Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (PER), maximum and minimum temperatures (Tmax and Tmin), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%–40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased precipitation. Hence our CEM and BRT models agree on the locations most at risk, but there is less consensus about the degree of risk at these locations. Our results help to identify locations where livelihoods of low-income farmers and regional food security may be threatened in the next few decades by climate changes. The use of more drought-resilient rice varieties and better irrigation infrastructure in these regions may help to reduce these impacts and reduce the vulnerability of farmers dependent on rainfed cropping.

A 3-year field study was conducted during 1998/99–2000/01 on the effect of tillage on crop growth, yield and nutrient use in wheat (Triticum aestivum L.) grown after different methods of rice (Oryza sativa L.) seeding. Treatments comprised three methods of rice seeding, viz. direct seeding (unpuddled), manual transplanting and mechanical transplanting by self-propelled rice transplanter as main plots and three tillage levels in wheat (conventional tillage (CT), reduced tillage (RT), zero tillage (ZT)) as subplots. Results indicated that tillage significantly decreased soil bulk density (1·59 Mg/m3) over the zero tillage system (1·69 Mg/m3). Greater root density in terms of root dry weight (7·50 Mg/20 cm row length) was recorded in CT and the lowest root dry weight (5·80 Mg/20 cm row length) was obtained in ZT during 2000/01. Significantly higher dry matter accumulation (254 g/m row) and leaf area index of wheat (3·04) were consistently recorded under direct seeding of rice, which was statistically different from the other methods of seeding adopted in the preceding rice crop. CT resulted in significantly higher dry matter (253 g/m row) and leaf area index of wheat (3·02) than RT and ZT respectively, during 2000/01. The highest mean yield of wheat (6·02 Mg/ha) was obtained in direct seeding of rice, followed by mechanical and manual transplanting. Among different tillage levels, CT recorded the highest mean yield of wheat (5·90 Mg/ha) followed by RT (5·82 Mg/ha) and ZT (5·40 Mg/ha). The yield reduction was in the order of 11·28 and 6·31% under ZT and RT, respectively. Soil chemical analysis showed that available soil N, P and K contents were affected significantly due to seeding method and tillage after each cycle of rice–wheat sequence. Significantly greater available soil N, P and K were recorded under direct seeding of rice followed by manual and mechanical transplanting. CT recorded significantly lower values of available soil N and higher values of soil P and K whereas ZT recorded higher values of available soil N and lower values of available soil P and K during the 3 years of study.

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The CERES (Crop Estimation through Resource and Environment Synthesis)-rice model incorporated in DSSAT version 4.5 was calibrated for genetic coefficients of rice cultivars by conducting field experiments during the kharif season at Jorhat, Kalyani, Ranchi and Bhagalpur, the results of which were used to estimate the gap in rice yield. The trend of potential yield was found to be positive and with a rate of change of 26, 36.9, 57.6 and 3.7 kg ha -1 year -1 at Jorhat, Kalyani, Ranchi and Bhagalpur districts respectively. Delayed sowing in these districts resulted in a decrease in rice yield to the tune of 35.3, 1.9, 48.6 and 17.1 kg ha -1 day -1 respectively. Finding reveals that DSSAT crop simulation model is an effective tool for decision support system. Estimation of yield gap based on the past crop data and subsequent adjustment of appropriate sowing window may help to obtain the potential yields.