Identifying important factors influencing corn yield and grain quality variability using artificial neural networks

Abstract

Soil, landscape and hybrid factors are known to influence yield and quality of corn (Zea mays L.). This study employed artificial neural network (ANN) analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on yield and grain quality in two Illinois, USA fields. About 7 to 13 important factors were identified that could explain from 61% to 99% of the observed yield or quality variability in the study site-years. Hybrid was found to be the most important factor overall for quality in both fields, and for yield as well in Field 1. The relative importance of soil and landscape factors for corn yield and quality and their relationships differed by hybrid and field. Cation exchange capacity (CEC) and relative elevation were consistently identified as among the top four most important soil and landscape factors for both corn yield and quality in both fields in 2000. Aspect and Zn were among the top five most important factors in Fields 1 and 2, respectively. Compound topographic index (CTI), profile curvature and tangential curvature were, in general, not important in the study site-years. The response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equations. We conclude that hybrid was more important than soil and landscape factors for consideration in precision crop management, especially when grain quality was a management objective.

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

“…Even for the ANN with 6 input variables, the results also were superior to those of the MLR with 10 input variables. The result was similar to the findings of Kaul et al (2005) and Miao et al (2006). Kaul et al (2005) used both soil productivity rates and climate variables for yield prediction and found that ANN had shown to be better tools than regression methods when analyzing corn and soybean yield data generated in field trials.…”

“…Kaul et al (2005) used both soil productivity rates and climate variables for yield prediction and found that ANN had shown to be better tools than regression methods when analyzing corn and soybean yield data generated in field trials. Miao et al (2006) employed ANN analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on corn yield and grain quality in two Illinois, USA fields, and the results indicated that the response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equation. The performance of ANN in the study was mainly attributed to the ability of ANNs to capture the nonlinear input-output relationship between crop growth and soil moisture and salinity, whereas MLRs were unable to reflect these complicated relationships due to their linear characteristics.…”

Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems

“…A ratio greater than 1.0 implied that, then, the variable made an important contribution to the variability in soil organic matter. The higher the ratio, the more important the variable (StatSoft, 2004;Miao, 2006).…”

Elimination of Iron Interference in the Molecular Spectrophotometric Determination of Aluminum in Soil Extracts Using Artificial Neural Networks

“…[20][21][22][23][24] This paper presents the development of an ANN calibration model to determine aluminum in the presence of iron in soil extracts, without any additional sample treatment, using the molecular spectral data of the complexes Al-XO and Fe-XO in the visible region. The ANN prediction ability was evaluated by comparison with ICP OES measurements.…”