Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901–2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3–4 years) and 4 to 5 consecutive drought years (once in 120 years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8–0.88) between observed and predicted data and low RMSE (2.6–2.7

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.

South interior Karnataka, being a major rainfed zone contributing to production of pigeonpea, finger millet,groundnut, etc., suffers from severe yield instability due to dependency on rainfall. As the distribution of rainfall(spatial and temporal) is erratic, droughts are becoming a common phenomenon and adversely affecting regional crop production by influencing soil moisture availability. Regression studies on per cent available soil moisture (PASM) and yield were conducted based on long-term field observations in 2014–19 on soil moisture and crop yields in finger millet, pigeonpea and groundnut at UAS-GKVK, Bengaluru. The outcomes indicated large yield variability due to minor soil moisture differences. In finger millet, at 58 PASM, 50% (1500 kg/ha) of normal yield was obtained and at 40 PASM, only 25% of normal yield was obtained. In pigeonpea, the crop yield recorded at 25 PASM was 8–18% (202–347 kg/ha) of the normal yield. In groundnut 50 PASM gave 41% of normal yield. The outcome indicated different soil moisture requirement for different crops, stressed the need for amending existing non-crop specific PASM ranges for drought declaration. Amendments were brought in by considering different PASM levels in these three crops finger millet: 20–40 as severe, 40–60 as moderate, >60 as no drought; in pigeonpea, 25–30 as severe, 30–60 as moderate, >60 as no drought; in groundnut, 35–50 as severe, 50–70 PASM as moderate, >70 as no drought. Since the practicality of the study was proven, the amendments were given in the drought manual published by ministry of farmers’ welfare.

The dry spells and rainfall deficit within crop season, play vital role in determining productivity of rainfed crops. Dry Spell Index (DSI) was formulated to quantify cumulative impact of dry spells during kharif season (Jun-Sep) on major rainfed crops of India. District-wise variability of DSI were analyzed across rainfed regions of India using rainfall data of 1636 stations. Comparison of DSI with Standardized Precipitation Index (SPI), hitherto, a widely used drought index showed that, central and eastern Karnataka, northern Rajasthan and western Gujarat are becoming wetter in terms of total seasonal rainfall as indicated by SPI, and becoming drier in terms of total dry spell duration within the season as per DSI. The impact of DSI on yield of major rainfed crops viz., cotton, groundnut, maize, pearl millet, pigeon pea and sorghum were estimated. The analysis showed that, the impact of dry spells integrated in form of the DSI on yields of six major rainfed crops was higher in comparison to total rainfall indicated by SPI for six major rainfed crops in India. Groundnut and pearl millet crops experienced higher duration of dry spells in comparison to other crops. The productivity of all the crops was significantly influenced by DSI across more than 65% growing regions. The yield loss was about 75-99% in 24% of sorghum, 23% of groundnut and 13% of pearl millet and it was about 50-74% in 44% of cotton, 24% of groundnut, 17% of maize, 16% each of pearl millet & sorghum and 12% of pigeon pea growing regions. We also found that by minimizing the cumulative impact of dry spells, yield can be increased twice in more than 55%, 49% and 42% areas of pearl millet, pigeon pea and groundnut growing regions, respectively. This study will help developing adaptation strategies to sustain crop production in rainfed regions of India.

Success of rainfed crop production is highly dependent on timely sowing/planting decisions. Variability in sowing/planting dates between years affect crop planning and thus could decrease farm profitability. Therefore, information on the optimum crop sowing window for the season will facilitate crop planning by farmers and other stakeholders. To determine optimum sowing window over 19 Agro-Ecological Regions (AER) of India, three methods viz., Soil Water Balance (SWB), Depth and modified Morris & Zandesta methods were evaluated to identify the most suitable method for working out the onset dates of the crop growing season. The onset dates of growing season determined by three methods were found to be similar over northeastern (hot sub-humid and warm per-humid eco-region), west coast (hot humid to per-humid), cold and hot arid eco-regions in India. However, differences in onset dates of the crop growing season were observed over hot semi-arid to sub-humid eco-region. SWB method was found to be the most suitable based on validating with observed crop sowing dates, false start and number of undefined onset years criteria. However, as SWB method is data-intensive, onset was determined using an alternative Modified Threshold Combination (MTC) method comprising 40 combinations of threshold values viz. rainfall amount, wet spell duration, dry spell duration and dry spell search period. The onset dates determined by MTC and SWB methods were validated and subsequently, appropriate threshold values were identified to work out onset dates in 19 AERs of India. This study revealed that the onset is not influenced by threshold combinations in per-humid, humid and sub-humid regions. However, in semi-arid and arid regions, critical evaluation of criteria for determining onset is vital to avoid false starts and undefined onset years. Information on agroclimatic onset dates for each AER is useful for improving practical utility/decision making, especially in the semi-arid and arid regions of India.

Crop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r(2) of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.

Prediction of local scale frost events can be helpful for farmers to minimize crop loss due to frost damage. This study aims to detect a temporal trend in the occurrence of frost events and develop frost prediction models using multivariate statistical techniques like logistic regression, artificial neural network model, and thumb rules for two diverse locations of India (Palampur and Ludhiana). In these statistical models, eight daily meteorological parameters viz., maximum temperature (Tmax), minimum temperature (Tmin), wind speed, precipitation, sunshine duration, cumulative pan evaporation, morning relative humidity (RH1), and afternoon relative humidity (RH2) 1 to 5 days preceding the frost events for the period of 2004–2016 and 1982–2013 at Palampur and Ludhiana, respectively were used. Principal Component Analysis was performed to select the weather parameter that has maximum effect on the occurrence of frost event. Ten different skill scores like accuracy, bias, and probability of false detection were used to evaluate the accuracy of frost prediction models. The Mann–Kendall trend test showed a significant increasing annual trend in the number of frost events at Ludhiana, with a remarkable increase in December. The results also showed that lower afternoon relative humidity 1-day preceding the frost event at Palampur and calm wind and lower evaporation 1-day preceding at Ludhiana augmented the occurrence of frost events. Among the techniques for developing frost prediction models, the logistic regression model performed better over artificial neural network and thumb rule-based models. The logistic regression model performed better for the plain region (Ludhiana) than for the hilly area (Palampur). The developed models are most suitable for predicting the radiation frost.

Abstract Assessment of soil moisture availability and timely declaration of drought are keys for exemplary relief assistance in water stressed regions. Percent available soil moisture (PASM) is one among several drought declaration indices, needs evaluation with respect to individual crop and cropping system, as the amount of water requirement varies with respect to crop and its growth stage. Analysis of yield variability due to PASM was carried out by employing correlation and linear regression analyses based on long term observations in experiments conducted at different dry farming locations of the peninsular India. The range of available soil moisture in order to obtain at least 50 per cent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeonpea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26 to 61 per cent. Establishment of these regression models helped in timely drought declaration / precise identification of drought hit areas and assuring feasible relief assistance. The outcomes of the study may be used for amending the existing drought norms (0–50; severe, 50–75; mild and 75–100; no drought) for provision of proportionate compensations to the farmers.

Studies on wheat-weather relationship were carried out at Pusa (25.98 oN, 85.67 oE, 52 m), Bihar situated in middle Gangetic plains of India, with three popular wheat cultivars viz. RW 3711, HD 2824 and HD273, grown under five fixed dates of sowing viz. 15 November, 25 November, 5 December, 15 December and 25 December, for five consecutive rabi seasons from 2011-12 to 2015-16. Thresholds of maximum temperature (Tmax), minimum temperature (Tmin) and bright sunshine hours (BSH), associated with higher productivity, occurring at different phenophases, were determined. Results revealed that temperature played a crucial role in achieving higher grain yield of wheat. Both T max and Tmin during flowering to milking and flowering to maturity phases increased with delayed sowing dates beyond 25 November with consequent reductions in grain yield. During 50 % flowering to milk stage, Tmax and Tmin above 24.6 oC and 11.6 oC, respectively, reduced grain yield below 4000 kg ha-1; significant reduction in grain yield was also noted beyond maximum temperature of 26.9 oC. During flowering to milk and flowering to maturity phases, Tmax and Tmin exhibited highly significant negative correlation with grain yield, indicating higher temperatures causing lower grain yield. W ith delayed sowing, sensitive phases of the crop experienced higher air temperatures which led to reduction in grain yield. An increase of Tmax from 29.2 to 32.1 oC during flowering to maturity phases reduced the wheat productivity drastically in this region of Indo-Gangetic plains. Grain yield declined by 399 kg ha-1 per 1 oC rise in Tmax during 50 % flowering to maturity stage. Considering grain yield vis-à-vis temperature regimes during flowering to maturity stage, the most important recommendation for the farmers of the region would be to finish wheat sowing before 25 November in order to enable them to escape terminal heat stress in wheat and thereby realizing higher grain yield. The anthesis-time management by manipulating sowing dates could be a realistic adaptation strategy for attaining optimum grain yield under changing climate scenario.

The weekly rainfall data for 36 years (1981-2016) recorded at Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani were analyzed for mean seasonal, weekly rainfall and also weekly rainfall probabilities. The mean seasonal rainfall was 796 mm, received in 38 rainy days. The seasonal rainfall indicated that there is 53% chance of receiving less than 700 mm with variable intensities and 36% chance of getting more than normal rainfall and 11% chance of seasonal rainfall, in between 700 mm to 800 mm. The mean weekly rainfall during crop season was 45.8 mm with a CV of 116%. Highest mean weekly rainfall was recorded 71.8 mm with SD (95.3) and CV (132.7%) in 30th MW. Sowing of Kharif crops should be undertaken during 24th MW to 27th MW. Significant and positive correlation between yield and rainfall was observed for Soybean, Pigeonpea, Black gram, Green gram and rice. The predictability of productivity of crops using seasonal rainfall is 10-20% variation in productivity for all the crops at the Centre. The El Nino episode was negatively influencing Southwest monsoon and annual rainfall as well as rainfall during the months of July and September. El Nino episodes exhibit more negative influence on productivity of all the crops except rice crop. Among the different categories of El Nino, weak events exerted more negative impact on productivity of short duration crops (i.e., sorghum, soybean and Black gram) as compared to moderate and strong El Nino events.

The study was conducted to analyse the rainfall pattern of dry farming zones of Southern Karnataka to arrive at proper date of sowing by considering parameters like threshold rainfall (20 mm), threshold dry day (2.5 mm) and threshold dry spell period (10 days) as a main defining parameters for decision making in sowing of major crops (finger millet, pigeonpea, groundnut, etc.). In all the three zones, the agro-climatic onset of cropping season was earlier as compared to meteorological onset (June 1st week) due to bimodal distribution of rainfall having its peaks in May and September month. In Central Dry Zone, Southern Dry Zone and Eastern Dry Zone, fourteenth June, thirteenth June and twenty-third May were the agro-climatic onset dates (average of all stations in each zone), respectively. Station wise analysis of the rainfall revealed different agro-climatic onset dates. Ninth May in central dry zone, eighth May in eastern dry zone and fifth May in southern dry zone were the earliest onset dates. These variations in between zonal and station specific onset dates were due to spatio-temporal variations in rainfall. Therefore, advancements in sowing of crops based on the agro-climatic onset should be taken into account for betterment of crop production.

The impact of El Nino-southern oscillation (ENSO) on rainfall and major Kharif crop production was analysed for the period 1985-2017. For this study daily rainfall and crop yield data was collected from India Meteorological Department, Pune and Department of Agriculture, Maharashtra respectively. The Impact of rainfall on productivity of major Kharif crops under different categories of El Nino episode was studied in different districts of Vidarbha region. The rainfall during summer, monsoon, post monsoon and winter seasons were 53.4, -6.1%, 1.1% and 41.2% less respectively during El Nino years compared to normal years in Amravati division. Similarly, in the Nagpur division SW monsoon was 33.3%, 12.2%, 12.3% and 68.3% less during summer, monsoon, post-monsoon and winter seasons respectively during El Nino years. The result showed that the SW monsoon rainfall and annual rainfall was less during El Nino years as compared to normal years. During El Nino years, rainfall in Amravati division was more in June and August and less in July and September compared to normal years. On the contrary, Nagpur district received less rainfall in all the months during El Nino years. Influence of El Nino episodes on productivity of different major Kharif crops of Vidarbha was highly influenced by El Nino episodes. In majority of the districts, Kharif crops produced tended to decline during strong El Nino years as compared to Weak and Moderate El Nino years. Among the different categories of El Nino, strong events exerted a more negative impact on major Kharif crop productivity in all the districts of Vidarbha region.

All India Coordinated Research Project on Agrometeorology (AICRPAM) of ICAR has started the micro-level Agromet Advisory Service (AAS) through its 25 cooperative centers across the country. Microlevel advisory based on weather forecast is the newer dimension of the AAS in the country. Studies on economic impact of these micro-level advisories are uncommon. Therefore, the present study was conducted using the field survey to assess the farmer’s perception and economic impact of micro-level AAS in Vijayapura and Anantapur centers on pilot basis. Two groups i.e. AAS and non-AAS farmers, consisting of 40 farmers in each group were selected through multi-stage stratified random sampling technique. The probit regression model was employed to assess the factors influencing willingness to pay (WTP) for AAS. Majority of farmers (65%) rated micro-level AAS as ‘very good’ on scale of ‘very poor’ to ‘very good’. Majority of non-AAS farmers were aware about micro-level AAS but lagged in adopting the service. It needs further detailed investigation of underlying causes of not adopting the service. Farming experience, education, land holding size and income were found to be most important factors influencing the farmer’s willingness for pay-based services. Results of economic impact revealed that there was 12 to 33 per cent increase in profit for AAS farmers as compared to non-AAS farmers.

Drought is a one of the most destructive climate-related hazards, it is in general unstated as a prolonged deficiency of precipitation. Drought features are thus recognized as important factors in water resources planning and management. The purpose of this study is to detect the changes in drought frequency, persistence, and severity in the Maharashtra. Drought probabilities for different talukas of Maharashtra based on SPI methodology was computed for three time scales (Annual - 12 months scale, Southwest monsoon - 4 months scale, and Post monsoon - 3 months scale) for 326 talukas for which daily rainfall data for 30 years or more was used. The main benefit of the application of this index is its versatility, only rainfall data are required to deliver five major dimensions of a drought: duration, intensity, severity, magnitude, and frequency. It is interesting to note that highest probability (89%) is observed in Shirur Anantpal and Udgir taluka of Latur district followed by Loha taluka in Nanded district and Kamptee taluka in Nagpur (89%). Though they are situated in a semi-arid climatic region, near normal conditions are being expected over these talukas. In talukas from coastal Kokan region where annual rainfall is higher, the occurrence of near normal condition is less. Probability of 70 and above has been considered generally as a benchmark for making decisions on agricultural operations, and this is observed in 128 talukas out of 328 talukas across the state for near normal condition. The Lowest probability for normal conditions is noted in Murtijapur talukas in Akola district (50%) followed by Sangrampur in Buldhana district (53%) and Digras in Yavatmal district (54%). The Highest probability of occurrence for moderately dry condition is seen in Manora in Washim district and Parseoni Nagpur district (21%) followed by 20 percent probability in Ralegaon in Yavatmal district and Chopda in Jalgaon district. It can be inferred that 2 out of 10 years moderate dry conditions can be expected in the above talukas. The probability of occurrence of severe and extremely dry conditions is almost nil in 86 and 139 talukas, respectively. The Highest probability (13%) under severe dry category has been noted in 2 talukas and extremely dry conditions may prevail with a 21% probability in Mauda taluka of Nagpur district.

In field trials of six years (2009-14) the influence of weather parameters on groundnut (The varieties: TMV-2, JL-24, K-134 and C-2) under varied sowing environments (July as normal sowing and August as late sowing months) was studied at Bengaluru, Karnataka in alfisols. Rainy days (RD), bright sun shine hours (BSS), total pan evaporation (EVP. in mm), growing degree days (GDD) and rainfall, the cumulative of all measured during crop period were found to influence significantly the growth and yield of ground nut in alfisols across the genotypes. Whereas, potential evapo-transpiration (PET) and length of growing period (LGP) during the cropping period did not show such significant influence. For achieving maximum yield, the optimum value of annual rainfall from the fitted quadratic curves, was found to be 650 mm. BSS, pan evaporation and GDD during the cropping period showed negative correlation with pod yield. Among the 11 multiple linear regression models established, model III was found to be the most reliable in judging the yield potential of groundnut in alfisols of southern Karnataka (Pod yield in kg/ha = -3058.24**+ 6.55 (RD) -2.01 ** (SSH) + 3.98* (GDD) -6.25 (evaporation in mm) + 8.01** (PET) -21.98** (LGP) + 3.38 ** (LAD) with R2 value of 0.86**. Model IV and XI were effective in predicting the yield (R2 =0.86**& 0.60**, respectively), however of the second order.

The state of undivided Andhra Pradesh, India, spent around US$ 20 million per annum on drought relief programs during the period 1998–2008. However, till date, no long-term drought plans are in place due to poor availability of information on drought severity at each mandal or county level. For assessing the vulnerability of mandals to different drought intensities, a simple and novel drought severity index called CRIDA Drought Severity Index, named after the affiliated institute, was developed. This index takes into account frequency and intensity of agricultural droughts at mandal level. The frequency and intensity were estimated using average moisture adequacy index (MAI) during the crop growing season for each mandal. The years with average MAI during crop season of > 0.75, < 0.75 to > 0.50, < 0.50 to > 0.25, and < 0.25 were classified as no drought, mild, moderate, and severe drought years, respectively. All the 1099 rural mandals of the state were categorized into four classes, viz. safe, less vulnerable, moderately vulnerable, and highly vulnerable. The spatial depiction of vulnerability of mandals to any of these four classes of agricultural droughts (using GIS), with and without considering the irrigation potential of these mandals showed that the south and southwestern regions of the state with low rainfall, poor water-holding capacity of soils, and limited irrigation potential are highly vulnerable to agricultural droughts. The methodology adopted may serve as a model for assessing drought vulnerability and planning of mitigation measures in other drought-prone states or countries.

A field experiment was conducted during with three sowing dates (23 June, 8 July and 23 July) with three varieties (JS 20-29, JS 20-34 and JS 97-52) kharif season of 2016 and 2017 at Jabalpur in eastern Madhya Pradesh for assessing crop weather relationship in soybean through thermal and radiation environments. The results revealed that early sown crop attained more accumulated heat units, and yield decreased with delay in sowing. The maximum and minimum temperatures during reproductive stage were positively correlated with seed yield while negatively associated with vegetative and pod development stages. Photosynthetic active radiation absorption (APAR) was maximum in June sowing in semi -determinate JS 97-52 variety at pod formation stage. Maximum leaf area index (LAI) exhibited in June sown for JS 97-52 variety during pod formation stage. Seed yield increase with increased in APAR and LAI during pod formation stage. Season length difference between normal and actual crop maturity period increased with the decrease in GDD thereby suggesting a decline in yield due to shortening of crop growing period.

Potato tuber yield were simulated at Jorhat, Assam under various Representative Concentration Pathways (RCPs) scenarios for 2020, 2050 and 2080 using DSSAT SUBSTOR-Potato model. The model was calibrated and validated for three potato cultivars, viz., Kufri Jyoti, Kufri Pokhraj and Kufri Himalini with the experimental data collected during 2014-15 and 2015-16. Results revealed that if planting is delayed beyond November, all these cultivars are likely to record drastic reduction in tuber yield. Cultivar Kufri Himalini may incur tuber yield loss of 64 per cent in 2020 to 75 per cent in 2080, followed by Kufri Jyoti (57.6% in 2020 to 71.5% in 2080) and Kufri Pokhraj (45.2% in 2020 to 56.2% in 2080). Among the cultivars, Kufri Pokhraj may remain a viable cultivar up to 2050, but Kufri Himalini may lose its sustainability by 2020 itself. Hence, adjustment of planting time and development of improved adaptive potato cultivars only will ascertain future potato production in this region.

Future climate change projections for India indicate distinct rise in temperature and increased variability in rainfall. This study aims to assess the impact of climate change on sorghum productivity in India in future climatic periods (2025, 2050 and 2075) using DSSAT-sorghum and suggest adaptation strategies to negate the negative impact of climate change on sorghum productivity in the future climates. Three CMIP-5 climate models (GFDL-ESM2M, MIROC5 and NorESM1-M) generated weather data for three future periods were used at various locations for kharif (Akola, Dharwad, Surat and Udaipur) and rabi (Bijapur, Dharwad, Rahuri and Solapur) seasons to simulate sorghum yields. Projected changes in day-night temperatures and rainfall during kharif and rabi growing seasons at these locations are diverse both in direction and magnitude. Increasing trend in rainfall is observed during both crop seasons towards the end of 21st century. Sorghum crop is likely to experience warmer temperature in the second half of the century and rise in minimum temperature is more explicit than maximum temperature at all the locations. Location specific management options can be adopted to mitigate the negative impacts of the change in climate in future projected scenarios, as they are found beneficial.