This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
FAO report 2018.OECD & Food and Agriculture Organization of the United Nations (2019), OECD-FAO Agricultural Outlook 2019–2028. (Rome: FAO: 2018).FAO report 2018.OECD & Food and Agriculture Organization of the United Nations (2019), OECD-FAO Agricultural Outlook 2019–2028Rome: FAO: 2018Search in Google Scholar
FAO report 2017.The future of food and agriculture– Trends and challenges. (Rome: FAO: 2017).FAO report2017The future of food and agriculture– Trends and challenges. (Rome: FAO: 2017)Search in Google Scholar
Gaber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, Falcucci A, Tempio G. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. (Rome: FAO: 2013).GaberPJSteinfeldHHendersonBMottetAOpioCDijkmanJFalcucciATempioGTackling climate change through livestock: a global assessment of emissions and mitigation opportunitiesRomeFAO2013Search in Google Scholar
Angel SP, Amitha JP, Rashamol VP, Vandana GD. Climate change and cattle production – impact and adaptation. J Vet Med Res 2018; 5: 1134.AngelSPAmithaJPRashamolVPVandanaGDClimate change and cattle production – impact and adaptationJ Vet Med Res201851134Search in Google Scholar
Grossi G, Goglio P, Vitali A, Williams AG. Livestock and climate change: impact of livestock on climate and mitigation strategies. Anim Front 2019; 9: 69–76.GrossiGGoglioPVitaliAWilliamsAGLivestock and climate change: impact of livestock on climate and mitigation strategiesAnim Front20199697610.1093/af/vfy034701546232071797Search in Google Scholar
FAO 2012. World agriculture towards 2030/2050, the 2012 revision. (Rome: FAO: 2012).2012World agriculture towards 2030/2050, the 2012 revision. (Rome: FAO: 2012)Search in Google Scholar
FAO 2013. Sustainable Food Consumption and Production. (Rome: FAO: 2013).2013Sustainable Food Consumption and Production. (Rome: FAO: 2013)Search in Google Scholar
Alexandratos N and Bruinsma J. World agriculture towards 2030/2050.The 2012 revision. ESA Working Paper No. 12–03. (Rome: FAO: 2012).AlexandratosNBruinsmaJWorld agriculture towards 2030/2050.The 2012 revision. ESA Working Paper No. 12–03RomeFAO2012Search in Google Scholar
FAO 2009. Global agriculture towards 2050. In How to Feed the World 2050 1–10. (Rome: FAO: 2009).2009Global agriculture towards 2050. In How to Feed the World 2050 1–10RomeFAO2009Search in Google Scholar
Foley JA, Ramankutty N, BraumanKA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C (2011) Solutions for a cultivated planet. Nat 2011 478: 337.FoleyJARamankuttyNBraumanKACassidyESGerberJSJohnstonMMuellerNDO’ConnellCRayDKWestPCBalzerC2011Solutions for a cultivated planetNat 201147833710.1038/nature1045221993620Search in Google Scholar
Delgado C, Rosegrant M, Steinfeld H, Ehui, S, Courbois C. Livestock to 2020: the next food revolution. IFPRI Food, Agriculture, and the Environment Discussion Paper 28. (Washington: IFPRI: 1999).DelgadoCRosegrantMSteinfeldHEhuiSCourboisCLivestock to 2020: the next food revolution. IFPRI Food, Agriculture, and the Environment Discussion Paper 28WashingtonIFPRI1999Search in Google Scholar
Wilkinson JM, Lee MRF. Review: Use of human-edible animal feeds by ruminant livestock. Animal 2018; 12(8): 1735–1743.WilkinsonJMLeeMRFReview: Use of human-edible animal feeds by ruminant livestockAnimal20181281735174310.1017/S175173111700218X28893333Search in Google Scholar
de Vries M, de Boer JM. Comparing environmental impacts for livestock products: a review of life cycle assessments. Livest Sci 2010 128: 1–1.deVries MdeBoer JMComparing environmental impacts for livestock products: a review of life cycle assessmentsLivest Sci20101281110.1016/j.livsci.2009.11.007Search in Google Scholar
ILCD. International reference life cycle data system (ILCD) handbook—general guide for life cycle assessment - detailed guidance. (Luxembourg: UE: 2010).ILCD. International reference life cycle data system (ILCD) handbook—general guide for life cycle assessment - detailed guidanceLuxembourgUE2010Search in Google Scholar
Borchers MR, Bewley JM. An assessment of producer precision dairy farming technology use, pre-purchase considerations, and usefulness. J Dairy Sci 2015; 98: 4198–4205.BorchersMRBewleyJMAn assessment of producer precision dairy farming technology use, pre-purchase considerations, and usefulnessJ Dairy Sci2015984198420510.3168/jds.2014-896325892693Search in Google Scholar
Caja G, Castro-Costa A, Knight CH. Engineering to support wellbeing of dairy animals. J Dairy Res 2016; 83: 136–147.CajaGCastro-CostaAKnightCHEngineering to support wellbeing of dairy animalsJ Dairy Res20168313614710.1017/S002202991600026127210489Search in Google Scholar
De Marchi M, Toffanin V, Cassandro M, Penasa M. Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits. J Dairy Sci 2014; 97: 1171–1186.DeMarchi MToffaninVCassandroMPenasaMInvited review: mid-infrared spectroscopy as phenotyping tool for milk traitsJ Dairy Sci2014971171118610.3168/jds.2013-679924440251Search in Google Scholar
Egger-Danner C, Cole JB, Pryce JE, Gengler N, Heringstad B, Bradley A, Stock KF. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animals 2015; 9: 191–207.Egger-DannerCColeJBPryceJEGenglerNHeringstadBBradleyAStockKFInvited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traitsAnimals2015919120710.1017/S1751731114002614429953725387784Search in Google Scholar
Gargiulo JI, Eastwood CR, Garcia SC, Lyons NA. Dairy farmers with larger herd sizes adopt moreprecision dairy technologies. J Dairy Sci 2018; 101: 5466–5473.GargiuloJIEastwoodCRGarciaSCLyonsNADairy farmers with larger herd sizes adopt moreprecision dairy technologiesJ Dairy Sci20181015466547310.3168/jds.2017-1332429525319Search in Google Scholar
Halachmi I, Guarino M, Bewley J, Pastell M. Smart animal agriculture: application of real-time sensors to improve animal wellbeing and production. Annu Rev Anim Biosci 2019; 7: 403–425.HalachmiIGuarinoMBewleyJPastellMSmart animal agriculture: application of real-time sensors to improve animal wellbeing and productionAnnu Rev Anim Biosci2019740342510.1146/annurev-animal-020518-11485130485756Search in Google Scholar
Hartung J, Banhazi T, Vranken, E, Guarino M. European farmers’ experiences with precision livestockfarming systems. Anim Front 2017; 7: 38–4.HartungJBanhaziTVrankenEGuarinoMEuropean farmers’ experiences with precision livestockfarming systemsAnim Front2017738410.2527/af.2017.0107Search in Google Scholar
Kaniyamattam K, De Vries A. Agreement between milk fat, protein, and lactose observations collected from the Dairy Herd Improvement Association (DHIA) and a real-time milk analyzer. J Dairy Sci 2014; 97: 2896– 2908.KaniyamattamKDeVries AAgreement between milk fat, protein, and lactose observations collected from the Dairy Herd Improvement Association (DHIA) and a real-time milk analyzerJ Dairy Sci2014972896–290810.3168/jds.2013-769024630652Search in Google Scholar
Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front Genet 2019; 10: 1197.KoltesJEColeJBClemmensRDilgerRNKramerLMLunneyJKMcCueMEMcKaySDMateescuRGMurdochBMA vision for development and utilization of high-throughput phenotyping and big data analytics in livestockFront Genet201910119710.3389/fgene.2019.01197693405931921279Search in Google Scholar
Lokhorst C, de Mol RM, Kamphuis C. Invited review: big data in precision dairy farming. Animals 2019; 13: 1519– 1528.LokhorstCdeMol RMKamphuisCInvited review: big data in precision dairy farmingAnimals2019131519–152810.1017/S1751731118003439658196430630546Search in Google Scholar
Neves RC, LeBlanc SJ. Reproductive management practices and performance of Canadian dairy herdsusing automated activity-monitoring systems. J Dairy Sci 2015; 98: 2801–2811.NevesRCLeBlancSJReproductive management practices and performance of Canadian dairy herdsusing automated activity-monitoring systemsJ Dairy Sci2015982801281110.3168/jds.2014-822125660748Search in Google Scholar
Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H. Invited review: Sensors to support healthmanagement on dairy farm. J Dairy Sci 2013; 98: 1928–1952.RuttenCJVelthuisAGJSteeneveldWHogeveenHInvited review: Sensors to support healthmanagement on dairy farmJ Dairy Sci2013981928195210.3168/jds.2012-610723462176Search in Google Scholar
Sauls JA, Voelz BE, Hill SL, Mendonça LGD, Stevenson JS. Increasing estrus expression in the lactating dairy cow. J Dairy Sci 2017; 100: 807–820.SaulsJAVoelzBEHillSLMendonçaLGDStevensonJSIncreasing estrus expression in the lactating dairy cowJ Dairy Sci201710080782010.3168/jds.2016-1151927889121Search in Google Scholar
Song X, Bokkers EAM, van Mourik S, Groot Koerkamp PWG, van der Tol PPJ. Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. J Dairy Sci 2019; 102: 4294–4308.SongXBokkersEAMvanMourik SGrootKoerkamp PWGvander Tol PPJAutomated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regionsJ Dairy Sci20191024294430810.3168/jds.2018-1523830879819Search in Google Scholar
Steeneveld W, Hogeveen H, Lansink AGJMO. Economic consequences of investing in sensor systems on dairy farms, Comput. Electron Agric 2015; 119: 3-39.SteeneveldWHogeveenHLansinkAGJMOEconomic consequences of investing in sensor systems on dairy farms, ComputElectron Agric2015119339Search in Google Scholar
Steeneveld W, Hogeveen H. Characterization of Dutch dairy farms using sensor systems for cow management. J Dairy Sci 2015; 98: 709-717.SteeneveldWHogeveenHCharacterization of Dutch dairy farms using sensor systems for cow managementJ Dairy Sci20159870971710.3168/jds.2014-859525465556Search in Google Scholar
Tse C, Barkema HW, DeVries TJ, Rushen J, Pajor EA. Effect of transitioning to automatic milking systems on producers’ perceptions of farm management and cow health in the Canadian dairy industry. J Dairy Sci 2017; 100: 2404–2414.TseCBarkemaHWDeVriesTJRushenJPajorEAEffect of transitioning to automatic milking systems on producers’ perceptions of farm management and cow health in the Canadian dairy industryJ Dairy Sci20171002404241410.3168/jds.2016-1152128109587Search in Google Scholar
Van De Gucht T, Saeys W, Van Nu el A, Pluym L, Piccart K, Lauwers L, Vangeyte J, Van Weyenberg S. Farmers’ preferences for automatic lameness-detection systems in dairy cattle. J Dairy Sci 2017; 100: 5746–5757.VanDe Gucht TSaeysWVanNu el APluymLPiccartKLauwersLVangeyteJVanWeyenberg SFarmers’ preferences for automatic lameness-detection systems in dairy cattleJ Dairy Sci20171005746575710.3168/jds.2016-1228528527794Search in Google Scholar
Van Hertem T, Maltz E, Antler A, Romanini CEB, Viazzi S, Bahr C, Schlageter-Tello A, Lokhorst C, Berckmans D, Halachmi I. Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity. J Dairy Sci 2013; 96: 4286–4298.VanHertem TMaltzEAntlerARomaniniCEBViazziSBahrCSchlageter-TelloALokhorstCBerckmansDHalachmiILameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activityJ Dairy Sci2013964286429810.3168/jds.2012-618823684042Search in Google Scholar
Van Hertem T, Rooijakkers L, Berckmans D, Peña Fernández A, Norton T, Berckmans D, Vranken E. Appropriate data visualisation is key to Precision Livestock Farming acceptance. Comput Electron Agric 2017; 138: 1–10.VanHertem TRooijakkersLBerckmansDPeñaFernández ANortonTBerckmansDVrankenEAppropriate data visualisation is key to Precision Livestock Farming acceptanceComput Electron Agric201713811010.1016/j.compag.2017.04.003Search in Google Scholar
Abeni F, Galli A. Monitoring cow activity and rumination time for an early detection of heat stress in dairy cow. Int J Biometeorol 2017; 61 (3): 417-425.AbeniFGalliAMonitoring cow activity and rumination time for an early detection of heat stress in dairy cowInt J Biometeorol201761341742510.1007/s00484-016-1222-z27498881Search in Google Scholar
Cabrera VE, Kalantari AS. Economics of production efficiency: Nutritional grouping of the lactating cow. J Dairy Sci 2016; 99: 825-841.CabreraVEKalantariASEconomics of production efficiency: Nutritional grouping of the lactating cowJ Dairy Sci20169982584110.3168/jds.2015-984626519971Search in Google Scholar
Carlström C, Pettersson G, Johansson K, Strandberg E, Stålhammar H, Philipsson J. Feasibility of using automatic milking system data from commercial herds for genetic analysis of milkability. J Dairy Sci 2013; 96: 5.CarlströmCPetterssonGJohanssonKStrandbergEStålhammarHPhilipssonJFeasibility of using automatic milking system data from commercial herds for genetic analysis of milkabilityJ Dairy Sci201396510.3168/jds.2012-622123706483Search in Google Scholar
Lawson LG, Pedersen SM, Sørensen CG, Pesonen L, Fountas S, Werner A, Oudshoorn FW, Herold L, Chatzinikos T, Kirketerp IM. A four nation survey of farm information management and advanced farming systems: A descriptive analysis of survey responses. Comput Electron Agric 2011; 7: 7–20.LawsonLGPedersenSMSørensenCGPesonenLFountasSWernerAOudshoornFWHeroldLChatzinikosTKirketerpIMA four nation survey of farm information management and advanced farming systems: A descriptive analysis of survey responsesComput Electron Agric2011772010.1016/j.compag.2011.03.002Search in Google Scholar
Pimentel D. Livestock production: energy inputs and the environment. Can Soc Anim Sci 1997; 47: 17–26.PimentelDLivestock production: energy inputs and the environmentCan Soc Anim Sci1997471726Search in Google Scholar
Aiking H. Future protein supply. Trends Food Sci Technol. 2011; 2 (2–3): 112-120.Aiking HFuture protein supplyTrends Food Sci Technol201122–311212010.1016/j.tifs.2010.04.005Search in Google Scholar
D’Agaro E. New Advances in NGS Technologies. In: New Trends In Veterinary Genetics. (London: Intech Editions: 2017).D’AgaroENew Advances in NGS Technologies. In: New Trends In Veterinary GeneticsLondonIntech Editions201710.5772/66924Search in Google Scholar
D’Agaro E. Artificial intelligence used in genome analysis studies. Eurobiotech J 2018; 2(2): 78-8.D’AgaroEArtificial intelligence used in genome analysis studiesEurobiotech J20182278810.2478/ebtj-2018-0012Search in Google Scholar
Berckmans D. General introduction to precision livestock farming. Anim Front 2017; 7: 6–1.BerckmansDGeneral introduction to precision livestock farmingAnim Front201776110.2527/af.2017.0102Search in Google Scholar
Rexroad C, Vallet J, Matukumalli LK, Reecy J, Bickhart D, Blackburn H, Boggess M, Cheng H, Clutter A, Cockett N. Genome to phenome: improving animal health, production, and well-being—a new USDA blueprint for animal genome research 2018-2027. Front Genet 2019; 10: 327.RexroadCValletJMatukumalliLKReecyJBickhartDBlackburnHBoggessMChengHClutterACockettNGenome to phenome: improving animal health, production, and well-being—a new USDA blueprint for animal genome research 2018-2027Front Genet20191032710.3389/fgene.2019.00327653245131156693Search in Google Scholar
van der Werf HMG, Garnett T, Corson MS, Hayashi K, Huisingh D, Cederberg C. Towards eco-efficient agriculture and food systems: theory, praxis and future challenges. J. Clean. Prod. 2014; 73: 1–9.vander Werf HMGGarnettTCorsonMSHayashiKHuisinghDCederbergCTowards eco-efficient agriculture and food systems: theory, praxis and future challengesJ. Clean. Prod2014731910.1016/j.jclepro.2014.04.017Search in Google Scholar
Connor EE, Hutchison JL, Norman HD, Olson KM, Van Tassell CP, Leith JM, Baldwin RLV. Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. J Anim Sci 2013; 91: 3978–3988.ConnorEEHutchisonJLNormanHDOlsonKMVanTassell CPLeithJMBaldwinRLVUse of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selectionJ Anim Sci2013913978398810.2527/jas.2012-597723658326Search in Google Scholar
de Haas Y, Windig JJ, Calus M, Dijkstra J, de Haan M, Bannink A, Veerkamp RF. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J Dairy Sci 2011 94: 6122–6134.deHaas YWindigJJCalusMDijkstraJdeHaan MBanninkAVeerkampRFGenetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selectionJ Dairy Sci2011946122613410.3168/jds.2011-443922118100Search in Google Scholar
Yan T, Mayne CS, Gordon FG, Porter MG, Agnew RE, Patterson DC, Ferris CP, Kilpatrick DJ. Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. J Dairy Sci 2010; 93: 2630–2638.YanTMayneCSGordonFGPorterMGAgnewREPattersonDCFerrisCPKilpatrickDJMitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cowsJ Dairy Sci2010932630263810.3168/jds.2009-292920494172Search in Google Scholar
Sere, C.; Steinfeld, H. World livestock production systems: current status, issues and trends. Animal Production and Health Paper No 127. (Rome: FAO: 1996).SereCSteinfeldHWorld livestock production systems: current status, issues and trends. Animal Production and Health Paper No 127RomeFAO1996Search in Google Scholar
OECD/FAO 2016. OECD-FAO Agricultural Outlook. OECD Publishing: Paris, France, 2016.OECD/FAO 2016OECD-FAO Agricultural Outlook. OECD PublishingParis, France2016Search in Google Scholar
USDA. USDA Nutritional Database for Standard Reference Release 27. USDA Agricultural Research Service. (Washington: USDA: 2014).USDAUSDA Nutritional Database for Standard Reference Release 27. USDA Agricultural Research ServiceWashingtonUSDA2014Search in Google Scholar
Flachowsky G, Meyer U, Südekum KH. Land Use for Edible Protein of Animal Origin—A Review. Animals 2017; 7(3): 25.FlachowskyGMeyerUSüdekumKHLand Use for Edible Protein of Animal Origin—A ReviewAnimals2017732510.3390/ani7030025536684428335483Search in Google Scholar
ISO 2006a. Environmental Management - Life Cycle Assessment-Principles and Framework. EN ISO 14040:2006. EN ISO 14040. (Geneva: ISO: 2006).ISO2006aEnvironmental Management - Life Cycle Assessment-Principles and Framework. EN ISO140402006EN ISO 14040. (Geneva: ISO: 2006)Search in Google Scholar
ISO 2006b. Environmental Management - Life Cycle Assessment-Requirements and Guidelines. EN ISO 14044:2006. EN ISO 14044:2006. (Geneva: ISO: 2006).ISO2006bEnvironmental Management - Life Cycle Assessment-Requirements and GuidelinesEN ISO140442006EN ISO 14044:2006. (Geneva: ISO: 2006)Search in Google Scholar
Pimentel D, Pimentel M. Sustainability of meat-based and plant-based diets and the environment. Am J Clin Nutr 2003; 78: 660-663.PimentelDPimentelMSustainability of meat-based and plant-based diets and the environmentAm J Clin Nutr20037866066310.1093/ajcn/78.3.660S12936963Search in Google Scholar
IPCC 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Inter-governmental Panel on Climate Change. (Cambridge (UK): Cambridge University Press: 2014).IPCC 2014Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Inter-governmental Panel on Climate ChangeCambridge (UK)Cambridge University Press2014Search in Google Scholar
Sonesoon U, Davis J, Ziegler F. Green-House Gases from livestock production EU27. (Göteborg: Swedish Institute for Food and Biotechnology: 2011).SonesoonUDavisJZieglerFGreen-House Gases from livestock production EU27GöteborgSwedish Institute for Food and Biotechnology2011Search in Google Scholar