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Agriculture is central to the Indian economy and suffers from widespread operational inefficiencies that could be corrected by the use of digital agriculture technologies (DA). We review and synthesise available literature concerning digital agriculture in India and anticipate its transformative potential in the coming decade. Although the initial growth of DA was more conspicuous in the downstream sectors and high-value crops, reaching smallholder farmers upstream is slowly emerging despite significant obstacles such as small fragmented holdings, inadequate data infrastructure and public policy, and unequal access to digital infrastructure. Agri-tech enables innovation at many locations within value chains, and a steady shift is occurring in change from individual farms to the whole value chain. Technology in the sector is progressing from information and communication technology-based solutions to Internet of Things and artificial intelligence–machine learning-enabled services. India’s public policy shows signs of a longstanding investment and collaboration in the sector, with an explicit focus on data infrastructure development. We find smallholder predominance, diversity in production systems, the predominance of commodity crops, proximity to urban markets, and public policy as the major factors of DA’s success in India. A stocktake of the available technologies and their applications by the public sector, tech giants, information technology leaders and agri-food tech startups in India strongly indicates a digital transformation of Indian agriculture. However, given the federal structure of governance and agriculture being a state (province) subject, we need to wait to see how DA policies are rolled out and taken up across the country.

Social Network Analysis (SNA) has received growing attention among diverse academic fields for studying ‘social relations’ among individuals and institutions. Unfortunately, its application has remained limited in the study of livelihood systems of rural poor. Complexity in rural livelihoods has increased sharply in the face of increased pressure on natural resources and rapid shift in farm-based to non-farm based employments. This poses great challenge to successful livelihood intervention in rural areas. On one hand, rural development/extension needs to cater to diverse information and service need of the rural people; on other hand, rural institutions need to deliver livelihood-sustaining services more efficiently, which often need institutional restructuring at multiple levels. To achieve these challenges, a strong innovative analytical tool is required for understanding the complexity of rural livelihoods and the associated role of rural institutions. SNA provides excellent scope to analyse such complex systems and interactions among their components. This article proposes an outline of using SNA in livelihood system analysis. The analysis can provide answer to many questions of practical importance – Who are the influential actors in a livelihood system? Which are the key institutions contributing towards sustainable livelihoods? How do these actors interact among themselves? This will help rural development administrators to deliver livelihood-supporting services more efficiently through informed targeting and capacity building.

Identifying productive, profitable and less risky cropping systems is important for sustaining farm-based livelihoods in the context of climatic uncertainties and market volatility in many developing nations. Reductionist field crop research identifies the best-bet solutions based on treatment replicates at a single point in time, which may not account for price instability under climatic uncertainties and volatile markets. Keeping this in mind, we estimated productivity, profitability and weather-related risk from eleven different rice-based cropping systems (eight existing and three potential systems) in the coastal region of West Bengal, India. Information on the crop management practices, yield and prices of the component crops of these eleven cropping systems, under ’best,’ ’normal’ and ‘worst’ situations (scenario), were collected through a ques-tionnaire survey on50 farms of Gosaba Block, West Bengal, India. Irrespective of the scenarios, the rice-lathyrus systems, followed by rice-onion and rice-lentil systems recorded the lowest rice-equivalent yields and system yields. However, the highest rice-equivalent yields and system yields were recorded for rice-chilli systems, followed by rice-tomato and rice-potato-green gram systems. Per hectare total paid-out cost (TPC) of rice-tomato systems was higher, followed by rice-chilli, rice-potato-green gram and rice-potato systems. However, irrespective of seasonal conditions (best, normal and worst), rice-chilli systems gave a higher net return followed by rice-tomato and rice-potato-green gram systems. The rice-fallow system recorded the lowest value for both parameters. Under the worst seasonal conditions, the rice-onion system gave a negative net return. Under all the scenarios, the highest B:C ratio was observed for rice-chilli, rice-tomato, rice-potato-green gram and rice-potato systems. The cumulative probability distribution of the rice-tomato system showed first-degree stochastic dominance over other systems and the rice-chilli system showed second-degree stochastic dominance over the rest of the systems. Only the rice-onion system had a small chance (< 1%) of a negative net return, while the rest of the cropping systems were highly likely to get a positive net return. Taking productivity, economics, and risk assessment of different rice-based systems into account, rice-vegetable systems, especially rice-tomato and rice-chilli among the existing systems and rice-potato-green gram system among the potential systems, can be recommended for sustainable intensification in these coastal saline tracts of the region. We also discuss additional socio-economic factors explored by in-depth in-terviews with the farmers, which might influence the adoption and upscaling of these cropping systems in the region.

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