Search Results (123)

This study, conducted in Southern Italy in 2023, investigated the effects of a dynamic agrivoltaics (AV) system on microclimate, water consumption, plant growth, and essential oil yield in six medicinal species: lavender (Lavandula angustifolia L. ‘Royal purple’), lemmon thyme (Thymus citriodorus (Pers.) Schreb. ar. ‘Aureus’), common thyme (Thymus vulgaris L.), rosemary (Salvia rosmarinus Spenn. ‘Severn seas’), mint (Mentha spicata L. ‘Moroccan’), and sage (Salvia officinalis L. subsp. Officinalis). Due to the rotating solar panels, two distinct ground zones were identified: a consistently shaded area under the panels (UP), and a partially shaded area between the panels (BP). These were compared to an adjacent full-sun control area (T). Microclimate parameters, including solar radiation, air and leaf infrared temperature, and soil temperature, were recorded throughout the cultivation season. Reference evapotranspiration (ET_O) was calculated using Turc’s method, and crop evapotranspiration (ET_C) was estimated with species-specific crop coefficients (K_C). Results showed significantly lower microclimatic values in the UP plot compared to both BP and especially T, resulting in ET_C reductions of 81.1% in UP and 13.1% in BP relative to T, an advantage in water-scarce environments. Growth and yield responses varied among species and treatment plots. Except for mint, all species showed a significant reduction in fresh biomass (40.1% to 48.8%) under the high shading of UP compared to T. However, no biomass reductions were observed in BP. Notably, essential oil yields were higher in both UP and BP plots (0.60–2.63%) compared to the T plot (0.51–1.90%). These findings demonstrate that dynamic AV systems can enhance water use efficiency and essential oil yield, offering promising opportunities for sustainable, high-quality medicinal crop production in arid and semi-arid regions. Full article

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ET_o when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ET_o estimation to overcome the lack of ground-truth data and upscale ET_o estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ET_o. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (k_Rs) yielded accurate results. ET_o estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d⁻¹), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d⁻¹ and 1.05 mm d⁻¹ for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ET_o. It was concluded that PMT-ET_o can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing. 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

A comparison of evapotranspiration between seasonally frozen and permafrost soils has important theoretical value for studying land surface processes and ecological environmental evolution on the Tibetan Plateau. In this work, the actual (ET_a) and reference (ET₀) evapotranspiration and crop coefficient (K_c) were calculated via eddy covariance data and meteorological gradient data from sites in the Naqu Prefecture and Tanggula Mountains. The variations, differences, and factors influencing the ET_a and ET₀ were analysed. The results revealed that at the two sites in 2008, the annual total ET_a values were 493.53 and 585.17 mm, which accounted for 83.58% and 144.39% of the total annual rainfall, respectively. The ET_a at the Naqu site was affected mainly by the Tibetan Plateau monsoon (TPM), whereas the ET_a at the Tanggula site was strongly affected by both the TPM and the freezing–thawing processes of the permafrost. The annual total ET₀ values at the two sites were 819.95 and 673.15 mm, respectively. The monthly total ET₀ at the Naqu site was greater than that at the Tanggula site. The ET_a and ET₀ values at the two sites were low in winter–spring, high in summer–autumn, and concentrated from May to October. When snow was present, the ET_a values at the Naqu site were relatively high, and the ET₀ values at both sites were very small and even negative at the Naqu site. The ET_a and ET₀ values at the two sites were significantly positively correlated with the net radiation (R_n), surface temperature (T₀), air temperature (T_a), water vapour pressure (e) and soil water content (smc), and negatively correlated with the wind speed (ws). The correlation between the ET_a and the T₀ at the Naqu site was the most significant, and the coefficient of partial correlation was 0.812; meanwhile, the correlation between the ET_a and the smc at the Tanggula site was the most significant, and the coefficient of partial correlation was 0.791. The R_n at the Naqu and Tanggula sites both had greater impacts on the ET₀. Full article

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article

The accuracy of the crop coefficient approaches recommended by the FAO-56 guidelines for evapotranspiration (ET) in saline environments is limited due to complex soil–water–crop interactions, highlighting the need for advanced methods to improve ET estimation for water management in saline-alkali lands. To improve ET estimation for wheat, maize, and soybean in the Yellow River Delta, China, three machine-learning algorithms—gradient-boosting decision tree (GBDT), random forest (RF), and extreme gradient-boosting regression (XGBoost)—were applied alongside single- and dual-crop coefficient approaches (K_c-ET_o). The results showed that increasing the input variables did not necessarily improve the ML model performance. The ML models outperformed K_c-ET_o approaches, particularly for summer crops (maize and soybean), with the mean absolute error reduced by 26.4% to 80.9%, R² increased by 5.6% to 11.2%, and root mean square error (RMSE) decreased by 22.4% to 98.1%. RF and XGBoost were more accurate than GBDT, with R² increasing by 3.2% to 5.4% and RMSE decreasing by 22% to 57%. Scenario simulations showed increased ET with intensified emission scenarios for RF and GBDT, similar to K_c-ET_o approaches. However, XGBoost simulated a significantly lower ET in high-emission scenarios, indicating potential unreliability for scenario predictions beyond the training dataset, especially in a saline-alkali environment with an increasingly complex background. Full article

How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (ET_c) measured by the Bowen ratio system as the reference standard. The reference crop evapotranspiration (ET_o) was calculated using the Penman formula, and the grape crop coefficient (K_c) was subsequently derived. The FAO-56 dual crop coefficient method was then employed to determine the soil evaporation coefficient (K_e) and the water stress coefficient (K_s), leading to the acquisition of the basal crop coefficient (K_cb). Concurrently, multispectral remote sensing images captured by unmanned aerial vehicle (UAV) were used to gather grape spectral data, from which the reflectance of multiple bands was extracted to compute four vegetation indices: the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Ratio Vegetation Index (RVI), and the Difference Vegetation Index (DVI). Relationship models between the grape basal crop coefficient (K_cb) and these vegetation indices were established using univariate linear regression, polynomial regression, and multiple linear regression. These models were then used to estimate vineyard evapotranspiration and validate the accuracy of the UAV multispectral remote sensing in estimating the grape K_cb. The results indicated that: (1) The growth stage, type of vegetation index, and modeling method were three significant factors influencing the fitting accuracies of the relationship models between the grape basal crop coefficient (K_cb) and vegetation indices. These model fitting accuracies had a notable impact on the estimation accuracies of evapotranspiration. (2) The application of UAV-based multispectral remote sensing to estimate the grape basal crop coefficient in the subhumid region of Northwest China was feasible. Compared to the K_cb values recommended by the FAO-56, the K_cb values derived from the UAV data improved the estimation accuracies of evapotranspiration by more than 11% in 2021 and 13% in 2022. Full article

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ET_c), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ET_a followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (K_c) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ET_a (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m⁻³. Good correlations were found between the K_c’s proposed by FAO and the NDVI evolution in the experimental plot, with an R² of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ET_a values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day⁻¹ and a low bias of 0.6 mm day⁻¹. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine K_c, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings. Full article

The production of high-quality coffee seedlings is essential to meet the demands of the coffee sector, requiring more efficient and sustainable water management practices. In this context, the use of hydroretentive polymers, particularly biodegradable ones, emerges as a promising alternative to optimize water use, reduce the environmental impact associated with synthetic polymers, and improve the agronomic traits of seedlings. Therefore, this study aimed to evaluate the effects of different irrigation intervals and hydroretentive polymer doses on the water consumption and agronomic characteristics of Coffea arabica L. seedlings. This study was conducted in a protected environment using a randomized block design with split plots and four replicates. The plots consisted of two irrigation intervals (2 and 4 days), and the subplots included four doses of hydroretentive polymer (0%, 0.25%, 0.5%, and 1%), applied in 0.5 dm³ polypropylene bags. Results showed that the 0.5% polymer dose combined with a 2-day irrigation interval resulted in the highest water consumption, while the combination of 0% polymer and a 4-day irrigation interval led to the lowest water consumption. The 0.25% hydroretentive polymer dose with irrigation every 2 days showed the best performance in gas exchange, promoting photosynthesis without causing water saturation. This management also promoted better seedling growth, increasing biomass, height, leaf area, and root volume compared to longer irrigation intervals. The crop coefficients (Kc × Ks) were 0.20, 0.28, and 0.45 during the periods of 0–50, 51–80, and 81–150 days after sowing, respectively. A dose of 0.25% hydroretentive polymer with a 2-day irrigation interval is recommended for the production of Arabica coffee seedlings, contributing to agricultural practices aligned with environmental preservation and productive efficiency. Full article

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions. Full article

The amount of irrigation needed can be determined using reference evapotranspiration (ETo), the crop coefficient (Kc), and the water deficit index. Reference evapotranspiration is typically calculated utilizing the Penman–Monteith (PM) model, which necessitates various meteorological parameters, including temperature, humidity, net radiation, and wind speed. In regions where meteorological stations are absent, alternative methods must be employed to estimate these parameters. This study employs a combination of estimated meteorological parameters derived from different methodologies to calculate both reference evapotranspiration and irrigation rates, subsequently evaluating the results through wheat irrigation experiments. The daily irrigation rate for the T1 treatment was computed using real-time meteorological data, resulting in the highest grain yield of 561.73 g/m² and an irrigation water use efficiency of 7.61 kg/m³. The irrigation rate for the T2 treatment was determined based on real-time net radiation alongside monthly average values of temperature, humidity, and wind speed. In comparison to T1, the irrigation amount, yield, and irrigation water use efficiency for T2 decreased by 1.59%, 2.96%, and 1.42%, respectively. For the T3 treatment, the irrigation amount was calculated using monthly average values of temperature, humidity, and wind speed, with net radiation derived from daily light duration. The yield for T3 decreased by 19.4% relative to T1, the irrigation amount decreased by 12.95% relative to T1, and the irrigation water use efficiency decreased by 7.45% relative to T1. In the case of the T4 treatment, monthly average values of temperature, humidity, and wind speed were utilized, while net radiation was calculated using the Hargreaves–Samani (HS) model in conjunction with real-time temperature data. The yield for T4 decreased by 8.75% relative to T1, the irrigation amount decreased by 5.58% relative to T1, and the irrigation water use efficiency decreased by 3.39% relative to T1. For the T5 treatment, similar monthly average values were employed, and net radiation was calculated using HS methodology combined with monthly average temperature data. The yield for T5 decreased by 11.96% relative to T1, the irrigation amount decreased by 6.07% relative to T1, and the irrigation water use efficiency decreased by 6.3% relative to T1. Furthermore, the yield for the CK treatment under conventional irrigation decreased by 20.89% compared to T1, while the irrigation amount increased by 1.57% compared to T1 and the irrigation water use coefficient decreased by 22.14% compared to T1. Above all, this article posits that in areas lacking meteorological stations, monthly mean meteorological data should be utilized for parameters such as temperature, humidity, and wind speed, while the HS model is recommended for calculating net radiation. Full article

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U²Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U²net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R²) of 0.953. The final R² between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize. Full article

Soil water balance (SWB) in woody crops is sometimes difficult to estimate with one-dimensional models because these crops do not completely cover the soil and usually have a deep root system, particularly when cropped under rainfed conditions in a Mediterranean climate. In this study, the actual crop evapotranspiration (ET_{c act}) is estimated with the soil water balance model SIMDualKc which uses the dual-K_c approach (relating the fraction of soil cover with the crop coefficients) to improve the estimation of the water requirements of a rainfed vineyard, using data from a deep soil profile. The actual basal crop coefficient (K_{cb act}) obtained using the SIMDualKc model was compared with the K_{cb act} estimated using the A&P approach, which is a simplified approach based on measurements of the fraction of ground cover and crop height. Spectral vegetation indices (VIs) derived from Landsat-5 satellite data were used to determine the fraction of ground cover (f_{c VI}) and thus the density coefficient (K_d). The SIMDualKc model was calibrated using available soil water (ASW) measurements down to a depth of 1.85 m, which significantly improved the conditions for using an SWB estimation model. The test of the model was performed using a different ASW dataset. A good agreement between simulated and field-measured ASW was observed for both data sets along the crop season, with RMSE < 12.0 mm and NRMSE < 13%. The calibrated K_cb values were 0.15, 0.60, and 0.52 for the initial, mid-season, and end season, respectively. The ratio between ET_{c act} and crop evapotranspiration (ET_c) was quite low between veraison and maturity (mid-season), corresponding to 36%, indicating that the rainfall was not sufficient to satisfy the vineyard’s water requirements. VIs used to compute f_{c VI} were unable to fully track the plants’ conditions during water stress. However, ingestion of data from remote sensing (RS) showed promising results that could be used to support decision making in irrigation scheduling. Further studies on the use of the A&P approach using RS data are required. Full article

Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ET_c), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ET_c. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values. Full article

Crop coefficient (K_c), the ratio of crop evapotranspiration (ET_c) to reference evapotranspiration (ET_o), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ET_c and growth-stage-specific K_c in flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of two temperature-based ET_o estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ET_c over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water in the 2021–2022 and 2022–2023 growing seasons, respectively. The ET_o over the growing season, computed using Penman–Monteith, Hargreaves–Samani, and Blaney–Criddle equations, were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021–2022, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022–2023, respectively. The result of global sensitivity analysis showed that the mean temperature is the main driving factor for estimated ET_o based on Blaney–Criddle and Hargreaves–Samani methods, but the sensitivity percentage for Blaney–Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves–Samani, given that Blaney–Criddle method is less accurate in ET_o estimation for this area, especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman–Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (K_c) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman–Monteith, 0.51, 1.0 and 0.46 based on Blaney–Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves–Samani. The results showed that the Hargreaves–Samani equation serves as an alternative tool to predict ET_o when fewer meteorological variables are available. The calculated local growth-stage-specific K_c can help improve irrigation water management in this region. Full article