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The agro-hydrological SWAP model was employed for simulation of evapotranspiration, yield, and water productivity of potato under six irrigation scenarios (100, 90, 80, 70, 60, and 50% potato water requirement (WR)) in Fariman, Ghoochan, and Golmakan in the northeast Iran. The results showed that the SWAP model well-simulated potato yield and water productivity. The model slightly overestimates the potato yield and underestimates the water productivity. The results revealed that irrigation scheduling is an important factor effecting on evapotranspiration, yield, and water productivity of potatoes. By decreasing irrigation water to 50% WR, potato evapotranspiration and yield decreased in all three study areas. However, potato water productivity increased in Fariman and Golmakan and decreased in Ghoochan, as irrigation volume decreased to 50% WR. In Fariman and Golmakan, irrigation at the rate of 80% WR led to the best irrigation management to have maximum water productivity (2.96 and 2.48 kg m−3, respectively) and acceptable potato yield (21,376.2 and 10,998.7 kg ha−1). In Ghoochan, by adopting the irrigation scenario at the level of 90% WR, the potato yield decreased by approximately 7.6% compared to the full irrigation conditions. However, the highest amount of water productivity (2.27 kg m−3) was achieved. HIGHLIGHTS The agro-hydrological SWAP model accurately simulated potato yield and water productivity.; Decreasing irrigation water to 50% of potato water requirement resulted in lower potato evapotranspiration and yield.; Irrigation scheduling significantly influenced evapotranspiration, yield, and water productivity of potatoes.; The SWAP model slightly overestimated potato yield and underestimated water productivity.;

Recently, the Agricultural Policy Extender (APEX) model was enhanced with a grazing module, and the modified grazing database, APEXgraze, recommends sustainable livestock farming practices. This study developed a combinatorial deterministic approach to calibrate runoff-related parameters, assuming a normal probability distribution for each parameter. Using the calibrated APEXgraze model, the impact of grazing operations on native prairie and cropland planted with winter wheat and oats in central Oklahoma was assessed. The existing performance criteria produced four solutions with very close values for calibrating runoff at the farm outlet, exhibiting equifinality. The calibrated results showed that runoff representations had coefficients of determination and Nash–Sutcliffe efficiencies >0.6 in both watersheds, irrespective of grazing operations. Because of non-unique solutions, the key parameter settings revealed different metrics yielding different response variables. Based on the least objective function value, the behavior of watersheds under different management and grazing intensities was compared. Model simulations indicated significantly reduced water yield, deep percolation, sediment yield, phosphorus and nitrogen loadings, and plant temperature stress after imposing grazing, particularly in native prairies, as compared to croplands. Differences in response variables were attributed to the intensity of tillage and grazing activities. As expected, grazing reduced forage yields in native prairies and increased crop grain yields in cropland. The use of a combinatorial deterministic approach to calibrating parameters offers several new research benefits when developing farm management models and quantifying sensitive parameters and uncertainties that recommend optimal farm management strategies under different climate and management conditions. [ABSTRACT FROM AUTHOR]

Recently, the Agricultural Policy Extender (APEX) model was enhanced with a grazing module, and the modified grazing database, APEXgraze, recommends sustainable livestock farming practices. This study developed a combinatorial deterministic approach to calibrate runoff-related parameters, assuming a normal probability distribution for each parameter. Using the calibrated APEXgraze model, the impact of grazing operations on native prairie and cropland planted with winter wheat and oats in central Oklahoma was assessed. The existing performance criteria produced four solutions with very close values for calibrating runoff at the farm outlet, exhibiting equifinality. The calibrated results showed that runoff representations had coefficients of determination and Nash–Sutcliffe efficiencies >0.6 in both watersheds, irrespective of grazing operations. Because of non-unique solutions, the key parameter settings revealed different metrics yielding different response variables. Based on the least objective function value, the behavior of watersheds under different management and grazing intensities was compared. Model simulations indicated significantly reduced water yield, deep percolation, sediment yield, phosphorus and nitrogen loadings, and plant temperature stress after imposing grazing, particularly in native prairies, as compared to croplands. Differences in response variables were attributed to the intensity of tillage and grazing activities. As expected, grazing reduced forage yields in native prairies and increased crop grain yields in cropland. The use of a combinatorial deterministic approach to calibrating parameters offers several new research benefits when developing farm management models and quantifying sensitive parameters and uncertainties that recommend optimal farm management strategies under different climate and management conditions.

Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns. [ABSTRACT FROM AUTHOR]

Adaptation to climate change is a major challenge facing the agricultural sector worldwide. Olive (Olea europaea L.) is a global, high value crop currently cultivated in 28 countries worldwide. Global data to assess the vulnerability of the crop to climate variability are scarce, and in some notable cases, such the United Nations Food and Agricutlure Organization database (FAO, 2006), qualitative assessments rather than quantitative indicators are provided. The aim of this study is to demonstrate a new approach to help overcome these constraints toward a globally applicable method to assess the adaptability of olive cultivars. The adaptability of 11 cultivars, widely used in 11 countries worldwide, was studied using a new generic approach based on the evaluation of soil hydrological regime against cultivar-specific hydrological requirements. The approach requires local data, notably on soil hydrological properties, but it is easily transferable to other countries and regions. We applied an agrohydrological model in 60 soil units to determine hydrological indicators both in a reference (1961-1990) and a future (2021-2050) climate case. We compared indicators with cultivar-specific requirements to achieve the target yield; requirements were established using experimental yield response curves. We estimated the probability of adaptation, i.e., the probability that a given cultivar attains the target yield, and we used it to evaluate the cultivar potential distribution in the study area. At the locations where soil hydrological conditions were favorable, the probabilities of adaptation of the cultivars were high in both climate cases. The results show that the area with suitable conditions for the target yield (area of adaptability) decreased under future climate for all the cultivars, with higher reduction for Frantoio and Maiatica and smaller reduction for Itrana, Nocellara, Ascolana, and Kalamata. These cultivars are currently grown in Argentina, United States (US), Australia, France, Greece, and Italy. Our results indicate also that these cultivars require higher available soil water to attain the target yield, i.e., we may expect similar vulnerability in other parts of the world. Based on these findings, we provide some specific recommendations for enrichment of global databases and for further developments of our approach, to increase its potential for global application. [ABSTRACT FROM AUTHOR]

• GHG emission value account for 75–80% of the total negative ESV in Northwest China. • Reducing irrigation by 33.3–53.3 % and N fertilization by 29.4–41.2 % can achieve REF trade-offs. • Wet years increased the water-saving potential but reduced the N-saving potential. Excessive irrigation and nitrogen (N) fertilization have caused severe reductions in farmland water and N productivity, and functional degradation of agroecosystems in arid to semi-arid regions. Thus, a process simulation-based framework for resource, food, and ecology (RFE) trade-offs was used to optimize the irrigation and N fertilization strategy (IRNF). A scenario study was conducted based on the framework that was developed in our previous companion study in the arid to semi-arid regions of Northwest China. Based on the local IRNF (i.e., 450 mm irrigation and 340 kg ha−1 N fertilization), reduced irrigation and N fertilization management scenarios were proposed considering eleven irrigation depth reduction cases and ten N fertilization reduction cases in three hydrological years (wet, normal, and dry). The integrated responses of spring wheat yield, water and N productivity, and ecosystem service value (ESV) to various IRNFs were quantified and assessed. The results showed that the local IRNF obtained higher yields and positive ESVs, but was also produced higher greenhouse gas (GHG) emissions, N losses, and water consumption, thus resulting in lower water and N productivity and higher negative ESVs. Among the negative ESVs, GHG emission value accounted for the largest proportion of the total negative ESV (75–80 %), followed by water consumption and fertilizer loss values. Compared with the local IRNF, the scenarios with 210 mm irrigation and 240 kg ha–1 N fertilization in wet years, 240 mm irrigation and 200 kg ha–1 N fertilization in normal years, and 300 mm irrigation and 200 kg ha–1 N fertilization in dry years could not only reduce negative ESVs, improve water and N productivity, and save water and fertilizer resources, but also maintain acceptable yields and positive ESVs. These recommendations reduced the total negative ESV by 4.8–7.7 %, increased water and N productivity by 4.0–8.9 % and 44–69 %, and saved irrigation water and N fertilizer resources by 33.3–53.3 % and 29.4–41.2 % in three hydrological years in relation to the local IRNF, respectively. This study indicates that the framework is a reliable tool for achieving farmland RFE trade-offs and generating appropriate IRNFs for different hydrological years in arid to semi-arid regions. Therefore, sustainable development goals in RFE systems may be achieved by optimizing irrigation and fertilization management. [ABSTRACT FROM AUTHOR]

Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.

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.

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

Soil salinization, severe NO 3 --N leaching and low water-fertilizer use efficiency are constraints to sustainable maize production in the upper Yellow River basin (YRB) of Northwest China. Agro-hydrological models have been proven to be a promising decision tool for water and fertilizer management of farmland. In this paper, the AHC (Agro-Hydrological & Chemical and Crop Systems Simulator) model was calibrated and validated by measured soil water, salt, nitrogen (N) and crop growth data and used for scenario analysis to search for the appropriate irrigation and N fertilization strategies for drip-irrigated saline maize farmland. The scenarios were designed with different soil matric potential thresholds (SMPT) for irrigation, different saline degrees (i.e., slightly and moderately saline soils), and with or without additional salt leaching (ASL) in different hydrological years (wet, normal and dry). The results indicated that increasing SMPT and ASL increased the frequency and total amount of irrigation, thus favoring soil salt leaching. NO 3 --N was leached out of the root zone soil as SMPT exceeded –20 kPa, especially for the cases with ASL. For the case of –20 kPa SMPT, compared with the irrigation scenario without ASL, the irrigation scenario with ASL could significantly increase maize yield and water use efficiency (WUE) in moderately saline farmland. However, for the case of –15 kPa SMPT, the irrigation scenario with ASL caused a significant decrease in WUE for both saline farmlands. Maize yield increased as the N application rate increased from 150 kg ha–1 to 250 kg ha–1 and remained stable as the N application rate continuously increased, resulting in high NO 3 --N leaching and low partial factor productivity of N fertilizer (PFP n). Comprehensively considering crop yield, WUE, PFP n , soil desalting and environmental impacts, in wet and normal years, the appropriate irrigation and N fertilization strategy is irrigation under –20 kPa SMPT and a 250 kg ha–1 N application rate without ASL for slightly saline maize farmland and with ASL for moderately saline maize farmland, respectively. In dry years, the appropriate irrigation and N fertilization strategy is irrigation without ASL under –15 kPa SMPT and a 250 kg ha–1 N application rate for both slightly and moderately saline maize farmlands in the study and relevant areas. • Increasing additional salt leaching favored soil desalting and maize production. • Excessive high soil matric potential thresholds caused severe NO3--N leaching. • Irrigation practices were optimized for two salinized soils in three hydrological years. • Proper nitrogen input rate was determined for food-resource-environmental trade-offs. [ABSTRACT FROM AUTHOR]

Unreasonable irrigation water saving and salinity control practices (IWS-SCPs) have resulted in a shallow groundwater table, severe soil salinization and low water use efficiency in the upper Yellow River basin (YRB), Northwest China. In our study, a framework was developed by coupling the agro-hydrological model, i.e., AHC (Agro-Hydrological & chemical and Crop systems simulator) with a decision-making method, i.e., VIKOR (VlseKriterijuska Optimizacija I Komoromisno Resenje), and then a comprehensive evaluation index was calculated to assess the IWS-SCPs for maize and sunflower in the upper YRB. The AHC model was calibrated and validated with two years of field measured data, and then it was used to perform the simulations of agro-hydrological processes corresponding to the designed scenarios considering the irrigation depth and groundwater table depth (GWD) under the conditions of different hydrological years and degrees of soil salinization. The simulated crop yield, soil salinity variation, and crop water productivity were used as the evaluation indicators to search for appropriate IWS-SCPs of the target crops. The results showed that the recommended IWS-SCPs for maize were those with GWDs between 2.1 − 2.3 m and irrigation depths approximately 10% lower for non-saline soils in wet years and 10% higher than the present irrigation for slightly and moderately saline soils in normal and dry years. Whereas, the IWS-SCPs with GWD between 1.5 − 2.2 m and an irrigation depth of approximately 10 − 30% lower than the present situation could be recommended for sunflower in wet years and normal years regardless of saline soils and in dry years of non-saline soils. The developed framework and the obtained results are expected to provide a decision tool and implications for improving irrigation efficiency and salinity control in the arid upper YRB as well as other areas with similar climate conditions and shallow groundwater. • Coupling AHC model and VIKOR method provided a decision tool for water saving and salinity control. • The framework could be used as a decision tool for irrigation management and salinity control. • The appropriate groundwater depth for maize is 0.6 m deeper than that for sunflower. • Approximately 10%− 30% of irrigation water can be saved for sunflower in the upper Yellow River basin. [ABSTRACT FROM AUTHOR]

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. • The optimality of the location of water quality monitoring stations is studied. • The location must maximize the economic value of the information. • The area studied is a catchment area in northeastern France. • The impact of different assumptions on the damage is discussed. • The assumption of the overall level of damage is crucial to the results. [ABSTRACT FROM AUTHOR]

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m²/m² to 0.2 and R² = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m²/m² and R² = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m²/m² are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments. [ABSTRACT FROM AUTHOR]

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

[Display omitted] • We develop a new monthly distributed agro-hydrological model for irrigation district. • It considers groundwater exchange between irrigated and non-irrigated lands. • ·It simulates main agro-hydrological processes well in Hetao irrigation district. • Interior and ditch drainages are both important in controlling groundwater table. • Evapotranspiration is about 95% of total irrigation and precipitation in average. Agro-hydrological processes in arid irrigation districts mainly include precipitation, water diversion, irrigation, drainage, evapotranspiration (ET), and soil water and groundwater flow, which interact with each other and are controlled by complex natural and anthropogenic drivers. To better understand the agro-hydrological processes in arid irrigation districts with shallow groundwater table, we developed a novel monthly distributed agro-hydrological model for irrigation districts (DAHMID) based on the concepts of canal command area (CCA) and sub-drainage command area (SDCA). The DAHMID model is driven by meteorology, irrigation, and evapotranspiration (ET) estimated by remote sensing-based ET model, and considers soil water and groundwater balances in both irrigated and non-irrigated lands and interior drainage between them. The model was applied to Hetao Irrigation District (HID), the largest irrigation district in arid region of China with a total irrigated area of 0.68 million ha. The DAHMID model was calibrated with groundwater table depth measurements in 13 CCAs of HID from 2008 to 2010, and validated from 2012 to 2013. Results depicted that the root mean square errors (RMSEs), normalized RMSEs (NRMSEs), Nash-Sutcliffe efficiency coefficients (NSEs), and coefficients of determination (r2) of groundwater table depth in both irrigated and non-irrigated lands for all CCAs were in the ranges of 0.19–0.34 m, 0.10–0.25, 0.30–0.82, and 0.68–0.91, respectively. The simulation results from 2008 to 2014 indicated that interior drainage from irrigated land to non-irrigated land is an important approach of drainage in HID, which is about 14.3% of total irrigation water diversion and 34.9% more than the drainage through ditches. The interior drainage process is basically similar to irrigation and ditch drainage processes, all reaching their peaks in May and October. ET is the major water consumption in HID, which is about 95% of total irrigation water diversion and precipitation in average. The net capillary rise of irrigated land is significantly less than that of non-irrigated land due to the impact of irrigation infiltration. The DAHMID model has less parameters and requires less inputs, and can be better applied to continuous simulation of agro-hydrological processes in irrigation districts in medium and long periods with satisfactory simulation accuracy. [ABSTRACT FROM AUTHOR]

Water shortage and poor soil fertility are the main factors restricting the sustainable development of peanut production in semi-arid areas of Northeast China. It is thus essential to have a deep understanding of the soil water-nitrogen dynamics and crop water/nitrogen use for developing water and nutrient strategies. Three levels of irrigation treatment (W 65 , 65% of field capacity; W 55 , 55% of field capacity; W 45 , 45% of field capacity) and a rain-fed treatment (CK) were implemented in field experiments conducted for peanut during the growing seasons of 2016 and 2017 in Liaoning, Northeast China. The AHC (Agro-Hydrological & chemical and Crop systems simulator) model was calibrated and validated, and then applied to assess peanut yield, water productivity (WP) and nitrogen use efficiency (NUE) for the present situation and future irrigation scenarios. The results indicated that the AHC model was capable of simulating soil water and nitrogen status and peanut growth. Simulations of soil water/nitrogen contents and crop growth indicators (Leaf area index, aboveground biomass, plant height and yield) fitted well with field observations. Simulated dynamics showed that 9–21% of the total water input and 14–27% of the total N input were leached out the root zone (0–60 cm). Rainfall was the main cause of water percolation and nitrogen leaching. The highest average yield (5701 kg ha–1) and NUE (26.77 kg kg–1) were obtained in the W 55 treatment. The WP was not obviously decreased under the W 55 treatment, and was only 4.1% lower than that of the CK treatment, in which the WP was highest. Based on scenario analysis with the consideration of crop yield, WP and NUE, the optimal irrigation amount of 80–97 mm is recommended for peanut cultivation in this region. We demonstrated that the AHC model could be used to develop water management strategies for peanuts in Northeast China to conserve water while sustaining agriculture. • AHC model showed good performances in simulating water and nitrogen dynamics and peanut growth. • Water percolation and nitrogen leaching mainly occurred due to heavy rainfall events. • Evapotranspiration and crop N uptake accounted for 75%− 87% and 74%− 97% of the total water and nitrogen inputs. • The 80–97 mm irrigation is recommended for peanut cultivation. [ABSTRACT FROM AUTHOR]

Abstract: Complete knowledge of all components of the water balance is essential to optimize water use in irrigated agriculture. However, most water balance components are very difficult to measure in terms of the required time interval and due to the complexity of the processes. An unsaturated zone model is a useful tool for predicting the effects of agricultural management on crop water use and can be used to optimize agricultural practices in view of minimizing the agricultural water use. For the irrigated areas in Minqin County of northwest China, the physically based one-dimensional agro-hydrological model SWAP (Soil, Water, Atmosphere and Plant) for water movement and crop growth was applied to reveal all the components of the water balance at multiple sites. This model has a varying level of abstraction referring to simulated processes in time and space. A combination of field, meteorological and aerial data was used as input to the model. Inverse modeling of evapotranspiration (ET) fluxes was followed to calibrate the soil hydraulic functions by using the parameter estimation package PEST. Surface Energy Balance System (SEBS) was used to estimate actual ET fluxes from NOAA AVHRR satellite images. Simulations were carried out for 15 different sites in Minqin County by using wheat (Triticum aestivum L.) as a test crop, but only three sites were selected for model calibration and evaluation. The period of simulation for the whole wheat growing season was from 1 April 2004 to 30 July 2004 and detailed analyses were performed for all sites. SWAP simulated soil water dynamics well and the distributed SWAP model is a useful tool to analyze all water balance components. [Copyright &y& Elsevier]

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]

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice. [ABSTRACT FROM AUTHOR]

European rice production is concentrated in limited areas of a small number of countries. Italy is the largest European producer with over half of the total production grown on an area of 220,000 hectares, predominantly located in northern Italy. The traditional irrigation management (wet seeding and continuous flooding until few weeks before harvest—WFL) requires copious volumes of water. In order to propose effective 'water-saving' irrigation alternatives, there is the need to collect site-specific observational data and, at the same time, to develop agro-hydrological models to upscale field/farm experimental data to a spatial scale of interest to support water management decisions and policies. The semi-distributed modelling system developed in this work, composed of three sub-models (agricultural area, groundwater zone, and channel network), allows us to describe water fluxes dynamics in rice areas at the irrigation district scale. Once calibrated for a 1000 ha district located in northern Italy using meteorological, hydrological and land-use data of a recent four-year period (2013–2016), the model was used to provide indications on the effects of different irrigation management options on district irrigation requirements, groundwater levels and irrigation/drainage network efficiency. Four scenarios considering a complete conversion of rice irrigation management over the district were implemented: WFL; DFL—dry seeding and delayed flooding; WDA—alternate wetting and drying; WFL-W—WFL followed by post-harvest winter flooding from 15 November to 15 January. Average results for the period 2013–2016 showed that DFL and WDA would lead to a reduction in summer irrigation needs compared to WFL, but also to a postponement of the peak irrigation month to June, already characterized by a strong water demand from other crops. Finally, summer irrigation consumption for WFL-W would correspond to WFL, suggesting that the considered winter flooding period ended too early to influence summer crop water needs. [ABSTRACT FROM AUTHOR]

ABSTRACT Simulations for root growth, crop growth, and N uptake in agro-hydrological models are of significant concern to researchers. SWMS_2D is one of the most widely used physical hydrologically related models. This model solves equations that govern soil-water movement by the finite element method, and has a public access source code. Incorporating key agricultural components into the SWMS_2D model is of practical importance, especially for modeling some critical cereal crops such as winter wheat. We added root growth, crop growth, and N uptake modules into SWMS_2D. The root growth model had two sub-models, one for root penetration and the other for root length distribution. The crop growth model used was adapted from EU-ROTATE_N, linked to the N uptake model. Soil-water limitation, nitrogen limitation, and temperature effects were all considered in dry-weight modeling. Field experiments for winter wheat in Bouwing, the Netherlands, in 1983-1984 were selected for validation. Good agreements were achieved between simulations and measurements, including soil water content at different depths, normalized root length distribution, dry weight and nitrogen uptake. This indicated that the proposed new modules used in the SWMS_2D model are robust and reliable. In the future, more rigorous validation should be carried out, ideally under 2D situations, and attention should be paid to improve some modules, including the module simulating soil N mineralization.