Overview ’, Quarterly Journal of International Agriculture , 42 / 2 : 127 – 48 .

Podhora A. et al.  ( 2013 ) ‘ The Policy-Relevancy of Impact Assessment Tools: Evaluating Nine Years of European Research Funding ’, Environmental Science and Policy , 31 : 85 – 95 .

Rodrigues G. S. , de Almeida Buschinelli C. C. , Dias Avila A. F. ( 2010 ) ‘ An Environmental Impact Assessment System for Agricultural Research and Development II: Institutional Learning Experience at Embrapa ’, Journal of Technology Management and Innovation , 5 / 4 : 38 – 56 .

Maredia M. , Pingali P. ( 2001 ) Environmental Impacts of Productivity-Enhancing Crop Research: A Critical Review . Durban : CGIAR .

FAO . ( 2011 ) ‘ Environmental Impact Assessment', Guideline for FAO field projects . Rome : Food and Agriculture Organization of the United Nations .

Miedzinski M. et al.  ( 2013 ) Assessing Environmental Impacts of Research and Innovation Policy .

Ervin D. E. , Glenna L. L. , Jussaume R. A. ( 2011 ) ‘ The Theory and Practice of Genetically Engineered Crops and Agricultural Sustainability ’, Sustainability , 3 / 6 : 847 – 74 .

Jacob K. et al.  ( 2012 ) ‘Sustainability in Impact Assessments - A Review of Impact Assessment Systems in selected OECD countries and the European Commission’ . Paris : Organisation for Economic Co-operation and Development .

Röling N. ( 2009 ) ‘ Pathways for Impact: Scientists' Different Perspectives on Agricultural Innovation ’, International Journal of Agricultural Sustainability , 7 / 2 : 83 – 94 .

Dentoni D. , Klerkx L. ( 2015 ) ‘ Co-managing Public Research in Australian Fisheries Through Convergence-Divergence Processes ’, Marine Policy , 60 : 259 – 71 .

Helming K. et al.  ( 2011 ) ‘ Ex Ante Impact Assessment of Policies Affecting Land Use, Part A: Analytical Framework ’, Ecology and Society , 16 / 1 : 27 .

Stads G. J. , Beintema N. ( 2015 ) ‘ Agricultural R&D Expenditure in Africa: An Analysis of Growth and Volatility ’, European Journal of Development Research , 27 / 3 : 391 – 406 .

Raitzer D. A. , Kelley T. G. ( 2008 ) ‘ Benefit-cost Meta-analysis of Investment in the International Agricultural Research Centers of the CGIAR ’, Agricultural Systems , 96 / 1-3 : 108 – 23 .

Andersen M. A. ( 2015 ) ‘ Public Investment in US Agricultural R&D and the Economic Benefits ’, Food Policy , 51 : 38 – 43 .

Lilja N. , Dixon J. ( 2008 ) ‘ Responding to the Challenges of Impact Assessment of Participatory Research and Gender Analysis ’, Experimental Agriculture , 44 / 1 : 3 – 19 .

Kristjanson P. et al.  ( 2009 ) ‘ Linking International Agricultural Research Knowledge with Action for Sustainable Development ’, Proceedings of the National Academy of Sciences United States of America , 106 / 13 : 5047 – 52 .

Upton S. , Vallance P. , Goddard J. ( 2014 ) ‘ From Outcomes to Process: Evidence for a New Approach to Research Impact Assessment ’, Research Evaluation , 23 : 352 – 65 .

The exact TOPIC query was: agricult* NEAR/1 (research* OR *scien* OR "R&D" OR innovati*) AND (research* OR *scien* OR "R&D" OR innovati*) NEAR/2 (impact* OR assess* OR evaluat* OR criteria* OR benefit* OR adoption* OR adaptation*)

The exact TITLE query was: agricult* AND (research* OR *scien* OR "R&D" OR innovati*) AND (impact* OR assess* OR evaluat* OR criteria* OR benefit* OR adoption* OR adaptation*)

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Agriculture and Food

Agriculture can help reduce poverty, raise incomes and improve food security for 80% of the world's poor, who live in rural areas and work mainly in farming. The World Bank Group is a leading financier of agriculture.

Healthy, sustainable and inclusive food systems are critical to achieve the world’s development goals. Agricultural development is one of the most powerful tools to end extreme poverty, boost shared prosperity, and feed a projected  10 billion people by 2050 . Growth in the agriculture sector is  two to four times more effective  in raising incomes among the poorest compared to other sectors.

Agriculture is also crucial to economic growth: accounting for 4% of global gross domestic product (GDP) and in some least developing countries,  it can account for more than 25% of GDP .

But agriculture-driven growth, poverty reduction, and food security are at risk: Multiple shocks – from COVID-19 related disruptions to extreme weather, pests, and conflicts – are impacting food systems. The goal of ending global hunger by 2030 is currently off track. Conflicts, climate change, and high food prices are driving food and nutrition insecurity, pushing millions into extreme poverty, and reversing hard-won development gains. Around a quarter of a billion people now face acute food insecurity .

The growing impact of climate change could further cut crop yields, especially in the world’s most food-insecure regions. At the same time, our food systems are responsible for about 30% of greenhouse gas emissions.

Current food systems also threaten the health of people and the planet and generate unsustainable levels of pollution and waste. One third of food produced globally is either lost or wasted. Addressing food loss and waste is critical to improving food and nutrition security, as well as helping to meet climate goals and reduce stress on the environment.

Risks associated with poor diets are also  the leading cause of death worldwide . Millions of people are either not eating enough or eating the wrong types of food, resulting in a  double burden of malnutrition  that can lead to illnesses and health crises. Food insecurity can worsen diet quality and increase the risk of various forms of malnutrition, potentially leading to undernutrition as well as people being overweight and obese. An estimated 3 billion people in the world cannot afford a healthy diet.

Last Updated: Mar 15, 2024

The World Bank Group provides knowledge, advice, and financial resources in low- and middle-income countries to transform food systems to reduce poverty and achieve green, resilient, and inclusive development.

Our work in food and agriculture focuses on: 

• Food and nutrition security , where we work with efforts to share information, and to rapidly provide resources where they are needed, while helping countries design the long-term reforms needed to build resilient food and nutrition systems.
• Climate-smart agriculture by working with client governments to provide solutions that address global climate priorities, while recognizing national contexts and development objectives.
• Data-driven digital agriculture by expand the frontier of financing and expertise for digital agriculture.
• Mobilizing capital for development in agriculture & food . We identify and leverage growth areas for productive investments, focusing on innovation and impact. And we design projects to ensure that financing boosts sustainable productivity gains, reaches smallholders and SMEs, and creates jobs to end poverty and hunger.
• Public policy and expenditure by working with governments to facilitate the adoption of more sustainable approaches, technologies, and practices, alongside policies that promote public and private sector investment.
• Sustainable and health diets to ensure that food can support a healthy population.

For fiscal year 2024, a total of $2.98 billion in new IBRD/IDA commitments to agriculture and related sectors are being delivered. Around half of this investment will directly support climate action.

 As part of a comprehensive, global response to the food and nutrition crises, the World Bank is scaling up its responses , making $45 billion available in 90 countries. Our intervention is expected to benefit 335 million people, equivalent to 44% of the number of undernourished people. More than half of the beneficiaries are women, who are disproportionately affected by the crisis. It includes both short term interventions such as expanding social protection, also longer-term resilience such as boosting productivity and climate-smart agriculture. The World Bank has also included food and nutriton security as part of the global challenges that it will address at scale.

Increasingly, the Bank supports country efforts to transform their food systems by taking a holistic look at public policies and spending for agriculture and food. A Multi-Donor Trust Fund,  Food Systems 2030 , provides a platform for change in this area.

In Angola, a project  co-financed by the World Bank and the French Agency for Development, contributed to the government economic diversification agenda by supporting the transition from subsistence to a more market-oriented, competitive agriculture sector. The project is helping producers or small and medium enterprises prepare and finance agriculture investments. As of December 2023, 268 projects have been approved, equivalent to about $37 million in agriculture investment. The project funded the first partial credit guarantees scheme ever dedicated to the agriculture sector in Angola – an innovation for the country’s agribusiness sector – mobilizing so far $4.1 million in private bank financing. 

In  Argentina , the Bank supported 14,630 families who benefited from better socioeconomic inclusion. Under the project, 2,409 families accessed water for human and animal consumption, also irrigation; 7,499 rural families improved their productive capacity; and over 900 families accessed infrastructure, equipment and training that improved their marketing. Based on the model of productive alliances, 2,801 families from different regions became beneficiaries by linking their production with the markets. Among the funded activities, the production of honey, orchards, forage, livestock, nuts, spices, yerba mate and tea, among others, stand out.

In Benin, between 2011-2021, the Agricultural Productivity and Diversification Project facilitated the adoption of productivity-enhancing technologies for 327,503 crop producers, leading to 135,549 hectares of land cultivated with improved technologies. The project interventions resulted in increased yields from 0.45 ton to 0.81 ton for cashew; from 1.2 tons to 2.97 tons for maize, from 4 tons to 6.2 tons for rice, and from 50 tons to 70 tons for pineapple. The project led to significant increases of milled rice and fish output. Combined with support for crop production and processing, support to exports has led to increases in the export of cashew and pineapple.

For the past 18 years, Bolivia has been developing a strategy to improve agricultural production and marketing through the productive alliances model. This model links small rural producers with markets, and facilitates their participation in value chains, and access to technical assistance and technology for better market access. Currently, over 2,600 productive alliances have been implemented, benefiting 107,308 producer families. In 2023, the third phase of productive alliances model was launched, expecting to have a significant impact on nearly 130, 000 rural producer’s communities, with a focus on food security, adoption of innovative practices for resilient agriculture and the increased participation of women producers.

A Bank-supported project implemented in partnership with the Government of Rio Grande do Norte, one of Brazil's poorest and most violent states, has aimed to improve agricultural productivity, the quality of and access to health, public security, education and public sector management across the state. The project has implemented 131 subprojects in family farming, renovated 274km of roads, renovated and strengthened the safety of an important dam, and built 22 modern, multi-service Citizen Centers.

In Bhutan,  a project  is supporting the government's efforts to reduce rural poverty and malnutrition through climate-smart agriculture. Irrigation technology and greenhouses introduced through the project have helped farmers to increase their access to local and export markets. More than 6,500 people have increased the quality and quantity of produce like rice, maize, potato, vegetables, quinoa, citrus, apples, and potatoes, as well as high-value spices such as cardamom and ginger. 

In Burkina Faso, the Bank supported the Burkina Faso Livestock Sector Development Project which ran from 2017 to 2022. By project completion, beneficiaries among selected value chains increased their yield by 8.4%. Yield increase for cattle, sheep, and egg production reached 6.76%, 11.93%, and 6.50%, respectively. Sales increased by 45% exceeding the target of a 30% increase. The volume of loans granted by partner financial institutions reached $5.02 million, exceeding the original target of $4.38 million. The project reached a total of 329,000 beneficiaries, out of which 138,314 were women and 112,573 were youth.

In the Central African Republic, through the Emergency Food Security Response project, 330,000 smallholder farmers received seeds, farming tools, and training in agricultural and post-harvest techniques. The project helped farmers boost their crop production and become more resilient to climate and conflict risks. Local food production increased by 250%, from 28,000 tons in September 2022 to 73,000 tons in June 2023. Moreover, 21,006 agricultural households received training on post-harvest loss management and provided equipment, such as mobile storage units, to enhance packaging of agricultural products, leading to higher selling prices.

In  Colombia, since 2010, the adoption of environmentally friendly silvopastoral production systems  (SPS) for over 4,100 cattle ranches has converted 100,522 hectares of degraded pastures into more productive landscapes and captured 1,565,026 tons of CO2 equivalent. In addition, almost 40,000 hectares of pastureland were transformed to SPS and 4,640 hectares into intensive Silvopastoral Production Systems (iSPS). Moreover, 4,100 direct farmers beneficiaries, of which 17% were women, were trained in SPS and iSPS, and over 21,000 farmers, technicians and producers were also trained, visited demonstration farms, and participated in workshops and events and technology brigades. A network of 116 plant nurseries were also established, which produced around 3.1 million fodder trees that were delivered to beneficiary farmers. 

In Cote d’Ivoire, between 2013 and 2017, the Agriculture Sector Project  boosted the productivity of 200,000 farmers and rehabilitated 6,500 kilometers of rural roads allowing farmers to better transport their products  and reduce post-harvest losses. To aid the cashew industry, the Bank also supported a research program that helped disseminate 209 genotypes of high-performing trees and establish 18 nurseries. The Bank-financed project also helped leverage $27.5 million in private investment to boost productivity on at least 26,500 hectares.

In Ethiopia, since 2015 a project has helped 2.5 million smallholder farmers increase agricultural productivity and commercialization by establishing market linkages, increasing access to agricultural public services, building smallholder farmer capacity in efficient water and crop management to implement climate change mitigation and adaptation, and improving diet diversification. The project has also been promoting the use of nutrition sensitive agriculture and gender and climate-smart agriculture including dietary diversity through nutrient-dense crops, livestock products, post-harvest processing/handling and social behavioral change communication, along with food safety and child and maternal health. The project has supported farmers increase yield in crops and livestock by 19% and 52% respectively and their revenue by 96.2%. To date the project has also provided 58,823 hectares of land with irrigation and water related services, and over 1.6 million farmers have adopted improved agriculture technologies promoted by the project. Nearly one million jobs for rural people, including for women and youth in fragile and conflict affected areas have been created as a result of the project interventions.

In Grenada , the World Bank supported local farmers and fisherfolk, along with aggregators and agro-processors to enhance their access to markets and sales from 2017 to 2023 through the OECS Regional Agriculture Competitiveness Project. The project provided vouchers to 206 farmers and fisherfolk and offered co-financing opportunities for 10 agro-processors, leading to significant improvements in their production facilities and market access. Additionally, 260 employees and 53 extension workers received training, improving their skills in agricultural production and market reach. Through the project, 150 producers adopted various climate-smart technologies, such as solar panels and rainwater harvesting systems, underscoring the project's dedication to sustainability and efficiency.

In Guinea, from 2018 to 2023, through the  Guinea Integrated Agricultural Development Project , local farmers increased agriculture's productivity, and sustainability. To help local communities, the project disseminated high-yielding seeds, improve irrigation, and trained women and youth to access funds to create jobs. The project also promoted the use of climate-smart, gender-sensitive digital technologies with local producers. The project has reached 149,000 farmers (of whom 38% are women and 30% are youth). The project’s results include a 30% increase in yield of rice and maize; a 42% increase in commodity sales; a 47,470-hectare area covered by improved technologies; over 97,000 users of improved technologies, and more than 2,000 jobs created for women and youth.

In Haiti , a World Bank project strengthened the institutional capacity of Haiti’s Ministry of Agriculture and Rural Development by accessing technologies to increase not only agricultural productivity and production but also improved livelihoods and resilience. The project developed irrigation and drainage on 2,244 hectares; established 115 farmer field schools, and trained facilitators in agricultural extension techniques. A total of 78,242 small producers increased their market access, half of whom were women; more than 3,368 private and public sector staff (including staff from the Ministry of Agriculture, municipal staff, among others) and 600 farmers were trained on surveillance and vaccination, the use of fruit fly traps, mealybugs control, and protection of animals against rabies and anthrax and more than 3.6 million animals were vaccinated.

In Honduras, since 2010 , 12,878 small farmers, of which 27% are women, have used productive alliances to improve productivity and access to markets, which has leveraged $33.5 million in finance from commercial banks and microfinance institutions. Under the project, gross sales of producer organizations rose by 25.3%. Also, support to Honduras’ Dry Corridor Alliance, has helped 12,202 households implement food security and agricultural business plans, and improved agricultural yields, nutrition, and food diversity of project beneficiaries.

In India, the  Assam Agribusiness and Rural Transformation Project  supported over 400,000 farm families and 1,270 businesses and over 100 of industry associations and producer organizations in improving their productivity and incomes and helping develop new marketing channels since 2017.

In  Kenya, since 2016,   1.5 million farmers , where over 60% are women, have increased their productivity , climate resilience and access to markets. The digital registry (including geo tagging) of these 1.5 million farmers enables them to access agro-weather and market advisories. In addition, the Bank is facilitating partnerships between the government and 26 ag-tech support agencies which enables almost 500,000 farmers to access a range of services (inputs, financial services and markets) by leveraging digital technologies.  

In Kosovo , the Bank provided 775 grants to farmers and 103 grants to agri-processors to increase production capacities and enhance market competitiveness in the livestock and horticulture sector. This was done through upgrading facilities, adopting new technologies, and introducing food safety and environmental standards. Further, support was provided for the rehabilitation of irrigation schemes covering an area of 7,750 hectares which had an impact on the production, yield, quality, and variety of products cultivated in the area.

In the Kyrgyz Republic, the Additional Financing to the Integrated Dairy Productivity Improvement Project is improving productivity through better technologies and breeds of dairy animals rather than increasing their numbers. The project provides training, artificial insemination services, and monitoring milk yields per cow and the quality of milk to processing companies. To date, 10,000 small farmers including 5,000 women farmers, have received training to enhance productivity and climate-smart agriculture. Over 13,000 cows received artificial insemination for breed improvement with positive pregnancy rate of 67.3% which is above the global average. With improved breeds of dairy animals, the market value of the crossbred calves is higher than local calves and the average milk yield per cow has increased by nearly 15%. The project has also established a digital tool to monitor milk quality which is being used by eight dairy processing companies. The project established 30 milk collection points through famers’ Jamaats that are equipped with refrigerated tanks and advanced testing equipment, strategically located to ensure consistent milk quality and timely delivery, especially during hot summers.

In Madagascar, since 2016 , the Bank has boosted the productivity of over 130,000 farmers. Sixty-thousand hectares of irrigated rice fields have been rehabilitated. The Bank also supported the cocoa sector through research, the development of certified seeds, and promotion of improved production and processing techniques. This allowed 4,000 cocoa producers to increase their incomes and increase production and export volumes by 50%. The Bank also financed the country’s largest land rights registration, facilitating the delivery of over 200,000 land certificates to farmers. 

In Mauritania, between 2016 and 2021, the intervention of the Sahel regional support project offered agricultural assets and services to more than 400,000 farmers/pastoralists, where nearly 30% are women. More than 1.9 million hectares of land under sustainable management practices, in addition to the construction of 133 vaccination parks and the realization of 118 water points (wells and boreholes) as well as other infrastructure of valorization and trade of animals were provided to agro-pastoralist communities. Additionally, from April 2023- June 2028, the Bank offered to support the  Agriculture Development and Innovation Support Project (PADISAM)  to improve land resources management and foster inclusive and sustainable commercial agriculture in selected areas of Mauritania. It is anticipated that by the end of the project, there will be 72,000 direct beneficiaries and about 5,000 Ha of land under sustainable landscape management practices.

Following Russia’s invasion of Ukraine and the resulting spikes in wheat prices in 2022, the World Bank provided emergency support to several countries in the Middle East and North Africa to mitigate the negative socio-economic consequences on the poor and vulnerable. These emergency projects secured access to affordable bread for over 89 million people across the region. In Lebanon, a project ($150 million) has been financing wheat imports that supports universal access to affordable Arabic bread for over a year to 5.36 million people living in Lebanon, of which 1 million are Syrian, Palestinian, and other refugees. In Egypt, a project helped procure around 1.15 million metric tons of wheat – equivalent to at least a 2-month supply to cover the needs of 72 million vulnerable people. A project in In Tunisia procured 160,099 metric tons of soft wheat, equivalent to seven weeks of bread supply for a population of 12 million.

In Moldova, since 2012 , the Bank has helped more than 7,500 farmers gain access to local and regional high-value markets for fresh fruit and vegetables and boosted land productivity through the promotion of sustainable land management practices on 120,000 hectares of farmland.

In  Montenegro , the bank, through the Second Institutional Development and Agriculture Strengthening (MIDAS2), helped the government launch the very first Instrument for Pre-accession Assistance for Agriculture and Rural Development (IPARD)-like agro-environmental measure in a manner compliant with EU requirements, increasing the amount of meadows and pasture lands recorded in the Land Parcel Identification System (LPIS) from 13,600 hectares (ha) to 92,000 ha. The Bank has also supported almost 4,000 farmers working on orchards, vineyards, livestock and aromatic plants, 224 agro-processors, and 59 farmers working on processing on-farm complying with the European Union requirements for food safety and 278 agricultural households adopting agro-environmental measures, improving their competitiveness and sustainability.

In Morocco , the Strengthening Agri-food Value Chains Program for Results has financed the construction of the first modern regional wholesale market in Rabat, which will improve the distribution of agricultural products throughout the region, benefiting more than 4.6 million inhabitants. The program also financed the establishment of the male sterile Ceratite production center, which will enable citrus producers in the Souss-Massa and Berkane regions, which represent 52% of national citrus production and generate about 6 million working days per year, to protect their production from damage caused by the Mediterranean fruit fly. The program also enabled more than 1,000 agri-food SMEs to obtain sanitary approval after upgrading, leading to an increase in employment by almost 61%. The program co-financed more than 70 units of packaging, cold storage and processing, which leveraged about US$86 million as private investment and led to an overall increase in production value of around 34%.

In Niger , through  the Climate Smart Agriculture Support Project , the World Bank supported over 370,000 farmers, where 145,000 of whom are women. The farmers benefited from the project’s investments in small and large-scale irrigation, improved climate-smart agriculture, and sustainable land management practices. Over 154,000 hectares of land were developed with sustainable land management practices, and 4,400 hectares of cropland were brought under irrigation. In collaboration with the International Crops Research Institute for the Semi-Arid Tropics and FAO, the project promoted good agriculture practices through farmer led e-extension services and technical assistance. The project investments led to significant increases in agriculture productivity: yields of cowpea, millet, and sorghum increased by 169, 164, and 142 percent, respectively. The project also strengthened the national climate information system by building the capacity of the National Meteorology Department (the project installed 30 meteorological stations and 600 rain gauges). Through its support to the Sahel Regional Center for Hydro and Agrometeorology, the project strengthened the early warning systems of national institutes such as National Meteorology and the National Hydrology Directorate.

In Nigeria,  APPEALS Project   was designed to enhance agricultural productivity of small and medium scale farmers and improve value addition along priority value chains. Since 2017, the project has demonstrated 204 improved technologies to 93,0009 farmers. Food crop production has surged, with 304,516 metric tons produced, representing 3.1% of the national output. Furthermore, the project has reached 61,171 farmers with processing assets to improve the quality of their produce. The project also trained 10,346 women and youth, including persons with disability, providing them with business, technical and life skills training, support to business planning and facilitation of business name registration, start-up grant to establish a commercially viable business, and mentorship to provide the beneficiaries with continued support from established agribusiness entrepreneurs. The project linked farmers to market through the facilitation of commercial partnerships resulting in a total of 327 business alliances with 147 off-takers already buying farmers’ produce across the 11 value chains, with a transaction worth of US$ 59.7 million. Similarly, the project has linked 200 agribusiness clusters to infrastructures which includes 55km rural farm access road, 75 aggregation and cottage processing centers, 102 solar-powered water intervention and energy supplies.

In Paraguay, since 2008, 20,863 farmers  increased their agricultural income by at least 30% and 18,951 adopted improved agricultural practices, boosting the productivity of their land.

In the  Philippines, since 2015 , the Bank helped raise rural incomes, enhance farm and fishery productivity, improve market access and mainstream institutional and operational reforms, as well as science-based planning for agricultural commodities in 81 provinces. The project has benefitted a total of 323,501 people–46% of them women–with farm roads, irrigation, and agricultural enterprise projects, boosting incomes by up to 36%. 

In  Rwanda, since  2010, the Bank helped support over 410,000 farmers – half are women – in improving their agricultural production by developing over 7,400 hectares for marshland irrigation, providing hillside irrigation on over 2,500 hectares, and several hundreds of farmers benefitted matching grants to support their investments in Farmer-Led Irrigation Development (FLID) technologies on over 1,200 hectares of their land. Interventions also included improving soil conservation and erosion on more than 39,000 hectares of hillside. Maize, rice, beans, and potato yields have all more than doubled and around 2.5 tons of vegetables are exported to Europe and the Middle-East every week from intervention areas, or locally, where more horticulture produce is sold to premium markets including 5-star hotels or the national airline, RwandAir. Less than two years after  one of the Bank supported projects  introduced greenhouse farming in its intervention areas to minimize the impacts of unfavorable weather conditions and better manage crop pests and diseases, by 2023, the demand for these technologies has seen a rapid increase in these areas and 132 units have been acquired and installed through the matching grants program under the project. Evidence shows relatively high revenues for farmers investing in greenhouse technology, with revenues increasing up to 15 times for vegetable growers.

Since 2019, the ongoing Serbia Competitive Agriculture Project has been supporting the government economic diversification and competitiveness agenda for small and medium scale farmers and their participation in a more market-oriented agriculture sector. The productive alliance model supported by the project has contributed to the improvement of the agri-food market linkages of 823 farmers, of which 330 are women farmers. Through the project, 4,356 farmers have received technical assistance to prepare their business ideas and plans (1,307 are women), while 1,319 business plans have received support in various forms, such as matching grants, technical assistance, and business development support. The farmers have signed their loans with commercial banks to invest in farm innovations, including equipment, on-farm irrigation, digital agriculture, climate-smart agriculture technologies. By providing co-financing with EUR 24.17 million in matching grants, the project-supported business plans have leveraged an additional EUR 24.17 million in private capital so far, including commercial loans to farmers at market interest rate from 11 local banks, and cash contributions from the beneficiary farmers. Amongst them, 1,117 beneficiary farmers are first-time users of credit.

In  Tajikistan , the Bank supported the establishment of 545 farmer groups in horticulture value chains, specifically apricot, apple, pear, lemon, cucumber, and tomato, and dairy value chain benefiting a total of 13,516 farmers out of which 48% were women. The Bank also supported the establishment of 342 productive partnerships benefitting 4,340 smallholder farmers. A total of 21,882 beneficiaries achieved an increase in commercial activity. The project supported training for 13, 516 farmers, on value chain development.

In  Tunisia, the Bank helped 113 remote rural villages improve  land management practices on 37,000 hectares of land to increase productivity and improve 930 kilometers of rural roads serving some 160 villages. 

In  Uruguay, since 2014, climate-smart agriculture techniques  have been adopted on 2.7 million hectares and adopted by 5,541 farmers, providing for a carbon sequestration potential of up to 9 million tons of CO2 annually.

In Uganda, since 2015 , the  Agriculture Cluster Development Project’s e-voucher scheme has leveraged over $12 million of farmer investments enabling over 450,000 farm households access and use improved agro-inputs resulting in higher farm yields. Provision of matching grants has enhanced storage capacity by 55,000MT, acquiring value addition equipment and machinery thereby facilitating Producer Organizations to add value and undertake collective marketing. Additional infrastructure support addressing road chokes has also led to improved market access.

The Bank has also made investments into strengthening regulatory and administrative functions of the Ministry of Agriculture through the development of IT Platforms and tools facilitating timely planning and decision making.

In the Uganda Multi-Sectoral Food and Nutrition Security Project, the Bank has supported enhanced knowledge on nutrition resulting in improved household nutrition and incomes for 1.55 million direct project beneficiaries.

In Uzbekistan, the Horticulture Development Project has helped create, 34,520 jobs, including 13,124 for women; increase beneficiary productivity by 24% and profitability by 124%, including through entry into new export markets. The  Livestock Sector Development Project  supported a sub-loans benefitting 560 large scale commercial livestock farmers, and a total of 135 value chain development projects benefiting 1,456 smallholder farmers (Dekhans). As a result, the share of improved and high yielding livestock breeds increased by 98.7%; increasing milk and meat productivity by 33% and 38% respectively. The Ferghana Valley Rural Enterprise Project has supported the establishment and operation of nine business incubation hubs in Andijan, Namangan, and Ferghana regions, to support local entrepreneurs in business plan preparation, and facilitated access to finance, technology infusion, also organized training among 5,000 project initiators in 36 districts of Ferghana Valley. The project, under its credit line activities, financed a total of 501 investment sub-projects with $119.6 million of the project fund, of which 77.8% were for small business entrepreneurs This created substantial number of new jobs, and increased the incomes of rural enterprises,

In Vietnam, since 2010, the Bank has promoted sustainable livelihoods by helping develop 9,000 “common interest groups” comprising over 15,500 households and partnering them with agricultural enterprises. The Bank also helped  over 20,000 farmers  improve their livestock production and benefited an additional 130,000 people through capacity building in food safety. 

Under the  West African Agricultural Productivity Program , the Bank supported a research and development effort that promoted technology generation, dissemination, and support to local farming systems in 13  ECOWAS  countries. The project reached over 2.7 million beneficiaries, 41% of whom were women. It also generated 112 technologies that reached over 1,850,000 hectares.

The Yemen Food Security Response and Resilience Project has directly benefited over 1 million beneficiaries to date. The project is focusing on resilience building amidst protracted crisis – including conflict, insecurity, and climate-related shocks. The project has created around 20,000 short-term jobs and benefited over 50,000 smallholder farm households through various agricultural infrastructure improvements. The project invested in the vaccination of 11 million small ruminants and treated a similar number for parasites. In addition to building resilience, as a short-term response, the project supported 20,000 vulnerable households with kitchen gardens and livestock kits, business development training and start-up grants to vulnerable women. Furthermore, the project facilitated a supplemental feeding program for over 740,000 most vulnerable beneficiaries.

Last Updated: Apr 09, 2024

The World Bank works with a range of partners to achieve ambitious development goals: transforming food systems, boosting food security and empowering smallholder farmers, to realize zero hunger and poverty by 2030. 

The World Bank Group is a joint convener, with the G7 Presidency, of the Global Alliance for Food Security (GAFS) . A key outcome of the Global Alliance is the  Global Food and Nutrition Security Dashboard , a key tool to fast-track a rapid response to the unfolding global food security crisis, designed to consolidate and present up-to-date data on food crisis severity, track global food security financing, and make available global and country-level research and analysis to improve coordination of the policy and financial response to the crisis.

The Bank hosts a  Multi-Donor Trust Fund,  Food Systems 2030 , that helps countries build better food systems, fostering healthy people, a healthy planet and healthy economies. The Trust Fund aims to deliver improved livelihoods and affordable, and nutritious diets for all, and progress towards the Sustainable Development Goals of zero poverty and hunger by 2030 and the climate goals of the Paris Agreement. Food Systems 2030 provides advice and analytical products to underpin policy options, funds to pilot innovative approaches, and information to build support for change in different country contexts. It engages with the private sector by supporting the design, piloting and de-risking of innovative public-private partnerships that advance development and climate goals.   

The  Global Agriculture and Food Security Program , a multilateral financing platform, is dedicated to improving food and nutrition security worldwide. Launched by the  G20 in the wake of the global response to the 2007–08 food price crisis, GAFSP works to build sustainable and resilient agriculture and food systems in the world’s poorest and most vulnerable countries. Since its inception in 2010, the Program has mobilized more than US$2 billion in donor funds to reach more than 16.6 million people. GAFSP provides financial and technical resources – investment grants, technical assistance, concessional finance, and advisory services – to demand-driven projects along the food chain to accelerate the transformation of food systems at scale.

The World Bank leads the  Food Systems, Land use and Restoration Global Platform (FOLUR) , financed by the Global Environment Facility, in partnership with UNDP, the UN Food and Agriculture Organization (FAO), the Global Landscapes Forum and the Food and Land-use Coalition. FOLUR is a $345 million, seven-year program that aims to improve the health and sustainability of landscapes that produce the world’s food. FOLUR targets sustainable production landscapes in 27 country projects for eight major commodities (livestock, cocoa, coffee, maize, palm oil, rice, soy, and wheat).

The World Bank chairs the System Council of  CGIAR , a global partnership that advances cutting-edge science to reduce rural poverty, increase food security, improve human health and nutrition, and ensure sustainable management of natural resources.

For more information, contact Clare Murphy-McGreevey on [email protected].

Last Updated: Sep 19, 2023

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The importance of research in agriculture

The eighth episode of tech terrain deliberates over the role research plays in agriculture and how it can increase productivity to meet the food demand of the world..

Since the start of the agricultural revolution, the sector has been defined by research and innovation, which include technology development that is adopted throughout the value chain, comprehensive and inclusive of digital solutions.

And this also includes the very important topic of ethical and safe practices – both for industry role players, consumers and the environment.

In this week’s episode of Tech Terrain, Tony Ndoro talks to two distinguished academics – Charlie Reinhardt, Professor of Agronomy,  Faculty of Agriculture: North-West University and Prof Lise Korsten of the Department of Plant and Soil Sciences at the University of Pretoria to share their views on the value of research for the sector.

And later on in the episode, Matome Ramokgopa, Managing Director: Enza Zaden South Africa discusses the application of agricultural research on the African continent.

A word from John Deere

You can also look forward to a discussion about the John Deere 6M tractor with Admire Mutsvairo, Business Operations Analyst for John Deere Sub-Saharan Africa (JD SSA) and Hein Snyman, Territory Sales Manager for JD SSA.

Visit  techterrain.co.za  to get access to all the content plus bonus material. New episodes exploring new and relevant themes will be released every Thursday at 16:00pm.

Powered by John Deere, in collaboration with Farmer’s Weekly and Brand Republic.

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Sustainable Agriculture Research and Education in the Field: A Proceedings (1991)

Chapter: introduction, introduction.

Charles M. Benbrook

These proceedings are based on a workshop that brought together scientists, farmer-innovators, policymakers, and interested members of the public for a progress report on sustainable agriculture research and education efforts across the United States. The workshop, which was held on April 3 and 4, 1990, in Washington, D.C., was sponsored by the Office of Science and Education of the U.S. Department of Agriculture and the Board on Agriculture of the National Research Council. The encouraging new science discussed there should convince nearly everyone of two facts.

First, the natural resource, economic, and food safety problems facing U.S. agriculture are diverse, dynamic, and often complex. Second, a common set of biological and ecological principles—when systematically embodied in cropping and livestock management systems—can bring improved economic and environmental performance within the reach of innovative farmers. Some people contend that this result is not a realistic expectation for U.S. agriculture. The evidence presented here does not support such a pessimistic assessment.

The report of the Board on Agriculture entitled Alternative Agriculture (National Research Council, 1989a) challenged everyone to rethink key components of conventional wisdom and contemporary scientific dogma. That report has provided encouragement and direction to those individuals and organizations striving toward more sustainable production systems, and it has provoked skeptics to articulate why they feel U.S. agriculture cannot—some even say should not—seriously contemplate the need for such change. The debate has been spirited and generally constructive.

Scholars, activists, professional critics, and analysts have participated in

this debate by writing papers and books, conducting research, and offering opinions about alternative and sustainable agriculture for over 10 years. Over the past decade, many terms and concepts have come and gone. Most people—and unfortunately, many farmers—have not gone very far beyond the confusion, frustration, and occasional demagoguery that swirls around the different definitions of alternative, low-input, organic, and sustainable agriculture.

Fortunately, though, beginning in late 1989, a broad cross-section of people has grown comfortable with the term sustainable agriculture. The May 21, 1990, issue of Time magazine, in an article on sustainable agriculture entitled “It's Ugly, But It Works” includes the following passage:

[A] growing corps of experts [are] urging farmers to adopt a new approach called sustainable agriculture. Once the term was synonymous with the dreaded O word—a farm-belt euphemism for trendy organic farming that uses no synthetic chemicals. But sustainable agriculture has blossomed into an effort to curb erosion by modifying plowing techniques and to protect water supplies by minimizing, if not eliminating, artificial fertilizers and pest controls.

Concern and ridicule in farm publications and during agribusiness meetings over the philosophical roots of low-input, sustainable, or organic farming have given way to more thoughtful appraisals of the ecological and biological foundations of practical, profitable, and sustainable farming systems. While consensus clearly does not yet exist on how to “fix” agriculture's contemporary problems, a constructive dialogue is now under way among a broad cross-section of individuals, both practitioners and technicians involved in a wide variety of specialties.

This new dialogue is powerful because of the people and ideas it is connecting. Change will come slowly, however. Critical comments in some farm magazines will persist, and research and on-farm experimentation will not always lead to the hoped for insights or breakthroughs. Some systems that now appear to be sustainable will encounter unexpected production problems. Nonetheless, progress will be made.

The Board on Agriculture believes that over the next several decades significant progress can and will be made toward more profitable, resource-conserving, and environmentally prudent farming systems. Rural areas of the United States could become safer, more diverse, and aesthetically pleasing places to live. Farming could, as a result, become a more rewarding profession, both economically and through stewardship of the nation's soil and water resources. Change will be made possible; and it will be driven by new scientific knowledge, novel on-farm management tools and approaches, and economic necessity. The policy reforms adopted in the 1990 farm bill, and ongoing efforts to incorporate environmental objectives

into farm policy, may also in time make a significant difference in reshaping the economic environment in which on-farm management decisions are made.

This volume presents an array of new knowledge and insight about the functioning of agricultural systems that will provide the managerial and technological foundations for improved farming practices and systems. Examples of the research projects under way around the country are described. Through exploration of the practical experiences, recent findings, and insights of these researchers, the papers and discussions presented in this volume should demonstrate the value of field- and farm-level systems-based research that is designed and conducted with ongoing input from farmer-innovators.

Some discussion of the basic concepts that guide sustainable agriculture research and education activities may be useful. Definitions of key terms, such as sustainable agriculture, alternative agriculture, and low-input sustainable agriculture, are drawn from Alternative Agriculture and a recent paper (Benbrook and Cook, 1990).

BASIC CONCEPTS AND OPERATIONAL DEFINITIONS

Basic concepts.

Sustainable agriculture, which is a goal rather than a distinct set of practices, is a system of food and fiber production that

improves the underlying productivity of natural resources and cropping systems so that farmers can meet increasing levels of demand in concert with population and economic growth;

produces food that is safe, wholesome, and nutritious and that promotes human well-being;

ensures an adequate net farm income to support an acceptable standard of living for farmers while also underwriting the annual investments needed to improve progressively the productivity of soil, water, and other resources; and

complies with community norms and meets social expectations.

Other similar definitions could be cited, but there is now a general consensus regarding the essential elements of sustainable agriculture. Various definitions place differing degrees of emphasis on certain aspects, but a common set of core features is now found in nearly all definitions.

While sustainable agriculture is an inherently dynamic concept, alternative agriculture is the process of on-farm innovation that strives toward the goal of sustainable agriculture. Alternative agriculture encompasses efforts by farmers to develop more efficient production systems, as well as

efforts by researchers to explore the biological and ecological foundations of agricultural productivity.

The challenges inherent in striving toward sustainability are clearly dynamic. The production of adequate food on a sustainable basis will become more difficult if demographers are correct in their estimates that the global population will not stabilize before it reaches 11 billion or 12 billion in the middle of the twenty-first century. The sustainability challenge and what must be done to meet it range in nature from a single farm field, to the scale of an individual farm as an enterprise, to the food and fiber needs of a region or country, and finally to the world as a whole.

A comprehensive definition of sustainability must include physical, biological, and socioeconomic components. The continued viability of a farming system can be threatened by problems that arise within any one of these components. Farmers are often confronted with choices and sacrifices because of seemingly unavoidable trade-offs—an investment in a conservation system may improve soil and water quality but may sacrifice near-term economic performance. Diversification may increase the efficiency of resource use and bring within reach certain biological benefits, yet it may require additional machinery and a more stable and versatile labor supply. Indeed, agricultural researchers and those who design and administer farm policy must seek ways to alleviate seemingly unwelcome trade-offs by developing new knowledge and technology and, when warranted, new policies.

Operational Definitions

Sustainable agriculture is the production of food and fiber using a system that increases the inherent productive capacity of natural and biological resources in step with demand. At the same time, it must allow farmers to earn adequate profits, provide consumers with wholesome, safe food, and minimize adverse impacts on the environment.

As defined in our report, alternative agriculture is any system of food or fiber production that systematically pursues the following goals (National Research Council, 1989a):

more thorough incorporation of natural processes such as nutrient cycling, nitrogen fixation, and beneficial pest-predator relationships into the agricultural production process;

reduction in the use of off-farm inputs with the greatest potential to harm the environment or the health of farmers and consumers;

productive use of the biological and genetic potential of plant and animal species;

improvement in the match between cropping patterns and the productive potential and physical limitations of agricultural lands; and

profitable and efficient production with emphasis on improved farm management, prevention of animal disease, optimal integration of livestock and cropping enterprises, and conservation of soil, water, energy, and biological resources.

Conventional agriculture is the predominant farming practices, methods, and systems used in a region. Conventional agriculture varies over time and according to soil, climatic, and other environmental factors. Moreover, many conventional practices and methods are fully sustainable when pursued or applied properly and will continue to play integral roles in future farming systems.

Low-input sustainable agriculture (LISA) systems strive to achieve sustainability by incorporating biologically based practices that indirectly result in lessened reliance on purchased agrichemical inputs. The goal of LISA systems is improved profitability and environmental performance through systems that reduce pest pressure, efficiently manage nutrients, and comprehensively conserve resources.

Successful LISA systems are founded on practices that enhance the efficiency of resource use and limit pest pressures in a sustainable way. The operational goal of LISA should not, as a matter of first principles, be viewed as a reduction in the use of pesticides and fertilizers. Higher yields, lower per unit production costs, and lessened reliance on agrichemicals in intensive agricultural systems are, however, often among the positive outcomes of the successful adoption of LISA systems. But in much of the Third World an increased level of certain agrichemical and fertilizer inputs will be very helpful if not essential to achieve sustainability. For example, the phosphorous-starved pastures in the humid tropics will continue to suffer severe erosion and degradation in soil physical properties until soil fertility levels are restored and more vigorous plant growth provides protection from rain and sun.

Farmers are continuously modifying farming systems whenever opportunities arise for increasing productivity or profits. Management decisions are not made just in the context of one goal or concern but in the context of the overall performance of the farm and take into account many variables: prices, policy, available resources, climatic conditions, and implications for risk and uncertainty.

A necessary step in carrying out comparative assessments of conventional and alternative farming systems is to understand the differences between farming practices, farming methods, and farming systems. It is somewhat easier, then, to determine what a conventional practice, method, or system is and how an alternative or sustainable practice, method, or system might or should differ from a conventional one. The following definitions are drawn from the Glossary of Alternative Agriculture (National Research Council, 1989a).

A farming practice is a way of carrying out a discrete farming task such as a tillage operation, particular pesticide application technology, or single conservation practice. Most important farming operations—preparing a seedbed, controlling weeds and erosion, or maintaining soil fertility, for example—require a combination of practices, or a method. Most farming operations can be carried out by different methods, each of which can be accomplished by several unique combinations of different practices. The manner in which a practice is carried out—the speed and depth of a tillage operation, for example—can markedly alter its consequences.

A farming method is a systematic way to accomplish a specific farming objective by integrating a number of practices. A discrete method is needed for each essential farming task, such as preparing a seedbed and planting a crop, sustaining soil fertility, managing irrigation, collecting and disposing of manure, controlling pests, and preventing animal diseases.

A farming system is the overall approach used in crop or livestock production, often derived from a farmer's goals, values, knowledge, available technologies, and economic opportunities. A farming system influences, and is in turn defined by, the choice of methods and practices used to produce a crop or care for animals.

In practice, farmers are constantly adjusting cropping systems in an effort to improve a farm's performance. Changes in management practices generally lead to a complex set of results—some positive, others negative—all of which occur over different time scales.

The transition to more sustainable agriculture systems may, for many farmers, require some short-term sacrifices in economic performance in order to prepare the physical resource and biological ecosystem base needed for long-term improvement in both economic and environmental performance. As a result, some say that practices essential to progress toward sustainable agriculture are not economically viable and are unlikely to take hold on the farm (Marten, 1989). Their contention may prove correct, given current farm policies and the contemporary inclination to accept contemporary, short-term economic challenges as inviolate. Nonetheless, one question lingers: What is the alternative to sustainable agriculture?

PUBLIC POLICY AND RESEARCH IN SUSTAINABLE AGRICULTURE

Farmers, conservationists, consumers, and political leaders share an intense interest in the sustainability of agricultural production systems. This interest is heightened by growing recognition of the successes achieved by innovative farmers across the country who are discovering alternative agriculture practices and methods that improve a farm's economic and environmental performance. Ongoing experimental efforts on the farm, by no

means universally successful, are being subjected to rigorous scientific investigation. New insights should help farmers become even more effective stewards of natural resources and produce food that is consistently free of man-made or natural contaminants that may pose health risks.

The major challenge for U.S. agriculture in the 1990s will be to strike a balance between near-term economic performance and long-term ecological and food safety imperatives. As recommended in Alternative Agriculture (National Research Council, 1989a), public policies in the 1990s should, at a minimum, no longer penalize farmers who are committed to resource protection or those who are trying to make progress toward sustainability. Sustainability will always remain a goal to strive toward, and alternative agriculture systems will continuously evolve as a means to this end. Policy can and must play an integral role in this process.

If sustainability emerges as a principal farm and environmental policy goal, the design and assessment of agricultural policies will become more complex. Trade-offs, and hence choices, will become more explicit between near-term economic performance and enhancement of the long-term biological and physical factors that can contribute to soil and water resource productivity.

Drawing on expertise in several disciplines, policy analysts will be compelled to assess more insightfully the complex interactions that link a farm's economic, ecological, and environmental performance. It is hoped that political leaders will, as a result, recognize the importance of unraveling conflicts among policy goals and more aggressively seizing opportunities to advance the productivity and sustainability of U.S. agriculture.

A few examples may help clarify how adopting the concept of sustainability as a policy goal complicates the identification of cause-and-effect relationships and, hence, the design of remedial policies.

When a farmer is pushed toward bankruptcy by falling crop prices, a farm operation can become financially unsustainable. When crop losses mount because of pest pressure or a lack of soil nutrients, however, the farming system still becomes unsustainable financially, but for a different reason. In the former example, economic forces beyond any individual farmer's control are the clear cause; in the latter case the underlying cause is rooted in the biological management and performance of the farming system.

The biological and economic performance of a farming system can, in turn, unravel for several different reasons. Consider an example involving a particular farm that is enrolled each year in the U.S. Department of Agriculture's commodity price support programs. To maintain eligibility for government subsidies on a continuing basis, the farmer understands the importance of growing a certain minimum (base) acreage of the same crop each year. Hence, the cropping pattern on this farm is likely to lead to a

buildup in soilborne pathogens that attack plant roots and reduce yields. As a result, the farmer might resort to the use of a fumigant to control the pathogens, but the pesticide might become ineffective because of steadily worsening microbial degradation of the fumigant, or a pesticide-resistant pathogen may emerge.

A solution to these new problems might be to speed up the registration of another pesticide that could be used, or relax regulatory standards so more new products can get registered, or both. Consider another possibility. A regulatory agency may cancel use of a fumigant a farmer has been relying upon because of food safety, water quality, or concerns about it effect on wildlife. The farmer might then seek a change in grading standards or an increase in commodity prices or program benefits if alternative pesticides are more costly.

Each of these problems is distinctive when viewed in isolation and could be attacked through a number of changes in policy. The most cost-effective solution, however, will prove elusive unless the biology of the whole system is perceptively evaluated. For this reason, in the policy arena, just as on the farm, it is critical to know what the problem is that warrants intervention and what the root causes of the problem really are.

Research Challenges

In thinking through agricultural research priorities, it should be acknowledged that the crossroads where the sciences of agriculture and ecology meet remain largely undefined, yet clearly promising. There is too little information to specify in detail the features of a truly sustainable agriculture system, yet there is enough information to recognize the merit in striving toward sustainability in a more systematic way.

The capacity of current research programs and institutions to carry out such work is suspect (see Investing in Research [National Research Council, 1989b]). It also remains uncertain whether current policies and programs that were designed in the 1930s or earlier to serve a different set of farmer needs can effectively bring about the types of changes needed to improve ecological management on the modern farm.

In the 1980s, the research community reached consensus on the diagnosis of many of agriculture's contemporary ills; it may take most of the 1990s to agree on cures, and it will take at least another decade to get them into place. Those who are eager for a quick fix or who are just impatient are bound to be chronically frustrated by the slow rate of change.

Another important caution deserves emphasis. The “silver bullet” approach to solving agricultural production problems offers little promise for providing an understanding of the ecological and biological bases of sustainable agriculture. The one-on-one syndrome seeks to discover a new

pesticide for each pest, a new plant variety when a new strain of rust evolves, or a new nitrogen management method when nitrate contamination of drinking water becomes a pressing social concern. This reductionist approach reflects the inclination in the past to focus scientific and technological attention on products and outcomes rather than processes and on overcoming symptoms rather than eliminating causes. This must be changed if research aimed at making agriculture more sustainable is to move ahead at the rate possible given the new tools available to agricultural scientists.

One area of research in particular—biotechnology—will benefit from a shift in focus toward understanding the biology and ecology underlying agricultural systems. Biotechnology research tools make possible powerful new approaches in unraveling biological interactions and other natural processes at the molecular and cellular levels, thus shedding vital new light on ecological interactions with a degree of precision previously unimagined in the biological sciences. However, rather than using these new tools to advance knowledge about the functioning of systems as a first order of priority, emphasis is increasingly placed on discovering products to solve specific production problems or elucidating the mode of action of specific products.

This is regrettable for several reasons. A chance to decipher the physiological basis of sustainable agriculture systems is being put off. The payoff from focusing on products is also likely to be disappointing. The current widespread pattern of failure and consolidation within the agricultural biotechnology industry suggests that biotechnology is not yet mature enough as a science to reliably discover, refine, and commercialize product-based technologies. Products from biotechnology are inevitable, but a necessary first step must be to generate more in-depth understanding of biological processes, cycles, and interactions.

Perhaps the greatest potential of biotechnology lies in the design and on-farm application of more efficient, stable, and profitable cropping and livestock management systems. For farmers to use such systems successfully, they will need access to a range of new information and diagnostic and analytical techniques that can be used on a real-time basis to make agronomic and animal husbandry judgments about how to optimize the efficiencies of the processes and interactions that underlie plant and animal growth.

Knowledge, in combination with both conventional and novel inputs, will be deployed much more systematically to avoid soil nutrient or animal nutrition-related limits on growth; to ensure that diseases and pests do not become serious enough to warrant the excessive use of costly or hazardous pesticides; to increase the realistically attainable annual level of energy flows independent of purchased inputs within agroecosystems; and to maximize a range of functional symbiotic relationships between soil micro-

and macrofauna, plants, and animals. Discrete goals will include pathogen-suppressive soils, enhanced rotation effects, pest suppression by populations of plant-associated microorganisms, nutrient cycling and renewal, the optimization of general resistance mechanisms in plants by cultural practices, and much more effective soil and water conservation systems that benefit from changes in the stability of soil aggregates and the capacity of soils to absorb and hold moisture.

Because of the profound changes needed to create and instill this new knowledge and skills on the farm, the recommendations in Alternative Agriculture (National Research Council, 1989a) emphasize the need to expand systems-based applied research, on-farm experimentation utilizing farmers as research collaborators, and novel extension education strategies—the very goals of the U.S. Department of Agriculture's LISA program.

Future research efforts—and not just those funded through LISA—should place a premium on the application of ecological principles in the multidisciplinary study of farming system performance. A diversity of approaches in researching and designing innovative farming systems will ensure broad-based progress, particularly if farmers are actively engaged in the research enterprise.

Benbrook, C., and J. Cook. 1990. Striving toward sustainability: A framework to guide on-farm innovation, research, and policy analysis. Speech presented at the 1990 Pacific Northwest Symposium on Sustainable Agriculture, March 2.

Marten, J. 1989. Commentary: Will low-input rotations sustain your income? Farm Journal, Dec. 6.

National Research Council. 1989a. Alternative Agriculture. Washington, D.C.: National Academy Press.

National Research Council. 1989b. Investing in Research: A Proposal to Strengthen the Agricultural, Food, and Environmental System. Washington, D.C.: National Academy Press.

Interest is growing in sustainable agriculture, which involves the use of productive and profitable farming practices that take advantage of natural biological processes to conserve resources, reduce inputs, protect the environment, and enhance public health. Continuing research is helping to demonstrate the ways that many factors—economics, biology, policy, and tradition—interact in sustainable agriculture systems.

This book contains the proceedings of a workshop on the findings of a broad range of research projects funded by the U.S. Department of Agriculture. The areas of study, such as integrated pest management, alternative cropping and tillage systems, and comparisons with more conventional approaches, are essential to developing and adopting profitable and sustainable farming systems.

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Agruculture Lore

Why Is Research Important In Agriculture

Research has become an indispensable part of modern agriculture. It is used to solve various problems faced by the agricultural sector, from pest control to land management. Research helps farmers stay ahead of the curve, allowing them to make better decisions, use up-to-date information and make the most out of their crops and land. It’s also essential in understanding the changing climate and developing better strategies for facing challenges brought about by weather conditions.

Research in agriculture helps in the development of new methods and techniques of farming, some of which are more efficient, cost-effective, and ecologically beneficial. These new techniques, if implemented in the right way, can help increase the productivity of a farm and lower the risk to crops. Newer methods are also required in order to tackle the ever-changing pest infestations, soil erosion and other threats.

Moreover, research helps identify suitable crops for particular soils and climates, and lets farmers know which fertilizers and insecticides should be used in order to yield better crops. Research is also necessary for the development of innovative and eco-friendly farming practices, such as more efficient irrigation systems, crop rotation, and sustainable management of natural resources.

Studies are imperative for efficient farm management, as they can provide farmers and agronomists with important indicators about the status of their crops and land. For example, research can help better identify the factors that can influence the growth of crops, such as temperature, soil composition, and amount of sunlight. With this data, farmers can make calculated decisions to maximize their yield and minimize crop damage, such as selecting the best time to sow, irrigate, and harvest.

Furthermore, research in agriculture can help to preserve and improve the quality of life of those who depend on it. Studies have shown that research-based improvements in agricultural methods can lead to higher incomes for farmers and other members of the agricultural workforce. Research has also identified innovative approaches for improving nutrition, which is especially beneficial for enhancing the quality of life in rural communities.

As you can see, research is extremely important in agriculture. It’s essential for the development of efficient and profitable farming methods, as well as for preserving and improving the quality of life of those who depend on farming as their livelihood.

Current trends in agricultural research

At present, agricultural research has advanced to encompass new technologies, such as the use of satellite imagery, advanced agricultural software, and precision farming. Data collected through research is used to better understand the factors that influence the growth and health of crops. This data is also analyzed to identify areas for improvement, such as the optimal arrangement of crops, or the appropriate use of fertilizers.

Furthermore, research is also used to investigate and develop beneficial training and educational opportunities for farmers. Research findings aid agricultural organizations in developing more effective methods for promoting the modern practices of farming. The use of information gleaned from research is also critical for the success of agricultural programs implemented by the government.

In addition to these areas of research, more resources are also being devoted to studying the various genetic components involved in crop production and environmental sustainability. This involves researching and developing new genetic material, as well as gene manipulation tactics. The potential of this kind of research is massive, as it could enable farmers to produce entirely new hybrid varieties of plants, specifically designed to resist certain pests or harsh climatic conditions.

Movements like organic agriculture are further expanding the scope of agricultural research, as a lot of studies are conducted to explore new agro-ecological methods and to understand their environmental, economic, and health benefits.

The impact of agricultural research

The impact of agricultural research is felt in every aspect of agricultural life, from the use of ingenious techniques to boost production to the implementation of improved safety protocols. The use of research-based findings can lead to more efficient cultivation methods and improved product yield.

At the same time, the use of research-based information also helps to minimize risks to farmers and their crops. With access to the right information and resources, farmers can better understand and combat environmental threats and make sound decisions when it comes to preparing fields and crops.

Agricultural research also helps advance food security initiatives, as it can provide us with the data needed to identify nutritious crops and healthy livestock breeds that can help feed a growing global population. Research also enables us to expand our knowledge about the nature of agricultural products and the ways in which we can best use them.

The advancements made in research have also modernized agricultural methods and processes, with studies leading to the introduction of sophisticated machines and equipment that are efficient, reliable, and controlled by smart technologies. Automation is being increasingly used to monitor and manage crop production, from the moment of sowing to the time of harvest. Research-based solutions also enable farmers to reduce labor costs and save resources.

The potential of agricultural research

Agricultural research is essential for maintaining our access to food and other resources. As such, research plays a significant role in global efforts to achieve food security, a sustainable environment, and a healthier population.

As the world’s population and demand for food continue to increase, it is essential that we not only focus our attention on optimizing existing methods, but also direct resources towards discovering new and better ones. Moreover, research is essential for mapping out the ways in which we can help protect our planet for future generations.

Although the application of research-based solutions is still in its early stages in many parts of the world, it can already have a massive positive impact. For example, studies have already identified the use of certain plants as a form of pest control, which could have a huge impact on reducing the use of insecticides, herbicides, and fungicides.

Research has also been instrumental in helping create healthier crops and livestock, as studies have shown that it is possible to develop and promote varieties that are resistant to certain diseases and pests. This could help farmers reduce their crop losses and, in turn, improve their incomes.

Research is an indispensable tool in modern agriculture, as it is the key in helping us advance in the areas like pest control, land management, developing new crops, and improving farm management. As our technology rapidly advances, research is also essential in monitoring, customizing, and improving agricultural methods to better serve the world’s population. Additionally, agricultural research might be the catalyst in enabling us to create a sustainable, healthy, and equitable global food system.

Eduardo Villanueva

Eduardo Villanueva is an expert on agricultural sciences, with decades of experience in the field. With a passion for teaching others, Eduardo has written extensively about topics related to sustainable agriculture and food security. His work aims to empower rural farmers and promote responsible farming practices that help preserve the environment for future generations. A dedicated family man, Eduardo lives in central Mexico with his wife and children. He is always looking for ways to connect people and knowledge to create positive changes in their local communities.

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Home / Blog

Why Is Agriculture Important? Benefits and Its Role

July 12, 2022 

Tables of Contents

What Is Agriculture?

Why is agriculture important, how is agriculture important, importance of agriculture in everyday life, how does agriculture affect the economy, importance of agricultural biodiversity, why is agriculture important for the future.

When people think of agriculture, they often envision crop farming: soil and land preparation and sowing, fertilizing, irrigating, and harvesting different types of plants and vegetation.

However, according to the U.S. Census Bureau’s North American Industry Classification System (NAICS) , crop farming is just one element of the Agriculture, Forestry, Fishing, and Hunting sector. Agriculture also encompasses raising livestock; industrial forestry and fishing; and agricultural support services, such as agricultural equipment repair and trucking operations.

Why is agriculture important? It helps sustain life by providing the food we need to survive. It also contributes $7 trillion to the U.S. economy. Despite agriculture’s importance, the Economic Policy Institute reports that farmworkers are among the lowest-paid workers in the U.S.

However, agriculture also provides opportunities for economic equity and helps people prosper around the world. For example, since 2000, the agricultural growth rate in Sub-Saharan Africa has surpassed that of any other region in the world (approximately 4.3% annually), contributing to the region’s economic gains, according to the United States Agency for International Development (USAID). While there’s been a global decline in agricultural jobs — from 1 billion in 2000 to 883 million in 2019, according to employment indicators from the Food and Agriculture Organization of the United Nations — agriculture remains the second-highest source of employment (26.7% of total work).

Agriculture is the practice of cultivating natural resources to sustain human life and provide economic gain. It combines the creativity, imagination, and skill involved in planting crops and raising animals with modern production methods and new technologies.

Agriculture is also a business that provides the global economy with commodities: basic goods used in commerce, such as grain, livestock, dairy, fiber, and raw materials for fuel. For example, fiber is a top crop in U.S. agricultural production , according to The Balance Small Business, and a necessary commodity for the clothing sector.

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Agriculture impacts society in many ways, including: supporting livelihoods through food, habitat, and jobs; providing raw materials for food and other products; and building strong economies through trade. Source: The Balance Small Business.

A key to why agriculture is important to business and society is its output — from producing raw materials to contributing to the global supply chain and economic development.

Providing Raw Materials

Raw materials are a core building block of the global economy. Without access to raw materials, manufacturers can’t make products. Nonagricultural raw materials include steel, minerals, and coal. However, many raw materials derive from agriculture — from lumber for construction materials to herbs for adding flavor to food. Corn, for example, is used to produce foods and serves as a foundation for ethanol, a type of fuel. Another example is resins : plant products used in various industrial applications, such as adhesives, coatings, and paints used in construction.

Creating a Strong Supply Chain

Importing and exporting goods such as agricultural products requires shipping methods such as ocean freight, rail, and trucking. Delays in shipping agricultural products from a Los Angeles port can create problems in China, and vice versa, impacting the global supply chain.

For example, sales of soybean crops from Iowa skyrocketed in 2021 due to various factors including delays in South American crop shipments, according to the Iowa Soybean Association. In this example, Iowa benefited from a competitive standpoint. However, delays in shipping crops could also be detrimental to regions expecting shipment, limiting availability of products on store shelves and affecting livelihoods.

Encouraging Economic Development

Agriculture impacts global trade because it’s tied to other sectors of the economy, supporting job creation and encouraging economic development. Countries with strong agricultural sectors experience employment growth in other sectors, according to USAID. Countries with agricultural productivity growth and robust agriculture infrastructure also have higher per capita incomes, since producers in these countries innovate through technology and farm management practices to boost agricultural productivity and profitability.

Resources on the Importance of Agriculture

The following resources provide information about the importance of agriculture as a source of raw materials and its impact on transportation and contribution to economic development:

• American Farm Bureau Federation, Fast Facts About Agriculture & Food : Provides various statistics demonstrating why agriculture is important.
• The Western Producer, “Suddenly Agriculture Is Important ”: Highlights agriculture’s role as a stable commodity provider even amid disruption.
• LinkedIn, “What Is Agriculture and Its Importance? ”: Discusses the importance of agriculture in 10 areas.

When global supply chains are disrupted , considerable attention is given to the technology sector. For example, the lack of computer chips — made from silicon, a nonagricultural raw material — limits a manufacturer’s ability to make computers, cars, and other products. This impacts many areas of society and business.

Agriculture also plays a central role in meeting consumer and business market demand in a world with interconnected economies. Here are different types of products derived from agriculture.

Fruits and Vegetables

Fruits and vegetables are essential sources of fiber, proteins, and carbohydrates in human diets. Vitamins, such as A, C, and E, and minerals, such as magnesium, zinc, and phosphorus, are naturally occurring in many fruits and vegetables. In addition to health benefits, fruits and vegetables add flavors to the human palette.

Animal Feed

Some fruits and vegetables are grown to provide feed for animals, from poultry to livestock. The American Industry Feed Association reports that about 900 animal feed ingredients are approved by law in the U.S. These include ingredients that come from agricultural production, including hay, straw, oils, sprouted grains, and legumes.

Natural Rubber Production

The number of vehicles in the world  is more than 1.4 billion, according to Hedges & Company market research. Every single one runs on rubber tires. According to GEP, the top rubber-producing countries are Thailand, Indonesia, and Malaysia — collectively representing approximately 70% of  global natural rubber production  — and about 90% of suppliers are small-scale farmers.

Cotton for Clothing

From cotton to clothes, the journey starts with agricultural production. Cotton is grown, harvested, and then processed, spun, and woven into fabric before it becomes a piece of clothing. Cotton production encompasses an expansive global supply chain, and according to Forum for the Future , it’s a leading commodity, making up approximately 31% of all textile fibers globally.

The U.S. Environmental Protection Agency (EPA) reports favorable economics of biofuels , produced from biomass sources including agricultural products such as corn, soybeans, sugarcane, and algae. The benefits include reduced greenhouse gas and pollutant emissions and the potential for increased incomes for farmers. However, biodiesel production requires the use of land and water resources that can affect food costs.

Industrial Products

Bio-based chemistry involves using raw materials derived from biomass to develop industrial products. Different industrial products derived from bio-based chemicals include bioplastics, plant oils, biolubricants, inks, dyes, detergents, and fertilizers. Bio-based chemicals and products offer an alternative to conventional products derived from petroleum products. Bio-based chemistry is considered a type of green chemistry because it promotes the reduction of environmental impacts in industrial production.

Pharmaceutical Products

For thousands of years, humans have turned to plants to help treat what ails them. For example, ginger, a plant root typically consumed in tea, can help aid digestion. Substances derived from plants and herbs can also help in healthcare. For example, extracted chemicals from the foxglove plant are used for digoxin, a drug used for heart failure. Another example is polylactic acid (PLA), a chemical produced when glucose is fermented into lactic acid in green plants. PLA has applications in tissue engineering, cardiovascular implants, orthopedic interventions, cancer therapy, and fabrication of surgical implants, according to a study published in Engineered Regeneration .

Agricultural products provide essential resources for daily activities, such as: getting ready for work in the morning, thanks to coffee and clothes; washing hands with soap; fueling vehicles to travel; preparing and eating food; and minding health through medicines and treatments. Sources: Commodity.com, the U.S. Environmental Protection Agency, ThoughtCo, and the U.S. Department of Agriculture.

For thousands of years, agriculture has played an important role in everyday life. Before agriculture, hunting and gathering enabled humans to survive. It wasn’t until the transition to the planned sowing and harvesting of crops that humans began to thrive. Humans developed tools and practices to improve agricultural output with more efficient means of sustaining themselves. From there, innovations that created industries led to the modern era.

Today, the importance of agriculture in everyday life can’t be minimized. Without the agriculture sector, activities such as getting dressed for work and cleaning the home wouldn’t be possible. Here are examples of the agricultural products we use in our everyday lives:

• Shelter . Wood and plant-based materials, such as bamboo, can be used for indoor décor and construction materials.
• Morning routine.  Mint is often an ingredient in toothpaste, adding flavor while brushing your teeth, and the caffeine in coffee that keeps you awake is derived from the coffee bean.
• Dressing up.  In addition to cotton, clothing can be manufactured from hemp, ramie, and flax. Bio-based materials can be used to produce grooming products such as skin creams and shampoos.
• Cleaning.  Two types of chemicals used in detergents, cleaning products, and bath or hand soap — surfactants and solvents — can be produced from biomass.
• Driving to work.  Plants make it possible to get to and from work. Think of rubber (sourced from rubber trees) and biodiesel fuel, which often includes ethanol (sourced from corn).
• Entertainment.  Paper from trees enables you to write, and some musical instruments, such as reed instruments, require materials made from plants.
• Education.  From pencils (still often made of wood) to paper textbooks, students rely on agricultural products every day.

Agriculture can have a significant effect on the economy. The U.S. Department of Agriculture (USDA) Economic Research Service reports that  agricultural and food sectors  provided 10% of all U.S. employment in 2020 — nearly 20 million full- and part-time jobs. Additionally, the USDA reported that  cash receipts from crops  totaled nearly $198 billion in 2020.  Animal and animal product receipts  weren’t far behind in 2020, totaling $165 billion.

The interdependence of the  food and agriculture sector  with other sectors, including water and wastewater systems, transportation systems, energy, and chemical, makes it a critical engine for economic activity, according to the Cybersecurity and Infrastructure Security Agency (CISA).

Agriculture also impacts economic development by contributing to the overall U.S. gross domestic product (GDP), directly and indirectly. It does so through farm production, forestry, fishing activities, textile mills and products, apparel and food and beverage sales, and service and manufacturing.

• Farm production.  The latest USDA data on  farming and farming income  report the U.S. had a little over 2 million farms, encompassing 897 million acres, in 2020. Farm production includes producing fruits, vegetables, plants, and varieties of crops to meet demand for agricultural products throughout the country and abroad.
• Forestry and fishing activities.  Agricultural activities include forestry and harvesting fish in water farms or in their natural habitat.  Agroforestry is focused on “establishing, managing, using, and conserving forests, trees and associated resources in a sustainable manner to meet desired goals, needs, and values,” according to the USDA. A form of fishing activity known as  aquaculture  involves the production of fish and other sea animals under controlled conditions to provide food.
• Textile mills and products.  The  S. cotton industry  produces $21 billion in products and services annually, according to the USDA. The industry has created various employment roles, such as growers, ginners, and buyers working on farms and in textile mills, cotton gins, offices, and warehouses.
• Apparel and food and beverage sales.  Since agriculture is a business, selling products made from agricultural production is essential. A key aspect of the sales component in agriculture is to help growers build capacity and understand the market dynamics to meet the needs of customers, many of whom care deeply about Food services and eating and drinking places accounted for 10.5 million jobs in 2020, the largest share among all categories within the agriculture and food sectors, according to the USDA.
• Manufacturing.  Agricultural products contribute to the manufacturing of a huge variety of goods, including food and beverage products, textiles, cleaning and personal products, construction materials, fuels, and more. According to the USDA, food and beverage manufacturing companies employ about 1.7 million people in the U.S.

Here’s how agriculture directly and indirectly contributes to the U.S. gross domestic product: farm production, forestry and fishing activities, textile mills and products, apparel and food and beverage sales, and service and manufacturing. Sources: American Farm Bureau Federation, the Bureau of Economic Analysis, and the USDA.

Here are ways agriculture and related industries impact economic development:

Agribusiness

Agribusiness  consists of the companies that perform the commercial activities involved in getting agricultural goods to market. It includes all types of businesses in the food sector, from small family farms to global agricultural conglomerates. In the U.S., farms contributed about $136 billion to GDP (about 0.6% of total GDP) in 2019, according to the USDA.

However, farms are just one component of agribusiness. Agribusiness also includes businesses involved in manufacturing agricultural equipment (such as tractors) and chemical-based products (like fertilizers) and companies involved in the production and refinement of biofuels. USDA data reports that in total, farms and related industries contributed more than $1.1 trillion to GDP, a little over 5% of the GDP, in 2019.

The  economics of agribusiness  also entails building production systems and supply chains that help maintain a country’s economic and social stability. Through the development of organizational and technological knowledge, agribusiness plays a vital role in protecting the environment and biodiversity near farms and using natural resources sustainably.

Food Security

Food security  is central to the agricultural industry:  Sustainable agriculture  is a key to fulfilling the United Nations’s Sustainable Development Goals (SDGs), including  SDG 2 :  Zero Hunger . In addition to food security, the agricultural sector raises the incomes among the poorest communities  up to four times more effectively  than other sectors, according to the World Bank.

Job Creation

Throughout the world, agriculture plays an important role in job creation. For example, agriculture accounts for 25% of exports in developing countries in Latin America, about 5% of their regional GDP, according to a report about  the importance of agribusiness  from BBVA, a corporate and investment bank. This activity is a source of economic activity and jobs in these countries. In the U.S., agriculture and related industries provide 19.7 million full- and part-time jobs, about 10.3% of all employment.

Resources on the Economic Impact of Agriculture

The following resources highlight agriculture’s impact on the economy, from how disruption affects the business and the benefits of the sector to people’s livelihoods:

• Economic Research Service, Farming and Farm Income : Provides an overview of trends in farming and economic development statistics.
• American Journal of Agricultural Economics, “The Importance of Agriculture in the Economy: Impacts from COVID-19” : Highlights why agriculture is important based on the impact of COVID-19’s disruptions to the sector.
• Canadian Journal of Agricultural Economics, “Agriculture, Transportation, and the COVID-19 Crisis” : Discusses how transportation services that COVID-19 has disrupted can impact agricultural supply chains.

Advanced farming equipment and the increased use of fertilizers and pesticides have resulted in higher crop yields. At the same time, they’ve impacted the environment, contributing to soil and water pollution and climate change. NASA projects a 24% decline in corn crop yields by 2030, thanks to climate change. However, ensuring a healthy biodiversity can help mitigate the impact. Here are some factors to consider:

• Sustainable agriculture.  Through  sustainable agricultural practices , farmers and ranchers help ensure the profitability of their land while improving soil fertility, helping promote sound environmental practices, and minimizing environmental impacts through  climate action .
• Climate change regulation.  The agricultural sector produced about 10% of U.S.  greenhouse gas emissions  in 2019, according to the EPA. Regulation and policy changes can help promote sustainable practices in the sector and provide guidance on agricultural adaptation to address the challenges that climate change poses.
• Agriculture technology and innovation.  From temperature- and moisture-sensing devices to GPS technologies for land surveys to robots,  agriculture technology  can result in higher crop yields, less chemical runoff, and lower impact on natural resources.

Agricultural Biodiversity Resources

Find information about agricultural biodiversity and its impacts in the following resources:

• Our World in Data, “Environmental Impacts of Food Production” : Discusses how sustainable agriculture offers a path to addressing food and nutrition issues.
• IBM, “The Benefits of Sustainable Agriculture and How We Get There” : Addresses how artificial intelligence (AI) and analytics technologies help farmers maximize food production and minimize their environmental impact.
• S. Environmental Protection Agency, The Sources and Solutions: Agriculture : Explains how agriculture can contribute to reducing nutrient pollution.
• FoodPrint, Biodiversity and Agriculture : Provides answers to what it will take to preserve the health of the planet to safeguard our own food supply.
• Brookings, “What Is the Future of Work in Agri-Food? ”: Discusses the future of agricultural automation and its impact on work.

Agriculture offers an opportunity to improve the lives of millions of food-insecure people and help countries develop economies that create jobs and raise incomes. Today’s agriculture also impacts future generations. To ensure the long-term success of the global agricultural sector, building a more sustainable economic system aligned with the U.N.’s Sustainable Development Goals is a crucial imperative to help create a more equitable society.

Infographic Sources

American Farm Bureau Federation, “Farm Contribution to Agricultural GDP at Record Low”

Bureau of Economic Analysis, “Gross Domestic Product (Third Estimate), Corporate Profits (Revised Estimate), and GDP by Industry, Second Quarter 2021”

Commodity.com, “Learn All About Agricultural Commodities and Market Trends”

Environmental Protection Agency, Commonly Consumed Food Commodities

The Balance Small Business, “What Is Agricultural Production?”

ThoughtCo, “List of Medicines Made From Plants”

USDA, Ag and Food Sectors and the Economy

USDA National Agricultural Library, Industrial, Energy, and Non-food Crops

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• v.111(6); Nov-Dec 2014

Why We Need GMO Crops in Agriculture

Within 35 years (2049) the global population will reach an estimated nine billion people. This presents a massive challenge to agriculture: how do we feed all of these people with nutritious food in a sustainable way?

Presently the yields of most major crops are stagnating while the demand for food, both grain and animal protein, is growing. To meet the challenge of improving yields requires a constant commitment to generating a steady supply of improved cultivars and lines for all major crops. Conventional breeding cannot keep pace with what is required; to meet the targets biotechnology and the production of genetically-modified (GM) crops is filling the gap. However, there are still concerns as to the safety of GM crops for human consumption and the environment. In this review I explore the need for GM crops, the way they are produced, and their impact and safety.

The future is very promising for GM technologies to meet the future global needs for food feed and fiber in a sustainable and responsible way. GM crops are only one part of the solution. To meet the targeted yields, nutritional quality, and sustainable production, we need all of the tools at our disposal including conventional and organic food production systems.

Introduction

In August of 2013 anti-GMO (Genetically-Modified Organisms) activists destroyed the Philippine Department of Agriculture’s field trials of Golden Rice, a rice variety genetically-modified to deliver high levels of β-carotene in the seed (See Figure 1 ). Within the scientific community there was a rapid and unprecedented condemnation of this action, led by a widely signed petition and a strongly worded letter to science in support of GMOs by a cadre of highly respected and prominent scientists. 1 This outrage and widespread condemnation by the scientific community had not been forthcoming following the many similar destructive acts perpetrated on research fields involving GMOs. However, the primary reason for such a vigorous response was that “Golden Rice” ( http://www.goldenrice.org/ ) was a community supported effort to meet a critical humanitarian need. β-carotene is a precursor of vitamin A, an essential component of rhodopsin the fundamental light absorbing pigment in the human eye. A chronic deficiency of vitamin A in the diet leads to blindness and a compromised immune system. It is an all too common affliction in the world’s poverty stricken and malnourished, claiming the sight of half a million children a year and the lives of almost half of them. According to a recent study 2 vitamin A supplements reduce the mortality rate in children aged six months to five years by 24% and deliver a large reduction in poor vision and blindness. Golden Rice was envisioned as a non-commercial venture to deliver a cheap and effective (easy to distribute and deliver) dietary source of vitamin A for areas of the world where rice is the staple and often the main source of nutrition. Golden Rice, developed by the research teams of Ingo Potrykus and Peter Beyer, has taken 25 years to reach the point where field trials can be undertaken. Nearly all scientific and regulatory hurdles have been successfully navigated. This effort took an unmatched partnership between public and private sectors to fund and required private concerns to agree to release the intellectual property rights free of charge for the many patented components involved in the gene constructs.

Golden Rice (far right, yellow color) was envisioned as a non-commercial venture to deliver a cheap and effective (easy to distribute and deliver) dietary source of vitamin A for areas of the world where rice is the staple. Source: Wikipedia

Why was the reaction to the Golden Rice incident limited to the scientific community? The answer to that question is a complex one, but at its root is a lack of understanding of both Genetically-Modified Organisms (GMOs) as they pertain to crops and the food supply and the depth of the problem that agriculture faces over the next two decades and beyond. I have deliberately focused on agriculture and plants in the preceding sentence because GMOs have been fully accepted in the medical arena. Recombinant proteins are widely used to develop effective treatments of a variety of diseases and ailments and there has been no effort to ban them or to vilify the practice of producing them. The prime example of this is the use of genetically-modified bacteria to produce human insulin 3 widely used in the treatment of diabetes. Biopharmaceuticals, the products obtained from the use of GMOs, were well established in the 1980s 4 and have since been fully accepted. Their benefits and risks are well understood. GM crops have not experienced this widespread acceptance and remain controversial for many people and advocacy groups. 5

The Need for GMOs

Before I discuss GM crops, how they are produced, what GM crops are currently grown and will be available in the future, I think it is important to understand why there is such a commitment to developing them.

At the time of writing, the global human population is approximately 7.15 billion according to the U.S. Census Bureau population clock ( http://www.census.gov/popclock/ ). The United Nations predicts, depending upon which growth model is used, that by 2030 (only 16 years from now) the global human population will be between 8.9 (high) to 7.9 (low) billion, and by 2050 somewhere between 10.9 and 8.3 billion (See Figure 2 ). The majority of the population growth will occur in what are now designated as developing countries ( http://www.landcommodities.com/farmland-supply-and-investment-fundamentals/ ). The U.N. Food and Agricultural Organization (FAO) reported that in 2012 a total of 868 million people were suffering from hunger and malnutrition, just over two-thirds of which (563 million) live in Asia and the Pacific and a quarter (234 million) in Sub-Saharan Africa. 6 Although these figures have declined from the 1,000 million people level recorded in 1990, there is still a long way to go. Consider that the death toll from hunger and malnutrition, a curable condition, is greater than “for AIDS, malaria and TB combined” ( http://www.wfp.org/stories/what-need-know-about-hunger-2012 ). This is a complex issue with many socioeconomic and political ramifications. A major factor that drives any realistic solution is the need to match the rate of increase in global demand with rate of increase in yields of staple crops (primarily grains), feed, and livestock (including fish). Tilman et. al 7 concluded that to provide sufficient food to cope with the increase in the global population, agricultural production would have to double by 2050. Even the more conservative FAO estimates that agricultural production must increase by at least 60% globally (77% in the developing economies) in the same time frame. 6 In practical terms, if we focus on just the major global crops: maize, wheat, rice, and soybean (66% of calories in the “global” diet) this would require an annual increase in yield of 2.4%. 8 On a global level the current rates of increase for these four crops are 1.6% for maize, 0.9% for wheat, 1% for rice, and 1.3% for soybean which is significantly less that what is required. 8 On a regional level there are areas of the world that will double agricultural yields by 2050, primarily in regions where population growth is somewhat stable. There are large portions of the world that will not be able to come close to such a goal even for one of the four major crops. Historical analyses of yield data from over 13,500 political units across the globe reveal areas where current yield gains have stagnated or declined under the current agricultural production systems. 9 , 10 To reverse these trends and to achieve the necessary yield gains on a yearly basis is a daunting task. It will require both an ongoing improvement in the genetics of our major crops (and livestock) and how we manage our cropping and animal production systems. These improvements will have to be tailored to regional and local needs and environments as well as ensure that the agricultural systems we put in place are sustainable. Such a task will require all of the tools we have at our disposal and the development of new ones.

Predictions of future global population growth.

Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2012 Revision, http://esa.un.org/unpd/wpp/index.htm

The problems we face are compounded by several complicating factors foremost of which is the finite amount of arable land that we have available for agriculture. The FAO baseline scenario predicts that by 2050 there will be approximately 0.18 hectares of arable land available for food production for each person on the planet, down from the current 0.242 hectare value. 11 The consensus of opinion, formulated from this FAO study is that the global yield increases required to meet future demands must be obtained from the same area of land that is currently under cultivation. Without the ability to farm more acreage yield increases must come from genetic improvement or greater agricultural inputs (fertilizer, water, and pest/weed management). The baseline scenario does not take into account additional demands on the land for biofuel feedstock production or possible alterations in land use driven by urbanization, desertification, salinization, and soil degradation. Also the increase in demand for animal protein in the diet alters land use from crops to pasture as prosperity comes to developing economies. All of these factors have the potential to further reduce the amount of arable land available for food production. Putative global climate change scenarios and water resource problems further complicate the task of maintaining arable lands for agriculture. These factors also add specific challenges to crop improvement through genetics and improved cropping systems as they directly affect crop yields rather than simply limit farmable acreage. 12

To meet the challenge of improving crop yields each season requires a constant commitment to generating a conveyor belt of improved cultivars and lines for all of the major crops. Such a commitment has been in place since organized breeding programs were established in the 18 th century even though genetic principles were not yet understood (pre-Mendel). Conventional breeding programs were able to sustain and advance yields for the staple grain crops to keep up with the demand for food in the developed countries of Europe and North America. In the late 1950s and early 1960s large areas of Asia were facing widespread famine. Fortuitously, in the 1940s a very insightful plant breeder, Norman Borlaug, had seen the danger of such a catastrophe occurring in Mexico and had initiated a breeding program for high-yielding and disease resistant wheats. His success in doing so, coupled with the development of new mechanized agricultural technologies and cropping systems, resulted in the aversion of famine in Mexico and allowed the country to becoming a net exporter of wheat by the early 1960s. 13 Borlaug, through his tireless advocacy and the foresight of Asian governments, was able to translate his success with wheat to the development of a high-yielding disease resistant rice cultivar, IR8, which was quickly adopted. Mass famine was averted and Borlaug was credited with saving a billion lives by his breeding and advocacy efforts. He was awarded the Nobel Peace Prize in 1970. It is clear that we are currently facing a similar crisis to the one that Borlaug saw coming in the 1940s. It is also clear that conventional breeding, as practiced in the 20 th century, will not be the entire solution this time around.

Conventional breeding relies on the introduction of new traits/genes into existing cultivars or commercial lines by sexual crosses i.e. crossing of one parental line with a second parental line that is expressing the desired trait (disease resistance, drought tolerance etc). Such a cross results in progeny that have inherited a complete set of genes from both parents so that although they have inherited the desired trait they have inherited a multitude of others, some of which may not be desirable and may reduce yield (a phenomenon called yield drag). To reduce yield drag breeders select progeny that best express the desired trait and cross it back to one of the parent plants in order to dilute out the negative traits inherited in the first cross (backcross). Through several iterations of this backcrossing scheme breeders eventually end up with a high yielding line that carries the desired trait. To achieve this requires many generations and several years (10 to 15years for wheat depending on the starting material) before lines can be tested in an agronomic setting or, as in the case for corn, used as a parental line in the production of commercial hybrids. Conventional breeding is also limited to what genetic variation is available in the gene pool of the crop or in a close relative that is sexually compatible. The search for genetic variation (gene variants) that can impact yield and productivity becomes more and more difficult and the incremental increases in yield become smaller and smaller with time. Yield is a complex phenotype and is the sum of the activity of a multitude of genes and rarely lends itself to rapid yield gains. Norman Borlaug’s lines dramatically altered crop yields not only by increasing the number of seeds per plants but also by adapting the plants to mechanized and high density cropping systems. Modern conventional breeding programs use varieties that are well adapted to modern production agriculture and thus yield gains based solely on plant performance are not as dramatic as those witnessed in the “Green Revolution” ( http://en.wikipedia.org/wiki/Green_Revolution ).

Modern breeding programs are also focused on nutritional and compositional qualities of the final product, whether it be grain, bean, fruit, or vegetative outputs. Variation can be increased using mutagenesis (chemical or radiation) but this is not selective and introduces many genetic changes into the crop and extends the breeding timeline. Currently our efforts fall short of the desired target yield increase rates (2.4% per year) if food production is to keep pace with the growing population. Modern breeding efforts are starting to be driven by molecular and genomics driven technologies, such as marker assisted breeding and genotyping-by-sequencing. These promise to dramatically reduce breeding timelines and fuel the rapid discovery of here-to-with untapped genetic variation. Thus although there are limitations, conventional breeding has a major role to play.

If we are to succeed in doubling global agricultural production for both crop and animal food commodities we need to be able to reduce the production timelines for both plant and animal breeding programs and to introduce new sources of genetic variation that improve yield potential, nutrition, and lower yield losses from disease and environmental factors such as changing climate and soil depletion. This is where biotechnology and the development of Genetically-Modified Organisms comes into its own and along with conventional breeding, molecular and genomic assisted crop/livestock improvement and novel genetic modification technologies, may be the vital tools that gets us to the critical goal of sustainable global food security. GMO technologies offer more rapid crop improvement, novel genetic strategies for crop improvement, and the ability to use genes from all sources regardless of origin from within the tree of life.

For the remainder of this review I will be concentrating the narrative on GM crops rather than the more universal use of the term GMO. For information regarding GM farm animals and fish I refer the reader to the recent review by Forabosco et al. 14

What Are GMOs?

The term Genetically-Modified Organism is amorphous and somewhat imprecise. All of our crops and livestock are GMOs in that their genetics have been manipulated and designed by man over the last 10,000 years or more. This has occurred to such an extent that most barely resemble their wild progenitors. The majority could not compete or survive long outside of an agricultural setting. The FAO and the European Commission define a GMO, and the products thereof, as being plants or animals that are produced through techniques in which the genetic material has been altered in a way that does not occur naturally by mating and/or natural recombination. Although this is a closer description of what is meant in the general usage of the term GMO, it would also encompass several crops that have long been accepted as conventional, e.g., Triticale. Triticale is a grain crop commonly used in bread and pasta that was developed to offer a more nutritious food source (higher protein and low gluten). It is totally “man-made.” It was first developed in the laboratory in 1884 by crossing wheat with rye to form a sterile hybrid which would not survive in nature. To produce the crop, fertility had to be restored, and this was achieved by chromosome doubling to form a stable polyploid plant with two copies of each of the parental genomes (rye and wheat). 15 This was achieved in the late 1930s using in vitro culture technology and treatment of embryos with the chemical colchicine, which interferes with the normal process of cell division (mitosis) to generate polyploid cells. Clearly, this is a crop that would fit the FAO definition of a GMO but it is not designated as such. Perhaps a better definition would be a modification to The Cartagena Protocol 16 definition for “living modified organisms,” which would then read, “Genetic Modified Organism” means any living organism that possesses a novel combination of genetic material obtained through the use of modern biotechnology.

I would suggest something new and less obtuse such as Biotechnologically Modified Organism (BMO) but for the sake of convention I will stick to the use of GMO as defined in the aforementioned modified Cartagena Protocol definition.

How Do We Produce GM Crops?

To transfer genes into a crop plant to generate a GMO, (a transgenic plant) generally requires a two-step process:

• Successful delivery of the gene into a plant cell(s), called transformation, and
• The regeneration of a transgenic plant, primarily in tissue culture, from the transformed cell(s).

The transferred gene, termed a transgene, is usually engineered to control when and in what tissue it is expressed so that the maximum benefit can be realized. Gene delivery into plant cells is generally achieved in one of two ways: either direct transfer of “naked” DNA or indirectly using a bacterial vehicle, the “natural genetic engineer,” Agrobacterium tumefaciens . Efficient regeneration protocols have been developed for almost all crop species but they will not be discussed here.

The direct delivery of DNA into plant cells leading to transformation has been achieved in several ways, electroporation into protoplasts (plant cells minus the rigid cell wall), microinjection, chloroplast transformation, silicon-carbide slivers (coated in DNA), mesophorus silica nanoparticles, and microparticle bombardment. 17 By far the most common and widely used technique for direct DNA transfer is particle bombardment. Microparticle bombardment, also known as biolistics or the “gene gun”, was first developed by Sanford in the late 1980s 18 using pressurized helium to fire gold or tungsten microparticles (diameter between .5 and 1.0 μm) coated with the engineered gene of interest as naked DNA into the plant tissue at high velocities. The pressure used to project the microparticles varied depending upon the target tissues but could go up as high as 2,200 psi: the higher the velocity of the particles, the deeper the penetration into the target tissue. The primary targeted tissues were embryonic tissues from the seed or meristems. The engineered gene was delivered as a high copy number plasmid (a circle of DNA capable of replicating in a bacterial host during the engineering process) and once in the cell was capable of integrating into the plant genome, often in multiple copies. Although the equipment has become more sophisticated and the microprojectiles have changed with time, microparticle bombardment still operates on the same principles as the original Sanford “gene gun”. Microparticle bombardment has been successfully used to produce transgenic plants in a wide-range of crops including all of the cereals, some tuber crops, and trees. It has the advantage over other methods in that it can be used to transfer large DNA fragments and has even been used to transfer whole chromosomes and multiple independently engineered genes at the same time. 19

Although micropartical bombardment has been successful, its use is greatly surpassed by the use of Agrobacterim tumefaciens in the commercial realm. A. tumefaciens is a soil bacterium that infects plants, generally where stem and roots meet (known as the crown in gardening terms), and alters the genome of the plant by inserting genes that cause cell proliferation. The cell proliferation forms a mass of cells (a gall or tumour) within which the bacteria live. Not only does A. tumefaciens “instruct” the plant to form a gall (crown-gall disease), it also provides the cells with genetic information to make opines, modified amino acids that the bacteria use as a nutrient source. It accomplishes this natural genetic engineering via a large tumour-inducing plasmid (Ti plasmid) that contains a section of DNA known as T-DNA (for transfer DNA). The T-DNA is flanked by two small (25 base pairs) direct repeats, known as the right (RB) and left border (LB) sequences, that act as insertion signals for the T-DNA transfer into the plant genome. The T-DNA contains genes that encode enzymes that synthesize plant hormones, auxin and cytokinin, that cause cell proliferation and tumour formation along with the genes that encode the enzymes for opine metabolism. The rest of the Ti plasmid, along with the bacterial chromosome, contains the virulence genes that control the ability of the bacteria to infect the plant tissue and to transfer the T-DNA into the nucleus of the target plant cell. A complete description of the mechanism by which A. tumefaciens infects plant tissues, transfers the T-DNA into the cell, and incorporates it into the plant genome can be found in Barampuram and Zhang. 17

The exploitation of A. tumefaciens as a possible means to insert a novel gene in to plants was first recognized in the late 1970s, 20 but the real explosion in the field occurred in the early 1980s with the advent of binary vectors. Binary vectors separated the T-DNA region onto a separate smaller plasmid away from the rest of the Ti plasmid, which remains as a separate vector within the Agrobacterium cell. The smaller binary vector was engineered so that it could replicate in both A. tumefaciens and E. coli , which greatly facilitated the construction and insertion of the target genes of interest between the RB and LB of the T-DNA. 17 Replacement of the T-DNA with engineered genes capable of expression in plant cells, “disarmed” the binary vector so that the infection and transfer of DNA to the plant by the bacteria no longer resulted in cell proliferation and opine biosynthesis.

Little has changed in this part of the process since these early days and we still use only a limited number of binary vector systems, more focus has been placed on increasing the efficiency of the transformation and new ways of introducing Agrobacteria to plant tissues. For the former the most successful development has been the introduction of explant wounding techniques that deliver A. tumefaciens deep into a wound site to promote closer contact of the bacteria to transformable plant cells. The “dip-wounding” technique for soybeans where explants are wounded with a blade covered in A. tumefaciens prior to placing them in a suspension of A. tumefaciens for cocultivation in tissue culture, increased transformation efficiency ten-fold. 17 The most promising of the latter efforts has been the development of methods that target the delivery of A. tumefaciens to transform developing ovules in situ , as a means of avoiding the need for regeneration of whole transgenic plants in tissue culture. Such an approach, known as the floral-dip method, has long been used to transform the model plant Arabidopsis thaliana for research purposes. More recently variations on this established method have been used to transform wheat, pakchoi and rape/canola by vacuum infiltration of flower buds, sorghum, corn, cotton, and wheat by application of A. tumefaciens directly to the pistil. 21

In generating transgenic plants (GMOs) the binary vector systems contained selectable markers, genes whose products allowed for the selection of transformed cells of the target tissue, and the tissues that were regenerated from them, away from non-transformed cells/tissues. These selectable marker genes were located along side the gene of interest within the T-DNA. The most commonly used selectable marker genes are those that infer antibiotic resistance or herbicide tolerance. 17 The inclusion of these selectable marker genes within the transferred DNA was considered to pose an unacceptable added environmental risk 22 , 23 and their elimination became a commercial priority.

There are a multitude of techniques, or in development, that have been mobilized in this effort. 23 The most effective and successful of these has been the use of site-specific recombinases. Site-specific recombinases are enzymes, common in bacterial and some eukaryotes such as yeast, which catalyze recombination of DNA between two enzyme specific recognition sites (short inverted repeat sequences). The two most common site-specific recombinases in use are the CRE- LOX and the FLP- FRT systems (the first triplet of letters designates the enzyme and the second triplet the recognition sites). The recombination activity that these enzymes catalyze is guided by the orientation of the recognitions sites, if they are oriented in tandem (in the same direction) the DNA between the two sites is excised, and if in opposite directions (facing each other) the DNA is inverted. The strategy for using these recombinases in producing marker-free GM crops (See Figure 3 ), in this example using CRE- LOX , involves:

The CRE-LOX system for removal of antibiotic marker genes from GM plants.

Each block represents the strands of the genomic DNA of the plant. The checkered boxes represent the recognition sites (LOX) for the CRE recombinase.

• engineering LOX sites (in tandem) either side of the marker gene within the binary vector gene construct;
• expression of the CRE recombinase enzymes in the transgenic plant or tissue after selection; and
• segregation of the marker free transgenic plants from the progeny for development of the GM crop.

These and other technologies to remove the antibiotic marker genes have been deployed even though there is no evidence that such genes pose a risk to either animals, including us, or microbial communities within the soil. The amount of DNA encoding the antibiotic gene that is ingested by an animal or us is an extremely small component (approximately 0.00005%) of the 0.1 to 1g of DNA a day consumed in a normal diet 24 , 25 and is subject to degradation by the same digestive processes as all DNA. The perceived risk for ingested antibiotic maker genes is that they might be incorporated either into the DNA of the animal ingesting the DNA or by microbes in the gut thus rendering them antibiotic-resistant. DNA is generally cleaved into very small fragments during digestion (and food processing) making it even less likely that a whole gene remains intact. This is true for all ingested DNA and so the likelihood that any gene is incorporated into the genome of the animal or the microbes in the gut is highly unlikely. 26 It is also well documented that the antibiotic resistance marker genes pose no health risk to humans or livestock, and that they are naturally present in environment and in gut flora, 27 so should such an unlikely gene transfer event into the genome of a gut microbe occur it would be of little consequence. To date there is no evidence that DNA absorbed through the intestines following ingestion can be integrated into the germ line of either humans or livestock.

Impact and Safety of GM Crops

It has been thirty years since the first genetically engineered plants were generated, and it has been eighteen years since the first introduction of a transgenic crop into U.S. agriculture. Since their emergence the acreage planted with GM crops has steadily increased such that in 2013, 433 million acres (175.2 hectares) of land were dedicated to their production, 56% of which were grown in developing countries. 28 As of 2013, a total more than four billion acres of GMA crops have been grown in 27 countries world-wide, primarily in corn, soybean and cotton, although new crops are being introduced at an increasing rate. The economic benefits of the deployment of these crops have been substantial. Mannion and Morse 29 report that on a global level, from 1996 to 2006, GM crops increased farm income by $40.7 billion, occurring in both developed (47%) and developing agricultures (53%). In the following six years (as of 2012) the global increase in farm income from GM crops had almost tripled that of the previous 10 years to reach $116 billion. 28 , 30 Both studies estimate that 42% of this economic gain is derived from the increased yield associated with lower weed and pest damage as well as superior genetics. The remaining 58% accrued from a decrease in production costs (decreased herbicide and pesticide costs and a reduction in tillage). These figures indicate that the underlying agronomic benefits derived from GM crops are equally impressive: with a global yield increase of 377 million tons from 1996 to 2012. In 2012 the increase in yield attributed to GM crops for the U.S. was 47 million tons. 28 , 30 Brooks and Barfoot 30 estimate that to attain an equal yield increase to that delivered by GM crops between 1996 and 2012, an additional 303 million acres (123 million hectares) of conventional crops would have been required. As James 28 postulates that to attain this extra land industrial nations would have to use marginal lands that are generally characterized by poor soils (requiring substantial inputs such as fertilizer and irrigation) and developing countries would primarily target tropical forests. Certainly such an added conversion of land to agricultural purposes would have serious ecological and environmental impacts regardless of what part of the world it is acquired.

It is well documented that the use of biotechnology is having an impact on the alleviation of poverty and to hunger in those developing countries, especially China and India (if one can still classify these two as developing), where development and deployment of GM crops has been adopted. 28 , 29 , 31 Economic gains are being translated into improving agriculture-based economies and higher and more stable yields are alleviating some of the concerns about food security.

GM crop production is a vital tool in the “agricultural toolbox” and along with advances in the development of the new genomics based genetic technologies that improve conventional crop production it may be realistic to expect to meet the aforementioned lofty goals. Organic crop production technologies, although generally delivering lower yields than conventional crops, 32 have an important role in yield improvement and stability efforts in areas where these technologies are optimal. To abandon any one of these efforts would be unwise and potentially catastrophic, especially without sound scientific reason, as agricultural production systems are complex and changing, more so now than ever before, as global climate change alters the “farming landscape.”

There are those that are adamantly opposed to the adoption of GM technologies in agriculture (though not in medicine) as a means of increasing yields and improving nutrition, and thus removing this key tool from the toolbox. The reasons for this opposition are complex and multifaceted, but from what is articulated and communicated by those who oppose GMOs, they are based on the perception that such crops pose an unacceptable risk to both human health and the environment. Such sentiment exists even though there have been no adverse health or environmental affects from the almost four billion acres of GMO crops grown since their introduction in 1996. Several National Research Council committees and European Commissions (as well as joint commissions) have concluded that with the extensive scientific inquiry into the safety issues surrounding the adoption of GM crops, genetic engineering using biotechnology is no different from conventional breeding in terms of unintended consequences to the environment or animal and human health. 33 The European Commission funded more 50 research programs from 2001–2010 to address concerns regarding the use of GM crops to reach this same determination. 34 Nicolia et al. 24 constructed a database of 1,783 scientific original research papers, reviews, relevant opinion articles, and reports published between 2002 and October of 2010 on GMO safety issues, and reviewed the contents to generate a comprehensive overview of the accumulated knowledge. The overall conclusion of this mammoth undertaking was that “the scientific research conducted so far has not detected any significant hazards directly connected with the use of GM crops.

At the present time, two types of GM crops dominate GMO crop plantings: 30

• herbicide-tolerant crops, primarily glyphosate (Roundup TM ) resistant, that express enzymes that are unaffected by the herbicide and thus bypass the native susceptible protein (5-enolpyruvoyl-shikimate-3-phosphate synthetase (EPSPS) in the case of glyphosate) or enzymes that degrade the herbicide; and
• insect-resistant crops, almost exclusively crops expressing the insecticidal crystal proteins (CRY) produced by Bacillus thuringiensis (Bt) a soil bacterium.

In both cases, the aim was to improve yields by limiting losses due to competition from weeds and damage from insect pests, reduce input costs for the farmer by better crop management, and to reduce both herbicide (by reducing the need for multiple sprayings) and insecticide use. As much of the debate as to the safety and impact of GMOs is focused on these two classifications, a detailed look at the adoption of these technologies and associated outcomes will serve to highlight some of the issues that fuel the ongoing debate.

Herbicide Tolerance

Herbicide-tolerant GM crops have been widely adopted in the U.S., such that >90% of corn, soybeans, and cotton are GM and herbicide-tolerant 28 and as other countries adopt GM technologies, the amount of acreage planted with herbicide-tolerant GM crops will continue to grow. In Canada 98% of the canola crop is GM. The perceived issues with herbicide-tolerant crops relate to the development of herbicide-resistant weeds (so called “superweeds”), transgene transfer (gene flow) to wild relatives or non-GMO crops close by, and environmental/ecological concerns that relate to biodiversity and chemical usage. All of these issues actually predate the adoption of GM crops. Herbicide resistant weeds have long been an issue for countries that rely on herbicides for weed control. 35 Gene transfer is not a unique property of GM crops and is equally an issue with herbicide tolerance (or any other trait) that is developed through conventional breeding methods. 36 Herbicide use, as is true for agriculture in general, has environmental and ecological consequences even for crops derived by conventional breeding programs. These considerations should be taken into account in any risk assessment for GMO crops.

Gene flow between closely related plant species (wild or cultivated) is a natural phenomenon that has been difficult to document and study but is known to occur in both directions. 37 It is somewhat ironic that the transgenes inserted into GM crops are ideal markers for documenting and studying this process. 38 The transfer of a transgene from a GMO crop to a wild relative depends on several factors: the reproductive strategy of the crop (open or self-pollination), the proximity of sexually compatible wild relatives, and the fertility/fecundity and fitness of the resultant hybrid. The fertility/fecundity and fitness of the hybrid is the controlling factor in establishing the presence of the transgene in the population of the wild relative of the GMO crop. To date, even with the large acreage of GM crops, this has only been observed in a small number of cases and only in the U.S. and Canada. 37 The glyphosate tolerance transgene from GM grasses grown in a field trial in Oregon escaped and has been incorporated into native creeping bentgrass populations 39 and the establishment of GM canola (rapeseed in the U.S.) along trucking routes in North Dakota has led to transgene transfer into non-GMO “feral” canola in these locations. 38 As far as can be determined there is no evidence that the establishment of the herbicide tolerance gene in these populations has had a detrimental effect and mitigation strategies have been identified. 40

Transgene flow from a GMO crop into a neighbouring field of an identical non-GMO crop is a problem for organic farming where registration as a non-GMO crop relies upon the lack of a transgene. This is also true for conventional farming operations that wish to take advantage of the non-GMO market. There are strategies to prevent this from occurring but as of yet they have not been deployed. 41 , 42 , 43 Prevention of this occurrence remains a crop management problem. Coexistence strategies for many crops have been investigated and deployment is driven not only by a scientific or social compulsion but also by economic feasibility factors. 24 These strategies include separation by a distance that negates pollen flow from one crop to another, harvesting practices that reduce residual seed accumulation, and transportation and other post-harvest containment practices. All of these present an economic challenge for producers where coexistence is desired.

Although herbicide tolerance in weeds resulting from transgene flow from a crop is rare and limited to a small number of crops and related weeds (and does not occur when the crop and weed are sexually incompatible), the development of herbicide tolerant weeds in agricultural settings is becoming a problem. The widespread adoption of glyphosate resistant GM crops in the U.S. and the reliance of upon the use a single herbicide for weed control established a strong selection pressure for weeds that have natural herbicide tolerance genes. This would occur whether or not the herbicide tolerance in the crop is GM or conventionally bred: as documented for the toxic herbicide atrazine for which GM derived resistance has not been employed. 44 The over reliance on a single herbicide as the main strategy for weed control will eventually limit the usefulness of both the herbicide and the tolerant GM crop. 45 There is a broad consensus in the agricultural scientific community that over reliance on a lone herbicide strategy is not sustainable. The problems associated with the evolution of herbicide tolerance in weeds can be mitigated or solved if GM herbicide tolerance is part of a broader integrated weed management program that incorporates crop rotation, herbicide tolerance gene-stacking technologies and field management technologies. 29 , 44 – 47

The adoption of GM herbicide tolerant crops does alter the biodiversity of plant populations (weeds) in agricultural ecosystems and some of the insects and other organisms that rely upon them but this is related to weed management and herbicide use not the GM crop. Alterations in biodiversity also occur in conventional agriculture where weed management strategies are employed. 48 Nevertheless there is great deal of evidence that the adoption of GM herbicide tolerant crops has had a beneficial impact on the environment. The conversion of natural habitat and ecosystems to urban development and agriculture is clearly the most detrimental aspect of human activity as it relates to environmental impact and loss of biodiversity. As yields increase with the adoption of GM crops, as discussed previously, the need to dedicate land for agriculture decreases. Apart from the reduced conversion of land to agricultural use the emergence of GM herbicide tolerant crops has accelerated and enabled the adoption of conservative tillage (no-till and reduced-till) practices. 30 , 45 , 48 Such practices enhance soil quality, reduce water run-off, conserves nutrients, increases water infiltration, and contributes to a reduction in greenhouse gases.

The GM herbicide tolerant crops have also been developed to enable the use of less toxic and more environmentally-friendly chemicals. Glyphosate and glufosinate (another GM targeted herbicide resistance), for example are both Class III herbicides (EPA), which are only slightly toxic and have low persistence in the soil and environment, averaging approximately 40 days. These herbicides have replaced herbicides either more toxic or that are known to contaminate and persist in groundwater. 33 In Argentina, glyphosate replaced several Class II herbicides (significantly toxic) by over 83% deployed on herbicide tolerant soybeans. 48 Thus the impact of targeting less toxic herbicides is a reduction in human exposure and a positive impact on environmental and human health.

With the global increase in acreage of GM herbicide tolerant crops there has been some concern that overall herbicide use would increase and thus the possible environmental impact would negate the value of planting GM crops and ultimately render them a detriment. Initially, at least for the U.S., the overall quantity of herbicide deployed in the environment was reduced but by 2010, when USDA stopped collecting usage data, the amount of herbicide used was approaching pre-GM levels. However, the quantity of herbicide used in an agricultural endeavour is not a satisfactory indicator of environmental impact as the new herbicides substituted for older more toxic chemicals. Kovach et al. 49 developed a metric entitled the Environmental Impact Quotient (EIQ) that utilizes toxicity and exposure data for each herbicide, pesticide, or fungicides to derive a single value that is effectively the average risk/impact of the farm worker, consumer, and ecological components of the agricultural production system. Brooke and Barfoot 30 describe a field EIQ that is derived by multiplying the EIQ for a herbicide (or a pesticide or fungicide) by the amount of the active ingredient applied per hectare and thus conventional herbicide (or pesticide or fungicide) usage can be directly compared usage in a GM crop production field. They point out that the EIQ is a useful, but not comprehensive indicator, for environmental impact but it is more informative than simply recording and comparing the quantity of a chemical used. Using the available data Brookes and Barfoot 30 report that both the amount of active ingredient used and the environmental load (EIQ) has been significantly reduced for all of the major GM crops (maize, soybean, and cotton) in all of the GM adopting countries between 1997 and 2012.

Insect Resistance

Insect-resistant GM crops have also been widely adopted in the U.S., over 90% of corn, soybeans, and cotton are GM for insect resistance, 28 and like herbicide-tolerant GM crops the insect-resistant GM crops are rapidly growing in acreage globally. As mentioned above the primary transgene used in the production of insect resistant GM crops is one that allows the synthesis of a CRY protein toxin from the bacteria Bacillus thurengensis (Bt). This toxin is relatively specific to key agronomic caterpillar and beetle pests that feed on the crop plants, affecting the gut cells of the insect and preventing digestion. The CRY toxins are specific to their target insects and are innocuous to vertebrates, including humans, and have no impact on the plant. They are also biodegradable and thus do not persist in the environment. 50 , 51 This made them ideal targets for GM technology to combat insect pests and the damage and the resultant yield reductions they cause. The recognition that these proteins were useful pesticides predated GMOs. Sprayable formulations, of both the crystal proteins and bacteria preparations (a microbial pest control agent [MCPA]), have long been used in agriculture. 51 It is one of the few pesticidal treatments available to an organic farmer. It is widely used today, making up over 90% of the MCPA market ( http://www.bt.ucsd.edu/organic_farming.html ).

The use of the same pesticide in GM crops that has long been used in organic and conventional agriculture took advantage of the wealth of EPA and FDA testing data for this toxin. This allowed government agencies worldwide to conclude that Bt GM crops are as safe for both human and animal consumption as well as the environment as conventional/organic crops that have been sprayed with the CRY protein or bacterial preparations. 33 In fact, because the Bt GM crop only delivers the CRY toxin to those insects that eat the crop, whether directly or in crop residue, it was considered less likely to cause environmental issues than spraying or dusting plants with the toxin or bacterial preparations. Nevertheless, as with herbicide-tolerant GM crops, concerns remain and for Bt GM crops these relate to the development of Bt-resistant insects, transgene transfer (gene flow) to wild relatives or non-GMO crops close by and environmental/ecological concerns that relate to biodiversity.

The concern for Bt GM crops in regards to gene flow is that unlike herbicide tolerant GM plants the transfer of insect-resistance to wild relatives theoretically could offer a selective advantage to the recipient from increased seed production as a result of reduced loss of vegetative tissue from herbivory. The transfer of insect resistance from a crop to a wild sexually compatible relative is not dependent on the transgene but would occur in conventional insect resistant crops, as discussed previously. After almost twenty years of cultivation there have not been any negative effects of gene transfer from a Bt GM crop to a wild relative. 29 , 45 In a review of the literature of gene flow, Chandler and Dunwell 37 uncovered reports of Bt gene transfer between Bt-canola ( Brassica napus ) and a related wild species Brassica rapa that indicated that plants that have the Bt gene are less fit than those that do not in the absence of the herbivorous insects but survived better than the non-Bt plants under heavy infestations. Other studies using similar populations did not see an increase in fitness in the hybrids. Snow et al. 52 crossed Bt-sunflowers with a weedy relative and demonstrated that the GM hybrids and offspring produced more seed than the non-GM siblings but as this was in a controlled experimental system, and it was not clear if fitness would be enhanced in an agricultural setting. The transfer of the Bt transgene from a GMO crop into a neighbouring field of the non-GM crop counterpart is, as described for herbicide tolerant GM crops, a specific concern for organic farming and requires specific management strategies to negate its occurrence.

As with all insecticides, insect populations that are resistant to the pesticide arise, and Bt crystal proteins are no exception. Long before Bt-GM crops emerged on the scene, the diamondback moth (or cabbage moth), an important pest of cruciferous crops developed resistance to Bt preparations repeatedly applied to fields of conventional crops. 53 Although the Bt toxin is only contained within the tissues of a Bt-GM crop and not applied as a spray in the field, it is not surprising that resistance to Bt-GM crops has emerged in the target insects; most recently in western corn root worm. 54 The strategy to combat the development of resistance to Bt in the targeted pests has been to establish Bt-GM crop free refuges, either within or adjacent to the Bt-GM crop. The refuge strategy works on the theory that if there is a large population of susceptible target insects close to the Bt-GM crop then the rare insect that survives feeding on the crop will, in all likelihood, mate with a susceptible insect that is feeding on the non-GM plants nearby. As most resistance genes tend to be recessive the hybrid offspring of such a mating would be susceptible to the Bt in the GM crop and would die. This has delayed the evolution of Bt-resistant pests. 33 This is not a perfect system and Bt-resistant insects have evolved. In some cases this has arisen because the level of Bt in the GM plants is not sufficient to kill the hybrid insects or because of the significant costs associated with establishing and maintaining refuges some producers fail to provide them at all or limit the size. Strategies to ensure that refuges are established and maintained are being implemented, including increasing the dose of CRY protein that the plant delivers, economic incentives, and “refuge in the bag” (adding non-GM seed to the bag of Bt-GM seed to ensure refuge establishment) may help further delay widespread resistance. Recently GM crops containing “stacked” Bt genes, more than one CRY protein gene, have been developed in the hope of eliminating or slowing insect resistance. New emerging technologies that utilize more than one mode of action (Bt plus another insecticide) as well as maintaining sufficiently large refuge areas may also help prevent or severely delay the development if pest resistance.

The deployment of Bt-GM crops has resulted in a significant decrease in the use of chemical pesticides in all countries where they have been adopted, along with the reduction in environmental impact and associated human exposure. 29 – 31 The reductions are both in quantities of active ingredient and the overall field EIQs for each major crop. In the U.S. the use of Bt-GM maize reduced the amount of pesticide used on corn to target corn borers and root-worms by 80% and the field EIQ load by 54%. Since 1966 the overall decrease in pesticide use on corn was 45% with a reduction of 38% for the field IEQ load. Where data is available, the reductions in total pesticide use and EIQ in all countries that have adopted Bt-maize cultivation. Similar figures are also available for Bt-cotton and other crops. 30 The beneficial economic, environmental, and human health effects resulting from a reduction in pesticide use (and reduced need for toxic pesticide alternatives) can be directly attributed to the ability of GM technologies to contain the pesticide within the plant that is targeted by specific insects (or other invertebrate pests) and to deliver the pesticide only to those pests that ingest the tissues of the plant. The reduction in the need to expose the environment and workers to chemical sprays is clearly a positive outcome of the deployment of GM crops.

The widespread use of broad-spectrum pesticides to combat agricultural pests has significant and negative effects on biodiversity at all levels in the agricultural ecosystem, from mammals to soil microbes and is well documented. 55 , 56 This is not the case for Bt-GM crops, where the consensus is that the effects on biodiversity have been positive. The debate on the possible impact of Bt-GM crops on biodiversity was fueled by early reports that laboratory-feeding experiments using Bt-pollen indicated that Bt-GM corn posed a serious threat to the conservation of monarch butterflies in the U.S. These reports spawned a series of field-based ecological impact studies that concluded that commercial large-scale cultivation of Bt-maize did not pose a significant threat to monarch populations and that the lab-based studies were flawed. 57 This initial flurry of environmental impact assessments of Bt-GM crops on biodiversity of both beneficial organisms (non-targeted) and the targeted pests has continued and the data collected is substantial. 48 The analysis of the literature and data leads to the following conclusions:

• The deployment of Bt-GM crops has had little or no effect on the biodiversity of soil organisms. There are some reports of changes in soil organisms, primarily soil microbes but these changes are indistinguishable or can be explained by the effect of temperature, soil type, or other unrelated parameters.
• No significant adverse effects of Bt-GM crops on the non-target organisms or beneficials have been detected in the field.
• Controlled lab or greenhouse studies only observe an effect of Bt-GM crops on the natural predators or parasites of the targeted pest if they are fed (or use as a host) an insect which is damaged by feeding on Bt-GM plants but not dead and that in the field these effects are not observed.
• In some areas where Bt-GM crops predominate the landscape, the populations of the targeted pests decline to levels that benefit nearby farms that grow non-GM crops of a reduction in the level and frequency of insecticide deployment.

In a more recent study of the impact of Bt-GM crops, using data collected between 1990 and 2010 at 36 sites across northern China, Lu et al. 58 demonstrated that with the adoption of Bt-cotton and the resulting decrease in insecticide use there was a major reduction in the target insect, the cotton bollworm, and an increase in abundance of several generalist predators (ladybugs, spiders, and lacewings). With the increase in the generalist predators they also saw a decrease in the cotton aphid populations that damage the plants but are not controlled by the Bt toxin. As reported by others they conclude that the impact on beneficial predators (generalists) provides a measure of biocontrol of plant pests that affect neighboring crops that are not necessarily GM.

Substantial Equivalence

A major paradigm in the risk assessment of GM crops, particularly for human consumption, is the concept of “substantial equivalence” which is based on the idea that a GM crop is directly comparable (within normal levels of variation) to its non-GM counterpart to ensure that there are no unintended hazards associated with the insertion of the transgene. The GM and non-GM plants are compared with regard to their agronomic and morphological characteristics prior to an in depth compositional analysis. The compositional comparisons encompass “those components in a particular food that may have a substantial impact in the overall diet” 59 present in the food/feed products that are derived from the GM crop. The analysis can include macro- and micronutrients, anti-nutrients, secondary metabolites, and toxins. The non-GM crop that is used as a point of comparison is presumed to be safe, as it will have had a history of successful and safe use as food or feed. Any difference in the composition of the GM crop must fall within the normal range of variability for the non-GM counterpart for it to be considered safe. If the differences fall outside the normal range then the GM crop must be further assessed for its safety. All of the GM crops adopted so far have been fully tested for substantial equivalence, and all have been graded as equivalent to their non-GM counterpart, and thus, safe. 60 The approaches to assess equivalence are constantly improving and there is a movement towards non-targeted approaches including “omics” based analyses (genomics, proteomics, metabolomics, etc.). In a recent review, Ricroch et al. 61 concluded that GM crop plants more closely resemble the parental line from which they were generated than do their conventional bred (or mutagenized) counterparts. The “omics” analyses would support the conclusion that the insertion of a transgene into a plant to generate a GM crop is neither inherently risky and nor does it present novel or greater sources of risk than conventional breeding. The use of “omics” in the normal testing for substantial equivalence is not yet part of a standard approach. 24

GM crops are more rigorously tested for safety than any conventionally bred crop (which are not tested), even though the genetic changes that are made in the production of GM crops are precisely assessed and minimal, and none have yet failed to pass this intense scrutiny, including golden rice.

On the Horizon

The focus of the discussion so far has been on the first generation of GM crops that are primarily targeted to two agronomic traits. At the present time the only other agricultural crop grown commercially is the GM papaya, the first GM tree crop, with resistance to the devastating papaya ringspot virus that threatened to wipeout the papaya industry in Hawaii. 62 This was the pioneering public institution driven (Cornell University, USDA-ARS) development of a GM crop, with cooperation from private industry, to combat a national crisis. The safety of GM papaya can be attested to by the fact that they have been approved for direct consumption in Japan, a difficult consumer market to penetrate with a GM product. However, with recent advances and the prior establishment of “transgenic pipelines” we are beginning to see other important traits being addressed using GM technologies. These next generation GM crops involve more than just the major crops such as corn, soybeans, and cotton, and have utilized genes from sources other than microbes, including genes that are derived from plant sources that can enable new trait development in our commercial crops. The farthest advanced is the new drought-tolerance technology that uses a bacterial gene (a protein that stabilizes RNA structure) that is in its first year of commercialization in the U.S., and under a public-private partnership in development for deployment in Africa. Those that are still in the pipelines for commercialization address such traits as: pest and disease resistance, photosynthetic efficiency, salinity, nutrient efficiency (nitrogen and phosphorus uptake), nitrogen fixation, modifications for biofuel production, and biofortification. The latter trait, biofortification, is where golden rice is leading the way and other nutrient deficiencies that significantly impact human health, such as vitamin A, iron, and zinc deficiencies, are all in the GM pipeline (see reference 45 for a comprehensive look at new technologies).

Each trait will undergo the rigorous testing that is demanded of commercial or public entities, so that any environmental or health safety issues are addressed and accounted for before release.

The Future of GM Technologies

The future is very promising for GM technologies to enhance our efforts to meet the future global needs for food, feed and fiber in a sustainable and responsible way. Conventional breeding methods, especially with the advent of genome level technologies, that are designed to both generate and exploit genetic variation in order to isolate effective alleles (variants) of genes that generate yield increases, disease resistance, pest resistance etc., also clearly play a role in this effort. Organic farming practices also have a place at the global table 63 where such practices make sense. Agriculture is a diverse endeavor, and if we are to be successful we need to embrace that diversity.

Melvin J. Oliver, PhD, is a Supervisory Research Geneticist (Plants), USDA, Agricultural Research Service, University of Missouri.

Contact: [email protected]

None reported.

Crop Physiology in Agricultural Research for Development: Enhancing Crop Improvement

Crop physiology plays a crucial role in agricultural research for development, as it seeks to enhance crop improvement and address the challenges faced by farmers worldwide. By understanding how crops respond to different environmental factors and stresses, researchers can develop strategies to optimize their growth and productivity. For instance, consider a case study where a group of researchers investigated the physiological responses of rice plants under drought conditions. Through their findings, they were able to identify key traits that contribute to drought tolerance in rice varieties, leading to the development of improved cultivars capable of withstanding water scarcity.

In recent years, there has been an increasing focus on utilizing crop physiology knowledge to drive sustainable agricultural practices. This is particularly important considering the ever-growing global population and the need for food security. Researchers are exploring various aspects of crop physiology such as photosynthesis efficiency, nutrient uptake mechanisms, and stress response pathways to uncover potential avenues for enhancing crop performance. By identifying genetic markers associated with desirable traits or developing innovative techniques like precision agriculture, scientists aim to improve both yield quantity and quality while minimizing resource inputs.

The integration of crop physiology into agricultural research for development not only benefits farmers but also contributes significantly toward achieving broader developmental goals. By improving crop resilience against biotic and abiotic stresses, researchers can help mitigate the impacts of climate change on agriculture and reduce crop losses. This can enhance food security, especially in regions prone to extreme weather events or facing water scarcity.

Furthermore, by optimizing crop physiology, researchers can also promote sustainable farming practices. For example, understanding nutrient uptake mechanisms can help develop strategies for efficient fertilizer use, minimizing environmental pollution caused by excess nutrient runoff. Similarly, studying photosynthesis efficiency can lead to the development of crops that require less water and energy inputs for growth.

Crop physiology research also plays a vital role in ensuring the nutritional quality of crops. By studying factors such as nutrient uptake and assimilation processes, scientists can identify ways to enhance the nutritional content of crops, leading to improved human health outcomes.

Overall, integrating crop physiology into agricultural research for development has the potential to significantly improve agricultural productivity, sustainability, and resilience. By understanding how crops function at a physiological level and developing innovative solutions based on this knowledge, researchers are driving progress towards achieving global food security goals while minimizing environmental impacts.

Crop Physiology: Understanding Plant Functions

Crop physiology is a crucial aspect of agricultural research that focuses on understanding the functions and processes within plants. By studying plant physiology, researchers gain valuable insights into how crops respond to their environment, allowing for the development of more efficient and resilient agricultural practices.

To illustrate the importance of crop physiology, consider the case study of drought-tolerant maize varieties. In regions where water scarcity is a major concern, such as Sub-Saharan Africa, farmers face significant challenges in growing sufficient food. However, through an understanding of crop physiology, scientists have been able to develop maize varieties that are better equipped to withstand periods of drought. These varieties exhibit traits such as enhanced root systems and improved water-use efficiency, enabling them to thrive even in arid conditions. This breakthrough highlights the practical implications of studying crop physiology in addressing real-world problems related to food security.

Understanding plant functions involves examining various physiological processes at play within crops. One key area of focus is photosynthesis—the process by which plants convert sunlight into energy through the synthesis of carbohydrates. Maximizing photosynthetic efficiency is essential for enhancing crop productivity. Furthermore, investigating factors affecting nutrient uptake and assimilation helps optimize fertilizer use and improve nutrient management strategies in agriculture.

• Crop physiology provides insights into plant responses to environmental stresses.
• It aids in developing stress-tolerant cultivars with improved yields.
• Understanding plant growth processes supports effective agronomic decision-making.
• Crop physiological studies contribute to sustainable farming practices.

Additionally, incorporating a table can enhance comprehension and emotional engagement:

In conclusion (without explicitly stating it), crop physiology plays a vital role in advancing agricultural research and development. By unraveling the complex mechanisms of plant functions, scientists can develop innovative strategies to improve crop production, enhance food security, and promote sustainable farming practices. In the subsequent section on “The Role of Crop Physiology in Agricultural Research,” we will explore how this field contributes to broader advancements in agriculture without missing a beat.

Role of Crop Physiology in Agricultural Research

In the previous section, we explored the intricacies of crop physiology and how it helps us understand plant functions. Now, let’s delve into the vital role that crop physiology plays in agricultural research for development.

To illustrate this role, consider a case study where researchers aimed to enhance drought tolerance in maize crops. By studying the physiological responses of different varieties of maize under water-deficient conditions, they were able to identify key traits associated with drought tolerance. This knowledge then guided breeding programs towards developing improved maize cultivars with enhanced resilience to drought stress.

The importance of crop physiology in agricultural research can be further exemplified through the following points:

Optimizing resource utilization: Through an understanding of plant physiology, scientists can determine how crops efficiently use resources such as water, nutrients, and sunlight. This knowledge enables them to develop management practices that maximize resource utilization while minimizing waste or environmental impact.

Enhancing crop productivity: Crop physiologists investigate various factors influencing yield potential, including photosynthetic efficiency, nutrient uptake, and reproductive processes. By identifying limiting factors and optimizing these processes, researchers can contribute to increasing crop yields and food security on a global scale.

Improving stress tolerance: Climate change poses significant challenges to agriculture by introducing new stresses such as heatwaves or prolonged periods of drought. Crop physiologists play a crucial role in understanding plant responses to stressors and devising strategies to enhance stress tolerance in crops.

Sustainable farming practices: With growing concerns about environmental sustainability, crop physiologists investigate methods for reducing inputs like fertilizers or pesticides without compromising yield or quality. They explore ways to improve nutrient-use efficiency and pest resistance through physiological studies.

Table 1 below highlights key contributions of crop physiology in agricultural research:

In conclusion, the role of crop physiology in agricultural research for development is indispensable. By understanding plant functions at a physiological level, researchers can optimize resource utilization, enhance crop productivity, improve stress tolerance, and promote sustainable farming practices.

[Transition] Now let’s shift our focus to the significance of crop physiology in maximizing crop yield potential.

Importance of Crop Physiology in Crop Yield

Enhancing Crop Improvement through Understanding Crop Physiology

In the previous section, we examined the role of crop physiology in agricultural research. In this section, we will explore the importance of crop physiology in achieving higher crop yields. To illustrate this, let’s consider a hypothetical case study involving rice cultivation.

Imagine two farmers, Farmer A and Farmer B, who both cultivate rice in similar conditions. However, Farmer A has a deeper understanding of crop physiology and implements appropriate strategies to optimize plant growth and development. As a result, Farmer A consistently achieves higher crop yields compared to Farmer B.

There are several key reasons why understanding crop physiology is crucial for enhancing crop improvement:

Efficient resource allocation: By understanding how plants absorb and utilize nutrients, water, and sunlight, farmers can allocate these resources more efficiently. This results in improved nutrient uptake and reduced wastage, leading to healthier plants with increased resistance to pests and diseases.

Climate adaptation: Climate change poses significant challenges to agriculture worldwide. However, by studying crop physiology, scientists can identify traits that enable crops to better withstand extreme weather conditions such as drought or heat stress. This knowledge allows breeders to develop climate-resilient varieties that maintain yield stability under changing environmental conditions.

Enhanced photosynthetic efficiency: Photosynthesis is the process by which plants convert sunlight into chemical energy necessary for growth. By gaining insights into the physiological processes involved in photosynthesis, researchers can devise methods to enhance its efficiency. This leads to increased biomass production and ultimately improves overall crop productivity.

To further emphasize these points, consider the following table showcasing the potential benefits of understanding crop physiology:

In summary, understanding crop physiology plays a crucial role in enhancing crop improvement. Through efficient resource allocation, climate adaptation, and improved photosynthetic efficiency, farmers can achieve higher yields and ensure food security. In the subsequent section on “Crop Physiology Techniques in Research,” we will delve into the specific methodologies used in studying and applying crop physiology principles for further advancements in agricultural practices.

Crop Physiology Techniques in Research

Section Title: Crop Physiology Techniques in Research

Having established the significance of crop physiology in enhancing crop yield, it is imperative to delve into the various techniques employed in agricultural research. These techniques enable scientists and researchers to gain a deeper understanding of plant functions and their responses to environmental factors, ultimately leading to improved crop production. This section will explore some key methodologies used in crop physiology research, highlighting their relevance and potential impact.

Methodologies utilized in crop physiology research encompass a diverse range of approaches that contribute to our knowledge of plant growth and development. One notable technique involves controlled environment chambers, where plants are grown under precisely regulated conditions such as temperature, humidity, light intensity, and photoperiods. By subjecting crops to specific stressors or simulating changes in climate scenarios, researchers can investigate physiological processes at different stages of growth and identify critical markers for enhanced productivity.

To elucidate the complex interactions between plants and their environments, advanced imaging technologies have emerged as powerful tools. For instance, chlorophyll fluorescence imaging provides insights into photosynthetic efficiency by measuring emission levels during photosynthesis. Additionally, thermal cameras capture infrared radiation emitted by plants, enabling researchers to assess variations in leaf temperature associated with water stress or disease.

In order to effectively analyze large datasets generated through modern technologies like genomics and transcriptomics, bioinformatics plays a crucial role. Through computational analysis methods and data mining algorithms, researchers can extract valuable information related to gene expression patterns underlying physiological traits. Such findings aid in identifying genes responsible for desirable characteristics like drought tolerance or resistance against pests.

These methodologies not only enhance our understanding of plant biology but also offer practical applications for sustainable agriculture. By providing valuable insights into how crops respond to changing environmental conditions, they pave the way for developing resilient varieties that can withstand abiotic stresses and diseases while maintaining optimal yields. Moreover, these advancements facilitate precision farming practices by allowing farmers to make informed decisions regarding irrigation, fertilization, and crop protection strategies.

By exploring the diverse techniques employed in crop physiology research, we can now move on to discussing their practical applications in sustainable agriculture. Understanding how plants respond to environmental stimuli provides a foundation for implementing effective strategies that maximize productivity while minimizing negative impacts on ecosystems.

Applications of Crop Physiology in Sustainable Agriculture

Enhancing Crop Improvement through Applications of Crop Physiology Techniques

In the previous section, we explored various crop physiology techniques that have been utilized in agricultural research. Now, we will delve into the practical applications of these techniques and discuss how they contribute to sustainable agriculture.

To illustrate the significance of crop physiology in agricultural research for development, let us consider a hypothetical case study involving wheat production. By applying physiological principles, researchers were able to identify specific traits related to drought tolerance in wheat varieties. This knowledge enabled breeders to develop new cultivars with enhanced drought resistance, thereby ensuring stable yields even under water-limited conditions.

The applications of crop physiology in sustainable agriculture are vast and encompass several key areas:

• Improved nutrient management: Through understanding plant nutrient uptake mechanisms and optimizing fertilization practices, farmers can minimize nutrient losses while maximizing crop productivity.
• Enhanced pest and disease control: Utilizing physiological insights into plant defense mechanisms enables the development of targeted strategies for managing pests and diseases without excessive reliance on chemical inputs.
• Efficient water use: By studying crop water requirements and implementing precision irrigation methods based on real-time monitoring systems, farmers can reduce water wastage and promote more efficient use of this precious resource.
• Climate change adaptation: With climate change posing significant challenges to agricultural productivity, understanding how crops respond physiologically to changing environmental conditions is crucial for developing resilient farming systems.

Table 1 below provides an overview of the contributions made by crop physiology techniques in each area mentioned above:

This comprehensive utilization of crop physiology techniques has the potential to revolutionize agricultural practices and contribute significantly to sustainable food production. By integrating scientific knowledge with practical applications, we can address pressing challenges in agriculture while minimizing negative environmental impacts.

Looking ahead, future perspectives in crop physiology research will focus on further enhancing our understanding of plant responses to complex environmental interactions and developing innovative approaches for optimizing productivity and resilience. In the subsequent section, we will explore these exciting prospects that lie ahead in crop physiology research.

[Transition Sentence] As we shift our focus towards future perspectives in crop physiology research, it is essential to recognize the evolving nature of this field and its pivotal role in shaping sustainable agricultural systems.

Future Perspectives in Crop Physiology Research

Section Title: Advancing Crop Physiology Research for Sustainable Agricultural Development

Building upon the applications of crop physiology in sustainable agriculture, it is imperative to explore the future perspectives and advancements in this field. By pushing the boundaries of our understanding, researchers can enhance crop improvement strategies and contribute to global food security. This section delves into emerging areas of research within crop physiology that hold promise for sustainable agricultural development.

Emerging Areas of Research:

Harnessing Plant-Microbe Interactions: Understanding the intricate relationships between plants and microbes has become a focal point in crop physiology research. Investigating how beneficial microorganisms positively influence plant growth and stress tolerance offers exciting possibilities for improving crop productivity sustainably. For instance, studies have demonstrated that certain rhizobacteria can promote nutrient uptake by plants through their ability to solubilize phosphorus or fix atmospheric nitrogen. Incorporating these microbial interactions into agricultural practices can reduce reliance on synthetic fertilizers while minimizing environmental impact.

Unraveling Epigenetic Mechanisms: Epigenetics, which refers to heritable changes in gene expression without alterations in DNA sequence, has emerged as a key area of interest in crop physiology research. Unlocking epigenetic mechanisms holds immense potential for enhancing plant adaptation to changing environments and improving stress tolerance. By manipulating epigenetic marks through advanced breeding techniques or genetic engineering, we may be able to develop crops with enhanced resistance to pests, diseases, and abiotic stresses such as drought or heat.

Exploiting Genetic Diversity: The vast genetic diversity present within plant species provides an invaluable resource for crop improvement efforts. Utilizing advanced genomic tools like next-generation sequencing enables scientists to identify desirable traits associated with specific genotypes more efficiently than ever before. Additionally, exploring wild relatives of domesticated crops unlocks access to novel genes that could confer improved resilience and productivity under challenging conditions. Integrating this knowledge into breeding programs allows breeders to develop new crop varieties that are better adapted to local climates, have increased nutritional content, or possess enhanced yield potential.

Enhancing Crop Water Use Efficiency: With water scarcity becoming an increasingly pressing issue in agriculture, improving crop water use efficiency has gained significant attention. Researchers are exploring physiological mechanisms that influence a plant’s ability to utilize water effectively and sustainably. By identifying genes associated with traits such as stomatal regulation, root architecture, or photosynthetic efficiency, scientists aim to breed crops that can thrive under limited water availability without compromising productivity. These efforts not only address the challenges of water scarcity but also contribute to sustainable farming practices by reducing excessive irrigation needs.

• Increased food security through improved crop productivity
• Reduced environmental impact from synthetic fertilizers
• Enhanced resilience against pests, diseases, and abiotic stresses
• Sustainable agricultural practices for future generations

In light of these emerging areas of research within crop physiology, it is evident that focusing on sustainable agricultural development holds immense promise for addressing global challenges related to food production and environmental sustainability. By harnessing the power of plant-microbe interactions, unraveling epigenetic mechanisms, exploiting genetic diversity, and enhancing crop water use efficiency, researchers can pave the way towards a more secure and resilient agricultural system. Through these advancements in crop physiology research, we can foster a brighter future where our agricultural practices align with the needs of both present and future generations.

Related posts:

• Biotechnology in Agricultural Research for Development: Advancing Crop Improvement
• Crop Agronomy in Agricultural Research for Development: An Overview of Crop Improvement

Crop Genetics for Agricultural Research: Enhancing Crop Improvement

Crop Improvement in Agricultural Research for Development: Enhancing Yield, Resilience, and Sustainability

Crop Rotation: Enhancing Soil Health Management in Agricultural Research for Development

Plant Breeding for Crop Improvement: Agricultural Research for Development

Genetic Engineering in Agricultural Research for Development: Crop Improvement

Crop Improvement in Agricultural Research for Development: Enhancing Yield,…

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• Open access
• Published: 23 April 2024

Rainfall’s impact on agricultural production and government poverty reduction efficiency in China

• Jianlin Wang 1 ,
• Zhanglan You 2 ,
• Pengfei Song 3 &
• Zhong Fang 4  

Scientific Reports volume  14 , Article number:  9320 ( 2024 ) Cite this article

Metrics details

• Environmental economics
• Sustainability

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Introduction

As it is at the center of the United Nation’s Sustainable Development Goals (SDG), overcoming poverty in all its manifestations continues to be one of the most significant hurdles humanity confronts. Currently, developing countries are faced with the enormous challenges of poverty eradication and agricultural development. As the world’s most populous country, since its reform and opening up, China has lifted millions out of poverty, but progress has been uneven 1 . According to the World Bank 2 , over the past four decades, China has reduced poverty by nearly 800 million people, accounting for more than 75 percent of global poverty reduction over the same period. However, this does not mean that China’s poverty reduction efforts have been completed. Relative poverty will persist in China for a long time, with some regions still facing serious risks and challenges of people returning to poverty after being lifted out of poverty.

The United Nations Development Programme (UNDP) 3 has emphasized that the emergence of new threats such as the impacts of climate change on rainfall necessitates additional efforts to uplift people from poverty. Climate change and its effects on rainfall patterns pose significant challenges to agricultural systems worldwide, with far-reaching implications for food security and poverty alleviation 4 . The significance of rainfall as a determinant of agricultural productivity is well-established 5 . Studies have consistently shown that agricultural output is profoundly influenced by variations in rainfall patterns. Research by Liu and Feng 6 and Wang et al. 7 have delved into the spatial and temporal dimensions of rainfall, emphasizing the need for precision in evaluating its impact on different crops and regions. The efficiency of rainfed agriculture, which is predominant in many parts of China, has become a crucial factor. Studies by Zhang et al. 8 and Chen et al. 9 provide insights into the efficiency of rainfed farming systems, exploring adaptive strategies such as improved water management and drought-resistant crop varieties. These findings contribute to a nuanced understanding of how farmers can enhance productivity in the face of changing rainfall patterns.

The nexus between rainfall variability and agricultural production serves as a focal point in understanding the challenges and opportunities facing Chinese farmers. Efficiency evaluations of these poverty reduction policies are critical to ensuring their effectiveness. Studies 10 , 11 underscore the spatial and temporal nuances of rainfall patterns, emphasizing the need for adaptive strategies. Efficiency evaluations reveal that technological interventions, such as precision agriculture and data-driven decision-making 12 , play a pivotal role in mitigating the impact of unpredictable rainfall, enhancing resource utilization, and improving crop yields. Research by Chen et al. 9 delves into the livelihood outcomes of farmers in regions prone to erratic rainfall, providing evidence of the direct linkages between agricultural efficiency, income stability, and poverty reduction. Therefore, the efficiency evaluation of the impact of rainfall on agricultural production and government poverty reduction in China unveils a complex and dynamic landscape. The intricate interplay between climatic factors, agricultural practices, and government policies forms a tapestry that shapes the socio-economic fabric of the nation.

Efficiency evaluations of government poverty reduction policies elucidate the successes and challenges of interventions aimed at improving the livelihoods of vulnerable populations. Research by Li et al. 10 indicates that rural infrastructure development projects contribute to poverty reduction by enhancing connectivity and creating opportunities. However, challenges persist, and the effectiveness of policies is contingent upon addressing regional disparities, administrative bottlenecks, and ensuring inclusive growth 7 . The efficiency evaluation extends beyond immediate poverty reduction outcomes to encompass the broader context of climate change adaptation and environmental sustainability. Studies 13 , 14 underscore the importance of integrating sustainable agricultural practices into poverty reduction strategies. By recognizing the intrinsic link between environmental resilience and poverty alleviation, efficiency evaluations provide insights into long-term strategies that promote both economic development and ecological sustainability.

The main contributions of this study are as follows: (1) From the perspective of sustainable agricultural development, a novel two-stage poverty research theoretical and analytical framework is constructed, divided into agricultural production and government poverty reduction stages, to obtain a more objective efficiency evaluation and better under-stand the current state of agricultural production and poverty reduction in China. (2) Considering public expenditure on central government poverty alleviation funds and local government agricultural support funds as inputs for the second stage, the number of rural residents guaranteed minimum living standards as an unexpected output, and the Sustainable Agricultural Development Infrastructure Index as an expected output, this study comprehensively evaluates the efficiency of China’s agricultural production and poverty reduction. (3) By combining the advantages of the meta-frontier entropy network DEA model, this study creatively improves the model by introducing rainfall as an exogenous variable. In addition, this study divides China’s 27 provinces into northern and southern provinces according to the “Qinling-Huaihe” line for a comparative study of environmental, economic, and governmental efficiency. This allows researchers and policymakers to perform a more detailed analysis of the efforts to eliminate poverty and provides valuable insights for policymakers to pro-mote inter-sectoral collaboration for sustainable agricultural development and optimize the use of public funds.

Literature review

Government poverty reduction efficiency.

Concerning the government’s role in poverty reduction, a significant number of scholars have dedicated their studies and achieved fruitful results. Two prominent schools of thought, represented by Douglas C. North and Commons, Mitchell, and Veblen, challenge neoclassical economics’ limited consideration of institutions and governance in the economy 15 . While these schools share a common emphasis on the importance of institutions, they differ in their methodologies 16 .

The empirical study’s conclusion, against the above backdrops, reveals contentious debates 17 and the definition of efficiency in government poverty reduction is still under research. Some results indicate that efficient governance acts as a prerequisite to poverty alleviation in various countries 2 , 15 , 18 , 19 , 20 , 21 , while some studies argue that the benefits of efficient government are primarily enjoyed by upper-income and middle-income groups, limiting their impact on poverty reduction and reaching the poor 22 , 23 , 24 . Recent research by Christiaensen and Martin 25 provides nuanced insights into how growth in agriculture, often spurred by government policies, is more poverty-reducing than equivalent growth in non-agricultural sectors. This highlights the need for targeted government policies that specifically address the needs of the poor, especially in rural areas where agriculture is a predominant livelihood.

Existing studies also show that poverty reduction and economic development bring rapid consumption of resources and environmental damage 26 , and poverty-stricken counties overlap highly with ecologically fragile areas geographically and spatially 27 , 28 , which are more likely to cause serious environmental quality deterioration problems in the process of poverty alleviation. At the same time, the policy of poverty alleviation requires ecological poverty alleviation, so it is of great significance to study the impact of poverty alleviation policy on the environment in poor areas to achieve sustainable development. Dai et al. 29 compared the poverty alleviation outcomes in two Chinese Provinces, Anhui and Guizhou provinces, and concluded that smaller, localized projects had a greater impact on reducing poverty than larger-scale infrastructure and industrial developments. Similarly, in their research, Fan and Chang-Kang 30 argued that the Chinese government’s emphasis on building major intercity highways did not alleviate poverty as efficiently as investing in the development of simpler roads in isolated regions would have. Yang et al. 31 utilized a meta-frontier undesirable dynamic two-stage DEA to assess anti-poverty policy efficiencies across Chinese provinces and underscored regional disparities in policy efficiency and highlighted the superior performance of poverty reduction efforts over agricultural productivity. While Yang et al. 31 provided a comprehensive analysis of policy efficiencies across Chinese provinces, their study primarily focuses on the economic aspects of these policies, with less emphasis on the fiscal support through central government and institutions at the local level on sustainable agricultural infrastructure development and environmental determinants that could significantly impact agricultural productivity and, consequently, poverty reduction efforts.

Thus, we extend the meta-frontier undesirable dynamic two-stage DEA framework used by Yang et al. 31 to include the index of infrastructure for sustainable agricultural development and environmental factors. In addition, very little research has paid attention to the impact of poverty alleviation policies on the number of rural residents guaranteed minimum subsistence allowance on the ground. This paper sought to bridge this gap by exploring how government efficiency in China shapes the distribution of fiscal support through central government and institutions at the local level on infrastructure for sustainable agricultural development and the number of rural residents guaranteed minimum subsistence allowance.

Agricultural production efficiency

From 2012 to 2016, China’s agricultural development was in the implementation period of the 12th Five-Year Plan. At that time, the Chinese government mainly focused on improving agricultural productivity and farmers’ income, as well as strengthening rural infrastructure construction. These policies laid the foundation for agricultural development in the 14th Five-Year Plan of China. However, in the past decade, China’s agricultural development direction has undergone significant changes. From the basis of simply improving productivity and income in the 12th Five-Year Plan to the 14th Five-Year Plan, China’s agricultural development has shifted towards a greater emphasis on modernization, quality and efficiency, and competitiveness. This shift in focus highlights the need for a dynamic analysis of China’s agricultural production efficiency.

The discourse on agricultural production efficiency robustly positions government interventions as pivotal elements for enhancing sectoral efficiency 32 , 33 . These studies highlight the transformative potential of policy frameworks and technological advancements in boosting agricultural outputs and, consequently, fostering economic development within rural landscapes. Ambali 32 explicates the direct correlation between policy induced modernization efforts and the increase in agricultural productivity in sub-Saharan Africa, while Apezteguía 33 outlines the positive impacts of modernization initiatives on the agrarian economies in Argentina. Complementing these perspectives, Sikandar et al. 34 expand the narrative by examining the panel data across developing economies in Latin America, Asia, and Eastern Europe, they underscores the critical influence of integrating developing economies into global food supply chains and highlights the necessity of capital infusion for achieving sustainable growth in the agricultural sector and effective poverty reduction.

In the context of China, various scholars contribute to this narrative by examining the specificities of government policies on agricultural efficiency and poverty reduction 35 , 36 , 37 . Jiang et al. 38 analyze the role of rural collective economy policies in enhancing common prosperity, revealing how farmland transfer and scale operation under these policies significantly improve agricultural efficiency and rural prosperity. Chen et al. 35 further refine this analysis by distinguishing the effects of farmland transfers on poverty vulnerability among smallholder households. They identify a significant reduction in poverty vulnerability households, underscoring the complexities of farmland transfer as a non-uniform poverty alleviation tool. Wang 39 found that market restrictions and household characteristics significantly influence efficiency, with education, family size, and income playing a key role. Hu & McAleer 36 estimated an increase in production efficiency over time but noted a growing gap between affluent coastal regions and the western hinterland. Complementing this narrative, Yan et al. 37 delve into the “Poverty Alleviation through Agriculture Project,” highlighting the challenges of translating enhanced agricultural efficiency into effective poverty alleviation, thereby stressing the need for a more nuanced understanding of the dynamics between agricultural productivity and poverty reduction mechanisms.

Despite these insights, a discernible gap persists in the literature concerning the direct translation of efficiency gains into tangible poverty reduction outcomes, particularly amidst the exacerbating challenges posed by climate change 40 . Noack and Larsen 40 critically assess the interplay between farm size, productivity, and poverty, underscoring the complex dynamics between these variables and the overarching influence of climatic variabilities. Their research suggests that while agricultural efficiency is a crucial component of rural economic development, its direct impact on poverty alleviation is mediated by a multitude of factors, including climate change, which remains under explored.

This gap highlights the need for a nuanced exploration of how agricultural efficiency gains, underpinned by government policies can translate into poverty reduction, particularly within the context of climate change. Our study aims to bridge this gap by integrating insights from both global and Chinese-specific literature, delving into the multifaceted relationship between agricultural efficiency, government interventions, and poverty outcomes within rural landscapes, especially under the looming shadow of climate-induced uncertainties.

Impact of rainfall on agricultural production and government poverty reduction

As a meteorological factor in the natural environment, rainfall is highly random 41 , 42 . It’s well established in the literature that rainfall has a significant impact on the agricultural production system that we analyze, thus satisfying exogeneity and correlation.

The impact of rainfall on agricultural production and government poverty reduction is a complex issue. Hagos et al. 43 , Kyei-Mensah et al. 44 and Fei and Lin 45 highlight the importance of agricultural water management technologies in mitigating the negative effects of rainfall variability on poverty and food insecurity. Similarly, Huang et al. 46 and Abdul Rahim 47 both found that irrigation and soil and water conservation significantly contribute to increased yields and incomes, particularly in poor areas, and help in poverty mitigation. However, Asiimwe 48 emphasize the need for a multi-faceted approach, including education, infrastructure, and land policies, to address the vulnerability of agricultural households to rainfall shocks and reduce food poverty. In the same vein, the research of Liu and Zeng 49 emphasized the importance of the agricultural products circulation infrastructure and effective government policies in poverty reduction,however, like the research of Asiimwe 48 overlooked the impact of rainfall. Cook et al. 50 stated the problem of rainwater and agricultural production is more about the intensity and timing of the rainfall rather than the scarcity of rainfall in terms of the poverty-stricken Gangsu Province in China. Xue et al. 28 showed that the spatial distribution of the agricultural water environmental efficiency in China is uneven, showing a gradual decrease from east to west. The results also showed that there is still a large gap in the research on the rainfall impact on agriculture and the economic situation of the whole country, considering China’s vast and diversified territory. While studies have shown significant impacts of rainfall variability on agricultural production 43 , 44 , there is a lack of research on integrating these impacts into government poverty alleviation policies and agricultural production strategies to enhance overall sustainability and efficiency 51 .

While the existing literature collectively highlights the nuanced relationship between rainfall, agricultural production, and government poverty reduction, it lacks a comprehensive perspective when discussing government poverty alleviation policies, agricultural production efficiency, and the impacts of climate change (especially rainfall) on these factors, particularly in terms of how these factors interact to affect sustainable agricultural development and poverty reduction. We thus add rainfall as an exogenous variable into the research model to explore the effects and fill the gap in the literature. Furthermore, as indicated by Xue et al. 28 , given the extensive and varied landscape of China, we categorize China’s 27 provinces into southern and northern regions according to the climatic boundary Qinling-Huaihe line for a wholistic comparative study of environmental, economic, and governmental efficiency.

Chung et al. 52 introduced the concept of an output-orientated distance function (DDF), which is an extended directionally orientated output distance function. The traditional DDF, as a radial measurement model, usually overestimates efficiency values. To address this issue, Chen et al. 53 established a non-oriented directional distance function, which leads to a more reasonable and accurate estimation of the efficiency value Fare et al. 54 proposed the Network Data Envelopment Analysis (Network DEA) model. Compared with the traditional DEA model that treats production technologies as black boxes, the Network DEA model focuses on illustrating these production technologies and explores in detail the input allocations and intermediate outputs that may have a potential impact on the In contrast to traditional DEA models that treat production technologies as black boxes, network DEA models focus on describing these production technologies and exploring in detail the potential impact that input allocations and intermediate outputs may have on the production process, rather than treating them as black boxes that cannot be measured. A basic type of network structure is the parallel system, where the DMUs of the production process consist of sub-units Kao 55 investigates the relationship between the underestimation of the efficiency values of the sub-systems and the underestimation of the efficiency of the whole system and proposes the use of a parallel DEA model to compute the overall and partial efficiency values Färe and Grosskopf 56 , using Dsingle- and two-level hierarchical models were proposed to calculate the efficiency of hierarchical and networked system subsystems, where each DMU was set up as a consecutive parallel subunit. In the network DEA model, a dynamic approach is allowed.

Therefore, extending the literatures reviewed, this study novelly include the index of infrastructure for sustainable agricultural development and environmental factors and adopt the DDF model as a research methodology. This allows more relevant, accurate, and reasonable evaluation results to make it more realistic which Fare and Grosskopf 57 failed to take into account the persistent effects across periods with different stages of parallel systems while considering different production technologies. To correct this deficiency, this study revised the traditional DDF model and combined the parallel DEA model of Shannon 58 Entropy and Kao 55 and the concept of common frontier (Meta frontier) proposed by O’Donnell et al. 59 to present the Meta entropy parallel two-stage dynamic DDF empirical study. The aim of this study is to evaluate the efficiency stage of agricultural production (SDG2, which aims to ensure food security) and the efficiency stage of government poverty alleviation (SDG1, which aims to eliminate all forms of poverty) in 27 provinces in China, in order to avoid underestimation or overestimation of efficiency values. Specifically, this paper first introduces the Entropy method and then builds a Meta-frontier entropy parallel two-stage dynamic DDF model.

Research methodology

The entropy method, detailed indicators.

In this study, on the basis of fully considering the impact of governmental inputs on agricultural infrastructure, and referring to the existing research results 60 , 61 , 62 , in the second stage of the model, the “index of infrastructure for sustainable development of agriculture” was used as the expected output to be included in the analytical framework. In the second stage of the model, the expected output of “Sustainable Agricultural Development Infrastructure Index” is incorporated into the analytical framework, which consists of five secondary indicators, including postal and telecommunication facilities, ecological and environmental facilities, water resources and water supply and drainage facilities, energy and power facilities, and road transportation facilities, and covers 22 specific sub-indicators. The subjective assignment method relies on the intention of the decision maker when assigning weights to the indicators, which is not objective, so the Shannon 58 Entropy method was used to synthesize the line item indicators into a single value (Table 1 is detailed indicators).

Entropy method steps

Step 1: Data standardization. Detailed indicators from the Government’s Poverty Reduction Phase output item “ Sustainable Agricultural Development Infrastructure Index “ for the 27 provinces of China were calculated using the following formula.

where \(r_{mn}\) is the standardised value of the nth indicator for the mth province, and \(\mathop {\min }\limits_{m} x_{mn}\) is the minimum value of the nth indicator for the mth province; \(\mathop {\max }\limits_{m} x_{mn}\) is the maximum value of the nth indicator for the mth province.

Step 2: Sum up the standardised values for each sub-indicator.

where \(P_{mn}\) denotes the proportion of the standardised value of the nth indicator to the sum of the standardised values for the m provinces.

Step 3: Calculate the entropy value for the nth indicator ( \(e_{n}\) ).

Step 4: Calculate the weight of the nth index.

The Entropy method described above allows us to introduce a dynamic DDF model of the common boundary network under exogenous DEA. The details of the model are described below:

Meta-frontier entropy network dynamic DDF under exogenous DEA model

Our assumption is that all provinces (P) consist of decision-making units (DMUs) due to differences in labor, capital, land, technology, or government governance efficacy, where cluster G contains P = P1 + P2 + ··· + Pg. We further set two phases of period t time (t = 1,…, T), respectively, and in each of them In each period, there are two different phases (i.e., efficiencies correspondingly): the efficiency of agricultural production and the efficiency of government support to agriculture.

There are I inputs in the agricultural production stage \(x_{ij1}^{tm} ,{ }\left( {i = 1, \ldots ,{\text{I}}} \right)\) and produces D desired outputs \(E1_{dj}^{tm} \left( {{\text{d}} = 1 \ldots \ldots {\text{D}}} \right)\) and produce D desired outputs; in the government expenditure phase there are C additional inputs \(f_{cj}^{tm} \left( {c = 1, \ldots ,{\text{C}}} \right)\) in the government spending stage and produce V desired outputs \(E2_{vj}^{tm} \left( {{\text{v}} = 1 \ldots \ldots {\text{V}}} \right)\) and G non-desired outputs \(U_{gj}^{tm} \left( {g = 1 \ldots \ldots G} \right)\) , where \({ }z_{ej}^{tm} \left( {e = 1 \ldots \ldots E} \right)\) are intermediate outputs that link (links) the agricultural production stage (Stage1) to the government farm support stage (Stage2).Finally \({ }L_{hj}^{tm} (h = 1 \ldots .H\) ) is a carry-over factor and has Q exogenous variables \({ }O_{qj}^{tm} \left( {q = 1 \ldots .Q} \right)\) . Under the common boundary, the decision unit z can choose the final output that is most favorable to its maximum value, therefore, the efficiency of DMUz under the common boundary can be solved by the following linear programming process.

Objective function:

DMU efficiency is:

Subject to:

Production efficiency stage and Government Poverty Reduction stage

Exogenous variables:

Link of the two stages.

Among them, the \({\upgamma }_{t}\) are the weights in period t, while \(w_{1}^{t}\) and \(w_{2}^{t}\) are the weights for the agricultural production efficiency stage and the government poverty reduction efficiency stage, respectively. Thus, for each time period \(w_{1}^{t}\) , \(w_{2}^{t}\) , \({\upgamma }_{t} \ge 1\) , and \(\mathop \sum \nolimits_{g = 1}^{G} \mathop \sum \nolimits_{t = 1}^{T} {\upgamma }_{tg} = 1\) .

We can calculate the following three efficiency groups by linear programming.

(1) Stage efficiency.

The efficiency of stage L (L = 1, 2) of the DMU to be evaluated is appraised relative to each period t (t = 1,…, T). The stage efficiency can be expressed as:

Stage1: Agricultural productivity values

Stage 2: Government efficiency value for poverty reduction

(2) Period efficiency value.

In this group, the overall efficiency of each period t of the DMU being evaluated is.

(3) Overall efficiency value.

The overall efficiency of the DMUs evaluated in this group. The overall efficiency is obtained by weighting the period efficiency values over t, e.g.

Group-Frontier Efficiency (GFE)

Under the cluster boundary, the decision unit can also choose the final output that is most favourable to its maximum value, therefore, the efficiency of the DMU under the cluster boundary can be solved by the following linear programming process.

(a) Objective function

The DMU efficiency is

Exogenous variables.

Link of the two periods:

We calculated the following three efficiency groups by linear programming.

(1) Stage efficiency

The efficiency of stage L (L = 1, 2) of the DMU to be evaluated is appraised relative to each period t (t = 1, . . . , T). The stage efficiency can be expressed as:

Stage1: Agricultural Productivity values

Stage2: Government efficiency value for poverty reduction

(3) Overall efficiency

The overall efficiency of the DMUs evaluated in this group. The overall efficiency is obtained by weighting the period efficiency values over t, e.g.…

Technology gap ratio (TGR)

The technical efficiency of Meta-frontier (MFE) is less than the technical efficiency of group-frontier (GFE) since g groups are included in the Meta-frontier model. The ratio is the Technology Gap Ratio (TGR):

Inputs, unexpected outputs, and expected output efficiencies

We adopted the total factor energy efficiency indicators proposed by Hu and Wang to overcome the possible bias of traditional efficiency indicators. These include eight key efficiency indicators, namely, the sown area of crops, the number of legal entities in agriculture, and financial expenditure as inputs, the gross agricultural product, the disposable income per capita of rural residents, the level of infrastructure for sustainable agricultural development, and the number of people covered by the minimum subsistence guarantee for rural residents as outputs, and the number of large and medium-sized tractors used in agriculture as a carry-over variable is also taken into account. Here, the symbol “i” stands for area, and “t” stands for time. The formula for calculating the efficiency value of the key indicators for each DMUit is as follows:

If the targeted inputs are equal to the actual inputs, the efficiency is 1; however, if the targeted inputs are less than the actual inputs, the efficiency is less than 1, indicating an overall lower efficiency. If the targeted expected output is equal to the actual expected output, the efficiency is 1; however, if the targeted expected output is greater than the actual expected output, the efficiency is less than 1, indicating overall inefficiency. If the targeted non-desired output is equal to the actual non-desired output, the efficiency is 1; however, if the targeted unexpected output is less than the actual unexpected output, the efficiency is less than 1, indicating overall inefficiency.

Empirical analysis

Data and variables.

Based on data availability and the results of the final model, we collected data for 2016–2020. Since the central financial poverty alleviation funds do not cover Beijing, Tianjin, and Shanghai, and the data for Tibet are seriously missing, we finally chose to use panel data to conduct an empirical study on 27 provinces, autonomous regions, and municipalities (excluding Hong Kong, Macao, and Taiwan) in China, with the raw data from the China Statistical Yearbook, the China Agricultural Yearbook, and the China Rural Statistics Yearbook in previous years. Yearbook, China Agricultural Yearbook, and China Rural Statistics Yearbook. The exogenous variable rainfall was obtained from the China National Meteorological Information Database. Details of the variables used in the study are shown in Table 2 :

Stage I: Agricultural Production stage

Input variables:

(A) Crop sown area (CA) is the area actually sown or transplanted with crops

(B) Agricultural business entity (ABE) is any individual or organization directly or indirectly engaged in the production, processing, marketing, and service of agricultural products. service of agricultural products.

Output variables:

(C) Agricultural GDP (AGDP) is the total amount of all products of agriculture, forestry, animal husbandry, and fishery expressed in monetary terms within a certain period (usually one year). It reflects the total scale and results of agricultural production. It reflects the total scale and results of agricultural production.

(D) Per capita disposable income of rural residents (RRDI) is the combination of final consumption expenditure and savings available to rural survey households, i.e., the income that survey households can use for discretionary purposes. Per capita disposable income of rural residents (RRDI) is the combination of final consumption expenditure and savings available to rural survey households, i.e., the income that survey households can use for discretionary purposes. Disposable income includes both cash and in-kind income.

Stage II: Government Poverty Reduction Stage

(E) Financial support for agriculture (FSA) is China’s national financial support for agriculture, rural areas, and farmers, the main means of national financial support for agriculture, rural areas, and farmers, and one of the important elements of the distribution relationship between the State and farmers. Financial support for agriculture (FSA) is China’s national financial support for agriculture, rural areas, and farmers, the main means of national financial support for agriculture, rural areas and farmers, and one of the important elements of the distribution relationship between the State and agriculture (FSA) is China’s national financial support for agriculture, rural areas and farmers, the main means of national financial support for agriculture, rural areas and farmers, and one of the important elements of the distribution relationship between the State and farmers, whose main forms of expression are capital input preferential policies and institutional construction.

Financial support for agriculture = central fiscal special funds for poverty alleviation + local government fiscal expenditure for agriculture.

(F) Sustainable Agricultural Development Infrastructure Index (ISA) is a comprehensive evaluation indicator synthesized by the entropy method: (1) postal and telecommunication facilities; (2) ecological facilities; (3) water resources, water supply, and drainage facilities; (4) energy and power facilities. (1) postal and telecommunication facilities; (2) ecological facilities; (3) water resources, water supply and drainage facilities; (4) energy and power facilities; and (5) road transportation facilities. (3) water resources, water supply, and drainage facilities; (4) energy and power facilities; and (5) road transportation facilities.

(G) Number of rural residents guaranteed minimum subsistence allowance (MSA) is a livelihood protection system introduced by the Chinese Government for rural residents whose annual per capita net household income is below the local minimum subsistence standard. A straightforward explanation is that “low security” is the same as the minimum subsistence guarantee. A straightforward explanation is that “low security” is the same as the minimum subsistence guarantee.

(H) Number of agricultural large and medium-sized tractors (ALMT) is the number of medium and large tractors used for agricultural production and other related farming activities.

(I) Annual rainfall(R) is a measure of how much precipitation falls on an area. Specifically, it is the depth to which liquid and solid (melted) precipitation falling from the sky to the ground has accumulated on the horizontal plane without evaporation, infiltration, or loss. Specifically, it is the depth to which liquid and solid (melted) precipitation falling from the sky to the ground has accumulated on the horizontal plane without evaporation, infiltration, or loss.

We used a modified non-expectation two-stage dynamic DDF model to analyze the estimation bias in the two-stage analysis using annual rainfall as an exogenous variable and disposable income per capita of rural residents as an intermediate linking variable. Based on these assumptions, we designed a Meta-frontier two-stage non-expectation dynamic DDF model under consideration of the effects of exogenous variables (see Fig.  1 ).

Model framework.

Descriptive statistics of relevant indicators such as inputs and outputs

Figure  2 presents input and output indicators, including input indicators for the agricultural production stage: total sown area of crops, number of legal entities in agriculture, and output indicators: gross agricultural product and disposable income per rural resident. The results of the statistical analysis of the Government’s additional input indicators for the poverty reduction stage: fiscal expenditure and output indicators: infrastructure for sustainable agricultural development and the number of rural inhabitants covered by the minimum subsistence guarantee.

Input–output statistics (2016–2020).

The main reason for the slow growth of the average total sown area (inputs) of crops in 2016–2020 is affected by the 1.8 billion mu of arable land red line policy. In 2016, China’s Ministry of Land and Resources issued the Adjustment Programme for the Outline of the National Overall Land Use Plan (2006–2020), in which it adjusted the indicators of arable land retention, basic farmland protection area, and the total scale of land used for construction at the national level and in all provinces (autonomous regions and municipalities), requiring that by 2020 the national arable land retention will be more than 1865 million mu and The basic farmland protection area will be over 1.546 billion mu, and the total scale of construction land will be controlled within 40.71939 million hectares (610.79 million mu). From the perspective of Agricultural business entity (input), the average and maximum values have increased year by year, while the minimum value has not changed significantly in the rest of the years, except for a slight increase in 2017. The maximum and average values of Agricultural GDP have shown a trend of faster growth, while the minimum value has grown slowly and fluctuated slightly. Per capita disposable income of rural residents’ maximum value, average value, and minimum value all show an increasing trend year by year. Financial support for agriculture’s maximum value, average value, and minimum value continue to increase, and the gap between the maximum value and the minimum value is getting bigger and bigger, which indicates that Financial support for agriculture has maintained a certain degree of growth trend. In terms of Infrastructure for sustainable agricultural development, the maximum value fluctuates from 2019–2020, the index started to increase in 2016, but in 2020 the index fell and hit a new low. Both its minimum and average values also fluctuate slightly. The maximum value of the non-expected output Number of rural residents guaranteed minimum subsistence allowance shows a gradual decline in 2016–2018, but then rebounds in 2019–2020, and the performance of the minimum value also fluctuates slightly, but in terms of the overall performance of the average value 2016–2019 declined yearly but regressed to the 2018 level in 2020 due to the new crown epidemic. The maximum, minimum, and average values of the carry-over variable Number of agricultural large and medium-sized tractors all show a year-on-year upward trend and peak in 2020, which also reflects the increase in the degree of mechanization of agricultural production in China.

This paper takes the Qinling-Huaihe line as the regional demarcation line between the north and the south of China, and according to the latitude of the provinces and the difference in the degree of regional economic development, the 27 provinces of China are explored based on retaining the complete provincial administrative units, and the statistics of input–output indexes of each province are shown in Table 3 :

Table 4 shows a comparison of key input–output indicators for the two regions for 2016–2020. We see that the Northern Area’s average Agricultural business entity inputs increase year on year, while Southern Areas fluctuate slightly. In terms of average Crop sown area inputs, Northern Areas has a clear comparative advantage over Southern Areas. In stage 1, Southern Areas outperform Northern Areas in terms of average agricultural GDP and per capita disposable income of rural residents, while in stage 2, the additional financial support for agriculture inputs, Northern Areas are significantly more favorable than Northern Areas. Support for agriculture inputs, Northern Areas is larger than Southern Areas, while in terms of output performance Southern Areas’ desired output Infrastructure for sustainable agricultural development is higher than that of Northern Areas in 2016, except that it is higher than that of Northern Areas in 2016. Development lags behind Northern Areas in 2017–2020, except for a slight lead in 2016, while in terms of non-desired outputs Rural residents guaranteed minimum subsistence allowance, Southern Areas’ farm household performance of the exogenous variable Rainfall is significantly higher in Southern Areas than in Northern Areas.

Comparative analysis of the overall efficiency of 27 provinces under two scenarios

We evaluate each DMU while considering (R) and excluding (R*) the effect of rainfall. Without considering exogenous variables, there are seven provinces with an overall efficiency of 0.8 or higher, with three of them in the position of the south and four in the north. After considering the exogenous variable rainfall, there are nine provinces with an overall efficiency of at least 0.8, four of which are located in the South and five in the North. The problem of underestimation of efficiency values is improved when we discuss rainfall as an exogenous variable in the model. A comparative comparison between the southern and northern regions clearly shows that the efficiency is significantly improved when exogenous variables are considered. This is shown in Table 5 :

As can be seen in Fig.  3 , there is a mechanism of interaction between rainfall and the efficiency of sustainable agricultural development. Changes in rainfall can indirectly reflect changes in the climate environment of the region. In terms of spatial distribution China’s rainfall as a whole shows a wet and rainy southeast, gradually decreasing towards the inland northwest, and the vast inland northwestern region (except for individual areas of northwestern Xinjiang) is characterized by a dry climate with little precipitation. This results in an overall increase in efficiency in both the southern and northern regions after considering rainfall as an exogenous influence. After considering rainfall as an exogenous variable, the average total efficiency score of the northern region is higher than that of the southern region in 2016, 2017, and 2018, while in 2019 and 2020 it is the southern region that has a higher total efficiency score than the northern region. Both the Southern and Northern regions showed some fluctuations in their average total efficiency scores, implying that there is still more room for improvement in both places. Without considering rainfall as an exogenous variable, the average total efficiency score of the southern region dropped significantly from 0.72 in 2016 to 0.63 in 2017, recovered slightly in 2018 and 2019, and then dropped again in 2020 to remain the same as in 2018. In contrast, the average total efficiency score for the northern region shows a decreasing trend from year to year. It declined from 0.71 in 2016 to 0.54 in 2020.

Meta-frontier Efficiency from 2016 to 2020 between the Northern Areas and Southern Areas. Rain: Rainfall.

Efficiency analysis between the agricultural production stage and government poverty reduction stage

Southern and northern provinces are generally more efficient at the agricultural production stage than at the government poverty reduction stage. The overall efficiency value of the southern provinces in the agricultural production stage is 0.743, with room for improvement. In the following, we will analyze the efficiency performance of these two stages in terms of efficiency. Agricultural production efficiency is an important criterion for evaluating the efficiency of sustainable agricultural development, so it is necessary to analyze the efficiency of the agricultural production stage. Table 6 shows that the performance of the south and the north during the period of 2016–2020 is good, with an overall average value of 0.787, which is closely related to China’s “Three Rural” policy regulation, the improvement of agricultural mechanization and the rapid development of rural revitalization. The efficiency of the agricultural production stage in the northern provinces is significantly higher than that in the southern provinces, with an average efficiency of 0.822. This is mainly since the northern provinces are China’s main grain-producing areas, for example, according to the data published by the National Bureau of Statistics of China: in 2021, the total population of the seven grain-producing provinces in the north of China was 398 million people, which accounted for 28 percent of the country’s total population. The total grain output, however, is as high as 683.118 billion jin, accounting for about 50 percent of the country. In terms of topography, China’s major plains are concentrated in the northern region, while the terrain in the southern region is mostly hilly. In addition, the area of arable land, per capita area of arable land, the degree of agricultural mechanization, agricultural population, and per capita food ownership have all contributed to the increase in agricultural GDP, which has led to a better performance in terms of efficiency in the agricultural production phase.

Effective allocation of government financial resources and macroeconomic growth affect sustainable agricultural development, and numerous literatures have found that government intervention is an important factor contributing to the variability of regional economic development in China. We introduce indicators related to sustainable agricultural development (fiscal expenditure, Infrastructure for sustainable agricultural development, and the number of rural residents with minimum subsistence guarantee) into the input and output elements of the government’s poverty reduction stage to assess the Chinese government’s public service function and sustainability. As can be seen from Table 7 , the overall average efficiency of the government’s poverty reduction stage is 0.666 lower than that of the agricultural production stage. From the evaluation results, the government poverty reduction efficiency of 0.679 in the southern provinces has a large room for improvement, and the efficiency value shows a U-shaped curve trend of decreasing and then increasing. The performance of the government’s poverty reduction efficiency in the northern provinces is 0.656, and its actual performance is also lower than expected compared to the more financial funds received from the Chinese government for poverty reduction, which actually contributes to the widening of the economic gap between the north and the south that has long been present in the Chinese economy.

An important feature of local government intervention in the microeconomic sector at all levels in China is administrative intervention in or control of the allocation and pricing of key factor markets within their jurisdictions, leading to distortions in factor markets as factor market reforms lag behind product market reforms. Specifically, due to the greater integration of southern China into the global industrial chain, supply chain, and value chain division of labor and trade system, under the dual effect of the historical tradition of being “relatively far away from the political center” and the mechanism of “external openness to force the reform of the internal market”, the factor market distortion is caused by the lagging behind of factor market reform compared with product market reform. The two factors are mutually reinforcing. In the Southern Plate, the government’s intervention and control of financial subsidies, financial markets, land markets, and other key factor resources are more in line with the principle of fair competition in the market, so that the development and operation mechanism of key factor markets are relatively perfect, and the dominant role of the market competition mechanism in the operation of the national economy is more prominent. On the contrary, in the face of the competitive pressure from the economic development and industrial development advantages of the southern sector, the government of the northern sector of China, in the process of attracting investments and promoting industrial development, is more inclined to adopt preferential policies and government subsidies that are contrary to the market competition mechanism, such as intervening in and controlling the distribution and pricing of specific key factors in the region. As a result, the market-oriented reform process, including product market-oriented reform and factor market-oriented reform, has lagged behind that of the Southern China region, which has resulted in the Southern China government’s poverty reduction efficiency being significantly higher than that of the Northern China government.

As can be seen in Fig.  4 , 13 of the 27 provinces are less efficient in the second stage than in the first stage, and 14 provinces are more efficient in the second stage than or the same as in the first stage. In the second stage, Yunnan province has the largest improvement in efficiency value of 0.712, which is 0.167 higher than the first stage. Jilin province has the largest regression in efficiency from an efficiency value of 1.000 in the agricultural production stage to 0.448 in the second stage. for both stages, Guangdong and Ningxia have an efficiency value of 1.

Efficiency of the Two-stage comparison of 27 provinces under exogenous variables rainfall.

Comparative analysis of the TGR in the region

As can be seen in Fig.  5 , there is a gap between the technical variance rates of the northern region and the southern region when rainfall is considered as an exogenous variable. However, the gap is not very large, and the technical discrepancy rate in the northern region is above 0.8. In 2019, the northern region is higher than the southern region, but the gap is the smallest. On the whole, the rate of technological difference is higher in the northern regions of China, and there is more room for improvement. Therefore, the authorities should take measures to strengthen the sustainable governance of agricultural economic development and implement more intensive governance in the northern region.

Technology gap from 2016 to 2020 between the North- and South Region.

Analysis of the efficiency values of major inputs and outputs

As can be seen from Table 8 , the efficiency performance of the main input–output indicators for 2016–2020 is inconsistent. Due to space constraints, we are unable to list the efficiency values for all input–output indicators. Therefore, we report the efficiency values of the key indicators in two stages.

Input indicators

(1) Crop sown area (CA). The CA efficiency in the northern provinces is higher than that in the southern provinces. In terms of agricultural resources, climatic conditions, terrain topography, and agricultural mechanization conditions, the comparative advantage of the north is more obvious, which increases efficiency. The CA efficiency of the north reached a peak of 0.906 in 2016; the lowest efficiency year was in 2018, with an efficiency of 0.855, while the other three years were around 0.9, and from the trend of evolution showed a U-shaped inter-annual fluctuation of decreasing then increasing Trend. The CA efficiency value in the South, on the other hand, after reaching a peak of 0.906 in 2016, shows a year-on-year decreasing trend, with more room for improvement. This is also consistent with previous research, which found that CA efficiency in the southern provinces during the agricultural production stage is affected by multiple factors such as plot geometry, operational behavior, cropping patterns, and the level of agricultural mechanization. Unlike the cropping pattern of large farms in northern China, the small-plot compact cropping pattern is typical of agriculture in southern China, which is the reason for the low CA efficiency.

(2) Agricultural business entity (ABE). The overall efficiency of the North and South is above 0.8 every year, in which the ABE efficiency of the South performs better than that of the South, exceeding 0.9 in four years and 1 in one year. The ABE efficiency of the North performs slightly worse, exceeding 0.9 in only one year and exceeding 0.8 in another four years. Considering the gap between the North and the South, in the early stage of China’s reform and opening-up, the North was the centre of China’s manufacturing industry, and the development of the Agricultural business entity development relied on factors and investment to drive ahead of the development of the South, but led to the insufficient endogenous impetus for market-oriented reforms in the North, and the South relied on shipping and Yangtze River inland navigation to rise rapidly through market mechanism innovation. As the market in the south is more perfect, the marketisation of agricultural products is also easier to form a scale compared to the north Agricultural business entity’s agglomeration effect has a huge impact on the agricultural economy. For example, the marketization of agricultural products can promote China’s agricultural market resources more optimized, thus playing a role in reducing costs, and then increasing the disposable income of farmers, farmers realize the benefits of agriculture and will be more willing to enter the labor market, can bring more and more inexpensive manpower costs for the Agricultural business entity, thus forming a virtuous circle. In turn, the efficiency of ABE is better in the South than in the North.

(3) Financial support for agriculture (FSA). Financial support for agriculture in general public budget expenditures reflects the actual financial resources at the disposal of local governments in China after receiving transfer payments from the central government, which can truly map the differences in financial supply capacity among localities and the degree of importance attached to the industry. From the results of descriptive statistics, the North has more financial resources in the initial allocation than the South. Then, the FSA efficiency in the secondary allocation needs to be improved, and there is a tendency to expand between the northern and southern regions. The FSA efficiency of the southern provinces is consistently greater than 0.9, with the highest value of 0.956 in 2019. the lowest value of 0.905 in 2020. while the highest FSA efficiency of the southern provinces was in 2016, with an efficiency of 0.901. the performance of the rest of the years is stable between 0.878 and 0.880, with a large room for improvement.

(4) Infrastructure for sustainable agricultural development (ISA). The difference between the southern region and the northern region is not obvious, and the performance results show that the lowest value of ISA efficiency in the southern region appeared in 2020 at 0.833, and the highest value appeared in 2017 at 0.885. The rest of the years fluctuated between high and low values, which indicates that the ISA efficiency is highly unstable, and there is room for improvement. In the northern region, the lowest value of ISA efficiency appeared in 2018 as 0.839, and the highest value appeared in 2016 as 0.888, showing a U-shaped curve trend of decreasing and then increasing, but there is also a high room for improvement.

Output indicators

(1) Agricultural GDP (AGDP). Both regions have efficiency values greater than 0.9 each year, with the Northern region performing better, with AGDP efficiency greater than 0.960 each year and reaching the optimal production frontier in two years, with an efficiency value of 1. However, there is also a downward trend, from 1 in 2016 and 2017 to 0.965 in 2020. This may be due to the increase in costs due to the NKP outbreak and the intense competition in the marketplace, which thereby reduces AGDP efficiency. The Southern region also performed better, with AGDP efficiencies greater than 0.930 each year and 1 year with an efficiency value of 1. However, what raises concern is that the Southern region’s AGDP efficiency performance shows a trend of incremental decline. This may be related to the fact that the coastal cities of Guangxi, Zhejiang, Fujian, and Jiangsu in the southern region are subject to more frequent natural disaster events. Mainland China is located low in the mid-latitudes, with the Pacific Ocean to the east and the world’s highest terrain, the Tibetan Plateau, to the west, and the feedback relationship formed by the land and sea atmospheric systems. The strength of the polar high and the subtropical high is directly related to the degree of influence of the winter and summer winds. The increase in water temperature in the Pacific Ocean and the role of the El Niño phenomenon on atmospheric circulation and the source of storms have led to numerous and frequent climatic and oceanic disasters in China in recent years, which have seriously affected the performance of China’s AGDP efficiency.

(2) Per capita disposable income of rural residents (RRDI). The performance of RRDI efficiency is positive. Except for 2020, the RRDI efficiency in both the Southern and Northern regions maintained an upward trend. The RRDI efficiency of the Southern region increased from 0.943 in 2016 to 0.989 in 2019, but the efficiency score in 2020 fell back to the 2017 level, which needs to be taken into account by the relevant authorities. In contrast, the RRDI efficiency in the northern region grew from 0.886 in 2016 to 0.965 in 2019, although it does not perform as well as the southern region in terms of specific efficiency scores. However, the northern region is higher than the southern region in terms of incremental performance and performs better in 2020, which reflects the greater resilience of RRDI efficiency in the northern region. However, some effective measures need to be taken to stabilize RRDI efficiency and stop its decline.

(3) Number of rural residents guaranteed minimum subsistence allowance (MSA). The performance of MSA shows that it is the most efficient of all the output indicators, and the efficiency of MSA in both the South and the North is very good. The annual efficiency of the South region in 2016–2020 is all above 0.970, with the highest efficiency of 1.000 and the lowest efficiency of 0.980. And the annual average efficiency of the North region is also above 0.970, with the highest efficiency of 1.000 and the lowest efficiency shows that the living conditions of the disadvantaged groups in rural areas have been significantly improved, which is also consistent with the fact that China’s decision on winning the battle against poverty, considered by the Political Bureau of the Central Committee of the Communist Party of China (CPC) in 2015, will result in a major historic achievement in China’s fight against poverty by 2020.

Causal network diagram analysis

In order to investigate the causal relationship between rainfall, poverty reduction efficiency in agricultural production and region, this paper uses Pearl 63 , who combined graph theory with Bayesian probability formulas, to propose the concept of Bayesian networks to analyze the causal relationship between the variables. Bayesian network (BN), is a directed acyclic graph that represents causal relationships between variables. The nodes represent the probability distributions of the variables, the directed edges between the nodes represent the causal relationships characterized by conditional probabilities, and the nodes are connected into a mesh by using the Bayesian conditional probability formula. The conditional probability distributions of the network nodes are obtained through prior knowledge and observation data, and the Bayesian network can calculate the posterior conditional probability distributions of other nodes to realize prediction or causal inference. Its expression is:

where \(R_{{{\text{BN}}}}\) denotes the Bayesian network structure; \(F\) is the set of directed edges; and \(X_{i}\) is the set of all nodes in the network.

It is known that the Bayesian network structure contains the conditional independence assumption, i.e., under the condition that the parent node is known, each node is independent of the nodes that are not its descendants, and the conditional independence assumption expression is:

where \(X_{i}\) denotes the parent node of \(X_{j}\) ; P ( \(X_{i}\) ) represents the probability of the event occurring in the parent node; and \(X_{n}\) represents the set of non \(X_{i}\) child nodes.

For the study of Bayesian network models, there are usually two methods, structure learning and parameter learning. Structure learning refers to the process of using specific algorithms to extract the internal topology between variables and optimize the local structure of the network with the help of known data and prior knowledge. Parameter learning is the process of determining the conditional probabilities of different nodes of a Bayesian network as well as specific parameters based on actual data as well as the experience of professionals. Rainfall, agricultural production and government poverty reduction is a system with a complex interaction mechanism, and the same evaluated unit may face different risks in different time and space, so it is extremely difficult to perform data statistics directly, which cannot meet the data set needed for Bayesian structured network learning and parameter learning. Therefore, this study mainly utilizes the way of parameter learning based on actual data to confirm the occurrence probabilities of each node P( \({X}_{i}\) ), P( \({Y}_{i}\) ) and P( \({Z}_{i}\) ). Since the nodes studied in this paper are all binary events, the probability that the studied node does not occur can be determined when the occurrence probability of the studied node is determined, as explained in Table  9 :

Where P(X = 1) denotes South, P(Y = 1) denotes Improved and P(Z = 1) denotes the effect of rainfall is considered. According to the results of parameter learning based on real data can be derived:

P (Y = 1|X = 1,Z = 1) = 0.1012

P (Y = 1|X = 1,Z = 0) = 0.0160

P (Y = 1|X = 0,Z = 1) = 0.1765

P (Y = 1|X = 0,Z = 0) = 0.0062

According to the above calculation process, we get the a priori probabilities and conditional probabilities of the variables, and substitute the probabilities of all the nodes into the convex combination of Fig.  6 , where A is a point that is free to move between the line segments (1) and (3), and B is a point that is free to move between the line segments (2) and (4), we can get the four statements.

Convex combination.

Statement 1

Point A > Point B, For Statement 1 to hold, it is implicitly necessary that point A be as close as possible to point (3), which means that the northern region has a higher probability of using rainfall as an exogenous variable compared to the southern region. That is: region → rainfall.

Statement 2

Point (3) > Point (4), the implication of which is that the northern region has a higher probability of improvement after considering rainfall than without considering rainfall. That is: region → rainfall → efficiency improvement.

Statement 3

Point (2) > Point (4), the implication of which is that the southern region has a higher probability of efficiency improvement than the northern region if rainfall is ignored. That is: region → efficiency improvement.

Statement 4

Point (3) > Point (1), the implication of which is that if rainfall is taken into account, the probability of efficiency improvement is higher in the Northern region than in the Southern region. I.e.: region → efficiency improvement.

Finally we used Netica 7.01 free version software ( https://norsys.com/download.html .) to complete the Bayesian network visualization and the results are shown in Fig.  7 :

Bayesian network for area, rainfall and efficiency improvements after calculating conditional probability tables (2016–2020).

Conclusions and policy recommendation

Empirical analysis conclusions.

In this paper, using rainfall as a proxy variable for the atmospheric environmental system, the dynamic DDF model of the common boundary network under the introduction of exogenous DEA was used to measure the agricultural production efficiency and government poverty reduction efficiency of 27 provinces in China from 2016 to 2020, and the dynamic changes in the efficiency of the agricultural production stage and the government poverty reduction stage in the southern region and the northern region of China were discussed, and the results of the study showed that:(1) rainfall as an exogenous variable has a significant effect on agricultural production efficiency. When rainfall is taken into account, the overall efficiency increases in both the southern and northern regions, suggesting that changes in rainfall indirectly reflect the impact of the climate environment on sustainable agricultural development. This provides a valuable reference for future agricultural planning and water management, emphasizing the importance of considering meteorological factors when assessing agricultural efficiency. (2) The relatively lower efficiency of government poverty reduction in the northern provinces compared to the southern provinces is associated with more frequent natural disaster events. This suggests that rainfall not only affects agricultural production but also has a significant impact on the efficiency of the government’s poverty reduction phase. In contrast, the U-curve trend in government poverty reduction efficiency in the southern provinces may be affected by the frequency of natural disasters. There is a need to focus on the root causes of the economic gap between the North and the South and formulate appropriate policies to enhance the efficiency of poverty reduction. (3) The study also reveals that there are differences in market-based reforms between the South and the North, with different levels of intervention in factor markets. The government of the southern region pays more attention to the principle of fair competition in the market, while the government of the northern region is more inclined to adopt preferential policies and subsidy strategies, which leads to a relative lag in the market-based reform process in the northern plate, thus affecting the government’s efficiency in poverty reduction. This signifies that more attention should be paid to the development and operation of the market mechanism in poverty reduction policy formulation to improve the government’s efficiency in poverty reduction.

Policy recommendations

Combined with the findings of this paper, the following policy implications can be drawn: (1) Enhancing Agricultural Production Efficiency and Sustainability. To improve agricultural production efficiency and sustainability, it is recommended to establish a nationwide meteorological information system. This system should monitor and predict rainfall patterns, facilitating the development of a responsive agricultural planning framework. Additionally, workshops for farmers on climate-smart agricultural practices should be organized to ensure adaptive and informed farming methods. (2) Improving Disaster Resilience in Northern Provinces. Addressing the relatively lower efficiency of government poverty reduction in northern provinces, particularly due to frequent natural disasters, requires the creation of a dedicated fund for disaster-resilient infrastructure. This fund should support initiatives such as early warning systems, community-based disaster preparedness training, and the construction of resilient shelters. Regular drills and simulations should also be conducted to enhance community response during natural disasters. (3) Rectifying Disparities in Market-Based Reforms. To rectify the disparities in market-based reforms between the Southern and Northern regions, a comprehensive review of market intervention policies in the northern provinces is recommended. Incentives should be provided to businesses adhering to fair competition principles, and training programs for government officials on market-oriented strategies and fair competition principles should be developed. (4) Promoting Fair Competition in Factor Markets. Promoting fair competition in factor markets in the northern region can be achieved through awareness campaigns and guidance to businesses on ethical market practices. The creation of a certification system for businesses adhering to fair competition principles, along with collaboration between the government and industry associations, can further ensure the enforcement of fair competition. (5) Efficiency in Poverty Reduction. Finally, to improve the efficiency of poverty reduction, it is crucial to establish a task force dedicated to studying and recommending improvements to market mechanisms in poverty reduction policies. Pilot projects in the northern provinces should focus on market-driven approaches, with subsidies aligned with fair competition principles. Regular assessments and adjustments to policies based on the performance of these pilot programs are essential for continual improvement.

Data Availability

Data is provided within the manuscript.

Khan, A. R. & Riskin, C. Inequality and Poverty in China in the Age of Globalization (Oxford University Press, 2001).

Book   Google Scholar  

World Bank. Four Decades of Poverty Reduction in China: Drivers, Insights for the World, and the Way Ahead (The World Bank, 2022). https://doi.org/10.1596/978-1-4648-1877-6 .

UNDP. Goal 1: No poverty | Sustainable Development Goals | United Nations Development Programme. Retrieved from https://www.undp.org/sustainable-development-goals/no-poverty (2016).

IPCC, (2018). An IPCC Special Report on the Impacts of Global Warming of 1.5°C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty, ed.

Li, P. & Huang, Y. Agricultural trade liberalization and China’s agricultural total factor productivity. East China Econ. Manag. 9 , 49–58. https://doi.org/10.19629/j.cnki.34-1014/f.210422013 (2021).

Article   Google Scholar  

Liu, Y. & Feng, C. What drives the fluctuations of “green” productivity in China’s agricultural sector? A weighted Russell directional distance approach. Resour. Conserv. Recycl. 147 , 201–213 (2019).

Wang, L., Tang, J., Tang, M., Su, M. & Guo, L. Scale of operation, financial support, and agricultural green total factor productivity: Evidence from China. Introd. J. Environ. Resour. Public Health 19 , 9043 (2022).

Zhang, Z., Hu, B. & Qiu, H. Comprehensive evaluation of resource and environmental carrying capacity based on SDGs perspective and Three-dimensional Balance Model. Ecol. Indic. 138 , 108788 (2022).

Chen, Y., Miao, J. & Zhu, Z. Measuring green total factor productivity of China’s agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO 2 emissions. J. Clean. Prod. 318 , 128543 (2021).

Article   CAS   Google Scholar  

Li, R. et al. Geological resources and environmental carrying capacity evaluation review, theory, and practice in China. China Geol. 1 , 4 (2018).

Google Scholar  

Li, X., Zhang, Y. & Liang, L. Measure of agricultural production input/output efficiency and the spatial disparity analysis in China. Custos E Agronegocio Line 13 , 408–420 (2017).

Zhang, L. C. & Dong, Y. G. The impact of foreign investment on food security in developing countries. J. South China Agric. Univ. 20 , 95–106 (2021).

Huang, Y. et al. Social impact assessment of photovoltaic poverty alleviation program in China. J. Clean. Prod. 290 , 125208 (2021).

Smith, L. & Frankenberger, T. R. Does resilience capcity reduce the negative impact of shocks on household food security? Evidence from the 2014 floods in northern Bangladesh. World Dev. 102 , 358–376 (2018).

Chhabra, M., Giri, A. K., & Kumar, A. (2023). Does good governance and trade openness contribute to poverty reduction in BRICS? An empirical analysis. Australian Economic Papers.

Rutherford, M. The old and the new institutionalism: Can bridges be built?. J. Econ. Issues 29 (2), 443–451. https://doi.org/10.1080/00213624.1995.11505681 (1995).

Zhan, W. & Li, G. Experience and effectiveness measurement of poverty reduction governance in China. Econ. Manag. 02 , 17–35 (2022).

Coccia, M. How a good governance of institutions can reduce poverty and inequality in society? In Legal-Economic Institutions, Entrepreneurship, and Management: Perspectives on the Dynamics of Institutional Change from Emerging Markets (eds Faghih, N. & Samadi, H.) 65–94 (Springer, 2021). https://doi.org/10.1007/978-3-030-60978-8_4 .

Chapter   Google Scholar  

Sittha, P. V. Governance and poverty reduction in Thailand. Modern Econ. 3 (5), 487–497. https://doi.org/10.4236/me.2012.35064 (2012).

Woldekidan, H. The role of foreign aid in reducing poverty: Time series evidence from Ethiopia. J. Econ. Int. Finance 7 (3), 59–71. https://doi.org/10.5897/jeif2015.0646 (2015).

Yan, F. Urban poverty, economic restructuring and poverty reduction policy in urban China: Evidence from Shanghai, 1978–2008. Dev. Policy Rev. 2018 (36), 465–481. https://doi.org/10.1111/dpr.12303 (2018).

Boullenois, C. Poverty alleviation in China: The rise of state-sponsored corporate paternalism. China Perspect. 2020 (2020–3), 47–56 (2020).

Fan, S., Zhang, L. & Zhang, X. Growth, Inequality, and Poverty in Rural China: The Role of Public Investments (Intl Food Policy Res Inst, 2002).

Ross, M. Is democracy good for the poor?. Am. J. Polit. Sci. 50 (4), 860–874. https://doi.org/10.1111/i.1540-5907.2006.00220.x (2006).

Christiaensen, L. & Martin, W. Agriculture, structural transformation and poverty reduction: Eight new insights. World Dev. 109 , 413–416. https://doi.org/10.1016/J.WORLDDEV.2018.05.027 (2018).

Mafi Gholami, D., Baharlouii, M. & Mahmoudi, B. Vulnerability assessment of mangroves to sea level rise. Environ. Res. 10 (19), 27–39 (2019).

Sun, S. K. et al. The temporal and spatial variability of water footprint of grain: A case study of an irrigation district in China from 1960 to 2008. J. Food Agric. Environ 10 , 1246–1251 (2012).

Xue, S., Yang, T., Zhang, K. & Feng, J. Spatial effect and influencing factors of agricultural water environmental efficiency in China. Appl. Ecol. Environ. Res. 16 , 4491–4504 (2018).

Dai, A., Bai, J. & He, W. Investigation and analysis on the effectiveness of targeted poverty alleviation in China—Based on some counties and Cities in Guizhou and Anhui Provinces. Modern Econ. Manag. Forum 3 (2), 138. https://doi.org/10.32629/memf.v3i2.782 (2022).

Fan, S., & Chan-Kang, C. Road development. Economic Growth and Poverty Reduction in China. IFPRI Research Report: 138 (2006).

Yang, G., Wang, Y., Chang, H. & Chen, Q. Evaluating anti-poverty policy efficiencies in China: Meta-frontier analysis using the two-stage data envelopment analysis model. China Agric. Econ. Rev. 14 (2), 416–442. https://doi.org/10.1108/CAER-10-2020-0254 (2022).

Ambali, O. I., Adegbite, D. A., Ayinde, I. A. & Awotide, D. O. Analysis of production efficiency of food crop farmers in Ogun State, Nigeria. ARPN J. Agric. Biol. Sci. 7 (9), 680–688 (2012).

Apezteguía, B. I. & Gárate, M. R. Technical efficiency in the Spanish agrofood industry. Agric. Econ. 17 (2–3), 179–189 (1997).

Sikandar, F., Erokhin, V., Wang, H., Rehman, S. & Ivolga, A. The impact of foreign capital inflows on agriculture development and poverty reduction: Panel data analysis for developing countries. Sustainability 13 (6), 3242. https://doi.org/10.3390/SU13063242 (2021).

Chen, J., Yang, M., Zhang, Z., Wang, Z. & Zhang, J. Can farmland transfer reduce vulnerability as expected poverty? Evidence from smallholder households in rural China. Front. Sustain. Food Syst. 7 , 1187359 (2023).

Hu, B. & McAleer, M. Estimation of Chinese agricultural production efficiencies with panel data. Math. Comput. Simul. 68 (5–6), 474–483 (2005).

Article   MathSciNet   Google Scholar  

Yan, S., Li, L., Sarkar, A. & Yang, G. Assessing the efficiency level of the “poverty alleviation through agriculture project”: A case study of fixed observation points in China. Front. Sustain. Food Syst. 6 , 1007915 (2022).

Jiang, F. et al. Effects of rural collective economy policy on the common prosperity in China: based on the mediating effect of farmland transfer. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2023.1302545 (2023).

Wang, J., Cramer, G. L. & Wailes, E. J. Production efficiency of Chinese agriculture: Evidence from rural household survey data. Agric. Econ. 15 (1), 17–28 (1996).

Noack, F. & Larsen, A. F. The effects of farm size on productivity and income distribution in agriculture: Evidence from Uganda. Agric. Econ. 50 (6), 711–726. https://doi.org/10.1111/agec.12530 (2019).

Zhao, L., Liu, M. & Song, Z. Regional-scale modeling of rainfall-induced landslides under random 872 rainfall patterns. Environ. Model. Softw. 155 , 105454 (2022).

Olayide, O. E. & Alabi, T. Between rainfall and food poverty: Assessing vulnerability to climate change in an agricultural economy. J. Clean. Prod. 198 , 1–10 (2018).

Hagos, F., Jayasinghe, G., Awulachew, S. B., Loulseged, M. & Yilma, A. D. Agricultural water management and poverty in Ethiopia. Agric. Econ. 43 (s1), 99–111. https://doi.org/10.1111/j.1574-0862.2012.00623.x (2012).

Kyei-Mensah, C., Kyerematen, R. & Adu-Acheampong, S. Impact of rainfall variability on crop production within the Worobong Ecological Area of Fanteakwa District, Ghana. Adv. Agric. 2019 , 1–7 (2019).

Fei, R. & Lin, B. Energy efficiency and production technology heterogeneity in China’s agricultural sector: A meta-frontier approach. Technol. Forecast. Soc. Chang 109 , 25–34 (2016).

Huang, Q., Rozelle, S., Lohmar, B., Huang, J. & Wang, J. Irrigation, agricultural performance and poverty reduction in China. Food Policy 31 (1), 30–52 (2006).

Abdul-Rahim, A. S., Sun, C. & Noraida, A. W. The impact of soil and water conservation on agricultural economic growth and rural poverty reduction in China. Sustainability 10 (12), 4444 (2018).

Asiimwe, J. B. (2007). Implications of rainfall shocks for household income and consumption in Uganda.

Liu, X. & Zeng, F. Poverty reduction in China: does the agricultural products circulation infrastructure matter in rural and urban areas?. Agriculture 12 (8), 1208 (2022).

Cook, S., Fengrui, L. & Huilan, W. Rainwater harvesting agriculture in Gansu Province, people’s Republic of China. J. Soil Water Conserv. 55 (2), 112–114 (2000).

Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11 (4), 306–312. https://doi.org/10.1038/s41558-021-01000-1 (2021).

Article   ADS   Google Scholar  

Chung, Y. H., Fare, R. & Grosskopf, S. Productivity and undesirable outputs: a directional distance function approach. J. Environ. Manag. 51 , 229–240 (1997).

Chen, P. C., Yu, M. M., Chang, C. C., Hsu, S. H. & Managi, S. The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO 2 emissions. Omega 53 , 30–40 (2015).

Färe, R., Grosskopf, S. & Whittaker, G. Network Data Envelopment Analysis. In Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis (eds Zhu, J. & Cook, Wade D.) 209–240 (Springer, 2007).

Kao, C. Efficiency decomposition in network data envelopment analysis: A relational model. Eur. J. Oper. Res. 192 , 949–962. https://doi.org/10.1016/j.ejor.2007.10.008 (2009).

Färe, R. & Grosskopf, S. Network DEA. Socio-Econ. Plann. Sci. 34 , 35–49 (2000).

Färe, R. & Grosskopf, S. Directional distance functions and slacks-based measures of efficiency. Eur. J. Oper. Res. 200 (1), 320–322 (2010).

Shannon, C. E. A mathematical theory of communication. Bell Syst. Technol. J. 27 , 623–656 (1948).

O’Donnell, C. J., Prasada Rao, D. S. & Battese, G. E. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir. Econ. 34 (2), 231–255 (2008).

Teruel, R. G. & Kuroda, Y. Public infrastructure and productivity growth in Philippine agriculture, 1974–2000. J Asian Econ. 16 (3), 555–576 (2005).

Geng, J. & Li, C. Empirical research on the spatial distribution and determinants of regional E-commerce in China: evidence from Chinese provinces. Emerg. Mark. Finance Trade 56 , 3117–3133 (2020).

Wang, J., Tong, J. & Fang, Z. Assessing the drivers of sustained agricultural economic development in China: Agricultural productivity and poverty reduction efficiency. Sustainability 16 (5), 2073 (2024).

Pearl, J. Probabilistic reasoning in intelligent systems: networks of plausible inference san mateo. Computer Science Artificial Intelligence. (1988).

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This research was funded by Fujian Social Science Foundation Youth Project (FJ2024C026) and Fuzhou Key Research Base of Social Sciences Min Merchants Research Center (2023FZB70) for financial support.

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Canr honors uyeh as 2024 global scholar in research.

April 19, 2024

CANR to honor Daniel Uyeh, Ph.D. as 2024 Global Scholar in Research

The Michigan State University College of Agriculture and Natural Resources has named Daniel Uyeh, Ph.D., as the 2024 Global Scholar in Research. Uyeh will be honored at the CANR Faculty and Staff Award Reception on May 2.

The Global Scholas Program supports established, early and mid-career faculty members with seed funding and travel support for two years. Selected annually by the CANR International Programs Office, scholars work to strengthen and expand their global linkages, networks and collaborative programs across three core missions of the college in diverse areas of research, education and outreach.

Daniel Uyeh, Ph.D., is an associate professor in the Department of Biosystems and Agricultural Engineering. Uyeh conducts multidisciplinary research in climate-smart decision support systems, with research focuses across climate-smart agriculture, decision support systems, system modeling and AI in agriculture. Uyeh’s work related to the development of such systems aims to enhance agricultural decision making, leading to better adaptation and resilience to forthcoming climate change. Prior to joining BAE, Uyeh was a research professor at the Upland Field Machinery Research Center, and researcher at the Smart Agriculture Innovation Center at Kyungwook National University.

The Global Scholars Program began in summer 2019 after  Karim Maredia  was named director of CANR international programs. The program centers around a faculty development initiative that grows the college’s global footprint and forms lasting international partnerships.

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What is top of mind for dairy executives in 2024?

As the global population grows, agriculture systems everywhere must find ways to feed humankind sustainably. The US dairy industry is no exception. C-suite and environmental, social, and governance (ESG) leaders in the sector are shaping dairy’s path to a sustainable future, but at the same time, they are being asked to account for new innovations, regulations, and economic factors.

To gain insight into how dairy executives’ priorities have shifted over time, McKinsey and the International Dairy Foods Association conducted their sixth annual survey of dairy executives in fourth quarter 2023 (see sidebar, “About the research”). 1 To explore the results from last year’s survey, see Christina Adams, Ludovic Meilhac, Kate Toews, and Roberto Uchoa, “ Top priorities for dairy executives in 2023 ,” McKinsey, March 27, 2023. In interviews and responses to survey questions, respondents shared what excites them about dairy, emerging challenges in the sector, and more.

About the research

In fourth quarter 2023, the International Dairy Foods Association and McKinsey jointly surveyed nearly 80 executives and company leaders in the dairy industry, which included a poll and interviews with 38 industry leaders. Participants came from a variety of company types (processors, producers, retailers, packaging companies, and more), ranging in size from large to small. The companies represented are primarily headquartered in the United States but include organizations from other parts of North America, Europe, and Oceania.

To ensure that respondents were not being asked to give feedback on areas outside their expertise, the survey differentiated among respondents based on their role and whether their company has a sustainability or environmental, social, and governance (ESG) strategy. Only those in C-level and vice president roles from relevant fields (strategy and operations) were asked questions about the company’s business strategy and operations, a new area of inquiry in the 2023 survey, and only those with an ESG or sustainability strategy were asked about sustainability.

To quantify interview data, keywords mentioned in question responses were coded, grouped by subject, and compiled to form totals.

A number of new findings emerged from this survey. The previous survey showed that concerns were divided between growth, resilience, and sustainability, but sustainability has moved to the center of the conversation. We have seen progress on sustainability commitments and actions, but challenges with addressing farm-level emissions remain. Other findings indicate that although there is much work to be done, dairy executives are optimistic about the future of the industry.

What excites leaders in dairy?

Executives expressed excitement about many aspects of the dairy industry, from international opportunities to novel technologies. As in the 2022 survey, a few topics stood out during our interviews: growth, product nutrition, and innovation (Exhibit 1).

Dairy executives were most excited about growth, including growth in the industry, companies, and consumer demand. More than half of the executives we interviewed talked about the potential for growth, and more than 40 percent cited growth as their top source of excitement. This share of executives is similar to that of the 2022 survey, reflecting the sector’s overall momentum: the retail value of the overall dairy market in the United States grew by 9 percent from 2021 to 2022 and by 7 percent from 2022 to 2023. 2 Euromonitor US Retail Market Size Database, December 2023. According to our interviews, much of this growth was driven by pricing, though executives expect future growth to be propelled more by volume. Growth forecasts vary by product, but dairy overall is expected to grow 4 percent annually from 2024 through 2027, with most growth coming from cheese and yogurt. 3 Euromonitor US Retail Market Size Database, December 2023.

Dairy is just scratching the surface of unlocking value for consumers around the world. Dairy executive

Product nutrition and innovation

Almost 30 percent of dairy executives we interviewed were most excited about the nutrition or “goodness” of their products, from the high nutrient density of yogurt to the potential for dairy protein ingredients. In addition, one-third of the executives we interviewed were excited about product innovations that allow them to better harness the nutrition of dairy. Excitement about innovation is not new—as far back as the 2018 Executive Sentiment Survey, product innovation was the second-most-cited response to the question, “What do you believe is your company’s top source of competitive advantage?” (17 percent).

What keeps dairy leaders up at night?

Although there is much to be excited about in dairy, industry leaders do not wear rose-tinted glasses when looking toward the future. When we asked dairy leaders about their biggest concerns (what keeps them up at night), the most frequent answers were sustainability and regulation (Exhibit 2).

Sustainability

When asked what keeps them up at night, executives were most likely to cite sustainability (19 percent of interviewees). About three-quarters of survey respondents said their sustainability efforts are motivated by their customers (retailers and other dairy vendors), and just under half said the same of consumers. This finding is relatively consistent with 2022 results. However, interviewed executives said that consumers may not be willing to pay more for sustainable products, raising understandable concerns about the costs associated with decarbonization and other sustainability initiatives. Still, some signals suggest that ESG could be growing in importance for consumers. In a recent joint analysis with Nielsen IQ, McKinsey found that yogurt and cheese products with ESG-related claims outperformed products without those claims. 4 “ Consumers care about sustainability—and back it up with their wallets ,” McKinsey, February 6, 2023.

Consumers are extremely interested in sustainability, but they are not necessarily willing to pay more. I’m not sure if this will ever change. People will tell you they are willing to pay for it, but they won’t. Dairy executive

Great progress has already been made in reducing the carbon intensity of dairy. In the United States, emissions per kilogram (kg) of milk dropped by 27 percent from 1995 to 2015, from 0.75 kg CO 2 equivalent (CO 2 e) per kg of milk to 0.55 kg CO 2 e per kg of milk. 5 FAO. Emissions intensities. License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/faostat/en/#data/EI. Date of Access: April 1, 2024. However, US dairy production has outpaced this rate of change (a 34 percent increase over the same period to 208 billion pounds), resulting in increased emissions overall. 6 National Agricultural Statistics Service survey on national milk production, from “Quick stats,” US Department of Agriculture, accessed April 10, 2024. There also remains great uncertainty about how to address Scope 3 emissions—that is, upstream emissions outside a company’s direct operational control, such as enteric emissions from cows and emissions from manure. 7 For more on Scope 3 emissions, see “ The Scope 3 challenge: Solutions across the materials value chain ,” McKinsey, May 5, 2023. Scope 3 emissions are inherently difficult to measure, particularly at the farm level.

One challenge when it comes to sustainability is how we control the cost, from carbon credits to biodigesters. The more go-betweens you have between the farmer and the processor, the more expensive it gets. Dairy executive

Many dairy leaders also cited regulation as a topic on their minds (13 percent). This makes sense, given that in the United States, national legislation affecting the dairy industry, including the Federal Milk Marketing Orders and the Farm Bill, have recently and will be revisited and renewed. Of particular interest is the Farm Bill, which covers a large suite of conservation programs and associated funding, including the $18 billion expansion of funds granted in the Inflation Reduction Act for agricultural practices that can mitigate and reduce greenhouse gas emissions (GHG). In addition, on March 6, 2024, the US Securities and Exchange Commission adopted rules to enhance and standardize climate related disclosures, adding pressure on sustainability transparency. 8 “SEC adopts rules to enhance and standardize climate-related disclosures for investors,” SEC, March 6, 2024.

The past few years have also seen new environmental regulations concerning dairy. For example, California Senate Bill 1383, enacted in 2020, requires dairies to reduce methane emissions by 40 percent from 2013 levels by 2030. Dairy leaders are also looking at environmental regulation abroad and considering what the impact could be if the United States follows suit. Examples include the European Green Deal and New Zealand’s upcoming carbon tax on farm emissions.

What priorities have changed in importance?

Since the 2022 survey, dairy executives’ leading priorities have shifted toward ESG topics. Talent saw the biggest increase in priority, rising from fourth to second place (Exhibit 3). Sustainability experienced the second-highest increase (sixth to fifth place), trading places with supply chain, one of 2022’s critical topics.

In the 2023 interviews, 45 percent of executives noted that talent is less of a concern now than it was in the past few years. This may be because leaders were particularly concerned about talent in 2022, with about 70 percent of executives sharing their concerns about labor in 2022 interviews. However, survey results indicate that the past year’s concern is this year’s strategic priority: in 2023, 60 percent of executives cited talent as a strategic priority, moving it two places up the rankings.

We also noted a shift in how executives view the labor challenge. One executive said, “It is more a generational change. I doubt the new people we hire will retire here.” In fact, a 2022 McKinsey survey of 1,763 Gen Z Americans (aged 18 to 24) found that 77 percent of them are searching for a new job. 9 “ How does Gen Z see its place in the working world? With trepidation ,” McKinsey, October 19, 2022. At the same time, dairy companies are trying to make the workplace more appealing to potential employees. Our interviews with dairy leaders revealed three common approaches to managing labor issues: compensation, culture, and process. Dairy companies are considering increasing wages and benefit packages, focusing on company culture, and investing in operational technology to help attract and retain talent.

We are focusing on making this a great place to work, connecting to purpose and values. Dairy executive

Environmental sustainability

In addition to keeping executives up at night, sustainability is a strategic priority. For example, 60 percent of processors said that reducing GHG emissions was a top three issue in their sustainability strategy, particularly since addressing Scope 3 and farm-level emissions is so challenging. According to our survey, 71 percent of companies are measuring farm emissions, but only 27 percent are changing procurement based on emissions, and an even smaller portion—7 percent—are mandating that farms inset their emissions reductions (rather than sell outside the value chain).

Sustainability is very high on our agenda. A mentality shift is needed to decarbonize the industry, and for a while, there has been talk but no action. Now we are seeing things intensify. Dairy executive

Companies are also considering their impact on natural capital. Forty-five percent of companies cited water usage as a top three issue in their sustainability strategy. And when asked about how they are addressing nature and natural capital, 44 percent said they were sourcing feed grown with regenerative-agriculture practices, and 20 percent said they were incorporating recommendations from the Taskforce on Nature-related Financial Disclosures.

To build and enact sustainability strategies, almost 90 percent of the surveyed dairy companies currently have a sustainability or ESG lead on staff. Of these leads, 77 percent are dedicated full-time employees, up from about 60 percent in 2022. These sustainability leaders will need their companies to support them in taking concrete actions if they want to reach net-zero emissions.

What’s next?

Moving forward, sustainability looms large. Indeed, decarbonization of the dairy industry is already intensifying. At the UN Climate Change Conference (COP28) in 2023, several dairy companies (representing about 5 percent of global milk intake) announced the formation of the newly established Dairy Methane Action Alliance. This effort builds on prior industry sustainability efforts, such as the US Dairy Net Zero Initiative, launched in 2020. 10 “U.S. Dairy Net Zero Initiative,” Dairy Management, accessed April 3, 2024. Members of the Dairy Methane Action Alliance plan to address methane from across their dairy supply chains, including Scope 3 emissions. 11 Simon Harvey, “COP28 - Nestlé, Danone among food signatories to Dairy Methane Alliance,” Just Food, December 5, 2023.

Alongside sustainability, companies are taking steps to address labor needs and a challenging inflationary environment, which will likely remain necessary for the foreseeable future. As we look to 2024 and beyond, executives should consider the following areas:

• A path to net zero, including measuring and addressing Scope 3 emissions, is complex. Meeting the dairy industry’s goal of net zero by 2050 will require industry-wide alignment and meaningful actions, such as working with farms to reduce emissions via initiatives like manure management or novel feed additives. Companies can help push the industry forward by building out transparency and measurement capabilities, such as via integrated enterprise resource planning systems (ERPs).
• The forces pushing sustainability in the industry include consumer preferences, shareholder and lender pressure, and regulatory action. Companies should carefully track shifts across these dimensions and stay ahead of trends and requirements.
• The labor market is shifting. Taking steps to appeal to the current generation of workers will be critical. This may include offering flexible work schedules and staying up to date on best-in-class benefit offerings.

With exciting prospects on the horizon, the dairy industry has an opportunity to seize the moment and develop a more sustainable approach to dairy production. Global pushes such as the newly announced Dairy Methane Action Alliance, requirements from shareholders and retailers, and consumer preferences mean that the time is now for the dairy industry to develop a unified plan to tackle some of its biggest sustainability obstacles. If leaders can lean into innovations and make progress on emissions, they can set themselves up well in the years to come.

Rudolf Henkell-von Ribbentrop is an associate partner in McKinsey’s Washington, DC, office; Ludovic Meilhac is a partner in the Stamford office; and Emmy Moore is a consultant in the Bay Area office.

The authors wish to thank Christina Adams, Melanie Lieberman, and Elizabeth Yablon for their contributions to this article.

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Fighting Hunger and Disease, One Strain of Rice at a Time

April 23, 2024

Supporting Louisiana’s Rice Farmers

The LSU AgCenter is Louisiana rice farmers’ MVP, or most valued partner, in research and crop variety development. From creating a new market for jasmine rice, to producing varieties of rice that are better for diabetics and more sustainable and resilient to changes in the environment, LSU has been critical to the Louisiana rice industry for more than 100 years. The research also has world-wide impact since one-fifth of the global population’s calories comes from rice.

More than 60 percent of the rice Louisiana farmers plant was developed by the LSU AgCenter, with a direct economic impact of $580 million. New varieties are being created all the time, drawing large federal grants to Louisiana while supporting the state’s farmers and agricultural industry.

Last year, an LSU-led team won a $10 million grant from the U.S. Department of Agriculture to develop climate-resilient rice that requires less land, less water and less energy to grow. Other AgCenter researchers have created a high-protein, low-glycemic rice variety called Frontière that now is being studied and used internationally, while helping Eunice area farmer Michael Frugé build a small business and brand, Parish Rice, together with his father.

“Having to watch your blood sugar is the number-one reason people shy away from rice, but we’re rice people, with our gumbo and our étouffée and all of those things,” Frugé said. “As a farmer, growing rice that allows people to eat their favorite meals makes me smile, and every variety of rice we grow comes from LSU’s rice research station in Crowley—every single one.”

The LSU AgCenter is now planning a research collaboration with LSU’s Pennington Biomedical to study Frontière’s health effects, which could allow doctors and dietitians to recommend it, “almost as medicine.”

Eunice area farmer Michael Frugé with LSU AgCenter researchers Ida Wenefrida and Herry Utomo, who developed the Frontière rice variety Frugé grows and sells as Parish Rice.

– Photo courtesy of Parish Rice

“The value of LSU’s rice breeding program? Invaluable. You can’t put a price tag on it. Between 60 and 70 percent of the rice we mill are LSU varieties. Without LSU’s breeding program, our industry as a whole would wither and die—it would be hard to survive without LSU.” Bobby Hanks, CEO of Supreme Rice in Crowley, the state’s largest rice mill

Director of Strategic Research Communications LSU Office of Research & Economic Development

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