Agmip Clip India Pmh

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    Crop livestock intensification in the

    face of climate change

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    Study sites

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    Fast track site

    NKAYI

    MATEBELELA

    ND

    SOUTH

    MA T EB E LE LAN D

    N O R TH

    M ASHO NAL AND

    C ENT RALM ASHO NAL AND

    W E S T

    MASH

    ONALAND

    EAST

    MA S VI N GO

    MAN

    ICAL

    AN

    D

    MI

    D

    LANDS

    HARARE

    BULAWAYO

    NKAYI

    DISTRICT

    VIII

    IXVI

    X

    XI

    XIII XIV XV

    XXIV

    XXV

    XXIII

    XVI

    IV

    III

    III

    V

    XII

    XXII

    XX

    XVII

    XVIII

    XIX

    XXVI

    VII

    District B oundary

    Ward Boundary

    Village Boundary

    Research Wards

    II Ward Name

    EXPLANATION

    0 20km

    Site selection:Southern Africa

    Village selection:

    8 villagesDistance from markets and roads

    Village level surveys:Focus group discussions

    ~ 30 farmers of different wealth, gender

    and age per each village (n=24)

    Household surveys:Quantitative interviews

    20 households per village, stratified by

    wealth (n=160)

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    Farm Systems Characteristics

    1. Population and strataPopulation:160 HH (20HH per 8 villages) in Nkayi, South West Zimbabwe

    Strata:Ownership of ruminants (TLU)

    2. Mixed crop livestock sub-systemsMaize and other crops: Grain and residues

    Cattle and other livestock: Milk, draft power, manure, milk

    3. Crop, livestock and outcome componentsProduction: Maize grain and residues, cattle milk and meat

    Gains and losses: Net returns on maize, other crops, cattle, other livestock

    Herd size Thresholds (TLU) % household

    No/few ruminants 0-0.49 29.4

    Small herd 0.5-5.4 41.3

    Large herd >5.4 29.4

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    Integrated crop-livestock systems

    Soil fertility

    Feedshortages

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    Food security

    0

    10

    20

    30

    40

    5060

    70

    80

    90

    100

    0 50 100 150 200 250 300 350 400 450

    Probabilityofexce

    edence(%)

    % grain requirement met

    Control

    Micro-dose

    Mz_muc

    100%

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    Feed gap analysis

    Monthly feed supply (pasture, green & crop residues, red)

    versus feed demand (black line) for an example household

    Base climate Future climate

    Feed gap analysis is translated into livestock cost/benefits:

    - Milk and meat production in case of feed surplus

    - Costs for stock feed in case of feed shortage

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    From feed gaps to livestock cost/benefitsFeed gap

    analysis is

    translated into

    livestockcost/benefits:

    - Milk and meat

    production in

    case of feed

    surplus

    - Costs forstock feed in

    case of feed

    shortage

    % change is used to calculate future livestock livestock cost/benefits based on observed data:

    cost/benefit_future = cost/benefit_observed * %change

    where %change = (modelled_future - modelled_historic)/modelled_historic

    milk meat concentrates

    base future base future base future

    HH id kg kg % change kg kg % change kg kg % change43102 1176 912 -22% 117 91 -22% 685 1448 111%

    43104 786 594 -24% 79 59 -24% 793 1388 75%

    43107 1176 912 -22% 117 91 -22% 685 1448 111%

    43119 1296 1092 -16% 130 109 -16% 394 923 134%

    43120 3542 2970 -16% 354 297 -16% 1136 2664 134%

    43202 0 0

    43203 1248 784 -37% 125 78 -37% 4929 6328 28%

    43204 0 0

    43205 212 110 -48% 21 11 -48% 1665 1979 19%

    43206 1968 1698 -14% 197 169 -14% 490 1208 147%

    43207 972 834 -14% 97 83 -14% 279 664 138%43208 1008 888 -12% 101 89 -12% 180 497 176%

    BENEFITS COSTS

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    -600

    -400

    -200

    0

    200

    400600

    0 50 100

    S2a S2b S2c S3

    Gain/loss diagram for each stratum from the TOA-MDanalysis

    $/yr

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    Household modelling: APSFarm

    Rodriguez, D., Cox, H., deVoil, P., Power, B. 2012. A whole farm modelling approach to understand impacts and increase preparedness to climate change in

    Australia. Ag. Systems

    Rodriguez D, deVoil P, Power B, Cox H, Crimp S, Meinke H (2011) The intrinsic plasticity of farm businesses and their resilience to change. An Australian

    example. Field Crops Res. 124, 157-170.

    Power, B., Rodriguez, D., deVoil, P., Harris, G., Payero, J., 2011. A multi-field bio-economic model of irrigated grain-cotton farming systems. Field Crop Res.

    124, 171-179.

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    LivSim (Rufino et al., 2008)

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    APSFarm-LivSimRelevantinterventions

    Profits Risks SustainabilityFood security

    Livestock No livestock Livestock

    Own and hired labour Labour constrained

    Less land constrained Land constrained

    More educated No educated

    Potential production

    M

    aizeharvested

    APSFarm-LivSim simulation

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    Expected outputs

    Detailed description of farming systems and developed

    crop-livestock management practices relevant to different

    household typologies Interactions and synergies of increased diversity and

    integration (agro-ecological and economic opportunities)

    and their contribution to reduce risk and increase system

    resilience explored through modelling