Advanced Institute on Climatic Variability and Food Security

1. Advanced Institute on Climatic Variability and Food Security Synthesis Report

2. Objectives of The Institute • MOTIVATION • Climate variability affects people. • There are opportunities to take advantage of the knowledge generated by different disciplines: • Climate Science • Agricultural Science • Socioeconomic science • The goal is to generate new knowledge and serve the needs of the people affected

3. Objectives of The Institute • STRATEGY • An effective action over highly complex systems calls for multidisciplinary work • But it is also necessary to have people trained to facilitate communication among disciplines and to carry out integrated research. • Holistic view of the system

4. Training Phase • Scientific basis of climatic predictability • ENSO • Downscaling • Translating climate variability into yield outcomes • Crop Modeling • Undestanding decission makers needs and perceptions • Socioeconomic approach • Identify cuantitative indicators of performance • Constraints of the system

5. Training Phase • Iterative process. • Lessos learned • Opportunities • Achivements. • Consistent improvements towards a main goal • Research Projects.

6. Samuel Adiku (Ghana) “Operationalizing ENSO-based climate forecasts for Agricultural Planning in Ghana” • Investigate tele-connections between OND ENSO and rainfall at nine farming zones. • Demonstrate the impact of ENSO on crop yields via crop modeling and explore management practices. • Develop a framework for operationalizing ENSO-based seasonal forecast. • ENSO impact on seasonal rainfall was significant • Short duration varieties were found to be more productive in zones with ENSO footprint

7. Pierre Sibiry Traore (Mali) “Seasonal forecasting and climate risk in the Sudano-Sahelian zone: Progress towards new opportunities for improving sorghum varieties” • Translating climate forecasts into enhanced food security in the Sahel • DSSAT family of cropping systems was improved to simulate sorghum and millet phenology and growth • Seasonal IRI probabilistic forecasts were evaluated against station and satellite data • Dynamic satellite time series were assembled to evaluate cotton growing belt

8. Trevor Lumsden (South Africa) “Application of seasonal climate forecasts to predict regional scale crop yields in south africa” • Reserch methods to produce crop yield forecasts for small scale agriculture in S.A. and evaluate quality of forecasts • Assess the potential application of crop yield forecasts to improve crop management decissions • Proposal submitted to carry out case studies at five sites where forecasts will be used to implement crop management decisions.

9. Milton Waiswa (Uganda) “Providing farmers with needed climatic information through linking indigenous and scientific climate knowledge systems” • Identify (validate) how farmers traditionally use local temperatures and winds to forecast rainfall onset. • Develop statistical models for forecasting rainfall onset • Linkages between indigenous and scientific climate knowledge systems • Models can be used to forecast the onset of rainfall 2-3months in advance

10. Kamalesh Kumar Singh (India) “Application of seasonal climate forecasts for sustainable agricultural prediction in Telangana sub-division of Andhra Pradesh, India” • Maximize crop yield through application of seasonal climate forecast in agriculture for selected locations. • Generate seasonal rainfall hindcast for selected locations. • Select sowing window for selected crops. • ECHAM model showed better rainfall hindcast at seasonal/sub-seasonal scale • Awareness was created among reserachers and end users about utility and limitations of seasonal climate forecast for application in agriculture

11. Rengalakshmi Raj (India) “Localized Climate Forecasting System: Seasonal Climate and Weather Prediction for Farm Level Decision Making” • Study seasonal climate variations, traditional farmers knowledge and coping strategies • Translate forecast information into farmer friendly versions for its practical use in livelihood enhancement • Framework for farmer friendly localized forecasting system • Social stratification of knowledge was documented • Special program on “climate, adaptation and vulnerability” was evolved

12. Rizaldi Boer (Indonesia) “Reducing climate risk in Chili and Potato production at Pengalengan District, West Java” • Study relations between climate forcing factors and rainfall variability • Evaluate use of CFF for planting strategy design • Develop models for optimum planting date and crop yields based on CFF indices • Rainfall variability was influenced not only by ENSO but also by Indian Dipole Mode • SOI and DMI can be used to predict optimum planting date and yield • Follow up project has been developed and submitted

13. Ramasamy Selvaraju (India) “Improving Food Security and Resource Use of Irrigated Crop Production Systems through Climate Forecasts in Southern India” • Assess and manage the impact of climate variability on the irrigated crop production systems to improve smallholder food security in a highly vulnerable semi-arid India. • Quantify the impact of ENSO on water availability and on crop yield through system simulation approaches • A generic water allocation crop choice framework prototype was developed for the case study region incorporating ENSO information.

14. Felino Lansigan (Philippines) “Delivering climate forecasts products to farmers: Agronomic and economic impacts of advanced climate information on corn production systems in Isabela, Philippines” • Determine perceptions and linkages of climate information with crop production systems • Evaluate agronomic and economic impacts of advanced information on corn production • Use of climate information to determine planting date has resulted in higher yields and higher net incomes • Department of agriculture is now funding the development of a crop forecasting system

15. Nageswara Rao (India) “Farmers’ Participatory Approach to Manage Climate Variability” • Provide seasonal climate Prediction to farmers based on coupled atmospheric General Circulation Model (GCM) output statistical (MOS) downscaling. • Provide forecast-based simulated crop management options for farmers’ choice, and evaluate the value of forecast to farmers with their participation. • Better Forecast skill was identified for Kurnool and Anantapur districts in AP, India by stepwise regression, and ENSO phase relationship with rainfall • Farmers were responsive to forecast decision options and took up double cropping in both the districts

16. Thuan Nguyen (Vietnam) “Application of Climate Prediction in Rice Production in the Mekong River Delta (Vietnam)” • Study relations between ENSO, rainfall and temperature in the region • Prepare/disseminate forecasts for two selected districts • Perform Rice crop simulation • Significant lag-time correlations between SST, SOI and climate variables was found • Forecast bulletins were issued and disseminated to farmers (Climate variables + water level and salinity) • Crop simulation is identified as useful tool for decision making, but requires validation

17. Alvaro Roel (Uruguay) “Towards the development of a Spatial Decision Support System (SDSS) for the Application of Climate Forecasts in Uruguayan Rice Production Sector” • Evaluate ENSO effects on Uruguayan rice production systems • Evaluate Ceres-Rice performance in recreating temporal and spatial variability • Simulate rice yields under different seasonal forecasts scenarios • Rice yield is affected by ENSO phases • Ceres-Rice was able to capture spatial and temporal yield variability

18. Francisco Meza (Chile) “Where and When do we need water?: Development of a regional crop yield and water demand model based on Sea surface temperature forecasts” • Characterize the main components of the agricultural hydrological cycle • Assess possible crop yield outcomes of irrigated sectors under ENSO scenarios under drought conditions • ENSO does play a role determining evapotranspiration rates • It is possible to use climate forecasts in water resources allocation at the farm level (Mathematical programming approach)

19. ATI Outcomes • Increase awareness about the impacts of climate variability and possibilities of adaptation • Contribution to scientific and technical literature • Regional capacity building throughout farmers participation and specific workshops • ATI network/colleagues