Critical weather limits for paddy rice under diverse ecosystems of India

Rice yields are largely influenced by variability in weather. Here, we demonstrate the effect of weather variables viz., maximum and minimum temperatures, rainfall, morning and evening relative humidity, bright sunshine hours on the yield of rice cv. Swarna, grown across five rice ecologies of India through field experiments during kharif (wet) season (Jun-Sept.). Critical thresholds of weather elements were identified for achieving above average, average and below average yield for each ecology. The investigation could determine how different weather elements individually and collectively affect rice yield in different rice ecosystems of India. While a sudden increase in minimum temperature by 8-10 °C (> 30 °C) during reproductive period resulted in 40-50 per cent yield reduction at Mohanpur, a sudden decrease (< 20 °C) caused yield decline at Dapoli. The higher yields may be attributed to a significant difference in bright sunshine hours between reproductive phases of above-average and below-average yield years (ranging from 2.8 to 7.8 hours during P5 stages and 1.7 to 5.1 during P4 stages). Rice cultivar Swarna performed differently at various sowing dates in a location as well as across locations (6650 kg ha-1 at Dapoli to 1101 kg ha-1 at Samastipur). It was also found that across all locations, the above average yield could be associated with higher range of maximum temperature compared to that of below average yield. Principal component analysis explained 77 per cent of cumulative variance among the variables at first growth stage, whereas 70 per cent at second growth stage followed by 74 per cent and 66 per cent at subsequent growth stages. We found that coastal locations, in contrast to inland ones, could maximize the yield potential of the cultivar Swarna, due to the longer duration of days between panicle initiation to physiological maturity. We anticipate that the location-specific thresholds of weather factors will encourage rice production techniques that are climate resilient

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

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

Asymmetrical trusted technology networks in developing economies: A case study on critical infrastructure in Bhutan

Developing Nations are subject to amplified challenges in terms of the integration of technology, and the exposure to non-domestic opportunism from larger neighboring economies. These challenges are recognizable as asymmetrical differences between what is seen as the normative list of critical infrastructures, and the specialisms that can dominate an emerging economy with early maturity technology networks. This paper discusses the case of Bhutan and demonstrates the need for strengthened approaches to trusted networks to ensure the reliability and continuity of the Nation\u27s critical infrastructures. The paper also links the importance of trusted information sharing networks as part of an overarching technology strategy that protects the Gross National Happiness of the nation

Coordinated research on agrometeorology: India perspective

The All India Coordinated Research Project on Agrometeorology (AICRPAM) was initiated in 1983 to utilize the climatic resource potential for better agricultural planning, enhanced productivity, profitability and sustainable livelihoods. The project has generated valuable research output in the areas of agroclimatic characterization, crop-weather relationship and weather effects on pests and diseases. Such information has been used for developing crop weather calendars, agroclimatic atlases, decision support systems, android apps, software for agromet data analysis, weather-based pest forewarning models, weather triggers for crop insurance etc. These products are being used for preparing agromet advisories and weather-related risk management systems. AICRPAM has completed forty years of its very meaningful existence with significant achievements and recommendations of practical value for the benefit of various stakeholders, particularly farmers. However, in view of the increase in intensity and frequency of the extreme weather events such as heat and cold waves, floods and droughts etc. under changing climatic conditions, the coordinated project envisages characterizing and identifying the hotspots, to minimize risks in crop production.

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Not AvailablePrediction 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.Not Availabl

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Not AvailableThe 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.Not Availabl

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Not AvailableThis 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.Not Availabl

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

Abstract 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

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file:///C:/Users/NS%20Raju/Downloads/Algorithmsforweather-basedmanagementdecisionsinmajor.pdfCrop 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 (r2 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 IndiaNot Availabl

Water Demand in Maize Is Projected to Decrease under Changing Climate in India

Crop stage-specific information on the impacts of projected climate change on crop and irrigation water requirements are essential for improving productivity. This study investigated the possible implications of projected climate change on the phenology, effective rainfall (Peff), crop (CWR) and irrigation water requirements (IWR) of maize in eight locations in India. CWR, Peff and IWR were estimated for seven crop stages viz., emergence, 5th leaf stage, tasseling, silking, milking, dough and maturity during the baseline (1980–2009) and near-century (2022–39) using climate data derived from a subset of 29 general circulation models. The results indicated that mean seasonal maximum temperature, minimum temperature and rainfall were projected to increase in all the locations. Hence, the total crop duration (3–7 days), CWR (8–69 mm) and IWR (1–54 mm) were projected to decrease. The study could identify the specific stages in which the greatest reduction in crop duration, CWR and IWR would occur. Such information will be of immense help to farmers and varietal improvement programs in the study regions in the near future