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The First Conference on Farmer-Centric On-farm Experimentation — Digital Tools for a Scalable Transformative Pathway which is scheduled for October 13th-15th 2021 in Montpellier in a hybrid form (in-person and virtual).
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 ...
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.
The gap is still huge between researchers and farmers. More needs to be done to give them opportunities to have a say in how research happens. However, co-design requires more funding, and more patience. Many companies took part in AgriTech4.0 which was an opportunity to learn about new developments in sensors and data management, and promote the importance of including farmers from an early stage of development. The goal is to help ensure technological solutions are useful, practical and easy to use in farm contexts, ultimately driving uptake of these technologies on farms. Access to the 45+ recorded presentations can be ...
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.]
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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 ...
Dr. Véronique Bellon Maurel, INRAE, Director of #DigitAg, France Dr. Nicolas Tremblay, International Society of Precision Agriculture (ISPA), Canada Prof. Simon Cook, Murdoch University, Centre for Digital Agriculture, Australia
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.]
CSIRO’s Digiscape Future Science Platform and Responsible Innovation Future Science Platform will be hosting a one-day Cutting Edge Science symposium on Responsible AgTech Innovation (27 May 2021 8:30 am – 5:00 pm AEST). The event will bring together key stakeholders to explore how the combination of science, agriculture and responsible innovation can strengthen Australian farming and land management.
There is a growing need to quantify complex interactions of processes for diverse environmental conditions and crop management realities. Any study is worth very little in itself unless its data is being agglomerated with others to express conclusions valid for real use. In order to tear the agronomic data Babel Tower down, there is little alternative but to converge on standards, at least for a minimal set of them. Do you use a standard to construct your agronomic databases? If so, which one? Please fill in this two-question survey. The OFE-C will use the results to start a ...
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.]
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://...
Farm Hack is a worldwide community of farmers that build and modify their own tools. They share their hacks online and at meet-ups. Their work is licensed under a Creative Commons Attribution 4.0 International License.
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.]
Trevisan, R.G., Bullock, D.S. & Martin, N.F. Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation. Precision Agric 22, 342–363 (2021).
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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.
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.
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.
The long-term agroecosystem research (LTAR) network consists of eighteen sites located across the US and are situated within USDA Agricultural Research Service (ARS) units, universities, and non-profit conservation organizations. Research approaches include long-term monitoring efforts, common experiments, and modeling conditions.Sites are able to produce data and metadata in specified formats from local data management systems to share across the network via information technology (IT). Their data management portal provides guidance for LTAR data managers and researchers for managing data and information generated from LTAR science endeavors. These are some of the tools available: Cataloging published data Documenting, integrating ...
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.
Our website features a map locating OFE-C members around the world. As we want to make sure it is working as intended, we would like to know if you experience problem using it. More specifically, if you have had the "Sorry Something Went Wrong" message: Did the reset button solved the problem? If not, have you successfully tried F5? What is your browser? Thanks for your help in making sure the OFE-C tools are useful to our members.
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.
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.
What aspect(s) of on-farm experimentation are of interest to you? Let us know by filling this one-question survey.
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.
A lot of agricultural experimentation in Africa is conducted on farms. What can the rest of the word learn from OFE with an African flavour? A good place to start was the 1st African Conference on Precision Agriculture held just last week from 8 to 10 December. The African Plant Nutrition Institute, the Mohammed VI Polytechnic University and the International Society of Precision Agriculture were behind this successful inaugural event. even hundred participants at 14 sites representing 51 countries were put together on this occasion. On-demand content will soon be made available on the conference website for participants.
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 Conference will be preceded by four thematic workshop webinars in May. These webinars will each be prepared by a dedicated working group. Value creation: through co-producing and sharing knowledge People and processes: avoiding the tech fallacy, building ecosystem innovation platforms Data and analytics: large-scale agronomy, experimental designs, aggregation, metadata Policy linkages: to support innovation, and for data governance
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).
To share global experience of farmer-centric On-Farm Experimentation (OFE) To explain the innovation process that OFE represents, as an alternative to the top-down, researcher-directed process that currently predominates How OFE creates value to sustain itself; The alternative innovation pathways of the OFE bottom-up processes; How OFE offers a pathway for valorising digital technologies in agriculture; and The interplay between OFE practice on the ground and supporting policy and investment.
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 four OFE2021 webinars recordings are now available at https://www.ispag.org/Events/OFE Value Creation. OFE creates value for varied stakeholders, value is shared, and scientific disciplines can contribute. May 10, 2021. People and Processes. People are the real key to digital transformation. Transformation occurs when individual changes scale up through their networks and their organizations. May 12, 2021. Data and Analytics. Appropriate procedures and well-targeted analytics can be deployed to exploit the valuable data and metadata collected on the farm. May 17, 2021. Policy Linkages. Currently, initiatives around OFE are happening in spite of funding mechanisms, career paths and norms favoring traditional ...
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!
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).
The conference is supported by the OECD Co-operative Research Program (CRP) “Biological resource management for sustainable agricultural systems,” under Theme 3 — Transformational technologies and innovation. It is organised by INRAE Montpellier and the On-Farm Experimentation Community (OFE-C) of the International Society of Precision Agriculture (ISPA). Support is also provided by Agropolis Foundation, Occitanie Region, MUSE (Montpellier University of Excellence), Agreenium, #Digitag, RMT NAEXUS, RMT Modelia and Occitanum.
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 International Data Week (IDW 2021) is scheduled for 8–11 November 2021 in Seoul, South Korea, in a hybrid form, under the theme “Data to Improve our World.” Open Science and the FAIR Principles are important enablers of discovery and innovation, particularly the use of Big Data and Artificial Intelligence. The IDW 2021 is supported by the International Science Council’s Committee on Data (CODATA) and World Data System (WDS), and the Research Data Alliance (RDA).
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.
The OFE-C is collecting and centralizing on-farm research guides in a repository. Do you have any to suggest? Please send the document or its path in an email. You will find a few examples of what we are looking for in the following articles.
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)]
The OFE-C will release in January a newsletter with information about on-farm experimentation. Should you have something to communicate to our readership, please submit to this email.
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.
This message has been sent to you because you are a member of, or have shown interest in OFE. If you know a colleague who would like to be kept in the loop, please transfer this link to fill the OFE-C Expression of Interest Signup. You will find the past Info editions at the same place. If you want to access to the full opportunities of the OFE-C and the ISPA please join.
The Yield Enhancement Network (YEN) in the UK supports arable crops innovators since 2012. Learn about the impressive achievements of YEN growers in the though 2020 season. Awards were given for best yields or best % or potential yield. See the recording here.
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 ...