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Field Characterization of Field Capacity and Root Zone Available Water Capacity for Variable Rate Irrigation

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Applied Engineering in Agriculture. 33(4): 559-572. (doi: 10.13031/aea.11963) @2017
Authors:   Tsz Him Lo, Derek M. Heeren, Luciano Mateos, Joe D. Luck, Derrel L. Martin, Keith A. Miller, J. Burdette Barker, Tim M. Shaver
Keywords:   Available water capacity, Electrical conductivity, Field capacity, Permanent wilting point, Spatial variability, Variable rate irrigation.

Abstract. Accurate spatial characterization of field capacity (FC) and root zone available water capacity (R) can enhance site-specific management practices—such as variable rate irrigation—to lower input costs, reduce contaminant leaching, and/or improve crop yield. Measuring the volumetric water content after wet soils drain following substantial precipitation can provide a field estimate of FC. The average FC (FCa) for the managed root zone was determined at thirty-two locations in a topographically variable field in south central Nebraska. The difference between FC and permanent wilting point estimates—computed using a pedotransfer function—yielded values for R for the observation locations. Sampling locations were too sparse for reliable interpolation across the field. Therefore, relationships between a surrogate, or predictor, variable and soil water properties were used to provide spatial distributions of FC and R for the field. Field estimates of FCa and R were more strongly correlated to elevation (correlation coefficient, r = -0.77 and -0.76, respectively) than to deep soil apparent electrical conductivity (r = -0.46 and -0.39, respectively). Comparing maps of FCa and R from gSSURGO to maps from field characterization yielded a root mean squared difference of 0.031 m3 m-3 for FCa and 34 mm for R. Sampling seven locations across the elevation range in this field produced FCa and R prediction functions that achieved 95% and 87%, respectively, of the reduction in the standard error achievable with a larger number of sampling locations. Spatial characterization of FCa and R depends on identifying a suitable predictor variable(s) based on field knowledge and available spatial data. Well-chosen variables may allow satisfactory predictions using several sampling locations that are distributed over the entire field. Ultimately, the costs and benefits of spatial characterization should be considered when evaluating site-specific water management.

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