Carbon Footprint Of Farms Research Articles

Optimization strategies for carbon neutrality in a maize-soybean rotation production system from farm to gate

Agricultural food production stands as a primary source of global greenhouse gas emissions, with residue management widely acknowledged as an effective avenue to achieve carbon neutrality. However, there is a lack of comprehensive assessment of the carbon footprint of agricultural residue production, processing, and recycling from farm to gate. Here we conducted a hybrid approach that integrated the DeNitrification-DeComposition (DNDC) model and life cycle carbon footprint to co-optimize water and residue management, targeting carbon neutrality and yield increase. The results revealed that controlled drainage with sub-irrigation (CDSI) served as a potent water management strategy to mitigate greenhouse gas emissions (15.65 %) while increasing yield (24.34 %) compared to free drainage. Regardless of the type of bioproduct, incorporating CDSI with downstream residue utilization exhibited substantial potential for carbon-negative emissions, reducing the average farm carbon footprint to −1538.15 kg CO2 eq yr−1. Among those scenarios, the CDSI with bioethanol scenario particularly stands out with its robust carbon mitigation capability (−1994.94 kg CO2 eq yr−1), driven by the enormous energy demand throughout the agricultural production process and the environmental friendliness of bioethanol, which substantially exceeds that of gasoline. However, challenges such as high plant construction costs and extended investment payback periods associated with residue conversion underscore the need for the urgent establishment of national subsidies and market mechanisms to foster carbon neutrality in agricultural production.

Farm Carbon Footprint Measurement Frameworks Based on the Digitization and Environmental Sustainability Paradigm

Abstract The use of digital technology in agriculture, such as sensors, satellites, and artificial intelligence, for developing sustainable agriculture and controlling the impacts of climate change, has been enhanced under the European Green Deal. Given the environmental exigencies, the agricultural sector must adapt to new technologies that ensure the sustainable development of the whole agri-food sector. This paper emphasizes the importance of measuring carbon footprints in agriculture as a tool for further improving the sustainability of the sector. Specifically, the research addresses the importance of sustainable technologies and practices, including agriculture, circular economy, and conservation agriculture, which play an important role in reducing carbon emissions from the agricultural sector. Thus, in the framework of the European Green Deal and digitalization, a bibliographic analysis was carried out using the Web of Science database to review the scientific interest on carbon footprint generated by farms. The aim of the paper is to tackle the significance given by the scientific community on the carbon footprint of farms in order to enhance the development of a comprehensive set of measures that can incorporate the sector’s social and environmental responsibility, and ensure further the right path towards a sustainable future without affecting the profitability of the small farmers.

Addressing dairy industry's scope 3 greenhouse gas emissions by efficiently managing farm carbon footprints

Corporate climate action is increasingly considered important in driving the transition towards a low-carbon economy. Especially in the food processing industry indirect greenhouse gas (GHG) emissions upstream in a producer's value chain (scope 3 emissions) often represent the largest portion of the GHG inventory. In order to manage those GHG emissions efficiently there is a need for simple GHG calculation models ready for large scale application on supplying farms. The aim of this study is to describe a procedure for the development of a simplified GHG calculation model specific for dairy farms based on detailed GHG emission calculations. The results presented are obtained from a case study of supplying farms of a Bavarian organic dairy. The simplified model shall reduce the number of input parameters significantly while representing the variations in GHG emissions across farms and allowing the quantification of GHG mitigation measures. The median farm carbon footprint (CCF) of the case study farms calculated with the detailed calculation model is 441.7 t CO2e/a (mean number of dairy cows per farm is 57). The total GHG mitigation potential per farm ranges from 6.51 t CO2e/a to 112.29 t CO2e/a. The simplification of the calculation model results in a less complex and time consuming questionnaire covering 57 parameters instead of up to 467 in the detailed model. The total CCF results of the simplified model give highly comparable results to the detailed calculation model (R2 = 99.7% , standard error = 2.3% of the mean CCF). The correlation between the total mitigation potential calculated per farm by the simplified and detailed calculation model is also high (R2 = 98.92%, standard error = 7.0% of the mean total mitigation potential per case study farm). For the quantification of some mitigation measures with the simplified model, attention must be paid to the question whether calculated GHG emission reductions are significantly higher than the standard error of GHG emissions calculated for the relevant life cycle stage. Summarizing the procedure for the development of a simplified GHG calculation model proposed in this paper, leads to a model that meets the defined requirements.

Managing food waste is key to tackling climate change

Managing food waste is key to tackling climate change

Carbon footprint of a typical pomelo production region in China based on farm survey data

Greenhouse gas emissions are a central environmental concern in China, and quantifying the carbon footprint of crop production can help identify key options for mitigating greenhouse gas emissions from agriculture. Using two-year farm survey data from a typical pomelo production area in China, the carbon footprint of a pomelo orchard was assessed by quantifying the greenhouse gas emissions from farming inputs and operations with a full life cycle assessment methodology. The carbon emission and carbon sequestration levels were 19.9 and 3.43 t CO2 eq ha−1, respectively. The farm carbon footprint and product carbon footprint were 16.5 t CO2 eq ha−1 and 0.33 t CO2 eq t−1, respectively, which were 3.8 and 1.5 times greater than the corresponding mean values for other fruit orchards in different areas of the world. Compared with farms with low yield and high carbon footprint, farms with high yield and low carbon footprint reduced nitrogen fertilizer input significantly, by 44.7%. Furthermore, the yield of farms which with high yield and low carbon footprint increased significantly by 69.4%, and the farm carbon footprint and product carbon footprint significantly decreased by 40.7% and 67.2%, respectively, compared to those of farms with low yield and high carbon footprint. The synthetic nitrogen fertilizer application rate was significantly linearly correlated with, and contributed 84.7% to the carbon footprint of pomelo production. The relationships between the carbon footprint and the nitrogen partial factor productivity also indicated that improving nitrogen utilization can effectively reduce greenhouse gas emissions. In addition, based on group analysis, the difference in carbon footprint among farm groups was also mainly due to nitrogen fertilization. Reducing costs can significantly improve carbon efficiency without affecting the economic benefit at similar yield levels. Therefore, the carbon intensity of pomelo production in China could be reduced primarily by reducing excessive nitrogen fertilizer application and potentially increasing nitrogen use efficiency. Apart from this, these differences could be implemented by modifying farming fertilization strategies and farm size. Overall, this study highlights the importance of strengthening nitrogen management in pomelo production based on agricultural practices as well as upscaling farm size, which could achieve higher yields with lower greenhouse gas emissions and thereby avoid tradeoffs between productivity and carbon costs.

Variation in the carbon footprint of milk production on smallholder dairy farms in central Kenya

Milk production by smallholders in Africa has a high carbon footprint (CF) and is predicted to increase significantly in the coming decades. This study, based on data from a sample of 382 farms in central Kenya, is the first assessment of the CF of milk production in Sub-Saharan Africa based on a large dataset of actual farm management practices. The aims of the study were (1) to determine whether there are significant differences in the CF of farms with different feeding systems (i.e., zero-grazing, grazing and mixed systems), and (2) to identify factors associated with variability in CF between farms. This analysis is used to identify options for mitigating GHG emissions from Kenya’s growing dairy production. Average CF ranged between 2.19 and 3.13 kg CO2e/kg fat and protein corrected milk (FPCM), depending on the GWPs and allocation method used. Analysis based on variability in farm management showed that CF was similar between farms with zero-grazing and mixed feeding systems, and significantly higher on farms with grazing only feeding systems, but no difference was detected when input parameter uncertainty was considered. At individual cow level, variation in milk yields explained more than 70% of the variation in GHG intensity. At farm level, milk yield explained less than half of variation in CF. CF was correlated with feed characteristics, manure management practices and herd size and composition. In particular, the level of concentrate use was positively correlated with CF, and was the most important factor explaining variation in CF not attributable to variation in milk yield. Our findings suggest that promoting balanced feed rations and feeding concentrate according to cows’ needs across the lactation cycle could provide opportunities to both increase milk production and reduce the CF of milk production on smallholder farms in central Kenya. Supporting smallholder farmers to implement these mitigation options will require interventions at several levels in feed supply chains in the dairy sector.

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.

Cows, Cash and Climate: Low Stocking Rates, High-Performing Cows, Emissions and Profitability across New Zealand Farms

Using the New Zealand Monitor Farm Data (NZMFD), this paper explores the cost-effectiveness of two mitigation options to reduce biological greenhouse gas (GHG) emissions on farms: reducing stocking rate (SR; the number of cows per effective hectare of dairy land); and increasing animal performance (AP; measured by production of milk solids (MS) per cow). These mitigation options have been defined as “no cost” because, if applied together, they could reduce the carbon footprint of farms while also maintaining or even improving profits (de Klein & Dynes, 2017). We evaluate the effect of these mitigation options on three main variables: milk profitability of the farm (cash operating surplus (COS)/ton of MS produced); emissions intensity (ton CO2eq/ton of MS produced); and the value of emissions (COS/ton CO2eq). The paper has two main findings: high-AP farms show significantly lower emissions intensities and higher milk profitability; and higher SRs on farms are significantly associated with lower emissions intensities while not being significantly associated with milk profitability or negatively associated with profit per hectare. These results imply that higher levels of AP reduce the GHG intensity of the farm and increase profit – a “no-cost” option – but unless either the SR or the area under dairy farming fall, an increase in AP will lead to an increase in absolute emissions. However, our results cast doubt on the idea that reducing SR is a no-cost way to achieve absolute emission reductions. The two options do seem to constitute a no-cost outcome when combined, but potentially the same mitigation could be achieved with lower loss of profit by reducing the area of dairy land while maintaining high SRs and increasing the performance of the animals.

Carbon and water footprints of major cereal crops production in China

Abstract Greenhouse gas (GHG) emissions and freshwater scarcity are central environmental concerns in China. They are closely linked to crop production. Based on a national-scale survey and statistics, we estimated the carbon footprint (CF) and water footprint (WF) of rice, wheat, and corn in China in 2011. The GHG emissions were 9.9, 3.9, and 3.7 tons of carbon dioxide equivalents per hectare (t CO2e ha−1) for rice, wheat, and corn, respectively. Carbon sequestration due to straw return mitigated 202–478 kg CO2e ha−1. Therefore, the farm CFs were 9.7, 3.5, and 3.4 t CO2e ha−1 for rice, wheat, and corn; while the product CFs were 1.4, 0.7, and 0.6 kg CO2e kg−1 respectively. The product WFs were 1.5, 1.1, and 1 m3 kg−1 for rice, wheat, and corn, respectively. Blue water made a greater contribution to WF in the north and northwest than to the other regions, and gray water occupied 27% of the total WF. Our results indicated that cereal production in China depended more on fertilization and irrigation than global average production requirements. The significantly positive relationships between the CF and WF indicated the possibility for simultaneous mitigation. Based on our results and other studies, we recommended potential practices, especially optimized fertilization and irrigation, and straw returns, to mitigate GHG emissions and water use to achieve environmentally sustainable agriculture in China.

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 2: Investigation and Modeling of Indirect Energy Requirements

Dairy cattle farms are continuously developing more intensive systems of management, which require higher utilization of durable and non-durable inputs. These inputs are responsible for significant direct and indirect fossil energy requirements, which are related to remarkable emissions of CO2. This study focused on investigating the indirect energy requirements of 285 conventional dairy farms and the related carbon footprint. A detailed analysis of the indirect energy inputs related to farm buildings, machinery and agricultural inputs was carried out. A partial life cycle assessment approach was carried out to evaluate indirect energy inputs and the carbon footprint of farms over a period of one harvest year. The investigation highlights the importance and the weight related to the use of agricultural inputs, which represent more than 80% of the total indirect energy requirements. Moreover, the analyses carried out underline that the assumption of similarity in terms of requirements of indirect energy and related carbon emissions among dairy farms is incorrect especially when observing different farm sizes and milk production levels. Moreover, a mathematical model to estimate the indirect energy requirements of dairy farms has been developed in order to provide an instrument allowing researchers to assess the energy incorporated into farm machinery, agricultural inputs and buildings. Combining the results of this two-part series, the total energy demand (expressed in GJ per farm) results in being mostly due to agricultural inputs and fuel consumption, which have the largest share of the annual requirements for each milk yield class. Direct and indirect energy requirements increased, going from small sized farms to larger ones, from 1302–5109 GJ·y−1, respectively. However, the related carbon dioxide emissions expressed per 100 kg of milk showed a negative trend going from class <5000 to >9000 kg of milk yield, where larger farms were able to emit 48% less carbon dioxide than small herd size farm (43 vs. 82 kg CO2-eq per 100 kg Fat- and Protein-Corrected Milk (FPCM)). Decreasing direct and indirect energy requirements allowed reducing the anthropogenic gas emissions to the environment, reducing the energy costs for dairy farms and improving the efficient utilization of natural resources.

Carbon footprint of grain crop production in China – based on farm survey data

Quantifying the carbon footprint of crop production can help identify key options to mitigate greenhouse gas emissions from agriculture. Using farm survey data from eastern China, the carbon footprints of three major grain crops (rice, wheat and maize) were assessed by quantifying the greenhouse gas emissions from individual inputs and farming operations with a full life cycle assessment methodology. The farm carbon footprint in terms of farm area was estimated to be 6.0 ± 0.1, 3.0 ± 0.2, and 2.3 ± 0.1 t CO2-eq ha−1, and the product carbon footprint in terms of grain produced was 0.80 ± 0.02, 0.66 ± 0.03, and 0.33 ± 0.02 t CO2-eq t−1 grain for rice, wheat and maize, respectively. Use of synthetic nitrogen fertilizers contributed 44–79% and mechanical operations 8–15%, of the total carbon footprints. Irrigation and direct methane emission made a significant contribution by 19% and by 25%, on average respectively for rice production. However, irrigation was only responsible for 2–3% of the total carbon footprints in wheat and maize. The carbon footprints of wheat and maize production varied among climate regions, and this was explained largely by the differences in inputs of nitrogen fertilizers and mechanical operations to support crop management. Moreover, a significant decrease (22–28%) in the product carbon footprint both of wheat and maize was found in large sized farms, compared to smaller ones. This study demonstrated that carbon footprint of crop production could be affected by farm size and climate condition as well as crop management practices. Improving crop management practices by reducing nitrogen fertilizer use and developing large scaled farms with intensive farming could be strategic options to mitigate climate change in Chinese agriculture.

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

PurposeLife cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.Materials and methodsData was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO2 equivalents (CO2-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was < ±5 %.Results and discussionThe combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO2-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO2-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R 2 = 0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.ConclusionsThe certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.

Farming and marketing system affects carbon and water footprint – a case study using Hokaido pumpkin

The objective of the study was to determine the carbon footprint for four farming and marketing systems, using primary data obtained on the farms for autumn pumpkin as an example and model crop. Area for the crop cultivation and weight, i.e. kilogram of saleable product, for the marketing phase were employed as the two functional units with system boundaries from seed acquisition to pumpkin disposal; offset was not used.In the farm carbon footprint (FCF), pumpkins from the organic farm with 240kg CO2eq/ha scored best due to the lowest (50kg N/ha) (organic) nitrogen application compared with twice that value (448kg CO2eq/ha) for the IP farm. This was due to nitrous oxide emissions, as a consequence of N fertiliser, with 75–99% of FCF in the cultivation phase; it contributed ca. 10% and plant protection (methiocarb in the ‘integrated production’; IP) <1% to the product carbon footprint (PCF). Neither the form (organic or inorganic) nor the amount of applied nitrogen (a 2.5 fold difference) largely influenced the final product carbon footprint.The large specialised farm showed the best product carbon footprint with 139g CO2eq/kg pumpkin due to use of potassium fertiliser and 3-fold larger yields (18t/ha versus 5.8t/ha in the organic [716g CO2eq/kg]) and, to a lesser extent, the scale of the farm business. On the other two farms, cultivation is more extensive with the main income not from pumpkin; any increase in farm size or their pumpkin acreage would not improve their efficacy and product carbon footprint. The imported organic Argentinean pumpkin scored second best with 243g CO2eq/kg despite the long-distance transport due the lower energy consumption of bulk sea freight. The private consumer shopping distance in both cases (retail versus farm shop) amounted to as much as ca. 89% of the product carbon footprint.For a cleaner production, carbon reduction potential appears in improving the potassium fertilisation in all farming systems, except for the large farm, and consumer behaviour regarding the means of transport for shopping.A simple water footprint assessment, based on irrigation and produce washing, resulted in 0.4 (IP), 0.9L (organic) to 9L (large scale) blue water/kg pumpkin.