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USDA Releases Agriculture Innovation Research Strategy Summary and Dashboard

(Washington, D.C., January 12, 2021) – Today, the U.S. Department of Agriculture (USDA) released its U.S. Agriculture Innovation Strategy Directional Vision for Research (PDF, 4.8 MB) summary and dashboard that will help to guide future research decisions within USDA. The strategy synthesizes the information USDA collected as part of a public announcement earlier this year engaging the public on research priorities under the Agriculture Innovation Agenda (AIA) .

“This initial report is a culmination of creative minds from across the agricultural community,” said Deputy Under Secretary Scott Hutchins, who leads USDA’s Research, Education, and Economics mission area, and who is responsible for research efforts under the AIA. “Innovation and ingenuity have always been key to solving critical agricultural production challenges and will also be critical for addressing new and emerging challenges on the horizon—and our stakeholders advocated for some truly bold goals. We believe this information and the AIA will create enthusiasm, bridge collaborations, drive constructive discussions, and spark imagination to convey the positive role innovation will play to help solve challenges that face our nation in meeting pressing demands to feed a growing population in a sustainable way,” said Hutchins.

USDA collected hundreds of responses through the RFI and stakeholder-led workshops. Respondents were asked to identify transformational research goals for the next era of agriculture productivity and environmental conservation. They were also asked to propose approaches to these opportunities around four innovation cluster areas (Genome Design, Digital Automation, Prescriptive Intervention, and Systems Based Farm Management), and to identify gaps, barriers, and hurdles to meeting these goals.

This report summarizes the extensive stakeholder input and defines discovery goals that will help inform research to best address the AIA for the next 10 to 30 years.

USDA developed the public dashboard to help sort the information collected from stakeholders. Stakeholders and customers can use the dashboard to take a deeper dive into the data to gain insights on agricultural innovation opportunities over three time horizons, including near-term solutions, longer-term transformational solutions, and next era concepts.

Both products are iterative and are starting points for moving the innovation community forward in reaching agricultural research goals and AIA outcomes. USDA will seek additional input from stakeholders to continue to shape and build the agriculture innovation strategy and fill any remaining gaps where more input is needed. Next steps include aligning existing USDA research programs against these bold innovation goals, while also linking existing research activities to the objectives to inform the broader innovation community of progress and enlist their engagement.

Background on USDA’s Agriculture Innovation Agenda (AIA):

The AIA is a Department-wide effort to align USDA’s resources, programs, and research to provide farmers with the tools they need to position American agriculture as a global leader in meeting future food, fiber, fuel, feed, and climate demands. As part of the AIA, USDA set ambitious goals to increase agricultural production by 40 percent, while cutting the environmental footprint of U.S. agriculture in half by 2050.

The AIA is comprised of four main components. The first component is to develop a U.S. Agriculture Innovation Strategy that aligns and synchronizes public and private-sector research. The second component is to align the work of our customer-facing agencies and integrate innovative technologies and practices into USDA programs. The third component is to conduct a review of USDA productivity and conservation data to improve our ability to track progress against our goals. Finally, USDA set benchmarks to hold us accountable. Learn more on USDA’s Agriculture Innovation Agenda website.

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Science Breakthroughs to Advance Food and Agricultural Research by 2030

For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate.

More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face.

Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030).

RESOURCES AT A GLANCE

• Press Release
• Report Highlights
• Interactive Overview of Breakthrough Opportunities

• Agriculture — Policy, Reviews and Evaluations
• Food and Nutrition — Policy, Reviews and Evaluations
• Earth Sciences — Policy, Reviews and Evaluations
• Environment and Environmental Studies — Policy, Reviews and Evaluations

Suggested Citation

National Academies of Sciences, Engineering, and Medicine. 2019. Science Breakthroughs to Advance Food and Agricultural Research by 2030 . Washington, DC: The National Academies Press. https://doi.org/10.17226/25059. Import this citation to: Bibtex EndNote Reference Manager

Publication Info

• Paperback:  978-0-309-47392-7
• Ebook:  978-0-309-47395-8

What is skim?

The Chapter Skim search tool presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter. You may select key terms to highlight them within pages of each chapter.

• Interactive Overview of Breakthrough Opportunities Read Description: This study identified five convergent breakthrough opportunities. Some are in early stages of development, while others are on the cusp of widespread application.

This brief video outlines some of the key background info and major conclusions of this report.

Report Release Webinar

The National Academies of Science, Engineering, and Medicine held a public release webinar for the report on Wednesday, July 18. The webinar featured a presentation and live Q&A by the co-chairs and two committee members of the report’s authoring committee:– Susan R. Wessler, NAS, University of California, Riverside (co-chair)– John D. Floros, New Mexico State University (co-chair)– Corrie Brown, University of Georgia– Gregory V. Lowry, Carnegie Mellon University

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OSU Extension Service

• OSU Extension Catalog

Experimenting on the Farm: Introduction to Experimental Design

Choosing a research question, selecting treatments, the importance of a control, replication and randomization.

• Things to consider

Plot layouts

Data collection, yield measurements, interpreting results.

• References and resources

A combine harvests plots in an on-farm experiment, leaving buffer strips between plots.

Introduction

Farmers often try new management practices, technologies or crops in small areas before making changes on a larger scale. On-farm trials help growers test if promising experiment station research results can be replicated on their farms. However, these on-farm trials must be carefully planned to ensure the results are valid.

This publication will describe how to design an on-farm experiment. You will learn about experimental design, including treatments, check plots and replication. Each of these aspects affects the quality of your experiment and determines the time and effort required. In the end, you will know how to design experiments that maximize your time and resources so you can make informed management decisions.

The goal of an experiment is to answer a question. On the farm, research questions are often generalized as, “Which management practice is best?” It is easier to plan your experiment if you state your question in a clear, detailed way. As you formulate your question, ensure you can answer the following:

• What management practice do I want to test?
• What effects do I expect that management practice to have?
• What metrics will I use to decide which management practice is best for my situation?

Your research question and available time and resources will dictate the measurements you take to answer your question. Yield is one of the most commonly measured results in on-farm research. However, the goal of many on-farm experiments is to increase profitability, which means you should keep track of any differences in costs related to inputs or management. A small decrease in yield might be acceptable if it comes with reduced input costs, environmental benefits, increased crop quality or other valuable outcomes. If you are testing an additional input product, consider what yield increase you need to justify the additional input cost. Other measurements can include crop quality, disease or pest pressure, soil quality or other results depending on your research question. Besides profit, consider what else is important to you. Practices that improve sustainability and soil health may not benefit your bottom line in the short term but can ensure your farm is in better shape for future generations.

To answer your research question, compare different management practices or treatments applied to adjacent plots. One treatment should be a control that is your standard management, while the other treatment is an alternative management practice that you want to test. Carefully plan how you will manage each treatment. Any difference between the treatments could affect yield or other outcomes.

Many management practices work well in some conditions but not in others. For example, adding fertilizer increases yield, but only if that nutrient is deficient and limiting yield. Researchers call this an interaction. When designing an experiment to test a management practice, do it under conditions where the difference will be most obvious. This is especially true for practices that make crops more stress-tolerant. Practices that are expected to increase soil water-holding capacity should be tested under conditions where crop growth is limited by water availability. You should test stress-tolerant crop varieties in situations where that stress is present. Products that claim to increase nutrient uptake should be tested on crops grown in nutrient-limited conditions. You can do this by applying less fertilizer or choosing a field with lower soil fertility.

Case study: a new soil amendment

Jane is just back from a growers’ meeting, where she learned about a new soil amendment. According to the marketing material, Mineral BOO$TER 10000 increased yields for farmers in other states. The dealer showed impressive photographs of healthy, dark green, vigorous crops and provided testimonials about the benefits in several different cropping systems. Feeling lucky, Jane entered the door prize raffle and won a bag of MB 10000 to use on her farm.

Back home, Jane was excited to try the amendment but could not decide how much to apply. The bag said to apply MB 10000 “as needed” with a suggested rate of 5–10 pounds per acre. But the label noted that higher rates could produce additional benefits. Jane is skeptical about silver bullets in farming, so she decided to test MB 10000 herself. She called the local Extension office for MB 10000 information and application recommendations.

Figure 1. The experimental plot layout Jane used to test MB 10000.

The county agent had not heard of MB 10000. He suggested Jane conduct a response trial on a representative field on her farm. He suggested splitting the difference between 5 and 10 pounds per acre and setting her spreader to apply 7.5 pounds per acre. Jane had been planning to use the same rate on half of the field, but the agent recommended she apply MB 10000 in three, replicated, spreader-width strips, leaving three untreated strips as a control. The agent recommended she apply twice the rate of MB 10000 (15 pounds per acre) for three strips of the fields as well (Figure 1). The agent encouraged Jane to apply all three treatments to adjacent strips because differences in soil or other factors would be more likely to affect the results if the control and 2x treatments were on opposite ends of the field.

This setup gave Jane a control (the no MB 10000 pass) to compare the base 7.5-pounds-per-acre standard treatment. If the MB 10000 had a beneficial effect, Jane would see improved yield in the standard rate over the control strip. Jane would also be able to answer the question, “If a little is good, then is more better?” If the strip in which she applied the 2x rate looks healthier or yields more than the standard rate, she might consider applying a rate greater than 7.5 pounds per acre the following year. With this experimental design, Jane could learn more than if she applied MB 10000 to half of a field. The Extension agent warned that any product needs to increase yield more than enough to pay for itself, otherwise it is a waste of money. Another year of data would probably be needed before using a product on the whole farm.

Imagine a farmer growing wheat using the same management for many years. He switches to a new practice and notices a yield increase. Without a control, or check plot, where wheat was grown using prior practices, he can’t be certain the new practice caused the yield increase. The weather or some other factor may have been responsible.

A control allows you to make a direct comparison between the old practice and the new one. Treat your control exactly like the other treatment plots, except for the difference or differences you are testing. This means the weather, soil, planting date, fertilizer, pest pressure, irrigation and application timings are all the same unless they are part of your research question.

Sometimes you may want to compare a set of management practices or systems with another. For example, transitioning from conventional tillage to strip tillage could affect planting time. In this case, compare your control (conventional tillage at your usual planting date) to a treatment (strip tillage with a different planting date). You wouldn’t be able to tell whether the planting date or tillage caused the difference between your treatment and control, but you could see which practices worked better. To know which of these—tillage or planting date—caused the difference between your treatment and control, change only one practice in your treatment relative to your control. If you are more interested in the overall outcome of a set of practices, multiple differences between the control and experimental treatments are OK.

Do I always need a control?

When trying a new crop, you may not need a control. If your question is, “How does this crop perform on my farm?” you don’t need a control. You are looking at the crop itself. You are not comparing it to other options. If you are deciding between a new and current crop, include both options in your trial.

Case study: a hidden variable

A grower wants to try a new humic acid-based biostimulant reported to increase nutrient uptake in grass seed production. The grower applies the product to three test strips throughout the field and will measure yield with a combine monitor. To keep management consistent across both treatments, the grower applies their standard nutrient program to the whole field.

The grower observes greener and taller plants and higher yields in the three treated strips. But the grower realizes the biostimulant contains 10% nitrogen and 3% potassium. The grower unknowingly changed multiple variables instead of one. Did the benefits come from the additional N and P? Would the grower get the same effect by adding N and P rather than the more expensive biostimulant?

The grower repeats the experiment with both treatments receiving the same amount of N and P. When the biostimulant product is applied, the grower applies fertilizer to the control plot that matches the amount of N and P in the biostimulant.

Including replication in your experiment means that the experiment is repeated multiple times, with multiple plots for each treatment. Replication should be combined with randomization, meaning that you flip a coin or use some other random method to decide what treatment is applied to each plot. Replication and randomization increase your chances of detecting a difference between treatments if one exists.

Figure 2. Hypothetical plot layouts for an experiment comparing a Treatment (T) and a Control (C) in a field that was partially damaged by a pest outbreak (yellow area). Experiments with 1 (un-replicated), 2, 4 or 8 replicates are shown. Black lines show plot boundaries, with each plot labeled with the treatment and the percentage of the damaged plot area. Each replicate is outlined with a thick black line and contains one T and one C plot. Below each panel, the percentage of the area assigned to each treatment that was damaged is shown. As the number of replicates increases, the damage tends to affect the treatments more evenly.

Figure 2 shows how a hypothetical experiment with different numbers of randomly arranged replicates would be affected by a pest outbreak in part of the field. If the experiment is not replicated, the pest damage is likely to affect one treatment more than the other, leaving you wondering if any difference in yield was caused by the treatments or by the pest damage. As the number of replicates increases, the damage is more evenly distributed across the treatments, so it is less likely to be causing a difference between the treatments.

Field trials are typically replicated across a field at multiple locations or over multiple years. Replication in at least one of these forms is usually necessary to build confidence in the results. The number of replicates determines how large of a difference between treatments you will be able to detect. If the treatment you are testing might cause a crop failure or a very large yield reduction, two replicates, or even an unreplicated study, provide the information you need to decide not to use that practice. If a treatment increases yield by 5%, you need many more replicates to show that difference conclusively.

The ideal number of replicates depends on how much variability there is in your field and how much difference there is between the new management practice and your control, These are not easy to know in advance. You can add replications by repeating the experiment over multiple years.

Things to consider:

• If you are testing a major change to your management system, start with a small pilot study. If the practice shows promise, test it again the following year with more replicates and larger plots.
• If you are testing a minor change to your management and you want to gain more information in a shorter time, start with at least four replications. Repeat the following year if you want more data.

Case study: what can you learn from an unreplicated study?

A farmer wanted to try planting a fall cover crop but did not have time for a full replicated study with all their other fall field preparations. They decided to plant a cover crop in one field but not in an adjacent one. The next spring, they planted onions in both fields. When harvest came, the field with the cover crop had higher yields than the adjacent field.

The farmer was cautious when drawing conclusions from this unreplicated trial. They had grown different crops in the fields before planting the cover crop, so they didn’t know whether the yield resulted from the cover crop or other pre-existing differences, such as soil type or prior management. Still, the grower felt they had gained useful information. The cover crop was successful, and the following crop performed well. If they have time this year, they will plant a cover crop in half of each field, leaving the other half as a check, for an experiment with two replicates.

Controlled and replicated trials are the best way to test new practices. But this approach may require more time and effort than you want. As long as you are cautious about your conclusions, unreplicated trials can help determine which treatments to investigate further. Never adopt a new whole-farm practice based on an unreplicated test.

How many replicates do I need?

Researchers in the dryland grain production region of the Inland Pacific Northwest wanted to know how many replicates were needed for on-farm trials. They used plots with one combined header (16–25 feet) wide with various lengths but did not apply any treatments. This experiment was replicated on 14 dryland wheat and barley fields across Oregon, Washington and Idaho. By comparing the differences in yield between adjacent plots, the researchers could measure how much variability there was in these fields. Increasing the plot length helped reduce the variability between neighboring plots. Based on their results, four replicates of plots 1,250 feet long in a lower variability field would be needed to detect a 4-bushel (about 8%) yield difference between treatments 80% of the time. These numbers will vary depending on your cropping system and combine header width, but four replicates are a good place to start. Longer plots produce better results than shorter plots, with little additional work. To get a better feel for the variability on your farm, compare the yields in adjacent combine passes in a uniform field area.

In field trials, divide plots into groups or “blocks” of adjacent plots with similar characteristics. You should end up with one block per replication of your experiment, and each block should have one plot for each treatment. Within each block, randomly choose what treatment goes in which plot. This helps evenly distribute the plots of each treatment across the field. If you have two treatments, you can flip a coin to choose which treatment goes in the first plot in the block and assign the other treatment to the second plot. For more treatments, you can draw treatments from a hat or use a random number generator.

A plot layout is the arrangement of plots across the field. When planning a plot layout, consider the plots' size and shape, the number of treatments and replications, and any variation in the field. If the number of replicates is the same, larger plots will give you a better idea of how the treatment performs at the field scale than smaller plots.

Choose plot dimensions that are easy to manage and harvest with your equipment. Long, narrow plots often work well. The plot width is determined by equipment widths. The plot can be the full length of the field. Treatments can bleed over into neighboring plots, causing the plot edges to behave differently than the centers. Products with a sprayer or spreader can drift onto neighboring plots, and the plot edges can behave differently than the centers without an obvious cause. Avoid these problems by including buffers on the plot edges where no data is collected. Since harvest equipment headers are often much narrower than spray booms or fertilizer spreaders, make the plot width match the width of your application equipment and the yield measurement area match your harvest equipment. If your application equipment is narrower than your harvest equipment, avoid skips and overlaps in the yield measurement area. Figure 3 shows an example of easy-to-manage plots with buffer areas.

Once you know the number of plots you need (number of treatments times number of replicates) and the dimensions of those plots, look for a field for the experiment. The ideal location has minimal variability and is average compared to the rest of your farm. Evaluate known variations across your farm or field. What parts of your field or farm tend to have higher or lower yields? Soil type, topography or microclimate cause variability that could interfere with your experiment. The tops of hills often differ from the bottoms of slopes. Northern exposures will tend to be cooler than southern exposures. Leveled fields can have variations caused by topsoil being removed from some areas and added to others. Edge effects are also a concern along the borders of fields, especially where it is difficult to broadcast fertilizers uniformly.

Figure 3a. An example of a simple and efficient layout for plots on farms. Treatments are applied to a full plot width, and yield measurements are made by harvesting a swath down the center of each plot. Any skips or carryovers between treatments will occur in the buffer areas on either side of the plot and will not affect the yield results. The yield measurement area of a plot should be harvested before the buffers to ensure that the full swather or combine header width is being cut. If treatments are applied with a sprayer, the plot width should equal the spray boom width. If a fertilizer spreader is used, plot widths should match the area covered by a spreader pass. If your equipment allows, plot widths that are slightly (1–2 feet) less than double the width of your harvesting equipment are efficient to harvest because the buffer areas between plots can be harvested in a single pass without leaving a narrow strip. Plots can be any length, up to the full length of the field. Figure 3b. The path a swather would take when harvesting plots. The center of each plot should always be cut before the buffer area for that plot.

Take detailed notes of your experiment. Before you start, write down a plan and note changes you make during the experiment. During the growing season, make and record your observations. Some important information to record includes:

• Plot maps that show which treatment was applied to each plot.
• Descriptions of the management used for each treatment, especially how treatments were managed differently.
• Costs associated with differences in management.
• Details of all management activities.
• Observations, including weather, any visible differences between the plots, pest or disease outbreaks, etc.
• Photos taken regularly (weekly or monthly).
• Measurements from each plot (keep the raw data, not just averages).
• Dates of management activities, field observations and weather events

Taking notes can seem time-consuming, but it is less work than repeating the study because you can’t remember which plot was which. Frequent observations and notes can lead to new insights you were not expecting.

Getting useful information from an experiment requires accurate data-collection methods, especially for yield. For crops harvested with a combine, yield monitors can give approximations of yield, but these estimates can be off by 7% or more, and errors can be much higher if you do not calibrate the yield monitor. Yield monitor accuracy is especially poor in crops that must be cleaned or dried before being sold. In grass seed, for example, the amount of cleanout during seed cleaning can vary from treatment to treatment, but a yield monitor would miss those differences. If you use a yield monitor, be sure to calibrate it.

A weigh wagon is a better way to measure yield. For each plot, harvest an area with known dimensions, then transfer the harvested crop into the weigh wagon to measure the weight. Check that the harvest equipment is empty before you harvest your first plot, and empty it between plots. You can get a small sample of the harvested crop from each plot to measure cleanout, moisture or quality. Calculate the area of the plot in acres, then divide the harvested weight by the plot area to get yield.

Yield calculation example: A combine with a 16-foot header was used to harvest a 100-foot strip. The harvested crop weighed 68 pounds.

Calculate how much area was harvested: 16 feet X 100 feet = 1,600 square feet

Convert the area in square feet to acres: 1,600 square feet ÷ 43,560 ft 2 /ac = 0.037 ac

Divide the harvested crop weight by plot area in acres: 68 lb ÷ 0.037 ac = 1,838 lb/ac

Once you have collected your data, you must interpret your results. Even well-designed experiments can produce misleading data if not carefully interpreted. For example, two identical treatments might appear different due to random chance, Likewise, random variability might hide a difference. Researchers often use replication and statistical analysis to understand an experiment’s random variability. When treatments differ by more than expected due to random chance, the difference is “statistically significant.” This means that it is reasonable to conclude that the difference is a real effect of the treatments rather than an artifact of random chance.

Deciding if you found a difference between treatments is an important step in interpreting your results, but common sense is also required. Consider your farm management when interpreting your data. How much better do you need that treatment to perform before you are willing to adopt it on your whole farm? The treatment you tested might cost more or take longer than your control or standard management practice. If a treatment saves money or time, a slight decrease in yield might even be acceptable.

In this section, we examine several hypothetical experiments (Figure 5) and explain the process for interpreting results. This should help you decide if your results show a clear difference between treatments, no difference between treatments, or insufficient information to conclude. We will use the same ideas that statisticians use, but we will not discuss how to do the mathematical calculations used in statistics. To learn more about statistical analysis, check out the resources section or reach out to your county Extension agent.

The average of several replicates is your best estimate for how a treatment performs, so start evaluating your results by averaging the replicates from each treatment. In Figure 5, these averages are shown by the dashed and dotted lines. Next, look at the difference between the averages of the treatments. If you were to test two treatments that did not differ, random chance is likely to produce small differences between the treatment averages in an experiment but less likely to cause big differences.

Figure 5. Experimental results for three different hypothetical experiments with four replicates. For each experiment, data from the Control is in red, while the Treatment is in blue. Each point represents the yield measured in one plot. The dashed red lines show the average yield for the Control plots in the experiment, while the dotted blue lines show the average for the Treatment plots. Graphs a, b and c show Experiments 1-3 respectively with one year of data, while d shows Experiment 3 after two additional years of data were collected. The results of Experiment 1 (a) suggest no meaningful difference between the treatments. Experiment 2 (b) shows strong evidence that Treatment 2 increased yield relative to the control. This would be considered a statistically significant difference. The results of Experiment 3 (c) are inconclusive after the first year. On average, Treatment 3 yielded 10 bushels more than the control, but because the yields varied widely, we cannot rule out the possibility that this difference was caused by random chance. The result is not statistically significant. Repeating Experiment 3 for two more years (d) showed that Treatment 3 consistently produces higher yields than the Control. With three years of data, there is strong evidence that Treatment 3 yields more than the control, and the result is statistically significant.

In Experiment 1 (Figure 5a), the average yield of the control plots was 130 bushels, while the average of the treatment plots was 132 bushels, a difference of 2 bushels. This small difference may be a result of random chance. In Experiment 2 (which tested a different treatment, Figure 5b), the control yielded 130 bushels on average, while the average yield of the treatment was 140 bushels, 10 bushels more than the control. The larger difference observed in Experiment 2 is likely caused by a difference between the treatments. It is a large enough difference that the increase in yield could pay for increased input costs.

The next step in analyzing your data is to decide how well the averages you measured in your experiment reflect how the treatments will perform (that is, if you were to use that management across your whole farm for many years). You can do this by looking at the amount of variability between replicates of the same treatment and the number of replicates.

Both Experiment 2 (Figure 5b) and Experiment 3 (Figure 5c) had the same difference between the average of the control and treatment, but the amount of variability was different. In Experiment 2, every treatment plot yielded higher than every control plot — strong evidence that you would see an increase in yield if you adopted the treatment across your farm. Experiment 3 is another story, with the yields from individual plots spread across a wide range. Results like those in Experiment 3 (a meaningful difference between the treatment averages but high variability among the individual plots) indicate that you should gather more data before concluding. Consider repeating the experiment for another year, possibly with more replications. Experiment 1 also has high variability, which combined with the minimal difference between the treatment averages. So, there is little evidence that the treatment had a meaningful effect on yield.

Figure 5d shows the results of Experiment 3 after two more years of data collection. The treatment continued to produce higher average yields than the control in both years, which should increase your confidence in that result. Repeating the experiment essentially increased the number of replications from three to nine. If you have more replicates, variability is less likely to affect the overall average of those replicates, so you can be more confident in the treatment averages than if you had fewer replications.

Key points:

• On-farm experiments can give you confidence about which new or novel practices you should adopt on your farm.
• Choose a field or site for your trial which is uniform and representative of most of your farm.
• When you try a new product or practice, ensure you have a “business as usual” control to compare to.
• Before you begin, ask yourself, “How will I know this new practice is better? What will I look for, and what will I measure?”
• If possible, replicate your treatments and controls so you can be confident that the results you see were caused by the treatments rather than some unknown variability in your field or unexpected pest outbreak.
• Take good notes, use marking flags, and take photographs so you don’t forget the important details.
• These types of on-farm trials can be quite interesting for science-minded Extension agents. They might be able to help with the design, measurements, or interpretation.

Resources and references

Chaney, D. 2017. How to Conduct Research on Your Farm or Ranch . Sustainable Agriculture Research and Education Technical Bulletin.

Colley, M., J. Dawson, J. Zystro, K. Healy, J. Myers, H. Behar and K. Becker. 2018. The Grower’s Guide to Conducting On-farm Variety Trials . Organic Seed Alliance.

Hilshey, B., S. Bosworth and R. Gilker. 2013 . A Practical Guide to On-farm Pasture Research . Sustainable Agriculture Research and Education.

Kyveryga, P. M., T.A. Mueller, N. Paul, A. Arp, and P. Reeg. 2015. Guide to On-Farm Replicated Strip Trials . Iowa Soybean Association.

Luck, B.D. 2017. Calibrate Your Yield Monitor for Greater Accuracy During Harvest . University of Wisconsin-Extension publication A4146.

Tarasoff, C. 2016. A Guide to On-Farm Demonstration Research – How to Plan, Prepare and Conduct Your Own On-Farm Trials . British Columbia Forage Council.

Farm Stat – Statistics Calculator for On-farm Trials . University of Nebraska – Lincoln.

Grower’s Guide to On-Farm Research . Nebraska On-Farm Research Network.

Carmer, S.G. and W.M. Walker. 1988. Significance from a statistician's viewpoint. Journal of Production Agriculture , 1(1), 27-33.

Wuest, S.B., B.C. Miller, J.R. Alldredge, S.O. Guy, R.S. Karow, R.J. Veseth, and D.J. Wysocki. 1994. Increasing plot length reduces experimental error of on‐farm tests. Journal of production agriculture , 7(2), 211-215.

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On-Farm Experimentation to transform global agriculture

• Myrtille Lacoste   ORCID: orcid.org/0000-0001-6557-1865 1 , 2 ,
• Simon Cook   ORCID: orcid.org/0000-0003-0902-1476 1 , 3 ,
• Matthew McNee 4 ,
• Danielle Gale   ORCID: orcid.org/0000-0003-3733-025X 1 ,
• Julie Ingram   ORCID: orcid.org/0000-0003-0712-4789 5 ,
• Véronique Bellon-Maurel 6 , 7 ,
• Tom MacMillan   ORCID: orcid.org/0000-0002-2893-6981 8 ,
• Roger Sylvester-Bradley 9 ,
• Daniel Kindred   ORCID: orcid.org/0000-0001-7910-7676 9 ,
• Rob Bramley   ORCID: orcid.org/0000-0003-0643-7409 10 ,
• Nicolas Tremblay   ORCID: orcid.org/0000-0003-1409-4442 11 ,
• Louis Longchamps   ORCID: orcid.org/0000-0002-4761-6094 12 ,
• Laura Thompson   ORCID: orcid.org/0000-0001-5751-7869 13 ,
• Julie Ruiz   ORCID: orcid.org/0000-0001-5672-2705 14 ,
• Fernando Oscar García   ORCID: orcid.org/0000-0001-6681-0135 15 , 16 ,
• Bruce Maxwell 17 ,
• Terry Griffin   ORCID: orcid.org/0000-0001-5664-484X 18 ,
• Thomas Oberthür   ORCID: orcid.org/0000-0002-6050-9832 19 , 20 ,
• Christian Huyghe 21 ,
• Weifeng Zhang 22 ,
• John McNamara 23 &
• Andrew Hall   ORCID: orcid.org/0000-0002-8580-6569 24  

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Restructuring farmer–researcher relationships and addressing complexity and uncertainty through joint exploration are at the heart of On-Farm Experimentation (OFE). OFE describes new approaches to agricultural research and innovation that are embedded in real-world farm management, and reflects new demands for decentralized and inclusive research that bridges sources of knowledge and fosters open innovation. Here we propose that OFE research could help to transform agriculture globally. We highlight the role of digitalization, which motivates and enables OFE by dramatically increasing scales and complexity when investigating agricultural challenges.

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Acknowledgements

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

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

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

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Transparency is important to us at the College of Agricultural and Environmental Sciences. Below you'll find a list of the funding sources that provide us with the tools to continue researching topics of importance to California and beyond.

$-.- Million - The Impact

Research in the UC Davis College of Agricultural and Environmental Sciences has an impact on California and beyond. Check out these highlighted articles for a more in-depth look at what we are doing in the fields of agriculture, the environment and the human and social sciences.

Mechanical Crop Harvest

Strawberry Breeding Program

Beverage Consumption Attitudes

Reducing Cattle Methane Emissions

Cultural Biases Impact Native Fish

Controlled Environment Agriculture

Managing Landscapes with Sheep Mowers

Identifying Food Biomarkers

Smoke-Taint Research During California Wildfires

Innovative Cooling Cubes

A prototype helper robot transporting fresh-picked strawberries.

Mechanical Crop Harvesting

Department: Biological and Agricultural Engineering

Harvesting crops and fresh market fruit is among the most labor-intensive elements of any agricultural operation in terms of cost and dependence on a seasonal and sometimes semi-skilled workforce.

Agricultural robotics can automate and simplify many processes, speeding up harvesting, reducing labor needs and increasing efficiency. Researchers used camera-based sensing, conventional picking bags and an algorithm to maximize performance and were successful in creating a functional robotic orchard platform that achieved up to 25 percent faster harvesting speeds compared to the standard non-automated platform.

UC Davis “Valiant” strawberry among new varieties developed by the Strawberry Breeding Program.

Department: Plant Sciences

Plant breeders are using genetics to create new varieties of germplasm that satisfy the needs of consumers, who want the fruit to be bright red, sweet and juicy; and growers, who want plants to be high-yielding and resistant to diseases. For more than 70 years, our researchers have revolutionized the strawberry industry by developing prominent strawberry varieties that support a market that leads to nearly $3 billion in sales annually.

Strawberries are vulnerable to soil-borne pathogens, which can destroy plants and greatly reduce yield. The goal of the Strawberry Breeding Program is to develop new varieties of strawberry plants that have higher quality berries, are less vulnerable to pests and diseases and can be grown more efficiently. CA&ES researchers have released seven new varieties in the past two years.

Student looks through the many choices of soft drink available at the Memorial Union.

Human and Social

Department: Human Ecology

Reducing the amount of sugar-sweetened beverages consumption has become a major public health priority. Sugary drinks have been shown to contribute to weight gain and obesity, tooth decay, type 2 diabetes and cardiovascular disease.

Researchers found that simple, bright yellow warning labels on beverages like fruit-flavored drinks, sweetened teas and flavored milk in a college cafeteria helped students reduce their reported consumption of drinks by 14.5 percent. The results signal that such labels could reduce sugar consumption in larger settings.

Cattle feed containing seaweed was given to dairy cows at UC Davis.

Environment

Department: Animal Science

As cattle digest their food throughout the day, they burp and exhale methane, a potent heat-trapping gas.

Adding a touch of seaweed to cattle feed can dramatically cut greenhouse gas emissions from dairy cows and livestock by up to 80 percent. This discovery can help California’s farmers meet new carbon emission standards to reduce methane by 40 percent by 2030, receive carbon credits using emission reduction protocols, and produce food sustainably to help feed the world.

Bowfin fish are native to North America; photo credit: Solomon David, Nicholls State University.

Department: Wildlife, Fish and Conservation Biology

Colonialist attitudes toward native fishes are rooted in elements of racism and sexism, and those attitudes continue to shape fisheries management today, often to the detriment of native fishes. Nearly all states have policies that encourage overfishing native species. Up to half of fish species globally are in some form of decline and 83 percent of native California fish species are declining.

Native fishes help ecosystems in many ways, including nutrient cycling and food chain support for other native species. Researchers offer several recommendations for how anglers and fisheries managers can shift to a new paradigm for integrating changes, including indigenous perspectives, bag limits, and supporting science on native fish.

Department of Plant Sciences Chair Gail Taylor inside the school’s vertical farming laboratory.

Traditional agricultural methods could be disrupted by drought, flood and extreme weather as climate change intensifies. Growing crops in a closed-loop system where lighting, water and ventilation are controlled provides a way to maintain food sources while also avoiding climate instability and reducing risk of foodborne pathogens.

Inside a 40-foot long shipping container, students are learning how to be next-generation farmers with skills in engineering, computer science and growing crops such as lettuce and other leafy greens. This laboratory helps us to better understand hydroponic growing systems to provide nutrition to a growing population.

Sheep graze on UC Davis campus.

This ongoing research project aims to determine if sheep, which intermittently graze a lawn on campus near the UC Davis Arboretum, can eat weeds and grass, fertilize the land and control pests as well as or better than using conventional landscaping methods.

Incorporating sheep into an existing urban green space has the potential to offer multiple operational, environmental and social benefits.

Students participating in a diet comparison project in the Nutrition department.

Department: Nutrition, Food Science and Technology

The foods we eat can directly affect overall health and disease risk. But it’s not one size fits all and new methods are needed to track how an individual responds to food.

The Dietary Biomarkers Development Center is using clinical trials to identify specific food biomarkers, which are indicators of what someone has eaten. The findings could lead to support personalized nutrition and culinary medicine.

Anita Oberholster, a viticulture and enology professor of cooperative extension, samples grapes that have been tainted by smoke from wildfires.

Department: Viticulture and Enology

California wildfires have been especially hard on the state’s wine industry, damaging billions of dollars in property and grapes; wildfires of 2020 alone cost the wine industry $3.7 billion in lost property, wine inventory, grapes and wine production. When grapes are exposed to smoke, they can sometimes impart unwanted flavors into finished wine and thus must be assayed to determine if they can be used.

With existing testing labs stretched beyond capacity, CA&ES partnered with CDFA to provide hundreds of growers and vintners with a rapid assay to test wine grapes for smoke taint and outreach support on how to quickly ferment grapes in small batches during COVID-19 lockdown to test for smoke exposure.

Jiahan Zou, a Ph.D. graduate student in the Department of Food Science and Technology, holding a new form of ice cube that doesn't melt, is reusable, plastic-free and compostable.

Department: Food Science and Technology

Ice is used to keep foods fresh but can lead to cross-contamination as it melts, potentially passing microbes to other foods or down the drain.

CA&ES researchers have developed a jelly ice cube that doesn’t melt, is reusable and antimicrobial. The jelly ice cubes offer an alternative to traditional ice and could potentially reduce water consumption, environmental impact and help manage food waste by controlling microbial contaminations.

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• Experimental Agriculture

Experimental Agriculture

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We’re delighted to announce that all articles accepted for publication in Experimental Agriculture   from 29 November 2024 will be ‘open access’; published with a  Creative Commons  licence and freely available to read online (see the journal’s  Open Access Options  page for available licence options).  We have an OA option for  every  author:  the costs of open access publication will be covered through  agreements between the publisher and the author’s institution , payment of APCs from grant or other funds, or else waived entirely, ensuring  every  author can publish and enjoy the benefits of OA.  

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• ISSN: 0014-4797 (Print) , 1469-4441 (Online)
• Editor: Dr Rafael Ribeiro University of Campinas (UNICAMP), Brazil
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Latest articles

Tailoring interventions through a combination of statistical typology and frontier analysis: a study of mixed crop-livestock farms in semi-arid zimbabwe.

• Frédéric Baudron , Sabine Homann-Kee Tui , João Vasco Silva , Irenie Chakoma , Dorcas Matangi , Isaiah Nyagumbo , Sikhalazo Dube
• Experimental Agriculture , Volume 60

Drought responses in Coffea arabica as affected by genotype and phenophase. II – photosynthesis at leaf and plant scales

• Miroslava Rakocevic , Evelyne Costes , Eliemar Campostrini , José Cochicho Ramalho , Rafael Vasconcelos Ribeiro

Exchangeable molybdenum concentration in lowland paddy fields of Sri Lanka as affected by the differences in agro-climatic zones, soil orders, and water sources

• Indeera Hettiarachchi , Mojith Ariyaratne , Upul Rathnayake , Ranga Madushan , Harsha Kadupitiya , Rohana Chandrajith , Lalith Suriyagoda

Characterisation of bitter vetch ( Vicia ervilia (L.) Willd) ecotypes: An ancient and promising legume

• Soumaya Boukrouh , Ali Noutfia , Nassim Moula , Claire Avril , Julien Louvieaux , Jean-Luc Hornick , Mouad Chentouf , Jean-François Cabaraux

Optimizing soil and plant functions: combinatory design of fertilizing resources assemblage for rainfed rice in Madagascar

• Manoa Raminoarison , Benoît Jaillard , Tantely Razafimbelo , Koloina Rahajaharilaza , Eric Blanchart , Jean Trap

Achieving super high yield in rice by simultaneously increasing panicle number and grain weight via improving pre-heading biomass production

• Min Huang , Zhengwu Xiao , Shengliang Fang , Hengdong Zhang , Longsheng Liu , Fangbo Cao , Jiana Chen

Low soil phosphorus availability has limited effects on wood traits in young plants of five eucalypt species

• Franklin Magnum de Oliveira Silva , Helena Augusto Gioppato , Alexandre Augusto Borghi , Sara Adrián López Andrade , Paulo Mazzafera

Impact of soil type and harvest season on the ratooning ability of sugarcane varieties

• Njabulo Eugene Dlamini , Angelinus C. Franke , Marvellous Zhou

Agriculture Blog Posts

International Day of Awareness of Food Loss and Waste

• 28 September 2024, Renewable Agriculture and Food Systems
• September 29th is designated as the International Day of Awareness of Food Loss and Waste by the United Nations General Assembly and is co-convened by the Food...

Cultivating Change: Indigenous Strategies for Sustainable Food Systems

• 09 August 2024, Renewable Agriculture and Food Systems
• Friday, August 9th was designated as the International Day of the World’s Indigenous Peoples in December of 1994 by the UN General Assembly resolution 49/214.…...

Energy supplementation of beef steers or inclusion of legumes in temperate pastures in crop-livestock integration area

• 31 July 2024, Luís Fernando Glasenapp de Menezes
• The intensification of production systems has become essential, as the profit margin in beef cattle farming has been steadily decreasing, requiring producers...

The Concept of a ‘Land Equivalent Ratio’ and Advantages in Yields from Intercropping

• R. Mead , R. W. Willey
• Experimental Agriculture , Volume 16 , Issue 3

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Louisiana Agricultural Experiment Station

Frances Gould

Rice harvest

The Louisiana Agricultural Experiment Station is the research arm of the LSU AgCenter. It is composed of academic departments , where experiment station scientists conduct research and hold joint teaching appointments in the LSU College of Agriculture , and research stations across Louisiana, where scientists develop new knowledge and technology to help our producers provide our citizens and those of the nation with a vast array of food, fiber and fuel.

The experiment station also includes the Audubon Sugar Institute , which focuses on sugar processing and biofuel production, and the Department of Agricultural Chemistry , which is jointly operated with the Louisiana Department of Agriculture and Forestry and focuses on analyses of feed, fertilizer, food and pesticides for regulatory and research purposes.

The need for research from state agricultural experiment stations is as important now as it has ever been. The constantly expanding world population demands that we increase our food and fiber production. The key to sustaining agricultural production to meet the expanding population is through research to refine existing technologies, develop new technology and advance knowledge. As agricultural research and development meets the challenges of the future, we must be mindful of the environment. All of us want cleaner water and air, sustainable forests, a stable coastline and other outdoor amenities.

Agricultural research in Louisiana is dynamic, and its breadth is extensive. The traditional areas remain the core programs in the Louisiana Agricultural Experiment Station. These areas are plant variety development, improved animal and plant production, environmentally sound pest management, forest management and wood product development, natural resources conservation and management, food technology and safety, and agricultural economics. However, offshoots of traditional research have led to exciting discoveries in human disease diagnostics and treatment, value-added products for oil drilling operations and recycling technology for contaminated wood products.

The research conducted by experiment station scientists contributes substantially to economic development in our state. Too often, these contributions are taken for granted. For example, the rice industry that sustains much of the economy of southwest Louisiana has benefited greatly from the research emanating from the Rice Research Station and several departments on campus. For more than 100 years, research at the station has brought forth new, improved varieties, better cultural practices and improved pest control. The same case could be made that experiment station research has allowed the sugarcane industry to continue to be a mainstay of our state’s economy.

Two specific events clearly illustrate the role of our organization in economic development in Louisiana. Lamb Weston, a division of ConAgra Foods, Inc., built a plant in northeast Louisiana to process sweet potatoes. Lamb Weston is the unit of ConAgra involved in producing sweet potato products (french fries and other items) for grocery stores and large restaurant chains. When state officials and industry leaders announced the plans to build the plant, they cited as one of the four reasons for locating in Louisiana was the Sweet Potato Research Station in Chase, La., near the proposed plant site in Delhi, La.

Another example of direct contribution to economic development involves a technique, patented by the LSU AgCenter, which can be used to turn used plastic motor oil containers and wood waste into a strong composite material that can be used in construction. When the material is added to the “mud” used in the oil drilling business, the material prevents the drilling mud from seeping away from the drill as an oil well is being dug. A start-up company licensed the product and markets it to energy companies. The product is being made by a moulding and millworks company in rural Louisiana, which has hired back employees laid off because of the housing slump to manufacture the product. This illustrates how research led to a new start-up company, allowed an existing company to rehire employees, developed a value-added product from waste material (while protecting the environment) and aided another major industry -- oil exploration -- in our state.

Have a question or comment about the information on this page?

Email frances gould, innovate . educate . improve lives.

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6 Fun Agriculture Science Experiments For Kids

By Sarah Hunt, AZFB Communications Intern

Looking for fun activities to occupy your kids this summer and help them learn something new? We have six different ag-related science experiments that will do just the trick!

• Grow an Avocado Tree

This experiment is so easy, and the kids will love watching their little avocado tree sprout and grow in your kitchen! With enough time, patience, sunlight, and water, you’ll be potting this plant in about a month.

• Greenhouse in a Soda Bottle

With the greenhouse like environment of a soda bottle, seeds will sprout much quicker with the retained moisture inside the bottle. Kids will enjoy helping put this little project together and watching the seed’s progress each day.

• Color Changing Flowers

I remember doing this experiment in elementary school and loving it! All you need is some white flowers, scissors, a cup, water, and some food coloring. Plus it makes a nice colorful centerpiece that your kids can make for your kitchen table!

• Ice Cream in a Bag

This is another one of my childhood favorites. It’s very simple and tasty, but can be time consuming. Make sure you have plenty of hands on deck to help you shake the bag so no one’s hands get too cold from the ice and rock salt!

• Make a Rubber Egg

This is a popular one! I did this years ago and thought it was so cool. Your kids will enjoy seeing the eggshell disappear over the allotted time and find this transformation fun and fascinating.

• Hydroponics

This one will take a little longer, but hey, you’ve got two whole months of summer break to do this with, right? This experiment will teach your kids how to grow plants in nutrient rich water instead of soil! It gives them a responsibility to check on it every day and helps them see another way that we can grow the food we eat every day.

Find more fun summer experiments and activities for kids on Fill Your Plate’s blog!

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Alabama Agricultural Experiment Station

Research Funding & Resources  /

The Alabama Agricultural Experiment Station conducts a variety of research to support the state’s agricultural industry and economy. AAES research scientists are from Alabama A&M University, Auburn University and Tuskegee University and from extension centers across Alabama. Research is also coordinated with scientists at other state universities, at universities throughout the southeast and at USDA Agricultural Research Service locations throughout the state and region. AAES research is conducted at 16 research centers — two in and near Auburn, and 14 throughout Alabama.

AAES research is supported by the National Institute of Food and Agriculture , whose mission is to advance knowledge for agriculture, the environment, human health and well-being and communities by supporting research, education and extension programs in the Land-Grant University System and other partner organizations.

Regional Centers

Plant science research center - auburn.

The only Alabama Ag Experiment Station research unit on the AU campus, the Plant Science Research Center offers scientists the opportunity to work in a glass greenhouse structure with state-of–the-art climate and environmental controls and an advanced data acquisition system. Current research areas include field crops, biofuels and vegetable and ornamental plant production.

Learn more about the  Plant Science Research Center

E.V. Smith Research Center

Located on Interstate 85 between Auburn and Montgomery, E.V. Smith Research Center is the most visible agricultural facility in Alabama. The only facility named for a former AAES director—Edwin V. Smith who served at Auburn from 1929 to 1972—it is also the largest and most comprehensive with research units in dairy cattle, beef cattle, horticulture, plant breeding, field crops and biosystems engineering.

Learn more about the E.V. Smith Research Center

Tennessee Valley Research & Extension Center - Belle Mina

The Tennessee Valley Research and Extension Center is located just north of the Tennessee River, a stone’s throw from Interstate 65, right in the heart of Alabama’s top cotton-producing region and directly on the cutting edge of cotton research. Though TVREC research projects frequently target corn, soybeans and wheat, cotton claims the lion’s share of attention, and it is for its cotton research that the center is nationally recognized.

Sand Mountain Research & Extension Center - Crossville

Agriculture is the economic lifeblood of northeast Alabama’s rural Sand Mountain region, and for eight decades now, the Sand Mountain Research and Extension Center has played a key role in strengthening the area’s farm sector.

The goal of SMREC research is to develop farm management practices that will help the region’s livestock, poultry, row crop and commercial vegetable producers—particularly small-scale farmers—to operate more efficiently, sustainably and profitably.

North Alabama Horticultural Research Center - Cullman

The name of the North Alabama Horticultural Research Center east of Cullman is a dead giveaway as to what the 159-acre center is all about. Its sole role is to conduct scientific studies and generate research data that benefits large- and small-scale commercial fruit and vegetable producers in the state’s northern counties.

The NAHRC is one of only two Alabama Ag Experiment Station outlying units with certified organic research plots and uses those plots almost exclusively for studies on organic production of the area’s top three vegetable crops—fresh-market tomatoes, peppers and sweet potatoes.

Upper Coastal Plain Agricultural Research Center - Winfield

The uneven terrain of the Upper Coastal Plain Agricultural Research Center could be challenging for row crop production, but for the cattle, pastureland and forage crop management research that takes center stage at the UCPARC, it’s ideal. The 735-acre UCPARC is located in northwest Alabama’s Marion County.

CHILTON RESEARCH & EXTENSION CENTER - Clanton

It’s fitting that the Chilton Research and Extension Center is located in the shadow of Clanton’s famous giant peach water tower just off of I-65, because at the CREC, peaches rule. When the center was established in 1944, its mission was to identify superior varieties and develop new management techniques that would help Chilton County’s peach producers maximize their profits. And that’s still its mission today. The CREC does work with other small fruits—most notably kiwifruit—but peaches have priority.

PIEDMONT RESEARCH UNIT - Camp Hill

A four-mile-long, eight-foot-high, fixed-knot steel fence constructed in 2007 at the Piedmont Research Unit in Camp Hill completely encloses 430 of the unit’s acres, and that land plus the deer that were on it when the final section of the fence went up comprise what has been dubbed the Auburn University deer lab.

In an exhaustive, multidisciplinary long-term study of the lab’s 40 captive white-tail herd, researchers will generate detailed information on deer biology and behaviors that is expected to significantly impact the scientific management of deer.

PRATTVILLE AGRICULTURAL RESEARCH UNIT - Prattville

Since 1928, the Prattville Agricultural Research Unit has specialized in small-plot research on traditional row crops, primarily cotton. But in 2008, the unit stepped outside the box and began construction of a wildflower garden. The garden is named in honor of Lady Bird Johnson, who until she was a young lady spent her summers with relatives in Autauga County and who later, as the nation’s first lady, was recognized for her environmental conservation and landscape beautification work.

BLACK BELT RESEARCH & EXTENSION CENTER - Marion Junction

Rolling prairie land and unique soil formations make the Black Belt Research and Extension Center in Marion Junction an excellent location for research on beef cattle and forages. The BBREC’s primary research focus is in the areas of grazing and animal breeding.

WIREGRASS RESEARCH & EXTENSION CENTER - Headland

A variety of soil types and land characteristics typical of the Coastal Plain allow the Wiregrass Research and Extension Center to address problems that challenge farmers in this region. While the center has been a leader in all types of agronomic research, it is renown for its focus on peanuts.

BREWTON AGRICULTURAL RESEARCH UNIT - BREWTON

At the Brewton Agricultural Research Unit, the research emphasis is on woody ornamentals, flowering annuals and vegetables, and the research is carried out with homeowners and landscapers in mind. Because hydrangeas, crape myrtles and shrub roses have been favorites for generations, many BARU studies focus on identifying production practices that can help the trio stay healthier and bloom longer and more prolifically.

The center also is involved in bioenergy research, with studies on switchgrass, bluestem and sorghum for use in biofuel production.

ORNAMENTAL HORTICULTURE RESEARCH CENTER - Spring Hill

The Ornamental Horticulture Research Center in Mobile is located in an area of Alabama where commercial nurseries abound, and that’s an ideal site for an applied-research facility dedicated primarily to supporting the commercial container-nursery industry. Research at the OHRC focuses on helping producers identify and address pest, disease and other production problems early on. At 17 acres, the OHRC is the smallest among the Alabama Ag Experiment Station’s outlying units.

GULF COAST RESEARCH & EXTENSION CENTER - Fairhope

With its location one mile east of Mobile Bay and 30 miles north of the Gulf of Mexico, the Gulf Coast Research and Extension Center in Fairhope enjoys a climate that’s ideal for a highly diversified research program, and the GCREC takes full advantage of that. The long growing seasons and mostly mild winters allow for research on all of Alabama’s major row crops as well as on turfgrass, vegetable and fruit crops, pecans, beef cattle and forage. The GCREC’s top goal is to help southwest Alabama producers maximize their income.

New Research

Researchers fight high mortality rate of largemouth bass

Research office, research funding & resources, colloquium series, grant proposal development guide, research focus areas, al agricultural experiment station (aaes), centers & institutes, student research.

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The Agricultural Research Service: A History of Innovation

This year marks the Agricultural Research Service's (ARS) 70th anniversary, making it a particularly appropriate time to look back at the founding of this revolutionary agency and its scientific accomplishments.

To fully appreciate the importance of ARS, we have to rewind to 1862, long before ARS was established. In 1862, the United States Department of Agriculture (USDA) was founded, and the history of U.S. federal agricultural research began.

Old USDA Administration Building looking South.

On May 15, 1862, during the American civil war, President Abraham Lincoln signed a bill establishing USDA – what he called "the People's Department". This bill was followed by the Morrill Land Grant College Act , granting each state 30,000 acres of land for the construction of "agricultural and mechanical schools" leading to the creation of land-grant colleges.

USDA grew out of the United States Patent Office’s Agricultural Division, which had been established in 1839 with the mission to acquire, propagate, evaluate, and distribute seeds and plants, and to collect agricultural statistics and production information.

On July 1, 1862, Isaac Newton, then chief of the Agricultural Division, was appointed by President Lincoln as the new Department's first commissioner. In the beginning, Newton headed a staff of 38 employees in the basement of the U.S. Patent Office. However, by 1889, the USDA occupied its own building with over 400 employees and had achieved cabinet status.

The passing of the Hatch Act in 1887 (and the establishment of the Office of Experiment Stations in 1888) provided $15,000 annually for the establishment of one or more agricultural experiment stations in each state with the purpose of conducting agricultural research and "dispersing useful and practical information connected with agriculture." Dr. Wilbur O. Atwater was the first director of the Federal Office of Experiment Stations — these stations became essential to agricultural research and development. 

Early USDA research wasn't solely in the agricultural realm. Scientists also worked diligently on human nutrition research.  

In May 1894, Atwater became USDA's first chief of nutrition and went on to oversee more than 300 food studies in 17 states. He was widely regarded as the father of modern nutrition research and education. To this day, calorie values for foods are calculated using the Atwater system — each gram of protein is said to contain 4 calories, each gram of fat 9 calories, and each gram of carbohydrates 4 calories.  

In the beginning, much of early research in USDA was conducted by scientists in state labs that later became part of the new scientific agency. Much of that research continues to benefit farmers, ranchers, and the public today with the USDA screwworm eradication program being one particularly noteworthy example.  

For years, screwworms (parasitic flies) were a devastating pest of the U.S. Southwest, Florida, and parts of Georgia. The larvae of the screwworm eat living tissue of people and other animals.

In 1937, USDA screwworm research was initiated in the Bureau of Entomology and Plant Quarantine at a USDA laboratory in Texas. USDA scientist Edward F. Knipling came up with the idea of flooding affected areas with sterilized male screwworm flies. The theory being the large numbers of released sterile male flies would mate with nonsterile female screwworm flies, resulting in a decrease of the population and over time driving the flies to extinction. In what is considered one of the greatest entomological success stories of all times, screwworms were eventually eradicated from the U.S., Mexico, and Central America by the 1980s.

Equally important research relates to the "Dust Bowl" of the 1930s. In the wake of a multiyear period of severe dust storms stretching across several states, the USDA Soil Conservation Service and the Texas Agricultural Experiment Station created a lab in Bushland, Texas. Years of poor agricultural practices and severe drought left the soil of extensive U.S. farmland exposed to severe wind erosion and resulting dust storms. The Bushland lab, working with experiment stations and other labs, began investigating methods to minimize the potential for another Dust Bowl. The results of their research were the forerunner of the highly successful practice of no-till and other forms of conservation tillage used today.

Andrew Moyer, in his Peoria laboratory, discovered the process for mass producing penicillin.

Perhaps one of USDA's greatest scientific achievements occurred in the 1940s during World War II. Penicillin, a mold with bacteria-killing powers, was discovered in 1928 by Scottish microbiologist Alexander Fleming. However, it was not until 1941, in the midst of WWII, that a team of scientists from the University of Oxford in England and scientists at what was then the USDA Northern Regional Research Laboratory in Illinois devised methods of growing the mold using deep-tank fermentation and producing penicillin yields that surpassed anything achieved before. Drug companies then used these methods just in time to produce enough penicillin to treat Allied soldiers wounded on D-Day in 1944.

On November 2, 1953, a Secretary's Memorandum redesignated the Agricultural Research Administration —established in 1942—as the Agricultural Research Service. This established ARS as USDA's chief scientific research agency with the job of "conducting research to develop & transfer solutions to agricultural problems of high national priority".

Over the years, many familiar products have resulted from ARS research, such as frozen foods, sunflower seed butter, Roma tomatoes, improved frozen orange juice concentrate, fire-resistant cotton and Pampers and lactose-free milk. Today, 2,000 ARS scientists at 90+ research locations are conducting impactful, revolutionary research in all areas of agriculture, with more than 600 research projects occurring at any given time. ARS has earned a worldwide reputation as a scientific organization delivering scientific solutions to national and global agricultural challenges.

To learn more, visit our new Research Timeline to read about the many agricultural research accomplishments ARS scientists have achieved from 1862 to the present. — By Nancy Vanatta , ARS Office of Communications.

U.S. Government Accountability Office

Precision Agriculture: Benefits and Challenges for Technology Adoption and Use

Precision agriculture uses technologies such as GPS or automation to make farms more efficient. For example, farmers can use auto-steering equipment to precisely plant a field, and activity monitors can help dairy farmers collect data about their cows' health.

In this Technology Assessment , we review the benefits and challenges of precision agriculture technologies. They can make farms more profitable and have environmental benefits—like reducing fertilizer runoff in waterways. But they can also be complex and have high up-front costs, making it hard for some farmers to access them. We examine policy options to help address these challenges.

Artist’s concept of an autonomous tractor working in a corn field

What GAO Found

Precision agriculture technologies can improve resource management through the precise application of inputs, such as water, fertilizer, and feed, leading to more efficient agricultural production. Precision agriculture can be implemented through a suite of technologies that can be used in isolation or in conjunction with other technologies. Examples of emerging precision agriculture technologies are in the table below.

Examples of emerging precision agriculture technologies

Source: GAO summary of literature, interviews, and agency documentation.  |  GAO-24-105962

While precision agriculture technologies, such as variable rate fertilizer applications and yield monitoring, have been available since the 1990s, only 27 percent of U.S. farms or ranches used precision agriculture practices to manage crops or livestock, based on 2023 U.S. Department of Agriculture (USDA) reporting.

Use of precision agriculture practices by U.S. farms, June 2022–June 2023

Federal agencies support precision agriculture adoption, research and development, education, and training. USDA supports precision agriculture technology adoption with financial assistance and loan programs, such as through payments for implementing practices that provide a conservation benefit. USDA and the National Science Foundation (NSF) have provided almost $200 million for precision agriculture research and development funding in fiscal years 2017—2021. This funding includes partnerships between the two agencies to support artificial intelligence (AI) research institutes.

Benefits to using precision agriculture technologies include: 

• Increased profits. Farmers can increase yields and thus profits with the same amount of inputs or achieve an equivalent yield with fewer inputs.
• Reduced application of crop inputs. Technologies can reduce the application of crop inputs such as fertilizer, herbicide, fuel, and water. They can also address water scarcity by promoting the efficient use of water in agriculture.
• Environmental benefits. Technologies can prevent excessive use of chemicals and nutrients in a field, potentially reducing runoff into soil and waterways.

Challenges limiting the broader adoption and use of precision agriculture include:

• High up-front acquisition costs. Acquisition costs for the latest technologies can be prohibitive for farmers with limited resources or access to capital.
• Farm data sharing and ownership issues. Concerns regarding farm data sharing and ownership can pose obstacles to the widespread use of AI in agriculture.
• Lack of standards. An absence of uniform standards can hamper interoperability between different precision agriculture technologies.

GAO examined three policy goals and associated options that could help address adoption challenges or enhance the benefits of precision agriculture technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. In addition, for each policy goal, policymakers may choose no additional policy interventions, maintaining the status quo by continuing existing activities.

Policy Goals and Options That Could Address Challenges or Help Enhance Benefits of Adoption and Use of Precision Agriculture Technologies

Source: GAO.  |  GAO-24-105962

Why GAO Did This Study

Precision agriculture involves collecting, analyzing, and taking actions based on data. It can help the agricultural sector meet increasing demand for food products, while also helping farmers improve efficiencies such as through reduced input costs.

The Advancing IoT for Precision Agriculture Act of 2021, contained in what is commonly referred to as the CHIPS and Science Act of 2022, included provisions for GAO to conduct a technology assessment and review federal programs.

This report examines (1) emerging precision agriculture technologies and precision agriculture technology adoption; (2) federal programs providing support for precision agriculture; (3) benefits and challenges of adopting and using precision agriculture technologies; and (4) policy options that could address challenges or help enhance benefits of adopting and using precision agriculture technologies.

To conduct this technology assessment and review, GAO reviewed scientific literature and other key reports; interviewed officials and representatives from government, industry, academia, and associations; conducted two site visits to observe technology operations and obtain stakeholder perspectives; and convened a 3-day virtual expert meeting in collaboration with the National Academies of Sciences, Engineering, and Medicine. GAO is identifying policy options in this report.

For more information, contact Brian Bothwell at (202) 512-6888 or [email protected] , or Steve D. Morris at (202) 512-3841, [email protected] .

How to Conduct Research on Your Farm or Ranch

Basics of experimental design.

or call (301) 779-1007 to order.

The previous section summarized the 10 steps for developing and implementing an on-farm research project. In steps 1 through 3, you wrote out your research question and objective, developed a hypothesis, and figured out what you will observe and measure in the field. Now you are ready to actually design the experiment. This section provides more detail on step 4 in the process.

Recall from the introduction that on-farm research provides a way of dealing with the problem of field and environmental variability. In comparing the effects of different practices (treatments), you need to know if the effects that you observe in the crop or in the field are simply a product of the natural variation that occurs in every ecological system, or whether those changes are truly a result of the new practices that you have implemented.

Take the simple example of comparing two varieties of tomatoes: a standard variety and a new one that you have just heard about. You could plant half of a field in the standard variety and the other half of the field in the new variety. You plant the tomatoes on exactly the same day, and you manage both halves of the field exactly the same throughout the growing season. Throughout the harvest period, you keep separate records of the yield from each half of the field so that at the end of the season you have the total yield for each variety. Suppose that under this scenario, the new variety had a 15 percent higher yield than your standard variety. Can you say for sure that the new variety outperforms your standard variety? The answer is no, because there may be other factors that led to the difference in yield, including:

• The new variety was planted in a part of the field that had better soil.
• One end of the field was wetter than the other and some of the tomatoes were infected with powdery mildew.
• Soil texture differences resulted in increased soil moisture from one end of the field to the other.
• Part of the field with the standard variety receives afternoon shade from an adjacent line of trees.
• Weed pressure is greater in one part of the field with the standard variety.
• Adjacent forest or wildlands are a source of pests that affect one end of the field more than the other.

With the right experimental design and statistical analysis, you can identify and isolate the effects of natural variation and determine whether the differences between treatments are “real,” within certain levels of probability. This section looks at three basic experimental design methods: the paired comparison, the randomized complete block and the split-plot design. Which one you choose depends largely on the research question that you are asking and the number of treatments in your experiment (Table 2).

The number of treatments in your experiment should be apparent from your research question and hypothesis. If that is not the case, then you will need to go back and refine your research question so that you have more clarity as to what you are testing. As previously noted, when identifying your research question (step 1), remember to keep things simple. Avoid over-complicating your experiment by trying to do too much at once. And, keep in mind that although the randomized complete block and split-plot designs provide more information than the paired comparison, they also require a larger field area, more management and more sophisticated statistics to analyze the data. Table 2 also lists the type of statistical analysis associated with each experimental design method. These statistical techniques are covered in the next section, Basic Statistical Analysis for On-Farm Research . First is a review of some basic experimental design terminology.

TABLE 2: Three Experimental Design Methods

Treatments: A treatment is the production practice that you are evaluating. Examples of treatments include choice of variety, different fertilizer rates, different fertilizer timing, choice of cover crops, different cover crop management strategies, timing of planting, type of tillage, different pest control methods or different irrigation strategies. For animal operations, treatments might be different feed rations, type of bedding, pasture versus confinement, grazing period, nutritional supplements, or disease/parasite controls. The choices are limitless given the complexity of farming. On-farm research usually compares just two or three practices. In most cases, one of the treatments is the standard practice, or what you usually do, and is known as the “control.”

Small-scale intensive onion production on plastic in Interlaken, NY. Cornell extension vegetable specialist Christine Hoepting found growers could improve yields and reduce bacteria incidence by using alternatives to black plastic mulch, and by increasing planting density. Courtesy Cornell University Cooperative Extension

Variable: In statistics, a variable is any property or characteristic that can be manipulated, measured or counted. In on-farm research, the independent variable is the different treatments (practices) you are applying, and the dependent variable is the effect or outcome you are measuring. What you measure in your particular experiment depends on what treatments you apply. Examples include crop yield, weed density, milk production or animal weight gain.

Plot: Plots are the basic units of a field research project—the specific-sized areas in which each treatment is applied. Replication: Replication means repeating individual treatment plots within the field research area. If you set up an experiment comparing two treatments, instead of setting out just one plot of Treatment A and one plot of Treatment B, you repeat the plots within the field multiple times. Replications reduce experimental error and increase the power of the statistics used to analyze data.

Block: It is usually not possible to find a perfectly uniform field in which to conduct the experiment, and some sources of variation simply cannot be controlled (e.g., slope or soil texture gradients). In order to address the problem of field variability, divide your field of interest into sections that have common slope and soil characteristics. Within each section—typically known as blocks—field conditions should be as uniform as possible. Taken together, however, all of your blocks should encompass the variability that exists across the research area. After delineating the areas for your blocks, make sure you include each treatment inside each block; that way, your blocks can serve as replications. In most on-farm research studies, four to six blocks are sufficient to provide a good level of confidence in the results. Figure 2 provides examples of how to use blocking to address field variability due to slope or soil type.

Agricultural research should usually be blocked because of field variability. If your field has a known gradient, such as a fertility or moisture gradient, it is best to place blocks to that conditions are as uniform as possible within each block. Figure 2a: On a slope, for example, each whole block should occupy about the same elevation. Treatments are randomized and run across the slope within each block. Figure 2b: Place whole blocks within different soil types. Figure 2c: If blocks cannot be used to account for variability, then each treatment should run across the whole gradient, as in all the way down the slope or all the way across the field. This arrangement can also be used for a completely randomized design (see Figure 3).

Randomization: In addition to replication, randomization is also important for addressing the problem of field variability, reducing experimental error and determining the true effect of the treatments you are comparing. Replications should be arranged randomly within the field. Or in the case of a blocked experimental design, treatment plots must be arranged randomly within each block. If you have three treatments, for example, you cannot place those treatments in the same left-to right sequence within each block. They must be arranged in a random order. This can be done using the flip of a coin, drawing numbers from a hat or using a random number generator for each block.

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We’re going to learn about a very small fungus, called yeast. Let’s find out how this special fungus helps us make bread.

Do different types of apples have the same number of seeds? Try this fun data analysis and collection experiment for elementary school students.

What happens if we put hydrogen peroxide and yeast together in a large quantity?

Use this project to fin dout if you are getting enough vitamins and minerals.

Try this experiment around your yard or school and see how biodiversity at the plant level affects biodiversity at the insect level.

For this experiment, we’re going to test the effect that high salt soil concentrations have on plant growth and root development.

This activity helps students learn more about different animal behaviors . Get creative painting or drawing!

This experiment helps students learn the importance of having clean hands.

This experiment helps students create a black light and use it to look at the fluorescence of different food items and plants .

This activity helps students learn more about different animal behaviors to ensure pets are happy and stress free.

These experiments help students learn about soil erosion.

This experiment helps students compare vitamin C content and rank orange varieties or juice brands from highest to lowest.

For this experiment, we’re going to test the effect a hard soil layer has on plant growth and root development.

This experiment helps students learn about growing plants with organic soil amendments.

With IICA's support, FORAGRO renews and strengthens its commitment to science, technology and agricultural innovation in the Americas

San José, 14 October 2024 (IICA) - The Forum of the Americas for Agricultural Research and Technological Development (FORAGRO), whose technical secretariat is managed by the Inter-American Institute for Cooperation on Agriculture (IICA), will be revitalized and its commitment to science, technology, and innovation will be strengthened, in order to increase its effectiveness and address challenges and opportunities facing modern agriculture.   This was defined in a virtual assembly convened by IICA, which brought together prominent representatives from the agri-food sector, including national and international research institutes, cooperation organizations, the private sector, and farmers' organizations. In the assembly, a roadmap was set to revitalize FORAGRO and connect it with other global forums through regional initiatives.   The forum was created in 1997.   "FORAGRO should become the space that creates synergies, bringing together efforts by involving public, academic, private, and civil society actors to build collective agendas. This forum will allow us to move away from immediate concerns and focus on future challenges and the strategic alliances necessary to integrate the region into global discussions," said Manuel Otero, IICA’s Director General, during the assembly.   IICA launched a comprehensive proposal with three priorities for revitalizing the forum. The first priority is to anticipate scientific and technological trends that will shape the future of agriculture in the region, enabling better planning and taking advantage of emerging opportunities. Secondly, it aims to unite efforts and foster close collaboration in the region and with other international actors, acknowledging that today's challenges are shared and require joint solutions.   The third priority is to defend and position agricultural science, technology, and innovation in major international decision-making forums, ensuring that Latin America and the Caribbean are represented in global agendas.   "IICA has science and innovation in its DNA. We have committed to bringing all results generated by FORAGRO to the discussions of the Inter-American Board of Agriculture (IABA, which includes the Ministers of Agriculture of the Americas and the highest governing body of IICA), ensuring that these advancements are known, supported, and have a significant impact," Otero added.   Recognition and Support   Hildegard Lingnau, Executive Secretary of the Global Forum on Agricultural Innovation and Research (GFAiR), congratulated FORAGRO for its revitalization, highlighting its crucial role in establishing the Global Consortium of National Agricultural Research Systems and its interaction with other regional forums worldwide.   Joaquín Lozano, Regional Director for Latin America and the Caribbean at CGIAR, expressed enthusiasm for participating in the revitalization of FORAGRO, stating, "We are committed and excited to engage not only with the four CGIAR centers in the Americas but also with our global capacity to collaborate in the region and bring the Latin American and Caribbean perspective to international discussions."   The meeting, which included key institutions such as the U.S. Department of Agriculture (USDA), Agriculture and Agri-Food Canada, the Brazilian Agricultural Research Corporation (EMBRAPA), the Tropical Agricultural Research and Higher Education Center (CATIE), and the Caribbean Agricultural Research and Development Institute (CARDI), among other relevant actors, also announced a face-to-face meeting in February 2025.   This event will follow up on the agreements reached during the virtual assembly and further strengthen the region's commitment to agricultural science, technology, and innovation.   The virtual assembly marked a significant step forward following the Regional Dialogue on Science, Technology, and Innovation, held in May 2023. This was a joint initiative of IICA, CGIAR, and the World Bank, with participation from organizations such as the Inter-American Development Bank (IDB), the Food and Agriculture Organization of the United Nations (FAO), and the Economic Commission for Latin America and the Caribbean (ECLAC). This dialogue identified the need to balance productivity and sustainability goals in agri-food systems and highlighted the gaps in funding for innovation.

More information: Muhammad Ibrahim, Director of Technical Cooperation at IICA and Executive Secretary of FORAGRO. [email protected]