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• Published: 23 December 2021

On-Farm Experimentation to transform global agriculture

• Myrtille Lacoste   ORCID: orcid.org/0000-0001-6557-1865 1 , 2 ,
• Simon Cook   ORCID: orcid.org/0000-0003-0902-1476 1 , 3 ,
• 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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The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information (sources of Figs. 1 – 3 ).

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Acknowledgements

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

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Myrtille Lacoste, Simon Cook & Danielle Gale

Montpellier Advanced Knowledge Institute on Transitions (MAK’IT), University of Montpellier, Montpellier, France

Myrtille Lacoste

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Roger Sylvester-Bradley & Daniel Kindred

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Contributions

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

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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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Experimental methods in agriculture

Andrea Onofri and Dario Sacco

Update: v. 1.1 (2023-12-06), compil. 2023-12-13

Introduction

This is the website for the book “Experimental methods in agriculture”, where we deal with the organisation of experiments and data analyses in agriculture and, more generally, in biology. Experiments are the key element to scientific progress and they need to be designed in a way that reliable data is produced. Once this fundamental requirement has been fulfilled, statistics can be used to summarise and explore the results, making a clear distinction between ‘signal’ and ‘noise’ and, hence, reaching appropriate conclusions.

In this book, we will try to give some essential information to support the adoption of good research practices, with particular reference to field experiments, which are used to compare, e.g., innovative genotypes, agronomic practices, herbicides and other weed control methods. We firmly believe that the advancement of cropping techniques should always be based on the evidence provided by scientifically sound experiments.

We will follow a ‘learn-by-doing’ approach, making use of several examples and case studies, while keeping theory and maths at a minimum level; indeed, we are talking to agronomists and biologists and not to statisticians! However, we will not totally remove theory: we think that being able to do some simple hand-calculations is the best way to master the process of data-analysis.

This website is (and will always be) free to use, and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. It is written in RMarkdown with the ‘bookdown’ package and it is rebuilt every now and then, to incorporate corrections and updates. This is necessary, as R is a rapidly evolving language.

This book is not written aiming at completeness, but it is finely tuned for a 6 ECTS introductory course in biometry, for master or PhD students. It is mainly aimed at building solid foundations for starting a job in the research field and, eventually, to be able to tackle more advanced statistical material.

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In Chapter 3 we will show how we can describe the experimental results, based on some simple stats, such as the mean, median, chi square value and Pearson correlation coefficient. In chapter 4 we will introduce some simple models, which we can use to describe the results of our experiments. Of course, the observed data come as the result of deterministic and stochastic processes and, therefore, we will also describe some stochastic models, with particular reference to the Gaussian Density function.

In Chapters 5 and 6 we will talk about statistical inference and Formal Hypothesis Testing. We will describe the basic concepts of confidence intervals, P-levels and error types and we will introduce t-tests and chi-square tests.

From Chapter 7 to Chapter 12 we will talk about the ANOVA, that is one of the most widely used techniques of data analysis. We will show one-way and two-ways ANOVA models and we will also introduce more complex designs, such as the split-plot and strip-plot. Chapter 13 and 14 will be devoted to describe, respectively, linear and nonlinear regression models. In the Chapters from 7 to 14, we will always start from a motivating example, so that the readers can have an idea of the experimental situation, before diving into the details. In the final chapter 15, we will provide exercises for all book chapters, which should help the readers to practice with what they have learned, while reading the book.

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In this book, we will work through all the examples by using the R statistical software, together with the RStudio environment. We selected such software for a number of reasons: first of all we like it very much and we think that it is a pleasure to use it, once the initial difficulties have been overcame! Second, it is freeware, which is fundamental for the students. Third, in recent years the software skills of students in master degree or PhD programmes have notably increased and writing small chunks of code is no longer a problem for most of them. Last, but not least, we have seen that some experience with R is a very often required skill when applying for a job. We should acknowledge that R and RStudio are two wonderful pieces of software and we are very much indebted to the whole community who is working to ensure their wide availability and freeware nature.

R is characterised by a modular structure and its basic functionalities can be widely extended by a set of add-in packages. As this is mainly an introductory course, we decided to stick to the main packages, which come with the basic R installation. However, we could not avoid the use of a few very important packages, which we will indicate later on. Of course, it is necessary to state that many of the tasks we perform in this book could be as well (or even better) performed by using additional packages, such as those included in the relatively new ‘tidyverse’ package. We should also mention that this book was built by using the ‘bookdown’ package and it is hosted on the blog ‘www.statforbiology.com’, which is built by using the ‘blogdown’ package. We will not use these two packages during the course, but we should mention that they are really useful.

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Andrea is Associate Professor at the Department of Agricultural, Food and Environmental Science, University of Perugia and he has taught ‘Experimental methods in Agriculture’ since 2000. Dario was Associate Professors at the Department of Agricultural, Forest and Food Sciences, University of Torino; he used to teach ‘Experimental Methods in Agriculture’ until 2020, when he suddenly died, far too early. Unfortunately, he could not see this book completed.

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American Journal of Experimental Agriculture (ISSN: 2231-0606)

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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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Experimental research on breakage characteristics of feed pellets under different loading methods.

1. Introduction

2. materials and methods, 2.1. samples and preparation, 2.2. experimental equipment, 2.3. experimental methods, 2.3.1. repeated compression, 2.3.2. repeated impacts, 2.4. evaluation of breakage characteristics, 2.4.1. size distribution function, 2.4.2. pulverization rate, 2.4.3. mass-specific energy, 2.4.4. fitting model, 3. results and discussion, 3.1. breakage behaviors of feed pellets, 3.2. particle size distribution, 3.3. energy and pulverization rate, 3.3.1. the influence of loading cycles, 3.3.2. relationship between energy and pulverization rate, 4. conclusions, supplementary materials, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Kong, X.; Cao, Q.; Niu, Z. Experimental Research on Breakage Characteristics of Feed Pellets under Different Loading Methods. Agriculture 2024 , 14 , 1401. https://doi.org/10.3390/agriculture14081401

Kong X, Cao Q, Niu Z. Experimental Research on Breakage Characteristics of Feed Pellets under Different Loading Methods. Agriculture . 2024; 14(8):1401. https://doi.org/10.3390/agriculture14081401

Kong, Xianrui, Qing Cao, and Zhiyou Niu. 2024. "Experimental Research on Breakage Characteristics of Feed Pellets under Different Loading Methods" Agriculture 14, no. 8: 1401. https://doi.org/10.3390/agriculture14081401

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Computer Science > Computer Vision and Pattern Recognition

Title: kan you see it kans and sentinel for effective and explainable crop field segmentation.

Abstract: Segmentation of crop fields is essential for enhancing agricultural productivity, monitoring crop health, and promoting sustainable practices. Deep learning models adopted for this task must ensure accurate and reliable predictions to avoid economic losses and environmental impact. The newly proposed Kolmogorov-Arnold networks (KANs) offer promising advancements in the performance of neural networks. This paper analyzes the integration of KAN layers into the U-Net architecture (U-KAN) to segment crop fields using Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the performance and explainability of these networks. Our findings indicate a 2\% improvement in IoU compared to the traditional full-convolutional U-Net model in fewer GFLOPs. Furthermore, gradient-based explanation techniques show that U-KAN predictions are highly plausible and that the network has a very high ability to focus on the boundaries of cultivated areas rather than on the areas themselves. The per-channel relevance analysis also reveals that some channels are irrelevant to this task.

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