IRRIGATION MANAGEMENT AND AUTOMATION FOR INCREASED WATER USE EFFICIENCY
Location: Soil and Water Management Research
Title: Radiation model for row crops: I. Geometric view factors and parameter optimization
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 25, 2011
Publication Date: January 5, 2012
Citation: Colaizzi, P.D., Evett, S.R., Howell, T.A., Li, F., Kustas, W.P., Anderson, M.C. 2012. Radiation model for row crops: I. Geometric view factors and parameter optimization. Agronomy Journal. 104(2):225-240..
Interpretive Summary: Water and fertilizer management of agricultural crops is important for maintaining farm profitability. Mathematical models for predicting crop growth and water use are becoming increasingly important tools for crop management; therefore, continued model improvement is essential in order for United States farmers to compete in a global market. Since solar radiation is required for crop growth and crop water use, it is important to accurately model the amount of solar radiation that is available to the crop. Most crops are planted in rows, but this complicates the computation of solar radiation available to the crop. We refined an existing solar radiation sub-model that is commonly used in crop growth and water use models. In Part 1, we described the new sub-model and determined the appropriate calibration constants.
Row crops with partial cover result in different radiation partitioning to the soil and canopy compared with full cover; however, methods to account for partial cover have not been adequately investigated. The objectives of this study were to: (i) develop geometric view factors to account for the spatial distribution of row crop vegetation; (ii) combine view factors with a widely used vegetation radiation balance model; and (iii) optimize three parameters required by the model that describe leaf angle, visible leaf absorption, and near-infrared leaf absorption. Measurements of transmitted and reflected shortwave irradiance for corn (Zea mays L.), grain sorghum [Sorghum bicolor (L.) Moench], and cotton (Gossypium hirsutum L.) were used to optimize parameters and evaluate the model. View factors were derived by modeling the crop rows as elliptical hedgerows. The optimized ellipsoid leaf angle parameter, visible leaf absorption, and near-infrared leaf absorption were 1.0, 0.85, and 0.20 for corn; 1.5, 0.82, and 0.20 for grain sorghum; and 3.0, 0.83, and 0.14 for cotton, respectively. Visible leaf absorption was similar for all crops. Near-infrared leaf absorption was the same for corn and grain sorghum but less for cotton. The only parameter that changed for each crop species was leaf angle. The optimized parameters for corn and grain sorghum were within the range of values recommended in previous studies, and the leaf angle parameter for cotton agreed with a previous study of cotton leaf angles. All parameters were distinctly identifiable, and no parameter correlation was observed.