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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.

An Inspiration List:

• Agricultural Research
• Current Agriculture Research Journal
• Agricultural Research & Technology
• Journal of Agriculture and Food Research
• Advances in Plants & Agriculture Research
• Journal of Bioscience and Agriculture Research
• Middle East Journal of Agriculture Research

Agriculture Research Paper Topics

• Agrochemical
• Aquaculture
• Biotechnology
• DDT (dichlorodiphenyltrichloroethane)
• Genetic engineering
• Organic farming
• Slash-and-burn agriculture

Four stages of agricultural development

Agriculture advanced in four major stages that were closely linked with other key historical periods. The first, the Neolithic or New Stone Age, marks the beginning of sedentary farming. Although much of this history is lost in antiquity, dating back 10,000 years or more, anthropologists believe farming arose because of increasing population. The major technological development of this ancient time was the plow. Appearing in Mesopotamia (an ancient region in southwest Asia) around 4000 B.C., the plow allowed farmers to plant crops in rows, saving time and increasing food production.

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The second major advance came as a result of Spanish explorer Christopher Columbus’s voyages to the New World in the late fifteenth century. The connecting of the New World and the Old World saw the exchange of farming products and methods. From the New World came maize (Indian corn), beans, the “Irish” potato, squash, peanuts, tomatoes, and tobacco. From the Old World came wheat, rice, coffee, cattle, horses, sheep, and goats. Several Native American tribes adopted new lifestyles, notably the Navajo as sheepherders and the Cheyenne as nomads (wanderers) who used the horse to hunt buffalo. In the twentieth century, maize is a staple food in Africa.

The Industrial Revolution of the eighteenth and nineteenth centuries both contributed to and was supported by agriculture. The greatest agricultural advances came in transportation, where canals, railroads, and then steamships made possible the shipment of food. This in turn increased productivity, but most important, it reduced the threat of starvation. Without these massive increases in food shipments, the exploding populations could not have been fed and the greatly increased demand for labor by emerging industries could not have been met.

As a consequence, the Industrial Revolution introduced major advances in farm technology, such as the cotton gin, mechanical reaper, threshing machine, mowing machine, improved plows, and, in the twentieth century, tractors and trucks. These advances enabled fewer and fewer farmers to feed larger and larger populations, freeing workers to fill demands for factory labor and the growing service industries.

Finally, scientific advances of the twentieth century—the refrigeration of meat, the development of hybrid crops, research into genetics— have greatly benefitted agriculture. Great potential exists for the development of crop and animal varieties with greatly improved dietary characteristics, such as higher protein or reduced fat.

Drawbacks to the rise of agriculture

The agricultural revolution is also associated with some of humankind’s darker moments. In the tropical and subtropical climates of the New World, slave labor was used extensively in farm fields in the eighteenth and nineteenth centuries. In the late twentieth century, the mass production of animals, especially in close quarters, has been extremely controversial. While farmers view new breeding practices as useful means to producing more food, animal rights activists protest them as showing a disregard for animals’ comfort and welfare. Additionally, the widespread use of fertilizers, pesticides, and other chemicals in agriculture have led to serious pollution crises in many areas of the world.

Famine throughout history shows mankind’s desperate dependence on agriculture. Advances in farming, especially in the last few centuries, have led to increases in population. Growing populations—made possible by food surpluses—have forced agricultural expansion onto less and less desirable lands. Because agriculture drastically simplifies ecosystems (communities of plants and animals) and greatly increases soil erosion, many areas such as the Mediterranean basin and tropical forestlands have severely deteriorated.

The future of agriculture

Some argue that the agricultural revolution masks the growing hazards of an overpopulated, increasingly contaminated planet. In the nineteenth and twentieth centuries, agriculture more than compensated for the population explosion. Through scientific advances in areas such as genetic engineering, there is hope that the trend will continue. However, the environmental effects of the agricultural progress could soon undermine any advances if they are not taken seriously.

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186 Agriculture Essay Topics & Research Questions + Examples

Are you looking for the best agriculture topics to write about? You’re at the right place! StudyCorgi has prepared a list of important agriculture research topics. On this page, any student can find essay questions and project ideas on various agricultural issues, such as food safety, genetically engineered crops, and sustainable farming practices.

👨‍🌾 TOP 7 Agriculture Research Topics – 2024

🏆 best essay topics on agriculture, 🎓 most interesting agriculture topics for college students, 👍 good agriculture research topics & essay examples, 💡 cool agricultural research topics for high school students, ❓ research questions about agriculture, 🔎 current agriculture research paper topics, 📝 agriculture argumentative essay topics, 🗣️ agriculture topics for speech.

• Agriculture and Its Role in Economic Development
• Globalization Impact on Sustainable Agriculture
• Food Safety Issues in Modern Agriculture
• Commercial Agriculture, Its Role and Definition
• Agricultural Biotechnology and Its Pros and Cons
• Agriculture: Personal Field Visit
• In Support of Robotics Use in Agriculture
• Agricultural Influences on the Developing Civil Society Agriculture had a significant influence on developing societies, ranging from creating trade to bringing industrialization, education, and social classes.
• Agriculture and Food in Ancient Greece The paper states that agricultural practices and goods from Greece extended to neighboring countries in the Mediterranean as the dominance increased.
• Soil: The Essential Aspect of Agriculture Soil is an integral part of human life as it determines one’s quality of life. The health of the soil is reduced by erosion and degradation due to human activities.
• Repeasantization: Impact on Agriculture The repeasantization led to fundamental changes that created a new system of agriculture that is still relevant today.
• Food and Agriculture of Ancient Greece The concepts of agriculture and cuisine both have a deep connection to Greek history, culture, development, and social trends.
• Food Safety: A Policy Issue in Agriculture Today Food safety constitutes proper preparation, storage and preservation of all foods. Markets are increasingly calling for improvement in the quality and safety standards of food crops.
• Population Growth and Agriculture in the Future The current industrial agriculture needs to be advanced and developed in combination with sustainable agricultural practices.
• Improving Stress Resistance in Agricultural Crops The essay suggests that stress-resistant crops are needed to ensure yield stability under stress conditions and to minimize the environmental impacts of crop production.
• 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.
• Industry and Agriculture: Use of Technology Industry and agriculture are among the areas that have experienced a vast rise in effectiveness and performance quality due to the integration of new types of technology into them.
• 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.
• 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.
• The Neolithic Era: Architecture and Agriculture The improvements to agriculture, society, architecture, and culture made during the Neolithic period had an undeniable impact on aspects of the world.
• Agriculture Development and Related Theories There are two main domestication models used to describe the development of agriculture: unconscious and conscious.
• 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.
• Agricultural Technology Implementation by Medieval Europeans and West Africans The paper examines how West Africans and Medieval Europeans were affected by their corresponding climates and why their methods were unique to their respective locations.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Impacts of Genetic Engineering of Agricultural Crops In present days the importance of genetic engineering grew due to the innovations in biotechnologies and Sciences.
• 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.
• 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.
• 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
• 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, 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.
• 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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• Published: 15 October 2018

Sustainable agriculture

Nature Sustainability volume  1 ,  page 531 ( 2018 ) Cite this article

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Achieving food security is possible, if we better understand the complexity of the agricultural system and re-design practices accordingly.

Early in September, the State of Food Security and Nutrition in the World report was released through a joint press conference at the FAO headquarters in Rome. The analysis ( The State of Food Security and Nutrition in the World FAO; 2018) is the outcome of a close collaboration between five United Nations agencies — the Food and Agriculture Organization, the International Fund for Agricultural Development, the United Nations Children’s Fund (UNICEF), the World Food Programme and the World Health Organization — and one of the key messages from the report is, perhaps not surprisingly, alarming: the world is not on track to meet the ‘Zero Hunger’ Sustainable Development Goal, SDG 2.

Alex Ramsay/Alamy Stock Photo

Drawing attention to the drivers of hunger and malnutrition, the report includes updated estimates of a number of indicators related to food security and its health implications, including the number of hungry people in the world, data on child stunting, adult obesity, and childhood obesity among others.

The report, which uses data from 2017, shows how hunger has increased worldwide for the third consecutive year, and childhood malnutrition has not improved or, even worse, in some cases has declined. Countries must take urgent action to meet SDG 2 by 2030.

This year’s edition of the annual report focuses on the need to build climate resilience for food security and nutrition. Acknowledging the dependency of our nutritional needs on the natural environment should indeed be a critical component of any food-policy strategy.

More specifically, the sustainability of the food system should be at the heart of the international food-security debate. Despite increasing hunger globally, the demand of food has been rising rapidly and has had a significant environmental cost: degradation of agricultural land, pollution of rivers and aquifers due to agro-chemicals, increased freshwater consumption, greenhouse-gas emissions from agriculture and land-use change, loss of agro-biodiversity and other negative consequences. All of these environmental impacts severely undermine our ability to continue to feed a growing population and ultimately will jeopardize the opportunity to meet SDG 2, unless more-sustainable food-production practices are embraced globally.

Enhanced agricultural productivity (intensification) has been a major response to the growing food demand, but intensification could be done better. In an Analysis by Pretty et al. published in our August issue, the authors underline how environmental considerations in agriculture intensification have been traditionally limited to minimizing negative impacts. Instead, with their analyses, they show that, for example, a move away from fertilizers to nitrogen-fixing legumes as part of rotations or intercropping could improve intensification without increasing environmental stress. Their point is that agriculture intensification can be sustainable if the system is adequately re-designed and if all players involved accept that no new designed system will succeed forever.

Increasing food production can impact conservation strategies — another issue likely to have long-term negative consequences for our ability to provide healthy levels of nutrition to all. Keesing and colleagues, in an Article in this issue, show however that a conflict between the two shouldn’t be the case. They analyse the potential trade-offs between management for wildlife and for livestock in an East African savannah, and find potential ecological and economic benefits from integrating the two.

However, even while considering effective strategies, it remains clear that human activities do affect biodiversity around the world, and that applies to agricultural practices as much as to other activities. In a Brief Communication in the August issue, Mehrabi et al. analyse the implications of the conservationists’ proposal to give back half the Earth’s surface to nature (the ‘Half-Earth’ project). Among other results, they find that, depending on the landscape conservation strategy, 23–25% of non-food calories and 3–29% of food calories from crops globally could be lost if the proposal were implemented. They do show that the trade-offs between agriculture and the Half-Earth proposal will be much lower if landscapes remain mosaics of shared land uses.

So, what are we left with? We can certainly improve agricultural practices as discussed earlier in this Editorial, and increase their sustainability. Will it be enough to achieve our societal goals? Perhaps we need to also look at our individual behaviour. We know that we need to manage our dependence on nature sustainably. And sustainable management hinges on deep understanding of human–nature relationships. Behavioural sciences can bring invaluable insights to our ways of mapping the complexity of such relationships. Going back to the sustainability of agricultural practices, the different ways in which individuals’ mindsets represent a system and the causal relationships among its components (mental models) can capture more or less complexity, as discussed in an Article by Levy and colleagues in our August issue. The authors show that, for example, mental models characterized by direct, unidirectional causation allow fast decision-making but might fail to anticipate consequences of actions in the presence of strong interdependences. Understanding these cognitive mechanisms has important implications for individual and collective decision-making about sustainable agriculture. And that is crucial to better orient food-security strategies. With the right set of changes in practices and interventions, informed by academic research and practice, there is still hope to achieve SDG 2 on time.

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45 Research Project Ideas in Agriculture – Innovative Approaches to Sustainable Farming

Explore 45 research project ideas in agriculture for sustainable farming.

Table of contents

Agriculture is a vast and dynamic field that plays a critical role in feeding the world’s population. As the global population continues to grow, the demand for food production is also increasing, making agriculture one of the most important sectors for ensuring food security and sustainable development. However, the challenges facing the agriculture industry today are numerous, ranging from climate change, soil degradation, water scarcity, and pest infestation to biodiversity loss and food waste.

To tackle these issues and promote sustainable agriculture, researchers and professionals in the field are continuously exploring new and innovative ways to improve agricultural practices, increase productivity, and reduce environmental impact. In this article, we will present 45 research project ideas in agriculture that can help address some of the most pressing issues facing the industry today.

These research projects cover a wide range of topics, from soil health and crop yields to livestock farming, aquaculture, and food systems, providing a comprehensive overview of the latest trends and innovations in agricultural research.

Whether you are a student, researcher, or professional in the field, these research project ideas can help guide your work and contribute to a more sustainable and resilient agriculture industry.

• Evaluating the effectiveness of natural pest control methods in agriculture.
• Investigating the effects of climate change on crop yields and food security.
• Studying the impact of soil quality on plant growth and crop yields.
• Analyzing the potential of precision agriculture techniques to increase yields and reduce costs.
• Assessing the feasibility of vertical farming as a sustainable solution to food production.
• Investigating the impact of sustainable agriculture practices on soil health and ecosystem services.
• Exploring the potential of agroforestry to improve soil fertility and crop yields.
• Developing strategies to mitigate the effects of drought on crop production.
• Analyzing the impact of irrigation management techniques on crop yields and water use efficiency.
• Studying the potential of biochar as a soil amendment to improve crop productivity.
• Investigating the effects of soil compaction on crop yields and soil health.
• Evaluating the impact of soil erosion on agriculture and ecosystem services.
• Developing integrated pest management strategies for organic agriculture.
• Assessing the potential of cover crops to improve soil health and reduce erosion.
• Studying the effects of biofertilizers on crop yields and soil health.
• Investigating the potential of phytoremediation to mitigate soil pollution in agriculture.
• Developing sustainable practices for livestock farming and manure management.
• Studying the effects of climate change on animal health and productivity.
• Analyzing the impact of animal feeding practices on meat quality and safety.
• Investigating the potential of aquaponics to increase food production and reduce environmental impact.
• Developing strategies to reduce food waste and loss in agriculture.
• Studying the effects of nutrient management practices on crop yields and environmental impact.
• Evaluating the potential of organic agriculture to improve soil health and reduce environmental impact.
• Investigating the effects of land use change on agriculture and biodiversity.
• Developing strategies to reduce greenhouse gas emissions from agriculture.
• Analyzing the impact of agricultural policies on food security and sustainability.
• Studying the potential of precision livestock farming to improve animal welfare and productivity.
• Investigating the impact of agrochemicals on soil health and biodiversity.
• Developing sustainable practices for fisheries and aquaculture.
• Studying the potential of bioremediation to mitigate pollution in aquaculture.
• Investigating the effects of climate change on fisheries and aquaculture.
• Developing strategies to reduce water pollution from agriculture and aquaculture.
• Studying the impact of land use change on water resources and aquatic ecosystems.
• Evaluating the potential of agroecology to promote sustainable agriculture and food systems.
• Investigating the impact of climate-smart agriculture practices on food security and resilience.
• Studying the potential of agrobiodiversity to improve crop productivity and resilience.
• Analyzing the impact of agricultural trade on food security and sustainability.
• Investigating the effects of urbanization on agriculture and food systems.
• Developing strategies to promote gender equity in agriculture and food systems.
• Studying the potential of agroforestry to promote biodiversity and ecosystem services.
• Analyzing the impact of food systems on public health and nutrition.
• Investigating the effects of climate change on pollination and crop yields.
• Developing strategies to promote agrotourism and rural development.
• Studying the potential of agroforestry to promote carbon sequestration and mitigate climate change.
• Analyzing the impact of agricultural subsidies on food security and sustainability.

I hope this article would help you to know the new project topics and research ideas in Agricultural.

• agriculture research
• crop yields
• food systems
• livestock farming
• Project Topics
• Research Ideas
• soil health
• sustainable farming

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• Understanding Poverty
• Agriculture and Food

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Marc Sadler

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Sustainable Agriculture Research & Education Program

• What is Sustainable Agriculture?

The goal of sustainable agriculture is to meet society’s food and textile needs in the present without compromising the ability of future generations to meet their own needs.

Practitioners of sustainable agriculture seek to integrate three main objectives into their work: a healthy environment, economic profitability, and social and economic equity. Every person involved in the food system—growers, food processors, distributors, retailers, consumers, and waste managers—can play a role in ensuring a sustainable agricultural system.

There are many practices commonly used by people working in sustainable agriculture and sustainable food systems. Growers may use methods to promote  soil health , minimize  water use , and lower  pollution levels  on the farm. Consumers and retailers concerned with sustainability can look for “ values-based ” foods that are grown using methods promoting  farmworker wellbeing , that are  environmentally friendly , or that strengthen the local economy. And researchers in sustainable agriculture often cross disciplinary lines with their work: combining biology, economics, engineering, chemistry, community development, and many others. However, sustainable agriculture is more than a collection of practices. It is also process of negotiation: a push and pull between the sometimes competing interests of an individual farmer or of people in a community as they work to solve complex problems about how we grow our food and fiber.

Topics in sustainable agriculture

• Addressing Food Insecurity
• Agritourism
• Agroforestry
• Conservation Tillage
• Controlled Environment Agriculture (CEA)
• Cooperatives
• Cover Crops
• Dairy Waste Management
• Direct Marketing
• Energy Efficiency & Conservation
• Food and Agricultural Employment
• Food Labeling/Certifications
• Food Waste Management
• Genetically Modified Crops
• Global Sustainable Sourcing of Commodities
• Institutional Sustainable Food Procurement
• Biologically Integrated Farming Systems
• Integrated Pest Management (IPM)
• Nutrition & Food Systems Education
• Organic Farming
• Precision Agriculture (SSM)
• Soil Nutrient Management
• Postharvest Management Practices
• Technological Innovation in Agriculture
• Urban Agriculture
• Value-Based Supply Chains
• Water Use Efficiency
• Water Quality Management
• Zero-Emissions Freight Transport

Directory of UC Programs in Sustainable Agriculture

This directory is a catalog of UC's programmatic activities in sustainable agriculture and food systems. All programs are sorted by activities and topic areas.

The Philosophy & Practices of Sustainable Agriculture

Agriculture has changed dramatically, especially since the end of World War II. Food and fiber productivity soared due to new technologies, mechanization, increased chemical use, specialization and government policies that favored maximizing production. These changes allowed fewer farmers with reduced labor demands to produce the majority of the food and fiber in the U.S.

Although these changes have had many positive effects and reduced many risks in farming, there have also been significant costs. Prominent among these are topsoil depletion, groundwater contamination, the decline of family farms, continued neglect of the living and working conditions for farm laborers, increasing costs of production, and the disintegration of economic and social conditions in rural communities.

Potential Costs of Modern Agricultural Techniques

A growing movement has emerged during the past two decades to question the role of the agricultural establishment in promoting practices that contribute to these social problems. Today this movement for sustainable agriculture is garnering increasing support and acceptance within mainstream agriculture. Not only does sustainable agriculture address many environmental and social concerns, but it offers innovative and economically viable opportunities for growers, laborers, consumers, policymakers and many others in the entire food system.

This page is an effort to identify the ideas, practices and policies that constitute our concept of sustainable agriculture. We do so for two reasons: 1) to clarify the research agenda and priorities of our program, and 2) to suggest to others practical steps that may be appropriate for them in moving toward sustainable agriculture. Because the concept of sustainable agriculture is still evolving, we intend this page not as a definitive or final statement, but as an invitation to continue the dialogue

Despite the diversity of people and perspectives, the following themes commonly weave through definitions of sustainable agriculture:

Sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs. Therefore,  stewardship of both natural and human resources  is of prime importance.  Stewardship of human resources  includes consideration of social responsibilities such as working and living conditions of laborers, the needs of rural communities, and consumer health and safety both in the present and the future.  Stewardship of land and natural resources  involves maintaining or enhancing this vital resource base for the long term.

A  systems perspective  is essential to understanding sustainability. The system is envisioned in its broadest sense, from the individual farm, to the local ecosystem,  and  to communities affected by this farming system both locally and globally. An emphasis on the system allows a larger and more thorough view of the consequences of farming practices on both human communities and the environment. A systems approach gives us the tools to explore the interconnections between farming and other aspects of our environment.

Everyone plays a role in creating a sustainable food system.

Making the transition to sustainable agriculture is a process.   For farmers, the transition to sustainable agriculture normally requires  a series of small ,  realistic   steps . Family economics and personal goals influence how fast or how far participants can go in the transition. It is important to realize that each small decision can make a difference and contribute to advancing the entire system further on the "sustainable agriculture continuum." The key to moving forward is the will to take the next step. Finally, it is important to point out that   reaching toward the goal of sustainable agriculture is the responsibility of all participants in the system ,  including farmers, laborers, policymakers, researchers, retailers, and consumers. Each group has its own part to play, its own unique contribution to make to strengthen the sustainable agriculture community. The remainder of this page considers specific strategies for realizing these broad themes or goals. The strategies are grouped according to three separate though related areas of concern:  Farming and Natural Resources ,  Plant and Animal Production Practices , and the  Economic, Social and Political Context . They represent a range of potential ideas for individuals committed to interpreting the vision of sustainable agriculture within their own circumstances.

• Farming and Natural Resources

When the production of food and fiber degrades the natural resource base, the ability of future generations to produce and flourish decreases. The decline of ancient civilizations in Mesopotamia, the Mediterranean region, Pre-Columbian southwest U.S. and Central America is believed to have been strongly influenced by natural resource degradation from non-sustainable farming and forestry practices. 

Water is the principal resource that has helped agriculture and society to prosper, and it has been a major limiting factor when mismanaged.

Water supply and use.  In California, an extensive  water storage and transfer system  has been established which has allowed crop production to expand to very arid regions. In drought years, limited surface water supplies have prompted overdraft of groundwater and consequent intrusion of salt water, or permanent collapse of aquifers. Periodic droughts, some lasting up to 50 years, have occurred in California.

Several steps should be taken to develop drought-resistant farming systems even in "normal" years, including both policy and management actions:

1) improving  water conservation  and storage measures,

2) providing incentives for selection of drought-tolerant crop species,

3) using  reduced-volume irrigation  systems,

4) managing crops to reduce water loss, or

5) not planting at all.

Water quality.  The most important issues related to water quality involve salinization and contamination of ground and surface waters by pesticides, nitrates and selenium. Salinity has become a problem wherever water of even relatively low salt content is used on shallow soils in arid regions and/or where the water table is near the root zone of crops. Tile drainage can remove the water and salts, but the disposal of the salts and other contaminants may negatively affect the environment depending upon where they are deposited. Temporary solutions include the use of salt-tolerant crops, low-volume irrigation, and various management techniques to minimize the effects of salts on crops. In the long-term, some farmland may need to be removed from production or converted to other uses. Other uses include conversion of row crop land to production of drought-tolerant forages, the restoration of wildlife habitat or the use of agroforestry to minimize the impacts of salinity and high water tables. Pesticide and nitrate contamination of water can be reduced using many of the practices discussed later in the  Plant Production Practices  and  Animal Production Practices  sections.

Wildlife . Another way in which agriculture affects water resources is through the destruction of riparian habitats within watersheds. The conversion of wild habitat to agricultural land reduces fish and wildlife through erosion and sedimentation, the effects of pesticides, removal of riparian plants, and the diversion of water. The plant diversity in and around both riparian and agricultural areas should be maintained in order to support a diversity of wildlife. This diversity will enhance natural ecosystems and could aid in agricultural pest management.

Modern agriculture is heavily dependent on non-renewable energy sources, especially petroleum. The continued use of these energy sources cannot be sustained indefinitely, yet to abruptly abandon our reliance on them would be economically catastrophic. However, a sudden cutoff in energy supply would be equally disruptive. In sustainable agricultural systems, there is reduced reliance on non-renewable energy sources and a substitution of renewable sources or labor to the extent that is economically feasible.

Many agricultural activities affect air quality. These include smoke from agricultural burning; dust from tillage, traffic and harvest; pesticide drift from spraying; and nitrous oxide emissions from the use of nitrogen fertilizer. Options to improve air quality include:

      - incorporating crop residue into the soil       - using appropriate levels of tillage       - and planting wind breaks, cover crops or strips of native perennial grasses to reduce dust.

Soil erosion continues to be a serious threat to our continued ability to produce adequate food. Numerous practices have been developed to keep soil in place, which include:

      - reducing or eliminating tillage       - managing irrigation to reduce runoff       - and keeping the soil covered with plants or mulch. 

Enhancement of soil quality is discussed in the next section.

• Plant Production Practices

Sustainable production practices involve a variety of approaches. Specific strategies must take into account topography, soil characteristics, climate, pests, local availability of inputs and the individual grower's goals.  Despite the site-specific and individual nature of sustainable agriculture, several general principles can be applied to help growers select appropriate management practices:

      - Selection of species and varieties that are well suited to the site and to conditions on the farm;       - Diversification of crops (including livestock) and cultural practices to enhance the biological and economic stability of the farm;       - Management of the soil to enhance and protect soil quality;       - Efficient and humane use of inputs; and       - Consideration of farmers' goals and lifestyle choices.

Selection of site, species and variety

Preventive strategies, adopted early, can reduce inputs and help establish a sustainable production system. When possible, pest-resistant crops should be selected which are tolerant of existing soil or site conditions. When site selection is an option, factors such as soil type and depth, previous crop history, and location (e.g. climate, topography) should be taken into account before planting.

Diversified farms are usually more economically and ecologically resilient.  While monoculture farming has advantages in terms of efficiency and ease of management, the loss of the crop in any one year could put a farm out of business and/or seriously disrupt the stability of a community dependent on that crop. By growing a variety of crops, farmers spread economic risk and are less susceptible to the radical price fluctuations associated with changes in supply and demand.

Properly managed, diversity can also buffer a farm in a biological sense. For example, in annual cropping systems,  crop rotation can be used to suppress weeds, pathogens and insect pests. Also, cover crops can have stabilizing effects on the agroecosystem by holding soil and nutrients in place, conserving soil moisture with mowed or standing dead mulches, and by increasing the water infiltration rate and soil water holding capacity.  Cover crops  in orchards and vineyards can buffer the system against pest infestations by increasing beneficial arthropod populations and can therefore reduce the need for chemical inputs. Using a variety of cover crops is also important in order to protect against the failure of a particular species to grow and to attract and sustain a wide range of beneficial arthropods.

Optimum diversity may be obtained by integrating both crops and livestock in the same farming operation. This was the common practice for centuries until the mid-1900s when technology, government policy and economics compelled farms to become more specialized. Mixed crop and livestock operations have several advantages. First, growing row crops only on more level land and pasture or forages on steeper slopes will reduce soil erosion. Second, pasture and forage crops in rotation enhance soil quality and reduce erosion; livestock manure, in turn, contributes to soil fertility. Third, livestock can buffer the negative impacts of low rainfall periods by consuming crop residue that in "plant only" systems would have been considered crop failures. Finally, feeding and marketing are flexible in animal production systems. This can help cushion farmers against trade and price fluctuations and, in conjunction with cropping operations, make more efficient use of farm labor.

Soil management

A common philosophy among sustainable agriculture practitioners is that a "healthy" soil is a key component of sustainability; that is, a healthy soil will produce healthy crop plants that have optimum vigor and are less susceptible to pests. While many crops have key pests that attack even the healthiest of plants, proper soil, water and nutrient management can help prevent some pest problems brought on by crop stress or nutrient imbalance. Furthermore, crop management systems that impair soil quality often result in greater inputs of water, nutrients, pesticides, and/or energy for tillage to maintain yields.

In sustainable systems, the soil is viewed as a fragile and living medium that must be protected and nurtured to ensure its long-term productivity and stability.   Methods to protect and enhance the productivity of the soil include:

      - using cover crops, compost and/or manures       - reducing tillage       - avoiding traffic on wet soils       - maintaining soil cover with plants and/or mulches

Conditions in most California soils (warm, irrigated, and tilled) do not favor the buildup of organic matter. Regular additions of organic matter or the use of cover crops can increase soil aggregate stability, soil tilth, and diversity of soil microbial life.

Efficient use of inputs

Many inputs and practices used by conventional farmers are also used in sustainable agriculture. Sustainable farmers, however, maximize reliance on natural, renewable, and on-farm inputs.  Equally important are the environmental, social, and economic impacts of a particular strategy. Converting to sustainable practices does not mean simple input substitution. Frequently, it substitutes enhanced management and scientific knowledge for conventional inputs, especially chemical inputs that harm the environment on farms and in rural communities. The goal is to develop efficient, biological systems which do not need high levels of material inputs.

Growers frequently ask if synthetic chemicals are appropriate in a sustainable farming system. Sustainable approaches are those that are the least toxic and least energy intensive, and yet maintain productivity and profitability. Preventive strategies and other alternatives should be employed before using chemical inputs from any source. However, there may be situations where the use of synthetic chemicals would be more "sustainable" than a strictly non-chemical approach or an approach using toxic "organic" chemicals. For example, one grape grower switched from tillage to a few applications of a broad spectrum contact herbicide in the vine row. This approach may use less energy and may compact the soil less than numerous passes with a cultivator or mower.

Consideration of farmer goals and lifestyle choices

Management decisions should reflect not only environmental and broad social considerations, but also individual goals and lifestyle choices. For example, adoption of some technologies or practices that promise profitability may also require such intensive management that one's lifestyle actually deteriorates. Management decisions that promote sustainability, nourish the environment, the community and the individual.

• Animal Production Practices

In the early part of this century, most farms integrated both crop and livestock operations. Indeed, the two were highly complementary both biologically and economically. The current picture has changed quite drastically since then. Crop and animal producers now are still dependent on one another to some degree, but the integration now most commonly takes place at a higher level-- between  farmers, through intermediaries, rather than  within  the farm itself. This is the result of a trend toward separation and specialization of crop and animal production systems. Despite this trend, there are still many farmers, particularly in the Midwest and Northeastern U.S. that integrate crop and animal systems--either on dairy farms, or with range cattle, sheep or hog operations.

Even with the growing specialization of livestock and crop producers, many of the principles outlined in the crop production section apply to both groups. The actual management practices will, of course, be quite different. Some of the specific points that livestock producers need to address are listed below.

Management Planning

Including livestock in the farming system increases the complexity of biological and economic relationships. The mobility of the stock, daily feeding, health concerns, breeding operations, seasonal feed and forage sources, and complex marketing are sources of this complexity. Therefore, a successful ranch plan should include enterprise calendars of operations, stock flows, forage flows, labor needs, herd production records and land use plans to give the manager control and a means of monitoring progress toward goals.

Animal Selection

The animal enterprise must be appropriate for the farm or ranch resources. Farm capabilities and constraints such as feed and forage sources, landscape, climate and skill of the manager must be considered in selecting which animals to produce. For example, ruminant animals can be raised on a variety of feed sources including range and pasture, cultivated forage, cover crops, shrubs, weeds, and crop residues. There is a wide range of breeds available in each of the major ruminant species, i.e., cattle, sheep and goats. Hardier breeds that, in general, have lower growth and milk production potential, are better adapted to less favorable environments with sparse or highly seasonal forage growth.

Animal nutrition

Feed costs are the largest single variable cost in any livestock operation. While most of the feed may come from other enterprises on the ranch, some purchased feed is usually imported from off the farm. Feed costs can be kept to a minimum by monitoring animal condition and performance and understanding seasonal variations in feed and forage quality on the farm. Determining the optimal use of farm-generated by-products is an important challenge of diversified farming.

Reproduction

Use of quality germplasm to improve herd performance is another key to sustainability. In combination with good genetic stock, adapting the reproduction season to fit the climate and sources of feed and forage reduce health problems and feed costs.

Herd Health

Animal health greatly influences reproductive success and weight gains, two key aspects of successful livestock production. Unhealthy stock waste feed and require additional labor. A herd health program is critical to sustainable livestock production.

Grazing Management

Most adverse environmental impacts associated with grazing can be prevented or mitigated with proper grazing management. First, the number of stock per unit area (stocking rate) must be correct for the landscape and the forage sources. There will need to be compromises between the convenience of tilling large, unfenced fields and the fencing needs of livestock operations. Use of modern, temporary fencing may provide one practical solution to this dilemma. Second, the long term carrying capacity and the stocking rate must take into account short and long-term droughts. Especially in Mediterranean climates such as in California, properly managed grazing significantly reduces fire hazards by reducing fuel build-up in grasslands and brushlands. Finally, the manager must achieve sufficient control to reduce overuse in some areas while other areas go unused. Prolonged concentration of stock that results in permanent loss of vegetative cover on uplands or in riparian zones should be avoided. However, small scale loss of vegetative cover around water or feed troughs may be tolerated if surrounding vegetative cover is adequate.

Confined Livestock Production

Animal health and waste management are key issues in confined livestock operations. The moral and ethical debate taking place today regarding animal welfare is particularly intense for confined livestock production systems. The issues raised in this debate need to be addressed.

Confinement livestock production is increasingly a source of surface and ground water pollutants, particularly where there are large numbers of animals per unit area. Expensive waste management facilities are now a necessary cost of confined production systems. Waste is a problem of almost all operations and must be managed with respect to both the environment and the quality of life in nearby communities. Livestock production systems that disperse stock in pastures so the wastes are not concentrated and do not overwhelm natural nutrient cycling processes have become a subject of renewed interest.

• The Economic, Social & Political Context

In addition to strategies for preserving natural resources and changing production practices, sustainable agriculture requires a commitment to changing public policies, economic institutions, and social values.  Strategies for change must take into account the complex, reciprocal and ever-changing relationship between agricultural production and the broader society.

The "food system" extends far beyond the farm and involves the interaction of individuals and institutions with contrasting and often competing goals including farmers, researchers, input suppliers, farmworkers, unions, farm advisors, processors, retailers, consumers, and policymakers. Relationships among these actors shift over time as new technologies spawn economic, social and political changes.

A wide diversity of strategies and approaches are necessary to create a more sustainable food system. These will range from specific and concentrated efforts to alter specific policies or practices, to the longer-term tasks of reforming key institutions, rethinking economic priorities, and challenging widely-held social values. Areas of concern where change is most needed include the following:

Food and agricultural policy

Existing federal, state and local government policies often impede the goals of sustainable agriculture. New policies are needed to simultaneously promote environmental health, economic profitability, and social and economic equity. For example, commodity and price support programs could be restructured to allow farmers to realize the full benefits of the productivity gains made possible through alternative practices. Tax and credit policies could be modified to encourage a diverse and decentralized system of family farms rather than corporate concentration and absentee ownership. Government and land grant university research policies could be modified to emphasize the development of sustainable alternatives. Marketing orders and cosmetic standards could be amended to encourage reduced pesticide use. Coalitions must be created to address these policy concerns at the local, regional, and national level.

Conversion of agricultural land to urban uses is a particular concern in California, as rapid growth and escalating land values threaten farming on prime soils. Existing farmland conversion patterns often discourage farmers from adopting sustainable practices and a long-term perspective on the value of land. At the same time, the close proximity of newly developed residential areas to farms is increasing the public demand for environmentally safe farming practices. Comprehensive new policies to protect prime soils and regulate development are needed, particularly in California's Central Valley. By helping farmers to adopt practices that reduce chemical use and conserve scarce resources, sustainable agriculture research and education can play a key role in building public support for agricultural land preservation. Educating land use planners and decision-makers about sustainable agriculture is an important priority.

In California, the conditions of agricultural labor are generally far below accepted social standards and legal protections in other forms of employment. Policies and programs are needed to address this problem, working toward socially just and safe employment that provides adequate wages, working conditions, health benefits, and chances for economic stability. The needs of migrant labor for year-around employment and adequate housing are a particularly crucial problem needing immediate attention. To be more sustainable over the long-term, labor must be acknowledged and supported by government policies, recognized as important constituents of land grant universities, and carefully considered when assessing the impacts of new technologies and practices.

Rural Community Development

Rural communities in California are currently characterized by economic and environmental deterioration. Many are among the poorest locations in the nation. The reasons for the decline are complex, but changes in farm structure have played a significant role. Sustainable agriculture presents an opportunity to rethink the importance of family farms and rural communities. Economic development policies are needed that encourage more diversified agricultural production on family farms as a foundation for healthy economies in rural communities. In combination with other strategies, sustainable agriculture practices and policies can help foster community institutions that meet employment, educational, health, cultural and spiritual needs.

Consumers and the Food System

Consumers can play a critical role in creating a sustainable food system. Through their purchases, they send strong messages to producers, retailers and others in the system about what they think is important.  Food cost and nutritional quality have always influenced consumer choices. The challenge now is to find strategies that broaden consumer perspectives, so that environmental quality, resource use, and social equity issues are also considered in shopping decisions. At the same time, new policies and institutions must be created to enable producers using sustainable practices to market their goods to a wider public. Coalitions organized around improving the food system are one specific method of creating a dialogue among consumers, retailers, producers and others. These coalitions or other public forums can be important vehicles for clarifying issues, suggesting new policies, increasing mutual trust, and encouraging a long-term view of food production, distribution and consumption.  

Contributors : Written by  Gail Feenstra , Writer; Chuck Ingels, Perennial Cropping Systems Analyst; and David Campbell, Economic and Public Policy Analyst with contributions from David Chaney, Melvin R. George, Eric Bradford, the staff and advisory committees of the UC Sustainable Agriculture Research and Education Program.

How to cite this page UC Sustainable Agriculture Research and Education Program. 2021. "What is Sustainable Agriculture?" UC Agriculture and Natural Resources. <https://sarep.ucdavis.edu/sustainable-ag>

This page was last updated August 3, 2021.

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Research and Science

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

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

USDA Science and Research Strategy, 2023-2026: Cultivating Scientific Innovation

The “ USDA Science and Research Strategy, 2023-2026: Cultivating Scientific Innovation (PDF, 21.4 MB)” presents a near-term vision for transforming U.S. agriculture through science and innovation, and outlines USDA’s highest scientific priorities. The S&RS is a call to action for USDA partners, stakeholders, and customers to join the conversation and help identify innovative research strategies that lead to real-world, practical solutions that help farmers, producers, and communities thrive.

Learn more and engage below:

USDA Science and Research Strategy

AGARDA: A Vision for Disruptive Science to Confront Audacious Challenges

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

Strengthening Our Research System

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

The Office of the Chief Scientist (OCS) coordinates USDA research, education and Extension with scientists and researchers across the federal government and university and private partners, to make the best use of taxpayer investments. In 2012, OCS continued focus on the Research, Education and Economics Action Plan (PDF, 486 KB) and identified seven priority research topics:

• Global Food Supply and Security
• Climate and Energy Needs
• Sustainable Use of Natural Resources
• Nutrition and Childhood Obesity
• Food Safety
• Education and Science Literacy
• Rural-urban Interdependence/Rural Prosperity

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

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

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

The National Institute of Food and Agriculture (NIFA) supports research, education and Extension programs in the Land-Grant University System and other partner organizations.

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

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

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

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

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

Improving Nutrition and Confronting Obesity

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

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

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

Conserving Natural Resources and Combating Climate Change

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

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

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

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

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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.

Past Papers

Agriculture Topics For Students: A Comprehensive Guide

As an educator, I firmly believe that agriculture topics for students play a pivotal role in their education. Agriculture, the backbone of our society, encompasses a wide range of relevant and essential subjects for students to understand.

In this comprehensive guide, I will delve into the importance of studying agriculture and the benefits of learning about agriculture, as well as provide valuable insights on agriculture research topics suitable for both students and high school students.

Table of Contents

The Importance of Studying Agriculture

Studying agriculture is vital for students as it allows them to develop a deep understanding of the fundamental concepts and principles that sustain our food systems. By learning about agriculture, students gain insights into the processes involved in food production, the importance of sustainable farming practices, and the challenges farmers face in an ever-changing world.

Moreover, agriculture education fosters essential skills such as critical thinking, problem-solving, and scientific inquiry.

Through hands-on experiences, students learn to apply theoretical knowledge to real-world situations, enabling them to become well-rounded individuals capable of making informed decisions about food, agriculture, and environmental issues.

Benefits of Learning about Agriculture

Learning about agriculture offers numerous benefits for students. Firstly, it promotes environmental awareness and instills a sense of responsibility towards the planet. By understanding the impact of agricultural practices on ecosystems, students can actively contribute to developing sustainable solutions that ensure the long-term viability of our natural resources.

Secondly, studying agriculture enhances students’ appreciation for farmers’ hard work and dedication. It exposes them to the challenges faced by those who work tirelessly to feed the world’s growing population. This understanding cultivates empathy and gratitude, encouraging students to support local farmers and make conscious choices that promote sustainable and ethical practices.

Lastly, agriculture education opens doors to a wide range of career opportunities. From agricultural engineering to food science, students with a background in agriculture have a wealth of career paths to choose from.

By immersing themselves in agriculture topics, students can explore their passions and develop skills that are highly demanded in the agricultural industry.

Agriculture Research Topics for Students

Research is an integral part of agriculture education , as it allows students to delve deeper into specific areas of interest and contribute to the body of knowledge in the field. Here are some agriculture research topics that students can explore:

• The impact of climate change on crop productivity
• The role of biotechnology in improving agricultural yields
• Sustainable farming practices for small-scale farmers
• The effects of pesticides on pollinators and biodiversity
• The importance of soil health in sustainable agriculture
• Urban agriculture and its potential for food security
• The benefits of organic farming for human health and the environment

These research topics offer a starting point for students to develop their research questions and methodologies. By selecting a topic aligned with their interests and passions, students are more likely to remain engaged and motivated throughout the research process.

Agriculture Research Topics for High School Students

High school students can also delve into agriculture research topics tailored to their understanding and academic capabilities. Here are some agriculture research topics suitable for high school students:

• The impact of food deserts on urban communities
• The role of genetically modified organisms in agriculture
• The importance of crop rotation in sustainable farming
• The effects of irrigation techniques on water conservation
• The potential of vertical farming in urban environments
• The benefits of community gardens for social cohesion
• The role of bees in pollination and food production

These research topics offer high school students the opportunity to explore agriculture-related subjects within the framework of their academic curriculum. By researching these topics, students can develop critical thinking skills and gain a deeper understanding of the complex interplay between agriculture, the environment, and society.

How to Choose the Right Agriculture Topic

Selecting the right agriculture topic is crucial for a successful research project. Here are some tips to help students choose the most suitable agriculture topic:

Identify your interests: Choose a topic that aligns with your passions and curiosity. This will ensure that you remain motivated and engaged throughout the research process.

Consider the scope: Select a topic that is neither too broad nor too narrow. It should be wide enough for in-depth research but narrow enough to be manageable within the given time frame.

Research the existing literature: Before finalizing a topic, review the literature to ensure enough research material is available. This will help you avoid redundant or unexplored areas of study.

Seek guidance: Consult your teachers, mentors, or agricultural professionals for their insights and recommendations. They can provide valuable advice and suggest potential research topics based on their expertise.

Resources for Finding Agriculture Research Topics

Finding the right agriculture research topic can sometimes be challenging. However, several resources help students search for a suitable topic. Here are some resources to consider:

Academic Journals: Browse through reputable academic journals in agriculture to identify current trends and potential research topics.

Online Databases: Use databases such as PubMed, Google Scholar, or Web of Science to search for agriculture-related articles, research papers, and literature reviews.

Professional Associations: Explore the websites of professional agricultural associations and organizations. They often provide valuable resources, research publications, and suggested research topics.

University Libraries: Visit your university library and consult with the librarians. They can guide you toward relevant books, journals, and databases to help you find the right agriculture research topic.

By utilizing these resources, students can broaden their knowledge base and discover exciting research topics that align with their academic interests.

Tips for Conducting Agriculture Research

Conducting agriculture research requires a systematic and organized approach. Here are some tips to help students conduct their research effectively:

Develop a research plan: Outline your research objectives, methodologies, and timelines. This will help you stay focused and organized throughout the research process.

Collect relevant data: Gather data from credible sources such as scientific journals, government reports, or agricultural research institutes. Ensure the data is pertinent to your research topic and supports your objectives.

Analyze the data: Use appropriate statistical tools or qualitative analysis techniques to analyze the collected data. This will allow you to draw meaningful conclusions and support your research findings.

Seek guidance and feedback: Regularly consult your teachers, mentors, or agricultural professionals for their advice and feedback on your research progress. They can provide valuable insights and help you refine your research methodology.

Maintain accurate records: Keep detailed records of your research process, including data, methodologies, and sources. This will ensure transparency and facilitate the writing process when presenting your research findings.

Presenting Your Agriculture Research Findings

Presenting your agriculture research findings is a crucial step in the research process. Here are some tips to help you effectively communicate your research:

Structure your presentation: Organize your research findings logically and coherently. Use clear headings and subheadings to guide your audience through your research process and conclusions.

Utilize visual aids: Incorporate graphs, charts, and images to represent your data and findings visually. Visual aids can enhance audience understanding and engagement.

Practice your presentation: Rehearse your presentation multiple times to ensure a smooth and confident delivery. Consider recording yourself to identify areas for improvement and refine your speaking skills.

Engage your audience: Encourage participation by asking questions, facilitating discussions, or incorporating interactive elements into your presentation. This will enhance audience engagement and promote a deeper understanding of your research findings.

Be prepared for questions: Anticipate potential questions and prepare thoughtful responses. This will demonstrate your expertise and enhance your credibility as a researcher.

Conclusion: The Impact of Agriculture Education on Students

In conclusion, studying agriculture topics is of paramount importance for students. It equips them with essential knowledge about food production, sustainability, and environmental stewardship and fosters critical thinking, problem-solving, and empathy.

By learning about agriculture, students develop an appreciation for the hard work of farmers, gain insights into global challenges, and explore a wide range of career opportunities.

Whether conducting research on agriculture topics or presenting their findings, students can actively contribute to the field of agriculture and positively impact society. Therefore, I encourage students to embrace agriculture education, choose research topics that align with their passions, and leverage the available resources to embark on a journey of discovery and growth.

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Hybrid intelligence can reconcile biodiversity and agriculture

by University of Hohenheim

A research team at the University of Hohenheim and Technical University of Munich has developed a new transdisciplinary approach to resolve the tradeoff between biodiversity and agricultural production.

Preserving biodiversity without reducing agricultural productivity: So far, these two goals have not be reconciled because the socio-ecological system of agriculture is highly complex, and the interactions between humans and the environment are difficult to capture using conventional methods.

Thanks to new technology, a research team at the University of Hohenheim in Stuttgart and the Technical University of Munich is showing a promising way to achieve both goals at the same time. The members of the team are focusing on further developing artificial intelligence in combination with collective human judgment: the use of hybrid intelligence. They have published their article in the current issue of Nature Food .

"Although we have more and more data sets at our disposal, we have not yet been able to use them to solve the problem. Available data from remote sensing , proximal sensing and statistical surveys are disconnected and highly fragmented," said Prof. Dr. Thomas Berger, agricultural economist at the University of Hohenheim and lead author of the publication.

"Another challenge is the different planning horizon: Agricultural practices are based on short- and medium-term economic objectives at the field and farm level, that is, on a scale of 1 hectare to 100 hectares. The long-term ecological effects, on the other hand, are evident at the landscape level of 100,000 hectares."

From an ecological point of view, it is therefore necessary to look at the landscape level and better understand the interactions of many farms in terms of space and time. "There is little cross-farm coordination for agri-environmental measures," stated Prof. Dr. Senthold Asseng from the Chair of Digital Agriculture at the Technical University of Munich.

Previous funding programs in agricultural and environmental policy were not designed to enable biodiversity-friendly synergies among farmers, between farmers and other stakeholders, and in science.

The problem is also very challenging from a social science perspective, according to Prof. Dr. Claudia Bieling from the Hohenheim Department of Societal Transition and Agriculture. "This is the classic situation of a social dilemma. Why should individual stakeholders forgo productivity on their own initiative when the common public good of biodiversity conservation benefits many other stakeholders free of charge?"

There are also similar situations that block progress in other economic sectors, e.g., in recycling and waste management as well as in energy and transport.

In order to capture the complexity of the problem and develop new intelligent solutions, joint expertise from the natural and social sciences, engineering, and computer science is required, as well as close cooperation between science and practice.

Technological progress enables new interaction between humans and machines

A 13-person team with precisely this expertise joined forces to develop a transdisciplinary approach—exploiting the new possibilities offered by artificial intelligence in merging and processing large volumes of data. The authors of the publication refer to this combination as "hybrid intelligence."

"By combining the intuitive abilities of humans with the computing power of modern computers and the analytical capabilities of artificial intelligence, for the first time we can develop human-machine systems that successfully address complexity in agriculture," said Prof. Berger.

One component of such systems are computer models with what the team refers to as "multi-agent technology" for the various ecological, social, and economic processes. By enriching these models with artificial intelligence , the research team aims to create a detailed, interactive image of reality in which various biodiversity measures and effects can be simulated and stakeholders can be supported in joint decision-making.

Group payments as a practical example of hybrid intelligence

The authors explain practical implementations in several applied examples, e.g., compensation payments to groups of farmers instead of individual farms.

"The EU provides various subsidies for species protection measures, for example by giving farmers money to set up flower strips," stated Prof. Asseng. "Up to now, farmers have planted the flower strips on their own and without coordinating with their neighbors. Overall, the flower strips are fragmented and have limited effectiveness."

Group payment programs for farmers who coordinate their flower strips at the landscape level with the use of hybrid intelligence are more promising.

In a first step, hybrid intelligence could analyze complex data on soil conditions , local biodiversity, and similar factors and thus identify the locations where cross-farm environmental measures would be particularly effective and crop losses as lowest as possible.

In a second step, AI systems could provide communication platforms that facilitate exchanging information and planning joint projects without excessive bureaucracy. "Another goal would be a fair balance among all parties involved, for example, through new auction mechanisms for subsidies," said Prof. Berger.

The virtual image of their economic and ecological environment would give actors from agriculture, consulting, and politics the opportunity to try out the measures before deciding whether to implement them. "This would make it easier to assess the impact on biodiversity and crop yields and minimize the costs for everyone involved," added Prof. Bieling.

Above all, AI could serve as an automated moderator that follows the discussions within the group and improves decision-making by contributing information or alternative perspectives. "We can currently see the capabilities of generative AI in language processing and generating new content with ChatGPT. This can be particularly useful to ensure that all relevant information is considered in group discussions and creative solutions are found," explained Prof. Dr. Henner Gimpel from the Department of Digital Management at the University of Hohenheim.

Trust and transparency remain crucial for success

If the approach is to be successful, it must be transparent and participatory. "The technology must be designed in such a way that people can trust it. The ethical use of the technology is also crucial," said Prof. Gimpel. Only if these conditions are met can hybrid intelligence systems develop their full potential and find broad acceptance.

According to Prof. Berger, hybrid intelligence holds the key to solving some of the most pressing issues in agriculture. "The prospects are very promising, but there is still a need for fundamental research in order to successfully develop this technology further and then implement it. To achieve this, we need the cooperation of all stakeholders from science, practice, and society."

Journal information: Nature Food

Provided by University of Hohenheim

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• Frontiers in Robotics and AI
• Field Robotics
• Research Topics

Robotics for Smart Farming

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Robotics in agriculture explores the potential of robotics and artificial intelligence to revolutionize the way farming is done. It looks at the possibilities for automation in crop production and livestock farming, as well as the implications for farming and rural communities. It examines the ways in which robotics could reduce costs, increase yields, and improve safety and sustainability. It also considers the potential risks and drawbacks associated with the use of robotics and AI in agriculture, such as the potential for job losses and the vulnerability of robotic systems to cyberattack. This Research Topic (Robotics for Smart Farming) aims to highlight the latest research in robotic technologies relevant to agriculture and farming processes. It will focus on agricultural robotics covering different fields of robotics, intelligent perception, manipulation, control, path planning, machine learning, and the applications of robotic and control systems in agriculture. The goal of this Research Topic is to explore the potential of robotics for smart farming and to bring together the latest developments in the field of robotics for agriculture and food production. We aim to provide a comprehensive overview of the current state of research and applications in this field, and to identify the challenges, opportunities and future trends in robotics for smart farming. We also aim to promote collaboration between researchers and practitioners, and to provide a platform for exchanging ideas and experiences. The scope of this Research Topic is to review the latest developments in the field of robotics for smart farming. We invite original research papers, review articles, and technical notes on topics related to the following, but not limited to: • Robotics and UAVs in Smart Farming • Robotics for crop production, harvesting, and post-harvest processing • Autonomous navigation and control of agricultural robots • Machine learning and artificial intelligence for agricultural robotics • Deep learning and reinforcement learning for agricultural robotics • Robotic Applications in Agriculture for Land Preparation before Planting • Robotic Applications in Agriculture for Sowing and Planting • Robotic Applications in Agriculture for Plant Treatment • Robotics for Yield Estimation and Phenotyping • Robotic Applications in Agriculture for Harvesting • Robotic Systems for Food Production • Robotic Livestock Farming • Robotic Fish Farming • Robotic Crop Plantation and Weeding • Robotic Harvesting • Robotic Crop Sensing and Monitoring • Robotic Disease Detection • Robotics in Precision Agriculture • Robotics in Food Processing • Social and ethical implications of robotics in agriculture

Keywords : Robotics, Smart Farming, Autonomous Navigation, Sensor Technologies, Machine Learning

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Food security in developed countries shows resilience to climate change

A study by the University of Southampton has found that market forces have provided good food price stability over the past half century, despite extreme weather conditions.

Research into US wheat commodities by economists at Southampton, in collaboration with UCL, also suggests high uncertainty about the state of future harvests hasn't destabilised the market.

Findings are published in the Journal of Economic Dynamics and Control .

Wheat is an important crop in the United States used for food production. A small fraction becomes animal feed and the crop isn't used to generate biofuel. The main buyers of wheat are flour mills, food processors, and direct consumers.

The researchers analysed data on American wheat production, inventories, crop area, prices and wider market conditions from 1950 to 2018, together with records of annual fluctuations in the weather for the same period. This showed strong evidence of an increase in weather and harvest variability from 1974 onwards.

"Before the mid-70s, oil was the dominant driver of wheat price fluctuations in the US, but after this point we see a much stronger influence coming from a wider set of factors that includes weather and food consumption," explains lead author Dr Vincenzo De Lipsis of the University of Southampton.

"Extreme weather events, such as droughts and floods, are becoming more frequent and intense across the world due to climate change. Understanding the impact of this variability on food commodity prices is crucial, as it could have serious implications for food security."

The authors found that in the US the market system around wheat has remained competitive, functioning well and adapting to the new uncertain climate conditions. The potential for weather fluctuations to adversely affect wheat prices has increased, but in reality this hasn't been passed on to the market. Wheat prices remain relatively stable, along with the price of associated goods.

The researchers found that this is mainly due to farmers and agricultural industries providing a buffer, smoothing out any bumps in the supply of grain to retailers and consumers, thus reducing shocks to the market that poor harvests may cause. This has been achieved by investment in substantial storage facilities, modern infrastructure and good transport links.

According to the study, the US wheat sector has demonstrated remarkable resilience and flexibility in adapting to the ever-increasing unpredictability of the climate and harvest by modifying its inventory management. At the same time, there is no indication that the wheat market is vulnerable to excessive volatility from the related financial futures market, which can often emerge in commodity markets in response to increased uncertainty regarding future production capacity.

Commenting on what policymakers can take from the research, Dr De Lipsis says: "We have shown that market forces provide a powerful stabilising mechanism to counter the increased variability in weather and harvest observed in the last half a century.

"The market mechanism is one of the most effective instruments that governments have available for climate change adaptation and food security. But for this to work effectively, we need a combination of factors in place: a well-functioning competitive commodity market, a modern infrastructure with extensive transport networks, sufficient food storage capacity and a liquid futures market.

"However, while the system in the US continues to be robust, it's hard to predict if storage mechanisms will work equally well if faced with unprecedented levels of weather variability -- the kind of extreme events that can potentially disrupt the transport network and the very infrastructure on which it is based."

The authors acknowledge that stability is easier to achieve in developed and more affluent countries, but say that their results underscore the need to prioritise investment in these key areas in developing regions to ensure a reliable and secure food supply in the future.

• Food and Agriculture
• Agriculture and Food
• Global Warming
• Severe Weather
• Resource Shortage
• World Development
• Public services
• Economic growth
• Meteorology
• Severe weather terminology (United States)
• Stock market

Story Source:

Materials provided by University of Southampton . Note: Content may be edited for style and length.

Journal Reference :

• Vincenzo De Lipsis, Paolo Agnolucci. Climate change and the US wheat commodity market . Journal of Economic Dynamics and Control , 2024; 161: 104823 DOI: 10.1016/j.jedc.2024.104823

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Strange & offbeat.

19th Annual RISE Symposium celebrates student research

The annual tradition presented opportunities for research, teaching and outreach.

Pasture 1 on the Santa Rita Experimental Range, southeast of Tucson

The  19 th  Annual Research Insights in Semi-Arid Ecosystems (RISE) Symposium  was held this past Saturday at the Marley Building on the UArizona Campus. The objectives of the symposium were to share recent results of scientific research in semiarid environments, with an emphasis on work conducted at the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) and the  University of Arizona Santa Rita Experimental Range  (SRER), and to encourage collaboration among researchers and students on future research and outreach activities. This year’s symposium presented opportunities for research, teaching and outreach. Speakers at the event covered a wide variety of topics, including results of completed and on-going work at WGEW and SRER.

Oral presentations started with updates and opportunities at the Santa Rita Experimental Range and  National Ecological Observatory Network  (NEON) by  Brett Blum,  director of the Southern Arizona Experiment Station, and  Abe Karam , NEON manager. Nico Franz, from the School of Life Sciences at Arizona State University, introduced the enormous number of holdings and research opportunities using the NEON Biorepository. Vanessa Prileson, from Pima County Natural Resources, Parks and Recreation, gave an overview of lands held by Pima County in title and easement for the Sonora Desert Conservation Plan, specifically opportunities on the ranch lands in that program.  Michael Johnson , an Indigenous Resilience Specialist in the  School of Natural Resources and the Environment  at the  College of Agriculture, Life and Environmental Sciences , and a 250th generation Hopi Farmer, described opportunities related to agricultural food storage and cultivation practices performed by the Hopi and how those practices reduced the vulnerability to the uncertainties of inter-annual growing conditions.  Joel Biederman , from the  USDA ARS Southwest Watershed Research Center , described discoveries and opportunities following the first three years of the RainMan project that measured vegetation and soil microbial responses to manipulation of rainfall amount and timing on the Santa Rita Experimental Range.

After lunch,  Phil Heilman , from the USDA ARS Southwest Watershed Research Center, described recent projects and opportunities at the Walnut Gulch Experimental Watershed near Tombstone. Eric Dhruv, from the Ironwood Tree Experience, described programs connecting diverse youth to biodiverse ecosystems and specifically the programs that occurred on the Santa Rita with local high school students.  Betsy Arnold , a professor in the  School of Plant Sciences  at the College of Agriculture, Life and Environmental Sciences, led us through recent discoveries and ongoing projects describing the abundance and role of soil microorganisms on the above ground vegetation at SRER and nearby locations, as well as a fascinating collection of photographs of common mushroom species.

Additionally, 18 short talks and accompanying posters showcasing work conducted at WGEW and SRER were presented by graduate and undergraduate students and submitted for a poster contest. The posters are available for viewing on the  RISE website .

Neda Arad, a student at the School of Plant Sciences, took first place in the graduate student category and won a prize of $500 for the poster titled Endophytes of native and introduced plants at the Santa Rita Experimental Range: High biodiversity contextualized by a continental-scale context.

Changpeng Fan, a Hydrology and Atmospheric Sciences student, won second place and $300 in the graduate student category for the poster titled Harness the power of NEON observations and artificial intelligence to predict the hydroclimate regulation on microbial functional composition across the CONUS.

Doan Goolsby, a student at the School of Natural Resources and the Environment, took first place and won a $300 prize in the undergraduate category for the poster titled The Impact of Increased Precipitation Variability on Soil Microbial Communities of the Desert Southwest Rangeland Ecosystem.

We rely on donations to cover the student poster contest awards and costs associated with running the symposium. Please consider making a donation of any amount to support the Symposium Program by visiting the  UA Foundation website .

News Topics

• Arizona Experiment Station

This outlet tells farmers' stories in Nigeria and offers advice for agriculture reporters

By arinze chijioke and muhammad auwal ibrahim apr 12, 2024 in specialized topics.

Over 70% of Nigerians work in agriculture. Despite its substantial contribution to the country’s economy, however, the sector remains under-covered in the news. 

Farmers’ stories often aren’t amplified, as a result, nor are their concerns conveyed to policymakers. Meanwhile, many lack sufficient knowledge about climate change and how to adapt to it. 

There is too little capacity and interest in newsrooms to cover agriculture, explained Abdulkareem Mojeed , an agricultural correspondent for the Premium Times Nigeria . 

“This is largely because agricultural stories would ordinarily take reporters to local/rural communities, where they engage with farmers [who are] often perceived as poor people in this part of the world, unlike the political beat, where they would be engaging the elites,” he said, adding that a lack of political interest also contributes to the coverage gap.

In 2023, Yunusa Ya’u , director at the Centre for Information, Technology and Development , launched Farmers Voice to provide more thorough reporting of agricultural issues and to amplify the voices of farmers by highlighting their challenges and successes alike.  

Here’s how this outlet is doing so, along with advice to help reporters improve their reporting on the beat: 

Taking action

The inspiration behind Farmers Voice stemmed from a recognition of the pivotal role agriculture plays in Nigeria’s economy, coupled with the lack of media attention the sector has commanded, said Buhari Abba , a copy editor at the outlet.

“We wanted to promote a knowledge-based agricultural sector through accurate reporting of the growths in the sector, keep farmers informed about innovative approaches and create awareness about what challenges they face,” said Abba.

Farmers Voice has reported on farming practices, market trends, policy changes and innovations. The newsroom connects farmers with prospective consumers, too, and presents their concerns to government officials in hopes to influence policy. "Our key priority areas include promoting sustainable farming methods, highlighting success stories and addressing challenges within the sector," said Abba.

Farmers Voice also organizes workshops during which experts offer guidance on agricultural practices and farm management. During one of its workshops last July, farmers from Kano State were educated on drip and sprinkler irrigation, greenhouse crop production, and pest and disease control.

“I have gained knowledge and insights on pest and disease management and how to enhance [my] farming practices and increase productivity,” said one local farmer, Muhammad Ismail, who attended the workshop. 

Telling the stories

Since its launch, Farmers Voice has published articles and research that capture the challenges faced by farmers and the need for the government to intensify investments in the sector to improve agriculture yield. 

For example,  one story looked at how hippopotamus attacks on farmlands in Gombe State were affecting agriculture, and how the state government is tackling this by hiring forest guards to use drones, fly boats and ride motorcycles to enhance surveillance of the waterways.

In another report , Farmers Voice reporters captured how bandit attacks thwarted plans by one agriculture expert to expand ginger production in Kaduna state. This article examined how local farmers generated N700 million [about US$525,000] in revenue from sugarcane sales.

Farmers Voices’ agriculture coverage has not come without its challenges. Financial sustainability is chief among them.

“There are a lot of stories we want to tell, but we cannot do that because we do not have the funds,” said Abba. “It is something we are hoping for in the future.”

The outlet has also had trouble accessing remote farming communities due to poor road infrastructure. “[Despite these challenges] we remain committed to delivering accurate and timely information to keep our readers abreast of what is happening in the agricultural sector,” said Abba. 

Tips for covering agriculture

Do your research.

Effective reporting on agriculture requires reporters to gain a solid grasp of the issues and policies related to it, said Mojeed: “Journalists should arm themselves with the required knowledge of the sector as effective reporting would keep people, farmers and even policymakers informed.”

Agriculture reporters should research governmental agriculture policies and related budgets. They can also visit the websites of relevant organizations such as the Food and Agricultural Organization of the United Nations and the African Development Bank .

Cultivate sources

Agricultural reporters must cultivate sources, especially among farmers, as they understand and experience certain issues firsthand and know how to tackle them.

"You must endeavor to understand the people, places and [agriculture]-related events; that is how you can build the confidence of your sources,” said Mojeed. “Field visits, for instance, help reporters understand the peculiarities of the environment."

Agricultural reporters should also speak with researchers and policymakers to better understand the issues that require coverage, said climate change expert Murtala Abdullahi . “Prioritizing agriculture reporting is important because it is one of the most powerful tools to end extreme poverty and increase prosperity, accounting for 4% of global GDP ,” he said.

Learn the relevant language, legislation and organizations

Agriculture, like other sectors, has a specific vocabulary that journalists on the beat should familiarize themselves with.

“To better understand their sources and to report on different topics, it is also important for reporters to know a lot of terminologies that they might not be used to,” said Lekan Otufodunrin ,  executive director of the Media Career Development Network .

Among the many terms Otufodunrin recommended agricultural reporters become familiar with:

• Agronomy : the science of crop production and managing soil
• Hydroponics : growing plants in fertilized water
• Crop rotation : planting different crops from year to year to keep soil healthy

Reporters must also keep abreast of legislation and policies that concern agriculture, and how they affect farmers, said Otufodunrin. One example is Nigeria’s National Policy for Agriculture .

Keep an eye, too, on how major financial institutions involve themselves in agriculture. For example, the Bank of Agriculture provides agricultural credit facilities to small and large-scale farmers in the country. The Anchor Borrowers’ Programme provides loans to smallholder farmers with the goal of boosting agricultural production, creating jobs, and more.

Photo by Taylor Siebert on Unsplash .

Read more articles by

Arinze Chijioke and Muhammad Auwal Ibrahim

Arinze Chijioke is a freelance journalist in Nigeria covering global health, climate change and the environment, human rights, business and economics. Muhammad Auwal Ibrahim is an award-winning investigative journalist and creative writer based in Gombe, Nigeria.

IJNet provides the latest tips, trends and training opportunities in eight languages . Sign up here for our weekly newsletter: