Location: Soil Management Research
Title: Empirical modeling of the impact of Mollisols variation on performance of a potential oilseed crop Authors
Submitted to: Australian Journal of Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 29, 2014
Publication Date: N/A
Interpretive Summary: The production potential of newly developed oilseed crops such as Cuphea depends on natural soil fertility and rates of fertilizer application, among other factors. We evaluated the effects of annual and inherent soil variation on the performance (seed yield, oil content and oil yield) of Cuphea grown on four soil series during two contrasting growing seasons. The annual variation had the largest effects on crop performance, followed by variation within soil series, especially on seed yield, followed by oil yield and oil content. Researchers and farmers will benefit from the guidelines we developed based on soil characteristics to design management strategies to optimize oil yield of the oilseed crop grown on soils with high inherent variability.
Technical Abstract: Production potential of many soils is affected by low supply of nutrients due to adverse constraints or spatio-temporal variation of soil physical and chemical properties. New oilseed crops differ in their nutrient needs for maximum performance in different soils and may not be able to economically compete with grain crops for fertile land. Spatial variation in physico-chemical properties within and among four soil series during two contrasting cropping seasons accounted for significant and decreasing amounts of variation in crop performance quantified by seed yield, oil yield and oil content in a semi domesticated oilseed crop. Spatially demarcated 36 grids within soil series accounted for more variation in crop performance and reacted more significantly to temporal variation than soil series. Nutrient ratios of four macronutrients (C, N, P, and S) in seed were slightly better predictors of oil content and oil yield than those in soil. Soil chemical properties, including nutrient contents, soil pH, soil water, and soil electrical conductivity, when used as covariates or predictors in calibration and validation regression models, provided new insights into the variation structure and prediction power of crop performance. Predictive models may help design management strategies to optimize oil content and oil yield of oilseed crops on different soil series.