Interpreting relationships between soil variables and soybean iron deficiency using factor analysis

Abstract

Iron chlorosis in soybean [Glycine max (L.) Merr.] can be difficult to predict and often depends on various soil factors. The objective of this study was to determine the underlying soil factors that are conducive to Fe chlorosis in soybean using a statistical factor analysis. This study was conducted at seven locations in western Kansas with intensive soil sampling to investigate the relationships between soil variables and the incidence of Fe chlorosis. The soil variables measured were pH, P, Fe, organic matter (OM), Ca, Mg, electrical conductivity (EC), NO[subscript]3–N, and calcium carbonate equivalent (CCE). Factor analysis was performed using the Varimax rotation and the Heywood convergence to obtain the best possible relationships. The factors were deemed significant if the Eigenvalues were >1. The factor analysis showed that two underlying factors can be selected to explain the incidence of Fe chlorosis in soybean. These factors are “plant chlorosis” (Factor 1) and “soil available Fe” (Factor 2). With regression analysis, these underlying factors were indicative of the chlorophyll meter (CM) readings at the V3 and V6 growth stages and in the grain yield (GY). However, soybean management practices, such as variety selection and the use of seed-applied Fe fertilizers were shown to affect the relationship between latent factors (from factor analysis) and soybean response. When seed-applied Fe fertilizers are used with tolerant and nontolerant soybean varieties, the overall effect of the undelaying soil factors seems irrelevant to soybean response.