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首页> 外文会议>Agriculture and Hydrology Applications of Remote Sensing; Proceedings of SPIE-The International Society for Optical Engineering; vol.6411 >Methodology for National Wheat Yield Forecast Using Wheat Growth Model, WTGROWS and Remote Sensing Inputs
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Methodology for National Wheat Yield Forecast Using Wheat Growth Model, WTGROWS and Remote Sensing Inputs

机译：利用小麦生长模型，WTGROWS和遥感输入预测全国小麦单产的方法

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Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtainedrnupto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.
机译：小麦是该国重要的粮食作物。由于生长地区的生物物理和社会经济状况各异，其生产力范围非常广泛。农作物的切割和抽样调查既费时又乏味，而且预报程序也要延迟。使用遥感，地面实况和盛行天气的CAPE方法论非常成功，但实际上是经验性的。在IARI-SAC联合研究计划中，尝试了将小麦动态生长模型与遥感输入和其他关系数据库层链接的可能性。 ITG开发的小麦生长模型WTGROWS与遥感和相关数据库的使用是动态的，只要收到天气，种植面积和化肥以及其他投入，就可以更新。通过使用WTGROWS，关系数据库层和卫星图像，以气象细分规模对三个季节的全国小麦单产进行了预报。 WTGROWS用于历史天气数据集（过去25年），通过关系数据库的相关增长率输入相关数据，并将其与气象部门的生产率趋势进行比较。获得每个分区的校准因子，以捕获其他生物和非生物胁迫，随后用于将每个分区的产量降低到现实水平。卫星图像用于计算每个分区的小麦种植面积。每个细分的气象数据均来自IMD（每周一次）。运行WTGROWS并获取到给定时间的实际天气数据，并在随后的时间段使用天气正常值，并准备了预报。每周更新一次，该方法可以非常准确地提前预测小麦单产。该程序显示了该国的作物生长监测系统（CGMS）的路径，该路径将用于土地使用规划和农业生产估算，尽管对于多种农业生态和广泛的生物物理和社会经济而言，这似乎很困难导致生产率差异趋势的角色。

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