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Developing decision support systems for crop yield forecasts

Code: 9781801463461
Lin Liu, University of Minnesota – Twin Cities, USA; and Bruno Basso, Michigan State University, USA

Chapter synopsis: 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.

DOI: 10.19103/AS.2021.0097.23
Table of contents 1 Introduction 2 Crop modelling for yield-forecasting systems 3 Statistical-based yield-forecasting systems 4 Lead time and accuracy of crop-yield forecasting models 5 Case study: linking in-season field survey with crop modelling to forecast maize yield in Tanzania 6 Conclusion 7 References

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