Yunish Khan, Vinod Kumar, P. Setiya, Anurag Satpathi
Early information exchange regarding predicted crop production could play a role in lowering the danger of food insecurity. In this study total six multivariate models were developed using past time series yield data and weather indices viz. SMLR, PCA-SMLR, ANN, PCA-ANN, SMLR-ANN and PCA-SMLR-ANN for three major soybean producing districts of Uttarakhand viz. Almora, Udham Singh Nagar and Uttarkashi. Further analysis was done by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield. Phenology wise average values were computed using the daily weather data. These average values are subsequently employed in the computation of both weighted and unweighted weather indices. The PCA-SMLR-ANN, SMLR-ANN and PCA-ANN models were found to be the best soybean yield predictor model for Almora, Udham Singh Nagar and Uttarkashi districts, respectively. The overall ranking based on the performances of the models for all locations can be given as: SMLR-ANN > PCA-ANN > PCA-SMLR-ANN ≈ ANN > PCA-SMLR > SMLR. The study results indicated that hybrid models outperformed the individual models well for all the study regions.
{"title":"Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in Uttarakhand","authors":"Yunish Khan, Vinod Kumar, P. Setiya, Anurag Satpathi","doi":"10.54386/jam.v25i3.2232","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2232","url":null,"abstract":"Early information exchange regarding predicted crop production could play a role in lowering the danger of food insecurity. In this study total six multivariate models were developed using past time series yield data and weather indices viz. SMLR, PCA-SMLR, ANN, PCA-ANN, SMLR-ANN and PCA-SMLR-ANN for three major soybean producing districts of Uttarakhand viz. Almora, Udham Singh Nagar and Uttarkashi. Further analysis was done by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield. Phenology wise average values were computed using the daily weather data. These average values are subsequently employed in the computation of both weighted and unweighted weather indices. The PCA-SMLR-ANN, SMLR-ANN and PCA-ANN models were found to be the best soybean yield predictor model for Almora, Udham Singh Nagar and Uttarkashi districts, respectively. The overall ranking based on the performances of the models for all locations can be given as: SMLR-ANN > PCA-ANN > PCA-SMLR-ANN ≈ ANN > PCA-SMLR > SMLR. The study results indicated that hybrid models outperformed the individual models well for all the study regions.","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":"45793865","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}
T. Ghosh, DEBASHIS CHAKRABORTY, BAPPA DAS, VINAY K. SEHGAL, DEBASHISH ROY, RAJKUMAR DHAKAR, KOUSHIK BAG
Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.
{"title":"Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm","authors":"T. Ghosh, DEBASHIS CHAKRABORTY, BAPPA DAS, VINAY K. SEHGAL, DEBASHISH ROY, RAJKUMAR DHAKAR, KOUSHIK BAG","doi":"10.54386/jam.v25i3.2254","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2254","url":null,"abstract":"Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.","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":"47477921","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}
Water, nutrients, and energy are the three main inputs in agricultural production and recently there has been a drop in the factor productivity of these inputs because of their improper management and deterioration of soil health. To maximize agricultural productivity while lowering strain on natural resources, the best synergistic combinations of tillage, residue, nitrogen, and water management should be identified for improving resource use efficiency of wheat. Hence, an attempt has been made to evaluate the impact of contrasting tillage, crop residue mulch, nitrogen, and irrigation interaction on yield, radiation productivity (RP), and water productivity (WP) of wheat in a split-factorial design. Results showed that wheat yield was higher under no-tillage (4.8%) than that of conventional tillage. Crop residue mulch (CRM) and higher nitrogen application enhanced RP, WP, and yield of wheat; although RP increased with increase in nitrogen application up to 100% recommended dose of nitrogen (RDN). CRM significantly reduced the seasonal evapotranspiration (6.0‒7.2%) as compared to residue removal treatment. Deficit irrigation enhanced the WP while it lowered the crop yield significantly. Therefore, wheat can be grown under no-tillage, CRM, 100% RDN with deficit irrigation to obtain higher WP but with full irrigation to obtain higher yield, and RP in the semiarid climate of India.
{"title":"Interactive effect of tillage, residue, nitrogen, and irrigation management on yield, radiation productivity and water productivity of winter wheat in semi-arid climate","authors":"SUJAN ADAK, KALIKINKAR BANDYOPADHYAY, R.N. SAHOO, PRAMEELA KRISHNAN, V.K. SEHGAL, S. NARESH KUMAR, S.P. DATTA, A. SARANGI, R.S. BANA, NANDITA MANDAL, PRIYA BHATTACHARYA, MD YEASIN","doi":"10.54386/jam.v25i3.2240","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2240","url":null,"abstract":"Water, nutrients, and energy are the three main inputs in agricultural production and recently there has been a drop in the factor productivity of these inputs because of their improper management and deterioration of soil health. To maximize agricultural productivity while lowering strain on natural resources, the best synergistic combinations of tillage, residue, nitrogen, and water management should be identified for improving resource use efficiency of wheat. Hence, an attempt has been made to evaluate the impact of contrasting tillage, crop residue mulch, nitrogen, and irrigation interaction on yield, radiation productivity (RP), and water productivity (WP) of wheat in a split-factorial design. Results showed that wheat yield was higher under no-tillage (4.8%) than that of conventional tillage. Crop residue mulch (CRM) and higher nitrogen application enhanced RP, WP, and yield of wheat; although RP increased with increase in nitrogen application up to 100% recommended dose of nitrogen (RDN). CRM significantly reduced the seasonal evapotranspiration (6.0‒7.2%) as compared to residue removal treatment. Deficit irrigation enhanced the WP while it lowered the crop yield significantly. Therefore, wheat can be grown under no-tillage, CRM, 100% RDN with deficit irrigation to obtain higher WP but with full irrigation to obtain higher yield, and RP in the semiarid climate of India.","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":"47647846","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}
An attempt was made to determine the most favourable abiotic factors influencing germination of urediniospores of different pathotypes of Puccinia species. The causal organism of rusts in wheat is Puccinia spp. Five pathotypes of Puccinia striiformis (46S119, 78S84, 110S84, 110S119, 238S119) causal organism of yellow rust and two pathotypes of Puccinia triticina (77-5 and 77-9) causal organism of brown rust in wheat were obtained from Department of Plant Pathology, Punjab Agricultural University, Ludhiana. The data related to spore germination on agar slides was analysed and the levels of urediniospores germination at different temperatures (5,10,15 and 20oC) and pH (5,6,7 and 8) for each pathotype was compared using analysis of variance. The most appropriate temperature and pH were later used to conduct an experiment to study effect of different light intensities (500, 750,1000 and 1250 lux) on spore germination of all the pathotypes under study. The data showed that on agar, 15°C was proved as most suitable for urediniospore germination for Puccinia striiformis. Mean per cent spore germination was highest over the temperature range 15°C (43.55%) for Pst pathoypes and dropped significantly at 10°C (37.97%), 20°C (29.66%) and 5°C (21.04%). Mean urediniospore germination for Puccinia triticina was highest at 20°C (43.89%) followed by 15°C (39.44%), 10°C (30.43) and 5°C (27.39% ). Experimental results revealed that per cent spore germination was better under pH 7 followed by 6, 5 and 8 for all the pathotypes considered for study. The highest urediniospore germination was observed for 1250 lux (46.54%) followed by 1000 lux (41.29%), 750 lux (38.42%) and 500 lux (27.60%).
{"title":"Effect of abiotic factors on pathotypes causing yellow and brown rust in wheat","authors":"S. Anand, S. Sandhu, P. S. Tak","doi":"10.54386/jam.v25i3.2140","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2140","url":null,"abstract":"An attempt was made to determine the most favourable abiotic factors influencing germination of urediniospores of different pathotypes of Puccinia species. The causal organism of rusts in wheat is Puccinia spp. Five pathotypes of Puccinia striiformis (46S119, 78S84, 110S84, 110S119, 238S119) causal organism of yellow rust and two pathotypes of Puccinia triticina (77-5 and 77-9) causal organism of brown rust in wheat were obtained from Department of Plant Pathology, Punjab Agricultural University, Ludhiana. The data related to spore germination on agar slides was analysed and the levels of urediniospores germination at different temperatures (5,10,15 and 20oC) and pH (5,6,7 and 8) for each pathotype was compared using analysis of variance. The most appropriate temperature and pH were later used to conduct an experiment to study effect of different light intensities (500, 750,1000 and 1250 lux) on spore germination of all the pathotypes under study. The data showed that on agar, 15°C was proved as most suitable for urediniospore germination for Puccinia striiformis. Mean per cent spore germination was highest over the temperature range 15°C (43.55%) for Pst pathoypes and dropped significantly at 10°C (37.97%), 20°C (29.66%) and 5°C (21.04%). Mean urediniospore germination for Puccinia triticina was highest at 20°C (43.89%) followed by 15°C (39.44%), 10°C (30.43) and 5°C (27.39% ). Experimental results revealed that per cent spore germination was better under pH 7 followed by 6, 5 and 8 for all the pathotypes considered for study. The highest urediniospore germination was observed for 1250 lux (46.54%) followed by 1000 lux (41.29%), 750 lux (38.42%) and 500 lux (27.60%).","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":"46362359","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 was conducted to investigate the response of stress irrigation management on chlorophyll content, water potential, photosynthetically active radiation (PAR) and canopy temperature in tomato during summer season. The main plot treatments consist of three drying cycles that is 7, 11 and 15 days and sub treatments include four irrigation levels viz.,60, 80, 100, and 120 % ETC. The control treatments i.e. drip irrigation with 100% ETC on every two alternate days. The results showed that the 7 days drying cycle showed maximum chlorophyll content, absorbed PAR and leaf water potential followed by 11 days drying cycle. Among the drip irrigation levels, the maximum drip irrigation levels 120 % ETC exhibited significantly maximum chlorophyll content, absorbed PAR and leaf water potential. However, it was at par with 100 % ETC and further 80 % ETC drip irrigation level also showed significant at 90 and 120 DAT. While in the case of difference between canopy and air temperature (Tc-Ta) less negative values were noted by 7 days drying cycle and 120% ETC drip irrigation level.
{"title":"Response of stress irrigation management on chlorophyll content, water potential, PAR and canopy temperature in tomato (Lycopersicum Esculentum Mill.)","authors":"K. Chavan, P. Bodake","doi":"10.54386/jam.v25i3.2172","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2172","url":null,"abstract":" This study was conducted to investigate the response of stress irrigation management on chlorophyll content, water potential, photosynthetically active radiation (PAR) and canopy temperature in tomato during summer season. The main plot treatments consist of three drying cycles that is 7, 11 and 15 days and sub treatments include four irrigation levels viz.,60, 80, 100, and 120 % ETC. The control treatments i.e. drip irrigation with 100% ETC on every two alternate days. The results showed that the 7 days drying cycle showed maximum chlorophyll content, absorbed PAR and leaf water potential followed by 11 days drying cycle. Among the drip irrigation levels, the maximum drip irrigation levels 120 % ETC exhibited significantly maximum chlorophyll content, absorbed PAR and leaf water potential. However, it was at par with 100 % ETC and further 80 % ETC drip irrigation level also showed significant at 90 and 120 DAT. While in the case of difference between canopy and air temperature (Tc-Ta) less negative values were noted by 7 days drying cycle and 120% ETC drip irrigation level.","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":"42515895","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. N. R. Kumar, Anurag Satpathi, M. Reddy, P. Setiya, A. Nain
Groundnut is a key oilseed crop in the world and India is one of the largest groundnuts producing country in terms of area and yield. Keeping that in view, five models were developed for five districts of Andhra Pradesh to forecast the groundnut yield viz., Stepwise Multiple Linear Regression (SMLR), Ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Artificial Neural Network (ANN). The historical data on the weather parameters are obtained from NASA POWER web portal and groundnut yields for these districts of the state during both Kharif and Rabi seasons obtained through Season and Crop Report, Government of Andhra Pradesh for the period, 2001 to 2020. In total 30 weather indices were generated through five weather variables. The assessment of models was done by fixing 75 % of the data for calibration and left 25 % data for validation. The findings inferred that based on the values of R2, RMSE, nRMSE and EF, Ridge regression, ELNET and ANN models showed better performance for Ananthapur, Chittoor and Kadapa districts and SMLR and LASSO models showed better performance for Kurnool and Nellore districts during both Kharif and Rabi seasons at calibration and validation stages.
{"title":"Development of groundnut yield forecasting models in relation to weather parameters in Andhra Pradesh, India","authors":"K. N. R. Kumar, Anurag Satpathi, M. Reddy, P. Setiya, A. Nain","doi":"10.54386/jam.v25i3.2194","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2194","url":null,"abstract":"Groundnut is a key oilseed crop in the world and India is one of the largest groundnuts producing country in terms of area and yield. Keeping that in view, five models were developed for five districts of Andhra Pradesh to forecast the groundnut yield viz., Stepwise Multiple Linear Regression (SMLR), Ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Artificial Neural Network (ANN). The historical data on the weather parameters are obtained from NASA POWER web portal and groundnut yields for these districts of the state during both Kharif and Rabi seasons obtained through Season and Crop Report, Government of Andhra Pradesh for the period, 2001 to 2020. In total 30 weather indices were generated through five weather variables. The assessment of models was done by fixing 75 % of the data for calibration and left 25 % data for validation. The findings inferred that based on the values of R2, RMSE, nRMSE and EF, Ridge regression, ELNET and ANN models showed better performance for Ananthapur, Chittoor and Kadapa districts and SMLR and LASSO models showed better performance for Kurnool and Nellore districts during both Kharif and Rabi seasons at calibration and validation stages.","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":"47848436","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}
PARTHA PRATIM MAITY, B. Chakrabarti, A. Bhatia, S N KUMAR, T. Purakayastha, D. Chakraborty, S. Adak, Arpit Sharma, S. Kannojiya
Greenhouse gas (GHG) emissions from anthropogenic activities are the most significant drivers of climate change, which has both direct and indirect effects on crop production. The study was conducted during the kharif season for two years inside the Open Top Chamber (OTC) at the Genetic-H field of ICAR-Indian Agriculture Research Institute (IARI) to quantify the effect of elevated CO2 and temperature on water use efficiency of rice varieties. There were two different CO2 concentrations i.e. ambient (410 ppm) and elevated (550 ± 25 ppm) and also two different temperature levels i.e. ambient and elevated (+2.5-2.9°C). Results suggested that warming caused more accumulated GDD in rice and which negatively affected the duration of both the varieties. In elevated CO2 plus high temperature interaction treatment net photosynthesis rate was more than that of chamber control. Stomatal conductance and transpiration rate reduced with co-elevation of CO2 and temperature. Co-elevation of CO2 and temperature, has also improved WUE (both instantaneous and intrinsic) through enhanced carbon assimilation and reduced stomatal conductance, thus, reducing the amount of water lost through transpiration, eventually improving WUE of the crop.
{"title":"Co-elevation of atmospheric CO2 and temperature affect instantaneous and intrinsic water use efficiency of rice varieties","authors":"PARTHA PRATIM MAITY, B. Chakrabarti, A. Bhatia, S N KUMAR, T. Purakayastha, D. Chakraborty, S. Adak, Arpit Sharma, S. Kannojiya","doi":"10.54386/jam.v25i3.2243","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2243","url":null,"abstract":"Greenhouse gas (GHG) emissions from anthropogenic activities are the most significant drivers of climate change, which has both direct and indirect effects on crop production. The study was conducted during the kharif season for two years inside the Open Top Chamber (OTC) at the Genetic-H field of ICAR-Indian Agriculture Research Institute (IARI) to quantify the effect of elevated CO2 and temperature on water use efficiency of rice varieties. There were two different CO2 concentrations i.e. ambient (410 ppm) and elevated (550 ± 25 ppm) and also two different temperature levels i.e. ambient and elevated (+2.5-2.9°C). Results suggested that warming caused more accumulated GDD in rice and which negatively affected the duration of both the varieties. In elevated CO2 plus high temperature interaction treatment net photosynthesis rate was more than that of chamber control. Stomatal conductance and transpiration rate reduced with co-elevation of CO2 and temperature. Co-elevation of CO2 and temperature, has also improved WUE (both instantaneous and intrinsic) through enhanced carbon assimilation and reduced stomatal conductance, thus, reducing the amount of water lost through transpiration, eventually improving WUE of the crop.","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":"48379265","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}
An experiment was conducted at the research farm of the Rajasthan Agricultural Research Institute, Durgapura, Jaipur, during Rabi, 2020–21 and 2021–22, to study the impact of meteorological parameters on the populations of the aphid, Lipaphis erysimi (Kalt) it’s associated natural enemies coccinellids, Coccinella septempunctata and syrphid flies, Xanthogramma scutellariae. The correlation coefficients with the pooled data, showed a substantial negative correlation of aphid population with temperature (r = -0.466 and -0.582*) as well as with average relative humidity (r =0.489*). C. septempunctata and X. scutellariae had positive significant correlations with L. erysimi (r = 0.965* and 0.988* respectively). The most significant variables for aphid populations, according to PC1 and PC2 (initial components of principal component analysis), are biotic factors and weather parameters.
{"title":"Population dynamics of aphid and their natural enemies in mustard based on meteorological parameters using principal component analysis","authors":"RAJ VEER YADAV, VIPIN KUMAR, RANI SAXENA","doi":"10.54386/jam.v25i3.2209","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2209","url":null,"abstract":"An experiment was conducted at the research farm of the Rajasthan Agricultural Research Institute, Durgapura, Jaipur, during Rabi, 2020–21 and 2021–22, to study the impact of meteorological parameters on the populations of the aphid, Lipaphis erysimi (Kalt) it’s associated natural enemies coccinellids, Coccinella septempunctata and syrphid flies, Xanthogramma scutellariae. The correlation coefficients with the pooled data, showed a substantial negative correlation of aphid population with temperature (r = -0.466 and -0.582*) as well as with average relative humidity (r =0.489*). C. septempunctata and X. scutellariae had positive significant correlations with L. erysimi (r = 0.965* and 0.988* respectively). The most significant variables for aphid populations, according to PC1 and PC2 (initial components of principal component analysis), are biotic factors and weather parameters.","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":"45643622","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}
{"title":"Assessing future precipitation and temperature changes for the Kesinga Basin, India according to CORDEX-WAS climate projections","authors":"Pereli Chinna Vani, B.C. SAHOO, J.C. PAUL, A.P. Sahu, A.K.B. MOHAPATRA","doi":"10.54386/jam.v25i3.2228","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2228","url":null,"abstract":"","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":"45292405","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}
Shweta Pokhariyal, N. R. Patel, A. Danodia, R. Singh
Global climate change expected to exacerbate the temperature extremes and intensity of heat waves in recent decades. The terrestrial biosphere plays a crucial role in absorbing carbon from the atmosphere. Therefore, understanding how terrestrial ecosystems respond to extreme temperatures is essential for predicting land-surface feedbacks in a changing climate. In light of this, a study was conducted to assess the effects of 2022 heat wave [March-May (MAM)] on carbon and water vapour fluxes. This study utilized the measurements obtained from the eddy covariance tower mounted within the sugarcane agroecosystem. The study period (MAM) was characterized into three events: Heat wave event 1 (HE1), Heat wave event 2 (HE2), Non heat wave event (NHE). The variation in carbon and water vapour fluxes, along with meteorological variables, during these events in 2020 and 2022 was further analysed. Our findings indicate that the heat wave caused a decrease in net ecosystem exchange (NEE), leading to an increase in atmospheric CO2 concentration during HE1, HE2 compared to NHE. In HE1, maximum NEE in 2020 and 2022 was -19.15 µmol m-2 s-1 and -13.21 µmol m-2 s-1, respectively. Furthermore, the heat wave events led to a decrease in latent heat flux (LE) and sensible heat flux (H), with changes of up to 5% in LE and 57% in H compared to the same period in 2020. These results highlight the significant impact of the heatwave on both carbon and energy fluxes. Overall, the present study provides a valuable reference for further climate change analysis, specifically focusing on both carbon and energy fluxes within sugarcane ecosystem.
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