ArticlePloS one2025
A machine learning approach for estimating forage maize yield and quality in NW Spain.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop models simulate crop growth and development according to different climatic, soil and crop management conditions. The CSM-CERES-Maize model (DSSAT) was adapted to simulate forage maize yields by calibrating the genetic parameters of six cultivars: SE1-200, SE2-300 and SE3-400 in three sites and three years in Asturias, and XU1-220, XU2-300 and XU3-400 in four sites and three years in Galicia. Calibration using the CSM-CERES-Maize model, together with the use of historical meteorological data (2000-2022) from the study sites, enabled simulation of forage maize yield (whole plant dry matter yield) and quality (whole plant net energy for lactation yield and whole plant crude protein yield) for six cultivars during the 23-year period. LightGBM models (a machine learning technique) were used with the simulated forage maize yield, quality data, historical weather, soil, and management data to capture non-linear relationships in the data and to identify the most influential variables for crop yield and quality predictions. The results of the model evaluation yielded an accuracy of 94.7%, (R2 score = 0.86) for forage maize yield, an accuracy of 94.0% (R2 score = 0.84) for the net energy for lactation yield and an accuracy of 93.0% (R2 score = 0.85) for the crude protein yield. Variable importance plots revealed Growing Season and Radiation from sowing to harvest to be the top two most influential predictor variables. In Asturias and Galicia, the cultivars with the longest cycle (cultivars cycle 400) are those with the highest values for the variables studied in the 23 years of historical meteorological data (average of three sites in Asturias and four sites in Galicia with three sowing dates in each site). The models will be available to make predictions for forage maize yield and quality by non-specialist users, using the geographical location of the crop field, cultivar type, sowing and harvest date and probable values of weather variables during the growing season as input data.
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