Projects

Project description

Climate-smart agriculture (CSA) interventions are those that increase productivity, adjust farming systems with respect to perceived or future projected climate change impacts, and reduce or remove (where possible) GHG emissions. Interventions can range from the implementation of new or changes in current agronomic practices, to the introduction of new information products such as seasonal climate forecasts. Due to the complex nature of agricultural systems, however, trade-offs and synergies arise between the three different CSA pillars for particular interventions.

Despite significant investment in CSA-related research and development by funding bodies, the CGIAR, the United Nations Food and Agriculture Organization (FAO), as well as national governments and non-governmental organisations, numerical evidence of trade-offs and synergies between CSA pillars for so-called CSA interventions is scarce or non-existent. Importantly, no studies have assessed these trade-offs and synergies across timescales (from interannual to multi-decadal).

The aim of this project, closely related to Core F2 Leadership, is to use models and data to generate evidence for CSA, focusing on quantifying trade-offs and synergies between the three CSA pillars (resilience, mitigation, and productivity) for select CSA practices. The project involves the innovative development and combination of models in order to evaluate the multiple objectives of CSA strategies in an integrated way. This modelling innovation will have wide application in the evaluation of CSA systems globally. The project will focus on two CSA practices, intermittent irrigation or Alternate Wetting and Drying (AWD) and Conservation Agriculture as they are, together with agroforestry, two of the most widely promoted practices under the Climate-Smart Agriculture context.

Some key research questions include:

• What are current and future risks facing farmer households in sub-Saharan Africa and Latin America?
• Which crop, livestock and soil emissions model prognostic variables are useful and robust as indicators for the different CSA pillars?
• What types of CSA practices can help manage the risks farmers face, and what are their impacts on the three CSA pillars?
• What are the trade-offs and synergies across space and time of different CSA interventions?

Research activities

Research will investigate e CSA practices under future climate scenarios using data from CSVs and crop-climate models, and will mainstream important findings onto both the CGIAR and broad scientific and policy contexts (e.g. the IPCC).

• Climate-smart management for lowland rice production: Alternate Wetting and Drying (AWD) for rice systems seeks to reduce water consumption and methane emissions by alternating the draining and re-flooding of the paddy field 1-2 weeks after transplanting. Advances in field experiments and modelling at the University of Leeds and the International Center for Tropical Agriculture (CIAT) now provide a foundation for the modelling of AWD with a CSA lens. CIAT, working in collaboration with FEDEARROZ, has generated AWD suitability maps for Colombia and has also conducted field measurements on GHG emissions under fully irrigated and AWD schemes and simulated rice system emissions using the Denitrification-Decomposition (DNDC) soils model. CIAT has also been conducting growth experiments to calibrate and evaluate the Oryza_v3 process-based crop model for both irrigated and rainfed rice. Additionally, a new version of the Oryza_v3 model is underway capable of simulating changes in GHGs together with crop yields. The project will assess AWD through a CSA lens in Latin America, quantifying the suitability and potential impact of AWD on rice CH4 emissions, productivity and resilience.
• Is conservation agriculture climate-smart? Conservation agriculture –i.e., minimum tillage, permanent soil cover, management of crop rotations, and soil fertility management– is also a practice being widely promoted as CSA, particularly in sub-Saharan Africa. The characteristics of conservation agriculture and their effects on soil properties that are relevant to crop productivity, resilience and mitigation, can be simulated with crop-climate and soil models. A literature review on available data and models will inform the implementation of a modelling framework to assess the three CSA pillars for conservation agriculture systems.
• Assessment of climate-smart breeding needs: Climate-smart breeding seeks to deliver more productive and resilient crops that keep pace with climate change. Delivery of new varieties is currently limited by the cost and time required for developing and selecting new breeding material. The breeding of new material for adaptation to broad geographic areas where multiple stresses occur can slow yield gain due to substantial GxE interactions. Therefore, understanding the stability (i.e. frequency of occurrence) and spatial distribution of seasonal patterns of heat and drought stresses, and assessing affected plant processes helps targeting breeding activities better. This activity uses calibrated crop models to develop targeted crop model simulations under a variety of climate scenarios, and address potential directions for breeding programs during the 21^st century.

Outputs

• Research on 'Timescales of transformational climate change adaptation in sub-Saharan African agriculture’and Climate-smart breeding
• Peer-reviewed studies on future breeding needs in Latin America
• Peer-reviewed studies on the assessment of alternate wetting and drying for rice in Latin America, and conservation agriculture for sub-Saharan Africa.
• Peer-reviewed studies on the predictability of temperature, precipitation and crop yield during the 21st Century, and the potential for reducing uncertainty in crop-climate model projections.

Partners

Led by the University of Leeds, in partnership with the International Center for Tropical Agriculture (CIAT) and CCAFS F2 Core Leadership.

Further information

For further information, please contact the project leader Andy Challinor (University of Leeds) at a.j.challinor@leeds.ac.uk