Introduction

Agriculture systems need a radical transformation to ensure sustainability in food production. In the recent past, it was difficult to get information to or from the farmers on their basic needs such as access to farm inputs, information about market trends, microfinance, or technical inputs (Trendov et al. 2019; Food and Agriculture Organization 2000; Blackmore 2000). To address these issues, digital inclusion of farmers is suggested to enhance their ability to connect with the knowledge, networks, and institutions (Gangwar et al. 2020; United Nations Development Program 2015; Food and Agriculture Organization 2013). The emerging digital technologies can contribute their role toward this transition by providing new ways of visualizing and measuring the impact of various farm practices, communicating required changes, and ensuring connectivity among participants of the food supply chain (Bonny 2017).

This work aims to sensitize agrarian communities toward digital technologies that create opportunities to empower them in addressing their farming needs, especially related to irrigation facilities, soil health, and crop management. Generally, farmers make farm management decisions based on an assessment of local conditions, previous experience, and desired outcomes (Verdouw et al. 2015). A conceptual framework of digital agriculture is shown in Fig. 1. It relies on a network of low-cost agriculture sensors (Stamenković et al. 2016; Ray 2017; Mahbub 2020). These systems can rapidly assess the cropland growing conditions and provide valuable inferences and operational insights (Zaks and Kucharik 2011; Zhang and Pierce 2013; Miller 2008). To increase production efficiency, recommendations derived from these systems can be supplemented with usual farm management practices.

Fig. 1
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A conceptual framework of digital agriculture services

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With the integration of distributed sensing capability, remote surveillance, and real-time data analysis, farm management information systems (FMISs) provide new insights into agroecological observations (Koksal and Tekinerdogan 2018; Sørensen et al. 2011). Previously, several researchers investigated the role of sensor-based digital agriculture innovations in farm management (Wang et al 2006; Panchard et al. 2008; Taylor et al. 2013; Srbinovska et al., 2014; Gutiérrez et al. 2014; Sakthipriya 2014; Wei et al. 2015; Ojha et al. 2015; Kavitha et al. 2018; Gsangaya et al. 2020). These innovations are also enabling farmers in bringing new opportunities for inclusiveness and sustainable development (Samans et al. 2017; El Bilali and Allahyari 2018).

For ensuring sustainability in agriculture, Patel et al. (2004) suggested the use of precision farming (PF) technologies. The term precision farming is defined as a comprehensive system designed to optimize agricultural production by carefully observing monitoring and controlling the cropland growing conditions to correspond to the unique requirements found in every farm field (Gack 2018; Zaks and Kucharik 2011). It is an interdisciplinary concept of integrating information and communication technologies in agriculture to improve the production efficiency of the cultivated crops (Gangwar et al. 2019; Kumar and Ilango 2018; Keshtgary and Deljoo 2012). The early adopters started with yield monitoring along with the spatial and temporal mapping of farm fields (Panchard et al. 2007; Blackmore 1994). It further continued with the variable rate application of irrigation, fertilizers, and pesticides (Khanna and Kaur 2019; Bonny 2017; Lawson et al. 2011).

Apart from technological interventions, green growth initiatives focus on environmental conservation (Tirado 2015). To address this concern, organic farming practices are found more resilient to weather extremes. In their work, the author (Pimentel et al. 2005) presented a comparison of organic and conventional farming methods and highlighted the environmental, energetic, and economic aspects of these two systems. It was reported that organic farming systems can improve production efficiency with minimum environmental impact.

During the period of the green revolution, a critical linkage between agriculture and environmental fragility was ignored. Alternative methods used for enhanced farm yields reduced the degree of resilience to climate change and environmental degradation (Shiva and Leu 2018). Authors in their work (Kim et al. 2008; Kirby et al. 2003) described the role of intensive use of chemical fertilizers and pesticides combined with poor irrigation management in severe water stress, pesticide contamination, and disruption of agroecosystems. Therefore, people are expressing their concern for the ‘ever-green revolution’ where the sustainability of farming landscapes is put on the highest priority (Klerkx et al. 2019).

The conducted techno-economic analysis is based on the data collected during three successive crop cycles from 2017 to 2020 from the farm fields of participating farmers from Farrukhabad and Dehradun districts in North India. It attempts to investigate the role of sensor-based information systems in enhancing the economic and environmental performance of farm management practices. During this study, the impact of technology diffusion on crop cultivation is assessed with the help of data related to input cost, crop water requirement, farm yield, and net profit. The financial figures described in this paper are presented in terms of Indian Rupee (INR).

The rest of the paper presents details of digital agriculture technologies and their role in sustainable farming. Section “Materials and methods” presents an overview of the materials and methods of digital agriculture. It includes a discussion on motivation for adopting digital agriculture interventions; ecological farming; precision irrigation and IoT-enabled precision farming (Fernandes et al 2013). Results and discussions related to the carried out techno-economic analysis of digital technologies are described in section "Results and discussion". Finally, "Conclusion" section concludes the entire discussion and presents a summary of the scope for future research.

Materials and methods

Digital agriculture platforms require the acquisition, integration, and processing of a vast collection of data streams coming from large-scale and heterogeneous sensor networks (Kumar and Ilango 2018; Keshtgary and Deljoo 2012; Wark et al. 2007). Decisions on the adoption of digital technologies are affected by multiple factors, such as their role in production efficiency, minimizing operating costs, reducing the risk of crop failure, and selling crops at profitable prices (Trendov et al. 2019). This requires effective management of input resources, data-driven targeted application of irrigation, fertilizers, and pesticides, crop health monitoring, and minimizing the impact of unpredictable variables, such as the weather, weeds, and pests (Verdouw et al. 2015).

The integration of information and communication technologies into the physical components enables the provision of digital services. These digital services are essential for the identification of characteristics and attributes of potential businesses. The ongoing digitalization process has influenced business models in multiple domains, ranging from e-commerce to smart living (Gack 2018). The implementation of digital technologies is changing the value creation stages for the participants of the food supply chain (Engdahl 2015).

With the help of data collected from the farmers, this work presents a comprehensive analysis of technical, economic, environmental, social, and behavioral aspects of adoption and management benefits of digital agriculture interventions (Cropin 2020; Dessart et al. 2019; Mwangi and Kariuki 2015). The quantitative and qualitative analyses of digital agriculture practices were carried out through detailed survey questionnaires and other forms of interactions.

The provided questionnaires inquired details about their landholdings, cultivated crops, irrigation facilities, fertilizers, input cost, net income, usage of digital technologies, farming apps, socioeconomic background, and educational qualification. Volunteers and trainers assisted farmers in understanding which actions to take, when, and where. During this process, the net values that digital technologies bring for the entire ecosystem are identified as:

  • Efficient use of farm resources, i.e., fertilizers, water, chemicals, fuel, etc.

  • Improving the quantity and quality of farm products.

  • Higher yield per hectare and risks mitigation.

  • Reducing the ecological footprints.

The approach followed in this analysis focuses on a framework of sustainability in agroecosystems that brings together economic, environmental, social, and technological aspects of food production. It highlights the impact of such intervention on food productivity, livelihood, income distribution, and the environment (Bonny 2017) and provides insight into areas where further research and development activities should be focused on.

Motivation for adoption of digital agriculture services

The agriculture sector is exposed to many challenges and risk-factors (Wei et al. 2015; Miller 2008). In such circumstances, the adoption of climate-smart PF technologies can significantly reduce the associated production risk (Aryal et al. 2020). These interventions help minimize the impact of unpredictable variables. From harvesting early to beat a hailstorm, farmers are relying on smartphones and sensor-based smart farm facilities. Based on these valuable inferences and operational insights, farmers are making more informed decisions about the selection of the crops and plan their strategies to break the monopolies in the food and agriculture industry (Klerkx et al. 2019).

Better resource planning is key to improve the production efficiency of the farming systems. Several authors have reported that mismanagement of land and water resources has resulted in poor agricultural productivity (Patel et al. 2004; Aryal et al. 2020). Wider application of technological interventions can certainly help in better utilization of farm resources, lowering production costs, and enhancing farm productivity. Excessive use of chemicals and inefficient use of water indicate that Indian farmers need assistance and guidance to manage their farm practices (Panchard et al. 2008).

For ensuring global food security, production of cereals is a critical issue. But, the water consumption levels for these crops are substantially high. A link between global cereal trade and water use was discussed in De Fraiture and Wichelns (2010). In another study, Yadav et al. (2013) expressed his concern over future water demands for agriculture. Figure 2 illustrates the crop water consumption for three main cereal crops in different countries. This chart highlights the excess use of water in India. These levels are alarmingly high when compared to other countries producing the same crops (De Fraiture and Wichelns 2010.

Fig. 2
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Crop water consumption for main cereal crops

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Capital-intensive chemical farming is the leading factor behind the water stress reported from multiple locations in India (Deb 2004; Shiva and Leu 2018). Most of the water is consumed in the industrial monocultures of water-intensive cereal crops and sugarcane (Yadav et al. 2013). A new study using satellite data indicates that the northwestern region of India is using more water than replenished during rains. The gravity recovery and climate experiment (GRACE) report of NASA indicates that current rates of water extraction in this region are not sustainable (Tapley et al. 2004).

The current crisis of Indian agriculture is very much similar to that which was reported 5000 years ago (Swaminathan 2007a) during the period of Indus valley civilization. In those days, a powerful agrarian society emerged. It was supported by large-scale and community-led irrigation facilities. The cities of that time were noted for their urban planning, clusters of large non-residential buildings, baked brick houses, water supply systems, elaborate drainage systems, and new techniques in handicraft. This early model of a hydraulic society led to socioeconomic development because it provided a significant increase in the food supply. It permitted population growth, urbanization, and the development of alternative economic activities.

Like other ancient systems, community members of Indus valley failed to manage the environmental issues. Their growth was constrained by the availability of water. The intensification of irrigated agriculture resulted in the form of water shortage and increased salinity. Degradation of water resources severely affected agricultural and other economic activities. Eventually, the sustainability of these communities was threatened (Barker and Molle 2004). It indicates that environmental sustainability is necessary and sufficient condition for equitable and sustainable human development.

One of the main reasons behind this current agrarian crisis in India is poor returns to cultivation. Poor infrastructure in rural areas and lack of policy framework for resource allocation are some other factors for the persistence of the crisis (Deb 2004; Shiva and Leu 2018). Two different dimensions of this crisis are identified as agricultural and agrarian. The first one is a developmental crisis, and the second one relates to the livelihood crisis threatening the survival of the vast majority of the population (Mishra and Reddy 2011).

Apart from irrigation woes, falling incomes, increasing cost of production, crop failure, price crash, indebtedness, and suicides have become the harsh reality of the socioeconomic farming landscapes in India and other parts of the world where industrial chemical farming is adopted. Intensification of agriculture has a direct relationship with the debt trap, with suicides as an extreme response (Shiva and Leu 2018). In many cases, farmers have to sell their land and even body organs like kidneys to pay off their loans.

Capital-intensive chemical farming is not at all a sustainable and commercially viable option. Price crash situations often reported from many parts of India, for tomato, onion, potato, and other vegetable crops increase the problem of farmers with small landholdings. Thousands of farmers have committed suicide because they were immersed in debt. They drowned in the debt because their crops were not sold at fair prices. Their farm yields were not enough for subsistence. Not only just this, but an increasing number of poor farmers are also losing their lands to work as laborers.

However, India is one of the fastest-growing market economies in the world. But, the share of the agriculture sector in the national gross domestic product (GDP) has declined significantly between 1980 and 2020. The growth rate of the national GDP has always been higher than the growth in agricultural GDP. During this period, the growth rate of the national GDP was around 7%, but the agricultural GDP growth rate was limited to only 3% (Mishra and Reddy 2011).

For ensuring sustainability in agriculture, technological and institutional interventions are needed. To improve the economic viability of farming, five major areas identified to address this ongoing crisis are water-efficient irrigation facilities, community participation in the development of irrigation infrastructure, deployment of modern technologies, effective post-harvest crop management, and enhanced access to the institutions (Swaminathan 2007a).

Ecological farming methods

The term ecology is the study of nature as a stable and orderly system, and agroecology refers to the ecosystem of the farming landscapes (Altieri 1995). It includes nutrient cycling, population regulation of livestock, biodiversity, energy flow, and dynamic equilibrium among all functional entities. The ecosystem of the farm fields is different from the natural ecosystem as it involves human interventions. It is based on biological interactions and synergies to support sustainable food production.

It is rightly said that nothing will go right for agriculture if farm ecology and economics go in the wrong direction (Swaminathan 2007b). The basic philosophy of ecological farming is deeply rooted in the coexistence of species as humans and other animals rely on other forms of life for food, clean air, clean water, and as a means of combating climate change (Deb 2004). Therefore, a sustainable agriculture model designed on the principles of agroecology would be one having the following basic characteristics.

  • Ecologically vibrant.

  • Economically viable.

  • Culturally justifiable.

The goal of ecological farming is to protect, restore, and promote sustainable use of terrestrial ecosystems, combat desertification, and soil-conservation, reverse land degradation, and promote biodiversity (Blandford et al. 2014). It creates a stable constellation of nature, society, and capital and promotes the basic idea of sustainable use of the vital resources that benefit planet earth and farmers rather than the chemical industry. Ecological farming emphasizes a stable and balanced approach among Nature Economy, People Economy, and Market Economy (Shiva and Leu 2018).

Attributes and characteristics of digital agriculture services

Farmers around the world are experiencing a paradigm shift where more and more technology is installed to gather information from their farm fields (Dessart et al. 2019; Mwangi and Kariuki 2015; Gaurav and Singh 2012). The digitalization of non-digital farming equipment is an important stimulus for technology developers (Gack 2018). For example, tractors and irrigation facilities became smart products when they are integrated with embedded systems. The identification of attributes of digital agriculture services is an integral part of this research.

Initially, farmers had many queries and doubts regarding cost and received benefits for their investments in digital agriculture services. But this collaborative and participatory research work helped them in the systematic identification of the associated benefits and net return (Cousins et al. 2013). The linkages between socioeconomic profile, cognitive capabilities, and technology adoption with the state of the environment in the selected areas of this study are identified through a series of interactions with participating farmers, volunteers, researchers, trainers, and technology developers.

Investments in digital agriculture innovations, input costs, and net returns for the cultivated crops are important parameters of this study. It was noticed that the cost factor is a major adoption barrier for the farmers and it adds difficulties for convincing them until and unless systematic information about received benefits is provided to them (Mwangi and Kariuki 2015; Long et al. 2016). The cost concept is an important parameter in deciding on digital agriculture interventions. Therefore, the total cost of technological interventions and overall received value needs to be examined carefully (Blandford et al. 2014).

The integration of digital technology into physical products enables the provision of digital services. The connectivity of physical farms with the digital world is subjected to many challenges. Normal equipment and farming gadgets can be transformed into smart equipment. These innovations can help in keeping the record of farming activities for future reference. Figure 3 illustrates the joining of digital interconnectivity and agriculture as digital farming. It is influenced by Industry 4.0 that includes the use of sensors, interconnected machinery, precision farming, and robotics to increase production efficiency (Gack 2018). Digital technologies are enhancing the value creation process in service management related to smart farming (Engdahl 2015).

Fig. 3
figure 3

The joining of digital interconnectivity and agriculture

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Digitization of farm fields is not a very costly affair. With an initial installation cost of INR 500,000 to 1,000,000, effective digitization of nearly 500 hectares is possible. A single weather station is capable of serving its purpose for nearly 100 to 200 villages. The digital information network for the same population may be created in a very cost-effective manner when the overall value addition in the entire process is considered (Cropin 2020). The cost of standalone sensors ranges from INR 500 to INR 5000; the cost for WSN-based systems is between INR 5000 to INR 30,000, and the cost of IoT-enabled sensors is in the range of INR 1500 to INR 50,000. However, when compared to the long-term benefits, this cost is quite negligible (Advancetech India Pvt. Ltd 2020).

The first phase of this study started with the selection of farmers. As far as educational background of the participating farmers is concerned, details are as follows: two are technical graduates, two are graduates in agriculture, four are graduates in other streams, and the remaining four are undergraduates. Because of their educational background and sensitivity to ecological and economic issues, it was easy to communicate and convince them for using the sensor-based digital agriculture interventions.

The area selected in the Farrukhabad district of Uttar Pradesh falls under the Upper Gangetic Plain agro-climatic zone with annual rainfall reported around 810 mm with an average of 67 rainy days. The total irrigated area in the district is 180,197 hectares. Out of it, 97.8% of this total irrigated area depends on bore wells and only 2.2% area is canal-irrigated (National Innovations in Climate-Smart Agriculture 2014a). Only 10,200 hectares area is rain-fed out of the total cultivated area of 210,900 hectares in the district.

In comparison with it, the second selected area comes in the Dehradun district of Uttarakhand that falls under the Western Himalayan Zone between the Ganga and Yamuna rivers with an annual average rainfall of 1896 mm with an average of 96 rainy days. Farmers have access to canals network and hardly need to irrigate their fields using bore-wells (National Innovations in Climate-Smart Agriculture 2014b).

Farmers from Dehradun use traditional seeds and rely on organic farming practices. It reduces their input cost and maintains soil fertility in the long term. In the Farrukhabad district, chemical farming is preferred and farmers purchase seeds from the seed-producing companies. However, some of the farmers with better educational exposure are adopting ecological farming methods. In both regions, farmers were encouraged to use PF interventions and other digital platforms to monitor, control, and manage their farm resources. This inter-state interaction helped them in learning many new things and in improving their farming practices.

The sensors deployed in the farm fields are capable of acquiring data related to weather information, soil moisture, soil pH, soil temperature, leaf wetness, and crop health. The participating farmers were updated with the 7-day weather forecast. They are also given information related to hailstorms, frost, heat-wave, and wind to make necessary arrangements for the protection of their crops. To improve their operational efficiency, farmers were instructed to align their field visits according to the issued alerts.

The training of farmers for digital agriculture platforms was the next phase of this work. Simultaneously, questionnaires were prepared to collect data about their farm practices and revenue generated for the selected crops. Once they were convinced to use sensors, field deployment was accomplished with the help of trainers and volunteers. Data from the farmers were collected regularly for every crop. Based on the received information, a systematic analysis was carried out.

Precision agriculture and precision irrigation

The chemical industry and seed-producing companies made large profits during the period of the green revolution. Not only farmers lost money from their pockets, but soil fertility also emerged as a big loss for them. Digital agriculture technologies are trying to reverse this loss. In a broader sense, digital agriculture interventions are not only helping them in soil fertility management and environmental conservation but also ensuring higher economic returns.

Agriculture equipment providers are looking for adding value to their customers with Internet connectivity to capitalize on the real economic value for their crop yields (Taylor et al. 2013). The emerging Internet of Things (IoT) technology is also contributing its role in the digital agriculture transformation. From soil health management to irrigation management, IoT solutions are ready to acquire information from the farm fields and issue alerts with adequate spatial and temporal resolution. The IoT technology is described as the Internet protocol (IP) based on smart networks of sensors and actuators.

With the help of smartphones and agriculture-based applications, farmers are getting updates regarding their farming practices. These sensor networks are embedded with data storage, computation, and communication capabilities to collect and disseminate the information. An IoT-enabled Smiledrive soil sensor deployed in a rice field in Dehradun (Smiledrive 2019) is shown in Fig. 4. The local connectivity of sensors to the access point (usually a mobile phone or an IoT Gateway) is managed by Bluetooth, whereas the global connectivity is taken care of by a 3G/4G mobile. The acquired farm management information provides valuable inferences and operational insights.

Fig. 4
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Field deployment of the IoT-enabled Smiledrive soil sensor

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A summary of farm parameters is presented in Table 1. Soil moisture is an important parameter to inform about irrigation, whereas fertility tells about the need for fertilizers. Soil-specific agroecological strategies for sustainable land use are extremely useful for soil fertility management. Even though there exist some adoption barriers, these interventions can help farmers in improving their farming practices. Farmers get inputs and updates about required interventions through their mobile phones.

Table 1 Acquired farm parameters using IoT enabled sensors
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Like precision farming, precision ırrigation (PI) is another domain where sensors-based information systems are playing a significant role. It worth mentioning here that the agriculture sector consumes 85% of the total available freshwater. With the growing demand for food, this crop water consumption may increase in the future (De Fraiture and Wichelns 2010). Hence, an upgradation of irrigation infrastructure is needed to improve water efficiency. For this purpose, sensor-based irrigation facilities are proving their worth. Farmers are expressing their interest in the adoption of water-efficient irrigation mechanisms as it reduces their input cost. These technology-supported irrigation systems reduce a significant amount of water usage when compared to traditional flood irrigation.

Participating farmers were encouraged to reduce chemical inputs and use organic farming methods as much as possible to optimize their overall water consumption. Organic farming uses water more efficiently because of better soil structure, higher levels of organic matter, humus, and charcoal (Pimentel et al. 2005). The open structure of the soil allows rainwater to replenish the groundwater, resulting in less water loss runoff. Humus can absorb water up to 30 times its total weight. That helps in reducing evaporation and leaching. Water-holding capacity is far better in the case of organic matter when compared to conventional chemical farming. It is also helpful in the prevention of land degradation and desertification.

Results and discussion

Assessment of economic feasibility or commercial viability is an essential process for successful commercialization, during the development or deployment of new products or services (Kumar et al. 2011). This exercise is commonly known as techno-economic analysis (TEA). The value creation process for digital agriculture is bidirectional and can be seen as an integration of the digital world into the physical world. It allows the development of new service concepts and the redesigning of existing services. Interactive sessions with farmers and technology developers helped in finding the value creation attributes of DAS platforms. These attributes are summarized in Table 2.

Table 2 Value creation attributes of digital agriculture services
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The commercial viability and perceived value for the farmers are identified as drivers for the adoption of such innovations (Long et al. 2016; Mwangi and Kariuki 2015). In the case of digital agriculture facilities, the entire input cost is divided into two parts: installation cost and operating cost. The installation cost includes hardware as well as software cost whereas operating cost includes annual maintenance charges, wages, and salaries of professionals and services taken from other agencies.

Many companies are offering their services based on annual maintenance charges costing around INR 500,000 to cover the field area of 500–1000 hectares. Hence, the approximate cost of crop monitoring is in the range of INR 500 to INR 1000 per hectare. Field executives continuously monitor the cropland growing conditions and share updates with the farmers (Cropin 2020).

As a PI intervention, farmers used the XH-M214 soil sensor-based automated irrigation system that takes the values of soil moisture and maintains relative humidity levels within the specified range. The sensor probe is inserted directly into the soil. The digital display unit shows the soil moisture level. When the moisture is below the lower threshold value pump starts watering and when it reaches the set value, the pump is switched off automatically. The value of volumetric soil water contents was controlled in the range of 10% to 60%. Readings for soil temperature, soil pH, and atmospheric conditions were also taken into the account before making any decision for irrigation. A significant reduction in water use was observed during the first crop cycle, and even better results were reported during the second and third crop cycles.

In this evaluation, twelve crops in four different categories are selected to examine the economic and environmental performance of digital agriculture innovations. These crops include cereals, pulses, cash crops, and fruits. As far as the crop cultivation area is concerned, these crops cover nearly 90% of the total cultivated land in the selected regions. Data related to the optimum use of water after PI Interventions are shown in Table 3.

Table 3 Crop water requirement for the selected crops
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Operational efficiency and complete traceability of farm operations are the main objectives of this analysis. Improvement in the farm yields (income) is giving farmers a fair chance to realize the value of digital agriculture interventions. The net benefits from digital agriculture innovations were estimated at current prices for the year 2019–2020. The financial figures presented here are based on the results of long-term experiments conducted at the farm fields of participating farmers. The impact of technological interventions on production cost is evaluated by estimating the relationship between input production cost and net return (Srivastava et al. 2017). In this study, production cost was estimated by the following cost function

$$\begin{aligned} {\text{Production cost}} & = {\text{f(crop yield}},{\text{ seed prices}},{\text{ fertilizer prices}},{\text{ labor wages}},{\text{ machine use prices}}, \\ \quad {\text{irrigation prices}},{\text{ animal use prices}},{\text{ market trend)}} \\ \end{aligned}$$
(1)

The last factor in this cost function, the market trend is the major cause of worry for farmers. Farmers are compelled to sell their produce at extremely low prices. Apart from falling incomes, the increasing cost of production, crop failure, and indebtedness have become the harsh reality of the socioeconomic farming landscapes. As the cost function suggests that the overall input cost shall be reduced to increase the commercial viability of a crop. This goal requires the effective use of technology and ecological farming practices. The value of aggregate farm produce becomes higher when crop farming is integrated with livestock farming as it improves the internal cycling of nutrients (Deb 2004).

When compared to the conventional farming methods, digital agriculture innovations helped farmers in the optimization of farm resources and helped them significantly reducing the input cost and ecological footprints. The economic and environmental performance of crop cultivation practices is compared with other similar studies (Kumar and Ilango 2018; Bonny 2017; Mwangi and Kariuki 2015; Kumar et al. 2011). The net benefits due to digital agriculture practices compared to the existing agricultural practices and corresponding farming requirements have been assessed.

If Table 4 is examined carefully, it infers that ‘output price to input cost ratio’ is in a wide range. Cranberries, Arhar, and Potato are the crops with higher ‘output price to input cost ratio,’ but the market trends are extremely uncertain. Sugarcane, wheat, and rice are the favorite choice of farmers with the highest assured returns. Remaining crops have other benefits, some are better in terms of lower input labor wages; for others, water consumption is low, while some other crops improve soil fertility.

Table 4 Comparison of cost of production and income for the selected crops in (INR)
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Farmers were also encouraged to opt for multi-culture instead of monoculture farming to reduce associated production risk. Farms with mixed cropping with lower chemical inputs have higher returns when compared with the monoculture. Net value received for sugarcane with mixed cropping is higher when compared to monoculture. Table 5 presents a comparative summary of sugarcane cultivation. It woths mentioning here that sugarcane is a major cash-crop in both regions.

Table 5 Comparison of cost of production and net income for sugarcane cultivation
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The net benefit of digital agriculture innovations and ecological farming practices is reported as 18,000 to 25,000 per hectare. Despite apparent and hidden benefits, farmers are reluctant toward adopting these innovative practices as they hardly receive incentives and appreciation from the market. Professional exposure and awareness among farmers toward sustainable farming methods can surely change this paradigm. Young graduate farmers are not only expressing their concern for the adoption of professional practices, they are also serious about bringing the ecology and economics of farming landscapes on the right track.

Conclusion

The connectivity of physical farms with the digital world helped farmers in keeping a record of activities for future reference. Additionally, digital innovation increased the opportunities for the identification of farming needs. The conducted techno-economic analysis of smart farm interventions assisted in the identification of value creation attributes of digital agriculture services. Different attributes that farmers value can help in the domestication of digital agriculture technologies. This analysis also provided some inferences into the areas where further research and development activities should be focused on.

As a concluding remark, it is worth mentioning that digital connectivity along with ecological farming is enabling farming communities in getting better returns for their hard work. Participating farmers received the net benefit in the range of INR 18,000–25,000 per hectare. However, the adoption of digital technologies will not ensure sustainable farming if they are not sustainably used and implemented on a large scale. At the same time, digital farming practices need to be adopted selectively in different soil conditions, irrigation facilities, and agroecological zones.

To complement the findings of this research work, technology developers, service providers, and researchers should focus on technologies that create opportunities to empower the small farmers who have limited access to technology and until recently had little chance of direct connectivity with the market. With a goal of affordable excellence, digital agriculture innovations should enhance the connectivity of the farmers with the knowledge, networks, and institutions. Digital agriculture innovations must focus on capacity building and social value creation as the ongoing agrarian crisis in India has many social aspects.