Search Results (44)

Drought stress critically constrains agricultural productivity in arid and semi-arid regions, necessitating the development of drought-tolerant crop varieties for sustainable food security. This study evaluated drought tolerance in 222 foxtail millet (Setaria italica) germplasms from diverse Chinese agroecological zones from 2021–2023 at a specialized identification site in Xinjiang. Field experiments used a randomized complete block design comparing normal irrigation (3000 m³/ha) with drought stress (1800 m³/ha) across 12 morpho-agronomic traits including plant height, spike characteristics, biomass, and yield components. Drought stress significantly reduced all parameters, with yield exhibiting the highest sensitivity (drought tolerance coefficient = 0.58). Principal component analysis indicated that the first three components explained 82.70% of phenotypic variance, with yield-related parameters contributing the most to genotypic differentiation. Integrated evaluation using comprehensive drought tolerance coefficient (DTC), drought resistance index (DRI), and D-values classified germplasms into five categories: highly resistant (4.50%), resistant (11.71%), moderately resistant (57.21%), sensitive (16.22%), and highly sensitive (10.36%). Correlation and stepwise regression analyses identified five critical indicators: stem basal thickness, single plant biomass, spike weight, grain weight per spike, and yield. The predictive model demonstrated exceptional accuracy (R² = 0.9998), enabling efficient screening using the targeted traits. The elite germplasms T125 (92) and Baogu 23 (135) consistently ranked as the most drought-tolerant across all methods. These findings establish a robust methodological framework for evaluating drought tolerance in foxtail millet and provide practical selection criteria for developing climate-resilient cultivars. The identified germplasms and evaluation indices significantly contribute to agricultural sustainability in water-limited environments, supporting food security in regions that are increasingly affected by climate-induced drought stress. Full article

Water scarcity, soil salinization, and desertification threaten sustainable agricultural ecosystems of Hetao irrigation district, Yellow River Basin (YRB). Precise quantification of soil water dynamics and plant water consumption processes is essential for the agricultural sustainability of the irrigation district. Therefore, this study mainly focused on the crop coefficients and water consumption processes of three representative plant types in the Hetao irrigation district, each corresponding to a specific land system: Helianthus annuus (cultivated land), Tamarix chinensis (wasteland), and Phragmites australis (lake). The SIMDualKc model was calibrated and validated based on situ observation data (soil water content and yield) during 2018 (conventional conditions), 2023 and 2024 (deep water-saving conditions). Results show strong agreement between simulated and observed soil moisture and crop yields. The results indicate that the process curves of K_cb (basal crop coefficient) and K_cbadj (adjusted crop coefficient) nearly overlapped for the three plant types in 2018 and 2023. However, under the deep water-saving project implemented in 2024, the K_cbadj process curves for all three plant types exhibited a significant reduction (approximately 15%). Soil evaporation fractions (E/ET_cadj) were stable at 19–30% during the 2018, 2023, and 2024. The contribution of capillary rise to ET reached 38.61–43.18% in cultivated land (Helianthus annuus), 41.52–48.93% in wasteland (Tamarix chinensis), and 38.08–46.57% in lake boundary areas (Phragmites australis), which underscores the significant role of groundwater recharge in sustaining plant water consumption. Actual-to-potential transpiration ratios (T_a/T_p) during 2023–2024 decreased by 3–11% for Helianthus annuus, 5–12% for Tamarix chinensis, and 23% for Phragmites australis compared to T_a/T_p values in 2018. Capillary rise decreased approximately 10% during the whole system. Deep water-saving practices increased the groundwater depth and restricted groundwater recharge to plants via capillary rise, thereby impairing plant transpiration and growth. These findings provide scientific support for sustainable agriculture and ecological security in the Yellow River Basin. Full article

Accurately partitioning actual evapotranspiration ET_{c act} into soil evaporation E_s and plant transpiration T_{c act} is crucial for improving water use efficiency and devising precise irrigation schedules. In water-saving irrigated rice fields, ET_{c act}, E_s and T_{c act} were estimated using a dual crop coefficient method based on three approaches: FAO56 adjusted, locally calibrated and leaf area index LAI-based coefficients. Continuous measurements of hourly and daily ET_{c act}, E_s and T_{c act} with weighing lysimeters were used to validate these coefficients. Results showed that hourly ET_{c act}, E_s and T_{c act} exhibited a distinct inverted “U” shape single-peak trend. Daily ET_{c act} and T_{c act}, along with the corresponding crop coefficients K_{c act} and basal crop coefficients K_{cb act}, initially increased and then decreased throughout the rice growth stages, while daily E_s and soil evaporation coefficient K_{e act} were high during the initial stage and gradually decreased as the development stage progressed. FAO56 adjusted coefficients consistently underestimated both hourly and daily ET_{c act}, E_s and T_{c act}. Locally calibrated basal crop coefficients K_{cb Cal} were determined as 0.28, 1.17 and 1.09 for the initial, mid-season and end-season stages, respectively, and locally calibrated turbulent transport coefficient of water vapor K_{cp Cal} (recommended as 1.2 by FAO) was determined to be 1.59. Based on these calibrated coefficients, estimates of hourly and daily evapotranspiration ET_{c Cal}, soil evaporation E_{s Cal} and plant transpiration T_{c Cal} performed poorly during the initial stage but showed improved accuracy during subsequent growth stages. Hourly and daily evapotranspiration and its components based on LAI-based coefficients exhibited similar performance in estimating measurements, albeit slightly inferior to FAO56 calibrated coefficients. Overall, both the FAO56 calibrated coefficients and LAI-based coefficients are recommended for estimating evapotranspiration and its components at daily and hourly scales. These research findings provide valuable insights for optimizing irrigation regimes and improving water use efficiency in rice cultivation. 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

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

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 (K_{c act}), the actual basal crop coefficient (K_{cb act}), and the actual crop evapotranspiration (ET_{c act}), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While K_{cb 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 K_{c 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 K_{c ini act} and from 0.80 to 1.06 for the K_{c 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 K_{cb act}, K_{c act}, and ET_{c 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. Full article

Water scarcity and quality are critical impediments to sustainable crop production. In this study, HYDRUS-2D was calibrated using field measurements of water contents and salinities in the soil under wine grapes irrigated with river water (Rw, 0.32 dS/m). The calibrated model was then used to evaluate the impact of (a) four different water qualities ranging from 0.32 (Rw) to 3.2 dS/m (brackish water, Gw) including blended (Mix) and monthly alternating (Alt) irrigation modes; (b) two rainfall conditions (normal and 20% below normal); and (c) two leaching options (with and without 30 mm spring leaching irrigation) during the 2017–2022 growing seasons. Irrigation water quality greatly impacted root water uptake (RWU) by wine grapes and other water balance components. Irrigation with brackish water reduced average RWU by 18.7% compared to river water. Irrigation with blended water or from alternating water sources reduced RWU by 8.8 and 7%, respectively. Relatively small (2.8–8.2%) average annual drainage (Dr) in different scenarios produced a very low (0.05–0.16) leaching fraction. Modeling scenarios showed a tremendous impact of water quality on the salts build-up in the soil. The average electrical conductivity of the saturated soil extract (EC_e) increased three times with Gw irrigation compared to Rw (current practices). Blended and alternate irrigation scenarios showed a 21 and 28% reduction in EC_e, respectively, compared to Gw. Irrigation water quality substantially impacted site-specific actual basal (K_{cb act}) and single (K_{c act}) crop coefficients of grapevine. Threshold leaching efficiency estimated in terms of the salt mass leached vs. added (LE_s; kg/kg) for salinity control (LE_s > 1) was achieved with LFs of 0.07, 0.12, 0.12, and 0.15 for the Rw, Mix, Alt, and Gw irrigations, respectively. Applying annual leaching irrigation (30 mm) before bud burst (spring) in the Mix and Alt with Rw and Gw scenarios was found to be the best strategy for managing irrigation-induced salinity in the root zone, lowering the EC_e to levels comparable to irrigation with Rw. Modeling scenarios suggested that judicious use of water resources and continuous root zone monitoring could be key for salinity management under adverse climate and low water allocation conditions. Full article

The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model uses the FAO56 dual crop coefficient and a daily time-step soil–water balance to simulate evapotranspiration and water content in the soil profile. This research calibrated the WINDS model for simulation of guayule under full irrigation. Using data from a furrow irrigated two-season guayule experiment in Arizona, this research developed segmented curves for guayule basal crop coefficient, canopy cover, crop height and root growth. The two-season guayule basal crop coefficient (K_cb) curve included first and second season development, midseason, late-season and end-season growth stages. For a fully irrigated guayule crop, the year one midseason K_cb was 1.14. The second year K_cb development phase began after the crop was semi-dormant during the first winter. The second year K_cb value was 1.23. The two-season root growth curve included a growth phase during the first season, no growth during winter, and a second growth phase during the second winter. A table allocated fractions of total transpiration to soil layers as a function of root depth. With the calibrated tables and curves, the WINDS model simulated soil moisture content with a root mean squared error (RMSE) of 1- to 3-% volumetric water content in seven soil layers compared with neutron probe water contents during the two-year growth cycle. Thus, this research developed growth curves and accurately simulated evapotranspiration and water content for a two-season guayule crop. Full article

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET₀) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates. Full article

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day⁻¹, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images. Full article

Citrus is one of the most valuable crops in Syria, with the largest production areas being in the coastal provinces of Tartus and Latakia, where this study was performed. A companion paper reported on the basal crop coefficients derived from the field water balance and on the performance assessment of various irrigation methods used in a citrus orchard located in the same region. That study evidenced the need for the improved management of irrigation water, mainly reducing water applications and increasing productivity, thus leading to the current research. The main objectives consisted of (i) providing a set of reliable basal (K_cb) and average (K_c) crop coefficients to be used in practice in the citrus orchards of the Syrian coastal area, while accounting for the diversity of characteristics observed; (ii) to estimate the seasonal consumptive use of typical orchards under different climate-demand and deficit-irrigation scenarios; and (iii) to assess possible water savings and related yield reductions. The previously calibrated water balance model SIMDualKc was used for these purposes. The computed K_cb values for the mid-season and average demand for water ranged from 0.52, when the plant density was low, to 0.84, when plant density was very high. The corresponding K_c values, which further reflected the impact of drip irrigation on controlling soil evaporation, were 0.72 and 0.97, respectively. Overall, the consumptive use of water was estimated to range from 867 to 1573 mm. The assessed water-saving scenarios consisted of adopting increased management-allowed depletion (MAD) thresholds relative to the p depletion fraction for no stress: MAD = 1.05, 1.10, 1.20, and 1.30 p. For trees under a very high climatic demand, water savings ranged from 12 to 34%, but the yield losses induced by the water deficits ranged from 8 to 48%. Although the selection of optimal strategies should be based upon economic terms, these may only be used when the Syrian economy recovers from civil war and the current crisis. The present results show the feasibility of adopting such MAD thresholds for building an irrigation management platform. The data provided by the current study are valuable because they can be efficiently used to support of the irrigation management of Syrian citrus production systems. Full article

Direct-seeded rice (DSR) has received much attention because of its advantages in having low labor costs compared to the traditional transplanted rice (TPR). Investigating the differences in evapotranspiration (ET) partitioning and crop coefficients (K_c) between DSR and TPR is essential in understanding how agricultural water demand is affected by crop rotation. In this study, the water fluxes of two-year (2017–2018) growing seasons were collected from a pair of eddy covariance (EC) towers for DSR and TPR in the Poyang Lake Basin, Southern China. This study aims to compare the seasonal characteristics of the ET components (evaporation, E, and transpiration, T) and dual crop coefficients (basal crop coefficient, K_s·K_cb, and soil/water crop coefficient, K_e) of DSR with those of TPR. The ET values for the 2017 and 2018 growing seasons were 374 mm and 436 mm for the DSR, respectively, and 309 mm and 342 mm for the TPR. The seasonal T/ET values in 2017 and 2018 were 0.40 and 0.46 for the DSR, respectively, and 0.49 and 0.52 for the TPR, indicating that the higher ET values for the DSR can be mostly attributed to E. The DSR had overall higher K_c values than the TPR because of free water evaporation during the initial stage and a higher plant density. Our results enrich the K_c dataset for DSR and have great implications for the sustainable irrigation of the Poyang Lake Basin in the future. Full article

Japanese plum (Prunus salicina) farming in the Western Cape (South Africa) is an important industry for the export market and job creation and is a large water user; however, adequate information on water requirements of this crop is not available in this semi-arid area. The objective of this study was to determine seasonal plum water requirements for the purpose of water use planning and allocation. The study made use of experimental data from four fully bearing, high-yielding plum orchards (cv African Delight and Fortune) in two major plum production regions (Robertson and Wellington). Crop water requirements and the soil water balance were modelled with the physically based HYDRUS-2D model. Seasonal crop water requirements were estimated to be between 524 mm (cv Fortune in Wellington) and 864 mm (cv African Delight in Robertson). Initial basal crop coefficients (Kcb) ranged between 0.98 and 1.01, whilst Kcb for the mid-stage averaged between 1.11 (cv African Delight in Robertson) and 1.18 (cv Fortune in Wellington). Modelling scenarios indicated that soil water redistribution beyond the root zone continues at reduced rates after the soil dries to levels below field capacity. Irrigation management needs to be balanced with other farming practices to reduce leaching and impacts on water resource quality, as well as with the economics of the farm. Full article

Accurate prediction of orchard evapotranspiration (ET) can optimize orchard water management. Based on the jujube (Zizyphus jujuba), ET was continuously measured from 2016 to 2019 using a large weighing lysimeter; the actual jujube ET was compared with the ET simulated with the Priestley–Taylor (P–T), Dual Crop Coefficient (Dual Kc), and Shuttleworth–Wallace (S–W) models, to verify the accuracy of the three models. The results showed that, from 2016 to 2019, the whole growth period of jujube ET was 532–592 mm and the crop coefficient was 0.85–0.93. The basal crop coefficients of the calibrated Dual Kc model were 0.4, 1.0, and 0.5 at the initial, middle, and ending growth stages, respectively. The overall simulation error of the Dual Kc model was low, and simulations were stable during the four years of the study. However, because of rough estimation the water stress coefficient (K_s) simulation accuracy will be reduced in the case of serious water shortage. The simulation error of the S–W model was greater than the simulation error of the Dual K_c model, and the simulations were unstable and vulnerable to interannual changes. The simulation error of the traditional P–T model was large. When the parameter “α” solution method was improved, the simulation accuracy was significantly improved, and the P–T model’s simulation accuracy was only slightly lower than that of the Dual Kc model. However, the model was easily affected by changes in net radiation and air temperature. Therefore, the Dual Kc model is recommended for estimating the ET of young jujube trees in arid areas. Full article

With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts. Full article