Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-24T18:09:34.606Z Has data issue: false hasContentIssue false
  • English
  • Français

An Optimal Irrigation Model: Theory, Experimental Results, and Implications for Future Research

Published online by Cambridge University Press:  29 April 2019

Shannon A. Boomgarden Open the ORCID record for Shannon A. Boomgarden [Opens in a new window] ,
Duncan Metcalfe  and
Ellyse T. Simons
Shannon A. Boomgarden*
Affiliation:
Natural History Museum of Utah, University of Utah, 301 Wakara Way, Salt Lake City, UT 84108, USA
Duncan Metcalfe
Affiliation:
Natural History Museum of Utah, University of Utah, 301 Wakara Way, Salt Lake City, UT 84108, USA
Ellyse T. Simons
Affiliation:
Natural History Museum of Utah, University of Utah, 301 Wakara Way, Salt Lake City, UT 84108, USA
*
(S.Arnold@utah.edu, corresponding author) https://orcid.org/0000-0003-2808-0172
Get access Rights & Permissions [Opens in a new window]

Abstract

A series of farming experiments was conducted between 2013 and 2017 in Range Creek Canyon, Utah, to better understand the opportunities and constraints faced by prehistoric farmers in the Southwest. The experiments were designed to collect data on the optimal amount of supplemental water that should be applied to maize fields given the costs in labor and benefits in greater yield. We investigate expected variation in water management strategies using an optimal irrigation model (OIM). The model makes clear that the payoff for farming is best understood as a continuum of relative success and that irrigation is one activity (probably of many) that may improve farming efficiency as well as increase harvest yields. The optimal harvest will always be less than the maximum harvest when there are significant operating costs associated with irrigation. Estimating the costs and benefits of irrigation in a specific area allows for an assessment of whether irrigation is expected, and if so, how much effort should be devoted to water management. A local dendroclimatological study is used to provide the prehistoric context for the Fremont who occupied Range Creek Canyon, and irrigation is expected even in periods of greater precipitation.

Entre los años 2013 y 2017 se llevó a cabo una serie de experimentos agrícolas en el cañón de Range Creek, Utah, para comprender mejor las oportunidades y limitaciones a las que se enfrentaron los agricultores prehistóricos del Suroeste. Los experimentos fueron diseñados para recolectar datos sobre la cantidad óptima del abasto de agua que debía aplicarse a los campos de maíz, considerando el costo de la mano de obra y los beneficios en la producción de la cosecha. Se investigó la variación esperada en las estrategias de gestión del agua utilizando un modelo de irrigación óptimo (OIM, por sus siglas en inglés). El modelo deja en claro que el beneficio obtenido en la agricultura es mejor entendido como un continuo de éxito relativo y que la irrigación es una actividad, probablemente una de muchas, que puede mejorar la eficiencia de la agricultura y aumentar el rendimiento en la cosecha. La cosecha óptima siempre será menor que la cosecha máxima cuando existan costos operativos significativos asociados con la irrigación. La estimación de los costos y beneficios de la irrigación en un área específica permite evaluar si esta debe llevarse a cabo, y de ser así, cuánto esfuerzo debe dedicarse a la gestión del agua. Se empleó un estudio dendroclimatológico para proporcionar el contexto prehistórico de los Fremont que habitaron el cañón de Range Creek y el uso de la irrigación es de esperarse incluso durante períodos de mayor precipitación.

Keywords

experimental archaeologyFremontRange Creek Canyonfarmingmaizeoptimal foraging theoryirrigationUtahexperimentos arqueologíaFremontRange Creek Canyonagrícolasmaízirrigaciónteoría de forrajeo óptimoUtah
Type
Articles
Information
American Antiquity , Volume 84 , Issue 2 , April 2019 , pp. 252 - 273
Copyright
Copyright © 2019 by the Society for American Archaeology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References Cited

Barlow, K. Renee 2002 Predicting Maize Agriculture among the Fremont: An Economic Comparison of Farming and Foraging in the American Southwest. American Antiquity 67:65–88.Google Scholar
Benson, Larry V. 2010 Factors Controlling Pre-Columbian and Early Historic Maize Productivity in the American Southwest, Part 1: The Southern Colorado Plateau and Rio Grande Regions. Journal of Archaeological Method and Theory 18:1–60.Google Scholar
Benson, Larry V., Ramsey, D.K., Stahle, David W., and Petersen, Kenneth L. 2013 Some Thoughts on the Factors that Controlled Prehistoric Maize Production in the American Southwest with Application to Southwestern Colorado. Journal of Archaeological Science 40:2869–2880.Google Scholar
Bettinger, Robert L., Winterhalder, Bruce, and McElreath, Richard 2006 A Simple Model of Technological Intensification. Journal of Archaeological Science 33:538–545.Google Scholar
Boomgarden, Shannon A. 2015 Experimental Maize Farming in Range Creek Canyon, Utah. PhD dissertation, Department of Anthropology, University of Utah, Salt Lake City.Google Scholar
Boserup, Ester 1965 The Conditions of Agricultural Growth: The Economics of Agrarian Change Under Population Pressure. Aldine, Chicago.Google Scholar
Davies, Nicholas B., Krebs, John R., and West, Stuart A. 2012 An Introduction to Behavioural Ecology, 4th edition. Wiley, Hoboken, New Jersey.Google Scholar
Davis, Owen K. 1994 The Correlation of Summer Precipitation in the Southwestern U.S.A. with Isotopic Records of Solar Activity During the Medieval Warm Period. Climate Change 26:271–287.Google Scholar
Doolittle, William 1984 Agricultural Change as an Incremental Process. Annals of the Association of American Geographers 74(1):124–137.Google Scholar
Dorshow, Wetherbee 2012 Modelling Agricultural Potential in Chaco Canyon during the Bonito phase: A Predictive Geospatial Approach. Journal of Archaeological Science 39:2098–2115.Google Scholar
English, Marshall, and Raja, Syed Navaidm1996 Perspectives on Deficit Irrigation. Agricultural Water Management 32:1–14.Google Scholar
English, Marshall J., Solomon, Kenneth H., and Hoffman, Glenn J. 2002 A Paradigm Shift in Irrigation Management. Journal of Irrigation and Drainage Engineering 128:267–277.Google Scholar
Gardner, Franklin P., Pearce, Robert B., and Mitchell, Roger L. 1985 Physiology of Crop Plants. Iowa State University Press, Ames.Google Scholar
Geib, Phil and Heitman, Carrie 2015 The Relevance of Maize Pollen to Assessing the Extent of Maize Production in Chaco Canyon. In Chaco Revisited: New Research on the Prehistory of Chaco Canyon, New Mexico, edited by Heitman, Carrie and Plog, Stephen, pp. 66–95. University of Arizona Press, Tucson.Google Scholar
Hart, Isaac 2016 After the Rain: Using Paleoclimatic and Paleoecological Methods to Inform Archaeological Investigation in Baja California and Range Creek Canyon, Utah. PhD dissertation, Department of Anthropology, University of Utah, Salt Lake City.Google Scholar
Kent, Susan 1992 Studying Variability in the Archaeological Record: An Ethnoarchaeological Model for Distinguishing Mobility Patterns. American Antiquity, 57:635–660.Google Scholar
Knight, Troy A., Meko, David M., and Baisan, Christopher H. 2010 A Bimillenial-Length Tree-Ring Reconstruction of Precipitation for the Tavaputs Plateau, Northeastern Utah. Quaternary Research 73(1):107–117.Google Scholar
Kohler, Timothy, Bocinsky, R. Kyle, Cockburn, Denton, Crabtree, Stefani, Varien, Mark, Kolm, Kenneth, Smith, Schaun, Ortman, Scott, and Kobti, Ziad 2012 Modeling Prehispanic Pueblo Societies in Their Ecosystems. Ecological Modelling 241:30–41.Google Scholar
Kranz, William, Irmak, Suat, van Donk, Simon, Yonts, Dean, and Martin, Derrel 2008 Irrigation Management for Corn. NebGuide-University of Nebraska-Lincoln Extension, Institute of Agriculture and Natural Resources. Electronic document, http://extensionpublications.unl.edu/assets/pdf/g1850.pdf, accessed March 20, 2019.Google Scholar
Lamb, Hubert H. 1977 Climate: Past, Present, and Futures. Methuen, London.Google Scholar
Morgan, Christopher 2015 Is It Intensification Yet? Current Archaeological Perspectives on the Evolution of Hunter-Gatherer Economies. Journal of Archaeological Research 22(4):1–51.Google Scholar
Muenchrath, Deborah A. 1995 Productivity, Morphology, Phenology, and Physiology of a Desert-Adapted Native American Maize (Zea mays L.) Cultivar. PhD dissertation, Department of Agronomy, Iowa State University, Ames.Google Scholar
Petersen, Kenneth L. 1994 A Warm and Wet Little Climatic Optimum and a Cold and Dry Little Ice Age in the Southern Rocky Mountains, U.S.A. Climatic Change 26(2):243–269.Google Scholar
Rhoads, Fred M., and Yonts, C. Dean 1991 Irrigation Scheduling for Corn: Why and How. In National Corn Handbook, Electronic document, http://corn.agronomy.wisc.edu/Management/pdfs/NCH20.pdf, accessed March 20, 2019.Google Scholar
Shaw, Roger H. 1988 Climate Requirement. In Corn and Corn Improvement, 3rd edition, edited by Sprague, G. F. and Dudley, J. W., pp. 609–638. American Society of Agronomy, Madison, Wisconsin.Google Scholar
Simons, Ellyse 2017 Experimental Irrigation in Range Creek Canyon. Poster presented at the Utah Professional Archaeological Counsel winter meeting, Price, Utah. Electronic document, https://www.researchgate.net/publication/315496742_Experimental_Irrigation_in_Range_Creek_Canyon, accessed March 20, 2019.Google Scholar
Stephens, David W., and Krebs, John R. 1986 Foraging Theory. Princeton University Press, Princeton, New Jersey.Google Scholar
Ugan, Andrew, Bright, Jason, and Rogers, Allen 2003 When Is Technology Worth the Trouble? Journal of Archaeological Science 30(10):1315–1329.Google Scholar
Supplementary material: File

Boomgarden et al. supplementary material

Boomgarden et al. supplementary material 1

Download Boomgarden et al. supplementary material(File)
File 9.9 MB