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Abstract
Bridging the gap: Integrating crop pests and pathogens into agricultural foresight models for food security assessments
Highlights
Regional and global economic models, combined with spatially distributed crop growth simulation models and hydrology models that simulate water supply and demand across sectors, represent the most widely used quantitative approach for addressing questions related to food security under alternative future scenarios
While we do not claim to comprehensively address “[...] the entire set of facets of crop losses [...] in a multiple pest-crop system” (Savary et al, 2012, p. 522), we explore how key dimensions of this challenge can be effectively addressed within an Agricultural Integrated Assessment Models” (AIAMs) approach – an approach we consider well-suited for global food security analyses
How can we develop an operational approach to model and assess the food security impacts of pests and pathogens (P&P) using AIAMs under future scenarios? Can we establish biophysical modelling requirements for this approach? Given the challenges in representing crop-pest-environment interactions, as outlined by various authors (e.g., Cunniffe et al, 2015), can existing biophysical P&P models meet these requirements? Additional critical questions arise, which transcend the biophysical dimension: how can human interventions, including management strategies, be effectively incorporated into AIAMs? What key elements, such as drivers and boundary conditions, should be integrated in future scenarios?
Summary
Regional and global economic models, combined with spatially distributed crop growth simulation models and hydrology models that simulate water supply and demand across sectors (among others), represent the most widely used quantitative approach for addressing questions related to food security under alternative future scenarios (e.g., for a recent reference, van Dijk et al, 2021). We note that several authors have already advocated for holistic approaches to assess the impact of P&P on crop yields and food security, highlighting the importance of integrating insights from both socioeconomic and biophysical disciplines (Antle et al, 2017; Savary & Willocquet, 2020; Savary et al, 2012; Singh et al, 2023) Slow progress on this front reflects the complex and diverse requirements associated with biophysical P&P modelling, in terms of crop loss data (Savary et al, 2019), environmental and agronomical variables, genetic factors, and the parameters needed to simulate P&Ps damage mechanisms.
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