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

• Background on CA and CF in Zambia • Data sources • Methodology • Adoption & Disadoption • Determinants of adoption and its

intensity • Conclusions

Outline

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

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…

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

MonzeMazabuka

Mambwe

Chilubi

Namwala

Katete

Chavuma Masaiti

Chiengi

Nakonde

Gwembe

Luangwa

Siavonga

Sina

zong

we

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 ChilangaDecember 2002

Scale 1: 2,500,000

RegionsI

IIa

IIb

III

LEGEND

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

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

Data Sources

• Rural Incomes and Livelihoods Surveys 2004 &

2008 (MAFF & FSRP/IAPRI)

• Historical Rainfall Estimates (NOAA-CPC)

• Soil Nutrient Availability (Harmonized World

Soil Database)

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*** 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

Adoption & Dis-adoption (0/1)

2004 No Yes Total 2004 No Yes TotalNo # 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

Adoption Intensity (area share)

Adoption intensity by land size

2004 2008 2004 2008<=1.5 0.41 0.64 0.47 0.571.5 - 2.5 0.28 0.48 0.43 0.522.5 - 5 0.24 0.37 0.40 0.465-20 0.18 0.17 0.35 0.35> 20 0.03 0.06 0.09 0.13

Land (ha)MSD Intensity CR Intensity

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 SS S if S

if S

≤

= < < ≥

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*** -0.3492008 dummy -0.663*** -0.075*Number of obs. 8,208 8,208

Determinants of Adoption Intensity Variables MSD CREducation (average) 0.021 0.014**Dependency ratio 0.017 0.006Land per capita -0.01 -0.025***Ag-wealth index 0.05 0.034***# Oxen owned -0.03 0.012**ASP district dummy -0.033 0.028Moderate 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*** 0.1532008 dummy -0.418*** 0.031**Number of obs. 8,208 8,208

Summary of Findings

MSD CR MSD CRSocio-economic variables (labor, educ, ag wealth) + +Soil constraints - +Delay in the onset of rains + + + +Extension coverage + + + +Historical rainfall variability + +

Adoption Intensity

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

THANK YOU!