Unpacking stakeholder perceptions on challenges for increasing adoption of solar-powered irrigation systems in India: A Q methodology study

Abstract

Solar-powered irrigation systems (SPISs) are instruments for sustainable energy transition in agriculture. Despite the government of India's efforts, the adoption of SPIS has remained low. This paper uses the Q method to examine shared stakeholder views on the challenges of increasing SPIS adoption. The aim is to understand barriers to and drivers for SPIS adoption, and the necessary institutional setting to scale up adoption. To capture the discourse on challenges to SPIS adoption, a Q sample of 20 statements was extracted after stakeholder interviews and expert ratings. The statements were Q sorted by 22 respondents across SPIS stakeholder groups. Factor analysis identifies two distinct perspectives on constraints to expanding SPIS: (1) economic and financial barriers; and (2) institutional and governance challenges. This calls for re-framing SPIS from the incentive side of things and convergence across concerned implementing agencies.

1. Introduction

Solar-powered irrigation systems (SPISs) are being promoted across the globe as an alternative to pumps powered by conventional energy sources. The declining cost of photovoltaic (PV) panels, the development of solar pumping systems suitable for small-scale irrigation, and global climate change commitments are the reasons for this renewed policy push (Purohit and Kandpal 2005; Shah et al. 2018; Lefore et al. 2021; Mana et al. 2021). India is also promoting SPIS as a strategy to meet the target of generating 450 GW of renewable energy and to meet at least 40 per cent of energy demand through non-fuel sources by the end of 2030. Agriculture is one of India's major users of electricity, consuming 17.5 per cent of generated energy, and usage has grown at an annual rate of 6.5 per cent over the last six years (GoI 2021). The ‘Pradhan Mantri Kisan Urja Suraksha Evam Utthaan Mahaabhiyan’ (PM KUSUM) scheme, launched in 2019, sets an ambitious target of installing 1.75 million stand-alone SPIS and aims to replace 1 million conventional grid-connected electric pumps with SPIS. Through the scheme, 60 per cent of the upfront SPIS cost is subsidized, and additional state subsidies are also provided (GoI 2019). In spite of the policy push and capital subsidies, SPIS adoption in India has been slow (Shah 2021). As with the renewable energy transition process, the transition toward SPIS involves multiple actors, starting with different government departments to equipment supply chains, and electricity distribution companies, financial institutions, and farmers. The different stakeholder groups can have different interests, and temporal and spatial trade-offs (Schmeer 2000; Eidt et al. 2002; Rastogi et al. 2010; Berardi 2013). Successful SPIS adoption requires that stakeholder objectives are compatible with the stated policy objectives.

Adoption and diffusion of agricultural innovations does not always follow a linear path, and financial incentives alone may not be sufficient for scaling up (Hermans et al. 2017; Dessart et al. 2019). To adopt an SPIS, there should be either a financial incentive or comparative advantage over the conventional electric pump. Also, it must be stressed that the enabling environment in terms of institutions, credit and service is important. Most adoption studies and even policies have an implicit conceptual assumption that prerequisites for adoption, such as service infrastructure and a functional market for spare parts exist (Ruzzante et al. 2021); however, these may or may not exist. Adoption of technologies is rooted in social context, with stakeholders or actors interacting by sharing knowledge and resources, yet holding different perceptions regarding the costs and benefits of the new technology (Eidt et al. 2002; Caird et al. 2008; Abrahamse and Steg 2013; Hermans et al. 2017; Lavoie and Wardropper 2021). Often a coalition of actors or stakeholders are needed to accelerate the adoption of innovations (Barett 2023). So, an understanding the slow pace of SPIS adoption requires a nuanced analysis of different stakeholders and their perceptions along the technology value chain, not just those of farmers. Understanding the perceptions and attitudes of key stakeholders is essential, as it affects policy outcomes and scalability (Burton 2004; Caird et al. 2008; Elmustapha et al. 2018; Ghadim and Pannell 1999; Khatri-Chhetri et al. 2019).

The literature on SPIS has mostly focused on understanding the financial viability of the technology in comparison to conventional pumps (Purohit and Kandpal 2005; Gopal et al. 2013; Welsh and Powell 2017). A few studies, in addition to financial viability, also consider environmental sustainability, impact on resource use, and include case studies on pilot projects (Gopal et al. 2013; Agrawal and Jain 2016; Jain and Shahidi 2018; Shah et al. 2018; Verma et al. 2018). Other studies, look at adoption and perceptions from the perspective of farmers alone (Kumar and Syan 2019). A few case studies do mention the constraints related to the equipment supply chain and the lack of service providers for SPIS repairs (Agrawal and Jain 2016; Jain and Shahidi 2018; Otoo et al. 2018; ICID 2019; Yashodha et al. 2021), but detailed studies on the challenges of SPIS expansion across stakeholders in the context of scaling up are missing.

Understanding the perceptions of stakeholders is complicated, particularly regarding technologies like SPIS, where there are large numbers of stakeholders and substantial heterogeneity within each stakeholder group. Stakeholders might disagree on what the goal of policy should be or on the means of achieving the goals, as shown by Buckwell et al. (2020), using the Q method approach, in the case of natural resource management for the Vanuatu region. The usual approach to understanding stakeholder perceptions through ranking, or using rating scales, may not be sufficient to uncover the subjectivity in perceptions.

In this context, the present paper applies the ‘Q method’ to analyse the perceived challenges of increasing SPIS adoption, and aims to reveal ‘distinct viewpoints’ across stakeholders. Understanding discourses among and across stakeholders on a given topic helps when implementing policies by identifying potential barriers to effective implementation and alignment with policies. At the same time, this can also help to identify the reasons for low adoption of SPIS in India. For instance, Curry et al. (2013) used the Q method to identify discourses among stakeholders concerning the environmental and resource dimensions of sustainability policies and to elaborate on how stakeholders understood the policies.

The strength of the method is in allowing us to use a transparent approach in classification, drawn from quantitative methods yet still nuanced in interpretation of respondents’ subjective perceptions by revealing segments of subjectivity. The Q method has been frequently used by researchers to examine stakeholder perceptions in different domains: natural resource management (Buckwell et al. 2020); sustainability and energy transformation (Cuppen et al. 2010; Curry et al. 2013; Horschig et al. 2020; Wolbertus et al. 2020; Venus et al. 2021; Derksen and Mithöfer 2022); healthcare (van Exel et al. 2015; Wouters et al. 2017, McHugh et al. 2019); and technology change and innovation systems (Bacher et al. 2014; Louah and Visser 2016). The Q method enables the unravelling of diverse stakeholder views; broad generalizations, perspectives, and agreement, as well as differences between perspectives (Guimaraes 2010; McHugh et al. 2019). Many studies have pointed to heterogeneity within stakeholder groups using the Q method (Bacher et al. 2014; Buckwell et al. 2020; Dieteren et al. 2023) and revealed that actor types may not be proxies for particular perspectives (Curry et al. 2013). The Q method has also been employed to understand discourses around policy issues (Frantzi et al. 2009; Curry et al. 2013; Ullah et al. 2017; Stevenson 2019). A nuanced understanding of different perspectives facilitates communication between stakeholders (Curry et al., 2013), helps to identify issues for resolution (Bacher et al., 2014), and helps to understand and incorporate community perspectives into policy (Buckwell et al., 2020). As Louah and Visser (2016) note, the Q method is also useful in the context of agricultural innovation systems (AISs) to understand stakeholder perceptions and incorporate them into policies to strengthen AIS.

The paper makes the following contributions: (1) From a policy perspective, we generate insights useful for understanding barriers to and drivers for SPIS adoption and the institutional setting required for scaling up, which is lacking in the current literature as well as the slow adoption of SPIS at present. (2) We demonstrate the utility of the Q method in examining stakeholder perceptions to generate more nuanced policy recommendations, which are very helpful in the adoption of other sustainable farming practices that involve multiple stakeholders, a multitude of perspectives, and conflicting trade-offs.

2. Context: SPISs in India

Irrigation in India is predominantly groundwater-based, with more than 60 per cent of irrigated areas using groundwater resources. India has approximately 31 million irrigation pumps, of which 21 million are conventional grid-connected electricity-powered irrigation pumps and almost 7 million are conventional diesel-powered irrigation pumps (IEA 2020). Electricity for irrigation in most states is either free or provided at a subsidized flat rate. Many states, like Karnataka and Gujarat, also have separate feeders to provide electricity for agriculture. Through this network of grids, some minimum hours of electricity are available for irrigation.

India began promoting SPIS from 2010, through various government programmes. In case of the standalone SPIS, the farmer has a solar panel installed in the field, which generates the decentralized energy required to pump during sunshine hours. However, a few studies have highlighted how such technology can incentivize farmers to over-exploit the groundwater (Shah et al. 2018; Verma et al. 2018; Bastakoti et al. 2020; Shah 2021). Groundwater is not metered or priced, and when coupled with an SPIS with zero marginal cost for energy and a reliable daytime power supply, farmers have no incentive to save water. SPIS also provides uninterrupted, autonomous, and decentralized daytime power, which can also contribute groundwater overdraft. In addition to using more water for existing crops, this can also lead to a change in cropping to more water-intensive crops, leading to over-extraction of groundwater. In India, groundwater over-exploitation is already a major crisis (Fishman et al. 2015; Kumar and Perry 2018; Balasubramanya et al. 2022; Gupta 2023), and SPIS could aggravate the issue.

Contrary to stand-alone SPIS, grid-connected SPIS are seen as a potential market-based solution to incentivize farmers to save energy and water. In this system, the SPIS are connected to existing power grids, with an option for farmers to sell the surplus energy generated from the solar panels to power distribution companies. Farmers will receive monetary compensation for solar energy not used to pump but sold to the system. This not only incentivizes farmers to use irrigation optimally but also prevents the wastage of surplus solar energy generated from the panel (Yashodha et al. 2021). Such systems have been piloted in different schemes in India, such as the Surya Raitha scheme in Karnataka, an Andhra Pradesh grid-connected solar pilot, and the SKY scheme in Gujarat. In 2019, the Indian government launched a massive solar pumps scheme, ‘Pradhan Mantri Kisan Urja Suraksha Evam Utthaan Mahaabhiyan (PM KUSUM)’ (GoI 2019). The PM KUSUM scheme aims to promote both stand-alone and grid-connected SPIS. For reasons of simplicity, we use the term SPIS for grid-connected SPIS in the methods and results section but to allow selective reading use the full name in the discussion and conclusion sections.

3. Conceptual framework

In the past, technology innovation and diffusion processes have often been studied as linear processes, whereby technology is transferred from a scientist and adopted by farmers (World Bank 2006; Spielman and Birner 2007). This top-down approach has been criticized (Spielman and Birner 2007), and ‘market push’, bottom-up approaches to understanding the diffusion of innovation are now used frequently. One such approach is AISs. AIS is both a process and an outcome, in which information is put to economic, social, or environmental use, including the integration of previous and new information. AIS can also be defined as a network of actors, individuals, and enterprises who are involved in bringing the policy or a product into economic or social use (Chinseu et al. 2022). The AIS framework recognizes that innovations are dynamic and complex, with many actors situated in different organizational, institutional, and social contexts; therefore, a linear analysis of adoption, in isolation, is incomplete. Different actors and stakeholders interact, co-create value, and facilitate negotiations across various levels, which are crucial for the adoption of the technologies by the ultimate stakeholder—farmer (Eidt et al. 2002).

The system of actors, roles, and interests determining how value is created along the chain must be studied in order to understand the opportunities and threats of potential interventions (Berardi 2013; Varvasovszsky and Brugha 2000). In many cases, collective action around the innovation opportunity does not emerge automatically and needs to be fostered by innovation brokering (Louah and Visser 2016). In the case of SPIS, there are many stakeholders, with conflicting motives, and the success of the policy depends on harnessing opportunities and addressing conflicts. Specifically, we examine stakeholder views of the challenges of expanding SPIS in India, which helps explain the slow uptake of the technology. Such understanding can enable the creation of a common space to negotiate and co-innovate, which is crucial for the success of innovation (Klerx et al. 2009; Klerkx et al. 2012).

4. Data and methods

A positivist approach to empirical social science involves measuring socio-economic or attitude-related variables and drawing inferences. Often, subjective views of a topic are lost in this approach of numerical reduction. The alternative epistemological standpoint of subjectivism involves examining a research question from the standpoint of the respondent (Midgley and Delprato 2017). This approach relies on a respondent's ability to present a coherent story or perspective on a topic using methods from interpretative epistemological approaches. The major concern with many such studies that use qualitative methods is what is known as the ‘mystery of classification’ (Brown 1993). Once the qualitative data is gathered, the classification into themes and the presentation of the data are challenging and are often left to the researcher's interpretation (Brown 1993; Baker et al. 2006). In such situations, the Q method provides a structured way of analysing multiple facets of subjectivity, provides a richer description of the discourse, and still provides a sense of coherence for potentially complex and contested topics (Baker et al., 2006). For example, Wolbertus et al. (2020) employed the Q method to examine perceptions of electric vehicle charging infrastructure; Vecchio et al. (2022) examined preferences for precision farming in Italy; and Wouters et al. (2017) examined patient preferences regarding end-of-life treatments in the Netherlands.

Furthermore, if topics of analysis include concepts unfamiliar to respondents, as with SPIS, expecting respondents to narrate a coherent and consistent story may be unrealistic. Respondents may not be able to think through different dimensions of the topic and present their viewpoints. In such cases, the Q method provides a useful framework to operationalize subjective perceptions (Baker et al. 2006). The Q sorting method was developed by William Stephenson as an approach for the scientific examination of subjectivity in human behaviour. Based on the guidelines provided in Brown (1993), van Exel and Gjalt (2005), Baker et al. (2006), and Dieteren et al. (2023), we implemented the Q method through the following steps: (1) defining and accessing the concourse; (2) development of the Q set or Q sample; (3) selecting the respondents (or P sample) and card-sorting task; and (4) by-person factor analysis and interpretation. These steps are explained in the following section, with reference to the present study. The Q method has an advantage over rating scales, as the sorting process requires respondents to contextualize each statement by comparing it with the other statements presented to them and then ranking the statements.

• (1) Defining and accessing the concourse: The universe of statements, opinions, and perspectives on a specific topic is called the concourse. To develop the concourse, we used multiple sources on the discourse related to the challenges of expanding SPIS in India. We carried out in-depth interviews with key informants from various stakeholder groups and also examined the literature on SPIS to extract various views of the challenges of SPIS expansion. Expert interviews (12 in total, details in Online Appendix 1) were transcribed, and phrases related to the challenges were extracted. The initial coding framework used is detailed in Online Appendix 2. In total, 51 statements were extracted (Online Appendix 3), which formed the basis for the final set of statements, also known as the Q sample.

• (2) Developing the Q set or Q sample: The initial items were classified into themes such as technological constraints, awareness and perception-related constraints, institutional constraints, financial constraints, and environmental concerns. These dimensions are not strict categorizations but only indicative, such that the final Q sample covers all the relevant parts of the topic, as suggested by McKeown and Thomas (2013). The purpose of classifying items into themes was to be comprehensive and representative of the discourse (Baker et al. 2006; Buckwell et al. 2020), but we did not follow systematic sampling from the themes or try to ensure a balance in terms of positive and negative statements. Compared to the usual application of Q sorting, in which statements are more attitudinal, in our case it was not appropriate to balance the statements based on Stephenson's use of Fisher's balanced block design (Sylvester 2019),¹ as our study's focus was the subjective perceptions of the challenges of SPIS expansion, which cannot be easily classified as positive or negative.

The related statements were merged, minimizing overlap, and a follow-up expert review was carried out: three SPIS experts (details given in Online Appendix 4) were asked if the statements were diverse enough to represent the breadth of the concourse on the topic, and additional statements were also considered. Finally, we selected 20 statements as the Q sample. The usual size of a Q sample is 20-to-50 statements, depending on the sophistication of the discourse on a topic (Baker et al. 2006; Dieteren et al. 2023). Given that the concept of SPIS is a niche area, and the discourse on the challenges of expansion is relatively concrete, the size of the Q sample is relatively small. However, as Watts and Stenner (2005) note, irrespective of any efforts, a Q sample can never be ‘complete’, as there is ‘always something else to be said’ about the topic. Although the task of selecting the statements is important, the choice has little effect on the outcome of the factor analysis, as even with 10 statements, there can be over 1.2 million unique ways of sorting the cards (Baker et al. 2006). We retained the statements in verbatim and natural language, as the Q method does not work well with standardized statements, which also means the statements can have more than one interpretation (Walder and Kantelhardt 2018).

• (3) Selecting the respondents (or the P sample) and the card-sorting task: The P sample consists of key informants from different stakeholder groups, in our case 22 respondents, whose details are provided in Table 2. In a Q survey, the aim of sampling is not to be representative, as generalization or prediction is not the goal of the method (Brown 1993; van Exel and Gjalt 2005; Watts and Stenner 2005). Instead, we purposively selected respondents who could have different perspectives (Dieteren et al. 2023). Each respondent was provided with a set of cards with statements from the Q sample in bilingual format, with a randomly assigned serial number. The respondent was asked to initially group the cards into three pools: agree, disagree, and neutral. Then the respondent was asked to arrange the cards in a quasi-normal distribution, with a bipolar agree-disagree, three-point continuum, as shown in Fig. 1. The choice of a respondent's arrangement of the Q sample in this distribution is called a Q sort. The sorting task is when the participants engage with the statements and rank them in comparison to others based on their own point of view, and the completed sort reflects what they think. The process of Q sorting is not a measurement; there is no right or wrong way of ordering. In fact, it is a set of items valued by respondents based on subjective criteria.

We programmed the sorting task using the Easyq websort software interface. The sorting was carried out face-to-face and was also self-administered. For the face-to-face interviews (e.g. with farmers), we used the web version of ‘Easyq’ to collect the data. For other respondents, we emailed the questionnaire with a unique ID and password. The survey consisted of three parts. In the first part, the objectives of the survey and the task were introduced, along with a request to sort the cards into three piles: agree, disagree, and neutral. In the second part, respondents were asked to sort the cards as explained above. In the third step, a few important socio-economic characteristics of the respondents were collected, along with follow-up questions from the sorting task. Specifically, we presented the cards that they ranked in the extremes (−3 disagree and +3 agree) and asked their specific reasons for placing them at the extreme ends of the distribution. This provided us with the additional information required to interpret the results.

• (4) The by-person factor analysis and interpretation: A Brown centroid factor analysis with varimax rotation was conducted, whereby the persons (not the items or the statements) were grouped into different clusters based on the correlation between the Q sorts. Based on van Exel et al. (2015), we initially extracted all the factors supported by the dataset, and examined the statistical properties and interpretability. We extracted two factors with at-least more than two respondents loading on them. For each of the factors extracted, factor scores for statements were calculated from the factor loadings of the individual and the score given to a respective factor in the Q sort. All the statements are sorted back into the grid, to represent the idealized Q sort for the factor: this is the general point of view expressed by the respondents loaded on to the factor. This idealized Q sort is then used to describe the factor or the specific point of view represented by the factor. Furthermore, we examined three statistical dimensions of factor analysis: (1) We retained only those factors with at least two Q sorts uniquely loading to them (so only two factors retained); (2) composite reliability—higher reliability indicates that the factors are consistent; and (3) factor correlations—lower factor correlations indicate that the factors are distinct and interpretable.

The interpretation of the factors is mainly based on the idealized Q sort for each factor and the dominant items in each of them. Apart from using the idealized Q sort to explain factors, we specifically focussed on two types of statements within each factor: characterizing statements, which are those ranked at the extremes (+3 or −3) in the idealized Q sort; and consensus or distinguishing statements. Consensus statements are those which are similarly placed across factors and do not have a statistically significant difference (P values of less than 0.05 and 0.01 are used) in scores across factors. The statements with significant P values are called distinguishing statements; these are statements that actually differentiate the factors. These statements, along with specific statements or comments made by respondents in the Q sort follow-up survey, were used for interpretations.

5. Results

The set of items and statements selected for inclusion in the Q sample is provided in Table 1. As highlighted in the methodology above, the statement selection is based on the criteria of being representative of the discourse on the topic. The statements represented different challenges of grid-connected SPIS expansion, as well as uncertainty among stakeholders about whether SPIS should be scaled up, due to the negative externalities (S6 and S7) and whether alternative technologies should be considered instead of SPIS (S9 and S13). These statements are included to represent the discourse against scaling up SPIS. The statements were also translated to a local language (Kannada), and randomly assigned statement numbers between 1 and 20.

The details of the respondents who participated in the sorting task are given in Table 2. We have included the respondents across six stakeholder groups. As highlighted in the method section, the focus was not just to represent all the stakeholder groups but to include those who could have different perspectives on the topic. Based on the in-depth interviews, we identified a lot of discussions and different perspectives on the policy options for SPIS among the scientific community. Hence, the sample consisted of a greater number of researchers, although they represented different research domains such as agricultural engineering, environmental sciences, and resource economics. The average age of respondents and professional experience ranged from 38–50 years to 7–22 years, respectively (Table 2). The range of experience relative to SPIS ranged from 2 to 11 years whereby farmers and electricity distributors were at the lower end of the distribution, while the Ministry of Renewable Energy and Pump Manufacturers were at the higher end of the distribution.

The Q sorts from the respondents were subjected to by-person factor analysis. Brown's centroid method was used for its suitability in Q method studies. The scree plot for the analysis is given in Fig. 2. Based on eigen value and at least two individuals loading on a factor to be considered, we extracted two factors. The factor loadings are given in Table 3. A low factor correlation and high composite reliability within factors (Table 3) indicates that the factors are distinct and can be interpreted separately. Idealized Q sort based on the factor scores is presented in Table 4 and forms the basis for interpretation. Since the factor loadings determine the weights in calculation of the factor scores, only the respondents with factor loadings of more than 0.5 were flagged (marked with *), as highlighted in Table 3.

5.1 Factor interpretation

Factors are considered the ‘bones of the concourse’ (Wolf 2004); they provide a structured view based on a self-referent, participant-led classification based on subjective expression embedded in the sorting exercise. Using the factor scores across the two extracted factors (Table 3, and the characterizing statements in each factor (scored as ±3), we have named the two factors in order to facilitate interpretations.

Perspective 1: Economic and financial barriers to scaling up SPIS

Looking at the factor scores from Table 4, the respondents loading on to this factor highlight the issue of high initial cost (S15, scored at +3), and difficulty in availing a loan (S5, scored at +2) as important constraints for expansion. A typical SPIS, with a 60 per cent subsidy for the upfront cost, would result in price of US|${\$}$|1,500, which is almost three-times the cost of a conventional electricity-powered irrigation pump. If the farmer has to pay this amount, liquidity would be a major constraint, particularly for small and marginal farmers, who might already have a current loan from the bank and could find it difficult to access another loan. Furthermore, the other items ranked positively are the lack of incentive to adopt SPIS as electricity is free (S12, scored at +2); the subsidy for the upfront cost and environmental benefits not being enough of an incentive (S18, scored at 1); and the lack of alternative technology that can use the surplus solar energy (S3, scored at +1). All of these statements highlight that if the farmer already has an existing irrigation pump, then in the present framing, there is little incentive for them to adopt. In essence, the respondents loading on to this factor focus on getting the incentive right and then addressing the financial constraints so that farmers can adopt SPIS. However, we also need to look at the statements ranked on the other extreme as well. Possible negative impacts like groundwater extraction due to SPIS (S16, scored at −3), and environmental pollution due to unused panels (S6, scored at −2), are regarded as the least important challenges from this point of view. Also, alternative options to SPIS, which can also produce green energy in the form of centralized solar parks (S9, scored at −1), and promoting the use of solar panels in fallow lands (S13, scored at −3), are considered less important challenges.

Perspective 2: Institutional and governance challenges for scaling up SPIS

The respondents loading on to this factor placed greater emphasis on addressing the institutional issues in scaling up SPIS. Lack of coordination between different departments implementing the scheme (S3, scored at +3), governance issues in last-mile delivery (S2, scored at +3), and a lack of regional-level planning (S17, scored at +2) highlight the pivotal point of view that institutional coordination and better governance are important in scaling up SPIS. This view highlights that the present implementation structure of the PM-KUSUM scheme for SPIS needs to focus more on achieving institutional convergence. Also, it is important to note that alternative technological solutions like greening the grid (S13, scored at 1) and solar parks (S9, scored at 0) are ascribed relatively greater importance. Perceptions of solar as a complex technology, which thus faces difficulty convincing farmers to adopt (S10, scored at −3), and farmers lacking awareness (S1, scored at −2) are viewed as less challenging issues during the expansion of SPIS. This point of view also postulates that technical challenges like the falling efficiency of solar panels (S19, scored at −3) and maintaining load balancing with grid connections (S20, scored at −1) can be easily addressed if the institutional and governance issues are taken care of.

5.2 Consensus statements: Points of agreement

The lack of local repair and maintenance (S4, scored +3 and +2), the subsidy for the upfront cost and the environmental benefits not being enough as incentives (S18, scored at +1 in both factors), and the difficulty in maintaining load balance at the grid (S20, scored at −1) are the three consensus statements placed similarly across the factors. Both points of view were unanimous that without repair and maintenance services at the local level, it will be impossible to scale up SPIS, and the present implementation structure gives this element no explicit consideration.

6. Discussion

In this paper, we examine stakeholder perceptions of the challenges of SPIS expansion in India using the Q method. We found two major points of view: economic and financial challenges; and institutional and governance challenges for scaling-up. Although the Q method does not enable us to predict the proportion of respondents falling into these competing and equivalent narratives, it also allows us to provide a richer description of stakeholder perceptions. In this section, we elaborate further on these two points of view using statements from the post-Q-sorting finishing interviews to provide a more nuanced description.

In the case of SPIS, like most technologies to mitigate climate change, the social benefit is large in terms of clean energy production, with no private financial benefit for adoption. The private benefits of SPIS are pronounced only in terms of reliable daytime power, and autonomy. However, even after a 60 per cent subsidy, the upfront cost of SPIS is almost three-times that of a regular electricity-powered irrigation pump. So, given this condition, a 60 per cent subsidy for upfront costs and a large social benefit may not be enough of an incentive to promote adoption and scaling-up, as highlighted in the first factor. Many papers have highlighted high upfront cost as a major obstacle to SPIS adoption in India and elsewhere (Jain and Shahidi 2018; Otoo et al. 2018; ICID 2019; Adhikari 2020; Bastakoti et al. 2020). On top of the high upfront cost, small and marginal farmers might also find it difficult to access loans from a bank, with limited assets to mortgage as security.

‘Right now, one solar irrigation pump costs approximately Rs 130,000. To give perspective, the electric pump costs around 30,000 (starting), good pumps maybe 60k. So why will a farmer want to pay twice the amount to get a solar pump? I understand that daytime power is good but if the farmer has to pay twice the money just for that one advantage, it is not going to work out.’ R17, farmer, local leader

In addition, there is no local repair and maintenance service infrastructure for SPIS in India, which was a consensus statement across the two factors. This is a chicken-egg story: the infrastructure would develop if there was SPIS adoption, yet to increase adoption, development of local service delivery is important. At the moment, the companies who install SPIS will provide five years of warranty, as local repair and services do not exist. However, what is more important is farmer perceptions of the timeliness of service.

‘For us, the most important thing is repair. Repair is costly for submersible pumps. If the pump breaks down frequently it will be very costly. Also, if the person is not available in time we may lose the crop. But in our district only one (person) is available.’ R2, farmer

‘Local repair services for me is the most important constraint in terms of SPIS. If the government doesn't spend on building capacity at local level, there won't be takers for SPIS. Anyone in the village can repair a diesel pump. But for SPIS, this is not the case. Even if a diesel pump stops working after three years, one can buy a new one for one-tenth of the cost of SPIS. So, without local (at least one per district) servicing centres, SPIS would never be taken up on a large scale.’ R24, scientist

The second factor highlights the issues of a lack of institutional convergence and the governance challenges of scaling up SPIS. Within the ambit of PM-KUSUM, it is the prerogative of the state to choose the implementing agency for the scheme. For example, in Karnataka, it is the Karnataka Renewable Energy Department Limited (KREDL) that implements the scheme. In our interviews and interactions, we found that there is no coordination with other concerned departments, like the Department of Agriculture, or any other line department engaged with farmers. Neither KREDL nor the electricity distribution companies (utilities) have experience dealing with farmers, nor do they engage in awareness-raising activities like those of the Department of Agriculture. Yashodha et al. (2021) also highlight this lack of coordination among different departments as a challenge.

‘Absolutely no coordination. Ministry of Agriculture is not even involved in the loop (in Karnataka). We understand farmers, we are the one who give the machinery and other things to them. But MNRE has not even kept us in the loop. At least to create awareness. I came to know about this by reading newspaper.’ R10, agricultural officer, Karnataka state

‘Since the scheme was initially implemented in the pilot mode, the number of pumps available for a region was very limited. So, neither awareness-creation nor publicity was given. When the technology is scaled up they did not make any changes to the institutional setup with which it was implemented in the first phase.’ R9, engineer, electricity distribution company, Karnataka state

Furthermore, if we expand SPIS, it is important that the grids are properly maintained so that the surplus electricity generated can be fed into the grid with farmers being paid for the units of energy delivered. This system not only incentivizes the farmer to save water, but also acts as a monetary incentive for adoption (depending on the Feed in Tariff—the per unit of electricity paid to farmer). The payment to farmers can also be seen as an element of climate justice, as the farmers are adopting technology to mitigate climate change. However, the major challenge, as highlighted by the second perspective, is the governance capabilities of the utilities to implement this scheme.

As mentioned before, in most states, the electricity supply for irrigation purposes is either free or charged at a flat rate. The utilities are compensated by the state governments in the form of subsidies. As the electricity supply for agriculture is not metered, in most states, the agriculture grids are poorly maintained. Transmission losses, voltage fluctuations, and frequent breakdowns of transformers in agricultural grids are common. For a grid-connected SPIS with a net metering facility, the grid needs to be very well maintained; many of the old grid infrastructures might require a complete overhaul for the system to work. Additionally, grid separation for agriculture is needed to implement this mechanism, as highlighted by Yashodha et al. (2021). If this is not achieved, the grids cannot take in the surplus power from solar pumps, and the farmers will not get paid. This system will also need substantial additional human resources at the level of utilities, without which the system cannot be scaled. If scaled, it might lead to failures, as in case of the Surya Raitha pilot scheme in Karnataka, India, where due to the use of inappropriate technical components in grid connection, the utilities were unable to take in the electricity, and could not fulfil the promise of paying the farmers. Consequently, the pumps are currently running as stand-alone SPIS. The governance and last mile delivery at the level of utilities need improvement along with convergence to accelerate the transition toward SPIS.

‘In my experience of handling projects on Solar irrigation pumps, even in states like Gujarat where the agricultural grids are better maintained compared to the other states, they still struggle in dealing with farmers. Often the agricultural grids are not well-maintained, and utilities have much less man power to deal with farmers in installation and maintenance.’ R26, researcher

6. Limitations of the study

The Q method is a small sample method focused on exploring the subjectivity of respondents, but at the cost of generalizability. The proportion of people ascribing to different views cannot be predicted from this method. This is mostly a descriptive method to facilitate a rich and nuanced description of different perspectives on a complex and contested topic like solar irrigation, but it cannot generate specific policy prescriptions.

7. Conclusion

We examined stakeholder perceptions of the challenges of scaling-up grid-connected SPIS in India using the Q method approach. We have identified two distinct viewpoints or perspectives on the challenges of expanding grid-connected SPIS in India; the economic and financial challenges; and the institutional and governance challenges. These are equivalently valid narratives, and if un-addressed they could potentially compromise policy objectives of scaling up. Based on the first viewpoint, policy makers and implementing agencies should focus on better positioning and framing SPIS considering the micro-level incentives for adoption. Removing the financial barriers by linking credit with SPIS can help to ease farmers’ liquidity constraint for adoption. At the same time, as indicated in second perspective that focusses more at factors at meso and macro level, policy makers and implementing agencies should also focus on achieving convergence between different government departments and organizations concerned. Specifically, the Department of Agriculture and Horticulture could play a more central role in implementing the scheme. This would also help in improving the last mile delivery, as these departments have experience of working closely with farming communities, unlike utilities. Development of local repair services is also crucial in scaling up the adoption of SPIS which was common for both the perspectives. Such a nuanced understanding of different perspectives facilitates communication and alignment between stakeholders and helps policy makers to incorporate community perspectives into the policy and Q method can be a useful analytical tool for this purpose. The paper also demonstrates that by considering perceptions of different stakeholders and heterogeneity in their perceptions, we can uncover different narratives that can facilitate creating a common space or actor coalitions to negotiate and co-innovate to foster adoption of the technology, which can be relevant for other countries trying to accelerate transition to solar energy in agriculture.

Footnotes

Acknowledgments

The authors would like to acknowledge and thank the financial support for the study from the Netaji Subhas ICAR-International Fellowship program and IRI THESys. The authors thank the editor and the reviewers for their critical comments, which helped to improve the paper. Earlier versions of the paper were presented at the Tropentag Conference 2023 and the EAAE PhD Workshop 2024 and greatly benefited from the discussions. The ethical clearance for the survey was obtained by the university ethics committee before starting the data collection process.

Data Availability

The authors confirm that the data supporting the findings of this study will be provided with supplementary materials.

References