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)..

Positive effect of climate change on cotton in 2050 by CO2 enrichment and conservation agriculture in Cameroon

This article predicts an unexpected positive effect of climate change on cotton production in Cameroon. Global warming could threaten cotton production in Africa due to increasing temperature and CO2, and rainfall uncertainties. This situation is worsened by the fact that most African farmers grow cotton as their cash crop and have few or no possible alternatives. Assessing the impact of climate change on cotton production is therefore critical. Here, we used CROPGRO, a process-based crop model that can simulate the main features of cotton growth and management. We applied this model to two regions in North Cameroon and a set of six regional climate projections combining general climate models and regional climate models from the ENSEMBLES project. The model was calibrated and validated with a data set of observations made in farmer fields from 2001 to 2005 and at an experimental station in 2010. Our results show unexpectedly that climate change from 2005 to 2050 in North Cameroon will have a positive effect on cotton yields with an increase of 1.3 kg ha(-1) year(-1) in yield, especially if conservation agriculture systems are adopted. The predicted increase of 0.05 A degrees C year(-1) in temperature will shorten crop cycles by 0.1 day year(-1) with no negative effect on yields. Moreover, the fertilizing effect of CO2 enrichment will increase yields by approximately 30 kg ha(-1). The rainfall pattern is likely to change, but the six regional models used to generate future weather patterns did not predict a decrease in rainfall. One model even forecast an increase in rainfall amounts. According to our findings, climate changes in North Cameroon can be anticipated by tailoring alternative cropping systems and adaptation techniques to cope with climate change

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

peer reviewedClimate effects on agriculture are of increasing concern in both the scientific and policy communities because of the growing population and the greater uncertainty in the weather during growing seasons. Changes in production are directly linked to variations in temperature and precipitation during the growing season and often to the offseason changes in weather because of soil water storage to replenish the soil profile. This is not an isolated problem but one of worldwide interest because each country has concerns about their food security. The Agricultural Model Intercomparison and Improvement Project (AgMIP) was developed to evaluate agricultural models and intercompare their ability to predict climate impacts. In sub-Saharan Africa and South Asia, South America and East Asia, AgMIP regional research teams (RRTs) are conducting integrated assessments to improve understanding of agricultural impacts of climate change (including biophysical and economic impacts) at national and regional scales. Other AgMIP initiatives include global gridded modeling, data and information technology (IT) tool development, simulation of crop pests and diseases, site-based crop-climate sensitivity studies, and aggregation and scaling