Person:
Craufurd, P.

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Craufurd
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Craufurd, P.
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Author: Chamberlin, J. ×

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Now showing 1 - 6 of 6
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    Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries
    (Elsevier B.V., 2023) Sida, T.S.; Gameda, S.; Chamberlin, J.; Andersson, J.A.; Getnet, M.; Woltering, L.; Craufurd, P.
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    How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia
    (Elsevier, 2021) Kosmowski, F.; Chamberlin, J.; Hailemariam Ayalew; Sida, T.S.; Abay, K.A.; Craufurd, P.
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    Adapting the QUEFTS model to predict attainable yields when training data are characterized by imperfect management
    (Elsevier, 2021) Ravensbergen, A.P.P.; Chamberlin, J.; Craufurd, P.; Shehu, B.M.; Hijbeek, R.
    Publication
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    Implications of intra-plot heterogeneity for yield estimation accuracy: evidence from smallholder maize systems in Ethiopia
    (Elsevier, 2021) Sida, T.S.; Chamberlin, J.; Hailemariam Ayalew; Kosmowski, F.; Craufurd, P.
    Publication
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    Farmers’ preferences for high-input agriculture supported by site-specific extension services: evidence from a choice experiment in Nigeria
    (Elsevier, 2019) Oyakhilomen Oyinbo; Chamberlin, J.; Vanlauwe, B.; Liesbet Vranken; Kamara, A.Y.; Craufurd, P.; Maertens, M.
    Agricultural extension to improve yields of staple food crops and close the yield gap in Sub-Saharan Africa often entails general recommendations on soil fertility management that are distributed to farmers in a large growing area. Site-specific extension recommendations that are better tailored to the needs of individual farmers and fields, and enabled by digital technologies, could potentially bring about yield and productivity improvements. In this paper, we analyze farmers' preferences for high-input maize production supported by site-specific nutrient management recommendations provided by an ICT-based extension tool that is being developed for extension services in the maize belt of Nigeria. We use a choice experiment to provide ex-ante insights on the adoption potentials of site-specific extension services from the perspective of farmers. We control for attribute non-attendance and account for class as well as scale heterogeneity in preferences using different models, and find robust results. We find that farmers have strong preferences to switch from general to ICT-enabled site-specific soil fertility management recommendations which lend credence to the inclusion of digital technologies in agricultural extension. We find heterogeneity in preferences that is correlated with farmers' resource endowments and access to services. A first group of farmers are strong potential adopters; they are better-off, less sensitive to risk, and are more willing to invest in a high-input maize production system. A second group of farmers are weak potential adopters; they have lower incomes and fewer productive assets, are more sensitive to yield variability, and prefer less capital and labor intensive production techniques. Our empirical findings imply that improving the design of extension tools to enable provision of information on the riskiness of expected outcomes and flexibility in switching between low-risk and high-risk recommendations will help farmers to make better informed decisions, and thereby improve the uptake of extension advice and the efficiency of extension programs.
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    A spatial framework for ex-ante impact assessment of agricultural technologies
    (Elsevier, 2019) Andrade, J.F.; Rattalino Edreira, J.I.; Farrow, A.; Van Loon, M.P.; Craufurd, P.; Rurinda, J.; Shamie Zingore; Chamberlin, J.; Claessens, L.; Adewopo, J.; Ittersum, M.K. van; Cassman, K.G.; Grassini, P.
    Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.
    Publication