Intercomparison of crop establishment methods for improving yield and profitability in the rice-wheat system of Eastern India.

Conventionally managed rice-wheat systems of the eastern Indo-Gangetic Plains (E-IGP) that rely on soil puddling for rice and intensive tillage for wheat are low-yielding and resource-inefficient, leading to low profitability. While a host of alternative tillage and crop establishment (TCE) methods have been advocated as solutions for sustainably enhancing productivity and profitability, few systematic comparisons of these methods are reported. To address this gap, a three-year field study was conducted in Bihar, India with the goal of identifying TCE methods for rice-wheat systems that are high yielding, less resource-intensive, and more profitable. The following systems were evaluated: 1) puddled transplanted rice (PTR) followed by (fb) conventional tillage wheat (CTW) or zero-tillage wheat (ZTW); 2) machine transplanted rice in non-puddled soil (MTR) fb ZTW; 3) the system of rice intensification (SRI) fb system of wheat intensification (SWI); and 4) dry-seeded rice (DSR) fb ZTW. Rice cultivar duration (short versus medium-duration) was incorporated as a subplot treatment in all systems. Rice yields were similar with all methods, except DSR yield was 11 % lower and MTR yield was 7% higher than PTR in the third year. Cost of production was US$ 149 and 77 ha-1 lower in DSR and MTR, respectively, and US$ 84 ha-1 higher in SRI than PTR. The gross margin and benefit-cost (B:C) ratio was highest in MTR followed by DSR and lowest in SRI. In wheat, ZT resulted in a higher yield than CTW, especially when ZTW was cultivated after non-puddled rice (e.g., DSR or MTR). ZTW reduced production costs by US$ 69 ha-1, whereas SWI increased it by US$ 139 ha-1 relative to CTW. The higher yield and lower cost of production resulted in a higher gross margin (US$ 82-355 ha-1 and US$ 129-409 ha-1 higher than CTW and SWI, respectively) and a higher B:C ratio in ZTW treatments than CTW and SWI. At the system level, MTR or DSR followed by ZTW had both superior crop yields and consistently higher gross margins (US $133 to 382 ha-1) than other practices. On the other hand, the SRI fb SWI system had no yield advantage and poorer economic performance than conventional practices. In all systems, the inclusion of a medium-duration rice hybrid resulted in higher rice and system yields. These results suggest that significant gains in profitability are possible with emerging TCE practices in rice-wheat systems, but alternatives such as the SRI and SWI will likely erode farmer incomes.

Large survey dataset of rice production practices applied by farmers on their largest farm plot during 2018 in India.

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. Each survey captured approximately 225 variables around rice production practices of farmers' largest plot starting with land preparation, establishment method, crop variety and planting time through to crop yield. Detailed modules captured fertilizer application, irrigation, weed management, biotic and abiotic stresses. Additional information was gathered on household demographics and marketing. Geo-points were recorded for each surveyed plot with an accuracy of <10 m. This dataset is generated to bridge a data-gap in the national system and generates information about the adoption of technologies, as well as enabling prediction and other analytics. It can potentially be the basis for evidence-based agriculture programming by policy makers.