Abstract
Climate change, extreme weather events, and water scarcity have severely impacted the agricultural sector. Under scarce conventional water supplies, a farm faces a decision between reducing production through deficit irrigation and leveraging alternative water and energy resources to continue producing large quantities of crops and these investments would have to be balanced against an unknown climate. Therefore, we develop a framework for farm investment decisions structured as a two-stage stochastic quadratically constrained linear program that maximizes farm profit over a 25-year period while considering an uncertain future climate and the costs of investing and operating various electricity and water technologies. We create four representative climate futures and two climate probability distributions that represent different beliefs that the decision maker might have about the likelihood of each climate scenario occurring. Then, we compare four solutions where decisions are made on information ranging from perfectly knowing the climate and weather to only the average precipitation. Our results show that expected profit and crop yield heavily depend on a decision maker’s given climate probability distributions. Aggressively preparing for an extreme climate can cause significant losses if a more moderate climate is realized. Furthermore, given a future climate, year-to-year weather variability can also corrode the potential cost savings from investing in alternative resources. The insights from this framework can help agricultural decision makers determine how to address climate uncertainty, water scarcity, and to a limited degree weather variability via investments in alternative water and electricity resources that can help improve resilience and fortify profits.
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References
Adamseged ME, Frija A, Thiel A (2019) Dynamics of rural livelihoods and rainfall variability in Northern Ethiopian Highlands. Clim Risk Manage. https://doi.org/10.1016/j.crm.2019.100195
Amosson S, Almas L, Girase JR, Vimlesh K, Marek T (2019) Economics of Irrigation Systems. Tech. rep, Texas AgriLife Extension
Anderson CJ, Kyveryga PM (2016) Combining on-farm and climate data for risk management of nitrogen decisions. Clim Risk Manage 13:10–18. https://doi.org/10.1016/j.crm.2016.03.002
Arroyo J, Shirazi S (2012) Cost of Brackish Groundwater Desalination in Texas. Tech. rep
Barrett JW, Skogerboe GV (1980) Crop production functions and the allocation and use of irrigation water. Agric Water Manage 3(1):53–64. https://doi.org/10.1016/0378-3774(80)90015-3
Bauer P, Thorpe A, Brunet G (2015) The quiet revolution of numerical weather prediction. Nature 525(7567):47–55
Blackmore I, Rivera C, Waters WF, Iannotti L, Lesorogol C (2021) The impact of seasonality and climate variability on livelihood security in the Ecuadorian Andes. Clim Risk Manage. https://doi.org/10.1016/j.crm.2021.100279
Blinco LJ, Lambert MF, Simpson AR, Marchi A (2017) Framework for the optimization of operation and design of systems with different alternative water sources. Earth Perspect. https://doi.org/10.1186/s40322-017-0038-2
Campana PE, Li H, Yan J (2013) Dynamic modelling of a PV pumping system with special consideration on water demand. Appl Energy 112(2013):635–645
Campana PE, Li H, Zhang J, Zhang R, Liu J, Yan J (2015) Economic optimization of photovoltaic water pumping systems for irrigation. Energy Convers Manage 95:32–41. https://doi.org/10.1016/j.enconman.2015.01.066
City of Austin (2019) City of Austin Fiscal Year 2019 Electric Tariff. Tech. rep, Austin City Council, Austin
Dastane N (1978) Effective rainfall in irrigated agriculture
Dinar A, Rhoades JD, Nash P, Waggoner BL (1991) Production functions relating crop yield, water quality and quantity, soil salinity and drainage volume. Agric Water Manage 19(1):51–66. https://doi.org/10.1016/0378-3774(91)90062-N
Foster T, Brozović N (2018) Simulating crop-water production functions using crop growth models to support water policy assessments. Ecol Econ 152(March):9–21. https://doi.org/10.1016/j.ecolecon.2018.05.019
Fu R, Feldman D, Margolis R (2018) U.S. Solar photovoltaic system cost benchmark: Q1 2018. Tech. rep., National Renewable Energy Laboratory, Golden, NREL/TP-6A20-72399
Garg MC, Joshi H (2015) Optimization and economic analysis for a small scale nanofiltration and reverse osmosis water desalination system. Water Sci Technol Water Supply 15(5):1027–1033
Ghasemi A (2018) Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid. Energy 142:1–13
Guido Z, Zimmer A, Lopus S, Hannah C, Gower D, Waldman K, Krell N, Sheffield J, Caylor K, Evans T (2020) Farmer forecasts: impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa. Clim Risk Manage. https://doi.org/10.1016/j.crm.2020.100247
Heady EO (1954) Simplified presentation and logical aspects of linear programming technique. J Farm Econo 36(5):1035
Jones E, van Vliet MT (2018) Drought impacts on river salinity in the southern US: implications for water scarcity. Sci Total Environ 644:844–853. https://doi.org/10.1016/J.SCITOTENV.2018.06.373
Jones EC, Leibowicz BD (2021) Co-optimization and community: maximizing the benefits of distributed electricity and water technologies. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2020.102515
Kannan S, Pokhrel C, McKenzie C (2019) Analysis of the effects of high precipitation in Texas on rainfed sorghum yields. Water 11(9):1920
Kleywegt AJ, Shapiro A, Homem-de Mello T (2006) The sample average approximation method for stochastic discrete optimization. SIAM J Optim 12(2):479–502
Laureti T, Benedetti I, Branca G (2021) Water use efficiency and public goods conservation: a spatial stochastic frontier model applied to irrigation in Southern Italy. Socio-Econom Plan Sci. https://doi.org/10.1016/J.SEPS.2020.100856
LBG-Guyton Associates (2003) Brackish Groundwater Manual for Texas Regional Water Planning Groups. Tech. rep, Texas Water Development Board
Leibowicz BD (2018) The cost of policy uncertainty in electric sector capacity planning: implications for instrument choice. Electr J 31(1):33–41
Masoner JR, Mladinich CS, Konduris AM, Smith SJ (2000) Water-Resources Investigations Report Comparison of Irrigation Water Use Estimates Calculated From Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season. Tech. rep., U.S. Department of the Interior, http://ok.water.usgs.gov
Moreno-Cruz JB, Keith DW (2013) Climate policy under uncertainty: a case for solar geoengineering. Clim Change 121(3):431–444. https://doi.org/10.1007/s10584-012-0487-4
New LL (2019) Pumping Plant Efficiency and irrigation Costs. Tech. rep., Texas Agricultural Extension Service, https://gfipps.tamu.edu/files/2019/12/L-2218.pdf
Nielsen-Gammon JW, Banner JL, Cook BI, Tremaine DM, Wong CI, Mace RE, Gao H, Yang ZL, Gonzalez MF, Hoffpauir R, Gooch T, Kloesel K (2020) Unprecedented drought challenges for Texas water resources in a changing climate: what do researchers and stakeholders need to know? Earth’s Future. https://doi.org/10.1029/2020EF001552
Nordhaus WD (1992) The ’DICE’ model: background and structure of a dynamic integrated climate-economy model of the economics of global warming. Cowles Foundation Discussion Papers. https://ideas.repec.org/p/cwl/cwldpp/1009.html
Palmer TN (2000) Predicting uncertainty in forecasts of weather and climate. Rep Progress Phys 63(2):71
Parker WS (2010) Predicting weather and climate: uncertainty, ensembles and probability. Stud Hist Philos Scie Part B 41(3):263–272. https://doi.org/10.1016/J.SHPSB.2010.07.006
Sengupta S (2021) It’s Some of America’s Richest Farmland. But What Is It Without Water?—The New York Times. https://www.nytimes.com/2021/06/28/climate/california-drought-farming.html
Shrum TR, Travis WR, Williams TM, Lih E (2018) Managing climate risks on the ranch with limited drought information. Clim Risk Manage 20:11–26. https://doi.org/10.1016/J.CRM.2018.01.002
Slingo J, Palmer T (2011) Uncertainty in weather and climate prediction. Philos Trans R Soc A Math Phys Eng Sci 369(1956):4751–4767
Smilovic M, Gleeson T, Adamowski J (2016) Crop kites: determining crop-water production functions using crop coefficients and sensitivity indices. Adv Water Resour 97:193–204. https://doi.org/10.1016/j.advwatres.2016.09.010
Texas Department of Agriculture (2021) Texas Ag Stats. https://www.texasagriculture.gov/About/TexasAgStats
United States Department of Agriculture (2017) 2017 Census of Agriculture Highlights | Irrigation and Water Management. Tech. rep., United States Department of Agriculture, www.nass.usda.gov/AgCensus
USDA Market News (2021) Wheat–Daily Price–Commodity Prices–Price Charts, Data, and News–IndexMundi. https://www.indexmundi.com/commodities/?commodity=wheat
Vitter J, Berhanu B, Deetjen TA, Leibowicz BD, Webber ME (2018) Optimal sizing and dispatch for a community-scale potable water recycling facility. Sustain Cities Soc 39:225–240
Werner K, Svedin L (2017) States, water, and climate: who’s planning for change? Clim Risk Manage 16:59–72. https://doi.org/10.1016/J.CRM.2017.03.005
Wheeler R, Lobley M (2021) Managing extreme weather and climate change in UK agriculture: impacts, attitudes and action among farmers and stakeholders. Clim Risk Manage 32:100313. https://doi.org/10.1016/j.crm.2021.100313
Zhang H, Oweis T (1999) Water-yield relations and optimal irrigation scheduling of wheat in the Mediterranean region. Agric Water Manage 38(3):195–211. https://doi.org/10.1016/S0378-3774(98)00069-9
Zhang J, Tian H, Yang J, Pan S (2018) Improving representation of crop growth and yield in the dynamic land ecosystem model and its application to China. J Adv Model Earth Syst 10(7):1680–1707. https://doi.org/10.1029/2017MS001253.https://www.onlinelibrary.wiley.com/doi/abs/10.1029/2017MS001253
Funding
We conducted this research at the University of Texas at Austin where it was supported by the National Science Foundation of the US through Award #1828974 (NRT-INFEWS: Graduate Student Education: Reducing Energy Barriers For Novel Water Supply Use in Sustainable Agriculture).
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Jones, E.C., Leibowicz, B.D. Climate risk management in agriculture using alternative electricity and water resources: a stochastic programming framework. Environ Syst Decis 42, 117–135 (2022). https://doi.org/10.1007/s10669-021-09838-8
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DOI: https://doi.org/10.1007/s10669-021-09838-8