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

Content Provider	Springer Nature Link
Author	Miao, Yuxin Mulla, David J. Robert, Pierre C.
Copyright Year	2006
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.
Starting Page	117
Ending Page	135
Page Count	19
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	7
Issue Number	2
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2006-04-07
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Corn grain quality Artificial neural networks Precision crop management Spatial variability Terrain analysis Remote Sensing/Photogrammetry Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Meteorology/Climatology Agriculture Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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