Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya
Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya
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DOI：
10.1016/j.agrformet.2004.02.006
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发表时间：
2004-09-20
影响因子：
6.2
通讯作者：
Indeje, M
中科院分区：
文献类型：
--
作者：
Hansen, JW;Indeje, M
通过在季节之前提供有关生长季节特征的信息，对季节性时间尺度的气候波动的预测为改善农业风险管理提供了机会，但前提是预测被转化为对管理替代方案的生产和经济结果的概率预测。如果作物模型要对这项任务作出贡献，就必须解决动态气候模型和过程一级作物模拟模型在空间和时间尺度上的不匹配问题。为将作物模型与动态季节气候预测联系起来而提出的方法包括历史相似物的分类和选择、随机分解、直接统计预测、概率加权历史相似物以及使用经校正的每日气候模型输出。在肯尼亚的半干旱地区，我们展示和评估的方法来预测田间规模的玉米产量，模拟CERES玉米与观察到的每日天气输入，在种植前，来自大气环流模型，ECHAM的降尺度的季节性降雨后报。我们考虑的方法是统计预测的非线性回归，概率加权历史类似物和随机分解，以预测田间规模的玉米产量模拟CERES玉米与观测到的每日天气输入。尺度缩小的ECHAM输出预测的总降水量的方差的36%和54%的降雨频率在12月至12月在该网站的方差。非线性回归显示最低，随机分解显示最高的总误差。所有的产量预测方法都表现出类似的随机误差，预测从28%到33%的方差与观测天气模拟产量。将降雨频率的可预测性纳入随机分解程序并没有改善产量预测。在这项研究的基础上，随机分解，直接统计预测和概率加权历史模拟都显示出将季节性气候预测转化为作物响应预测的潜力。(C)2004 Elsevier B.V.保留所有权利。
By providing information about growing season characteristics in advance of the season, predictions of climate fluctuations at a seasonal time-scale offer opportunity to improve agricultural risk management, but only if forecasts are translated into probabilistic forecasts of production and economic outcomes of management alternatives. A mismatch between the spatial and temporal scale of dynamic climate models and process-level crop simulation models must be addressed if crop models are to contribute to the task. Methods proposed for linking crop models with dynamic seasonal climate forecasts include classification and selection of historic analogs, stochastic disaggregation, direct statistical prediction, probability-weighted historic analogs, and use of corrected daily climate model output. For a semi-arid location in Kenya, we demonstrate and evaluate methods to predict field-scale maize yields, simulated by CERES-maize with observed daily weather inputs, in response to downscaled seasonal rainfall hindcasts available prior to planting, derived from an atmospheric general circulation model, ECHAM. The methods we considered were statistical prediction by non-linear regression, probability-weighted historic analogs and stochastic disaggregation to predict field-scale maize yields simulated by CERES-maize with observed daily weather inputs. Downscaled ECHAM output predicted 36% of the variance of total precipitation and 54% of the variance of rainfall frequency in October-December at the site. non-linear regression showed the lowest, and stochastic disaggregation showed the highest overall error. All of the yield forecasting methods showed similar random error, predicting from 28 to 33% of the variance of yields simulated with observed weather. Incorporating the predictability of rainfall frequency into the stochastic disaggregation procedure did not improve yield predictions. Based on this study, stochastic disaggregation, direct statistical prediction and probability-weighted historic analogs all show potential for translating seasonal climate forecasts into predictions of crop response. (C) 2004 Elsevier B.V. All rights reserved.