Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production
Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production
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DOI：
10.1016/s0167-8809(01)00358-9
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发表时间：
2002-12-01
影响因子：
6.6
通讯作者：
Jones, JW
中科院分区：
文献类型：
--
作者：
Jagtap, SS;Jones, JW
尽管有许多作物生长模型可用，但应用它们来预测区域产量及其变异性的经验有限。主要困难是可用数据的空间和时间尺度与作物模拟模型输入要求之间存在严重不匹配。本研究通过将 CROPGRO-大豆模型与天气、土壤、管理和品种的低分辨率区域数据库联系起来，开发并测试了预测大豆 (Glycine max L. Merrill) 产量和产量的操作程序。历史上观察到的人口普查产量被去趋势化，以消除技术变化的影响，然后使用面积加权方法聚合到 0.5 度单元格（约 50 公里网格单元）的规模（(g) 上限）。使用九个输入组合（3 个品种、3 个种植日期、1 个土壤和 1 个初始条件）模拟网格单元内的空间产量变异性（(y) over cap），对这些输入组合进行平均，以与每个单元和年份的汇总普查产量进行比较。通过最小化校正的 (y) 上限和 (g) 上限之间的均方根误差 (RMSE) 来估计良率偏差。产量修正系数需要针对特定地点，以考虑约束和管理的空间变化。超过 75% 的网格单元的良率修正系数在 0.40 到 0.50 之间。校正后，(y) 上限和 (g) 上限的拟合优度的成功率与 100 相似，而在 95% 置信限下的方差则为 80%。实际产量的 17 年平均值预测准确，斜率为 0.95，截距较小（-0.025），R-2 为 0.95。验证后，规定的因素测试误差为 14%，在环境保护局 (1982) 制定的 16% 指导方针内，该指导方针是模型有资格进行管理应用的可接受标准。 1991 年、1992 年、1993 年、1994 年和 1995 年的中位 RMSE 分别为 15%、8%、32%、7% 和 85%。 1993 年和 1995 年主要是严重缺水。我们的结论是，特定于电网的产量校正方法可以有效地校正模拟产量中的偏差，并使用现成的输入准确预测年际变化。未来需要采取措施，纳入动态考虑产量对病虫害敏感性的程序。模型的测试和改进应继续发挥其潜力。 (C) 2002 Elsevier Science B.V. 保留所有权利。
In spite of the availability of numerous crop-growth models, there has been limited experience in applying them to predict regional production and its variability. The main difficulty is a substantial mismatch between spatial, and temporal scales of available data and crop simulation model input requirements. This study developed and tested an operational procedure to predict soybean (Glycine max L. Merrill) yield and production by linking the CROPGRO-soybean model with a low resolution regional database of weather, soils, management, and varieties. Historically observed census yields were detrended to remove effects of changes in technology and then aggregated to a scale of 0.5degrees cell (about a 50 km grid cell) ((g) over cap) using an area-weighting approach. Spatial yield variability within a grid cell was simulated ((y) over cap) using nine input combinations (3 varieties, 3 planting dates, 1 soil and 1 initial condition) which were averaged for comparison with aggregated census yield, in each cell and year. Yield bias was estimated by minimizing the root mean squared error (RMSE) between corrected (y) over cap and (g) over cap. The yield correction factor needs to be site-specific to account for spatial variations in constraints and management. Yield correction factor ranged from 0.40 to 0.50 in more than 75% of grid cells. When corrected, the success rate for the goodness of fit of (y) over cap and (g) over cap was similar to 100 and 80% for variance at the 95% confidence limit. The 17-year mean of actual yield was accurately predicted with a slope of 0.95, small intercept (-0.025) and R-2 of 0.95. When validated, the prescribed factor test error was 14%, within the 16% guideline set by the Environmental Protection Agency (1982) as an acceptable criteria for a model to qualify for management application. Median RMSE were 15, 8, 32, 7 and 85% for 1991, 1992, 1993, 1994 and 1995, respectively. Years 1993 and 1995 were dominated by high water stress. We conclude that the grid-specific yield correction approach can effectively correct bias in simulated yields and accurately predict interannual variability using readily available inputs. Future steps are needed to incorporate procedures that account dynamically for yield susceptibility to pests and diseases. Testing and improvement of the model should continue to realize its potential. (C) 2002 Elsevier Science B.V. All rights reserved.