Estimating irrigation demand in the Ogallala Aquifer: Measured vs. Surveyed data

• In Event: Biodiversity, Water Resources, and Global Environmental Change

Abstract

Water pricing is among the most widely advocated tools among economists for managing aquifer depletion, yet its effectiveness hinges on a single parameter: the own-price elasticity of irrigation water demand. If farmers are unresponsive to price signals (that is, if demand is inelastic), pricing policies will fail to achieve conservation goals while imposing substantial costs on agricultural producers. Producing accurate elasticity estimates is therefore not an academic exercise; it is a prerequisite for effective water governance in stressed aquifer systems. Past studies have found a broad spectrum of irrigation demand elasticities ranging from highly inelastic to elastic, especially at the extensive margin. Two seminal studies that used panel data from the Kansas region overlying the Ogallala aquifer found total elasticity estimates of -0.10 (Hendricks and Peterson, 2012) and -0.26 (Pfeiffer and Lin, 2014). This paper addresses a critical but underexplored source of estimation error: data quality. The majority of studies estimating irrigation demand rely on the USDA's Irrigation Water Management Survey (IWMS), which consists of repeated cross-sectional surveys subject to omitted variable bias and measurement error. We compare elasticity estimates derived from this survey data against those from metered water use records drawn from Kansas's Water Information Management and Analysis System (WIMAS), which captures measured pumping data. To our knowledge, no prior study has conducted a direct comparison of survey- and meter-based elasticity estimates for the same geography and time period.Our study focuses on western Kansas, overlying the Ogallala Aquifer, one of the most economically significant and rapidly depleting groundwater systems in the world. The aquifer supports irrigated agriculture across five states, yet is depleting at rates that threaten the region's long-term agricultural viability. Using farm-level data from 1998 to 2023, we estimate demand elasticities at both the intensive margin and extensive margin, following the empirical framework of Pfeiffer and Lin (2014). The extensive margin is the crop choice and crop acreage decision which is estimated as a simultaneous equation model where the acreage planted to each crop is censored by sample selection. The intensive margin of the groundwater extraction decision is the water demand (acre-ft applied per acre) conditional on crop choice, which is estimated using OLS. If metered and survey-based estimates diverge systematically, the policy implications are substantial. State and district-level water management plans, including groundwater allocation limits, tiered pricing structures, and pumping caps, are routinely calibrated using demand elasticity estimates drawn from survey data. Estimates that overstate responsiveness will generate overly optimistic projections of conservation outcomes; those that understate it may lead policymakers to bypass pricing tools in favor of more costly regulatory interventions. By benchmarking widely-used survey data against high-frequency meter records, this study provides policymakers and researchers with direct evidence on the reliability of demand estimates underlying current water policy frameworks. The findings have direct implications for water rights administration, drought contingency planning, and the design of conservation programs across the High Plains and other groundwater-dependent agricultural regions.

Author

• Harikrishnan Santhosh, University of Georgia
  Presenting Author