Agricultural greenhouse gas emissions of an Indian village - Who's to blame: crops or livestock?

A carbon footprint assessment, combining various scales of analysis and including a territorial assessment, is proposed to estimate the greenhouse gas (GHG) emissions from crops and livestock in an Indian village impacted by both Green (for crops) and White (for milk) revolutions. It is based on the GHG assessment of 10 cropping systems, 8 livestock farming systems and 9 production systems using the comparative agriculture and Life Cycle Assessment (LCA) approaches. Results show that mineral fertilisation, irrigation and methane from paddy fields are the main drivers of emissions at plot level. Livestock farming systems emit from 4.7 tCO2eq/female to 8.6 tCO2eq/female, enteric fermentation being the first source of emission. Disparities at farm level are huge, ranging from 9 to 733 tCO2eq. At village level, emissions yield 37 tCO2eq/ha and livestock contributes to 60 % of GHG emissions. The high GHG emissions are a legacy of the Green and White Revolutions: the livestock population is high, fed on highly emissive fodder and concentrates and produces little milk. The results enhance our understanding of the share of carbon emissions from crops and livestock at farm and territorial level. They pinpoint the environmental and socio-economic downsides of livestock farming intensification.

Emergy evaluation of contrasting dairy systems at multiple levels.

Emergy accounting (EmA) was applied to a range of dairy systems, from low-input smallholder systems in South Mali (SM), to intermediate-input systems in two regions of France, Poitou-Charentes (PC) and Bretagne (BR), to high-input systems on Reunion Island (RI). These systems were studied at three different levels: whole-farm (dairy system and cropping system), dairy-system (dairy herd and forage land), and herd (animals only). Dairy farms in SM used the lowest total emergy at all levels and was the highest user of renewable resources. Despite the low quality of resources consumed (crop residues and natural pasture), efficiency of their use was similar to that of industrialised inputs by intensive systems in RI, PC and BR. In addition, among the systems studied, SM dairy farms lay closest to environmental sustainability, contradicting the usual image of high environmental impact of cattle production in developing countries. EmA also revealed characteristics of the three intensive systems. Systems from RI and PC had lower resource transformation efficiency and higher environmental impacts than those from BR, due mainly to feeding strategies that differed due to differing socio-climatic constraints. Application of EmA at multiple levels revealed the importance of a multi-level analysis. While the whole-farm level assesses the overall contribution of the system to its environment, the dairy-system level is suitable for comparison of multi-product systems. In contrast, the herd level focuses on herd management and bypasses debates about definition of system boundaries by excluding land management. Combining all levels highlights the contribution of livestock to the global agricultural system and identifies inefficiencies and influences of system components on the environment.

Pluri-energy analysis of livestock systems--a comparison of dairy systems in different territories.

This paper introduces a generic assessment method called pluri-energy analysis. It aims to assess the types of energy used in agricultural systems and their conversion efficiencies. Four types of energy are considered: fossil energy, gross energy contained in the biomass, energy from human and animal labor and solar energy. The method was applied to compare smallholder low-input dairy-production systems, which are common in developing countries, to the high-input systems encountered in OECD countries. The pluri-energy method is useful for analyzing the functioning of agricultural systems by highlighting their modes of energy management. Since most dairy systems in South Mali (SM) are low-input systems, they are primarily based on solar and labor energy types and do not require substantial fossil-energy inputs to produce milk. Farms in Poitou-Charentes (PC) and Bretagne (BR) show intermediate values of fossil-energy use for milk production, similar to that found in the literature for typical European systems. However, fossil-energy use for milk production is higher on PC than BR farms because of a higher proportion of maize silage in the forage area; grazing pastures are more common on BR farms. Farms on Reunion Island (RI) require a relatively large amount of fossil energy to produce milk, mainly because the island context limits the amount of arable land. Consequently, milk production is based on large imports of concentrated feed with a high fossil-energy cost. The method also enables assessment of fossil-energy-use efficiency in order to increase the performance of biological processes in agricultural systems. Comparing the low-input systems represented by SM to the high-input systems represented by RI, PC and BR, an increase in solar-energy conversion, and thus land productivity, was observed due to intensification via increased fossil-energy use. Conversely, though fossil-energy use at the herd level increased milk productivity, its effect on gross-energy conversion by the herd was less evident. Partitioning the total on-farm gross energy produced among animal co-products (milk, meat and manure) highlights the major functions of SM herds, which are managed to produce organic crop fertilizers.

Evaluating the ability of current energy use assessment methods to study contrasting livestock production systems.

Environmental impact assessment of agriculture has received increased attention over recent decades, leading to development of numerous methods. Among them, three deal with energy use: Energy Analysis (EA), Ecological Footprint (EF) and Emergy synthesis (Em). Based on a review of 197 references applying them to a variety of agricultural systems, this paper evaluates their ability to assess energy use. While EF assesses energy use as land use via a global accounting approach in which energy is only one component of the assessment, EA and Em are energy-focused and appear more appropriate to highlight ways to increase energy-use efficiency. EA presents a clear methodology via fossil energy use and its associated impacts but does not consider all energy sources. With inclusion of natural and renewable resources, Em focuses on other energy resources, such as solar radiation and energy from labour, but does not present impact indicators nor establish a clear link between activities and their environmental impacts. Improvements of the EA and Em methods could increase their ability to perform realistic and unbiased energy analysis or the diversity of livestock systems encountered in the world. First, to consider all energy sources, as Em does, EA could include solar radiation received by farm surfaces and energy expenditure by humans and animals to accomplish farm operations. Second, boundaries of the studied system in EA and Em must include draft animals, humans and communal grazing lands. Third, special attention should be given to update and locally adapt energy coefficients and transformities.