N. Hussain, A. Hussain, M. A. Bhat, O. A. Wani, A. Hussain, T. Bhat, A. H. Mir, F. Wani, S. Kouser, N. Fatima, Mansoor Hussain, S. Hussain
In order to investigate the "Effect of Establishment method and Planting date on phenology, yield, and agrometeorological indices for sweet corn," a field experiment was carried out at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Wadura, Sopore experimental farm of the Division of Agronomy, over the course of two sessions in Kharif 2020 and 2021. The experiment had two components: a distinct sowing date with a 20-day interval and two establishment methods (direct seeding and transplanting). The initial planting day was (25th of April, 2nd was 15th of May and third was 5th of June during both the years) Three replications in RCBD were confirmed. Following transplanting with the first date of sowing, direct seeding required the most days to attain different phenological stages and accumulate the most heat units. Transplanting with the initial date of sowing resulted in noticeably greater HUE, HTUE, PTUE, and HyTUE, resulting in the largest green cob and biological yield as compared to other dates of sowing and direct seeding. As a result, given the weather in Kashmir It was discovered that planting on the first day of sowing increased sweet corn yields economically.
{"title":"Heat unit requirement of sweet corn under different planting methods and dates in temperate Kashmir, India","authors":"N. Hussain, A. Hussain, M. A. Bhat, O. A. Wani, A. Hussain, T. Bhat, A. H. Mir, F. Wani, S. Kouser, N. Fatima, Mansoor Hussain, S. Hussain","doi":"10.54386/jam.v25i3.2251","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2251","url":null,"abstract":"In order to investigate the \"Effect of Establishment method and Planting date on phenology, yield, and agrometeorological indices for sweet corn,\" a field experiment was carried out at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Wadura, Sopore experimental farm of the Division of Agronomy, over the course of two sessions in Kharif 2020 and 2021. The experiment had two components: a distinct sowing date with a 20-day interval and two establishment methods (direct seeding and transplanting). The initial planting day was (25th of April, 2nd was 15th of May and third was 5th of June during both the years) Three replications in RCBD were confirmed. Following transplanting with the first date of sowing, direct seeding required the most days to attain different phenological stages and accumulate the most heat units. Transplanting with the initial date of sowing resulted in noticeably greater HUE, HTUE, PTUE, and HyTUE, resulting in the largest green cob and biological yield as compared to other dates of sowing and direct seeding. As a result, given the weather in Kashmir It was discovered that planting on the first day of sowing increased sweet corn yields economically. \u0000 ","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44804638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HEMMAREDDY THIMMAREDDY, R. H. PATIL, K. G. SUMESH, GANAJAXI MATH, MAHANTESH B. NAGANGOUDAR
Greengram is one of the major protein rich grain legumes predominately cultivated in North Interior Karnataka (NIK). The study aimed at determining the water requirement of greengram variety DGGV- 2 using CROPWAT model that helps the farmers of NIK consisting of 12 districts in tapping the potential yields of this crop through proper irrigation management. The decadal analysis for 60 years was done under past (1991-2020) and projected climate (2021-2050) as per the recommended practices of UAS, Dharwad across four dates of sowing from 07th June to 28th June at weekly interval. The average crop evapotranspiration (ETc), effective rainfall (ER) and irrigation requirement (IR) under past climates (1991-2020) for NIK were 246, 269.3 and 37.4 mm, respectively. An increase of 26.8 mm in ETc, 21.6 mm in ER and decrease of 0.3 mm in IR were simulated under projected climates. Sowing late i.e., on 28th June under projected climate (2021-2050) simulated the lowest water requirement and irrigation requirement for all the 12 districts of NIK. The spatial distribution of ETc, ER and IR for all the 12 districts of NIK were interpreted under both past and projected climates using ArcGIS software.
绿豆是主要种植在卡纳塔克邦北部内陆地区的富含蛋白质的豆类作物之一。本研究旨在利用CROPWAT模型确定绿绿品种DGGV- 2的需水量，帮助由12个区组成的NIK的农民通过适当的灌溉管理来挖掘该作物的潜在产量。60年的年代际分析是在过去(1991-2020)和预测气候(2021-2050)下进行的，按照UAS的推荐做法，在达尔瓦德播种的四个日期(6月7日至6月28日，每周间隔一次)进行。在过去气候条件下(1991-2020)，NIK的平均作物蒸散量(ETc)、有效降雨量(ER)和灌溉需要量(IR)分别为246、269.3和37.4 mm。在预估气候条件下，ETc增加26.8 mm, ER增加21.6 mm, IR减少0.3 mm。在预测气候条件下(2021-2050)，6月28日播种较晚，是NIK所有12个区的最低需水量和灌溉需水量。利用ArcGIS软件对12个区在过去和未来气候条件下的ETc、ER和IR空间分布进行了解译。
{"title":"Spatial estimation of water requirement in greengram under changing climates of North Interior Karnataka","authors":"HEMMAREDDY THIMMAREDDY, R. H. PATIL, K. G. SUMESH, GANAJAXI MATH, MAHANTESH B. NAGANGOUDAR","doi":"10.54386/jam.v25i2.1954","DOIUrl":"https://doi.org/10.54386/jam.v25i2.1954","url":null,"abstract":"Greengram is one of the major protein rich grain legumes predominately cultivated in North Interior Karnataka (NIK). The study aimed at determining the water requirement of greengram variety DGGV- 2 using CROPWAT model that helps the farmers of NIK consisting of 12 districts in tapping the potential yields of this crop through proper irrigation management. The decadal analysis for 60 years was done under past (1991-2020) and projected climate (2021-2050) as per the recommended practices of UAS, Dharwad across four dates of sowing from 07th June to 28th June at weekly interval. The average crop evapotranspiration (ETc), effective rainfall (ER) and irrigation requirement (IR) under past climates (1991-2020) for NIK were 246, 269.3 and 37.4 mm, respectively. An increase of 26.8 mm in ETc, 21.6 mm in ER and decrease of 0.3 mm in IR were simulated under projected climates. Sowing late i.e., on 28th June under projected climate (2021-2050) simulated the lowest water requirement and irrigation requirement for all the 12 districts of NIK. The spatial distribution of ETc, ER and IR for all the 12 districts of NIK were interpreted under both past and projected climates using ArcGIS software.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47339090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The better adaptation and mitigation options can help to curtail the effects of climate change on livestock performance. To reduce poverty and promote sustainable development through livestock production, favorable policies and action-oriented research are urgently required to address the pertinent issue. For effective adaptation and mitigation measures to address climate change and livestock production, these measures should be scaled up through policy. For example, understanding farmers’ perceptions and including them in policy development can improve food security and environmental conservation by promoting widespread practice adoption. In addition, a comprehensive view of costs, time, and effort required from the producer need to be included to the policy framework to maintain sustainable and resilient production systems.
{"title":"Resilience of livestock production under varying climates","authors":"Sohan Vir, Singh, Surender Singh","doi":"10.54386/jam.v25i2.2015","DOIUrl":"https://doi.org/10.54386/jam.v25i2.2015","url":null,"abstract":"The better adaptation and mitigation options can help to curtail the effects of climate change on livestock performance. To reduce poverty and promote sustainable development through livestock production, favorable policies and action-oriented research are urgently required to address the pertinent issue. For effective adaptation and mitigation measures to address climate change and livestock production, these measures should be scaled up through policy. For example, understanding farmers’ perceptions and including them in policy development can improve food security and environmental conservation by promoting widespread practice adoption. In addition, a comprehensive view of costs, time, and effort required from the producer need to be included to the policy framework to maintain sustainable and resilient production systems.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45024797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jute crop cultivated in Cooch Behar suffers a substantial amount of physical and economical loss every year due to several major insect pest infestation such as Yellow Mite (Polyphagotarsonemus latus Banks) and Jute Semilooper (Anomis sabulifera Guen). Constructed seasonal plots reveal that for Yellow Mite pest incidence is maximum at 55 DAS, while for Jute Semi Looper it is at 45 DAS. Correlation analysis indicate that the weather parameters such as minimum temperature at current week, maximum RH at one week lag, minimum temperature, minimum and maximum RH at two week lag are significantly correlated with the incidence of Yellow Mite, while in case of Jute Semilooper maximum temperature, minimum and maximum RH at two week lag are significantly correlated. Different forecasting models like ARIMA, ARIMAX, SARIMA, SARIMAX and SVR have been fitted and validated using RMSE values. In case of Jute Semilooper, SARIMAX model is found to be the best fitted model followed by SVR and SARIMA. Similarly, for Yellow Mite ARIMAX model produces the least RMSE value followed by SVR and ARIMA. Following successful model validation, forecasting is done for the year 2022 using the best fitted models.
{"title":"Prediction of major pest incidence in Jute crop based on weather variables using statistical and machine learning models: A case study from West Bengal","authors":"PRAHLAD SARKAR, PRADIP BASAK, CHINMAYA SUBHRAJYOTI PANDA, DEB SANKAR GUPTA, MRINMOY RAY, SABYASACHI MITRA","doi":"10.54386/jam.v25i2.1915","DOIUrl":"https://doi.org/10.54386/jam.v25i2.1915","url":null,"abstract":"Jute crop cultivated in Cooch Behar suffers a substantial amount of physical and economical loss every year due to several major insect pest infestation such as Yellow Mite (Polyphagotarsonemus latus Banks) and Jute Semilooper (Anomis sabulifera Guen). Constructed seasonal plots reveal that for Yellow Mite pest incidence is maximum at 55 DAS, while for Jute Semi Looper it is at 45 DAS. Correlation analysis indicate that the weather parameters such as minimum temperature at current week, maximum RH at one week lag, minimum temperature, minimum and maximum RH at two week lag are significantly correlated with the incidence of Yellow Mite, while in case of Jute Semilooper maximum temperature, minimum and maximum RH at two week lag are significantly correlated. Different forecasting models like ARIMA, ARIMAX, SARIMA, SARIMAX and SVR have been fitted and validated using RMSE values. In case of Jute Semilooper, SARIMAX model is found to be the best fitted model followed by SVR and SARIMA. Similarly, for Yellow Mite ARIMAX model produces the least RMSE value followed by SVR and ARIMA. Following successful model validation, forecasting is done for the year 2022 using the best fitted models.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44075845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
EAJAZ AHMAD DAR, GERRIT HOOGENBOOM, ZAHOOR AHMAD SHAH
The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R2), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R2 ~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R2 ~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R2 ~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R2 of 0.99. The lowest R2 and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R2 as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices.
{"title":"Meta analysis on the evaluation and application of DSSAT in South Asia and China: Recent studies and the way forward","authors":"EAJAZ AHMAD DAR, GERRIT HOOGENBOOM, ZAHOOR AHMAD SHAH","doi":"10.54386/jam.v25i2.2081","DOIUrl":"https://doi.org/10.54386/jam.v25i2.2081","url":null,"abstract":"The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R2), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R2 ~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R2 ~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R2 ~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R2 of 0.99. The lowest R2 and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R2 as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices. ","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42378682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elephant foot yam and taro are the two important aroids of tropical tuber crops, considered as underutilized in the context of climate change and food security. The present study focused to quantify the future climate suitability of aroids for future climate scenarios 2030, 2050, and 2070 for the two representative concentration pathways (RCP 4.5 and RCP 8.5). The district-wise future climate suitability of elephant foot yam and taro using MaxEnt across India was quantified. The percentage increase in climatically suitable area for taro is 49% and the same for elephant foot yam is 46% which is higher compared to those of tropical root crops. A total of 218 districts were identified as highly suitable for the cultivation of elephant foot yam for different RCPs across India. A total of 209 districts were observed as highly suitable for taro cultivation across India for the two RCPs. The information about the districtlevel suitability can assist decision-makers to understand the possible shifts in the climate suitability of aroids in India in the context of food security as they have higher productivity compared to other major food grain crops.
{"title":"Future climate suitability of underutilized tropical tuber crops-‘Aroids’ in India","authors":"RAJI PUSHPALATHA, SUNITHA S, SANTHOSH MITHRA VS, BYJU GANGADHARAN","doi":"10.54386/jam.v25i2.2152","DOIUrl":"https://doi.org/10.54386/jam.v25i2.2152","url":null,"abstract":"Elephant foot yam and taro are the two important aroids of tropical tuber crops, considered as underutilized in the context of climate change and food security. The present study focused to quantify the future climate suitability of aroids for future climate scenarios 2030, 2050, and 2070 for the two representative concentration pathways (RCP 4.5 and RCP 8.5). The district-wise future climate suitability of elephant foot yam and taro using MaxEnt across India was quantified. The percentage increase in climatically suitable area for taro is 49% and the same for elephant foot yam is 46% which is higher compared to those of tropical root crops. A total of 218 districts were identified as highly suitable for the cultivation of elephant foot yam for different RCPs across India. A total of 209 districts were observed as highly suitable for taro cultivation across India for the two RCPs. The information about the districtlevel suitability can assist decision-makers to understand the possible shifts in the climate suitability of aroids in India in the context of food security as they have higher productivity compared to other major food grain crops.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46559230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW–39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of co-efficient of variation (CV) showed low CV (<60%) in Pd and high CV (>60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (<40%) in Pw. The drought prone and moisture sufficient zones were indentified based on the analysis of long-term frequency distribution of dry-wet spells and trend. Inter-comparison of IPs between CHIRPs with IMD (Indian Meteorological Department) and NOAA CPC (National Oceanic and Atmospheric Administration/Climate Prediction Centre) showed encouraging results. The study provides baseline reference for climate-resilient agricultural crop planning with respect to food security.
{"title":"Assessing the long-term fluctuations in dry-wet spells over Indian region using Markov model in GEE cloud platform","authors":"INDRANI CHOUDHURY, BIMAL BHATTACHRYA","doi":"10.54386/jam.v25i2.2184","DOIUrl":"https://doi.org/10.54386/jam.v25i2.2184","url":null,"abstract":"The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW–39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of co-efficient of variation (CV) showed low CV (<60%) in Pd and high CV (>60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (<40%) in Pw. The drought prone and moisture sufficient zones were indentified based on the analysis of long-term frequency distribution of dry-wet spells and trend. Inter-comparison of IPs between CHIRPs with IMD (Indian Meteorological Department) and NOAA CPC (National Oceanic and Atmospheric Administration/Climate Prediction Centre) showed encouraging results. The study provides baseline reference for climate-resilient agricultural crop planning with respect to food security.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48814797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rijwana Parwin, M. Ramadas, Aakanksha Agrawal, Akash Devendra Atnurkar
warming and anthropogenic climate change are global drivers of changes in rainfall pattern, hydrologic processes, streamflows, groundwater level, water resources availability, and frequency and intensity of hydroclimatic extremes (droughts, floods, heat waves), and are also likely to impact water quality, agricultural productivity, food security, socio-economic development, and community resilience at local-to-regional levels (IPCC, 2021). Though rainfed agriculture is most prevalent in India, irrigation using surface and groundwater resources is also practised in many places to meet the growing demands of production, for instance, in non-rainy (Rabi) season. Especially since the crop evapotranspiration and irrigation demand depend on local climate (temperature, rainfall, evapotranspiration, among other factors), the impacts of climate change on agrarian activities and irrigation water requirement also need to be investigated in a regional context. Hence, it is necessary to understand and evaluate the impacts of climate change on the different resource systems and to adapt to the uncertainties of future climate by means of sustainable practices (Rehana and Mujumdar, 2013; Aswathi et al., 2022; Abrha and Hagos, 2022). Sustainable water management in future in agricultural communities can be possible by adopting integrated resources management, precision agriculture, and decision support systems for irrigation scheduling, based on regional level studies and analyses.
{"title":"Impacts of climate change on future crop water demand in an agricultural watershed in Mayurbhanj district of Odisha, India","authors":"Rijwana Parwin, M. Ramadas, Aakanksha Agrawal, Akash Devendra Atnurkar","doi":"10.54386/jam.v25i2.1952","DOIUrl":"https://doi.org/10.54386/jam.v25i2.1952","url":null,"abstract":"warming and anthropogenic climate change are global drivers of changes in rainfall pattern, hydrologic processes, streamflows, groundwater level, water resources availability, and frequency and intensity of hydroclimatic extremes (droughts, floods, heat waves), and are also likely to impact water quality, agricultural productivity, food security, socio-economic development, and community resilience at local-to-regional levels (IPCC, 2021). Though rainfed agriculture is most prevalent in India, irrigation using surface and groundwater resources is also practised in many places to meet the growing demands of production, for instance, in non-rainy (Rabi) season. Especially since the crop evapotranspiration and irrigation demand depend on local climate (temperature, rainfall, evapotranspiration, among other factors), the impacts of climate change on agrarian activities and irrigation water requirement also need to be investigated in a regional context. Hence, it is necessary to understand and evaluate the impacts of climate change on the different resource systems and to adapt to the uncertainties of future climate by means of sustainable practices (Rehana and Mujumdar, 2013; Aswathi et al., 2022; Abrha and Hagos, 2022). Sustainable water management in future in agricultural communities can be possible by adopting integrated resources management, precision agriculture, and decision support systems for irrigation scheduling, based on regional level studies and analyses.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46916520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. K. K. Singh, Kripan Ghosh, S. C. Bhan, Priyanka Singh, Lata Vishnoi, R. Balasubramanian, S. Attri, S. Goroshi, R. Singh
India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) in collaboration with Indian Council of Agriculture Research (ICAR), State Agriculture Universities (SAUs) , Indian Institute of Technology (IITs) and other organizations is rendering weather forecast based District level Agrometeorological Advisory Service (AAS) for benefits of farmers in the country under the centrally sponsored scheme ‘Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) ’ of MOES. AAS, popularly known as Gramin Krishi Mausam Sewa (GKMS) provides advance weather information along, with crop specific agromet advisories to the farming community by using state of the art instruments and technology through efficient delivering mechanism of the information which ultimately enables farmers to take appropriate actions at farm level. The various components of GKMS viz. observing weather, its monitoring and forecast; crop specific advisory bulletin generation and dissemination; outreach and feedback have been/are being digitized to support integrating all the components of information generation and action suggested linked to these information. An Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS. This includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc. to translate weather forecast into actionable farm advisories for efficient farm level decision making in India. Apart from this, effort is being made to develop recent technology driven tools to estimate future yield of crops and prepare an irrigation schedule without a need of multiple parameters.
{"title":"Decision support system for digitally climate informed services to farmers in India","authors":"K. K. K. Singh, Kripan Ghosh, S. C. Bhan, Priyanka Singh, Lata Vishnoi, R. Balasubramanian, S. Attri, S. Goroshi, R. Singh","doi":"10.54386/jam.v25i2.2094","DOIUrl":"https://doi.org/10.54386/jam.v25i2.2094","url":null,"abstract":"India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) in collaboration with Indian Council of Agriculture Research (ICAR), State Agriculture Universities (SAUs) , Indian Institute of Technology (IITs) and other organizations is rendering weather forecast based District level Agrometeorological Advisory Service (AAS) for benefits of farmers in the country under the centrally sponsored scheme ‘Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) ’ of MOES. AAS, popularly known as Gramin Krishi Mausam Sewa (GKMS) provides advance weather information along, with crop specific agromet advisories to the farming community by using state of the art instruments and technology through efficient delivering mechanism of the information which ultimately enables farmers to take appropriate actions at farm level. The various components of GKMS viz. observing weather, its monitoring and forecast; crop specific advisory bulletin generation and dissemination; outreach and feedback have been/are being digitized to support integrating all the components of information generation and action suggested linked to these information. An Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS. This includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc. to translate weather forecast into actionable farm advisories for efficient farm level decision making in India. Apart from this, effort is being made to develop recent technology driven tools to estimate future yield of crops and prepare an irrigation schedule without a need of multiple parameters.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70725892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Building resilience to climate change through on farm management techniques such as crop diversification, and water management as supplemental irrigation is vital for sustainable agriculture. In the present study, soybean (Glycine Max L.) based cropping systems (sole crop, and intercropped with cotton or pigeon pea) through different combinations of cultivation practices (flatbed, raised bed) and irrigation levels (Rainfed, 66%ETc, 100%ETc and methods (drip, sprinkler) were studied in randomized block design with three replications during kharif season of 2019-20 and 2020-21. Plant growth parameters viz. plant height and dry weight were recorded maximum in rainfed soybean as sole crop, while the number of branches/plant were recorded maximum in sole soybean crop irrigated at 100%ETc. Grain yield (5.37 t ha-1), and water productivity (0.47 kg m-3) were maximum in soybean intercropped with cotton. Overall, cotton+soybean irrigated at 66% ETc can be adopted by farmers to achieve optimal productivity without significant yield penalty.
通过作物多样化等农场管理技术以及作为补充灌溉的水资源管理来增强对气候变化的抵御能力，对可持续农业至关重要。本研究采用随机区组设计，在2019- 2020年和2020-21年收获季，采用3个重复试验，研究了大豆(Glycine Max L.)为基础的种植制度(单作、间作棉花或木豆)，通过不同的栽培方式(平耕、垄作)和灌溉水平(旱作、66%等、100%等)以及方法(滴灌、喷灌)的组合。单茬旱作大豆植株生长参数(株高和干重)最高，单茬大豆单株枝数最高。大豆间作棉花籽粒产量最高(5.37 t hm -1)，水分生产力最高(0.47 kg m-3)。综上所述，农民可以采用66% ETc灌溉棉花+大豆，在不造成显著减产的情况下实现最优生产力。
{"title":"Techno economic feasibility of soybean based cropping systems under varying climates in Madhya Pradesh","authors":"K.V. RAMANA RAO, YOGESH A RAJWADE, NEELENDRA SINGH VERMA, Deepika Yadav, VINAY NANGIA","doi":"10.54386/jam.v25i2.1737","DOIUrl":"https://doi.org/10.54386/jam.v25i2.1737","url":null,"abstract":"Building resilience to climate change through on farm management techniques such as crop diversification, and water management as supplemental irrigation is vital for sustainable agriculture. In the present study, soybean (Glycine Max L.) based cropping systems (sole crop, and intercropped with cotton or pigeon pea) through different combinations of cultivation practices (flatbed, raised bed) and irrigation levels (Rainfed, 66%ETc, 100%ETc and methods (drip, sprinkler) were studied in randomized block design with three replications during kharif season of 2019-20 and 2020-21. Plant growth parameters viz. plant height and dry weight were recorded maximum in rainfed soybean as sole crop, while the number of branches/plant were recorded maximum in sole soybean crop irrigated at 100%ETc. Grain yield (5.37 t ha-1), and water productivity (0.47 kg m-3) were maximum in soybean intercropped with cotton. Overall, cotton+soybean irrigated at 66% ETc can be adopted by farmers to achieve optimal productivity without significant yield penalty.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46615303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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