Maize and Bean Productivity changes in East Africa due to climate change Objectives: To look at: 1.System-based differences in productivity of maize and bean in CLIP domain due to climate change 2.What could be done to adjust for these changes in the future

To estimate productivity we need to know where in reality the crops are grown in a country For simulations we used length of growing season -LGP (>60days) and FAO soil suitability indices –Problem: Grossly over estimates cropped area Does not say where maize and bean crops are grown in a country Could use Global Land cover Classification (GLC 2000) –Problem: underestimates because of its inability to pick small-mosaics of crop land In this study we used : –modified live-stock systems classification of Sere and Steinfeld (1996) by incorporating cropping systems from Dixon and Gulliver (2001) leading to mixed crop-livestock systems –Overlaid Maize and Bean mask to the mixed crop-livestock systems to identify cropped area –This corresponded to systems classification: Mixed Rain fed Systems

MRA Mixed Rain fed Arid-semiarid MRH Mixed Rain fed Humid/sub-humid MRT Mixed Rain fed Temperate/tropical highland Mixed Rain fed Systems

Maize cropping intensity (You & Wood, 2006)

System areas for Burundi, Kenya, Rwanda, Tanzania and Uganda Percentage Land Area Total Area (km 2 ) MixedLivestockCroppingOther Burundi ,278 Kenya ,721 Rwanda ,580 Tanzania ,138 Uganda ,318 Average or Total ,819,034

Assumptions used in the analyses Maize is main crop grown at the start of growing season Bean comes only in those pixels where growing season is long enough for maize and then for bean Looking at indicative and directional change in productivity as affected by climate change Current national productivity is for from FAO Looking at national productivity and disaggregated productivity in mixed systems MRA, MRH and MRT

GCMs used HadCm3 ECHam4 CCSM (will include in future analysis) SRES Scenarios: A1F1 (high emission) and A1B (low emission)

Percentage production changes from current to 2030 and 2050 (HadCM3 model, Scenario A1) by country and system National ProductionMRTMRHMRA Burundi Kenya Rwanda Tanzania Uganda Maize

BEANS National ProductionMRTMRHMRA Burundi Kenya Rwanda Tanzania Uganda Percentage production changes from current to 2030 and 2050 (HadCM3 model, Scenario A1) by country and system

Considerable spatial variation MRTMRHMRA Maize in Kenya Increase in MRT but decrease in MRH and MRA

Considerable Temporal switches (besides spatial effects) Bean in Tanzania MRTMRHMRA MRH: 7% increase in 2030 from baseline (2000) followed by a 8% decrease in 2050 MRA: 5% increase in 2030 from baseline (2000) followed by a 12% decrease in 2050

Two basic types of response to overcome climate change effects on crop productivity 1.Internal shift: To offset projected decrease internal shifts of production from lower producing systems to higher producing systems e.g. Shifting bean production from MRH (-11%) to MRT (+21%) system in Kenya (caution: It might have to replace / compete with high value crops presently grown in MRT) 2. Agronomic or Management adjustments: Maize and Bean yield decreased in all systems in 2050 in Uganda. –No internal shift possible to overcome since all systems are affected –Are there any management adjustments possible to deal with such changes? o Can evaluate several options such as application of fertilizer, use longer duration crop varieties, irrigation etc. o Validate with FEWS yield and precip data? Chris Funk

To respond to overcome climate change effects on crop productivity : Need to look at high-resolution impacts as they vary both spatially and temporally Coping options vary depending on location and situation There is no single blanket response