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Information on the water quality of rivers can be used to judge the effectiveness of past policies or to guide future environmental policies. Consequently, the location of water quality monitoring stations (WQMSs) plays an important role in river pollution control. In the 2000s, a literature developed on the optimization of WQMS location to identify pollution hot spots, average quality, or to minimize the detection time of a potential source of accidental pollution. This article is part of a new literature aimed at locating WQMSs in order to optimize the economic value of information (EVOI) generated by water quality monitoring networks (WQMNs). The field of study is a catchment in northeastern France where the purpose of quality measurement is to define a policy of reduction of agricultural nitrogen fertilizers in order to reach the standard of 50 mg/l of nitrate at the WQMS. Agro-hydrological and economic models estimate the net benefit of input reduction depending on the location of the WQMS on the basis of different assumptions concerning the ecological damage generated by nitrate. We show that the magnitude of the ecological damage and, consequently, the perception of the contamination generated by nitrate in water, play a decisive role on the optimal location of the WQMS, as well as on the benefit of the economic optimization of locations, compared to traditional optimization. Locating WQMSs in a way that maximizes EVOI will be more attractive for very high or very low levels of damage. However, in this context, linking damage to nitrate concentration or to concentration coupled with riparian population density alone will have little impact., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Purpose: In a context of flourishing eco-labelling programs and environment policy for food products, LCA application to agricultural systems faces the challenges of being operational, accurate and exhaustive. This is particularly challenging for the newly developing LCA and ISO-compliant water footprinting, with many LCIA methods only recently developed, but no dedicated inventory method. To support the inventory of elementary water flows, LCA practitioners have a variety of tools available, ranging from databases (e.g. World Food LCA Database) to complex agro-hydrological models. To allow all LCA practitioners to fulfil their diverse agri-food LCA objectives, a review of available inventory tools for field water flows and recommendations are needed.Methods: The selection of the appropriate method and tool for the inventory of field water flows in agri-food LCA studies depends on the objectives of the LCA study, data and resources available (time and skills). We analysed water inventory and agri-food LCA databases by evaluating the models on which they rely and their input data. Then, we explored the use of agro-hydrological models for LCA aiming at discriminating between different cropping system practices (LCA-based eco-design).Results and discussion: Water inventory and agri-food LCA databases provide estimates of theoretical water consumed by a crop and rely on data and methods that have limitations, making them suitable only for background agricultural LCAs. In addition, databases do not support the application of water availability footprint indicators (assessing quantitative water use and water quality alteration). For the LCA-based eco-design of cropping systems, the inventory of water flows should be based on a model simulating evapotranspiration, deep percolation and runoff accounting for crop specificities, pedo-climatic conditions and agricultural management. In particular, the model should account for possible water, salinity and nutrient stresses; assess evaporation and transpiration separately; and estimate runoff and drainage according to the system specificities. Yield should not be estimated with a model but with primary data. Recommended and default data sources are provided for each input parameter.Conclusions: The FAO AquaCrop model represents a good trade-off between accuracy, simplicity and robustness for LCA-based eco-design of cropping systems. However, this model is not yet applicable for perennial crops. Beyond a single model selection, this is a modelling approach that we characterised in this work. [ABSTRACT FROM AUTHOR]

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling. [ABSTRACT FROM AUTHOR]