Aishi Mukherjee, S. Banerjee, Sarathi Saha, Rajib Nath, Manish Kumar Naskar, A. Mukherjee
The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indrira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture. Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE
{"title":"Developing weather-based biomass prediction equation to assess the field pea yield under future climatic scenario","authors":"Aishi Mukherjee, S. Banerjee, Sarathi Saha, Rajib Nath, Manish Kumar Naskar, A. Mukherjee","doi":"10.54386/jam.v26i1.2461","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2461","url":null,"abstract":"The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indrira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture. \u0000Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"113 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089653","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}
Shankarappa Sridhara, SOUMYA B. R., Girish R. Kashyap
Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages.
{"title":"Multistage sugarcane yield prediction using machine learning algorithms","authors":"Shankarappa Sridhara, SOUMYA B. R., Girish R. Kashyap","doi":"10.54386/jam.v26i1.2411","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2411","url":null,"abstract":"Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"17 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084952","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}
Amit Kumar, A. Sarangi, D. K. Singh, I. Mani, K. K. Bandhyopadhyay, S. Dash, M. Khanna
Estimation of pan evaporation (Epan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75
{"title":"Evaluation of soft-computing techniques for pan evaporation estimation","authors":"Amit Kumar, A. Sarangi, D. K. Singh, I. Mani, K. K. Bandhyopadhyay, S. Dash, M. Khanna","doi":"10.54386/jam.v26i1.2247","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2247","url":null,"abstract":"Estimation of pan evaporation (Epan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75<NSE 1; PBias< 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86), RMSE (0.93 to 1.22), PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of Epan for the study region.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"75 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087398","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}
S. S. Lakhawat, Vikas Sharma, T. Singh, Prakash Patil, S. Priyadevi, S. Gutam
A field experiment was conducted for three years (2019-20, 2020-21 and 2021-22) on 4 years old guava orchard established at 3×2 m spacing with drip irrigation treatments at two locations viz. Udaipur Rajasthan and, Rewa, Madhya Pradesh. Plant growth, yield contributing parameters, fruit yield and water use efficiency was significantly affected by different pan evaporation-based drip irrigation levels (70, 80, 90 & 100% of Epan) over local control. In existing climatic conditions of Udaipur and Rewa regions, the daily irrigation water requirement of high-density planting guava tree was varied from 7.8 to 26.3 and 4.5 to 26.5 liter/plant/day, respectively. Among all the pan evaporation-based drip irrigation levels, the irrigation supplied at 80% and 90% of daily pan evaporation were found as best approach for irrigating high density plantation (HDP) guava orchard through drip irrigation in Udaipur & Rewa regions with maximum fruit yield (37.3 & 30.7tha-1), irrigation water use efficiency (0.359 & 0.263tha-1-cm) along with significant water saving (29.2 & 22.2%), respectively over local control. Results will help farmers, policy makers and irrigation managers to conserve available fresh water resources in water scares regions of Rajasthan and Madhya Pradesh.
{"title":"Effects of pan evaporation-based drip irrigation levels on performance of guava grown in Udaipur and Rewa regions of India","authors":"S. S. Lakhawat, Vikas Sharma, T. Singh, Prakash Patil, S. Priyadevi, S. Gutam","doi":"10.54386/jam.v26i1.2306","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2306","url":null,"abstract":"A field experiment was conducted for three years (2019-20, 2020-21 and 2021-22) on 4 years old guava orchard established at 3×2 m spacing with drip irrigation treatments at two locations viz. Udaipur Rajasthan and, Rewa, Madhya Pradesh. Plant growth, yield contributing parameters, fruit yield and water use efficiency was significantly affected by different pan evaporation-based drip irrigation levels (70, 80, 90 & 100% of Epan) over local control. In existing climatic conditions of Udaipur and Rewa regions, the daily irrigation water requirement of high-density planting guava tree was varied from 7.8 to 26.3 and 4.5 to 26.5 liter/plant/day, respectively. Among all the pan evaporation-based drip irrigation levels, the irrigation supplied at 80% and 90% of daily pan evaporation were found as best approach for irrigating high density plantation (HDP) guava orchard through drip irrigation in Udaipur & Rewa regions with maximum fruit yield (37.3 & 30.7tha-1), irrigation water use efficiency (0.359 & 0.263tha-1-cm) along with significant water saving (29.2 & 22.2%), respectively over local control. Results will help farmers, policy makers and irrigation managers to conserve available fresh water resources in water scares regions of Rajasthan and Madhya Pradesh.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"120 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088308","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}
V. Kalaimathi, V. GEETHALAKSHMI, P. PARASURAMAN, P. KATHIRVELAN, C. SWAMINATHAN
A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community.
我们利用各种科学计量指标对 2008 年至 2022 年期间《农业气象学杂志》（JAM）上发表的科学文章进行了定量分析。研究采用了多种指标，包括年度研究成果、高引用率来源、作者排名、贡献和简介、合作趋势、高贡献国家、高引用率论文、常用搜索关键词和全球合作图谱。本研究采用了文献目录分析法，只关注 Scopus 数据库中的数据。在整个研究期间，该期刊的 h 指数（17）、g 指数（21）和总被引次数为 3238 次，显示出相当大的影响力。潘迪五世发表的研究论文数量最多（46 篇），被引用次数最多（236 次），在众多作者中脱颖而出。在论文数量和引用次数方面，印度遥遥领先，旁遮普农业大学以 744 篇论文位居首位。通过联合引用网络分析发现了四个集群，其中Allen RG是被引用次数最多的作者。研究还强调了印度作者合作最多这一事实。这项分析对于评估 JAM 的影响力非常重要，它提供了有关科学界值得关注的研究趋势和发展的深刻信息。
{"title":"A bibliometric analysis of the Journal of Agrometeorology (JAM) from 2008 to 2022","authors":"V. Kalaimathi, V. GEETHALAKSHMI, P. PARASURAMAN, P. KATHIRVELAN, C. SWAMINATHAN","doi":"10.54386/jam.v26i1.2525","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2525","url":null,"abstract":"A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" April","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092803","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}
Kaushik Maity, S. Banerjee, Manish Kumar Naskar, Sarath Chandran, Sarathi Saha, A. Mukherjee, Kushal Sarmah
West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region
{"title":"Variation of standardized precipitation index (SPI) over southern West Bengal and its effect on jute yield","authors":"Kaushik Maity, S. Banerjee, Manish Kumar Naskar, Sarath Chandran, Sarathi Saha, A. Mukherjee, Kushal Sarmah","doi":"10.54386/jam.v10i1.2328","DOIUrl":"https://doi.org/10.54386/jam.v10i1.2328","url":null,"abstract":"West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"107 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088479","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}
This study analyzes the long-term spatio-temporal changes and trend analysis in rainfall using the data from 1901 to 2020 and its impact on vegetation from 2000 to 2020 across districts of Uttarakhand. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test was performed to analyze the rainfall trend. Results show that the most of the districts exhibited significant negative trend of rainfall in monsoon, except two districts. Out of 13 districts, 4 districts recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for 7 districts of Uttarakhand. Furthermore, the significant negative trend (-2.23) was recorded for overall monsoon rainfall of Uttarakhand. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the districts of Uttarakhand. A significant decline rainfall was detected after 1960 while after 1970 interannual variability of rainfall was recorded to be increased. The analysis of month wise cumulative gross primary productivity (GPP) for 13 districts with rainfall trends shows that there is significant impact of rainfall trend on GPP during month of June and it gradually reduces for subsequent monsoon months. It was observed that the GPP of region is increasing at rate of 9.1 gCm-2d-1 in the region since 2000. Based on sensitivity analysis, the GPP of cropped area of region is more sensitive towards rainfall than forest area of Uttarakhand.
{"title":"Trend analysis and change-point detection of monsoon rainfall in Uttarakhand and its impact on vegetation productivity","authors":"Priyanka Swami","doi":"10.54386/jam.v26i1.2214","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2214","url":null,"abstract":"This study analyzes the long-term spatio-temporal changes and trend analysis in rainfall using the data from 1901 to 2020 and its impact on vegetation from 2000 to 2020 across districts of Uttarakhand. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test was performed to analyze the rainfall trend. Results show that the most of the districts exhibited significant negative trend of rainfall in monsoon, except two districts. Out of 13 districts, 4 districts recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for 7 districts of Uttarakhand. Furthermore, the significant negative trend (-2.23) was recorded for overall monsoon rainfall of Uttarakhand. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the districts of Uttarakhand. A significant decline rainfall was detected after 1960 while after 1970 interannual variability of rainfall was recorded to be increased. The analysis of month wise cumulative gross primary productivity (GPP) for 13 districts with rainfall trends shows that there is significant impact of rainfall trend on GPP during month of June and it gradually reduces for subsequent monsoon months. It was observed that the GPP of region is increasing at rate of 9.1 gCm-2d-1 in the region since 2000. Based on sensitivity analysis, the GPP of cropped area of region is more sensitive towards rainfall than forest area of Uttarakhand.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"107 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088493","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}
Priya Bhattacharya, K. Bandyopadhyay, P. Krishnan, P. Maity, T. Purakayastha, A. Bhatia, B. Chakraborty, S. Kumar, S. Adak, R. Tomer, Meenakshi
A two-year field study was carried out at the Indian Agricultural Research Institute New Delhi, from rabi 2020-21 to 2021-22, with the aim of examining the impacts of tillage and residue management on yield, greenhouse gases (GHGs) emissions, global warming potential (GWP) and carbon efficiency ratio (CER) of wheat in a split plot design. The results indicated that both tillage and residue management significantly influenced the grain and biomass yield of wheat. In comparison to conventional tillage (CT), no-tillage (NT) resulted in a substantial reduction of CO2-C emissions by 19.9%, while it led to a notable increase of N2O-N emissions by 11.6%. However, there was a notable and significant rise in GHG emissions with crop residue mulching, registering on an average 20.79% higher emissions compared to residue removal for both the years. The GWP was overall lower in case of NT as compared to CT plots. The highest CER was observed in NTR+ (3.07) during 2020-21 and in NTR0 (3.12) during 2021-22 due to lower CO2 emissions and higher C fixation in both years. Therefore, it may be recommended that wheat can be cultivated in a semi-arid environment with no tillage and residue mulching to provide a comparable yield in addition to lower GHG emissions and GWP and higher CER compared to the farmers’ practice of CT and residue removal.
{"title":"Impact of tillage and residue management on greenhouse gases emissions and global warming potential of winter wheat in a semi-arid climate","authors":"Priya Bhattacharya, K. Bandyopadhyay, P. Krishnan, P. Maity, T. Purakayastha, A. Bhatia, B. Chakraborty, S. Kumar, S. Adak, R. Tomer, Meenakshi","doi":"10.54386/jam.v25i4.2337","DOIUrl":"https://doi.org/10.54386/jam.v25i4.2337","url":null,"abstract":"A two-year field study was carried out at the Indian Agricultural Research Institute New Delhi, from rabi 2020-21 to 2021-22, with the aim of examining the impacts of tillage and residue management on yield, greenhouse gases (GHGs) emissions, global warming potential (GWP) and carbon efficiency ratio (CER) of wheat in a split plot design. The results indicated that both tillage and residue management significantly influenced the grain and biomass yield of wheat. In comparison to conventional tillage (CT), no-tillage (NT) resulted in a substantial reduction of CO2-C emissions by 19.9%, while it led to a notable increase of N2O-N emissions by 11.6%. However, there was a notable and significant rise in GHG emissions with crop residue mulching, registering on an average 20.79% higher emissions compared to residue removal for both the years. The GWP was overall lower in case of NT as compared to CT plots. The highest CER was observed in NTR+ (3.07) during 2020-21 and in NTR0 (3.12) during 2021-22 due to lower CO2 emissions and higher C fixation in both years. Therefore, it may be recommended that wheat can be cultivated in a semi-arid environment with no tillage and residue mulching to provide a comparable yield in addition to lower GHG emissions and GWP and higher CER compared to the farmers’ practice of CT and residue removal.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"61 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139196614","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}
AMMAIYAPPAN A., V. Geethalakshmi, K. BHUVANESWARI, M.K. KALARANI, N. THAVAPRAKAASH, M. PRAHADEESWARAN
Rainfed sorghum production is profoundly vulnerable to climate variability. Sowing the crop at an appropriate time could be one of the most crucial climate-resilient options to improve the yield. The well-calibrated and validated CERES-Sorghum model was employed to study the rainfed sorghum response to varied environments over the long term (1983–2021) and to determine the optimum sowing window at Coimbatore, Tamil Nadu. The CERES-Sorghum model was used for automatic-planting with a different minimum threshold of 50,60,70 and 80 percent soil water content at 15 cm soil depth under various sowing windows from 1stSeptember to 13th October at a 7-day interval. The model results of automatic planting event indicated the best performance of 1st September sowing window at 50 percent soil water content over 39 years under semi-arid environment. The temperature rise of 1˚C exhibited no significant influence on sorghum grain yields at all sowing windows and a slight reduction in yield was observed at an elevated 2˚C temperature. A further rise in temperature reduced the yield drastically on September month sowings. Across the sowing window, first week sowing window (1st to 7th September) yield was higher under current climatic conditions. The yield of 1st September sowing window remained higher in the elevated temperature conditions as well as in both deficit and excess rainfall conditions than other sowings. In current and future climatic conditions, 1st September sowing window would be the best sowing time to mitigate climate risk in rainfed sorghum.
{"title":"Long-term response of rainfed sorghum to diverse growing environments and optimal sowing window at Coimbatore","authors":"AMMAIYAPPAN A., V. Geethalakshmi, K. BHUVANESWARI, M.K. KALARANI, N. THAVAPRAKAASH, M. PRAHADEESWARAN","doi":"10.54386/jam.v25i4.2362","DOIUrl":"https://doi.org/10.54386/jam.v25i4.2362","url":null,"abstract":"Rainfed sorghum production is profoundly vulnerable to climate variability. Sowing the crop at an appropriate time could be one of the most crucial climate-resilient options to improve the yield. The well-calibrated and validated CERES-Sorghum model was employed to study the rainfed sorghum response to varied environments over the long term (1983–2021) and to determine the optimum sowing window at Coimbatore, Tamil Nadu. The CERES-Sorghum model was used for automatic-planting with a different minimum threshold of 50,60,70 and 80 percent soil water content at 15 cm soil depth under various sowing windows from 1stSeptember to 13th October at a 7-day interval. The model results of automatic planting event indicated the best performance of 1st September sowing window at 50 percent soil water content over 39 years under semi-arid environment. The temperature rise of 1˚C exhibited no significant influence on sorghum grain yields at all sowing windows and a slight reduction in yield was observed at an elevated 2˚C temperature. A further rise in temperature reduced the yield drastically on September month sowings. Across the sowing window, first week sowing window (1st to 7th September) yield was higher under current climatic conditions. The yield of 1st September sowing window remained higher in the elevated temperature conditions as well as in both deficit and excess rainfall conditions than other sowings. In current and future climatic conditions, 1st September sowing window would be the best sowing time to mitigate climate risk in rainfed sorghum.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"846 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139204930","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}
Indian Physicist Dr C.V. Raman, the founder of the Raman Spectroscopy, is the only Indian who received Nobel Prize in Science. Raman trained almost 100 scientists in his laboratory who influenced the development of science and technology in India. Dr L A Ramdas was one of them who began his research career under Raman in the beginning of 1920s. Not only, he coined the term ‘Raman Effect’, but also studied the scattering of light in gases and vapours. The present book written by Dr Rajinder Singh, presents Ramdas’s work on light scattering in association with Raman, his venture in establishing a new field namely, Agricultural Meteorology, and subsequently the discovery of Ramdas Layer, named after him.
{"title":"C.V. Raman's Student L.A. Ramdas - From Agricultural Meteorology to Discovery of Ramdas Layer","authors":"Hardev Singh Virk","doi":"10.54386/jam.v25i4.2393","DOIUrl":"https://doi.org/10.54386/jam.v25i4.2393","url":null,"abstract":"Indian Physicist Dr C.V. Raman, the founder of the Raman Spectroscopy, is the only Indian who received Nobel Prize in Science. Raman trained almost 100 scientists in his laboratory who influenced the development of science and technology in India. Dr L A Ramdas was one of them who began his research career under Raman in the beginning of 1920s. Not only, he coined the term ‘Raman Effect’, but also studied the scattering of light in gases and vapours. The present book written by Dr Rajinder Singh, presents Ramdas’s work on light scattering in association with Raman, his venture in establishing a new field namely, Agricultural Meteorology, and subsequently the discovery of Ramdas Layer, named after him.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139199069","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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