Developing decision support systems for crop yield forecasts

Lin Liu, B. Basso
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Abstract

This chapter discusses existing yield forecasting systems in which the yield forecasts are driven by integration of different data sources, such as output of crop modeling, remote sensing and gridded climate datasets. It first provides overviews of the two predominant modeling approaches— crop simulation modeling and statistical modeling— to forecasting crop yield, with an emphasis on their respective use for operational crop yield forecasting systems. The chapter then briefly describes the accuracy and lead time of the existing yield forecasting models. Lastly, it provides a case study that integrates digital tools, field surveys, and crop modeling to provide on-time maize yield forecasts in small fields in Tanzania. The chapter concludes with a summary and future perspectives for research.
开发作物产量预测决策支持系统
本章讨论了现有的产量预测系统,其中产量预测是由不同数据源的集成驱动的,例如作物模型的产量、遥感和网格化气候数据集。它首先概述了预测作物产量的两种主要建模方法-作物模拟建模和统计建模,重点介绍了它们各自在业务作物产量预测系统中的应用。然后简要介绍了现有产量预测模型的准确性和提前期。最后,它提供了一个案例研究,将数字工具、实地调查和作物建模集成在一起,为坦桑尼亚的小块田地提供及时的玉米产量预测。本章最后对研究进行了总结和展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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