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Design of Digital Agricultural Extension Tools: Perspectives from Extension Agents in Nigeria
Author(s) -
Oyinbo Oyakhilomen,
Chamberlin Jordan,
Maertens Miet
Publication year - 2020
Publication title -
journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.157
H-Index - 61
eISSN - 1477-9552
pISSN - 0021-857X
DOI - 10.1111/1477-9552.12371
Subject(s) - extension (predicate logic) , scope (computer science) , agricultural extension , computer science , interface (matter) , preference , ex ante , attendance , production (economics) , knowledge management , agriculture , economics , microeconomics , geography , economic growth , archaeology , programming language , bubble , maximum bubble pressure method , parallel computing , macroeconomics
Abstract Given the marked heterogeneous conditions in smallholder agriculture in Sub‐Saharan Africa, there is a growing policy interest in site‐specific extension advice and the use of digital extension tools to provide site‐specific information. Empirical ex‐ante studies on the design of digital extension tools and their use are rare. Using data from a choice experiment in Nigeria, we elicit and analyze the preferences of extension agents for major design features of ICT‐enabled decision support tools (DSTs) aimed at site‐specific nutrient management extension advice. We estimate different models, including mixed logit, latent class and attribute non‐attendance models. We find that extension agents are generally willing to use such DSTs and prefer a DST with a more user‐friendly interface that requires less time to generate results. We also find that preferences are heterogeneous: some extension agents care more about the effectiveness‐related features of DSTs, such as information accuracy and level of detail, while others prioritise practical features, such as tool platform, language and interface ease‐of‐use. Recognising and accommodating such preference differences may facilitate the adoption of DSTs by extension agents and thus enhance the scope for such tools to impact the agricultural production decisions of farmers.