Digital agriculture in India: Transforming farm economics in the 21st century: A review

Introduction

Despite being one of the major economic contributors in India, with 42% employment generation and providing about 18% of the gross domestic product of the country, agriculture in India continues to be plagued by multifarious problems such as diminishing sizes of farms, climatic uncertainties, water crisis, soil pollution and fluctuating commodity prices (Economic Survey of India, 2023; Ministry of Agriculture, 2024) [25,52] . This is because the average size of farms owned by farmers in India is only 1.08 hectares, which makes it very difficult for farmers to maximize their resource potential (Agriculture Census, 2021; Chand et al., 2015; Jain et al., 2016) [2,15,18,38] ). In order to address this issue, a revolution has come in the form of digital agriculture, which is defined as the use of ICTs in agriculture for optimizing agricultural processes (Trendov et al., 2019; Wolfert et al., 2017) [75,80] . The integration of technologies such as mobility solutions, remote sensing through satellites, big data, IoT and artificial intelligence has made it possible for India to embrace digital agriculture (Farooq et al., 2019; Lioutas et al., 2019) [28, 46] . In fact, India is poised to take advantage of digital agriculture owing to 1.2 billion mobile subscribers and rural internet proliferation (TRAI, 2024) [74] . This review synthesizes empirical evidence on digital agriculture's economic impact in India, examining technology adoption patterns, productivity gains, cost savings and income effects. We analyze the evolution of digital agricultural technologies from basic SMS-based advisories to sophisticated AI-powered decision support systems. The objectives are threefold: (1) to map the digital agriculture landscape in India, (2) to assess economic outcomes associated with various digital interventions and (3) to identify barriers and enablers for scaling digital agriculture across diverse agro-ecological zones.

Evolution of Digital Agriculture in India

The emergence of digital agriculture in India was marked in the early years of the new millennium with projects such as the e-Choupal project of the ITC Limited, where agricultural knowledge and market connectivity were made possible using internet kiosks (Banerji & Meenakshi, 2008) [5] .

Early Initiatives (2000-2010)

The emergence of digital agriculture in India was marked in the early years of the new millennium with projects such as the e-Choupal project of the ITC Limited, where agricultural knowledge and market connectivity were made possible using internet kiosks (Banerji & Meenakshi, 2008) [5] . The Kisan Call Centres established in 2004 offered advisory services related to agriculture through telephone, in local languages, to more than 100 million farmers (Mittal et al., 2010; Aker et al., 2016; Singh et al., 2008) [4,55,56,71] .

Mobile Revolution (2010-2015)

The increasing availability of mobile phones triggered another phase of digital agriculture services, including SMSbased advisory services that offered information about weather conditions, commodity prices and agronomic practices (Cole & Fernando, 2021; Fafchamps & Minten, 2012) [21,27] . The Soil Health Card program, launched in 2015, used technology to make soil testing and fertilizer advice available to over 220 million farmers (Brar et al., 2015) [11] .

Smart Agriculture Era (2015-2024)

There has been tremendous progress in digital agriculture technology in recent years. The Digital India campaign by the government and agricultural applications have changed the way services are provided (Bharadwaj et al., 2013) [8] . Systems like eNAM (National Agriculture Market) have enabled digitization of agricultural commodity exchanges in more than 1,000 market centers (Chand, 2016; Acharya & Agarwal, 2011) [1,15] . The precision agriculture technology such as use of drones, satellite-based farming and AI-based decision-making systems is becoming more popular (Kamilaris et [23,42,51] .

Key Digital Agriculture Technologies and Platforms 3.1 Precision Agriculture Technologies

Precision agriculture involves various technologies that allow site-specific crop management using spatial and temporal variations in agricultural practices (Zhang et al., 2002) [83] . In India, the use of satellite-based remote sensing technology has become an economically affordable method of crop monitoring and yield estimation (Chakraborty et al., 2005; Patel et al., 2009) [14,63] . Various studies have shown that satellite imaging technology can boost the productivity of wheat production from 12 to 18% through optimal irrigation and fertilization (Lobell et al., 2009) [48] .

Agri-drone technology is used in crop dusting, field surveying and crop monitoring purposes. The Kisan Drone scheme provides subsidies to farmers in the purchase of drones and helps in saving around 25-30% pesticides and 40% labor charges among others (Ministry of Agriculture, 2022; Mogili & Deepak, 2018) [54,57] . Sensor networks installed in agricultural fields using IoT technology to measure soil moisture, temperature and nutrient content help in implementing precision irrigation, thus saving water usage up to 30-40% (Venkatesan & Kathrine, 2019; Kumar et al., 2017) [45,78] .

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Mobile-Based Advisory Systems

With mobile applications, information regarding agriculture is no longer limited. The Kisan Suvidha app helps more than 50 million farmers get information on weather, market prices, pests and experts' advice (Veeranna et al., 2017) [77] .

Examples of private companies providing such services include CropIn, DeHaat and Plantix, which employ the use of artificial intelligence in diagnosing plant diseases with 85-95% accuracy by image recognition (Ramesh & Vydeki, 2020; Saleem et al., 2019) [65,69] . Research shows that adoption of mobile advisories increases crop yields by 10-15% and reduces crop loss by 20% (Aker, 2011; Nakasone et al., 2014) [3,59] . [64,67] .

Digital Market Platforms

Farm Management Information Systems

An integrated farm management software platform encompasses several applications ranging from planning of crops, procurement of inputs, monitoring the production process and managing finances. [19,76] . Likewise, Cropin and Gramophone provide digital solutions for input, output and crop management processes (Krishna, 2016) [43] .

Economic Impact of Digital Agriculture 4.1 Productivity Enhancement

Research has shown that there is a positive correlation between yield improvement and the use of digital agriculture. In an analysis carried out among 15,000 farmers in several states, the adoption of digital advisory services led to a 17% increase in rice yield, a 14% increase in wheat yield and a 22% increase in cotton yield (Birthal et al., 2015) [9] . The use of precision fertilizer management through soil health cards improved crop yields by 8-12% (Nayak et al., 2019) [60] .

Cost Reduction and Resource Efficiency

The adoption of digital technology greatly enhances the efficiency in input usage and cuts production costs. The use of precision irrigation technologies led to a 30-45% reduction in water usage without compromising on productivity (Evans & Sadler, 2008) [26] . Artificial intelligence pest and disease identification technology led to a 20-40% decrease in pesticide usage, thus reducing costs and adverse effects on the environment (Barbedo, 2018; Bock et al., 2010) [6,10] . Soil and nutrient analysis using digital technology led to a 15-25% cut in fertilizer costs due to proper fertilization (Jat et al., 2011) [39] .

Income and Profitability

The resultant impact of higher productivity and reduction in costs leads to significant financial benefits. Studies have shown that the use of digital agriculture resulted in a 20-35% increase in net farm income as compared to non-users (Rao & Rogers, 2006; Dey & Upadhyaya, 1996) [22,66] . The use of digital market platforms helped farmers achieve 8-15% higher prices due to improved market knowledge and lower transaction costs (Goyal, 2010) [36] . The ROI (return on investment) was in the range of 2.5:1 to 4.5:1 with payback periods of 1-2 cropping cycles (Jensen, 2007;

Svensson & Yanagizawa, 2009) [40,72] .

Adoption Patterns and Determinants

The adoption of digital agriculture in India is highly heterogeneous concerning geography, farm size and demographics. A survey conducted nationally involving 25,000 farmers found the overall adoption rate to be around 28% with variations of 45% in advanced states such as Punjab and Haryana and 12-15% in the eastern and northeastern areas (NABARD, 2023) [58] . The farm size is another important factor influencing adoption rates. Larger farms (over 2 hectares) exhibit an adoption rate of 60%, whereas marginal farmers (less than 1 hectare) have only 18% adoption rates (Gautam & Ahmed, 2019) [32] . This difference is mainly attributed to the economic aspect and scalability concerns. Similarly, educated farmers (secondary level education) are twice as likely to adopt digital agriculture than those with primary education and young farmers (below 40 years old) are thrice as likely to adopt the technology than older farmers (above 55 years old) (Wossen et al., 2017; Foster & Rosenzweig, 2010) [31,82] . Infrastructure development is positively correlated with adoption. The adoption rate for farmers with good mobile network connections (4G) is around 55% compared to 15% in areas with poor connectivity (Goedde et al., 2015) [33] . The same can be said about electrification, smartphone ownership and access to agricultural markets (Doss, 2006; Maertens & Barrett, 2013) [24,49] .

Challenges and Barriers to Adoption 6.1 Infrastructure Deficits

Despite progress in rural digitalization, infrastructure gaps remain formidable. Only 37% of rural India has reliable 4G coverage, limiting real-time data access for precision agriculture (TRAI, 2023) [73] . Power availability for charging devices and operating IoT sensors is inconsistent, with average power outages of 8-12 hours daily in many agricultural regions (Central Electricity Authority, 2023) [13] .

Digital Literacy and Skill Gaps

Low digital literacy poses a significant barrier. Survey data indicates that 62% of farmers lack basic smartphone operation skills and 78% cannot navigate app-based interfaces independently (NSSO, 2019) [62] . Language barriers compound the problem -while many apps support regional languages, technical agricultural terminology often lacks proper local translation (Burrell & Oreglia, 2015) [12] .

Affordability and Access

Technology costs remain prohibitive for smallholders. Precision agriculture equipment (sensors, drones) requires initial investments of 50,000-500,000, beyond reach for most farmers ₹ (Trendov et al., 2019) [75] . While smartphone-based solutions are more affordable, data costs burden poor farmers -estimated at 200-500 monthly for regular app usage (Chowdhury ₹ et al., 2014) [20] .

Data Privacy and Security Concerns

Farmer concerns about data misuse hinder adoption. Studies report that 45% of farmers express apprehension about sharing farm data with private companies, fearing exploitation or loss of bargaining power (Wiseman et al., 2019) [79] . Absence of clear data governance frameworks and farmer data rights exacerbates trust deficits (Ferris & Rahman, 2016) [30] .

Policy Landscape and Government Initiatives

The Indian government has made various efforts toward digital agriculture. For example, the Digital Agriculture Mission from 2021 to 2025, costing 2,817 crores, intends to construct a ₹ common farmers' database, connect agricultural databases and expand digital agricultural advisory services (Ministry of Agriculture and Farmers Welfare, 2021) [2,52] . Meanwhile, the AgriStack framework seeks to build an integrated digital system that connects land records, crop information and farmers' profiles to offer tailored services (NITI Aayog, 2021) [61] .

Other examples include state-level initiatives supporting national programs. In Maharashtra, the project named PROUD leverages artificial intelligence (AI) and satellite data for crop insurance assessment and benefits six million farmers (Government of Maharashtra, 2023) [35] . In Andhra Pradesh, the RythuBharosa portal offers soil testing, weather forecast and marketing linkage services, serving four and half million farmers (Government of Andhra Pradesh, 2022) [34] . Still, lack of coordination between central and statelevel programs, scattered databases and poor interoperability impede their efficiency (Chand et al., 2015) [18] .

Future Directions and Emerging Technologies

The future of digital agriculture is the integration of new technologies. Blockchain-based supply chains will ensure increased traceability and more benefits for farmers, with pilot programs reporting a 20% increase in price capture (Kshetri, 2018) [44] . State-of-the-art AI algorithms utilizing deep learning and computer vision can detect diseases accurately up to 95% of the time, potentially avoiding 30-40% of the loss in crops (Ferentinos, 2018) [29] . A climate-smart agriculture approach utilizing technology is another promising area of research. The use of weather models, crop simulation tools and sensors can assist farmers in dealing with climatic changes (Lipper et al., 2014) [47] . AI-driven voice assistants speaking local languages will remove language barriers, with prototype assistants able to answer questions 85% of the time (Martin & Hanington, 2012) [50] . [7,81] .

Key policy recommendations include

Conclusion and Policy Recommendations

The present review presents a large body of evidence that showcases the positive effects of digital agriculture technologies on farm economics in India. Precision agriculture, mobile advisories and digital marketplaces provide between 15% and 35% increases in farmer revenues thanks to higher efficiency and reduced expenses. When the technologies are used together, their economic benefits become even more significant. Nevertheless, several challenges need to be overcome to realize the full potential of digital agriculture technologies.

In particular, the current infrastructure inadequacies, poor digital literacy, high costs and data management issues disproportionately affect small farmers, comprising about 86% of all Indian farmers. Consequently, digital agriculture can potentially worsen existing disparities unless some interventions are made.

Future research needs to explore the following areas:

Sustainable effects of digital agriculture on farm economics; environmental consequences of the precision technologies' implementation; and implications of digital agriculture for social equity. To prove causality, researchers need to employ robust experimental methods. Furthermore, further research into farmers' decision making, technological adaptation to local conditions and new business models is also needed.

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