Investigating the mitigation of climate change effects using different carbon footprint calculators of greenhouse gas emissions from agricultural activities in dry areas.

WHETHER IMPROVED LAND USAGE PATTERNS AND LAND MANAGEMENT PRACTICES IMPROVE SOIL CARBON SEQUESTRATION AND MITIGATION OF CARBON EMISSIONS IN DRY AREAS? A RESEARCH PROPOSAL
Name of the Professor
Course
15th February 2018
Whether Improved Land usage patterns and Land Management Practices Improve Soil Carbon Sequestration and Mitigation of Carbon Emissions in Dry Areas? A Research Proposal
Introduction
Background
The growing menace of global warming over the past decades has raised significant concerns across policymakers, environmentalists, federal governments, and ecologists around the world (Post et al., 2001, p.73, Rypdal, and Winiwarter, 2001, p.107, Winiwarter and Rypdal, 2001, p. 5425). Greenhouse emissions and the resultant changes in global climatic patterns is well-recognized (Bernoux et al., 2005, p. 31, Millar, C., Stephenson, N., & Stephens, 2007, p.2145). Hence, there has been a global consensus in reducing carbon emissions into the atmosphere (Baumert, Herzog, and Pershing, 2005, n.p., Cherubini et al., 2009, p.434, Salome et al., 2010, p. 416). Different authors have highlighted the importance of innovative and improved land-management practices in reducing carbon emissions into the atmosphere (Veldkamp et al., 2001, p.1, Wang, 2005, p.739, Birdsey, Pregitzer, & Lucier, 2006, p. 1461). Chuai et al. (2015) acknowledged that land use change or improved land-management practices not only influence carbon storage in terrestrial ecosystems but indirectly affects carbon emissions that stem from anthropogenic causes (p.

77). Integrated land management practices may pave the roadmap in reducing carbon emissions and the menace of global warming in the coming future (Cubasch et al., 2001, p. 526, Henault et al., 1998, p. 299, Lapola et al., 2010, p. 3388, Weber and Mathews, 2008, p. 3508). Such assumptions stem from the promises that superior land management practices has in offer (Fontaine et al., 2007, p.277, Lal, 2006, p.197). The major aim of any land management practice in mitigating carbon emissions into the atmosphere relies on increased sequestration of carbon into the soil or into the trees. Both trees and soils are considered as potent natural carbon sinks, which can significantly reduce the emission of carbon and its compounds into the atmosphere (Bellamy et al., 2005, p. 245, Pouyat et al., 2002, p.107, Powlson, Whitmore, and Goulding, 2011, p. 42, Nowak and Crane, 2002, p. 381, Bourne, 2009, p. 49).
Chuai et al. (2015) explored the data on energy consumption, industrial productions, waste management, soil organic carbon content, vegetation statistics, and land-use images to estimate total carbon emissions in coastal Jiangsu, China (p.77). The authors assigned carbon emission items to different land use types and constructed a linear regression model of carbon emissions on the respective variables. Such regression models have been successfully implemented by different authors (Ramirez et al., 2008, p. 8263). Chuai et al. (2015) showed that carbon emissions in coastal Jiangsu were significantly higher compared to rest of China. The authors highlighted that energy consumption contributed to the most of such emissions, while contribution by animals and cattle seconded the list (p.77).
Chuai et al. (2015) further showed that urban land accounted significantly higher compared to their rural counterparts in contributing to the carbon emissions (p.77). The authors noted that from 1985 to 2000 most of the croplands in coastal Jiangsu gave head to built-up land and also accounted for the largest percentage of total transferred area which accounted for the increase in carbon emissions between the referred time-periods. Moreover, the authors contended that the limitation on urban land usage would play a key role in mitigating carbon emissions into the atmosphere. Chuai et al. (2015) emphasized that optimal land usage practices could control and reduce carbon emissions in the future (p.77). Hence, the authors voiced that policymakers and land-usage managers should implement stringent policies for ensuring optimized land usage patterns in coastal Jiangsu. On the other hand, studies suggest that limiting urbanization and promoting forestry is challenging (Ramachandran et al., 2009, p.172, Van der Werf et al., 2009, p. 737). Daniels (2010, p.463) showed that fiscal and procedural incentives for controlling carbon emissions could be one of the solutions in promoting forestry and limiting the possibilities of urbanization in non-productive lands. However, the author did highlight the success of such initiatives in mitigating the intensity of carbon emissions in California, United States.
Rationale
Although different studies have highlighted the importance of superior land-usage pattern in reducing the intensity of carbon emissions, however; very few studies have explored the role of monitoring carbon emission as a function of integrated land management and its impact on mitigation of greenhouse gases. Across various developed and developed nations, agricultural land or unutilized land is giving head to human habitations and the United Kingdom is no exception to such observation. Such changes in land usage structure could have an ecological impact, which may influence carbon footprints and greenhouse emissions from the specific land. Hence, there has been a global initiative to mitigate greenhouse emissions stemming from altered land usage. Carbon footprint calculator is one such modality that can help to predict or estimate greenhouse emissions as function of different land usage activities. Such calculators also estimate the carbon footprints that stem from energy usage, crop dynamics, or livestock management. Hence, carbon footprint calculators are effective in estimating the total carbon footprint that stems from different activities as a function of land or usage. Different studies have endorsed the use of carbon footprint calculators in estimating carbon emissions (Hillier et al., 2011, n.p, Virtanen et al., 2010, n.p, Saif et al., 2015, p.633, Akhtar et al., 2013, p. 302). From a broader perspective, the proposed study could be considered a stepping stone in mitigating the menace of global warming. The findings of the proposed study could help policy makers, agricultural scientists, land reformists, industrialists, governments, and allied stakeholders in reducing carbon emissions into the atmosphere. The proposed study would also help to explore the validity and reliability of online carbon-emission calculators that are routinely used to assess carbon emissions from land-usage activities. Such findings would help concerned stakeholders to adopt the online carbon-emission calculator that exhibits the highest goodness of fit model in relation to the independent variables that influence carbon emissions.
1.3. Aims and Objectives
The proposed study aims to explore the following research questions. The main research question that would be explored in this present study is “whether changes in land usage pattern influence greenhouse emission in the United Kingdom?” However, different sub-research questions would be also explored to answer the main research question in a comprehensive manner. The sub-research questions and their respective hypothesis that would be explored in the proposed study are as follows:
Whether improved land management practices (for example, re-vegetation of degraded/unproductive lands, cop rotation, and manipulation of livestock feeds) could significantly ensure enhanced soil carbon sequestration and mitigation of climatic changes caused due to increased greenhouse emissions?”
Ha = Improved land management practices (for example, re-vegetation of degraded/unproductive lands, cop rotation, and manipulation of livestock feeds) could significantly ensure enhanced soil carbon sequestration and mitigation of climatic changes caused due to increased greenhouse emissions (p<0.05).
Whether changes in land usage pattern in the United Kingdom significantly alter greenhouse emissions as estimated by carbon footprint calculators?
Ha= Changes in land usage pattern in the United Kingdom significantly alter greenhouse emissions as estimated by carbon footprint calculators (p<0.05).
Whether changes in land usage pattern in the United Kingdom significantly alter greenhouse emissions as reported in the national databases?
Ha= Changes in land usage pattern in the United Kingdom significantly alter greenhouse emissions as reported in the national databases?
Whether the greenhouse emissions reported in the national databases significantly correlate with the greenhouse emissions estimated through the carbon footprint calculators?
Ha= greenhouse emissions reported in the national databases significantly correlate with the greenhouse emissions estimated through the carbon footprint calculators (p<0.05)
Whether greenhouse emissions that are estimated by the carbon footprint calculators could be significantly predicted from the land usage patterns?
Ha= greenhouse emissions that are estimated by the carbon footprint calculators could be significantly predicted from the land usage patterns (p<0.05).
Whether greenhouse emissions that are reported in national databases could be significantly predicted from the land usage patterns?
Ha= Greenhouse emissions that are reported in national databases could be significantly predicted from the land usage patterns (p< 0.05).
Whether there is significant difference in goodness of fit between the two predictions?
Ha= There is significant difference in goodness of fit between the two predictions (p< 0.05).
2.0. Methodology
2.1. Overview
The proposed research would be based on a methodology triangulation approach. The methodology triangulation would include a systemic review of evidence-based literature, a content analysis (based on the data that are available in government websites, industrial reports, and public documents) and an integration of the systematic review and the content analysis. Different authors have emphasized the importance of integrating primary data with secondary data in addressing a specific research question. Such strategy improves the validity and reliability of a specific research or study. Moreover, such analyses help to address the confounding effects of different variables on the specific end-points that are explored in a proposed research. The methodology triad for the proposed study is presented in fig 1.
Integration and Appraisal of the Primary and Secondary data
Content Analysis
(Primary Data)
Systematic Review
(Secondary Data)

Fig 1: Methodology triangulation for the Proposed Study
The different land management practices that would be explored in the systemic review and the content analysis would be broadly classified under four categories; Crop Data, Livestock Data, Land Usage Data, and Greenhouse Emission Data. Such categorization is based upon the deliverables of the carbon footprint calculators, the environmental reports published by the federal governments for the general public, the national databases, and the end-points considered for the proposed study. The Crop data that would be assessed will include harvested and marketable yield product weights, area of cultivated land, manipulation of cultivated land (reducing the depth of soil cultivation to optimize energy use), status of fertilizer application (including both type and rate), status of pesticide applications (both time and rate), status of crop rotation, energy use (type and amount in KWH), transportation logistics, methods applied to improve carbon sequestration is soils and vegetation (for example use of non-productive land with re-vegetation). The Livestock data would include herd pr flock size, feed and manipulation of such feeds (for example use of feed additives), manure management strategies (for example application of springtime manures to improve nutrient availability), energy use (type and amount in KWH), and transportation logistics. The Land usage data would be primarily obtained from the national databases of the respective countries. The greenhouse emissions would be collected from the federal websites of the respective countries across different time periods.
2.2. Tasks
2.2.1. Systematic review of Literature
The systemic review of literature would be based on a keyword search strategy. Different keywords would be connected with Boolean connectors to retrieve the appropriate articles. The keywords and Boolean connectors that would be used for retrieving the relevant articles from different websites would include greenhouse emissions OR carbon footprints AND land usage OR land management AND Scotland OR England OR Wales AND Ireland. Articles that are published in English and during the last twenty years would be only included for the review. The retrieved articles would be sorted and appraised based on a thematic analysis. The thematic analysis would include country-specific articles on land usage, land management, greenhouse emissions and carbon footprint estimation.
2.2.2. Content Analysis
The greenhouse emission data, the land-usage data, and the regulatory guidelines on mitigation of greenhouse houses for the selected countries (England, Wales, Scotland, and Ireland) would be obtained for different time-periods from the websites and national databases of the respective countries.
2.2.2.1. Land usage data
For example, the land usage data for England would be retrieved from: https://www.gov.uk/government/statistics/land-use-change-statistics-2015-to-20162.2.2.2. Greenhouse Emission Data
The greenhouse emission statistics for the United kingdom would be obtained from;
https://www.gov.uk/government/collections/uk-greenhouse-gas-emissions-statistics2.2.2.3. Carbon footprint Data
The country-specific land usage patterns would be fed into the carbon footprint calculators would be used to estimate the carbon emissions (footprints). The carbon footprint calculators that would be used in the proposed research include https://www.carbonfootprint.com/calculator.aspx, https://www.carbonfootprint.com/calculator.aspx, CFF carbon calculator (http://www.cffcarboncalculator.org.uk/), and EX-ACT (http://www.fao.org/tc/exact/ex-act-home/fr/) (Colomb et al., 2012, p.4).
2.3. Statistical Tests and Software
The findings of the proposed study would be presented as descriptive statistics. Such descriptive statistics would be further used to conduct different inferential statistics. The inferential statistics that would be used in the proposed study are multiple comparison tests (t-tests and chi-square tests), correlation analysis, logistic regression analysis, and ANOVA. The t-tests and chi-square tests would be used to compare the greenhouse emissions and carbon footprints for changes in the land usage pattern for across different time periods. For this present study, the land usage and greenhouse emission data of the UK would be compared between 2015-2016 and 2016-2017. To recall, the data on green house emissions, land management practices, and regulatory standards for different countries would be obtained from the national databases. Pearson’s correlation coefficients would be estimated for exploring the relation between the greenhouse emissions and carbon emissions as estimated by the carbon footprint calculators. Logistic regression models would be constructed to predict the greenhouse emissions or carbon footprints as a function of land usage patterns for each country. The land usage patterns that would be explored would be categorized either qualitatively or quantitatively. The qualitative land-management practices would be assigned dummy ranks. Such assumptions would help to interpret the qualitative end-points quantitatively and also for framing the logistic regression models.
The logistic regression model would help to identify the land-management practices that translated into reduced carbon emissions or vice-versa. The statistical tests of inference considered for the proposed study would be interpreted at the 0.05 level of significance. The statistical tests of inference would be used to test the main and sub-research questions. Finally, the results of such tests would be interpreted based on the acceptance or rejection of the null (Ho) and alternative (Ha) hypothesis respectively. All statistical tests for the resent study would be conducted with the IBM-SPSS (version 18) software.
3.0. Project Management
3.1. Schedule
The timeline for the proposed research is estimated to be three months. The schedule for the proposed research is broadly divided into three tasks; systemic review of evidence-based literature, content analysis from national databases, and an integration of the systematic review and the content analysis.
3.2. Milestones
The different milestones for the proposed research are presented as a function of time and key deliverables in Table 1.
  1st Month 2nd month 3rd month
Systematic review    
Content Analysis      
Integration of the systematic review and content analysis    
Appraisal of primary and secondary data  
Writing of final dissertation  
Table 1: Represents the Gantt chart on Milestones and deliverables for the proposed study.
3.3. Deliverables
The proposed research would help to identify the land usage pattern that translated into superior mitigation of carbon emissions. Moreover, the study would also help to identify the carbon emission tools that are more valid and reproducible.
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