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Article: Criteria based decisions for determining agroclimatic onset of the crop growing season

Bal, Santanu Kumar / Manikandan, N. / Sandeep, V.M. / Vijaya Kumar, P. / Lunagaria, M.M. / Subba Rao, A.V.M. / Pramod, V.P. / Singh, V.K.

Agricultural and forest meteorology. 2022 Apr. 15, v. 317

2022

Abstract: 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 ... ...

Abstract	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.
Keywords	coasts ; cold ; crop production ; ecoregions ; farm profitability ; forests ; meteorology ; rain ; soil water balance ; stakeholders ; India
Language	English
Dates of publication	2022-0415
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	409905-9
ISSN	0168-1923
ISSN	0168-1923
DOI	10.1016/j.agrformet.2022.108903
Database	NAL-Catalogue (AGRICOLA)

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

Bal, Santanu Kumar / Pramod, V P / Sandeep, V M / Manikandan, N / Sarath Chandran, M A / Subba Rao, A V M / Vijaya Kumar, P / Vanaja, M / Singh, V K

Scientific reports

2023 Volume 13, Issue 1, Page(s) 7789

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. ... ...

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.
Language	English
Publishing date	2023-05-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-34194-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating area-specific adaptation strategies for rainfed maize under future climates of India.

Subba Rao, A V M / Sarath Chandran, M A / Bal, Santanu Kumar / Pramod, V P / Sandeep, V M / Manikandan, N / Raju, B M K / Prabhakar, M / Islam, Adlul / Naresh Kumar, S / Singh, V K

The Science of the total environment

2022 Volume 836, Page(s) 155511

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Adaptation, Physiological ; Agriculture ; Climate Change ; Crops, Agricultural ; Zea mays
Language	English
Publishing date	2022-04-28
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.155511
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Article: Assessing impact of dry spells on the principal rainfed crops in major dryland regions of India

Bal, Santanu Kumar / Sandeep, V.M. / Kumar, P. Vijaya / Rao, A.V.M. Subba / Pramod, V.P. / Manikandan, N. / Rao, Ch. Srinivasa / Singh, Naveen P. / Bhaskar, S.

Agricultural and forest meteorology. 2022 Feb. 15, v. 313

2022

Abstract: 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 ... ...

Abstract	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.
Keywords	Cenchrus americanus ; arid lands ; corn ; cotton ; drought ; forests ; meteorological data ; meteorology ; peanuts ; pigeon peas ; rain ; India
Language	English
Dates of publication	2022-0215
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	409905-9
ISSN	0168-1923
ISSN	0168-1923
DOI	10.1016/j.agrformet.2021.108768
Database	NAL-Catalogue (AGRICOLA)

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

Subba Rao, A.V.M. / Sarath Chandran, M.A. / Bal, Santanu Kumar / Pramod, V.P. / Sandeep, V.M. / Manikandan, N. / Raju, B.M.K. / Prabhakar, M. / Islam, Adlul / Naresh Kumar, S. / Singh, V.K.

Science of the total environment. 2022 Aug. 25, v. 836

2022

Abstract	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.
Keywords	climate ; climate change ; corn ; environment ; fertilizer rates ; irrigation ; meteorological data ; India
Language	English
Dates of publication	2022-0825
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.155511
Database	NAL-Catalogue (AGRICOLA)

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Article: Temporal trends in frost occurrence and their prediction models using multivariate statistical techniques for two diverse locations of Northern India

Bal, Santanu Kumar / Dhakar, Rajkumar / Kumar, P. Vijaya / Mishra, Adita / Pramod, V. P. / Chandran, M. A. Sarath / Sandeep, V. M. / Rao, A. V. M. Subba / Gill, K. K. / Prasad, Rajendra

Theoretical and applied climatology. 2021 Nov., v. 146, no. 3-4

2021

Abstract: 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 ... ...

Abstract	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.
Keywords	climatology ; crop losses ; evaporation ; frost ; frost injury ; neural networks ; prediction ; principal component analysis ; probability ; regression analysis ; relative humidity ; solar radiation ; temperature ; wind speed ; India
Language	English
Dates of publication	2021-11
Size	p. 1097-1110.
Publishing place	Springer Vienna
Document type	Article
ZDB-ID	1463177-5
ISSN	1434-4483 ; 0177-798X
ISSN (online)	1434-4483
ISSN	0177-798X
DOI	10.1007/s00704-021-03786-8
Database	NAL-Catalogue (AGRICOLA)

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Article: Algorithms for weather‐based management decisions in major rainfed crops of India: Validation using data from multi‐location field experiments

Vijaya Kumar, P. / Bal, Santanu Kumar / Dhakar, Rajkumar / Sarath Chandran, M. A. / Subba Rao, A. V. M. / Sandeep, V. M. / Pramod, V. P. / Malleswari, S. N. / Sudhakar, G. / Solanki, N. S. / Shivaramu, H. S. / Lunagaria, M. M. / Dakhore, K. K. / Londhe, V. M. / Singh, Mahender / Kumari, Pragyan / Subbulakshmi, S. / Manjunatha, M. H. / Chaudhari, N. J.

Agronomy journal. 2021 Mar., v. 113, no. 2

2021

Abstract: 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. ... ...

Abstract	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 (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 India.
Keywords	Cajanus cajan ; Gossypium ; agrometeorology ; agronomy ; cotton ; crop management ; drought ; monsoon season ; peanuts ; phenology ; pigeon peas ; prediction ; protocols ; research projects ; India
Language	English
Dates of publication	2021-03
Size	p. 1816-1830.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	410332-4
ISSN	1435-0645 ; 0002-1962
ISSN (online)	1435-0645
ISSN	0002-1962
DOI	10.1002/agj2.20518
Database	NAL-Catalogue (AGRICOLA)

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

Sarath Chandran, M A / Subba Rao, A V M / Sandeep, V M / Pramod, V P / Pani, P / Rao, V U M / Visha Kumari, V / Srinivasa Rao, Ch

International journal of biometeorology

2017 Volume 61, Issue 6, Page(s) 1063–1072

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2017-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	280324-0
ISSN	1432-1254 ; 0020-7128
ISSN (online)	1432-1254
ISSN	0020-7128
DOI	10.1007/s00484-016-1286-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Projecting future changes in water requirement of grain sorghum in India

Sandeep, V. M. / Rao, B. Bapuji / Bharathi, G. / Rao, V. U. M. / Pramod, V. P. / Chowdary, P. S. / Patel, N. R. / Kumar, P. Vijaya

Journal of agrometeorology

2017 Volume 19, Issue 3, Page(s) 217

Language	English
Document type	Article
ZDB-ID	2235052-4
ISSN	0972-1665
Database	Current Contents Nutrition, Environment, Agriculture

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Article: Impact of climate change on sorghum productivity in India and its adaptation strategies

Sandeep, V. M. / Rao, V. U. M. / Bapujirao, B. / Bharathl, G. / Pramod, V. P. / Chowdary, P. Santhibhushan / Patel, N.R. / Mukesh, P. / Vijayakumar, P.

Journal of agrometeorology

2018 Volume 20, Issue 2, Page(s) 89

Language	English
Document type	Article
ZDB-ID	2235052-4
ISSN	0972-1665
Database	Current Contents Nutrition, Environment, Agriculture

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