Knowledge Management in Agriculture

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		MJ Woodward-Greene	--	2037	2023-04-24 18:31:21	\|
2	format correction	Dean Liu	-6 word(s)	2031	2023-04-25 02:54:34	\|

This entry is adapted from the peer-reviewed paper 10.3390/land12030588

Achieving global food security requires better use of natural, genetic, and importantly, human resources—knowledge. Technology must be created, and existing and new technology and knowledge deployed, and adopted by farmers and others engaged in agriculture. This requires collaboration amongst many professional communities world-wide including farmers, agribusinesses, policymakers, and multi-disciplinary scientific groups. Each community having its own knowledge-associated terminology, techniques, and types of data, collectively forms a barrier to collaboration. Knowledge management (KM) approaches are being implemented to capture knowledge from all communities and make it interoperable and accessible as a “group memory” to create a multi-professional, multidisciplinary knowledge economy.

agriculture communities of practice food security geographic information systems (GIS) knowledge management ShEx data interoperability USDA National Agricultural Library

1. Agricultural Thesauri

Bridging different cultures and facilitating international knowledge flows is essential for facilitating interoperability across location-specific knowledge repositories, and a basis for information search and discovery across multi-lingual boundaries. Thesaurus-driven and other efforts in agriculture employing linked data and semantic web standards have proven invaluable for data and knowledge interoperability and discovery especially across cultures and continents ^[1].

Early KM initiatives in agriculture include the three major agricultural thesauri. (1) AGROVOC, from the Food and Agriculture Organization (FAO) of the United Nations, which was started in the 1980s to facilitate international knowledge flows. This initiative started in the form of print catalogues for describing documents, etc., and evolved into a multi-lingual thesaurus and controlled vocabulary consisting of over 40,000 concepts and 900,000 terms in 41 languages (https://www.fao.org/agrovoc/about, accessed on 1 February 2023). (2) The CAB Thesaurus (https://www.cabi.org/cabthesaurus, accessed on 1 February 2023) in the UK, which has been in use since 1983 and currently contains over three million English terms. And (3), the National Agricultural Thesaurus (https://agclass.nal.usda.gov, accessed on 1 February 2023) from the USDA, National Agricultural Library (NAL), which is of the same era. The first digital National Agricultural Thesaurus version appeared in 2002 in English and has been available in Spanish since 2008. All three of these agricultural thesauri have historically been used for subject indexing at each of their home institutions, for total coverage estimated at over 25 million bibliographic records. This is in addition to other organizations using the terms for their subject indexing ^[2].

The FAO, CABI, and NAL have a long history of collaboration and innovation to enhance semantic web interoperability. The concept data in the three thesauri use persistent uniform resource identifiers (URIs) as a single label representing each concept in all its forms (languages, synonyms, related terms, etc.) and these URI are linked by extensive mappings of mutual concepts between all three thesauri. Together the FAO, CABI and NAL curators developed the Global Agricultural Concept Space or ‘GACS’ as a namespace of concepts relevant to food and agriculture, which included the creation of GACS first and only sub-scheme, GACS Core (http://browser.agrisemantics.org/gacs/en/, accessed on 1 February 2023). The fundamental idea behind selection of terms in GACS is essentially a Venn diagram consisting of the most frequently used (i.e., important) concepts in agriculture from these three resources, based on the subject indexing of the millions of records managed by FAO, CABI, and NAL. Curation of GACS ceased in 2016, but the vision for GACS lives on in National Agricultural Library Thesaurus (NALT), which was first published as the NALT Concept Space (NALT) in 2022, with its first sub-scheme, NALT Core, based on GACS.

With the advent of graph technology, whether as RDFlabelled property graph databases such as Neo4j—or Wikidata type graphs, the data sources include more and more entities, and as many relationships between them as possible, for an incredible, complex web of knowledge. The ability to add information, including properties that are optional, i.e., not constrained by rows and columns order, can be challenging for data consumers. Shape Expressions (ShEx) allows applications and users to declare what should be in the RDF—and validate against that standard ^[3].

2. Embedding Knowledge Management in Ag Research Institutions: USDA

The Agricultural Research Service (ARS) is USDA’s main in-house research agency, and one of the largest agricultural research organizations in the world. Like the multi-national corporations of the 1990s, USDA-ARS faces geographic and cultural challenges regarding agency data, information, and knowledge use. This is due to the wide-ranging locations of its research centers within the United States, and throughout the world, and the multi-disciplinary scientific research efforts conducted by its scientists and collaborators; with each scientific discipline having its own culture (own knowledge with associated terminology, techniques, and forms of data and models).

Partnerships for Data Innovations (PDI) was formed out of recognition that USDA-ARS needed to scale through better data management to meet the demands of the agricultural community. The ARS scientists and collaborators produce large volumes of Big Data (data that varies in volume, variety, velocity) from many sources (institutional center-based and farm-based) that is siloed, geographically dispersed, and unmanaged. The PDI is a USDA-ARS enterprise-wide research architecture initiative, and associated staff, implemented to efficiently leverage geographically dispersed ARS- and collaborator- multidisciplinary research operations and accelerate agricultural research through standardization, automation, and integration of this data ^[4]. It is also designed to balance the need for expediting on-farm research while addressing the concerns around de-identifying farm geospatial data to protect privacy and individual producer competitive advantage ^[5]. Finally, PDI is by design a partnership between government, academia, and the agricultural business community attempting to ensure information and knowledge flows between these aspects of the agricultural enterprise. In other words, PDI is creating a test bed for the KM concepts presented.

While PDI emerged out of the need to capture and curate unmanaged, highly valuable Big Data from scientists, it is morphing into a KM initiative providing easy-to-use tools for the next generation of scientists and collaborators to capture data, information, and knowledge as a byproduct of their daily work. The KM approaches are needed because (1) as mentioned above, agency knowledge and information assets are currently siloed within locations and scientific disciplines, (2) the task of finding desired experts and desired knowledge and information needed by scientists, policymakers, and farmers and other end-users can be a challenge, and (3) tacit knowledge possessed by agency staff and collaborators is not being captured and is at risk of being lost due to retirements, etc. Relying on the previously discussed approaches from the business world, the formalisms from academia, and the ICT technology developed for KM, the KM at the ARS is effectively becoming the process of capturing tacit and explicit knowledge from scientists and collaborators and transforming it into managed explicit agricultural knowledge products for eventual curation at the NAL where it will be available for use, with permissions, by members of the agricultural enterprise.

PDI is also working with NAL to support development of standard data shapes, utilizing NALT concepts and iterative communications with research community domains to expand on the ‘simple’ semantics provided by NALT, to develop more complex standard data shapes expressions with standard data shape languages being standardized in W3C and the IEEE (IEEE https://standards.ieee.org/ieee/3330/11119/). NAL is using Shape Expression Language (ShEx) to build standard shapes to validate data as it is incoming or queried for aggregation of properties. Working with researchers to model their data relationships in graph form. These data shapes combined with NALT URIs will enable data interoperability beyond the constraints of any closed data base or source.

3. Ensuring Knowledge Exchange with and within the Farmer Community

Location-specific knowledge of intended beneficiaries, such as farmers, has often been overlooked despite farmers and other intended beneficiaries being best suited to determine which solutions are most pertinent to their specific needs ^[6]^[7]. Farmers have knowledge about local production contexts and practices and are themselves key sources of innovation and adaptations of technology to local conditions as part of their farming process ^[7]^[8]^[9]. Farmers need to be considered generators of tacit knowledge as well as users of explicit knowledge from academic, business, and governmental institutions. Importantly, integration of farmer participation and knowledge into solution development has been shown to increase farmer adoption of new technologies or solutions ^[10]^[11]^[12]. Solutions to agricultural problems are only impactful if they are used.

Grass-roots research and development efforts that embed farmers in research programs are being utilized to coalesce farmer and scientific knowledge communities ^[9]^[13]^[14]^[15]^[16]. On-farm research, where scientific research occurs in farmer fields, is being used to embed scientific research in farm management. This research occurs at scales meaningful to farmers, acknowledges specific farming realities, and creates value through co-learning and the combination of knowledge pools. On-farm experimentation initiatives involve well over 30,000 farms in more than 30 countries globally ^[9]. Another approach to coalescing farmer and scientific knowledge has been implemented at the International Maize and Wheat Improvement Center (CIMMYT) in Mexico. Here hubs are developed to build a network of farmers, farm advisors, scientists, research centers, and other actors that collaborate around local solutions to enhance productivity and sustainability of cropping systems. Hub participants implement and adapt best practices resulting from research programs and compare them with conventional practices. In this way, long-term knowledge and methods developed by generations of farmers is integrated with modern scientific methodology and technology ^[14]^[17]. In China, scientists have been embedded in villages among farmers to facilitate knowledge exchange between scientific and farmer communities in the Science and Technology Backyard program ^[16]. By living among farmers, scientists have been able to identify local factors that contributed to yields that were lower than attainable yields (e.g., use of seed varieties not suited for local conditions, improper seed planting density, incorrect tillage depth, improper sowing and harvest dates, improper fertilizer regimen); attainable yield being yield achieved using optimal cropping system management. When these limitations and farmers’ concerns were addressed, farmers adopted the recommended management practices and improved yield from 68% of attainable yield to 97% ^[16].

Farmers use many sources of knowledge (e.g., agriculture extension systems, farm advisors, NGOs, regulatory agencies), but for many, informal participatory farmer networks are key. Informal networks that include farmers lead to learning and innovation as well as adoption and successful implementation of new solutions and technologies ^[7]^[18]^[19]^[20]. Farmer Field Schools have been implemented where groups of farmers meet regularly to gain knowledge and adopt new farming practices; farming practices that can result in higher yields, increased sustainability, and higher incomes ^[21]. For example, Farmer Field Schools have been used extensively in implementation of Integrated Pest Management (IPM) approaches for more sustainable control of plant pathogens and pests worldwide ^[7]^[18]^[21]. The IPM approaches substitute agronomic and biological approaches for pesticides, but also require more information and management skills of farmers to implement and manage effectively. Farmer Field Schools and their use to collectively create and deploy knowledge of agroecology, problem solving, skills and their group building and development of social capital for collective decision making are one of the important underpinnings leading to development and spread of IPM. Collective information and knowledge matter greatly for IPM approaches, as coordinated, community-scale decision making by many farmers whose farms together cover large landscapes is necessary for successful outcomes ^[18]. Farmer Field Schools have been implemented where millions of smallholder farmers participated across Asia, Africa, and Latin America. Other participatory learning frameworks have also been rolled-out in developed countries such as the United Kingdom, Denmark, and the United States ^[7]^[18]. To facilitate the acquisition of tacit knowledge from farmers, just as embedding scientists facilitated adoption of new practices, it may be useful to embed knowledge engineers in these informal participatory farmer networks to facilitate knowledge capture using published methodologies ^[22]^[23]^[24].

Going forward, possibilities exist for using Humanistic AI to relay knowledge to and from farmers and other end-users in the agricultural enterprise. Consider Apple’s Siri, the first commercially successful, scalable application of many of the KM approaches presented. When coupled with the more formal approaches, conversational systems like Siri provide an opportunity to automate knowledge exchange to both expedite the dissemination of new knowledge from agricultural science to the farmer and facilitate the capture of structured field data from farmers ^[25]. New, Ag Tech startups like Dexer (https://www.dexerspeed.com/, accessed on 1 February 2023) will catalyze the adoption of these new conversational systems by being built on a strong KM foundation from supporting agricultural institutions. Regardless, the technology is now mature enough for cost conscious industries like agriculture to make investments in tailoring it to agriculture.

References

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1. Agricultural Thesauri

2. Embedding Knowledge Management in Ag Research Institutions: USDA

3. Ensuring Knowledge Exchange with and within the Farmer Community

References