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The uncertain impacts of climate changes on the crop water requirements disarrange the on-farm balance of water supply and demand, thus here needs to adapt a cropping pattern to sustain the Northwestern China Agro-system. Balancing the fluctuations of on-farm available water associated with the uncertainty of crops water requirements could be considered as a complex optimization model. Since the classical optimization models exhibited many problems on the farm studies, e.g., the lack of accurate information, the applied limitations, and short-time accuracy, this study has utilized the fuzzy rule-based system (FRBS). The part of this challenge is back to estimating the sum of water use based on the crops water requirements which resulted from assuming a resilience characteristic to adapt the crops with the water resource capacity under uncertain climatic changes. This study aimed to develop a methodology for operationalizing a coequality approach to balance the crop water requirement with on-farm available water and how selection of the regional suitable crops has been defined. The results of this methodology showed that agriculture sustainability in this region is achieved by planting the irrigated crops whose crop coefficients range from 0.39 to 0.64 which adapted their water demand to the water resources capacity under uncertain conditions. The optimal range of crop coefficients achieved based on this fuzzy-probabilistic approach can be the reference point of water allocation in this irrigation district, since they also are investigated based on the economic factors that are a kind of crop-pattern for sustaining the balance of water supply and demand. This cropping pattern will be a helpful government policy for the sustainability of the Gansu Provinceis the conversion factor, the Agro-system. [ABSTRACT FROM AUTHOR]

[Display omitted] Evapotranspiration (ET), which represents water loss due to soil evaporation and crop transpiration, is a critical hydrological parameter for managing available water resources in irrigation systems. Traditional methods for monitoring actual evapotranspiration (ETa) involve field measurements. While accurate, they lack scalability, are labor-intensive, and incur high costs. Remote sensing satellites can help address these practical challenges by providing high-resolution imagery for spatially explicit mapping and near-real-time monitoring of ETa. This study aimed to develop simple yet robust models for estimating ETa using Sentinel-2 (S2A and S2B) satellite vegetation indices (VIs)—the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—and the Google Earth Engine (GEE) cloud platform for irrigated sugarcane plantations of the Metehara Sugarcane Estate in the semiarid landscape of the Ethiopian Rift Valley. Six empirical ET-VI models that combined NDVI-based proxies (NDVI Kc , NDVI*, and NDVI* scaled) and EVI-based proxies (EVI Kc , EVI*, and EVI* scaled) for the crop coefficient (Kc) with the reference ET (ETo) were developed and evaluated for growing seasons between 2020 and 2022. Model validation using independently estimated sugarcane ET (ET sugarcane) and open-access remote sensing ET, Actual EvapoTranspiration and Interception (ETIa) showed that all ET-VI models captured spatiotemporal dynamics in the consumptive fraction of sugarcane water use, with a higher coefficient of determination (R2) of ≥ 0.91. However, comparative analyses of ETa retrieval models indicated improved accuracy of the ET-EVI models (root mean square error (RMSE) of ± 8 mm for ET sugarcane and ± 4 mm for ETIa) compared with the ET-NDVI models. Among the EVI models, ET-EVI Kc achieved the highest R2 of 0.98, RMSE of ≤ 30 mm, and percentage bias (PBIAS) of ≤ 15 %. The results also revealed a strong correlation between the scaled VI-derived models and the reference ETIa (R2 = 0.94–0.97), which best explained the field-by-field variability, with the ET-EVI* scaled model achieving a lower RMSE of 18 mm than the ET-NDVI* scaled model (RMSE= 32 mm), while both the models showed similar levels of bias (∼17 %). Moreover, compared to the referenced ET sugarcane , the bias was minimal at − 9 % for ET-NDVI* scaled and − 1 % for ET-EVI* scaled. At the field scale, the NDVI and EVI models estimated the mean monthly ETa ranging from 99 to 129 mm m−1 and 89 to 148 mm m−1, respectively, with total annual averages of 1188–1537 mm yr−1 and 1296–1566 mm yr−1. In this context, the modeled ETa provided improved insights into consumptive water use in irrigated sugarcane plantations with limited field measurements. The statistical model evaluation metrics indicated that ET-EVI Kc was the optimal model in characterizing ET sugarcane , outperforming the ET-NDVI Kc and ET-EVI* scaled models, which ranked second by > 6 %, and ET-NDVI* scaled model , which ranked third by > 20 %. Our findings demonstrate the potential of multispectral VI-driven models as cost-effective and practical tools for the rapid estimation and mapping of ETa, thereby supporting the development of sustainable water conservation practices. A major advantage of the empirical modeling framework presented in this study is the straightforward parametrization of spatially consistent Kc distributions using remote sensing VIs and local weather station data. However, further improvements and operational applications of standardized VI-based ET models in croplands of other large irrigation schemes in semiarid regions should consider atmospheric impacts, variations in scene characteristics, and bare ground/soil exposure. [ABSTRACT FROM AUTHOR]

Crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ETc and growth-stage-specific Kc 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 ETo estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ETc 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 ETo 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 ETo 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 ETo 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 (Kc) 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 ETo when fewer meteorological variables are available. The calculated local growth-stage-specific Kc can help improve irrigation water management in this region. [ABSTRACT FROM AUTHOR]

The goal of this study is to investigate the usefulness of the relatively new 30 m spatial and <5.7-day temporal resolution Harmonized Landsat Sentinel-2 (HLS) dataset for calculating vegetation index-based crop coefficients (KcVI) for estimating field scale crop evapotranspiration (ETc). Increased spatial and temporal resolution ETc estimates are needed for improving irrigation scheduling, monitoring impacts of water conservation programs, and improving crop yield. The crop coefficient (Kc) method is widely used for estimating ETc. Remote sensing vegetation indices (VI) are highly correlated to Kc and allow the creation of a KcVI but the approach is limited by the availability of high temporal and spatial resolutions. We selected and calculated sixteen commonly used VIs using HLS data and regressed them against field-measured ET for alfalfa in the Mesilla Valley, New Mexico to create linear KcVI models. All models showed good agreement with Kc (r2 > 0.67 and RMSE < 0.15). ETc prediction resulted in an MAE ranging between 0.35- and 0.64-mm day−1, an MSE ranging between 0.20- and 0.75-mm day−1 and an MAPD ranging between 10.0 and 16.5%. The largest differences in predicted ETc occurred early in the growing season and during cutting periods when the spectral signal could be influenced by soil background or irrigation events. The results suggest that applying the KcVI approach to the HLS dataset can help fill in the data gap in remote sensing ET tools. Future work should focus on assessing additional crops and integration into other tools such as the emerging OpenET platform. [ABSTRACT FROM AUTHOR]

Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established Kc coefficients in these environments. Alternate Kc estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict Kc as a function of LAI, with the Kc estimates derived from the best-performing model then being used in conjunction with in situ measurements of ETo to estimate ETa. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R2 values of 0.05 and 0.68, respectively, when compared against measured Kc. Comparisons between estimated and measured ETa yielded RMSE and R2 values of 0.51 mm d−1 and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions. [ABSTRACT FROM AUTHOR]

Accurate information regarding crop evapotranspiration (ETc) and its components is essential for proper water management. Salinity has become increasingly serious in many parts of the world, affecting the use of saline water in irrigation for various biochemical processes, hence the importance of understanding the effects of soil and water salinity on ETc. This study aimed to determine the transpiration (Tr) and soil evaporation (Es) components that constitute ETc in tomatoes irrigated by drip irrigation and to investigate the effects of irrigation water salinity levels on ETc and its components, as well as on the crop coefficient (Kc) used to determine ETc. In addition, the reference evapotranspiration (ETo) values determined by the measurements obtained with the lysimeter system were compared with the ETo values calculated using the Almeria (ETo_Alm), FAO Radiation (ETo_FAO-Rad), and Hargreaves (ETo_Harg) models. During the spring growing period of 2022, tomatoes were cultivated under four different irrigation water salinity levels: S0 = 0.7 (control), S1 = 2.5 (low), S2 = 5.0 (medium), and S3 = 7.5 (high) dS m−1. The Tr values in S0, S1, and S2 treatments were measured by the sap flow method. Radiation-based ETo prediction models had strong relationships with lysimeter-measured ETo values (R2 > 0.977 and RMSE < 0.53 mm). The results indicated that increasing salinity levels led to a decrease in both Tr and ETc while causing an increase in Es. In the S0, S1, and S2 treatments, Es accounted for 16.3, 45.0, and 36.2%, respectively. Actual Kc values also decreased with increasing salinity. The results indicate that different actual Kc values can be used when calculating evapotranspiration in decision-making processes in greenhouse tomato production in Antalya and when irrigation water with different salinity levels is used. Additionally, actual ETc responses of tomatoes to salinity can be used to manage saline water in irrigation scheduling. [ABSTRACT FROM AUTHOR]

Cover plants, among the most commonly used plants in the realm of green spaces, are typically characterized by shallow roots. In this research, a simple and flexible algorithm has been introduced for calculating the basal crop coefficient and evaporation coefficient of shallowrooted plants without the use of (micro-) lysimeters. The presented algorithm requires measurements of moisture at three depths for short-term calculations and only surface layer moisture monitoring for long-term calculations. Using this algorithm, it is possible to calculate the potential evaporation and transpiration of the plant. Furthermore, the presented algorithm is independent of time steps. To evaluate this algorithm, nine experimental plots were utilized, including six Frankenia plots with full coverage and three bare soil plots during the peak water demand period. All experiments were conducted at the educational site of irrigation systems in the vicinity of the meteorological site of Ferdowsi University of Mashhad (FUM). For this purpose, the water content and irrigation of these plots was fulfilled (at most) every 48 hr. The results indicated that the presented algorithm has good capabilities for estimating the basal crop and evaporation coefficients. Additionally, the basal crop coefficient for Frankenia plant was found to be 1, and the evaporation coefficient was 0.58. Therefore, this method can be employed for estimating the water requirements of different plants without using (micro-) lysimeters. [ABSTRACT FROM AUTHOR]

This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ETa) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that provides the best ETa estimates to improve irrigation water management and help make irrigated agriculture sustainable. The RS platforms used in the study included Landsat-8 (30 m pixel spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and a small uncrewed aerial system or sUAS (0.03 m). Two-source surface energy balance (TSEB) models, implementing the series and parallel surface resistance approaches, were used in this study to estimate hourly maize ETa. The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, CO, USA, in 2020 and 2021. Each research site had different irrigation systems. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Maize ETa predictions were compared to observed maize ETa data from an eddy covariance system installed at each research site. Results indicated that the MSR5 proximal platform (1 m) provided optimal RS data for the TSEB algorithms. The MSR5 "point-based" nadir-looking surface reflectance data and surface radiometric temperature combination resulted in the smallest error when predicting hourly (mm/h) maize ETa. The mean bias and root mean square errors (MBE and RMSE, respectively), when predicting maize hourly ETa using the MSR5 sensor data, were equal to −0.02 (−3%) ± 0.07 (11%) mm/h MBE ± RMSE and −0.02 (−3%) ± 0.09 (14%) mm/h for the TSEB parallel and series approaches, respectively. The poorest performance, when predicting hourly TSEB maize ETa, was from Landsat-8 (30 m) multispectral data combined with its original thermal data, since the errors were −0.03 (−5%) ± 0.16 (29%) mm/h and −0.07 (−13%) ± 0.15 (29%) mm/h for the TSEB parallel and series approaches, respectively. These results indicate the need to develop methods to improve the quality of the RS data from sub-optimal platforms/sensors/scales/calibration to further advance sustainable irrigation water management. [ABSTRACT FROM AUTHOR]

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP. [ABSTRACT FROM AUTHOR]

In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment.

【Objective】 Crop coefficient and soil evaporation are two important parameters for calculating evapotranspiration in terrestrial ecosystems but they are dependent on many factors. In this paper, we investigate their correlation for a forage grassland. 【Method】 The experiment was conducted in a forage grassland (Triticale and Oat) irrigated by sprinkler irrigation at the Lhasa Valley. Evapotranspiration (ET) was measured from a micro-lysimeter and the potential evapotranspiration was calculated using the PM method. The ratio of the former to the latter was the ground-truth crop coefficient (Kc). This crop coefficient was used to evaluate the single-crop coefficient estimated using the modified PM method. We also measured the inter-plant soil evaporation (E) using an in-house instrument to estimate the E/ET ratio. The relationship between Kcand E/ET, as well as their dependence on leaf area index (LAI) and SPAD were established. 【Result】 Using the correction method recommended by FAO-56, the crop coefficient of Triticale and Oat in the Lhasa valley at initial, middle and end of their growth stage was Kc ini=0.45, Kc mid=1.22 and Kc end=0.49 for the Triticale, and Kc ini=0.94, Kc mid=1.16 and Kc end=0.31 for the Oat. For the Triticale, soil evaporation accounted for 44.8% of the evapotranspiration during the whole growth, and 93.4%, 94.8%, 61.8%, 22.2% and 13.8% during the seedling-overwintering, overwintering-replanting, replanting-jointing, jointing-heading and heading-mowing stage, respectively. For the Oat, soil evaporation accounted for 98.9%, 79.1%, 31.7% and 16.8% of the evapotranspiration from seedling to tillering, tiller to jointing, jointing to heading and heading to mowing stages, respectively. Overall, soil evaporation accounted for more than 44% of the evapotranspiration and it is hence potential to improve water use efficiency by reducing soil respiration. The measured crop coefficient and the ratio of soil evaporation to evapotranspiration were significantly correlated with LAI and SPAD linearly or nonlinearly. 【Conclusion】 The measured crop coefficient, ratio of soil evaporation to evapotranspiration of the two-season forage grassland are significantly correlated to LAI and SPAD. Adding a crop indicator to the single crop coefficient can improve the accuracy of the calculated evapotranspiration. Through mathematical models, the crop indicators can simulate evapotranspiration.

The experiment was conducted using a lysimeter in June, 2014 in Saki (Derived Savanna) and Ile-Ife (Tropical Rainforest) to determine the crop coefficients and the effects of Urea fertilizer on the yield and water productivity of Amaranthus viridis in Nigeria. The loamy sandy soil in the top 15 cm was sampled, air-dried and analysed. Eighteen (18) kg of the air-dried and sieved soils were weighed into the thirty-two (32) lysimeters. Drill method was used for the sowing and the seeds were spread within 2 cm in the lysimeter. Urea fertilizer was applied at 0, 40, 80 and 160 kg N/ha twice during the growing season. The crop coefficients at the initial, mid, and late stages ranged from 0.15 to 0.41 in Ile-Ife, while in Saki, they ranged from 0.29 to 1.15 under Urea fertilizer. In Saki, the peak crop evapotranspiration was 1.21 mm/day for 0 kg N/ha of Urea while in the Ile-Ife, the crop evapotranspiration was 1.83 mm/day for 160 Kg N/ha of Urea. Water productivity in Saki and Ile-Ife were 45.47 kg/ha mm and 18.58 kg/ha mm respectively for 160 kg N/ha. This implies that Amaranthus viridis required more water during canopy expansion and flowering in Ile-Ife than in Saki. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the water balances of Alpine grassland ecosystems, it is crucial to know the abiotic and biotic drivers of evapotranspiration. The abiotic drivers are very heterogeneous in mountain regions because elevation, slope and aspect control incoming Rs, and atmospheric layering affect air temperature, humidity and wind distribution. In a study with 24 lysimeter plots distributed over a study area of approx. 300 km2 in the Eastern Alps, we covered a wide range of topographic conditions. We investigated the effects of abiotic drivers on evapotranspiration by measuring evaporation from a free‐water body (Ew). For the biological modulation of crop evapotranspiration (ETc), we analysed two different grassland types (at peak biomass and at low biomass) and calculated the respective crop coefficients (Kc). Results showed that primarily physical drivers such as the accumulated solar radiation from sunrise to measurement (Rs_acc), followed by atmospheric pressure (P), wind speed (u) and vapour pressure deficit (VPD) influence both Ew and ETc. Moreover, ETc is also significantly influenced by standing biomass and the grassland type (i.e., resource use strategies of the vegetation types) and by the geographic location along the valley (i.e., entrance, middle and head of the valley). We suppose plant stress and/or ground winds to be the underlying factor for the significance of the geographic location, yet further research is needed. The current study helps towards a better understanding of the water balance in alpine grassland ecosystems, but we also show that some spatial drivers cannot yet be adequately addressed. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

The pyfao56 software package is a Python-based implementation of the standardized evapotranspiration (ET) methodologies described in Irrigation and Drainage paper No. 56 of the Food and Agriculture Organization of the United Nations, commonly known as FAO-56. This update improved pyfao56 by 1) adding an optional surface runoff methodology, 2) adding an extensive algorithm for automating the computation of irrigation schedules, 3) considering irrigation losses due to irrigation system inefficiencies, 4) adding an optional method to compute the transpiration reduction coefficient (Ks) based on the curvilinear approach from AquaCrop, 5) incorporating a module for computing 15 goodness-of-fit statistics between simulated and measured data, and 6) computing a cumulative seasonal water balance summary. Most of these updates arose from user requests to add new features or options, and the collaborations demonstrated the value of community-based development for rapid improvement and generalization of scientific software.

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated ‘Pink Lady’ apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Zom), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards.

Crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ETc and growth-stage-specific Kc 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 ETo estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ETc 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 ETo 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 ETo 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 ETo 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 (Kc) 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 ETo when fewer meteorological variables are available. The calculated local growth-stage-specific Kc can help improve irrigation water management in this region.

Accurate information regarding crop evapotranspiration (ETc) and its components is essential for proper water management. Salinity has become increasingly serious in many parts of the world, affecting the use of saline water in irrigation for various biochemical processes, hence the importance of understanding the effects of soil and water salinity on ETc. This study aimed to determine the transpiration (Tr) and soil evaporation (Es) components that constitute ETc in tomatoes irrigated by drip irrigation and to investigate the effects of irrigation water salinity levels on ETc and its components, as well as on the crop coefficient (Kc) used to determine ETc. In addition, the reference evapotranspiration (ETo) values determined by the measurements obtained with the lysimeter system were compared with the ETo values calculated using the Almeria (ETo_Alm), FAO Radiation (ETo_FAO-Rad), and Hargreaves (ETo_Harg) models. During the spring growing period of 2022, tomatoes were cultivated under four different irrigation water salinity levels: S0 = 0.7 (control), S1 = 2.5 (low), S2 = 5.0 (medium), and S3 = 7.5 (high) dS m−1. The Tr values in S0, S1, and S2 treatments were measured by the sap flow method. Radiation-based ETo prediction models had strong relationships with lysimeter-measured ETo values (R2 > 0.977 and RMSE < 0.53 mm). The results indicated that increasing salinity levels led to a decrease in both Tr and ETc while causing an increase in Es. In the S0, S1, and S2 treatments, Es accounted for 16.3, 45.0, and 36.2%, respectively. Actual Kc values also decreased with increasing salinity. The results indicate that different actual Kc values can be used when calculating evapotranspiration in decision-making processes in greenhouse tomato production in Antalya and when irrigation water with different salinity levels is used. Additionally, actual ETc responses of tomatoes to salinity can be used to manage saline water in irrigation scheduling.

The goal of this study is to investigate the usefulness of the relatively new 30 m spatial and cVI) for estimating field scale crop evapotranspiration (ETc). Increased spatial and temporal resolution ETc estimates are needed for improving irrigation scheduling, monitoring impacts of water conservation programs, and improving crop yield. The crop coefficient (Kc) method is widely used for estimating ETc. Remote sensing vegetation indices (VI) are highly correlated to Kc and allow the creation of a KcVI but the approach is limited by the availability of high temporal and spatial resolutions. We selected and calculated sixteen commonly used VIs using HLS data and regressed them against field-measured ET for alfalfa in the Mesilla Valley, New Mexico to create linear KcVI models. All models showed good agreement with Kc (r2 > 0.67 and RMSE < 0.15). ETc prediction resulted in an MAE ranging between 0.35- and 0.64-mm day−1, an MSE ranging between 0.20- and 0.75-mm day−1 and an MAPD ranging between 10.0 and 16.5%. The largest differences in predicted ETc occurred early in the growing season and during cutting periods when the spectral signal could be influenced by soil background or irrigation events. The results suggest that applying the KcVI approach to the HLS dataset can help fill in the data gap in remote sensing ET tools. Future work should focus on assessing additional crops and integration into other tools such as the emerging OpenET platform.

An accurate assessment of crop water requirements during the crop growth period can help organize irrigation schedules and investigate the hydrological environments in irrigation districts, especially in shallow groundwater districts. In irrigation scheduling, crop coefficients, representing three development stages, play a critical role in modeling evapotranspiration. Therefore, in this study, the crop coefficient (Kc) of sunflowers under drip irrigation in different hydrological years in the Hetao irrigation district with shallow groundwater is determined. Based on the analysis of rainfall frequency, the experimental situations of 2012, 2013, and 2014 are adopted as high, normal, and low flow years. Using the water balance method, groundwater recharge and crop evapotranspiration in different hydrological years were investigated. The results showed that the groundwater recharge in a high-flow year was larger than that in normal and low-flow years with values of 67.47, 66.75, and 42.61 mm, respectively. Crop evapotranspiration is directly related to the irrigation amount at the same growth stage. The Kc of sunflowers in a high-flow year is higher by 0.1 and 0.15 than that in normal and low-flow years. The Kc of sunflowers under drip irrigation is lower than that under surface irrigation by 8.9% in the initial and late stages of sunflower growth, but higher than that by 10.6% in the mid-stage, which is due to differences in small water potential differences and less evapotranspiration in the initial and late stages under drip irrigation. The study is of great significance for formulating a reasonable schedule of drip irrigation with shallow groundwater and improving the field environment. [ABSTRACT FROM AUTHOR]

Crop irrigation requirements are usually estimated based on crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETo) and crop coefficient (Kc). There is a lack of knowledge on the irrigation requirements of tropical forage crops in Brazil, contrasting with the increasing use of irrigation in pastures. The effort of this study was to investigate what would be the water needs of tropical forages in Southern Brazil, based on a robust experimental database. The study was carried out in São Paulo State-Brazil using different forages species and their combinations [Guinea grass (GG); Guinea grass + black oat + ryegrass (GOR); Bermuda grass (BG), and Bermuda + black oat + ryegrass (BOR)]. The experimental fields were fully irrigated, and the Kc values were derived from ETc measurements on lysimeters; ETo was estimated using daily data from a nearby weather station and the standard FAO56 parameterization. Mean daily ETc values for GG, GOR, BG and BOR were 4.1, 2.9, 3.6, and 3.4 mm, respectively, and respective mean Kc values were 0.99, 0.90, 1.0, and 0.94. Average Kc values for all plots decreased as ETo increased, producing a negative Kc-ETo relationship, mainly when ETo reached values greater than 5 mm d−1. This was most likely due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high ETo. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETo to improve the required irrigation depth. [ABSTRACT FROM AUTHOR]

Estimating actual crop evapotranspiration is vital in water-scarce environment affected by climate change, particularly for optimizing irrigation and enhancing crop yield. This research focuses on assessing crop water and irrigation requirement for major crops across six districts of Madhya Pradesh, India, spanning diverse agro-climatic regions. Employing CLIMWAT 2.0 and CROPWAT 8.0 software, calculated crop evapotranspiration and devised irrigation strategies tailored to local climatic conditions. The FAO-Penman- Montieth (FAO-PM) equation for reference evapotranspiration (ET0), aiding in crop water requirement computation and irrigation planning. Our findings reveal substantial variations in crop water requirements across crops and districts. For instance, soybean in Indore requires the highest water input at 380 mm, while in Guna, was least at 303 mm. Wheat, on the other hand, register the highest water needs in Khandwa at 510.6 mm and the lowest in the Neemuch district at 370.8 mm, particularly during the rabi season. Besides that, this study underscores the need for district-specific considerations, taking into account climate and soil characteristics when formulating water management strategies. Employing efficient irrigation practices and techniques to manage water stress becomes imperative for optimizing crop yield and achieving economic returns. Implementing customized approaches to enhance water use efficiency and promote sustainability in agricultural production is crucial. These research outcomes provide valuable insights for policymakers, agricultural practitioners, and water resource managers to develop context-specific water management strategies. [ABSTRACT FROM AUTHOR]

Accurate estimation of the blue and green water requirements of the various crops cultivated in a water basin is essential for planning specific plant/crop irrigation schedules. In this study, the crop water requirements of the major crops grown in the Wami/Ruvu basin were determined using the FAO-CROPWAT model based on meteorological parameters: monthly maximum and minimum temperature, wind speed, mean relative humidity, sunshine hours, rainfall data, and effective rainfall for 23 hydro-meteorological stations distributed in the study area. The studied crops are rice, maize, beans, tomatoes, and sugarcane. The crop reference evapotranspiration (ETo) and actual evapotranspiration (ETc) for each crop were determined using crop coefficients (Kc) of various growth stages of crops. The results indicated that the total annual reference evapotranspiration (ETo) was 1604 mm, whereas an average ETo per month was 134 mm. The highest total irrigation water requirements were recorded in sugarcane, followed by rice, while the lowest water requirements were observed in beans and tomatoes. The results presented in this study may facilitate plant-specific water irrigation implementation and maximize crop production while conserving the environment. [ABSTRACT FROM AUTHOR]

Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established Kc coefficients in these environments. Alternate Kc estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict Kc as a function of LAI, with the Kc estimates derived from the best-performing model then being used in conjunction with in situ measurements of ETo to estimate ETa. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R2 values of 0.05 and 0.68, respectively, when compared against measured Kc. Comparisons between estimated and measured ETa yielded RMSE and R2 values of 0.51 mm d−1 and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions.

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Estimating actual crop evapotranspiration is vital in water-scarce environment affected by climate change, particularly for optimizing irrigation and enhancing crop yield. This research focuses on assessing crop water and irrigation requirement for major crops across six districts of Madhya Pradesh, India, spanning diverse agro-climatic regions. Employing CLIMWAT 2.0 and CROPWAT 8.0 software, calculated crop evapotranspiration and devised irrigation strategies tailored to local climatic conditions. The FAO-Penman-Montieth (FAO-PM) equation for reference evapotranspiration (ET0), aiding in crop water requirement computation and irrigation planning. Our findings reveal substantial variations in crop water requirements across crops and districts. For instance, soybean in Indore requires the highest water input at 380 mm, while in Guna, was least at 303 mm. Wheat, on the other hand, register the highest water needs in Khandwa at 510.6 mm and the lowest in the Neemuch district at 370.8 mm, particularly during the rabi season. Besides that, this study underscores the need for district-specific considerations, taking into account climate and soil characteristics when formulating water management strategies. Employing efficient irrigation practices and techniques to manage water stress becomes imperative for optimizing crop yield and achieving economic returns. Implementing customized approaches to enhance water use efficiency and promote sustainability in agricultural production is crucial. These research outcomes provide valuable insights for policymakers, agricultural practitioners, and water resource managers to develop context-specific water management strategies.

Ghana's agriculture is primarily rain-fed, making it vulnerable to the impact of climate variability and climate change. This study examined the trends and impact of climatic variability and evapotranspiration on the crop evapotranspiration of selected staple crops across three districts in the Upper East region of Ghana over a 19-year period. Data on agro-climatic conditions (rainfall, temperature, relative humidity, and sunlight) in the Upper East region were obtained from the Ghana Meteorological Agency (GMet) from 2000 to 2018. Given the non-conformity of the response variables to the normality assumption, the Quantile Regression (QR) was employed to examine the impact of climatic variability on different crop evapotranspiration scenarios (low = 10 %, typical = 50 %, and high = 90 %) of the selected crops. The analyzes disclosed a significant difference in crop evapotranspiration between the districts, except for maize, which remained consistent. There was an increase in crop evapotranspiration trend for all, but the maize crop informs the potential improvement of crop evapotranspiration notwithstanding the climatic variability. The study offers a vital insight into the complex dynamics of crop evapotranspiration in response to climatic variability and evapotranspiration. It is recommended, therefore, to consider advances in crop varieties through continuous investment in crop breeding research programmes to improve drought tolerance crops and develop regional water balance techniques.

An accurate assessment of crop water requirements during the crop growth period can help organize irrigation schedules and investigate the hydrological environments in irrigation districts, especially in shallow groundwater districts. In irrigation scheduling, crop coefficients, representing three development stages, play a critical role in modeling evapotranspiration. Therefore, in this study, the crop coefficient (Kc) of sunflowers under drip irrigation in different hydrological years in the Hetao irrigation district with shallow groundwater is determined. Based on the analysis of rainfall frequency, the experimental situations of 2012, 2013, and 2014 are adopted as high, normal, and low flow years. Using the water balance method, groundwater recharge and crop evapotranspiration in different hydrological years were investigated. The results showed that the groundwater recharge in a high-flow year was larger than that in normal and low-flow years with values of 67.47, 66.75, and 42.61 mm, respectively. Crop evapotranspiration is directly related to the irrigation amount at the same growth stage. The Kc of sunflowers in a high-flow year is higher by 0.1 and 0.15 than that in normal and low-flow years. The Kc of sunflowers under drip irrigation is lower than that under surface irrigation by 8.9% in the initial and late stages of sunflower growth, but higher than that by 10.6% in the mid-stage, which is due to differences in small water potential differences and less evapotranspiration in the initial and late stages under drip irrigation. The study is of great significance for formulating a reasonable schedule of drip irrigation with shallow groundwater and improving the field environment.

Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R2 values ranging from 0.35 to 0.93 (average R2 = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation. • High-throughput phenotyping of plant physiological traits such as ET at the plot-scale is difficult. • We developed a new method to estimate plot-scale ET using high resolution phenotyping data. • The method also used weather data, two-source energy balance, reference ET, and crop coefficient. • Estimated plot-scale ET correlated strongly with corn and soybean grain yield with average R2=0.71. • This study filled an important gap to enhance research in crop water use and drought response. [ABSTRACT FROM AUTHOR]

In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (D r , the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named "pyfao56" was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and D r based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the "Parameters" class defines variables affecting soil water balance and ET and the "Weather" class specifies relevant meteorological data. Other optional input data objects include the "Irrigation" class for irrigation events, the "Soil_Profile: class for defining stratified soil layer data, and the "Update" class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ET ref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily D r , ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 "customload" functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (K cb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide. [Display omitted] • An open-source, customizable FAO-56 water balance package is described. • The Python package was tailored for research use, named LIRFIP. • Data from full and limited irrigation field experiments are simulated. • Open-source code encourages and facilitates further customization and collaboration. [ABSTRACT FROM AUTHOR]

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated 'Pink Lady' apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Z om ), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)