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Aslihan Arslan
(Co-authors: Nancy McCarthy, Leslie Lipper, Solomon Asfaw and Andrea Cattaneo)
Global Science Conference on...
• Background on CA and CF in Zambia
• Data sources
• Methodology
• Adoption & Disadoption
• Determinants of adoption and i...
Background on CA
• Conservation agriculture (CA) aims to sustainably improve farm
productivity, profits and food security ...
CA in Zambia
• Promoted to smallholders in ZM as Conservation
Farming (CF):
1. Reduced tillage (<15 % of the area)
2. Prec...
Mpika
Solwezi
Sesheke
Kaoma
Serenje
Kalabo
Chama
Mkushi
Mumbwa
Kasempa
Lukulu
Chinsali
Mwinilunga
Kalomo
Senanga
MufumbweZ...
Classic Barriers to Adoption
• Risky new technology
• Credit const.
• Time lag
• Labor const.
• Seed market const.
• Agro-...
Barriers for CA
• Farm size in Africa & education in North America
(Knowler & Bradshaw, ’07)
• Lack of infrastructure, exi...
Data Sources
• Rural Incomes and Livelihoods Surveys 2004 &
2008 (MAFF & FSRP/IAPRI)
• Historical Rainfall Estimates (NOAA...
Shifting Rainy Season Onset
9
Map here
Tillage & Crop Management
Practices 2004 2008
Hand hoeing 0.60 0.44***
Planting basins 0.03 0.02***
Zero tillage 0.11 0.03...
Adoption & Dis-adoption (0/1)
2004 No Yes Total 2004 No Yes Total
No # 3,498 165 3,663 No # 1,071 755 1,826
% 95.5 4.5 100...
Adoption Intensity (area share)
Adoption intensity by land size
2004 2008 2004 2008
<=1.5 0.41 0.64 0.47 0.57
1.5 - 2.5 0....
Empirical Approach
1. Decision to Adopt:
Conditional Maximum Likelihood (CMLE) Probit model
(Chamberlain, ’80)
2. Intensit...
Determinants of Adoption
Variables MSD CR
# Adults (age>=15) 0.005 0.029*
Education (average) 0.025 0.033**
Ag-wealth inde...
Determinants of Adoption Intensity
Variables MSD CR
Education (average) 0.021 0.014**
Dependency ratio 0.017 0.006
Land pe...
Summary of Findings
MSD CR MSD CR
Socio-economic variables
(labor, educ, ag wealth) + +
Soil constraints - +
Delay in the ...
Conclusions
• Simple cross-sectional analyses of adoption &
barriers fail to capture the real determinants
• High levels o...
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Adoption and Intensity of Adoption of Conservation Farming Practices in Zambia

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www.fao.org/climatechange/epic
Full paper: http://www.fao.org/docrep/017/aq288e/aq288e.pdf

This presentation outlines an analysis of the determinants and the intensity of adoption of two components (i.e. the use of zero tillage and planting basins) of Conservation Farming in Zambia. We find a strong and robust relationship between the district level variation in historical rainfall during the growing season and adoption as well as the intensity of adoption of these practices in Zambia. This finding suggests that farmers are using these practices as a strategy to mitigate the risk of rainfall variability, providing evidence – albeit indirectly – of a synergy between these practices and adaptation to climate variability.

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Adoption and Intensity of Adoption of Conservation Farming Practices in Zambia

  1. 1. Aslihan Arslan (Co-authors: Nancy McCarthy, Leslie Lipper, Solomon Asfaw and Andrea Cattaneo) Global Science Conference on Climate Smart Agriculture March 20-22, UC Davis Adoption and Intensity of Adoption of Conservation Farming Practices in Zambia
  2. 2. • Background on CA and CF in Zambia • Data sources • Methodology • Adoption & Disadoption • Determinants of adoption and its intensity • Conclusions Outline
  3. 3. Background on CA • Conservation agriculture (CA) aims to sustainably improve farm productivity, profits and food security by combining (FAO 2012): 1. Minimum mechanical soil disturbance 2. Permanent organic soil cover 3. Crop rotation • Born out of ecological & economic hardships in the US in ’30s • Popular during the oil crisis in ’70s • Mainly large commercial farms in Brazil, South Africa & Zimbabwe • Promoted by many in SSA as a solution to soil degradation & low productivity
  4. 4. CA in Zambia • Promoted to smallholders in ZM as Conservation Farming (CF): 1. Reduced tillage (<15 % of the area) 2. Precise permanent planting basins/ripping of soil with a ‘Magoye ripper’ 3. Leaving of crop residues on the field 4. Rotation of cereals with legumes 5. Dry season land preparation (CFU, 2007) • MoAL adopted as priority in 1999: ZNFU, GART, CFU • Int’l support: SIDA, Norad, FAO, World Bank, WFP, EU, IFAD…
  5. 5. Mpika Solwezi Sesheke Kaoma Serenje Kalabo Chama Mkushi Mumbwa Kasempa Lukulu Chinsali Mwinilunga Kalomo Senanga MufumbweZambezi Lundazi Kaputa Kazungula Isoka Kabompo Mansa Mongu Mbala Nyimba Itezhi-Tezhi Shangombo Samfya Kasama Chibombo Chongwe Mungwi Kapiri Mposhi Luwingu Mporokoso Petauke Kafue Choma Lufwanyama Mpongwe Mpulungu Chipata Mwense Kawambwa Milenge Monze Mazabuka Mambwe Chilubi Namwala Katete Chavuma Masaiti Chiengi Nakonde Gwembe Luangwa Siavonga Sinazongwe Nchelenge Chadiza Kabwe Livingstone Lusaka Urban Chililabombwe MufuliraChingola Kalulushi Kitwe Luanshya Ndola 200 0 200 400 Kilometers N EW S Agro-Ecological Regions District boundary KEY Source: Soil Survey, Mt. Makulu Chilanga December 2002 Scale 1: 2,500,000 Regions I IIa IIb III LEGEND
  6. 6. Classic Barriers to Adoption • Risky new technology • Credit const. • Time lag • Labor const. • Seed market const. • Agro-ecological const. • Tenure security • Opportunity costs of residues
  7. 7. Barriers for CA • Farm size in Africa & education in North America (Knowler & Bradshaw, ’07) • Lack of infrastructure, existing livestock mgmt norms, imperfect input & credit, land tenure (Nkala et al.’11) • Zambia: Opportunity cost of crop residue, land and labor constraints , distance to markets, extension (Umar et al.’11; Baudron et al.’07, Chomba ’04; Haggblade&Tembo,’03) • BUT: Most studies are subject to small samples, selection bias or both
  8. 8. Data Sources • Rural Incomes and Livelihoods Surveys 2004 & 2008 (MAFF & FSRP/IAPRI) • Historical Rainfall Estimates (NOAA-CPC) • Soil Nutrient Availability (Harmonized World Soil Database)
  9. 9. Shifting Rainy Season Onset 9 Map here
  10. 10. Tillage & Crop Management Practices 2004 2008 Hand hoeing 0.60 0.44*** Planting basins 0.03 0.02*** Zero tillage 0.11 0.03*** Ploughing 0.29 0.31* Ripping 0.02 0.01*** Ridging/bunding 0.23 0.41*** Crop residue left in the field 0.74 n.a. CF Practices Analyzed Min. Disturbance (P. basins/zero tillage) 0.14 0.05*** Rotation (diff crops for 3 years) 0.57 0.56
  11. 11. Adoption & Dis-adoption (0/1) 2004 No Yes Total 2004 No Yes Total No # 3,498 165 3,663 No # 1,071 755 1,826 % 95.5 4.5 100 % 58.7 41.3 100 Yes # 505 19 524 Yes # 822 1,539 2,361 % 96.4 3.6 100 % 34.8 65.2 100 2008 National transition matrix, Minimum Soil Disturbance (MSD) National transition matrix, Crop Rotation (CR) 2008
  12. 12. Adoption Intensity (area share) Adoption intensity by land size 2004 2008 2004 2008 <=1.5 0.41 0.64 0.47 0.57 1.5 - 2.5 0.28 0.48 0.43 0.52 2.5 - 5 0.24 0.37 0.40 0.46 5-20 0.18 0.17 0.35 0.35 > 20 0.03 0.06 0.09 0.13 Land (ha) MSD Intensity CR Intensity
  13. 13. Empirical Approach 1. Decision to Adopt: Conditional Maximum Likelihood (CMLE) Probit model (Chamberlain, ’80) 2. Intensity of Adoption: Correlated Random Effects Tobit (Wooldridge, ’02) & Pooled Fractional Probit (Papke&Wooldridge, ’08) * it it it iC X u vβ= + + * it it it iS X u vβ= + + * * * * 0 0 0 1 1 1 it it it it it if S S S if S if S  ≤  = < <  ≥
  14. 14. Determinants of Adoption Variables MSD CR # Adults (age>=15) 0.005 0.029* Education (average) 0.025 0.033** Ag-wealth index 0.093 0.086** # Oxen owned -0.058 0.050*** ASP district dummy -0.048 0.094* Moderate soil constraint -0.123 0.109* Rain onset delay 0.860** 0.861*** Received MSD/CR extension (% SEA) 1.533*** 0.724*** RFE CV (1996-2011) 8.140*** -0.349 2008 dummy -0.663*** -0.075* Number of obs. 8,208 8,208
  15. 15. Determinants of Adoption Intensity Variables MSD CR Education (average) 0.021 0.014** Dependency ratio 0.017 0.006 Land per capita -0.01 -0.025*** Ag-wealth index 0.05 0.034*** # Oxen owned -0.03 0.012** ASP district dummy -0.033 0.028 Moderate soil const. -0.106* 0.062*** Severe soil const. -0.034 0.083*** Rain onset delay 0.664** 0.325*** Received MSD/CRextension (% SEA) 1.058*** 0.196*** RFE CV (1996-2011) 6.264*** 0.153 2008 dummy -0.418*** 0.031** Number of obs. 8,208 8,208
  16. 16. Summary of Findings MSD CR MSD CR Socio-economic variables (labor, educ, ag wealth) + + Soil constraints - + Delay in the onset of rains + + + + Extension coverage + + + + Historical rainfall variability + + Adoption Intensity
  17. 17. Conclusions • Simple cross-sectional analyses of adoption & barriers fail to capture the real determinants • High levels of dis-adoption of CF practices in ZM • CF seems suitable only under certain agro- ecological conditions • Suggestive evidence of adaptation benefits to highly variable & delayed rainfall • Extension coverage is critical, but effects of subsidized inputs/incentives need to be understood
  18. 18. THANK YOU!

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