Barrier identification and prioritisation for the deployment of solar photovoltaic systems in dairy farming in India using the ISM–MICMAC approach

ABSTRACT

Dairy farming needs a strong foundation of energy security in rural India for its sustainability. Solar Photovoltaic (PV) systems in dairy farming act as a catalyst to ensure Green Energy Measures (GEMs) and to achieve the various missions of the Indian Government. The deployment of solar PV systems to fulfil energy needs in Indian dairy farming has several issues and barriers. In this investigation, various foremost barriers creating hindrances in deployment of PV systems in Indian dairy farms have been identified. Contextual relationships between identified barriers have been established by Interpretive Structural Modelling (ISM) and classified using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC). It is used to determine the ranks and types of hindrances and to develop a structural model. The strongest driving force barriers are high initial capital cost, storage devices barrier, policy and regulatory issues, less education and weak affordability in rural and long payback periods. The strongest dependent force barriers include societal acceptance, fewer concerns about ecological and environmental issues, and a lack of entrepreneurs and innovations in the rural sector. Finally, the future roadmap to address the obstacles in exploitation of PV systems in context of dairy sector has been described.

KEYWORDS:

• Rural
• dairy farming
• solar energy
• solar photovoltaic
• barriers
• interpretive structural modelling
• India

1. Introduction

Almost two-thirds of the Indian population primarily reside in rural areas and primarily depend on farming and its allied sector to earn their livelihood. The population of India is increasing persistently and led to a population dividend demanding more healthy nutrients and employability (Ramesh, Srivastava, and Jaspal 2017; Mensah 2019; World population prospects 2017). Animal husbandry plays a pivotal role in rural livelihood and self-employability with a major contribution to agricultural Gross Domestic Product (GDP). In sustainable growth of the economy, rural development is a cornerstone for the balanced socio-economic growth of the country. In absolute terms, agriculture’s GDP is growing, but its share in the total gross GDP of the Indian economy is decreasing which influences the rural population (Ali 2007; Taneja 2021). The present Indian Government has an ambitious action plan to double the income of farmers by 2022. It may be achieved by an increased focus on the animal husbandry sector and by replacing the production-centric perspective of dairy farming with an income-centric perspective. It is growing significantly at a sustainable rate and is in advance among all subsectors of agriculture. The animal husbandry sector as the economic backbone of rural India plays an indispensable role in risk mitigation, absorbing income jerks arising due to crop failure and minimising the losses to the farmers (Ramesh 2016).

Undoubtedly, India is the topmost producer and consumer of milk in the world. The small family farms dominate Indian agriculture and are involved in the rearing of milch animals. The small milk groups contribute the highest 66.6% share of the value of the product within livestock farming. Dairy farming is vital means to provide livelihoods and nutritional security to the rural and urban population. It is the oldest and largest growing enterprise that can address the demands of the growing population. In fact, it is considered an important economic activity to increase farmers’ income and also ensure self-employment for the rural population. In rural India, income is mainly generated from agriculture and its allied sector but currently it is facing numerous issues and challenges. Indeed, in the last three decades the need for food grain and milk products has continuously expanded. It is the need of hours to cope with the increase in the utility of milk and its products by strengthening dairy farming across India. Presently, the dairy sector faces several challenges such as a lack of uninterrupted power supply, lack of skilled labour to manage routine dairy farming activities, increase the cost of fodder and livestock health issues, to name only a few. It is the need of the hour to address the issues and challenges and devise an optimal framework for the survival of the dairy sector in rural India (Mayank et al. 2019; India 2020 Energy Policy Review 2020; Charles Rajesh Kumar and Majid 2020; Nacer, Hamidat, and Nadjemi 2016).

Dairy farming is the oldest grassroot occupation for the major workforce of rural India which comprise largest livestock in the world. Primarily, grid supply based on conventional energy resources and fossil fuels is a major source of electric supply in dairy farms in rural India. 24 × 7 access to an uninterrupted power supply is a vital input for the survival and growth of the dairy sector. Dairy farms in rural India realise an acute shortage of electricity. The deployment of green stand-alone energy resources, such as solar, bio-energy or hybrid combination, can address the energy crises to meet routine dairy farming activities. Solar PV system deployment can be an optimal and critical solution to meet energy demands in rural dairy farms. The potential application of the deployment of solar PV systems in dairy farming is depicted in Figure 1.

Figure 1. Potential applications of deployment of solar PV systems in dairy farming (Desai Deepak et al. 2013).

The Government of India has a strong willingness to broaden the base of green electric power generation in the rural and agriculture sectors. But, due to various obstructions in the deployment of PV systems in rural and dairy sectors, renewable energy utilisation is far from the reach of the rural segment of society involved in dairy farming. In fact, the rural population, including women, landless labourers, and marginal farmers, includes a stumpy level of education and economic constraints on resources. Undoubtedly, this section of the community deviates from the deployment of cutting-edge technology in the dairy sector which is the major resource of their livelihood. The generation of electricity using distributed solar PV offer vast potential in dairy sectors. Solar energy in dairy farming can be used for different activities such as lighting, milking, cooling, cleaning, air conditioning, water, etc. (Bey et al. 2016). Automatic milking systems are benign activities to minimise labour, and the timing of milking. It has the revolutionary potential for dairy growth (Calcante, Francesco, and Tangorra 2016). Solar energy has the potential to change the face and pace of dairy farming. But, there are several peculiar barriers to the deployment of PV systems in dairy farming. In the succeeding section, efforts have been made to recognise various sensible impediments to the deployment of solar PV systems in dairy farming in rural India.

2. Investigation of barriers-literature background

The demand for electric energy in rural India is one major anxiety which needs more focus and attention for rural prosperity and socio-economic growth. In this context, green energy measures through solar PV deployment may be a viable solution. However, there are several issues and challenges in the deployment of solar PV systems in diverse applications in rural India to meet its daily energy needs. Indeed, the exploitation of solar PV systems in rural areas is still in its pre-mature state and requires investigation of numerous barriers in its diffusing path. The foremost step in the assimilation of solar PV systems in rural applications is to determine various unidentified critical barriers along with their ranking, interlinking and their types which restrict their exploitation. In this direction, in the last two decades, numerous investigations have been executed by several researchers locally and globally to identify various obstructions in the utilisation of GEMs in diverse applications and a few of them are presented in the following paragraphs (Calcante, Francesco, and Tangorra 2016; Desai Deepak et al. 2013; Yadav, Jadhav, and Choughgule 2016; Alka and Yash 2021; Divya et al. 2012; Ratnakar Mane 2013; Lipismita and Pattanaik 2014; Yadav, Jadhav, and Choughgule 2016; Rajeshwaran and Gopal 2021; Chand et al. 2017; Chopde, Datir, and Patil 2019; Ravi and Satish 2017; Vekariya, Rajput, and Vataliya 2021; Raj and Sehgal 2015; Vinutha et al. 2016; Neerkuzhi and Jenson 2013; Porchelvi Shophia and Sathya 2016; Chopde Santosh, Patil Madhav, and Adil 2016; Sudarshan, Naga Chaitanya, and Pakkiraiah 2019; Mayank et al. 2019; Mukul, Amandeep, and Ruchika 2020; Alka and Yash 2021; Upton 2010; Calcante, Francesco, and Tangorra 2016; Nikbakht et al. 2017; Sarnavi et al. 2017; Zhang et al. 2017; Maameur et al. 2017; Kaewprathum et al. 2019; Ashkan et al. 2019; Borek and Romaniuk 2020; Seetharaman, Nitin Patwa, and Gupta 2019).

Parbhu, Narayanan, and Mathew (2008) explored the status of the Indian solar renewable energy industry and determined various important barriers viz. financial investment, the role of government leadership and legislation, research and development, and public consciousness. In that investigation, these barriers are explored very well that is still necessary to describe which barriers are most decisive for the installation of solar renewable power (Parbhu, Narayanan, and Mathew 2008). MA Choudary, Raza, and Haya (2009) explored various effective barriers to the realisation of solar technologies and determined that government participation, public awareness, relevant education and skill development, research and development are crucial barriers to the expansion of solar renewable power in developing countries (Choudary, Raza, and Haya 2009). However, the work explored a few barriers and some numerous issues and challenges that exist in the diffusion of solar renewable technology. MF Ansari et al. (2013) conducted a critical investigation and identified 13 prominence barriers in the diffusion of solar power installation using the ISM and MICMAC methodology. It also proposed various suggestions to address barriers to the use of solar power in the context of India (Fahim et al. 2019). Dulal et al. (2013) proposed various obstructions in the dispersion of renewable energy in the context of Asia. It also highlights the government intervention and support to counter these barriers (Dulal et al. 2013). Suzuki (2013) conducted a crucial study on the roles of institutions of national and international importance to decrease the intensity of barriers in the deployment of GEMs in Asia (Suzuki 2013).

Kapoor (2014) explored and reviewed several major barriers in the path of fast diffusion of solar power in the Indian context to achieve its proposed goals to meet the objective of the renewable energy policy of India (Kapoor 2014). Sun and Nie (2015) proposed the role of two major policy measures which include feed-in tariff and renewable portfolio standards to control the cost and base of expansion of GEMs with the stimulation of research and development (Sun and Nie 2015). Nagamani et al. (2015) et al. investigated several critical barriers such as the lack of a strong manufacturing base of PV modules, huge investment costs, and lack of land in spurring the diffusion of solar power in the Indian outlook (Luthra et al. 2016). Philipp Blechinger, Richter, and Renn (2015) conducted a major study on barriers and their solutions in the diffusion of renewable energy technologies for distributed energy generations (Blechinger, Richter, and Renn 2015). Luthra et al. (2016) determined and analysed 16 such obstructions in the development of the diffusion of solar power, and also found the prominence barriers using the fuzzy DEMATEL methodology (Moallemi et al. 2017). PKS Rathore, Rathore, and Singh (2018) carried out a prominent analysis of five crucial obstacles in the deployment of solar PV systems from the Indian perspective. The authors also proposed a rating of hindrances and found that lack of regularity, legislation policy, and technological issues are prominent barriers to the diffusion of solar power (Rathore, Rathore, and Singh 2018). Moallemi et al. (2017) explored several dominant barriers in the expansion and development of solar power in India and the authors strongly recommended the reinforced political willpower and need for deep government interventions (Moallemi et al. 2017). Hairat and Ghosh (2017) explored various challenges in the path of the exponential proliferation of solar power in the context of India and assessed that techno-economic challenges are critical in this context (Hairat and Ghosh 2017). Punia Sindhu, Nehra, and Luthra (2016) identified and evaluated numerous critical barriers using ISM and fuzzy MICMAC approach in the context of rural India and also proposed mechanism for the abatement of these identified barriers (Punia Sindhu, Nehra, and Luthra 2016). J. Jeslin Drusila Nesalar et al. (2017) conducted a study about the status, prospects and barriers in the diffusion of renewable green energy in the context of Tamilnadu state from the Indian perspective (Nesamalar, Venkatesh, and Raja 2017). Seetharaman, Nitin Patwa, and Gupta (2019) explored various key barriers, namely, economic, socio-cultural, institutional and technological, policy, and regulatory barriers creating hindrance in the use of GEMs in the Indian context (Seetharaman, Nitin Patwa, and Gupta 2019). Agbonifo (2021) proposed various obstacles and possibilities in clean energy developments in the context of global energy politics (Agbonifo 2021).

The above studies highlight the present status, prospects, identification of barriers and their redressal necessity for the emergence and incorporation of RES in the Indian context (Punia Sindhu, Nehra, and Luthra 2016; Fahim et al. 2019; Karakaya and Sriwannawit 2015; Streimikiene, Balezentis, and Alebaite 2020; Stigka, Paravantis, and Mihalakakou 2014; Dulal et al. 2013; Nesamalar, Venkatesh, and Raja 2017; Byrnes et al. 2013; Suzuki 2013; Ohunakin et al. 2014; Sun and Nie 2015; Agbonifo 2021; Blechinger, Richter, and Renn 2015; Nasirov, Silva, and Agostini 2015; Olowu et al. 2018; Emodi, Yusuf, and Boo 2014; Rathore, Rathore, and Singh 2018; Sindhu, Nehra, and Luthra 2016; Chaianong and Pharino 2015; Chauhan and Saini 2015). However, specific studies on the identification of obstructions in the deployment of solar PV systems in the dairy sector are rarely reported. The adoption of green solar power is rapidly growing and emerging in India with an installed capacity of 61966.36 MW as on 30 November 2022. It reflects the commitment of central governments towards increasing the share of GEMs, technology advancement, leadership, public-private sector participation and the base and pace of solar PV systems. But, the adoption of green energy generation using solar PV systems in the rural sector and particularly in dairy farms have yet to develop its pace of growth in the diffusion of PV technology due to several critical hindrances. Although several studies were undertaken in the past, to recognise and rank various barriers in the deployment of RES in rural India, identification of barriers and their contextual relationship in the context of dairy farm in rural India is yet to be carried out. So, this investigation is an attempt in this direction to fulfil the research gap by identifying various barriers restricting the deployment of dairy farms in rural India.

3. Identification of barriers

Barrier identification and prioritisation are pre-requisite in the deployment of PV systems in dairy farms. Therefore, in the present investigation, 18 significant barriers, as depicted in Table 1, have been identified and brief outline of this is given in the following subsection.

Table 1. Barrier identification of solar PV System deployment in dairy farming (Padmanathan et al 2021; The Energy Progress Report Tracking SDG 7 2020; Chatterjee et al. 2019; El-houari et al. 2019; Ahmed et al. 2019; Renewables 2019 global status report 2021; Garlet et al. 2019; Hernández-Callejo, Gallardo-Saavedra, and Alonso-Gómez 2019; Peimani 2018; Shahsavari and Akbari 2018; Alexandros and Vassilis 2020; Horváth and Szabó 2018; La Camera 2018; Masson and Kaizuka 2019; Serafeim et al. 2019; Kevin et al. 2018; Aly et al. 2019; Sindhu, Nehra, and Luthra 2017; Sindhu et al. 2017; Jordan and Kurtz 2021; Yenneti 2016; Khoury et al. 2016; Kannan and Vakeesan 2018; Novacheck and Johnson 2015; Almasoud and Gandayh Hatim 2015; Rachchh, Manoj, and Tripathi 2016; Kapoor 2014; Vijay and Mansoor 2013; Strielkowski et al. 2019; REN-21 2020; Alireza and Nwaoha 2013; Khatib, Mohamed, and Sopian 2013; Mekhilef, Saidur, and Safari 2011; Timilsina, Kurdgelashvili, and Narbel 2011; Byrnes and Brown 2020; Deutsche Gesellschaft für Internationale Zusammenarbeit 2021; Parida, Iniyan, and Goic 2011; Dutzik et al. 2021; Jawaharlal Nehru National Solar Mission towards Building Solar India 2010; Painuly, J. P. et al. 2002, van Campen, guldi, and best 2000).

S. No.	Barriers	Barriers Ellipsis
1.	High Initial Capital Cost of the Solar PV System	B1
2.	Long Payback Period	B2
3.	Issues of Finance Mechanism	B3
4.	Lack of Awareness of Technology	B4
5.	Market Uncertainty	B5
6.	Storage Device Barriers	B6
7.	Lack of Solar Irradiations Data	B7
8.	Issues of Security and Safety Implication	B8
9.	Low Efficiency and Issues of Reliability	B9
10.	Lack of Research and Development	B10
11.	Lack of Skilled Human Resources	B11
12.	Lack of Local Infrastructure	B12
13.	Policy and Regulatory Issues	B13
14.	Fewer concerns about Ecological and Environmental Issues	B14
15.	Lack of Entrepreneurs and Innovations in Rural	B15
16.	Hindrances of Societal Acceptance	B16
17.	The dominance of Fossil Fuel Power Generation	B17
18.	Less Education and Weak Affordability in Rural	B18

3.1. High initial capital cost of the solar PV system (B1)

It is a key barrier to the deployment of PV systems in dairy farms in the Indian rural perspective. The purchasing cost of panels is gently declining continuously. But the cost of solar panels is still high in concern of dairy farming. The capital cost of establishing PV projects includes the cost of devices that exceeds 50% of the entire cost of setting up PV systems during commissioning and soft costs such as funding and system design for the rest (Khoury et al. 2016). In India, the base of manufacturing of PV modules and the balance of the system are weak and solar panels are mainly imported from outside countries. Although the manufacturing of PV panels is growing it needs more attention. The taxation on PV modules and weak manufacturing base lead to the high cost of solar systems. In fact, rural populations involved in dairy farm activities are many landless, small, and marginalised farmers having weak economic positions and mainly lacking to afford PV systems in dairy farms. The economic position of small farmers is a vital weakness to afford PV systems in dairy farms. But, as per global trends, India has become the cheapest developer of solar PV power generation.

Figure 2 indicates that there is a decrease in the investment cost of solar energy systems between 2010 and 2020. The investment cost of the PV system is decreasing due to bulk investments and advancements in technology (Sun and Nie 2015). Moreover, the installed capacity of India is also growing exponentially.

Figure 2. Installed solar PV power in India and capital cost (Ohunakin et al. 2014).

However, the deployment of solar PV systems in dairy farming is insufficient and at a premature level.

3.2. Long payback period (B2)

A longer payback period is also one of the important obstructions to the exploitation of PV systems in a dairy farm (Kannan and Vakeesan 2018). It is well recognised that dairy farming is labour-intensive farming with low outcomes. Initially, the exploitation of solar PV systems in dairy farms is not instant to increase the yield of dairy farms. But, it is used to improve the sustainability and well-being of livestock in dairy farming. The components of the payback are the gross cost of the system, upfront incentives such as tax credits, local incentives, saving yearly electricity bills, and income from solar renewable energy certificates. The variation between the gross cost of the PV system and upfront incentives is known as the combined cost and the sum of saving per year and additional incentives termed as an annual benefit (Novacheck and Johnson 2015). The ratio of combined cost to annual benefit is called the payback period. It is expanded due to the high investment in solar PV systems, low efficiency of PV panels, maintenance, and operating cost. Besides these, it is also influenced by the location of installation and the direction of solar panels. Environmental factors such as temperature, dirt, shading, and manufacturing untolerances also increase the reimbursement duration of the solar PV systems of dairy farms.

3.3. Issues of finance mechanism (B3)

Financial barriers comprise non-availability of reasonable infrastructure and ambiguous financing facilities create hindrances in the deployment of PV systems in dairy farms (Almasoud and Gandayh Hatim 2015; Rachchh, Manoj, and Tripathi 2016). Rural dairy farming is facing financial constraints due to a lack of a strong financial institutional framework for financing solar PV systems in dairy farming (Kapoor 2014). It involves high investment risks and the challenging issue of high rates of interest (Vijay and Mansoor 2013). Lack of availability of capital from financial institutions is the major concern in the exploitation of solar PV systems in Indian dairy farms. The enterprises, industrialists, banks, and governments are different stakeholders to finance solar PV power systems for rural applications such as dairy farming. The development of the integration of solar PV systems into dairy farms is still at its premature level in India. Solar PV systems are cost-intensive investments and need more capital to deploy in dairy farms. The cost of PV systems is high due to the cost of solar panels, storage devices and the balance of the system. Furthermore, the cost is escalated by maximum power point tracker controllers, electric wires, earthing, net metering, etc. A solar PV system with a balance of systems needs vast funds for its development. It is necessary to overcome issues of financial mechanisms in the context of the deployment of solar PV systems at grass root level in dairy farms. Innovative digital financial regulations may escalate solar PV technology installations, its broad manufacturing base, research and development activities, and training institutions. Reforms in banking sectors are needed to endorse the dissemination of solar energy in dairy farming.

At present, sectoral categorisation of banks perceives solar energy as a subpart of power farming. Under that, for public and private banks, the finance limit is broadly used by thermal power plants and merely a marginal share of the finance accessible for PV projects. In India, solar PV energy reveals exponential growth and presents stunning revenue to the government. Thus, it is necessary to categorise solar PV power as a distinct sector. It provides active pathways for more access to finance and resolves the mechanism of finance procurement for solar PV projects. The governments can also consider according to priority farming status solar energy and its strategic importance. Orientation towards rural and dairy farming of bond markets will facilitate securing reasonable funding for solar PV projects in the future. Rural-based banking systems have the potential to avail more finance at a competitive outlay to thrust solar energy for dairy farmers (Alka and Yash 2021).

3.4. Lack of awareness of technology (B4)

Undoubtedly, two-third of the Indian population lives in rural and their livelihood depends on dairy farming and the agriculture sector. Rural people are still unaware of the benefit of solar PV systems in dairy farms. The solar system plays an indispensable role to generate on-farm electricity and results in saving electricity bills or dependency on the national power grid. Different types of PV systems are available which can resolve the problem of the non-availability of electricity at remote dairy farms. The deployment of solar PV systems in dairy farms will generate electricity almost for almost 25 years. There are no moving parts in PV systems, hence no wear and tear of dynamic parts, and sound pollution is also not produced by the PV system. The untapped potential of solar PV systems in dairy farming is still premature, but its growth is expected to grow in rural India during 2021–2030 (Lipismita and Pattanaik 2014).

3.5. Market uncertainty (B5)

The sustainable developments of solar energy need a clear and consistent market policy. Market hindrance is also one of the vital barriers which restrict the exploitation of solar PV systems in dairy farms. Market hindrances, moreover, comprise the deficit of skilful market managers, and accessibility of information regarding markets intended for dairy farming and solar PV systems. The solar PV technology market size and its expansion grow, but it is required to divert towards the weaker section of dairy farmers. Subsidy available to solar PV systems is also facing various challenges due to the ambiguity of the market (Lipismita and Pattanaik 2014).

3.6. Storage device barriers (B6)

The output energy of PV panels is intermittent in nature because of the variation of solar irradiations, operating temperature, deposition of dirt, shadowing effects, etc. A battery backup system is essential for 24 × 7 uninterrupted supply of power even when no solar irradiations are available (Strielkowski et al. 2019). Incorporation of solar energy into the power generation system is difficult for large-scale renewable energy deployment because of intermittent characteristics. Advanced storage systems would be important in facilitating the widespread adoption of intermittent solar energy. The cost of PV systems is further raised many folds due to the assimilation of storage utilities to meet peak power demand in intermittent and uncertain conditions (REN-21 2020). The financial and technological risks of investing in battery storage, due to their short life span and the heavy cost, are one of the major obstacles (Alka and Yash 2021).

3.7. Lack of solar irradiation data (B7)

The deficiency of experimental data on sun irradiations is also a major barrier to PV system deployment in dairy farms in the Indian context. Techno-economic feasibility of solar PV systems is only possible using the detailed availability of solar and weather statics. Solar renewable energy projects are site-dependent in nature. Reliable solar irradiation data and weather data are the first and foremost requirements for the realisation of PV systems on dairy farms (Alireza and Nwaoha 2013). There is a need for the establishment of data assessment and collection centres to obtain reliable data on solar radiation. The accessibility of reliable information resources can support the downsizing of barriers in the context of capital cost, reliability, and efficiency of the deployment of solar PV systems in dairy farms (REN-21 2020; Alireza and Nwaoha 2013; Khatib, Mohamed, and Sopian 2013).

3.8. Issues of security and safety implication (B8)

In rural, the security and safety implications of solar PV system deployment in dairy farms are also a considerable barrier. The rural population involved in dairy farming is afraid of stealing or breaking various solar PV components by unlawful elements. Proper earthing and optimum wiring are needed in solar PV systems, but lack of them can harm house members of dairy farmers, animals, birds, etc. But solar PV system deployment in dairy farms has the least risks to human health and the environment. Moreover, India is not rich in producing silicon wafers and imported high-cost wafers which need more safety implications. Therefore, to minimise the cost of imported silicon, India should strengthen the manufacturing stage with safety implications. During the developing stage of solar panels the use of diverse solvents such as hydrofluoric, nitric acid, and sodium hydroxide is a vital cause of risk to health. Manufacturing silicon from silicon dioxide produces harmful by-products viz. crystalline silica dust. It gives rise to harmful diseases such as cancer and silicosis (Punia Sindhu, Nehra, and Luthra 2016).

3.9. Low efficiency and lack of reliability (B9)

The weak chain of advanced technology implementation at a large scale in semiconductor fabrication, manufacturing of PV panels, lithium storage batteries, and additional components of PV systems are critical barriers to the growth of deployment (Punia Sindhu, Nehra, and Luthra 2016). Low efficiency and lack of reliability are significant barriers to the diffusion of PV technology in dairy farms. The subsequent section gives a brief account of these barriers (Byrnes and Brown 2020).

3.9.1. Low efficiency of the solar PV system

The outcome of PV energy production is affected by the primary manufacturing level and the commissioning level. The fabrication of the PV panels includes thin film semiconductor materials, poly-crystalline silicon, and mono-crystalline silicon. At present, the efficiency of mono-crystalline Si-based PV panels is approximately 22%, and a thin film is 4-12% (Deutsche Gesellschaft für Internationale Zusammenarbeit 2021). The approximate efficiency of PV modules is of order 15-20%. But multi-junction-based PV cells may have an efficiency of up to 47% (Parida, Iniyan, and Goic 2011). The solar PV system output increases with an increase in efficiency. The efficiency of different types of solar cells is presented in Table 2.

Table 2. Photovoltaic cells’ critical features (Agbonifo 2021).

S. No.	Materials	Type of PV	Efficiency	Production method	Strength	Weakness
1.	Si	Mono-crystalline	16% and 25%.	Single crystal growth method	97% production	high cost
2.	ASi	Multi-crystalline (amorphous silicon)	14-18%	Cast solidification process	Less expensive	LESS efficiency
3.	GaAs	III-V compound semiconductor PV cells	40% and more	GaAs on the Ge substrates	High efficiency	High cost
4.	Cadmium Telluride (CdTe), and Copper-Indium-(Gallium)-Diselenide	thin film	4-7%	Depositing extremely thin layers of PV semiconductor materials	Less expensive	Extremely low efficiency

At the deployment level of the PV system, the environmental variables such as operating temperature, dust on panels, shadowing sources, and ohmic losses of connecting wires and protection diodes are significant causes of energy losses. Moreover, losses in batteries, inverters, and additional incorporated conditioning devices further increase energy losses and decrease the efficiency of PV systems (Dutzik et al. 2021; Jawaharlal Nehru National Solar Mission towards Building Solar India 2010).

3.9.2. Reliability issues

The inverter is one of the primary causes of energy losses and is supposed to be the weakest link in the PV system. The proper functioning of the inverter has been halted by different factors such as disturbances from the grid, lack of understanding of the production level, reclosing, and the problems linked to interconnections. These factors may cause the frequent failure of the inverter (van Campen, guldi, and best 2000). The assessment of the efficiency of the inverter is fluctuating in behaviour due to maximum power extraction from the controller. The maximum power output of the controller is dependent on climatic conditions, solar irradiation, and the efficiency of solar cells (Akinyele and Rayudu 2016). Moreover, the different components of reliability issues are depicted in Figure 3.

Figure 3. Various factors effects reliability of solar PV SYSTEMS (Mekhilef, Saidur, and Safari 2011).

3.10. Lack of research and development (B10)

The deployment of solar PV systems requires more research and development activities in the field of dairy farms and other areas including milk processing, transportation, etc. In this context, estimation of dairy load profile and energy audits of large dairy to save electric bills require novel studies in India. Milk processing is also one of the major areas of concern that needs more scientific investigation for the best utilisation of dairy products. Besides this, the allocation of budgets for research and development activities of green energy projects is also a matter of concern that needs to be addressed for the growth of agriculture and dairy farming (Kapoor 2014). It is time to execute the second milk operation with sustainable energy which requires focus research and development in this pathway.

3.11. Lack of skilled human resources (B11)

The lack of availability of a skilled labour force is one prime concern in the exploitation of solar PV systems in dairy farms. Trained and skilful persons in the renewable supply chain are required for the operation and maintenance of solar PV systems (Lu and Yang 2010; Lee et al. 2017). Setting up new training institutes for solar PV technology in rural areas further escalates green energy deployment in dairy farms. It will enhance the skill of rural youth and increase employability. The trained worker and experts are not intended to visit and work in remote and local areas until they are lured for more perks and incentives. Due to this, the proper functioning of small solar PV systems in dairy farming in remotes fails (Kapoor 2014).

3.12. Lack of local infrastructure (B12)

The lack of rural infrastructure is also a critical obstacle in the exploitation of solar PV systems in dairy farms (Lu and Yang 2010). The small-scale dairy farmers in rural India are facing the challenges of poor dairy farm infrastructure, limited access to electricity, limited road access, and transportation facilities. These barriers include less access to required components, scattered population, long-distance transmission, and limited rural infrastructures (Blenkinsopp, Coles, and Kirwan 2013). Solar PV systems in dairy farms located in rural areas need improved design and construction to counter issues and challenges of rural conditions (Reddy 2016). Deployment of solar PV systems in dairy farming connected to the grid needs a sufficient infrastructure for the layout of hassle-proof metering and billing system.

3.13. Policies and regulatory issues (B13)

The deployment of solar PV systems in dairy farms has a major barrier to policies and regulatory mechanisms. In this direction, formidable barriers such as weak execution of policies, negligent actions by legislators, lack or shortfall of explicit and encouraging solar energy policies for dairy farming, awkward legislative framework, and scarcity of support for recommending technology play critical roles in the promotion of solar PV system in dairy farms (Strielkowski et al. 2019). As per the demand of farmers, cognizability in directions and legitimate regulatory agreements are the pertinent factors that need to stimulate thought processing. The state government’s sanction process does not allow dairy farmers to obtain clearances through a single window which flatters longer the development cycle (Chauhan and Saini 2015).

3.14. Lack of awareness of ecological and environmental issues (B14)

In rural areas, dairy farmers are facing various critical issues and challenges in direct or indirect ways. The energy demand of dairy farmers also influences our environment. Rural dairy farmers are least aware of the impact of greenhouse gaseous emission and its consequence on weather, climate, and ecosystem. Thus, there is a need to sensitise rural dairy farmers about the consequence of greenhouse gaseous emissions and motivate them for adopting green energy measures. The deployment of solar PV systems such as in dairy farms not only results in meeting energy needs but also abates the emission of greenhouse gases. Energy from PV projects is considered environmentally beneficial because the emission of harmful gases such as carbon dioxide is negligible compared to the traditional source of energy depending on non-renewable fuel sources. Consequently, there is a dire need to examine the environmental and social interests acquired by deploying this advantageous resource of energy to engage rural society in the direction of PV projects for dairy (Donastorg, Renukappa, and Suresh 2017). Another issue in this regard is the disposal of waste in PV projects such as storage devices, panels, and components of systems (Yenneti 2016). Furthermore, Solar PV panel manufacturers also utilise solders having lead and other metals to connect solar PV cells, which could harm the surroundings and pose a well-being risk if leaked (Ministry of New and Renewable Energy 2021).

3.15. Lack of entrepreneurs and innovations (B15)

The growth and utilisation of solar PV systems are hindered in dairy farms due to a lack of entrepreneurs and innovations in rural India. The entrepreneurs in PV systems are either urban-centric or localised in particular states across India. The diffusion of solar PV systems in dairy farms is very limited due to the lack of rural entrepreneurs in the solar market. Remote villages are not familiar with the significance of these renewable energy projects and are almost away from the benefits of state-of-the-art technology in energy production. Dairy farming is a traditional cash-earning activity in rural but it faces the critical challenge of entrepreneurs and innovations. High risks of recovery of investment of PV projects in remote rural dairy farming are discouraging the investors and promoters. Lack of confidence and trust between rural communities and investors is also a hindrance to the growth of solar PV systems in dairy farming.

3.16 Hindrances of societal acceptance (B16)

Public perception and recognition of the imminent solar PV system potential for dairy farming are crucial to overcoming social obstacles (Kapoor et al. 2014). The behaviour of dairy farmers, such as their attitude, perception, gender, age, intelligence, etc., determines the acceptance of solar PV system deployment in dairy farms (Lipismita and Pattanaik 2014). Moreover, the role of the internet, information communication technology, multimedia, print media, etc. can change the level of acceptance of solar PV technology use in dairy farms (Kulkarni 2019).

3.17. The dominance of fossil fuel power generation (B17)

In general, subsidies and a strong market base of fossil fuels-based conventional energy are formidable barriers to the deployment of PV systems in dairy farming. In India, coal is the only natural resource and abundant fossil fuel. According to the Ministry of Power, presently India’s total power generation capacity is 409161.21 MW and almost 236018.90 MW (57.68%) is generated by thermal power. The renewable energy source, including small hydropower, bioenergy, solar and wind energy, contributes to 119512.13 MW (29.20%) as on 31 November 2022 (146). Furthermore, the majority of the coal is found in the country’s eastern regions, necessitating long-distance transportation, mostly by diesel-powered trains. Around 70% of India’s oil is imported, putting a strain on the country’s hard currency. As a result, efforts are being required to reduce coal-fired power plants’ negative environmental and ecological effects. Installed electricity generating capacity is growing at a rapid rate (around 8–10 %/year). Due to this, it is necessary to consider pollution factor and ecological factors. Fossil fuels have low direct costs but high community payouts, while PV systems have more direct costs but low community payouts. PV systems are at a disadvantage because investment costs and system funding are depending on personal high expenses. The regime is concerned with low-cost electricity generation technologies, and it is discernment among government officials that PV system is prohibitively costly.

3.18 Less education and weak affordability in rural (B18)

The less education and affordability of rural society are major barriers to the deployment of PV systems in dairy farming. The major segment of the rural community involved in dairy farming is illiterate or marginally literate. Primarily, the weaker section of rural society, women and labourers with less education are involved in dairy farming. Less education, gender-based discrimination, weaker section of society are also barriers to the deployment of solar PV systems in dairy farms (Lipismita and Pattanaik 2014).

4. Materials and methods

In the preceding section, various barriers hindering the development of solar PV systems in dairy farms have been determined from different sources. These barriers have relations to each other. The relationship between various identified barriers and their prominence can be determined by using ISM. The subsequent subsections provide the details of the ISM analysis of barriers to the deployment of solar PV systems in dairy farming.

4.1. Outline of interpretive structural modelling

Solar PV power implementation has a complex web of barriers that are dynamic in nature. As a result, any mechanism that facilitates the understanding and management of these barriers is required (Mohammed, Parag, and Tara 2016). ISM can outline a concise description of the structure of the barriers under consideration, as well as an organised, directional framework for these complex barriers to solar PV system deployment in dairy farms. ISM is essentially an organised application of the fundamental notion of graph theory and Boolean algebra commenced by J. Warfield in 1973 (Al-Badi, Malik, and Gastli 2009; Owen 2006). ISM is a simple quantitative process for recognising the relationship between explicit variables that define a complex problem or issue and r assemble a plan of action to resolve it.

4.2 Development of contextual relations of barriers using ISM

The interpretive structural modelling methodology for the identification of ranks and types of barriers in the deployment of solar PV systems in dairy farming is represented in Figure 4 (Mainali and Silveira 2015; Beck and Martinot 2004).

Figure 4. Flowchart of ISM for contextual relations between barriers using ISM methodology rules (Punia Sindhu, Nehra, and Luthra 2016; Fahim et al. 2019; https://powermin.gov.in/en/content/power-sector-glance-all-india).

4.2.1. Structural self-interaction matrix (SSIM) of Barriers

Different modes of information are exploited to develop the contextual association between barriers. The resources of information include extensive literature analysis and field visits to dairy farms, etc. Besides this, a field survey is conducted using direct contact with dairy farmers, and PV systems investors and valuable information is collected.

The contextual relationship between barriers is represented by symbols $\mathrm{V},\mathrm{A},\mathrm{X},\mathrm{\&}\mathrm{O}$ and $i\mathrm{th}$ number represents barriers in a ${}^{\mathrm{\prime }}i{\mathrm{th}}^{\mathrm{\prime }}$ row and j number for a ${}^{\mathrm{\prime }}j\mathrm{t}{\mathrm{h}}^{\mathrm{\prime }}$ column in an SSIM matrix (Al-Badi, Malik, and Gastli 2009; Owen 2006; Mainali and Silveira 2015; Beck and Martinot 2004). The contextual relationship symbols in the present study are defined as follows (Punia Sindhu, Nehra, and Luthra 2016; Fahim et al. 2019; https://powermin.gov.in/en/content/power-sector-glance-all-india):

1. V: Stands for ith barriers that lead to jth barriers.

2. A: Represents jth barriers that lead to ith barriers.

3. X: the ith barriers lead to jth barriers and jth barriers lead to ith barriers.

4. O: the ith barriers do not lead to jth barriers and jth barriers do not lead to ith barriers.

Self-structure interaction matrix has been developed using the judgments of academic professionals and industry experts as depicted in Table 3. Cell (3, 8) in the SSIM has the symbol ‘V’ to contextual relation between finance issues and cost barriers.

Table 3. Self-structural interaction matrix of barriers.

S. No.	Barriers	B18	B17	B16	B15	B14	B13	B12	B11	B10	B9	B8	B7	B6	B5	B4	B3	B2
1.	High Initial Capital Cost of the Solar PV System	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{X}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{A}$	A	A	$\mathrm{V}$	A	$\mathrm{V}$
2.	Long Payback Period	$\mathrm{A}$	$\mathrm{O}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$
3.	Issues of the Finance Mechanism	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	A	$\mathrm{X}$	$\mathrm{V}$
4.	Lack of Awareness of Technology	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{X}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$
5.	Market Uncertainty	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{X}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{O}$	$\mathrm{A}$	$\mathrm{A}$
6.	Storage Device Barriers	$\mathrm{A}$	$\mathrm{X}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{O}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$
7.	Lack of Solar Irradiations Data	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{O}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$
8.	Issues of Security and Safety Implication	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	O	$\mathrm{O}$	$\mathrm{X}$	$\mathrm{A}$	$\mathrm{O}$
9.	Low Efficiency and Issues of Reliability	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{O}$	$\mathrm{A}$	$\mathrm{A}$
10.	Lack of Research and Development	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$
11.	Lack of Skilled Human Resources	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{A}$	$\mathrm{A}$
12.	Lack of Local Infrastructure	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{A}$
13.	Policy and Regulatory Issues	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$	$\mathrm{V}$
14.	Fewer concerns about Ecological and Environmental Issues	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$
15.	Lack of Entrepreneurs and Innovations in Rural	$\mathrm{A}$	$\mathrm{A}$	$\mathrm{A}$
16.	Hindrances of Societal Acceptance	$\mathrm{A}$	$\mathrm{A}$
17.	The dominance of Fossil Fuel Power Generation	$\mathrm{A}$
18.	Less Education and Weak Affordability in Rural

The high cost of SPV systems leads to financial issues. The cells (1, 13) show the symbol as contextual relation between cost and policy issues. The cost of PV projects is the barrier and leads to policy issues. The cells (1, 10) depict a contextual relationship cost of solar PV systems and research and development in this field. These barriers lead to each other. The ‘O’ in cells (2, 3) depicts the contextual relation between issues of payback period and finance mechanism hindrances. To develop SSIM, all contextual pair-wise comparison of barriers has relations equal to $N\ast (N-1)/2.$

4.2.2. Initial reachability matrix of barriers

The initial reachability matrix is developed by substituting symbols $(V,A,X,O)$ of SSIM with 1 or 0. The rules to obtain the initial reachability matrix are as follows (Punia Sindhu, Nehra, and Luthra 2016; Fahim et al. 2019; https://powermin.gov.in/en/content/power-sector-glance-all-india):

1. The symbol ${}^{\mathrm{\prime }}{V}^{\mathrm{\prime }}$ of the cell $(i,j)$ has been replaced by binary digits $1$ or $0$ such that digit 1 in the cell $(i,j)$ and digit 0 in the cell $(j,i)$ are filled.

2. The symbol ${}^{\mathrm{\prime }}{A}^{\mathrm{\prime }}$ of a cell $(i,j)$ has been changed into binary digits as $0$ in the cell $(i,j)$ and 1 in the cell $(j,i)$.

3. The ${}^{\mathrm{\prime }}{X}^{\mathrm{\prime }}$ relation of the cell $(i,j)$ is replaced by binary digits such as 1 in the cell $(i,j)$ and 1 in the cell $(j,i)$.

4. The ${}^{\mathrm{\prime }}{\mathrm{O}}^{\mathrm{\prime }}$ relation of the cell $(i,j)$ is replaced with 0 in both the cell $(i,j)$ and the cell $(j,i)$

v. The initial reachability matrix of barriers to solar PV system deployment in dairy farming is depicted in Table 4.

Table 4. Initial reachability matrix of barriers

Name of Barriers	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	B15	B16	B17	B18
High Initial Capital Cost of the Solar PV System (B1)	1	1	0	1	0	0	0	1	0	0	1	0	1	1	1	1	0	0
Long Payback Period (B2)	0	1	0	0	0	0	0	0	0	1	0	0	1	1	1	1	0	0
Issues of Finance Mechanism (B3)	1	1	1	1	1	0	1	1	0	1	1	1	0	1	1	1	0	0
Lack of Awareness of Technology (B4)	0	1	0	1	0	0	0	1	0	0	0	0	0	1	1	1	0	0
Market Uncertainty (B5)	1	1	1	1	1	0	0	0	0	0	1	0	0	1	1	1	0	0
Storage Device Barriers (B6)	1	1	1	1	1	1	0	1	1	0	1	0	1	1	1	1	1	0
Lack of Solar Irradiations Data (B7)	1	1	0	1	1	1	1	1	1	0	0	0	1	1	1	1	0	0
Issues of Security and Safety Implication (B8)	0	1	0	1	0	0	0	1	0	0	1	0	0	1	1	1	0	0
Low Efficiency and Issues of Reliability (B9)	1	1	1	1	1	0	0	0	1	0	0	0	1	1	1	1	1	0
Lack of Research and Development (B10)	1	1	0	1	1	1	1	1	1	1	1	0	0	1	1	1	0	0
Lack of Skilled Human Resources (B11)	1	1	0	1	0	0	1	1	1	0	1	0	0	1	0	1	0	0
Lack of Local Infrastructure (B12)	1	1	0	1	1	0	0	0	0	1	1	1	0	1	1	1	0	0
Policy and Regulatory Issues (B13)	0	0	1	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1
Fewer concerns about Ecological and Environmental Issues (B14)	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
Lack of Entrepreneurs and Innovations in Rural (B15)	1	0	0	1	1	0	0	0	0	0	1	0	0	1	1	0	0	0
Hindrances of Societal Acceptance (B16)	1	0	0	1	1	0	0	0	0	0	0	0	0	1	1	1	0	0
The dominance of Fossil Fuel Power Generation (B17)	1	0	1	1	1	1	1	1	0	1	1	1	0	1	1	1	1	0
Less Education and Weak Affordability in Rural (B18)	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1

4.2.3. Final reachability matrix development using different types of barriers

In interpretive structural modelling, the final reachability matrix is derived from the initial reachability matrix. It is developed by utilising the transitivity rule. It is stated as if barrier A is related to barriers B and B is related to the C barrier, then the A and C barriers are also related to each other (Al-Badi, Malik, and Gastli 2009; Owen 2006; Mainali and Silveira 2015; Beck and Martinot 2004).

Driving power: It is the sum of ‘1’ in each row of the final reachability matrix for selected barriers.

Dependence power: It is the sum of ‘1’ in each column of the final reachability matrix for selected barriers.

The Reachability set: The digits ‘1’ in each row comprise the reachability set of that row.

The Antecedent set: Boolean digits ‘1’ in each column incorporate the antecedent set of that column.

The Intersection set: The intersection set gives the idea about common barriers in the antecedent and reachability set.

In Table 5, the deriving barrier power and dependence barrier power have been determined using the final reachability matrix.

Table 5. Final reachability matrix of barriers (* represent transistivity relation among barriers)

Name of barriers	B1	B2	B3	B4	B5	B6	B7	B8	B9	B10	B11	B12	B13	B14	B15	B16	B17	B18	Driving Forces
High Initial Capital Cost of the Solar PV System (B1)	1	1	1*	1	1*	0	1*	1	1*	1*	1	1*	1	1	1	1	1*	1*	17
Long Payback Period (B2)	1*	1	1*	1*	1*	1*	1*	1*	1*	1	1*	1*	1	1	1	1	1*	1*	18
Issues of Finance Mechanism (B3)	1	1	1	1	1	0	1	1	0	1	1	1	0	1	1	1	0	0	13
Lack of Awareness of Technology (B4)	1*	1	0	1	1*	1*	1*	1	1*	1*	1*	0	1*	1	1	1	0	0	14
Market Uncertainty (B5)	1	1	1	1	1	0	1*	1*	1*	1*	1	0	1*	1	1	1	0	0	14
Storage Devices Barriers (B6)	1	1	1	1	1	1	1*	1	1	1*	1	1*	1	1	1	1	1	1*	18
Lack of Solar Irradiations Data (B7)	1	1	0	1	1	1	1	1	1	1*	1*	1*	1	1	1	1	1*	1*	17
Issues of Security and Safety Implication (B8)	0	1	0	1	0	0	0	1	0	1*	1	0	1*	1	1	1	0	0	09
Low Efficiency and Issues of Reliability (B9)	1	1	1	1	1	0	0	1*	1	0	0	0	1	1	1	1	1	0	12
Lack of Research and Development (B10)	1	1	1*	1	1	1	1	1	1	1	1	0	1*	1	1	1	1*	0	16
Lack of Skilled Human Resources (B11)	1	1	1*	1	0	0	1	1	1	0	1	0	0	1	1*	1	0	0	11
Lack of Local Infrastructure (B12)	1	1	0	1	1	0	0	1*	0	1	1	1	0	1	1	1	0	0	11
Policy and Regulatory Issues (B13)	1*	1*	1	1	1	1*	1	1*	1*	1	1	1	1	1	1	1	1	1	18
Fewer concerns for Ecological and Environmental Issues (B14)	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	01
Lack of Entrepreneurs and Innovations in Rural (B15)	1	1*	0	1	1	0	0	1*	0	0	1	0	1*	1	1	1*	0	0	10
Hindrances of Societal Acceptance (B16)	1	1*	0	1	1	0	0	1*	0	0	1*	0	0	1	1	1	0	0	09
Dominance of Fossil Fuel Power Generation (B17)	1	1*	1	1	1	1	1	1	1*	1	1	1	0	1	1	1	1	0	16
Less Education and Weak Affordability in Rural (B18)	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	17
Dependent Forces	16	17	11	17	15	08	12	17	12	13	16	09	11	18	17	17	09	06	251/251

4.3. Level partitioning

The level of barriers is obtained using partitioning. The level partition incorporates intersection, antecedent, and reachability sets. The reachability set is a bundle of barriers that are influenced by it and the barrier itself.

For a barrier, an antecedent set is a group of barriers that influence it and the barrier itself. Reachability, antecedent, and intersection sets for all barriers are obtained from the reachability matrix. The outcomes of these sets are depicted in Table 6. The rank or level of a barrier is determined using reachability set as subset of antecedent set.

Table 6. Ranking of barriers using level partitioning (level 1).

Barriers	Reachability Sets	Antecedent Sets	Intersection Sets	Rank
B1	1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18	1,2,3,4,5,6,7,9,10,11,12,13,15,16,17,18	1,2,3,4,5,7,9,10,11,12,13,15,16,17,18
B2	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18
B3	1,2,3,4,5,7,8,10,11,12,14,15,16	1,2,3,4,5,9,10,11,13,17,18	1,2,3,4,5,10,11
B4	1,2,4,5,6,7,8,9,10,11,13,14,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,6,7,8,9,10,11,13,15,16
B5	1,2,3,4,5,7,8,9,10,11,13,14,15,16	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18	1,2,3,4,5,7,8,9,10,11,13,15, 16
B6	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18	2,4,6,7,10,13,17,18	2,4,6,7,10,13,17,18
B7	1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18	1,2,3,4,5,6,7,10, 11,17,18	1,2,4,5,6,7,10,11,17,18
B8	2,4,8,10,11,13,14,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	2,4,8,10, 11,13,15,16
B9	1,2,3,4,5,8,9,13,14,15,16,17	1,2,4,5,6,7,9,10,11,13,16,17	1,2,4,5,9,13,17
B10	1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17	1,2,3,4,5,6,7,8,10,12,13,17,18	1,2,3,4,5,6,7,8,10, 13,17
B11	1,2,3,4,7,8,9,11,14,15,16	1,2,3,4,5,6,7,8,10,11,12,13,15	1,2,3,4,7,8,11,15
B12	1,2,4,5,8,10,11,12,14,15,16	1,2,3,6,7,12,13,17	1,2,12
B13	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18	1,2,4,5,6,7,8,9,10,13,15	1,2,4,5,6,7,8,9,10,13,15
B14	14	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18	14	1st
B15	1,2,4,5,8,11,13,14,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,13,15,16
B16	1,2,4,5,8,11,14,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,15,16
B17.	1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17	1,2,6,7,9,10,13,17,18	1,2,6,7,9,10,17
B18.	1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17,18	1,2,6,7,13,18	1,2,6,7,18

If the reachability and intersection sets have the same barriers then level first or topmost level has been assigned to those variables according to the notion of level partitioning. After finding rank 1, it has not carried out further iterations and it is removed. The second level of partition iteration is depicted in Table 7.

Table 7. Ranking of barriers using level partitioning (level 2).

S. No.	Reachability Sets	Antecedent Sets	Intersection Sets	Levels
1	1,2,3,4,5,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,9,10,11,12,13,15,16,17,18	1,2,3,4,5,7,9,10,11,12,13,15,16,17,18
2	1,2,3,4,5,6,7,8,9,10,11,12,13,,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18
3	1,2,3,4,5,7,8,10,11,12,,15,16	1,2,3,4,5,9,10,11,13,17,18	1,2,3,4,5,10,11
4	1,2,4,5,6,7,8,9,10,11,13,,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,6,7,8,9,10,11,13,15,16	2nd
5	1,2,3,4,5,7,8,9,10,11,13,,15,16	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18	1,2,3,4,5,7,8,9,10,11, 13,15,16
6	1,2,3,4,5,6,7,8,9,10,11,12,13,,15,16,17,18	2,4,6,7,10,13,17,18	2,4,6,7,10,13,17,18
7	1,2,4,5,6,7,8,9,10,11,12,13,,15,16,17,18	1,2,3,4,5,6,7,10,11,17,18	1,2,4,5,6,7,10,11,17,18
8	2,4,8,10,11,13,,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	2,4,8,10,11,13,15,16	2nd
9	1,2,3,4,5,8, 9,13,,15,16,17	1,2,4,5,6,7,9,10,11,13,16,17	1,2,4,5,9,13,17
10	1,2,3,4,5,6,7,8,9,10,11,13,,15,16,17	1,2,3,4,5,6,7,8,10,12,13,17,18	1,2,3,4,5,6,7,8,10,13,17
11	1,2,3,4,7,8,9,11,,15,16	1,2,3,4,5,6,7,8,10,11,12,13,15	1,2,3,4,7,8,11,15
12	1,2,4,5,8,10,11,12,,15,16	1,2,3,6,7,12,13,17	1,2,12
13	1,2,3,4,5,6,7,8,9,10,11,12,13,,15,16,17,18	1,2,4,5,6,7,8,9,10,13,15	1,2,4,5,6,7,8,9,10,13,15
15	1,2,4,5,8,11,13,,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,13,15,16	2nd
16	1,2,4,5,8,11,,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,15,16	2nd
17.	1,2,3,4,5,6,7,8,9,10,11,12,,15,16,17	1,2,6,7,9,10,13,17,18	1,2,6,7,9,10,17
18.	1,2,3,4,5,6,7,8,9,10,11,12,,15,16,17,18	1,2,6,7,13,18	1,2,6,7,18

The level partition of barriers for level 3 is depicted in Table 8.

Table 8. Ranking of barriers using level partitioning (level 3).

S. No.	Reachability Sets	Antecedent Sets	Intersection Sets	Levels
1	12,17,18	1,2,3,4,5,6,7,9,10,11,12,13,15,16,17,18	12,17,18	3rd
2	3,,12,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	3rd
3	3,12,	1,2,3,4,5,9,10,11,13,17,18	1,2,3,4,5,10,11
5	,3,	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18	1,2,3,4,5,7,8,9,10,11,13,15,16	3rd
6	3,12,17,18	2,4,6,7,10,13,17,18	2,4,6,7,10,13,17,18
7	12,17,18	1,2,3,4,5,6,7,10,11,17,18	1,2,4,5,6,7,10,11,17,18
9	3,,17	1,2,4,5, 6, 7,9,1,0,11,13,16,17	1,2,4,5,9, 13,17
10	3,17	1,2,3,4,5,6,7,8,10,12, 13, 17,18	1,2,3,4,5,6,7,8,10, 13, 17	3rd
11	3,	1,2,3,4,5,6,7,8,10,11,12,13,15	1,2,3,4,7,8,11,15	3rd
12	12,14,15,16	1,2,3,6,7,12,13,17	1,2,12
13	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,8,9,10,13,15	1,2,4,5,6,7,8,9,10,13,15	3rd
17.	,3,12,,17	1,2,6,7,9,10,13,17,18	1,2,6,7,9,10,17
18.	3,12,,17,18	1,2,6,7,13,18	1,2,6,7,18

5. Outcomes of ISM and MICMAC analysis

The iteration process of finding level is repeated until entire ranks are invented and all ranked barriers are summed up in Table 9.

Table 9. Details of ranking of barriers using level partitioning

S. No.	Reachability Set	Antecedent Set	Intersection Set	Level
1	3,12,17,18	1,2,3,4,5,6,7,9,10,11,12,13,15,16,17,18	1,2,3,4,5,7,9,10,11,12,13,15,16,17,18	3rd
2	3,12,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	3rd
3	3,	1,2,3,4,5,9,10,11,13,17,18	1,2,3,4,5,10,11	4th
4	1,2,4,5,6,7,8,9,10,11,13,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,6,7,8,9,10,11,13,15,16	2nd
5	,3,	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18	1,2,3,4,5,7,8,9,10,11,13,15,16	3rd
6		2,4,6,7,10,13,17,18	2,4,6,7,10,13,17,18	4th
7		1,2,3,4,5,6,7,10,11,17,18	1,2,4,5,6,7,10,11,17,18	4th
8	2,4,8,10, 11, 13,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	2,4,8,10, 11, 13,15,16	2nd
9		1,2,4,5, 6, 7,9,1,0,11,13,16,17	1,2,4,5,9, 13,17	4th
10	3,17	1,2,3,4,5,6,7,8,10,12, 13, 17,18	1,2,3,4,5,6,7,8,10, 13, 17	3rd
11	3,	1,2,3,4,5,6,7,8,10,11,12,13,15	1,2,3,4,7,8,11,15	3rd
12	12,14,15,16	1,2,3,6,7,12,13,17	1,2,12	5th
13	1,2,3,4,5,6,7,8,9,10,11,12,13,,15,16,17,18	1,2,4,5,6,7,8,9,10,13,15	1,2,4,5,6,7,8,9,10,13,15	3rd
14		1,2,3,4,5,6,7,8,9,10,11,12,13,,15,16,17,18		1st
15	1,2,4,5,8,11,13,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,13,15,16	2nd
16	1,2,4,5,8,11,,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18	1,2,4,5,8,11,15,16	2nd
17.	,3	1,2,6,7,9,10,13,17,18	1,2,6,7,9,10,17	4th
18.		1,2,6,7,13,18	1,2,6,7,18	4th

The diagraphs’ structural model of barriers has been generated from the final reachability matrix in the form of vertices and edges.

The interpretive structure model of barriers has been designed for the deployment of PV systems in dairy farming as presented in Figure 5.

Figure 5. ISM-based diagraph structural model

Tables 6–9 provide the level or ranking of barriers. Similar rank barriers are placed together horizontally. Firstly, the first rank barrier B14 is placed at the apex of the digraph followed by the second rank barriers and so on. The model has five partition levels from top to bottom. The ISM-based diagram of the structural model has been developed for identifying the rank of barriers in the exploitation of PV systems in dairy farms.

The diagraph structural model reveals that less concern towards environmental and ecological issues and challenges are one of the top barriers for dairy farmers. In rural, dairy farmers are primarily reluctant to adopt new and renewable energy generated using solar PV panels. At the second rank, the barriers are less aware of PV technology among dairy farmers, safety concerns, a weak web of solar PV entrepreneurship, and less social acceptance among dairy farmers. At the third rank of the ISM graph model, the barriers are the high initial capital cost of solar PV systems, long payback period, small market size, lack of research and development, and less availability of skilful human resources. In the fourth level of the ISM graph structure, the barriers are the pathetic finance mechanism, low education, and affordability of dairy farmers, issues of policy and regulations viz. subsidy, feed-in tariff or another incentive, non-availability of solar irradiations data for solar PV model design, monopoly or strongholds of fossil fuels and thermal-based energy, low efficiency of solar PV panels and less reliability of solar PV systems. The fifth rank of the ISM graph model outcome includes the barriers that are weak extensions of local infrastructures including transport, public health issues, roads, streets, cleaning issues, mobile towers, the range of mobile signals, etc.

5.1 MICMAC analysis of barriers’ interaction validation model

MICMAC analysis is applied to decide special types of barriers such as linkage, autonomous, dependent, and independent barriers. The barrier identification is based on the quantised value of driving and the dependent force of the barrier interlinking. The estimation of the effective value of each barrier is presented in Table 5. Based on the driving force and dependent force of a barrier, its type is determined. A brief outline of the type of barrier is presented in Table 10.

Table 10. Categorisation of barriers [Hernández-Callejo, Gallardo-Saavedra, and Alonso-Gómez 2019; Horváth and Szabó 2018]

S.No.	Barriers	Briefs about types of barriers
1.	Autonomous barrier	Barrier incorporates weak dependence power and driving power. It is comparatively incoherent from the system, and these barriers have a few associations that can be exceptionally strong.
2.	Linkage Barrier	The barrier has both strong dependence and driving power. These factors are unstable. Action on this barrier would have a consequence on others and also feedback affects itself.
3.	Dependent Barrier	The barrier has dominant dependence power and low driving power.
4.	Independent Barrier	The barrier comprises dominant deriving power and miniature dependence power. It is known as a key barrier.

Table 11 presents the barrier classification based on MICMAC analysis and details of outcomes are depicted in Figure 6.

Figure 6. Types of barriers based on MICMAC analysis.

Table 11. Type of barriers based on MICMAC analysis.

Barriers to SPV deployment in dairy farming	Dependent forces	Driving forces	Types of barrier
High Initial Capital Cost of the Solar PV System (B1)	16	17	Linkage
Long Payback Period (B2)	17	18	Linkage
Issues of Finance Mechanism (B3)	11	13	Linkage
Lack of Awareness of Technology (B4)	17	14	Linkage
Market Uncertainty (B5)	15	14	Linkage
Storage Device Barriers (B6)	08	18	Independent
Lack of Solar Irradiations Data (B7)	12	17	Linkage
Issues of Security and Safety Implication (B8)	17	09	Dependent
Low Efficiency and Issues of Reliability (B9)	12	12	Linkage
Lack of Research and Development (B10)	13	16	Linkage
Lack of Skilled Human Resources (B11)	16	11	Linkage
Lack of Local Infrastructure (B12)	09	11	Independent
Policy and Regulatory Issues (B13)	11	18	Linkage
Fewer concerns about Ecological and Environmental Issues (B14)	18	01	Dependent
Lack of Entrepreneurs and Innovations in Rural (B15)	17	10	Linkage
Hindrances of Societal Acceptance (B16)	17	09	Dependent
The dominance of Fossil Fuel Power Generation (B17)	09	16	Independent
Less Education and Weak Affordability in Rural (B18)	06	17	Independent

Linkage barriers are high initial investment cost, lack of awareness, long payback period, issues of finance mechanism, lack of solar irradiations data, issues of efficiency, security implication, safety issues, lack of strong research and development, lack of skilled human resource, issues of entrepreneur and innovations in dairy farming, etc.

6. Conclusion

In the present study, driving, linkage, dependent and independent barriers to the deployment of solar PV systems in dairy farms have been determined. Driving force barriers comprise the high initial investment cost of the solar PV system, long payback period, storage devices barriers, policy and regulatory issues, less education, and weak affordability. Vital dependent force barriers have hindrances of societal acceptance, fewer concern towards ecological and environmental issues, lack of entrepreneurs and innovations in rural, long payback periods, etc. The independent barriers involve storage battery challenges and issues, power generation from fossil fuel, lack of local infrastructure, etc. The barriers are dynamic in nature, and the interaction between these depends upon various factors such as technology advancement, growth in research and development, type of application, and location to name a few.

In a nutshell, a major impetus is required to eliminate driving barriers as identified. The promotion of stand-alone solar PV systems for electric power supply plays a vital role in meeting the energy demands of dairy farms in rural India. Deployment of the PV system in rural India can act as a selective catalyst to minimise prolonged deprived rural electrification of the dairy sector. Undoubtedly, the assimilation of solar PV systems into dairy farms would be a outcome for sustainable socio-economic growth in rural India. To fulfil the ever-growing energy needs of dairy farms, it is imperative to fully utilise the potential of solar PV systems. In this direction, the effective subsidy and policy formulation to deploying solar PV systems in the dairy sector can fulfil the present and future energy needs of the major part of rural India if exploited optimally. But, various major barriers such as high initial investment prices, political, regulatory, institutional, education, infrastructure, financial, technical, solar irradiation data, and many more hinder the path of deployment of solar PV systems. In rural India, efforts are underway for integrating solar PV systems in dairy farms but it is at a slow rate due to less awareness, the poor socio-economic status of the rural population, and financial constraints.

Renewable energy from the solar PV system can play a significant role in obtaining safe and secure energy in the present and future in agriculture and dairy farm. The deployment of solar PV systems in dairy farms has vital potential to address various challenges such as depleting the nature of fossil fuels and their rising cost, environmental concerns, employability, sustainable socio-economic development, poverty, immigration, health, and nutrition crisis, etc. The growth and deployment of solar energy in the dairy sector can be succeeded by devising viable policy formulation and implementation policies as suggested through the outcomes of the interpretive structural modelling.

6.1. Limitation of the study

The present work is an effort towards the evaluation and prioritisation of barriers in the deployment of solar PV systems in the rural dairy sector. It can play a crucial role to minimise the intensity of various obstacles in the deployment of the emerging solar PV technology sector in grassroots dairy farms. The limitations of the study are as follows:

1. ISM and MICMAC technique is a hypothetical concept and the present model of ISM and MICMAC analysis is based upon the subjective judgment of academia and industry experts.

2. The quantification of barriers and validation of the model in real circumstances needs to be carried out.

3. The 18 barriers are identified for this work. The number of barriers may increase but the implementation of ISM and MICMAC techniques becomes more cumbersome. Although there are some simulation packages which may be utilised to implement the ISM model in due course of studies.

In summary, the outcomes of the present study have a small deviation from other similar studies involving the implementation of renewable energy in rural India as well as the number of barriers evaluated, and experts’ opinions of the linkage of barriers.

7. Recommendations

Brief outlines of proposed measures for reducing the intensity of barriers are summarised below:

1. Political willpower and commitment might give support to developers, investors, and entrepreneurs for installing solar PV projects in the dairy sector. Strong and practical implementation of government policies is the need of hour to ensure developers, investors, and entrepreneurs in relation to assured returns or risk involved.

2. Financial assistance from various agencies such as NABARD, world and Asian development banks and rural and cooperative banks play an indispensable role in the penetration of PV systems at the grass root level. Moreover, a low-interest loan at a subsidised rate to the dairy farmer for solar PV systems deployment can escalate its exploitation in the dairy sector. The setting up of financial institutions in rural areas with ease of credit access facilities to the dairy farmer can further escalate the pace of deployment.

3. Mobile, internet, television, radio, and print media can play a significant role in information dissemination by sharing success stories of the operation of solar PV systems in rural. Gram Panchayat in each village can also be a prominent link to enhance the promotion of solar PV deployment in rural dairy farms.

4. Government machinery should also encourage information diffusion in regard to expense, availability of incentives, benefits, efficiency, maintenance, and operation activities of solar PV systems to dairy farmers, developers, financial institutions, innovators, operators, and entrepreneurs.

5. The dairy farmer’s perception may not change at once due to a lack of awareness and reluctant mindsets in rural. Union, state, and local governments would have the patience to spend adequate time and finances to promote the deployment of PV systems in dairy farming for rural communities. It is possible by using innovative ideas, and easiness in rules and regulations. Single window clearance may also play a significant role in supporting developers in integrating PV systems with dairy farming. Efforts should also be made to set up new training institutes to address the shortage of lacking skilled human resource requirements in the supply chain of renewable energy resources for its diffusion in the dairy sector. It will propagate the benefits, accelerate research and development activities, generate employment, and create awareness in rural about solar renewable energy deployment.

In a nutshell, to eliminate the intensity of barriers to solar PV systems’ installation in dairy farms, prominence focus on government regulations, research and development activities, and wider diffusion of awareness among rural masses is the need of the hour. Vital efforts are required from the union and state government to realise PV systems as the main source of power in the dairy sector, which include

1. The need of enormous pace towards cleaner, affordable and secure energy by deploying solar PV systems in dairy farming.

2. The necessity of foremost power reforms leads to greater efficiency and strengthening of the manufacturing of solar panels, batteries, and the balance of systems.

3. Devise energy security a priority in rural applications and especially agriculture and dairy farming in states and union territories.

4. The need for efforts for significant progress in socio-economic sustainable development.

5. Adoption of power technology and modernisation enable ‘Make in India’.

6. Working in the direction of extra-robust solar irradiation data and policy governance to deploy solar power in dairy farming.

7. Establishment of permanent energy policy co-ordination in state and central governments.

8. Encouraging investment in solar PV system installations in rural dairy farms.

9. Prioritise actions to foster greater energy security by deploying solar PV systems in rural.

10. Making strong the fragile solar energy infrastructure for rural applications.

The unrealised potential of biogas energy along with solar PV system deployment may bring a renewable energy revolution in the dairy sector in rural India. Indeed, it plays a critical role to achieve the objective of the central government of India to raise the income of farmers and rural communities. Let us join hands together to address the impediments in the deployment of PV systems in rural areas and also grab the untapped potential of a dairy farm by deploying biogas and solar PV systems in the dairy sector in rural India.

8. Future Scope

The future scope in the context of the present study is as follows:

• Validation of the present outcomes of ISM and MICMAC analysis of obstruction for solar PV systems’ deployment in dairy farms may be improved by comparing and modifying the existing outcome using system dynamics modelling and structural equation modelling for further testing.

• The quantification of the barriers may be obtained using an analytical hierarchical process.

The solar PV systems deployment in the dairy sector is a prominent step to create a self-reliant India and to achieve its prominent mission such as ‘stand up India’, ‘energy to all’, ‘make in India’, ‘skill India mission’, ‘Swachh Bharat mission’, ‘digital India mission’.

Disclosure statement

No potential conflict of interest was reported by the author(s).