Using GIS Datasets to Classify Maize‐Growing Regions in Mexico and Central America

Abstract

AbstractThe environments in which maize (Zea mays L.) germplasm originated and in which it is evaluated can substantially affect results from germplasm evaluations, thus influencing where the germplasm will eventually be used. The adaptation classifications normally used (e.g., temperate, tropical, subtropical, and highland) are imprecise. Our objectives were to (i) apply multivariate statistical techniques to spatial GIS (geographic information system) datasets of agroclimatic data to group similar maize‐growing regions in Mexico and Central America and then (ii) use the groups to refine the mega‐environments developed by CIMMYT maize breeders to help manage their germplasm. Data for this region were extracted from a GIS‐compatible global spatial climate dataset. Variables analyzed (based on long‐term monthly averages) included mean maximum and minimum monthly air temperatures, absolute maximum and minimum air temperatures based on each year's monthly data, mean monthly temperatures and precipitation, total precipitation, and mode of the elevations in the grid. The best grouping of similar regions resulted when cluster analysis on 7 mo of growing season data (April through October) was used to obtain 25 groups. The 25 groups were then classified into 10 maize ecologies corresponding to CIMMYT's mega‐environments. The ecologies included three lowland, three highland, two subtropical, and two transitional from subtropical to highland. The technique will be an important aid in classifying and using northern Latin America's large quantity of diverse maize germplasm.