Abstract
Abstract. Sustainable intensification (SI) is a viable pathway to increase agricultural production and improve ecosystem health. Scaling SI technologies in locations with similar biophysical conditions enhance adoption. This paper employs novel extrapolation detection (ExeDet) algorithm and gridded bioclimatic layers to delineate extrapolation domains for improved maize variety (SC719) and inorganic fertilizers (YaraMila-CEREAL® and YaraBela-Sulfan®) in Tanzania. Suitability was based on grain yields recorded in on-farm trials. The ExeDet algorithm generated three maps: (1) the dissimilarity between bioclimatic conditions in the reference trial sites and the target extrapolation domain (Novelty type-1), (2) the magnitude of novel correlations between covariates in extrapolation domain (Novelty type-2) and (3) the most limiting covariate. The novelty type1 and 2 maps were intersected and reclassified into five suitability classes. These classes were cross-tabulated to generate extrapolation suitability index (ESI) for the candidate technology package. An impact based spatial targeting index (IBSTI) was used to identify areas within the zones earmarked as suitable using ESI where the potential impacts for out scaling interventions can be maximized. Application of ESI and IBSTI is expected to guide extension and development agencies to prioritize scaling intervention based on both biophysical suitability and potential impact of particular technology package. Annual precipitation was most limiting factor in largest area of the extrapolation domain. Identification of the spatial distribution of the limiting factor is useful for recommending remedial measures to address the limiting factor that hinder a technology to achieve its full potential. The method outlined in this paper is replicable to other technologies that require extrapolation provided that representative reference trial data and appropriate biophysical grids are available.
Highlights
Food insecurity is a prevalent problem in sub-Sahara Africa (SSA) and the situation is worsened by increasing human population
Scaling involves a degree of extrapolation to areas where the range of environmental variables is beyond that observed in the reference trial sites or areas with new combinations of environmental variables (Zurell et al 2012, Owens et al 2013, Mesgaran et al 2014)
This paper generates extrapolation suitability index (ESI) map as a simple method for visualizing risk associated with extrapolating agronomic technologies beyond the environmental conditions observed in their trial sites
Summary
Food insecurity is a prevalent problem in sub-Sahara Africa (SSA) and the situation is worsened by increasing human population (van Ittersum et al 2016). Adoption of improved crop varieties that are high yielding and tolerant to drought, pests and diseases is one of the promising pathway to increase food production (Asfaw et al 2012, Kassie et al 2013, Kassie et al 2014, Fisher et al 2015). This paper generates extrapolation suitability index (ESI) map as a simple method for visualizing risk associated with extrapolating agronomic technologies beyond the environmental conditions observed in their trial sites. This is demonstrated using agronomic technology package comprising of improved. This study posits that accounting for correlation structure between biophysical covariates improves delineation of the risk of extrapolating agronomic technologies to novel environments
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