The main purpose of this article is to estimate the impact of the direct rice sowing (DRS) technology on irrigation water saving in the Indo-Gangetic plains. For this study, a comprehensive data set was collected from the rice-wheat area of the Pakistani Punjab. In total, 238 farmers were interviewed from the three major rice-producing districts i.e. Gujranwala, Sheikhupura and Hafizabad. The empirical analysis was carried out by employing the propensity score matching approach to correct for potential sample selection bias that may arise due to systematic differences between the participants and non-participants. The empirical results indicate that the DRS technology is a water saving technology and, on average, the adopters need four less irrigation as compared to the traditional transplanting method. The DRS technology is also labour saving and requires less labour than the conventional rice sowing technology. The water productivity of the DRS technology is also higher as compared to the conventional transplanting method. The DRS technology also has a beneficial yield impact on the subsequent wheat crop. However, the major problem with the DRS technology is weed infestation which needs to be addressed. Farm size analysis indicates that DRS technology has a positive impact for all farmers and particularly on the small and medium scale farmers.

INTRODUCTION

Rice and wheat are of central importance in meeting food needs of the growing population in the world. These two cereal crops provide 45% of the digestible energy and 30% of the total protein in the human diet, as well as substantially contributing to livestock feed (Evans 1993). Currently the rice-wheat cropping system is being practiced on about 12% of the total farm land of Pakistan. The average rice and wheat yield in the country per hectare is 2.2 tons and 2.45 tons, respectively, which is low compared to potential yields. Progressive farmers are obtaining a per hectare yield of 4.50 and 4.58 tons for wheat and rice crops. Rice and wheat are important staple food crops in Pakistan. These crops are hardly meeting the food requirement of the country. To meet the food demand of the increasing population in the country, crop production will have to increase accordingly.

The biggest threat to sustaining or increasing the productivity of rice-wheat systems is water shortage. Water resources in Pakistan, both surface and groundwater, have become insufficient to meet the growing demands of the irrigated agriculture sector (Government of Pakistan 2009). The per capita water availability has reduced from 5,600 cubic meter to about 1,000 cubic meter from 1951 to 2010. The present overall shortfall of about 11% will increase to about 31% by 2025 (Government of Pakistan 2009). The supply of fresh water to the agriculture sector will be reduced in the future due to the increasing demand and competition from environmental, industrial and domestic sectors. The major challenge for the agriculture sector during the 21st Century is to produce more food with less water. Water savings from rice-based cropping systems will be of significant importance, as nearly 50% of the freshwater used in Asian agriculture is utilized for rice production (Gleick 1993). To meet the increasing demand for food, and cope with an increasing scarcity of water, more rice needs to be produced using less water (Guerra 1998).

The shortfall can be met either by constructing new storage reservoirs or by improving the efficiency of the existing water-use practices. Both are equally important; however, the construction of new storage reservoirs requires huge financial investment along with other constraints such as: limited availability of potential sites, population displacement, and environmental and socio-political issues. Therefore, proper management of the existing water resources appears to be an immediate option. Under the present water scarcity conditions, it becomes even more important to use water judiciously and increase the water productivity (Bouman & Tuong 2001; Hussain et al. 2007; Mendola 2007). Increasing demands for water, its limited availability and environmental concerns make it essential to utilize water efficiently. In view of diminishing water resources, farmers will likely continue to rely on improved technologies and water management practices to conserve water.

Different resource conservation technologies, including crop establishment and irrigation management practices, are being developed and promoted in the Indo-Gangetic plains. The crop establishment technologies include the double zero tillage and bed planting for rice and wheat, zero tillage for wheat and direct seeding for rice. Irrigation management strategies for rice crops include the alternate drying and wetting regime and the saturation regime. Adoption of these technologies is expected to result in enhanced crop production and water savings. Additionally, it is hoped they will address a range of other issues, including emerging labor shortages, poverty reduction and environmental sustainability.

Rice-wheat has emerged as the most widespread crop production system in the Indo-Gangetic Plains and the national rice-wheat area is estimated to be around 10 million hectares (Paroda et al. 1994; Yadav et al. 1998; Timsina & Connor 2001; Gupta et al. 2003; Hobbs & Morris 2013).

The Indo-Gangetic Plains are home to an ancient civilization and the livelihoods of millions of people depend on these fertile plains. With the availability of high-yielding rice and wheat varieties and improved production methods, rice-wheat has become the most dominant cropping system and, in the irrigated areas, double cropping is commonly practiced. In recent years, however, the productivity of rice is affected due to labor, water and power shortages. An alternate rice establishment is the direct seeding of rice.

In the past not many studies have specifically focused on the water saving aspect of the direct rice sowing (DRS) technology. In the current study the impact is estimated on water demand, cost of irrigation and labor demand. The rest of the paper is organized as follows: in Section 2 propensity score matching (PSM) approach is presented in Section 3, data collection methodology and the description of variables is presented; in Section 4, the empirical results are presented and the paper concludes with some policy recommendations in Section 5.

PROPENSITY SCORE METHOD

It follows that the expected treatment effect for the treated population is of primary significance. This effect may be given as: 
formula
1
where τ is the average treatment effect for the treated (ATT), R1 denotes the value of the outcome for adopters of the new technology and R0 is the value of the same variable for non-adopters. As noted a major problem is that we do not observe . Although the difference can be estimated, it is potentially a biased estimator.
In the absence of experimental data, the PSM model can be employed to account for this sample selection bias (Dehejia & Wahba 2002). The PSM is defined as the conditional probability that a farmer adopts the new technology, given pre-adoption characteristics (Rosenbaum & Rubin 1983). To create the condition of a randomized experiment, the PSM employs the unconfoundedness assumption, also known as the conditional independence assumption, which implies that once Z is controlled for, technology adoption is random and uncorrelated with the outcome variables. The PSM can be expressed as: 
formula
2
where I = {0,1} is the indicator for adoption and Z is the vector of pre-adoption characteristics. The conditional distribution of Z, given p(Z) is similar in both groups of adopters and non-adopters.

Unlike the parametric methods mentioned above, PSM requires no assumption about the functional form in specifying the relationship between outcomes and predictors of outcome. The drawback of the approach is the strong assumption of unconfoundness. There may be systematic differences between outcomes of adopters and non-adopters even after conditioning because selection is based on unmeasured characteristics. However, (Jalan & Ravallion 2003) pointed out that the assumption is no more restrictive that those of the IV approach employed in cross-sectional data analysis. In a study by (Michalopoulos et al. 2004) to assess which non-experimental method provides the most accurate estimates in the absence of random assignment, they conclude that propensity score methods provided a specification check that tended to eliminate biases that were larger than average. On the other hand, the fixed effects model did not consistently improve the results.

Average treatment effects

After estimating the propensity scores, the average treatment effect for the treated (ATT) can then be estimated as: 
formula
3

Several techniques have been developed to match adopters with non-adopters of similar propensity scores like nearest neighbor matching (NNM), kernel-based matching (KBM), radius matching and caliper matching. It is always better to use more than one approach as the performance of different matching estimators varies case-by-case and largely depends on the data structure at hand.

DATA AND DESCRIPTION OF VARIABLES

The conventional method of rice transplanting includes nursery growing and then the transplanting of the nursery in a prepared field with standing water. For the conventional method more time, irrigation water and labor is required. The DRS is a comparatively new technology. In the case of DRS technology, comparatively less irrigation water is required. In addition in DRS technology, the labor demand is also less and the new technology is time saving. In spite of all these advantages, the new technology is facing the serious challenge of weed infestation. DRS technology means the rice is sown in the dry soil. After field preparation at optimum moisture for seed germination. The fields are prepared in June and the crop is sown with pre-sowing irrigation to establish it before the onset of the monsoons.

The study was carried out in the rice-wheat area of the Pakistani Punjab. From the rice-wheat area, three main rice producing districts i.e. Sheikhupura, Hafizabad and Gujranwala were purposively selected for the current study, as mostly in these three districts the DRS technology has been adopted. As the technology is at its initial stages, the data was collected by employing purposive random sampling techniques and the selection of farmers was mostly made with the help of the PARC Rice Research Institute situated at Kala Shah Kaku, Punjab-Pakistan. The data was collected by employing a comprehensive questionnaire covering different aspects of socioeconomic information, land holdings, cropping patterns, rice varieties and the comparison of the conventional and dry rice methods, as well as the impact of DRS on the subsequent wheat crop. From the three districts, in total 238 farmers were interviewed.

The data and description of variables is presented in Table 1. The mean age of the farmers was about 47 years. The mean education level was about 10 years of schooling. The mean experience of the respondents was about 25 years. The mean male child was about 1.96 males per household. The mean female child was about 2.29 female children per household. The mean adult males were about 1.61 males per family. The mean adult females were about 1.61 females per family. The old age males were on average 0.61 males per family. The old aged females were about 0.387 females per family. The mean family size per household was about nine family members per household. The mean male workers working on farm were about 1.58 males participating in farming activities. The mean female workers were about 0.48 female workers participating in farming activities. The highest education in the family was about 11 years of schooling. The mean owned area was about 18.72 acres; the rented area on average was 7.12 acres, the rented out area was about 2.90 acres. There was negligible shared in and shared out areas. Overall operational land holdings per household were about 23 acres per household. The area under wheat and rice crop was about 16 acres on average. About 27% of the households in the study area have their own tube wells, while 31% of the households have their own tractors. About 41% of the households have access to extension services while 15% of the households have availed themselves of credit facilities. About 68% of the households have road access. The average distance to market was seven kilometers. The average household income was rupees 13,400 per month.

Table 1

Socioeconomic characteristics of the Surveyed Household

Variable Description Mean Std. 
Age Age of the rice producers in number of years 46.80 2.17 
Education Education of the sample respondents in years 10.09 0.74 
Experience Experience of the sample respondents in years 24.67 2.58 
Male child Number of male children in the household 1.967 0.326 
Female child Number of female children in the household 2.29 0.41 
Adult male Number of adult males in the household 1.612 0.29 
Female adult Number of female adults in the household 1.614 0.210 
Old age male Number of old age males in the household 0.612 0.078 
Old age female Number of old aged females in the household 0.387 0.082 
Family size Number of total family members in the household 9.19 0.88 
Male worker Number of male workers working on the farm 1.580 0.206 
Female worker Number of female workers working on farm 0.483 0.121 
Highest education Years of highest education in the family 11.25 0.66 
Own area Number of acres owned by the household 18.72 3.77 
Rented in Area rented in by the household 7.12 3.94 
Rented out Area rented out by the household 2.90 2.13 
Shared in Area shared in by the household 0.064 0.06 
Shared out Area shared out by the household 0.057 0.003 
Operational land Operational land holding of the farmer in acres 22.53 4.32 
Wheat area Area under wheat crop in acres 16.22 2.40 
Rice area Area under rice crop in acres 16.26 2.79 
Tube well 1 if household has tube well ownership, 0 otherwise 0.27 0.22 
Tractor 1 if household has tractor ownership, 0 otherwise 0.31 0.26 
Extension 1 if household has extension contact, 0 otherwise 0.41 0.28 
Credit facility 1 if household has availed credit facility, 0 otherwise 0.15 0.31 
Road access 1 if household has road access, 0 otherwise 0.68 0.24 
Market distance Distance to market in kilometers 7.26 2.83 
Income Average monthly income of the household in rupees 13,400 598 
Production cost Average cost of production in rupees 10,173 258 
Variable Description Mean Std. 
Age Age of the rice producers in number of years 46.80 2.17 
Education Education of the sample respondents in years 10.09 0.74 
Experience Experience of the sample respondents in years 24.67 2.58 
Male child Number of male children in the household 1.967 0.326 
Female child Number of female children in the household 2.29 0.41 
Adult male Number of adult males in the household 1.612 0.29 
Female adult Number of female adults in the household 1.614 0.210 
Old age male Number of old age males in the household 0.612 0.078 
Old age female Number of old aged females in the household 0.387 0.082 
Family size Number of total family members in the household 9.19 0.88 
Male worker Number of male workers working on the farm 1.580 0.206 
Female worker Number of female workers working on farm 0.483 0.121 
Highest education Years of highest education in the family 11.25 0.66 
Own area Number of acres owned by the household 18.72 3.77 
Rented in Area rented in by the household 7.12 3.94 
Rented out Area rented out by the household 2.90 2.13 
Shared in Area shared in by the household 0.064 0.06 
Shared out Area shared out by the household 0.057 0.003 
Operational land Operational land holding of the farmer in acres 22.53 4.32 
Wheat area Area under wheat crop in acres 16.22 2.40 
Rice area Area under rice crop in acres 16.26 2.79 
Tube well 1 if household has tube well ownership, 0 otherwise 0.27 0.22 
Tractor 1 if household has tractor ownership, 0 otherwise 0.31 0.26 
Extension 1 if household has extension contact, 0 otherwise 0.41 0.28 
Credit facility 1 if household has availed credit facility, 0 otherwise 0.15 0.31 
Road access 1 if household has road access, 0 otherwise 0.68 0.24 
Market distance Distance to market in kilometers 7.26 2.83 
Income Average monthly income of the household in rupees 13,400 598 
Production cost Average cost of production in rupees 10,173 258 

Source: Survey results, 2011.

The difference in key characteristics of the households having adopted the DRS technology and not adopted it is presented in Table 2. The mean area allocated to conventional rice was about 10.18 acres and the mean area allocated to direct seeding of rice was 7.88 acres and the difference in the area was 2.3 acres, and the difference is significant. The demand for seed rate was higher in the case of direct seeding of rice as compared to conventional rice sowing. The direct seeding of rice required 11 kgs more seed as compared to conventional rice sowing and the results are highly significant at the 1% level of significance. Most importantly, there is also difference in demand for irrigation water. The direct seeding of rice on average requires four times less irrigation as compared to conventional rice sowing and the results are significant at the 10% level of significance. The transplanting cost is high in the case of conventional rice sowing and by adopting the direct seeding of rice the farmers can save up to rupees 1,716 per acre as transplanting cost. The labor demand is less for the direct seeding of rice as compared to the conventional rice sowing technology and the results are significant at the 5% level of significance. The DAP and the urea fertilizer requirements are also high for the direct seeding of rice sowing technology as compared to the conventional rice planting method. The weedicide application costs are also high for the dry rice sowing technology and farmers have to spend about rupees 1,100 for weedicide application. Most importantly the farmers who adopted DRS technology were getting about 360 kgs higher yields as compared to traditional rice sowing. The difference in income is negative and significant, indicating that households practicing the DRS technology have higher income levels in the range of rupees 2,456 per month as compared to households practicing traditional methods of rice cultivation. The difference in family size is positive and non-significant indicating that adopters and non-adopters have almost similar family size. The difference in tube well ownership is negative and non-significant. The difference in tractor ownership is positive and significant at the 5% level of significance, indicating that households having practiced the traditional methods of rice sowing have higher tractor ownership and vice versa. The difference in extension services is negative and significant at the 10% level of significance, indicating that farmers practicing the direct seeding of rice sowing technology have more contact with the extension services and vice versa. The implication of this finding are that for the up scaling of DRS technology the agricultural extension services needs to play an important role. The difference in credit facility is positive and non-significant, indicating that farmers practicing the traditional rice sowing methods are availing themselves of higher credit facilities and vice versa. DRS adopters had better road access (is negative and significant at the 10% level of significance). Similarly the difference in market distance is positive and significant at the 10% level of significance, indicating that conventional rice farmers have to travel longer distances. The total cost of production in case of conventional method is rupees 12,150 and in case of DRS technology is 9,560. The difference is positive and significant. The lower cost of production is also an incentive for the small scale farmers to adopt the new technology.

Table 2

Difference in key characteristics of direct seeding of rice and conventional rice sowing

Variable Conventional rice Direct rice Difference t-values 
Area (acres) 10.182 7.882 2.3 0.92 
Seed Rate (kgs) 2.921 13.906 −10.984*** −18.76 
Irrigation (number) 21.64 17.55 4.09* 1.73 
(Trans)Planting Cost (rupees) 2147.02 430.55 1716.47*** 7.85 
Labor Demand (number) 4.05 1.13 2.92*** 2.77 
DAP Application (bags) 0.046 0.482 −0.437*** −4.26 
Urea Application (bags) 1.075 1.992 −0.898*** −4.31 
Weedicide Cost (rupees) 187.7 1283.2 −1095.4*** −7.97 
Rice Yield (kgs) 1,400 1,800 400 −2.24 
Income (rupees) 12,762 15,218 −2,456* −1.69 
Family Size (number) 9.36 8.93 0.43 0.12 
Tube well (dummy) 0.23 0.29 −0.06 −1.24 
Tractor (dummy) 0.35 0.24 0.11** 1.97 
Extension (dummy) 0.34 0.47 −0.13* −1.81 
Credit facility (dummy) 0.17 0.12 0.05 1.32 
Road access (dummy) 0.57 0.74 −0.17* −1.94 
Market distance (km) 7.93 6.55 1.38* 1.75 
Cost of production per acre 12,150 9,560 2,590** 2.16 
Variable Conventional rice Direct rice Difference t-values 
Area (acres) 10.182 7.882 2.3 0.92 
Seed Rate (kgs) 2.921 13.906 −10.984*** −18.76 
Irrigation (number) 21.64 17.55 4.09* 1.73 
(Trans)Planting Cost (rupees) 2147.02 430.55 1716.47*** 7.85 
Labor Demand (number) 4.05 1.13 2.92*** 2.77 
DAP Application (bags) 0.046 0.482 −0.437*** −4.26 
Urea Application (bags) 1.075 1.992 −0.898*** −4.31 
Weedicide Cost (rupees) 187.7 1283.2 −1095.4*** −7.97 
Rice Yield (kgs) 1,400 1,800 400 −2.24 
Income (rupees) 12,762 15,218 −2,456* −1.69 
Family Size (number) 9.36 8.93 0.43 0.12 
Tube well (dummy) 0.23 0.29 −0.06 −1.24 
Tractor (dummy) 0.35 0.24 0.11** 1.97 
Extension (dummy) 0.34 0.47 −0.13* −1.81 
Credit facility (dummy) 0.17 0.12 0.05 1.32 
Road access (dummy) 0.57 0.74 −0.17* −1.94 
Market distance (km) 7.93 6.55 1.38* 1.75 
Cost of production per acre 12,150 9,560 2,590** 2.16 

Note: Results are Significantly different from zero at ***,**,* at 1, 5 and 10 percent levels, respectively.

EMPIRICAL RESULTS

Impact of direct seeding of rice on water saving

The impact was estimated by employing the PSM and results are presented in Table 3. Two matching algorithms i.e. NNM and KBM are employed to carry out the empirical analysis. The average treatment effect for the treated (ATT) results for the number of irrigation employed are negative and significant at the 1% level of significance, implying that the adopters of the DRS technology use less irrigation as compared to non-adopters. The average treatment effect for the treated is actually the difference in the outcome of the similar adopters and non-adopters of the technology. The number of irrigation is less – in the range of 4.86–5.37 for the adopters of the DRS technology. The policy implication of this finding can be that the adoption of the DRS technology can help in water saving. The labor demand of DRS technology is also less – in the range of 3.15–3.55 persons especially during the paddy transplanting time. During the transplanting time the farmers face scarcity of skilled labour as its always difficult to find the skilled labour. The results for the labor demand are significant at the 1% level of significance, indicating that adopters of DRS technology require less labor as compared to non-adopters. The results for irrigation costs are negative and significant, indicating that the adopters of the DRS technology incur less cost for irrigation as compared to non-adopters. The irrigation cost is less – in the range of rupees 1,650–1,836 rupees per acre. The policy implication of the less irrigation cost indicate that DRS technology is affordable among all the farming community. From the empirical results, it can be concluded that the DRS technology is saving expenditure on irrigation and less expenditure is required generally.

Table 3

Impact on water saving

Matching Algorithms Outcome Caliper/Bandwidth ATT t-values Critical level of hidden Bias Number of Treated Number of Control 
NNM Irrigation Number 0.03 −5.37*** 2.53 1.25–1.30 110 118 
NNM Labor requirement 0.10 −3.55*** 2.79 1.35–1.40 112 108 
NNM Irrigation cost 0.05 −1650.55* 1.84 1.55–1.60 120 118 
KBM Irrigation Number 0.01 −4.86** 2.05 1.45–1.50 120 118 
KBM Labor requirement 0.01 −3.15*** 2.55 1.10–1.15 115 103 
KBM Irrigation cost 0.01 −1836.07*** 3.27 1.15–1.20 115 118 
Matching Algorithms Outcome Caliper/Bandwidth ATT t-values Critical level of hidden Bias Number of Treated Number of Control 
NNM Irrigation Number 0.03 −5.37*** 2.53 1.25–1.30 110 118 
NNM Labor requirement 0.10 −3.55*** 2.79 1.35–1.40 112 108 
NNM Irrigation cost 0.05 −1650.55* 1.84 1.55–1.60 120 118 
KBM Irrigation Number 0.01 −4.86** 2.05 1.45–1.50 120 118 
KBM Labor requirement 0.01 −3.15*** 2.55 1.10–1.15 115 103 
KBM Irrigation cost 0.01 −1836.07*** 3.27 1.15–1.20 115 118 

Note: ATT stands for Average Treatment affect for the Treated. NNM stands for Nearest Neighbor Matching and KBM stands for Kernel-Based Matching. The results are significantly different from zero at ***,**,* at 1, 5 and 10 percent levels, respectively. The irrigation number and labour requirements are per acre basis and irrigation cost is rupees per acre.

As the main purpose of the PSM is to balance the covariates before and after matching, a number of balancing tests are employed and the results are presented in Table 4. The balancing tests employed are the median absolute bias before and after matching; the value of R2 before and after matching and the joint significance of the covariates before and after matching. The median absolute bias is quite high before matching and is in the range of 18.52–24.36. After matching, a considerable amount of bias has been reduced and the bias is in the range of 4.23–6.29. The percentage bias reduction is in the range of 72%–79%, hence implying that after matching the adopters and non-adopters are very similar to each other. The value of R2 is quite high before matching and is quite low after matching, again implying that after matching the adopters and non-adopters are very similar to each other. The p-value of joint significance of covariates indicates that after matching there are no systematic differences between the participants and non-participants. The results of PSM are in line with the previous studies such a Ali & Abdulai (2010) and Ali & Erenstein (2013).

Table 4

Indicators of covariates balancing before and after matching

Matching Algorithm Outcome Median absolute bias (before matching) Median absolute bias (after matching) (Total) % bias reduction Pseudo R2 (unmatched) Pseudo R2 (matched) p-value of LR (unmatched) p-value of LR (matched) 
NNM Irrigation number 24.36 6.29 74.17 0.426 0.001 0.03 0.572 
NNM Labor requirement 22.69 5.31 76.59 0.347 0.004 0.075 0.330 
NNM Irrigation cost 18.52 4.98 73.11 0.472 0.003 0.091 0.280 
KBM Irrigation number 22.69 6.06 73.29 0.510 0.002 0.043 0.483 
KBM Labor requirement 19.47 5.38 72.36 0.426 0.001 0.052 0.592 
KBM Irrigation cost 20.52 4.23 79.38 0.540 0.005 0.036 0.462 
Matching Algorithm Outcome Median absolute bias (before matching) Median absolute bias (after matching) (Total) % bias reduction Pseudo R2 (unmatched) Pseudo R2 (matched) p-value of LR (unmatched) p-value of LR (matched) 
NNM Irrigation number 24.36 6.29 74.17 0.426 0.001 0.03 0.572 
NNM Labor requirement 22.69 5.31 76.59 0.347 0.004 0.075 0.330 
NNM Irrigation cost 18.52 4.98 73.11 0.472 0.003 0.091 0.280 
KBM Irrigation number 22.69 6.06 73.29 0.510 0.002 0.043 0.483 
KBM Labor requirement 19.47 5.38 72.36 0.426 0.001 0.052 0.592 
KBM Irrigation cost 20.52 4.23 79.38 0.540 0.005 0.036 0.462 

Note: NNM stands for Nearest Neighbor Matching and KBM stands for Kernel-Based Matching.

Farm size water saving analysis

The farm size water saving analysis is presented in Table 5. The ATT results for the irrigation number was about 5.23 for small farmers indicating that adopters of the DRS technology were using less irrigation as compared to non-adopters and the results are highly significant at the 1% level of significance. The ATT results for the labor savings are about 2.17 laborers and the results are significant at the 5% level of significance. The irrigation cost is less – about rupees 2,614 – and the results are significant at the 5% level of significance. For medium-sized farmers, the irrigation usage is less by 4.34 irrigation and the results are highly significant at the 1% level of significance. The labor requirements are less (about 1.78 laborers for irrigation purposes) and the results are significant at the 10% level of significance. The cost of irrigation is less by rupees 2,890 and the results are significant at the 10% level of significance. For large farmers, the irrigation numbers are about 3.75 and the results are significant at the 1% level of significance. The labor requirements are less by 2.21 laborers and the results are significant at the 5% level of significance. The irrigation cost is less by rupees 2,172 but the results are non-significant. The empirical results indicate that the new technology is beneficial across farm sizes and particularly benefits small and medium farms. The DRS technology can decrease the costs to a great extent, especially for small and medium farmers. Previous studies have shown that economic factors and development in rice production technologies have been the major drivers leading to the adoption of direct-seeding methods for rice establishment in place of transplanting in Asia (Pandey & Velasco 2002) From the empirical results this can be concluded that the DRS technology is more beneficial for the small scale farmers followed by the medium and large scale farmers. However for wider dissemination effective extension services are needed. Increasing water scarcity issues can give a further boost to DRS technology.

Table 5

Farm Size Wise Analysis of Water Saving

Matching Algorithms Outcome Bandwidth ATT t-values Critical level of hidden Bias Number of Treated Number of Control 
Small 
 KBM Irrigation Number 0.03 −5.23*** −3.87 1.55–1.60 23 20 
 KBM Labor requirement 0.03 −2.17** −2.30 1.20–1.25 24 18 
 KBM Irrigation cost 0.03 −2614.72** −1.98 1.45–1.50 18 22 
Medium 
 KBM Irrigation Number 0.03 −4.34*** −2.51 1.30–1.35 36 41 
 KBM Labor requirement 0.05 −1.78* −1.65 1.25–1.30 38 37 
 KBM Irrigation cost 0.01 −2890.11* −1.76 1.45–1.50 34 26 
Large 
 KBM Irrigation Number 0.02 −3.75*** −2.79 1.75–1.80 17 35 
 KBM Labor requirement 0.05 −2.21** −2.13 1.10–1.15 22 29 
 KBM Irrigation cost 0.02 −2172.22 −1.56 – 17 24 
Matching Algorithms Outcome Bandwidth ATT t-values Critical level of hidden Bias Number of Treated Number of Control 
Small 
 KBM Irrigation Number 0.03 −5.23*** −3.87 1.55–1.60 23 20 
 KBM Labor requirement 0.03 −2.17** −2.30 1.20–1.25 24 18 
 KBM Irrigation cost 0.03 −2614.72** −1.98 1.45–1.50 18 22 
Medium 
 KBM Irrigation Number 0.03 −4.34*** −2.51 1.30–1.35 36 41 
 KBM Labor requirement 0.05 −1.78* −1.65 1.25–1.30 38 37 
 KBM Irrigation cost 0.01 −2890.11* −1.76 1.45–1.50 34 26 
Large 
 KBM Irrigation Number 0.02 −3.75*** −2.79 1.75–1.80 17 35 
 KBM Labor requirement 0.05 −2.21** −2.13 1.10–1.15 22 29 
 KBM Irrigation cost 0.02 −2172.22 −1.56 – 17 24 

Note: ATT stands for Average Treatment affect for the Treated. KBM stands for Kernel-Based matching. The results are significantly different from zero at ***,**,* at 1, 5 and 10 percent levels, respectively. The irrigation number and labour requirements are per acre basis while irrigation cost is rupees per acre.

Impact of direct seeding of rice on water productivity

The results regarding water productivity are presented in Table 6. The mean number of irrigation for direct seeding of rice are 18, while the mean number of irrigation for conventional rice sowing are 22. The mean depth of irrigation for direct seeding of rice sowing technology is 70 mm and for conventional rice sowing is 65 mm. The total depth of irrigation for direct seeding of rice is 1,260 mm and for conventional rice sowing is 1,430 mm. The mean rainfall levels are the same for both methods, i.e. 623 mm rainfall is received during the season. The water saving for direct seeding of rice on average is about 15%. The total water application for direct seeding of rice is 1,883 mm and for conventional rice sowing is 2,053 mm. The paddy yield for direct seeding of rice is 1,800 kgs per acre and, for conventional rice sowing, the paddy yield is 1,440 kgs per acre, while the yield in tons is about 4 tons per acre for DRS and 3.2 tones per acre for conventional rice sowing. The water productivity for direct seeding of rice is 0.21 and for conventional rice sowing is 0.16. The empirical results indicate higher water productivity for direct seeding of rice sowing technology. The water productivity results indicates that DRS adopters are getting higher rice yield while using less irrigation.

Table 6

Water productivity per acre

Planting Technique Number of Irrigation Ave. Depth of Irrigation (mm) Total Irrigation depth (mm) Rainfall (mm) Water Savings (%) Total Water Application (mm) Paddy Yield (kgs/acre) Paddy Yield (T/ha) Water Productivity (Kg/m3) 
Direct Seeding 18 70 1,260 623 15 1,883 1,800 4.00 0.21 
Conventional 22 65 1,430 623 2,053 1,440 3.00 0.16 
Planting Technique Number of Irrigation Ave. Depth of Irrigation (mm) Total Irrigation depth (mm) Rainfall (mm) Water Savings (%) Total Water Application (mm) Paddy Yield (kgs/acre) Paddy Yield (T/ha) Water Productivity (Kg/m3) 
Direct Seeding 18 70 1,260 623 15 1,883 1,800 4.00 0.21 
Conventional 22 65 1,430 623 2,053 1,440 3.00 0.16 

Variety wise comparative analysis of irrigation water requirements

The variety wise difference in water requirements is presented in Table 7. The number of irrigation water requirements are higher for conventional rice sowing as compared to the direct seeding of rice. The variety wise comparative analysis indicates that the irrigation water requirements are the highest for super kernel and Basmati rice 386, followed by the coarse and C515 varieties while the irrigation requirements are the least for hybrid varieties. The variety wise results indicates that DRS technology is effective for all the rice varieties grown in Pakistan.

Table 7

Variety wise difference in water requirements

Variety Direct Seeding of Rice Conventional Rice Sowing Difference Average cost of single Irrigation Total Expenditure on Irrigation (Direct Seeding) Total Expenditure on Irrigation (Conventional) Difference t-values 
Super Kernel 18 22 372 6,696 8,184 1,488*** 2.91 
Basmati Rice 386 17 22 372 6,324 8,184 1,860** 2.37 
Coarse 17 20 372 6,324 7,440 1,116* 1.67 
Hybrid 13 16 372 4,836 5,952 1,116 1.39 
C515 15 17 372 5,580 6,324 744 1.23 
Variety Direct Seeding of Rice Conventional Rice Sowing Difference Average cost of single Irrigation Total Expenditure on Irrigation (Direct Seeding) Total Expenditure on Irrigation (Conventional) Difference t-values 
Super Kernel 18 22 372 6,696 8,184 1,488*** 2.91 
Basmati Rice 386 17 22 372 6,324 8,184 1,860** 2.37 
Coarse 17 20 372 6,324 7,440 1,116* 1.67 
Hybrid 13 16 372 4,836 5,952 1,116 1.39 
C515 15 17 372 5,580 6,324 744 1.23 

Note: The results are significant at ***, **, * at 1, 5 and 10 percent levels, respectively.

Impact on the subsequent wheat crop

The impact of DRS technology on the subsequent wheat crop is presented in Table 8. The difference in plowing is negative but non-significant. The irrigation requirements for wheat sown on DRS plots are quite high as compared to wheat sown on conventional rice plot. The difference in irrigation requirements are 1.33 per season and the results are significant at the 5% level of significance. The labor requirements are somewhat higher for wheat planted on conventional plots and the difference in labor requirements are significant. Most importantly, the wheat yield is higher on the plots following DRS compared to plots where conventional rice was grown and the difference in yield was up to 6 maunds per acre and the difference is significant at the 1% level of significance. The results are in line with the previous studies which have shown that direct-seeding methods produce higher income and are labor saving. Replacing transplanting rice with the DRS can also help to increase the soil fertility. The policy implication of this finding is that DRS has positive impact on the following wheat crop, however the benefits needs to be disseminated to the farming community. In addition the DRS technology is also time saving and the transplanting time is saved. As compared to conventional method this saves about 3–4 weeks time.

Table 8

Impact on following Wheat Crop

Activity Dry Rice Sowing Conventional Rice Difference t-values 
Plowing 2.74 3.38 −0.64 −1.02 
Irrigation 6.24 4.91 1.33** 2.15 
Labor 3.55 4.70 −1.15* −1.74 
Wheat Yield 43.64 37.51 6.13*** 2.63 
Activity Dry Rice Sowing Conventional Rice Difference t-values 
Plowing 2.74 3.38 −0.64 −1.02 
Irrigation 6.24 4.91 1.33** 2.15 
Labor 3.55 4.70 −1.15* −1.74 
Wheat Yield 43.64 37.51 6.13*** 2.63 

Note: The results are significantly different from zero at ***,**, * at 1,5 and 10 percent levels, respectively.

CONCLUSION

In the rice-wheat area of the Pakistani Punjab, the direct seeding of rice is new technology. The problem of water scarcity is increasing in the study area. The direct seeding of rice is a water-saving technology and, hence, is becoming popular among rice farmers in the study area. The direct seeding of rice on average requires 4–5 less irrigation than conventional rice transplanting. Most important is the difference in the yield between the direct seeding of rice and conventional practice. The rice yield for the direct seeding of rice is 1,800 kgs per acre and, for conventional rice transplanting it is 1,440 kgs per acre. The farmers are getting 360 kgs per acre higher yields with direct rice sowing. The labor demand is more for conventional rice planting than for the direct seeding of rice. The farm categories wise analysis indicates that the new technology is particularly beneficial for small and medium scale farmers. The DRS technology can save the irrigation costs of the rural farm households. The comparative analysis of the water productivity of the DRS technology and conventional rice sowing indicates that the DRS technology has higher water productivity as compared to conventional rice sowing technology. The water productivity for conventional rice sowing is about 0.17 while, for the DRS technology, it is about 0.21, indicating higher productivity levels for DRS technology. The DRS technology has a positive yield impact on the subsequent wheat crop. However the main problem for DRS technology are the weed control problems, which calls for further research. The variety wise comparative analysis indicates that hybrid varieties need less water than the conventional varieties. The economic impact of the spread of direct seeding has been positive overall. The spread of direct-seeding methods are aided by the availability of chemical methods of weed control, the increasing water scarcity and the rising cost and lower availability of farm labor. However, extension services are needed for the wider dissemination of this technology among the farming community. As the empirical results also indicated that farmers having access to agricultural extension services have more adopted the DRS technology as compared to farmers having no extension access. Similarly the farmers' access to credit facility can help to increase the probability of DRS technology adoption. The policy implication of these findings are that in the end, DRS technology is beneficial for all the farmers particularly for the small and medium scale farmers. The small scale farmers can improve their livelihood through the adoption of the DRS technology.

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