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156 Hot Agriculture Research Topics For High Scoring Thesis

Are you preparing an agriculture research paper or dissertation on agriculture but stuck trying to pick the right topic? The title is very important because it determines how easy or otherwise the process of writing the thesis will be. However, this is never easy for many students, but you should not give up because we are here to offer some assistance. This post is a comprehensive list of the best 156 topics for agriculture projects for students. We will also outline what every part of a thesis should include. Keep reading and identify an interesting agriculture topic to use for your thesis paper. You can use the topics on agriculture as they are or change them a bit to suit your project preference.

What Is Agriculture?

Also referred to as farming, agriculture is the practice of growing crops and raising livestock. Agriculture extends to processing plants and animal products, their distribution and use. It is an essential part of local and global economies because it helps to feed people and supply raw materials for different industries.

The concept of agriculture is evolving pretty fast, with modern agronomy extending to complex technology. For example, plant breeding, agrochemicals, genetics, and relationship to emerging disasters, such as global warming, are also part of agriculture. For students studying agriculture, the diversity of the subject is a good thing, but it can also make selecting the right research paper, thesis, or dissertation topics a big challenge.

How To Write A Great Thesis: What Should You Include In Each Section?

If you are working on a thesis, it is prudent to start by understanding the main structure. In some cases, your college/ university professor or the department might provide a structure for it, but if it doesn’t, here is an outline:

• Thesis Topic This is the title of your paper, and it is important to pick something that is interesting. It should also have ample material for research.
• Introduction This takes the first chapter of a thesis paper, and you should use it to set the stage for the rest of the paper. This is the place to bring out the objective of the study, justification, and research problem. You also have to bring out your thesis statement.
• Literature Review This is the second chapter of a thesis statement and is used to demonstrate that you have comprehensively looked at what other scholars have done. You have to survey different resources, from books to journals and policy papers, on the topic under consideration.
• Methodology This chapter requires you to explain the methodology that was used for the study. It is crucial because the reader wants to know how you arrived at the results. You can opt to use qualitative, quantitative, or both methods.
• Results This chapter presents the results that you got after doing your study. Make sure to use different strategies, such as tables and graphs, to make it easy for readers to understand.
• Discussion This chapter evaluates the results gathered from the study. It helps the researcher to answer the main questions that he/she outlined in the first chapter. In some cases, the discussion can be merged with the results chapter.
• Conclusion This is the summary of the research paper. It demonstrates what the thesis contributed to the field of study. It also helps to approve or nullify the thesis adopted at the start of the paper.

Interesting Agriculture Related Topics

This list includes all the interesting topics in agriculture. You can take any topic and get it free:

• Food safety: Why is it a major policy issue for agriculture on the planet today?
• European agriculture in the period 1800-1900.
• What are the main food safety issues in modern agriculture? A case study of Asia.
• Comparing agri-related problems between Latin America and the United States.
• A closer look at the freedom in the countryside and impact on agriculture: A case study of Texas, United States.
• What are the impacts of globalisation on sustainable agriculture on the planet?
• European colonisation and impact on agriculture in Asia and Africa.
• A review of the top five agriculture technologies used in Israel to increase production.
• Water saving strategies and their impacts on agriculture.
• Homeland security: How is it related to agriculture in the United States?
• The impact of good agricultural practices on the health of a community.
• What are the main benefits of biotechnology?
• The Mayan society resilience: what was the role of agriculture?

Sustainable Agricultural Research Topics For Research

The list of topics for sustainable agriculture essays has been compiled by our editors and writers. This will impress any professor. Start writing now by choosing one of these topics:

• Cover cropping and its impact on agriculture.
• Agritourism in modern agriculture.
• review of the application of agroforestry in Europe.
• Comparing the impact of traditional agricultural practices on human health.
• Comparing equity in agriculture: A case study of Asia and Africa.
• What are the humane methods employed in pest management in Europe?
• A review of water management methods used in sustainable agriculture.
• Are the current methods used in agricultural production sufficient to feed the rapidly growing population?
• A review of crop rotation and its effects in countering pests in farming.
• Using sustainable agriculture to reduce soil erosion in agricultural fields.
• Comparing the use of organic and biological pesticides in increasing agricultural productivity.
• Transforming deserts into agricultural lands: A case study of Israel.
• The importance of maintaining healthy ecosystems in raising crop productivity.
• The role of agriculture in countering the problem of climate change.

Unique Agriculture Research Topics For Students

If students want to receive a high grade, they should choose topics with a more complicated nature.This list contains a variety of unique topics that can be used. You can choose from one of these options right now:

• Why large-scale farming is shifting to organic agriculture.
• What are the implications of groundwater pollution on agriculture?
• What are the pros and cons of raising factory farm chickens?
• Is it possible to optimise food production without using organic fertilisers?
• A review of the causes of declining agricultural productivity in African fields.
• The role of small-scale farming in promoting food sufficiency.
• The best eco-strategies for improving the productivity of land in Asia.
• Emerging concerns about agricultural production.
• The importance of insurance in countering crop failure in modern agriculture.
• Comparing agricultural policies for sustainable agriculture in China and India.
• Is agricultural technology advancing rapidly enough to feed the rapidly growing population?
• Reviewing the impact of culture on agricultural production: A case study of rice farming in Bangladesh.

Fun Agricultural Topics For Your Essay

This list has all the agricultural topics you won’t find anywhere else. It contains fun ideas for essay topics on agriculture that professors may find fascinating:

• Managing farm dams to support modern agriculture: What are the best practices?
• Native Americans’ history and agriculture.
• Agricultural methods used in Abu Dhabi.
• The history of agriculture: A closer look at the American West.
• What impacts do antibiotics have on farm animals?
• Should we promote organic food to increase food production?
• Analysing the impact of fish farming on agriculture: A case study of Japan.
• Smart farming in Germany: The impact of using drones in crop management.
• Comparing the farming regulations in California and Texas.
• Economics of pig farming for country farmers in the United States.
• Using solar energy in farming to reduce carbon footprint.
• Analysing the effectiveness of standards used to confine farm animals.

Technology And Agricultural Related Topics

As you can see, technology plays a significant role in agriculture today.You can now write about any of these technology-related topics in agriculture:

• A review of technology transformation in modern agriculture.
• Why digital technology is a game changer in agriculture.
• The impact of automation in modern agriculture.
• Data analysis and biology application in modern agriculture.
• Opportunities and challenges in food processing.
• Should artificial intelligence be made mandatory in all farms?
• Advanced food processing technologies in agriculture.
• What is the future of genetic engineering of agricultural crops?
• Is fertiliser a must-have for success in farming?
• Agricultural robots offer new hope for enhanced productivity.
• Gene editing in agriculture: Is it a benefit or harmful?
• Identify and trace the history of a specific technology and its application in agriculture today.
• What transformations were prompted by COVID-19 in the agricultural sector?
• Reviewing the best practices for pest management in agriculture.
• Analysing the impacts of different standards and policies for pest management in two countries of your choice on the globe.

Easy Agriculture Research Paper Topics

You may not want to spend too much time writing the paper. You have other things to accomplish. Look at this list of topics that are easy to write about in agriculture:

• Agricultural modernization and its impacts in third world countries.
• The role of human development in agriculture today.
• The use of foreign aid and its impacts on agriculture in Mozambique.
• The effect of hydroponics in agriculture.
• Comparing agriculture in the 20th and 21st centuries.
• Is it possible to engage in farming without water?
• Livestock owners should use farming methods that will not destroy forests.
• Subsistence farming versus commercial farming.
• Comparing the pros and cons of sustainable and organic agriculture.
• Is intensive farming the same as sustainable agriculture?
• A review of the leading agricultural practices in Latin America.
• Mechanisation of agriculture in Eastern Europe: A case study of Ukraine.
• Challenges facing livestock farming in Australia.
• Looking ahead: What is the future of livestock production for protein supply?

Emerging Agriculture Essay Topics

Emerging agriculture is an important part of modern life. Why not write an essay or research paper about one of these emerging agriculture topics?

• Does agriculture help in addressing inequality in society?
• Agricultural electric tractors: Is this a good idea?
• What ways can be employed to help Africa improve its agricultural productivity?
• Is education related to productivity in small-scale farming?
• Genome editing in agriculture: Discuss the pros and cons.
• Is group affiliation important in raising productivity in Centre Europe? A case study of Ukraine.
• The use of Agri-Nutrition programs to change gender norms.
• Mega-Farms: Are they the future of agriculture?
• Changes in agriculture in the next ten years: What should we anticipate?
• A review of the application of DNA fingerprinting in agriculture.
• Global market of agricultural products: Are non-exporters locked out of foreign markets for low productivity?
• Are production technologies related to agri-environmental programs more eco-efficient?
• Can agriculture support greenhouse mitigation?

Controversial Agricultural Project For Students

Our team of experts has searched for the most controversial topics in agriculture to write a thesis on. These topics are all original, so you’re already on your way towards getting bonus points from professors. However, the process of writing is sometimes not as easy as it seems, so dissertation writers for hire will help you to solve all the problems.

• Comparing the mechanisms of US and China agricultural markets: Which is better?
• Should we ban GMO in agriculture?
• Is vivisection a good application or a necessary evil?
• Agriculture is the backbone of modern Egypt.
• Should the use of harmful chemicals in agriculture be considered biological terror?
• How the health of our planet impacts the food supply networks.
• People should buy food that is only produced using sustainable methods.
• What are the benefits of using subsidies in agriculture? A case study of the United States.
• The agrarian protests: What were the main causes and impacts?
• What impact would a policy requiring 2/3 of a country to invest in agriculture have?
• Analysing the changes in agriculture over time: Why is feeding the world population today a challenge?

Persuasive Agriculture Project Topics

If you have difficulty writing a persuasive agricultural project and don’t know where to start, we can help. Here are some topics that will convince you to do a persuasive project on agriculture:

• What is the extent of the problem of soil degradation in the US?
• Comparing the rates of soil degradation in the United States and Africa.
• Employment in the agricultural sector: Can it be a major employer as the population grows?
• The process of genetic improvement for seeds: A case study of agriculture in Germany.
• The importance of potatoes in people’s diet today.
• Comparing sweet potato production in the US to China.
• What is the impact of corn production for ethanol production on food supply chains?
• A review of sustainable grazing methods used in the United States.
• Does urban proximity help improve efficiency in agriculture?
• Does agriculture create economic spillovers for local economies?
• Analysing the use of sprinkle drones in agriculture.
• The impact of e-commerce development on agriculture.
• Reviewing the agricultural policy in Italy.
• Climate change: What does it mean for agriculture in developed nations?

Advanced Agriculture Project Topics

A more difficult topic can help you impress your professor. It can earn you bonus points. Check out the latest list of advanced agricultural project topics:

• Analysing agricultural exposure to toxic metals: The case study of arsenic.
• Identifying the main areas for reforms in agriculture in the United States.
• Are developed countries obligated to help starving countries with food?
• World trade adjustments to emerging agricultural dynamics and climate change.
• Weather tracking and impacts on agriculture.
• Pesticides ban by EU and its impacts on agriculture in Asia and Africa.
• Traditional farming methods used to feed communities in winter: A case study of Mongolia.
• Comparing the agricultural policy of the EU to that of China.
• China grew faster after shifting from an agro to an industrial-based economy: Should more countries move away from agriculture to grow?
• What methods can be used to make agriculture more profitable in Africa?
• A comprehensive comparison of migratory and non-migratory crops.
• What are the impacts of mechanical weeding on soil structure and fertility?
• A review of the best strategies for restoring lost soil fertility in agricultural farmlands: A case study of Germany.

Engaging Agriculture Related Research Topics

When it comes to agriculture’s importance, there is so much to discuss. These engaging topics can help you get started in your research on agriculture:

• Agronomy versus horticultural crops: What are the main differences?
• Analysing the impact of climate change on the food supply networks.
• Meat processing laws in Germany.
• Plant parasites and their impacts in agri-production: A case study of India.
• Milk processing laws in Brazil.
• What is the extent of post-harvest losses on farming profits?
• Agri-supply chains and local food production: What is the relationship?
• Can insects help improve agriculture instead of harming it?
• The application of terraculture in agriculture: What are the main benefits?
• Vertical indoor farms.
• Should we be worried about the declining population of bees?
• Is organic food better than standard food?
• What are the benefits of taking fresh fruits and veggies?
• The impacts of over-farming on sustainability and soil quality.

Persuasive Research Topics in Agriculture

Do you need to write a paper on agriculture? Perfect! Here are the absolute best persuasive research topics in agriculture:

• Buying coffee produced by poor farmers to support them.
• The latest advances in drip irrigation application.
• GMO corn in North America.
• Global economic crises and impact on agriculture.
• Analysis of controversies on the use of chemical fertilisers.
• What challenges are facing modern agriculture in France?
• What are the negative impacts of cattle farms?
• A closer look at the economics behind sheep farming in New Zealand.
• The changing price of energy: How important is it for the local farms in the UK?
• A review of the changing demand for quality food in Europe.
• Wages for people working in agriculture.

Work With Experts To Get High Quality Thesis Paper

Once you pick the preferred topic of research, it is time to get down and start working on your thesis paper. If writing the paper is a challenge, do not hesitate to seek thesis help from our experts. We work with ENL writers who are educated in top universities. Therefore, you can trust them to carry out comprehensive research on your paper and deliver quality work to impress your supervisor. Students who come to us for assistance give a high rating to our writers after scoring top grades or emerging top in class. Our trustworthy experts can also help with other school assignments, thesis editing, and proofreading. We have simplified the process of placing orders so that every student can get assistance quickly and affordably. You only need to navigate to the ordering page to buy a custom thesis paper online.

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130 Agriculture Research Topics To Write An Excellent Paper

The preparation of an agriculture research paper involves several nuances and complexities. The first aspect is technical requirements, such as text formatting, structure, and source list. It's also important to choose those agriculture topics that you can analyze and find expert material. Any research paper is based on theses and statements, which are supported by evidence and factual information.

This is especially important when you tend to choose agricultural controversial topics. Then you need to find studies with verified information and prepare arguments for your paper. The whole process of work requires meticulous data collection and analysis of alternative sources. Then choosing any agricultural essay topics won't seem like a heady decision.

Your academic paper may relate to environmental factors, the economic feasibility of starting a farm, or the nuances of breeding. The main plus is that you can choose any of the agricultural related topics for research preparation. Here are 130 options for you.

Fisheries And Aquaculture

Such agricultural research paper topics allow revealing the topic of fishery and agricultural procurement. Students can concentrate on many aspects of the payback of farms and fisheries. The topics are quite extensive, and you can find a lot of research on the Internet for choosing trust sources.

• Trout breeding in freshwaters.
• Effect of algae on oxygen levels in fish rates.
• Seasonal spawning of oceanic fish.
• Prohibited fishing waters in the United States.
• Exploration of the Pacific Ocean.
• The impact of cyclones on fishing.
• Poisonous fish and the reasons for their breeding in North America.
• Seasonal diseases of trout.
• Sea horse: A case study.
• Risk analysis of water quality in aquaculture.

Plant Science And Crop Production

Crop Production agricultural research topics and plant science are not the easiest, but they contain a ton of information on the Internet. It is not a problem to find research by leading scientists and create your own research paper based on their statistics. The plus is that you don't have to start from scratch.

• Innovative plant breeding.
• Reclamation as a method of increasing yields.
• Hybrid plants of Montana.
• Citrus growing methods.
• Technical cannabis and plantations in the USA.
• Analysis of the yield of leguminous crops.
• Method for creating genetically modified plants.
• Field analysis of wheat for pesticides.
• New plants and methods of growing them.
• Hybrids and cold-resistant plants.

Topics in Agricultural Science

Agriculture essay topics like this allow you to select a specific aspect to research. You can concentrate on vegetation breeding or high tech greenhouse methodology. A large amount of research is a definite plus because you can build your theses on the basis of available data, criticizing or supporting research by scientists.

• Harvesting robots.
• Methodology for improving agricultural performance.
• The influence of technology on the growth of grain crops.
• How important is the timely irrigation of fields?
• Climatic changes and impact on yield.
• Breeding earthworms.
• Hydroponic gardening.
• Genetically modified organisms and their distribution.
• Starting a garden.
• How can we make medicine from plants?

Topics in Agronomy

Agronomy agriculture projects for students allow you to consider the aspects of growing crops in conditions with a specific soil type and natural characteristics. You can base your claims on statistics with the ability to draw on facts from other research. For example, this is relevant for papers examining the fertility of the topsoil.

• Choosing the type of soil for the cornfield.
• Innovative land reclamation.
• New branches in agronomy.
• Phosphate-free fertilizers.
• Hydroponics and greenhouses.
• Hybrid yield analysis.
• Methodology for assessing agronomic losses.
• Stages of preparing a field for harvesting.
• The role of GMOs in the fight against insect pests.
• Cultivation of technical hemp and soil fertilization methods.

Topics in Animal Breeding And Genetics

Agriculture related topics are interesting because you can touch on aspects of genetics and breeding. Students can concentrate on specific aspects of species modification and animal rearing. The research paper will look more convincing when there are references to real scientific papers with statistics and experimental results.

• Breeding new types of sheep.
• Breeding bulls and genetic engineering.
• The influence of selection on the growth of the animal population.
• Proper nutrition for livestock in winter.
• Vitamin complexes for animals.
• Genetic changes in chickens for resistance to cold.
• Nuances of animal genetic modifications.
• Stages of caring for newborn kittens.
• What is a negative selection?
• Basic methods of genetic experiments on animals.

Topics in Animal Production And Health

Such agriculture research paper topics are especially interesting because you can write about farming aspects in the context of raising animals, vegetables, and various crops. It is broad enough, so you will not be limited by narrow boundaries and will be able to consider many aspects of your research paper.

• Environmental threats to the oversupply of the sheep population.
• The role of livestock in marginal areas.
• Livestock digitalization.
• Animal selection for meat preparation.
• Analysis of livestock farms.
• Animal production evaluation technique.
• Cow health during calving.
• The importance of animal vaccination.
• Technical aspects of the medical treatment of animals.
• Environmental aspects of animal husbandry.

Topics in Ecotourism And Wildlife

Ecotourism is gaining momentum all over the world. The new trend is aimed at bringing people closer to nature and exploring the beauty of different countries. This issue will be of interest to those who want to talk about wildlife and nature reserves. The topic is quite extensive, so students will not have problems with preparing a research paper.

• Minnesota and Eco-Tourism.
• The influence of wolves on the formation of the local ecosystem.
• Recreational tourism in the USA.
• Methods for preparing resorts for eco-tourism.
• Lakes and environmental factors.
• A technique for preserving wildlife in its original form.
• Classic models of eco-tourism.
• Stages of creating ecological reserves.
• The role of tourism in the restoration of the ecological environment.
• The main factors of wildlife conservation.
• The legislative framework for wildlife protection.
• The nuances of creating a farm in reserve.
• Consolidation of resources for the development of a livestock farm.

Topics in Farm Management

Managing a farm can be a complex and multifaceted process. Many students may choose this topic to talk about aspects of breeding and breeding pets or crops. The topic is quite extensive and allows you to touch on any aspect of the farmer's activities related to the production and sale of products.

• Farm methods to improve performance.
• Stages of creating a livestock farm.
• Farm success analysis forms.
• Management of the process of planting crops.
• The role of modern equipment in cow milking.
• Farm reporting and profitability analysis.
• Breeding exotic animals.
• Rabbit population management.
• Statistical methodology for farm control.
• Stages of the animal population control on the farm.

Topics in Fisheries And Aquaculture

A similar topic is associated with fish farming, introductory aquaculture, and general aquaculture. Quite a few students can prepare a good research paper if they turn to other people's research and use it as a basis to prove or disprove their own claims and theories. It is also a good opportunity to select food related research topics as you can touch upon the aspect of fish farming and marketing.

• Creation and management of a fish rate.
• Sturgeon breeding and distribution.
• Methods for improving the ecological state of water bodies.
• Planting plants in reservoirs for liquid purification.
• Fish spawning control.
• The aquaculture aspect and social trends.
• Methods for increasing fish resources.
• Breeding in the fishing industry.
• Methods for creating a fish farm.
• River resource monitoring and digitalization.

Topics in Agric Business And Financial Management

Control of a livestock or vegetable enterprise depends on many factors, so such a topic's choice will be extremely relevant. The student's most important task is to bring only proven facts and arguments of his own judgments. These agriculture topics for students include an overview of many business processes and farm management.

• The farm cost reduction methodology.
• US agricultural financing sector.
• Agricultural business practices.
• Data analysis and farming development.
• Financial management of small livestock farms.
• Impact of drought on yield.
• Cost and payback of farms.
• Selecting a region for creating a farm.
• A method for analyzing animal resources on a farm.
• Management of automated farming enterprises.
• Local farming business.
• Key factors of farm management.
• Farm reports and breeding work.

Topics in Agric Meteorology And Water Management

Meteorological aspects are very important for the management of a company or agricultural enterprises. Another aspect of this topic is water management, which may also be interesting for those who are going to reveal the nuances of fish farming in local waters. The topic will be especially interesting for those who want to connect their lives with agronomy and a similar field.

• Cattle breeding methodology.
• Pig breeding methods.
• Water management to maximize profits.
• The choice of a reservoir for growing fish.
• Analysis of the ecological situation in water bodies.
• Farm equipment management techniques.
• Water supply for farm households.
• Analysis and selection of a farm development methodology.
• Finding the right methods for creating protected reservoirs.
• Stages of development of a water farm.

Other Agric Topics

Sometimes choosing a specific topic can be difficult. This is because students are not quite sure which study to base their paper on. You can take a neutral topic that has no specific relation to breeding, meteorology, or farming aspects in such cases.

• Innovative farming methods.
• Choosing the right water farm management model.
• The nuances of trout breeding.
• Population control and livestock farm development plan.
• Financial analytics and purchase of farm animals.
• The self-sufficiency period of the fish farm.
• How to create fish spawning tanks?
• Selection of breeds of cows for farming.
• Methodology for calculating farm risks.
• Time management and selection of plants for the plantation.
• Features of the legal registration of a farm household.
• Modern agricultural drones.
• The difference between Ayn Rand's anthem and George Orwell's animal farm.
• Animal rights vs. animal welfare.

How to Write a Good Agriculture Research Paper?

One of the main life hacks for getting a high mark is choosing controversial agricultural topics. Choosing this option allows students to consider an interesting statement and back it up with real facts. A paper-based on real statistics with proof of student work is valued above all else.

But even when choosing a good topic, you still need to prepare the right outline for writing your research paper. The introduction should be of the highest quality as well as the final paragraph since these are the main parts that affect the assessment. Real facts and statistics must support all the statements above if you are talking about specific figures. Many colleges and universities have their own paper requirements as well as the nuances of the design of research work. You must consider each parameter in order to get the best result.

If it is difficult to find controversial topics in agriculture and write a high-quality research paper, we can help you with this issue. Our  best essay writing service has been in operation for many years and provides writing assistance for many types of essays, research papers, and theses. We will help you synchronize your preparation process and create an expert paper that gets high marks. You can switch to other tasks and get the opportunity to free up some time to study other disciplines.

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• Published: 23 December 2021

On-Farm Experimentation to transform global agriculture

• Myrtille Lacoste   ORCID: orcid.org/0000-0001-6557-1865 1 , 2 ,
• Simon Cook   ORCID: orcid.org/0000-0003-0902-1476 1 , 3 ,
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Restructuring farmer–researcher relationships and addressing complexity and uncertainty through joint exploration are at the heart of On-Farm Experimentation (OFE). OFE describes new approaches to agricultural research and innovation that are embedded in real-world farm management, and reflects new demands for decentralized and inclusive research that bridges sources of knowledge and fosters open innovation. Here we propose that OFE research could help to transform agriculture globally. We highlight the role of digitalization, which motivates and enables OFE by dramatically increasing scales and complexity when investigating agricultural challenges.

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Acknowledgements

This study was funded by the Premier’s Agriculture and Food Fellowship Program of Western Australia. This Fellowship is a collaboration between Curtin and Murdoch Universities and the State Government. The Fellowship is the centrepiece of the Science and Agribusiness Connect initiative, made possible by the State Government’s Royalties for Regions program. Additional support was provided by the MAK’IT-FIAS Fellowship programme (Montpellier Advanced Knowledge Institute on Transitions – French Institutes for Advanced Study) co-funded by the University of Montpellier and the European Union’s Horizon 2020 Marie Skłodowska-Curie Actions (co-fund grant agreement no. 945408), the Digital Agriculture Convergence Lab #DigitAg (grant no. ANR-16-CONV-0004) supported by ANR/PIA, and the Elizabeth Creak Charitable Trust. Contributions toward enabling workshops were made by the USDA (USDA AFRI FACT Los Angeles 2017), the International Society for Precision Agriculture (ICPA Montreal 2018 OFE-C, On-Farm Experimentation Community), the National Key Research and Development Program of China (2016YFD0201303) and ADAS (Cambridge 2018), the European Conference for Precision Agriculture (ECPA Montpellier 2019) and the OECD Co-operative Research Program for ‘Biological resource management for sustainable agricultural systems – Transformational technologies and innovation’ towards ‘#OFE2021, the first Conference on farmer-centric On-Farm Experimentation – Digital Tools for a Scalable Transformative Pathway’. L. Tresh assisted with the design and preparation of Figs. 2 and 3. Members of the #OFE2021 Working Groups also contributed their experiences and insights.

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Myrtille Lacoste, Simon Cook & Danielle Gale

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Contributions

M.L. and S.C. developed the study concept. M.M., D.G., J.I., V.B.-M., T.M., R.S.-B. and A.H. contributed additional concept development. M.L. and D.G. obtained the data and prepared the results. M.L., M.M., L.T., D.K., F.O.G., B.M., V.B.-M., J.R., C.H. and W.Z. contributed data. M.L. wrote the manuscript with input from all other authors.

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Correspondence to Myrtille Lacoste .

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Lacoste, M., Cook, S., McNee, M. et al. On-Farm Experimentation to transform global agriculture. Nat Food 3 , 11–18 (2022). https://doi.org/10.1038/s43016-021-00424-4

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Received : 13 August 2020

Accepted : 05 November 2021

Published : 23 December 2021

Issue Date : January 2022

DOI : https://doi.org/10.1038/s43016-021-00424-4

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Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris benos.

1 Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; [email protected] (L.B.); [email protected] (A.C.T.); [email protected] (G.D.); [email protected] (D.K.)

Aristotelis C. Tagarakis

Georgios dolias, remigio berruto.

2 Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy; [email protected]

Dimitrios Kateris

Dionysis bochtis.

3 FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

1. Introduction

1.1. general context of machine learning in agriculture.

Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earth’s population, climate changes [ 1 ], natural resources depletion [ 2 ], alteration of dietary choices [ 3 ], as well as safety and health concerns [ 4 ]. As a means of addressing the above issues, placing pressure on the agricultural sector, there exists an urgent necessity for optimizing the effectiveness of agricultural practices by, simultaneously, lessening the environmental burden. In particular, these two essentials have driven the transformation of agriculture into precision agriculture. This modernization of farming has a great potential to assure sustainability, maximal productivity, and a safe environment [ 5 ]. In general, smart farming is based on four key pillars in order to deal with the increasing needs; (a) optimal natural resources’ management, (b) conservation of the ecosystem, (c) development of adequate services, and (d) utilization of modern technologies [ 6 ]. An essential prerequisite of modern agriculture is, definitely, the adoption of Information and Communication Technology (ICT), which is promoted by policy-makers around the world. ICT can indicatively include farm management information systems, humidity and soil sensors, accelerometers, wireless sensor networks, cameras, drones, low-cost satellites, online services, and automated guided vehicles [ 7 ].

The large volume of data, which is produced by digital technologies and usually referred to as “big data”, needs large storage capabilities in addition to editing, analyzing, and interpreting. The latter has a considerable potential to add value for society, environment, and decision-makers [ 8 ]. Nevertheless, big data encompass challenges on account of their so-called “5-V” requirements; (a) Volume, (b) Variety, (c) Velocity, (d) Veracity, and (e) Value [ 9 ]. The conventional data processing techniques are incapable of meeting the constantly growing demands in the new era of smart farming, which is an important obstacle for extracting valuable information from field data [ 10 ]. To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence [ 11 ], by taking advantage of the exponential computational power capacity growth.

There is a plethora of applications of ML in agriculture. According to the recent literature survey by Liakos et al. [ 12 ], regarding the time period of 2004 to 2018, four generic categories were identified ( Figure 1 ). These categories refer to crop, water, soil, and livestock management. In particular, as far as crop management is concerned, it represented the majority of the articles amongst all categories (61% of the total articles) and was further sub-divided into:

• Yield prediction;
• Disease detection;
• Weed detection;
• Crop recognition;
• Crop quality.

The four generic categories in agriculture exploiting machine learning techniques, as presented in [ 12 ].

The generic categories dealing with the management of water and soil were found to be less investigated, corresponding cumulatively to 20% of the total number of papers (10% for each category).

Finally, two main sub-categories were identified for the livestock-related applications corresponding to a total 19% of journal papers:

• Livestock production;
• Animal welfare.

1.2. Open Problems Associated with Machine Learning in Agriculture

Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [ 30 ], or dealing with methods to analyze hyperspectral and multispectral data [ 31 ].

Although ML in agriculture has made considerable progress, several open problems remain, which have some common points of reference, despite the fact that the topic covers a variety of sub-fields. According to [ 23 , 24 , 28 , 32 ], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [ 15 , 21 , 22 , 23 ]. Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ]. Another important open problem is that the vast majority of farmers are non-experts in ML and, thus, they cannot fully comprehend the underlying patterns obtained by ML algorithms. For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [ 25 , 30 , 31 ]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [ 15 , 16 , 21 ]. Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [ 19 , 22 , 24 , 33 ]. As stated in [ 12 ], currently, all of the efforts pertain to individual solutions, which are not always connected with the process of decision-making, as seen for example in other domains.

1.3. Aim of the Present Study

As pointed out above, because of the multiple applications of ML in agriculture, several review studies have been published recently. However, these studies usually concentrate purely on one sub-field of agricultural production. Motivated by the current tremendous progress in ML, the increasing interest worldwide, and its impact in various do-mains of agriculture, a systematic bibliographic survey is presented on the range of the categories proposed in [ 12 ], which were summarized in Figure 1 . In particular, we focus on reviewing the relevant literature of the last three years (2018–2020) for the intention of providing an updated view of ML applications in agricultural systems. In fact, this work is an updated continuation of the work presented at [ 12 ]; following, consequently, exactly the same framework and inclusion criteria. As a consequence, the scholarly literature was screened in order to cover a broad spectrum of important features for capturing the current progress and trends, including the identification of: (a) the research areas which are interested mostly in ML in agriculture along with the geographical distribution of the contributing organizations, (b) the most efficient ML models, (c) the most investigated crops and animals, and (d) the most implemented features and technologies.

As will be discussed next, overall, a 745% increase in the number of journal papers took place in the last three years as compared to [ 12 ], thus justifying the need for a new updated review on the specific topic. Moreover, crop management remained as the most investigated topic, with a number of ML algorithms having been exploited as a means of tackling the heterogeneous data that originated from agricultural fields. As compared to [ 12 ], more crop and animal species have been investigated by using an extensive range of input parameters coming mainly from remote sensing, such as satellites and drones. In addition, people from different research fields have dealt with ML in agriculture, hence, contributing to the remarkable advancement in this field.

1.4. Outline of the Paper

The remainder of this paper is structured as follows. The second section briefly describes the fundamentals of ML along with the subject of the four generic categories for the sake of better comprehension of the scope of the present study. The implemented methodology, along with the inclusive criteria and the search engines, is analyzed in the third section. The main performance metrics, which were used in the selected articles, are also presented in this section. The main results are shown in the fourth section in the form of bar and pie charts, while in the fifth section, the main conclusions are drawn by also discussing the results from a broader perspective. Finally, all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , in accordance with their field of application, and presented in the Appendix A , together with Table A10 and Table A11 that contain commonly used abbreviations, with the intention of not disrupting the flow of the main text.

2. Background

2.1. fundamentals of machine learning: a brief overview.

In general, the objective of ML algorithms is to optimize the performance of a task, via exploiting examples or past experience. In particular, ML can generate efficient relationships regarding data inputs and reconstruct a knowledge scheme. In this data-driven methodology, the more data are used, the better ML works. This is similar to how well a human being performs a particular task by gaining more experience [ 34 ]. The central outcome of ML is a measure of generalizability; the degree to which the ML algorithm has the ability to provide correct predictions, when new data are presented, on the basis of learned rules originated from preceding exposure to similar data [ 35 ]. More specifically, data involve a set of examples, which are described by a group of characteristics, usually called features. Broadly speaking, ML systems operate at two processes, namely the learning (used for training) and testing. In order to facilitate the former process, these features commonly form a feature vector that can be binary, numeric, ordinal, or nominal [ 36 ]. This vector is utilized as an input within the learning phase. In brief, by relying on training data, within the learning phase, the machine learns to perform the task from experience. Once the learning performance reaches a satisfactory point (expressed through mathematical and statistical relationships), it ends. Subsequently, the model that was developed through the training process can be used to classify, cluster, or predict.

An overview of a typical ML system is illustrated in Figure 2 . With the intention of forming the derived complex raw data into a suitable state, a pre-processing effort is required. This usually includes: (a) data cleaning for removing inconsistent or missing items and noise, (b) data integration, when many data sources exist and (c) data transformation, such as normalization and discretization [ 37 ]. The extraction/selection feature aims at creating or/and identifying the most informative subset of features in which, subsequently, the learning model is going to be implemented throughout the training phase [ 38 ]. Regarding the feedback loop, which is depicted in Figure 2 , it serves for adjustments pertaining to the feature extraction/selection unit as well as the pre-processing one that further improves the overall learning model’s performance. During the phase of testing, previously unseen samples are imported to the trained model, which are usually represented as feature vectors. Finally, an appropriate decision is made by the model (for example, classification or regression) in reliance of the features existing in each sample. Deep learning, a subfield of ML, utilizes an alternative architecture via shifting the process of converting raw data to features (feature engineering) to the corresponding learning system. Consequently, the feature extraction/selection unit is absent, resulting in a fully trainable system; it starts from a raw input and ends with the desired output [ 39 , 40 ].

A graphical illustration of a typical machine learning system.

Based on the learning type, ML can be classified according to the relative literature [ 41 , 42 ] as:

• Supervised learning: The input and output are known and the machine tries to find the optimal way to reach an output given an input;
• Unsupervised learning: No labels are provided, leaving the learning algorithm itself to generate structure within its input;
• Semi-supervised learning: Input data constitute a mixture of labeled and non-labeled data;
• Reinforcement learning: Decisions are made towards finding out actions that can lead to the more positive outcome, while it is solely determined by trial and error method and delayed outcome.

Nowadays, ML is used in facilitating several management aspects in agriculture [ 12 ] and in a plethora of other applications, such as image recognition [ 43 ], speech recognition [ 44 ], autonomous driving [ 45 ], credit card fraud detection [ 46 ], stock market forecasting [ 47 ], fluid mechanics [ 48 ], email, spam and malware filtering [ 49 ], medical diagnosis [ 40 ], contamination detection in urban water networks [ 50 ], and activity recognition [ 51 ], to mention but a few.

2.2. Brief Description of the Four Generic Categories

2.2.1. crop management.

The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [ 52 ]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income. The above aspects constitute key goals of precision agriculture.

Yield Prediction

In general, yield prediction is one of the most important and challenging topics in modern agriculture. An accurate model can help, for instance, the farm owners to take informed management decisions on what to grow towards matching the crop to the existing market’s demands [ 20 ]. However, this is not a trivial task; it consists of various steps. Yield prediction can be determined by several factors such as environment, management practices, crop genotypic and phenotypic characteristics, and their interactions. Hence, it necessitates a fundamental comprehension of the relationship between these interactive factors and yield. In turn, identifying such kinds of relationships mandates comprehensive datasets along with powerful algorithms such as ML techniques [ 53 ].

Disease Detection

Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality and quantity at production, storage, and transportation level. At farm level, reports on yield losses, due to plant diseases, are very common [ 54 ]. Furthermore, crop diseases pose significant risks to food security at a global scale. Timely identification of plant diseases is a key aspect for efficient management. Plant diseases may be provoked by various kinds of bacteria, fungi, pests, viruses, and other agents. Disease symptoms, namely the physical evidence of the presence of pathogens and the changes in the plants’ phenotype, may consist of leaf and fruit spots, wilting and color change [ 55 ], curling of leaves, etc. Historically, disease detection was conducted by expert agronomists, by performing field scouting. However, this process is time-consuming and solely based on visual inspection. Recent technological advances have made commercially available sensing systems able to identify diseased plants before the symptoms become visible. Furthermore, in the past few years, computer vision, especially by employing deep learning, has made remarkable progress. As highlighted by Zhang et al. [ 56 ], who focused on identifying cucumber leaf diseases by utilizing deep learning, due to the complex environmental background, it is beneficial to eliminate background before model training. Moreover, accurate image classifiers for disease diagnosis need a large dataset of both healthy and diseased plant images. In reference to large-scale cultivations, such kinds of automated processes can be combined with autonomous vehicles, to timely identify phytopathological problems by implementing regular inspections. Furthermore, maps of the spatial distribution of the plant disease can be created, depicting the zones in the farm where the infection has been spread [ 57 ].

Weed Detection

As a result of their prolific seed production and longevity, weeds usually grow and spread invasively over large parts of the field very fast, competing with crops for the resources, including space, sunlight, nutrients, and water availability. Besides, weeds frequently arise sooner than crops without having to face natural enemies, a fact that adversely affects crop growth [ 18 ]. In order to prevent crop yield reduction, weed control is an important management task by either mechanical treatment or application of herbicides. Mechanical treatment is, in most cases, difficult to be performed and ineffective if not properly performed, making herbicide application the most widely used operation. Using large quantities of herbicides, however, turns out to be both costly and detrimental for the environment, especially in the case of uniform application without taking into account the spatial distribution of the weeds. Remarkably, long-term herbicide use is very likely to make weeds more resistant, thus, resulting in more demanding and expensive weed control. In recent years, considerable achievements have been made pertaining to the differentiation of weeds from crops on the basis of smart agriculture. This discrimination can be accomplished by using remote or proximal sensing with sensors attached on satellites, aerial, and ground vehicles, as well as unmanned vehicles (both ground (UGV) and aerial (UAV)). The transformation of data gathered by UAVs into meaningful information is, however, still a challenging task, since both data collection and classification need painstaking effort [ 58 ]. ML algorithms coupled with imaging technologies or non-imaging spectroscopy can allow for real-time differentiation and localization of target weeds, enabling precise application of herbicides to specific zones, instead of spraying the entire fields [ 59 ] and planning of the shortest weeding path [ 60 ].

Crop Recognition

Automatic recognition of crops has gained considerable attention in several scientific fields, such as plant taxonomy, botanical gardens, and new species discovery. Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [ 61 , 62 ]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [ 63 ]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular. As in the above sub-categories, the advancement on computer software and image processing devices combined with ML has led to the automatic recognition and classification of crops.

Crop Quality

Crop quality is very consequential for the market and, in general, is related to soil and climate conditions, cultivation practices and crop characteristics, to name a few. High quality agricultural products are typically sold at better prices, hence, offering larger earnings to farmers. For instance, as regards fruit quality, flesh firmness, soluble solids content, and skin color are among the most ordinary maturity indices utilized for harvesting [ 64 ]. The timing of harvesting greatly affects the quality characteristics of the harvested products in both high value crops (tree crops, grapes, vegetables, herbs, etc.) and arable crops. Therefore, developing decision support systems can aid farmers in taking appropriate management decisions for increased quality of production. For example, selective harvesting is a management practice that may considerably increase quality. Furthermore, crop quality is closely linked with food waste, an additional challenge that modern agriculture has to cope with, since if the crop deviates from the desired shape, color, or size, it may be thrown away. Similarly to the above sub-section, ML algorithms combined with imaging technologies can provide encouraging results.

2.2.2. Water Management

The agricultural sector constitutes the main consumer of available fresh water on a global scale, as plant growth largely relies on water availability. Taking into account the rapid depletion rate of a lot of aquifers with negligible recharge, more effective water management is needed for the purpose of better conserving water in terms of accomplishing a sustainable crop production [ 65 ]. Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [ 66 ]. Recent research on precision agriculture offers the potential of variable rate irrigation so as to attain water savings. This can be realized by implementing irrigation at rates, which vary according to field variability on the basis of specific water requirements of separate management zones, instead of using a uniform rate in the entire field. The effectiveness and feasibility of the variable rate irrigation approach depend on agronomic factors, including topography, soil properties, and their effect on soil water in order to accomplish both water savings and yield optimization [ 67 ]. Carefully monitoring the status of soil water, crop growth conditions, and temporal and spatial patterns in combination with weather conditions monitoring and forecasting, can help in irrigation programming and efficient management of water. Among the utilized ICTs, remote sensing can provide images with spatial and temporal variability associated with the soil moisture status and crop growth parameters for precision water management. Interestingly, water management is challenging enough in arid areas, where groundwater sources are used for irrigation, with the precipitation providing only part of the total crop evapotranspiration (ET) demands [ 68 ].

2.2.3. Soil Management

Soil, a heterogeneous natural resource, involves mechanisms and processes that are very complex. Precise information regarding soil on a regional scale is vital, as it contributes towards better soil management consistent with land potential and, in general, sustainable agriculture [ 5 ]. Better management of soil is also of great interest owing to issues like land degradation (loss of the biological productivity), soil-nutrient imbalance (due to fertilizers overuse), and soil erosion (as a result of vegetation overcutting, improper crop rotations rather than balanced ones, livestock overgrazing, and unsustainable fallow periods) [ 69 ]. Useful soil properties can entail texture, organic matter, and nutrients content, to mention but a few. Traditional soil assessment methods include soil sampling and laboratory analysis, which are normally expensive and take considerable time and effort. However, remote sensing and soil mapping sensors can provide low-cost and effortless solution for the study of soil spatial variability. Data fusion and handling of such heterogeneous “big data” may be important drawbacks, when traditional data analysis methods are used. ML techniques can serve as a trustworthy, low-cost solution for such a task.

2.2.4. Livestock Management

It is widely accepted that livestock production systems have been intensified in the context of productivity per animal. This intensification involves social concerns that can influence consumer perception of food safety, security, and sustainability, based on animal welfare and human health. In particular, monitoring both the welfare of animals and overall production is a key aspect so as to improve production systems [ 70 ]. The above fields take place in the framework of precision livestock farming, aiming at applying engineering techniques to monitor animal health in real time and recognizing warning messages, as well as improving the production at the initial stages. The role of precision livestock farming is getting more and more significant by supporting the decision-making processes of livestock owners and changing their role. It can also facilitate the products’ traceability, in addition to monitoring their quality and the living conditions of animals, as required by policy-makers [ 71 ]. Precision livestock farming relies on non-invasive sensors, such as cameras, accelerometers, gyroscopes, radio-frequency identification systems, pedometers, and optical and temperature sensors [ 25 ]. IoT sensors leverage variable physical quantities (VPQs) as a means of sensing temperature, sound, humidity, etc. For instance, IoT sensors can warn if a VPQ falls out of regular limits in real-time, giving valuable information regarding individual animals. As a result, the cost of repetitively and arduously checking each animal can be reduced [ 72 ]. In order to take advantage of the large amounts of data, ML methodologies have become an integral part of modern livestock farming. Models can be developed that have the capability of defining the manner a biological system operates, relying on causal relationships and exploiting this biological awareness towards generating predictions and suggestions.

Animal Welfare

There is an ongoing concern for animal welfare, since the health of animals is strongly associated with product quality and, as a consequence, predominantly with the health of consumers and, secondarily, with the improvement of economic efficiency [ 73 ]. There exist several indexes for animal welfare evaluation, including physiological stress and behavioral indicators. The most commonly used indicator is animal behavior, which can be affected by diseases, emotions, and living conditions, which have the potential to demonstrate physiological conditions [ 25 ]. Sensors, commonly used to detect behavioral changes (for example, changes in water or food consumption, reduced animal activity), include microphone systems, cameras, accelerometers, etc.

Livestock Production

The use of sensor technology, along with advanced ML techniques, can increase livestock production efficiency. Given the impact of practices of animal management on productive elements, livestock owners are getting cautious of their asset. However, as the livestock holdings get larger, the proper consideration of every single animal is very difficult. From this perspective, the support to farmers via precision livestock farming, mentioned above, is an auspicious step for aspects associated with economic efficiency and establishment of sustainable workplaces with reduced environmental footprint [ 74 ]. Generally, several models have been used in animal production, with their intentions normally revolving around growing and feeding animals in the best way. However, the large volumes of data being involved, again, call for ML approaches.

3.1. Screening of the Relative Literature

In order to identify the relevant studies concerning ML in respect to different aspects of management in agriculture, the search engines of Scopus, Google Scholar, ScienceDirect, PubMed, Web of Science, and MDPI were utilized. In addition, keywords’ combinations of “machine learning” in conjunction with each of the following: “crop management”, “water management”, “soil management”, and “livestock management” were used. Our intention was to filter the literature on the same framework as [ 12 ]; however, focusing solely within the period 2018–2020. Once a relevant study was being identified, the references of the paper at hand were being scanned to find studies that had not been found throughout the initial searching procedure. This process was being iterated until no relevant studies occurred. In this stage, only journal papers were considered eligible. Thus, non-English studies, conferences papers, chapters, reviews, as well as Master and Doctoral Theses were excluded. The latest search was conducted on 15 December 2020. Subsequently, the abstract of each paper was being reviewed, while, at a next stage, the full text was being read to decide its appropriateness. After a discussion between all co-authors with reference to the appropriateness of the selected papers, some of them were excluded, in the case they did not meet the two main inclusion criteria, namely: (a) the paper was published within 2018–2020 and (b) the paper referred to one of the categories and sub-categories, which were summarized in Figure 1 . Finally, the papers were classified in these sub-categories. Overall, 338 journal papers were identified. The flowchart of the present review methodology is depicted in Figure 3 , based on the PRISMA guidelines [ 75 ], along with information about at which stage each exclusive criterion was imposed similarly to recent systematic review studies such as [ 72 , 76 , 77 , 78 ].

The flowchart of the methodology of the present systematic review along with the flow of information regarding the exclusive criteria, based on PRISMA guidelines [ 75 ].

3.2. Definition of the Performance Metrics Commonly Used in the Reviewed Studies

In this subsection, the most commonly used performance metrics of the reviewed papers are briefly described. In general, these metrics are utilized in an effort to provide a common measure to evaluate the ML algorithms. The selection of the appropriate metrics is very important, since: (a) how the algorithm’s performance is measured relies on these metrics and (b) the metric itself can influence the way the significance of several characteristics is weighted.

Confusion matrix constitutes one of the most intuitive metrics towards finding the correctness of a model. It is used for classification problems, where the result can be of at least two types of classes. Let us consider a simple example, by giving a label to a target variable: for example, “1” when a plant has been infected with a disease and “0” otherwise. In this simplified case, the confusion matrix ( Figure 4 ) is a 2 × 2 table having two dimensions, namely “Actual” and “Predicted”, while its dimensions have the outcome of the comparison between the predictions with the actual class label. Concerning the above simplified example, this outcome can acquire the following values:

• True Positive (TP): The plant has a disease (1) and the model classifies this case as diseased (1);
• True Negative (TN): The plant does not have a disease (0) and the model classifies this case as a healthy plant (0);
• False Positive (FP): The plant does not have a disease (0), but the model classifies this case as diseased (1);
• False Negative (FN): The plant has a disease (1), but the model classifies this case as a healthy plant (0).

Representative illustration of a simplified confusion matrix.

As can be shown in Table 1 , the aforementioned values can be implemented in order to estimate the performance metrics, typically observed in classification problems [ 79 ].

Summary of the most commonly used evaluation metrics of the reviewed studies.

Other common evaluation metrics were the coefficient of correlation ( R ), coefficient of determination ( R 2 ; basically, the square of the correlation coefficient), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), which can be given via the following relationships [ 80 , 81 ]:

where X t and Z t correspond to the predicted and real value, respectively, t stands for the iteration at each point, while T for the testing records number. Accordingly, low values of MAE, MAPE, and MSE values denote a small error and, hence, better performance. In contrast, R 2 near 1 is desired, which demonstrates better model performance and also that the regression curve efficiently fits the data.

4.1. Preliminary Data Visualization Analysis

Graphical representation of data related to the reviewed studies, by using maps, bar or pie charts, for example, can provide an efficient approach to demonstrate and interpret the patterns of data. The data visualization analysis, as it usually refers to, can be vital in the context of analyzing large amounts of data and has gained remarkable attention in the past few years, including review studies. Indicatively, significant results can be deduced in an effort to identify: (a) the most contributing authors and organizations, (b) the most contributing international journals (or equivalently which research fields are interested in this topic), and (c) the current trends in this field [ 82 ].

4.1.1. Classification of the Studies in Terms of Application Domain

As can be seen in the flowchart of the present methodology ( Figure 3 ), the literature survey on ML in agriculture resulted in 338 journal papers. Subsequently, these studies were classified into the four generic categories as well as into their sub-categories, as already mentioned above. Figure 5 depicts the aforementioned papers’ distribution. In particular, the majority of the studies were intended for crop management (68%), while soil management (10%), water management (10%), and livestock management (12% in total; animal welfare: 7% and livestock production: 5%) had almost equal contribution in the present bibliographic survey. Focusing on crop management, the most contributing sub-categories were yield prediction (20%) and disease detection (19%). The former research field arises as a consequence of the increasing interest of farmers in taking decisions based on efficient management that can lead to the desired yield. Disease detection, on the other hand, is also very important, as diseases constitute a primary menace for food security and quality assurance. Equal percentages (13%) were observed for weed detection and crop recognition, both of which are essential in crop management at farm and agricultural policy making level. Finally, examination of crop quality was relatively scarce corresponding to 3% of all studies. This can be attributed to the complexity of monitoring and modeling the quality-related parameters.

The classification of the reviewed studies according to the field of application.

In this fashion, it should be mentioned again that all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , depending on their field of application, and presented in the Appendix A . The columns of the tables correspond (from left to right) to the “Reference number” (Ref), “Input Data”, “Functionality”, “Models/Algorithms”, and “Best Output”. One additional column exists for the sub-categories belonging in crop management, namely “Crop”, whereas the corresponding column in the sub-categories pertaining to livestock management refers to “Animal”. The present systematic review deals with a plethora of different ML models and algorithms. For the sake of brevity, the commonly used abbreviations are used instead of the entire names, which are summarized in Table A10 and Table A11 (presented also in the Appendix A ). The list of the aforementioned Tables, along with their content, is listed in Table 2 .

List of the tables appearing in the Appendix A related to: (a) the categories and sub-categories of the machine learning applications in agriculture ( Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 ) and (b) the abbreviations of machine learning models and algorithms ( Table A10 and Table A11 , respectively).

4.1.2. Geographical Distribution of the Contributing Organizations

The subject of this sub-section is to find out the geographical distribution of all the contributing organizations in ML applications in agriculture. To that end, the author’s affiliation was taken into account. In case a paper included more than one author, which was the most frequent scenario, each country could contribute only once in the final map chart ( Figure 6 ), similarly to [ 83 , 84 ]. As can be gleaned from Figure 6 , investigating ML in agriculture is distributed worldwide, including both developed and developing economies. Remarkably, out of the 55 contributing countries, the least contribution originated from African countries (3%), whereas the major contribution came from Asian countries (55%). The latter result is attributed mainly to the considerable contribution of Chinese (24.9%) as well as Indian organizations (10.1%). USA appeared to be the second most contributing country with 20.7% percentage, while Australia (9.5%), Spain (6.8%), Germany (5.9%), Brazil, UK, and Iran (5.62%) seem to be particularly interested in ML in agriculture. It should be stressed that livestock management, which is a relatively different sub-field comparing to crop, water, and soil management, was primary examined from studies coming from Australia, USA, China, and UK, while all the papers regarding Ireland were focused on animals. Finally, another noteworthy observation is that a large number of articles were a result of international collaboration, with the synergy of China and USA standing out.

Geographical distribution of the contribution of each country to the research field focusing on machine learning in agriculture.

4.1.3. Distribution of the Most Contributing Journal Papers

For the purpose of identifying the research areas that are mostly interested in ML in agriculture, the most frequently appeared international journal papers are depicted in Figure 7 . In total, there were 129 relevant journals. However, in this bar chart, only the journals contributing with at least 4 papers are presented for brevity. As a general remark, remote sensing was of particular importance, since reliable data from satellites and UAV, for instance, constitute valuable input data for the ML algorithms. In addition, smart farming, environment, and agricultural sustainability were of central interest. Journals associated with computational techniques were also presented with considerable frequency. A typical example of such type of journals, which was presented in the majority of the studies with a percentage of 19.8%, was “ Computers and Electronics in Agriculture ”. This journal aims at providing the advances in relation to the application of computers and electronic systems for solving problems in plant and animal production.

Distribution of the most contributing international journals (published at least four articles) concerning applications of machine learning in agriculture.

The “ Remote Sensing ” and “ Sensors ” journals followed with approximately 11.8% and 6.5% of the total number of publications, respectively. These are cross-sectoral journals that are concentrated on applications of science and sensing technologies in various fields, including agriculture. Other journals, covering this research field, were also “ IEEE Access ” and “ International Journal of Remote Sensing ” with approximately 2.1% and 1.2% contribution, respectively. Moreover, agriculture-oriented journals were also presented in Figure 7 , including “ Precision Agriculture ”, “ Frontiers in Plant Science ”, “ Agricultural and Forest Meteorology ”, and “ Agricultural Water Management ” with 1–3% percentage. These journals deal with several aspects of agriculture ranging from management strategies (so as to incorporate spatial and temporal data as a means of optimizing productivity, resource use efficiency, sustainability and profitability of agricultural production) up to crop molecular genetics and plant pathogens. An interdisciplinary journal concentrating on soil functions and processes also appeared with 2.1%, namely “ Geoderma ”, plausibly covering the soil management generic category. Finally, several journals focusing on physics and applied natural sciences, such as “ Applied Sciences ” (2.7%), “ Scientific Reports ” (1.8%), “ Biosystems Engineering ” (1.5%), and “ PLOS ONE ” (1.5%), had a notable contribution to ML studies. As a consequence, ML in agriculture concerns several disciplines and constitutes a fundamental area for developing various techniques, which can be beneficial to other fields as well.

4.2. Synopsis of the Main Features Associated with the Relative Literature

4.2.1. machine learning models providing the best results.

A wide range of ML algorithms was implemented in the selected studies; their abbreviations are given in Table A11 . The ML algorithms that were used by each study as well as those that provided the best output have been listed in the last two columns of Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 . These algorithms can be classified into the eight broad families of ML models, which are summarized in Table A10 . Figure 8 focuses on the best performed ML models as a means of capturing a broad picture of the current situation and demonstrating advancement similarly to [ 12 ].

Machine Learning models giving the best output.

As can be demonstrated in Figure 8 , the most frequent ML model providing the best output was, by far, Artificial Neural Networks (ANNs), which appeared in almost half of the reviewed studies (namely, 51.8%). More specifically, ANN models provided the best results in the majority of the studies concerning all sub-categories. ANNs have been inspired by the biological neural networks that comprise human brains [ 85 ], while they allow for learning via examples from representative data describing a physical phenomenon. A distinct characteristic of ANNs is that they can develop relationships between dependent and independent variables, and thus extract useful information from representative datasets. ANN models can offer several benefits, such as their ability to handle noisy data [ 86 ], a situation that is very common in agricultural measurements. Among the most popular ANNs are the Deep Neural Networks (DNNs), which utilize multiple hidden layers between input and output layers. DNNs can be unsupervised, semi-supervised, or supervised. A usual kind of DNNs are the Convolutional Neural Networks (CNNs), whose layers, unlike common neural networks, can set up neurons in three dimensions [ 87 ]. In fact, CNNs were presented as the algorithms that provide the best output in all sub-categories, with an almost 50% of the individual percentage of ANNs. As stressed in recent studies, such as that of Yang et al. [ 88 ], CNNs are receiving more and more attention because of their efficient results when it comes to detection through images’ processing.

Recurrent Neural Networks (RNNs) followed, representing approximately 10% of ANNs, with Long Short-Term Memory (LSTM) standing out. They are called “recurrent” as they carry out the same process for every element, with the previous computations determining the current output, while they have a “memory” that stores information pertaining to what has been calculated so far. RNNs can face problems concerning vanishing gradients and inability to “memorize” many sequential data. Towards addressing these issues, the cell structures of LSTM can control which part of information will be either stored in long memory or discarded, resulting in optimization of the memorizing process [ 51 ]. Moreover, Multi-Layer Perceptron (MLP), Fully Convolutional Networks (FCNs), and Radial Basis Function Networks (RBFNs) appeared to have the best performance in almost 3–5% of ANNs. Finally, ML algorithms, belonging to ANNs with low frequency, were Back-Propagation Neural Networks (BPNNs), Modular Artificial Neural Networks (MANNs), Deep Belief Networks (DBNs), Adaptive-Neuro Fuzzy Inference System (ANFIS), Subtractive Clustering Fuzzy Inference System (SCFIS), Takagi-Sugeno Fuzzy Neural Networks (TS-FNN), and Feed Forward Neural Networks (FFNNs).

The second most accurate ML model was Ensemble Learning (EL), contributing to the ML models used in agricultural systems with approximately 22.2%. EL is a concise term for methods that integrate multiple inducers for the purpose of making a decision, normally in supervised ML tasks. An inducer is an algorithm, which gets as an input a number of labeled examples and creates a model that can generalize these examples. Thus, predictions can be made for a set of new unlabeled examples. The key feature of EL is that via combining various models, the errors coming from a single inducer is likely to be compensated from other inducers. Accordingly, the prediction of the overall performance would be superior comparing to a single inducer [ 89 ]. This type of ML model was presented in all sub-categories, apart from crop quality, perhaps owing to the small number of papers belonging in this subcategory. Support Vector Machine (SVM) followed, contributing in approximately 11.5% of the studies. The strength of the SVM stems from its capability to accurately learn data patterns while showing reproducibility. Despite the fact that it can also be applied for regression applications, SVM is a commonly used methodology for classification extending across numerous data science settings [ 90 ], including agricultural research.

Decision Trees (DT) and Regression models came next with equal percentage, namely 4.7%. Both these ML models were presented in all generic categories. As far as DT are concerned, they are either regression or classification models structured in a tree-like architecture. Interestingly, handling missing data in DT is a well-established problem. By implementing DT, the dataset can be gradually organized into smaller subsets, whereas, in parallel, a tree graph is created. In particular, each tree’s node denotes a dissimilar pairwise comparison regarding a certain feature, while each branch corresponds to the result of this comparison. As regards leaf nodes, they stand for the final decision/prediction provided after following a certain rule [ 91 , 92 ]. As for Regression, it is used for supervised learning models intending to model a target value on the basis of independent predictors. In particular, the output can be any number based on what it predicts. Regression is typically applied for time series modeling, prediction, and defining the relationships between the variables.

Finally, the ML models, leading to optimal performance (although with lower contribution to literature), were those of Instance Based Models (IBM) (2.7%), Dimensionality Reduction (DR) (1.5%), Bayesian Models (BM) (0.9%), and Clustering (0.3%). IBM appeared only in crop, water, and livestock management, whereas BM only in crop and soil management. On the other hand, DR and Clustering appeared as the best solution only in crop management. In brief, IBM are memory-based ML models that can learn through comparison of the new instances with examples within the training database. DR can be executed both in unsupervised and supervised learning types, while it is typically carried out in advance of classification/regression so as to prevent dimensionality effects. Concerning the case of BM, they are a family of probabilistic models whose analysis is performed within the Bayesian inference framework. BM can be implemented in both classification and regression problems and belong to the broad category of supervised learning. Finally, Clustering belongs to unsupervised ML models. It contains automatically discovering of natural grouping of data [ 12 ].

4.2.2. Most Studied Crops and Animals

In this sub-section, the most examined crops and animals that were used in the ML models are discussed as a result of our searching within the four sub-categories of crop management similarly to [ 12 ]. These sub-categories refer to yield prediction, disease detection, crop recognition, and crop quality. Overall, approximately 80 different crop species were investigated. The 10 most utilized crops are summarized in Figure 9 . Specifically, the remarkable interest on maize (also known as corn) can be attributed to the fact that it is cultivated in many parts across the globe as well as its versatile usage (for example, direct consumption by humans, animal feed, producing ethanol, and other biofuels). Wheat and rice follow, which are two of the most widely consumed cereal grains. According to the Food and Agriculture Organization (FAO) [ 93 ], the trade in wheat worldwide is more than the summation of all other crops. Concerning rice, it is the cereal grain with the third-highest production and constitutes the most consumed staple food in Asia [ 94 ]. The large contribution of Asian countries presented in Figure 6 , like China and India, justifies the interest in this crop. In the same vein, soybeans, which are broadly distributed in East Asia, USA, Africa, and Australia [ 95 ], were presented in many studies. Finally, tomato, grape, canola/rapeseed (cultivated primarily for its oil-rich seed), potato, cotton, and barley complete the top 10 examined crops. All these species are widely cultivated all over the world. Some other indicative species, which were investigated at least five times in the present reviewed studies, were also alfalfa, citrus, sunflower, pepper, pea, apple, squash, sugarcane, and rye.

The 10 most investigated crops using machine learning models; the results refer to crop management.

As far as livestock management is concerned, the examined animal species can be classified, in descending order of frequency, into the categories of cattle (58.5%), sheep and goats (26.8%), swine (14.6%), poultry (4.9%), and sheepdog (2.4%). As can be depicted in Figure 10 , the last animal, which is historically utilized with regard to the raising of sheep, was investigated only in one study belonging to animal welfare, whereas all the other animals were examined in both categories of livestock management. In particular, the most investigated animal in both animal welfare and livestock production was cattle. Sheep and goats came next, which included nine studies for sheep and two studies for goats. Cattles are usually raised as livestock aimed at meat, milk, and hide used for leather. Similarly, sheep are raised for meat and milk as well as fleece. Finally, swine (often called domestic pigs) and poultry (for example, chicken, turkey, and duck), which are used mainly for their meat or eggs (poultry), had equal contribution from the two livestock sub-categories.

Frequency of animal species in studies concerning livestock management by using machine learning models.

4.2.3. Most Studied Features and Technologies

As mentioned in the beginning of this study, modern agriculture has to incorporate large amounts of heterogeneous data, which have originated from a variety of sensors over large areas at various spatial scale and resolution. Subsequently, such data are used as input into ML algorithms for their iterative learning up until modeling of the process in the most effective way possible. Figure 11 shows the features and technologies that were used in the reviewed studies, separately for each category, for the sake of better comprehending the results of the analysis.

Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.

Data coming from remote sensing were the most common in the yield prediction sub-category. Remote sensing, in turn, was primarily based on data derived from satellites (40.6% of the total studies published in this sub-category) and, secondarily, from UAVs (23.2% of the total studies published in this sub-category). A remarkable observation is the rapid increase of the usage of UAVs versus satellites from the year 2018 towards 2020, as UAVs seem to be a reliable alternative that can give faster and cheaper results, usually in higher resolution and independent of the weather conditions. Therefore, UAVs allow for discriminating details of localized circumscribed regions that the satellites’ lowest resolution may miss, especially under cloudy conditions. This explosion in the use of UAV systems in agriculture is a result of the developing market of drones and sensing solutions attached to them, rendering them economically affordable. In addition, the establishment of formal regulations for UAV operations and the simplification and automatization of the operational and analysis processes had a significant contribution on the increasing popularity of these systems. Data pertaining to the weather conditions of the investigated area were also of great importance as well as soil parameters of the farm at hand. An additional way of getting the data was via in situ manual measurements, involving measurements such as crop height, plant growth, and crop maturity. Finally, data concerning topographic, irrigation, and fertilization aspects were presented with approximately equal frequency.

As far as disease detection is concerned, Red-Green-Blue (RGB) images appear to be the most usual input data for the ML algorithms (in 62% of the publications). Normally, deep learning methods like CNNs are implemented with the intention of training a classifier to discriminate images depicting healthy leaves, for example, from infected ones. CNNs use some particular operations to transform the RGB images so that the desired features are enhanced. Subsequently, higher weights are given to the images having the most suitable features. This characteristic constitutes a significant advantage of CNNs as compared to other ML algorithms, when it comes to image classification [ 79 ]. The second most common input data came from either multispectral or hyperspectral measurements originated from spectroradiometers, UAVs, and satellites. Concerning the investigated diseases, fungal diseases were the most common ones with diseases from bacteria following, as is illustrated in Figure 12 a. This kind of disease can cause major problems in agriculture with detrimental economic consequences [ 96 ]. Other examined origins of crop diseases were, in descending order of frequency, pests, viruses, toxicity, and deficiencies.

Distribution of the most usual output features of the machine learning algorithms regarding: ( a ) Disease detection and ( b ) Crop quality.

Images were also the most used input data for weed detection purposes. These images were RGB images that originated mainly from in situ measurements as well as from UGVs and UAVs and, secondarily, multispectral images from the aforementioned sources. Finally, other parameters that were observed, although with lower frequency, were satellite multispectral images, mainly due to the considerably low resolution they provide, video recordings, and hyperspectral and greyscale images. Concerning crop recognition, the majority of the studies used data coming mostly from satellites and, secondarily, from in situ manual measurements. This is attributed to the fact that most of the studies in this category concern crop classification, a sector where satellite imaging is the most widely used data source owing to its potential for analysis of time series of extremely large surfaces of cultivated land. Laboratory measurements followed, while RGB and greyscale images as well as hyperspectral and multispectral measurements from UAVs were observed with lower incidence.

The input data pertaining to crop quality consisted mainly of RGB images, while X-ray images were also utilized (for seed germination monitoring). Additionally, quality parameters, such as color, mass, and flesh firmness, were used. There were also two studies using spectral data either from satellites or spectroradiometers. In general, the studies belonging in this sub-category dealt with either crop quality (80%) or seed germination potential (20%) ( Figure 12 b). The latter refers to the seed quality assessment that is essential for the seed production industry. Two studies were found about germination that both combined X-ray images analysis and ML.

Concerning soil management, various soil properties were taken into account in 65.7% of the studies. These properties included salinity, organic matter content, and electrical conductivity of soil and soil organic carbon. Usage of weather data was also very common (in 48.6% of the studies), while topographic and data pertaining to the soil moisture content (namely the ratio of the water mass over the dry soil) and crop properties were presented with lower frequency. Additionally, remote sensing, including satellite and UAV multispectral and hyperspectral data, as well as proximal sensing, to a lesser extent, were very frequent choices (in 40% of the studies). Finally, properties associated with soil temperature, land type, land cover, root microbial dynamics, and groundwater salinity make up the rest of data, which are labeled as “other” in the corresponding graph of Figure 11 .

In water management, weather data stood for the most common input data (appeared in the 75% of the studies), with ET being used in the vast majority of them. In many cases, accurate estimation of ET (the summation of the transpiration via the plant canopy and the evaporation from plant, soil, and open water surface) is among the most central elements of hydrologic cycle for optimal management of water resources [ 97 ]. Data from remote sensors and measurements of soil water content were also broadly used in this category. Soil water availability has a central impact on crops’ root growth by affecting soil aeration and nutrient availability [ 98 ]. Stem water potential, appearing in three studies, is actually a measure of water tension within the xylem of the plant, therefore functioning as an indicator of the crop’s water status. Furthermore, in situ measurements, soil, and other parameters related to cumulative water infiltration, soil and water quality, field topography, and crop yield were also used, as can be seen in Figure 11 .

Finally, in what concerns livestock management, motion capture sensors, including accelerometers, gyroscopes, and pedometers, were the most common devices giving information about the daily activities of animals. This kind of sensors was used solely in the studies investigating animal welfare. Images, audio, and video recordings came next, however, appearing in both animal welfare and livestock production sub-categories. Physical and growth characteristics followed, with slightly less incidence, by appearing mainly in livestock production sub-category. These characteristics included the animal’s weight, gender, age, metabolites, biometric traits, backfat and muscle thickness, and heat stress. The final characteristic may have detrimental consequences in livestock health and product quality [ 99 ], while through the measurement of backfat and muscle thickness, estimations of the carcass lean yield can be made [ 100 ].

5. Discussion and Main Conclusions

The present systematic review study deals with ML in agriculture, an ever-increasing topic worldwide. To that end, a comprehensive analysis of the present status was conducted concerning the four generic categories that had been identified in the previous review by Liakos et al. [ 12 ]. These categories pertain to crop, water, soil, and livestock management. Thus, by reviewing the relative literature of the last three years (2018–2020), several aspects were analyzed on the basis of an integrated approach. In summary, the following main conclusions can be drawn:

• The majority of the journal papers focused on crop management, whereas the other three generic categories contributed almost with equal percentage. Considering the review paper of [ 12 ] as a reference study, it can be deduced that the above picture remains, more or less, the same, with the only difference being the decrease of the percentage of the articles regarding livestock from 19% to 12% in favor of those referring to crop management. Nonetheless, this reveals just one side of the coin. Taking into account the tremendous increase in the number of relative papers published within the last three years (in particular, 40 articles were identified in [ 12 ] comparing to the 338 of the present literature survey), approximately 400% more publications were found on livestock management. Another important finding was the increasing research interest on crop recognition.
• Several ML algorithms have been developed for the purpose of handling the heterogeneous data coming from agricultural fields. These algorithms can be classified in families of ML models. Similar to [ 12 ], the most efficient ML models proved to be ANNs. Nevertheless, in contrast to [ 12 ], the interest also been shifted towards EL, which can combine the predictions that originated from more than one model. SVM completes the group with the three most accurate ML models in agriculture, due to some advantages, such as its high performance when it works with image data [ 101 ].
• As far as the most investigated crops are concerned, mainly maize and, secondarily, wheat, rice, and soybean were widely studied by using ML. In livestock management, cattle along with sheep and goats stood out constituting almost 85% of the studies. Comparing to [ 12 ], more species have been included, while wheat and rice as well as cattle, remain important specimens for ML applications.
• A very important result of the present review study was the demonstration of the input data used in the ML algorithms and the corresponding sensors. RGB images constituted the most common choice, thus, justifying the broad usage of CNNs due to their ability to handle this type of data more efficiently. Moreover, a wide range of parameters pertaining to weather as well as soil, water, and crop quality was used. The most common means of acquiring measurements for ML applications was remote sensing, including imaging from satellites, UAVs and UGVs, while in situ and laboratory measurements were also used. As highlighted above, UAVs are constantly gaining ground against satellites mainly because of their flexibility and ability to provide images with high resolution under any weather conditions. Satellites, on the other hand, can supply time-series over large areas [ 102 ]. Finally, animal welfare-related studies used mainly devices such as accelerometers for activity recognition, whereas those ones referring to livestock production utilized primary physical and growth characteristics of the animal.

As can be inferred from the geographical distribution (illustrated in Figure 6 ) in tandem with the broad spectrum of research fields, ML applications for facilitating various aspects of management in the agricultural sector is an important issue on an international scale. As a matter of fact, its versatile nature favors convergence research. Convergence research is a relatively recently introduced approach that is based on shared knowledge between different research fields and can have a positive impact on the society. This can refer to several aspects, including improvement of the environmental footprint and assuring human’s health. Towards this direction, ML in agriculture has a considerable potential to create value.

Another noteworthy finding of the present analysis is the capturing of the increasing interest on topics concerning ML analyses in agricultural applications. More specifically, as can be shown in Figure 13 , an approximately 26% increase was presented in the total number of the relevant studies, if a comparison is made between 2018 and 2019. The next year (i.e., 2020), the corresponding increase jumped to 109% against 2019 findings; thus, resulting in an overall 164% rise comparing with 2018. The accelerating rate of the research interest on ML in agriculture is a consequence of various factors, following the considerable advancements of ICT systems in agriculture. Moreover, there exists a vital need for increasing the efficiency of agricultural practices while reducing the environmental burden. This calls for both reliable measurements and handling of large volumes of data as a means of providing a wide overview of the processes taking place in agriculture. The currently observed technological outbreak has a great potential to strengthen agriculture in the direction of enhancing food security and responding to the rising consumers’ demands.

Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.

In a nutshell, ICT in combination with ML, seem to constitute one of our best hopes to meet the emerging challenges. Taking into account the rate of today’s data accumulation along with the advancement of various technologies, farms will certainly need to advance their management practices by adopting Decision Support Systems (DSSs) tailored to the needs of each cultivation system. These DSSs use algorithms, which have the ability to work on a wider set of cases by considering a vast amount of data and parameters that the farmers would be impossible to handle. However, the majority of ICT necessitates upfront costs to be paid, namely the high infrastructure investment costs that frequently prevent farmers from adopting these technologies. This is going to be a pressing issue, mainly in developing economies, where agriculture is an essential economic factor. Nevertheless, having a tangible impact is a long-haul game. A different mentality is required by all stakeholders so as to learn new skills, be aware of the potential profits of handling big data, and assert sufficient funding. Overall, considering the constantly increasing recognition of the value of artificial intelligence in agriculture, ML will definitely become a behind-the-scenes enabler for the establishment of a sustainable and more productive agriculture. It is anticipated that the present systematic effort is going to constitute a beneficial guide to researchers, manufacturers, engineers, ICT system developers, policymakers, and farmers and, consequently, contribute towards a more systematic research on ML in agriculture.

In this section, the reviewed articles are summarized within the corresponding Tables as described in Table 2 .

Crop Management: Yield Prediction.

Acc: Accuracy: CA: Conservation Agriculture; CI: Crop Indices; CEC: Cation Exchange Capacity; CCC: Concordance Correlation Coefficient; DOY: Day Of Year; EC: Electrical Conductivity; HD: Heading Date; HDM: Heading Date to Maturity; K: Potassium; Mg: Magnesium; N: Nitrogen; OLI: Operational Land Imager; P: Phosphorus; RGB: Red-Green-Blue; S: Sulphur; SOM: Soil Organic Matter; SPAD: Soil and Plant Analyzer Development; STI: Soil Texture Information; STD: Standard Deviation; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Disease Detection.

Acc: Accuracy; AUC: Area Under Curve; CR: Cedar Rust; ExGR: Excess Green Minus Excess Red; FS: Frogeye Spot; H: Healthy; mAP: mean Average Precision; RGB: Red-Green-Blue; S: Scab; TYLC: Tomato Yellow Leaf Curl; UAV: Unmanned Aerial Vehicle; VddNet: Vine Disease Detection Network.

Crop Management: Weed Detection.

Acc: Accuracy; AUC: Area under Curve; IoU: Intersection over Union; mAP: mean Average Precision; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Crop Recognition.

Acc: Accuracy; IoU: Intersection over Union; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle.

Crop Management: Crop Quality.

Acc: Accuracy; DSM: Detection and Segmentation Module; EDG: Estimated Dimensions Geometry; IVTD: In Vitro True Digestibility; RGB; Red-Green-Blue; MMD: Manually Measured Dimensions; mAP: mean Average Precision; PSO: Particle Swarm Optimization; RGB; Red-Green-Blue; SAE: Stacked AutoEncoder; VI: Vegetation Indices; WF: Wavelet Features.

Water management.

Acc: Accuracy; CC: Coefficient of Correlation; ET: Evapotranspiration; ET o : reference EvapoTranspiration; ROC: Receiver Operating Characteristic; ME: Model Efficiency; NSE: Nash-Sutcliffe model efficiency Coefficient; POD: Probability Of Detection.

Soil management.

ACCA: Aminoyclopropane-1-carboxylate; AUC: Area Under Curve; BP: Bacterial Population; CC: Coefficient of Correlation; CCC: Concordance Correlation Coefficient; CCE: Calcium Carbonate Equivalent; ET: EvaporoTransporation; MIR: Mid InfraRed; NSE: Nash-Sutcliffe model efficiency Coefficient; NIR: Near-InfraRed; PS: Phosphate Solubilization; PWP: Permanent Wilting Point; RPIQ: Ratio of Performance to Interquartile Range; RPD: Relative Percent Deviation; SOC: Soil Organic Carbon; WI: Willmott’s Index.

Livestock Management: Animal Welfare.

AUC: Area Under Curve; Cont: Contagious; DE: Digestible Energy; ED: Energy Digestibility; ENV: Environmental; DWT: Discrete Wavelet Transform; MFCCs: Mel-Frequency Cepstral Coefficients; NIR: Near InfraRed; NPV: Negative Predictive Value; PTZ: Pan-Tilt-Zoom; PPV: Positive Predictive Value; RGB: Red-Green-Blue; RR: Respiration Rate; ST: Skin Temperature.

Livestock Management: Livestock Production.

ACFW: Adult Clean Fleece Weight; ADG: Average Daily Gain; AFD: Adult Fibre Diameter; AGFW: Adult Greasy Fleece Weight; ASL: Adult Staple Length; ASS: Adult Staple Strength; BBFT: Bacon/BackFat Thickness; BCS: Body Condition Score; CCW: Cold Carcass Weights; CTLEAN: Computed Tomography Lean Meat Yield; DBT: Deep Body Temperature; EMA: Eye Muscle Area; GWAS: Genome-Wide Association Studies; GRFAT: Greville Rule Fat Depth; HER: Human Error Range; IMF: IntraMuscular Fat; HCW: Hot Carcass Weight; LW: Loin Weight; MS: Marbling Score; MT: Muscle Thickness; REIMS: Rapid Evaporative Ionization Mass Spectrometry; RGB: Red-Green-Blue; SMY: Saleable Meat Yield.

Abbreviations for machine learning models.

Abbreviations for machine learning algorithms.

Author Contributions

Conceptualization, D.B.; methodology, L.B., G.D., R.B., D.K. and A.C.T.; investigation, L.B. and G.D.; writing—original draft preparation, L.B. and A.C.T.; writing—review and editing, L.B., G.D., D.K., A.C.T., R.B. and D.B.; visualization, L.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.

This work has been partly supported by the Project “BioCircular: Bio-production System for Circular Precision Farming” (project code: T1EDK- 03987) co-financed by the European Union and the Greek national funds through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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114 Agriculture Essay Topic Ideas & Examples

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Agriculture plays a vital role in the development and sustainability of societies around the world. From crop cultivation to animal husbandry, agriculture encompasses a wide range of practices that affect our food production, environment, and economy. If you're looking for essay topics related to agriculture, we've compiled a comprehensive list of 114 ideas and examples to inspire your writing.

• The impact of climate change on agriculture: challenges and adaptation strategies.
• The role of genetically modified organisms (GMOs) in modern agriculture.
• Organic farming: benefits, challenges, and future prospects.
• The use of pesticides in agriculture: balancing productivity and environmental concerns.
• Agricultural subsidies: their impact on farmers and the economy.
• The importance of soil health for sustainable agriculture.
• Precision farming: the integration of technology in agricultural practices.
• The role of women in agriculture: empowerment and gender equality.
• Urban agriculture: promoting food security in cities.
• The impact of globalization on agriculture: opportunities and threats.
• The role of agricultural education in shaping the future of farming.
• Food waste in agriculture: causes, consequences, and solutions.
• Sustainable livestock production: balancing meat consumption and environmental impact.
• The role of small-scale farmers in global food production.
• The ethics of animal welfare in modern farming practices.
• Agricultural trade policies: implications for developing countries.
• The impact of deforestation on agricultural practices.
• The role of agricultural biotechnology in feeding a growing population.
• The challenges and benefits of aquaculture in meeting global seafood demand.
• The impact of agricultural practices on water resources.
• The role of agricultural cooperatives in supporting small-scale farmers.
• The future of vertical farming: opportunities and limitations.
• The impact of agricultural pollution on human health.
• Agroforestry: integrating trees into agricultural landscapes.
• The role of agricultural extension services in rural development.
• The potential of hydroponics in urban agriculture.
• The impact of industrial agriculture on biodiversity.
• The role of agricultural research and development in innovation.
• The influence of social media on consumer perceptions of agriculture.
• The challenges and opportunities of agricultural mechanization in developing countries.
• The role of agricultural insurance in mitigating risks for farmers.
• The impact of land tenure systems on agricultural productivity.
• The role of agricultural cooperatives in sustainable development.
• The potential of vertical farming to reduce food miles and carbon footprint.
• The impact of agricultural subsidies on food prices for consumers.
• The role of urban agriculture in community development.
• The importance of seed banks in preserving agricultural biodiversity.
• The impact of agricultural practices on pollinators and ecosystem services.
• The role of agricultural drones in precision farming.
• The challenges and benefits of transitioning to regenerative agriculture.
• The impact of agricultural practices on soil erosion.
• The role of agricultural education in fostering entrepreneurship.
• The potential of agricultural waste management in bioenergy production.
• The impact of agricultural practices on rural livelihoods.
• The role of agricultural cooperatives in improving market access for small-scale farmers.
• The challenges and benefits of transitioning to organic dairy farming.
• The impact of climate-smart agriculture on resilience and adaptation.
• The role of agricultural biotechnology in improving crop yields.
• The potential of agroecology in sustainable farming.
• The impact of agricultural practices on air quality.
• The role of agricultural research in addressing food security challenges.
• The challenges and benefits of transitioning to sustainable palm oil production.
• The impact of agricultural practices on wildlife conservation.
• The role of agricultural cooperatives in promoting fair trade.
• The potential of precision livestock farming in improving animal welfare.
• The impact of agricultural practices on rural migration patterns.
• The challenges and benefits of transitioning to organic vegetable farming.
• The role of agricultural biotechnology in addressing malnutrition.
• The potential of urban rooftop gardens in enhancing food security.
• The impact of agricultural practices on groundwater contamination.
• The role of agricultural entrepreneurship in rural development.
• The challenges and benefits of transitioning to agroforestry systems.
• The impact of agricultural practices on food safety.
• The role of agricultural cooperatives in empowering marginalized communities.
• The potential of hydroponics in space agriculture.
• The impact of agricultural practices on indigenous food systems.
• The challenges and benefits of transitioning to sustainable cotton production.
• The role of agricultural biotechnology in reducing post-harvest losses.
• The potential of vertical farming in food deserts.
• The impact of agricultural practices on rural poverty alleviation.
• The role of agricultural cooperatives in promoting climate-smart agriculture.
• The challenges and benefits of transitioning to organic wine production.
• The impact of agricultural practices on soil degradation.
• The role of agricultural education in promoting sustainable farming practices.
• The potential of aquaponics in sustainable food production.
• The impact of agricultural practices on food sovereignty.
• The challenges and benefits of transitioning to sustainable coffee farming.
• The role of agricultural biotechnology in reducing pesticide use.
• The potential of urban agriculture in reducing food waste.
• The impact of agricultural practices on indigenous land rights.
• The role of agricultural cooperatives in promoting gender equality.
• The challenges and benefits of transitioning to organic beekeeping.
• The impact of agricultural practices on rural resilience.
• The role of agricultural extension services in promoting climate resilience.
• The potential of rooftop farming in urban sustainability.
• The impact of agricultural practices on food culture.
• The challenges and benefits of transitioning to sustainable cocoa production.
• The role of agricultural biotechnology in improving nutritional quality.
• The potential of vertical farming in disaster-prone areas.
• The impact of agricultural practices on food sovereignty in indigenous communities.
• The role of agricultural cooperatives in promoting sustainable seafood.
• The challenges and benefits of transitioning to organic tea production.
• The impact of agricultural practices on rural social capital.
• The role of agricultural extension services in promoting sustainable water management.
• The potential of hydroponics in space exploration.
• The impact of agricultural practices on food justice.
• The challenges and benefits of transitioning to sustainable sugar production.
• The role of agricultural biotechnology in reducing food waste.
• The potential of urban agriculture in promoting social cohesion.
• The impact of agricultural practices on land rights in developing countries.
• The role of agricultural cooperatives in promoting sustainable palm oil.
• The challenges and benefits of transitioning to organic cotton farming.
• The impact of agricultural practices on rural cultural heritage.
• The role of agricultural extension services in promoting sustainable energy use.
• The potential of aquaponics in sustainable urban development.
• The impact of agricultural practices on food sovereignty in marginalized communities.
• The challenges and benefits of transitioning to sustainable chocolate production.
• The role of agricultural biotechnology in improving drought tolerance.
• The potential of vertical farming in post-disaster recovery.
• The impact of agricultural practices on food security in conflict zones.
• The role of agricultural cooperatives in promoting sustainable timber production.
• The challenges and benefits of transitioning to organic coffee farming.
• The impact of agricultural practices on rural cultural landscapes.
• The role of agricultural extension services in promoting sustainable waste management.

These essay topic ideas cover a wide range of aspects related to agriculture, providing a plethora of opportunities for research and critical analysis. Whether you're interested in environmental sustainability, social justice, or technological innovation, there is a topic here that will inspire your writing and contribute to the ongoing dialogue about the future of agriculture.

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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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186 Agriculture Essay Topics & Research Questions + Examples

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• Agricultural Role in African Development Diao et al. attempt to determine the role of agriculture in overcoming the challenge of poverty in rural areas of Africa compared to alternative theories of economic growth.
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• Agriculture Development and Related Theories There are two main domestication models used to describe the development of agriculture: unconscious and conscious.
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• Agricultural Traditions of Canadians In Canada there is a very good agricultural education, so young people can get higher education in agriculture and use it on their own farms.
• Agriculture: Application of Information Technology IT application in agriculture has contributed to food security in most modern communities. Farming has become easier than before as new inventions are made.
• Sharecropping. History of Racial Agriculture Sharecropping became a variation of racialized agriculture, that which has negative impact on the capabilities of the black population to generate and pass down wealth.
• Agriculture the Backbone of Ancient Egypt’s Economy In pre-industrial societies, agriculture was the backbone of most economies. This is true in ancient times and very much evident in ancient Egypt.
• Hunting and Gathering Versus Agricultural Society The hunting and gathering society is considered the most equitable of all seven types, while the agricultural community gives rise to the development of civilization.
• The Agriculture Industry’s Digital Transformation This study seeks to explore the dynamics of digital technology in agriculture over the past two decades, focusing on the perspectives and perceptions of the farmers.
• Colonialism and Economic Development of Africa Through Agriculture The colonial period is characterized by the exploitation of the agricultural sector in Africa to make a profit and provide Western countries with raw materials.
• The Big History of Civilizations – Origins of Agriculture: Video Analysis This paper aims to analyze the origins of agriculture – what was a foraging economy and way of life like, as well as compare foragers and farmers.
• History of Agricultural Technology Development Agricultural technologies were majorly developed during the Medieval period to ensure sufficient product yields for growing populations around the world.
• Impacts of Genetic Engineering of Agricultural Crops In present days the importance of genetic engineering grew due to the innovations in biotechnologies and Sciences.
• Agriculture in Honduras: Existing Challenges and Possible Solutions This paper tackles the issue of existing challenges and possible solutions to the problems of agriculture in Honduras.
• Virtual Water Savings and Trade in Agriculture The idea of virtual water was initially created as a method for assessing how water-rare nations could offer food, clothing, and other water-intensive products to their residents.
• Market Revolution: Agriculture and Global Trade In the era of traders, the vast land area and rich natural resources created many economic opportunities. Most people lived in rural areas and were engaged in agriculture.
• Agriculture and Food Production in the Old Kingdom
• Agriculture and the Transition to the Market in Asia
• Agrarian Reform and Subsistence Agriculture in Russia
• Agriculture, Nutrition, and the Green Revolution in Bangladesh
• Agriculture Business and Management
• Agriculture, Horticulture, and Ancient Egypt
• Agriculture and Food Production in the Old Kingdom of Egypt
• Administrative and Transaction-Related Costs of Subsidising Agriculture
• Agriculture and Economic Growth in Argentina, 1913-84
• Agriculture and Economic Development in Brazil, 1960-1995
• Agriculture and Greenhouse Gas Cap-And-Trade
• Croatian Agriculture Towards World Market Liberalization
• Adapting Credit Risk Models to Agriculture
• Agriculture and European Union Enlargement
• Agriculture and Food Security in Pakistan
• Cash Flows and Financing in Texas Agriculture
• Current Problems With Indian Agriculture
• Agriculture and Its Drain on California
• Agriculture and the Economic Life of India
• Agriculture and Global Climate Stabilization
• Achieving Regional Growth Dynamics in African Agriculture
• Agriculture and Non-agricultural Liberalization in the Millennium Round
• Corporate Agriculture and Modern Times
• Agriculture and Rural Employment Agricultural in Bolivia
• Climatic Fluctuations and the DI¤Usion of Agriculture
• Agriculture Global Market Briefing
• Agriculture and the Industrial Revolution of the Late 1700s
• Agriculture and Animal Husbandry in Ecuador
• Biofuels, Agriculture, and Climate Change
• Aggregate Technical Efficiency and Water Use in U.S. Agriculture
• Agriculture, Water, and Food Security in Tanzania This paper evaluates the strategies applicable to the development and further maintenance of agriculture, water, and food security in Tanzania.
• The Australian Agriculture Company’s Financial Analysis The Australian Agriculture Company shows a positive sign for investment due to its financial analysis indicating company resilience and strong prospects of growth.
• Governmental Price Control in Agricultural Sector The consequences of real-life governmental price control are the evolutionary nature of transformations in the agricultural sector.
• Aspects of Pesticide Use in Agriculture This paper investigates socio-environmental factors connected with pesticide use in agriculture and food production. It has a destructive impact on the environment
• Agriculture-Led Food Crops and Cash Crops in Tanzania This paper aims to explore the contributions of the agriculture sector in Tanzania to the country’s industrialization process by using recent data about its food and cash crops.
• The Impact of Pesticides’ Use on Agriculture Pesticides are mostly known for their adverse effects and, therefore, have a mostly negative connotation when discussed among general audiences.
• Cuisine and Agriculture of Ancient Greece There are many reasons for modern students to investigate the development of cuisine and agriculture in Ancient Greece.
• Agriculture and Food Safety in the United States Agriculture in the United States has grown progressively centralized. The shortcomings in the 2018 U.S. farm legislation resulted in multiple challenges in the food system.
• Sustainable Agriculture and Future Perspectives Sustainable agriculture is essential to the earth’s environment. When farmers take care of their land and crops, they are taking care of environmental sustainability.
• Agricultural Adaptation to Changing Environments The paper discusses the impact of climate change on agriculture in Canada. This phenomenon is real and has affected the industry over at least the last three decades.
• Trade Peculiarities in Food and Agriculture Food trading is a peculiar area, as food is the basis for surviving the population. The one who controls food production and trading routes, also controls all populations.
• Multinational Agricultural Manufacturing Companies’ Standardization & Adaptation The most popular approaches that multinational companies use to serve their customers from various countries are standardization and adaptation.
• Sustainable Agriculture Against Food Insecurity The paper argues sustainable agriculture is one way to reduce food insecurity without harming the planet because the number of resources is currently decreasing.
• Impacts of Climate Change on Agriculture and Food This paper will examine four aspects of climate change: variation in the rainfall pattern, water levels, drought, temperature, and heatwaves.
• Canadian Laws Regarding Agricultural Sector The unions in Canada are the concept over which there has been an excessive dispute involving court proceedings and questioning the constitutional rights of citizens.
• Food Additives Use in Agriculture in the United States Food additives in agriculture become a debatable issue because their benefits do not always prevail over such shortages like health issues and environmental concerns.
• Radio-Frequency Identification in Healthcare and Agriculture Specifically, radio-frequency identification (RFID) has gained traction due to its ability to transmit data over distance.
• Mechanism of US Agricultural Market The fact that lower interest rates increased the number of potential customers for real estate in the 2000s shows that housing prices should have increased.
• A Biological Terror Attack in Agriculture The United States is highly vulnerable to terror attacks of biological nature in agriculture yet such an occurrence can cripple the economy.
• The Economics of Race, Agriculture and Environment This research paper is going to answer the question; do public policies reduce or enhance racial inequality in agricultural and environmental affairs?
• Impact of Bioterrorism on the U.S Agriculture System The paper describes that the term bioterrorism has several definitions depending upon the origin of the attack but in general terms, it refers to any form of terrorist attack.
• The Effects of Genetic Modification of Agricultural Products Discussion of the threat to the health of the global population of genetically modified food in the works of Such authors as Jane Brody and David Ehrenfeld.
• Climate Change and Its Potential Impact on Agriculture and Food Supply The global food supply chain has been greatly affected by the impact of global climate change. There are, however, benefits as well as drawbacks to crop production.
• Agriculture and Mayan Society Resilience The Yucatan peninsula had a vast landscape which was good for agriculture thus making agriculture to be the main economic base for the Mayans.
• Climate Changes Impact on Agriculture and Livestock The project evaluates the influences of climate changes on agriculture and livestock in different areas in the Kingdom of Saudi Arabia.
• Homeland Security in Agriculture and Health Sectors Lack of attention to the security and protection of the agricultural sector in the U.S. economy can create a serious threat to the health and safety of the population.
• Water Savings and Virtual Trade in Agriculture Water trade in agriculture is not a practice that is unique to the modern generation. The practice was common long before the emergence of the Egyptian Empire.
• Virtual Water Trade and Savings in Agriculture This essay discusses the savings associated with virtual water trade in agriculture and touches on the effects of a shift to local agricultural production on global water savings.
• Virtual Water Trade of Agricultural Products Virtual water trade is a concept associated with globalization and the global economy. Its rise was motivated by growing water scarcity in arid areas around the world.
• European Invasion and Agriculture in the Caribbean The early invasion of the Europeans in the Caribbean did not prompt the employment of the slave trade in the agricultural activities until the development of the sugar plantations.
• Freedom in American Countryside and Agriculture This paper portrays how freedom has been eliminated in the countryside by the state agriculture department, and whether the farmer has a moral right to do his farming practices.
• Agricultural Problems in Venezuela Agriculture has been greatly underdeveloped in Venezuela, yet it is a country that has vital minerals and resources required for the global economy.
• America’s Agriculture in the Period of 1865-1938 This paper analyzes America’s contribution in prevention of natural calamities, decline of soil quality, promotion of production outlay and provision of sufficient food.
• Capital Taxes and Agriculture
• Canadian Trade With the Chinese Agriculture Market
• Agriculture and Its Impact on Economic Development
• Bacteriocins From the Rhizosphere Microbiome From an Agriculture Perspective
• Agriculture and Its Impact on Financial Institutions
• Agriculture, Fisheries, and Food in the Irish Economy
• Adoption and Economic Impact of Site-Specific Technologies in U.S. Agriculture
• Cash Rents and Land Values in U.S. Agriculture
• Crises and Structural Change in Australian Agriculture
• Biotechnology and Its Application in Agriculture
• Alternative Policies for Agriculture in Europe
• Agriculture and Food Security in Asia by 2030
• Agriculture and Coping Climate Change in Nepal
• Agriculture and Ethiopia’s Economic Transformation
• Culture: Agriculture and Egalitarian Social
• Adaptation, Climate Change, Agriculture, and Water
• Agriculture and the Literati in Colonial Bengal, 1870 to 1940
• Agriculture and Barley Farming Taro
• Agriculture and Agricultural Inputs Markets
• Agriculture and Environmental Challenges
• Challenges for Sustainable Agriculture in India
• Agriculture and German Reunification
• Agriculture and Tourism Relationship in Malaysia Tourism
• 21st Century Rural America: New Horizons for U.S. Agriculture
• Canadian Agriculture and the Canadian Agricultural Industry
• California Agriculture Dimensions and Issues
• Advancements and the Development of Agriculture in Ancient Greece and Rome
• Agriculture and Early Industrial Revolution
• Aztec: Agriculture and Habersham County
• Agriculture and Current Deforestation Practices
• How Has Agriculture Changed From Early Egypt, Greece, and Rome to the Present?
• What Are the Advantages of Using Pesticides on Agriculture?
• Are Digital Technologies for the Future of Agriculture?
• How Did Agriculture Change Our Society?
• Does Agriculture Help Poverty and Inequality Reduction?
• Can Agriculture Prosper Without Increased Social Capital?
• Are Mega-Farms the Future of Global Agriculture?
• How Can African Agriculture Adapt to Climate Change?
• Does Agriculture Really Matter for Economic Growth in Developing Countries?
• Can Conservation Agriculture Save Tropical Forests?
• How Can Sustainable Agriculture Be Better for Americans?
• Are U.S. and European Union Agriculture Policies Becoming More Similar?
• Should Pollution Reductions Count as Productivity Gains for Agriculture?
• Can Market Access Help African Agriculture?
• How Does Genetic Engineering Affect Agriculture?
• Does Individualization Help Productivity of Transition Agriculture?
• Can Spot and Contract Markets Co-Exist in Agriculture?
• How Has Biotechnology Changed Agriculture Throughout the Years?
• Does Trade Policy Impact Food and Agriculture Global Value Chain Participation of Sub-Saharan African Countries?
• Can Sustainable Agriculture Feed Africa?
• How Can Multifunctional Agriculture Support a Transition to a Green Economy in Africa?
• Does Urban Agriculture Enhance Dietary Diversity?
• How Did Government Policy, Technology, and Economic Conditions Affect Agriculture?
• Can the Small Dairy Farm Remain Competitive in US Agriculture?
• What Are the Main Changes in French Agriculture Since 1945 and What Challenges Does It Face Today?
• How Can Marketing Theory Be Applied to Policy Design to Deliver Sustainable Agriculture in England?
• Will African Agriculture Survive Climate Change?
• How Has Agriculture Changed Civilizations?
• Does Urban Agriculture Improve Food Security?
• Can US and Great Plains Agriculture Compete in the World Market?
• The effect of climate change on crop yields and food security.
• Sustainable agricultural practices for soil health.
• Precision agriculture techniques and applications.
• The impact of genetically engineered organisms on crop yields and safety.
• The benefits of agroforestry systems for the environment.
• Current challenges in water management in agriculture.
• The environmental impact of organic farming.
• The potential of urban agriculture to address food insecurity.
• Food waste in the agricultural supply chain.
• Comparing the effectiveness of aquaponic and hydroponic systems.
• Organic vs. conventional farming.
• Can regenerative agriculture combat climate change?
• Agricultural subsidies: pros and cons.
• Should harmful pesticides be banned to protect pollinators?
• Should arable land be used for biofuels or food production?
• Do patent protections of seeds hinder agricultural innovation?
• Agricultural robots: increased efficiency or displaced rural labor?
• Should GMO labeling be mandatory?
• Do the benefits of pesticides outweigh their potential health harms?
• Is it unsustainable to grow water-intensive crops in arid regions?
• The economics of organic farming.
• The need for climate-adaptive crops.
• The role of bees in agriculture and threats to their survival.
• Smart agriculture: transforming farming with data and connectivity.
• The journey of food in modern agricultural supply chains.
• The role of agri-tech startups in agricultural innovation.
• Youth in agriculture: inspiring the next generation of farmers.
• Why should we shift to plant-based meat alternatives?
• The importance of preserving indigenous agricultural practices.
• Smart irrigation systems: optimizing water use in agriculture.

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StudyCorgi . "186 Agriculture Essay Topics & Research Questions + Examples." March 1, 2022. https://studycorgi.com/ideas/agriculture-essay-topics/.

StudyCorgi . 2022. "186 Agriculture Essay Topics & Research Questions + Examples." March 1, 2022. https://studycorgi.com/ideas/agriculture-essay-topics/.

These essay examples and topics on Agriculture were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on January 21, 2024 .

Agriculture and Food Technology Research Paper Topics

See our collection of agriculture and food technology research paper topics . This page lists 19 topics and provides an overview of agriculture and food technology development.

1. Activated Carbon

Activated carbon is made from any substance with a high carbon content, and activation refers to the development of the property of adsorption. Activated carbon is important in purification processes, in which molecules of various contaminants are concentrated on and adhere to the solid surface of the carbon. Through physical adsorption, activated carbon removes taste and odor-causing organic compounds, volatile organic compounds, and many organic compounds that do not undergo biological degradation from the atmosphere and from water, including potable supplies, process streams, and waste streams. The action can be compared to precipitation. Activated carbon is generally nonpolar, and because of this it adsorbs other nonpolar, mainly organic, substances. Extensive porosity (pore volume) and large available internal surface area of the pores are responsible for adsorption. Activated carbon also found wide application in the pharmaceutical, alcoholic beverage, and electroplating industries; in the removal of pesticides and waste of pesticide manufacture; for treatment of wastewater from petroleum refineries and textile factories; and for remediation of polluted groundwater. Although activated carbons are manufactured for specific uses, it is difficult to characterize them quantitatively. As a result, laboratory trials and pilot plant experiments on a specific waste type normally precede installation of activated carbon facilities.

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Get 10% off with 24start discount code, 2. biological pest control.

Insect outbreaks have plagued crop production throughout human history, but the growth of commercial agriculture since the middle of the nineteenth century has increased their acuteness and brought forth the need to devise efficient methods of insect control. Methods such as the spraying of insecticides, the application of cultural methods, the breeding of insect-resistant plants, and the use of biological control have increasingly been used in the twentieth century. Traditionally limited to checking the populations of insect pests through the release of predatory or parasitic insects, biological control now refers to the regulation of agricultural or forest pests (especially insects, weeds and mammals) using living organisms. It also includes other methods such as the spraying of microbial insecticides, the release of pathogenic microorganisms (fungi, bacteria or viruses), the release of male insects sterilized by radiation, the combination of control methods in integrated pest management programs, and the insertion of toxic genes into plants through genetic engineering techniques. Biological control is also directed against invasive foreign species that threaten ecological biodiversity and landscape esthetics in nonagricultural environments.

3. Crop Protection and Spraying

Humans have controlled agricultural pests, both plants and insects, that infest crops with a variety of biological and technological methods. Modern humans developed spraying pest management techniques that were based on practical solutions to combat fungi, weeds, and insects. Ancient peoples introduced ants to orchards and fields so they could consume caterpillars preying on plants. Chinese, Sumerian, and other early farmers used chemicals such as sulfur, arsenic, and mercury as rudimentary herbicides and insecticides. These chemicals were usually applied to or dusted over roots, stems, or leaves. Seeds were often treated before being sowed. As early as 200 BC, Cato the Censor promoted application of antipest oil sprays to protect plants in the Roman Republic. The nineteenth century potato famine and other catastrophic destruction of economically significant crops including vineyard grapes emphasized the need to improve crop protection measures. People gradually combined technological advances with biological control methods to initiate modern agricultural spraying in the late nineteenth century. Such crop protection technology was crucial in the twentieth century when large-scale commercial agriculture dominated farming to meet global demands for food. Individual farms consisted of hundreds to thousands of acres cultivated in only one or two crop types. As a result, spraying was considered essential to prevent devastating economic losses from pest damage associated with specific crops or locales.

4. Dairy Farming

Throughout the world, especially in the Northern Hemisphere, milk, cheese, butter, ice cream, and other dairy products, have been central elements of food production. Over the centuries improvements in cattle breeding and nutrition, as well as new dairy techniques, led to the increased production of dairy goods. Hand-operated churns and separators were used to make butter and cream, and those close to a barnyard had access to fresh milk. By the late nineteenth century, new science and technology had begun to transform dairy production, particularly in the U.S. and Europe. Rail transportation and iced and refrigerated boxcars made it easier to transport milk to more distant markets. Successful machinery for separating milk from cream came from the DeLaval Corporation in 1879, and the Babcock butterfat tester appeared in 1890. The first practical automated milking machines and commercial pasteurization machines were in use in the decades before 1900. Louis Pasteur’s contribution to the dairy industry— discovering the sterilization process for milk— was substantial. By heating milk, pasteurization destroys bacteria that may be harmful to humans. The pasteurization process also increases the shelf life of the product by eliminating enzymes and bacteria that cause milk to spoil. Milk is pasteurized via the ‘‘batch’’ method, in which a jacketed vat is surrounded by heated coils. The vat is agitated while heated, which adds qualities to the product that also make it useful for making ice cream. With the ‘‘continuous’’ method of pasteurization, time and energy are conserved by continuously processing milk as a high temperature using a steel-plated heat exchanger, heated by steam or hot water. Ultra-high temperature pasteurization was first used in 1948.

5. Farming and Agricultural Methods

Agriculture experienced a transformation in the twentieth century that was vital in increasing food and fiber production for a rising global population. This expansion of production was due to mechanization, the application of science and technology, and the expansion of irrigation. Yet these changes also resulted in the decimation of traditional agricultural systems and an increased reliance on capital, chemicals, water, exploitative labor conditions, and the tides of global marketing. A sign of the transformation of agriculture in the twentieth century was the shift from China and India as countries often devastated by famine to societies that became exporters of food toward the end of the century. As the world’s technological leader, the U.S. was at the vanguard of agricultural change, and Americans in the twentieth century experienced the cheapest food in the history of modern civilization, as witnessed by the epidemic of obesity that emerged in the 1990s. Unfortunately, this abundance sometimes led to overproduction, surplus, and economic crisis on the American farm, which one historian has labeled ‘‘the dread of plenty.’’

6. Farming and Growth Promotion

Early in the twentieth century, most farmers fed livestock simple mixtures of grains, perhaps supplemented with various plant or animal byproducts and salt. A smaller group of scientific agriculturalists fed relatively balanced rations that included proteins, carbohydrates, minerals, and fats. Questions remained, however, concerning the ideal ratio of these components, the digestibility of various feeds, the relationship between protein and energy, and more. The discoveries of various vitamins in the early twentieth century offered clear evidence that proteins, carbohydrates, and fats did not supply all the needs of a growing animal. Additional research demonstrated that trace minerals like iron, copper, calcium, zinc, and manganese are essential tools that build hemoglobin, limit disease, and speed animal growth. Industrially produced nonprotein nitrogenous compounds, especially urea, have also become important feed additives. The rapid expansion of soybean production, especially after 1930, brought additional sources of proteins and amino acids within the reach of many farmers. Meanwhile, wartime and postwar food demands, as well as a substantial interest in the finding industrial uses for farm byproducts, led to the use of wide variety of supplements—oyster shells, molasses, fish parts, alfalfa, cod liver oil, ground phosphates, and more.

7. Farming Mechanization

Mechanization of agriculture in the twentieth century helped to dramatically increase global production of food and fiber to feed and clothe a burgeoning world population. Among the significant developments in agricultural mechanization in the twentieth century were the introduction of the tractor, various mechanical harvesters and pickers, and labor-saving technologies associated with internal combustion engines, electric motors, and hydraulics. While mechanization increased output and relieved some of the drudgery and hard work of rural life, it also created unintended consequences for rural societies and the natural environment. By decreasing the need for labor, mechanization helped accelerate the population migration from rural to urban areas. For example, in 1790, 90 percent of Americans worked in agriculture, yet by 2000 only about 3 percent of the American workforce was rural. Blessed with great expanses of land and limited labor, technologically inclined Americans dominated the mechanization of agriculture during the twentieth century. Due to mechanization, irrigation, and science, the average American farmer in 1940 fed an estimated ten people, and by 2000 the number was over 100 people. Yet even as mechanization increased the speed of planting and harvesting, reduced labor costs, and increased profits, mechanization also created widespread technological unemployment in the countryside and resulted in huge losses in the rural population.

8. Fertilizers

As the twentieth century opened, fertilizers were a prominent concern for farmers, industrialists, scientists, and political leaders. In 1898, British scientist William Crookes delivered a powerful and widely reported speech that warned of a looming ‘‘famine’’ of nitrogenous fertilizers. According to Crookes, rising populations, increased demand for soil-depleting grain products, and the looming exhaustion of sodium nitrate beds in Chile threatened Britain and ‘‘all civilized nations’’ with imminent mass starvation and collapse. Yet Crookes also predicted that chemists would manage to discover new artificial fertilizers to replace natural and organic supplies, a prophecy that turned out to encapsulate the actual history of fertilizers in the twentieth century. In addition to obvious links to increased agricultural production, the modern fertilizer industry has been linked with a number of concerns beyond the farm. For example, the short-lived phosphate boom on the Pacific island of Nauru offers a telling case study of the social consequences and environmental devastation than can accompany extractive industries. Further, much of the nitrogen applied to soils does not reach farm plants; nitrates can infiltrate water supplies in ways that directly threaten human health, or indirectly do so by fostering the growth of bacteria that can choke off natural nutrient cycles. To combat such threats, the European Union Common Agricultural Policy includes restrictions on nitrogen applications, and several nations now offer tax incentives to farmers who employ alternative agricultural schemes. Nevertheless, the rapidly growing global population and its demand for inexpensive food means that artificial fertilizer inputs are likely to continue to increase.

9. Fish Farming

Controlled production, management, and harvesting of herbivorous and carnivorous fish has benefited from technology designed specifically for aquaculture. For centuries, humans have cultivated fish for dietary and economic benefits. Captive fish farming initially sustained local populations by supplementing wild fish harvests. Since the 1970s, aquaculture became a significant form of commercialized farming because wild fish populations declined due to overfishing and habitat deterioration. Growing human populations increased demand for reliable, consistent sources of fish suitable for consumption available throughout the year. Fish farming technology can be problematic. If genetically engineered fish escape and mate with wild fish, the offspring might be unable to survive. Cultivated fish live in crowded tanks that sometimes cause suffocation, diseases, and immense amounts of waste and pollutants. Antibiotic use can sometimes result in resistant microorganisms. Coastal fish farms, especially those for shrimp, can be environmentally damaging if adjacent forests are razed.

10. Foods Additives and Substitutes

Advances in food and agricultural technology have improved food safety and availability. Food technology includes techniques to preserve food and develop new products. Substances to preserve and enhance the appeal of foods are called food additives, and colorings fit into this category of additives that are intentionally included in a processed food. All coloring agents must be proven to be safe and their use in terms of permitted quantity, type of food that can have enhanced coloring, and final level is carefully controlled. Fat substitutes on the other hand are technically known as replacers in that they replace the saturated and/or unsaturated fats that would normally be found in processed food as an ingredient or that would be added in formulation of a processed food. Usually the purpose is to improve the perceived health benefit of the particular food substance. Technically speaking, substitutes are not additives but their efficacy and safety must be demonstrated.

11. Food Preparation and Cooking

Twentieth century technological developments for preparing and cooking food consisted of both objects and techniques. Food engineers’ primary objectives were to make kitchens more convenient and to reduce time and labor needed to produce meals. A variety of electric appliances were invented or their designs improved to supplement hand tools such as peelers, egg beaters, and grinders. By the close of the twentieth century, technological advancements transformed kitchens, the nucleus of many homes, into sophisticated centers of microchip-controlled devices. Cooking underwent a transition from being performed mainly for subsistence to often being an enjoyable hobby for many people. Kitchen technology altered people’s lives. The nineteenth-century Industrial Revolution had initiated the mechanization of homes. Cooks began to use precise measurements and temperatures to cook. Many people eagerly added gadgets to their kitchens, ranging from warming plates and toasters to tabletop cookers. Some architects designed kitchens with built-in cabinets, shelves, and convenient outlets to encourage appliance use. Because they usually cooked, women were the most directly affected by mechanical kitchen innovations. Their domestic roles were redefined as cooking required less time and was often accommodated by such amenities as built-in sinks and dishwashers. Ironically, machines often resulted in women receiving more demands to cook for events and activities because people no longer considered cooking to be an overwhelming chore.

12. Food Preservation by Cooling and Freezing

People have long recognized the benefits of cooling and freezing perishable foods to preserve them and prevent spoilage and deterioration. These cold storage techniques, which impede bacterial activity, are popular means to protect food and enhance food safety and hygiene. The food industry has benefited from chilled food technology advancements during the twentieth century based on earlier observations. For several centuries, humans realized that evaporating salt water removed heat from substances. As a result, food was cooled by placing it in brine. Cold storage in ice- or snow-packed spaces such as cellars and ice houses foreshadowed the invention of refrigerators and freezers. Before mechanical refrigeration became consistent, freezing was the preferred food preservation technique because ice inhibited microorganisms. Freezing technology advanced to preserve food more efficiently with several processes. Blast freezing uses high-velocity air to freeze food for several hours in a tunnel. Refrigerated plates press and freeze food for thirty to ninety minutes in plate freezing. Belt freezing quickly freezes food in five minutes with air forced through a mesh belt. Cryogenic freezing involves liquid nitrogen or Freon absorbing food heat during several seconds of immersion.

13. Food Preservation by Freeze Drying, Irradiation, and Vacuum Packing

Humans have used processes associated with freeze-drying for centuries by placing foods at cooler high altitudes with low atmospheric pressure where water content is naturally vaporized. Also called lyophilization, freeze-drying involves moisture being removed from objects through sublimation. Modern freeze-drying techniques dehydrate frozen foods in vacuum chambers, which apply low pressure and cause vaporization. Irradiation is less successful than freeze-drying. Prior to irradiation, millions of people worldwide became ill annually due to contaminated foods with several thousand being hospitalized or dying due to food-borne pathogens. By exposing food to an electron beam, irradiation enhances food safety. Irradiated human and animal feed, especially grain, can be transported over distances and stored for a long duration without spoiling or posing contamination hazards. The radura is the international food packaging symbol for irradiation. Vacuum-packing food technologies involve a process that removes empty spaces around foods being packaged. Vacuum technology uses environments artificially modified to have atmospheric pressures that are lower than natural conditions. Vacuum packing extends the shelf life of food. The U.K. Advisory Committee on the Microbiological Safety of Foods warned that anaerobic pathogens such as C. botulinum can grow in vacuum-packed foods. Because vacuum packing often results in rubbery sliced cheese, some manufacturers use the modified atmosphere packaging (MAP) system, which utilizes gases to fill spaces so that cheese can mature to become tastier inside packaging.

14. Irrigation Systems

Since the onset of human civilization, the manipulation of water through irrigation systems has allowed for the creation of agricultural bounty and the presence of ornamental landscaping, often in the most arid regions of the planet. These systems have undergone a widespread transformation during the twentieth century with the introduction of massive dams, canals, aqueducts, and new water delivery technology. In 1900 there were approximately 480,000 square kilometers of land under irrigation; by 2000 that total had surged to 2,710,000 square kilometers, with India and China as the world leaders in irrigated acreage. Globally, the agriculture industry uses about 69 percent of the available fresh water supplies, producing 40 percent of the world’s food on just about 18 percent of the world’s cropland. (It takes 1000 tons of water to produce 1 ton of grain.) New technologies to monitor evaporation, plant transpiration, and soil moisture levels have helped increase the efficiency of irrigation systems. The US is the world leader in irrigation technology, exporting upward of $800 million of irrigation equipment to the rest of the world each year, with the sales of drip irrigation equipment increasing 15 to 20 percent per annum in the 1990s. Golf course and landscape irrigation are also an increasing part of the irrigation technology market. Intense competition for water from cities and for environmental restoration projects might mean a reduction in irrigated agriculture in future years. At the same time, salinization of fields, infiltration of aquifers by sea water, and depleted water availability could lead to a reduction in land under irrigation worldwide.

15. Nitrogen Fixation

In 1898, the British scientist William Crookes in his presidential address to the British Association for the Advancement of Science warned of an impending fertilizer crisis. The answer lay in the fixation of atmospheric nitrogen. Around 1900, industrial fixation with calcium carbide to produce cyanamide, the process of the German chemists Nikodemus Caro and Adolf Frank, was introduced. This process relied on inexpensive hydroelectricity, which is why the American Cyanamid Company was set up at Ontario, Canada, in 1907 to exploit the power of Niagara Falls. Electrochemical fixing of nitrogen as its monoxide was first realized in Norway, with the electric arc process of Kristian Birkeland and Samuel Eyde in 1903. The nitrogen monoxide formed nitrogen dioxide, which reacted with water to give nitric acid, which was then converted into the fertilizer calcium nitrate. The yield was low, and as with the Caro–Frank process, the method could be worked commercially only because of the availability of hydroelectricity.

16. Pesticides

A pesticide is any chemical designed to kill pests and includes the categories of herbicide, insecticide, fungicide, avicide, and rodenticide. Individuals, governments, and private organizations used pesticides in the twentieth century, but chemical control has been especially widespread in agriculture as farmers around the world attempted to reduce crop and livestock losses due to pest infestations, thereby maximizing returns on their investment in seed, fuel, labor, machinery expenses, animals, and land. Until the twentieth century, cultural pest control practices were more popular than chemicals. Cultural methods meant that farmers killed pests by destroying infested plant material in the fields, trapping, practicing crop rotation, cultivating, drying harvested crops, planting different crop varieties, and numerous other techniques. In the twentieth century, new chemical formulations and application equipment were the products of the growth in large-scale agriculture that simultaneously enabled that growth. Large scale and specialized farming provided ideal feeding grounds for harmful insects. Notable early efforts in insect control began in the orchards and vineyards of California. Without annual crop rotations, growers needed additional insect control techniques to prevent build-ups of pest populations. As the scale of fruit and nut production increased in the early decades of the century, so too did the insect problem.

17. Processed and Fast Food

Convenience, uniformity, predictability, affordability, and accessibility characterized twentieth-century processed and fast foods. Technology made mass-produced fast food possible by automating agricultural production and food processing. Globally, fast food provided a service for busy people who lacked time to buy groceries and cook their meals or could not afford the costs and time associated with eating traditional restaurant fare. As early as the nineteenth century, some cafeterias and restaurants, foreshadowing fast-food franchises, offered patrons self-service opportunities to select cooked and raw foods, such as meats and salads, from displays. Many modern cafeterias are affiliated with schools, businesses, and clubs to provide quick, cheap meals, often using processed foods and condiments, for students, employees, and members. Food-processing technology is designed primarily to standardize the food industry and produce food that is more flavorful and palatable for consumers and manageable and inexpensive for restaurant personnel. Food technologists develop better devices to improve the processing of food from slaughter or harvesting to presentation to diners. They are concerned with making food edible while extending the time period it can be consumed. Flavor, texture, and temperature retention of these foods when they are prepared for consumers are also sought in these processes. Microwave and radio frequency ovens process food quickly, consistently, and affordably. Microwaves are used to precook meats before they are frozen for later frying in fast-food restaurants. Nitrogen-based freezing systems have proven useful to process seafood, particularly shrimp. Mechanical and cryogenic systems also are used. The dehydrating and sterilizing of foods remove contaminants and make them easier to package. Heating and thawing eliminate bacteria to meet health codes. These processes are limited by associated expenses and occasional damage to foods. Processing techniques have been adapted to produce a greater variety of products from basic foods and have been automated to make production and packaging, such as mixing and bottling, efficient enough to meet consumer demand.

18. Synthetic Foods, Mycoprotein and Hydrogenated Fats

Food technologists developed synthetic foods to meet specific nutritional and cultural demands. Also referred to as artificial foods, synthetic foods are meat-free and are designed to provide essential fiber and nutrients such as proteins found in meats while having low saturated fat and lacking animal fat and cholesterol. These foodstuffs are manufactured completely from organic material. They have been manipulated to be tasty, nutritionally sound with major vitamins and minerals, have appealing textures, and safe for consumption. Synthetic foods offer people healthy dietary choices, variety, and convenience. Mycoprotein is created from Fusarium venenatum (also known as Fusarium graminearum), a small edible fungi related to mushrooms and truffles that was initially found in the soil of a pasture outside Marlow in Buckinghamshire, England. Concerned about possible food shortages such as those experienced in World War II Europe; as global populations swelled postwar, scientists began investigating possible applications for this organism as a widely available, affordable protein source. Scientists at one of Britain’s leading food manufacturers, Rank Hovis McDougall, focused on mycoprotein from 1964. At first, they were unable to cultivate fungus to produce mycoprotein in sufficient quantities for the envisioned scale of food production. Food technologists devoted several years to establishing procedures for growing desired amounts of mycoprotein. They chose a fermentation process involving microorganisms, somewhat like those historically used to create yogurt, wine, and beer. Food technologists create hydrogenated fats by processing vegetable oils, consisting of glycerides and fatty acids, with chemicals to achieve certain degrees of hardening. Partial hydrogenation stiffens oils, while full hydrogenation converts liquid oils into solid fat. The hydrogenation process involves moving hydrogen gas through heated oils in vats containing metals, usually copper, nickel, or zinc. When the metal reacts to the gas, it acts as a catalyst to relocate hydrogen molecules in the oil to create different, stiffer molecular shapes. This chemical reaction creates trans fats. Saturation of fats in these synthetic molecules increases according to the degree of hydrogenation achieved.

19. Transportation of Foodstuffs

Twentieth century foodstuffs were transported by land on vehicles and trains, by air on cargo planes, and by water on ships or barges. Based on innovations used in previous centuries, engineers developed agricultural technology such as refrigerated containers to ship perishable goods to distant markets. Technological advancements enabled food transportation to occur between countries and continents. International agreements outlined acceptable transportation modes and methods for shipping perishables. Such long-distance food transportation allowed people in different regions of the world to gain access to foodstuffs previously unavailable and incorporate new products they liked into their diets. Refrigerated trailers dominate road food transportation methods. This transportation mode minimizes food vulnerability to shipment damage from being harvested to placement on grocery shelves. Refrigerated transport enables fresh produce from milder climates to be shipped out-of-season to colder locations. Refrigeration is achieved by mechanical or cryogenic refrigeration or by packing or covering foods in ice. Ventilation keeps produce cool by absorbing heat created by food respiration and transferred through the walls and floor from the external air beneath and around the shipping trailer. Food technologists design packaging materials for food transportation. Most produce is shipped in corrugated and fiberboard cardboard boxes that are sometimes coated with wax. Wooden and wire-bound crates are also used in addition to bushel hampers and bins. Mesh plastic, burlap, and paper bags hold produce. Meat is often vacuum packed on plastic trays that are placed in wooden lugs. Foods are occasionally wrapped in plastic liners or packed in ice to withstand damage in transit and limit evaporation.

Agriculture and Food Technology

In late-twentieth century Western societies, food was available in abundance. Shops and supermarkets offered a wide choice in products and brands. The fast-food industry had outlets in every neighborhood and village. For those in search of something more exclusive, there were smart restaurants and classy catering services. People chose what they ate and drank with little awareness of the sources or processes involved as long as the food was tasty, nutritious, safe, and sufficient for everyone. These conditions have not always been met over the last century when food shortages caused by economic crises, drought, or armed conflicts and war, occurred in various places. During the second half of the twentieth century, food deficiency was a feature of countries outside the Western world, especially in Africa. The twentieth century also witnessed a different sort of food crisis in the form of a widespread concern over the quality and safety of food that mainly resulted from major changes in production processes, products, composition, or preferences.

Technology plays a key role in both types of crises, as both cause and cure, and it is the character of technological development in food and agriculture that will be discussed. The first section examines the roots of technological developments of modern times. The second is an overview of three patterns of agricultural technology. The final two sections cover developments according to geographical differences.

Before we can assess technological developments in agriculture and food, we must define the terms and concepts. A very broad description of agriculture is the manipulation of plants and animals in a way that is functional to a wide range of societal needs. Manipulation hints at technology in a broad sense; covering knowledge, skills, and tools applied for production and consumption of (parts or extractions of) plants and animals. Societal needs include the basic human need for food. Many agricultural products are food products or end up as such. However, crops such as rubber or flax and animals raised for their skin are only a few examples of agricultural products that do not end up in the food chain. Conversely, not all food stems from agricultural production. Some food is collected directly from natural sources, like fish, and there are borderline cases such as beekeeping. Some food products and many food ingredients are artificially made through complicated biochemical processes. This relates to a narrow segment of technology, namely science-based food technology.

Both broad and narrow descriptions of agriculture are relevant to consider. In sugar production for example, from the cultivation of cane or beets to the extraction of sugar crystals, both traditional and science-based technologies are applied. Moreover, chemical research and development resulted in sugar replacements such as saccharin and aspartame. Consequently, a randomly chosen soft drink might consist of only water, artificial sweeteners, artificial colorings and flavorings, and although no agriculture is needed to produce such products, there is still a relationship to it. One can imagine that a structural replacement of sugar by artificial sweeteners will affect world sugar prices and therewith the income of cane and beet sugar producers. Such global food chains exemplify the complex nature of technological development in food and agriculture.

The Roots of Technological Development

Science-based technologies were exceptional in agriculture until the mid-nineteenth century. Innovations in agriculture were developed and applied by the people cultivating the land, and the innovations related to the interaction between crops, soils, and cattle. Such innovation is exemplified by farmers in Northern Europe who confronted particular difficulties caused by the climate. Low temperatures meant slow decomposition of organic material, and the short growing season meant a limited production of organic material to be decomposed. Both factors resulted in slow recuperation of the soil’s natural fertility after exploitation. The short growing season also meant that farmers had to produce enough for the entire year in less than a year. Farmers therefore developed systems in which cattle and other livestock played a pivotal role as manure producers for fertilizer. Changes in the feed crop could allow an increase in livestock, which produced more manure to be used for fertilizing the arable land, resulting in higher yields. Through the ages, farmers in Northern Europe intensified this cycle. From about the 1820s the purchase of external supplies increased the productivity of farming in the temperate zones. Technological improvements made increases in productivity not only possible but also attractive, as nearby markets grew and distant markets came within reach as a result of the nineteenth century transportation revolution.

An important development at mid-nineteenth century was the growing interest in applying science to agricultural development. The two disciplines with the largest impact were chemistry and biology. The name attached to agricultural chemistry is Justus von Liebig, a German chemist who in the 1840s formulated a theory on the processes underlying soil fertility and plant growth. He propagated his organic chemistry as the key to the application of the right type and amount of fertilizer. Liebig launched his ideas at a time when farmers were organizing themselves based on a common interest in cheap supplies. The synergy of these developments resulted in the creation of many laboratories for experimentation with these products, primarily fertilizers. During the second half of the nineteenth century, agricultural experiment stations were opened all over Europe and North America.

Sometime later, experimental biology became entangled with agriculture. Inspired by the ideas of the British naturalist Charles Darwin, biologists became interested in the reproduction and growth of agricultural crops and animals. Botany and, to a lesser extent, zoology became important disciplines at the experimental stations or provided reasons to create new research laboratories. Research into the reproductive systems of different species, investigating patterns of inheritance and growth of plant and animal species, and experimentation in cross-breeding and selection by farmers and scientists together lay the foundations of genetic modification techniques in the twentieth century.

By the turn of the century, about 600 agricultural experiment stations were spread around the Western world, often operating in conjunction with universities or agricultural schools. Moreover, technologies that were not specifically developed for agriculture and food had a clear impact on the sector. Large ocean-going steamships, telegraphy, railways, and refrigeration, reduced time and increased loads between farms and markets. Key trade routes brought supplies of grain and other products to Europe from North America and the British dominions, resulting in a severe economic crisis in the 1880s for European agriculture. Heat and power from steam engines industrialized food production by taking over farm activities like cheese making or by expanding and intensifying existing industrial production such as sugar extraction. The development of synthetic dyes made crop-based colorants redundant, strongly reducing or even eliminating cultivation of the herb madder or indigo plants. These developments formed the basis of major technological changes in agriculture and food through the twentieth century.

Patterns of Technology Development

The twentieth century brought an enormous amount of technology developed for and applied to agriculture. These developments may be examined by highlighting the patterns of technology in three areas—infrastructure, public sector, and commercial factory—as if they were seen in cross section. The patterns are based on combined material and institutional forces that shaped technology.

A major development related to infrastructure concerns mechanization and transport. The combustion engine had a significant effect on agriculture and food. Not only did tractors replace animal and manual labor, but trucks and buses also connected farmers, traders, and markets. The development of cooling technology increased storage life and the distribution range for fresh products. Developments in packaging in general were very important. It was said that World War I would have been impossible without canned food. Storage and packaging is closely related to hygiene. Knowledge about sources and causes of decay and contamination initiated new methods of safe handling of food, affecting products and trade as well as initiating other innovations. In the dairy sector, for example, expanding markets led to the growth and mergers of dairy factories. That changed the logistics of milk collection, resulting in the development of on-farm storage tanks. These were mostly introduced together with compression and tube systems for machine milking, which increased milking capacity and improved hygiene conditions. A different area of infrastructure development is related to water management. Over the twentieth century, technologies for irrigation and drainage had implications for improved ‘‘carrying capacity’’ of the land, allowing the use of heavy machinery. Improved drainage also meant greater water discharge, which in turn required wider ditches and canals. Water control also had implications for shipping and for supplies of drinking water that required contractual arrangements between farmers, governing bodies, and other agencies.

During the twentieth century, most governments supported their agricultural and food sectors. The overall interest in food security and food safety moved governments to invest in technologies that increased productivity and maintained or improved quality. Public education and extension services informed farmers about the latest methods and techniques. Governments also became directly involved in technological development, most notably crop improvement. Seed is a difficult product to exploit commercially. Farmers can easily put aside part of the harvest as seed for the next season. Public institutes for plant breeding were set up to improve food crops—primarily wheat, rice, and maize—and governments looked for ways to attract private investment in this area. Regulatory and control mechanisms were introduced to protect commercial seed production, multiplication, and trade. Private companies in turn looked for methods to make seed reproduction less attractive to farmers, and they were successful in the case of so-called hybrid maize. The genetic make-up of hybrid maize is such that seeds give very high yields in the first year but much less in the following years. To maintain productivity levels, farmers have to purchase new seed every season. Developments in genetic engineering increased the options for companies to commercially exploit seed production.

Most private companies that became involved in genetic engineering and plant breeding over the last three decades of the twentieth century started as chemical companies. Genetic engineering allowed for commercially attractive combinations of crops and chemicals. A classic example is the herbicide Roundup, developed by the chemical company Monsanto. Several crops, most prominently soy, are made resistant to the powerful chemical. Buying the resistant seed in combination with the chemical makes weed control an easy job for farmers. This type of commercial development of chemical technologies and products dominated the agricultural and food sector over the twentieth century. Artificially made nitrogen fertilizers are one such development that had a worldwide impact. In 1908, Fritz Haber, chemist at the Technische Hochschule in Karlsruhe, fixed nitrogen to hydrogen under high pressure in a laboratory setting. To exploit the process, Haber needed equipment and knowledge to deal with high pressures in a factory setting, and he approached the chemical company BASF. Haber and BASF engineer Carl Bosch built a crude version of a reactor, further developed by a range of specialists BASF assigned to the project. The result was a range of nitrogen fertilizer products made in a capital and knowledge-intensive factory environment. This type of development was also applied to creating chemicals such as DDT for control of various pests (dichloro-diphenyltrichloroethane), developed in 1939 by Geigy researcher Paul Mu¨ ller and his team. DDT may exemplify the reverse side of the generally positive large-scale application of chemicals in agricultural production—the unpredictable and detrimental effects on the environment and human health.

The commercial factory setting for technology development was omnipresent in the food sector. The combination of knowledge of chemical processes and mechanical engineering determined the introduction of entirely new products: artificial flavorings, products, and brands of products based on particular food combinations, or new processes such as drying and freezing, and storing and packaging methods.

Patterns of Technology Development in the Western World

Technological developments in agriculture and food differ with regard to geography and diverging social and economic factors. In regions with large stretches of relatively flat lands, where soil conditions are rather similar and population is low, a rise in productivity is best realized by technologies that work on the economies of scale. The introduction of mechanical technologies was most intensive in regions with these characteristics. Beginning early in the twentieth century, widespread mechanization was a common feature of Western agriculture, but it took different forms. In the Netherlands, for example, average farm size was relatively small and labor was not particularly scarce. Consequently, the use of tractors was limited for the first half of the twentieth century as emphasis was placed on improved cultivation methods. Tractors became widely used only after the 1950s when equipment became lighter and more cost-effective and labor costs rose sharply. The result was an overall increase of farm size in these regions as well. The Dutch government changed the countryside with a land policy of connecting and merging individual parcels as much as possible. This huge operation created favorable conditions for expansion; but where the land was already under cultivation, the only way to expand was to buy up neighboring farms. The effect was a considerable reduction in the number of farm units. An exception to this process was the Dutch greenhouse sector, in which improvements in construction, climate regulation, and introduction of hydroponic cultivation, increased production without considerable growth of land per farm unit.

The Dutch greenhouse sector is also an exemplary case of technological support in decision making and farm management. In Western countries a vast service sector emerged around agriculture and food. This process in fact started early in the twentieth century with the rise of extension services, set up as government agencies or private companies. Experimental methods based on multivariate statistics, developed by the British mathematician Karl Fisher, are the major tool in turning results of field experiments into general advisories. In keeping with the development of modern computers, digital models of crop growth and farming systems became more effective. Computer programs help farmers perform certain actions and monitor other equipment and machinery; yet even in the most technologically advanced greenhouses, the skilled eye of the farmer is a factor that makes a considerable difference in the quality and quantity of the final product.

The means by which agriculture in the West raised productivity have been questioned. Doubts about the safety of food products and worries over the restoration of nature’s capacity became recurrent issues in public debate. Moreover, technological advances in tandem with subsidies resulted in overproduction, confronting national and international governing bodies with problems in trade and distribution, and a public resistance against intensive agriculture, sometimes called agribusiness. Technology is neither good nor bad; much of the knowledge underlying technologies with a detrimental effect also helps detect polluting factors and health hazards. Although a substantial part of research and technological efforts are aimed at replacing and avoiding harmful factors, many such ‘‘clean’’ technologies are commercially less interesting to farmers and companies. Subsidies and other financial arrangements are again being used to steer technology development, this time in the direction of environmentally friendly and safe forms of production.

Patterns of Technology Development in Less Developed Countries

From the beginning of the twentieth century, scientific and technological developments in the agricultural and food sector were introduced to less developed countries either by Western colonizing powers or by other forms of global interaction. The search for improved farming methods and new technology were mostly institutionalized at existing botanical gardens and established in previous centuries. Plant transfer and economic botany were a major modality of twentieth century technological improvement in less developed countries.

The early decades of the century featured an emphasis on technological improvement for plantation agriculture. Plantation owners invested in scientific research for agriculture, often supported by colonial administrations. The gradual abolition of slavery during the nineteenth century, increasing labor costs, was a reason to invest in technology. Other factors were more specific to particular sectors; for example, the rise of European beet sugar production encouraging cane sugar manufacturers to invest in technological improvement. Another example was the emergence of the automobile industry, which initiated a boom in rubber production.

Most colonial administrations launched programs, based on the combination of botanical and chemical research, to improve food crop production in the first decades of the twentieth century. It was recognized that dispersion of new technologies to a small number of plantation owners was different from initiating change among a vast group of local food crop producers. The major differences concerned the ecology of farming (crop patterns and soil conditions) and the socioeconomic conditions (organization of labor or available capital). Agronomists had to be familiar with local farming systems, occasionally resulting in pleas for a technology transfer that would better meet the complexity of local production. The overall approach, however, was an emphasis on improvement of fertilization and crop varieties. Transfer of the Western model gained momentum in the decades after World War II. Food shortages in the immediate postwar years encouraged European colonial powers to open up large tropical areas for mechanized farming. Unfortunately, the result was largely either a short-lived disaster, as in the case of the British-run groundnut scheme in Tanzania, or a more enduring problem, as in case of the Dutch-run mechanized rice-farming schemes in Surinam. The 1940s also saw the beginnings of a movement that came to be known as the ‘‘green revolution.’’ Driven by the idea that hunger is a breeding ground for communism, American agencies initiated a research program for crop improvement, primarily by breeding fertilizer-responsive varieties of wheat and rice. Agencies were put together in a Consultative Group on International Agricultural Research (CGIAR). Technological progress was realized by bringing together experts and plant material from various parts of the world. Modified breeding techniques and a wide availability of parent material resulted in high-yielding varieties of wheat and rice. Encouraged by lucrative credit facilities, farmers, especially in Asia, quickly adopted the new varieties and the required chemicals for fertilization and pest control. Research on the adoption process of these varieties made clear that many farmers modified the seed technology based on specific conditions of the farming systems. In areas where such modifications could not be achieved—primarily rice growing regions in Africa—green revolution varieties were not very successful. Based on these findings, CGIAR researchers began to readdress issues of variation in ecology and farming systems. This type of research is very similar to that done by colonial experts several decades earlier. However, because of decolonization and antiimperialist sentiments among Western nations, much of this earlier expertise has been neglected. This is just one of the opportunities for further research in the domain of agriculture and food technology.

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Explore 118+ Agriculture Research Topics for Innovation and Possibilities.

Dec 20, 2023 | 0 comments

Dec 20, 2023 | Topics | 0 comments

Embarking on a journey through agriculture research topics unveils a realm of possibilities and innovation that underpin our food systems and farming practices. Have you ever wondered what areas researchers delve into to improve our agricultural landscape? Well, get ready for a glimpse into intriguing research topics in agriculture that scientists are currently exploring. From sustainable development to genetic improvement of crops and livestock, the field is diverse and crucial for tackling our planet’s challenges. So, what exactly are these agriculture research topics, and how do they contribute to making our food systems more resilient and sustainable? Let’s uncover the answers to these questions as we explore the exciting world of agricultural research.

How do you select the best Agriculture Research Paper topic?

Choosing the right agriculture research paper topic is like picking the perfect crop to plant – it requires careful consideration and a bit of know-how. So, how do you select the best agriculture research paper topic that stands out in the field? Let’s break it down:

• Passion Points: Ask yourself, “What aspects of agriculture am I most passionate about?” Whether it’s sustainable farming, animal breeding, or soil health, picking a topic you’re genuinely interested in makes the research journey more exciting.
• Identify Gaps: Consider the gaps in current knowledge. Where does agriculture need more insight? Think about the questions that intrigue you – those uncharted territories where you can contribute valuable information.
• Feasibility: Assess the feasibility of your chosen topic. Are there enough resources and data available to support your research? Avoid topics that might be too complex or lack the necessary information.
• Relevance: Ask yourself, “How relevant is my chosen topic to the current issues in agriculture?” Staying up-to-date with industry trends ensures your research contributes to solving real-world problems.
• Impactful Research: Consider the potential impact of your research. Will it bring about positive changes in farming practices or contribute to sustainable agriculture? Aim for topics that have practical implications and can make a difference.
• Consult Experts: Don’t hesitate to consult with teachers, experts, or researchers in the field. They can provide valuable insights, helping refine your topic and ensure it aligns with current research priorities.
• Stay Flexible: Be open to adjusting your topic as you delve deeper into the research. The initial idea might sometimes evolve, leading to a more refined and focused research question.
• Ask for Feedback: Seek feedback from peers or mentors. Present your chosen topic and gather input – a fresh perspective can help you fine-tune your focus.

Best Agriculture Research Topics

• Integrating Sustainable Agriculture Practices for Enhanced Food Security
• Assessing the Impact of Climate Change on Livestock Production Systems
• Innovations in Irrigation Techniques to Promote Sustainable Food Systems
• Enhancing Food Safety Protocols in Agricultural Supply Chains
• Conservation Strategies for Biodiversity in Agroecosystems
• Pest Management Approaches for Sustainable Crop Yield Improvement
• The Role of Ecological Practices in Mitigating Agricultural Waste
• Sustainable Livestock Farming: Balancing Productivity and Environmental Impact
• Evaluating the Ecological Impact of Pesticide Use in Modern Agriculture
• Promoting Sustainable Food Systems through Community-Based Agriculture Initiatives

Agricultural Economics Research Topics

• Market Dynamics and Price Volatility in Agricultural Commodities
• Economic Impacts of Climate Change on Agricultural Production Systems
• Policy Interventions for Promoting Sustainable Agriculture and Rural Development
• Assessing the Role of Technology Adoption in Agricultural Productivity
• Economic Analysis of Precision Farming Technologies and Practices
• Income Inequality in Agricultural Communities: Causes and Remedies
• The Role of Agricultural Trade in Global Economic Development
• Economic Evaluation of Ecosystem Services in Agricultural Landscapes

Agricultural Engineering Research Topics

• Innovative Engineering Approaches for Sustainable Water Management in Agriculture
• Precision Agriculture Technologies: Advancements and Implementation Challenges
• Automated Systems for Crop Monitoring and Yield Prediction
• Energy-Efficient Solutions in Agricultural Machinery and Equipment
• Sensor Technologies for Real-time Monitoring of Soil Health and Crop Conditions
• Robotics and Automation in Agricultural Practices: Opportunities and Limitations
• Waste-to-Energy Technologies for Sustainable Agricultural Operations
• Engineering Solutions for Mitigating the Impact of Climate Change on Farming Systems

Interesting Agriculture Research Topics For Students

• Microbial Applications for Enhancing Nutrient Cycling in Agricultural Systems
• Sustainable Agricultural Practices in Rural Areas: A Case Study Analysis
• Greenhouse Gas Emissions in Urban Agriculture: Assessing Sustainability
• Innovative Agricultural Water Management Techniques for Increased Productivity
• Fertility Management Strategies for Sustainable Crop Production Systems
• Exploring the Role of Nutrient-Rich Food Products in Improving Human Health
• Assessing the Environmental Impact of Agricultural Waste in Production Systems
• Integrating NIFA Initiatives for Advancing Food and Agriculture Research
• Enhancing Agricultural Productivity through Technology-driven Production Systems
• The Intersection of Food Security and Sustainability in Modern Agricultural Practices

Agriculture-Related Research Paper Topics

• Analytical Approaches to Assessing the Environmental Sustainability of Local Food Systems
• The Impact of Bioenergy Production on Biodiversity in Agricultural Landscapes
• Intervention Strategies for Addressing Depletion of Crop Varieties in Modern Agriculture
• Exploring the Role of Agricultural Enterprises in Rural Development
• Assessing the Ecological Consequences of Invasive Species in Food Production Systems
• Local Food Initiatives and Their Influence on the Global Food Supply Chain
• Investigating the Analytical Methods for Monitoring and Improving Food Supply Chain Efficiency
• Biodiversity Conservation in Agricultural Landscapes: A Focus on Crop Varieties
• The Intersection of Rural Development and Environmental Sustainability in Agriculture
• Examining the Impact of Intervention Programs on Sustainable Food Production Practices

List of Agriculture Research Paper Topics

• Sustainable Development Strategies in High-Yielding Agriculture
• Integrated Pest Management Approaches for Crop Improvement
• Organic Farming and Its Impact on Soil Health and Fertility
• Assessing the Ecological and Economic Dimensions of Soil Degradation
• National and International Perspectives on Water Management Practices in Agriculture
• USDA Initiatives for Promoting Sustainable Agriculture in Rural Communities
• Balancing High-Yielding Crop Practices with Ecological Considerations
• Exploring the Relationship Between Soil Fertility and Agricultural Productivity

Expanded Agriculture Research Paper Topics

• Enhancing Crop Productivity through Innovative Input Strategies
• The Role of Forestry Practices in Sustainable Agriculture
• Microorganism Diversity and its Impact on Soil Health and Crop Yield
• Advancements in Horticulture Techniques for Improved Crop Management
• Wastewater Reuse in Agriculture: Challenges and Opportunities
• Physiological Mechanisms Underlying Crop Responses to Environmental Stress
• Engineering Approaches for Efficient Water Management in Agriculture
• Genomic Applications for Crop Improvement and Biotic Stress Resistance

Agricultural Research Topics in Animal Breeding And Genetics

• Genomic Selection and its Application in Animal Breeding Programs
• Genetic Improvement of Livestock for Enhanced Productivity and Disease Resistance
• Molecular Markers and their Role in Characterizing Genetic Diversity in Animal Populations
• Selective Breeding for Improved Reproductive Performance in Farm Animals
• Genomic Tools for Identifying and Managing Genetic Disorders in Livestock
• Application of Quantitative Genetics in Improving Feed Efficiency in Farm Animals
• Genetic and Genomic Approaches to Enhance Heat Tolerance in Livestock
• Advances in Marker-Assisted Selection for Traits of Economic Importance in Animal Agriculture

Agriculture Related Research Topics in Plant Science And Crop Production

• Innovative Approaches to Enhance Crop Productivity in Sustainable Agriculture
• Genetic Modification for Crop Resistance to Biotic and Abiotic Stresses
• Precision Farming Technologies for Optimal Resource Utilization in Crop Production
• Investigating the Impact of Climate Change on Crop Physiology and Yield
• Sustainable Management of Soil Health for Improved Crop Production
• Functional Genomics in Understanding Plant Responses to Environmental Challenges
• Development and Deployment of High-Yielding Crop Varieties with Desired Traits
• Exploring Novel Strategies for Integrated Pest Management in Crop Agriculture

Agriculture Research Project Topics in Fisheries And Aquaculture

• Sustainable Aquaculture Practices: Balancing Production and Environmental Conservation
• Genetic Improvement of Aquatic Species for Enhanced Aquaculture Productivity
• Aquatic Ecosystem Health and Its Impact on Fisheries Sustainability
• Innovative Technologies for Water Quality Monitoring in Aquaculture Systems
• Socio-economic Impacts of Aquaculture on Local Communities
• Development and Optimization of Feed Formulations for Aquaculture Species
• Disease Management Strategies in Aquatic Organisms: A Focus on Probiotics and Immunostimulants
• Assessing the Ecological Impact of Aquaculture Practices on Coastal and Inland Water Bodies

Topics in Agricultural Science

• Understanding the Physiology of Insect Species in Agricultural Ecosystems
• Sensitive Information Handling in Agricultural Science Research
• Addressing Water Scarcity Challenges in Agricultural Practices
• Livelihood Impact of Agricultural Practices on Local Communities
• Manure Management Strategies for Sustainable Agriculture
• Energy Production from Agricultural Waste: Biochemical Approaches
• Exploring Nutrient Composition in Plants for Improved Crop Yield
• Cover Crops and Medicinal Herbs: Contributions to Sustainable Agriculture in a Growing World Population

Agricultural Economics Research Topics in Farm Management

• Economic Analysis of Disease Management Strategies for Plant Pathogens in Crop Production
• Cost-Benefit Analysis of Precision Farming Technologies in Livestock Rearing
• Financial Viability of Integrated Pest Management Practices in Farm Management
• Evaluating the Economic Impact of Climate Change on Crop Rearing Systems
• Adoption and Economic Implications of Sustainable Agriculture Practices in Livestock Farms
• Farm-Level Decision-Making for Efficient Resource Allocation in Rearing Operations
• Economic Evaluation of Technology Adoption for Disease Control in Plant Pathogen Management
• Assessing the Profitability and Sustainability of Diversification Strategies in Farm Enterprises

Topics in Agric Meteorology And Water Management

• Climate Variability and its Impact on Agricultural Water Management
• Precision Irrigation Technologies for Efficient Water Use in Agriculture
• Modeling and Simulation of Meteorological Factors in Crop Growth
• Weather Forecasting for Optimal Decision-Making in Agriculture
• Integrated Water Resource Management for Sustainable Agriculture
• Evaluating the Impact of Climate Change on Water Availability for Agriculture
• Meteorological Approaches to Assessing Drought Risk in Agricultural Regions
• Remote Sensing Applications in Monitoring and Managing Agricultural Water Resources

Agriculture Research Paper Topics in Agronomy

• Optimizing Crop Rotation Systems for Sustainable Agronomic Practices
• Soil Health Assessment Techniques for Precision Agriculture
• Evaluating the Impact of Cover Crops on Weed Management in Agronomic Systems
• Enhancing Nitrogen Use Efficiency in Crop Production through Agronomic Practices
• Investigating the Role of Plant-Microbe Interactions in Crop Health and Yield
• Sustainable Management of Agricultural Residues for Improved Soil Quality
• Precision Farming Technologies for Efficient Resource Utilization in Agronomy
• Agronomic Approaches to Mitigate the Effects of Climate Change on Crop Production

Get Help With Your Agriculture Research Paper

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Which topic is best for research in agriculture?

Determining the best research topic in agriculture depends on your interests and the current needs of the industry, ranging from sustainable practices to genetic improvements in crops and livestock.

What are the research paper topics on organic farming?

Research paper topics on organic farming can include soil health in organic systems, the impact of organic practices on crop yield, and the economic viability of organic farming compared to conventional methods.

What are some of the projects in agriculture?

Projects in agriculture cover a broad spectrum, such as precision farming using technology, sustainable water management practices, genetic improvement of crops, and innovative approaches to pest management.

What is a research topic example?

An example of a research topic could be “Assessing the Impact of Climate Change on Crop Productivity” or “Exploring Sustainable Livestock Farming Practices for Environmental Conservation.”

With a passion for helping students navigate their educational journey, I strive to create informative and relatable blog content. Whether it’s tackling exam stress, offering career guidance, or sharing effective study techniques

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Top 9 Agriculture Research Paper Topics

Agriculture, for many people, is not the most interesting of topics. Turning a topic that revolves around farming, plants, growth, agricultural technology and other agricultural concerns may seem nearly impossible to some. However, there are actually dozens of intriguing and valid agriculture research paper topics crafted by professional term paper writers from usessaywriters.com that students can choose from. These topics are both relevant to the agricultural field, and highly intriguing – even to those that may not have any love for agriculture! If you’re looking for a great agriculture research paper topic, check out the top nine topics listed below.

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• Agricultural technology . Technology is popping up in every single academic and work field under the sun, but agriculture is benefiting very obviously. Much of today’s agricultural technology is intriguing and effective. Exploring technological advancement in agriculture, as well as its economic and social effects, makes for a great paper.
• Renewable energy . Astoundingly, many agricultural products are now being considered as possible energy sources – for example, a corn-based ethanol as a replacement for gasoline. Agriculture is fast becoming an important factor in the future of clean, renewable and alternative energy.
• A dying art – the agricultural work force . In reality, there is an insanely small amount of people that work in agriculture. This tiny minority works to feed nations, and exploring this workforce – as well as the future of such a workforce – leads to startling discoveries.
• Environmental issues . There has always been debate over the rules of agricultural expansion and environmental protection. When does agriculture rule, and when does the environment? How does agriculture affect ecosystems and the surrounding land? What are the environmental implications of commercial agriculture?
• Supply lines or finances . Ever consider the lengthy supply lines of agriculture? Every consider how much money goes in to producing agricultural products? Look it up – you’ll find some fascinating and unbelievable research that could contribute to your paper.
• Animal rights . Much of agricultural is based on animal products, and animal rights are a serious and constant issue in this market. Address laws, regulations, standards and conditions regarding animals in the agricultural business – you’ll find some interesting thesis starters.
• Pesticides . The effect of pesticides, safe choices for pesticides and alternative methods to pesticides have all been hot topics for many years. Pick something in this real to research for an interesting paper.
• Organic vs. Inorganic . There has been a rise in awareness on what is ‘organic’ vs. ‘inorganic.’ What are the health, financial, and business implications of this recent schism? How might it affect individual farmers or even larger productions?
• Cultural and political effects . Believe it or not, agriculture shapes much of today’s political and social movements. Find those connections for an interesting research paper read.

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Posted by November 4th, 2021

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