GLEAM: an example of the potential contribution of livestock modelling to sustainable intensification
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1 GLEAM: an example of the potential contribution of livestock modelling to sustainable intensification Carbon Management Centre International Conference Sustainable intensification: The pathway to low carbon farming? Edinburgh th September 2013 Michael MacLeod a,b*, Pierre Gerber b, Theun Vellinga c, Carolyn Opio b, Alessandra Falcucci b, Giuseppe Tempio b, Klaas Dietze b, Guya Gianni b, Benjamin Henderson b, Harinder Makkar b, Anne Mottet b, Tim Robinson d, Henning Steinfeld b, Olaf Thieme b and Viola Weiler b a. SRUC; b. FAO; c. WUR; d. ILRI *SRUC, Edinburgh, Scotland, EH9 3JG, michael.macleod@sruc.ac.uk 1
2 Overview Rationale for FAO developing GLEAM - Global Livestock Environmental Accounting Model Overview of the model Explanation of key features of the model GLEAM and sustainable intensification 2
3 Why study global livestock? Global production 2005 (Sources: FAO (2013a,b), OECDStat) Global production 2005 (Based on data extracted 21/1/13 from OECDStat) Livestock commodities are a key element in our diet The sector accounts for 40% of agricultural GDP It employs 1.3bn, and provides livelihoods for many (~1bn) of the world s poor (Steinfeld et al. 2006, pxx) Crucially, it is a rapidly changing sector > implications for food security, livelihoods and environment 3
4 Implications of growth in the livestock sector Either improve the efficiency of production or face proportionate increases in emissions Figures based on: Alexandratos, N. and J. Bruinsma. 2012, Table 3.4, 3.5 Satisfying the growing demand for animal food products while at the same time sustaining the productive assets of the natural resource base soil, water, air, biodiversity and coping with climate change and vulnerability Pingali and McCullough (2010, p9) 4
5 FAO s analysis of global livestock emissions LLS initiated debate Follow-up analysis to inform debate Improved accuracy More informative types of data GLEAM developed to do analysis GLEAM v LLS method differ (see FAO 2013c)
6 What is GLEAM? Global Livestock Environmental Accounting Model GIS-based model: Basic spatial unit 0.05x0.05 degree cell (~5x5km at the equator) Input data layers (e.g. animals distributions, crop areas and yields, etc.) Calculation routines written in Python Procedures for running the model and extracting and checking results Models the main livestock production activities and related resource flows in all countries. Covers the main 11 global livestock commodities, and predominant production systems. System boundary: cradle to retail point. Impact categories: current version (v1.0) focuses on GHG emissions; includes the main GHG emissions categories (soil carbon fluxes not arising from LUC to be included in V2.0) 6
7 GLEAM structure Source: FAO 2013a 7
8 Examples of GLEAM output Global pig meat production and emissions Within region variation in emissions intensity Regional breakdown of emissions intensity GIS enables results to be aggregated as desired Source: FAO (2013a) 8
9 How can GLEAM support sustainable intensification? Descriptive modelling How and why does the impact of different commodities and production systems vary? Predictive modelling What happens to the emissions intensity if: Sexed semen is used to reduce unwanted dairy calves (see poster by Eory et al.) Synthetic N is replaced with manure N Soy in the ration is replaced with substitutes Or if the wider context changes: Nuclear power stations are replaced with coal? Fertiliser producers widely adopt Best Available Technology? Different food packaging is introduced? Recalculate with interventions Parameterise interventions Production, emissions and EI Identification of potential interventions Analyse patterns and identify hotspots Investigation of why and how hotspots occur What features enable GLEAM to answer these types of questions? 9
10 Highlight 2 key features of GLEAM Tier 2 approach to emissions from livestock and manure management enables exploration of variation and predictive modelling Herd module enables definition of livestock populations 10
11 Tier 2 approach to emissions from livestock and manure management IPCC (2006) Tier 1 v Tier 2 v Tier 3 The T2 approach reflects animal populations, productivity, diet quality and management circumstances T2 provides more accurate (and informative) estimates of feed intake and emissions than T1, but requires a lot of additional data e.g. for manure management. 11
12 Tier 1 v Tier 2: manure management Manure methane EFs for pigs (kgch4/hd/year) Sow Market swine Tier 1 Tier 2* Tier 1 Tier 2* Denmark UK Tier 1 default EF for Western Europe Tier 2 context-specific EF calculated for each pig cohort *calculated using GLEAM Manure EF VSx and Nx Manure management DMI N retention Weight, growth rate, activity level etc. Ration properties Growth rate etc. Ration composition Tier 2 enables investigation of drivers of emissions and predictive modelling Tier 2 should be more accurate, but is only as good as the input data how do we acquire the required data on e.g., livestock populations, diet quality and manure management? 12
13 Livestock populations Challenge: Emissions and production varies markedly between different animals categories > need to know herd structure. On-farm emissions: UK conventional pig farm (kgco 2 e/head/year) Sows Sow replacements Boars Boar replacements Pigs reared for meat 2,298 1,993 1,859 2,129 1,501 On-farm emissions: UK dairy farm (kgco 2 e/head/year) Cow Cow replacements Bull Bull replacements Surplus calves (F) Surplus calves (M) 11,207 3,671 4,999 4,128 3,100 3,093 Approach: Herd module determines the herd structure using data on key herd parameters, e.g. mortality, fertility, growth and replacement rates. Benefits Enables determination of herd structures where these are not known (particularly important for ruminants and intermediate/backyard pigs) Enables output and emissions to be calculated for each cohort Enables investigation of the effect of varying key herd parameters, useful for modelling effects of mitigation measures or disease on EI etc. 13
14 Conclusions GLEAM can provide useful insights for SI, but some notes of caution GHG is not the only impact Input data quality can be a limiting factor Requires further development and validation wasn t initially designed to do predictive modelling Current applications at SRUC Developing MACCs for Scottish farm types Analysis of the relationship between animal health and GHG emissions (mastitis and Johne s in Scottish dairy; AAT in Kenyan dairy) Continuing development of GLEAM by FAO, notably within LEAP, the Livestock Environmental Assessment and Performance Partnership 14
15 Acknowledgements This work was conducted at the Animal Production and Health Division (AGA) of FAO and co-financed by the Mitigation of Climate Change in Agriculture (MICCA) programme. 15
16 References Alexandratos, N. and J. Bruinsma World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. Curran, M.A. (2006) LIFE CYCLE ASSESSMENT: PRINCIPLES AND PRACTICE Cincinnati: US EPA (available at: FAO (2010). Greenhouse Gas Emissions from the Dairy Sector: A Life Cycle Assessment. Food and Agriculture Organization, Rome, Italy. FAO (2013a) Greenhouse Gas Emissions from Pig and Chicken Supply Chains: A Global Life Cycle Assessment, Michael MacLeod, Pierre Gerber, Theun Vellinga, Carolyn Opio, Alessandra Falcucci, Giuseppe Tempio, Benjamin Henderson, Anne Mottet and Henning Steinfeld Rome: FAO FAO (2013b) Greenhouse Gas Emissions from Ruminant Supply Chains: A Global Life Cycle Assessment, by Carolyn Opio, Pierre Gerber, Theun Vellinga, Michael MacLeod, Alessandra Falcucci, Benjamin Henderson, Anne Mottet, Giuseppe Tempio and Henning Steinfeld Rome: FAO FAO (2013c) Livestock s Role in Tackling Climate Change: A Global Assessment of Emissions and Mitigation Opportunities, by Pierre Gerber, Henning Steinfeld, Benjamin Henderson, Carolyn Opio, Anne Mottet, Jeroen Dijkman, Alessandra Falcucci, and Giuseppe Tempio. Rome: FAO IPCC (2007) Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Pingali, P. and McCullough, E. (2010) Drivers of change in global agricultural livestock systems. In Livestock in a Changing Landscape Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., & de Haan, C. (2006). Livestock s long shadow Environmental issues and options. Rome, Italy: FAO. 16
17 HOW: Overview of GLEAM (FAO s Global Livestock Environmental Assessment model) MacLeod et al. (2013) 17
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