Accounting for multi-functionality of sheep farming in the carbon footprint of lamb: A comparison of three contrasting Mediterranean systems

Carbon foot prints (CFs) studies based on life cycle assessment between sheep farming systems and green house gases (GHG) emissions is one of the best indicators to quantify the amount of GHG emissions per kg of product. Therefore, a life cycle assessment (LCA) study was conducted for three different sheep farming systems i.e. intensive system (stall fed only), semi-intensive (grazing with supplementation) and extensive system (grazing only) under semiarid region of India to assess the carbon cost of sheep rearing. The total CFs were estimated to be 16.9, 15.8 and 17.1 kg CO2-eq in intensive, semi-intensive and extensive system of grazing indicating semi-intensive system to be most carbon (C) efficient. For 1kg mutton production in semi-intensive and intensive system, around 30% and 24% CFs were contributed from enteric fermentation and feed respectively, whereas, in extensive system, the contribution of enteric fermentation increased up to 50%. The carbon foot prints analysis gives an insight of carbon inputs used but the amount of CO2 sequestered in soil making LCA a holistic approach for estimating GHG emissions from livestock.

How does soil carbon sequestration affect greenhouse gas emissions from a sheep farming system? Results of a life cycle assessment case study

The carbon cost of impaired welfare on sheep farms.

Reply to L Aleksandrowicz et al.

Southland has witnessed a pronounced change in its agricultural landscape in recent years.Greater profitability of dairy relative to sheep farming has led to a large number of dairy conversions over the last 20 years, with the scope for further substantive conversions into the future.The economic and social benefits have been extensively reported, but less is understood about the environmental impacts associated with this land use change.To investigate the potential effect of land use change from sheep and beef to dairy on economic and environmental outcomes in the Southland region of New Zealand, farm-scale enterprise simulation models were linked with spatially explicit land resource information.By overlaying individual farm parcels with land resource information, land area and topography data for each farm were attained.Estimated pasture production (PP) for each land use capability (LUC) Class provided indicative data for the modelling exercise on the productive use of the land across the region.The approach provided a method for the expansion of farm scale modelling to a regional scale.A representative DairyNZ Production System 3 was used to investigate the influence of increasing dairy cow numbers and associated inputs at the farm level.A representative sheep and beef farm was also modelled.To account for a dairy support area, used to carry dry cows during the winter, a second step involved the modelling of a larger System 3 dairy farm that included a milking platform area and an adjacent support area.This farm system was considered for regional up-scaling to allow for a more comprehensive capture of nutrient losses and financial outcomes.Estimates of annual nitrogen (N) leaching values from dairy farms ranged from 21 to 44 kg N/ha, and were higher for farms with greater pasture production potential, due to the greater amount of N cycling and increased number of urine patches from the higher number of livestock numbers carried.Annual N leaching from the sheep and beef farms ranged from 8 to 17 kg N/ha.Annual greenhouse gas (GHG) emissions were also higher from farms with greater productive potential, ranging from 7.1 to 15.4 t CO 2 -e/ha for dairy and from 2.1 to 6.9 t CO 2 -e/ha for sheep and beef farms.In contrast to leaching, GHG emissions were higher from poorly-drained soils compared with well-drained soils; annual nitrous oxide (N 2 O) emissions accounted for 22% and 35% of total GHG emissions from dairy farms on well-and poorly-drained soils, respectively, and up to 40% from sheep and beef farms on poorly-drained soils.The new dairy farms resulting from conversion would largely fall in an N leaching range of 25 to 31 kg N/ha and have GHG emissions of 7.0 to 10.5 t CO 2 -e/ha.Depending on future regional regulations that may be implemented, a large number of potential dairy farms might leach more N than the allowable limit, and mitigation techniques will need to be implemented.A shift in land use from the current 15% of land area under dairying to a potential 46% led to a large increase in regional profit (76%).The environmental impact from this land use change, however, became substantial, with regional nitrate leaching increasing by 34% and GHG emissions by 24%.Conversion of more farms into dairying increased farm profit, N leaching and GHG emissions in the region compared with the current situation.It must be noted, however, that the up-scaling of potential dairy conversion was based on land resources defined by the productive potential of the landscapes found in Southland and that the actual level of conversion could differ substantially if additional or different farming scenarios were tested.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statisticalmore » sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.« less

The present study was conducted in 2008 to document the indigenous knowledge prevalent in sheep and goat farming systems in Tamil Nadu, India. Fifty sheep farmers in Tirunelveli district and fifty goat farmers in Erode district were selected by proportionate random sampling method and the data were collected through an interview schedule and analysed and ranked for getting inferences. The results revealed that in sheep farming system, the traditional practices such as lamb hut (100%), washing the sheep in ponds for tick removal (100%), selecting good breeding rams (96%), sheep penning (94%), and twinning (76%) were known to sheep farmers. In goat farming system, rearing bucks for religious purpose (100%), kid house (100%), tethering (96%), and locally made shelter (84%) were known to goat farmers. A close look at the indigenous practices in sheep and goat farming systems indicates the area-specific nature of these practices/beliefs.

Addressing the social life cycle inventory analysis data gap: Insights from a case study of cobalt mining in the Democratic Republic of the Congo

Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China – using a life cycle assessment approach

Direct and indirect impacts of crop–livestock organization on mixed crop–livestock systems sustainability: a model-based study

In the Netherlands, the Dutch Retail Broiler (DRB) and Better Life one Star (BLS) production systems have been introduced with the aim to improve broiler welfare. Simultaneously, retailers set targets for reduction of greenhouse gas (GHG) emissions in the whole broiler production chain. The GHG emissions of DRB and BLS may differ from conventional systems because of differences in slaughter age, feed intake, and diet composition. The aim of this study was to estimate GHG emissions of the conventional, DRB, and BLS production systems. A deterministic, spreadsheet based model was developed that included the breeder, hatchery, and broiler farm stages. First, the model calculates feed intake of different diets and energy use, based on performance objectives and literature. Selection of feed ingredients for the different types of diets was based on least cost formulation with nutritional constraints for each diet. Second, GHG emissions were estimated from cradle to broiler farm gate for processes along the broiler production chain by using life cycle assessment, and expressed as kg CO2-equivalents per kg live weight (kg CO2-eq/kg LW). Results showed that BLS (3.55 kg CO2-eq/kg LW) had lower GHG emissions compared to conventional (3.65 kg CO2-eq/kg LW) and DRB (3.98 kg CO2-eq/kg LW) at the broiler farm gate. Emissions from land use change (LUC) from feed production, mainly from soybean products, had highest impact on total GHG emissions (>50%) for the systems and these soybean products had the lowest inclusion in the diets of the BLS production system. Sensitivity analyses showed that variation in slaughter weight and feed intake could result in overlap of GHG emissions between systems. When soybean products were sourced from a country with low LUC emissions, conventional (1.37 kg CO2-eq/ kg LW) had the lowest GHG emissions and BLS (1.79 kg CO2-eq/kg LW) the highest. This study showed that origin of and including or excluding LUC emissions from soybean production results in different conclusions for achieving the GHG emissions reduction targets set by retailers.

The objective of this study was to estimate the effects of dairy diets, manure-handling methods, and interactions with the bio-fuels industry on the net energy intensity, greenhouse gas (GHG) emissions, and land use for milk production in Wisconsin. Five dairy diets supplemented with varying amounts of co-products from corn ethanol and soybean biodiesel production were modeled in two manure management scenarios: with and without on-farm biogas generation. The diets were characterized by different inclusion of soybean meal (SBM) and dry distillers grains with solubles (DDGS), balanced with different types forages. A partial life cycle assessment (LCA) of milk production from cradle to farm gate was performed. Milk production was used as the primary output for this analysis, since the dairy industry will remain the primary agricultural enterprise in Wisconsin for the foreseeable future. The boundaries of the milk production system were expanded to include bio-fuels production. The production of bio-fuels (corn ethanol and biodiesel) was scaled to meet the dietary requirements of each selected dairy ration. The choice of dairy ration had a substantial effect on GHG emissions and net energy intensity per energy corrected milk (ECM) produced. Land use for the integrated dairy and bio-fuels production systems ranged from 1.68 m2/kg ECM to 2.01 m2/kg ECM. Accounting for bio-fuels credits but without biogas generation, net energy intensity ranged from 0.83 MJ/kg ECM to 1.34 MJ/kg ECM, and GHG emissions ranged from 0.69 kg CO2-eq/kg ECM to 0.80 kg CO2-eq/kg ECM, depending on the diet. The average effects of including anaerobic digesters for on-farm biogas generation were reductions in GHG emissions by 0.24 kg CO2-eq/kg ECM, and in net energy intensity by 2.84 MJ/kg ECM.

Background: Current scientific literature suggests healthy dietary patterns may have less environmental impact than current consumption patterns, but most of the studies rely on theoretical modeling. The aim of this study was to assess the impact on resources (land, water, and energy) use and greenhouse gas (GHG) emissions of healthy dietary patterns in a sample of Italian adults. Methods: Participants (n = 1806) were recruited through random sampling in the city of Catania, southern Italy. Dietary consumption was assessed through a validated food frequency questionnaire (FFQ); dietary patterns were calculated through dietary scores. The specific environmental footprints of food item production/processing were obtained from various available life-cycle assessments; a sustainability score was created based on the impact of the four environmental components calculated. Results: The contribution of major food groups to the environmental footprint showed that animal products (dairy, egg, meat, and fish) represented more than half of the impact on GHG emissions and energy requirements; meat products were the stronger contributors to GHG emissions and water use, while dairy products to energy use, and cereals to land use. All patterns investigated, with the exception of the Dietary Approach to Stop Hypertension (DASH), were linearly associated with the sustainability score. Among the components, higher adherence to the Mediterranean diet and Alternate Diet Quality Index (AHEI) was associated with lower GHG emissions, dietary quality index-international (DQI-I) with land use, while Nordic diet with land and water use. Conclusions: In conclusion, the adoption of healthy dietary patterns involves less use of natural resources and GHG emissions, representing eco-friendlier options in Italian adults.

Climate-smart livestock production at landscape level in Kenya