1932

Abstract

Food, energy, and water (FEW) systems play a fundamental role in determining societal health and economic well-being. However, current and expected changes in climate, population, and land use place these systems under considerable stress. To improve policies that target these challenges, this review highlights the need for integrating biophysical and economic models of the FEW nexus. We discuss advancements in modeling individual components that comprise this system and outline fundamental research needs for these individual areas as well as for model integration. Though great strides have been made in individual and integrated modeling, we nevertheless find a considerable need for improved integration of economic decision-making with biophysical models. We also highlight a need for improved model validation.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-resource-100516-033533
2017-10-05
2024-06-22
Loading full text...

Full text loading...

/deliver/fulltext/resource/9/1/annurev-resource-100516-033533.html?itemId=/content/journals/10.1146/annurev-resource-100516-033533&mimeType=html&fmt=ahah

Literature Cited

  1. Adams RM, Rosenzweig C, Peart RM, Ritchie JT, McCarl BA. et al. 1990. Global climate change and US agriculture. Nature 345:219–24 [Google Scholar]
  2. Arnold JG, Fohrer N. 2005. SWAT2000: current capabilities and research opportunities in applied watershed modeling. Hydrol. Process. 19:3563–72 [Google Scholar]
  3. Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ. et al. 2012. SWAT: model use, calibration, and validation. Trans. ASABE 55:41491–508 [Google Scholar]
  4. Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment part I: model development. J. Am. Water Resour. Assoc. 34:173–89 [Google Scholar]
  5. Bates JM, Granger CW. 1969. The combination of forecasts. Oper. Res. Q. 20:4451–68 [Google Scholar]
  6. Borah DK, Yagow G, Saleh A, Barnes PL, Rosenthal W. et al. 2006. Sediment and nutrient modeling for TMDL development and implementation. Trans. ASABE 49:4967–86 [Google Scholar]
  7. Bosch NS, Allan JD, Selegean JP, Scavia D. 2013. Scenario-testing of agricultural best management practices in Lake Erie watersheds. J. Great Lakes Res. 39:429–36 [Google Scholar]
  8. Bouraoui F, Grizzetti B. 2014. Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture. Sci. Total Environ. 468–469:1267–77 [Google Scholar]
  9. Bressiani DDA, Gassman PW, Fernandes JG, Garbossa LHP, Srinivasan R. et al. 2015. A review of soil and water assessment tool (SWAT) applications in Brazil: challenges and prospects. Int. J. Agric. Biol. Eng. 8:39–35 [Google Scholar]
  10. Britz W, Hertel TW. 2011. Impacts of EU biofuels directives on global markets and EU environmental quality: an integrated PE, global CGE analysis. Agric. Ecosyst. Environ. 142:1–2102–9 [Google Scholar]
  11. Carpenter SR, Ludwig D, Brock WA. 1999. Management of eutrophication for lakes subject to potentially irreversible change. Ecol. Appl. 9:751–71 [Google Scholar]
  12. Chen X, Huang H, Khanna M, Önal H. 2014. Alternative fuel standards: welfare effects and climate benefits. J. Environ. Econ. Manag. 67:241–57 [Google Scholar]
  13. Clark TE, McCracken MW. 2013. Advances in forecast evaluation. Handbook of Economic Forecasting 2 G Elliot, A Timmermann 1107–1201 North-Holland, Neth.: Elsevier [Google Scholar]
  14. Cohen A, Keiser DA. 2015. The effectiveness of overlapping pollution regulations: evidence from the ban on phosphate in dishwasher detergent Work. Pap. 14034 Iowa State Univ. Ames: [Google Scholar]
  15. Corradi V, Swanson NR. 2006. Predictive density evaluation. Handb. Econ. Forecast. 1:197–284 [Google Scholar]
  16. Dalin C, Konar M, Hanasaki N, Rinaldo A, Rodriguez-Iturbe I. 2012. Evolution of the global virtual water trade network. PNAS 109:165989–94 [Google Scholar]
  17. Daniel EB, Camp JV, LeBoeuf EJ, Penrod JR, Dobbins JP, Abkowitz MD. 2011. Watershed modeling and its applications: a state-of-the-art review. Open Hydrol. J. 5:26–50 [Google Scholar]
  18. de Brauwere A, Ouattara NK, Servais P. 2014. Modeling fecal indicator bacteria concentrations in natural surface waters: a review. Crit. Rev. Environ. Sci. Technol. 44:2380–453 [Google Scholar]
  19. Diebold FX, Mariano RS. 1995. Comparing predictive accuracy. J. Bus. Econ. Stat. 13:3253–63 [Google Scholar]
  20. Douglas-Mankin KR, Srinivasan R, Arnold JG. 2010. Soil and Water Assessment Tool (SWAT) model: current developments and applications. Trans. ASABE 53:51423–31 [Google Scholar]
  21. Earnhart D. 2004a. Panel data analysis of regulatory factors shaping environmental performance. Rev. Econ. Stat. 86:1391–401 [Google Scholar]
  22. Earnhart D. 2004b. Regulatory factors shaping environmental performance at publicly-owned treatment plants. J. Environ. Econ. Manag. 48:1665–81 [Google Scholar]
  23. Elliott G, Timmermann A. 2008. Economic forecasting. J. Econ. Lit. 46:13–56 [Google Scholar]
  24. Elobeid A, Carriquiry M, Dumortier J, Rosas F, Mulik K. et al. 2013. Biofuel expansion, fertilizer use and GHG emissions: unintended consequences of mitigation policies. Econ. Res. Int 2013:708604 [Google Scholar]
  25. Fabiosa JF, Beghin JC, Dong F, Elobeid A, Tokgöz S, Yu T. 2010. Land allocation effects of the global ethanol surge: predictions from the international FAPRI model. Land Econ 86:4687–706 [Google Scholar]
  26. Farmer JD, Foley D. 2009. The economy needs agent-based modelling. Nature 460:685–86 [Google Scholar]
  27. Farrell AE. 2006. Ethanol can contribute to energy and environmental goals. Science 311:506–8 [Google Scholar]
  28. Fleming P, Lichtenberg E, Newburn DA. 2015. Agricultural cost sharing and conservation practices for nutrient reduction in the Chesapeake Bay watershed Presented at Annu. Meet. Agric. Appl. Econ., Assoc. West. Agric. Econ. Assoc. San Francisco: [Google Scholar]
  29. Foley JA, Levis S, Costa MH, Cramer W, Pollard D. 2000. Incorporating dynamic vegetation cover within global climate models. Ecol. Appl. 10:1620–32 [Google Scholar]
  30. Freeman AM III, Herriges JA, Kling CL. 2014. The Measurement of Environmental and Resource Values: Theory and Methods Washington, DC: RFF Press, 3rd ed.. [Google Scholar]
  31. Gao L, Li D. 2014. A review of hydrological/water-quality models. Front. Agric. Sci. Eng. 1:4267–76 [Google Scholar]
  32. Gassman PW, Reyes MR, Green CH, Arnold JG. 2007. The soil and water assessment tool: historical development, applications, and future research directions. Trans. ASABE 50:41211–50 [Google Scholar]
  33. Gassman PW, Sadeghi AM, Srinivasan R. 2014. Applications of the SWAT model special section: overview and insights. J. Environ. Q. 43:11–8 [Google Scholar]
  34. Gassman PW, Wang Y. 2015. IJABE SWAT Special Issue: innovative modeling solutions for water resource problems. Int. J. Agric. Biol. Eng. 8:31–8 [Google Scholar]
  35. Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF. 2012. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar. Pollut. Bull. 64:112409–20 [Google Scholar]
  36. Gleckler PJ, Taylor KE, Doutriaux C. 2008. Performance metrics for climate models. J. Geophys. Resour. 113:D06104 [Google Scholar]
  37. Gneiting T, Katzfuss M. 2014. Probabilistic forecasting. Annu. Rev. Stat. Appl. 1:125–51 [Google Scholar]
  38. Goetz RU, Zilberman D. 2000. The dynamics of spatial pollution: the case of phosphorus runoff from agricultural land. J. Econ. Dyn. Control 24:143–63 [Google Scholar]
  39. Gonzalez-Ramirez MJ, Kling CL, Arbuckle JG Jr.. 2015. Cost-share effectiveness in the adoption of cover crops in Iowa Presented at Annu. Meet. Agric. Appl. Econ., Assoc. West. Agric. Econ. Assoc. San Francisco: [Google Scholar]
  40. Gopalakrishnan S, Smith MD, Slott JM, Murray AB. 2011. The value of disappearing beaches: a hedonic pricing model with endogenous beach width. J. Environ. Econ. Manag. 61:297–310 [Google Scholar]
  41. Greenstone M, Hanna R. 2014. Environmental regulations, air and water pollution, and infant mortality in India. Am. Econ. Rev. 104:103038–72 [Google Scholar]
  42. Hansen LP, Sargent TJ. 2007. Recursive robust estimation and control without commitment. J. Econ. Theory 136:11–27 [Google Scholar]
  43. Hansen LP, Sargent TJ. 2008. Robustness Princeton, NJ: Princeton Univ. Press [Google Scholar]
  44. Hansen LP, Sargent TJ. 2014. Uncertainty Within Economic Models Singapore: World Sci. [Google Scholar]
  45. Harvey DI, Leybourne SJ, Newbold P. 1998. Tests for forecast encompassing. J. Bus. Econ. Stat. 16:2254–59 [Google Scholar]
  46. Hayes DJ, Babcock BA, Fabiosa J, Tokgoz S, Elobeid A. et al. 2009. Biofuels: potential production capacity, effects on grain and livestock sectors, and implications for food prices and consumers. J. Agric. Appl. Econ. 41:21–27 [Google Scholar]
  47. Hendricks NP, Sinnathamby S, Douglas-Mankin K, Smith A, Sumner DA, Earnhart DH. 2014. The environmental effects of crop price increases: nitrogen losses in the US corn belt. J. Environ. Econ. Manag. 68:3507–26 [Google Scholar]
  48. Hertel TW, Burke MB, Lobell DB. 2010a. The poverty implications of climate-induced crop yield changes by 2030. Glob. Environ. Change 20:4577–85 [Google Scholar]
  49. Hertel TW, Golub A, Jones A, O'Hare M, Plevin R, Kammen D. 2010b. Effects of US maize ethanol on global land use and greenhouse gas emissions: estimating market-mediated responses. Bioscience 60:3223–31 [Google Scholar]
  50. Hertel TW, Villoria NB. 2014. GEOSHARE: geospatial open source hosting of agriculture, resource and environmental data for discovery and decision making Work. Pap. Purdue Univ. West Lafayette, IN: https://mygeohub.org/resources/977/download/GEOSHARE_Prospectus-Final.pdf [Google Scholar]
  51. Hoekstra AY, Mekonnen MM. 2012. The water footprint of humanity. PNAS 109:93232–37 [Google Scholar]
  52. Housh M, Cai X, Ng T, McIsaac G, Ouyang Y. et al. 2014. System of systems model for analysis of biofuel development. J. Infrastruct. Syst. 21:3 https://doi.org/10.1061/(ASCE)IS.1943-555X.0000238 [Crossref] [Google Scholar]
  53. Housh M, Yaeger M, Cai X, McIsaac G, Khanna M. et al. 2015. Managing multiple mandates: a system of systems model to analyze strategies for producing cellulosic ethanol and reducing riverine nitrate loads in the Upper Mississippi River Basin. Environ. Sci. Technol. 49:1911932–40 [Google Scholar]
  54. Howard G, Roe BE. 2013. Stripping because you want to versus stripping because the money is good: a latent class analysis of farmer preferences regarding filter strip programs Presented at AAEA/CAES Joint Annu. Meet. Washington, DC: [Google Scholar]
  55. Howitt R, Medellin-Azuara J, MacEwan D, Lund J, Sumner D. 2014. Economic analysis of the 2014 drought for California agriculture Rep., Cent Watershed Sci., Univ. Calif. Davis: [Google Scholar]
  56. Hudiburg TW, Wang W, Khanna M, Long SP, Dwivedi P. et al. 2016. Impacts of a 32 billion gallon bioenergy landscape on land and fossil fuel use in the US. Nat. Energy 1:15005 [Google Scholar]
  57. Iho A. 2010. Essays on Socially Optimal Phosphorus Policies in Crop Production Jokioinen, Finl.: MTT Agrifood Res. [Google Scholar]
  58. Iho A, Laukkanen M. 2012. Precision phosphorus management and agricultural phosphorus loading. Ecol. Econ. 77:91–102 [Google Scholar]
  59. Imbens GW, Wooldridge JM. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:15–86 [Google Scholar]
  60. Jayakrishnan R. 2005. Advances in the application of the SWAT model for water resources management. Hydrol. Process. 19:749–62 [Google Scholar]
  61. Jiang Y, Nan Z, Yang S. 2013. Risk assessment of water quality using Monte Carlo simulation and artificial neural network method. J. Environ. Manag. 122:130–36 [Google Scholar]
  62. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD. et al. 2003. The DSSAT cropping system model. Eur. J. Agron. 18:235–65 [Google Scholar]
  63. Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ. et al. 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18:267–88 [Google Scholar]
  64. Keiser DA, Shapiro JS. 2016. Consequences of the Clean Water Act and the demand for water quality Work. Pap. Iowa State Univ. Ames: [Google Scholar]
  65. Khanna M, Zilberman D. 2012. Modeling the land-use and greenhouse-gas implications of biofuels. Clim. Change Econ. 3:1250016 [Google Scholar]
  66. Khanna M, Zilberman D, Crago C. 2014. Modeling land use change with biofuels. Oxford Handbook of Land Economics JM Duke, JJ Wu 210–35 Oxford, UK: Oxford Univ. Press [Google Scholar]
  67. Kilian L. 2013. Structural vector autoregressions. Handbook of Research Methods and Applications in Empirical Macroeconomics N Hashimzade, M Thornton 515–54 Cheltenham, UK: Edward Elgar [Google Scholar]
  68. Kling C, Phaneuf D, Zhao J. 2012. From Exxon to BP: Has some number become better than no number?. J. Econ. Perspect. 26:43–26 [Google Scholar]
  69. Knapp KC, Schwabe KA. 2008. Spatial dynamics of water and nitrogen management in irrigated agriculture. Am. J. Agric. Econ. 90:524–39 [Google Scholar]
  70. Konar M, Hussein Z, Hanasaki N, Mauzerall DL, Rodriguez-Iturbe I. 2013. Virtual water trade flows and savings under climate change. Hydrol. Earth Syst. Sci. Discuss. 10:167–101 [Google Scholar]
  71. Krysanova V, Arnold JG. 2008. Advances in ecohydrological modelling with SWAT—a review. Hydrol. Sci. J. 53:5939–47 [Google Scholar]
  72. Krysanova V, White M. 2015. Advances in water resources assessment with SWAT—an overview. Hydrol. Sci. J. 60:5771–83 [Google Scholar]
  73. LaBeau MB, Robertson DM, Mayer AS, Pijanowskic BC, Saad DA. 2014. Effects of future urban and biofuel crop expansions on the riverine export of phosphorus to the Laurentian Great Lakes. Ecol. Model. 277:27–37 [Google Scholar]
  74. Laborde D. 2011. Assessing the land use change consequences of European biofuel policies Rep., ATLASS Consort., Int. Food Policy Res. Inst. Washington, DC: [Google Scholar]
  75. Landry CE, Hindsley P. 2011. Valuing beach quality with hedonic property models. Land Econ 87:92–108 [Google Scholar]
  76. Lipscomb M, Mobarak AM. 2014. Decentralization and water pollution spillovers: evidence from the redrawing of county boundaries in Brazil Work. Pap. Yale Univ. New Haven, Conn.: [Google Scholar]
  77. Liu J, Mooney H, Hull V, Davis SJ, Gaskell J. et al. 2015. Systems integration for global sustainability. Science 347:62251258832 [Google Scholar]
  78. Lobell DB, Banziger M, Magorokosho C, Vivek B. 2011. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1:42–45 [Google Scholar]
  79. Lotze-Campen H, von Lampe M, Kyle P, Fujimori S, Havlik P. et al. 2014. Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric. Econ. 45:103–16 [Google Scholar]
  80. Manski C. 1990. Nonparametric bounds on treatment effects. Am. Econ. Rev. Pap. Proc. 80:319–23 [Google Scholar]
  81. Manski C. 1995. Identification Problems in the Social Sciences Cambridge, MA: Harvard Univ. Press [Google Scholar]
  82. McCarl BA, Schneider UA. 2001. Greenhouse gas mitigation in U.S. agriculture and forestry. Science 294:2481–82 [Google Scholar]
  83. McLellan E, Robertson D, Schilling K, Tomer M, Kostel J. et al. 2015. Reducing nitrogen export from the corn belt to the Gulf of Mexico: agricultural strategies for remediating hypoxia. J. Am. Water Resour. Assoc. 51:1263–89 [Google Scholar]
  84. Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S. et al. 2012. The North American regional climate change assessment program: overview of phase I results. Bull. Am. Meteorol. Soc. 93:91337–62 [Google Scholar]
  85. Mérel P, Howitt R. 2014. Theory and application of positive mathematical programming in agriculture and the environment. Annu. Rev. Resour. Econ. 6:451–70 [Google Scholar]
  86. Nair SS, King KW, Witter JD, Sohngen BL, Fausey NR. 2011. Importance of crop yield in calibrating watershed water quality simulation tools. J. Am. Water Resour. Assoc. 47:1285–97 [Google Scholar]
  87. Ng TL, Eheart JW, Cai X, Braden JB. 2011. An agent-based model of farmer decision-making and water quality impacts at the watershed scale under markets for carbon allowances and a second-generation biofuel crop. Water Resour. Res. 47:9 https://doi.org/10.1029/2011WR010399 [Crossref] [Google Scholar]
  88. Nordhaus WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies New Haven, CT: Yale Univ. Press [Google Scholar]
  89. Nordhaus WD, Boyer J. 2000. Warming the World: Economic Models of Global Warming Cambridge, MA: MIT Press [Google Scholar]
  90. Olmstead SM, Muehlenbachs LA, Shih J-S, Chu Z, Krupnick AJ. 2013. Shale gas development impacts on surface water quality in Pennsylvania. PNAS 110:134962–67 [Google Scholar]
  91. Paris Q, Howitt RE. 1998. An analysis of ill-posed production problems using maximum entropy. Am. J. Agric. Econ. 80:124–38 [Google Scholar]
  92. Pautsch G, Kurkalova L, Babcock BA, Kling C. 2001. The efficiency of sequestering carbon in agricultural soils. Contemp. Econ. Policy 19:123–34 [Google Scholar]
  93. Pelikan J, Britz W, Hertel TW. 2015. Green light for green agricultural policies? An analysis at regional and global scales. J. Agric. Econ. 66:11–19 [Google Scholar]
  94. Plantinga AJ, Mauldin T, Miller DJ. 1999. An econometric analysis of the costs of sequestering carbon in forests. Am. J. Agric. Econ. 81:812–24 [Google Scholar]
  95. Preston SD, Alexander RB, Wolock DM. 2011. Sparrow modeling to understand water-quality conditions in major regions of the United States: a featured collection introduction. J. Am. Water Resour. Assoc. 47:5887–90 [Google Scholar]
  96. Preston SD, Alexander RB, Woodside MD, Hamilton PA. 2009. SPARROW MODELING—enhancing understanding of the nation's water quality Factsheet 2009–3019, US Geol. Surv., US Dep. Inter. Washington, DC: [Google Scholar]
  97. Rabotyagov S, Campbell T, White M, Arnold J, Atwood J. et al. 2014. Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone. PNAS 111:18530–35 [Google Scholar]
  98. Randhir TO, Hertel TW. 2000. Trade liberalization as a vehicle for adapting to global warming. Agric. Resour. Econ. Rev. 29:159–72 [Google Scholar]
  99. Ranson M, Stavins RN. 2015. Linkage of greenhouse gas emissions trading systems: learning from experience. Clim. Policy 16:284–300 [Google Scholar]
  100. Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ. et al. 2013. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric. Forest Meteorol. 170:166–82 [Google Scholar]
  101. Rosenzweig C, Parry ML. 1994. Potential impact of climate change on world food supply. Nature 367:133–38 [Google Scholar]
  102. Rossi B. 2014. Density forecasts in economics, forecasting and policymaking Work. Pap. ICREA, Univ. Pompeu Fabra Barcelona: [Google Scholar]
  103. Rudik I. 2016. Optimal climate policy when damages are unknown SSRN Work. Pap Iowa State Univ. Ames: https://doi.org/10.2139/ssrn.2516632 [Crossref] [Google Scholar]
  104. Schlenker W, Hanemann WM, Fisher AC. 2005. Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 108:395–406 [Google Scholar]
  105. Schlenker W, Hanemann WM, Fisher AC. 2006. The impact of global warming on US agriculture: an econometric analysis of optimal growing conditions. Rev. Econ. Stat. 88:1113–25 [Google Scholar]
  106. Schlenker W, Roberts MJ. 2009. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PNAS 106:3715594–98 [Google Scholar]
  107. Schmitz C, van Meijl H, Kyle P, Nelson GC, Fujimori S. et al. 2014. Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45:69–84 [Google Scholar]
  108. Schwarz GE, Alexander RB, Smith RA, Preston SD. 2011. The regionalization of national-scale SPARROW models for stream nutrients. J. Am. Water Resour. Assoc. 47:51151–72 [Google Scholar]
  109. Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A. et al. 2008. Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319:1238–40 [Google Scholar]
  110. Segarra E, Ethridge DE, Deussen CR, Onken AB. 1989. Nitrogen carry-over impacts in irrigated cotton production, Southern High Plains of Texas. West. J. Agric. Econ. 14:20300–9 [Google Scholar]
  111. Shimshack JP, Ward MB. 2008. Enforcement and over-compliance. J. Environ. Econ. Manag. 55:190–105 [Google Scholar]
  112. Sigman H. 2002. International spillovers and water quality in rivers: Do countries free ride. Am. Econ. Rev. 92:41152–59 [Google Scholar]
  113. Sigman H. 2005. Transboundary spillovers and decentralization of environmental policies. J. Environ. Econ. Manag. 50:82–101 [Google Scholar]
  114. Sims CA. 1980. Macroeconomics and reality. Econometrica 48:11–48 [Google Scholar]
  115. Smith RA, Schwarz GE, Alexander RB. 1997. Regional interpretation of water-quality monitoring data. Water Resour. Res. 33:2781–98 [Google Scholar]
  116. Smith VK, Wolloh CV. 2012. Has surface water quality improved since the Clean Water Act? NBER Work. Pap. 18192 [Google Scholar]
  117. Sprague LA, Gronberg JAM. 2012. Relating management practices and nutrient export in agricultural watersheds of the United States. J. Environ. Q. 41:61939–50 [Google Scholar]
  118. Stavins RN. 1999. The costs of carbon sequestration: a revealed-preference approach. Am. Econ. Rev. 89:994–1009 [Google Scholar]
  119. Taheripour F, Hertel TW, Liu J. 2013. The role of irrigation in determining the global land use impacts of biofuels. Energy Sustain. Soc. 3:11–18 [Google Scholar]
  120. Timmermann A. 2006. Forecast combinations. Handb. Econ. Forecast. 1:135–96 [Google Scholar]
  121. Tuppad P, Douglas-Mankin KR, Lee T, Srinivasan R, Arnold JG. 2011. Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: extended capability and wider adoption. Trans. ASABE 54:51677–84 [Google Scholar]
  122. Uhlig H. 2005. What are the effects of monetary policy on output? Results from an agnostic identification procedure. J. Monet. Econ. 52:2381–419 [Google Scholar]
  123. West KD. 1996. Asymptotic inference about predictive ability. Econometrica 64:51067–84 [Google Scholar]
  124. Williams JR, Arnold JG, Kiniry JR, Gassman PW, Green CH. 2008. History of model development at Temple, Texas. Hydrol. Sci. J. 53:5948–60 [Google Scholar]
  125. Wilson RS, Howard G, Burnett EA. 2014. Improving nutrient management practices in agriculture: the role of risk-based beliefs in understanding farmers’ attitudes toward taking additional action. Water Resour. Res. 50:6735–46 [Google Scholar]
  126. Xabadia A, Goetz RU, Zilberman D. 2006. Control of accumulating stock pollution by heterogeneous producers. J. Econ. Dyn. Control 30:1105–30 [Google Scholar]
  127. Xabadia A, Goetz RU, Zilberman D. 2008. The gains from differentiated policies to control stock pollution when producers are heterogeneous. Am. J. Agric. Econ. 90:1059–73 [Google Scholar]
  128. Xie Y, Zilberman D. 2016. Theoretical implications of institutional, environmental, and technological changes for capacity choices of water projects. Water Resour. Econ. 13:19–29 [Google Scholar]
  129. Zhao J. 2015. Coupled landscape, atmosphere, and socioeconomic systems (CLASS) in the High Plains region Presented at Nat. Sci. Found. FEW Workshop, Oct 12–13 Ames, Iowa: [Google Scholar]
/content/journals/10.1146/annurev-resource-100516-033533
Loading
/content/journals/10.1146/annurev-resource-100516-033533
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error