What do we have to adapt to?Analysis of climate impacts on agriculture

in the past and future

Hideki KanamaruFAO

(Hideki.Kanamaru@fao.org)

GACSA Annual Forum – Speakers Corner15 June 2016

Evidence-based CSA• Any CSA programme/project should be supported by

robust evidences.

• Evidence about:– Past climate, and its impacts on local agriculture

– Future projected climate and its impacts on local agriculture

– Characterization of vulnerability to climate change and other factors(social, environmental, etc)

– Current GHG emissions

– Identification and appraisal of potential CSA practices, including cost-benefit – Adaptation/Mitigation/Food security

– Effectiveness of CSA interventions (monitoring and evaluation)

Climate and climate impacts on agriculture

• We need to know what has been happening to climate and local agriculture.

• We also need to know what will happen in the future.

– Near-term: within a range of past climate variabitlity

– Medium- to long-term: we may experience something thathad not happened before

Choice of CSA options (local level). Strategic decisionmaking (regional, national levels).

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)

• Different methodologies/approaches/models for producing different information

Evidence about past: Dry-spells during Reproduction of maize

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)

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

(Chitedze)

General Agriculture-centric Crop specific

Annual average rainfall

Length of potentialgrowing season

Planting date

Monthly average rainfall

Number of rain eventsin season

“False starts” to season

Monthly average temperature

Average volume of rain events in season

Dry spells during reproductive period

Monthly average Tmax

/ Tmin

Size of gaps between rain events in season

Number of “hot” Tmax

/ Tmin during reproductive period

Some examples of indices

Future impacts and vulnerabilityTop-down and bottom-up

MOSAICC

• Modeling System for Assessment of Agricultural Impacts of Climate Change

• 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

National scale with sub-national disaggregation – rainfall projection

Rainfed rice yield change 2011-2040 vs 1971-2000

Peru – corn yield projection

Conclusion

• In order to enable CSA, we need to know what we have to adapt to.

• Define what evidences are necessary to support CSA

• Identify information gaps

• Choose methodology that can fill the gaps

• For example, FAO has standard methodologies applicable to different agroecosystems to look at past and future climate impacts on agriculture