Lm. Pollak et Jd. Corbett, USING GIS DATASETS TO CLASSIFY MAIZE-GROWING REGIONS IN MEXICO AND CENTRAL-AMERICA, Agronomy journal, 85(6), 1993, pp. 1133-1139
The environments in which maize (Zea mays L.) germplasm originated and
in which it is evaluated can substantially affect results from germpl
asm 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 w
ere to (i) apply multivariate statistical techniques to spatial GIS (g
eographic information system) datasets of agroclimatic data to group s
imilar maize-growing regions in Mexico and Central America and then (i
i) use the groups to refine the mega environments developed by CIMMYT
maize breeders to help manage their germplasm. Data for this region we
re extracted from a GIS-compatible global spatial climate dataset. Var
iables analyzed (based on long-term monthly averages) included mean ma
ximum and minimum monthly air temperatures, absolute maximum and minim
um air temperatures based on each year's monthly data, mean monthly te
mperatures and precipitation, total precipitation, and mode of the ele
vations in the grid. The best grouping of similar regions resulted whe
n cluster analysis on 7 mo of growing season data (April through Octob
er) was used to obtain 25 groups. The 25 groups were then classified i
nto 10 maize ecologies corresponding to CIMMYT's mega environments. Th
e ecologies included three lowland, three highland, two subtropical, a
nd two transitional from subtropical to highland. The technique will b
e an important aid in classifying and using northern Latin America's l
arge quantity of diverse maize germplasm.