Role of Modelling in International Crop Research: Overview and Some Case Studies

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.

Value-Ag: An integrated model for rapid ex-ante impact evaluation of agricultural innovations in smallholder systems

“…While Value-Ag does not directly extend to institutions, services or markets, it captures some of the broader socio-economic influences via the adoption component of the framework, as well as sensitivity analysis. Overall, the strengths of the proposed approach in terms of systems integration, quantification of change, scaling potential and contribution towards achieving some of the global SDGs 1 could have wide-ranging applicability in the developing world where mixed smallholder farming systems prevail (Dixon et al, 2010;Reynolds et al, 2018).…”

CGIAR modeling approaches for resource‐constrained scenarios: II. Models for analyzing socioeconomic factors to improve policy recommendations

“…Forecasting of plausible and probable upcoming events is a crucial step to avoid or reduce the magnitude of future problems, thus enabling better decision making on strategic issues. A good way of examining potential future scenarios is by using agronomic and socioeconomic modeling approaches (Reynolds et al, 2018;Rosegrant et al, 2017). Modeling…”

“…Agri-food systems evaluated by the different CGIAR centers produce a range of products that generate positive environmental, social, and economic impacts at different scales (Bobojonov & Aw-Hassan, 2014;CGIAR Independent Science and Partnership Council, 2011). The socioeconomic impacts of CGIAR-generated activities can be evaluated and predicted using different socioeconomic modeling tools such as foresight analysis of agricultural systems under global change scenarios and the consequences of potential food system and farming system shocks, among others (Godfray et al, 2016;Komarek, Thurlow, Koo, & De Pinto, 2019;Komarek & Msangi, 2019;Boussios et al, 2019;Yigezu, Aw-Hassan, Shideed, Sommer, & El-Shater, 2014;Ates et al, 2018;Frija & Telleria, 2016;Reynolds et al, 2018). Simulation and scenario analysis (IPCC, 2019;Riahi et al, 2017) either focus on what the future has in store for humanity to identify research priorities today or look at (potential) emerging technologies to assess how they fit into dynamic complex agri-food systems including the analysis of the appropriateness of technology for farming systems and livelihood strategies (Rosegrant et al, 2017).…”

The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals

“…Recent examples of these modelling approaches show that procedures are also well-tested and accurate, even when applied to developing countries (e.g. Reynolds et al, 2018;Hack-ten Broeke et al, 2019).…”