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Climate Services for Agriculture and Food Security: A Stakeholder perspective. Pramod Aggarwal CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, New Delhi-110012, India. Climatic risks are increasing with time.
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Climate Services for Agriculture and Food Security: A Stakeholder perspective Pramod Aggarwal CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, New Delhi-110012, India
Climatic risks are increasing with time Number of weather-related disasters (1905-2015) Climatic risks are increasing with time Source: The human cost of weather-related disasters:1995-2015. CRED and UNISDR, 2016
Number of people affected by weather-related disasters (1995-2015) Source: The human cost of weather-related disasters:1995-2015. CRED and UNISDR, 2016
Food security and climate change; Present case, worst case and best case scenario
Climate services for agriculture and food security: What do Stakeholders need?
Climate services for agriculture and food security: What do Stakeholders need? Some examples
Agro-advisories: Intelligent Agricultural Systems Advisory Tool (iSAT) developed by ICRISAT • Started as a ‘sowing app’ in 2016 – 30% in yields of users (n=300) • Developed a pre-season decision tree to inform crop planning • Developed a weekly decision tree integrating forecasts, crop and soil scenarios and systems information – messages sent via SMS • Piloted 2017-19 with ~2100 farmers in Anantapur (Social media +40k) Weather advisories via SMS are nothing new but ‘integration’ to deliver real time, context specific advice are.
Pests and diseases monitoring: Plantix- the mobile crop doctor* * Plantix as an example; many companies provide such knowledge Link this with weather forecasts for pests and diseases EWS
Harnessing Big Data and Advanced Analytics for Improved Crop Insurance • Acquiring data Farmers practices and crop growth (digital geo-referenced pictures) crowdsourced by mobile Apps, sensors in farmer’s fields, gridded data, VIs, satellite rainfall, UAVs • Assimilating data Models, statistics, geospatial techniques • Analyzing data Assessment models, optimization, machine learning • Application ICT, Mobile Apps, multiple data for MRV, digital banking
Crop-loss Assessment Monitor (CAM): A Web-based Multi-Model Tool for Harnessing Technologies and Big Data for Improved Crop Insurance Insurance scheme design and analyses Multiple models Loss Assessment Model inputs Weather derivative Model Weather from Satellite, Observatory And Models Prevented Sowing Indemnity Level Sowing Failure Vegetation Indices Model Sum Insured Satellite and UAV signals Mid-season Loss Insurance Premium Statistical Yield Model Crowd sourced farmer’s practices Yield Loss at Maturity Crop Modelling Insurance claims Soil database Claim ratio Post Harvest loss Scalable Yield Model Historical Crop Yield Calibration and validation at sentinel sites
Climate Adaptation: Climate-smart villages:Pulling the pieces together to support national adaptation agenda Adapted technologies + Climate-specific management + Seasonal agroclimatic forecasts + Efficient resource use + Enabling environment NAPs and NAMAs Adapted technologies + Climate-specific management + Seasonal agroclimaticforecasts + Efficient resource use Adapted technologies + Climate-specific management + Seasonal agroclimatic forecasts Adapted technologies + Climate-specific management Adapted technologies Baseline Climate smartness
Improved Crop Varieties: Big data for stress-tolerant varieties Global Seed Distribution Network of Wheat and Maize
Detecting Climate Adaptation with Mobile Network dataBangladesh (Lu et al., 2016) Assessed data of mobility and calling behavior of 5.1 million mobile users before, during and after a cyclone
Smart Farms*: Big data analytics brings knowledge at your doorstep *Main page of Cropin company just for illustration purpose; many companies provide such knowledge
Climate services in agriculture and food security: Several exciting opportunities but challenges remain • Big data Big opportunities • Information and skills mismatch between data scientists and domain experts • Data rights and data sharing • Producer-user linkages • Moral hazards in crowdsourcing data • Inadequate capacity • Scaling up • Climate finance
Assumption is that knowledge is the main limiting factor…. Address generic vulnerability issues simultaneously – poverty, literacy, governance, etc., which limit utilization of knowledge even today and will do so in future as well