Global Futures and Strategic Foresight @ ICRISAT Progress update of 2015 deliverables

S Nedumaran

Research Program – Markets, Institutions ad PoliciesICRISAT | India26 May 2015

ICRISAT is one among 5 CG center started GFP

Multidisciplinary team created and institutionalized (14 member team)

Supporting priority setting, foresight and scenarios analysis for CRP DC and GL

Collaboration with other CRPs and Global Projects like AgMIP (data sharing, model enhancement, capacity building)

Global Futures and Strategic Foresight @ ICRISAT

Multi-disciplinary team @ ICRISAT

Achievements in GFSF @ ICRISAT

Crop model improvement completed – Sorghum, Groundnut and Chickpeas

Abiotic stress tolerant virtual cultivars evaluated for Sorghum and Groundnuts

Technology reports for ICRISAT mandate crops

Contributed to CRP GL and DC priority setting and justifying country focus

Contributed subnational level data for SPAM model improvement

Capacity building:

1 training program on foresight model and its application for ESA economists

1 DSSAT crop model training conducted for CG and NARS partners

Publications:

• 5 Journal articles (ISI journal)

• 1 Book Chapter

• 3 ICRISAT working/discussion paper

Data/tools management

• Populating the Dataverse with socio-economic and experimental data - http://dataverse.icrisat.org/

• Spatial Tool: http://spatial-tools.icrisat.ac.in/

Update on Progress in 2015 deliverables

Millets disaggregation IMPACT

SPAM (2005) used to estimate the production share of pearl millets and small millets (other millets)

Validating the production shares – with national statistics and experts knowledge

Modification of the CERES-Pearl millet model

Model deficiencies in the default version:Did not simulate heat tolerance of pearl millet, i.e., changes in seed set at high temperaturesUnderestimated yields at low plant density or wider row-spacingUnderestimated leaf area index of tillersOverestimated yield loss when drought stressed during panicle initiation to flowering or during flowering to seed-filling stages Overestimated harvest index under extended day length conditions

Relationship of seed set with mean air temperature in pearl millet

Source: Gupta et al. (2015)

Code and parameter changes in the model

All relationships determining heat tolerance in millet incorporated

Model code changed for better estimation of light extinction coefficient (LIFAC) to improve model response to plant population and row spacing

An additional genetic coefficient (GT) was added and other related changes made to improve LAI response of tillers

Effect of water stress on daily panicle growth (GROPAN) and panicle weight (PANWT) reduced by excluding TURFAC (turgor factor) in the GROPAN equation

Effect of day-length on harvest index still needs to be attempted

Collaborating with CRP DC in conducting field experiments

Collaborate with UF and DSSAT foundation (USAID-ICRISAT Linkage fund)

Simulated number of seeds/plant with increasing mean temperature

Simulated number of grain yield with increasing mean temperature

Spatial crop modeling – Case Studies

Case study:1 – Groundnut, India

Used well calibrated groundnut cultivars

Sowing window, fertilizer inputs across location using experts opinion

Needs to develop better soil profile raster for improving the simulations

Spatial Groundnut yield South Asia (Kg/ha)

Base climate

GFDL MIROC

Groundnut yield masked to crop area

Remote sensing data was used to develop the crop area extends

Clip the yields to that pixel

Crop extend maps can be used to update the SPAM maps

Work in progress in spatial crop modeling in 2015

Crop type masking for all ICRISAT mandate crops for South Asia was completed (Gumma et al.,)

Integrated assessment of climate change impacts and various NRM interventions will be studied for South Asia for groundnut and chickpea

Improvements in the global-scale crop modeling simulations by providing better inputs –case study groundnut (sowing window/soil etc.,)

Estimating total welfare benefits (direct and spillover) using Single-multi market Economic surplus model

To assess the spillover potential of technologies to inform decision making

Research domains for 5 ICRISAT mandate crops completed

Modified version of excel based ACIAR spillover model was developed

Estimation of welfare benefits for 5 crops completed

A proposal was submitted to CABI to publish a edited book

Title: DECISION SUPPORT FOR INTERNATIONAL AGRICULTURAL RESEARCH: Linking Impact and Spillover Assessments to Priority Setting

Global Millet Research Domains

Nedumaran et al., 2013

Welfare benefits for Millets – Real world vs Ideal world (US$ M)

776.3 MillionIRR – 17.7%

Nedumaran et al., 2013

Integrating gender and nutrition in foresight modeling

Identification and compilation of database from various sources - national, sub-national, micro-level 1. BMI - percentage of population by gender, rural & urban level, age cohort for Southeast Asia and African

regions

2. Nutritional status of children 0-5 years for Southern Asia, Sub-Saharan Africa, Northern Africa and Western Asia regions

3. % of children malnourished - who are exclusively breastfed, breastfed with complementary food, still breastfeeding, at country level

4. % of population below international poverty line of US$1.25 per day, % of central government expenditure (1997–2006*) allocated to health and education (India)

5. Data on women education, contraceptive prevalence and Antenatal care, delivery care coverage , maternal mortality ratio from 2000-2007 (India)

6. % of children underweight by gender, rural ,urban and income level for all regions of the world. (Source: UNICEF)

7. NFHS and NSSO – India

8. DHS – Global download and compilation in progress

9. Data on sanitation indicators

2. Next step - Developing an index Developing a composite index to be used in the foresight model

• Computing WEAI – for 3 states in India• Developing a index for nutritional status for boys, girls, men and women • Linking Sanitation indicators to nutrition status – micro level, national level

(India and Bangladesh)

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Nutritional status of children under five years of age using Height for age (stunting) indicator, 2013-2014

Severe Stunting (< median -3SD)Moderate Stunting (< median -2SD to >= median -3SD)Normal (>= median -2SD)

DSSAT training – Completed (March, 2015) Gridded spatial crop model training (Jan 2015 and follow up April 2015)

Spatial Tool: http://spatial-tools.icrisat.ac.in/

Publication: 1 Journal article published in 2015 (ISI journal) 2 working paper (under review)

Capacity Building, data tools and Publication

Thank you!

ICRISAT is a member of the CGIAR Consortium