Abstract
Low technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.
Highlights
Agricultural production and yields in developing countries have been lower than those of developed countries over the past few decades
This study aims to address the limitations of generic datadriven extension tools by suggesting an approach to inform advisors to “diagnose” farm-specific agricultural issues more quantitatively
Further special classes of farms are identified; the “Best Practice Farms” which can serve as a source of successful traditional or modern knowledge, “Critical Farms” which perform relatively poorly and would need critical focus urgently, and “Quick Improvement Farms” with low Water Use Efficiency (WUE) despite relatively better Soil Quality Index (SQI)
Summary
Agricultural production and yields in developing countries have been lower than those of developed countries over the past few decades. Agricultural technologies, along with agricultural knowledge are disseminated using agricultural extension services (or advisories) by governments and international organizations to farmers and rural inhabitants worldwide (Anderson and Feder, 2007; Nyarko and Kozári, 2021). Despite considerable investment and experience over decades (Anderson and Feder, 2007), there has been limited evidence to support the impact of agricultural extension on agricultural knowledge, technology adoption and improved productivity (Aker, 2011). In the developing world, agricultural extension has been described as “failing” (Government of Malawi, 2000), “moribund” (Eicher, 2001), “in disarray or barely functioning at all” (Rivera et al, 2001), or ineffective in responding to farmer demands and technological challenges (Ahikiriza et al, 2021). Risk preferences, education, access and affordability of information and learning (Aker, 2011) can result in technology adoption slowing down and becoming more discontinuous, further threatening agricultural productivity (Oduniyi, 2021)
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