Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India.

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc.

Pest scenario of Helicoverpa armigera (Hub.) on pigeonpea during future climate change periods under RCP based projections in India.

Gram pod borer, Helicoverpa armigera (Hub.) is the major insect pest of pigeonpea and prediction of number of generations (no. of gen.) and generation time (gen. time) using growing degree days (GDD) approach during three future climate change periods viz., Near (NP), Distant (DP) and Far Distant (FDP) periods at eleven major pigeonpea growing locations of India was attempted. Multi-model ensemble of Maximum (Tmax) and Minimum (Tmin) temperature data of four Representative Concentration Pathways viz., RCP 2.6, 4.5, 6.0 and 8.5 of Coupled Model Inter comparison Project 5 (CMIP5) models was adopted here. The increase in projected Tmax and Tmin are significant during 3 climate change periods (CCPs) viz., the NP, DP and FDP over base line (BL) period under four RCP scenarios at all locations and would be higher (4.7-5.1 °C) in RCP 8.5 and in FDP. More number of annual (10-17) and seasonal (5-8) gens. are expected to occur with greater percent increase in FDP (8 to 38%) over base line followed by DP (7 to 22%) and NP (5to 10%) periods with shortened annual gen. time (4 to 27%) across 4 RCPs. The reduction of crop duration was substantial in short, medium and long duration pigeonpeas at all locations across 4 RCPs and 3 CCPs. The seasonal no.of gen. is expected to increase (5 to 35%) with shortened gen. time (4 to 26%) even with reduced crop duration across DP and FDP climate periods of 6.0 and 8.5 RCPs in LD pigeonpea. More no. of gen. of H. armigera with reduced gen. time are expected to occur at Ludhiana, Coimbatore, Mohanpur, Warangal and Akola locations over BL period in 4 RCPs when normal duration of pigeonpeas is considered. Geographical location (66 to 72%), climate period (11 to 19%), RCPs (5-7%) and their interaction (0.04-1%) is vital and together explained more than 90% of the total variation in future pest scenario. The findings indicate that the incidence of H. armigera would be higher on pigeonpea during ensuing CCPs in India under global warming context.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India.

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9-6.0 °C) and minimum temperatures (1.1-6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980-2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

Pest scenario of Spodoptera litura (Fab.) on groundnut under representative concentration pathways (RCPs) based climate change scenarios.

Multi-model ensemble of Maximum (Tmax) and Minimum (Tmin) temperature data of four Representative Concentration Pathways viz., RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 of Coupled Model Intercomparison Project 5 (CMIP5) models were generated for ten major groundnut growing locations of the India to predict the number of generations of Spodoptera litura (Fab.) using Growing Degree Days approach during three future climate viz., Near (NF), Distant (DF) and Very Distant (VDF) periods and were compared over 1976-2005 baseline period (BL). Projections indicate significant increase in Tmax (0.7-4.7 °C) and Tmin (0.7-5.1 °C) in NF, DF and VDF periods under the four RCP scenarios at the ten groundnut growing locations. Higher percent increase of the number of generations of S. litura was predicted to occur in VDF (6-38%) over baseline, followed by DF (5-22%) and NF (4-9%) periods with reduction of generation time (5-26%) across the four RCP scenarios. Reduction of crop duration was higher (12-22 days) in long duration groundnut than in medium and short duration groundnut. Decrease in crop duration was higher in VDF (12.1-20.8 days) than DF (8.26-13.15 days) and NF (4.46-6.15 days) climate change periods under RCP 8.5 scenario. Increase in number of generations of S. litura was predicted even with altered crop duration of groundnut. Among locations, more number of generations of S. litura with reduced generation time are likely at Vridhachalam and Tirupathi locations. Geographical location (74-77%) and climate period (15-19%), together explained over 90 percent of the total variation in the number of generations and generation time of S. litura. These findings suggest that the incidence of S. litura on groundnut could be higher in future.

Indian summer heat wave of 2015: a biometeorological analysis using half hourly automatic weather station data with special reference to Andhra Pradesh.

Heat wave is a hazardous weather-related extreme event that affects living beings. The 2015 summer heat wave affected many regions in India and caused the death of 2248 people across the country. An attempt has been made to quantify the intensity and duration of heat wave that resulted in high mortality across the country. Half hourly Physiologically Equivalent Temperature (PET), based on a complete heat budget of human body, was estimated using automatic weather station (AWS) data of four locations in Andhra Pradesh state, where the maximum number of deaths was reported. The heat wave characterization using PET revealed that extreme heat load conditions (PET >41) existed in all the four locations throughout May during 2012-2015, with varying intensity. The intensity and duration of heat waves characterized by "area under the curve" method showed good results for Srikakulam and Undi locations. Variations in PET during each half an hour were estimated. Such studies will help in fixing thresholds for defining heat waves, designing early warning systems, etc.