Regional Environmental Change

, Volume 17, Issue 1, pp 129–141 | Cite as

Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies

  • Anne Mottet
  • Benjamin Henderson
  • Carolyn Opio
  • Alessandra Falcucci
  • Giuseppe Tempio
  • Silvia Silvestri
  • Sabrina Chesterman
  • Pierre J. Gerber
Original Article

Abstract

Livestock can contribute to climate change mitigation by reducing their greenhouse gas emissions and by increasing soil carbon sequestration. Packages of mitigation techniques can bring large environmental benefits as illustrated in six case studies modeled in the Global Livestock Environmental Assessment Model developed by FAO. With feasible technical interventions in livestock production systems, the mitigation potential of each of the selected species, systems and regions ranges from 14 to 41 %. While comparably high mitigation potentials were estimated for ruminant and pig production systems in Asia, Latin America and Africa, large emission reductions can also be attained in dairy systems with already high levels of productivity, in OECD countries. Mitigation interventions can lead to a concomitant reduction in emissions and increase in production, contributing to food security. This is particularly the case for improved feeding practices and better health and herd management practices. Livestock systems also have a significant potential for sequestrating carbon in pasturelands and rangelands through improved management, as illustrated in two of the six case studies in this paper.

Keywords

Climate change Mitigation Livestock systems Productivity Packages of options 

Introduction

Tackling climate change has become extremely urgent, as the window of opportunity for achieving climate targets is closing (Stocker 2013). Impacts of climate change on agri-food systems could be “manageable” up to 2050, though costly, but if no action is taken, the period 2050–2080 is likely to be much more challenging (Nelson et al. 2010). The urgent need to reduce greenhouse gas (GHG) emissions is an unprecedented challenge for the international community, but can be addressed with suitable policies to support innovations and investments to reduce emissions, improve resilience and increase productivity.

In order to have traction among policy-makers, mitigation policies targeting food value chains need to be consistent with the overall development goals of a country, and they must be part of a wider vision of how the agricultural sector should and could develop. A key requirement for participation of developing countries, where most of the mitigation potential in food production is found (Gerber et al. 2011), is the creation of strategies that can serve both development and mitigation objectives.

While livestock’s role in achieving food security is well established (FAO 2011; Herrero et al. 2013a), the sector’s sustainable development in the context of a 70 % rise in demand for animal products between 2010 and 2050 (Alexandratos and Bruinsma 2012; Steinfeld et al. 2006) is a key issue. Livestock matters to climate change. The sector contributes to 14.5 % of total human-induced GHG emissions (IPCC 2014; Gerber et al. 2013). Feed production that releases mainly N2O and CO2, and enteric fermentation from ruminants (CH4) are the two main sources of emissions, responsible for 45 and 39 % of sector emissions, respectively. GHG emissions from livestock can, however, be reduced by one-third through efficiency improvements (Gerber et al. 2013). Technologies and practices that help reduce emissions exist but could be applied more widely.

While many attempts have been made to quantify the mitigation potential of single technologies, few systematic studies exist for assessing “best-bet” options in different production systems and regions, and their impact on food security (Doreau et al. 2014). To improve our understanding of sustainability in food chains and better inform policy-makers, we cannot rely on country averages. Instead, spatially explicit and disaggregated data by production systems and product type are required to identify hot spots in the value chain where most efforts should be targeted (Herrero et al. 2013b).

This paper presents the results of a modeling effort to identify low emission pathways for a growing global livestock sector. Six practical case studies in selected regions and livestock production systems were developed to explore how mitigation could be achieved in practice. Each case study provides an illustration of possible mitigation interventions, based on our understanding of the main emissions drivers and related technical entry points for mitigation.

Method

Main features of the Global Livestock Environmental Assessment Model

Emissions from livestock production systems were estimated using the Global Livestock Environmental Assessment Model (GLEAM, Gerber et al. 2013, www.fao.org/gleam). GLEAM is a spatially explicit model that represents biophysical processes and activities along livestock supply chains using a life cycle assessment approach. The model considers all the main sources of emissions, from feed production to enteric methane, manure emissions, embedded energy and post-farm emissions, and has a high level of quantitative details on herd production functions and resource flows. It includes a farming system typology that was adapted from Seré and Steinfeld (1996), based on the feed-base and the agro-ecological conditions. GLEAM incorporates over 14,000 discrete supply chains, defined as unique combinations of commodity, farming system, country and agro-ecological zone. The model differentiates 11 main livestock commodities: meat and milk from cattle, sheep, goats and buffalo; meat from pigs and meat and eggs from chickens. Ruminant production is differentiated into mixed and grazing systems; pig production into backyard, intermediate and industrial systems; and chicken production into backyard, layers and broilers.

GLEAM utilizes geo-referenced data to compute emissions from the livestock sector. Data on production practices and productivity were collected at different levels of aggregation: production system, country level, agro-ecological zones or a combination thereof. Additional data, such as livestock numbers, pasture and availability of feedstuff, were available in the form of Geographical Information System (GIS) grids (raster layers). Data collection involved a combination of research, direct communication with experts, and access to public and commercially available life cycle inventory packages.

The full description of GLEAM including variables and equations is available in Supplementary Information and from http://www.fao.org/gleam/resources.

Production and emission profiles in regional case studies

Regions and systems were selected based on two main criteria: their overall contribution to GHG emissions and their a priori potential for emission reduction. Six combinations of regions and systems were assessed, covering a broad range of agro-ecological conditions and production systems. Five of the six case studies focused on ruminants given their large relative contribution to overall emissions. The baseline production systems characteristics, GHG emissions and production levels are taken from GLEAM 1.0 (Gerber et al. 2013) for the 2005 reference year.

Mixed dairy cattle production in South Asia

With about 12 % of global production, South Asia is one of the world’s major cattle milk-producing regions (FAOSTAT 2015). In India, which produces 75 % of the regional output, milk production is expected to grow by 3 %/year over the period 2011–2020. South Asia accounts for 23 % of global GHG emissions from dairy mixed systems. The main source of emissions is enteric CH4, with about 60 % of total emissions. N2O from feed production (applied and deposited manure and synthetic fertilizer) accounts for 17 %. Emissions from manure, both CH4 and N2O, are less important, with about only 7 % of total emissions.

The average emission intensity (Ei) is estimated at 5.5 kgCO2-eq/kg Fat and Protein Corrected Milk (FPCM) compared with the global average of 2.7 kgCO2-eq/kgFCPM. The main reasons for high emission intensities are poor feed quality (average feed digestibility of 54 %, mainly based on crop residues and fodder), a large breeding overhead (57 % of dairy herd composed of non-milk-producing animals compared with global average of 41 %) and high mortality rates (31.1 % for calves and 8.1 % for other animals—heifers, young bulls and adults—compared with global averages of 17.8 and 6.7 %). Poor feed quality also affects animal productivity: Milk yields are low (about 965 kg/cow/year, compared with a global average of 2269 kg) and animals grow slowly, leading to older ages at first calving.

Commercial pig production in East and Southeast Asia

East Asia and Southeast Asia account for 50 % of global pork production (FAOSTAT 2015). In the past three decades, there has been a fourfold increase in pig production in the region, mostly in intermediate and industrial systems in China, which now account for about 30 and 40 % of production, respectively. These systems are likely to continue to grow (FAO 2011).

Intermediate and industrial pig systems in the region emit 320 million tonnes (Mt) CO2-eq/year with a regional averages of emission intensity (6.7 kgCO2-eq/kg Carcass Weight (CW) for intermediate pig production systems and 6 kgCO2-eq/kgCW for industrial pig production systems), close to global averages, given the region’s massive share of global pig production. Higher emission intensity in intermediate systems is due to lower animal and herd performance. In particular, late age at first sowing (1.25 year) and weaning age (40 days) result in a greater breeding overhead. High mortality rates result in further “non-productive emissions.” Lower feed quality and daily weight gains (0.66 kg/day) lead to longer production cycles.

The main sources of emissions are feed production (60 %), half of them coming from energy use (field operations, transport and processing and fertilizer production). Emissions of N2O (from manure or synthetic N application to feed crops) and CO2 from LUC (related to imported soy) are also significant sources of GHGs in these systems. Methane emissions from manure account for 14 % of total emissions in industrial systems and 21 % in intermediate systems, due to both storage in liquid forms and the warm climates found in the region.

Specialized beef production in South America

The South American specialized beef sector produces 17 % of global beef production from both beef and dairy herds. South American specialized beef emits about 1 billion tCO2-eq/year, contributing 54 % to emissions from global specialized beef production and 15 % to emissions from the entire global livestock sector.

Emissions mainly arise from t enteric fermentation (30 %), feed production, primarily from manure deposited on pasture (23 %), and LUC (40 %). Emission intensities are 100 kgCO2-eq/kgCW, higher than the global average of 68 kgCO2-eq/kgCW. The main reasons for the high level of emission intensities are LUC due to deforestation caused by the expansion of grazing lands, deposition of manure on grasslands and a large breeding overhead.

Small ruminant production in West Africa

The small ruminant sector of West Africa produces about half of total ruminant meat and one-third of total milk produced in the region (FAOSTAT 2015). Due to their hardiness, small ruminants are well suited to the region, and they are an important and relatively low-risk source of food and income for vulnerable households (Kamuanga et al. 2008).

Emission intensities in the region are 36 kgCO2-eq/kgCW and 8.2 kgCO2-eq/kgFPCM, far higher than global averages (23 kgCO2-eq/kgCW and 6.8 kgCO2-eq/kgFPCM. High emission intensities can be explained by low herd productivity, caused by poor animal health (mortality rates for adult and young animals are 9.5 and 26.0 %, respectively, compared with global average rates of 8.8 and 20.6 %) and nutrition (average feed digestibility of 55 % compared with global average of 59 % for small ruminants).

Mixed dairy production in OECD countries

While countries belonging to OECD account for only 20 % of the global number of dairy cows, they produce a massive 73 % of global milk. In these countries, mixed systems dominate, accounting for 84 % of milk production. Within the OECD, the European Union produces 37 % of global milk and North America 22 %. Driven by growing demand for dairy products, milk production has been increasing in North America and in Oceania since the 1980s, but it has remained stable in the European Union as a result of the quota policy. Mixed dairy systems are different within OECD countries, but most of them share high productivity levels and a capacity to adopt mitigations options.

The average emission intensity of is lower than the world average (1.7 and 2.7 kg CO2-eq/kgFPCM, respectively). However, mixed dairy systems in OECD countries still account for 391 MtCO2-eq, representing 28 % of total emissions from global milk production, and 6 % of total emissions from the global livestock sector. The main sources of emissions are enteric fermentation (30 to 42 %) despite high averages in feed digestibility (72 to 77 %), manure management (up to 17 % in North America) and energy emissions related to feed production, farm and post-farm activities.

Mixed dairy production in East Africa

With about 10 % of the world dairy cows, East Africa produces only 2 % of global milk (FAOSTAT 2015). Dairy production in the region is developing fast, and mixed crop–livestock systems are predominant, accounting for about 75 % of total milk production. Kenya is the largest producer with 37 % of total milk produced in East Africa and a dynamic dairy sector that has increased by 60 % since 1990, as a response to growing domestic demand.

East Africa accounts for only 5 % of global GHG emissions from mixed dairy systems, but emission intensity is nearly 4 times higher than the global average for mixed dairy systems (10.4 and 2.7 kgCO2e/kgFPCM). Enteric methane is the largest source of emissions for these systems, with about 60 % of total emissions, as a result of low feed quality (average feed digestibility of 54 % compared with the global average of 59 %) and poor animal performance (low growth rates and milk yields), leading to a large breeding herd overhead (72 % of the mixed dairy herd of non-milk-producing animals compared with a global average of 41 %).

Selection of mitigation options

Mitigation options were selected according to their a priori mitigation potential and their applicability to the respective regions and production systems. They focus on packages of available techniques that have proven to be effective over the short to medium term and that are expected to provide important productivity benefits. Interventions were also selected in view of their anticipated economic feasibility, their positive implications on productivity and considering potential trade-offs with other environmental concerns, based on the literature.
  • Improving feed quality is considered by many to be one of the most effective ways of mitigating enteric methane emissions (Hristov et al. 2013; Beauchemin et al. 2008; Monteny et al. 2006; Boadi et al. 2004). Improvements in feed digestibility can be achieved through the processing of locally available crop residues (e.g., treatment of straw with urea—Walli 2011—drying, grinding and pelleting) and use of improved forages such as mixes including leguminous grass or trees (Mohamed Saleem 1998; Mekoya et al. 2008; Oosting et al. 2011). Better feed quality also leads to better animal and herd performance, through higher growth rates.

  • Preventive health measures such as vaccination, stress reduction (provision of shade and water) and low input breeding strategies contribute to reducing mortality rates and increasing fertility rates, thus improving animal and herd performance through increasing the share of producing animals in the herd (Hristov et al. 2013).

  • The relative share of productive cohorts in the herd can also be increased through improvements in reproduction management. Semen sexing and artificial insemination can reduce the unproductive male cohort.

  • Among the various feed supplements that reduce enteric CH4 emissions, lipids such as linseed oils or cotton seed oil are increasingly mentioned as the most feasible, despite their cost (Beauchemin et al. 2008). Added to the ration of lactating cows (up to 8 % of dry matter—DM), they can result in enteric methane abatement of 10 to 30 % (Nguyen 2012; Grainger and Beauchemin 2011; Rasmussen and Harrison 2011). Although several meta-analyses report a positive impact on productivity (Rabiee et al. 2012; Chilliard and Ferlay 2004), some results indicate negative impact on DM intake and milk production (Martin et al. 2008). In practice, supplementation is generally not provided to the entire lactating herd, but to the animals with above-average milk yields.

  • Anaerobic digesters, designed to treat liquid manure, are one of the most promising practices for mitigating CH4 emissions from manure (Safley and Westerman 1994; Masse et al. 2003a, 2003b). When correctly operated, anaerobic digesters are also a source of renewable energy in the form of biogas, which is 60 to 80 % CH4, depending on the substrate and operational conditions (Roos et al. 2004).

  • Adequate grazing management can contribute to carbon sequestration in soils (Soussana et al. 2004). Improving balance between forage growth/grass availability and grazing, increasing animal mobility and rest periods can all have a positive impact on forage production and soil carbon sequestration. In addition, the digestibility of grasses can be improved through practices that reduce cell-wall concentration (Jung and Allen 1995), including sowing of better quality pastures (Hristov et al. 2013; Alcock and Hegarty 2011; Wilson and Minson 1980). For example, according to Thornton and Herrero (2010), the replacement of native Cerrado grasses with more digestible Brachiaria decumbens has been estimated to increase daily growth rates in beef animals by 170 %. Improved grassland management can also improve nutrition, resulting in faster animal growth and earlier age at first calving, but also in increased cow fertility and reduced mortality of calves and mature animals, thus improving animal and herd performances (Hristov et al. 2013).

  • Adopting energy-efficient technologies and using low carbon energy reduces emissions from feed production, farm management and post-farm activities. Decreasing the emission intensity of energy requires decarbonizing power generation, which can be achieved through a significant switch to renewable energy production and wider carbon capture and storage (IEA 2008). In the BLUE Map scenario, IEA calculates that emissions in 2050 are reduced by 50 % compared with 2005 through reduction in energy Ei and gains in efficiency in all economic sectors (1.7 %/year). Similarly, Kimura (2012) examined ta Business-As-Usual (BAU) scenario that reflects each country’s current goals and action plans, and an Alternative Policy Scenario (APS) that includes additional voluntary goals and action plans currently under consideration. A partial shift from coal and oil to renewable and nuclear energy sources and the adoption of clean coal technologies and carbon capture and storage can reduce emissions from energy consumption per Gross Domestic Product (GDP) by 23 % for BAU and 46 % for APS.

Modeling assumptions

Baseline emissions were computed in GLEAM for the reference year 2005, whereas a short- to medium-term time horizon was assumed for mitigation options (up to 2020–2030). The mitigation potential was assessed for constant final output of meat and milk, which allows clear comparison of mitigation effects across systems and practices. Most of the options explored in this assessment having positive impacts on productivity, the potential for increased production was also estimated. The method to model each of the options in GLEAM is detailed in the Supplementary Information (Tables 4 to 9). The general approach was to model improved practices by either using the literature to improve the GLEAM input parameters or to partially align them with the best performances found at regional level.

The impact of improved feed quality on animal performance for ruminants was modeled by assuming that every 1 % increase in digestibility leads to a 4 % increase in growth rate, 5 % increase in milk yields and 4 % decrease in age at first parturition, which are quite conservative assumptions (Keady et al. 2012; Steen 1987; Manninen et al. 2011; Scollan et al. 2001; Bertelsen et al. 1993).

The adoption of improved reproduction practices was modeled in India only by modifying calves female-to-male sex ratio, from 50:50 in baseline to 80:20, and using the assumption that 50 % of the farms use artificial insemination (NDDB 2013) and 25 % use sexed semen (Rath and Johnson 2008; DeJarnette et al. 2009; Norman et al. 2010; Borchersen and Peacock 2009). The use of feed supplements was modeled by reducing enteric methane emissions of half of the lactating cows by 10 and 30 %, to provide a range of efficiency similar to that reported in the literature. For manure management, the amount of treated in liquid form (pits, lagoons, daily spread) was decreased, the biogas produced by anaerobic digestion was estimated and the equivalent CO2 emissions saved from fossil fuel substitution was calculated (under the different energy efficiency improvement scenarios).

Estimates of soil carbon sequestration in grasslands are taken from Henderson et al. (2015a), who relied on the Century model.

Reductions in emissions from energy were modeled following IEA (2008) and Kimura (2012). Given that the main part occurs off-farm (fertilizer and food industries, transport of feed and products, etc.), it was assumed that the energy use efficiency achieved on an economy-wide level applies also to livestock production.

Results

Mitigation

An overall reduction of 14–41 % of baseline emissions can be achieved in the regions and systems studied (Table 1; Fig. 1). Detailed maps of case studies are provided in Supplementary Information (Figures 2 to 7).
Table 1

Results of GLEAM modeling of mitigation packages by system and region

 

Mixed dairy production in South Asia

Absolute potential (Mt CO2-eq)

120

Share of baseline emissions

38 %

Share of total livestock emissions

2 %

Of which

Improved feed quality

30.4 %

Improved herd structure

7.6 %

Production system

Pig production in East and Southeast Asia

Intermediate

Industrial

Total commercial

Energy scenario

BAU

APS

BAU

APS

BAU

APS

Absolute potential (Mt CO2-eq)

26

33

21

33

47

66

Share of baseline emissions

26 %

33 %

16 %

25 %

20 %

28 %

Share of total livestock emissions

0.4 %

0.5 %

0.3 %

0.5 %

0.7 %

0.9 %

Of which

Reduced CH4 from manure

7.0 %

7.0 %

4.2 %

4.2 %

5.4 %

5.4 %

Energy produced by biogas

2.2 %

2.0 %

1.7 %

1.4 %

1.9 %

1.7 %

Energy use efficiency

5.0 %

9.9 %

9.6 %

19.3 %

7.6 %

15.3 %

Feed quality and animal performancesa

12.2 %

13.9 %

5.3 %

6.0 %

Agro-ecological zone

Specialized beef production in South America

Temperate

Humid

Arid

Total

Absolute potential (Mt CO2-eq)

9.2–13

156–255

24–42

190–310

Share of baseline emissions

40–57 %

17–29 %

16–29 %

18–29 %

Share of total livestock emissions

0.1–0.2 %

2.2–3.6 %

0.3–0.6 %

2.7–4.4 %

Of which

Improved feed quality

4.4–10 %

3.6–8.9 %

3.5–8.9 %

3.6–9.0 %

Improved fertility

7.5–12 %

3.7–5.7 %

3.2–5.4 %

3.7–5.8 %

Reduced mortality

20–28 %

9.4–13 %

8.0–13 %

9.4–13 %

Soil C sequestration

7.5 %

0.8 %

1.6 %

1.0 %

Species

Small ruminants in West Africa

Sheep

Goats

Total

Absolute potential (Mt CO2-eq)

4.7–7.1

3.0–4.9

7.7–12

Share of baseline emissions

33–49 %

21–33 %

27–41 %

Share of total livestock emissions

0.1 %

0.1 %

0.2 %

Of which

Improved feed quality

4.7–12 %

5.4–13 %

5.0–13 %

Improved fertility

6.0–6.7 %

1.9–2.5 %

4.0–4.6 %

Reduced mortality

11–19 %

5.0–9.2 %

7.9–14 %

Soil C sequestration

11 %

8.4 %

9.7 %

Region

Mixed dairy production in OECD countries

North America

Western Europe

Oceania

All OECD

Absolute potential (Mt CO2-eq)

25–28

21–26

2–4

54–66

Share of baseline emissions

25–28 %

11–14 %

11–17 %

14–17 %

Share of total livestock emissions

0.4 %

0.4 %

0.1 %

0.9 %

Of which

Fat supplementation

1.5–4.4 %

1.2–3.6 %

3.1–9.3 %

1.5–4.5 %

Manure management

12.7 %

2.8 %

3.2 %

4.9 %

Biogas production

4.4 %

2.4 %

0.7 %

2.4 %

Energy use efficiency

6.2 %

4.8 %

4.2 %

5.0 %

 

Mixed dairy production in East Africa

Absolute potential (Mt CO2-eq)

13–31

Share of baseline emissions

10–24 %

Share of total livestock emissions

0.2–0.4 %

of which

Improved feed quality

6–14 %

Improved herd structure

4–10 %

aOnly for intermediate systems

BAU Business-As-Usual, APS Alternative policy scenario

Fig. 1

Summary of mitigation options and potential for greenhouse gas emission reduction in % of baseline emissions in 6 regional case studies

In low productivity systems, improved digestibility of feed has the highest mitigation potential, mainly as a result of a reduction in animal numbers: Yield gains allow the same milk production to be achieved with fewer animals. For example, the mitigation effect of improved feeding is 85 Mt CO2-eq in India, 71 % of total mitigation effect in the region. In East Africa, it reaches 14 % of baseline emissions under the most optimistic assumptions, two-thirds of the total mitigation potential. In intermediate pig production systems, about half of the mitigation is achieved by improving feed quality and animal performance.

Lower mortality rates contribute the most to mitigation in West Africa (8–4 %) and South America (9–13 %). The combined effect of lower mortality rates and higher growth and fertility rates due to better nutrition reduces the required number of replacement females (44 % under the most optimistic scenario in South America and 13 % in East Africa).

Soil carbon sequestration can contribute significantly to mitigation, especially in temperate climatic zones. In South America, total annual sequestration of soil carbon is estimated to be 11 MtCO2-eq, and on about 80 million ha in West Africa, soil carbon sequestration makes the second largest contribution for small ruminants (upper range), offsetting almost 10 % of total emissions.

The combination of reduced emissions from manure storage and emission savings from energy substitution is significant in Oceania and North America (3.9–17.1 %) and in pig production in Asia (5.9–9.2 %).

Energy use efficiency is the most effective intervention to reduce emissions in pig industrial systems, but also in mixed dairy systems in OECD countries, given the share of products that are processed.

Though it ranges between 2 and 7 Mt CO2-eq, the impact of dietary lipids appears to be modest in dairy production in Western Europe and Northern America when expressed in relative terms, but is more significant in Oceania (up to 9.1 %).

To explore the impact of a higher feed emission intensity, the mitigation potential of improved feed quality was recalculated for pig production in East and South Asia with 0.9 kg CO2-eq/kgDM instead of 0.79 kg CO2-eq/kgDM. Results indicate a mitigation potential of 17 % of baseline emissions under the BAU energy scenario, and 24 % under the APS scenario, which remains important.

Productivity and emission intensities

Many mitigation options can also increase production. This is particularly the case for improved feeding practices, health and herd management. When holding the number of adult females constant, output is estimated to increase in each of the six case studies in which mitigation options improve animal performance (Table 2). Naturally, the absolute mitigation potentials are lower than when output is held constant. Nonetheless, simultaneous expansion of output and reduction in emissions happens in five of the six case studies.
Table 2

Potential for increased outputs of animal products and mitigation estimated for constant and increased output

System

Increase in output (Mt FPCM or CW)

Mitigation (absolute potential Mt CO2-eq or share of baseline emissions)

Emission intensity (kg CO2-eq/kg FPCM or CW)

With constant output

With increased output

Baseline

Mitigation scenario

Mixed dairy South Asia

13 or 24 %

120 or 38 %

72 or 23 %

5.7

3.6

Commercial pigs East and Southeast Asia

3 or 7 %

47–66 or 20–28 %

34–54 or 14–23 %

4.7

3.4–3.8

Specialized beef South America

2.8–5.0 or 27–48 %

190–310 or 18 %–29 %

−63 to −65 or −6 %

100

72–83

Small ruminants West Africa

0.12–0.26 or 19–40 % (meat)

0.03–0.10 or 5–14 % (milk)

8–12 or 27–41 %

2–5 or 27 %–41 %

36 (meat)

8.2 (milk)

22–29 (meat)

5.3–6.8 (milk)

Mixed dairy OECD

None

54–66 or 14 %–17 %

1.7

1.4–1.5

Mixed dairy East Africa

6–18 %

13–31 or 10–24 %

6–13 or 5–10 %

10.4

8.0–9.4

Ruminant sectors experience the largest increases in output and smallest reductions in emissions, due to the importance of mitigation measures that boost animal productivity. By contrast, the commercial pig sector experiences small output increases, but larger emission reductions due to the greater importance of energy efficiency and “end of pipe” mitigation practices.

Discussion

The most promising mitigation strategies discussed in this paper are summarized in Table 3. Although perhaps the most widely tested option (Hristov et al. 2013), supplementation of ruminants with grain was excluded from this analysis due to concerns about its economic feasibility and its potential to threaten food security. Moreover, a much broader analysis would have been required, accounting for the indirect impacts that substantial increase demand for concentrate feed would have on agriculture and LUC emissions, which were considered to be beyond the scope of this study. Other options, such as breeding improvements to increase animal productivity or anti-methanogen vaccines, were not included given the medium-term time horizon of the study. Possible vaccines are considered to have great potential in extensive ruminant systems, as they would require very infrequent inoculations and minimal management (Whittle et al. 2013; Moran et al. 2008; Beach et al. 2008). However, they still require large research efforts and are unlikely to be commercially available in the near future (Hristov et al. 2013). A number of controversial growth-promoting compounds, such as ionophores and bovine somatotropin (bST), which have been estimated to be effective mitigation options (US EPA 2006; Moran et al. 2011; Smith et al. 2007), were also excluded due to bans on their use in important markets (e.g., European Union) and uncertainties about their human health implications. Supplementation with synthetic amino acids, such as lysine in pig production, was also omitted in view of its cost, although it is often described as increasing efficiency and manure NH3 and N2O mitigation (Hristov et al. 2013).
Table 3

Most promising mitigation strategies at different level in livestock supply chains

Sector

Level of implementation

Mitigation strategies

Ruminants

Animal

Feed digestibility

Feed balancing

Health

Genetics

Herd

Overhead herd and production ratio

Production unit/farm

Grazing management

Supply chain

Energy use efficiency

Waste minimization & recycling

Monogastrics

Animal

Feed balancing

Health

Genetics

Production unit/farm

Source low Ei feed & energy

Supply chain

Energy use efficiency

Waste minimization & recycling

The mitigation potential estimated in this study is in line with the emission targets of a number of national voluntary schemes, mostly in OECD countries. For example, the Innovation Center for US Dairy (2008) announced a reduction in emissions by 25 % between 2009 and 2020 The Milk Roadmap prepared by the Dairy UK Supply Chain Forum (2008) expressed the intention to cut emissions from dairy farming by 20–30 % between 1990 and 2020, and to improve the energy efficiency of the industry by 15 % (1.3 %/year). The French plan “Carbon dairy” aims to reduce emissions by 20 % by 2020 (http://www.carbon-dairy.fr/). The Brazilian government is committed to a carbon sequestration target of 83–104 Mt CO2-eq through the restoration of 15 million hectares of degraded grassland, between 2010 and 2020, in its Low-Carbon Agriculture Program (ABC), a similar potential to the one estimated in this study, though based on different assumptions.

As illustrated by this study, a strong correlation can be found between mitigation and productivity gains, especially among ruminant systems operating at low productivity (Gerber et al. 2011). Improving resource use efficiency is recognized as a key approach for increasing the sustainability of livestock production (Herrero et al. 2013b). Therefore, options that boost productivity, such as improving feed quality and health management, have higher mitigation potential than “end of the pipe” options, such as improving manure management. This strategy, often referred to as “bridging the yield gap” (Foley 2011; Cassman et al. 2003), has been highlighted as one of the most promising approaches to improving the sustainability of food value chains and improving food security (West et al. 2014).

This assessment illustrates the technical mitigation potential of livestock in six case studies based on plausible practices and adoption rate in the midterm. The use of a modeling approach, relying on most accurate data available and harmonized methods, allows to consistently estimate mitigation potentials along livestock supply chains, and across a range of systems and agro-ecological contexts. It shows that mitigation is achievable in all regions and production systems, without system shift. The supply chain approach enables to model the effectiveness of comprehensive packages of mitigation techniques and practices (e.g., combinations of herd management, feeding practices and manure management), taking into account the interaction between practices and possible leakage along the chain, including post-farm processes. This exercise is complementary to direct measurements, generating specific insights but usually focusing on individual interventions, failing to grasp the full mitigation effect and the heterogeneity characteristic of the agricultural sector. It is, to the author’s knowledge, the most spatially detailed broad scale assessment, covering a diversity of regions but maintaining a very consistent basis for comparison given that all systems are modeled in the same framework. It allows for better prioritization, design and targeting of packages of interventions and technologies best suited to address the diversity of livestock production systems.

Tier 2 estimates of livestock emissions come with uncertainty that can be attributed to data on population data, production practices and performance, including feeding strategy. The use of Tier 2 methodology first requires a detailed characterization of the cattle population in different cohorts. These data are not available in a large number of countries and were modeled for this study using a number of herd parameters obtained through data collection and literature reviews. In addition, there is a scarcity of published data on production practices, dietary information, DM intake and animal performance, which may contribute to the uncertainty of model prediction. Uncertainty analyses (Monte Carlo) were conducted in a selection of countries and production systems, including on the land-use change assumptions. Results are presented in detail in Opio et al. (2013) and MacLeod et al. (2013). Coefficient of variation for emission intensities ranges from 24 to 26 % for ruminants and from 10 to 16 % for monogastrics. Relatively, few parameters are responsible for most of the variance. Key parameters contributing to uncertainty in ruminant production are feed digestibility, the emission factor used for N2O from manure deposited on pasture, milk yields for dairy systems and land-use change assumptions in grazing systems. For monogastrics, uncertainty is mainly due to the emission factor for N2O emissions from organic and synthetic N application on feed crops and N application rates. Daily weight gain (productivity) is an important source of uncertainty in backyard and intermediate systems, while assumptions from land-use change associated with soybean production are significant in industrial monogastric systems. The sensitivity analysis conducted for the LUC approach showed that results are strongly driven by the time frame considered and the previous use of land prior to soy beans (forests or pastures) (Opio et al. 2013; MacLeod et al. 2013).

This work does not include considerations about possible barriers to adoption. In the absence of financial incentives (e.g., mitigation subsidies) or regulations to limit emissions, most producers are unlikely to invest in mitigation practices unless they increase profits or provide other production benefits such as risk reduction. In this respect, cost–benefit analyses (Henderson et al. 2015b; US EPA 2013; Beach et al. 2008) are needed to estimate the emission reductions that could be achieved in an economically viable way. Henderson et al. (2015b), building on the same approach and model and focusing on a subset of mitigation options, showed that most promising practices could abate up to 379 MtCO2-eq/year, 11 % of annual global ruminant emissions. They estimated that around two-thirds of this potential are achievable at a carbon price of $20 tCO2-eq-1, a price level that has been observed in Kyoto-compliant carbon markets in the past. They also showed that some mitigation practices were more cost-effective than others. Practices which either sequester soil carbon (e.g., legume sowing and grazing management) or are based on the processing of crop straws to improve their nutritive value tended to contribute the most to mitigation at carbon prices up to $20 tCO2-eq-1. Likewise, mitigation was more affordable in some production systems than others. For instance, marginal abatement costs were generally lower for dairy than other ruminant sectors, due to higher returns from investments. In addition, other barriers will play a major role on the uptake of these practices, such as technical capacity of producers, extension agents and institutions, and the availability of capital and infrastructure to support adoption of the selected mitigation measures (de Garcia et al. 2014; Sánchez et al. 2014). Gerber et al. (2013) have explored the policy implications and requirements to overcome these barriers, but further research is needed for a better assessment of the “policy mitigation potential” of the livestock sector.

The adoption of GHG mitigation interventions may have side effects (positive or negative) on other environmental impacts (e.g., preservation of water resources and LUC), animal welfare and wider development goals (e.g., food security and equity), which need to be assessed and integrated as part of livestock sector policies. These factors are not modeled in this assessment; however, the selection of mitigation practices and, in some cases, assumptions about their level of adoption were made in view of some of these constraints and issues. For example, by improving animal and herd productivity, most of the selected mitigation practices have the capacity to simultaneously increase production and reduce emissions, and thus avoid conflicts between environmental and food security objectives.

This study based on an attributional Life Cycle Assessment (LCA) has limited capacity to explore the consequences of large changes in production practices. For example, improving feed quality in pig production in East and Southeast Asia could result in increasing feed emission intensity. Addressing this matter would require engaging in consequential LCA in particular to predict supply responses and changes in trade flows caused by the change in feeding practices. The selection of mitigation practices in this assessment was made within the range of options that can be modeled in an attributional environment. However, results indicate that with a feed emission intensity of 0.9 kg CO2-eq/kgDM instead of 0.79 kg CO2-eq/kgDM, mitigation potential remains important.

Conclusions

Improving efficiency in livestock supply chains can appreciably reduce GHG emissions and, at the same time, increase animal product supply. While the mitigation potential of a selection of best-bet practices seems to be significant, institutional, economic and behavioral barriers to adoption tend to reduce this mitigation potential.

Efficiency gains are recognized to be very promising for improving the sustainability in food value chains and food security and important mitigation efforts are being carried out on local industry scales. However, international mechanisms for GHG emission accounting and reduction cannot currently reflect these mitigation and productivity efforts since methodologies for national inventories do not account for them.

Realizing the technical mitigation potential described will therefore require adequate policy frameworks to overcome barriers to adoption, education, awareness raising and incentives for technology transfer but also improved monitoring and reporting methods and data. Due to the size and complexity of the livestock sector, the design and implementation of cost-effective and equitable mitigation strategies and polices can only be achieved through concerted action by all stakeholder groups. Moreover, given the global public good nature of climate change mitigation and overcoming the sector’s socioeconomic challenges, collective global action is both welcome and needed. Because of the increasing global economic integration of livestock sector supply chains, unilateral actions to mitigate GHG emissions will be much less effective than more internationally coordinated actions. In addition, unilateral policies invariably raise issues about competitiveness and fairness for sectors that are exposed to international trade. Global multi-stakeholder initiatives can support integration and mainstreaming of mitigation and development objectives pursued by sector stakeholders, especially in terms of methodology harmonization for GHG accounting and LCAs, and advocacy for their inclusion in international mechanisms.

Notes

Acknowledgments

This research was supported by the AnimalChange Project (FP7/2007-2013, Grant Agreement No. 266018), the Mitigation of Climate Change in Agriculture project (MICCA) and the CGIAR research program on Climate Change Agriculture and Food Security (CCAFS) and benefited from valuable comments from Henning Steinfeld, Tim Robinson, Jeroen Dijkman, Caroline Chaumont, Harinder Makkar and two anonymous reviewers.

Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Anne Mottet
    • 1
  • Benjamin Henderson
    • 2
  • Carolyn Opio
    • 1
  • Alessandra Falcucci
    • 1
  • Giuseppe Tempio
    • 1
  • Silvia Silvestri
    • 3
  • Sabrina Chesterman
    • 4
  • Pierre J. Gerber
    • 1
    • 5
  1. 1.UN Food and Agriculture OrganizationRomeItaly
  2. 2.Commonwealth Scientific and Industrial Research Organization, Queensland Bioscience PrecinctSt LuciaAustralia
  3. 3.Centre for Agriculture and Biosciences InternationalNairobiKenya
  4. 4.International Livestock Research InstituteNairobiKenya
  5. 5.Animal Production System GroupWageningen UniversityWageningenThe Netherlands

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