Calculation of new enteric methane emission factors for small ruminants in western Kenya highlights the heterogeneity of smallholder production systems
J. P. Goopy
A B C G , P. W. Ndung’u A C , A. Onyango A D , P. Kirui A E and K. Butterbach-Bahl A F
A Mazingira Centre, International Livestock Research Institute, Old Naivasha Road, Uthiru, Nairobi, Kenya.
B School of Agriculture and Food, University of Melbourne, Parkville, Vic. 3010, Australia.
C University of Pretoria, Department of Animal and Wildlife Sciences, Private Bag X20, Hatfield, Pretoria, South Africa.
D University of Hohenheim, Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), Stuttgart, Germany.
E Department of Animal Production, University of Nairobi, P.O. Box 29053-00625, Kangemi, Nairobi, Kenya.
F Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany.
G Corresponding author. Email: manofcows@yahoo.com
Animal Production Science 61(6) 602-612 https://doi.org/10.1071/AN19631
Submitted: 1 November 2019 Accepted: 5 December 2020 Published: 25 February 2021
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: African livestock play a critical role in food security and the wider economy, while accounting for >70% of African agricultural greenhouse gas emissions. Accurate estimates of greenhouse gas emissions from livestock are required for inventory purposes and to assess the efficacy of mitigation measures. While there is an increasing number of studies assessing methane (CH4) emissions of cattle, little attention has been paid to small ruminants (SR).
Aims: Enteric CH4 emissions were assessed from 1345 SR in three counties of western Kenya to develop more accurate emission factors (EF) for enteric CH4 from sheep and goats.
Methods: Using on-farm animal activity data, feed samples were also analysed to produce estimates of feed digestibility by season and region. The combined data were also used to estimate daily CH4 production by season, location and class of animal to produce new EF for annual enteric CH4 production of SR.
Key results: Mean dry-matter digestibility of the feed basket was in the range of 58–64%, depending on region and season (~10% greater than Tier I estimates). EF were similar for sheep (4.4 vs 5 kg CH4/year), but lower for goats (3.7 vs 5 kg CH4/year) than those given for SR in developing countries in Intergovernmental Panel on Climate Change (Tier I) estimates.
Conclusions: Published estimates of EF for SR range widely across Africa. In smallholder systems in western Kenya, SR appear to be managed differently from cattle, and EF appear to be driven by different management considerations.
Implications: The findings highlighted the heterogenous nature of SR enteric emissions in East Africa, but also suggested that emissions from SR are quantitatively less important than other estimates suggest compared with cattle.
Keywords: agricultural systems, climate, goats, methane, sheep.
References
AOAC International (2005) Official methods of analysis of AOAC International. (AOAC International: Rockville, MD)Benaouda M, Martin C, Li X, Kebreab E, Hristov AN, Yu Z, Yáñez-Ruiz DR, Reynolds CK, Crompton LA, Dijkstra J, Bannink A, Schwarm A, Kreuzer M, McGee M, Lund P, Hellwing ALF, Weisbjerg MR, Moate PJ, Bayat AR, Shingfield KJ, Peiren N, Eugène M (2019) Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: animal categories and dietary mitigation strategies. Animal Feed Science and Technology 255, 114207
| Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: animal categories and dietary mitigation strategies.Crossref | GoogleScholarGoogle Scholar |
Brett D, Corbett J, Inskip M (1972) Estimation of the energy value of ewe milk. In ‘Proceedings of the Australian Society of Animal Production’. (Citeseer)
Cashburn G (2016) ‘How to tell the age of sheep.’ (Government of New South Wales Department of Primary Industries: Wagga Wagga, NSW, Australia)
Charmley E, Williams SRO, Moate PJ, Hegarty RS, Herd RM, Oddy VH, Reyenga P, Staunton KM, Anderson A, Hannah MC (2016) A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science 56, 169–180.
| A universal equation to predict methane production of forage-fed cattle in Australia.Crossref | GoogleScholarGoogle Scholar |
Defar G, Mengistu A, Berhane G (2018) Estimation of livestock methane emissions in the extensive crop–livestock farming areas of Bale highland, Oromia, Ethiopia. Preprints 2018, 2018100104.
Dong H, Mangino J, McAllister T, Hatfield J, Johnson D, Lassey K, de Lima M, Romanovskaya A (2006) Chapter 10: emissions from livestock and manure management. In ‘IPCC guidelines for national greenhouse gas inventories. Vol. 4: agriculture, forestry, and other land use’. pp. 10.1–10.87. (IPCC: Paris, France) Available at http://www. ipcc-nggip. iges. or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock. pdf [Verified 13 May 2016]
Du Toit C, Van Niekerk WA, Meissner H (2013) Direct greenhouse gas emissions of the South African small stock sectors. South African Journal of Animal Science 43, 340–361.
| Direct greenhouse gas emissions of the South African small stock sectors.Crossref | GoogleScholarGoogle Scholar |
Freer HDM, Nolan JV (Eds) (2007) ‘Nutrient requirements of domesticated ruminants.’ (CSIRO Publishing: Melbourne, Vic., Australia)
Goopy JP, Onyango AA, Dickhoefer U, Butterbach-Bahl K (2018) A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa: results for Nyando, western Kenya. Agricultural Systems 161, 72–80.
| A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa: results for Nyando, western Kenya.Crossref | GoogleScholarGoogle Scholar |
Goopy JP, Korir D, Pelster D, Ali AIM, Wassie SE, Schlecht E, Dickhoefer U, Merbold L, Butterbach-Bahl K (2020) Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. British Journal of Nutrition 123, 1239–1246.
| Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle.Crossref | GoogleScholarGoogle Scholar |
Herrero M, Thornton PK, Kruska R, Reid RS (2008) Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030. Agriculture, Ecosystems & Environment 126, 122–137.
| Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030.Crossref | GoogleScholarGoogle Scholar |
Herrero M, Thornton PK, Notenbaert AM, Wood S, Msangi S, Freeman HA, Bossio D, Dixon J, Peters M, van de Steeg J, Lynam J, Rao PP, Macmillan S, Gerard B, McDermott J, Seré C, Rosegrant M (2010) Smart investments in sustainable food production: revisiting mixed crop–livestock systems. Science 327, 822–825.
| Smart investments in sustainable food production: revisiting mixed crop–livestock systems.Crossref | GoogleScholarGoogle Scholar | 20150490PubMed |
Herrero M, Havlík P, Valin H, Notenbaert A, Rufino MC, Thornton PK, Blümmel M, Weiss F, Grace D, Obersteiner M (2013) Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proceedings of the National Academy of Sciences of the United States of America 110, 20888–20893.
| Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems.Crossref | GoogleScholarGoogle Scholar | 24344273PubMed |
Jaetzold R, Schmidt H, Hornetz B, Shisanya C (1983) ‘Farm management handbook of Kenya. Vol. II: natural conditions and farm management information. Part A: west Kenya (Nyanza and Western provinces); and Part B: central Kenya (Rift Valley and Central provinces).’ (Ministry of Agriculture: Nairobi, Kenya)
Jahnke H, Tacher G, Kiel P, Rojat D (1988) Livestock production in tropical Africa, with special reference to the tsetse-affected zone. Livestock Production in Tsetse-affected Areas of Africa 3–21.
McPeak JG, Little PD (2006) ‘Pastoral livestock marketing in eastern Africa: research and policy challenges.’ (Intermediate Technology Publications: London, UK)
Morand-Fehr P, Sauvant D (1980) Composition and yield of goat milk as affected by nutritional manipulation 1. Journal of Dairy Science 63, 1671–1680.
| Composition and yield of goat milk as affected by nutritional manipulation 1.Crossref | GoogleScholarGoogle Scholar |
Ndao S, Moulin C-H, Traoré EH, Diop M, Bocquier F (2019) Contextualized re-calculation of enteric methane emission factors for small ruminants in subhumid western Africa is far lower than previous estimates. Tropical Animal Health and Production 51, 919–928.
| Contextualized re-calculation of enteric methane emission factors for small ruminants in subhumid western Africa is far lower than previous estimates.Crossref | GoogleScholarGoogle Scholar | 30565185PubMed |
Ndung’u PW, Bebe BO, Ondiek JO, Butterbach-Bahl K, Merbold L, Goopy JP (2019) Improved region-specific emission factors for enteric methane emissions from cattle in smallholder mixed crop: livestock systems of Nandi County, Kenya. Animal Production Science 59, 1136–1146.
| Improved region-specific emission factors for enteric methane emissions from cattle in smallholder mixed crop: livestock systems of Nandi County, Kenya.Crossref | GoogleScholarGoogle Scholar |
Oddy V, Robards G, Low S (1983) Prediction of in vivo dry matter digestibility from the fibre and nitrogen content of a feed, feed information and animal production. In ‘Proceedings of the second symposium of the International Network of Feed Information Centres’. (Eds GE Robards, RG Packham) (Commonwealth Agricultural Bureaux: Farnham Royal, Slough, UK).
Onyango AA, Dickhoefer U, Rufino MC, Butterbach-Bahl K, Goopy JP (2019) Temporal and spatial variability in the nutritive value of pasture vegetation and supplement feedstuffs for domestic ruminants in western Kenya. Asian-Australasian Journal of Animal Sciences 32, 637
| Temporal and spatial variability in the nutritive value of pasture vegetation and supplement feedstuffs for domestic ruminants in western Kenya.Crossref | GoogleScholarGoogle Scholar | 30056650PubMed |
Patra AK, Lalhriatpuii M, Debnath BC (2016) Predicting enteric methane emission in sheep using linear and non-linear statistical models from dietary variables. Animal Production Science 56, 574–584.
| Predicting enteric methane emission in sheep using linear and non-linear statistical models from dietary variables.Crossref | GoogleScholarGoogle Scholar |
Radostits O, Bell J (1970) Nutrition of the pre-ruminant dairy calf with special reference to the digestion and absorption of nutrients: a review. Canadian Journal of Animal Science 50, 405–452.
| Nutrition of the pre-ruminant dairy calf with special reference to the digestion and absorption of nutrients: a review.Crossref | GoogleScholarGoogle Scholar |
Reed JD, Soller H, Woodward A (1990) Fodder tree and straw diets for sheep: intake, growth, digestibility and the effects of phenolics on nitrogen utilisation. Animal Feed Science and Technology 30, 39–50.
| Fodder tree and straw diets for sheep: intake, growth, digestibility and the effects of phenolics on nitrogen utilisation.Crossref | GoogleScholarGoogle Scholar |
Svinurai W, Mapanda F, Sithole D, Moyo EN, Ndidzano K, Tsiga A, Zhakata W (2018) Enteric methane emissions and their response to agro-ecological and livestock production systems dynamics in Zimbabwe. The Science of the Total Environment 616–617, 710–719.
| Enteric methane emissions and their response to agro-ecological and livestock production systems dynamics in Zimbabwe.Crossref | GoogleScholarGoogle Scholar | 29122353PubMed |
Tubiello F, Salvatore M, Cóndor Golec R, Ferrara A, Rossi S, Biancalani R, Federici S, Jacobs H, Flammini A (2014) ‘Agriculture, forestry and other land use emissions by sources and removals by sinks.’ (Statistics Division, Food and Agriculture Organization: Rome, Italy)
Zhou G, Munga S, Minakawa N, Githeko AK, Yan G (2007) Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands. The American Journal of Tropical Medicine and Hygiene 77, 29–35.
| Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands.Crossref | GoogleScholarGoogle Scholar | 17620627PubMed |
