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Li Y, McIntyre KM, Rasmussen P, Gilbert W, Chaters G, Raymond K, Jemberu WT, Larkins A, Patterson GT, Kwok S, Kappes AJ, Mayberry D, Schrobback P, Acosta MH, Stacey DA, Huntington B, Bruce M, Knight-Jones T, Rushton J. Rationalising development of classification systems describing livestock production systems for disease burden analysis within the Global Burden of Animal Diseases programme. Res Vet Sci 2024; 168:105102. [PMID: 38215653 DOI: 10.1016/j.rvsc.2023.105102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/05/2023] [Accepted: 11/27/2023] [Indexed: 01/14/2024]
Abstract
The heterogeneity that exists across the global spectrum of livestock production means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. To ensure that burden of disease estimates are specific to the represented livestock population and people reliant upon them, livestock populations need to be systematically classified into different types of production system, reflective of the heterogeneity across production systems. This paper explores the data currently available of livestock production system classifications and animal health through a scoping review as a foundation for the development of a framework that facilitates more specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global scale. The proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems. The developed framework will fill a major gap in how livestock production and health are currently approached and analysed.
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Affiliation(s)
- Yin Li
- Global Burden of Animal Diseases (GBADs) Programme; Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, 4067 Brisbane, Australia; School of Veterinary Medicine and Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Australia.
| | - K Marie McIntyre
- Global Burden of Animal Diseases (GBADs) Programme; School of Natural and Environmental Sciences, Newcastle University, UK; Institute of Infection and Global Health, University of Liverpool, IC2 Building, 146 Brownlow Hill, Liverpool L3 5RF, UK
| | - Philip Rasmussen
- Department of Veterinary and Animal Sciences, Section for Animal Welfare and Disease Control, University of Copenhagen, Copenhagen, Denmark; Section for Epidemiology, University of Zurich, Zurich, Switzerland
| | - William Gilbert
- Global Burden of Animal Diseases (GBADs) Programme; Institute of Infection and Global Health, University of Liverpool, IC2 Building, 146 Brownlow Hill, Liverpool L3 5RF, UK
| | - Gemma Chaters
- Global Burden of Animal Diseases (GBADs) Programme; Institute of Infection and Global Health, University of Liverpool, IC2 Building, 146 Brownlow Hill, Liverpool L3 5RF, UK
| | - Kassy Raymond
- Global Burden of Animal Diseases (GBADs) Programme; School of Computer Science, University of Guelph, Canada
| | - Wudu T Jemberu
- Global Burden of Animal Diseases (GBADs) Programme; International Livestock Research Institute, P O Box 5689, Addis Ababa, Ethiopia; University of Gondar, P. O. Box 196, Gondar, Ethiopia
| | - Andrew Larkins
- Global Burden of Animal Diseases (GBADs) Programme; School of Veterinary Medicine and Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Australia
| | - Grace T Patterson
- Global Burden of Animal Diseases (GBADs) Programme; School of Computer Science, University of Guelph, Canada
| | - Stephen Kwok
- Global Burden of Animal Diseases (GBADs) Programme; School of Veterinary Medicine and Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Australia
| | - Alexander James Kappes
- Global Burden of Animal Diseases (GBADs) Programme; School of Economic Sciences & Paul G. Allen School for Global Health, Washington State University, USA
| | - Dianne Mayberry
- Global Burden of Animal Diseases (GBADs) Programme; Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, 4067 Brisbane, Australia
| | - Peggy Schrobback
- Global Burden of Animal Diseases (GBADs) Programme; Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, 4067 Brisbane, Australia
| | - Mario Herrero Acosta
- College of Agriculture and Life Sciences, Cornell University, 250C Warren Hall, Ithaca, NY 14853, USA
| | - Deborah A Stacey
- Global Burden of Animal Diseases (GBADs) Programme; School of Computer Science, University of Guelph, Canada
| | - Benjamin Huntington
- Global Burden of Animal Diseases (GBADs) Programme; Institute of Infection and Global Health, University of Liverpool, IC2 Building, 146 Brownlow Hill, Liverpool L3 5RF, UK
| | - Mieghan Bruce
- Global Burden of Animal Diseases (GBADs) Programme; School of Veterinary Medicine and Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Australia
| | - Theodore Knight-Jones
- Global Burden of Animal Diseases (GBADs) Programme; International Livestock Research Institute, P O Box 5689, Addis Ababa, Ethiopia
| | - Jonathan Rushton
- Global Burden of Animal Diseases (GBADs) Programme; Institute of Infection and Global Health, University of Liverpool, IC2 Building, 146 Brownlow Hill, Liverpool L3 5RF, UK
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Xie K, Liu F, Zhang C, Hou F. Nitrogen utilisation, energy utilisation and methane emissions of sheep grazing in two types of pasture. Animal 2023; 17:100705. [PMID: 36724585 DOI: 10.1016/j.animal.2023.100705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Livestock grazing plays a significant role in maintaining grasslands and promoting animal production globally. To understand the livestock performance in sown pasture (SP) vs native pasture (NP) is important to ensure more effective grassland-livestock interactions with minimal environmental impact. A 2 (treatment) * 2 (period) Latin Square design experiment was conducted with 10 growing Hu sheep ♂ × thin-tailed Han sheep ♀ rams grazed perennially SP vs NP in an inland arid region of China. The objectives were to evaluate the effects of grazing management on nutrient digestibility, nitrogen (N) and energy utilisation and methane (CH4) emission. The N intake, N retained and energy intake (gross energy (GE), and digestible and metabolisable energy) of sheep grazing in SP were significantly increased compared with those grazing in NP. There were significant linear relationships between DM intake (DMI) (g/kg BW or g/kg BW0.75) or CH4 (g/kg BW or g/kg BW0.75) emissions and forage nutrient and GE concentrations within each grassland type. The linear regression analysis indicated that forage CP or ether extract concentration was a good predictor for DMI (g/kg BW or g/kg BW0.75) (R2 = 0.756 or 0.752), and CH4 emission could be predicted using forage nutrient and GE concentrations (R2 = 0.381-0.503). These results suggest that DMI and CH4 emissions per unit metabolic BW were accurately predicted by multiple-factor combinations of forage nutrients, including ether extract and CP paired with GE. The present output could provide useful information for the development of sustainable sheep grazing systems in the inland arid regions of the world.
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Affiliation(s)
- Kaili Xie
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
| | - Fuyao Liu
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
| | - Cheng Zhang
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
| | - Fujiang Hou
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.
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Integrated Carbon Footprint and Economic Performance of Five Types of Dominant Cropping Systems in China’s Semiarid Zone. SUSTAINABILITY 2022. [DOI: 10.3390/su14105844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Crop production requires large areas of land and makes an important contribution to greenhouse gas emissions. Cleaner production of all crop types could be of great significance to realizing carbon neutrality as soon as possible. The present study adopted life cycle assessment (LCA) combined with the profit accounting method of input-output to compare the differences in greenhouse gas emissions in the entire life cycle of apple (Malus pumila Mill.), grain maize (Zea mays L.), wheat (Triticum aestivum L.), silage maize (Zea mays L.), and alfalfa (Medicago sativa Linn.) production in eastern Gansu Province with three functional units, including per ha of land, per ton of product, and per 10,000 yuan of output value. The results showed that apple had the largest carbon footprint per ha. Wheat had the largest carbon footprint per ton of product and per 10,000 yuan output. The results of LCA inventory sensitivity analysis showed that the main sources of greenhouse gas emissions for all crops were the production process of agricultural materials such as chemical fertilizer, machinery, and agricultural film. In particular, the excessive input of chemical fertilizer was the driving factor resulting in greenhouse gas emissions. Based on the study results, this paper also puts forward certain suggestions on the future land use of the cropping systems in the study area.
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