INRA, Paris, France.
UMR Ecosystème Prairial, INRA, Clermont-Ferrand, France.
Glob Chang Biol. 2018 Feb;24(2):e603-e616. doi: 10.1111/gcb.13965. Epub 2017 Nov 24.
Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N O emissions. Yield-scaled N O emissions (N O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N O emissions at field scale is discussed.
模拟模型被广泛用于预测农业生产力和温室气体排放。然而，在多物种农业背景下，对于影响粮食安全和气候变化缓解的变量，（简化的）模型集合模拟的不确定性尚未得到系统评估。我们报告了一项国际模型比较和基准测试活动，展示了多模型集合预测小麦、玉米、水稻和温带草原生产力和氧化亚氮（N O）排放的潜力。使用多阶段建模方案，从盲模拟（第 1 阶段）到部分（第 2-4 阶段）和完全校准（第 5 阶段），24 个基于过程的生物地球化学模型单独或作为集合进行了评估，以对抗跨越四大洲的四个温带草原和五个旱地作物轮作站点的长期实验数据。通过参考观测产量和 N O排放的实验不确定性进行了比较。结果表明，在跨越站点和作物/草原类型的情况下，23%-40%的未经校准的单个模型在观测产量的两个标准差（SD）内，而 42（水稻）到 96%（草原）的模型在观测 N O排放的一个标准差（SD）内。在第 1 阶段，由三个预测误差最低的模型组成的集合分别在 44%和 33%的作物和草原生长周期内，在实验不确定性内预测了产量和 N O排放。部分模型校准（第 2-4 阶段）显著降低了作物粮食产量（从第 1 阶段的 36%平均降低到 4%）和草原生产力（从 44%降低到 27%）以及对 N O排放的影响。模型集合以平均 3 个模型准确地对作物物种和田间站点的产量标准化的 N O排放（N O排放除以作物产量）进行了排名。讨论了使用基于过程的模型集合预测田间生产力和 N O排放的潜力。
