Using multivariate adaptive regression splines (MARS) to identify relationships between soil and corn (Zea mays L.) production properties

Abstract

Over-application of agricultural fertilizers can contribute to degradation of surface water quality. Factors governing crop establishment and yield must be identified in order to efficiently manage N application rates in corn (Zea mays L.) production systems. Spatial data sets of corn establishment and grain yields, and soil physical and chemical parameters were obtained for two corn production systems on a poorly drained clay loam soil in eastern Ontario, Canada, during low yielding conditions in 2000. The multivariate adaptive regression splines (MARS) automated regression data mining method was used to determine the dominant factors affecting both crop establishment and yield from these data sets. The analysis using MARS suggests that soil water content and cone penetration resistance are more important than elevation and spring mineral soil N (NH4+ + NO3−) in predicting crop establishment and grain yield. The MARS approach proved to be a useful method for identifying relationships between potential yield-governing variables. It also helped elucidate potential cause and effect processes, and in so doing, helped identify areas within the field where soil physical parameters may have been more important than nutrients in governing corn yield. Key words: Mulitvariate adaptive regression splines, corn yield, cone penetration resistance, soil water content, soil N, topography