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On-Farm Experimentation Community
The OFE-C is a new community gathering OFE researchers and practitioners interested in sharing and compiling existing resources online e.g.:
• guidelines to implement OFE with farmers
• examples of OFE, experiences and lessons learnt
• statistical solutions to analyze OFE data
• best practices to solve specific OFE problems
• examples of OFE, experiences and lessons learnt
• statistical solutions to analyze OFE data
• best practices to solve specific OFE problems
The community focuses on: 1) compiling practical, applied, proven-to-be-useful resources; 2) connecting practitioners e.g., OFE networks, extension personnel, analysts. The objective is to provide OFE practitioners with a “1-go-to” source of information.
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OFE-C Infos / Newsletters
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.
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://...more
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).]
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, ...more
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.
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 « ...more
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!
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 ...more
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.
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. ...more
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.]
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.
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.
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.
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!
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)]
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.
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.
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.
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.
“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 ...more
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:
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 ...more
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.
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.
“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 ...more
This guide from the Organic Farming Research Foundation (OFRF) is available to farmers for planning, carrying out, and analyzing experiments.
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, ...more
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.
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/...more
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 ...more
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.
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).
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.”
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 ...more
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.
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.
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 ...more
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. ...more
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 ...more
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 ...more