Carbon Footprint Of Agriculture Research Articles

The Future of Agriculture in A Carbon Constrained World

In a world grappling with the far-reaching consequences of climate change and a growing imperative to lower emissions of greenhouse gases, agriculture’s potential finds itself at a critical juncture. This paper explores the intricate dynamics of agriculture in a carbon-constrained world, presenting a nuanced analysis of the multifaceted challenges and opportunities that lie ahead. With a focus on sustainable practices, carbon sequestration, and innovative technologies, this study elucidates the pathways through which agriculture can transition towards a low-carbon and climate-resilient future. From precision farming and agroforestry to advanced breeding techniques and digital agriculture, a spectrum of strategies emerges to mitigate the carbon footprint of agriculture while concurrently enhancing productivity and food security. Moreover, the paper underscores the indispensable role of policy frameworks, international cooperation, and public-private partnerships in ushering in a transformative era for agriculture that aligns harmoniously with global climate goals. This review explores carbon foot printing methodologies in agriculture and their implications for sustainable practices. Additionally, it highlights role of carbon foot printing in informing decision-makers, examines emerging trends like precision agriculture, and addresses associated challenges. This review identifies these gaps and offers recommendations for future research. In elucidating these key themes, this paper advances to a thorough comprehensive understanding of the imperative to embrace innovation and sustainability as guiding principles for the future of farming in a carbon-constrained world.

Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.

Agricultural carbon footprint, energy utilization and economic quality: What causes what, and where?

China, being a significant agricultural country and the world's greatest carbon emitter, is today faced with the combined problems of improving agricultural economic quality and reducing carbon emissions, all while dealing with resource constraints. Exploring the nexus and internal mechanism between agricultural carbon footprint (CF), energy utilization and economic quality thereby implies practical significance. For these reasons, the current study intends to explore the nexus and internal mechanism between agricultural CF, energy utilization, and economic qualities of main grain-planting provinces in China. Multi-approaches like, Autoregressive Distributed Lag (ARDL) model, the Granger Causality Test based on the Vector Error Correction Model (VECM), and Impulse Response and Variance Decomposition (IRVD) methods, and a time-series data in the duration from 1997 to 2019 are employed. The findings indicate that: 1) Agricultural CF, energy utilization and economic quality demonstrate an increasing trend; 2) There exists environmental Kuznets curve (EKC) in agricultural CF in China's main grain-planting provinces; 3) Energy utilization negatively influenced agricultural CF in both the short-term and long-term; 4) In the short-term and long-term, a bidirectional causality exist among agricultural CF and economic quality, as well as a unidirectional causality from energy utilization to agricultural CF and economic quality.

Enhanced plant growth in the presence of earthworms correlates with changes in soil microbiota but not nutrient availability

Earthworms enhance plant growth but the precise mechanism by which this occurs is not known. An understanding of the mechanism could potentially support changes in agricultural management reducing fertiliser usage and therefore costs and the carbon footprint of agriculture. We conducted a factorial experiment in which 5 strains of wheat were grown in the presence and absence of earthworms under regular watering and droughted conditions. The different wheat strains all responded in a similar fashion. Plant biomass was greater in the presence of earthworms and under regular watering. The presence of earthworms reduced the impact of drought on plant biomass and also slowed down the rate of drying of the droughted soils. Plant nutrient content (N, P, Si) showed no consistent pattern with treatments but plant total N, P and Si mirrored plant biomass and decreased in the order earthworm-present watered > earthworm-present droughted > earthworm-absent watered > earthworm-absent droughted. Nutrient availability in the soil, as assessed by chemical extractions showed no consistent pattern with treatments. Differential gene expression of plants was greater between watering treatments than between earthworm treatments. Genes that were differentially expressed between the earthworm treatments predominantly related to plant defences, abiotic stress and control of plant growth though a couple were linked to both nitrogen cycling and stress responses. The soil microbiome of the earthworm-present treatments was more associated with nutrient-rich environments, the promotion of plant growth and the suppression of plant pathogens whilst that of the earthworm-absent treatments included a variety of plant pathogens. Our data are consistent with enhanced plant growth being due to changes in the microbiome brought about by earthworm processing of the soil rather than changes in nutrient availability directly due to earthworm activity.

Selected Developments in Biotechnology Law and the Biotechnology Industry

Selected Developments in Biotechnology Law and the Biotechnology Industry

Reducing Carbon Footprint of Agriculture—Can Organic Farming Help to Mitigate Climate Change?

In the face of a changing climate, intensive efforts are needed for limiting the global temperature increase to 1.5 °C. Agricultural production has the potential to play an important role in mitigating climate change. It is necessary to optimize all of the agricultural practices that have high levels of greenhouse gas (GHG) emissions. Among the plant production processes, mineral fertilization is of the greatest importance in the formation of the carbon footprint (CF) of crops. There are many possibilities for reducing GHG emissions from the application of fertilizers. Further benefits in reducing the CF can be obtained through combining tillage treatments, reduced and no-till technologies, and the cultivation of catch crops and leguminous plants. Organic farming has the potential for reducing GHG emissions and improving organic carbon sequestration. This system eliminates synthetic nitrogen fertilizers and thus could lower global agricultural GHG emissions. Organic farming could result in a higher soil organic carbon content compared to non-organic systems. When used together with other environmentally friendly farming practices, significant reductions of GHG emissions can be achieved.

Agricultural carbon footprint and food security: an assessment of multiple carbon mitigation strategies in China

PurposeTo achieve the dual goals of decarbonization and food security, this paper examines China's carbon footprint reduction in 2050 based on current mitigation strategies.Design/methodology/approachConsidering publications as featured evidence, this study develops an investigation of agricultural decarbonization in China. First, the authors summarize the mitigation strategies for agricultural greenhouse gas (GHG) emissions in the existing literature. Second, the authors demonstrate the domestic food production target in 2050 and the projection target's projected life-cycle-based GHG emissions at the commodity level. Lastly, the authors forecast China's emission removal in the agri-food sector in 2050 concerning current mitigation strategies and commodity productions. The authors highlight the extent to which each mitigation strategy contributes to decarbonization in China.FindingsPractices promoting sustainable development in the agri-food sector significantly contribute to GHG emission removal. The authors find mitigation strategies inhibiting future GHG emissions in the agri-food sector comprise improving nitrogen use efficiency in fertilizers, changing food consumption structure, manure management, cover crops, food waste reduction, dietary change of livestock and covered manure. A 10% improvement in nitrogen use efficiency contributes to 5.03% of GHG emission removal in the agri-food sector by 2050. Reducing food waste and food processing from 30% to 20% would inhibit 1.59% of the total GHG emissions in the agri-food sector.Originality/valueThis study contributes to policy discussions by accounting for agricultural direct and indirect emission components and assessing the dynamic changes in those related components. This study also extends existing research by forecasting to which extent the decarbonization effects implemented by current mitigation strategies can be achieved while meeting 2050 food security in China.

Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect

Due to the combined effects of carbon emission and carbon sink, agriculture is acknowledged as an essential contributor to achieve the Chinese government's carbon neutrality goal of 2060, and carbon footprint (CF) and carbon footprint intensity are substantial indicators to reveal the carbon emission level. For these reasons, the Theil index technique and extended STIRPAT model were employed to evaluate their spatiotemporal heterogeneity and influencing factors using panel data from 31 provinces for the period 1997–2019. The findings revealed that the CF showed an increasing trend with an annual growth rate of 24.6 %. The carbon footprint intensity (CFI) indicated an evident spatiotemporal heterogeneity and transferred over time, with an average growth rate of 19.82 %. The CFI Theil index and its contribution rate both confirmed that intra-regional difference is the main source of the overall difference, among which, the CFI Theil index displayed the distribution feature of “western (11.50 %) > central (11.12 %) > eastern (10.56 %) > northeast (6.61 %). The contribution rate of CFI illustrated the spatial pattern of “eastern (33.74 %) > central (21.07 %) > western (19.87 %) > northeast (5.24 %). Furthermore, the influencing effects of GDP per capita, planting structure, population density and urbanization level on CF and CFI also demonstrate evident spatiotemporal heterogeneity.

Benefits and Risks of Intercropping for Crop Resilience and Pest Management.

To combat climate change, farmers must innovate through ecological intensification to boost food production, increase resilience to weather extremes, and shrink the carbon footprint of agriculture. Intercropping (where alternative crops or noncrop plants are integrated with cash crops) can strengthen and stabilize agroecosystems under climate change by improving resource use efficiency, enhancing soil water holding capacity, and increasing the diversity and quality of habitat for beneficial insects that provide pollination services and natural pest control. Despite these benefits, intercropping has yet to be widely adopted due to perceived risks and challenges including decreased crop yield, increased management complexity, a steep learning curve for successful management, and increased susceptibility to pests. Here, we explore the major benefits of intercropping in agricultural systems for pest control and climate resilience reported in 24 meta-analyses, while addressing risks and barriers to implementation. Most studies demonstrate clear benefits of intercropping for weed, pathogen, insect pest control, relative yield, and gross profitability. However, relatively few studies document ecosystem services conferred by intercrops alongside labor costs, which are key to economic sustainability for farmers. In addition to clearer demonstrations of the economic viability of intercropping, farmers also need strong technical and financial support during the adoption process to help them troubleshoot the site-specific complexities and challenges of managing polycultures. Ecological intensification of agriculture requires a more strategic approach than simplified production systems and is not without risks and challenges. Calibrating incentive programs to reduce financial burdens of risk for farmers could promote more widespread adoption of intercropping.

Climate neutral in agricultural production system: a regional case from China.

The concept of climate neutral has been introduced in the agricultural production system to re-examine the connotation of agricultural carbon footprint (CF). According to the integrated accounting framework of the agricultural CF we built, then selected a case from China, and carried out the climate economic effect quantitative analysis of the agricultural production system. The results indicated that CO2 emissions accounted the largest percentage of total carbon emissions by 52.05%, which was driven strongly by the application of agricultural fertilizers and consumption of diesel oil and CH4 emissions (ME) from cattle fed intestinal fermentation, and the driving force behind carbon sequestration was derived from the woody cash crops of carbon sequestration by vegetation and the input of residual carbon from straw returning to field and root stubble in the soil carbon pool. The carbon sink finally realized in the agricultural production system and the agricultural CF index reflected the surplus of 1.801 Mt C in the study area. In addition, we used the indicators of carbon density, carbon intensity, and carbon efficiency to judge the trade-offs of cost-benefit between the agroecosystem and economic system, so as to put forward some potential mitigation strategies for the study area. The mitigative effect of agricultural production system on climate neutral need to be further estimated in a more rigorous manner while controlling for more uncertainties in the future.

Bioremediation: How to Decrease Greenhouse Gas Emissions Through Cattle

Introduction: The concentration of greenhouse gases within the atmosphere is currently on the rise. With the increase in human population comes a growing need for greater food security in order to support our population. The agricultural industry has a carbon footprint comparable to that of other extensive producers such as oil production facilities, waste sectors and industrial processors. This study aims to establish an experimental design to modify the current microbiome in cattle, as cattle farming practices account for a majority of agricultural methane production in North America. Methods: Young Canadian Holstein cattle with developing ruminal microbiomes will be placed into control and treatment groups and studied over the duration of 4 weeks. Ruminococcus hydrogenotrophicus will be inserted into the rumen of the experimental groups of cattle. Results: Results will generate findings on the basis of reducing methanogenic activity among the rumen of cattle, and therefore the results may propose a feasible answer towards the decreasing of the agricultural carbon footprint among the cattle industry. Discussion: The ability to decrease or eliminate the methane produced from agriculture would be a significant finding in current global studies. Through/by combining knowledge from environmental biology, agricultural science, microbiology and animal biology, solutions can be found for reducing the agricultural carbon footprint. Conclusion: The use of bioremediation methods to decrease methane production is a new area of research. By cultivating a microorganism that can compete with the methanogenic bacteria within the rumen of cows, less methane yield is plausible. Ultimately, leading to a proposed answer to curb climate change values to a decreasing end.

Agriculture and animal husbandry increased carbon footprint on the Qinghai-Tibet Plateau during past three decades

Agriculture and animal husbandry are important sources of greenhouse gas (GHG) emission and contribute to climate change, but the carbon sink function of cropland and grassland plays an important role in mitigating climate changes. Based on ecological footprint methodology, this study comprehensively considered carbon emission and carbon sequestration in order to calculate the carbon footprints from agriculture and animal husbandry, and analyzed spatiotemporal distribution of carbon emission, carbon sequestration, and carbon footprint on the QTP. The results showed that during the period of 1990–2015, the carbon emissions of agriculture and animal husbandry on the QTP increased by 163% and 41%, respectively. Agricultural carbon sequestration showed an increasing trend with a growth rate of 45%, and animal husbandry carbon sequestration showed that it increased by 40% in the period of 1990–2005 and then decreased by 28% in the period of 2005–2015. The carbon footprint of agriculture reduced by 23% from 1990 to 1995, then grew by 112% from 1995 to 2015, and the animal husbandry carbon footprint reduced by 18% from 1990 to 2005 then grew by 71% from 2005 to 2015; both of these phenomena showed the downward trends first before trending upward. The high carbon emission and sequestration regions of agriculture were mainly distributed in the southeastern and northwestern areas of the QTP, and the high carbon footprint regions of agriculture were centered in the southeastern and northeastern areas. The high carbon emission regions of animal husbandry were located in southeastern areas, while the high carbon sequestration regions were in the southeastern, southwestern and middle areas. Additionally, the high carbon footprint regions were in southwestern areas. All measures mentioned showed a significant variation in spatial distribution. The economic and population growth contributed to the increase in carbon footprint despite technological advances. This study propose options to reduce the carbon emission and suggestions to improve carbon sink function. This study provides an important basis for policy formulation and planning for sustainable agriculture on the QTP.

Optimal agricultural spreading scheduling through surrogate-based optimization and MINLP models

The most commonly used definition of climate smart agriculture (CSA) is provided by the Food and Agricultural Organisation of the United Nations, which defines CSA as “agriculture that sustainably increases productivity, enhances resilience, reduces/removes greenhouse gas where possible, and enhances achievement of national food security and development goals”. In this definition, the principal goal of CSA is identified as food security and development, while productivity, adaptation, and mitigation are identified as the three interlinked pillars necessary for achieving this goal. In the context provided by the CSA, soils are seen as a lever to improve the carbon footprint of agriculture, namely through their role as carbon sinks. Improving soils and in particular agricultural soils’ content in soil organic carbon (SOC) in one of the measures enabling to improve the environmental impact of agricultural practices. In this context, composts can be seen as an important feedstock for sustainable farming. To support the development of organic amendment strategies enabling to increase soils’ SOC content, this work proposes a novel methodology to optimize the monthly scheduling of composts and mineral fertilizers amendments. The schedule proposed maximizes soil health - via improved SOC content - while ensuring optimal gross operating surplus from agriculture. This problem is subjected to certain operational, regulatory and soil-dynamics constraints which leads to a complex optimization problem and has to be solved in a relatively short time period for decision-making purposes. This is a nonlinear optimization problem (NLP) which is based on a soil-simulation model from which the analytic functions are not explicitly available for the optimization model. Operational and regulatory constraints are explicitly defined and integer and continuous variables are needed in the modeling. In order to effectively solve it in a deterministic way, a novel surrogate-modeling approach for the objective functions and constraints is proposed. Another novelty comes from the implementation of a continuous-variable-reduction procedure in order to build effective surrogates. The optimisation model results obtained from a real case study show that local optimal solutions can be identified with short computation times. The evaluated scenario shows that the optimized strategy could increase by 8% the carbon in soil after 13 years while increasing by 7% the estimated agricultural profit when compared to expert based application schedules.

A Climate Smartness Index (CSI) Based on Greenhouse Gas Intensity and Water Productivity: Application to Irrigated Rice

Efforts to increase agricultural productivity, adapt to climate change, and reduce the carbon footprint of agriculture are reflected in a growing interest in climate-smart agriculture (CSA). Specific indicators of productivity, adaptation and mitigation are commonly used in support of claims about the climate smartness of practices. However, it is rare that these three objectives can be optimized simultaneously by any one strategy. In evaluating the relative climate smartness of different agricultural practices, plans and policies, there is a need for metrics that can simultaneously represent all three objectives and therefore be used in comparing strategies that have different benefits and trade-offs across this triad of objectives. In this context, a method for developing a Climate Smartness Index (CSI) is presented. The process of developing the index follows four steps: (1) defining system specific climate smartness; (2) selecting relevant indicators; (3) normalizing against reference values from a systematic literature review; and (4) aggregating and weighting. The CSI presented here has been developed for application in a systematic review of rice irrigation strategies and it combines normalized water productivity (WP) and greenhouse gas intensity (GHGI) The CSI was developed for application to data from published field experiments that assessed the impact of water management practices in irrigated rice, focusing on practices heralded as climate-smart strategies, such as Alternate Wetting and Drying (AWD). The analysis shows that the CSI can provide a consistent judgment of the treatments based on the evidence of water efficiency and reduced greenhouse gas intensity reported in such studies. Using a measurable and replicable index supports the aim of generating a reliable quantification of the climate smartness of agricultural practices. The same four step process can be used to build metrics for a broad range of CSA practice, policy and planning.

Agricultural carbon footprint is farm specific: Case study of two organic farms

Making reliable inferences from agricultural carbon footprint (aCF) estimates require increased understanding of factors behind differences in carbon footprint (CF) of different farms and systems. Establishing sources and drivers of differences in aCF accounting can improve the level of accuracy of aCF modeling and help identify meaningful areas for decision-making for greenhouse gas mitigation efforts. Currently, a lack of consideration for different styles, sizes and location of organic farms in many studies comparing CF for agricultural products from organic and conventional farming systems limit our understanding of the factors behind the observed inconsistencies in their results. This study used OFoot tool, a Life Cycle Assessment and CropSyst based carbon footprint calculator for organic farms, to estimate the aCF of two certified organic farms growing several crops in common; one is a small-scale farm located in a maritime climate and the other is a large-scale farm located in a semi-arid climate. The study identified the hotspots of each farm's CF and the underlying reasons for differences in hotspots of the two farms and those of two mutual crops, onion and winter squash, grown by the farms.The CF of the small scale and the large scale farms were 7144 and 3410 kg CO2e ha-1 yr-1 respectively. Identified hotspots differ between the two farms as well. Critical CF hotspots in the small scale farm were fuel use (47.6%), greenhouse facilities (11.3%), and net soil emissions (10.3%), whereas those in the large scale farm were electricity used for irrigation (47.5%), fuel use (26.1%), and soil amendments (20.1%). The estimated CFs of onion and winter squash produced in the small scale farm were 0.188 and 0.276 kg CO2e per kg of crops, respectively, whereas the CFs of the same crops produced in the large-scale farm were 0.05 and 0.07 kg CO2e per kg of crops, respectively. This study identified farm size and site-specific soil and climatic conditions as factors influencing decision-making that have great impact on agricultural CF of the whole farms and individual crops of onion and squash. This study reiterates the importance of using site-specific emission factors in developing models and tools for aCF estimation and echoes the need for whole-farm CF analysis in understanding the context behind major hotspots in the CF of farms and farms products.

Increasing carbon footprint of grain crop production in the US Western Corn Belt

Global agriculture is challenged to increase soil carbon sequestration and reduce greenhouse gas emissions while providing products for an increasing population. Growing crop production could be achieved through higher yield per hectare (i.e. intensive farming) or more hectares (extensive farming), which however, have different ecological and environmental consequences. Multiple lines of evidence indicate that expanding cropland for additional production may lead to loss of vegetation and soil carbon, and threaten the survival of wildlife. New concerns about the impacts of extensive farming have been raised for the US Corn Belt, one of the world’s most productive regions, as cropland has rapidly expanded northwestward unto grasslands and wetlands in recent years. Here we used a process-based ecosystem model to distinguish and quantify how natural drivers as well as intensive and extensive farming practices have altered grain production, soil carbon storage, and agricultural carbon footprint in the US Western Corn Belt since 1980. Compared to the period 1980–2005, we found that cropland expansion more than tripled in the most recent decade (2006–2016), becoming a significant factor contributing to growing grain production. Land use change in this period led to a soil carbon loss of 90.8 ± 14.7 Tg (1 Tg = 1012 g). As a result, grain production in this region shifted from carbon neutral to a carbon loss of 2.3 kg C kg−1 grain produced. The enlarging negative carbon footprint (ΔC/ΔP) indicates the major role that cropland expansion has had on the carbon cost of grain production in this region. Therefore, we should be more cautious to pursue high crop production through agricultural cropland conversion, particularly in those carbon-rich soils.

Improving carbon footprinting of agricultural systems: Boundaries, tiers, and organic farming

PurposeThe purpose of this commentary is to call for consistent and improved methodology for agricultural carbon footprint (CF) studies. MethodsThe methods of published agricultural CF studies were compared to identify areas of inconsistency. Organic agriculture has been proposed as an approach to reduce net agricultural greenhouse gas (GHG) emissions and sequester carbon. Therefore we used organic agriculture as a focal system to explore the impact on CF estimates of using inconsistent boundaries, soil emission accounting, and emission factor (EF) tiers. Results and discussionStudies of agricultural CF use inconsistent boundaries and most use EFs based on national averages or regional models. As a result the local and farm-to-farm variability of EFs are obscured and the comparability of CFs from different studies is dubious. We propose three principles for agricultural CF calculation: use of consistent broad agricultural system CF boundaries, incorporation of soil emissions and sequestration, and development and use of fine-scale EFs for agricultural inputs. The potential use of organic practices in GHG mitigation efforts, along with the annual inspection process for certified organic farms, justify the future use of organic farms as a longitudinal national or international study population using the proposed principles. ConclusionsUsing different boundaries, or generalized vs. site-specific EFs, can give not only different levels of precision but also fundamentally different answers. Policy based on averaged data or incomplete estimates may be misdirected. To support effective policy and individual decision-making that reduce GHG emissions and/or sequester more carbon, accurate and consistent assessments of the GHG emissions of agricultural practices and systems at a finer temporal and spatial scale are needed.

What's food and nutrition security got to do with wildlife conservation?

ABSTRACT We review the linkages between food security, nutrition and wildlife conservation in the early 21st century. Declines in wildlife populations and habitats have occurred in parallel with increasing human population and the global emergence of the double burden of under- and over-nutrition. Nutrition-sensitive landscapes and nutrition- and gender-sensitive value chains are key to delivering optimal food and nutrition security and environmental outcomes. Neglected or underutilized crops and sustainable harvest of wild food have the potential to play a number of roles in the improvement of food security that include being: (a) a way to reduce the risk of over-reliance on very limited numbers of major crops and animals; (b) a way to increase sustainability of agriculture through a reduction in the carbon footprint of agriculture and maintenance of biodiversity; (c) a contribution to food quality; and (d) a way to preserve and celebrate cultural and dietary diversity. Dietary diversity and reduced gree...

Allocation factors and issues in agricultural carbon footprint: a case study of the Canadian pork industry

The choice of the calculation pathways used to estimate the environmental impact of human activities is of importance since it could modify the results of such studies. This is the case for the Life Cycle Analysis (LCA) which is now commonly used to perform environmental assessments: the allocation methods used have an important impact on calculations and can potentially affect the final results. This could have a very negative impact on the LCA in terms of adoption and trust in the results. In the current study, the Canadian swine sector has been used as a case study and the carbon footprint of pork production has been estimated regionally for the year 2006. In this study, these calculations were performed using different allocation approaches to study the impact and usefulness of each method. No-allocation, economic-allocation, and mass-allocation approaches were used. Owing to climate and production-type specificities, calculations were done for eastern and western Canada in addition to the national estimates. Total greenhouse gas emissions were higher in the east (3.5 Mt CO2e) than in the west (3.1 Mt CO2e). However, the carbon footprint followed an opposite trend. Considering the primal cut products and, in turn, the mass allocation, the economic allocation and no allocations, the CFs were 2.6 kgCO2e, 3.8 kgCO2e and 4.0 kgCO2e per kg of product for the east and 3.2 kgCO2e, 4.7 kgCO2e and 5.0 kgCO2e per kg of product for the west. The current study shows that, in fact, allocation methods are not interchangeable and should be selected based on the specificity of each study: the no-allocation approach can be used to analyze on-farm production, economic allocation is oriented to market studies, and mass allocation is well suited to environmental sustainability assessments.

Carbon Footprint Analysis for Mechanization of Maize Production Based on Life Cycle Assessment: A Case Study in Jilin Province, China

The theory on the carbon footprint of agriculture can systematically evaluate the carbon emissions caused by artificial factors from the agricultural production process, which is the theoretical basis for constructing low-carbon agriculture and has important guiding significance for realizing low-carbon agriculture. Based on farm production survey data from Jilin Province in 2014, this paper aims to obtain a clear understanding of the carbon footprint of maize production through the following method: (1) one ton of maize production was evaluated systematically by using the Life Cycle Assessment (LCA); (2) the carbon emissions of the whole system were estimated based on field measurement data, (3) using the emission factors we estimated Jilin’s carbon footprint for the period 2006–2013, and forecasted it for the period from 2014 to 2020 using the grey system model GM (1, 1).