Dynamic Irrigation Management Under Weather Uncertainty and Soil Heterogeneity

Published Online:https://doi.org/10.1287/msom.2020.0187

References

  • Akkaya D, Bimpikis K, Lee H (2021) Government interventions to promote agricultural innovation. Manufacturing Service Oper. Management 23(2):437–452.AbstractGoogle Scholar
  • Alfandari L, Lemalade JL, Nagih A, Plateau G (2011) A MIP flow model for crop-rotation planning in a context of forest sustainable development. Ann. Oper. Res. 190(1):149–164.CrossrefGoogle Scholar
  • Alizamir S, Iravani F, Mamani H (2019) An analysis of price vs. revenue protection: Government subsidies in the agriculture industry. Management Sci. 65(1):32–49.LinkGoogle Scholar
  • Allen R, Pereira L, Smith M (1998) Crop evapotranspiration—Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, United Nations Food and Agriculture Organization, Rome.Google Scholar
  • Anvari S, Mousavi SJ, Morid S (2017) Stochastic dynamic programming-based approach for optimal irrigation scheduling under restricted water availability conditions. Irrigation Drainage 66(4):492–500.CrossrefGoogle Scholar
  • Archibald TW, Marshall SE (2018) Review of mathematical programming applications in water resource management under uncertainty. Environ. Model. Assessment 23(6):753–777.CrossrefGoogle Scholar
  • Barker JB, Bhatti S, Heeren DM, Neale CMU, Rudnick DR (2019) Variable rate irrigation of maize and soybean in west-central Nebraska under full and deficit irrigation. Frontiers Big Data 2:34.CrossrefGoogle Scholar
  • Barton B, Clark S (2014) Water and climate risks facing US corn production: How companies and investors can cultivate sustainability. Report, Ceres, Boston.Google Scholar
  • Becker R (2021) California enacted a groundwater law 7 years ago but wells are still drying up and the threat is spreading. CalMatters (August 21), https://tinyurl.com/4jry635m.Google Scholar
  • Beyer D, Cheng F, Sethi SP, Taksar M (2010) Markovian Demand Inventory Models (Springer, New York).CrossrefGoogle Scholar
  • Bland A (2023) Ground zero: Rain brings little relief to California’s depleted groundwater. CalMatters (February 7), https://tinyurl.com/4p7r9smz.Google Scholar
  • Blank HG (1975) Optimal irrigation decisions with limited water. PhD thesis, Colorado State University, Fort Collins.Google Scholar
  • Borodin V, Bourtembourg J, Hnaien F, Labadie N (2016) Handling uncertainty in agricultural supply chain management: A state of the art. Eur. J. Oper. Res. 254(2):348–359.CrossrefGoogle Scholar
  • Boyabatli O, Nasiry J, Zhou Y (2019) Crop planning in sustainable agriculture: Dynamic farmland allocation in the presence of crop rotation benefits. Management Sci. 65(5):2060–2076.AbstractGoogle Scholar
  • Boyabatli O, Shao L, Zhou Y (2023) Integrated optimization of farmland cultivation and fertilizer application: Implications for farm management and food security. Working paper, Singapore Management University, Singapore.Google Scholar
  • Bras RL, Cordova JR (1981) Intraseasonal water allocation in deficit irrigation. Water Resources Res. 17(4):866–874.CrossrefGoogle Scholar
  • Burns C, MacDonald J (2018) America’s diverse family farms, 2018 edition. Economic Information Bulleting No. 203, U.S. Department of Agriculture Economic Research Service, Washington, DC.Google Scholar
  • Cagle S (2020) Lost communities: Thousands of wells in rural California may run dry. Guardian (February 28), https://tinyurl.com/yr7dx44w.Google Scholar
  • Chea T (2022) California wells run dry as drought depletes groundwater. AP News (October 4), https://tinyurl.com/4bjymvvj.Google Scholar
  • Chhatre A, Devalkar S, Seshadri S (2016) Crop diversification and risk management in Indian agriculture. Decision 43(2):167–179.CrossrefGoogle Scholar
  • Daccache A, Knox JW, Weatherhead EK, Daneshkhah A, Hess TM (2015) Implementing precision irrigation in a humid climate—Recent experiences and ongoing challenges. Agricultural Water Management 147:135–143.CrossrefGoogle Scholar
  • Dawande M, Gavirneni S, Mehrotra M, Mookerjee V (2013) Efficient distribution of water between head-reach and tail-end farms in developing countries. Manufacturing Service Oper. Management 15(2):221–238.LinkGoogle Scholar
  • Doorenbos J, Kassam AH (1979) Yield response to water. FAO Irrigation and Drainage Paper No. 33, United Nations Food and Agriculture Organization, Rome.Google Scholar
  • dos Santos LMR, Michelon P, Arenales MN, Santos RHS (2011) Crop rotation scheduling with adjacency constraints. Ann. Oper. Res. 190(1):165–180.CrossrefGoogle Scholar
  • Dotterer LJ (2014) Optimizing water use through management of spatiotemporal variation using site specific technologies. Master’s thesis, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • Dukes MD, Zotarelli L, Liu GD, Simonne EH (2018) Principles and practices of irrigation management for vegetables. University of Florida Extension Publication No. IFAS AE260, University of Florida, Gainesville.Google Scholar
  • Eisenhauer DE, Martin DL, Heeren DM, Hoffman GJ (2021) Irrigation Systems Management, 1st ed. (ASABE, St. Joseph, MI).Google Scholar
  • Enciso JM, Porter D, Evett SR, Peries X, Peters T (2012) Irrigation monitoring with soil water sensors. Texas A&M Agrilife Extension Report No. E-618, Texas A&M, College Station.Google Scholar
  • English DJ (1990) Deficit irrigation I: Analytical framework. J. Irrigation Drainage Engrg. 116(3):399–412.CrossrefGoogle Scholar
  • Evans R (2001) Center pivot irrigation. Technical report, Northern Plains Agricultural Research Laboratory, U.S. Department of Agriculture Agricultural Research Service, Washington, DC.Google Scholar
  • Evett SR, Colaizzi PD, Schwartz RC, O’Shaughnessy SA (2014) Soil water sensing. Focus on variable rate irrigation. Proc. 26th Annual Central Plains Irrigation Conf. (Central Plains Irrigation Association (CPIA), Colby, KS), 99–109.Google Scholar
  • Federgruen A, Lall U, Şimsek AS (2019) Supply chain analysis of contract farming. Manufacturing Service Oper. Management 21(2):361–378.LinkGoogle Scholar
  • Fontanet M, Scudiero E, Skaggs TH, Fernàndez-Garcia D, Ferrer F, Rodrigo G, Bellvert J (2020) Dynamic management zones for irrigation scheduling. Agricultural Water Management 238:106–207.CrossrefGoogle Scholar
  • Food and Agriculture Organization (2018) The Future of Food and Agriculture Alternative Pathways to 2050 (United Nations Food and Agriculture Organization, Rome).Google Scholar
  • Glen JJ (1987) Mathematical models in farm planning: A survey. Oper. Res. 35(5):641–666.LinkGoogle Scholar
  • Greenwood DJ, Zhang K, Hilton HW, Thompson AJ (2010) Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology. J. Agricultural Sci. 148(1):1–16.CrossrefGoogle Scholar
  • Gupta S, Dawande M, Janakiraman G, Sarkar A (2017) Distressed selling by farmers: Model, analysis, and use in policy-making. Production Oper. Management 26(10):1803–1818.CrossrefGoogle Scholar
  • Haneveld WKK, Stegeman AW (2005) Crop succession requirements in agricultural production planning. Eur. J. Oper. Res. 166(2):406–429.CrossrefGoogle Scholar
  • Harper S (2016) Real-time control of soil moisture for efficient irrigation. Master’s thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Heeren D (2016) Variable rate irrigation for mining undepleted soil water. Internat. Committee Irrigation Drainage World Irrigation Forum, https://tinyurl.com/rdru3vhj.Google Scholar
  • Heeren D (2022) Personal communications. Biological Systems Engineering Department, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • Howard BC (2014) California drought spurs groundwater drilling boom in Central Valley. Accessed April 16, 2020, https://tinyurl.com/beezyuam/.Google Scholar
  • Huang H, Adamchuk V, Madramootoo C, Yari A (2015) Economic optimization of the levels of control in variable rate irrigation (VRI). 2015 ASABE IA Irrigation Sympos. Emerging Tech. Sustainable Irrigation (American Society of Agricultural and Biological Engineers, St. Joseph, MI), 1–15.Google Scholar
  • Huh WT, Lull U (2013) Optimal crop choice, irrigation allocation, and the impact of contract farming. Production Oper. Management 22(5):1126–1143.CrossrefGoogle Scholar
  • Ioslovich I, Borshchevsky M, Gutman P (2009) On optimal irrigation scheduling. Dynam. Discrete Continuous Impulsive Systems 19:303–310.Google Scholar
  • Iqbal J, Thomasson JA, Jenkins JN, Owens PR, Whisler FD (2005) Spatial variability analysis of soil physical properties of alluvial soils. Soil Sci. Soc. America J. 69(4):1338–1350.CrossrefGoogle Scholar
  • Irfan U (2023) Why all that rain in California won’t solve its drought. Vox (March 10), https://tinyurl.com/2s3zfunc.Google Scholar
  • Irmak S (2015) Interannual variation in long-term center pivot irrigated maize evapotranspiration and various water productivity response indices. J. Irrigation Drainage Engrg. 141(5):04014068.CrossrefGoogle Scholar
  • Irmak S, Rudnick DR (2014) Corn soil water extraction and effective rooting depth in a silt-loam soil. NebGuide No. G2245, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • Irmak S, Odhiambo LO, Kranz WL, Eisenhauer DE (2011) Irrigation efficiency and uniformity, and crop water use efficiency. NebGuide No. EC732, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • Irmak S, Burgert M, Yang H, Cassman K, Walters D, Rathje W, Payero J, et al. (2012) Large-scale on-farm implementation of soil moisture-based irrigation management strategies for increasing maize water productivity. Trans. ASABE 55(3):881–894.CrossrefGoogle Scholar
  • Jasechko S, Perrone D (2020) California’s Central Valley groundwater wells run dry during recent drought. Earth’s Future 8:1–12.CrossrefGoogle Scholar
  • Kazaz B (2004) Production planning under yield and demand uncertainty with yield-dependent cost and price. Manufacturing Service Oper. Management 6(3):209–224.LinkGoogle Scholar
  • Kazaz B, Webster S (2011) The impact of yield dependent trading costs on pricing and production planning under supply uncertainty. Manufacturing Service Oper. Management 13(3):404–417.LinkGoogle Scholar
  • Klocke NL, Currie RS, Tomsicek DJ, Koehn J (2011) Corn yield response to deficit irrigation. Trans. ASABE 54(3):931–940.CrossrefGoogle Scholar
  • Kranz W, Irmak S, van Donk S, Yonts C, Martin D (2008) Irrigation management for corn. NebGuide No. G1850, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • La Ganga M, L LG, James I (2021) A frenzy of well drilling by California farmers leaves taps running dry. Los Angeles Times (December 16), https://tinyurl.com/2ezmp893.Google Scholar
  • Li Q, Hu G, Jubery T, Ganapathysubramanian B (2017) A farm-level precision land management framework based on integer programming. PLoS One 12(3):e0174680.CrossrefGoogle Scholar
  • Lo TH, Heeren DM, Martin DL, Mateos L, Luck JD, Eisenhauer D (2016) Pumpage reduction by using variable-rate irrigation to mine undepleted soil water. Trans. ASABE 59(5):1285–1298.CrossrefGoogle Scholar
  • Lowe TJ, Preckel PV (2004) Decision technologies for agribusiness problems: A brief review of selected literature and a call for research. Manufacturing Service Oper. Management 6(3):201–208.LinkGoogle Scholar
  • Maatman A, Schweigman C, Ruijs A, van Der Vlerk MH (2002) Modeling farmers’ response to uncertain rainfall in Burkina Faso: A stochastic programming approach. Oper. Res. 50(3):399–414.LinkGoogle Scholar
  • McGuckin JT, Mapel C, Lansford R, Sammis S (1987) Optimal control of irrigation scheduling using a random time frame. Amer. J. Agricultural Econom. 69(1):123–133.CrossrefGoogle Scholar
  • Melvin S, Martin D (2018) In-canopy vs above-canopy sprinklers, which is better suited to your field? Proc. 30th Annual Central Plains Irrigation Conf. (Central Plains Irrigation Association (CPIA), Colby, KS), 157–165.Google Scholar
  • Miller K (2015) Estimating potential water pump reductions based on soil water content, geospatial data layers, and variable rate irrigation pivot control resolution. Master’s thesis, University of Nebraska–Lincoln, Lincoln.Google Scholar
  • Naadimuthu G, Raju KS, Lee ES (1999) A heuristic dynamic optimization algorithm for irrigation scheduling. Math. Comput. Model. 30(7–8):169–183.CrossrefGoogle Scholar
  • O’Shaughnessy SA, Evett SR, Colaizzi PD, Andrade MA, Marek TH, Heeren DM, Lamm FR, LaRue JL (2019) Identifying advantages and disadvantages of variable rate irrigation—An updated review. Appl. Engrg. Agriculture 35(6):837–852.CrossrefGoogle Scholar
  • Porteus E (2002) Foundations of Stochastic Inventory Theory (Stanford University Press, Stanford, CA).CrossrefGoogle Scholar
  • Rao NH, Sharma PBS, Chander S (1988) A simple dated water-production function for use in irrigated agriculture. Agricultural Water Management 13(1):25–32.CrossrefGoogle Scholar
  • Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8(6):e66428.CrossrefGoogle Scholar
  • Rhenals AE, Bras RL (1981) The irrigation scheduling problem and evapotranspiration uncertainty. Water Resources Res. 17(5):1328–1338.CrossrefGoogle Scholar
  • Sarker R, Quaddus M (2002) Modelling a nationwide crop planning problem using a multiple criteria decision making tool. Computers Indust. Engrg. 42(2):541–543.CrossrefGoogle Scholar
  • Sethi SP, Cheng F (1997) Optimality of (s, S) policies in inventory models with Markovian demand. Oper. Res. 45(6):931–939.LinkGoogle Scholar
  • Shan B, Guo P, Shanshan G, Zhong L (2019) A price-forecast-based irrigation optimization model under the response of fruit quality and price to water. Sustainability 11(7):2124.CrossrefGoogle Scholar
  • Shani U, Tsur Y, Zemel A (2004) Optimal dynamic irrigation schemes. Optimal Control Appl. Methods 25(2):91–106.CrossrefGoogle Scholar
  • Shani U, Tsur Y, Zemel A, Zilberman D (2009) Irrigation production functions with water-capital substitution. Agricultural Econom. 40(1):55–66.CrossrefGoogle Scholar
  • Sing A (2014) Irrigation planning and management through optimization modelling. Water Resources Management 28(1):1–14.CrossrefGoogle Scholar
  • Sommer L (2023) 3 Reasons why California’s drought isn’t really over, despite all the rain. NPR (March 23), https://tinyurl.com/2fsv2xae.Google Scholar
  • Song JS, Zipkin P (1993) Inventory control in a fluctuating demand environment. Oper. Res. 41(2):351–370.LinkGoogle Scholar
  • Steduto P, Hsiao T, Fereres E, Raes D (2012) Crop yield response to water. FAO Irrigation and Drainage Paper No. 66, United Nations FAO, Rome, Italy.Google Scholar
  • Sunantara JD, Ramirez JA (1997) Optimal stochastic multicrop seasonal and intraseasonal irrigation control. J. Water Resources Planning Management 123(1):39–48.CrossrefGoogle Scholar
  • Sundaramoorthi D, Dong L (2019) Machine-learning-based simulation for estimating parameters in portfolio optimization: Empirical application to soybean variety selection. Working paper, Washington University in St. Louis, St. Louis, MO.Google Scholar
  • U.S. Department of Agriculture National Agricultural Statistics Service (2013) 2012 Census of Agriculture. 2013 Farm and Ranch Irrigation Survey (U.S. Department of Agriculture National Agricultural Statistics Service, Washington, DC).Google Scholar
  • Varzi MM (2016) Crop water production functions—A review of available mathematical method. J. Agricultural Sci. 8(4):76–83.CrossrefGoogle Scholar
  • Vico G, Porporato A (2010) Traditional and microirrigation with stochastic soil moisture. Water Resources Res. 46(3):W03509.CrossrefGoogle Scholar
  • Walker A (2015) You can still find groundwater in California, but it’ll cost you. Accessed April 16, 2020, https://tinyurl.com/236fc5dr.Google Scholar
  • Waller P, Yitayew M (2016) Irrigation and Drainage Engineering, 1st ed. (Springer, Cham, Switzerland).CrossrefGoogle Scholar
  • Weintraub A, Romero C (2006) Operations research models and the management of agricultural and forestry resources: A review and comparison. Interfaces 36(5):446–457.LinkGoogle Scholar
  • Zhang N, Wang M, Wang N (2002) Precision agriculture—A worldwide overview. Comput. Electronics Agriculture 36(2):113–132.CrossrefGoogle Scholar
  • Zipkin PH (2000) Foundations of Inventory Management (McGraw-Hill, New York).Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.