Optimal Water Management and Sustainability in Irrigated Agriculture

Faced with severe global shortage of water and soil resources, studies on the integrated effect of water and nitrogen on tomato cultivation are urgently needed for sustainable agriculture. Two successive greenhouse experiments with three irrigation regimes (1, 2/3, 1/3 full irrigation) and four nitrogen levels (1, 2/3, 1/3, 0 nitrogen) were conducted; plant growth, fruit yield and quality were surveyed; and comprehensive quality and net profit were evaluated. The results show that water and nitrogen deficit decreased plant growth, evapotranspiration and yield while increasing production efficiency and fruit comprehensive quality. An antagonism effect from water and nitrogen application was found in tomato yield, organic acid, solids acid ratio, vitamin C and lycopene, whereas synergistic impact was observed in total soluble solids content. Water deficit had more significant effect on tomato yield and fruit quality parameters compared with that of nitrogen deficiency. Synthesizing the perspectives of yield, quality, resource productivity, market price index and profits, 1/3 full irrigation and 2/3 full nitrogen was the best strategy and could be recommended to farmers as an effective guidance for tomato production. Full article

We conducted a two-year field experiment on winter wheat (Triticum aestivum L.) from 2016–2018 to compare the effects of reducing nitrogen application rate in spring under three irrigation methods on grain yield (GY), water and nitrogen use efficiency in the North China Plain (NCP). Across the two years, GY of conventional irrigation (CI), micro-sprinkling irrigation (SI) and drip irrigation (DI) decreased by 6.35%, 9.84% and 6.83%, respectively, in the reduced nitrogen application rate (N45) than the recommended nitrogen application rate (N90). However, micro-irrigation (SI and DI) significantly increased GY relative to CI under the same nitrogen application rate, and no significant difference was observed in GY between SI and DI under N45, while SI obtained the highest GY under N90. The difference among different treatments in GY was mainly due to the variation in grain weight. The seasonal evapotranspiration (ET) in N45 was decreased more significantly than N90, and there was no significantly difference in ET among different irrigation methods under N45, but micro-irrigation significantly decreased the ET relative to CI under N90. Micro-irrigation significantly improved water use efficiency (WUE) compared to CI at the same nitrogen application rate. Under N45, compared with CI, WUE in SI and DI increased by 9.09% and 4.70%, respectively; however, the WUE increased by 15.9% and 7.23%, respectively, under N90. Reducing nitrogen application rate did not have a significant impact on WUE under CI, but it did have a substantial negative impact on SI and DI. Nitrogen accumulation in wheat plants at maturity (NAM) in N45 deceased significantly compared with N90 under the same irrigation method. Compared with CI under the same nitrogen application rate, micro-irrigation treatments significantly increased NAM, while SI was the largest. In comparison to N90, under three irrigation methods, N45 significantly increased nitrogen fertilizer use efficiency (N_fUE). The highest N_fUE was attained in SI, followed by DI, while CI was the lowest. Moreover, N45 significantly decreased soil NO₃⁻-N accumulation (SNC) in three irrigation methods, and micro-irrigation significantly decreased the SNC in deep soil layers compared with CI when nitrogen is applied at the same level. Overall, micro-irrigation with a reduced nitrogen application rate in spring can achieve a relatively higher production of winter wheat while increasing the use efficiency of water and nitrogen and reducing soil NO₃⁻-N leaching into deep soil layers in the NCP. Full article

Shallow groundwater is considered an important water resource to meet crop irrigation demands. However, limited information is available on the application of models to investigate the impact of irrigation schedules on shallow groundwater depth and estimate evaporation while considering the interaction between meteorological factors and the surface soil water content (SWC). Based on the Richards equation, we develop a model to simultaneously estimate crop water consumption of shallow groundwater and determine the optimal irrigation schedule in association with a shallow groundwater depth. A new soil evaporation function was established, and the control factors were calculated by using only the days after sowing. In this study, two irrigation scheduling methods were considered. In Method A, irrigation was managed based on the soil water content; in Method B, irrigation was based on the crop water demand. In comparison with Method B, Method A was more rational because it could use more groundwater, and the ratio of soil evaporation to total evapotranspiration was low. In this model, the interaction between meteorological factors and the SWC was considered to better reflect the real condition; therefore, the model provided a better way to estimate the crop water consumption. Full article

Land and water are two major inputs for crop production. Simulation modelling was used to determine crop sequences that maximise farm return. Crop yield was determined for different irrigation scheduling scenarios based on the fraction of available soil water (FASW). Farm returns ($ ML⁻¹ and $ ha⁻¹) were evaluated for seven crop sequences. Three irrigation water price scenarios (dry, median, wet) were considered. The yield of summer crops increased with irrigation. For winter crops, despite increase in irrigation, the yield would not increase. The optimum irrigation (ML ha⁻¹) was: soybean 8.2, maize 10.4, wheat 2.5, barley 3.1, fababean 2.5, and canola 2.7. The water productivity curve of summer crops has a parabolic shape, increasing with FASW, reaching a maximum value at FASW 0.4–0.6, and then decreasing. The water productivity of winter crops decreases as FASW increases following a power function. Gross margins are positive when water is cheap ($60 ML⁻¹) and when water has a median price ($124 ML⁻¹). When water is expensive ($440 ML⁻¹), positive gross margin would be obtained only for the continuous wheat scenario. Deficit irrigation of summer crops leads to significant yield loss. Supplemental irrigation of winter crops results in the highest gross margin per unit of water. Full article

Accurately forecasting reference evapotranspiration (ET₀) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture has been adapted and deployed for multivariate input time series forecasting (transformers) using past values of ET₀ and temperature-based parameters (28 input configurations) to forecast daily ET₀ up to a week (1 to 7 days). Additionally, it has been compared to standard machine learning models such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), convolutional neural network (CNN), long short-term memory (LSTM), and two baselines (historical monthly mean value and a moving average of the previous seven days) in five locations with different geo-climatic characteristics in the Andalusian region, Southern Spain. In general, machine learning models significantly outperformed the baselines. Furthermore, the accuracy dramatically dropped when forecasting ET₀ for any horizon longer than three days. SVM, ELM, and RF using configurations I, III, IV, and IX outperformed, on average, the rest of the configurations in most cases. The best NSE values ranged from 0.934 in Córdoba to 0.869 in Tabernas, using SVM. The best RMSE, on average, ranged from 0.704 mm/day for Málaga to 0.883 mm/day for Conil using RF. In terms of MBE, most models and cases performed very accurately, with a total average performance of 0.011 mm/day. We found a relationship in performance regarding the aridity index and the distance to the sea. The higher the aridity index at inland locations, the better results were obtained in forecasts. On the other hand, for coastal sites, the higher the aridity index, the higher the error. Due to the good performance and the availability as an open-source repository of these models, they can be used to accurately forecast ET₀ in different geo-climatic conditions, helping to increase efficiency in tasks of great agronomic importance, especially in areas with low rainfall or where water resources are limiting for the development of crops. Full article

Predicting the impact of future climate change on food security has important implications for sustainable food production. The 26 meteorological stations’ future climate data in the study area are assembled from four global climate models under two representative concentration pathways (RCP4.5 and RCP8.5). The future maize yield, actual crop evapotranspiration (ET_a), and water use efficiency (WUE) were predicted by calibrated AquaCrop model under two deficit irrigation (the regulated deficit irrigation (RDI) at jointing stage(W1), filling stage(W2)), and full irrigation (W3) during the three periods (2021–2040, 2041–2060, and 2061–2080). The result showed that the maize yields under W1, W2, and W3 of RCP4.5 were 2.8%, 2.9%, and 2.5% lower than those in RCP8.5, respectively. In RCP8.5, the yield of W3 was 1.9% and 1.4% higher than W1 and W2, respectively. Under the RCP4.5, the ET_a of W1, W2, and W3 was 481.32 mm, 484.94 mm, and 489.12 mm, respectively. Moreover, the ET_a of W1 was significantly lower than W2 under the RCP4.5 and RCP8.5 (p > 0.05). In conclusion, regulated deficit irrigation at the maize jointing stage is recommended in the study area when considering WUE. Full article

Mexico, as many countries, relies on its aquifers to provide at least 60% of all irrigation water to produce crops every year. Often, the water withdrawal goes beyond what the aquifer can be replenished by the little rainfall. Mexico is a country that has experienced a successful process of regional development based on the adoption of intensive agricultural systems. However, this development has occurred in an unplanned way and displays shortcomings in terms of sustainability, particularly in the management of water resources. This study analysed the case of Costa de Hermosillo, which is one of the Mexican regions in which this model of intensive agriculture has been developed and where there is a high level of overexploitation of its groundwater resources. Based on the application of a qualitative methodology involving different stakeholders (farmers, policymakers, and researchers), the main barriers and facilitators for achieving sustainability in water resources management have been identified. A series of consensus-based measures were contemplated, which may lead to the adoption of sustainable practices in water management. Useful lessons can be drawn from this analysis and be applied to other agricultural areas where ground and surface water resources are overexploited, alternative water sources are overlooked, and where stakeholders have conflicting interests in water management. Full article

Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θ_r) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale. Full article

Water is a key factor in global food security, which is critical to agriculture. The use of mathematical models is a strategy for managing water use in agriculture, and it is an effective way to predict the effect of irrigation management on crop yields if the accuracy of these models is demonstrated. The CROPWAT and SALTMED models were tested in this study, with water quantities applied to surface and drip irrigation (SI and DI) systems to estimate irrigation scheduling and wheat yield. For this purpose, field experiments were conducted for two consecutive years to study the effects of irrigation water levels of 80%, 100%, and 120% crop evapotranspiration (I₈₀, I₁₀₀, and I₁₂₀) on the yield and water productivity (WP) of wheat in SI and DI systems. Irrigation treatments affected yield components such as plant height, number of spikes, spike length, and 1000-kernel weight, though they were not statistically different in some cases. In the I₈₀ treatment, the biological yield was 12.8% and 8.5% lower than in the I₁₀₀ and I₁₂₀ treatments, respectively. I₁₀₀ treatment under DI resulted in the highest grain yield of a wheat crop. When DI was applied, there was a maximum (22.78%) decrease in grain yield in the I₈₀ treatment. The SI system was more water-consuming than the DI system was, which was reflected in the WP. When compared with the WP of the I₈₀ and I₁₀₀ treatments, the WP was significantly lower (p < 0.05) in the I₁₂₀ treatment in the SI or DI system. To evaluate irrigation scheduling and estimate wheat yield response, the CROPWAT model was used. Since the CROPWAT model showed that increasing irrigation water levels under SI for water stress coefficient (K_s) values less than one increased deep percolation (DP), the I₁₂₀ treatment had the highest DP value (556.15 mm on average), followed by the I₁₀₀ and I₈₀ treatments. In DI, I₁₀₀ and I₁₂₀ treatments had K_s values equal to one throughout the growing seasons, whereas the I₈₀ treatment had K_s values less than one during wheat’s mid- and late-season stages. The I₁₀₀ and I₈₀ treatments with DI gave lower DP values of 93.4% and 74.3% compared with that of the I₁₂₀ treatment (on average, 97.05 mm). The I₁₂₀ treatment had the lowest irrigation schedule efficiency in both irrigation systems, followed by the I₁₀₀ and I₈₀ treatments. In both seasons, irrigation schedule deficiencies were highest in the I₈₀ treatment with DI (on average, 12.35%). The I₈₀ treatment with DI had a significant yield reduction (on average, 21.9%) in both seasons, while the irrigation level treatments with SI had nearly the same reductions. The SALTMED model is an integrated model that considers irrigation systems, soil types, crops, and water application strategies to simulate soil water content (SWC) and crop yield. The SALTMED model was calibrated and validated based on the experimental data under irrigation levels across irrigation systems. The accuracy of the model was assessed by the coefficients of correlation (R), root mean square errors (RMSE), mean absolute errors (MAE), and mean absolute relative error (MARE). When simulating SWC, the SALTMED models’ R values, on average, were 0.89 and 0.84, RMSE values were 0.018 and 0.019, MAE values were 0.015 and 0.016, and MARE values were 8.917 and 9.133%, respectively, during the calibration and validation periods. When simulating crop yield, relative errors (RE) for the SALTMED model varied between −0.11 and 24.37% for biological yield and 0.1 and 19.18% for grain yield during the calibration period, while in the validation period, RE was in the range of 3.8–29.81% and 2.02–25.41%, respectively. The SALTMED model performed well when simulating wheat yield with different water irrigation levels under SI or DI. Full article

The growing global water shortage is an increasing challenge for the agricultural sector, which aims to produce sufficient quantity and quality of food and animal feed. In our study, effluent water from an intensive African catfish farm was irrigated on grain sorghum plants in four consecutive years. In our study the effects of the effluent on the N, P, K, Na content of the seeds, on the phenological parameters (plant height, relative chlorophyll content), the green mass, and on the grain yield of three varieties (‘Alföldi 1’, ‘Farmsugro 180’ and ‘GK Emese’) were investigated. Five treatments (Körös River (K) water and effluent (E) water: 30 and 45 mm weekly irrigation water dose; non-irrigated control) were applied with micro-spray irrigation. Compared to non-irrigated plants, effluent water did not negatively affect the N, P, K and Na contents of the grain crop. In terms of phenological parameters, the quality of the irrigation water (150–230 cm) had no negative effect on any of the measured parameters compared to the control (133–187 cm) values. In terms of biomass in 2020, grain yields were 89–109 g/plant with variety Alföldi 1, 64–91 g/plant with variety Farmsugro 180, and 86–110 g/plant with GK Emese. Full article

Irrigation and fertilization technologies need to be adapted to climate change and provided as effectively and efficiently as possible. The current study proposed pocket fertigation, an innovative new idea in providing irrigation water and fertilization by using a porous material in the form of a ring/disc inserted surrounding the plant’s roots as an irrigation emitter equipped with a “pocket”/bag for storing fertilizer. The objective was to evaluate the functional design of pocket fertigation in the specific micro-climate inside the screenhouse with a combination of emitter designs and irrigation rates. The technology was implemented on an experimental field at a lab-scale melon (Cucumis melo L.) cultivation from 23 August to 25 October 2021 in one planting season. The technology was tested at six treatments of a combination of three emitter designs and two irrigation rates. The emitter design consisted of an emitter with textile coating (PT), without coating (PW), and without emitter as a control (PC). Irrigation rates were supplied at one times the evaporation rate (E) and 1.2 times the evaporation rate (1.2E). The pocket fertigation was well implemented in a combination of emitter designs and irrigation rates (PT-E, PW-E, PT-1.2E, and PW-1.2E). The proposed technology increased the averages of fruit weight and water productivity by 6.20 and 7.88%, respectively, compared to the control (PC-E and PC-1.2E). Meanwhile, the optimum emitter design of pocket fertigation was without coating (PW). It increased by 13.36% of fruit weight and 14.71% of water productivity. Thus, pocket fertigation has good prospects in the future. For further planning, the proposed technology should be implemented at the field scale. Full article

Irrigated almond orchards in Spain are increasing in acreage, and it is pertinent to study the effect of deficit irrigation on the presence of pests, plant damage, and other arthropod communities. In an orchard examined from 2017 to 2020, arthropods and diseases were studied by visual sampling under two irrigation treatments (T1, control and T2, regulated deficit irrigation (RDI)). Univariate analysis showed no influence of irrigation on the aphid Hyalopterus amygdali (Blanchard) (Hemiptera: Aphididae) population and damage, but Tetranychus urticae Koch (Trombidiformes: Tetranychidae) damage on leaves was significantly less (50–60% reduction in damaged leaf area) in the T2 RDI treatment compared to the full irrigation T1 control in 2019 and 2020. Typhlocybinae (principal species Asymmetrasca decedens (Paoli) (Hemiptera: Cicadellidae)) population was also significantly lower under T2 RDI treatment. Chrysopidae and Phytoseiidae, important groups in the biological control of pests, were not affected by irrigation treatment. The most important diseases observed in the orchard were not, in general, affected by irrigation treatment. The multivariate principal response curves show significant differences between irrigation strategies in 2019 and 2020. In conclusion, irrigation schemes with restricted water use (such as T2 RDI) can help reduce the foliar damage of important pests and the abundance of other secondary pests in almond orchards. Full article

Precise forecasting of reference evapotranspiration (ET₀) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET₀ utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET₀ time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET₀ forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET₀ with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station. Full article

The assessment of the suitability and status of irrigation water quality from the aspect of its potential negative impact on soil salinization and mapping of spatial distribution within the area of the three Morava rivers, which includes the South, West, and Great Morava basins, was the purpose of this research. A total of 215 samples of irrigation water were tested, and their quality was evaluated based on the analysis of the following parameters: pH, electrical conductivity (EC), total dissolved salt (TDS), sodium adsorption ratio (SAR), and content of SO₄²⁻, Cl⁻, HCO₃⁻, CO₃ ²⁻, Mg²⁺, Ca²⁺, Na⁺, and K⁺. The results showed that the average content of ions was as follows: Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻ > CO₃²⁻. The assessment of irrigation water suitability was determined by calculating the following indices: percentage sodium (Na %), residual sodium carbonate (RSC), permeability index (PI), magnesium hazard (MH), potential salinity (PS), Kelley’s index (KI), total hardness (TH), irrigation water quality index (IWQI). Based on Wilcox’s diagram, the USSL diagram, and the Doneen chart, it was concluded that most of the samples were suitable for irrigation. Using multivariate statistical techniques and correlation matrices in combination with other hydrogeochemical tools such as Piper’s, Chadha’s, and Gibbs diagrams, the main factors associated with hydrogeochemical variability were identified. Full article