Irrigation Scenarios Research Articles

Spatial and temporal sensitivity of water footprint assessment in crop production to modelling inputs and parameters

Owing to increasingly limited water resources, evaluating large-scale regional crop water footprints (WFs) within acceptable errors is the basis for efficiency and productivity improvements for sustainable agriculture. However, quantitative sensitivity analysis of outputs to inputs and parameters of advanced and widely used crop WF accounting models are still lacking. In this study, we evaluated the spatial and temporal sensitivity of the blue and green WFs of crops to key input variables based on year-by-year simulations for wheat, rice, maize, and soybean production in China from 2010 to 2019. We employed one of the most widely used AquaCrop models to test different water supply and irrigation scenarios. Nine input variables or model parameters were selected, including reference evapotranspiration (ETo), crop transpiration coefficient (KcTr), soil evaporation coefficient (KE), maximum canopy cover (MCC), precipitation (PR), canopy decline coefficient (CDC), planting density of the crop (DC), reference harvest index (HI0), and normalised water productivity (WP*). The results showed that crop WFs are generally more sensitive to ETo among the input variables and KcTr and KE among the model parameters. We also found that the blue WF was more sensitive than the green WF. In most cases, WFs in rainfed scenarios were more sensitive to changes in input variables compared to WFs of irrigated crops. Of the four studied crops, the WFs of maize and wheat were more sensitive than the WFs of rice and soybean. Wheat, maize, and soybean also showed a visible spatial variation in the sensitivity of the WF to each input variable. Overall, the WF sensitivity was influenced by crop type, water supply, irrigation method, and regional climate features. The current analysis provides a feasible approach to comprehensively identify the key inputs and parameters to be calibrated for accurate crop WF accounting.

Eddy covariance quantification of corn water use and yield responses to irrigations on farm‐scale fields

AbstractFurrow irrigations dominate row‐crop irrigation scenarios in many regions worldwide. In this study, corn (Zea mays L.) yield and evapotranspiration (ET) in an all‐furrow irrigation (FI) were compared against irrigations applied through alternate furrows irrigation (SFI, skip‐furrow irrigation) and rainfed (RF) systems on farm‐scale fields in 2017 and 2019 {corn phases of a soybean [Glycine max (L.) Merr.]–corn rotation} in clay soil in the Lower Mississippi Delta region, United States. Evapotranspiration was monitored using the eddy covariance (EC) method. The average corn yield in the SFI was significantly (12.1 Mg ha−1) higher (4.9%) than in the FI (11.7 Mg ha−1). Corn yield in RF (10.2 Mg ha−1) was significantly lower (10.7%) than in the FI. However, the leaf area index (LAI) in SFI was lower than in FI. Seasonal average ET was 556, 573, and 540 mm in FI, SFI, and RF, respectively. The average water use efficiencies (WUEs) were 0.021, 0.021, and 0.019 Mg ha−1 mm−1, respectively (10.5 % lower in RF than FI and SFI). This investigation revealed that adapting the SFI irrigation regime in the corn cropping system could produce grain yields equal to or higher than corn grown under the conventional FI, saving ∼40% of irrigation water. The farm‐scale studies conducted in this investigation gave better confidence to recommend SFI to replace conventional FI systems in the region for water conservation in corn cropping systems. Further investigations may be needed to evaluate the viability of SFI in other contrasting soils and climates and recommend the system for adoption by the farming community.

Evaluating The Impact of Deficit Irrigation Scenarios on Paddy Rice at The Nile Northern Delta, Egypt

Evaluating The Impact of Deficit Irrigation Scenarios on Paddy Rice at The Nile Northern Delta, Egypt

Multi-objective winter wheat irrigation strategies optimization based on coupling AquaCrop-OSPy and NSGA-III: A case study in Yangling, China

The contradiction between crop water requirements and water supplies in Guanzhong Plain of Northwest China restricts the production of local winter wheat. The optimization of irrigation strategies considering multiple-objectives is of great significance to alleviate water crisis and sustainability of winter wheat production. This paper considered three typical hydrological years (dry year, normal year, and wet year), and a simulation optimization model coupling AquaCrop and NSGA-III was developed using Python language. The multi-objective optimization problem considered four objectives: (1) maximize crop yield (Y), (2) minimize irrigation water (IW), (3) maximize irrigation water productivity (IWP), and (4) maximize water use efficiency (WUE). The TOPSIS-Entropy method was then adopted for decision-making based on the Pareto fronts which were generated by multi-objective optimization, thus facilitating the optimization of the irrigation strategies. The results show that AquaCrop model could accurately simulate the growth process of winter wheat in the study area, the relative error is acceptable. The R2 of canopy cover (CC) is 0.75 and 0.61, and above ground biomass production (B) is 0.94 and 0.93, respectively. In the Pareto fronts, the difference between the maximum and minimum yield of winter wheat is 9.48 %, reflecting the diversity of multi-objective optimization results. According to the analysis results of this paper, the performance of different irrigation scenarios in each typical year varies greatly. The performance of the optimization in dry years is significantly better than that in normal years and wet years. The optimization of irrigation strategies and comparison of different scenarios play a positive role in improving the local water use efficiency, the winter wheat yield, as well as the sustainable development level of water resources.

Development and Evaluation of Irrigation Management Strategies for Higher Yield and Water Productivity of Wheat: A Simulation Modelling Approach

: In the present study, CERES-wheat model was calibrated and validated for predicting growth and yield of wheat by using three years of experimental data (2016-17, 2017-18 and 2019-20) under different irrigation treatments. Further the model was run with twenty years of weather data (2000-01 to 2019-20) with optimum management practices under different irrigation scenarios for developing the irrigation management strategies for obtaining higher grain yield and irrigation water productivity (IWP) under dry, wet and normal rainfall conditions. The results indicated that the model slightly overestimated the maximum leaf area index (LAI) and consequently the grain yield in water stress condition; however, overall, model could excellently simulate anthesis and maturity with RMSE<4 days, RMSEn<10% and d>0.90, as well as grain yield (RMSE= 163-204 kg/ha, RMSEn =3-6%, d = 0.93-0.98) for three, four and five irrigations from CRI to jointing, CRI to anthesis and CRI to dough stage, respectively. The IWP was increased with the decrease in number of irrigations. The maximum yield was obtained when the four irrigations applied from CRI to Anthesis under all-weather years (Dry, wet and normal), but it was as par with the three irrigations applied at CRI, Tillering and Jointing or CRI, Tillering and Anthesis stage for wet years. Under three irrigations scenario the maximum yield of 5050-5302 kg/ha and irrigation water productivity (IWP) of 33.7-35.3 kg/ha-mm was noticed when three irrigations applied at CRI, Tillering and Jointing followed by at CRI, Tillering, Anthesis stage and CRI, Jointing and Anthesis stage and under wet year no difference was noticed in yield and IWP whether three irrigations were applied at CRI, Tillering and Jointing or CRI, Tillering and Anthesis stage and under two irrigations scenario, the maximum grain yield as well as IWP was obtained when applied at CRI and Tillering followed by CRI and Jointing stage.

Towards an operational irrigation management system for Sweden with a water–food–energy nexus perspective

The 2018 drought in Sweden prompted questions about climate-adaptation and -mitigation measures – especially in the agricultural sector, which suffered the most. This study applies a water–food–energy nexus modelling framework to evaluate drought impacts on irrigation and agriculture in Sweden using 2018 and 2019 as case studies. A previous water–food–energy nexus model was updated to facilitate an investigation of the benefits of data-driven irrigation scheduling as compared to existing irrigation guidelines. Moreover, the benefits of assimilating earth observation data in the crop model have been explored. The assimilation of leaf area index data from the Copernicus Global Land Service improves the crop yield estimation as compared to default crop model parameters. The results show that the irrigation water productivities of the proposed model are measurably improved compared to conventional and static irrigation guidelines for both 2018 and 2019. This is mostly due to the advantage of the proposed model in providing evapotranspiration in cultural condition (ETc)-driven guidelines by using spatially explicit data generated by mesoscale models from the Swedish Meteorological and Hydrological Institute. During the drought year 2018, the developed model showed no irrigation water savings as compared to irrigation scenarios based on conventional irrigation guidelines. Nevertheless, the crop yield increase from the proposed irrigation management system varied between 10% and 60% as compared to conventional irrigation scenarios. During a normal year, the proposed irrigation management system leads to significant water savings as compared to conventional irrigation guidelines. The modelling results show that temperature stress during the 2018 drought also played a key role in reducing crop yields, with yield reductions of up to 30%. From a water–food–energy nexus, this motivates the implementation of new technologies to reduce water and temperature stress to mitigate likely negative effects of climate change and extremes. By using an open-source package for Google Earth®, a demonstrator of cost-effective visualization platform is developed for helping farmers, and water- and energy-management agencies to better understand the connections between water and energy use, and food production. This can be significant, especially during the occurrence of extreme events, but also to adapt to the negative effects on agricultural production of climate changes.

Estimating crop yield under conditions of soil water deficit and salinity stress with crop water productivity model

The aim of this study was to simulate grain yield, biomass production, canopy cover and water productivity of winter wheat grown under soil water deficit and salinity stress by AquaCrop model. Five different irrigation strategies (S100 - S75 - S50 – S25 and S0) and 5 different irrigation water salinity levels (T1 = 0.3 dS m-1, T2 =5 dS m-1, T3 = 7.5 dS m-1, T4 = 10 dS m-1, T5 = 15 dS m-1) were used with the model to estimate deficit irrigation and salinity stress scenarios. According to estimation of the model the grain and biomass yields were fluctuated in the range of 5.43-8.00 t ha-1 and 12.84-17.67 t ha-1 at irrigation treatments. The application of 25%, 50% and 75% level of deficit irrigation, grain yield reduction was obtained 5%, 13% and 26% respectively. It was compared to the T1 (control) treatment, a low value of 3% was obtained for the T2 treatment. Yield loss of T3 and T4 salinity treatments were found to be 19% and 43% respectively. The crop yield reduction was dramatically (86%) at 15 dS m salinity level of irrigation water. The lowest yield was obtained at all salinity levels in I25 treatment, where 75% water saved. The highest and lowest water productivity was 1.28 kg m-3 and 1.20 kg m-3 respectively. It is possible to irrigate much more areas saving water with deficit irrigation and also the yields obtained from these areas were 2.17, 6.17 and 17.2 tons more than the yields obtained from areas irrigated with full irrigation. For, sustainable water management in agriculture area, using simulation model such as AquaCrop is useful tolls to estimate effect of applied water depth and quality of irrigation water on crop yield.

Evapotranspiration rates and evapotranspirative cooling of green façades under different irrigation scenarios

Urban population growth has resulted in land use changes and the replacement of many vegetated areas with hard surfaces that have a higher heat capacity than vegetation and have negatively impacted the urban microclimate. Green façades are a method of reintroducing vegetation into city landscapes to help remediate the urban hydrological cycle closer to predevelopment conditions by improving evapotranspiration. This study quantifies the impact of green façade irrigation regimes on evapotranspiration rates and evapotranspirative cooling effects across the day. Rates of evapotranspiration were calculated from façade’s water balances, under sunny, dry weather conditions with three different irrigation regimes. In this experiment both non-deciduous native (Australian) and deciduous non-native (non-Australian) plant species, with two different façade orientations were used to explore their evapotranspiration rates and impact on evapotranspirative cooling. Evapotranspiration rates were lower under limited-watered conditions for the north-facing green façade, planted with non-Australian plants, followed by the normal-watered and well-watered conditions. In the west-facing green façade that was planted with Australian plants (known to thrive with minimal irrigation), the lowest evapotranspiration rates were recorded for normal-watered, followed by limited-watered, and well-watered scenarios. The highest evapotranspirative cooling of the green façades occurred under well-watered conditions, with an observed maximum evapotranspirative cooling of −4.4 °C and −1.7 °C for the north- and west-facing façade. The results highlighted the ability of Australian waterwise plants to transpire more (both evapotranspiration rate and diurnal evapotranspiration) and provide more evapotranspirative cooling under normal and limited-watered conditions. The findings from this study will contribute to more effective design of sustainable green façades, with recommendations relating to irrigation rates and plant species.

Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

Assessing the feasibility of sprinkler irrigation schemes at the regional scale using a distributed agro-hydrological model

Quantitatively assessing the feasibility of sprinkler irrigation at the regional level from the perspectives of water, food and economy could provide the basis for decision-making on how to implement sprinkler irrigation according to local conditions to alleviate the severe contradiction between groundwater shortages and food production. In this study, the distributed agro-hydrological model, after establishment, calibration and validation, was applied to the eastern central part of the North China Plain (NCP), where deep groundwater has been seriously overexploited. The model was used to simulate 11 sprinkler irrigation scenarios for winter wheat for 20 rotation years and to analyze the effects on crop yield, water productivity (WP), soil water balance and farmers’ net income. Then, the irrigation method and the corresponding irrigation scheme were selected from the sprinkler and surface irrigation scenarios to maximize the yield or WP of winter wheat or farmers’ net income under three irrigation quotas, three precipitation levels and 17 soil texture profiles. Next, the sprinkler irrigation scheme was optimized for each precipitation level to achieve the highest WP under the constraint of a specific threshold of irrigation quota reduction compared with the current irrigation schedule, and the effects on water saving and reducing deep groundwater exploitation were evaluated. The main results were as follows. (1) On average, the reduction in the irrigation quota within the set range decreased the winter wheat yield and WP under the 11 scenarios; with respect to the specific irrigation quota, the average differences in the yield and WP of winter wheat between sprinkler and surface irrigation did not exceed 300 kg hm−2 and 0.10 kg m−3, respectively. The application of sprinkler irrigation to winter wheat led to the stability or increase in yield and WP of the following summer maize, and the spatial–temporal differences in the yields and WPs of the two crops decreased under sprinkler irrigation compared with surface irrigation. (2) Less transpiration and soil water consumption and greater evaporation were estimated for the winter wheat season under sprinkler irrigation than under surface irrigation. The income increase under sprinkler irrigation was less than the cost increase, leading to a negative change in farmers’ net income. (3) To maximize the yield, WP or farmers’ net income, surface irrigation is recommended for winter wheat in most of the study area, while sprinkler irrigation is recommended in the area with sandy soils in each layer or sandy soils in the 0–70 cm layer and loamy soils in the >70 cm layer. (4) The fixed sprinkler irrigation scenarios dominated the optimized irrigation schemes, and when the specific threshold was 60%, compared with the current irrigation schedule, the optimized sprinkler irrigation schemes achieved 123% of the target value of reducing deep groundwater exploitation (i.e., 6.05 × 108 m3) in the normal season, with a yield reduction of 74.3%.

Evaluation of sugarcane irrigation using AquaCrop model and remote sensing

AbstractIran has an arid to semi‐arid climate where water resources are limited. Irrigation planning plays an essential role in the growth and development of crops. This study aims to enhance the current status of irrigation scheduling and improve farm water management on sugarcane farms in Khuzestan province, Iran. The AquaCrop model and the surface energy balance algorithm for land (SEBAL) were used to simulate sugarcane growth and performed well in estimating actual evapotranspiration and final yield based on the calibration and validation results. Four irrigation scenarios were considered: scenario 1, irrigation according to the current situation; scenario 2, irrigation based on maximum allowable depletion (40%–55%); scenario 3, irrigation based on field capacity; and scenario 4, deficit irrigation (causes 5% reduction in yield). Water efficiency based on water consumption (WPIR), transpiration rate (WPTr), and evapotranspiration rate (WPET) were estimated to compare the scenarios in terms of water consumption. Scenario 3 was the selected scenario in which WPIR, WPTr, and WPET were increased about 108.3%, 2.1%, and 1.0%, respectively. Finally, the scenarios were evaluated economically, and the results showed that improving irrigation management slightly increased the benefit.

Modelling groundwater flow in the Plaine du Nord–Massacre shallow aquifer, Haiti

To assist in the sustainable management of water resources, a groundwater model of the shallow groundwater in the Plaine du Nord–Massacre transboundary aquifer system was developed. The Plaine du Nord–Massacre Aquifer is one of the largest aquifer systems of Haiti, supposedly bearing a high potential for water supply and irrigation. The shallower part is already tapped by hundreds of domestic wells and hand-pumped public and private boreholes. In the framework of the AVANSE project (Appui à la Valorisation du Potentiel Agricole du Nord, pour la Sécurité Économique et Environnementale), a USAID funded project aiming at developing sustainable agriculture in the region, a groundwater model was built to assist in decision making. The finite-difference-based MARTHE software was used for the model. Due to the lack of monitoring data, the calibration of the model could only be carried out under steady-state hydraulic conditions. It should thus be considered as a first step towards a better characterisation of the aquifer’s potential. However, the model computed a groundwater balance at the scale of the aquifer showing that significant water resources can be easily mobilized if one carefully manages to rule out local operational difficulties. The simulated groundwater drawdowns using transient simulation of irrigation scenarios are pluri-metric, reaching 6–7 m. New irrigation wells must be deeper than the current average depth of wells to support agricultural development projects.

Understanding the Relationship between Water Availability and Biosilica Accumulation in Selected C4 Crop Leaves: An Experimental Approach.

Biosilica accumulation in plant tissues is related to the transpiration stream, which in turn depends on water availability. Nevertheless, the debate on whether genetically and environmentally controlled mechanisms of biosilica deposition are directly connected to water availability is still open. We aim at clarifying the system which leads to the deposition of biosilica in Sorghum bicolor, Pennisetum glaucum, and Eleusine coracana, expanding our understanding of the physiological role of silicon in crops well-adapted to arid environments, and simultaneously advancing the research in archaeological and paleoenvironmental studies. We cultivated ten traditional landraces for each crop in lysimeters, simulating irrigated and rain-fed scenarios in arid contexts. The percentage of biosilica accumulated in leaves indicates that both well-watered millet species deposited more biosilica than the water-stressed ones. By contrast, sorghum accumulated more biosilica with respect to the other two species, and biosilica accumulation was independent of the water regime. The water treatment alone did not explain either the variability of the assemblage or the differences in the biosilica accumulation. Hence, we hypothesize that genetics influence the variability substantially. These results demonstrate that biosilica accumulation differs among and within species and that water availability is not the only driver in this process.

Modeling and assessing water and nitrogen use and crop growth of peanut in semi-arid areas of Northeast China

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 (W65, 65% of field capacity; W55, 55% of field capacity; W45, 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 W55 treatment. The WP was not obviously decreased under the W55 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.

Effects of superhydrophobic sand mulching on evapotranspiration and phenotypic responses in tomato (Solanum lycopersicum) plants under normal and reduced irrigation.

Irrigated agriculture in arid and semi-arid regions is a vital contributor to the global food supply. However, these regions endure massive evaporative losses that are compensated by exploiting limited freshwater resources. To increase water-use efficiency in these giga-scale operations, plastic mulches are utilized; however, their non-biodegradability and eventual land-filling renders them unsustainable. In response, we have developed superhydrophobic sand (SHS) mulching technology that is comprised of sand grains or sandy soils with a nanoscale coating of paraffin wax. Here, we investigate the effects of 1cm-thick SHS mulching on the evapotranspiration and phenotypic responses of tomato (Solanum lycopersicum) plants as a model system under normal and reduced irrigation inside controlled growth chambers. Experimental results reveal that under either irrigation scenario, SHS mulching suppresses evaporation and enhances transpiration by 78% and 17%, respectively relative to the unmulched soil. Comprehensive phenotyping revealed that SHS mulching enhanced root xylem vessel diameter, stomatal aperture, stomatal conductance, and chlorophyll content index by 21%, 25%, 28%, and 23%, respectively, in comparison with the unmulched soil. Consequently, total fruit yields, total dry mass, and harvest index increased in SHS-mulched plants by 33%, 20%, and 16%, respectively compared with the unmulched soil. We also provide mechanistic insights into the effects of SHS mulching on plant physiological processes. These results underscore the potential of SHS for realizing food-water security and greening initiatives in arid regions.

Integrated Modeling of Agronomic and Water Resources Management Scenarios in a Degraded Coastal Watershed (Almyros Basin, Magnesia, Greece)

The scope of this study is to assess the effects of agronomic and water resources management scenarios on groundwater balance, seawater intrusion, and nitrate pollution and the comparison of the developed scenarios relative to the current crop production and water management regime in the coastal agricultural Almyros basin in the Thessaly region, Greece. Agronomic and water resources scenarios have been simulated and analyzed for a period of 28 years, from 1991 to 2018. The analysis has been conducted with the use of an Integrated Modeling System for agricultural coastal watersheds, which consists of coupled and interlinked simulation models of surface water hydrology (UTHBAL), reservoir operation (UTHRL), agronomic/nitrate leaching model (REPIC), and groundwater models for the simulation of groundwater flow (MODFLOW) and contaminant transport of nitrates (MT3DMS) and chlorides (SEAWAT). The pressure on water resources has been estimated with the Water Exploitation Index (WEI+) and the reservoirs’ operation with the Reliability index to cover the water demands. The indices of Crop Water Productivity, Nitrogen Use Efficiency, and Economic Water Productivity have been used to quantify the benefits and the feasibility of the alternative scenarios. The best results for the sustainability of water resources are achieved under the deficit irrigation and rain-fed scenario, while the best results for water resources and the local economy are achieved under deficit irrigation and reduced fertilization scenario.

Applying novel eco-exergoenvironmental toxicity index to select the best irrigation system of sunflower production

The goal of this study is to create a novel eco-exergoenvironmental toxicity index to identify the most sustainable irrigation system among surface irrigation and drip irrigation for sunflower production in Kurdistan, Iran. This research emphasizes on choosing a sustainable irrigation agricultural system with an invented index, which considers all aspects. For this purpose, life cycle assessment (USEtox 2.0) is applied for evaluating toxicity factors and results show that the environmental situation of drip irrigation is better than that of surface irrigation. Exergy analysis results reveal that total exergy demands are 287,421 and 211,458 Megajoule per ten ton of sunflower for surface irrigation and drip irrigation scenarios, respectively. Nitrogen has the highest share (more than 50%) in all factors and exergy categories. Eco-efficiency of all toxicity factors of drip irrigation (with net profit $7417.86) is more than those of surface irrigation (with net profit $6614.35). The analytic hierarchy process analysis indicates that human toxicity, cancer has the first rank of priority based on expert choices. Based on the novel index results, sustainable rates are 1.690 and 1.169 for surface irrigation and drip irrigation, respectively. Thus, drip irrigation is an eco-exergoenvironmental-friendly scenario in comparison to surface irrigation.

Climate-driven trends in agricultural water requirement: an ERA5-based assessment at daily scale over 50 years

The impact of climate variability on the water requirements of crops is a key issue in a globalized world with unprecedented population and unevenly distributed water resources. Changes of hydro-climatic forcings may have significant impacts on water resources use, considering the possible effects on irrigation requirements and crop water stress. In this work, a comprehensive estimation of crop water requirements over the 1970–2019 period is presented, considering 26 main agricultural products over a 5 arcmin resolution global grid. The assessment is based on a daily-scale hydrological model considering rainfed and irrigated scenarios, driven by hydro-climatic forcings derived from ERA5, the most recent climate reanalysis product within the Climate Change Service of the Copernicus Programme. Results show the heterogeneous impact of climate variability on harvested areas of the world, quantified by water stressed days and irrigation requirement rates. Increases of irrigation requirement rates were found on more than 60% of irrigated lands, especially in regions like South Europe, North-East China, West US, Brazil and Australia, where the mean rate increased more than 100 mm yr−1 from 1970s to 2010s. The daily analysis of water requirements shows that crops require significantly more days of irrigation per season, especially in Europe, Africa and South-East Asia. Statistically significant trends of water stress duration were found over 38% of rainfed croplands, while only 6% of croplands has been affected by negative trends and shorter stress duration, mainly in India, Malaysia, North Europe and coastal regions of central western Africa.

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (Tsoil°) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and Tsoil°, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha−1) and net income (US $3165 ha−1), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha−1 mm−1) and net water production (0.94 $ m−3). These results provided farmers guidelines to adopt deficit irrigation strategies.

AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity

Water shortage is the main constraint resulting in low crop yields in arid and semiarid areas, where irrigation is essential to sustain agricultural production. Unreasonable irrigation will not effectively improve crop yield but will cause a waste of water resources and a decrease in water productivity. The objectives of this study were intended to investigate the variability of yield and water productivity (WP) as a function of irrigation amount, time, and frequency and to explore optimal irrigation of winter wheat for improving grain yield and water productivity over a 60-year of long-term meteorological data (1961—2020). A water-driven model, AquaCrop model, was used to reproduce crop growth and yield responses to changes in water supply, with a total of 625 irrigation scenarios of irrigation amount and time. The results showed that there has been no significant change in seasonal ET0 (reference evapotranspiration) of winter wheat over the last 60 years. Instead, seasonal precipitation during the winter wheat growing cycle displayed a decreasing trend, potentially increasing the risk of meteorological drought. Despite the crop ET has remained stable at around 370 mm for the past 60 seasons, the grain yield and WP have steadily increased as the cumulative temperature increased. In all three typical meteorological years, crop average yield and ET were considerably improved under irrigation conditions compared with that in non-irrigated (rainfed). The total irrigation amounts of 90, 120, and 150 mm with two irrigation times in the wet, normal, and dry years, respectively, could achieve higher yields as well as the WP and IWP. Consequently, the optimal irrigation schedules in the wet, normal, and dry years were determined to be first irrigation in the wintering stage with 90 mm and second irrigation in the jointing stage with 0, 30, 60 mm, respectively. This work demonstrates that the AquaCrop model has a reliable accuracy for revealing crop growth and production under water deficit conditions and provides a technical means of application for formulating optimal irrigation schedules.