Effects of Weather Variability and Soil Parameter Uncertainty on the Soil-Crop-Climate System

Angelos L. Protopapas Polytechnic University, Brooklyn, New York

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Rafael L. Bras Massachusetts Institute of Technology, Cambridge, Massachusetts

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Abstract

The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.

Abstract

The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.

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