Aslihan Arslan(Co-authors: Nancy McCarthy, Leslie Lipper, Solomon Asfaw, Andrea Cattaneo and

Misael Kokwe)

1st Africa Congress on Conservation Agriculture19.03.2014

Lusaka, Zambia

Food security and adaptation in the context of potential CSA practices in

Zambia

• CSA & CA• Background• Data sources• Climate variables• Descriptive stats• Results• Conclusions

Outline

FAO CSA 2010 definition:

Agriculture that sustainably increases productivity, resilience (adaptation), reduces/removes GHGs (mitigation), and enhances achievements of national food security and development goals.

Climate Smart Agriculture

• CSA:• is an approach to achieve agricultural development

under climate change• CA:

• has the potential to contribute to CSA pillars• different impacts in different locations & experimental

vs. farmer plots• barriers to adoption (e.g. opp cost of residue, time

delay)• needs to be studied under farmer conditions & climate

change lens

CSA = CA?

Questions Addressed1. What are the impacts of CSA practices on maize yields per

hectare in Zambia?2. What are the impacts of CSA practices on the probability of

very low yields and on the yield shortfall?

Practices Studied:1. Minimum Soil Disturbance (MSD)2. Crop Rotation (CR)3. Legume Intercropping (LEGINT)4. Inorganic Fertilizer Use (INOF)5. Improved Maize Seeds (IMPS) CSA??

• RILS 2004 and 2008: supplemental surveys (CSO/FSRP) to the annual post-harvest surveys (PHS)– Both nationally representative– Around 4,000 households interviewed in both years– 4,138 & 4,354 maize plots in 1st and 2nd rounds– Econometric analyses of productivity and probability

of low production controlling for a large set of relevant socio-economic, climate and agro-ecological variables

Data Sources 1

RILS Enumeration Areas & AER

• Rainfall (1983-2012): Dekadal (10 days) rainfall data from Africa Rainfall Climatology v2 (ARC2) of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC)

• Temperature (1989-2010): Dekadal avg, min & max temperatures of the European Centre for Medium-Range Weather Forecasts (ECMWF)

• Soil: Soil nutrient availability and soil pH levels from the Harmonized World Soil Database (HWSD)

Data Sources 2

• Rainfall: 1. Growing Season Total (and its square)2. Onset of the rainy season: 2 dekads of >=50mm rainfall

after October 1.3. Dry spells: # dekads with <20mm rain during

germination&ripening4. False onset: 1 dekad with <20mm rain after the onset

• Temperature:1. Growing season average2. Growing season max3. Indicator if Tmax=28 degrees

References: Tadross et al. 2009. “Growing-season rainfall and scenarios of future change in southeast Africa:

implications for cultivating maize. “ Climate Research 40: 147-161.Thornton P., Cramer L. (eds.) 2012. “Impacts of climate change on the agricultural and aquatic

systems and natural resources within the CGIAR’s mandate.” CCAFS Working Paper 23.

Climate Variables

Maize Yields by AER & Year0

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02.0

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0 2000 4000 6000 0 2000 4000 6000

2004 2008Maize Yields by AER Maize Yields by AER

AER I AER IIa

AER IIb AER III

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Graphs by year

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2004 2008Season Rainfall by AER Season Rainfall by AER

AER I AER IIa

AER IIb AER III

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Graphs by year

Season total rainfall by AER & year

Average Temperature by AER & year0

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2004 2008Season Avg. Temp. by AER Season Avg. Temp. by AER

AER I AER IIa

AER IIb AER III

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Graphs by year

Max Temperature by AER & year0

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2004 2008Season Max. Temp. by AER Season Max. Temp. by AER

AER I AER IIa

AER IIb AER III

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Graphs by year

CoV of Rainfall & Onset by AER0

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20

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AER I AER IIa AER IIb AER III

CoV of Rainfall by AER

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AER I AER IIa AER IIb AER III

CoV of Rain onset (1983-2012) by AER

Shares of maize plots under each practice

Year 2004 2008 TotalMSD 0.030*** 0.043*** 0.037CR 0.239*** 0.361*** 0.301LEGINT 0.047*** 0.029*** 0.038INOF 0.374 0.391 0.382HYBM 0.436*** 0.476*** 0.457MSD+CR 0.009*** 0.021*** 0.015MSD+LEGINT 0.001 0.001 0.001MSD+INOF 0.010 0.008 0.009MSD+HYBM 0.010 0.010 0.010CR+LEGINT 0.007 0.007 0.007CR+INOF 0.087*** 0.143*** 0.115CR+HYBM 0.079*** 0.146*** 0.113LEGINT+INOF 0.011** 0.007** 0.009LEGINT+HYBM 0.014*** 0.006*** 0.010INOF+HYBM 0.217*** 0.259*** 0.238CR+INOF 0.052*** 0.098*** 0.075LEGINT+INOF+HYBM 0.007*** 0.003*** 0.005* significant at 10%; ** significant at 5%; *** significant at 1%

Maize yields by practice & year

Average maize yields (kg./ha) by practice and year

No Yes No YesMSD 1,580 1,495 1,551 1,317

CR 1,538 1,703 1,513 1,589LEGINT 1,576 1,619 1,538 1,629

INOF 1,320 2,011 1,206 2,060HYBM 1,417 1,786 1,229 1,884

2004 2008

Econometric AnalysesThe methodology we use… • Avoids confounding factors that affect average

yield comparisons (e.g. farmer characteristics, plot characteristics, labor availability, other input use)

• Helps us identify the average impact of a practice on yields and probability of very low production

• Interaction terms between climate variables and practices help us identify how the average impacts vary with climatic conditions

Yieldp(low yield)

Yield shortfall

MSDCR -LEGINT + -

INOF + - -IMPSEED + - -CR*CoV Rain +INOF*CoV Rain + +

INOF*False onset - + +IMPS*False onset + -

IMPS*tmax ≥28°C - + +Fertilizer on time + - -Rainfall + -Max temp ≥ 28°C + -

Summary of robust findings

Conclusions- yield effects• Climatic shock variables significantly change the

impacts of practices• Rainfall variability drives yield effects: In high

variability areas…• Crop rotation has positive effects • Inorganic fertilizer & hybrids not effective

• Legume intercropping has robust yield impacts• No significant impact of minimum soil

disturbance on yield outcomes• Timely fertilizer delivery most important

Broader implications• Data used are from years with limited rainfall

stress • Our analysis shows that some climate related

variables determine which practices will yield best results

• Taking climate variables into consideration in developing strategies to support agricultural productivity increases is essential.

• Our results suggest SLM/CA practices could play an important role in responding to CC.

THANK YOU!

APPENDIXIndependent variables used in empirical models

Variables 2004 2008 Signif. Age of household head 49.50 52.48 *** Education (average) 5.23 5.47 *** # of adults (age>=15) 4.58 3.91 *** Share of ill adults 0.07 0.02 *** Female headed 0.21 0.21 *** Total maize area (ha) 1.09 1.52 *** Wealth index 0.21 0.18

# of oxen owned 0.78 1.18 *** Organic fertilizer applied 0.12 0.12

# of weedings applied 1.72 1.70 Tilled before rainy season 0.37 0.33 ***

Policy Variables ASP Dummy 0.50 0.53 **

Had fertilizer on time 0.29 0.34 *** Geo-referenced Variables

Growing season rainfall (100mm.) 8.62 8.19 *** CoV of growing season rainfall (1983-2012) 0.20 0.21 *** False onset of rainy season 0.63 0.19 *** Growing season avg. temperature (°C) 21.96 22.27 *** Growing season max. temperature ≥ 28°C 0.14 0.18 *** Moderate nutrient constraint 0.35 0.34

Severe/very severe nutrient constraint 0.35 0.34 Average soil pH 5.59 5.61 Observations (# maize plots) 4,138 4,354

Province 2004 2008 Central 0.43 0.54 Copperbelt 0.45 0.49 Land size 2004 2008 Eastern 0.28 0.30 <=1.5ha 0.22 0.25 Luapula 0.33 0.16 1.5-2.5ha 0.28 0.30 Lusaka 0.45 0.54 2.5-5ha 0.34 0.36 Northern 0.31 0.35 5-20ha 0.46 0.46 Northwestern 0.11 0.20 >20ha 0.55 0.53 Southern 0.29 0.31 Total 0.29 0.32 Western 0.05 0.03 Total 0.29 0.32

Fertilizer timeliness by province & land size

Further EPIC Work• Similar analyses on the impacts of sustainable land

management practices on yields, incomes and food security in Tanzania, Malawi, Uganda, Niger, Nigeria, Ethiopia with detailed climate data

• Analyses of climatic shocks and welfare in these countries

• Work with ministries of agriculture in Malawi & Zambia to design CSA policies

• Support to MS and PhD students to work on CSA• Investment proposals for CSA (potentially targeting

GCF/GEF for funding)