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Assessing vulnerability to climate change and variability is an important first step in evolving appropriate adaptation strategies to changing climate. Such an analysis also helps in targeting adaptation investments, specific to more vulnerable regions. Adopting the definition of vulnerability given by IPCC, vulnerability was assessed for 572 rural districts of India. Thirty eight indicators reflecting sensitivity, adaptive capacity and exposure were chosen to construct the composite vulnerability index. Climate projections of the PRECIS model for A1B scenario for the period 2021-2050 were considered to capture the future climate. The data on these indicators were normalized based on the nature of relationship. They were then combined into three indices for sensitivity, exposure and adaptive capacity, which were then averaged with weights given by experts, to obtain the relative vulnerability index. Based on the index, all the districts were divided into five categories with equal number of districts. One more district was added to 'very high' and 'high' categories. The analysis showed that districts with higher levels of vulnerability are located in the western and peninsular India. It is also observed that the highly fertile Indo-Gangetic Plains are relatively more sensitive, but less vulnerable because of higher adaptive capacity and lower exposure.

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)—2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1–2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18–22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods.

Introduction The impact of climate change on agriculture and food security has been examined quite thoroughly by researchers globally as well as in India. While existing studies provide evidence on how climate variability affects the food security and nutrition, research examining the extent of effect vulnerability of agriculture to climate change can have on nutrition in India are scarce. This study examined a) the association between the degree of vulnerability in agriculture to climate change and child nutrition at the micro-level b) spatial effect of climate vulnerability on child nutrition, and c) the geographical hotspots of both vulnerability in agriculture to climate change and child malnutrition. Methods The study used an index on vulnerability of agriculture to climate change and linked it to child malnutrition indicators (stunting, wasting, underweight and anaemia) from the National Family Health Survey 4 (2015–16). Mixed-effect and spatial autoregressive models were fitted to assess the direction and strength of the relationship between vulnerability and child malnutrition at macro and micro level. Spatial analyses examined the within-district and across-district spill-over effects of climate change vulnerability on child malnutrition. Results Both mixed-effect and spatial autoregressive models found that the degree of vulnerability was positively associated with malnutrition among children. Children residing in districts with a very high degree of vulnerability were more like to have malnutrition than those residing in districts with very low vulnerability. The analyses found that the odds of a child suffering from stunting increased by 32%, wasting by 42%, underweight by 45%, and anaemia by 63% if the child belonged to a district categorised as very highly vulnerable when compared to those categorised as very low. The spatial analysis also suggested a high level of clustering in the spatial distribution of vulnerability and malnutrition. Hotspots of child malnutrition and degree of vulnerability were mostly found to be clustered around western-central part of India. Conclusion Study highlights the consequences that vulnerability of agriculture to climate change can have on child nutrition. Strategies should be developed to mitigate the effect of climate change on areas where there is a clustering of vulnerability and child malnutrition.

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