Yield prediction across a target production zone with varying strategies of agronomic practices has been a challenging problem to plant breeders when testing new genotypes for release. This study focused on comparing the importance of a new bioclimatic index called biological windows and six other traditional environmental indices as predictor variables of maize yields across sites (farmers' fields) and years, using a simple linear regression model. The yield data were collected for six hybrids evaluated in strip tests at 57 to 186 sites throughout Iowa during 1987-1994. The biological windows index was based on the Newhall Simulation Model and estimated the number of days the soil was moist and above 5°C. The environmental indices were July precipitation, temperature, the product of July precipitation and temperature, and the equivalent values for August. Because the actual values for the indices were not recorded at each site, all the indices were estimated for each site as the weighted averages of the data from 112 Iowa weather stations. Across years and within the Iowa sites, the mean percentiles of R-square distributions showed that biological windows had less predictive value for maize yields than the more traditional indicators such as August precipitation and temperature. For all indices, across years and within sites had much greater mean R-squares than across sites and within years, which had very low predictive values. For predicting yield across years within sites, there appeared to be an advantage in using August precipitation or the product of August precipitation and temperature over the live other indices. The R-square values for these two indices were at least 0.60 in 80% of the test sites for five hybrids.
ASJC Scopus subject areas
- Agronomy and Crop Science