Adoption and intensity of adoption of conservation farming practices in Zambia

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Although the transition matrices paint a striking picture of dis-adoption, they do not provide information on the drivers of adoption and the intensity of it. To address these questions in our econometric analysis, we draw on the rich literature on technology adoption to select a comprehensive set of explanatory variables that are known to affect farmers’ decisions to adopt an agricultural technology. These variables include both production and consumption variables given the fact that most smallholders operate under imperfect market conditions that make consumption and production decisions non-separable (de Janvry, Fafchamps, and Sadoulet 1991). Table 10 summarizes our explanatory variables by CF status and year. Adopters of CF (by both definitions) tend to come from households with significantly smaller number of adults, lower wealth indices, and less livestock in both years. 18,19 They also tend to cultivate significantly smaller land. The share of adopters that use plough on at least one field is significantly lower than that of non-adopters in both years and under both definitions of CF. Three quarters of both adopters and non-adopters of CF1 left the crop residues on the field in 2004. Based on the stricter definition of CF, the share of adopters that left the crop residues on the field is significantly higher than that of non-adopters of CF2 in 2004. A significantly higher share of CF1 adopters practiced crop rotation in 2004, whereas the difference was not significant in 2008. This difference in rotation practice between adopters and non-adopters of CF2 is, however, significant in both years. The share of CF1 adopters that practiced intercropping and intercropping with legumes was significantly higher than that of non-adopters in 2004. In 2008, the difference was not significant for CF1 and switched sign for CF2. 18 The agricultural wealth index is created using Principal Component Analysis (PCA) using the number of ploughs, harrows, cultivators, rippers, tractors, hand tractors, carts, pumps, mills, expellers, sprayers and shellers owned by the household. The non-agricultural wealth index is created using PCA based on the number of bikes, motorcycles, cars, lorries, trucks, televisions and wells owned by the household. 19 Tropical Livestock Unit (TLU) is calculated using the numbers of livestock owned by the household as: TLU=(cattle+oxen)*0.5 + pigs*0.2 + (goats+sheep+chickens)*0.1. 11
Table 10. Explanatory Variables by CF Status and Year 2004 2008 2004 2008 CF1 NoCF1 CF1 NoCF1 CF2 NoCF2 CF2 NoCF2 Socio-economic variables Female head 0.22 0.19 ** 0.25 0.22 ** 0.2 0.21 0.26 0.24 Widow head 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.02 Polygamous head 0.11 0.16 *** 0.09 0.15 *** 0.1 0.13 ** 0.16 0.12 # adults (age>=15) 4.12 4.55 *** 3.44 3.76 *** 4.01 4.32 *** 3.55 3.61 Age head 49.80 49.05 52.68 52.43 49.89 49.46 52.29 52.55 Education (average) 4.94 5.10 ** 5.04 5.32 *** 5.22 4.97 ** 4.75 5.21 *** Dependency ratio 0.94 0.94 1.18 1.17 0.98 0.94 1.11 1.18 Chronically ill (% adults) 0.07 0.06 * 0.02 0.02 0.06 0.07 0.02 0.02 Ag-wealth index -0.10 0.65 *** -0.16 0.6 *** -0.09 0.23 *** 0.04 0.26 *** Wealth index 0.03 0.26 *** -0.06 0.15 *** 0.09 0.12 -0.04 0.05 * TLU (total) 1.40 4.58 *** 1.1 4.28 *** 1.57 2.75 *** 1.65 2.85 *** # oxen owned 0.20 1.13 *** 0.17 1.13 *** 0.13 0.61 *** 0.37 0.69 *** Land cultivated (ha) 2.17 2.82 *** 2.98 4.34 *** 2.46 2.41 3.01 3.74 ** Land title dummy Tillage/cropping practices n.a. n.a. 0.04 0.03 n.a. n.a. 0.02 0.04 Hand-hoe 0.92 n.a. 0.93 n.a 0.61 0.6 0.31 0.44 *** Plough 0.09 0.65 *** 0.09 0.51 *** 0.06 0.33 *** 0.12 0.32 *** Ripping/ridging/bunding 0.20 0.30 *** 0.36 0.54 *** 0.33 0.22 *** 0.48 0.45 Crop residue left on field 0.74 0.74 0.75 0.73 0.81 0.73 *** 0.75 0.74 Rotation 0.64 0.46 *** 0.56 0.57 0.84 0.53 *** 0.7 0.56 *** Intercropping 0.22 0.12 *** 0.11 0.11 0.46 0.14 *** 0.07 0.11 ** Intercropping with legumes 0.09 0.03 *** 0.04 0.03 0.22 0.04 *** 0.02 0.03 * Hybrid seeds (% land) 0.23 0.39 *** 0.22 0.35 *** 0.16 0.31 *** 0.28 0.29 Average # weedings per field Policy variables 1.63 1.47 *** 1.7 1.56 *** 1.39 1.6 *** 1.84 1.61 *** ASP district dummy Received minimum tillage 0.47 0.43 *** 0.43 0.5 *** 0.52 0.45 *** 0.52 0.46 extension (% in the SEA) 0.29 0.30 0.32 0.36 *** 0.23 0.3 *** 0.54 0.33 *** Weather variables RFE (growing season av.) 922.05 884.48 *** 927.5 888.49 *** 953.4 901.25 *** 850.8 909.3 *** RFE CV (1996-2011) 0.26 0.28 *** 0.26 0.27 *** 0.24 0.27 *** 0.27 0.26 *** Observations 2571 1560 1903 2179 524 3607 184 3898 Source: Author calculations based on RILS. 12
Page 1 and 2: Adoption and intensity of adoption
Page 3 and 4: Adoption and intensity of adoption
Page 5 and 6: EXECUTIVE SUMMARY This paper contri
Page 7 and 8: CONTENTS ACKNOWLEDGEMENTS .........
Page 9 and 10: ACRONYMS ACT African Conservation T
Page 11 and 12: Existing empirical analyses of CF i
Page 13 and 14: 2003; Tarawali et al. 1999; Steiner
Page 15 and 16: use of the panel structure in their
Page 17 and 18: Table 2. Various Tillage and Crop M
Page 19: Table 5. Adoption and Intensity of
Page 23 and 24: 4.1. Adoption of CA 4. METHODOLOGY
Page 25 and 26: v X X X N X 2 i | ( i1, i2,..., iT
Page 27 and 28: The coefficient of the agricultural
Page 29 and 30: With regard to CF2, the most robust
Page 31 and 32: Table 14. Additional Household and
Page 33 and 34: own land. However, the coefficient
Page 35 and 36: The results should be interpreted w
Page 37 and 38: CSO 2004. Rural Income and Liveliho
Page 39 and 40: Papke, Leslie E. and Jeffrey M. Woo

Adoption and intensity of adoption of conservation farming practices in Zambia

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