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!