Implications of less tail end water on livelihoods of small farmers in Pakistan

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1 Article Implications of less tail end water on livelihoods of small farmers in Pakistan Outlook on Agriculture 2017, Vol. 46(1) ª The Author(s) 2017 Reprints and permission: sagepub.co.uk/journalspermissions.nav DOI: / journals.sagepub.com/home/oag Akhter Ali 1, Dil Bahadur Rahut 2 and Muhammad Imtiaz 1 Abstract In Pakistan, about 80% of the cropped area is irrigated using canal irrigation, and water availability is closely linked to the location of the farm. Using data collected from 950 farmers through a field survey covering four provinces (Punjab, Sindh, Khyber Pakhtunkhwa and Balochistan), this study aimed to assess the impact of location, that is, head versus tail on water availability and its impact on crop yield, household income, food security and poverty levels. The censored least absolute deviation was used to estimate farmer participation in water markets, and the propensity score matching was used to assess impacts on yield of wheat and rice, household income and poverty levels as well as land rent and water scarcity. The results show that farmers situated at the head of the water source have higher wheat and rice yields in the range of 2 3 maunds per acre. Household income levels are higher in the range of PKR ,673, and poverty levels are lower (þ3% to 5%). The land rent at the head is higher compared to the tail while water scarcity is also less at the head. The study indicated that farmers status plays a major role in land location and access to irrigation water. Keywords land location, income, poverty, propensity score matching Introduction In irrigated agriculture, surface irrigation is one of the critical components of food production. Globally, about 17% of agricultural land is irrigated and contributes to about 40% of global cereal production (Dams WCD, 2000). Surface water is a cheaper source of water for irrigation compared to groundwater (Shah, 2000). The provision of sufficient quantities of surface water can help to increase crop production and household income and reduce poverty levels (Barker et al., 2000; Chambers, 1988). Previous research on water allocation has reported that farmers at the bottom or tail end of a canal often receive a disproportionately small amount of irrigation water and sometimes no water (Chambers, 1988; Shah, 2000). The top or head end farmers, however, receive a disproportionately large share of canal water (Chambers, 1988; Shah, 2000). Overall, irrigated agriculture needs to produce more food with less water (Wheeler and Kay, 2010). Many basins worldwide are facing water shortages due to increased demand for water from all sectors (Mollinga et al., 2006). In Pakistan, agriculture constitutes about 21.8% of the gross domestic product (GDP) and employs 44.7% of the workforce. More than two-thirds of Pakistan s population live in rural areas, and their livelihoods continue to revolve around agriculture and allied activities (Government of Pakistan, ). Agriculture dominates foreign trade through the export of raw products such as rice, cotton (semi-processed and processed) and leather products. The share of primary commodities and processed and semi-processed products constitutes almost 60% of total exports. There have been some structural changes over time, but the contribution of agro-based products has more or less sustained its position. Agriculture thus provides the foundation for the economic development and growth of Pakistan (Government of Pakistan, 2003). Pakistan has the world s largest canal irrigation system with 80% of the cultivated area being irrigated by canals. The distribution of canal water in Pakistan is skewed, with the tail land receiving less water compared to land located at the head and middle parts of the canal. As a consequence of the inequality in the distribution of canal water, the use of groundwater at the tail end is expected to be higher than it is on the land situated at the head. Pakistan is classified as a water-stressed country, as per capita water availability is around 1223 m 3 per year, and by 2017, per capita water availability is expected to be less than 1000 m 3 per year (Ahmad, 2007). Pakistan also needs to double its annual food production every 15 years in order to meet the 1 International Maize and Wheat Improvement Center (CIMMYT), National Agricultural Research Center, Islamabad, Pakistan 2 International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico Corresponding author: Akhter Ali, International Maize and Wheat Improvement Center (CIMMYT), National Agricultural Research Center, Park Road, Islamabad, Pakistan. akhter.ali@cgiar.org

2 Ali et al. 37 Table 1. Data and description of variables. Variable Description Mean Standard deviation Farm location 1 if the farm is situated at head, 0 otherwise Water revenue Rate of water revenue in the area in rupees Tube well bore depth Depth of the tube well bore in meters Tube well power source 1 if the power source is electricity, 0 otherwise Tube well ownership 1 if the household owns a tube well, 0 otherwise Water rate Average per acre water rate for 1 irrigation in rupees Water sold/purchased 1 if the household sold/purchased water, 0 otherwise Water suitable 1 if the water is suitable for irrigation, 0 otherwise Water scarcity 1 if the household is facing water scarcity, 0 otherwise Agricultural extension 1 if the household has access to agri. extension, 0 otherwise Cotton yield Cotton yield in maunds per acre Sugarcane yield Sugarcane yield in maunds per acre Wheat yield Wheat yield in maunds per acre Maize yield Maize yield in maunds per acre Rice yield Rice yield in maunds per acre Gender 1 if the respondent is male, 0 otherwise Age of farmer Age of the farmer in years Education Education of the farmer in number of years Experience Experience of the farmer in number of years Marital status 1 if the respondent is married, 0 otherwise Land ownership Mean land owned by the farmer in number of acres Tenancy status 1 if the farmer is owner, 0 otherwise Soil quality 1 if the soil is of good quality, 0 otherwise Income Income per annum of the household in rupees 127, Metal road 1 if the household has access to metal road, 0 otherwise Pesticide dealer 1 if the household has access to pesticide dealer, 0 otherwise Output market 1 if the household has access to output market, 0 otherwise NGOs 1 if the household has membership of NGOs, 0 otherwise Tractor 1 if the household owns a tractor, 0 otherwise Tube well 1 if the household has a tube well, 0 otherwise Car 1 if the household owns a car, 0 otherwise Television 1 if the household owns a television, 0 otherwise Water scarcity 1 if the household faces water scarcity, 0 otherwise Punjab 1 if the respondent is from Punjab province, 0 otherwise Sindh 1 if the respondent is from Sindh province, 0 otherwise KPK 1 if the respondent is from KPK province, 0 otherwise Balochistan 1 if the respondent is from Balochistan province, 0 otherwise NGOs: non-governmental organizations; KPK: Khyber Pakhtunkhwa. changing food requirements of the increasing population. However, due to increased salinity and waterlogging problems, irrigation water scarcity issue is becoming a major and severe issue (Afzal, 1996; Briscoe and Qamar, 2006; World Bank, 1996). In Pakistan, agriculture is dependent on irrigation, perhaps more so than anywhere else in the world. Irrigation is used on more than 80% of all arable land and produces more than 90% of the total food and feed (Government of Pakistan, 2008). A substantial amount of the water resources in Pakistan is used for crop production (Shaikh, 2000). Due to increased competition from other industries, the availability of irrigation water is declining, particularly at the tail ends (Ma et al., 2008). Hence, water scarcity is more acute on land at the tail end compared to the head of the canals. Pakistan is currently facing serious water scarcity problems, and farmers complement both the canal water (surface) and groundwater to overcome their water shortage problem. The objective of this article was twofold: First, it assesses the determinants of participants in the water market by farm household; and second, it estimates the impacts of land location along the irrigation canals on the livelihoods of small farmers in Pakistan. Methodology We consider a rural farm household (H) that directly uses irrigation water. It is assumed that usage increases with the availability of water (W A ) compared to non-availability of water (W NA ). The availability of irrigation water is closely linked to the location of the land (L). Water availability is greater at the head end (L H ) compared to the tail end (L T ): UðL H Þ > UðL T Þ: ð1þ Equation (1) indicates that the usage (U) at the head is higher compared to the tail end. This is due to higher crop yield, higher household income and lower poverty levels: UðL H Þ½Cy; I; PŠ > UðL T Þ½Cy; I; PŠ: ð2þ

3 38 Outlook on Agriculture 46(1) If there are systematic differences between farmers at the head end of the canal and the tail end, then the ordinary least square (OLS) estimates might lead to biased estimates. Hence, to overcome the problem of the biased estimate, the empirical analysis is carried out by employing the propensity score matching (PSM) approach. The expected treatment effect for the treated population is of particular significance in the PSM approach. This effect may be given as τj I¼1 ¼ EðτjI ¼ 1Þ ¼EðR 1 ji ¼ 1Þ EðR 0 ji ¼ 1Þ; ð3þ where τ is the average treatment effect for the treated (ATT), R 1 denotes the value of the outcome for the farmers situated at the head end and R 0 is the value of the same variable for the farmers situated at the tail end. The main difficulty is that EðR 0 ji ¼ 1Þ cannot be observed. Although the difference ½τ e ¼ EðR 1 ji ¼ 1Þ EðR 0 ji ¼ 0Š can be estimated, it is potentially a biased estimator. The PSM model can be used to account for the sample selection bias if experimental data is not available (Dehejia and Wahba, 2002). To create the condition of a randomized experiment, the PSM uses the unconfoundedness assumption, also called the conditional independence assumption, which implies that once Z is controlled for, the location of the farm is random and uncorrelated with the outcome variables. The PSM can thus be expressed as pðzþ ¼ PrfI ¼ 1jZg ¼ EfIjZg: ð4þ Unlike parametric methods, PSM requires no assumption about the functional form in specifying the relationship between outcomes and predictors of outcome. 1 In the absence of random assignment, the propensity score methods provide a specification check that tends to eliminate biases that were larger than average (Michalopoulos et al., 2004). However, the fixed effects model did not consistently improve the results. After calculating the propensity scores, the ATT can then be estimated as τ ¼ EfR 1 R 0 ji ¼ 1g ¼EfEfR 1 R 0 ji ¼ 1; pðzþgg ¼ EfEfR 1 ji ¼ 1; pðzþg EfR 0 ji ¼ 0; pðzþgji ¼ 0g: ð5þ As it eliminates the tails of the distribution of p(x), the quality of the matches improves, but this may lead to considerable reduction in the sample. However, the non-parametric matching methods can only be meaningfully applied to regions of overlapping support (Heckman et al., 1997): 0 < PðD ¼ 1jX Þ < 1: ð6þ It ensures that a sample with the same X values has a positive probability of being both situated at the head end and the tail end (Heckman et al., 1999). In the literature, a number of matching algorithms have been proposed; however, the selection of a matching approach often does not make much difference (Smith and Todd, 2001). In small samples, the choice of matching approach can be important (Heckman et al., 1997). Practically, it seems reasonable to try different approaches because, as noted earlier, the performance of different Table 2. Differences in key characteristics of farmers situated at the head and the tail. Variable Situated at head Situated at tail Difference t Values Yields Cotton yield a 2.63 Sugarcane yield a 3.26 Wheat yield a 2.91 Maize yield a 2.55 Rice yield a 3.04 Demographic Gender Age b 2.07 Education c 1.68 Experience a 2.56 Marital status Land ownership and quality Land ownership b 2.19 Tenancy status c 1.82 Soil quality a 2.51 Wealth and assets Income 154, ,836 48,520 a 3.10 Tractor a 2.57 Tube well a 2.74 Car b 2.13 Television b 1.98 Accessibility, infrastructure and institution Metal road Agricultural extension Pesticide dealer Output market NGOs NGOs: non-governmental organizations. a Significant at 1% level. b Significant at 5% level. c Significant at 10% level. matching estimators varies case by case and depends in large part on the data structure at hand (Zhao, 2003). Nearest neighbour, caliper and radius, kernel and local linear and stratification matching are the most widely used matching algorithms. Nearest neighbour matching (NNM) is the most straightforward and involves choosing individuals from two groups (i.e. situated at head and tail) that are closest in terms of propensity scores as matching partners. Several variants of the NNM have been proposed in the literature, including NNM matching with and without replacement. In the former, an untreated individual can be used more than once as a match, whereas, in the latter, it is considered only once. Let T be the set of treated units and C the set of control units, and Yi T and Yj C be the observed outcomes of the treated and control units, respectively. C(i) isthesetof control group, which is matched to the treated unit i with an estimated propensity score value of p i. NNM sets CðiÞ ¼min j kp i p j k: ð7þ The representation in equation (7) indicates that two groups, that is, those situated at the head end and tail end,

4 Ali et al. 39 Table 3. The censored least absolute deviation estimates of water market participation (sales and purchase of water). Variable Coefficient t Value Yield Cotton yield 0.04 a 2.71 Sugarcane yield 0.02 b 1.82 Wheat yield 0.03 a 2.53 Maize yield 0.01 c 1.97 Rice yield 0.03 a 3.14 Demographic Gender 0.02 b 1.68 Age Education 0.02 b 1.70 Marital status Land ownership Land ownership 0.03 b 1.81 Tenancy status 0.02 c 2.17 Wealth and assets Tractor 0.04 a 2.91 Tube well 0.03 a 3.15 Car 0.02 c 2.20 Television 0.03 c 1.85 Accessibility, infrastructure and institution Agricultural extension 0.03 c 1.98 Constant 0.03 b 1.68 Initial sample size 950 Final sample size 682 Pseudo R a Significant at 1% level. b Significant at 10% level. c Significant at 5% level. can be matched only, that is, matching without replacement. In another scenario matching with replacement, a control group can be matched more than once. The trimming option further improves the matching quality, that is, double precision. Data and description of variables This study used a comprehensive primary data set collected through a field survey covering all four major provinces in Pakistan including Punjab, Sindh, Khyber Pakhtunkhwa (KPK) and Balochistan. A detailed questionnaire was designed for data collection, which included information on a number of socio-economic, household and farm level characteristics. The multistage random sampling approach was employed to collect the data. At the first stage, all four provinces were selected; in the second stage, 61 districts were chosen; in the third stage, we selected 119 blocks/ tehsils; in the fourth stage, we selected 275 villages; in the final stage, 950 households were sampled. The sample ensured representation of both groups of farmers situated at the head end and the tail. The survey was implemented by a team of local enumerators who were trained prior to the exercise, between September and December Table 1 presents descriptive statistics for variables used in the analysis. The water scarcity problem was faced by 56% of households surveyed. Of the surveyed households, 38% were from the Punjab province, 27% from Sindh, 23% Table 4. The propensity score matching estimates of land location (i.e. 1 ¼ head, 0 ¼ tails; logit estimates). Variable Coefficient t Value Demographic and human capital Age of farmer a 1.93 Education b 2.13 Gender Farmer status b 2.18 Family system Marital status Land Land ownership c 2.71 Tenancy status Soil quality c 3.25 Wealth and assets Tractor Tube well Car c 2.90 Television Accessibility and infrastructure Metal road a 1.76 Pesticide dealer Output market NGOs Location Punjab a 1.76 Sindh c 2.42 KPK c 3.89 Constant c 4.85 Number of observations 917 LR w Prob. > w Pseudo R NGOs: non-governmental organizations; KPK: Khyber Pakhtunkhwa; LR: likelihood ratio. a Significant at 10% level. b Significant at 5% level. c Significant at 1% level. from KPK and 11% from Balochistan. The differences in the main characteristics of the farmers situated at the head end and tail end are presented in Table 2. The results indicated that there are significant differences in crop yield, land and asset ownership between those households with the farm land at the head end of the irrigation canal and those with farm land at the tail end of the irrigation canal. Results and discussion Participation in water market: Censored least absolute deviation The outputs on determinants of water market participation (sales and water purchase) are given in Table 3. The censored least absolute deviation (CLAD) was employed to estimate farmer participation in water markets. The CLAD is a censored regression model, and the estimates are robust compared to the Tobit model. A number of independent variables were included in the model on the basis of the review of the literature and descriptive statistics analyses given in Tables 1 and 2. The agricultural extension contact was included as a dummy variable, and the coefficient was

5 40 Outlook on Agriculture 46(1) Table 5. Impact of head canal location on farmer s livelihood. a Outcome Caliper/bandwidth ATT t Values Critical level of hidden bias Number of treated Number of control Matching algorithm: NNM Wheat yield b Rice yield c Income b Poverty d Land rent Water scarcity b Matching algorithm: KBM Wheat yield d Rice yield b Income ,367 b Poverty Land rent d Water scarcity b Matching algorithm: RM Wheat yield c Rice yield d Income ,283 b Poverty c Land rent Water scarcity c ATT: average treatment affect for the treated; NNM ¼ nearest neighbour matching; RM ¼ radius matching; KBM: kernel-based matching. a For the NNM and RM, calipers are reported, while for the KBM, bandwidths are reported. b Significant at 1% level. c Significant at 10% level. d Significant at 5% level. Table 6. Indicators of covariate balancing before and after matching. Outcome Median absolute bias before matching Median absolute bias after matching Percentage bias reduction Value of R 2 before matching Value of R 2 after matching LR w 2 before matching LR w 2 after matching Matching algorithm: NNM Wheat yield Rice yield Income Poverty Land rent Water scarcity Matching algorithm: KBM Wheat yield Rice yield Income Poverty Land rent Water scarcity Matching algorithm: RM Wheat yield Rice yield Income Poverty Land rent Water scarcity NNM: nearest neighbour matching; KBM: kernel-based matching; RM: radius matching. positive and significant at 5%, signifying that farmers in contact with agricultural extension services were more likely to participate in the water market. The yield coefficients for cotton, sugarcane, wheat, maize and rice were positive and significant, suggesting those farm households with higher yield and income were more likely to participate in the water market. The results indicated that young, male-headed household and educated farmers with more

6 Ali et al Untreated Treated Effect on wheat yield Untreated: Off support Untreated: On support Effect on rice yield Untreated: Off support Effect on household income Untreated: On support Untreated: Off support Untreated: On support Effect on poverty Untreated: Off support Untreated: On support Effect on land rent Untreated: Off support Untreated: On support Effect on water scarcity Figure 1. Imposition of the common support condition and indicators of the covariates balancing. land holdings and more assets are situated at the head end and have more access to water. The policy implications of the study are that farmers need to be encouraged to participate in water markets (sale or purchase of water) as they can help in increasing crop yields. Impact of location on farm household welfare: PSM estimates The results of the PSM estimates are presented in Table 4. The dependent variable is a dummy, that is, 1 if the household is situated at the head and has easy access to water and 0 if the household is located at the tail and faces water scarcity. The PSM was carried out by employing three different matching algorithms, that is, NNM, kernel-based matching and radius matching. In the case of PSM, the ATT indicates the difference in outcomes of similar farmers situated at the head end and tail end. The results are presented in Table 5. The ATT results for the wheat and rice yield are positive and significant, indicating that farmers situated at the head end have higher wheat and rice yields, in the range of maunds and

7 42 Outlook on Agriculture 46(1) maunds per hectare, respectively. The household income levels are higher in the range of PKR ,367. The poverty levels are lower, in the range of 3 %, for farmers situated at the head end compared to farmers situated at the tail end. The headcount index of poverty was estimated based on the poverty line of US$2. Those households with per capita income of less than US$2 were categorized as poor. This study has important policy implications that water management at the institutional level is necessary to enable the tail end farmers to access water for irrigation and enable them to sustain or increase their crop yields and income levels. After the PSM analysis, a number of balancing tests were employed to check the matching quality; the results are presented in Table 6. The balancing tests indicated that before matching there were systematic differences between head end and tail end farmers and after matching both the groups are quite similar to each other. The indicators of covariate balancing are also presented in Figure 1. Concluding comments The location of land along the canal affects a household s access to irrigation water. The household situated at the head has easy access to water compared to households located at the tail end. The aim of this article was to assess the determinants of the farm household participants in the water market as well as the impact of the location of the farm at the canal head on the livelihood of the farmers in Pakistan. The PSM approach was employed to account for systematic differences between the farms situated at the head end and the tail end. The balancing tests indicated that the PSM property is robust and satisfactory. The PSM results indicate that the location of the farm land, that is, head versus tail, has a significant impact on crop yield, household incomes and poverty levels. In addition, the location of the land affects the land rental value and water access. Farmers situated at the head end of the canal have higher wheat and rice yields due to water availability compared to households located at the tail end of the canal. Hence, the location of the farm land along the irrigation canal is crucial for the rural household s food security and poverty reduction. The farmers with farm land at the tail end have less surface irrigation water, and they supplement their supply with groundwater; hence, they own more tube wells. As a consequence of higher yields and higher land rental value, farmers at the head end had higher income and lower poverty levels. The study suggests that the irrigation policy should provide support to farmers at the tail end in the form of subsidies for alternate water resource investment including, for example, tube well installation. Introducing policies that protect the interest of the tail end farmers and enable them to increase crop yields, household food security and income levels are of particular importance for ensuring food security, reducing inequality and poverty. Acknowledgements The authors thank the Consortium Research Program (CRP) on WHEAT and MAIZE for supporting this study. Authors Note The contents and opinions expressed herein are those of the author(s) and do not necessarily reflect the views of USAID, or the authors institution, and shall not be used for advertising or product endorsement purposes. The usual disclaimer applies. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was made possible through the support provided by the United States Agency for International Development (USAID) funded Agricultural Innovation Program (AIP) for Pakistan. Note 1. The drawback of the approach is the strong assumption of unconfoundness. As argued by Smith and Todd (2005), there may be systematic differences between outcomes of adopters and non-adopters even after conditioning because selection is based on unmeasured characteristics. However, Jalan and Ravallion (2003) point out that the assumption is no more restrictive than those of the IV approach employed in cross-sectional data analysis. 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