climate change adaptation strategies in semi-arid zimbabwe for sustainable intensification of...
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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 SecurityNH Hotel Leeuwenhorst
29. September – 02. October, 2013
• 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
Geneticintensification
Modern technologies
Ecological intensification
Systems integration
Sustainable intensificationof 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
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
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/ha52kgN/ha
Maize-Mucuna rotation
Effects on on-farm maize and Mucuna production
Livestock modelLivsim (Rufino et al.)
Feed gaps
On-farm feed production (crop residues, forages)
Effects on livestock production (milk, off-take, mortality rates)
Economic modelTOA-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 populationsTypes of households
∎ 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
Projected temperature changes • Strong signal that temperature will increase (by +2 - +3.3oC)• Temperature increase is all year, esp. during the early growing seasonProjected 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
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
Feed gapsLivestock modeling
Feed gaps
Baseline scenario (C0F0M0)
Fertilizer scenario (C0F52M0) Mucuna scenario (C0F0M1)
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
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
BeefMilk
1.31
1.431.05
Exogenous growth (%) based on RAPs Mid term
Maize Other crops
3020
CattleOther livestock
1010
-2000
-1500
-1000
-500
0
500
1000
1500
2000
0 10 20 30 40 50 60 70 80 90 100
Eco
no
mic
loss
es
(USD
/far
m)
Percentage of farm population
GCME_no RAPs GCMK_no RAPs
GCME_with RAPs GCMK_with RAPs
Economic modeling1. 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
2. Benefits of CC adaptationsScenarios: 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
Op
po
rtu
nit
y co
sts
(USD
/far
m)
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
3. Impact of CC adaptations by farm types
GCME GCMKStratum Small Medium Large Total Small Medium Large Total
Net losses per farm (USD/farm)CC_no RAPs 11 2 165 37 -5 -35 -23 -20CC_with RAPs -32 -161 -80 -91 -47 -211 -303 -159F17 -6 -87 -251 -85 -2 -194 -199 -113F52 19 -160 -149 -82 22 -158 -136 -77Mucuna -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 87CC_with RAPs 100 95 59 85 100 93 55 83F17 100 90 47 79 100 82 43 75F52 100 85 48 78 100 82 46 76Mucuna 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
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
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!
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