Productivity Index Model Modified to Estimate Variability of Montana Small Grain Yields

Abstract

AbstractThe productivity index (PI) model estimates crop productivity by characterization of the soil rooting environment and evaluates a soil's vulnerability by simulated removal of surface soil and consideration of three soil properties, available water‐holding capacity, bulk density, and pH. Although the PI model has performed well in the Corn Belt and elsewhere, further testing is required to demonstrate its applicability in northern Great Plains small‐grain environments. Evaluation of PI model performance in Cascade County, Montana, revealed a weak relationship between PI and small‐grain yield that may have been due to shortcomings in the SOILS‐5 database. Therefore, soils and crop data were collected in August and September 1987 from four fields in Hill and Jefferson Counties to evaluate model performance and indicate appropriate changes in the model's design to allow its use in Montana. Results indicate that model performance can be improved by addition of the content and location of organic matter and CaCO3 in the soil profile. Productivity indices were first generated with the original model. The regression of small‐grain yield against PI accounted for 64, 67, 63, and 1% of the variability in yield within Fields 1 through 4, respectively. When the modified PI values were used, R2 increased an average of 44% and accounted for 77, 69, 60, and 75% of the variation in grain yield within Fields 1 through 4, respectively. Moreover, R2 increased slightly (0.69 to 0.75) for all fields collectively when cropping history was considered. These results have encouraged further efforts to develop a modified and improved version of the PI model that uses the SOILS‐5 database for county‐scale assessments in northern Great Plains small‐grain environments.