Accounting for correlation among environmental covariates improves delineation of extrapolation suitability index for agronomic technological packages

Abstract

This paper generates an extrapolation suitability index (ESI) to guide scaling-out of improved maize varieties and inorganic fertilizers. The bestbet technology packages were selected based on yield gap data from trial sites in Tanzania. A modified extrapolation detection algorithm was used to generate maps on two types of dissimilarities between environmental conditions at the reference sites and the outlying projection domain. The two dissimilarity maps were intersected to generate ESI. Accounting for correlation structure among covariates improved estimate of risk of extrapolating technologies. The covariate that highly limited the suitability of specific technology package in each pixel was identified. The impact based spatial targeting index (IBSTI) identified zones that should be prioritized to maximize the potential impacts of scaling-out technology packages. The proposed indices will guide extension agencies in targeting technology packages to suitable environments with high potential impact to increase probability of adoption and reduce risk of failure.

Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa

“…Maps on spatial-temporal variations and trends of monthly and annual rainfall generated in this study would be key inputs in spatially explicit models (e.g. Muthoni et al 2017;Rubiano et al 2016) that generate specific recommendation domains for specific basket of technologies based on crop trials in diverse rainfall regimes.…”

“…Rainfall is the most important limiting factor in rain-fed farming systems in Africa (Niles et al 2015) since it determines availability of soil moisture required for potential productivity. The amount and distribution of rainfall determines suitability of crop varieties and related agronomic management at different locations (Muthoni et al 2017). Low or sub-optimal rainfall cause agricultural drought that retard plant growth and reduced yields (Zampieri et al 2017;Zipper et al 2016) while extremely high rainfall events cause floods that destroy crops.…”

Effect of Seasonal Drought on the Agronomic Performance of Four Banana Genotypes (Musa spp.) in the East African Highlands

“…Therefore, rainfall is the most important limiting factor in rain-fed farming in Africa [2]. The amount and distribution of rainfall versus the evaporative demand and root depth determine the suitability of crop varieties and related agronomic management [3,4]. Drought arises when the evaporative demands are too high, resulting in a water deficit that delays plant growth and reduces crop yields [5].…”

Extrapolation suitability index for sustainable vegetable cultivation in Babati district, Tanzania

“…Geospatial data and tools are applied to generate recommendation domains for sustainable agricultural intensification technologies as they offer the speed, flexibility, and power to synthesize big data on land resources (Hyman et al, 2013;Usha and Singh, 2013;Muthoni et al, 2019). Spatial frameworks for mapping crop suitability follow a 'top-down' or a 'bottom-up' approach.…”

“…The top-down approach utilizes a multi-criteria analysis to classify remote sensing data into clusters based on predetermined criteria or expert knowledge but without direct reference to the on-farm trial sites (e.g., Tesfaye et al, 2015;Muthoni et al, 2017). The bottom-up approach utilizes data from field trials as the benchmark of the suitable environmental conditions for agronomic technology, followed by a spatial search of other areas with a similar context in the wider geographical area (Rubiano et al, 2016;Muthoni et al, 2019). The top-down approach is the most applied to generate the spatial recommendation domains for agronomic technologies (Tesfaye et al, 2015;Notenbaert et al, 2016), primarily due to the increasing free availability of gridded biophysical and socio-economic layers.…”