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Integrated hydro-economic models have been widely applied to water management problems in regions of intensive groundwater-fed irrigation. However, policy interpretations may be limited as most existing models do not explicitly consider two important aspects of observed irrigation decision making, namely the limits on instantaneous irrigation rates imposed by well yield and the intraseasonal structure of irrigation planning. We develop a new modeling approach for determining irrigation demand that is based on observed farmer behavior and captures the impacts on production and water use of both well yield and climate. Through a case study of irrigated corn production in the Texas High Plains region of the United States we predict optimal irrigation strategies under variable levels of groundwater supply, and assess the limits of existing models for predicting land and groundwater use decisions by farmers. Our results show that irrigation behavior exhibits complex nonlinear responses to changes in groundwater availability. Declining well yields induce large reductions in the optimal size of irrigated area and irrigation use as constraints on instantaneous application rates limit the ability to maintain sufficient soil moisture to avoid negative impacts on crop yield. We demonstrate that this important behavioral response to limited groundwater availability is not captured by existing modeling approaches, which therefore may be unreliable predictors of irrigation demand, agricultural profitability, and resilience to climate change and aquifer depletion.

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Reliable accounting of agricultural water use is critical for sustainable water management. However, the majority of agricultural water use is not monitored, with limited metering of irrigation despite increasing pressure on both groundwater and surface water resources in many agricultural regions worldwide. Satellite remote sensing has been proposed as a low-cost and scalable solution to fill widespread gaps in monitoring of irrigation water use in both developed and developing countries, bypassing the technical, socioeconomic, and political challenges that to date have constrained in situ metering. In this paper, we show through a systematic meta-analysis that the relative accuracy of different satellite-based irrigation water use monitoring approaches remains poorly understood, with evidence of large uncertainties when water use estimates are validated against in situ irrigation data at both field and regional scales. Subsequently, we demonstrate that water use measurement errors result in large economic welfare losses for farmers and may negatively impact ability of policies to limit acute and nonlinear externalities of irrigation abstraction on both the environment and other water users. Our findings highlight that water resource planners must consider the trade-offs between accuracy and costs associated with different water use accounting approaches. Remote sensing has an important role to play in supporting improved agricultural water accounting-both independently and in combination with in situ monitoring. However, greater transparency and evidence is needed about underlying uncertainties in satellite-based models, along with how these measurement errors affect the performance of associated policies to manage different short-and long-term externalities of irrigation water use. Despite the importance of monitoring for water management, the overwhelming majority of agricultural water use worldwide-both from groundwater and surface water-remains unmetered (OECD, 2015). For example, a recent report by the Murray-Darling Basin Commission in Australia highlighted that around 30% of the total surface water abstractions were unmetered (MDMA, 2017), with monitoring gaps of up to 75% in some parts of the basin (Grafton, 2019; Hanemann & Young, 2020). Similarly, estimates from the U.S. Department of Agriculture (USDA, 2019) show only 36% of groundwater irrigation wells in the United States are equipped with flow meters (Figure 1), with large monitoring gaps in states such as California and Texas that have experienced severe aquifer depletion over recent decades (Scanlon et al., 2012). In low-income countries, gaps in agricultural water use accounting are even more pronounced, with almost nonexistence

Six domestic hens were exposed to a series of five pairs of two-key concurrent variable-interval schedules with a range of changeover delays from no delay to 15 s. Times spent responding on each alternative and total, within_, and post-changeover-delay response ratios were analyzed in terms of the generalized matching law. The sensitivity parameters, a, for response and time data were generally low when no changeover delay was programmed but were not 0.0. They were higher for all other changeover-delay values, with some tendency to increase as the changeover delay lengthened at very short delays. Within-delay responding was insensitive to reinforcement-rate differences at all changeover delays (a values close to 0.0). As a result of this insensitivity, post-changeover-delay responding was more sensitive to reinforcement-rate changes than was total responding. Interchangeover intervals increased systematically with changeover-delay duration. Responding, particularly after the changeover delay, was well predicted by an equation based on a reinforcer-loss model.

Six hens were exposed to several concurrent (second-order) variable-interval schedules in which the response requirements on the alternatives were varied. The response requirements were one key peck versus five key pecks, one key peck versus one door push, and five key pecks versus one door push. Response-and time-allocation ratios undermatched the obtained reinforcement ratios but were well described by the generalized matching law. Time and response bias estimates from two pairs of response requirements were used to predict bias in the third pairing. The predicted values were close to those obtained; this result supports the notion that both numerically and topographically different responses act as constant sources of bias within the generalized matching law. The differences between the response and time biases could be accounted for by the different times needed to complete each response requirement. The results also suggest that the door push is a useful operant for research with domestic hens.