R. N. Singh, J. Mukherjee, Sonam, AMRESH CHAUDHARY, ABIRA BANERJEE, A.K.Singh, K. S. Reddy
Micrometeorology plays a pivotal role in advancing our understanding of agricultural systems by unraveling intricate interactions between climate dynamics and crop performance. This article presents a comprehensive analysis of the literature published on crop micrometeorology and indexed in Scopus database from 2000 to 2023. The query yielded only 146 documents, which were subsequently subjected to analysis using an R-based bibliometric tool to assess annual scientific production trend, document types, citation, and keyword analysis. The results revealed zero growth rate of the topic with an average 47.36 citations and total citation of 6536 in the analysis period. USA dominates the number of publications (28.1%), followed by China (17.8%), Japan (11.6%) and Australia (8.9%). India stood at 10th position with only 8 documents contributing 5.5% of the total publications included in the study. The key domains of current research in the realm of crop micrometeorology identified through bibliometric analysis were evapotranspiration, energy balance, gas emissions, and modelling based studies, which are discussed in details in the article. As climate change and global food security becomes more critical, this analysis highlights the role of micrometeorological works within the realm of climate change and crop studies.
{"title":"Exploring the landscape of contemporary crop micrometeorology: A bibliometric investigation","authors":"R. N. Singh, J. Mukherjee, Sonam, AMRESH CHAUDHARY, ABIRA BANERJEE, A.K.Singh, K. S. Reddy","doi":"10.54386/jam.v25i3.2320","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2320","url":null,"abstract":"Micrometeorology plays a pivotal role in advancing our understanding of agricultural systems by unraveling intricate interactions between climate dynamics and crop performance. This article presents a comprehensive analysis of the literature published on crop micrometeorology and indexed in Scopus database from 2000 to 2023. The query yielded only 146 documents, which were subsequently subjected to analysis using an R-based bibliometric tool to assess annual scientific production trend, document types, citation, and keyword analysis. The results revealed zero growth rate of the topic with an average 47.36 citations and total citation of 6536 in the analysis period. USA dominates the number of publications (28.1%), followed by China (17.8%), Japan (11.6%) and Australia (8.9%). India stood at 10th position with only 8 documents contributing 5.5% of the total publications included in the study. The key domains of current research in the realm of crop micrometeorology identified through bibliometric analysis were evapotranspiration, energy balance, gas emissions, and modelling based studies, which are discussed in details in the article. As climate change and global food security becomes more critical, this analysis highlights the role of micrometeorological works within the realm of climate change and crop studies.","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":"43643274","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}
Sathyamoorthy N.K, G. V, Ramanathan Sp, S. T, S. M, P. C, G. T.
{"title":"Assessment of growth and productivity of pearl millet (Pennisetum glaucum L.) with varied sowing environments and nitrogen concentrations using AquaCrop model","authors":"Sathyamoorthy N.K, G. V, Ramanathan Sp, S. T, S. M, P. C, G. T.","doi":"10.54386/jam.v25i3.2215","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2215","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":"43490824","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}
P. H. Rank, D. Vaghasiya, M. Lunagaria, R. Patel, M. Tiwari, H. Rank
An assessment of climate chnage and its impacts on water fluxes in the Shingoda basin of the Saurashtra region having 14% agriculture and 75% forest were made through analysis of time series (1951-2100) of bias corrected maximum/minimum temperature and rainfall (RCP4.5), rreference evapotranspiration (ETo), evapotranspiration (ETc) and runoff. Results showed significant climate changes in the basin, with day mean temperature rising from 24.4°C in the second half of the 20th century to 26.5°C and 27.9°C in the first and second half of the 21st century, respectively. During the first and second half of the 21st century, seasonal rainfall increased by 23.0% and 46.33%, and runoff rose by 46.78% and 86.40% compared to the second half of the 20th century. However, annual reference evapotranspiration (ETo) decreased by -1.41% and -6.5%, and crop evapotranspiration (ETc) decreased by -3.2% and -9.8% in the same periods. The analysis also revealed a deficit of -16.10% in downward water flux (rainfall) in the first half of the 20th century, followed by a surplus of 8.46% and 28.37% compared to the upward flux (ETc) in subsequent periods. The upward water flux deficit during 2nd half of 20th century were supported by evidence of depleted groundwater levels and seawater intrusion in the study area.
{"title":"Climate change impacts on water flux dynamics in Shingoda basin having agriculture and forest ecosystems: A comprehensive analysis","authors":"P. H. Rank, D. Vaghasiya, M. Lunagaria, R. Patel, M. Tiwari, H. Rank","doi":"10.54386/jam.v25i3.2284","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2284","url":null,"abstract":"An assessment of climate chnage and its impacts on water fluxes in the Shingoda basin of the Saurashtra region having 14% agriculture and 75% forest were made through analysis of time series (1951-2100) of bias corrected maximum/minimum temperature and rainfall (RCP4.5), rreference evapotranspiration (ETo), evapotranspiration (ETc) and runoff. Results showed significant climate changes in the basin, with day mean temperature rising from 24.4°C in the second half of the 20th century to 26.5°C and 27.9°C in the first and second half of the 21st century, respectively. During the first and second half of the 21st century, seasonal rainfall increased by 23.0% and 46.33%, and runoff rose by 46.78% and 86.40% compared to the second half of the 20th century. However, annual reference evapotranspiration (ETo) decreased by -1.41% and -6.5%, and crop evapotranspiration (ETc) decreased by -3.2% and -9.8% in the same periods. The analysis also revealed a deficit of -16.10% in downward water flux (rainfall) in the first half of the 20th century, followed by a surplus of 8.46% and 28.37% compared to the upward flux (ETc) in subsequent periods. The upward water flux deficit during 2nd half of 20th century were supported by evidence of depleted groundwater levels and seawater intrusion in the study area.","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":"48426773","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}
WOFOST and InfoCrop crop growth simulation models were used to assess the impact of climate change on potato cultivars and to develop adaptation strategies for future climatic scenarios (2030, 2050 and 2080) under representative concentration pathways (RCP’s) 4.5 and 6.0 in Bihar. Potato cultivars belonging to late (Kufri Badshah), medium (Kufri Jyoti) and early (Kufri Pukhraj) maturity groups were selected. The simulated results revealed variations in potential productivity of potato under both RCP’s (4.5 & 6.0) with baseline yields of 43.80 t ha-1 for Kufri Badshah, 41.5 t ha-1 for Kufri Jyoti and 43.6 t ha-1 for Kufri Pukhraj. Under RCP 4.5, elevated concentration of CO2 projected to increase the productivity of Kufri Badshah, Kufri Jyoti, and Kufri Pukhraj. However, a decline in yield is expected when individual effect of temperature is considered for future climatic scenarios (2030, 2050 & 2080). However, these yield loss is negated when combined effect of CO2 and temperature is considered by 1.3, 0.7 and 0.3 % in 2030, by -0.4, -1.1 and -2.2 % in 2050 and by 3.5, 4.4 and 5.9 % in 2080, respectively. Likewise, for RCP 6.0, combined effect of CO2 and temperature offset the yield losses by 2.6, 2.4 and 2.3% in 2030, 2.1, 1.7 and 1.1 in 2050 and 1.1, -0.1 and -1.8 in 2080. In addition, selection of suitable cultivars, shifting the date of planting and proper irrigation and nitrogen management practices can counterbalance the yield losses.
WOFOST和InfoCrop作物生长模拟模型用于评估气候变化对马铃薯品种的影响，并制定比哈尔邦代表性浓度途径（RCP）4.5和6.0下未来气候情景（2030、2050和2080）的适应策略。选择属于晚熟（Kufri Badshah）、中熟（Kufri Jyoti）和早熟（Kufri-Pukhraj）的马铃薯品种。模拟结果显示，在两种RCP（4.5和6.0）下，Kufri Badshah的基准产量为43.80 t ha-1、Kufri Jyoti的41.5 t ha-1和Kufri Pukhraj的43.6 t ha-1，马铃薯的潜在生产力发生了变化。根据RCP 4.5，CO2浓度的升高预计将提高Kufri Badshah、Kufri Jyoti和Kufri Pukhraj的生产力。然而，当考虑到未来气候情景（2030年、2050年和2080年）温度的单独影响时，预计产量会下降。然而，当2030年二氧化碳和温度的综合影响分别为1.3%、0.7%和0.3%，2050年为-0.4%、-1.1%和-2.2%，2080年为3.5%、4.4%和5.9%时，这些产量损失被抵消。同样，对于RCP 6.0，二氧化碳和温度的综合影响在2030年抵消了2.6%、2.4%和2.3%的产量损失，在2050年抵消了2.1、1.7和1.1，在2080年抵消了1.1、-0.1和-1.8。此外，选择合适的品种、改变种植日期以及适当的灌溉和氮管理实践可以抵消产量损失。
{"title":"Climate change impact on potato (Solanum tuberosum) productivity and relative adaptation strategies","authors":"ANCHAL RANA, VIJAY KUMAR DUA, NIRMLA CHAUHAN, PARESH CHAUKHANDE, Meena Kumari","doi":"10.54386/jam.v25i3.2181","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2181","url":null,"abstract":"WOFOST and InfoCrop crop growth simulation models were used to assess the impact of climate change on potato cultivars and to develop adaptation strategies for future climatic scenarios (2030, 2050 and 2080) under representative concentration pathways (RCP’s) 4.5 and 6.0 in Bihar. Potato cultivars belonging to late (Kufri Badshah), medium (Kufri Jyoti) and early (Kufri Pukhraj) maturity groups were selected. The simulated results revealed variations in potential productivity of potato under both RCP’s (4.5 & 6.0) with baseline yields of 43.80 t ha-1 for Kufri Badshah, 41.5 t ha-1 for Kufri Jyoti and 43.6 t ha-1 for Kufri Pukhraj. Under RCP 4.5, elevated concentration of CO2 projected to increase the productivity of Kufri Badshah, Kufri Jyoti, and Kufri Pukhraj. However, a decline in yield is expected when individual effect of temperature is considered for future climatic scenarios (2030, 2050 & 2080). However, these yield loss is negated when combined effect of CO2 and temperature is considered by 1.3, 0.7 and 0.3 % in 2030, by -0.4, -1.1 and -2.2 % in 2050 and by 3.5, 4.4 and 5.9 % in 2080, respectively. Likewise, for RCP 6.0, combined effect of CO2 and temperature offset the yield losses by 2.6, 2.4 and 2.3% in 2030, 2.1, 1.7 and 1.1 in 2050 and 1.1, -0.1 and -1.8 in 2080. In addition, selection of suitable cultivars, shifting the date of planting and proper irrigation and nitrogen management practices can counterbalance the yield losses.","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":"49364063","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}
AJITH S, MANOJ KANTI DEBNATH, DEB SANKAR GUPTA, PRADIP BASAK, SUBHENDU BANDYOPADHYAY, SHYAMAL KHEROAR, RAGINI HR
Rapeseed-mustard (Brassica spp.) is one of the important edible oilseeds crops in India. The same level of weather condition impacts the growth and establishment of rapeseed-mustard plant differently in different stages of crop which lead to large intra-seasonal yield variations. Hence it is essential to give weightage to weekly weather conditions while fitting predictive model. In this present study, path-coefficient based weighted index was proposed along with existing unweighted and correlation based weighted index. The performance of penalized regression models viz. Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET) were compared with Multiple Linear Regression (MLR) for predicting rapeseed-mustard yield using weather-indices. The results revealed that the path-coefficient based weighting of weather parameters to the yield were stable than correlation based weighted-indices. Path-coefficient based weighted indices of maximum temperature, minimum temperature and windspeed were important variables in projection of yield. The performance of MLR was poor during validation of model due to overfitting issue. The performance of penalized models was stable in both calibration and validation of the model. The LASSO and ENET models that accompanied with coefficient shrinkage and variable selection were found to be the best fitted models for predicting Rapeseed-Mustard yield.
{"title":"Comparative evaluation of penalized regression models with multiple linear regression for predicting rapeseed-mustard yield: Weather-indices based approach","authors":"AJITH S, MANOJ KANTI DEBNATH, DEB SANKAR GUPTA, PRADIP BASAK, SUBHENDU BANDYOPADHYAY, SHYAMAL KHEROAR, RAGINI HR","doi":"10.54386/jam.v25i3.2185","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2185","url":null,"abstract":"Rapeseed-mustard (Brassica spp.) is one of the important edible oilseeds crops in India. The same level of weather condition impacts the growth and establishment of rapeseed-mustard plant differently in different stages of crop which lead to large intra-seasonal yield variations. Hence it is essential to give weightage to weekly weather conditions while fitting predictive model. In this present study, path-coefficient based weighted index was proposed along with existing unweighted and correlation based weighted index. The performance of penalized regression models viz. Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET) were compared with Multiple Linear Regression (MLR) for predicting rapeseed-mustard yield using weather-indices. The results revealed that the path-coefficient based weighting of weather parameters to the yield were stable than correlation based weighted-indices. Path-coefficient based weighted indices of maximum temperature, minimum temperature and windspeed were important variables in projection of yield. The performance of MLR was poor during validation of model due to overfitting issue. The performance of penalized models was stable in both calibration and validation of the model. The LASSO and ENET models that accompanied with coefficient shrinkage and variable selection were found to be the best fitted models for predicting Rapeseed-Mustard yield.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70725903","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}
Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.
{"title":"Advancements in remote sensing based crop yield modelling in India","authors":"N. R. Patel, Shweta Pokhariyal, R. P. Singh","doi":"10.54386/jam.v25i3.2316","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2316","url":null,"abstract":"Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in 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":"45401557","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. Wise, Estella Baziotopoulos, Catherine Zhang, Myles Leaming, Li Shen, Jamie Selby-Pham
Water is a valuable and limited resource, which is becoming increasingly under pressure due to the impacts of climate change and over utilization by the agricultural industry. Cotton is the predominant natural fibre utilized within textiles and is a highly water-intensive crop, thereby contributing to the negative environmental impacts of water use in agriculture, such as depletion of water from ecosystems and other uses, land degradation, and dissemination of pollutants. Accordingly, there is significant interest in establishing alternative natural fibre sources, which have lower water requirements. Cannabis sativa (hemp) fibre is becoming an increasingly popular fibre alternative and is purported to require less water during its cultivation. Accordingly, herein data was compared across 28 prior published sources, which identified that hemp has a 38% lower crop water requirement (CWR), 60% lower water footprint (WF), 84% lower crop irrigation requirement (CIR), and 91% lower irrigated water footprint (IRF) as compared to cotton. Therefore, these results support hemp as a water-efficient environmentally sustainable alternative to cotton for fibre cultivation.
{"title":"Comparative study of water requirements and water footprints of fibre crops hemp (Cannabis sativa) and cotton (Gossypium hirsutum L.)","authors":"K. Wise, Estella Baziotopoulos, Catherine Zhang, Myles Leaming, Li Shen, Jamie Selby-Pham","doi":"10.54386/jam.v25i3.2260","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2260","url":null,"abstract":"Water is a valuable and limited resource, which is becoming increasingly under pressure due to the impacts of climate change and over utilization by the agricultural industry. Cotton is the predominant natural fibre utilized within textiles and is a highly water-intensive crop, thereby contributing to the negative environmental impacts of water use in agriculture, such as depletion of water from ecosystems and other uses, land degradation, and dissemination of pollutants. Accordingly, there is significant interest in establishing alternative natural fibre sources, which have lower water requirements. Cannabis sativa (hemp) fibre is becoming an increasingly popular fibre alternative and is purported to require less water during its cultivation. Accordingly, herein data was compared across 28 prior published sources, which identified that hemp has a 38% lower crop water requirement (CWR), 60% lower water footprint (WF), 84% lower crop irrigation requirement (CIR), and 91% lower irrigated water footprint (IRF) as compared to cotton. Therefore, these results support hemp as a water-efficient environmentally sustainable alternative to cotton for fibre cultivation.","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":"48605964","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. Govindaraj, N. Maragatham, S. Ramanathan, V. Geethalakshmi, M. K. Kalarani
{"title":"Calibration and validation of APSIM maize simulation model for different date of sowing","authors":"T. Govindaraj, N. Maragatham, S. Ramanathan, V. Geethalakshmi, M. K. Kalarani","doi":"10.54386/jam.v25i3.2212","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2212","url":null,"abstract":"","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41425169","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}
Climate change has an impact on agricultural activity because of its direct reliance on climate change. There are two types of relationships between agriculture and climate change, and they are extremely important, particularly for developing and underdeveloped or low-income countries, which rely heavily on agriculture for subsistence and lack adaptation infrastructure when compared to developed countries. Geographically high-latitude places that already have low temperatures might benefit from a prolonged growing season when temperatures rise due to climate change. GHG emissions such as carbon dioxide, nitrous oxide, and methane have an impact on agricultural lands. Gases have an impact on climate through emitting greenhouse gases. Emissions are mostly caused by tillage operations, fossil fuels, fertilized agricultural soils, and farm animal waste, and have a significant impact on the agriculture industry. Agriculture, on the other hand, might be a solution to climate change by lowering emissions and extensively implementing mitigation and adaptation measures. Best management approaches such as use of microbial inoculants to reduce fertilizer inputs, carbon sequestration and methane oxidation has potential to reduce greenhouse gases from agro-ecosystem.
{"title":"Climate change and agricultural ecosystem: Challenges and microbial interventions for mitigation","authors":"R. Vyas, Y. K. Jhala","doi":"10.54386/jam.v25i3.2305","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2305","url":null,"abstract":"Climate change has an impact on agricultural activity because of its direct reliance on climate change. There are two types of relationships between agriculture and climate change, and they are extremely important, particularly for developing and underdeveloped or low-income countries, which rely heavily on agriculture for subsistence and lack adaptation infrastructure when compared to developed countries. Geographically high-latitude places that already have low temperatures might benefit from a prolonged growing season when temperatures rise due to climate change. GHG emissions such as carbon dioxide, nitrous oxide, and methane have an impact on agricultural lands. Gases have an impact on climate through emitting greenhouse gases. Emissions are mostly caused by tillage operations, fossil fuels, fertilized agricultural soils, and farm animal waste, and have a significant impact on the agriculture industry. Agriculture, on the other hand, might be a solution to climate change by lowering emissions and extensively implementing mitigation and adaptation measures. Best management approaches such as use of microbial inoculants to reduce fertilizer inputs, carbon sequestration and methane oxidation has potential to reduce greenhouse gases from agro-ecosystem. ","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":"47145050","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}
In the arid zone, one of the ways to provide animals with feed is the organization of forested pastures, the productivity of which largely depends on weather conditions. Our study analyzes changes in meteorological conditions and hydrothermal coefficient (HTC) during the growing season April-October from 2018 to 2022 and their impact, on natural and forest-reclaimed pastures of the sandy Bazhigan massif of Northwestern Caspian Sea. Pasture productively was negatively correlated with the temperature and positively correlated with the precipitation. The relationship between hydrothermal coefficient (HTC) and productivity of different types of pastures has been established with coefficient of determination of R2 of 0.765 under pasture with different density and R2 of 0.879 under natural pasture. Results showed that the atmospheric humidification is the determining factor of stable pasture productivity in the conditions of climate change in the arid zone of Russia.
{"title":"Relationship between hydrothermal coefficient (HTC) and productivity of pastures in the arid zone of Northwestern Caspian Sea","authors":"L. Rybashlykova, S. N. Sivceva, T. F. Mahovikova","doi":"10.54386/jam.v25i3.2220","DOIUrl":"https://doi.org/10.54386/jam.v25i3.2220","url":null,"abstract":"In the arid zone, one of the ways to provide animals with feed is the organization of forested pastures, the productivity of which largely depends on weather conditions. Our study analyzes changes in meteorological conditions and hydrothermal coefficient (HTC) during the growing season April-October from 2018 to 2022 and their impact, on natural and forest-reclaimed pastures of the sandy Bazhigan massif of Northwestern Caspian Sea. Pasture productively was negatively correlated with the temperature and positively correlated with the precipitation. The relationship between hydrothermal coefficient (HTC) and productivity of different types of pastures has been established with coefficient of determination of R2 of 0.765 under pasture with different density and R2 of 0.879 under natural pasture. Results showed that the atmospheric humidification is the determining factor of stable pasture productivity in the conditions of climate change in the arid zone of Russia.","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":"45874872","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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