Explore the vast range of titles available.

Application of advanced techniques to ensure the environment sustainability and freshwater ecosystem conservation is a paramount importance. Industrial sector is mostly accused for the increased water pressure with high water consumption rates and increased grey water footprints. Industries must take action toward implementing water conservation strategies to enhance natural water cycle, increase water-use efficiency and address future challenges. The significance of water footprint as a driving force to water conservation is spotlighted with severe water scarcity. “Sustainable Development Goals (SDGs)” proposed by the United Nations are focused on creating a sustainable way of life to reduce the impacts on ecology and achieve economic and social benefits. Water is a central part in sustainable development; thus, all SDGs are interlinked with sustainable water use. The paper reviews incorporating the concept of water footprint to achieve SDGs in relation to industrial water conservation and the future pathways that lead to sustainable water resource management.

Difficulties in direct monitoring of nitrate balance in agricultural fields reveal the importance of modeling and quantifying the affecting parameters on nitrate balance. We constructed meta-models for APSIMX-Sugarcane using the treed gaussian process and conducted a global sensitivity analysis for nitrate uptake and leaching under three conditions: (1) bare land (BL) to examine the influence of soil hydraulic characteristics, (2) N-free treatment under radiation use efficiency (RUE) ranges (i) 1.2–1.8 [N-free(a)] and (ii) 1.8–2.5 [N-free(b)], and (3) urea conditions to examine the influence of plant growth. Generated meta-models showed good accuracy (for all conditions: R2 > 0.70; NRMSE < 16%; AI > 0.90). The most influential parameters (sensitivity indices ≥ 0.02) were as follows: for leached NO3−N in BL: the parameter rerated to saturated flow-proportion of water between saturation and field capacity (SWCON) of all soil layers; for NO3− uptake and leached NO3−N in N-free(a) and urea: RUE of the phenological stage (PS) 3 (RUE3) and 4, tt_emerg_to_begcane, green_leaf_no, and y_n_conc_crit_leaf of PS 4 (NCL4); in N-free(b): RUE3, NCL4, and SWCON of soil layers 0–15 cm; 15–30 cm, which confirmed that influential parameters were depended on N-stress. The outcomes of this study are useful for enhancing the accuracy and efficiency of crop modeling.

Groundwater pollution by nitrate leaching from sugarcane fields in Okinawa is recognized as a critical issue. Controlled release fertilizer (CRF) has the potential to reduce N leaching from cropping systems. The study focused on confirming the effectiveness of CRF at balancing sugarcane yield and reducing nitrate leaching from sugarcane fields via a water footprint (WF) approach. A lysimeter study was conducted using four treatments: (i) bare land, (ii) P and K fertilization without N, (iii) urea fertilization, and (iv) CRF application. According to the results, for both plant cane and ratoon, the total sugarcane dry weight obtained for CRF was higher compared to urea application. The cumulative nitrate-N leaching of the plant cane season for all treatments was higher than of the ratoon season. For the total crop cycle (plant cane plus ratoon), heavier nitrate-N leaching was observed in the urea-applied condition than in the CRF-applied condition. For both crop seasons, the total sugarcane WF of the CRF application (plant cane: 192.33 m3/t, ratoon: 190.47 m3/t) was lower than that of the urea application (plant cane: 233.47 m3/t, ratoon: 237.59 m3/t). WF values indicated that the CRF application had a lower impact on the groundwater of the area.

The global optimization of parameters in process-based crop models is often considered computationally expensive. Gaussian process (GP) emulation is a widely used method for reducing the computational burden of the optimization process. Total above-ground biomass and cane dry weight of three Thai sugarcane cultivars (KK3, LK92-11 and 02-2-058) collected under rainfed and irrigated conditions were used to optimize cultivar-specific parameters in the Agricultural Production Systems sIMulator (APSIM)-Sugarcane crop model through a GP emulation. GP emulators were trained and validated to approximate APSIM-Sugarcane model and then used for optimizing the cultivar-specific parameters through the differential evolution algorithm. Resulting optimized parameters allowed to obtain simulations that quite well approximated the observed biomass and CDW (validation results between simulated and observed yields: R2 0.93–0.98; normalized root mean squared error: 5–22%; Willmott’s agreement index: 0.87–0.99). The best parametrization was obtained under the lowest water stressed conditions. Based on these results, we suggest that GP emulation can be efficiently implemented for the parameterization of computationally expensive simulators.

Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.