Purpose: The key informants in a village setup were studied to understand the existing social and knowledge systems of farmers, their structure, and relationships between different actors. The purpose is to identify different channels of information and use them as a means to disseminate agricultural technologies and related information to farmers. Design: We use the network map analysis in a case study approach as a tool to demonstrate the linkages between the key actors and stakeholders in the information network of farmers. Findings: The government institutions are well networked among themselves but have limited interactions with non-government sources. Farmers in Bihar have strong linkages with few network actors, who are important nodes in the social knowledge network. The study showed heterogeneity and complexity in the network shape and structure across different districts. Practical Implications: Knowledge networks and social networks are the drivers of information sharing and play a significant role in the diffusion of agricultural technology and related knowledge. Understanding these networks provides a platform for introducing the agricultural technologies and getting connected to a wider group of farming communities. Theoretical Implications: The study shows that the network is formed by different actors and their role determine the nature and shape of the network. Knowledge of this heterogeneity is important in designing or revamping agricultural information systems. Originality: Information and knowledge networks are least explored in the agricultural information dissemination process. Some studies have shown the role of actors and social networks, but this study uniquely explores and presents the heterogeneity of these networks.

This dataset provides detailed information on rice production practices being applied by farmers during 2018 rainy season in India. Data was collected through computer-assisted personal interview of farmers using the digital platform Open Data Kit (ODK). The dataset, n = 8355, covers eight Indian states, viz., Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Odisha, Punjab, Uttar Pradesh and West Bengal. Sampling frames were constructed separately for each district within states and farmers were selected randomly. The survey was deployed in 49 districts with a maximum of 210 interviews per district. The digital survey form was available on mobile phones of trained enumerators and was designed to minimize data entry errors.

Far removed from the agricultural fire “hotspots” of Northwestern India, rice residue burning is on the rise in Eastern India with implications for regional air quality and agricultural sustainability. The underlying drivers contributing to the increase in burning have been linked to the adoption of mechanized (combine) harvesting but, in general, are inadequately understood. We hypothesize that the adoption of burning as a management practice results from a set of socio-technical interactions rather than emerging from a single factor. Using a mixed methods approach, a household survey (n = 475) provided quantitative insights into landscape and farm-scale drivers of burning and was complemented by an in-depth qualitative survey (n = 36) to characterize decision processes and to verify causal inferences derived from the broader survey. For communities where the combine harvester is present, our results show that rice residue burning is not inevitable. The decision to burn appears to emerge from a cascading sequence of events, starting with the following: (1) decreasing household labor, leading to (2) decreasing household livestock holdings, resulting in (3) reduced demands for residue fodder, incentivizing (4) adoption of labor-efficient combine harvesting and subsequent burning of loose residues that are both difficult to collect and of lower feeding value than manually harvested straw. Local demand for crop residues for livestock feeding plays a central role mediating transitions to burning. Consequently, policy response options that only consider the role of the combine harvester are likely to be ineffective. Innovative strategies such as the creation of decentralized commercial models for dairy value chains may bolster local residue demand by addressing household-scale labor bottlenecks to maintaining livestock. Secondary issues, such as timely rice planting, merit consideration as part of holistic responses to “bend” agricultural burning trajectories in Eastern India towards more sustainable practices.

Approximately 7,600 wheat plots were surveyed and geo-tagged in the 2017-18 winter or rabi season in Bihar and eastern Uttar Pradesh (UP) in India to capture farmers’ wheat production practices at the landscape level. A two-stage cluster sampling method, based on Census data and electoral rolls, was used to identify 210 wheat farmers in each of 40 districts. The survey, implemented in Open Data Kit (ODK), recorded 226 variables covering major crop production factors such as previous crop, residue management, crop establishment method, variety and seed sources, nutrient management, irrigation management, weed flora and their management, harvesting method and farmer reported yield. Crop cuts were also made in 10% of fields. Data were very carefully checked with enumerators. These data should be very useful for technology targeting, yield prediction and other spatial analyses.

In 2022, the Indian state of Bihar experienced its sixth driest year in over a century. To document the consequences and farmer responses to the meteorological drought, real-time survey data was collected across 11 districts of Bihar. We then developed a causal machine learning model to quantify drought impacts on rice production and to characterize how access to affordable irrigation from electric pumps mitigated productivity losses. This model addresses the empirical challenge of conducting a counterfactual causal analysis when a factor like drought affects nearly all sampled farmers. In the 2022 event, drought led to rice acreage reduction, transplanting delays, damage to seedling nurseries, and higher use rates of supplemental irrigation. For fields that were planted, average yield losses from water stress were estimated as 0.94 t/ha (∼23 % yield loss) with these losses reduced by 0.3 t/ha in fields with access to electric tubewells. Agronomic management practices such as earlier transplanting were also identified as complementary strategies that increased the adaptation value of investments in irrigation. To reduce the impact of drought in Bihar, additional investments in electric irrigation infrastructure are needed along with focused extension efforts and decision support systems that empower farmers to make economically and sustainably rational use of available water resources to maintain yield and profitability.

Sub-optimal wheat productivity in the eastern Indo-Gangetic plain of India can largely be attributed to delayed sowing and the use of short duration varieties. The second week of November is the ideal time for sowing wheat in eastern India, though farmers generally plant later. Late-sowing farmers tend to prefer short-duration varieties, leading to additional yield penalty. To validate the effect of timely sowing and the comparative performance of long- and short-duration varieties, multi-location on-farm trials were conducted continuously over five years starting from 2016–2017. Ten districts were selected to ensure that all the agro-climatic zones of the region were covered. There were five treatments of sowing windows: (T1) 1 to 10 November, (T2) 11–20 November, (T3) 21 to 30 November, (T4) 1–15 December, and (T5) 16–31 December. Varietal performance was compared in T3, T4, and T5, as short-duration varieties are normally sown after 20 November. There is asymmetry in the distribution of samples within treatments and over the years due to the allocation of fields by farmers. Altogether, the trial was conducted at 3735 sites and captured 61 variables, including yield and yield attributing traits. Findings suggested that grain yields of long-duration wheat varieties are better even under late sown scenarios.