Advertisement
Advertisement

More Related Content

Slideshows for you(20)

Similar to Climate change adaptation strategies in semi-arid Zimbabwe for sustainable intensification of crop-livestock systems(20)

Advertisement

More from ICRISAT(20)

Recently uploaded(20)

Advertisement

Climate change adaptation strategies in semi-arid Zimbabwe for sustainable intensification of crop-livestock systems

  1. Climate change adaptation strategies in semi-arid Zimbabwe for sustainable intensification of crop-livestock systems Sabine Homann-Kee Tui, Patricia Masikati, Katrien Descheemaker, Lieven Claessens, Olivier Crespo, Andre van Rooyen 1st Int. Conference on Food Security NH Hotel Leeuwenhorst 29. September – 02. October, 2013
  2. • Increasing populations • Diminishing p. c. food production • Dwindling natural resource base • Climate change Introduction Sustainable intensification of smallholder farming systems in Southern Africa Source: Alex Rouane Median temperature change (oC), Mid-Century, RCP 8.5, S- Africa Median precipitation change (%), Mid-Century, RCP 8.5, S- Africa
  3. Genetic intensification Modern technologies Ecological intensification Systems integration Sustainable intensification of mixed crop-livestock systems: concept Socio-economic Intensification Resilience  Increase production on existing land  Efficient and prudent use of inputs  Increasing the stock of natural capital Production Income Nutrition Source: Mariana Rufino Adapted from MPR, 2013
  4. Objectives 1. Describe current farming systems and propose a farming systems typology 2. Assess potential impact of climate change on future smallholder crop livestock systems 3. Assess potential economic benefits of selected climate change adaptations • Fertilizer applications on maize • Maize-Mucuna rotation
  5. MethodsClimate data Historical (1980-2010): Mid century (2040-2070): RCP 8.5 (CMIP5) 20 GCMs Projected changes in temperature, precipitation Crop Model APSIM 5 GCMs 0kgN/ha (FP) 17kgN/ha 52kgN/ha Maize-Mucuna rotation Effects on on-farm maize and Mucuna production Livestock model Livsim (Rufino et al.) Feed gaps On-farm feed production (crop residues, forages) Effects on livestock production (milk, off- take, mortality rates) Economic model TOA-MD (Antle et al.) 2GCMs HH survey data (n=160) Relative yields Prices, costs Regional RAPs (Global and regional models) Economic trade-offs of climate change and adaptation strategies on entire farms Economic impacts Heterogeneous populations Types of households
  6. ∎ HISTORICAL A = ACCESS1-0 B = bcc-csm1-1 C = BNU-ESM D = CanESM2 E = CCSM4 F = CESM1-BGC G = CSIRO-Mk3-6-0 H = GFDL-ESM2G I = GFDL-ESM2M J = HadGEM2-CC K = HadGEM2-ES L = inmcm4 M = IPSL-CM5A-LR N = IPSL-CM5A-MR O = MIROC5 P = MIROC-ESM Q = MPI-ESM-LR R = MPI-ESM-MR S = MRI-CGCM3 T = NorESM1-M Climate data RCP8.5 GCMs for Nkayi in Zimbabwe historical - mid century averaged over the growing season
  7. Projected temperature changes • Strong signal that temperature will increase (by +2 - +3.3oC) • Temperature increase is all year, esp. during the early growing season Projected precipitation changes • No strong signal on precipitation changes (-0.7mm/day - +0.5mm/day) • Precipitation decreases esp during earlier rainy season RCP 8.5 mid century temperature scenarios for all GCMs in Nkayi, Zimbabwe RCP 8.5 mid century precipitation scenarios for all GCMs in Nkayi, Zimbabwe
  8. Effects of CC and technologies on maize grain and mucuna yields •Maize grain yields decrease up to >20% •Mucuna biomass decrease up to > 20%, but still provides high feed biomass •Fertilizer and Mucuna applications offset CC effects •Uncertainty among GCMs Crop modeling
  9. Feed gaps Livestock modeling Feed gaps Baseline scenario (C0F0M0) Fertilizer scenario (C0F52M0) Mucuna scenario (C0F0M1)
  10. Effects of CC and technologies on livestock performance •Marginal effect of CC on milk production and mortality rates •Increased milk production and reduced mortality under fertilizer and Mucuna scenarios •Uncertainty among GCM
  11. Farming systems parameters Current systems Small farms Medium farms Large farms Share of HH (n=160) % 43 38 19 Crop production (maize + other crops) • Cultivated land • Maize yields • Net returns ha/farm (sd) Kg/ha (sd) USD/farm (sd) 1.3 (0.7) 497 (444) 90 (85) 1.8 (0.8) 826 (623) 217 (152) 2.5 (1.4) 675 402) 110 (131) Livestock production (cattle + other livestock) • Herd size • Milk yield • Net returns TLU/farm (sd) l/cow/day (sd) USD/farm (sd) 0.3 (0.4) 0 9 (23) 5.8 (2.5) 0.8 (0.7) 495 (322) 15.5 (4.9) 1.3 (0.8) 1376 (586) Off-farm income USD/farm (sd) 246 (250) 324 (288) 345 (431) Selected producer prices (USD/kg) based on RAPs Current Mid term Maize grain (residues) Other crops grain (residues) Mucuna biomass 0.20 (0.04) 0.25 (0.04) 0.17 0.22 (0.04) 0.26 (0.04) 0.18 Beef Milk 1.3 1 1.43 1.05 Exogenous growth (%) based on RAPs Mid term Maize Other crops 30 20 Cattle Other livestock 10 10
  12. -2000 -1500 -1000 -500 0 500 1000 1500 2000 0 10 20 30 40 50 60 70 80 90 100 Economiclosses(USD/farm) Percentage of farm population GCME_no RAPs GCMK_no RAPs GCME_with RAPs GCMK_with RAPs Economic modeling 1. Impact of CC on future agricultural production systems Scenarios: C0F0M0 – C1F0M0 with/without RAPs • 44 to 56% farms are negatively affected by CC - Livestock reduces negative effects of CC • Net losses from CC are marginal (6 to -3%) • Economic development can offset CC impact • Uncertainty among GCMs % gainers % losers
  13. 2. Benefits of CC adaptations Scenarios: C1F1M0, C1F2M0, C1F0M1 – with RAPs -2000 -1500 -1000 -500 0 500 1000 1500 2000 0 10 20 30 40 50 60 70 80 90 100 Opportunitycosts(USD/farm) Percentage of farm population GCMEF17 GCMEF52 GCMEMuc GCMKF17 GCMKF52 GCMKMuc •CC adaptation technologies benefit most farms •Net effects of fertilizer applications are small •Maize-Mucuna rotation provides higher benefits, against less risk % adopters % non-adopters
  14. 3. Impact of CC adaptations by farm types GCME GCMK Stratum Small Medium Large Total Small Medium Large Total Net losses per farm (USD/farm) CC_no RAPs 11 2 165 37 -5 -35 -23 -20 CC_with RAPs -32 -161 -80 -91 -47 -211 -303 -159 F17 -6 -87 -251 -85 -2 -194 -199 -113 F52 19 -160 -149 -82 22 -158 -136 -77 Mucuna -80 -235 -493 -219 -109 -368 -331 -251 Poverty rate (% of population living < 1 USD/day) CC_no RAPs 100 98 62 86 100 98 66 87 CC_with RAPs 100 95 59 85 100 93 55 83 F17 100 90 47 79 100 82 43 75 F52 100 85 48 78 100 82 46 76 Mucuna 100 84 40 75 100 74 39 71 • Net effects from CC are marginal for small farms - any positive incentive will improve their system • Medium and better off farms benefit more from CC adaptations, but also face higher risk • CC adaptation can reduce the proportion of people below poverty rate by 10-15% - but poverty remains high
  15. New routes for sustainable intensification? – CC impacts on entire farm net benefits are small in semi-arid Zimbabwe – Greater impact on farm households’ well being through • Economic changes – policy and institutional interventions • Alternative income options – opportunities for reinvestments – Transition towards CC resilient + profitable farming systems requires a drastic shift • From high risk to more diversified systems (maize-cattle→maize-cattle –mucuna-smallstock) • Coupling crop livestock systems: livestock as currency for intensification • CC adaptation technologies tailored to farm types
  16. ICRISAT is a member of the CGIAR Consortium Picture here Thank you! This work was supported by the CGIAR Research Program CCAFS. Data were used from previous CGIAR-SLP. Special kuddos to Roberto Valdiva!
Advertisement