García-Cortés Silverio, Menéndez-Díaz Agustín, Bande-Castro María José, Carballal-Samalea Alfonso, Martínez-Fernández Adela, Oliveira-Prendes Jose Alberto
Cartographic Engineering Area, University of Oviedo, Asturias, Spain.
Construction and Manufacturing Engineering Dept, University of Oviedo, Asturias, Spain.
PLoS One. 2025 Aug 12;20(8):e0326364. doi: 10.1371/journal.pone.0326364. eCollection 2025.
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.
作物模型根据不同的气候、土壤和作物管理条件模拟作物的生长和发育。对CSM-CERES-玉米模型（DSSAT）进行了调整，通过校准六个品种的遗传参数来模拟饲用玉米产量：在阿斯图里亚斯的三个地点进行了三年的SE1-200、SE2-300和SE3-400，以及在加利西亚的四个地点进行了三年的XU1-220、XU2-300和XU3-400。使用CSM-CERES-玉米模型进行校准，并结合研究地点的历史气象数据（2000 - 2022年），能够模拟23年期间六个品种的饲用玉米产量（全株干物质产量）和品质（全株泌乳净能产量和全株粗蛋白产量）。使用LightGBM模型（一种机器学习技术）结合模拟的饲用玉米产量、品质数据、历史天气、土壤和管理数据，以捕捉数据中的非线性关系，并确定对作物产量和品质预测最有影响的变量。模型评估结果显示，饲用玉米产量的准确率为94.7%（R2分数 = 0.86），泌乳净能产量的准确率为94.0%（R2分数 = 0.84），粗蛋白产量的准确率为93.0%（R2分数 = 0.85）。变量重要性图显示，生长季和从播种到收获的辐射是最具影响力的两个预测变量。在阿斯图里亚斯和加利西亚，在23年历史气象数据中（阿斯图里亚斯三个地点和加利西亚四个地点，每个地点有三个播种日期），周期最长的品种（品种周期400）是所研究变量值最高的品种。这些模型将可供非专业用户使用，以作物田的地理位置、品种类型、播种和收获日期以及生长季天气变量的可能值作为输入数据，来预测饲用玉米的产量和品质。
