TY - GEN A1 - Kayatz, Benjamin A1 - Baroni, Gabriele A1 - Hillier, Jon A1 - Lüdtke, Stefan A1 - Freese, Dirk A1 - Wattenbach, Martin T1 - Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements T2 - Agricultural Water Management N2 - Assessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation. This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision support tool for crop water management under data scarcity. This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios. Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70 mm, CFTWlocal: 1.79 mm), while, however, the median model bias is much greater (CFTWglobal: 18.6%, CFTWlocal: 4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant overestimation of irrigation water requirements (median difference: 110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil for some European growing seasons. We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the future. Y1 - 2024 U6 - https://doi.org/10.1016/j.agwat.2024.108803 SN - 0378-3774 VL - 296 PB - Elsevier BV ER - TY - GEN A1 - Kayatz, Benjamin A1 - Baroni, Gabriele A1 - Hillier, Jon A1 - Lüdtke, Stefan A1 - Heathcote, Richard A1 - Malin, Daniella A1 - Tonder, Carl van A1 - Kuster, Benjamin A1 - Freese, Dirk A1 - Hüttl, Reinhard F. A1 - Wattenbach, Martin T1 - Cool Farm Tool Water: A global on-line tool to assess water use in crop production T2 - Journal of Cleaner Production N2 - The agricultural sector accounts for 70% of all water consumption and poses great pressure on ground water resources. Therefore, evaluating agricultural water consumption is highly important as it allows supply chain actors to identify practices which are associated with unsustainable water use, which risk depleting current water resources and impacting future production. However, these assessments are often not feasible for crop producers as data, models and experiments are required in order to conduct them. This work introduces a new on-line agricultural water use assessment tool that provides the water footprint and irrigation requirements at field scale based on an enhanced FAO56 approach combined with a global climate, crop and soil databases. This has been included in the Cool Farm Tool e an online tool which already provides metrics for greenhouse gas emissions and biodiversity impacts and therefore allows for a more holistic assessment of environmental sustainability in farming and agricultural supply chains. The model is tested against field scale and state level water footprint data providing good results. The tool provides a practical, reliable way to assess agricultural water use, and offers a means to engage growers and stakeholders in identifying efficient water management practices. Y1 - 2019 U6 - https://doi.org/10.1016/j.jclepro.2018.09.160 SN - 0959-6526 VL - 207 SP - 1163 EP - 1179 ER -