Peter Jones

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 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 five or ten 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.

Jones, P. G. and P. K. Thornton. 2002. Spatial modeling of risk in natural resource management. Conservation Ecology 5(2): 27.

A software package to generate daily weather data for Latin America and Africa is described. The program is based on a stochastic weather generator that uses a third‐order Markov process to model daily weather data. The model has been fitted to data from more than 9200 stations with long runs of daily data throughout the world. The climate normals for these stations were assembled into 664 groups using a clustering algorithm. For each of these groups, rainfall model parameters are predicted from monthly means of rainfall, air temperature, diurnal temperature range, and station elevation and latitude. The program identifies the cluster relevant to any required point using interpolated climate surfaces at a resolution of 10 min of arc (18 km²) and evaluates the model parameters for that point. The application currently contains surfaces for Latin America and Africa, and other regions will later be added. Use of the software is demonstrated by generating daily weather data files for running one of the DSSAT crop models.