Pub Date : 2017-12-16DOI: 10.9734/BJECC/2017/37999
Mallikarjun G. Handiganoor, S. B. Patil, S. Vasudevan
{"title":"Response of Pigeonpea (Cajanus cajan L.) to Seed Polymerization with Micronutrients and Foliar Spray at Different Growth Stages","authors":"Mallikarjun G. Handiganoor, S. B. Patil, S. Vasudevan","doi":"10.9734/BJECC/2017/37999","DOIUrl":"https://doi.org/10.9734/BJECC/2017/37999","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131478960","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}
Pub Date : 2017-12-16DOI: 10.9734/BJECC/2017/38328
Salmawati, K. Sasaki, S. Yuichi
Aims: To estimate the surface CO2 flux derived from CO2 concentration profiles and to validate the results by previous data of surface CO2 flux obtained from the measurements using close-chamber method. Study Design: The measurement of soil CO2 concentration profile, soil properties, and soil temperature was carried out to estimate surface CO2 flux using the derived model of mass balance equation. The results were subsequently compared with measurements of surface CO2 flux using close-chamber method. Place and Duration of Study: INAS field located in Ito Campus of Kyushu University (Japan) from November 2015 to March 2016. Methodology: CO2 gas was sampled in four different depths to analyze its concentration within the soil layer. Soil temperature was monitored throughout the measurement and soil properties such as density, porosity and moisture content were measured as well to estimate the diffusion rate. Derived from mass balance equation, the surface CO2 flux was estimated. It was validated using the previous measurement data of surface CO2 flux using close-chamber method that had been conducted formerly at the same location. Results: A total of seven measurements of soil CO2 concentration profile showed that the CO2 concentration increased with soil depth and it was fitted with logarithmic trend (R 2 = 0.981 in average). A range of CO2 concentration values was measured at each depth, i.e., 1300 to 8700 ppm at 0.1 m depth; 2500 to 10800 ppm at 0.2 m depth; 4200 to 13200 ppm at 0.3 m depth; and Original Research Article Salmawati et al.; BJECC, 7(4): 214-222, 2017; Article no.BJECC.2017.017 215 5800 to 16500 ppm at 1.0 m depth. High CO2 concentration in 0.1 m soil depth indicated high surface CO2 flux. Conclusions: Soil CO2 concentration in INAS field increased following a logarithmic trend. Based upon this trend, an equation to estimate the surface CO2 flux was proposed using derived model from mass balance equation and gas diffusion model. The estimated surface CO2 flux was compared and showed a good agreement with measured one. The equation presented herein is potentially suitable to estimate the surface CO2 flux.
{"title":"Estimating surface CO2 flux based on soil concentration profile","authors":"Salmawati, K. Sasaki, S. Yuichi","doi":"10.9734/BJECC/2017/38328","DOIUrl":"https://doi.org/10.9734/BJECC/2017/38328","url":null,"abstract":"Aims: To estimate the surface CO2 flux derived from CO2 concentration profiles and to validate the results by previous data of surface CO2 flux obtained from the measurements using close-chamber method. Study Design: The measurement of soil CO2 concentration profile, soil properties, and soil temperature was carried out to estimate surface CO2 flux using the derived model of mass balance equation. The results were subsequently compared with measurements of surface CO2 flux using close-chamber method. Place and Duration of Study: INAS field located in Ito Campus of Kyushu University (Japan) from November 2015 to March 2016. Methodology: CO2 gas was sampled in four different depths to analyze its concentration within the soil layer. Soil temperature was monitored throughout the measurement and soil properties such as density, porosity and moisture content were measured as well to estimate the diffusion rate. Derived from mass balance equation, the surface CO2 flux was estimated. It was validated using the previous measurement data of surface CO2 flux using close-chamber method that had been conducted formerly at the same location. Results: A total of seven measurements of soil CO2 concentration profile showed that the CO2 concentration increased with soil depth and it was fitted with logarithmic trend (R 2 = 0.981 in average). A range of CO2 concentration values was measured at each depth, i.e., 1300 to 8700 ppm at 0.1 m depth; 2500 to 10800 ppm at 0.2 m depth; 4200 to 13200 ppm at 0.3 m depth; and Original Research Article Salmawati et al.; BJECC, 7(4): 214-222, 2017; Article no.BJECC.2017.017 215 5800 to 16500 ppm at 1.0 m depth. High CO2 concentration in 0.1 m soil depth indicated high surface CO2 flux. Conclusions: Soil CO2 concentration in INAS field increased following a logarithmic trend. Based upon this trend, an equation to estimate the surface CO2 flux was proposed using derived model from mass balance equation and gas diffusion model. The estimated surface CO2 flux was compared and showed a good agreement with measured one. The equation presented herein is potentially suitable to estimate the surface CO2 flux.","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124474347","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}
Pub Date : 2017-12-16DOI: 10.9734/BJECC/2017/35336
M. Siddique, J. Sultana, M. Abdullah, K. Azad
Revised universal soil loss equation (RUSLE2) was applied to assess the soil loss in an agricultural field of Uccle, Belgium. Determination of soil loss required lots of information and data sets from various variables related to RUSLE2 in different formats scales. The effect of each factor affection soil loss and or erosion was estimated. Soil loss was influenced by soil properties (textural class), rainfall, topography (slope gradient), crop management and conservation practices (soil cover, type of tillage). The influence of erosion control practices (up and down slope ploughing, perfect contouring and buffer strip) on soil loss was also analysed. Results indicated that among three textural class of soils highest loss found in the silty soil followed by loamy sand and clayey soil had the least soil loss. This showed that the silty soil had the highest erodibility. It was evident from the modelling that as the slope steepness and slope length increased the soil loss increased, but when the slope steepness and slope length were reduced the soil loss decreased. Soil cover and tillage contributed greatly in soil erosion. The bare soil (silt) had the highest soil loss 22 Mg hayr but the dense grass cover had the lowest soil loss of 0.034 Mg ha yr. While the conventional tillage had higher soil loss 15 Mg hayr compared with the Original Research Article Siddique et al.; BJECC, 7(4): 252-260, 2017; Article no.BJECC.2017.020 253 conservation tillage 11 Mg hayr. In case of conservation practices, filter strips had the lowest soil loss from detachment of 4.4 Mg hayr but the most important is that despite the detachment very little soil leaves the field as indicated by the slope delivery 0.00092 Mg hayr. Ploughing up and down the slope resulted in the highest soil (39 Mg hayr) loss and should be discouraged. These results will be used for soil protection measures and land use planning in agriculture.
{"title":"Modelling of Soil Loss through RUSLE2 for Soil Management in an Agricultural Field of Uccle, Belgium","authors":"M. Siddique, J. Sultana, M. Abdullah, K. Azad","doi":"10.9734/BJECC/2017/35336","DOIUrl":"https://doi.org/10.9734/BJECC/2017/35336","url":null,"abstract":"Revised universal soil loss equation (RUSLE2) was applied to assess the soil loss in an agricultural field of Uccle, Belgium. Determination of soil loss required lots of information and data sets from various variables related to RUSLE2 in different formats scales. The effect of each factor affection soil loss and or erosion was estimated. Soil loss was influenced by soil properties (textural class), rainfall, topography (slope gradient), crop management and conservation practices (soil cover, type of tillage). The influence of erosion control practices (up and down slope ploughing, perfect contouring and buffer strip) on soil loss was also analysed. Results indicated that among three textural class of soils highest loss found in the silty soil followed by loamy sand and clayey soil had the least soil loss. This showed that the silty soil had the highest erodibility. It was evident from the modelling that as the slope steepness and slope length increased the soil loss increased, but when the slope steepness and slope length were reduced the soil loss decreased. Soil cover and tillage contributed greatly in soil erosion. The bare soil (silt) had the highest soil loss 22 Mg hayr but the dense grass cover had the lowest soil loss of 0.034 Mg ha yr. While the conventional tillage had higher soil loss 15 Mg hayr compared with the Original Research Article Siddique et al.; BJECC, 7(4): 252-260, 2017; Article no.BJECC.2017.020 253 conservation tillage 11 Mg hayr. In case of conservation practices, filter strips had the lowest soil loss from detachment of 4.4 Mg hayr but the most important is that despite the detachment very little soil leaves the field as indicated by the slope delivery 0.00092 Mg hayr. Ploughing up and down the slope resulted in the highest soil (39 Mg hayr) loss and should be discouraged. These results will be used for soil protection measures and land use planning in agriculture.","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117014658","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}
Pub Date : 2017-12-16DOI: 10.9734/BJECC/2017/37044
Yuehua Wu, Xiaoying Sun, E. Chan, Shanshan Qin
In some environmental problems, it is required to find out if new influences (e.g., new influences on the ozone concentration) occurred in one area of the region (named as a treatment area) have affected the measurements there substantially. For convenience, the area of the region that is free of influences is named as the control area. To tackle such problems, we propose a change-point detection approach. We first introduce a general spatio-temporal autoregressive (GSTAR) model for the environmental data, which takes into account effects of different spatial location surroundings, seasonal cyclicities, temporal correlations among observations at the same locations and spatial correlations among observations from different locations. An EM-type algorithm is provided for estimating the parameters in a GSTAR model. We then respectively model the data collected from the treatment and control areas of the region by the GSTAR models. If new influences occurred in the treatment area are not negligible, there should be detectable changes in the time-dependent regression coefficients in the GSTAR model for that area compared to those in the GSTAR model for the control area. A change-point detection method can be applied to the differences of regression coefficient estimates of these two models. We illustrate our method through one real data example of daily ozone concentration measurements and one simulated data example with two scenarios.
{"title":"Detecting Non-negligible New Influences in Environmental Data via a General Spatio-temporal Autoregressive Model","authors":"Yuehua Wu, Xiaoying Sun, E. Chan, Shanshan Qin","doi":"10.9734/BJECC/2017/37044","DOIUrl":"https://doi.org/10.9734/BJECC/2017/37044","url":null,"abstract":"In some environmental problems, it is required to find out if new influences (e.g., new influences on the ozone concentration) occurred in one area of the region (named as a treatment area) have affected the measurements there substantially. For convenience, the area of the region that is free of influences is named as the control area. To tackle such problems, we propose a change-point detection approach. We first introduce a general spatio-temporal autoregressive (GSTAR) model for the environmental data, which takes into account effects of different spatial location surroundings, seasonal cyclicities, temporal correlations among observations at the same locations and spatial correlations among observations from different locations. An EM-type algorithm is provided for estimating the parameters in a GSTAR model. We then respectively model the data collected from the treatment and control areas of the region by the GSTAR models. If new influences occurred in the treatment area are not negligible, there should be detectable changes in the time-dependent regression coefficients in the GSTAR model for that area compared to those in the GSTAR model for the control area. A change-point detection method can be applied to the differences of regression coefficient estimates of these two models. We illustrate our method through one real data example of daily ozone concentration measurements and one simulated data example with two scenarios.","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122760769","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}
Pub Date : 2017-12-16DOI: 10.9734/BJECC/2017/36547
V. Venkataramana, A. Fauzie, Sreenivasa
{"title":"Characterization of Particulate Matter in Urban Environments and Its Effects on the Respiratory System of Mice","authors":"V. Venkataramana, A. Fauzie, Sreenivasa","doi":"10.9734/BJECC/2017/36547","DOIUrl":"https://doi.org/10.9734/BJECC/2017/36547","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058591","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}
Pub Date : 2017-01-10DOI: 10.9734/BJECC/2017/35017
Tyhra Carolyn Kumasi
{"title":"Conflict Resolution and Collective Action for Ecological Restoration in the Highlands of Northern Ethiopia","authors":"Tyhra Carolyn Kumasi","doi":"10.9734/BJECC/2017/35017","DOIUrl":"https://doi.org/10.9734/BJECC/2017/35017","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537783","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}
Pub Date : 2017-01-10DOI: 10.9734/bjecc/2017/31732
Gurdeep Singh, B. Lone, S. Qayoom, Purshotam Singh, Z. Dar, Sandeep Kumar, Asma Fayaz, K. Singh, A. Hussain
{"title":"Soil Water and Nitrogen Balance Study of Maize Using CERES Maize Model in DSSAT","authors":"Gurdeep Singh, B. Lone, S. Qayoom, Purshotam Singh, Z. Dar, Sandeep Kumar, Asma Fayaz, K. Singh, A. Hussain","doi":"10.9734/bjecc/2017/31732","DOIUrl":"https://doi.org/10.9734/bjecc/2017/31732","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132033486","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}
Pub Date : 2017-01-10DOI: 10.9734/BJECC/2017/34589
Alan Smith
{"title":"An Analysis of Climate Forcings from the Central England Temperature (CET) Record","authors":"Alan Smith","doi":"10.9734/BJECC/2017/34589","DOIUrl":"https://doi.org/10.9734/BJECC/2017/34589","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130569333","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}
Pub Date : 2017-01-10DOI: 10.9734/BJECC/2017/27897
K. K. D. A. Serique, J. Monteiro, A. Darwich, F. Aprile
{"title":"Pairing Quantitative and Qualitative Analyses during Flooding Event in Mamiá Lake (Amazon River)","authors":"K. K. D. A. Serique, J. Monteiro, A. Darwich, F. Aprile","doi":"10.9734/BJECC/2017/27897","DOIUrl":"https://doi.org/10.9734/BJECC/2017/27897","url":null,"abstract":"","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122954875","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}
Pub Date : 2017-01-10DOI: 10.9734/bjecc/2017/33843
Landon Stevens, A. Wardle, Ryan M. Yonk
Increasingly, countries are gathering to address concerns surrounding climate change. The 2015 United Nations Conference of Parties, COP21, saw the emergence of a landmark agreement for collective global action. The tagline arising from this agreement was "Long live the planet. Long live humanity. Long live life itself." Indeed, this agreement should positively benefit the planet, but comes with myriad costs associated with such efforts. Just how these agreements are funded, managed, and regulated are crucial to understanding the broader impacts on individual parties. This paper evaluates the impacts of trade-offs made when considering long-term climate goals over short-term well-being for individual nations and citizens. The paper identifies considerations for officials in countries facing issues associated with energy poverty when crafting global climate agreements (GCAs). The primary question this paper asks is: “What role, if any, should poorer nations play in global climate agreements?” After reviewing the status of global CO2 emissions and the efficacy of GCAs, we argue that involving developing countries in GCAs is not beneficial in accomplishing global CO2 mitigation goals. In fact, when low-income countries are party to GCAs their role is either purely symbolic or works counter to other development goals.
{"title":"Analyzing the Role of Poor and Developing Nations in Global Climate Agreements","authors":"Landon Stevens, A. Wardle, Ryan M. Yonk","doi":"10.9734/bjecc/2017/33843","DOIUrl":"https://doi.org/10.9734/bjecc/2017/33843","url":null,"abstract":"Increasingly, countries are gathering to address concerns surrounding climate change. The 2015 United Nations Conference of Parties, COP21, saw the emergence of a landmark agreement for collective global action. The tagline arising from this agreement was \"Long live the planet. Long live humanity. Long live life itself.\" Indeed, this agreement should positively benefit the planet, but comes with myriad costs associated with such efforts. Just how these agreements are funded, managed, and regulated are crucial to understanding the broader impacts on individual parties. This paper evaluates the impacts of trade-offs made when considering long-term climate goals over short-term well-being for individual nations and citizens. The paper identifies considerations for officials in countries facing issues associated with energy poverty when crafting global climate agreements (GCAs). The primary question this paper asks is: “What role, if any, should poorer nations play in global climate agreements?” After reviewing the status of global CO2 emissions and the efficacy of GCAs, we argue that involving developing countries in GCAs is not beneficial in accomplishing global CO2 mitigation goals. In fact, when low-income countries are party to GCAs their role is either purely symbolic or works counter to other development goals.","PeriodicalId":373103,"journal":{"name":"British Journal of Environment and Climate Change","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134024419","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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