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PREDICTING YIELD LOSS IN INDETERMINATE SOYBEAN FROM POD DENSITY USING SIMULATED DAMAGE STUDIES
Author(s) -
Singer J. W.,
Malone R. W.,
Meek D. W.,
Drake D.
Publication year - 2004
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2004.5840
Subject(s) - point of delivery , odocoileus , mathematics , yield (engineering) , biomass (ecology) , statistics , linear model , coefficient of determination , agronomy , ecology , biology , materials science , metallurgy
Developing relationships between seed yield and pod density can be useful for predicting yield loss in soybean [ Glycine max (L.) Merr.] damaged by deer ( Odocoileus virginianus ). The objectives of this research were to (i) develop a modeling tool using differences between biomass removal treatments and controls for pod density and seed yield to quantify yield loss and (ii) assess the tool using double cross‐validation. Model development using linear and polynomial exponential (PE) equations was accomplished using 1998–2001 data from studies examining different biomass removal treatments, varieties, and row spacings. The PE model had a slightly higher coefficient of determination ( R 2 = 0.93) than the linear model ( R 2 = 0.92). Double cross‐validation of both models produced strong relationships with high coefficients of determination and predictive ability; however, the model performance statistics indicated that the PE model had higher coefficients of determination, lower mean bias error, and more robust slope estimates than the linear model. Depending on the end‐user, the simplicity of the linear model should be carefully considered in weighing the benefits of each tool. Nevertheless, these approaches provide robust tools that are not sensitive to moderate abiotic fluctuations, varying cultural practices, and a wide range of temporal biomass removal. Validating the relationship using additional data should be the next step before implementation.