From climate information to to climate impacts on agriculture and

food security

Evidence-base to support NAP

• Evidence about what have been happening to climate and agriculture in the country: impacts, risks, vulnerabilities (B.1 and B.2)

• Evidence about what is expected to happen in the future: impacts, risks, vulnerabilities (B.1 and B.2)

• Evidence about identification and appraisals of adaptation practices (B.3)

• Evidence about effectiveness of adaptation interventions

--> Different methodologies/approaches/models for strengthening each evidence.

Evidence about past and future• What are we trying to adapt to?

• Looking at climate from agriculture perspective

• Forms bases for NAP - justify projects / programmes / investments, prioritize areas and sub-sectors for interventions

• Provide counterfactuals/baseline for tracking adaptation

• Adaptation planning – iterative process with periodical review of new evidence, science, and outcomes form adaptation activities

• Country ownership, capacities

Evidence about past: Dry-spells during Reproduction

Number of dry-spells during the reproductive period of the 120-day and 90-day growing season

120-day Maize

1965 1970 1975 1980 1985 1990 1995 2000 2005 20100

0.5

1

1.5

2

2.5

3Number of dry spells in reproductive (120) period (trend=0.02, pval=0.01)

90-day Maize

1965 1970 1975 1980 1985 1990 1995 2000 2005 20100

0.5

1

1.5

2

2.5

3Number of dry spells in reproductive (90) period (trend=−0.02, pval=0.02)

Chitedze

What information do you need?• Parameters to define types of

information/evidence

– Biophysical/geophysical/socioeconomic/economic, etc

– Quantitative/qualitative

– Sub-sectors (crops, pasture, livestock, fisheries, forest, economy, market, water, etc)

– Spatial scale (global, regional, national, sub-national, local)

– Temporal scales (intra-seasonal, seasonal, a few yrs, 10, 30, 50, 100 yrs, centuries)

Top-down and bottom-up

MOSAICC

• A capacity development tool

• Integrated modeling system for inter-disciplinary assessments

• National and sub-national scales, medium- to long-term

Downscaled climate projections under

various climate scenarios

Crop yield projections

under climate scenarios

Simulation of the country’s hydrology

and estimation of water resources

Economic impact and analysis of

policy response at national level

Forest productivity

changes under climate scenarios

Precipitation projections for SE Asia

A regional agriculture impact example:Average change in rice yield in Asia

Masutomi et al., 2009

National scale with sub-national disaggregation – rainfall projection

Rainfed rice yield change 2011-2040 vs 1971-2000

Peru – corn yield projection

Conclusion

• Define what evidences/information are necessary to support NAP

• Identify information gaps

• Choose methodology that can fill the gaps

• Data availability and quality – lack of data can be complemented by global dataset (to some extent)

• Other sub-sectors than crops – livestock, fisheries, forestry, and to food security

• Strengthening country capacities

• Addressing uncertainties

• Validation against local knowledge and perception