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Empirical modeling of the impact of Mollisol soils variation on performance of Cuphea: A potential oilseed crop

Abdullah A. Jaradat, Jana Rinke
Australian Journal of Crop Science 2014 v.8 no.7 pp. 1093-1113
Cuphea, Mollisols, chemical constituents of plants, electrical conductivity, grain crops, lipid content, mathematical models, oilseed crops, plant nutrition, regression analysis, seed yield, soil nutrients, soil pH, soil water, soil-plant interactions, temporal variation
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.