Affiliation:
1. Embrapa Soybean – Soil Science and Plant Nutrition Londrina Brazil
2. School of Sciences and Engineering São Paulo State University ‐ mathematical modeling Tupã Brazil
3. College of Agricultural and Technological Sciences São Paulo State University Júlio de Mesquita Filho – Crop Science Dracena Brazil
4. Embrapa Soybean – Plant Physiology Londrina Brazil
Abstract
AbstractMathematical models that incorporate biotic and abiotic attributes are important tools for improving fertilizer use efficiency and reducing production costs for soybean [Glycine max (L.) Merrill] crop. In this study, artificial neural networks (ANNs) were used to estimate soybean grain yield (GY) under subtropical conditions in Brazil from plant morphological and nutritional data collected from 16 cultivars in two growing seasons. The ANNs were adequately trained, with a mean squared error of approximately 10−5 between the outputs obtained (via ANN) and desired (via experimental field), equivalent to a mean percentage error of 70.1 kg ha−1 (1.6%), confirming their efficacy as a tool to estimate GY. Smaller plant height, higher foliar calcium, magnesium and chlorophyll concentrations, and greater numbers of grains per pod and branches per plant were associated with higher GY, whereas oil content, crude protein content, and foliar manganese and potassium concentrations had no predicted effects on GY.
Subject
Agronomy and Crop Science
Cited by
1 articles.
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