Rainfed rice environments typically have low productivity due to multiple abiotic stresses (drought, poor soils) and are characterized by much uncertainty, particularly with regard to the timing, duration, and intensity of rainfall. Climate change poses the threat of extreme weather events that, in turn, create uncertainties in rainfall: too much or too little of it, or rain coming at the wrong time, can worsen the productivity of rice farming in rainfed areas.
The IRRI-Japan collaborative research project on Climate Change Adaptation in Rainfed Rice Areas (CCARA) was launched to develop the weather-rice-nutrient integrated decision support system (WeRise). It builds on existing knowledge, database, and tools and integrates seasonal weather forecast and real-time weather data with the crop growth models and nutrient management tools.
WeRise hopes to help farmers with their crop production decisions by providing crucial weather information such as the start and end of the rainy season and the distribution of rainfall during the cropping season. It could also give advice on the optimum planting times, which variety to use, and the amount and timing of fertilizer application in rainfed rice areas under current and future climate conditions.
The WeRise prototype is now ready for field testing in CCARA’s project sites in Laos and Indonesia.
Theme 1: Development of a seasonal weather forecasting model and analysis of abiotic stress conditions caused by climate change
1.1. Generating seasonal weather forecasts
A weather forecast model will be selected and its ability to predict seasonal weather (rainfall patterns, temperature) over land in rainfed lowland rice- growing environments will be tested. For this purpose, the current spatial scale of the model will be downscaled mathematically. Testing will be carried out at two “benchmark sites” in rainfed lowland rice environment sites in Southeast Asia (see Theme 3.1).
1.2. Assessment of predicted weather on rice growth and development
The predicted patterns of rainfall, water shortages, and high temperature will be analyzed, and the effects of these on rice growth and yield formation will be explored by linking the weather forecast model with a crop growth model.
Theme 2: Development of rice genotypes suitable for abiotic stresses caused by climate change
2.1. Evaluation and selection of favorable genotypes under rainfed lowland conditions
The IRRI germplasm collection, which covers a wide variation of rice genotypes, and introgression lines developed under Phase IV* of the IRRI‐Japan Collaborative Research Project will be screened for adaptation to drought under rainfed lowland conditions. Depending on weather forecasts for the benchmark sites (see subtheme 1.1), tolerance to high temperature may be included into the screening. The selected genotypes will be evaluated for agronomic performance at the benchmark sites.
Development of Integrated Rice Cultivation System under Water‐Saving Conditions (9 August 2005-8 August 2010)
2.2. Genetic analysis for tolerance to drought
New hybrid populations from promising genotypes selected in subtheme 2.1 will be developed. Genetic analyses and identification of QTLs for tolerance to drought and, potentially, to high temperatures (see subtheme 2.1), will be performed and DNA markers for marker- assisted breeding (MAB) will be developed.
2.3. Development of near- isogenic lines (NILs) for tolerance to drought
NILs will be produced from crosses between IR64 and selected germplasm. Homozygosity and agronomic traits of the NILs will be determined.
Theme 3: Development of fertilizer management technologies to mitigate stresses caused by climate change
3.1. Selection of benchmark sites and characterization of these sites
Benchmark sites and local partnerships in rainfed lowland rice areas will be identified to test the accuracy of weather forecasts generated by the weather forecast model (from Theme 1), evaluate improved rice germplasm (from Theme 2), develop and validate improved fertilizer management technologies (from subtheme 3.2) and, finally, test the integrated DSS (from Theme 4). The first site will be in southern Lao PDR (Savannaketh and Khammouane provinces), while the second site will be in Central Java in Indonesia. Site characterization parameters will include soil characteristics, current rice varieties, and management practices used. Production constraints will be identified.
3.2. Identification and validation of key technical options for soil nutrient management
The effects of soil characteristics on soil fertility status, especially nitrogen, will be assessed and improved fertilizer strategies for coping with variable rainfall patterns will be developed. Improved fertilization strategies will take into account the availability of seasonal weather forecasts, as generated by the weather forecast model, to make specific recommendations according to predicted weather. Fertilizer management recommendations will feed into the DSS developed under Theme 4.
Theme 4: Development of an integrated decision support system (DSS)
4.1. Development of a DSS for nutrient management and agronomic practices for rainfed rice
Science‐based decision support will help farmers in rainfed environments make improved crop management decisions such as choice of variety, planting time, planting method (transplanting or direct seeding), and fertilizer application. The integrated DSS will be developed by building on existing knowledge, database, and tools and integrating seasonal weather forecast and real-time weather data with the crop growth models and nutrient management tools.
4.2. Integration and validation of the DSS with weather forecasting system
Seasonal weather forecasts (generated under Theme 1) will feed the DSS for nutrient management and agronomic practices to help farmers cope with climate change. The developed integrated DSS will take climate change into account through its capability of generating seasonal weather forecasts.
Theme 5: Capacity building
Training courses on the weather forecasting system and DSS for counterpart scientists, including local staff from the benchmark sites, will be organized in Years 3 and 5 of the project.