The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)..

Impact of ET0 method on the simulation of historical and future crop yields : a case study of millet growth in Senegal

The reference evapotranspiration (ET0) is an integrated climatic variable from which many crop models derive simulated crop yields. In most of these models, different equations are parameterized leaving the choice of the equation to the user. However, the impact of the choice of the ET0 equations on crop yield prediction has been little studied. The present study proposes a sensitivity analysis of the impact of the choice of the ET0 equation on simulated millet yields using SARRA-H crop model over 12 Senegalese stations representative of the Sudano-Sahelian climate conditions of West Africa. Priestley-Taylor, a modified Priestley-Taylor and Hargreaves equations lead to simulated yields up to 19% than those calculated using the Penman-Monteith equation. Despite high biases in wind speed, among the tested methods, the Penman-Monteith method remains the most robust to derive ET0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET0 formulation introduces uncertainties representing 8% of baseline yield regardless of precipitation changes; for wet conditions these uncertainties approach 30% of the overall climate change impact. The choice of ET0 equation is increasingly important, with local temperature changes out to 4 degrees C, while extreme changes above 6 degrees C depend less on the ET0 equation

ClimaVista : une plateforme web pour une meilleure gestion intégrée des pratiques agricoles

Résumé du posterNational audienc

La Smart Agriculture au service de la gestion des risques phytosanitaires agricoles

Résumés court et étendu. Session : Politique publique et préventionNational audienc

Drought risk management using satellite-based rainfall estimates

In this chapter, we present an overview of the role of satellite-based rainfall estimates (SREs) in drought risk management applications, ranging from simple anomaly and index-based approaches to cross-cutting drought early warning systems (EWS) and financial instruments such as weather index-based insurance (WII) schemes. We contend that meteorological, hydrological, agricultural, and socioeconomic are aspects – not types – of drought, and a universally acceptable drought definition is not a prerequisite for the effective and efficient assessment of the impacts of drought using SREs and other satellite-based datasets and/or models. This is illustrated through examples from the work of the co-authors, as well as the wider community. The chapter concludes with a synthesis of the challenges for SREs and the current trends in the development and application of SREs in drought risk management, including an outlook of the priorities for future research and applications