Spatial analysis to support geographic targeting of genotypes to environments

Abstract

Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.

Improvement of spatial modelling of crop suitability using a new digital soil map of Tanzania

“…Crop suitability models can be potentially useful tools for strategic spatial planning of agricultural activities, including risk assessments, as these models are capable of reproducing regional and global trends in crop production (Zullo et al 2006;Zabel et al 2014). Crop suitability model outcomes can also provide crop breeders with decision support for targeting genotypes to environments (Hyman et al 2013), or for identifying future requirements in crop adaptation (Beebe et al 2011;Jarvis et al 2012). A wide variety of algorithms exist, as illustrated by the following examples (see also Soberón and Nakamura 2009).…”

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

“…Moreover, trials are conducted by different entities following different protocols that hinder inter-comparison. Moreover, the geographical coordinates for trial sites data are often held outside public domain or are imprecise thus leading to error propagation in spatial models (Hyman et al 2013). Establishing a platform to harmonize protocols for maize trials data collection, archiving and open sharing would greatly improve delineation of extrapolation domains.…”

“…One of main challenge is lack of relevant tools to guide spatial targeting of new technologies especially in data limited and heterogeneous environments in Africa (Kumar and Jhariya 2015;Tesfaye et al 2016). Recently, the application of geospatial tools to generate extrapolation domains for agronomic technologies has been accentuated by increased availability of big spatial data on climate, soils and topography and robust modelling algorithms (Akıncı et al 2013;Elsheikh et al 2013;Hyman et al 2013). Most studies, for generating extrapolation domains for agronomic technologies apply a top-down geospatial approach that utilize machine learning algorithms to classify gridded environmental variables to relatively similar non-contiguous zones (e.g.…”

Improving agricultural knowledge management: The AgTrials experience

“…Genotypes can be targeted to the environments where they are most likely to succeed based on their performance in crop trials ( Hyman et al , 2013). Appropriately-targeted cultivars can improve yields substantially ( Annicchiarico et al , 2005; Annicchiarico et al , 2006), particularly when combined with site similarity methods, yielding information on analogous sites ( Jarvis et al , 2014; Jones et al , 2005; Ramírez et al , 2011).…”