Does Zero Tillage Improve the Livelihoods of ... - Wiley Online Library

Journal of Agricultural Economics, Vol. 67, No. 1, 2016, 154–172 doi: 10.1111/1477-9552.12133

Does Zero Tillage Improve the Livelihoods of Smallholder Cropping Farmers? Tamer El-Shater, Yigezu A. Yigezu, Amin Mugera, Colin Piggin, Atef Haddad, Yaseen Khalil, Stephen Loss and A. Aw-Hassan1 (Original submitted October 2014, revision received April 2015, accepted July 2015.)

Abstract The biophysical benefits of zero tillage (ZT) are well documented in the literature. However, the literature on its economic benefits, especially in the context of small and medium-scale farmers in the temperate developing world is scanty. Using a study of 621 wheat farmers in Syria, we provide empirical evidence on the impacts of adoption of ZT on farm income and wheat consumption. We use propensity score matching (PSM) and endogenous switching regression (ESR) approaches to account for potential selection biases. After controlling for confounding factors, we find that adoption of the ZT technology leads to a US$ 189/ha (33%) increase in net crop income and a 26 kg (34%) gain in per capita wheat consumption per year (adult equivalent) – an indication of meaningful changes in the livelihoods of the farm households. Besides the biophysical and environmental benefits documented elsewhere, our results suggest that adoption of ZT can also be justified on economic and food security grounds. Therefore, ZT can have sizeable impacts in transforming the agricultural sector in the temperate developing world provided that the technology is well promoted and adopted. Keywords: Consumption; cropping farms; endogenous switching regression; farm income; livelihoods; propensity score matching; small holders; Syria; zero tillage. JEL classifications: Q12, Q15, Q16, Q24.

1

Yigezu A. Yigezu and all other authors, except Amin Mugera, are with the International Center for Agricultural Research in the Dry Areas (ICARDA), Amman, Jordan. E-mail: y.yigezu@ cgiar.org for correspondence. Amin Mugera is at The Institute of Agriculture of the University of Western Australia. The authors would like to thank the Australian Centre for International Agricultural Research (ACIAR) for funding this research, and anonymous referees for their valuable comments on an earlier draft.

Ó 2015 The Agricultural Economics Society

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1. Introduction Conservation agriculture (CA) is a label used by United Nations Food and Agriculture Organization (FAO) and a variety of other international development agencies to describe a package of agricultural production practices that maintain soil structure and improve productivity. The key components of the package include zero tillage (ZT; or at least minimum soil disturbance), retention of crop residues for soil cover (mulching), and rotation (or sometimes intercropping) of cereals with legumes or other crops (Kassam et al., 2009). Zero tillage, also called no tillage (NT), promotes cropping with minimum soil disturbance and stubble retention, and early sowing which is important for good yields, especially in rainfed and dryland environments. Other ‘improved’ crop management technologies such as optimising seeding rates, fertiliser applications, and integrated pest and disease management can be built into a productive, profitable and sustainable ZT system. However, tillage has been an integral part of conventional cropping for centuries. Whilst ZT is used quite widely in developed countries around the world, it is often looked upon with scepticism in developing countries, mainly due to lack of information and evidence on its benefits relative to conventional tillage (Belloum, 2007). It is estimated that about 9% of global cropland was cultivated using a complete CA package in 2012, while more land was cropped using some of the components of the CA package (Friedrich et al., 2012). ZT has been widely adopted in North America, South America and Australia (Fulton, 2010; Lewellyn et al., 2012), with varying levels of crop residue retention and rotation. With few local exceptions, South Asia, West Asia and Africa have not yet benefitted from advances in CA technology in general and ZT in particular (Giller et al., 2009). Zero tillage conserves soil moisture and organic matter and reduces fuel, labour and machinery costs (Ribera et al., 2004). In addition, a reduction in wind and water erosion and an increase in soil organic matter and carbon provide significant environmental benefits. With its capacity for moisture conservation and cost savings, ZT can often lead to higher yields and increased net returns with reduced variability of yield and income, which is particularly important in dryland farming. ZT can also lead to benefits for smallholder farmers and consumers in low and middle income countries in Asia and Africa (ICARDA, 2012). With this premise, a number of efforts have been made by the governments of Syria and Iraq to introduce ZT with other components of CA using local resources and funding from international development organisations including the Arab Agency for Agricultural International Development (AAAID), Arab Center for Studies of Arid Zones (ACSAD) and Australian Centre for International Agricultural Research (ACIAR) and Australian Agency for International Development (AusAID). Given its recent introduction, adoption and impacts of ZT in Iraq are relatively low. However, in Syria, ZT has been well received by a relatively large number of farmers in a fairly short time; it was introduced through the ACIAR-AusAID funded project in early 2005. The success of the ACIAR-AusAID project in promoting the adoption of ZT in Syria may be attributed mainly to: (i) research trials and farmer field demonstrations that provide exposure to the technology, (ii) extensive and successful development of local capacity to fabricate and market effective ZT seeders at affordable prices, and (iii) an extensive and participatory extension programme with local agriculture and NGO groups where interested farmers could ‘borrow’ a ZT seeder without charge and choose the components of the ZT technology ‘package’ to try in their own fields.


162

Tamer El-Shater et al. Eðy1 jD ¼ 1Þ ¼ X1 x1 þ r1e k1

ð7Þ

Eðy0 jD ¼ 0Þ ¼ X0 x0 þ r0e k0

ð8Þ


ð9Þ


ð10Þ

Finally, we calculate the average treatment effect on the treated (ATT) as the difference between (7) and (10) and the average treatment effect on the non-adopters (ATU) as the difference between (9) and (8). We also compute the effect of base heterogeneity for the group of adopters (BH1) as the difference between (7) and (9), and for the group of non-adopters (BH2) as the difference between (10) and (8). A number of factors such as varieties used and the amounts of fertilisers, seed, labour and tillage are important in determining yield, which in turn will affect income and consumption. Moreover, whether farmers participated only by hosting demonstration trials, only by attending field days, or both, can have effects on farmers’ adoption decision for they are included in the estimation of both the PSM and ESR (Table S2 of the online Appendix). A check on the Variance Inflation Factor (VIF) showed that the data are free from multicollinearity, with VIF values ranging between 1.02 and 2.26 which are much less than the VIF threshold of 10 (Leahy, 2001). To create a more homogeneous dataset, all the continuous variables (such as income, consumption, farmer age, experience, distance to the nearest input market, area, value of assets, and all quantities of inputs) are logged. Yigezu et al. (2015) have measured the impacts of ZT using the Heckman (Heckit) model, but found that their inverse mills ratio (IMR) estimate was insignificant, signalling the limited power of the Heckit model to detect or cope with bias. Our PSM and ESR approaches here are particularly potent in correcting for biases related to both observable and unobservable factors. 5. Results 5.1. Results from propensity score matching The results of the probit model for the PSM method are presented in Table 1. As the main objective of this paper is to measure impacts, the adoption results are discussed here only briefly. Tests of goodness-of-fit show joint significance of explanatory variables with Wald v2 test statistically significant at 1% level. The coefficients on level of education (1.04) and the dummy variables for farmers’ participation in field days (2.47), hosting demonstration trials (3.22) and participation in both (4.56) are large, positive, and significantly different from zero. These results indicate a strong association between exposure to technology and adoption, emphasising the importance of participatory methods and cost-free access to technology components for first time users. In addition, farmers who have prior knowledge about the technology are more likely to adopt, consistent with other previous studies (Kaliba et al., 2000; Kristjanson et al., 2005; Shiferaw et al., 2008). The estimated summary statistics for the propensity scores for the entire sample are reported in Table 2. The results suggest that the common support region is between 0.02 and 1.00. For sound comparison of effects between adopters and non-adopters, predicted propensity scores should satisfy a common support condition. Therefore 72



10.28(0.24)* 0.63(0.28) 0.15(0.01)*

9.90(0.71)*

0.37(0.82) 0.38(0.02)*

17.02(8.06)* 41.89

14.82(3.23)†

0.06(0.03)†

0.41(0.31)

0.05 (0.05)

0.81(0.45)

0.03(0.02)

0.16 (0.06)*

2.81(0.02)†

0.03(0.02)†

0.67 (0.28)* 0.36 (0.02)*

3.42 (0.64)*

0.32 (0.21)

0.01(0.01)

0.12(0.13)

0.01(0.003)†

0.28(0.06)*

0.12(0.07)

0.04(0.01)*

Consumption equation for adopter Coef.

Adoption of ZT (No = 0, Yes = 1) Coef.

Net income equation for non-adopters Coef.

Notes: *, †, and ‡ represent significance at 1%, 5% and 10% levels, respectively. Number in parentheses are the standard errors. Source: Model results.

Quantity of fertilisers used (kg/ha) Quantity of labour used (hour/ha) Wheat variety used (0 = local, 1 = improved) Quantity of herbicides used (litre/ha) Constant Log likelihood q r

Explanatory variables

Net income equation for adopters Coef.

Table 4 (Continued)

0.19 (0.33) 0.54 (0.02)*

3.02(0.86)*

0.23(0.10)†

0.07(0.07)

0.02(0.05)

0.02(0.01)†

Consumption equation for non-adopter Coef.

22.35(8.34)* 423.41

15.83(3.46)*

0.56 (0.47)

3.07(0.73)*

0.09(0.08)


166 Tamer El-Shater et al.

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Tamer El-Shater et al.

Farmers in the study area graze their livestock on green barley and in bad seasons, they can graze the entire barley crop – making the measurement of impacts on barley producers very difficult. Consequently, only the observations relating to the 621 wheat farmers in the sample (308 from the random sample and 313 from the purposive sample) were used for analysis, with barley growers excluded. Details of the sampling design are summarised in Table S1 of the online Appendix. For the purpose of this study, adoption is defined as the use of the ZT technology for at least 1 year without any support from the project. On-farm demonstration trials are considered pre-testing, not real adoption. Hence, among the farmers who tested the ZT through support from the project, only those who continued to use ZT even after the project withdrew its support are considered as adopters, while the rest are non-adopters. Hence, the variable ‘area under ZT’ in Table S2 of the online Appendix refers only to the area cultivated using ZT by farmers who do not receive any support from the project. All of the 313 wheat farms which were purposively selected had been exposed to ZT through their involvement in the project; 257 (82%) of them have tried ZT on their own farms with support from the project while the rest had participated only in field days. Of these farmers, 36 (14%) did not continue using ZT after the project withdrew its support. None of the 308 randomly selected farms were exposed to the ZT through the project. Nonetheless, 52% had some knowledge about ZT, either from farmer-tofarmer information exchange or from the local extension offices. Only 15 (5%) of these farmers adopted ZT (Table S3 of the online Appendix). A structured survey questionnaire was used to collect data on farmer and farm characteristics, production data, including tillage cost, seeding cost, cost of planting, cost of fertilisers, cost of herbicides, cost of insecticides, cost of weeding, cost of harvesting, cost of transport (inclusive of labour and machinery costs) and quantities of grain and biomass outputs (descriptive statistics provided in Table S2 of the online Appendix). The sample farms were small to medium size with an average size of 27.5 ha. The average farmer has 3.5 years of schooling and 26 years of farming experience. Among the 621 sample wheat producers, 197 (32%) only hosted on-farm demonstrations/ tests, 56 (9%) participated in field days only, and 60 (10%) had engaged in both. Of the total sample of 621 farmers, 40% were adopters of the new ZT technology, using it for an average of 2.1 years. There are no significant differences between adopters and non-adopters in terms of their farming experience, agro-ecological zones, distance to the nearest market, and average value of total assets. Adopters and non-adopters differ significantly in terms of many other variables including their participation in field days, hosting demonstration trials, total and wheat area, knowledge about ZT, duration since they heard about ZT and input levels (Table S2 of the online Appendix). Of particular interest are the differences in yield and the two impact indicators namely, net wheat income and per-capita wheat consumption. The average yield among adopters of ZT is 1.7 tons/ha while that of non-adopters is 1.2 tons/ha, representing about 40% difference. Moreover, with an average net wheat income of about 37,995 Syrian pounds (SYP) or US$ 760/ha, adopters of ZT earn 45% (US$ 243) more net wheat income than non-adopters who earn only 27,335 SYP or US$ 537/ha. The typical member of an adopter family consumes about 80 kg of wheat per year while the corresponding figure for a non-adopter family is 49 kg/year showing that adopter families consume almost double the amount of non-adopter families. The distributions of the two Ó 2015 The Agricultural Economics Society

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outcome indicators (net income and wheat consumption from own production) are provided in Figures 1 and 2, respectively. 4. Methodology

1 .5 0

Kernel density

1.5

The decision to adopt a new technology may be affected by a number of farm, farmer, agro-ecological and socio-economic characteristics as well as farmers’ perception of the inherent features of the technology. Among farmer characteristics, a number of studies find that sex, age, education, experience and farmers’ perceptions about the technology are important (Knowler and Bradshaw, 2007; Vitale et al., 2011; Baumgart-Getz et al., 2012). Farm characteristics such as the size, location, soil properties, slope, proximity to homestead, access to irrigation and the agro-ecological and socioeconomic conditions of the area where the farm is located have also been found to affect adoption (D’Emden et al., 2008; Gedikoglu and McCann, 2012). After adoption, a number of factors can create confounding errors in the measurement of impacts. Income from wheat production (income) and per-capita wheat consumption from own production (consumption) are used as impact indicators. Income is measured as the difference between total revenue from sales of wheat and wheat residue and total production cost. In Syria, all wheat production is purchased by the government at a fixed price. Hence, that price is used for computing revenue. Production cost includes: tillage, seed, fertiliser, herbicides, insecticides, transport and the cost of planting, weeding, harvesting and threshing. Consumption is measured as the residual from the total quantity of wheat produced after deducting quantities sold and kept as seed for next season. Therefore, per-capita wheat consumption from own production is computed as total family wheat consumption from own production divided by total household size adjusted for adult equivalent using weights provided by Claro et al. (2010). The inventory carried forward from previous year and total purchase of wheat and wheat products were assumed to be zero.

6

7

8

9

10

Net income (natural logs) Adopters

Non-adopters

kernel = epanechnikov, bandwidth = 0.1109

Figure 1. Distribution of net income by adoption status


11

160

1 0

.5

Kernel density

1.5

2


2

3

4

5

6

Consumption (natural logs) Adopters

Non-adopters

kernel = epanechnikov, bandwidth = 0.1740

Figure 2. Distribution of wheat consumption by adoption status

4.1. Propensity score matching Propensity score matching (PSM) is one of the multivariate methods that can be used to construct treated and matched control samples that have similar distributions on many covariates (e.g., Rosenbaum and Rubin, 1983; Heckman et al., 1998; Daheja and Wabha, 2002; Caliendo and Kopeinig, 2008). The statistical comparison group is constructed based on a model of the probability of participating in the treatment – the propensity score – using observed characteristics. The propensity scores are used to estimate treatment effects. The most common treatment effects in the literature include the average treatment effect (ATE), which is the average treatment effect for the whole sample, the average treatment effect on the treated (ATT), which is the participation effect, and the average treatment effect on the untreated (ATU) which is the non-participation effect. While PSM does not involve parametric or distributional assumptions (Heckman and Vytlacil, 2007), it does not account for unobservable or unobserved variables which affect adoption (i.e., it requires a conditional independence assumption). Brief implementation details are provided in Appendix S1 (online). Following Millimet and Tchernis (2013), we also use the bias minimising treatment effect (BMTE) estimator that provides a range of treatment effects estimates intended to estimate the average effects of the treatment when the conditional independence assumption (CIA) for PSM fails and appropriate exclusion restrictions are unavailable (see Appendix S2 online for more explanation). It is theoretically expected that farmers will decide to participate in the technology adoption programme when the expected (but unobserved) utility of participation (D1 ) is greater than the utility of non-participation (D0 ). Participation in the programme is observable as a dichotomous choice: D = 1 if D1 [ D0 and D = 0 if D1 \D0 , modelled as: Di ¼ Zi b þ ei with Di ¼ 1 if Di [ D0 ; otherwise Di ¼ 0


ð1Þ


161

where Z represents a matrix of the explanatory variables, ß is a vector of parameters to be estimated and e is a vector representing normally distributed error term with mean zero and variance r2e 4.2. Endogenous switching regression The difference in the outcomes of interest between adopters and non-adopters may not only be due to observable heterogeneity but also due to unobserved heterogeneity. Therefore, we use an endogenous switching regression (ESR) to account for both observable and unobservable endogeneity of the adoption decision by simultaneously estimating the adoption function (equation (1)) and the outcome equation of interest for each group. Following Di Falco et al. (2011) and Shiferaw et al. (2014) the ESR can be estimated as follows: y1 ¼ X1 x1 þ 1 if D ¼ 1

ð2Þ

y0 ¼ X0 x0 þ 0 if D ¼ 0

ð3Þ

where yi is a vector of dependent variables representing outcomes for adopters (y1) and non-adopters (y0), Xi is a matrix of explanatory variables, xi is a vector of parameters to be estimated, and ϵ1, and ϵ0 are error terms. The error terms from the three equations ϵ, ϵ1 and ϵ0 are assumed to have a trivariate normal distribution with mean vector zero and the following covariance matrix: 2 2 3 r0 r10 r0e ð4Þ covðe; 1 0 Þ ¼ 4 r10 r21 r1e 5 r0e r1e r2e where r2e is the variance of the selection equation (equation (1)), r20 and r21 are the variances of the outcome equations for non-adopters and adopters while rϵ0ɛ and r1e represent the covariance between ϵ1 and ϵ0 If ɛ is correlated with 1 and ϵ0, the expected values of ϵ1 and ϵ0 conditional on the sample selection are non-zero: /ðZi xi Þ ¼ r1e k1 UðZi xi Þ

ð5Þ

/ðZi xi Þ ¼ r0e k0 1 UðZi xi Þ

ð6Þ

Eð1 jD ¼ 1Þ ¼ r1e Eð0 jD ¼ 0Þ ¼ r0e

where / and U are the probability density and the cumulative distribution function of the standard normal distribution, respectively. If rϵ1ɛ and rϵ0ɛ are statistically significant, this would indicate that the decision to adopt and the outcome variable of interest are correlated suggesting evidence of sample selection bias. Therefore, estimating the outcome equations using ordinary least square (OLS) would lead to biased and inconsistent results and Heckman procedures (Heckman, 1979) are normally used. In the face of heteroscedastic error terms, the full information maximum likelihood (FILM) estimator can be used to fit an endogenous switching regression that simultaneously estimates the selection and outcome equations to yield consistent estimates. The ESR can be used to compare the actual expected outcomes of adopters (7) and non-adopters (8), and to investigate the counterfactual hypothetical cases that the non-adopters did adopt (9) and the adopters did not adopt (10) as follows:


162

Tamer El-Shater et al. Eðy1 jD ¼ 1Þ ¼ X1 x1 þ r1e k1

ð7Þ


ð8Þ


ð9Þ


ð10Þ

Finally, we calculate the average treatment effect on the treated (ATT) as the difference between (7) and (10) and the average treatment effect on the non-adopters (ATU) as the difference between (9) and (8). We also compute the effect of base heterogeneity for the group of adopters (BH1) as the difference between (7) and (9), and for the group of non-adopters (BH2) as the difference between (10) and (8). A number of factors such as varieties used and the amounts of fertilisers, seed, labour and tillage are important in determining yield, which in turn will affect income and consumption. Moreover, whether farmers participated only by hosting demonstration trials, only by attending field days, or both, can have effects on farmers’ adoption decision for they are included in the estimation of both the PSM and ESR (Table S2 of the online Appendix). A check on the Variance Inflation Factor (VIF) showed that the data are free from multicollinearity, with VIF values ranging between 1.02 and 2.26 which are much less than the VIF threshold of 10 (Leahy, 2001). To create a more homogeneous dataset, all the continuous variables (such as income, consumption, farmer age, experience, distance to the nearest input market, area, value of assets, and all quantities of inputs) are logged. Yigezu et al. (2015) have measured the impacts of ZT using the Heckman (Heckit) model, but found that their inverse mills ratio (IMR) estimate was insignificant, signalling the limited power of the Heckit model to detect or cope with bias. Our PSM and ESR approaches here are particularly potent in correcting for biases related to both observable and unobservable factors. 5. Results 5.1. Results from propensity score matching The results of the probit model for the PSM method are presented in Table 1. As the main objective of this paper is to measure impacts, the adoption results are discussed here only briefly. Tests of goodness-of-fit show joint significance of explanatory variables with Wald v2 test statistically significant at 1% level. The coefficients on level of education (1.04) and the dummy variables for farmers’ participation in field days (2.47), hosting demonstration trials (3.22) and participation in both (4.56) are large, positive, and significantly different from zero. These results indicate a strong association between exposure to technology and adoption, emphasising the importance of participatory methods and cost-free access to technology components for first time users. In addition, farmers who have prior knowledge about the technology are more likely to adopt, consistent with other previous studies (Kaliba et al., 2000; Kristjanson et al., 2005; Shiferaw et al., 2008). The estimated summary statistics for the propensity scores for the entire sample are reported in Table 2. The results suggest that the common support region is between 0.02 and 1.00. For sound comparison of effects between adopters and non-adopters, predicted propensity scores should satisfy a common support condition. Therefore 72



163

Table 1 Estimation of propensity scores: Probit model Adoption of conservation tillage (0 = No, 1 = Yes) Zone (1 = farm is in Zone 1, 0 = otherwise) Participated in field days only (0 = No, 1 = Yes) Hosted demonstration trials only (0 = No, 1 = Yes) Both demonstration trials and field days (0 = No, 1 = Yes) Level of education (years) Experience (years) Total area (ha) Distance to nearest input market (km) Knowledge of ZT technology (0 = No, 1 = Yes) Value of total assets (SYP) Quantity of seed used (kg/ha) Quantity of fertilisers used (kg/ha) Quantity of labour used (hour/ha) Wheat variety used (0 = local, 1 = improved) Quantity of herbicides used (litre/ha) Constant Log likelihood

Coeff.

Marginal effects

0.06 (0.45) 2.47 (0.74)* 3.22 (0.63)*

0.002 0.07 0.09

4.56 (1.04)*

0.13

0.12 (0.13) 0.02 (0.02) 0.001 (0.004) 0.03 (0.03) 0.27 (0.08)* 2.54E07 (1.42E07) 0.03 (0.01)* 0.01 (0.002)* 0.11 (0.02)* 0.25 (0.42) 7.14 (1.57)* 4.34 (2.03)* 31.8

0.003 0.001 0.00 0.001 0.01 0.00 0.001 0.00 0.003 0.01 0.20

Notes: Dependent variable is adoption of zero tillage (No = 0, and Yes = 1). Parameter significance: *(1%). Numbers in parentheses are the standard errors. Source: Survey data.

observations whose predicted propensity scores are less than 0.02 were discarded from the analysis. Three procedures (the reduction in the mean standardised biases between the matched and unmatched households, the equality of means using t-test, and the v2 test for joint significance for all the variables) were used to check the balancing of propensity scores and the relevant variables in both control and treatment groups. Among three matching algorithms considered, the Caliper (0.25) was found to fit the data better than the Nearest Neighbour and Kernel bandwidth algorithms (Table S4 in the online Appendix). In the first procedure, the difference between the treatment and control groups propensity score (the standardised difference) before and after matching was 91.4%. This shows that sample differences in the unmatched data significantly exceeded those in the matched cases, indicating the success of the matching procedure (Rosenbaum and Rubin, 1985). The two-sample t-test was used to check if there are significant differences in covariate means for both groups. Several variables exhibited statistically significant differences in the unmatched data but all covariates were balanced after matching. Comparison of pseudo-R2s for before and after matching to check the balancing of propensity score and the relevant variables also revealed that there are no systematic differences in the distribution of covariates between both groups after matching (Table S5 in the online Appendix). Ó 2015 The Agricultural Economics Society

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Tamer El-Shater et al. Table 2 Mean of estimated propensity scores

Group

Obs

Mean

Min

Max

Total households Non-adopters Adopters

621 372 249

0.40 0.03 0.96

1.68E113 1.68E113 0.03

1.00 0.97 1.00

Source: Model results.

These tests confirm that the matching procedure was able to balance the characteristics of the adopter and the matched non-adopter (control) groups, allowing reliable comparison of observed outcomes between the two groups. 5.1.1. Impact estimation: Estimation of treatment effects After controlling for observable confounding factors, we found statistically significant differences in net income and annual per capita wheat consumption between adopter and non-adopter households. The results (Table 3) show that, on average, adoption of ZT raised net wheat income by 28% or 7,519 SP/ha (US$ 150.4/ha)4 and per capita wheat consumption by 70% or 36.5 kg/year. These results suggest that the adoption of ZT indeed leads to improvements in the livelihoods of the farm households. However, given that PMS does not correct for biases from unobservable factors, we also use the endogenous switching regression to control for these unobservable factors. 5.2. Results from the endogenous switching regression Results of the endogenous switching regression (ESR) are presented in Table 4. Once again, as the main objective of this study is to measure impact, results of the ESR regression are discussed only briefly. Tests of goodness-of-fit show that the selected Table 3 The average impact of treatment on the treated ATT for net income and wheat consumption (Using propensity score matching)

Group

Treatment group

Net income(SP/ha) Unmatched 37,995 ATT 34,579

Control group

Average treatment effect on the treated (ATT)

SE

t-stat

27,335 27,061

10,660 7,519

967 3,390

11.0* 2.22†

2.8 17.2

11.3* 2.13†

Wheat consumption(kg/year adult equivalent) Unmatched 79.6 48.6 ATT 88.4 51.9

31.0 36.5

Note: * and † represent significance at 1% and 5%, respectively. Source: Model results. 4

During the study period, the conversion rate was 1 US$ for about 50 Syrian pounds (SP).


Zone (1 = farm is in Zone 1, 0 = otherwise) Participated in field days only (0 = No, 1 = Yes) Hosted demonstration trials only (0 = No, 1 = Yes) Both demonstration trials and field days (0 = No, 1 = Yes) Level of education (years) Experience (years) Total area (ha) Prior knowledge of ZT technology (0 = No, 1 = Yes) Distance to nearest input market (km) Value of total assets (SYP) Quantity of seed used (kg/ha)


0.41(0.53) 0.43(0.42) 0.43(0.27) 0.19(0.07)*

– – – 0.010(0.02) 0.02(0.02) 0.01(0.01) – 0.03(0.01)* 0.01(0.01) 0.04(0.04)

–

–


–

0.12(0.05)†

0.17(0.05)* 0.04(0.03) –

0.02(0.01)†

0.01(0.01)

0.11(0.10)

0.01(0.04) 0.20(0.13)

0.23 (0.08)† 4.10(1.09)*

0.01(0.04)

0.01(0.06) 0.003(0.03) –

0.01(0.06)

–

–

–

0.03(0.07)


0.01(0.03)

0.03 (0.04)

0.06 (0.04) 0.01(0.02) –

0.05 (0.05)

–

–

–

0.03 (0.05)


0.25(0.10)

0.33(0.36)

4.78(1.25)*

3.35(0.81)*

2.99(0.96)*

0.04(0.48)

0.09(0.02)*

0.13(0.06)†




Full information maximum likelihood estimates of the endogenous switching regression model

Table 4

4.48(1.15)*

0.46 (0.34)

0.38 (0.32)

0.79(0.41)‡ 0.48 (0.19)* 0.21(0.07)*

0.59(0.44)

4.25 (1.41)*

3.12 (0.65)*

3.05 (0.80)*

0.09(0.50)


Does zero tillage improve livelihoods? 165


10.28(0.24)* 0.63(0.28) 0.15(0.01)*

9.90(0.71)*

0.37(0.82) 0.38(0.02)*

17.02(8.06)* 41.89

14.82(3.23)†

0.06(0.03)†

0.41(0.31)

0.05 (0.05)

0.81(0.45)

0.03(0.02)

0.16 (0.06)*

2.81(0.02)†

0.03(0.02)†

0.67 (0.28)* 0.36 (0.02)*

3.42 (0.64)*

0.32 (0.21)

0.01(0.01)

0.12(0.13)

0.01(0.003)†

0.28(0.06)*

0.12(0.07)

0.04(0.01)*




Notes: *, †, and ‡ represent significance at 1%, 5% and 10% levels, respectively. Number in parentheses are the standard errors. Source: Model results.

Quantity of fertilisers used (kg/ha) Quantity of labour used (hour/ha) Wheat variety used (0 = local, 1 = improved) Quantity of herbicides used (litre/ha) Constant Log likelihood q r



Table 4 (Continued)

0.19 (0.33) 0.54 (0.02)*

3.02(0.86)*

0.23(0.10)†

0.07(0.07)

0.02(0.05)

0.02(0.01)†


22.35(8.34)* 423.41

15.83(3.46)*

0.56 (0.47)

3.07(0.73)*

0.09(0.08)


166 Tamer El-Shater et al.

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covariates provide a good estimate of the conditional density of adoption and joint significance of explanatory variables with Wald v2 test statistic significant at 1%. The correlation coefficients (q) between the error-terms in the selection and the outcome equations is significantly different from zero for adopters in the consumption equation and non-adopters in the income equation – implying that the switch is indeed endogenous. For instance, since rho is positive and significantly different from zero for the non-adopters in the income equation, the model suggests that an individual who did not adopt the ZT technology had a higher income than any individual randomly drawn from the whole sample, while the insignificant rho for the adopters indicates that the incomes of those who adopted ZT are no different from that of an individual randomly drawn from the whole sample. Results indicate that the zone in which the farm is located, farmer education and experience, distance to nearest market and quantities of inputs had significant influence on net income. On the other hand, only the input variables were found to have significant effect on consumption. Prior knowledge of ZT was used as an exclusion variable in the selection equation and is found to be positive and significant in the two estimated ESR models. Table 5 presents the expected net income and wheat consumption under actual and counterfactual conditions from the ESR method. Simple comparison of observed outcomes of adopters and non-adopters alone can be misleading as it suggests that on average the adopting households’ net income of 10,662 SP/ha is 39% higher than the non-adopters. However, the correct comparison is between the observed outcomes for adopters (a, Table 5) and the counterfactual case (c, Table 5), which shows that by adopting the technology, the adopter farm households are earning on the average 9,494 SYP or US$ 189/ha (33% higher) net income. Similarly, comparing the expected net income in the counterfactual case (d) and observed outcome (b), by not adopting the ZT technology, non-adopters are forgoing 32% of net income. These results imply that adoption of ZT significantly increases net income. The results generated by the bmte Stata command (StataCorp, 2009) for a suite of estimators also show that the adoption of ZT can lead to significant increases in net Table 5 The average impact of treatment on the treated (ATT) for net income and for wheat consumption (Endogenous switching regression model) Decision stage Subsamples effects Average expected net income (SP/ha) Farm households that adopted Farm households that did not adopt Heterogeneity effects

To adopt

Not to adopt

Treatment

(a) 37,994.5 (d) 36,249.7 1,744.8

(c) 28,500.3 (b) 27,332.2 1,168

9,494.2*** 8,917.4*** 576.8

(c) 48 (b) 41.2 6.8

25.5*** 16.3*** 9.2

Average expected consumption (kg/year) adult equivalent Farm households that adopted (a) 73.5 Farm households that did not adopt (d) 57.5 Heterogeneity effects 16

Note: ***, **, * represent the statistically significant difference between decision the two decision stages (To adopt and not to adopt) at 0.01, 0.05 and 0.1 levels, respectively. Source: Model results.


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Tamer El-Shater et al. Table 6 Bias-minimising treatment-effects estimation (BMTE)

Estimator OLS

ATE

CF

Standard ordinary least-squares Inverseprobabilityweighted estimators Minimum-biased (MB) estimator Control function

KV-IV

Klein and Vella

10,095.1

BVN

Heckman’s bivariate normal Bias-corrected estimator using the same cut-off levels (a) The biasminimising propensity score

IPW

MB (0.50)

MB-BC (0.5)

P*

BC-IPW

ATT

ATU

10,315.2

10,315.2

10,315.2

9,764.6

8,695.6

9,967.6

9,764.6

8,695.6

9,967.6

9,429.3

9,609.1

8,856.3

9,160.8 F = 19.8 P = 0.0000 10,095.1 F = 4,388 P = 0.000 9132.9

7,218.7

7,022.7

4,051.5

0.93

0.5

0.5

1.4E+04

3.7E+03

2.5E+04

10,095.1

8,803.9

income and wheat consumption (Table 6). From among the results generated by bmte for the different estimators, the control function (CF) and the OLS estimators provide the closest results to the ESR results for net wheat income and wheat consumption respectively – indicating that the CF and OLS estimators correct for biases better than the others. The results of the adjusted potential heterogeneity in the sample show that farm households who actually adopted would have had a net wheat income which is significantly higher than the farm households that did not adopt under both decision stages (i.e., under adoption and non-adoption scenarios). This highlights that important sources of heterogeneity mean that adopters obtain higher net income than non-adopters irrespective of their adoption status. Nevertheless, the farm households who adopted are still better off adopting than not-adopting. The high transitional heterogeneity (TH) explains the difference in the ATT estimates from these ESR and PSM approaches compared with the Heckit model results reported in Yigezu et al. (2015). This is evidence for the presence of unobserved heterogeneity, which is not properly accounted for other than with the ESR approach. Results also show that adoption of ZT increased per capita wheat consumption by 34% (25.5 kg/year). While there are differences in absolute values, the direction of change in the ESR results is consistent with those from PSM. If the farmers who did not adopt ZT were to adopt, they would have had an increase in per capita wheat Ó 2015 The Agricultural Economics Society


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consumption of 16 kg, 39%. The consumption effects from the Heckman model (Yigezu et al., 2015) were much lower than those from the PSM and ESR showing an underestimation by the previous study. 6. Conclusions Increasing agricultural productivity is crucial for improving the welfare of rural households and requires increased efforts to provide technologies that enhance yield and conserve natural resources. We used a sample of farms from Syria to evaluate the impacts of adopting the ZT technology on farm household welfare, measured by net crop (wheat) income and per capita wheat consumption (to account for the improvement in production over and above that which is sold and also improvement in quality of food consumed). The PSM and ESR estimation methods provide estimates of the true effects of technology adoption by correcting for different types of selection bias, and suggest that the results are robust. Results from both estimation methods suggest that the adoption of ZT leads to increases in net wheat crop income ranging from US$ 150–189 (33%) and per capita wheat consumption from 16 to 26 kg/year adult equivalent, (at least 34%). Those values represent a meaningful change in the welfare of small and medium-scale farmers in Syria. Along with the positive biophysical and environmental benefits of the adoption of ZT documented elsewhere, our results suggest that ZT is a robust technology which can be justified on economic, food security, biophysical and environmental grounds. This suggests that a wider adoption of ZT has the potential of improving the productivity, profitability and sustainability of the agricultural cropping sector in Syria and the wider West Asian region where small to medium-scale farms dominate. Our results also strongly suggest that appropriate exposure to ZT technology through participatory demonstrations or farmer testing along with associated field days as used in the development project in Syria are an effective way to raise community awareness, experience and adoption of the technology. The budget required for implementing such a policy in Syria is estimated at about US$ 792,000 which is mainly for the purchase of 132 locally manufactured ZT seeders for creating free access for first-time users. With such an investment, up to 70% adoption of ZT might be achieved in 10 years. Given the short duration of the introduction of ZT in Syria, a random sample of 308 wheat farms did not result in an adequate number of adopters of ZT in the sample. Hence, we had to make a decision to include a purposive sample of 313 wheat growers who had participated in the ZT popularisation project. The best way of measuring the impacts of ZT would have been to employ experimental methods with complete randomisation involving panel data (baseline and follow up) to capture the differences in livelihood indicators by comparing both before and after as well as with and without the technologies. However, the fact that the data used for this study come from a single snap-shot survey involving purposive sampling has compelled us to use quasi-experimental approaches which are intended to ‘reduce’ and not completely remove biases introduced by ‘observable’ and ‘unobservable’ factors. The Syrian government provides price support for wheat and also subsidies for inputs such as fertilisers and fuel (for irrigation pumps) which have distortionary effects in the market. While we acknowledge that this is an empirical question which will require the simulation of different scenarios and further analysis – a task beyond the scope of this paper – we believe that in the absence of government interventions in Ó 2015 The Agricultural Economics Society

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both the input and output markets, ZT would still remain beneficial in improving the livelihoods of wheat farmers in Syria and other similar regions. This is because the major benefits of the adoption of ZT come from cost (tillage and labour) savings rather than increase in revenues and hence withdrawal of input subsidies would increase the cost savings by adopters of ZT relative to non-adopters thereby leading to increased net gains. Supporting Information Additional Supporting Information may be found in the online version of this article: Appendix S1. Steps followed for the implementation of the propensity score matching (PSM). Appendix S2. Bias-minimising treatment-effects estimation (BMTE). Table S1. Survey details. Table S2. Characteristics of adopters and non-adopter sample farm households. Table S3. Comparison of characteristics of sample farm households in the random and purposive samples. Table S4. Performance of different matching estimators. Table S5. Propensity score and covariate balance test of variables. References Baumgart-Getz, A., Prokopy, L. S. and Floress, K. ‘Why farmers adopt best management practices in the States, United: A meta-analysis of the adoption literature’, Journal of Management Environmental, Vol. 96, (2012) 17–25. Belloum, A. ‘Conservation agriculture in the Arab world between concept and application’, in B. I. Stewart, A. F. Asfary, A. Belloum, K. Steiner and T. Friedrich (eds.), Conservation Agriculture for Sustainable Land Management to Improve the Livelihood of People in Dry Areas (2007). Available at: http://www.fao.org/ag/ca/doc/CA%20Workshop%20procedding% 2008-08-08.pdf (last accessed September, 2014). Caliendo, M. and Kopeinig, S. ‘Some practical guidance for the implementation of propensity score matching’, Journal of Economic Surveys, Vol. 22, (2008) pp. 31–72. Claro, R. M., Levy, R. B., Bandoni, D. H. and Mondini, L. ‘Per-capita versus adult-equivalent estimates of calorie availability in household budget surveys’. Cadernos de Sa ude P ublica, Vol. 26, (2010) pp. 2188–2195. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.) (Hillsdale, NJ: Erlbaum, 1988). Daheja, R. and Wabha, S. ‘Propensity score matching methods for non-experimental causal studies’, Review of Economics and Statistics, Vol. 84, (2002) pp. 151–161. D’Emden, F. H., Llewellyn, R. S. and Burton, M. P. ‘Factors influencing adoption of conservation tillage in Australian cropping regions’, The Australian Journal of Agricultural and Resource Economics, Vol. 52, (2008) pp. 169–182. Di Falco, S., Veronesi, M. and Yesuf, M. ‘Does adaptation to climate change provide food security? A microperspective from Ethiopia’, American Journal of Agricultural Economics, Vol. 93, (2011) pp. 829–846. Friedrich, T., Derpsch, R. and Kassam, A. ‘Overview of the global spread of conservation agriculture’, Field Actions Science Reports, Special Issue 6, (2012). Institut Veolia Environnement, Paris, France. Available at: http://factsreports.revues.org/1941 (last accessed 13 December 2013). Fulton, M. ‘Foreword’, in C. Lindwall and B. Sonntag (eds.). Landscapes Transformed: The History of Conservation Tillage and Direct Seeding (Saskatoon, Saskatchewan: Knowledge



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