Review of current models and approaches used for maize crop yield forecasting in sub-Saharan Africa and their potential use in early warning systems

Agriculture is the mainstay of many developing economies, and successful production is intricately linked to food security, economic development, and regional stability. Estimates of crop yield for strategic grain crops, such as maize (Zea mays L.) have been used in national food security planning to develop response strategies in years of shortfalls and secure markets in years of surplus. Past studies have shown that despite the potential of models in maize crop yield assessment, they have not been effectively used in understanding seasonal and annual pro-duction dynamics. Thus, stakeholders require the availability of accurate and timely data on maize production potential and hence the development and application of crop yield models for maize yield estimation. However, current methods of assessing maize crop yields are based on field assessments, which are expensive, laborious and inaccurate. This mixed methods paper, therefore, aimed to; (i) review information sources for maize crop yield assessments, looking at their strengths, limitations, and potential for application in sub-Saharan Africa, (ii) perform trend and distribution analyses of publications in maize crop yield simulation, and (iii) discuss the challenges in the application of models in agriculture planning in the African agriculture systems. The general aim was to understand these crop yield assessments and the current approaches in maize yield estimation methods, and their potential use in the early warning system. The study narrowed the review to crop growth simulation models and explored the different growth simulation models and their potential integration into real-time monitoring frameworks for grain crop assessments. It was observed in this review, using graphical pre-sentation of trend and distribution analysis of one thousand three hundred and thirty-thre scientific publications, that there is an increase in research interest in crop simulation modelling, with current research being done mostly in developed countries. However, the application of models in maize crop yield assessment is dependent on the availability of data, modelled crop characteristics, model calibration requirements, technical capacity, and model implementation costs. Therefore, it was concluded that using crop yield estimation models integrating remote sensing is an important step in local, national and regional agricultural planning in the sub-region and beyond.

Automated summary

Agriculture is crucial for developing economies, and accurate crop yield estimation is vital for food security and economic stability. Current methods for assessing maize crop yields are expensive and inaccurate. This study explores the potential of crop growth simulation models in providing accurate and timely data for maize yield estimation. The study concludes that integrating remote sensing in crop yield estimation models is an important step in agricultural planning.