A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Abstract

Effective site-specific management requires an understanding of the soil and environmental factors influencing crop yield variability. Moreover, it is necessary to assess the techniques used to define these relationships. The objective of this study was to assess whether statistical models that accounted for heteroscedastic and spatial-temporal autocorrelation were superior to ordinary least squares (OLS) models when evaluating the relationship between corn (Zea mays L.) yield and soil attributes in Brazil. The study site (10 by 250 m) was located in Sao Paulo State, Brazil. Corn yield (planted with 0.9-m spacing) was measured in 100 4.5- by 10-m cells along four parallel transects (25 observations per transect) during six growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. Ordinary least squares, generalized least squares assuming heteroscedasticity (GLS(he)), spatial-temporal least squares assuming homoscedasticity (GLS(sp)), and spatial-temporal assuming heteroscedasticity (GLS(he-sp)) analyses were used to estimate corn yield. Soil acidity (pH) was the factor that most influenced corn yield with time in this study. The OLS model suggested that there would be a 0.59 Mg ha(-1) yield increase for each unit increase in pH, whereas with GLS(he-sp) there would be a 0.43 Mg ha(-1) yield increase, which means that model choice impacted prediction and regression parameters. This is critical because accurate estimation of yield is necessary for correct management decisions. The spatial and temporal autocorrelation assuming heteroscedasticity was superior to the OLS model for prediction. Historical data from several growing seasons should help better identify the cause and effect relationship between crop yield and soil attributes.