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The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

Published online by Cambridge University Press:  24 August 2020

H. M. Menendez III
Affiliation:
Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
L. O. Tedeschi
Affiliation:
Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
Corresponding

Abstract

Perception of freshwater use varies between nations and has led to concerns of how to evaluate water use for sustainable food production. The water footprint of beef cattle (WFB) is an important metric to determine current levels of freshwater use and to set sustainability goals. However, current WFB publications provide broad WF values with inconsistent units preventing direct comparison of WFB models. The water footprint assessment (WFA) methodologies use static physio-enviro-managerial equations, rather than dynamic, which limits their ability to estimate cattle water use. This study aimed to advance current WFA methods for WFB estimation by formulating the WFA into a system dynamics methodology to adequately characterize the major phases of the beef cattle industry and provide a tool to identify high-leverage solutions for complex water use systems. Texas is one of the largest cattle producing areas in the United States, a significant water user. This geolocation is an ideal template for WFB estimation in other regions due to its diverse geography, management-cultures, climate and natural resources. The Texas Beef Water Footprint model comprised seven submodels (cattle population, growth, nutrition, forage, WFB, supply chain and regional water use; 1432 state variables). Calibration of our model replicated initial WFB values from an independent study by Chapagain and Hoekstra in 2003 (CH2003). This CH2003 v. Texas production scenarios evaluated model parameters and assumptions and estimated a 41–66% WFB variability. The current model provides an insightful tool to improve complex, unsustainable and inefficient water use systems.

Type
Modelling Animal Systems Research Paper
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Aivazidou, E, Tsolakis, N, Vlachos, D and Iakovou, E (2018) A water footprint management framework for supply chains under green market behaviour. Journal of Cleaner Production 197, 592606.CrossRefGoogle Scholar
Atzori, AS, Canalis, C, Francesconi, AHD and Pulina, G (2016) A preliminary study on a new approach to estimate water resource allocation: the net water footprint applied to animal products. Agriculture and Agricultural Science Procedia 8, 5057.CrossRefGoogle Scholar
Baudracco, J, Lopez-Villalobos, N, Holmes, CW, Comeron, EA, MacDonald, KA, Barry, TN and Friggens, NC (2012) E-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding. Animal: An International Journal of Animal Bioscience 6, 980993.CrossRefGoogle ScholarPubMed
Berdahl, JD and Redfearn, DD (2007) Grasses for Semiarid Areas. In Barnes, RF, Nelson, CJ, Moore, KJ and Collins, M (eds). Forages I: The Science of Grassland Agriculture, 6th edn, Blackwell Publishing, 221244.Google Scholar
Boulay, AM, Bare, J, Benini, L, Berger, M, Lathuillière, MJ, Manzardo, A, Margni, M, Motoshita, M, Núñez, M, Pastor, AV, Ridoutt, B, Oki, T, Worbe, S and Pfister, S (2018) The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). International Journal of Life Cycle Assessment 23, 368378.CrossRefGoogle Scholar
Chapagain, AK and Hoekstra, AY (2003) Virtual Water Flows between Nations in Relation to Trade in Livestock and Livestock Products.Google Scholar
Colorado State University (2019) Beef Cutout Calculator, Available at http://beefcutoutcalculator.agsci.colostate.edu/.Google Scholar
Commonwealth Scientific and Industrial Research Organization (1990) Feeding Standards for Australian Livestock. Melbourne, Australia: Commonwealth Scientific and Industrial Research Organization.Google Scholar
Commonwealth Scientific and Industrial Research Organization (2007) Nutrient Requirements of Domesticated Ruminants. Collingwood, VIC: Commonwealth Scientific and Industrial Research Organization.Google Scholar
Conrad, SH (2004) The Dynamics of Agricultural Commodities and Their Responses to Disruptions of Considerable Magnitude. Proceedings of the 22nd International Conference of the System Dynamics Society, 115.Google Scholar
Dieter, CA, Maupin, MA, Caldwell, RR, Harris, MA, Ivahnenko, TI, Lovelace, JK, Barber, NL and Linsey, KS (2018) Estimated Use of Water in the United States in 2015: U.S. Geological Survey Circular 1441.CrossRefGoogle Scholar
Doreau, M, Corson, MS and Wiedemann, SG (2012) Water use by livestock: a global perspective for a regional issue? Animal Frontiers 2, 916.CrossRefGoogle Scholar
Environmental Protection Agency (2019) Level III and IV Ecoregions of the Continental United States, Available at https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states.Google Scholar
FAO (2017) Water for Sustainable Food and Agriculture Water for Sustainable Food and Agriculture.Google Scholar
Food and Agriculture Organization (2016) Guidelines for Assessment-Environmental Performance of Large Ruminant Supply Chains. Rome, Italy: Food and Agriculture Organization.Google Scholar
Food and Agriculture Organization (2019a) ETC-Single Crop Coefficient (Kc), Chapter 6. Available at http://www.fao.org/docrep/X0490E/x0490e0b.htm#chapter.Google Scholar
Food and Agriculture Organization (2019b) Guidelines for Assessment-Water Use in Livestock Production Systems and Supply Chains. Available at http://www.fao.org/3/ca5685en/ca5685en.pdf.Google Scholar
Food and Agriculture Organization of the United Nations (2016) Food Outlook: Biannual Report on Global Food Markets. Food and Agriculture Organization of the United Nations Available at https://www.weltagrarbericht.de/fileadmin/files/weltagrarbericht/GlobalAgriculture/02Hunger/FoodOutlook10_2016.pdfGoogle Scholar
Ford, A (2010) Modeling the Environment: An Introduction to System Dynamics Models of the Environmental Systems., 2nd edn, Washington, D.C.: Island Press, 267288.Google Scholar
Forrester, JW (1961) Industrial Dynamics. Waltham, Massachusetts: Pegasus Communications.Google Scholar
Fox, DG, Tedeschi, LO, Tylutki, TP, Russell, JB, Van Amburgh, ME, Chase, LE, Pell, AN and Overton, TR (2004) The Cornell net carbohydrate and protein system model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology 112, 2978.CrossRefGoogle Scholar
Grant, WE, Pedersen, EK and Marin, SL (1997) Ecology and Natural Resource Management: Systems Analysis and Simulation. New York: John Wiley & Sons.Google Scholar
Ha, K (2018) Livestock, Dairy, and Poultry Outlook. U.S. Export of Animal Proteins: Broiler Exports Represent Largest Volume Share, While Beef Exports Comprise Greatest Volume Share. Washington, DC: United States Department of Agriculture-Economic Research Service.Google Scholar
Heflin, KR (2015) Life-Cycle Greenhouse-Gas Emissions of Five Beef Production Systems Typical of the Southern High Plains. West Texas: A&M University.Google Scholar
Herring, AD (2014) Beef Cattle Production Systems. Oxfordshire, United Kingdom: CAB International.CrossRefGoogle Scholar
Hoch, T and Agabriel, J (2004) A mechanistic dynamic model to estimate beef cattle growth and body composition: 2. Model evaluation. Agricultural Systems 81, 1735.CrossRefGoogle Scholar
Hoekstra, AY and Hung, PQ (2002) Virtual Water Trade. A Quantification of Virtual Water Flows Between Nations in Relation to International Crop Trade. Delft, Netherlands: IHE Delft Institute for Water Education.Google Scholar
Hoekstra, AY and Mekonnen, MM (2012) The water footprint of humanity. Proceedings of the National Academy of Sciences of the United States of America 109, 32323237.CrossRefGoogle ScholarPubMed
Hoekstra, AY, Chapagain, AK, Aldaya, MM and Mekonnen, MM (2011) The Water Footprint Assessment Manual: Setting a Global Standard. London, United Kingdom: Earthscan.Google Scholar
Kannan, N, Osei, E, Gallego, O and Saleh, A (2017) Estimation of green water footprint of animal feed for beef cattle production in Southern Great Plains. Water Resources and Industry 17, 1118.CrossRefGoogle Scholar
Legesse, G, Ominski, KH, Beauchemin, KA, Pfister, S, Martel, M, McGeough, EJ, Hoekstra, AY, Kroebel, R, Cordeiro, MRC and McAllister, TA (2017) BOARD-invited review: quantifying water use in ruminant production. Journal of Animal Science 95, 20012018.Google ScholarPubMed
Leon-Velarde, CU and Quiroz, R (1999) Modeling cattle production systems: integrating components and their interactions in the development of simulation models. In The Third International Symposium on Systems Approaches for Agricultural Development, pp. 112.Google Scholar
Mcbride, WD and Mathews, K (2011) United States Department of Agriculture The Diverse Structure and Organization of U.S. Beef Cow-Calf Farms.CrossRefGoogle Scholar
Meadows, DL (1970) Dynamics of Commodity Production Cycles. Cambridge, Massachusetts: Wright-Allen Press.Google Scholar
Mekonnen, M and Hoekstra, AY (2010) The Green, Blue and Grey Water Footprint of Farm Animals and Animal Products. Delft, Netherlands: IHE Delft Institute for Water Education.Google Scholar
Mekonnen, M and Hoekstra, AY (2011) National Water Footprint Accounts: The Green, Blue and Grey Water Footprint of Production and Consumption. Delft, Netherlands: IHE Delft Institute for Water Education.Google Scholar
Mekonnen, MM and Hoekstra, AY (2012) A global assessment of the water footprint of farm animal products. Ecosystems 15, 401415.CrossRefGoogle Scholar
Mekonnen, MM, Neale, CMU, Ray, C, Erickson, GE and Hoekstra, AY (2019) Water productivity in meat and milk production in the US from 1960 to 2016. Environment International 132, Article 105084, 10.1016/j.envint.2019.105084.Google ScholarPubMed
Menendez, HM, Atzori, AS and Tedeschi, LO (2020 a) The Conceptualization and Preliminary Evaluation of a Dynamic, Mechanistic Mathematical Model to Assess the Water Footprint of Beef Cattle Production. bioRxiv, Available at https://doi.org/10.1101/2020.04.14.028324.Google Scholar
Menendez, HM, Wuellner, MR, Turner, BL, Gates, RN, Dunn, BH and Tedeschi, LO (2020 b) A spatial landscape scale approach for estimating erosion, water quantity, and quality in response to South Dakota grassland conversion. Natural Resource Modeling 33, e12243.CrossRefGoogle Scholar
Mubako, ST and Lant, CL (2013) Agricultural virtual water trade and water footprint of U.S. States. Annals of the Association of American Geographers 103, 385396.CrossRefGoogle Scholar
National Academies of Science Engineering and Medicine (2016) Nutrient Requirements of Beef Cattle, 8th Edn, Washington, DC: The National Academies Press.Google Scholar
National Oceanic and Atmospheric Administration (2019 a) Data Tools: Historical Palmer Drought Indices. Available at https://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/.Google Scholar
National Oceanic and Atmospheric Administration (2019 b) Data Tools: Find a StationTitle. Available at https://www.ncdc.noaa.gov/cdo-web/datatools/findstation.Google Scholar
Oltjen, JW, Bywater, AC, Baldwin, RL and Garrett, WN (1986) Development of a dynamic model of beef cattle growth and composition. Journal of Animal Science 62, 8697.Google Scholar
Parker, DB, Perino, LJ, Auvermann, BW and Sweeten, JM (2000) Water use and conservation at Texas High Plains beef cattle feedyards. Applied Engineering in Agriculture 16, 7782.Google Scholar
Philipp, D, Putman, B and Thoma, G (2019) ASAS-CSAS annual meeting symposium on water use efficiency at the forage-animal interface: life cycle assessment of forage-based livestock production systems. Journal of Animal Science 97, 18651873.CrossRefGoogle ScholarPubMed
Rahmandad, H, Oliva, R and Osgood, ND (2015) Analytical Methods for Dynamic Modelers. Cambridge, Massachusetts: MIT Press.CrossRefGoogle Scholar
R Core Team (2019) A language and environment for statistical computing.Google Scholar
Redfearn, DD and Nelson, CJ (2003) Grasses for Southern Areas. In Barnes, RF, Nelson, CJ, Collins, M and Moore, KJ (eds). Forages II: An Introduction to Grassland Agriculture, 6th edn, Ames Iowa: Blackwell Publishing, 149170.Google Scholar
Ridoutt, BG and Pfister, S (2013) A new water footprint calculation method integrating consumptive and degradative water use into a single stand-alone weighted indicator. International Journal of Life Cycle Assessment 18, 204207.CrossRefGoogle Scholar
Rotz, CA, Asem-Hiablie, S, Dillon, J and Bonifacio, H (2015) Cradle-to-farm gate environmental footprints of beef cattle production in Kansas, Oklahoma, and Texas. Journal of Animal Science 93, 25092519.CrossRefGoogle ScholarPubMed
Rotz, CA, Asem-Hiablie, S, Place, S and Thoma, G (2019) Environmental footprints of beef cattle production in the United States. Agricultural Systems 169, 113.CrossRefGoogle Scholar
Ruelle, E, Delaby, L, Wallace, M and Shalloo, L (2016) Development and evaluation of the herd dynamic milk model with focus on the individual cow component. Animal: An International Journal of Animal Bioscience 10, 19861997.CrossRefGoogle ScholarPubMed
Steinfeld, H, Gerber, P, Wassenaar, TD, Castel, V, Rosales, M, Rosales, M and de Haan, C (2006) Livestock's Long Shadow: Environmental Issues and Options. Rome, Italy: Food and Agriculture Organization.Google Scholar
Sterman, JD (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, Massachusetts: McGraw-Hill Companies Incorporated.Google Scholar
Teague, WR, Grant, WE, Kreuter, UP, Diaz-Solis, H, Dube, S, Kothmann, MM, Pinchack, WE and Ansley, RJ (2008) An ecological economic simulation model for assessing fire and grazing management effects on mesquite rangelands in Texas. Ecological Economics 64, 611624.CrossRefGoogle Scholar
Tedeschi, LO (2019a) ASN-ASAS Symposium: future of data analytics in nutrition: mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics. Journal of animal science 97, 19211944.CrossRefGoogle Scholar
Tedeschi, LO (2019b) Relationships of retained energy and retained protein that influence the determination of cattle requirements of energy and protein using the California Net Energy System. Translational Animal Science 3, 10291039.CrossRefGoogle Scholar
Tedeschi, LO and Fox, DG (2020) The Ruminant Nutrition System: Volume I – An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants. Ann Arbor, MI: XanEdu.Google Scholar
Tedeschi, LO and Menendez, HM III. (2020) Mathematical Modeling in Animal Production. In Bazer, FW, Lamb, GC and Wu, G (eds). Animal Agriculture. London, UK: Elsevier, 431453.Google Scholar
Tedeschi, LO, Fox, DG and Guiroy, PJ (2004) A decision support system to improve individual cattle management. 1. A mechanistic, dynamic model for animal growth. Agricultural Systems 79, 171204.CrossRefGoogle Scholar
Tedeschi, LO, Muir, JP, Riley, DG and Fox, DG (2015) The role of ruminant animals in sustainable livestock intensification programs. International Journal of Sustainable Development and World Ecology 22, 452465.Google Scholar
Tedeschi, LO, Almeida, AKD, Atzori, AS, Muir, JP, Fonseca, MA and Cannas, A (2017a) A glimpse of the future in animal nutrition science. 1. Past and future challenges. Revista Brasileira de Zootecnia 46(5), 438451.Google Scholar
Tedeschi, LO, Fonseca, MA, Muir, JP, Poppi, DP, Carstens, GE, Angerer, JP and Fox, DG (2017b) A glimpse of the future in animal nutrition science. 2. Current and future solutions. Revista Brasileira de Zootecnia 46(5), 452469.CrossRefGoogle Scholar
Tedeschi, LO, Molle, G, Menendez, HM, Cannas, A and Fonseca, MA (2019) The assessment of supplementation requirements of grazing ruminants using nutrition models. Translational Animal Science 3, 811823.CrossRefGoogle ScholarPubMed
Texas Water Development Board (2017) Water for Texas 2017 State Water Plan. Austin, Texas: Texas Water Development Board, 57133.Google Scholar
Texas Water Development Board (2019) Water Data for Texas, Texas Water Development Board. Available at https://www.twdb.texas.gov/groundwater/data/index.asp.Google Scholar
Thornley, JH (1998) Grassland Dynamics: An Ecosystem Simulation Model. New York, New York: CAB International.Google Scholar
Tinsley, TL, Chumbley, S, Mathis, C, Machen, R and Turner, BL (2019) Managing cow herd dynamics in environments of limited forage productivity and livestock marketing channels: an application to semi-arid Pacific island beef production using system dynamics. Agricultural Systems 173, 7893.CrossRefGoogle Scholar
Turner, BL, Rhoades, RD, Tedeschi, LO, Hanagriff, RD, McCuistion, KC and Dunn, BH (2013) Analyzing ranch profitability from varying cow sales and heifer replacement rates for beef cow-calf production using system dynamics. Agricultural Systems 114, 614.CrossRefGoogle Scholar
United States Department of Agriculture-National Agricultural Statistical Survey (2019) Quick Stats, Available at https://quickstats.nass.usda.gov/.Google Scholar
United States Department of Agriculture (2016) Overview of the United States Cattle Industry. Washington, D.C. United States Department of Agriculture. Available at https://downloads.usda.library.cornell.edu/usda-esmis/files/8s45q879d/9z903258h/6969z330v/USCatSup-06-24-2016.pdfGoogle Scholar
Xu, H and Wu, M (2018) A first estimation of county-based greenwater availability and its implications for agriculture and bioenergy production in the United States. Water 10, 148. https://doi.org/10.3390/w10020148.Google Scholar
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