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The paper explores how agricultural water pricing could contribute to lowering water demand when uses are unobserved (asymmetric information). The topic of the paper is justified by the fact that most water authorities worldwide do not control water uses at the farm scale. The study draws inspiration from the pricing policies of a Reclamation and Irrigation Board in Northern Italy. It analyses the optimal design of current tariff strategies with respect both to the actual regulator's goals and the cost recovery objective of an ideal regulator driven by European Water Framework Directive principles and having full information. The analysis is based on the logic of a Principal-Agent model implemented as a mathematical non-linear programming model. Given the current pricing structure and assuming zero transaction costs, the results show a relevant increase in net benefits for the ideal scenario with respect to the actual one as water use costs increase. Benefits differences between the two scenarios mark a limit in value below which mechanisms able to solve the existing asymmetries between the principal and the agents are economically desirable. The study concludes by showing that the current regulator's discriminatory strategy (pricing structure) would be better justified with higher levels of cost for water use. However, the existence of non-zero transaction costs related to the control of water uses points to the need for further research in order to analyze incentive mechanisms in the absence of water metering. [ABSTRACT FROM AUTHOR]

The Piedmont Region in NW Italy has recently deployed an ambitious and pioneering agricultural water pricing reform aimed at integrating and effectively enforcing EU's Water Framework Directive principles of cost recovery, polluter-pays and affordability. This paper develops a multi-model ensemble framework encompassing 5 mathematical programming models (2 Positive Mathematical Programming models, 2 Positive Multi-Attribute Utility Programming models and 1 Weighted Goal Programming model) that represent the observed behavior of socioeconomic agents to: 1) simulate the impacts of the Piedmontese water pricing reform on land use allocation and management, water conservation, profit and water tariff revenue; 2) sample modeling uncertainty through the ensemble spread; and 3) explore potential tipping points through use of scenario-discovery techniques. Our research suggests that the key challenge to the reform lies in the management of rice fields, an extensive (17% of the agricultural area), water-demanding and relatively low-added-value crop that nonetheless delivers significant ecosystem services (e.g. water retention) of historical and cultural relevance to the region. The ensemble experiment suggests that rice agriculture rapidly dwindles in the price range 0.012–0.074 EUR/m3 depending on the model. Before reaching this tipping point, agricultural water pricing can reduce withdrawals up to 1.7%–9.5%, while reducing profit between 4.9% and 5.6% and achieving a 57- to 65-fold increase in water tariff revenue. • We combine a multi-model ensemble experiment with scenario discovery techniques. • Our approach samples modeling and scenario uncertainty to identify tipping points. • Our approach is illustrated using the agricultural water pricing in Piedmont (IT). • Results show historical rice fields dwindle in the price range 0.014–0.074 EUR/m3. • Conserving rice fields considerably limits pricing water conservation potential. [ABSTRACT FROM AUTHOR]

Water is considered the most critical resource for sustainable agricultural development worldwide. Water scarcity has become an issue, especially in arid and semi-arid areas. There is the need to develop efficient management options to reduce water consumption and waste. In order to measure irrigated crop technical efficiency, a stochastic frontier production method – in which the inefficiency component is heteroscedastic – is used with the aim to assess what crop type and production technique revealed highest efficiency. The model is applied to a case study in southern Italy by using 2016 data from the EU Farm Accountancy Data Network. Our results show that the assumption of heteroscedasticity in the one-sided error term in the stochastic frontier is valid: individual farms' characteristics critically influence technical efficiency. Processing tomato is the most efficient crop production system; organic farms tend to have a lower level of technical efficiency compared to conventional farms; and fertigation system is found to increase the level of technical efficiency. • Efficient agriculture water management through improved water use is a critical response to growing water scarcity. • This paper aims at measuring technical efficiency of irrigated crop production in Italy giving emphasis on the water use input. • The adopted stochastic approach allows us to incorporate the one-sided component error which reflects farms inefficiency. • Model results indicate that comparable technical efficiency scores among farms does exist. [ABSTRACT FROM AUTHOR]

The hydrant pressure head in an on-demand water distribution system can be subject to high fluctuation depending on the discharge flowing inside the pipes, with consequent impacts on the performance of on-farm irrigation systems. In this work, an Italian water distribution system was analyzed using the AKLA model at upstream discharges of 1,200 and 600 L·s-1 to estimate the range of hydrant pressure variation. A computer model was developed, calibrated, and used to evaluate the performance of a drip irrigation system by relating the on-farm network with the hydrant characteristic curve at a certain operating status. The flow regulator within the hydrant played an important role in stabilizing the performance of the network at hydrant pressures higher than 27 m. At lower hydrant pressures, to apply the same amount of water, irrigation time must be extended by 17 and 95% for pressure heads of 20 and 12 m, respectively. These approaches described have great utility to ensure adequate irrigation management when water is delivered by pressurized on-demand systems. [ABSTRACT FROM AUTHOR]