Decision Support Tools for Site-Specific Fertilizer Recommendations and Agricultural Planning in Selected Countries in Sub-Sahara Africa

MacCarthy, Dilys S.; Kihara, Job; Masikati, Patricia; Adiku, S.G.K.

doi:10.1007/978-3-319-58792-9_16

Cited by 16 publications

(14 citation statements)

References 82 publications

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“…Inadequate use of crop models is attributed to a lack of application in policy formulation and low capacity (MacCarthy, Kihara, Masikati, & Adiku, 2017). Using crop models as decision support tools by both end‐users, researchers, planners, and policymakers could lead to efficient use of nutrients in agricultural productivity.…”

Section: Resultsmentioning

confidence: 99%

Projecting maize yield under local‐scale climate change scenarios using crop models: Sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates

Chisanga

Phiri

Chinene

et al. 2020

Food and Energy Security

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Section: Resultsmentioning

confidence: 99%

Projecting maize yield under local‐scale climate change scenarios using crop models: Sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates

Chisanga

Phiri

Chinene

et al. 2020

Food and Energy Security

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“…Improving soil fertility is challenging because of the large spatio-temporal heterogeneity in biophysical and socio-economic conditions of smallholder farming systems (Tittonell et al, 2010; Vanlauwe et al, 2015b; Njoroge et al, 2017; MacCarthy et al, 2018). Given an average low level of input use, it is often argued that smallholder farmers in SSA need to intensify the use of external inputs, especially inorganic fertilizer, in order to improve yields and productivity (Chianu and Tsujii, 2005; Duflo et al, 2011; Wiredu et al, 2015; Sheahan and Barrett, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…a region, a district or a province (Tittonell and Giller, 2013; Kihara et al, 2016; Shehu et al, 2018). Such agricultural extension practices fail to take into account the heterogeneous and complex biophysical and socio-economic conditions of smallholder farming (MacCarthy et al, 2018; Kihara et al, 2016). Site-specific agricultural extension, on the other hand, includes recommendations that are tailored to the situation of an individual farmer or field.…”

Section: Introductionmentioning

confidence: 99%

Farmers' preferences for high-input agriculture supported by site-specific extension services: Evidence from a choice experiment in Nigeria

Oyinbo

Chamberlin

Vanlauwe

et al. 2019

Agricultural Systems

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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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“…Traditional extension systems in Sub-Saharan Africa (SSA) provide very general agronomic advice, such as fertiliser use recommendations, across wide and highly heterogeneous environments (Shehu et al, 2018;Theriault et al, 2018). Such information fails to take into account the spatial and temporal variability in biophysical and socio-economic conditions within a given national or regional context (MacCarthy et al, 2018;Jayne et al, 2019). The use of digital decision support tools (DSTs), enabled by modern information and communication technology such as smartphones and tablets, is increasingly promoted for more effective delivery of agronomic information tailored to the site-specific conditions of individual farmers (Bernet et al, 2001;Kragt and Llewellyn, 2014;MacCarthy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Such information fails to take into account the spatial and temporal variability in biophysical and socio-economic conditions within a given national or regional context (MacCarthy et al, 2018;Jayne et al, 2019). The use of digital decision support tools (DSTs), enabled by modern information and communication technology such as smartphones and tablets, is increasingly promoted for more effective delivery of agronomic information tailored to the site-specific conditions of individual farmers (Bernet et al, 2001;Kragt and Llewellyn, 2014;MacCarthy et al, 2018). A growing number of agronomy DSTs are being developed or have recently been developed in SSA, including tools specific for maize ('Maize-Variety-Selector', 'Maize-Seed-Area'), for rice ('RiceAdvice', 'WeedManager'), for cassava ('Akilimo'), for cocoa ('CanOvaLator') and for crops in general ('Farmbook', 'Fertilizer Optimizer', 'FAMEWS').…”

Section: Introductionmentioning

confidence: 99%

Design of Digital Agricultural Extension Tools: Perspectives from Extension Agents in Nigeria

Oyinbo¹,

Chamberlin²,

Maertens³

2020

J Agricultural Economics

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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.

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Decision Support Tools for Site-Specific Fertilizer Recommendations and Agricultural Planning in Selected Countries in Sub-Sahara Africa

Cited by 16 publications

References 82 publications

Projecting maize yield under local‐scale climate change scenarios using crop models: Sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates

Projecting maize yield under local‐scale climate change scenarios using crop models: Sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates

Farmers' preferences for high-input agriculture supported by site-specific extension services: Evidence from a choice experiment in Nigeria

Design of Digital Agricultural Extension Tools: Perspectives from Extension Agents in Nigeria

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