Lalić B, Firanj Sremac A, Dekić L, Eitzinger J, Perišić D
Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq. 8, 21000 Novi Sad, Serbia.
Republic Hydrometeorological Service of Serbia, Kneza Višeslava 66, 11000 Belgrade, Serbia.
J Agric Sci. 2018 Jul;156(5):645-657. doi: 10.1017/S0021859617000788. Epub 2017 Dec 11.
A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006-2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006-2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.
基于作物模型输出（CMO）估计集合的概率作物预报，能在月度或季节尺度上提供大量关于绿水成分（降水和蒸散）、作物产量及绿水足迹（GWF）的可能实现情况和概率预报。本文展示了一项正在进行的关于将集合预报概念应用于农业生产的研究的部分结果。介绍了利用集合季节天气预报作为作物模型输入气象数据，在不扰动初始土壤或作物条件的情况下生成集合CMO所使用的方法，并对其准确性进行了测试，同时展示了测试结果。所选案例研究是针对2006 - 2014年期间奥地利和塞尔维亚冬小麦生长情况，使用天狼星作物模型进行模拟。收集了2006 - 2014年期间6个月（3月1日至8月31日）的历史季节预报数据，这些数据来自欧洲中期天气预报中心和气象档案与检索系统。冬小麦物候动态、产量和GWF的季节集合预报结果显示估计范围较窄。这些结果表明，在农业中使用季节天气预报及其在概率作物预报中的应用，可以优化田间作业（如土壤耕作、植物保护、施肥、灌溉），并提前几周或几个月利用作物发育和产量的预测。
