Mohammad Faiz Alam
Michael McClain
Alok Sikka2
Saket Pande- 1Department of Water Management, Delft University of Technology, Delft, Netherlands
- 2International Water Management Institute, Delhi, India
- 3IHE Delft Institute for Water Education, Delft, Netherlands
The adoption of agricultural water interventions for climate change adaptation has been slow and limited despite their established efficacy and benefits. While several studies have identified socio-economic, biophysical, technological and institutional factors that influence adoption, psychological factors have often been overlooked. This study examines the socio-economic and psychological factors, using RANAS behavioral model, that influence the adoption of agricultural water interventions in the semi-arid region of Saurashtra in India. Two contrasting and dominating agricultural water interventions in the area: drip irrigation and borewells are evaluated. Despite subsidies being available for drip irrigation systems, the adoption rate remains low (~16% adopting rate) compared to borewells (~24.5% adoption) with no subsidies reflecting farmer’s preference for supply augmentation measures over demand management. Incorporating psychological factors in the analysis improved the explanatory power of the logistic model by almost threefold, underscoring the significance of psychological factors in explaining farmers’ adoption decisions. Based on the logistic model, major factors determining farmers adoption behaviour identified are farmer’s perceived ability, risk preference and positive beliefs about the technologies along with socio-economic (e.g., land size) and biophysical factors (e.g., proximity to water). The study recommends a multi-pronged approach to increase the adoption of interventions, including augmenting subsidies with efforts on extension services, post-adoption services, training, and awareness campaigns to build farmers’ capacity and raise awareness.
1 Introduction
Agriculture with a strong dependence on weather is highly vulnerable to climate change (FAO, 2021; Sikka et al., 2022). With changing climate intensifying hydroclimatic extremes of floods and drought, adaptation in agriculture is extremely important (United Nations, 2019; IPCC, 2022). Without adaptation, agricultural yields could decrease by 30% by 2050, impacting livelihoods and food security, especially in less developed countries where smallholder farmers have limited capacity to adapt (GCA and WRI, 2019). Given the centrality of water in climate change adaptation efforts, climate smart agriculture water management is critical to building water resilience and adapting to climate change (Sikka et al., 2022). The efficacy and benefits of a range of climate smart agriculture water interventions for adaptation have been widely reported and established (Evans and Giordano, 2012; Alam et al., 2021; Sikka et al., 2022).
The successful scaling of these interventions is needed to achieve transformational and visible impacts in building climate change adaptation (Aggarwal et al., 2018; Sikka et al., 2022). However, the widespread adoption of agricultural water management interventions and technologies has been slow and limited (Shiferaw et al., 2009; Palanisami et al., 2015; Alam et al., 2021). Multiple studies over time and in different contexts have evaluated the factors influencing the uptake of different adaptation strategies and technologies in agriculture (Palanisami et al., 2011; Reddy, 2016; Pathak et al., 2019; Balasubramanya et al., 2023). Several factors including socio-economic (e.g., land size, experience), biophysical (e.g., soil), technology (e.g., cost, availability), and institutional (e.g., capacity building, subsidies) have been identified (Pathak et al., 2019; Nair and Thomas, 2022; Balasubramanya et al., 2023).
However, psychological factors have been often overlooked in many studies (Namara et al., 2007; Nair and Thomas, 2022; Balasubramanya et al., 2023). This is a gap as several studies have shown that psychological factors significantly influence the adoption of interventions (Daniel et al., 2020; Hatch et al., 2022; Alam et al., 2022a). For instance, farmers’ perceived behavioral control, belief about cost and benefits, and risk perception have been shown to significantly influence their adoption decisions (Yazdanpanah et al., 2014; Arunrat et al., 2017; Alam et al., 2022a,b). Thus, neglecting psychological factors can lead to a lack of understanding of why some farmers adopt interventions while others do not, despite similar socio-economic and environmental conditions.
Several behavioral theories, grounded in social science, exist to evaluate the influence of psychological factors on farmers’ adoption behaviors (Schlüter and Pahl-Wostl, 2007; An, 2012; Alam et al., 2022b). The risk, Attitude, Norms, Abilities, and Self-regulation (RANAS) model (Mosler, 2012) is one among them. The RANAS model assumes that multiple socio-psychological factors (i.e., risk, attitude, norm, ability, and self-regulation) impact behavioral outcomes (i.e., behavior, intention, use, and habit). Although initially developed for the WASH sector, the RANAS model is being increasingly used to understand farmer irrigation behavior or adoption of water management interventions (Stocker and Mosler, 2015; Daniel et al., 2020; Hatch et al., 2022; Klessens et al., 2022; Alam et al., 2022a). RANAS’s strength is that it combines important socio-psychological factors from other important behavioral theories, can be adapted for a range of behaviors, and provides a systematic approach with a standardized questionnaire (Callejas Moncaleano et al., 2021).
This study, using the RANAS behavioral model, examines the factors that influence the adoption of agricultural water interventions in a semi-arid region (Saurashtra) in India. Specifically, adoption of two dominant and contrasting agricultural water interventions in the region: drip irrigation and borewells are analyzed. Drip irrigation, increasing efficiency of on farm water application, is a demand management strategy and is extensively promoted by the government with enabling policies and subsidies (Nair and Thomas, 2022; Sikka et al., 2022). While micro irrigation generally consists of both drip and sprinkler irrigation, in the studied region, drip irrigation is the dominant form, and therefore, we have used the terms “drip” and “micro irrigation” interchangeably in the paper. On the other hand, drilling borewells to tap deeper aquifers is a supply-augmenting intervention that farmers adopt in response to the drying of wells or depletion of aquifers (Kattumuri et al., 2017; Patil et al., 2019). Access to groundwater has played a crucial role in expanding irrigation and production globally, especially in South Asia (Mukherji, 2020) and now increasingly in Africa (Cobbing and Hiller, 2019). However, over time, this has led to the depletion of aquifers (Mukherji, 2020).
This paper evaluates the factors that govern the adoption of drip irrigation and borewells in the Saurashtra region. The findings of this study provide insights into the key factors that need to be addressed to promote the adoption of water interventions among farmers. It informs the development of effective policies and programs to improve water management in the region and elsewhere.
2 Study area
The study area is the Kamadhiya catchment located in the Saurashtra region of Gujarat state in India (Figure 1). The region is characterized by a semi-arid climate, low rainfall [average of 638 mm year−1 (1983–2015)] with high evaporation and high-water demand. There is large intra- and inter-year rainfall variability that impacts the agriculture in the region, which covers ~70% of the catchment area (Alam et al., 2022c). More than 90% of the rainfall is concentrated in the four monsoon months of June to September (Pai et al., 2014). The main crops grown in the region are cotton and groundnut during the Kharif season (the monsoon season, starting in June and ending in October) and chickpea and wheat during the Rabi season (the post-monsoon season, starting in November and ending in February/March). The lack of water during the post-monsoon season limits crop intensity (Alam et al., 2022c).
Figure 1. (A) Location of Kamdhiya catchment, Bhadar basin, Saurashtra region and Gujarat state in India. (B) Sampled villages for household survey in Kamadhiya catchment. In light blue color are the villages that lie downstream of dams (shown in dark blue) or near to the main stem of the river (stream order > = 4).
Groundwater is the main source of irrigation in the region, covering ~82% of the irrigated area. Aquifers of the region are represented by parent basalt rocks of the deccan trap with low primary porosity and hydraulic conductivity (Kulkarni et al., 2000; Mohapatra, 2013). The storage of these aquifers is primarily limited to water-bearing zones mostly confined to upper shallow (15–30 m) weathered and fractured zones of hard rock (Kulkarni et al., 2000; Mohapatra, 2013). Groundwater from the top 15–30 m of weathered upper parts is tapped by open large diameter (4–8 m) dug wells usually 15–30 m deep (Kulkarni et al., 2000; Mohapatra, 2013). The groundwater availability in upper weathered zone remains limited in the post-monsoon season and is mostly depleted by the end of the year because of the limited extent and storage of aquifers (Foster, 2012; Alam et al., 2022c) thus limiting cultivated area in post monsoon seasons (Alam et al., 2022c). Groundwater availability in deeper aquifers is limited and dependent on nature and the degree of vertical and horizontal joints and fractures (Kulkarni et al., 2000; Foster, 2012). The deeper aquifer is tapped by deep (~100–300 m) borewells.
2.1 Agricultural water management interventions in the region
The vulnerability of the agriculture sector is high in India with large part of the country under arid and semi-arid climate and half of the cropped area being rainfed (Alam et al., 2021; Sikka et al., 2022). Saurashtra region with low and highly variable rainfall faces frequent droughts and associated production losses (Alam et al., 2022c). Governmental and non-governmental organizations have been promoting a range of interventions in the area to mitigate the impact of short and unpredictable monsoons. The key interventions in the region include supply augmentation through check dams, which are community water harvesting structures largely built on common land through state resources (Alam et al., 2022a) and increasing the efficiency of water use through drip irrigation (Namara et al., 2007; GGRC, 2023). The impact of check dams and farmers’ perception on check dams has been evaluated earlier (Alam et al., 2022a,c). On other hand, field visits have shown that farmers increasingly are drilling deeper borewells to supplement water from shallow dugwells.
2.1.1 Drip irrigation
Drip irrigation involves applying water and nutrients directly to the crop root zone. Multiple studies have evaluated the benefits of drip irrigation, which includes water savings, yield enhancement, labor savings, efficient fertilizer use, and reduced weed and pest infestation among others (Namara et al., 2007; Palanisami et al., 2011; Singh, 2013; Reddy, 2016). In India, the government has been running capital subsidy programs for more than a decade to increase the adoption of micro-irrigation (including drip), starting with the national mission on micro-irrigation and currently continuing with Pradhan Mantri Krishi Sinchai Yojana (PMKSY—Prime minister Farm Irrigation scheme; DAC&FW, 2017; Nair and Thomas, 2022). Additionally, non-governmental organizations have also invested (funds, knowledge transfer, and training) to increase the uptake of micro-irrigation (Panda, 2003). In the region, Gujarat state government has set up a special purpose vehicle, Gujarat Green Revolution Company (GGRC) limited in 2004–05, to expand the area under micro irrigation in the state (GGRC, 2023). A subsidy of 50% is provided (limited to ~$750/per hectare) with an additional subsidy of 25% for tribal and scheduled caste farmers (GGRC, 2023).
However, despite being subsidized and with widely reported benefits, multiple studies over time have shown that the adoption of micro-irrigation has remained low (Namara et al., 2007; Palanisami et al., 2011; Nair and Thomas, 2022). The micro-irrigation has been adopted in less than 15% of the potential area in India (Suresh and Samuel, 2020). The question then becomes why?
2.1.2 Borewells
Borewells are narrow, deep wells drilled into the ground using a tube (Steinhübel et al., 2020) to tap deeper aquifers (~100–300 m), in contrast to large diameter shallow (~15–30 m) dug wells. Although dugwells remain the primary source of irrigation, farmers in the study region have increasingly been using borewells to supplement their shallow dugwells. Unlike drip irrigation, borewell drilling in the region is not supported by government subsidies but is being taken up by farmers as a supply augmentation strategy (Kulkarni et al., 2000; Mudrakartha, 2012).
Farmers drill borewells to hedge against production risks associated with low rainfall years, particularly during the dry seasons after the monsoon when the shallow weathered aquifer (15–30 m) in the region dries out (Steinhübel et al., 2020). The drilling of borewells or digging deeper wells as an adaptation strategy in response to droughts or declining groundwater levels has been observed in other parts of the country as well (van Steenbergen, 2006; Mudrakartha, 2007; Jain et al., 2015; Singh et al., 2018; Steinhübel et al., 2020). However, the hard rock aquifers of the region are characterized by low primary porosity and a heterogeneous and low-density fracture network thus leading to high borewell drilling failure rates (Foster, 2012; Robert et al., 2018). Even if borewells are successfully drilled, their yields are low and can only supplement the water supply from dug wells.
3 Methodology
3.1 Household survey
The primary data were collected through a household survey in December 2021. A total of 492 farmers were interviewed across 24 villages in the Kamadhiya catchment (Figure 1). The 24 villages were sampled using a multistage random sampling method. Initially, 24 out of 88 villages within the Kamadhiya catchment were chosen through regular distribution sampling. Thereafter, in each selected village, 20–22 farmers were surveyed using proportionate random sampling. This method involved taking random samples from stratified groups in the same proportion as their representation in the total population (Alam et al., 2022a). The farmers were stratified into four groups: marginal (<1 ha), small (1–2 ha), medium (2–4 ha) and large farmers (>4 ha) based on farmers’ land area in the administrative blocks where villages are located. The structured interviews, translated into the local language (Gujarati), lasted approximately 45–50 min and were carried out by a trained team of 10 enumerators native to the region.
3.2 Questionnaire
The structured survey questionnaire consisted of two parts, (1) farmers’ socio-economic factors and (2) farmers’ perception of drip irrigation and borewells and RANAS related questions regarding the adoption of the irrigation technologies. Farmer socio-economic data included information on farmer’s age, gender, number of household members, farming experience, area of land owned, main income sources, livestock, house type, and ownership of material assets (e.g., TV, scooter, car). In addition, data on farmers’ cropping practices were also collected.
The questions on drip irrigation and borewells consisted of a mix of informative questions (e.g., cost, subsidy, benefits) and farmers’ perceptions about the benefits of each. Questions on RANAS sociopsychological factors (i.e., R-risk, A-attitude, N-norm, A-ability, and S-self-regulation) towards the adoption of drip and bore wells were measured with two to four questions on a five-point Likert scale (Supplementary Table S1).
3.3 Data analysis
A descriptive analysis is carried out to understand farmers’ socioeconomic profile in the region and their perception of the benefits and impacts of drip irrigation and borewells. This is followed by a binary logistic regression analysis to understand the main determinants of farmers’ behavior toward the adoption of drip irrigation and borewells. Separate logistic regression is carried out for both drip and borewells considering their contrasting roles and assuming that adoption of one technology does not necessarily influence adoption of the other. This is supported by field observations indicating farmers consider them as individual technologies catering to separate goals.
Binary logistic regression is a statistical method that estimates the probability of one of two possible outcomes, based on a set of predictor variables and is appropriate when the dependent variable has only two outcomes (e.g., such as yes/no or adopters/non-adopters; Tranmer and Elliot, 2008; Harris, 2021). To account for variations in village size, sampling weights (farmers interviewed in each village divided by the village population) were derived and used in the analysis. The effects (coefficients) estimated by a logistic regression are interpreted as changes in the log-odds of the outcome variable for one-unit change in the predictor variables, with other variables held constant. A positive and significant coefficient indicates an increased likelihood of the outcome, while a negative and significant coefficient indicates a decreased likelihood. The magnitude of the coefficient indicates the strength of the association (Tranmer and Elliot, 2008; Harris, 2021). Binary logistic regression has been used widely across fields and in a range of studies to evaluate the adoption of water management technologies (Namara et al., 2007; Singh, 2013; Patil et al., 2019; Raut et al., 2021; Yifru and Miheretu, 2022).
3.4 Definition and selection of variables
The dependent variable was whether a farmer has adopted drip irrigation (or borewells) or not. A value of 1 was assigned to all the farmers who use drip irrigation and 0 to those who use other irrigation methods. The use of sprinkler irrigation was negligible in the area. For borewells, a value of 1 was assigned to all the farmers who have installed a borewell in addition to dugwell(s) and 0 to those who only have dugwells. The farmers that only had borewells as their primary source of irrigation were excluded from the logistic regression of borewell adoption.
The RANAS model factors (risk, Attitude, Norms, Abilities, and Self-regulation) represent different multiple socio-psychological factors of farmers. Risk factors represent a person’s understanding and awareness of the health risk; Attitude factors represent a person’s positive or negative stance towards a behavior; Norm factors represent the perceived social pressure towards a behavior; Ability factors represent a person’s confidence in her or his ability to practice a behavior and self-regulation factors represent a person’s attempts to plan and self-monitor behavior and to manage conflicting goals and distracting cues (Mosler, 2012). RANAS sociopsychological factors (i.e., R-risk, A-attitude, N-norm, A-ability, and S-self-regulation) were measured with 2–4 questions on five-point Likert scales (Supplementary Table S1).
For RANAS factors measured with three or more questions (measuring common latent variables) principal component analysis (PCA) was used to address multicollinearity (Daniel et al., 2020). Table 1 gives the results of the PCA and redefined psychological factors for all RANAS factors measured with three or more questions. For instance, the three questions on perceived ability (financial, knowledge, operate) to adopt drip irrigation were renamed as “ability” since they all loaded on the first principal component (Table 1). Likewise, the five questions related to risk were renamed as perceived vulnerability and severity, which loaded on the first two principal components, separating risk and impact factors (Table 1). Table 2 lists the final RANAS factors retained for the binary logistic regression.
Table 1. Principal component analysis (PCA) on RANAS factor question for Drip and borewell.
Table 2. Description and summary statistics (mean and percentage) of the variables used in the binary logistics model.
The selection of explanatory variables, in addition to RANAS psychological variables, was done based on previous studies that have shown that the adoption of practices is influenced by a range of socio-economic factors including farmers’ economic, social, and demographic factors (Namara et al., 2007; Nair and Thomas, 2022; Yifru and Miheretu, 2022). This included farmer owned land, age, farming experience, education, income from agriculture and wealth (defined by ownership of assets). Additionally, based on field visit observations, number of livestock, distance from check dam and proximity to river or dams (Shown in Figure 1B) were identified as important factors and were added. The location of the administrative block in which farmers are located was added to account for other unobserved factors. A multi-collinearity analysis was carried out among the socioeconomic variables and any variables with a high degree of correlations (threshold of 0.6) were removed. Final socioeconomic and biophysical variables retained for the binary logistic regression are presented in Table 2.
4 Results and discussion
4.1 Descriptive statistics
The average landholding in the catchment was reported to be 2.9 hectares (median = 2.0 hectares). Small farmers (1–2 ha) represented the highest share of sample farmers (31.7%) followed by medium (2–4 ha; 27.8%) and large (> 4 ha; 23.4%) and marginal (< 1 ha; 17.5%) farmers. More than 60% of farmers were above the age of 40 and had 8 years or less of schooling. Agricultural income from crop production (99.2% of farmers) and livestock rearing (71.7%) were the main sources of income. Further description of socioeconomic statistics can be found in Supplementary Table S2.
Table 3 gives a summary of agriculture and irrigation characteristics in the region. Cotton and groundnut are the main Kharif crops (~98% area) covering 44 and 54% of the Kharif cultivated area, respectively. Rabi cultivated area is limited (~46% of Kharif cultivated area), with chickpea (49%), cumin (24%), and wheat (15%) being the main crops. Cultivation is negligible in the area from March to May. Overall ~97% of the farmers reported having access to irrigation, with groundwater (~96%) being the main source of irrigation.
Table 3. Agriculture and Irrigation characteristics for the main crops in the region.
For the Kharif crops, ~80% of farmers indicated that they irrigate always (every year) whereas ~10% indicated that irrigation is needed only in dry years. On the other hand, almost all farmers indicated that they irrigate their crop always (every year) in the post-monsoon Rabi season reflecting the lack of rainfall. About two-thirds of the farmers indicated that their irrigation source is not sufficient (not sufficient or only a little sufficient) in dry years, which shows limited groundwater storage in the region. Regarding the irrigation schedule, most farmers indicated they irrigated when they felt the need.
4.2 Adoption of drip irrigation and borewells
4.2.1 Drip irrigation
Overall adoption of drip irrigation is low in the catchment with only 16.5% of the farmers using drip irrigation systems. The use of drip irrigation is mainly for the cotton crop (10.4%) followed by small areas under groundnut cultivation (2.7%; Table 2). This is despite the subsidy program by the government with farmers reporting an average of ~50% subsidy for drip irrigation systems. Also, both cotton and groundnut, dominating the cropping area are cash crops and are suitable for drip irrigation. Studies have shown that adopting drip irrigation has technical and economic benefits, including water savings and increased physical and economic water productivity for both crops (Namara et al., 2007; Singh, 2013). For Rabi crops, the use of drip irrigation remains negligible. Micro irrigation remains less suitable for cereals and pulses (Namara et al., 2007; Singh, 2013), which could explain negligible use in the Rabi season. The main irrigation method reported was conventional flood irrigation for all crops except groundnut where both furrow and flood irrigation are used (Table 3).
The statistical tests (t-test and chi-square; Table 2) showed that adopters were significantly (p < 0.05) wealthier and earned a higher percentage of their income from agriculture. Similarly, adopters’ have higher landholdings, with significantly more large farmers being adopters and significantly fewer marginal farmers being non-adopters. With respect to the psychological factors, adopters show significantly higher ability, positive belief about the utility of the drip irrigation technology, and societal norms towards drip irrigation systems.
4.2.2 Borewells
In the catchment, 57.3, 12.8, and 24.6% of farmers reported owning only a dugwell, a borewell and a borewell in addition to a dug well, respectively. The latter group of farmers who own a borewell in addition to a dugwell (24.6% of farmers) are referred to as adopters and those who own only a dugwell are referred to as non-adopters.
The average depth of borewells was reported to be ~115 meters (ranging from 45 to 300 meters) against the average depth of ~20 m for dugwells. This shows that borewells are accessing deeper groundwater. The average age of borewells is ~12 years against ~25 years for dugwells, which shows that the drilling of borewells has started more recently. The drilling of borewells is capital intensive. The average cost of drilling a borewell and associated pump (~6 HP) was reported to be ~120,000 INR (~1,450 USD). The drilling of borewells was also associated with high failure rates. The farmers who owned a borewell reported drilling on average 2.3 bore wells (range 1–12) to get a successful bore. This was also reflected in farmers’ reported reason for not owing a borewell, with 42% saying that it is too expensive and 35% saying it is too difficult to drill one. Additionally, 10% of farmers reported trying for one but not having success.
The main benefits of borewells as reported by farmers, both adopters and non-adopters, was the protection against drought (86%), followed by an increase in the Rabi (post-monsoon) cropping area (53.3%). This corroborates observations from the field studies that demonstrate that borewells are primarily adoption measures against low water availability in the dry or post-monsoon season (Birkenholtz, 2009). This is also reflected in the crop data reported by the farmers. On average, borewell owners reported cultivating 53.7% of their Kharif area in the Rabi season as opposed to 40.9% by non-adopters.
The statistical tests (t-test and chi-square) showed that adopters were significantly wealthier (p < 0.05). However, no significant difference in landholdings between the adopters and non-adopters was found. Also, the adopters have a higher perceived ability and more positive belief toward borewells than non-adopters.
4.3 Factors influencing the adoption
Table 4 presents the results of binary logistic regression for drip irrigation and borewells, with two regression models implemented for each technology. Model 1 included both socio-economic and psychological factors, while Model 2 considered only socio-economic factors. The results revealed that incorporating psychological factors improved the model’s explanatory power by almost threefold for adopting both drip and borewells.
Table 4. Results of binary logistic regression of farmer’s decision to adopt drip irrigation and borewells.
For drip irrigation, Model 1 yielded a pseudo-R2 of 0.31, with an overall accuracy of 88.4% and an area under the ROC Curve (AUC) of 87.1%, indicating satisfactory model performance. In contrast, Model 2 (only socio-economic factors) produced a lower pseudo-R2 of 0.12, with corresponding reductions in overall accuracy (84.2%) and AUC (75.9%). Similarly, for borewells, Model 1 generated a pseudo-R2 of 0.21, with an overall accuracy of 76.9% and an AUC of 78.9%. Model 2 had a lower pseudo-R2 of 0.07, with corresponding reductions in overall accuracy (69.6%) and AUC (69.7%).
These findings underscore the significance of psychological factors in explaining farmers’ adoption decisions, as they influence their attitudes, beliefs, perceptions, and motivations towards new technologies or practices. While previous studies on adoption have often overlooked the role of psychological factors (Namara et al., 2007; Nair and Thomas, 2022), our results demonstrate that considering these factors can facilitate a better understanding of farmers’ adoption decisions. This can help extension workers, researchers, and policymakers develop effective strategies to promote the adoption of new technologies among farmers. In the following section, we have discussed results from the model 1 which combines both socio-economic and psychological factors.
4.3.1 Land size and wealth
Earlier studies have widely reported that larger or wealthier farmers are more likely to adopt both drip irrigation and bore well technologies, as both require significant capital investments (Namara et al., 2007; Singh et al., 2018; Patil et al., 2019; Nair and Thomas, 2022). This is reflected in results which show that small, medium, and large farmers are 246% ([odds ratio − 1]*100), 189% (at 10% significance level) and 366% more likely to adopt borewells as compared to marginal farmers, respectively. The influence of land size is not visible for drip irrigation. However, wealth (an indicator of capital) shows significant positive but small (~16%) positive influence on drip irrigation adoption. Additionally, the ownership of more livestock significantly increases the adoption of borewells by 9% and could be explained by the need to fulfill the water needs of livestock.
4.3.2 Proximity to water (river, dam, and check dams)
The impact of proximity to water sources, such as the main river and dam, on the adoption of drip and borewell irrigation is significant, but in opposite directions. In contrast to a 242% increase in the adoption of borewells, the likelihood of adopting drip irrigation decreases by approximately 81% in villages with proximity to rivers or dams. This could be due to the increased recharge in downstream villages near rivers and dams, which increases the success rate of borewell drilling and the availability of groundwater, prompting more farmers to adopt borewell irrigation. However, this also suggests that the increased availability of water (absence of water scarcity) may make farmers less inclined to adopt drip irrigation. This observation reflects the presence of supply–demand feedback, where increased water supply leads to an increase in demand (Scott et al., 2014; Di Baldassarre et al., 2018) and less adoption of demand management measures. The impact of check dams’ proximity on adoption is negligible, indicating their limited and short-lived storage (Alam et al., 2022a).
4.3.3 Perceived ability
A strong perception of one’s ability to practice (operate, maintain, and financially afford) drip irrigation translates to a 125% greater likelihood of its adoption. With lack of technical knowledge and support after adoption along with high cost of maintenance (e.g., replacement of parts) being major constraints for adoption (Nair and Thomas, 2022), it is natural that those who have more confidence in their ability to do so adopt more. Low adoption in the region is also due to farmers’ perceived financial inability to afford drip irrigation systems, as reflected in the low score (mean score = 1.28) on the perceived financial ability data. In comparison, farmers reported higher capacity to install (mean score = 1.65) and operate and maintain (mean score = 1.89) the systems.
Farmers reported the average cost of drip installation to be ~65,000 INR (~790 USD) /hectare and after an average subsidy of 50%, this would translate to a farmer share of ~32,500 INR (~395USD) /hectare. This upfront investment in combination with a lack of belief in benefits may be limiting farmers’ adoption of drip irrigation systems. However, it could also be due to institutional and operational issues in the subsidy programs (e.g., delay in subsidy disbursement, the requirement to pay full cost upfront, and cumbersome paperwork) that have been highlighted by several studies (Chandran Madhava and Surendran, 2016; Malik et al., 2018; Misquitta and Birkenholtz, 2021; Nair and Thomas, 2022). While the Gujarat state special purpose vehicle, Gujarat Green Revolution Company (GGRC), to increase adoption has been highlighted as a relatively successful model with good institutional mechanism (Pullabhotla et al., 2012), the case of institutional issues needs to be further investigated.
Other than financial ability, limited capacity to operate and maintain drip irrigation has been highlighted as a key barrier to adoption (Palanisami et al., 2011; Cremades et al., 2015; Nair and Thomas, 2022). Thus, farmers who have higher perception of their capability to operate and maintain also adopt more (Table 4). For drip irrigation, the lack of capacity has been related with a lack of extension services and post-adoption support with frequent issues of clogging of filters and drippers in drip irrigation systems (Palanisami et al., 2011; Nair and Thomas, 2022). Field visits have shown that issues associated with clogging along with challenges for storing drip systems due to damage caused by rodents that gnaw the drip irrigation tubings creating holes were reiterated by farmers and hinders adoption.
In contrast to drip irrigation, perceived ability (financial and knowledge to install) did not significantly influence the adoption of borewells. This could be as with high uncertainty of successful borewell drilling, higher perceived financial and capacity/knowledge to install a borewell does not necessarily lead to higher adoption. This is similar to findings from Ethiopia where a reduction in ambiguities related to well drilling was found to be one of the main factors influencing the adoption of groundwater irrigation (Balasubramanya et al., 2023).
4.3.4 Attitude towards technology
Results show that for drip irrigation and borewells, positive belief about the reliability and benefits of the technology translates to a 108 and 116% increase in the likelihood of adoption, respectively. This corroborates the observation from earlier studies that have also shown the importance of positive belief in increasing the adoption of micro-irrigation in India (Hatch et al., 2022), China (Wang et al., 2020) and Iran (Nejadrezaei et al., 2018). Nair and Thomas (2022), based on their review of micro-irrigation adoption in India, also observed that awareness regarding the benefits of drip irrigation is central to increasing adoption. Similarly, Reddy (2016), evaluating the Andhra Pradesh Micro Irrigation Project program, also found that awareness activities (television and radio programs, live demonstrations) played a key role in the success of the program. Interestingly, higher education negatively influences the adoption of drip irrigation (Table 4) showing that more years of education does not necessarily lead to more awareness about drip irrigation benefits and higher adoption.
4.3.5 Perceived risk and impact
The results show that for drip irrigation, interestingly, an increase in perceived vulnerability and associated impact severity translates to a 53 and 38% decrease (at 10% significance level) in the likelihood of drip irrigation adoption, respectively. Whereas for borewells, the impact of perception of risk and vulnerability on adoption is not significant. Theoretically, both drip irrigation and borewells may act as risk-reducing strategies under conditions of water scarcity by using water more efficiently and augmenting the supply of water from deeper aquifers, respectively. Thus, intuition may suggest that an increase in perceived vulnerability and associated impacts should be associated with an increase in adoption of both. This has been observed in other studies where farmers choose to adopt the new technology/practices (e.g., crop insurance, efficient irrigation) to hedge/reduce the risk (Koundouri et al., 2006; Saqib et al., 2016).
The contrasting impact of perceived risk and vulnerability on the adoption of drip irrigation and borewell technologies reveals the differing nature of these technologies as perceived by farmers. Field observations indicate that farmers do not see drip irrigation as a solution for water scarcity as in times of water scarcity (as in dry years), drip irrigation is considered redundant (without any irrigation water). Thus, while the perceived threat of water scarcity is higher, adoption of drip irrigation remains low due to farmers’ perception of the technology’s benefits and costs. This suggests a lack of awareness about the benefits of drip irrigation as a risk-reducing strategy, as well as a perceived imbalance between the cost of adoption and the benefits it provides. Additionally, frequent climate threats, such as drought in the region, can lead to losses in crop yields and revenue, reducing farmers’ financial capacity to invest in risk-reducing strategies (Alam et al., 2022c).
Additionally, the common pool nature of groundwater where the same aquifer is accessed by multiple users creates challenges for adoption of demand management strategies such as drip irrigation (Gardner et al., 1990; Asprilla-Echeverria, 2021). This is because saving water in one’s well using drip irrigation does not necessarily translate to actual savings for the farmer if other farmers continue to abstract without drip irrigation.
4.3.6 Societal norm
The societal norms, perceived social pressure towards a behavior, have a positive influence on farmers’ adoption behavior by affecting their perception of confidence, the benefits of adoption, norm conformity, learning, and perceived risk reduction (Daxini et al., 2019; Streletskaya et al., 2020; Qiu et al., 2021; Hatch et al., 2022). The results suggest that an increase in societal norms leads to a 57% increase in the likelihood of adopting borewell irrigation but has no significant impact on drip irrigation adoption. The positive impact of societal norms on borewell adoption may be due to farmers’ perception of the success of borewells in nearby farms. However, the study was not able to determine why the same impact does not hold for drip irrigation adoption.
In addition, the study found that the opinions of government and NGOs do not significantly influence the adoption of drip or borewell irrigation. This may be because most farmers rely on neighboring farmers (71.8%), agro-dealers and private companies (56.9%), and lead farmers (39.1%) for information, while less than a quarter of farmers reported government or NGOs as their source of information. This finding highlights the importance of considering these channels while designing awareness and extension activities for promoting technology adoption.
4.3.7 Other factors
Action planning significantly increases borewell adoption by farmers. Access to information on external factors such as drilling contractors, engineers, and technicians is a key determinant of adoption. However, the observed association may be explained by reverse causality, as borewell owners are more knowledgeable about the necessary resources for drilling (Daniel et al., 2020). For drip irrigation, farming experience shows a slightly positive (4% increase for each unit increase in farming experience) impact on adoption. Household size and income from farming did not have any influence on the adoption of both drip irrigation and borewell irrigation.
4.4 Discussion and recommendations
Our findings show that although subsidies (50–70%) are available for drip irrigation systems, adoption rates remain low (approximately 16% adoption rate). In contrast, the adoption rate for borewells, which require more capital investment and have no subsidies, is higher (approximately 24.5%). This suggests that farmers prioritize augmenting their water supply and view borewells as a more effective means of mitigating water scarcity or intensifying cultivation. This trend is consistent with observations from Patil et al. (2019) in another water-stressed area of Southern India, where the uptake of water-saving technologies was low, and farmers chose water-intensive crops and unregulated pumping, which exacerbates water stress.
The results indicate that the availability of water (proximity to dam and river) and higher perception of risk negatively affect the adoption of drip irrigation. This reflects that farmers may not necessarily perceive drip technology as a risk-reducing strategy, thereby hindering adoption. Furthermore, limited financial and technical capacity is another obstacle to adoption. Thus, a multi-pronged approach is necessary to build farmers’ capacity to adopt drip irrigation (including alternative financial mechanisms and capacity building) and to raise awareness of its benefits.
Although subsidies have been shown to positive impact adoption (Heumesser et al., 2012; Cremades et al., 2015), our results indicate that in the region, subsidies alone are not enough to promote the adoption of drip irrigation. Alternative financial mechanisms may be required, such as increasing subsidies or providing low-interest or interest-free loans to cover the unsubsidized cost (Palanisami et al., 2011; Nair and Thomas, 2022). An example of this is the Aga Khan Rural Support Programme (AKSRP) in the study region which provided added subsidies and interest-free loans (with delayed repayment) to cover the unsubsidized cost (Panda, 2003). Similarly, other studies have shown the positive impact of easy access and low-interest loans on adoption (Abate et al., 2016; Balasubramanya et al., 2023). For example, Abate et al. (2016) showed in Ethiopia the positive impact of microfinance institutions and member-owned financial cooperatives on the adoption of agricultural technologies by alleviating credit constraints. Alternative financial mechanisms should be accompanied by supporting farmers to easily access the subsidy schemes by making the process faster and more flexible in terms of meeting farmers’ requirements (Singh, 2013; Malik et al., 2018).
Additionally, capacity building efforts should prioritize building farmers’ confidence in operating and maintaining drip irrigation systems. Research has shown that capacity building for farmers is an effective strategy for technology adoption across various countries and technologies (Arslan et al., 2014; Cremades et al., 2015; Zakaria et al., 2020; Nair and Thomas, 2022). This can be achieved through various means such as training programs, community-based approaches like farmer field schools, access to replacement parts, and post-adoption extension services. The Indian government’s operational guidelines for the micro-irrigation subsidy scheme also emphasize the need for capacity building, including organizing training programs and exposure visits (DAC&FW, 2017). In the study region, farmers have expressed concerns about dripper clogging and rodent damage to drip systems, which underscores the need for targeted training on these issues. Capacity building can also involve creating a network of local professionals who can provide on-site training and technical assistance to farmers.
In addition to the aforementioned capacity building efforts, it is essential to provide farmers with information on the benefits of drip irrigation, including increased crop yield and reduced water usage, to reinforce and strengthen positive attitudes and societal norms towards drip irrigation. This is crucial as farmers with higher risk perception are less likely to adopt drip irrigation due to lack of trust in the technology’s ability to mitigate risk. Studies in multiple countries have shown that increasing awareness through training, demo farms, and social learning can positively influence adoption rates (Genius et al., 2014; Hunecke et al., 2017; Nejadrezaei et al., 2018; Wang et al., 2020). Ways to achieve this could include increasing access to information through local government institutions, education campaigns, workshops, and field visits. Government guidelines also recommend awareness raising through print and electronic media and publicity campaigns at block/ district/state level (DAC&FW, 2017).
To enhance the influence of extension services such as capacity building and awareness raising, it is important to have a presence and build trust in social, formal, or informal networks (targeting and influencing social norm) such as cooperative organizations and farmers’ user groups, rather than focusing solely on individuals (Genius et al., 2014; Hunecke et al., 2017). While the government’s official guidelines for promoting micro-irrigation recommend both capacity building and awareness raising (DAC&FW, 2017), low capacity and awareness in the region indicates a need to intensify efforts (Figure 2).
Figure 2. Summary of recommendations for increasing adoption of drip irrigation.
However, the increasing adoption of borewells in the region is a cause for concern. While access to borewells may lead to higher availability of water, it comes with social costs. Borewell drilling is capital-intensive and risky in the region, with no guarantee of success. This means that smaller and marginal farmers may not be able to tap the resource, thus exacerbating socioeconomic disparities in the region, as discussed in studies by Patil et al. (2019) in a similar hard rock aquifer area in Southern India and Birkenholtz (2009) in adjoining state of Rajasthan. The financial risks associated with borewells mean that farmers may fall into severe indebtedness with no access to low-interest loans or other safety nets, as observed by Reddy (2012) in a hard rock aquifer region in Southern state of Andhra Pradesh. Our data also show that farmers drill an average of 2.3 borewells (range 1–12) to get a successful borewell. To mitigate the risks and uncertainties associated with borewells, it is essential to provide farmers with information on the underlying hydrogeology, as the hydrogeology in the region is complex.
Additionally, over-extraction of groundwater through borewells can lead to severe depletion and degradation of deeper aquifers. It is not clear whether shallow and deeper aquifers are connected and if connected, tapping deeper aquifers may have a negative influence on shallow water sources. Also, over-extraction of groundwater through borewells can lead to a decline in water levels, making it more difficult and expensive to extract water in the future. Moreover, this strategy may become maladaptive in the long run, as found in the study by Jain et al. (2015) in Gujarat state where study area is located. Also, depletion of groundwater can increase energy consumption for pumping leading to a vicious cycle of increased energy demand, higher costs, and further depletion of groundwater resources. Further research is required to understand the hydrogeology of deeper aquifers in the region.
Finally, the common pool nature of groundwater may hinder adoption at the individual level of demand management interventions (Gardner et al., 1990; Asprilla-Echeverria, 2021). Given that farmers tap into a shared resource, cooperation at the village level and incentivization may be required to realize the benefits of drip adoption at the individual level. This is necessary to avoid the free rider problem. Also, while including psychological factors in the analysis enhances understanding, RANAS theory may not account for all psychological factors that hinder adoption, such as perceived fairness and technology acceptance (Contzen et al., 2023). Future studies could consider adding more factors to RANAS theory or testing alternative psychological theories to gain a deeper understanding of adoption barriers.
5 Conclusion
Increasing the adoption of agricultural water interventions by farmers is critical to adapting to water scarcity and ensuring the food and economic security of millions of farmers. However, despite the availability of a range of interventions and successful pilots, adoption remains low. This study assessed socioeconomic, biophysical and psychological factors influencing the adoption of two contrasting adaptation strategies, drip irrigation (demand management) and borewells (supply augmentation), in a semi-arid catchment in India. While drip irrigation is being promoted with government subsidies, borewells are being taken up by farmers using their own resources. The results show that psychological factors play a significant role in the adoption of both technologies, and incorporating these factors improved model explanatory power by almost threefold. The findings show that despite subsidies, drip irrigation adoption lags behind borewells, suggesting farmers’ preference for supply augmentation measures. Farmers’ perceived ability and positive beliefs about the benefits of drip systems are significant factors in adoption. Based on the results, the study suggests that a multi-pronged approach is necessary to increase the adoption of drip irrigation, including augmenting subsidies with efforts on extension services, post-adoption services, training, and awareness campaigns to build farmers’ capacity and raise awareness. On the other hand, the increasing adoption of borewells is concerning, with implications for increasing socioeconomic inequality, indebtedness, and threatening deeper aquifers. Overall, it is critical to devise strategies that look beyond the socioeconomic factors to increase fair access to water resources while safeguarding against the overexploitation of groundwater.
Data availability statement
The datasets generated during and/or analyzed during the current study are available from https://doi.org/10.4121/e5dc84d4-e22e-41c3-aa41-fbbe7ec74d83.
Ethics statement
The studies involving humans were approved by Human Research Ethics Committee TU Delft (http://hrec.tudelft.nl/). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
MA: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing. MM: Supervision, Writing – original draft, Writing – review & editing. AS: Supervision, Writing – original draft, Writing – review & editing. SP: Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frwa.2024.1444423/full#supplementary-material
References
Abate, G. T., Rashid, S., Borzaga, C., and Getnet, K. (2016). Rural finance and agricultural technology adoption in Ethiopia: does the institutional Design of Lending Organizations Matter? World Dev. 84, 235–253. doi: 10.1016/j.worlddev.2016.03.003
Aggarwal, P., Jarvis, A., Campbell, B., Zougmoré, R., Khatri-Chhetri, A., Vermeulen, S., et al. (2018). The climate-smart village approach: framework of an integrative strategy for scaling up adaptation options in agriculture. Ecol. Soc. 23:14. doi: 10.5751/ES-09844-230114
Alam, M. F., Mandave, V., Sikka, A. K., and Sharma, N. (2021). “Enhancing water productivity through on-farm water management” in Water, climate change, and sustainability. eds. V. P. Pandey, S. Shrestha, and D. Wiberg (USA: John Wiley & Sons, Inc.), 109–124.
Alam, M. F., McClain, M. E., Sikka, A., Daniel, D., and Pande, S. (2022a). Benefits, equity, and sustainability of community rainwater harvesting structures: An assessment based on farm scale social survey. Front. Environ. Sci. 10:1043896. doi: 10.3389/fenvs.2022.1043896
Alam, M. F., McClain, M., Sikka, A., and Pande, S. (2022b). Understanding human–water feedbacks of interventions in agricultural systems with agent based models: a review. Environ. Res. Lett. 17:103003. doi: 10.1088/1748-9326/ac91e1
Alam, M. F., Pavelic, P., Villholth, K. G., Sikka, A., and Pande, S. (2022c). Impact of high-density managed aquifer recharge implementation on groundwater storage, food production and resilience: a case from Gujarat, India. J. Hydrol. Reg. Stud. 44:101224. doi: 10.1016/j.ejrh.2022.101224
An, L. (2012). Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25–36. doi: 10.1016/j.ecolmodel.2011.07.010
Arslan, A., McCarthy, N., Lipper, L., Asfaw, S., and Cattaneo, A. (2014). Adoption and intensity of adoption of conservation farming practices in Zambia. Agric. Ecosyst. Environ. Eval. Cons. Agric. Small-scale Farmers Sub-Saharan Africa South Asia 187, 72–86. doi: 10.1016/j.agee.2013.08.017
Arunrat, N., Wang, C., Pumijumnong, N., Sereenonchai, S., and Cai, W. (2017). Farmers’ intention and decision to adapt to climate change: a case study in the Yom and Nan basins, Phichit province of Thailand. J. Clean. Prod. 143, 672–685. doi: 10.1016/j.jclepro.2016.12.058
Asprilla-Echeverria, J. (2021). The social drivers of cooperation in groundwater management and implications for sustainability. Groundw. Sustain. Dev. 15:100668. doi: 10.1016/j.gsd.2021.100668
Balasubramanya, S., Buisson, M.-C., Mitra, A., and Stifel, D. (2023). Price, credit or ambiguity? Increasing small-scale irrigation in Ethiopia. World Dev. 163:106149. doi: 10.1016/j.worlddev.2022.106149
Birkenholtz, T. (2009). Irrigated landscapes, produced scarcity, and adaptive social institutions in Rajasthan, India. Ann. Assoc. Am. Geogr. 99, 118–137. doi: 10.1080/00045600802459093
Callejas Moncaleano, D. C., Pande, S., and Rietveld, L. (2021). Water use efficiency: a review of contextual and behavioral factors. Front. Water 3:91. doi: 10.3389/frwa.2021.685650
Chandran Madhava, K., and Surendran, U. (2016). Study on factors influencing the adoption of drip irrigation by farmers in humid tropical Kerala, India. Int. J. Plant Prod. 10, 347–364. doi: 10.22069/ijpp.2016.2902
Cobbing, J., and Hiller, B. (2019). Waking a sleeping giant: realizing the potential of groundwater in Sub-Saharan Africa. World Dev. 122, 597–613. doi: 10.1016/j.worlddev.2019.06.024
Contzen, N., Kollmann, J., and Mosler, H.-J. (2023). The importance of user acceptance, support, and behaviour change for the implementation of decentralized water technologies. Nat. Water 1, 138–150. doi: 10.1038/s44221-022-00015-y
Cremades, R., Wang, J., and Morris, J. (2015). Policies, economic incentives and the adoption of modern irrigation technology in China. Earth Syst. Dynam. 6, 399–410. doi: 10.5194/esd-6-399-2015
DAC&FW (2017). Operational guidelines of per drop more crop (Micro irrigation) component of PMKSY. Department of Agriculture, Cooperation & Farmer Welfare (DAC&FW), Ministry of Agriculture & farmers welfare, government of India. New Delhi Available at: https://pmksy.gov.in/microirrigation/Archive/Revised%20PDMC%20GL%202021.pdf (Accessed March 08, 2024).
Daniel, D., Pande, S., and Rietveld, L. (2020). The effect of socio-economic characteristics on the use of household water treatment via psychosocial factors: a mediation analysis. Hydrol. Sci. J. 65, 2350–2358. doi: 10.1080/02626667.2020.1807553
Daxini, A., Ryan, M., O’Donoghue, C., and Barnes, A. P. (2019). Understanding farmers’ intentions to follow a nutrient management plan using the theory of planned behaviour. Land Use Policy 85, 428–437. doi: 10.1016/j.landusepol.2019.04.002
Di Baldassarre, G., Wanders, N., AghaKouchak, A., Kuil, L., Rangecroft, S., Veldkamp, T. I. E., et al. (2018). Water shortages worsened by reservoir effects. Nat. Sustain. 1, 617–622. doi: 10.1038/s41893-018-0159-0
Evans, A E V, and Giordano, M (2012). Investing in agricultural water management to benefit smallholder farmers in Ethiopia AgWater solutions project country synthesis report (international water management institute (IWMI). doi: 10.5337/2012.215
FAO (2021). The Impact of Disasters on Agriculture and Food Security. Rome: Food and Agriculture Organization of the United Nations (FAO).
Foster, S. (2012). Hard-rock aquifers in tropical regions: using science to inform development and management policy. Hydrogeol. J. 20, 659–672. doi: 10.1007/s10040-011-0828-9
Gardner, R., Ostrom, E., and Walker, J. M. (1990). The nature of common-pool resource problems. Ration. Soc. 2, 335–358. doi: 10.1177/1043463190002003005
GCA and WRI (2019). Adapt now: A global call for leadership on climate resilience. Rotterdam: Global Center on Adaptation and World Resources Institute.
Genius, M., Koundouri, P., Nauges, C., and Tzouvelekas, V. (2014). Information transmission in irrigation technology adoption and diffusion: social learning, extension services, and spatial effects. Am. J. Agric. Econ. 96, 328–344. doi: 10.1093/ajae/aat054
GGRC (2023). Performance highlights of the micro irrigation scheme implemented by GGRC. Gujarat Green Revolution Company Limited. Available at: https://ggrc.co.in/webui/Content.aspx?PageId=5#:~:text=90%2C000%2F%2D%20per%20hectare%2C%20whichever,per%20hectare%2C%20whichever%20is%20less (Accessed January 18, 2023)
Harris, J. K. (2021). Primer on binary logistic regression. Fam. Med. Commun. Health 9:e001290. doi: 10.1136/fmch-2021-001290
Hatch, N. R., Daniel, D., and Pande, S. (2022). Behavioral and socio-economic factors controlling irrigation adoption in Maharashtra, India. Hydrol. Sci. J. 67, 847–857. doi: 10.1080/02626667.2022.2058877
Heumesser, C., Fuss, S., Szolgayová, J., Strauss, F., and Schmid, E. (2012). Investment in irrigation systems under precipitation uncertainty. Water Resour. Manag. 26, 3113–3137. doi: 10.1007/s11269-012-0053-x
Hunecke, C., Engler, A., Jara-Rojas, R., and Poortvliet, P. M. (2017). Understanding the role of social capital in adoption decisions: An application to irrigation technology. Agric. Syst. 153, 221–231. doi: 10.1016/j.agsy.2017.02.002
IPCC (2022) in Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of working group II to the sixth assessment report of the intergovernmental panel on climate change. eds. H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, and A. Alegría, et al. (NY, USA: Cambridge University Press. Cambridge University Press, Cambridge, UK and New York), 3056.
Jain, M., Naeem, S., Orlove, B., Modi, V., and DeFries, R. S. (2015). Understanding the causes and consequences of differential decision-making in adaptation research: adapting to a delayed monsoon onset in Gujarat, India. Glob. Environ. Chang. 31, 98–109. doi: 10.1016/j.gloenvcha.2014.12.008
Kattumuri, R., Ravindranath, D., and Esteves, T. (2017). Local adaptation strategies in semi-arid regions: study of two villages in Karnataka, India. Clim. Dev. 9, 36–49. doi: 10.1080/17565529.2015.1067179
Klessens, T. M. A., Daniel, D., Jiang, Y., Van Breukelen, B. M., Scholten, L., and Pande, S. (2022). Combining water resources, socioenvironmental, and psychological factors in assessing willingness to conserve groundwater in the Vietnamese mekong delta. J. Water Resour. Plan. Manag. 148:05021034. doi: 10.1061/(ASCE)WR.1943-5452.0001516
Koundouri, P., Nauges, C., and Tzouvelekas, V. (2006). Technology adoption under production uncertainty: theory and application to irrigation technology. Am. J. Agric. Econ. 88, 657–670. doi: 10.1111/j.1467-8276.2006.00886.x
Kulkarni, H., Deolankar, S. B., Lalwani, A., Joseph, B., and Pawar, S. (2000). Hydrogeological framework of the Deccan basalt groundwater systems, west-Central India. Hydrogeol. J. 8, 368–378. doi: 10.1007/s100400000079
Malik, R. P. S., Giordano, M., and Rathore, M. S. (2018). The negative impact of subsidies on the adoption of drip irrigation in India: evidence from Madhya Pradesh. Int. J. Water Res. Dev. 34, 66–77. doi: 10.1080/07900627.2016.1238341
Misquitta, K., and Birkenholtz, T. (2021). Drip irrigation as a socio-technical configuration: policy design and technological choice in Western India. Water Int. 46, 112–129. doi: 10.1080/02508060.2020.1858696
Mohapatra, B. (2013). District groundwater brochure, Rajkot district, Gujarat. Central ground water board west central region, Ministry of Water Resources, Government of India. Ahmedabad. Available at: http://cgwb.gov.in/District_Profile/Gujarat/Rajkot.pdf.
Mosler, H.-J. (2012). A systematic approach to behavior change interventions for the water and sanitation sector in developing countries: a conceptual model, a review, and a guideline. Int. J. Environ. Health Res. 22, 431–449. doi: 10.1080/09603123.2011.650156
Mudrakartha, S. (2007). "To adapt or not to adapt: the dilemma between long-term resource management and short-term livelihood," IWMI books, reports H040050, International Water Management Institute. Available at: https://publications.iwmi.org/pdf/H040050.pdf (Accessed April 09, 2024).
Mudrakartha, Srinivas (2012). Groundwater recharge management in Saurashtra, India: Learnings for water governance. PhD Thesis. Jagatpura, Jaipur, India: Suresh Gyan Vihar University Mahala, p. 363.
Mukherji, A. (2020). Sustainable groundwater Management in India Needs a water-energy-food Nexus approach. Appl. Econ. Perspect. Policy 44, 394–410. doi: 10.1002/aepp.13123
Nair, J., and Thomas, B. K. (2022). Why is adoption of micro-irrigation slow in India? A review. Dev. Pract. 33, 76–86. doi: 10.1080/09614524.2022.2059065
Namara, R. E., Nagar, R. K., and Upadhyay, B. (2007). Economics, adoption determinants, and impacts of micro-irrigation technologies: empirical results from India. Irrig. Sci. 25, 283–297. doi: 10.1007/s00271-007-0065-0
Nejadrezaei, N., Allahyari, M. S., Sadeghzadeh, M., Michailidis, A., and El Bilali, H. (2018). Factors affecting adoption of pressurized irrigation technology among olive farmers in northern Iran. Appl Water Sci 8:190. doi: 10.1007/s13201-018-0819-2
Pai, D.S., Latha, Sridhar, and Rajeevan, M., Sreejith, O.P., Satbhai, N.S., and Mukhopadhyay, B., (2014). Development of a new high spatial resolution (0.25° X 0.25°) Long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM 65, 1–18, 1(January 2014).
Palanisami, K., Kumar, D. S., Malik, R. P. S., Raman, S., Kar, G., and Mohan, K. (2015). Managing water management research: analysis of four decades of research and outreach programmes in India. Econ. Polit. Wkly. 50, 33–43.
Palanisami, K., Mohan, K., Kakumanu, K. R., and Raman, S. (2011). Spread and economics of Micro-irrigation in India: evidence from nine states. Econ. Polit. Wkly. 46, 81–86.
Panda, Sudarsan (2003). Enhancing entrepreneurship in Micro irrigation, mainly in drip irrigation: A case study of AKRSP(I)‘s Junagadh Programme area. Aga Khan Rural Support Programme (India) Ahmedabad. Available at: https://www.akrspindia.org.in/uploadcontent/resourcemenu/resourcemenu_36.pdf (Accessed May 15, 2024).
Pathak, H. S., Brown, P., and Best, T. (2019). A systematic literature review of the factors affecting the precision agriculture adoption process. Precis. Agric. 20, 1292–1316. doi: 10.1007/s11119-019-09653-x
Patil, V. S., Thomas, B. K., Lele, S., Eswar, M., and Srinivasan, V. (2019). Adapting or chasing water? Crop choice and farmers’ responses to water stress in Peri-urban Bangalore, India. Irrig. Drain. 68, 140–151. doi: 10.1002/ird.2291
Pullabhotla, H. K., Kumar, C., and Verma, S. (2012). Micro-irrigation subsidies in Gujarat and Andhra Pradesh [India] implications for market dynamics and growth. IWMI-Tata Water Policy Res. Highlight 43:9.
Qiu, W., Zhong, Z., and Huang, Y. (2021). Impact of perceived social norms on farmers’ behavior of cultivated land protection: an empirical analysis based on mediating effect model. Int. J. Low-Carbon Technol. 16, 114–124. doi: 10.1093/ijlct/ctaa043
Raut, N., Shakya, A., Gurung, S., and Dahal, B. M. (2021). Adoption of a multiple use water system (MUWS) to ensure water security for Nepalese hill farmers. Water Policy 23, 239–254. doi: 10.2166/wp.2021.066
Reddy, A.A.A., (2012). Structure of indebtedness of households in semi-arid tropics of India. doi: 10.2139/ssrn.2160334
Reddy, K. Y. (2016). Micro-irrigation in participatory mode pays huge dividends—Apmip experiences, India. Irrig. Drain. 65, 72–78. doi: 10.1002/ird.2039
Robert, M., Bergez, J.-E., and Thomas, A. (2018). A stochastic dynamic programming approach to analyze adaptation to climate change – application to groundwater irrigation in India. Eur. J. Oper. Res. 265, 1033–1045. doi: 10.1016/j.ejor.2017.08.029
Saqib, S. E., Ahmad, M. M., Panezai, S., and Ali, U. (2016). Factors influencing farmers’ adoption of agricultural credit as a risk management strategy: the case of Pakistan. Int. J. Disaster Risk Reduct. 17, 67–76. doi: 10.1016/j.ijdrr.2016.03.008
Schlüter, M., and Pahl-Wostl, C. (2007). Mechanisms of resilience in common-pool resource management systems: an agent-based model of water use in a river basin. Ecol. Soc. 12:23. doi: 10.5751/ES-02069-120204
Scott, C. A., Vicuña, S., Blanco-Gutiérrez, I., Meza, F., and Varela-Ortega, C. (2014). Irrigation efficiency and water-policy implications for river basin resilience. Hydrol. Earth Syst. Sci. 18, 1339–1348. doi: 10.5194/hess-18-1339-2014
Shiferaw, B., Okello, J., and Reddy, V. R. (2009). “Challenges of adoption and adaptation of land and water management options in smallholder agriculture: synthesis of lessons and experiences” in Rainfed agriculture: Unlocking the potential. eds. S. P. Wani, J. Rockström, and T. Oweis (UK: CABI), 258–275.
Sikka, A. K., Alam, M. F., and Mandave, V. (2022). Agricultural water management practices to improve the climate resilience of irrigated agriculture in India. Irrig. Drain. 71, 7–26. doi: 10.1002/ird.2696
Singh, O. P. (2013). Hydrological and farming system impacts of agricultural water management interventions in North Gujarat. Indian J. Agric. Econ. 68, 292–312. doi: 10.22004/ag.econ.206336
Singh, C., Rahman, A., Srinivas, A., and Bazaz, A. (2018). Risks and responses in rural India: implications for local climate change adaptation action. Clim. Risk Manag. 21, 52–68. doi: 10.1016/j.crm.2018.06.001
Steinhübel, L., Wegmann, J., and Mußhoff, O. (2020). Digging deep and running dry—the adoption of borewell technology in the face of climate change and urbanization. Agric. Econ. 51, 685–706. doi: 10.1111/agec.12586
Stocker, A., and Mosler, H. (2015). Contextual and sociopsychological factors in predicting habitual cleaning of water storage containers in rural Benin. Water Resour. Res. 51, 2000–2008. doi: 10.1002/2014WR016005
Streletskaya, N. A., Bell, S. D., Kecinski, M., Li, T., Banerjee, S., Palm-Forster, L. H., et al. (2020). Agricultural adoption and behavioral economics: bridging the gap. Appl. Econ. Perspect. Policy 42, 54–66. doi: 10.1002/aepp.13006
Suresh, A., and Samuel, M. P. (2020). Micro-irrigation development in India: challenges and strategies. Curr. Sci. 118:1163. doi: 10.18520/cs/v118/i8/1163-1168
Tranmer, M., and Elliot, M. (2008). Binary logistic regression. Cathie Marsh for Census Surv. Res. 20, 3–43.
van Steenbergen, F. (2006). Promoting local management in groundwater. Hydrogeol. J. 14, 380–391. doi: 10.1007/s10040-005-0015-y
Wang, Y., Long, A., Xiang, L., Deng, X., Zhang, P., Hai, Y., et al. (2020). The verification of Jevons’ paradox of agricultural water conservation in Tianshan District of China based on water footprint. Agric. Water Manag. 239:106163. doi: 10.1016/j.agwat.2020.106163
Yazdanpanah, M., Hayati, D., Hochrainer-Stigler, S., and Zamani, G. H. (2014). Understanding farmers’ intention and behavior regarding water conservation in the middle-east and North Africa: a case study in Iran. J. Environ. Manag. 135, 63–72. doi: 10.1016/j.jenvman.2014.01.016
Yifru, G. S., and Miheretu, B. A. (2022). Farmers’ adoption of soil and water conservation practices: the case of Lege-Lafto watershed, Dessie Zuria District, south Wollo, Ethiopia. PLoS One 17:e0265071. doi: 10.1371/journal.pone.0265071
Keywords: agriculture water management, drip, borewell, adoption, RANAS, subsidy
Citation: Alam MF, McClain M, Sikka A and Pande S (2024) Subsidies alone are not enough to increase adoption of agricultural water management interventions. Front. Water. 6:1444423. doi: 10.3389/frwa.2024.1444423
Edited by:
Giulio Castelli, University of Florence, ItalyReviewed by:
A. Amarender Reddy, National Institute of Agricultural Extension Management (MANAGE), IndiaAlessandra Scardigno, Istituto Agronomico Mediterraneo di Bari, Italy
Copyright © 2024 Alam, McClain, Sikka and Pande. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mohammad Faiz Alam, m.f.alam@tudelft.nl; m.alam@cgiar.org