Search Results (54)

Reference evapotranspiration (ET_o) is essential for irrigation scheduling, water resources management, and climate-related applications, but the FAO Penman–Monteith (FAO-PM) method is often constrained by limited meteorological data availability. This study evaluated four machine learning (ML) algorithms, Kernel Approximation Regression (KAR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Random Forest (RF), for reproducing FAO-PM ET_o under different levels of geographical and meteorological information availability in the Czech Republic. Daily observations from 59 meteorological stations (1980–2024) were used to develop eight input scenarios. Model performance was evaluated using a station-wise chronological train–test framework and station-based analyses. The results showed that predictor availability had a greater influence on model performance than model selection. The geographical-information scenario produced the lowest performance, whereas substantial improvements were achieved when meteorological variables were incorporated. Among the single-variable meteorological scenarios, relative humidity provided the greatest improvement in agreement with the FAO-PM ET_o benchmark. Across all input scenarios and ML algorithms, testing performance ranged from R² = 0.683 to 0.998 and RMSE = 0.076 to 0.939 mm d⁻¹, indicating progressively improved agreement with FAO-PM ET_o as additional meteorological information became available. The reduced-input scenarios therefore provide a practical approach for approximating FAO-PM ET_o at stations represented during model development when some meteorological inputs are unavailable. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

Reference evapotranspiration (ET₀) is most commonly estimated using the FAO-56 Penman–Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET₀, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET₀ using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET₀ prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations were developed and tested. The random forests, gradient boosting and k-nearest neighbors models were used as a benchmark, and model performance was evaluated using R², NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R² = 0.97, NSE = 97.07%, RMSE = 0.23 mm/day, and MAE = 0.21 mm/day. The results showed that a limited number of input variables can be used to estimate ET₀ with high accuracy, achieving an R² value of 0.95 using only three input variables. Therefore, the findings of this study may contribute to more accurate and cost-effective irrigation scheduling and water balance estimation, providing practical benefits for agricultural water management and farmers in Serbia. Full article

Background/Objectives: Onion (Allium cepa) peems are an underutilized by-product rich in polyphenols. This study evaluated the physicochemical profile, and bioactive potential (antidiabetic, antimicrobial, antioxidant, and anticoagulant) of Moroccan red onion peels using integrated in vivo, in vitro, and in silico approaches. Methods: Moisture, pH, ash content, and mineral elements were determined, followed by phytochemical screening and three extractions: decoction E0, aqueous Soxhlet E1, and hydroethanolic Soxhlet E2 (70/30; ethanol/water, v/v). The measurement of polyphenols, flavonoids, and tannins was carried out using colorimetric methods, while the molecular profile was studied by high-performance liquid chromatography coupled to ultraviolet detection and electrospray ionization mass spectrometry (HPLC/UV-ESI-MS). Biological activities were determined using 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power, and total antioxidant capacity assays (in vitro antioxidant); microdilution (antimicrobial); prothrombin time and activated partial thromboplastin time (anticoagulant); and α-amylase/α-glucosidase enzymatic inhibition and oral glucose tolerance tests on normoglycemic rats. Also, acute toxicity was evaluated, and molecular interactions between these proteins and ligands (docking, molecular dynamics, and MM-PBSA) were analyzed. Results: Physicochemical analyses showed an acidic pH (3.06) and high ash content (15.21%), with the concentration of regulated elements remaining within FAO/WHO limits. The extractive content was between 6.90% E0 and 19.18% E2. The E1 extract had the maximum amount of total polyphenols (178.95 mg GAE/g); on the other hand, E2 was the richest in flavonoids by 121.43 mg QE/g. The HPLC/ESI-MS analysis of E0 revealed 20 compounds, among which flavonoids (84.93%) were predominant, with isorhamnetin (30.26%), followed by quercetin and its glycosylated forms. E1 showed the most potent antioxidant effects (IC₅₀ DPPH, 22.38 µg/mL, as that of ascorbic acid). The antibacterial activity of E0 was especially potent towards Enterobacter cloacae and Pseudomonas aeruginosa (MIC 75 µg/mL). A mild dose-dependent anticoagulant effect was seen. Antidiabetic activity was found to be outstanding: α-amylase (IC₅₀ 62.75 µg/mL) and α-glucosidase (IC₅₀ 8.49 µg/mL, stronger than acarbose) inhibitions were corroborated in vivo by a considerable decrease in the glycemic area under the curve. The molecular docking study in silico demonstrated strong molecular interactions, especially for quercetin 4′-O-glucoside with good binding energies. Conclusions: A. cepa peels from Morocco can be considered a safe plant matrix containing bioactive flavonoids with strong antioxidant and selective antimicrobial activities and promising antidiabetic effects, supported by molecular modeling. Full article

This study compares the performance of empirical and hybrid deep learning (DL) models in estimating daily potential evapotranspiration (PET) in the Nakdong River Basin (NRB), South Korea, with the FAO-56 Penman–Monteith (PM) method as a reference. Two empirical models, Priestley–Taylor (P-T) and Hargreaves–Samani (H-S), and two DL models, a standalone Long Short-Term Memory (LSTM) network and a hybrid Convolutional Neural Network Bidirectional LSTM with an attention mechanism, were trained on a meteorological dataset (1973–2024) across 13 meteorological stations. Four input combinations (C1, C2, C3, and C4) were tested to assess the model’s robustness under varying data availability conditions. The results indicate that empirical models performed poorly, with a basin-wide RMSE of 5.04–5.79 mm/day and negative NSE (−10.37 to −13.99), and are therefore poorly suited to NRB. In contrast, DL models achieved significant improvements in accuracy. The hybrid CNN-BiLSTM Attention Mechanism (C1) produced the highest performance, with R² = 0.820, RMSE = 0.672 mm/day, NSE = 0.820, and KGE = 0.880, which was better than the standalone LSTM (R² = 0.756; RMSE = 0.782 mm/day). The generalization of heterogeneous climates was also verified through spatial analysis, in which the NSE at the station level consistently exceeded 0.70. The hybrid DL model was found to be highly accurate in representing the temporal variability and seasonal patterns of PET and is therefore more suitable for operational hydrological modeling and water-resource planning in the NRB. Full article

Accurate estimation of reference evapotranspiration (ET_o) is essential for irrigation planning in semi-arid Mediterranean regions. This study evaluated temperature-based and neural network models for estimating daily ET_o and its accumulated values over multiple timescales, using data from two lysimeter stations in Albacete and Badajoz, Spain. Model performance was assessed against the FAO56 PM equation and against lysimeter measurements to quantify the joint effect of benchmark choice and temporal aggregation. Under FAO56 PM benchmarking, RRMSE for temperature-based models in Albacete decreased from about 0.18 for daily ET_o to around 0.08 for monthly accumulated ET_o, while complex models achieved daily RRMSE near 0.06–0.07, and all models exhibited RRMSE below 0.08 at monthly and longer scales. When lysimeter ET_o was used as the benchmark, errors increased and became more variable in winter, with daily RRMSE often exceeding 0.22, indicating reduced lysimeter reliability under cold, calm conditions. Overall, extending the estimation interval from daily to multi-day periods markedly reduced errors and narrowed differences among models. These results show that, in the semi-arid Mediterranean environments studied, temperature-based models can provide operationally reliable estimates at irrigation-relevant timescales, while FAO56 PM offers a more robust primary benchmark than lysimeter measurements for winter irrigation planning. Full article

Reference crop evapotranspiration (ET₀) is crucial for water management. Although the FAO56-PM method is widely used to estimate ET₀, its input variables of solar radiation (R_s) and net longwave radiation (R_nl) are not readily available. Currently, mere calibration of the formula for R_s is assumed to be effective in improving FAO56-PM’s performance. To test this hypothesis, all input variables for FAO56-PM were measured in Lhasa, Linzhi, and Bange over the Tibetan Plateau (TP) to assess how different calculation methods affect ET₀ estimates. Compared to the original FAO56-PM, calibration of both R_s and R_nl formulas yielded the best model performance. Mere calibration of the formula for R_nl notably improved ET₀ estimation accuracy, while mere calibration of the formula for R_s reduced its accuracy. The general calibration model was slightly less effective than calibration of both R_s and R_nl formulas but obviously outperformed the original FAO56-PM. This model showed that ET₀ increased from east to west, ranging from 569.4 mm/year to 1118.5 mm/year. Trend analysis indicated rapid increases in ET₀ in the eastern region and significant decreases in the western region of the TP over recent decades. The findings are useful for the regional application of FAO56-PM to achieve sustainable development in Tibet. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article

Planosols are seasonally waterlogged soils characterized by an abrupt transition from coarse-textured surface horizons to dense, clay-enriched subsoils. Despite the increased agricultural expansion in the Planosol landscapes, these soils have been largely overlooked in Ethiopia. The FAO soil map of Ethiopia (1:200,000 scale) does not recognize the presence of Planosols. In contrast, the more recent digital soil map of Ethiopia, EthoSoilGrids v1.0, at a 250 spatial resolution, was not detailed enough to capture Planosol landscapes, reflecting their historical undersampling in the legacy data. To address this gap, we conducted a thorough mapping and characterization of Planosols in the Omo-Gibe basin, southwestern Ethiopian highlands. Using over 200 auger observations, 74 georeferenced soil profiles, 296 laboratory analyses, and Random Forest modeling, we produced a 30 m-resolution soil-landscape map. Our results show that Planosols cover about 18% of the basin, a substantial extent previously unrecognized in national exploratory maps. Morphologically, these soils exhibit abrupt textural change from the coarse-textured, light grey Ap/Eg horizon (about 30–40 cm thick) to a very clayey, grey–black Bssg/Bt horizon occurring below 40 cm depth. Analytical data on selected parameters show the following pattern: low clay contents (20–29%) and acidic pH (5.2–5.8) with relatively low CEC values (11–26 cmol/kg) in the surface horizons (Ap/Eg), but pronounced clay increase (37–74%), higher bulk density (1.3 g/cm³), higher pH (up to 6.5), and substantially higher CEC (37–47 cmol/kg) in the sub-surface horizons (Bss/Bt). In terms of soil fertility, Planosols are low in SOC, TN, and exchangeable K contents, but micronutrient levels are variable—high in Fe-Mn-Zn and low in B and Cu. The findings confirm the diagnostic features of WRB Planosols and align with regional East African averages, underscoring the reproducibility of our approach. By rectifying long-standing misclassifications and generating fine-scale, field-validated evidence on soil fertility constraints and management options, this study establishes a strong foundation for targeted soil management in Ethiopia. It offers transferable insights for Planosol-dominated agroecosystems across Eastern Africa. Globally, the dataset contributes to enriching the global scientific knowledge and evidence base on Planosols, thereby supporting their improved characterization and management. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

The aim of this study was to investigate the method of estimating actual crop evapotranspiration (ET_cact) in a greenhouse using other measured meteorological parameters when solar radiation (R_s) data are missing. The study estimated ET_cact of greenhouse green peppers by combining solar radiation estimation models with the Penman–Monteith (PM) model and evaluated model performance. The results showed that the prediction accuracy of the temperature-based solar radiation model was higher than the model based on sunshine hours in the Hexi Corridor region. The effect of the insulation cover on the incident solar radiation in the greenhouse is modeled by introducing a ramp function. In terms of crop coefficients (K_cb), the initial K_cb value of green peppers in the 2023 growing season was generally consistent with the updated FAO-56 standard values, whereas the initial K_cb values (0.17) were higher than the standard values in the 2023–2024 growing season. During the two growing seasons, the mid-stage K_cb values were 1.01 in the 2023 growing season and 0.82 in the 2023–2024 growing season. The study also found that PM–RT4, PM–RT5, and PM–RT6 models were all able to accurately predict the ET_cact of greenhouse green peppers during the 2023 growing season. The PM–RT4 model performed well in both growing seasons, with R² = 0.8101 in the 2023 growing season and R² = 0.7561 in the 2023–2024 growing season. Our research supports the PM–RT4 model as appropriate to estimate green pepper actual evapotranspiration in Gobi solar greenhouses (GSGs) and may be further used to improve irrigation scheduling for green peppers grown in GSGs. Full article

In arid and semi-arid climates, where water scarcity is a persistent challenge, accurately estimating reference evapotranspiration (ET) becomes essential for sustainable water management and agricultural planning. The objectives of this study are to compare hourly ET among P–M ASCE, P–M FAO, and P–M KSA mathematical models. In addition to the accuracy assessment of daily ET derived from hourly timestep calculations for the P–M ASCE, P–M FAO, and P–M KSA. To achieve these goals, a total of 525,600-min data points from the Riyadh region, KSA, were used to compute the reference ET at multiple temporal resolutions: hourly, daily, hourly averaged over 24 h, and daily as the sum of 24 h values, across all selected Penman–Monteith (P–M) models. For hourly investigation, the comparison between reference ET computed as average hourly values and as daily/24 h values revealed statistically and practically significant differences. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001) with R² of 94.75% for ASCE, 94.87% for KSA at h_plt = 50 cm, 92.41% for FAO, and 92.44% for KSA at h_plt = 12 cm. For daily investigation, comparing the sum of 24 h ET computations to daily ET measurements revealed an underestimation of daily ET values. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001), with R² exceeding 90% for all studied reference ET models. This comprehensive approach enabled a rigorous evaluation of reference ET dynamics under hyper-arid climatic conditions, which are characteristic of central Saudi Arabia. The findings contribute to the growing body of literature emphasizing the importance of high-frequency meteorological data for improving ET estimation accuracy in arid and semi-arid regions. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

Estimating the actual evapotranspiration (ET_{c act}) of cropland in arid areas, exploring the time trend, and analyzing periodic variation are the key to long-term assessment of water resource availability and regional drought. The Penman formula has a strong ability to characterize reference crop evapotranspiration (ET_o). However, the application of this formula may be limited in the absence of a complete set of climate data. While previous studies have investigated K_{c act} in China, few have employed localized K_c values to systematically analyze long-term periodic fluctuations in ET_{c act} under climate variability conditions. Therefore, this study aimed to evaluate the applicability of nine ET_o estimation models in the Loess Plateau of China, calculate actual crop coefficients (K_{c act}) for spring maize and winter wheat, and examine the temporal trend and periodicity of ET_{c act} for long-term (1961–2018) continuous cropping of spring maize and winter wheat in the study area. The Mann–Kendall test and continuous wavelet transform (CWT) were used to obtain the temporal trend and periodicity of ET_{c act}. The results were as follows: (1) Priestley–Taylor (Prs–Tylr), based on radiation, and the 1985 Hargreaves–Samani (Harg), based on temperature, can be used when meteorological data are limited. It should be noted that among the models evaluated in this study, except for FAO56-PM, only the Harg equation is compatible with K_c-ET_o due to established conversion factors. (2) The K_{c act} of spring maize at the seeding–jointing stage and the earning–filling stage was 12% and 10% lower than the value recommended by FAO, respectively. For K_{c act} of winter wheat, it was 65% higher, 31% lower, and 85% higher than the FAO experience values in the rejuvenation–jointing stage, heading–grouting stage, and grouting–harvest stage. (3) Winter wheat, through its ET_{c act} cycle synchronized with precipitation and excellent water balance, can effectively alleviate regional drought. It is recommended to be included in the promotion of drought resistance policies. Full article

This study addresses the challenge of estimating reference crop evapotranspiration (ET_O) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was applied to three irrigation districts: Moda (MD), Jiangbei (JB), and Manla (ML). Using ET_O values calculated by the FAO-56 Penman–Monteith (FAO-56PM) model as a benchmark, the performance and applicability of the LASSO-BP model were assessed. Short-term ET_O predictions for the three districts were also conducted using the mean-generating function optimal subset regression algorithm. The results revealed significant multicollinearity among six meteorological factors (maximum temperature, minimum temperature, average temperature, average relative humidity, sunshine duration, and average wind speed), as identified through tolerance, variance inflation factor (VIF), and eigenvalue analysis. The LASSO-BP model effectively captured the interannual variation of ET_O, accurately identifying peaks and troughs, with trends closely aligned with the FAO-56PM model. The model demonstrated strong performance across all three districts, with evaluation metrics showing MAE, RMSE, NSE, and R² values ranging from 4.26 to 9.48 mm·a⁻¹, 5.91 to 11.78 mm·a⁻¹, 0.92 to 0.96, and 0.82 to 0.94, respectively. Prediction results indicated a statistically insignificant declining trend in annual ET_O across the three districts over the study period. Overall, the LASSO-BP model is a reliable and accurate tool for estimating ET_O in Xizang Plateau irrigation districts with limited meteorological data. Full article