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Abstracts for #OFE2021 are accepted until June 30, 2021. Submit your 150-word abstract for an e-presentations on On-Farm Experimentation activities or research. Topics addressing lessons learnt as well as research results are suitable and encouraged.
GARDIAN is CGIAR’s (Consultative Group on International Agricultural Research) flagship data harvesters. It enables the discovery of publications and datasets from across the thirty-odd institutional publications and data repositories from CGIAR Centers and beyond. Actually, most data and publications are not stored in it but in other public databases and repositories. GARDIAN a key component of the Platform’s objective to establish the infrastructure, tools, and approaches to making CGIAR data Findable, Accessible, Interoperable, Reusable (FAIR). GARDIAN employs text mining to enrich the associated metadata to enhance discovery, and will soon test data mining techniques with cleaned, ...
A guide in 6 steps: 1) define the study question; 2) choose treatments; 3) how and where to conduct the study; 4) choose variables to measure; 5) conduct the experiment and analyze the results; 6) share your results.
Farmers often feel that they do not get the value back after sharing their data. The GODAN (Global Open Data for Agriculture and Nutrition) organization has recently made available an Agricultural Data Codes of Conduct Toolkit. By using the toolkit, they can understand and control what is done with the data, who can do what, and so on. They feel engaged, considered and this strengthens the farmer value structure. The toolkit allows farmers to select clauses that might be of relevance and to easily produce a printable and saveable Code of Conduct that provides the conceptual basis for general, scalable ...
Accurate interpretation is the key to getting value from OFEs—good interpretation helps farmers learn more from each OFE, and manage with greater certainty as a result. Sadras and co-authors [Making Science More Effective for Agriculture: Advances in Agronomy, 163:153—77] call for an expanded role for agronomic logic to solve global crop production challenges. Yet many OFEs generate insights of complex and variable crop behaviour that call for stronger engagement of agronomy with these farmer-driven operations. In fact, some data scientists believe analysis can proceed without theory—an approach Taguchi adopted for dealing with complex systems. As we ...
This review provides guidance for the most commonly encountered methodological issues when analyzing yield stability in LTEs. Consistent use of the suggested guidelines and recommendations may provide a basis for robust analyses of yield stability in LTEs and to subsequently design stable cropping systems that are better adapted to a changing climate. Reckling, M., Ahrends, H., Chen, TW. et al. Methods of yield stability analysis in long-term field experiments. A review. Agron. Sustain. Dev. 41, 27 (2021). https://doi.org/10.1007/s13593-021-00681-4
The Agricultural Research Data Network (ARDN) provides tools and protocols to allow researchers to not only share their data, but to make their data interoperable and reusable. Additional tools allow end users of the data to combine and reformat ARDN data for quantitative analysis and modeling.
The Living Laboratories Initiative is an integrated approach to agricultural innovation that brings farmers, scientists, and other partners together to co-develop, test, and monitor new practices and technologies in a real-life context.
A new paper proposes a spatially varying local cokriging method for large on-farm experimentation data which could lead to high-resolution site-specific farming treatment recommendations. Its accuracy of spatial prediction is compared with five other techniques. The open source code is accessible via a user-friendly interface of Quantum GIS. [Huidong Jin, K. Shuvo Bakar, Brent L. Henderson, Robert G.V. Bramley, David L. Gobbett. 2021. An efficient geostatistical analysis tool for on-farm experiments targeted at localised treatment. Biosystems Engineering 205:121–136, ISSN 1537–5110.]
The Australian Farm Data Code aims to promote adoption of digital technology, by ensuring that farmers have comfort in how their data is used, shared and managed. It is intended to inform the service providers who manage data on behalf of farmers, and a tool for farmers to evaluate their policies.
A virtual Workshop on Big Data Promises and Obstacles: Agricultural Data Ownership and Privacy was hosted by the Digital Agriculture “UASPSE” (Unmanned Aircraft Systems, Plant Sciences and Education) project, the University of Minnesota College of Food, Agricultural and Natural Resources Sciences and PepsiCo. Recordings of the presentations: Cultivating Trust in Technology-Mediated Sustainable Agricultural Research The Law and Economics of Agricultural Data Privacy FAIR to FAIRS: Data Security by Design for the Global Burden of Animal Diseases Big Data, Data Privacy, and Plant and Animal Diseases Research Unmanned Aircraft Systems in Agriculture: Data Issues of Privacy, Ownership, and ...
26 June 2018, Montreal, Quebec, Canada The meeting started at 6:35 pm with 11 people in attendance. After introductions, a discussion occurred about changing the focus of the OFDS Community from sharing of field-scale trial data collected on production farms to methods, protocols and analysis of on-farm experimentation. The rationale for the change provided by Nicolas Tremblay and Tom Morris was that the proposed new Consortium for On-Farm Experimentation with leadership by Simon Cook would benefit from having a scientific home in ISPA, and because some of the objectives of the OFDS group have been shifted to an OFDS Working Group in ...
Here is an exceptional opportunity to let the scientific community know about your On-Farm Experimentation (OFE) work. Contributions are most welcome to this Virtual Issue focusing on recent advances relative to Farmer-Centric On-Farm Experimentation (OFE). It coincides with OFE2021, the 1st Farmer-Centric On-Farm Experimentation Conference (13–15 October 2021) and is open to everyone. Indeed, participation in OFE2021 is not mandatory to submit OFE-related work to this special issue. Submissions end on March 1, 2022. The conference was supported by the OECD—CRP Sustainable Agricultural Systems, DigitAg, INRAE and the International Society of Precision Agriculture (ISPA).
You might consider submitting your work to the Special Issue in Agronomy for Sustainable Development. This virtual issue coincides with OFE2021, the 1st Farmer-Centric On-Farm Experimentation Conference (13–15 October 2021) and is open to everyone. Submissions end on March 1, 2022.
Crowdsourcing, understood as outsourcing tasks or data collection by a large group of non-professionals, is increasingly used in scientific research and operational applications. Close connections with the farming sector, including extension services and farm advisory companies, could leverage the potential of crowdsourcing for both agricultural research and farming applications. [Julien Minet, Yannick Curnel, Anne Gobin, Jean-Pierre Goffart, François Mélard, Bernard Tychon, Joost Wellens, Pierre Defourny. Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. Computers and Electronics in Agriculture 142, Part A (2017): 126-138.]
To address the global food system challenges effectively, we must overcome fragmentation within and across sectors to act in a transdisciplinary fashion, bringing together the natural and social sciences with data and technology to drive food systems towards more favorable potential futures for humanity and the planet. The Global Coalition for Digital Food Systems Innovation participated to the “Forum COP26 Live”. The recording is available here. The Coalition involves FAO, World Bank, CGIAR Platform for Big Data in Agriculture, Mineral at X, Google, Digital Green, GEOGLAM, Hewlett Packard Enterprise, VARDA, Consumers International, Bayer Foundation, Mercy Corps, World ...
We launched a quick survey in the OFE-C info letter no. 5. To the question, “Do you use a standard for your agronomic data? » 85% answered, “No, but I would be interested,” nobody simply answered, “no” and 15% answered, “Yes.” Among the latter, the following standards were suggested: AgMIP / ICASA: Porter, C.H., C. Villalobos, D. Holzworth, R. Nelson, J.W. White, I.N. Athanasiadis, S. Janssen, D. Ripoche, J. Cufi, D. Raes, M. Zhang, R. Knapen, R. Sahajpal, K.J. Boote, J.W. Jones. 2014. Harmonization and translation of crop modeling data to ensure interoperability. ...
Data Sharing Toolkit could contribute to unlocking greater food security. CABI and the Open Data Institute (ODI) has launched a Data Sharing Toolkit which could contribute to greater food security in Sub-Saharan Africa and South Asia through better access to data on soil health, agronomy and fertilizer.
ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi‐sites and multi‐year summaries. [Laurent et al. 2021. Research Synthesis Methods 12(1). https://doi.org/10.1002/jrsm.1440.]
Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a ...
Dr. David Bullock made a presentation entitled “Contributing to an International Cyber-Infrastructure for On-farm Precision Experimentation” before the OFE2021 “Farmer-Centric On-Farm Experimentation” Conference and the University of Bonn PhenoRob Institute. The purpose of the trip was to publicize DIFM’s latest efforts and seek collaboration with researchers in the European Union. The DIFM team has also created a multistate Research Project, titled NC1210: Frontiers in On-Farm Experimentation which will enable researchers from all across the United States to collaborate and host meetings on an annual basis.
Have a look at this classic 2006 guide (Designing Your Own OFE - Bramley) for farmers and their advisers on precision agriculture-based field experiments - their design, and the important issues to be considered in analysing the results. The guide was published by the Grains Research & Development Corporation (GRDC) of the Australian Government.
In our data-rich world, identifying optimal systems for sustainable intensification or diversification is lacking a data management system across spatial and temporal resolutions including workflows, interpretation methodology, and a delivery structure. This paper offers solutions for developing a platform for bridging component parts (encompassing multiple scales and disciplines) to analyze system functionality for greater resiliency. [Tulsi P. Kharel Amanda J. Ashworth Phillip R. Owens Michael Buser. 2020. Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions. Agronomy Journal 112 (5): 4498–4510. https://doi.org/10.1002/agj2.20285].
Have a look at a very packed page on the challenge around data with clarifications on the lexicon for terms such as “metadata, interoperability, governance, cleaning and big data.” We learn that “over the last two years, a CODATA-led pilot project has developed, tested and refined methods for aligning metadata specifications, taxonomies and ontologies to address these problems in a consensual fashion.”
The one-size-fits-all approach of research has had success but advances are slowing. “How well crops and livestock grow depends on the interaction of genes, management and environment. As weather patterns fluctuate, gains in production will depend ever more on innovating in context. Big knowledge flowing from institutes to farm must be complemented by local knowledge.” Small-scale agricultural innovation will boost yields and protect the planet. See this Nature Comment.
Precision farming experiments are generally incompatible with conventional statistical methods and alternative models of response variables (e.g. yield) must be estimated if the effect of the management decision is to be distinguished from other sources of variation. The model-based statistical analyses of these experiments require assumptions regarding the variation of the response variable. When these assumptions are inappropriate (e.g. if the correlation between response variable measurements is poorly modelled) then the inferences from the experiments can be unreliable. Marchant, B. et al. Establishing the precision and robustness of farmers’ crop experiments. Field Crops Res. 230, 31-45, doi:10.1016/...
There is a need to shift the focus from individual studies to the accumulating body of evidence concerning the agronomic and environmental benefits of innovative farming practices. Systematic reviews, evidence mapping, on-farm research, and meta-analyses are available for the integration of results but they are not yet used as frequently as one might expect. Both qualitative (systematic reviews, evidence maps, farm surveys) and quantitative syntheses (meta-analyses, modeling) have been published in a special issue of the European Journal of Agronomy. [Makowski, D. Editorial of the special issue “Evidence synthesis in agronomy”. European Journal of Agronomy 122 (2021) 126183. ISSN 1161-0301. https://...
On-farm experimentation (OFE) and precision agriculture technologies could be a potent mix for driving change in agricultural systems. Many of us recognize the significant opportunity in large, tech heavy and digitally enabled cropping enterprises. However, most of the world’s agricultural land is characterized by extensive, tech-poor livestock systems (LS). “OFE in LS” could help to introduce appropriate digital technologies in a way that is meaningful to farmers. Have a look at this recording from Matthew McNee, agronomy advisor in the Falkland Islands.
A Farmer-Led Innovation Network (FLIN) was established in October 2018 to share knowledge and experiences and provide a collective advocacy voice for farmers in the UK. The main aim is to understand, learn from and “power-up” farmer-led innovation initiatives and increase their economic, environmental and social impact across the industry.
A Farmer-Led Research Webinar was conducted last month by the School of Environmental Design and Rural Development, University of Guelph. The webinar mentioned the need for scientific rigor, yet keeping a balance between practical and robust protocol, on the one hand, and keeping data collection and research flexible, on the other hand. The recording is now available.
“Testing of only one variable at the same time,” has quite recently been described as one of the criteria that a scientific field trial has to satisfy. In projects involving cooperation between farmers and scientists, scientists have sometimes been “frustrated” with farmers whose experiments have not satisfied the one-variable requirement. Reportedly, this is “one of the points that has [led] research station scientists to dismiss farmer innovation.” This study investigates methodological and philosophical issues pertaining to farmers’ experiments such as the choice of interventions to be tested, the planning of experiments, and the ...
“Testing of only one variable at the same time,” has sometimes been described as one of the criteria that a scientific field trial has to satisfy. In projects involving cooperation between farmers and scientists, scientists have sometimes been “frustrated” with farmers whose experiments have not satisfied the one-variable requirement. Reportedly, this is “one of the points that has [led] research station scientists to dismiss farmer innovation.” This study investigates methodological and philosophical issues pertaining to farmers’ experiments such as the choice of interventions to be tested, the planning of experiments, and the means ...
This study investigates methodological and philosophical issues pertaining to farmers’ experiments, including the choice of interventions to be tested, the planning of experiments, and the use of control fields and other means to deal with confounding factors. [Hansson, Sven Ove. 2019. “Farmers’ Experiments and Scientific Methodology.” European Journal for Philosophy of Science 9 (3): 32. https://doi.org/10.1007/s13194-019-0255-7.]
This webinar reflects on how to achieve sustainable productivity gains through investments in soil health and knowledge. Reports have been commissioned on pioneering efforts in East and Southern Africa to engage and empower farmers and communities through approaches that specifically support disadvantaged youth and women. Highlights include innovations in extension, soil health monitoring and agricultural policy around sustainable intensification.
By working together with other farmers, suppliers, agronomists and scientists, farmers can use their own trials to bring fast learning, new findings and best practice for themselves and the industry at large, an approach ADAS calls “Agronōmics”. GPS and other modern technologies, along with thorough trial protocols, can make farm trialling straight forward and routine. Decisions and innovations can then become thoroughly validated and tailored to real farming conditions. This Guide to Farmer’s Crop Trials outlines processes leading to successful farm-trialling and how to avoid the pitfalls.
Building an “OFE wiki How-to”: seeking reports and guidelines about On-Farm Experimentation The ISPA Community OFE (On-Farm Experimentation) is creating an online “OFE wiki How-to” to support On-Farm Experimentation initiatives worldwide. We are looking for: Relevant material aimed at practitioners to (re-)publish (or link toward) e.g. design manuals, implementation guidelines for farmers, best practice recommendations, scripts and protocols for analysts, statistical solutions and packages, accounts of experiences and lesson learnt for extension personnel, etc. People motivated to volunteer time and effort to help setup, compile, organise, write-up the Wiki (excellent opportunity for ...
The EFAO is a research program led by farmers which combine their curiosity with scientific rigour to answer challenging on-farm questions. Their website features an open access source to EFAO research protocols, reports and publications. Their research library lists a few on-farm research guides, two of them to be found below:
Farmers today face a complicated set of expectations while trying to make a living. These challenges are complex, yet most agricultural research has approached them from a reductionist standpoint. The handbook delivers guidance on how to form effective interdisciplinary and multi-stakeholder teams and how to plan, implement and analyze system experiments. The Sustainable Agriculture Research and Education (SARE) program is a decentralized competitive grants and education program.
Heterogeneous spatial datasets are those for which the observations of different datasets cannot be directly compared because they have not been collected under the same set of acquisition conditions, with consistent sensors or under similar management practices, among others. This paper details and compares four automated methodologies that could be used to harmonize heterogeneous spatial agricultural datasets so that the data can be analyzed and mapped conjointly. [Leroux, C., Jones, H., Pichon, L. et al. Automatic harmonization of heterogeneous agronomic and environmental spatial data. Precision Agric 20, 1211–1230 (2019). https://doi.org/10.1007/s11119-019-09650-0]
The OFE-C is seeking professionals and researchers dealing with data from on-farm experimentations or their analysis. We want to identify requirements and valid procedures leading to guidelines and eventually policy development. Volunteers will help select topics to cover in a webinar sometime this spring and the best presenters for that purpose. The workload will not be substantial. Please volunteer or suggest someone you know here.
Do farmers and researchers have the same criteria for gauging the success of an experimental trial in commercial conditions? Having the priorities of the farmers in mind, how should the researchers adapt their experimental approaches and analytics? White peg research or else? We are starting a structured thinking process on this question in order to frame the debate and develop consensual guidelines. Should you have elements to provide or want to be involved, drop us a line here.
Mobilising co-innovation involves a complex interplay between contextual forces and facilitation processes. This interplay shapes the core co-innovation processes of joint framing, testing of solutions and creating new knowledge. The interplay between contextual and facilitation processes requires an adaptive approach to research design and management. [Ingram, J., Gaskell, P., Mills, J. & Dwyer, J. How do we enact co-innovation with stakeholders in agricultural research projects? Managing the complex interplay between contextual and facilitation processes. J. Rural Stud. 78, 65-77, doi:10.1016/j.jrurstud.2020.06.003 (2020).]
This Sustainable Agriculture Research and Education (SARE) technical bulletin provides detailed instruction for crop and livestock producers, as well as educators, on how to conduct research at the farm level using practical strategies and peer-reviewed research findings. It also includes a comprehensive list of in-depth resources and real-life examples in order to stimulate on-farm research ideas and provide guidance.
Smallholder farmers need to find a way past the status quo and a path to modernizing their operations. The mapping system—iSDAsoil— provides African soil properties at 30m resolution, and advisory services possible at the level of the single small farm. iSDA’s ultimate goal is to help smallholders develop long-term sustainable businesses. It was founded by three research institutes—Rothamsted Research, the World Agroforestry Centre (ICRAF) and the International Institute of Tropical Agriculture (IITA).
A digital geographic dataset is a representation of some model of the world for use in computer analysis and graphic display of information. To ensure that data are not misused, the assumptions and limitations affecting the creation of data must be fully documented. The objective of this part of ISO 19115 is to provide a model for describing information or resources that can have geographic extents. ISO 19115-1:2014 defines the schema required for describing geographic information and services by means of metadata.
On-Farm Experimentation Community (OFE-C) co-leads, Simon Cook and Nicolas Tremblay are among the authors of a newly released and highly collaborative Nature Food paper on OFE. This timely work acknowledges and celebrates the diversity of approaches and views on farmer-centric OFE internationally. As the visionary Professor Simon Cook put it, “OFE is the result of accumulated changes across several domains that individually may not be spectacular, but collectively realize a change substantial enough to acknowledge and start articulating.” OFE indeed occurs at the intersection of discussions in agricultural sciences that are necessary to move forward, from best ...
The Krishi Vigyan Kendra (KVK) knowledge network is an integral part of the National Agricultural Research System (NARS) in India. It aims at the assessment of location-specific technology modules in agriculture and allied enterprises, through technology assessment, refinement and demonstrations. KVKs have been functioning as Knowledge and Resource Centres of agriculture technology supporting initiatives of public, private and voluntary sectors. This initiative has the potential of becoming very large and increasingly farmer-driven, leading researchers to consider farmers first.
The scientists, together with producers and their communities will co-create applicable solutions to improve the environmental performance of farms and watersheds and help improve the health of Lac Saint-Pierre, an enlargement of the St. Lawrence River in Quebec.
A GODAN webinar with Professor Sabina Lionelli and Dr. Hugh Williamson from Exeter University on Making Crop Data Responsible and Reliable that took place recently. The speakers concentrated on how social intelligence fuels ethical data management strategies for precision agriculture. The recording of the event is now available to watch here.
Minutes of First On-Farm Data Sharing Community Meeting – ICPA – St. Louis, MO Union Station Hotel, Grand Ballroom B, 1 August 2016, 7:40 pm to 8:40 pm Attendees: Nicolas Tremblay, David Clay, Peter Kyveryga, Ignacio Ciampitti, Scott Murrell, Tom Morris, Gordon Reichert, Gary Hatfield, David Bonfil, Clive Blacker, Richard Heath, Rodrigo Tression, Lucas Haag, Suzanne Fey, Nicole Rabe, David Krueger, Cornelia Weltzien, Marilyn Kot, Guillermo Balboa. Tom Morris and Nicolas Tremblay thank everyone for attending the first meeting of the OFDS community. Many great ideas were provided and discussed. We will keep you informed about activities of the community by email. If you ...
The study of a corpus of 954 articles published by INRA scientists from 2007 to 2017 concludes that MCDA studies will need to include participatory science to involve stakeholders (i.e., public authorities, governmental agencies) and end users (i.e., farmers, producers, industry, consumers) in the construction of the multi-criterion evaluation but also in the resulting decisions.
Michael Kremer got a share of the 2019 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for his work on experimental approaches to alleviating global poverty. He notably showed innovative uses of randomized control trials to answer key development questions related to agriculture. Kremer recently gave a lecture at the FAO (Food and Agriculture Organization of the United Nations) to show how mobile technologies and digital agriculture can create innovations reaching out to smallholders as well. Kremer also addressed the role of higher-resolution weather information, customized pest-control advice and the opportunities to improve supply chains and extension services. ...
The On-Farm Experimentation Community makes available here the recordings of the #OFE2021 Webinar Series. These four online sessions took place in May 2021 to discuss a range of key topics prepared by the conference committee together with dedicated working groups. Learn more about #OFE2021.
This open access 2019 special issue of Agronomy Journal 111(6): 2633–2768 is a must read. It contains papers that show how to improve data analyses and summarization of a large number of experiments containing similar treatments across years and locations.
We are putting together the #OFE2021, the First Conference on Farmer-Centric On-Farm Experimentation—Digital Tools for a Scalable Transformative Pathway. The conference will be preceded by four preparatory webinars: Value creation: Monday, May 10, 2021 People and processes: Wednesday, May 12, 2021 Data and analytics: Monday, May 17, 2021 Policy linkages: Wednesday, May 19, 2021 The times will correspond to 8 to 10 a.m. in Chicago (Central Daylight Time), 3 to 5 p.m. in Paris and 6:30 to 8:30 p.m. in India. Check the calendar on the ISPA home page for updates.
This 2018 book chapter by Kyveryga et al. is about On-Farm Replicated Strip Trials. It provides a brief overview of how to plan, design, and conduct on-farm replicated strip trials. Practical considerations are listed when using different types of equipment. Examples are presented on how to summarize data from individual locations, as well as how to interpret experiments conducted. Applicable keywords are data analyses, economic analysis, environmental conditions, modern precision agriculture equipment, on‐farm replicated strip trials, research hypothesis, result interpretations, sustainable farming, within‐field management history, within‐field variability.
This guide from the Organic Farming Research Foundation (OFRF) is available to farmers for planning, carrying out, and analyzing experiments.
Dr. David Charles, retired from Nottingham University in the UK is passionate with the history of agriculture. Here are two excerpts of what you can find in Dr. Charles short paper on the ISPA website. Early Precision Agriculture: In the 6th century A.D. Pope Gregory and Archbishop Augustine organised England on a system of parishes. Within these parishes there were usually three large communal fields divided into strips. Village families held randomised replicated strips in the three fields with the intention of giving them all fair shares of good and bad land. Early On-Farm Experimentations: Thomas Coke of ...
The Open Ag Data Alliance is an open project designed to bring interoperability, security, and privacy to agricultural data. The purpose of the Open Ag Data Alliance is to develop a standard API framework for automated data exchange. If a person has data stored in one place, and would like an app or service to be able to access it, they need only know the top-level domain where their data sits in order for the app or service to use it, providing permission when setting up the connection.
This Laurent et al. paper shows how to prevent farmers from overoptimistic expectations that a significant effect at the overall population level will lead with high certainty to a yield gain on their own farms. [Laurent, A., Kyveryga, P., Makowski, D. & Miguez, F. A Framework for Visualization and Analysis of Agronomic Field Trials from On‐Farm Research Networks. Agron. J. 111, 2712-2723, doi:10.2134/agronj2019.02.0135 (2019).]
The proceedings from the 1st African Conference on Precision Agriculture (AfCPA) are now available for download as a PDF (29 MB). The 1st AfCPA was held from 8-10 December 2020 under the hospices of the African Plant Nutrition Institute (APNI) in partnership with Mohammed VI Polytechnic University (UM6P) and the International Society of Precision Agriculture (ISPA).
This paper explores statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Cho, Jason B., Joseph Guinness, Tulsi Kharel, Ángel Maresma, Karl J. Czymmek, Jan van Aardt, and Quirine M. Ketterings. 2021. "Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials" Agronomy 11, no. 10: 2042. https://doi.org/10.3390/agronomy11102042
The Global Open Data for Agriculture and Nutrition (GODAN) conducted a virtual Workshop on December 11, 2020, to offer an opportunity to find out more about the Agricultural Data Codes of Conduct Toolkit and GODAN’s work on Data Ethics. The toolkit provides a guide to data management best practice for any individuals or organizations (farmers, agri-businesses, associations, regional or national governments…) who collect, manage or share agricultural data. The recording of the 90-minute workshop which gathered about 300 participants can be found here.
The outcomes of on-farm experiments can support farmers’ decision-making processes, while inappropriate procedures would result in incorrect interpretations. Conventional statistical approaches (e.g., ordinary least squares regression) may not be appropriate for on-farm experiments because they are not capable of accurately accounting for the underlying spatial variations in a particular response variable (e.g., yield data). A combination of a repeated design and an anisotropic model is required to improve the precision of the experiments. [Tanaka,T.S.T. 2020. Assessment of research frameworks for on-farm experimentation through a simulation study of wheat yield in Japan . Preprint 12741.]
We are seeking free-of-right photos illustrating co-learning by scientists, farmers and professionals around on-farm experimentation and digital opportunities in a broad range of systems and contexts. If you have pictures that eloquently illustrate this idea that you are willing to share, please drop us an email. It will be greatly appreciated!
As the use of smartphone technology is becoming increasingly popular in the agricultural context, there is a need to consider how farmers have adapted to this form of technology. The current study examined the factors which influence Irish farmers’ engagement with smartphone use and new smartphone apps and explored the supports required by farmers to successfully engage with smartphone apps for agriculture use. Kenny, U. and Regan, Á., 2021. Co-designing a smartphone app for and with farmers: Empathising with end-users’ values and needs. Journal of Rural Studies, 82, pp.148-160.
Site-specific information about crop responses to agronomic treatments is needed. Geographically weighted regression was applied to generate local regression coefficients, which were used to delineate response zones in fields. This is a way to reevaluate expectations on variable rate prescriptions guided largely by soil and variability. Trevisan, R.G., Bullock, D.S., Martin, N.F. Site-Specific Treatment Responses in On-Farm Precision Experimentation. Preprints 2019, 2019020007 (doi: 10.20944/preprints201902.0007.v1).
From Fisher in 1926 to nowadays much needs to change in the analysis of agricultural experimentations. Charles (2021) guest editorial in The Journal of Agricultural Science focuses on the 20th century. Even before the digital age, experiments intended to resolve difference questions were replaced by experiments designed to answer questions about the magnitude of differences and responses to treatments. The review raises a question: namely is it time to revisit Bayesian statistics on the grounds that visionaries and innovators are prone to subjectivity? [Charles D. (2020). Guest Editorial: The analysis of agricultural experiments: a brief history of the techniques of the 20th century. ...
Fragmented and unclear data governance arrangements may weaken farmers’ willingness to adopt digital solutions. This, in turn, may reduce the availability and accessibility of agricultural data for policymaking, for the agricultural innovation system, and for developing services for farmers. This OECD report focuses on farmers’ concerns around access, sharing and use of agricultural data and explores whether and how existing policy frameworks and other sectoral initiatives can help to foster greater trust. [Jouanjean, M., et al. (2020), “Issues around data governance in the digital transformation of agriculture: The farmers’ perspective,” OECD Food, Agriculture and Fisheries Papers, ...
The LTAR network integrates question-driven research projects with common measurements on multiple agroecosystems (croplands, rangelands, and pasturelands) and develops new technologies to address agricultural challenges and opportunities. The LTAR network provides common measurements and data streams that complement other federally funded national networks. Their data management working group strives to make LTAR data aligned with the FAIR guiding principles, to be findable, accessible, interoperable, and reusable. The LTAR network fosters data sharing principles and guidelines with the intent that all LTAR data will be available for research collaboration and the development of agroecosystem management recommendations and education.
With over 10,000 members from 145 countries, the Research Data Alliance (RDA) provides a neutral space to develop and adopt infrastructure that promotes data-sharing and data-driven research to enable the open sharing and re-use of data. RDA has a grass roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. Generic topics of its interest are social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects. The RDA is constituted of different elements, ...
Acknowledging how farmers learn is a forced passage to the impact of knowledge generation and the way to link extension to research. This Janvry et al. (2016) paper presents an interesting perspective. It presents a few concepts such as “private learning” (learning-by-doing) by Bayesian updating. This consists of direct learning from own individual actions over time. There is also “social learning” (learning from others) with Bayesian updating and aggregation of observations collected from others according to a chosen pattern of weights.
All data scientists know the importance of good and unambiguous definitions of data dimensions, crucial to all phases of data analysis. However, semantics is often left implicit in the data, the semantic resources used to create the data are not easily accessible, or available in non-standard formats, non (easily) machine-readable – all factors hampering the possibility of reusing data in information systems or integrating it with other datasets and ultimately limiting the interoperability of data. This paper presents recommendations to engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the « ...
What is farmer-led research? What are some examples and the benefits? The Ecological Farmers Association of Ontario (EFAO) has experience and share its learned lessons in this guidebook.
The UK has launched the Transforming Agricultural Innovation for People, Nature and Climate campaign to catalyze a step change in agricultural innovation. The series of webinars will present the findings of five evidence reviews commissioned under the Research, Development and Deployment (RD&D) strand of the Sustainable Agriculture component of the COP26 Nature Campaign. The second webinar in the series focuses on agroecology and climate change adaptation and mitigation. Agroecology is increasingly promoted as a means to transform food systems globally, yet the evidence for generating large-scale impacts on climate change adaptation and mitigation in developing countries has been ...
Data from commercial oil palm operations were analyzed for a whole plantation to rank individual blocks according to their ability to respond to applied fertilizer. The ranking was used to guide fertilizer management by diverting fertilizer from unresponsive blocks to those that are more responsive. Although the inferences lack statistical validity, they appear robust from a practical viewpoint. They are easy to evaluate in the field, since they require no upscaling from or interpretation of experimental data. [Oberthür, T. et al. Plantation Intelligence applied Oil Palm operations: unlocking value by analyzing commercial data. The Planter 93, 339–351 (2017)]
A Precision Ag Insight webcast on Maximizing the Value of On-Farm Research was presented on December 8th 2016 by Godsey Precision Ag. You can find the slides and the recording here.
The OFE-C is consolidating occurrences of farmer-led research, farmer-centric on-farm experimentation, living labs, or the like. Our goal is to map and feature these initiatives all around the world. Drop us a short notice about what and whom you know!
Thanks to the many who have answered our quick survey posted in the On-Farm Experimentation Community Info No. 1. We asked you to select any combination among the following themes: Creation/sharing of value and intellectual property Farmer-centric, co-learning and social aspects Data, metadata, analytics, modelling, artificial intelligence Transformation through policy, legislation and investment All aspects generated interest, but primarily the data and analytics, and the farmer-centric ones. We will soon come back to you with more about how we intend to make progress along those lines.
Farmers struggle to use data for decision-making. A survey of over 1500 farmers demonstrated high rates of data collection but low rates of data usage. Participants to the conference “Identifying Obstacles to Applying Big Data in Agriculture” defined scenarios in which on-farm decisions could benefit from the application of Big Data. Common obstacles identified included errors in the data, inaccessibility of the data, unusability of the data, incompatibility of data generation and processing systems, the inconvenience of handling the data, the lack of a clear return on investment (ROI) and unclear ownership. One solution: Standards or guidelines for farmers ...