Abstract
Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.
Highlights
Current rates of yield growth and overall production are not sufficient to satisfy future demand [1]and climate change is predicted to add at least another 60 million hungry people by 2050 [2].Rosegrant et al [3] predicted that—in the absence of unprecedented, coordinated measures to raise productivity—consumers will pay double for staple foods by 2050 in real terms
Newer developments include the use of remote-sensed data and mobile phone technology linked to simulation models [5]; improved geographic information system (GIS) techniques [6]; strategic crossing and selection models making use of high-throughput genotyping and phenotyping to increase genetic gains [7]; as well as data sharing and standardization in the new era of big data [8]
This paper briefly outlines the history of crop modelling, including major theoretical advances and their practical applications by international crop research centres of the CGIAR
Summary
Current rates of yield growth and overall production are not sufficient to satisfy future demand [1]. This paper briefly outlines the history of crop modelling, including major theoretical advances and their practical applications by international crop research centres of the CGIAR. Crop modelling in the target environments of the CGIAR centres has its own unique challenges in addition to the fact that many of the regions face direct climate change and other resource limitations, e.g., related soil erosion and unavailability of good quality water. While the expansion of crop modelling approaches is still somewhat limited by the availability of quality data for calibration, validation, and evaluation, recent progress in the development of large-scale agro-ecological datasets, facilitated by global initiatives such as AgMIP [10] and the Big Data Platform in Agriculture [8], are helping fill the data gap. While this review takes stock of the state-of-the-art of crop related modelling within CGIAR, it highlights where improvements are desirable and feasible
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