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The Crop Water Stress Index (CWSI) is a widely used method for quantifying crop water status and predicting yield. However, its evaluation across different irrigation methods and its stage‐specific response to crop yield is rarely evaluated. In this study, controlled field experiments were conducted on winter wheat using drip irrigation (DI) and flood irrigation (FI) during the 2021–2022 and 2022–2023 seasons in western Uttar Pradesh, India. The irrigation treatments included 50% MAD (maximum allowable depletion) (DI), 55% MAD (DI), 60% MAD (DI), 50% MAD (FI), local farmer's field replication (FI), rain‐fed, and well‐watered treatment (DI). The derived mean CWSI values for the irrigation treatments ranged from 0.03 to 0.66 in season 1 and 0.06 to 0.57 in season 2 across treatments. The seasonal mean CWSI for 50% MAD (DI) was 0.12 (season 1) and 0.11 (season 2), while 50% MAD (FI) yielded higher mean CWSI values of 0.29 (season 1) and 0.22 (season 2). The 50% MAD (DI) treatment produced the highest grain yield and water use efficiency in both seasons. A comprehensive analysis of stage‐specific CWSI values and grain yields revealed that grain yield was more sensitive to post‐heading CWSI as compared to pre‐heading CWSI values. Among the growth stages, CWSI values during the flowering stage were the most critical for predicting wheat yield. The study recommends that the CWSI values in the flowering and post‐heading stages are more relevant in predicting wheat yield accurately as compared to the pre‐heading and seasonal mean CWSI. [ABSTRACT FROM AUTHOR]

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Abstract Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package—including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station—attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

The crop water stress index (CWSI) is an important technique for determining stress levels in the plant and directing irrigation management techniques. To determine the CWSI for sunflower, a pot-based research trial was carried out in the research field of the Department of Soil and Water Engineering, PAU, Ludhiana (India) during the summer of 2023. The sensor-based irrigation scheduling was carried out on the basis of the depletion of total available soil moisture (TASM). The drip irrigation treatments consist of I1 (full irrigation), I2 (20% depletion of TASM), and I3 (40% depletion of TASM). The results revealed that the highest amount of water applied under I1 was recorded at 484.4 mm, while I2 (387.5 mm) and I3 (290.7 mm) during the growing season of sunflower. The results revealed that for kernel diameter and seed weight, I1 and I2 were statistically non-significant to each other while I1 and I3 were statistically significant to each other. The highest water productivity recorded for I2, followed by I3. The overall findings revealed that an average CWSI value of 0.85 for the sunflower crop falls within the range of lower and upper baselines. The response of physico-chemical properties of sunflower seeds showed a high correlation with the draught condition. HIGHLIGHTS Investigated crop water stress (CWSI) of sunflower under varying soil moisture depletion.; To develop lower and upper limits of baselines for fully irrigated and under stressed conditions.; To analyse physico-chemical changes of sunflower seed and oil.; To develop sensor-based irrigation scheduling for sunflower crops under deficit condition.; To analyse the water productivity of sunflower under various irrigation regimes.;

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R² values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds. [ABSTRACT FROM AUTHOR]

Upland cotton (Gossypium hirsutum L.) is increasingly being managed under subsurface drip irrigation (SDI) in the semiarid Texas High Plains (THP). Scheduling irrigation using a thermally based crop water stress index (CWSI) has the potential to overcome limitations of soil water-based methods and facilitate efficient use of water. The objectives of this study were to develop a CWSI calculation procedure using stability correction, determine an appropriate window for averaging CWSI during daytime hours, compare CWSI with soil water-based indicators of crop water stress, and evaluate methods for estimating crop evapotranspiration (ET) using measured canopy temperatures. Infrared thermometer measurements of canopy temperature and corresponding cotton water use derived from soil water measurements were acquired under two irrigation levels (100% and 33%) under SDI during three growing seasons. A theoretical approach was used to calculate the CWSI using the Businger-Dyer stability correction. Basing daily CWSI on short 1- or 2-h periods in the afternoon overestimated crop water stress. In contrast, CWSI averaged during daytime periods with 0.25-h solar radiation and air temperature exceeding 0.3 MJ m-2 and 24°C, respectively, was strongly correlated (R2=0.76) with the soil water depletion-based stress coefficient Ks with a plausible lower canopy resistance of 25 s m-1. The CWSI was weakly correlated (R2=0.48) to the fraction of plant available water, with the CWSI exhibiting the greater increase of these two indices in response to irrigation events. Using the proposed CWSI scaling at different locations for canopies with dissimilar cover fractions yielded satisfactory estimates of crop ET (RMSE=0.74 mm d-1) compared with soil water balance-calculated ET (ETswb). When summed over weekly intervals, energy balance predictions of 0.25-h crop transpiration inferred from canopy temperature measurements had an acceptable agreement with ETswb (RMSE=0.92 mm d-1). Employing the CWSI to trigger irrigation will require targeted field studies to evaluate thresholds and strategies to optimize water management. [ABSTRACT FROM AUTHOR]

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments. [ABSTRACT FROM AUTHOR]

Centella asiatica (Indian pennywort) is a green leafy vegetable containing centelloside' (triterpenoid), a key phytochemical component in traditional medicine. Being a glycophytic species, they exhibit decline in growth performance and yield traits when subjected to water-deficit (WD) conditions. Glycine betaine (GB) is a low molecular-weight organic metabolite that plays a crucial role in abiotic stress conditions in higher plants. The objective of this study was to investigate the potential of GB in alleviating water-deficit stress (in terms of morphological and physiological responses) in two different genotypes of Indian pennywort, "Nakhon Pathom" (NP; high centelloside-yielding genotype) and "Pathum Thani" (PT; low centelloside-yielding genotype). The genotypes of Indian pennywort were propagated by stolon cutting and transplanted into plastic bags containing 2 kg of garden soil. At the flower-initiation stage (30 days after transplantation), uniform plant material was treated exogenously with 0 (control), 25, and 50 mM GB at 100 mL per plant (one-time foliar spray) and then divided into two groups, 1) well watered (WW; irrigated daily with 400 mL fresh water; 98% field capacity) and 2) water deficit (WD; withheld water for 14 days; 72% field capacity). Foliar application of GB (25 mM) significantly improved leaf osmotic potential in NP under WD conditions via osmotic adjustment by free proline and fructose. Differences in leaf temperature (Tleaf) between WD and WW in NP were maximized (+ 1.93 °C) and the gap of Tleaf was reduced in the case of 25–50 mM GB application. Similarly, crop water stress index (CWSI) in NP and PT plants under WD condition was significantly increased by 1.95- and 1.86-fold over the control, respectively; however, it was significantly decreased by exogenous GB application. Increasing Tleaf and CWSI in drought-stressed plants was closely related to stomatal closure, leading to reduced gas exchange parameters, i.e., stomatal conductance (gs), transpiration rate (E), net photosynthetic rate (Pn), and intercellular CO2 concentration (Ci), and consequently decreased plant biomass and total centelloside yield. Overall physiological, morphological, and secondary metabolite traits were enhanced in NP under WD conditions using 25 mM GB exogenous application compared with the control. The study highlights the significance of GB in Indian pennywort production under limited water irrigation (water deficit) with higher vegetable yield and phytochemical stabilization. [ABSTRACT FROM AUTHOR]

The lower limit temperature in the crop water stress index (CWSI) model refers to the canopy temperature (Tc) or the canopy-air temperature differences (dT) under well-watered conditions, which has significant impacts on the accuracy of the model in quantifying plant water status. At present, the direct estimation of lower limit temperature based on data-driven method has been successfully used in crops, but its applicability has not been tested in forest ecosystems. We collected continuously and synchronously Tc and meteorological data in a Quercus variabilis plantation at the southern foot of Taihang Mountain to evaluate the feasibility of multiple linear regression model and BP neural network model for estimating the lower limit temperature and the accuracy of the CWSI indicating water status of the plantation. The results showed that, in the forest ecosystem without irrigation conditions, the lower limit temperature could be obtained by setting soil moisture as saturation in the multiple linear regression model and the BP neural network model with soil water content, wind speed, net radiation, vapor pressure deficit and air temperature as input parameters. Combining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured Tc and dT could realize the non-destructive, rapid, and automatic diagnosis of the water status of Q. variabilis plantation. Among them, the CWSI obtained by combining the lower limit temperature determined by the dT under well-watered condition calculated by the BP neural network model and the upper limit temperature was the most suitable for accurate monitoring water status of the plantation. The coefficient of determination, root mean square error, and index of agreement between the calculated CWSI and measured CWSI were 0.81, 0.08, and 0.90, respectively. This study could provide a reference method for efficient and accurate monitoring of forest ecosystem water status. [ABSTRACT FROM AUTHOR]

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat (Triticum aestivum L.) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling.

Safflower is known as a tolerant plant to abiotic stress factors. This study was conducted to introduce some physiological indices to improve drought-tolerant safflower genotypes for cultivation in marginal and arid areas. Six safflower genotypes were studied for two years (2017–2019) in the East Azarbaijan Agricultural and Natural Resources Research and Education Centre of Iran under non-stressed and low-water conditions from flowering to seed maturity. The occurrence of water deficits led to a significant decrease in relative water content (RWC), stomatal conductance (gs), osmotic adjustment (Oadj), water potential (WP) and agronomic water use efficiency (WUEa) and an increase in the water stress index (CWSI). In addition, the values of these traits differed significantly between the safflower genotypes. The correlations between the physiological traits and seed yield were significant. The regression relationships between seed yield and the above traits showed that CWSI, WP and WUEa had a strong relationship with seed yield under normal (R2 = 0.854, 0.801 and 0.856, respectively) and water deficit conditions (R2 = 0.931, 0.877 and 0.900, respectively). It can be concluded that the CWSI, WP and WUEa indices are able to select high-yielding and drought-tolerant safflower genotypes for the late season. Among the components of seed yield, the number of capitula per plant (r = 0.86) and seeds per capitula (r = 0.92), which were positively and significantly correlated with seed yield, played the main roles in the formation of seed yield. The Golemehr and Mec.295 genotypes achieved higher seed yields under normal (4676 and 4961 kg h−1, respectively) and water deficit conditions (3211 and 3385 kg h−1, respectively) and can be recommended for cultivation in marginal and arid areas. [ABSTRACT FROM AUTHOR]

Sugar beet is an economically important crop in the semi-arid Intermountain Western U.S., with seasonal water use ranging from 500 to 900 mm. Sugar beet is a deep-rooted crop (1.5-2 m) in unrestricted soil profiles that can utilize stored soil water to reduce seasonal irrigation requirements. Effective use of stored soil water below 0.6 m requires precise irrigation scheduling and knowledge of soil water availability below 0.6 m, which is usually unknown due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Deficit irrigation (DI) management of sugar beet using a thermal-based crop water stress index (CWSI) has the potential to overcome soil water monitoring limitations and facilitate the utilization of stored soil water to reduce seasonal irrigation requirements. The objective of the research summarized in this paper was to implement and evaluate the effect of automated DI scheduling of sugar beet using three daily average CWSI thresholds (0.2, 0.35, and 0.55) on seasonal irrigation requirement, crop evapotranspiration, seasonal soil water depletion, root yield, estimated recoverable sugar (ERS) yield, and water use efficiency compared to full irrigation. There were no significant differences in root and ERS yield between full irrigation and 0.2 CWSI DI treatment, while seasonal ET was significantly decreased, seasonal soil water extraction was significantly increased, and seasonal irrigation depths were reduced from 133 to 185 mm. Root and ERS yield water production functions were curvilinear with a downward concave. Root and ERS yield water use efficiencies were constant or increased slightly for crop evapotranspiration reductions up to 85% of full irrigation evapotranspiration. The results indicate that irrigating when the average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. [ABSTRACT FROM AUTHOR]

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μ m o l   C O 2 · m − 2 · s − 1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI. [ABSTRACT FROM AUTHOR]

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of ‘Calatina’ olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

Evaluation of the genotypes of an agronomic or horticultural crop for their drought tolerance ability is of utmost importance to ensure sustainable production and food security. Although a number of okra [Abelmoschus esculentus (L.) Moench] genotypes are commonly grown in Thailand, most of which have not been evaluated for their drought tolerance ability. The present study evaluated 17 of the most popular okra genotypes for their drought tolerance ability where plants were subjected to two water regimes of well–watered (WW) and water–deficit (WD; withholding irrigation for 10 days) at the vegetative stage. A significant variation among genotypes was observed in terms of growth and physio–biochemical response (15–70% reduction in shoot dry matter at WD compared with WW for different genotypes, 45% average reduction in membrane stability index for all genotypes, 62–89% reduction in net photosynthetic rate at WD compared with WW for different genotypes, and 7–146% increase in crop water stress index at WD compared with WW for different genotypes). Based on Ward's multivariate cluster analyses of growth and physio–biochemical traits, the tested genotypes were classified into drought tolerant, moderately drought tolerant, and drought sensitive ones. Among the 17 tested genotypes, five genotypes (Four Compass 443, Green Nam Tao, Dark Green Okra No.5, Maejo 70, and Jusco) were found drought tolerant, six genotypes (Dow Morakot, King Star, Queen Star, Hisao, Bhalsar, and Maejo 49) were found moderately drought tolerant, while the remaining six genotypes (Best Green 5, TVRC 064, River Green No.1, Taratip, Lucky Five 473, and Chia Tai) were found drought sensitive. This study provides a base for okra growers to select the most feasible genotype for their areas based on the condition of irrigation water availability. The findings will also serve as firsthand information for the plant breeders to screen the newly–released genotypes for their drought tolerance ability. [ABSTRACT FROM AUTHOR]

The availability of field experimental data plays a pivotal role in advancing agricultural research, particularly in the Mediterranean, where farmers face significant challenges due to water scarcity and changing climatic conditions. We present a multi-year homogenized dataset of agro-physiological traits collected on industrial tomatoes and focused on the effect of deficit irrigation (DI). The dataset has been compiled over nine years and comprises 100 experimental plots, where 32 DI strategies have been tested. Visual observations on tomato phenology and qualitative and quantitative production data have been collected in field and laboratory surveys, complemented with detailed information on pedo-climatic conditions and irrigation scheduling (timing and volume). Researchers can find in this dataset a rich source for calibrating and evaluating agro-physiological models and a reference basis to study the relationships between DI strategies, weather variability, and the performance of tomato growing systems. Agronomists from the public and private sectors can gain domain knowledge to support local farmers with the best DI strategies to achieve high yields while optimizing water use. Moreover, this dataset serves as ground truth for digital decision support systems, which need real-world data to enhance their accuracy in guiding farmers on efficient water use. This data source is intended to become a crucial asset for researchers, agronomists, and decision-makers in the Mediterranean as it bridges the gap between research and practice in an area where farmers are already striving with water scarcity for industrial tomato cultivation.

Accurate assessment and monitoring of plant water stress are essential for optimizing irrigation strategies, improving water use efficiency. This article explores the multifaceted issue of water stress, encompassing both agricultural and environmental contexts. It emphasizes the pivotal role of precise water stress detection in effectively managing water resources and fostering sustainable agricultural practices. The primary focus is on the progression of sensors designed specifically to detect water stress, with particular attention given to two approaches: Vapor Pressure Deficit (VPD) and Crop Water Stress Index (CWSI). The article thoroughly investigates the underlying principles, operational mechanisms, advantages, and limitations of these sensor technologies. It vividly showcases their wide-ranging applications across agriculture, horticulture, and environmental monitoring, elucidating their significance in each domain. Moreover, it delves into the integration of VPD and CWSI sensors and introduces emerging technologies like thermal imaging and chlorophyll fluorescence sensors, expanding the horizon of water stress detection methodologies. Addressing the challenges linked to calibration and data interpretation, the article proposes potential pathways for future research endeavors. In essence, the overarching goal of this article is to propel the development of advanced sensor technologies, ultimately facilitating precise water stress detection. It aims to bolster sustainable water resource management practices while fortifying resilient agricultural methods in the face of evolving environmental challenges. VPD and CWSI-based approaches offer precise water stress insights in agriculture, aiding irrigation management.

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments.

Frequent monitoring of crop moisture levels can significantly improve crop production efficiency and optimise water resource utilisation. The aim of the present study was to generate moisture status maps using thermal infrared imagery, centring on the development of a predictive model for the cotton leaf water potential. The model was constructed using particle swarm optimisation (PSO) in conjunction with the least squares support vector machine (LS-SVM). Traditional SVM models suffer from high computational complexity, long training times, and inequality constraints in predicting leaf water potential. To address such issues, the PSO algorithm was introduced to improve the performance of the LS-SVM model. The PSO-optimised LS-SVM model exhibited notable improvements in performance when evaluated on two distinct test datasets (Alaer and Tumushuke). The research results indicate that the predictive accuracy of the PSO-LS-SVM model significantly improved, as evidenced by an increase of 0.05 and 0.04 in the R 2 values, both of which reached 0.95. This improvement is reflected in the corresponding RMSE values, which were reduced to 0.100 and 0.103. Furthermore, a model was established based on data from three cotton growth stages, achieving high predictive accuracy even with fewer training samples. By using the PSO-LS-SVM model to predict leaf water potential information, the predicted data were mapped onto drone images, enabling the transformation of the leaf water potential from a point to an area. The present findings contribute to a more comprehensive understanding of the cotton leaf water potential by visually representing the spatial distribution of crop water status on a large scale. The results hold substantial significance for the improvement of crop irrigation management. [ABSTRACT FROM AUTHOR]

Accurate assessment and monitoring of plant water stress are essential for optimizing irrigation strategies, improving water use efficiency. This article explores the multifaceted issue of water stress, encompassing both agricultural and environmental contexts. It emphasizes the pivotal role of precise water stress detection in effectively managing water resources and fostering sustainable agricultural practices. The primary focus is on the progression of sensors designed specifically to detect water stress, with particular attention given to two approaches: Vapor Pressure Deficit (VPD) and Crop Water Stress Index (CWSI). The article thoroughly investigates the underlying principles, operational mechanisms, advantages, and limitations of these sensor technologies. It vividly showcases their wideranging applications across agriculture, horticulture, and environmental monitoring, elucidating their significance in each domain. Moreover, it delves into the integration of VPD and CWSI sensors and introduces emerging technologies like thermal imaging and chlorophyll fluorescence sensors, expanding the horizon of water stress detection methodologies. Addressing the challenges linked to calibration and data interpretation, the article proposes potential pathways for future research endeavors. In essence, the overarching goal of this article is to propel the development of advanced sensor technologies, ultimately facilitating precise water stress detection. It aims to bolster sustainable water resource management practices while fortifying resilient agricultural methods in the face of evolving environmental challenges. VPD and CWSI-based approaches offer precise water stress insights in agriculture, aiding irrigation management. [ABSTRACT FROM AUTHOR]

Thermal infrared imaging can be expected to rapidly and cost-saving monitor the soil moisture content in the largescale farmland. But it is still unclear on the impact of lighting conditions on thermal infrared images. This study aims to explore the impact of direct sunlight or shadow occlusion of the crop canopy and soil on the UAV thermal infrared remote sensing, in order to diagnose the crop water stress and monitor soil moisture content. Summer corn with different irrigation treatments was taken as the research object. The thermal infrared images were divided into four parts: illuminated canopy, shaded canopy, illuminated soil, and shaded soil. The light temperature was extracted from the higher temperature, whereas, the shadow temperature was assumed as the lower temperature. Temperature extraction was used to calculate the 11:00, 13:00, and 15:00 canopy temperature difference (difference between canopy temperature and atmospheric temperature, ΔT), crop water stress index (CWSI), and evaporative fraction (ratio of latent heat flux to effective energy, EF). A comparison was made on three changes in the monitoring effect of the index on soil moisture content after using light temperature (ΔTL, CWSIL, EFL) and shadow temperature (ΔTS, CWSIS, EFS ) at different times. The results show that: 1) There was the variation in the monitoring effects of the three indices over time. The EF monitoring effect was better at 11:00 and 15:00, the crop water stress index monitoring effect was better at 13:00. There was the less change in the ΔT monitoring over time. The temperature index should be selected for monitoring soil moisture content using field conditions and the time of flying the drone; 2) The monitoring effect was improved the most at 11:00 in the jointing period, after distinguishing light temperature and shadow temperature. The R² of EF, EFS, and EFL were 0.54, 0.65, and 0.78, respectively. The R² of CWSI, CWSIS, and CWSIL were 0.47, 0.64, and 0.70, respectively. There was no significant light temperature in the period of tasseling and filling, whereas, the index monitoring effect was significantly reduced using shadow temperature. There was the largest decrease in the CWSIS at 13:00, compared with the CWSI, where the R² decreases were 0.11 and 0.06, respectively. Therefore, it was very necessary to choose the clear and cloudless weather and avoid cloudy days, when shooting thermal infrared images. Furthermore, the impact of lighting conditions on thermal infrared images was also change with the growth period; 3) The best monitoring soil moisture content was obtained using EFL at 11:00 in the jointing and tasseling period, and CWSI at 13:00 in the filling period, where the R² of predicting soil moisture content were 0.75, 0.75, and 0.89, respectively. In addition, the soil moisture content was also dominated the monitoring effect. When the soil volumetric moisture content was around 0.23, both EFL and CWSI shared the more accurate prediction. Different time points, light temperature and shade temperature were utilized to analyze the monitoring effect of three temperature indexes on soil moisture content. The better monitoring time and index were determined in the three growth periods of summer corn. The finding can provide a strong reference for the UAV thermal infrared monitoring of soil moisture content. [ABSTRACT FROM AUTHOR]

The crop water stress index is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN) are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, and canopy temperature). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. For the lower CWSI baseline of rice, a linear relationship between air and canopy temperature difference (Tc-Ta) and, air vapour pressure deficit (AVPD) was developed, whereas air temperature plus 3°C was taken for the upper CWSI baselines. The performance of SOM and FF-BP-ANN were compared by computing Nash–Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R2). It is found that FF-BP-ANN (R2 = 0.97, NSE = 0.92, d = 0.95, RMSE = 0.006) performs better than SOM (R2 = 0.88, NSE = 0.87, d = 0.9, RMSE = 0.075). [ABSTRACT FROM AUTHOR]

Identifying the irrigation-induced cooling effects from a particular plant species used for urban groundcovers while optimizing the rates of irrigation applications is important in regions with hot and dry summers. A 2-year (2020–21) study was conducted in Riverside, CA, USA, to evaluate the effect of irrigation rates on the canopy temperature dynamics of 10 urban groundcovers. Four reference evapotranspiration (ETo)-based irrigation treatments (20%, 40%, 60%, and 80% ETo) and 10 groundcovers were laid in a randomized complete block design and replicated three times. The effect of irrigation rates on the difference between canopy–air temperature (ΔT), leaf area index (LAI), and stomatal conductance (gs) were evaluated. All response variables were collected between May and October 2020 and 2021. The crop water stress index for five groundcovers was also computed. The ΔT was affected (P < 0.05) by irrigation rates, and groundcovers, including Rhagodia spinescens and Baccharis × ‘Starn Thompson’, maintained the canopy temperature less than the ambient air temperature for all irrigation rates imposed. For most of the groundcovers, the ΔT yielded a strong relationship with LAI (r = –0.41 to –0.73), and gs (r = –0.35 to –0.60). Crop water stress index also showed a strong correlation to normalized difference vegetation index (r = 0.42 to –0.72) and gs (r = –0.57 to –0.64). Irrigation-included cooling was evident in most groundcovers irrigated at higher rates; however, Rhagodia spinescens and Baccharis × ‘Starn Thompson’ were found to perform well in cooling ability and maintaining the canopy growth as evidenced by LAI. Our study showed that proper plant selection and irrigation management could help maintain green spaces and mitigate the urban heat island effect while conserving irrigation water. [ABSTRACT FROM AUTHOR]

Featured Application: Integrating infrared thermography with olive tree traits and their stressors enhances centennial olive tree monitoring and management in Mediterranean traditional agroecosystems. Centennial olive trees, integral components of traditional Mediterranean agroecosystems, hold immense value as repositories of biodiversity and cultural heritage due to their physiological attributes and life history, making them crucial for the conservation of High Nature Value ancient olive orchards. However, they are increasingly confronted with physiological challenges exacerbated by various biotic and abiotic stressors jeopardizing their health and productivity, underscoring the urgency for ongoing monitoring and conservation measures to secure their long-term existence. To monitor these challenges, in recent years, the adoption of non-invasive techniques like infrared thermography (IRT) has become prevalent. In this study, we aimed to comprehensively assess the health state of traditional centennial olive trees, with a particular focus on their productivity. To achieve this, we monitored 44 centennial olive trees from a traditional olive grove on the island of Naxos, Greece, a representative location for Mediterranean olive groves, during the period from 2017 to 2020. We established connections between a set of trunk and canopy thermal and humidity indices, phenotypic traits, and the two most prevalent stressors affecting olive trees not only within the context of the island but also more broadly in similar Mediterranean environments worldwide: the olive leaf spot disease (OLS) and crop water stress, assessed through the crop water stress index (CWSI). To evaluate their interrelationships, we initially assessed intraspecific thermal and humidity pattern variations, and we developed linear and logistic regression models to gain insights into the factors influencing olive tree productivity, water stress, and the OLS presence. Results indicated that combining thermal and humidity indices can substantially explain olive tree productivity, water stress, and OLS, providing a valuable tool for assessing and monitoring the health and overall state of centennial olive trees, while offering a comprehensive approach to understanding the complex interactions shaping traditional olive grove dynamics. By identifying key indicators such as tree thermal patterns and water stress levels, olive growers and conservationists can make informed decisions to enhance the vitality and longevity of these culturally and ecologically significant trees. [ABSTRACT FROM AUTHOR]

Safflower is known as a tolerant plant to abiotic stress factors. This study was conducted to introduce some physiological indices to improve drought-tolerant safflower genotypes for cultivation in marginal and arid areas. Six safflower genotypes were studied for two years (2017–2019) in the East Azarbaijan Agricultural and Natural Resources Research and Education Centre of Iran under non-stressed and low-water conditions from flowering to seed maturity. The occurrence of water deficits led to a significant decrease in relative water content (RWC), stomatal conductance (gs), osmotic adjustment (Oadj), water potential (WP) and agronomic water use efficiency (WUEa) and an increase in the water stress index (CWSI). In addition, the values of these traits differed significantly between the safflower genotypes. The correlations between the physiological traits and seed yield were significant. The regression relationships between seed yield and the above traits showed that CWSI, WP and WUEa had a strong relationship with seed yield under normal (R2 = 0.854, 0.801 and 0.856, respectively) and water deficit conditions (R2 = 0.931, 0.877 and 0.900, respectively). It can be concluded that the CWSI, WP and WUEa indices are able to select high-yielding and drought-tolerant safflower genotypes for the late season. Among the components of seed yield, the number of capitula per plant (r = 0.86) and seeds per capitula (r = 0.92), which were positively and significantly correlated with seed yield, played the main roles in the formation of seed yield. The Golemehr and Mec.295 genotypes achieved higher seed yields under normal (4676 and 4961 kg h−1, respectively) and water deficit conditions (3211 and 3385 kg h−1, respectively) and can be recommended for cultivation in marginal and arid areas.

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

The main limitation of conventional methods generally used for monitoring the crop water stress lies in the expenditure of time and laboriousness (e.g., stem water potential, Ψstem). In this sense, infrared thermography can assist to identify the crop water status for precise irrigation purposes. However, this method requires high cost equipment and heavy systems for real-time analysis of the results. The aim of this research was to evaluate the reliability of a portable low-cost sensor (FLIR One Pro), connectable to a smartphone, for determining the water status of orange trees subjected to different treatments (i.e., full irrigation versus regulated deficit irrigation with or without soil mulching). The thermal information obtained from FLIR One Pro was compared with the data acquired with a professional thermal camera (Optris Xi 400) for two consecutive years (2021–2022). In addition, the reliability of the low-cost sensor was assessed, in respect to the loss of accuracy due to the sensor’s price reduction, in identifying the crop water stress index (CWSI) and the upper and lower baselines. A good agreement was obtained between the canopy temperature (Tc) and the references leaves temperatures (dry leaf, Td; and wet leaf, Tw) provided by both sensors, resulting in coefficient of determination (R2) of 0.89, 0.82 and 0.75, respectively. The CWSI comparing the two sensors provided a R2 of 0.75. No influence of the agrometeorological conditions and overheating of the low-cost sensor on the Tc was found, however it is advisable to keep the low-cost sensor under homogeneous weather conditions during the acquisition process. No correlation was found between the CWSI and the Ψstem, due to the isohydric behavior of citrus species. Finally, this study opens new insights for spreading the use of low-cost thermal sensors for speeding up the crop water status monitoring under field conditions and supporting the adoption of precision irrigation criteria.

Improving soil fertility by adopting several sustainable management practices that are a prime concern for plant growth. Organic waste addition to agricultural soil is becoming a common practice that improves physical, chemical and hydrodynamic properties and minimizes environmental impact. The use of some alternative nutrition sources as biochar and compost has been tested on two-year-old olive varieties 'Chemlali' and 'Koroneiki.' Three treatments were established "Cont" soil didn't amended, "Comp" soil amended with Compost (90% soil+10% compost), and "Bioc" soil amended with biochar (90% soil+10% biochar). All treatments were conducted under deficit irrigation receiving 50% of daily evapotranspiration. We evaluated soil–plant water status and relationships between all studied parameters. Results showed that soil water content was the lowest in non-amended treatments and did not exceed 10%. During experiment, compost amendment induced a better water plant status for both studied varieties less than −2 MPa for mean seasonal leaf water potential, while the biochar amendment induced a weak stress level indicated by the lower potential that reached a minimum value equal to −2.9 MPa. Crop Water Stress Index on biochar-amended soil was more important than that measured on other treatments which confirms the sensitivity of biochar treatment to hard conditions. High negative relationships have been found between soil water statuses and plant water statuses. The highest determination coefficient was between crop water stresss index and leaf water potential was about 0.85. In conclusion, the use of organic amendment is an approach for sustainable olive orchards under low water avaibility. [ABSTRACT FROM AUTHOR]

Crop water stress indices (CWSIs) quantify plant water status based on measurement of plant temperature. The goal of CWSI formulation is to normalize measured leaf temperatures based on reference temperatures to remove sensitivity to ambient environmental conditions (e.g., air temperature, humidity, radiation), while retaining sensitivity to plant water status as reflected by stomatal conductance. This study sought to better understand the sensitivity of these temperatures to ambient environmental conditions, and ultimately how they influence various CWSIs. The surface energy balance was modeled to simulate the impacts of input parameter variation on leaf temperature and reference surface temperatures used to calculate four different CWSIs. The performance of the CWSIs were assessed based on their ability to maximize sensitivity to stomatal conductance while minimizing the relative sensitivity to ambient environmental conditions. The sensitivity analyses indicated that all four CWSIs performed poorly in shaded conditions, as they had relatively low sensitivity to stomatal conductance and were sensitive to all environmental parameters. Two CWSIs had high sensitivity to stomatal conductance, and low sensitivity to all environmental parameters except wind speed. None of CWSIs could remove sensitivity to all environmental parameters while retaining sensitivity to stomatal conductance.

Crop water stress indices (CWSIs) quantify plant water status based on measurement of plant temperature. The goal of CWSI formulation is to normalize measured leaf temperatures based on reference temperatures to remove sensitivity to ambient environmental conditions (e.g., air temperature, humidity, radiation), while retaining sensitivity to plant water status as reflected by stomatal conductance. This study sought to better understand the sensitivity of these temperatures to ambient environmental conditions, and ultimately how they influence various CWSIs. The surface energy balance was modeled to simulate the impacts of input parameter variation on leaf temperature and reference surface temperatures used to calculate four different CWSIs. The performance of the CWSIs were assessed based on their ability to maximize sensitivity to stomatal conductance while minimizing the relative sensitivity to ambient environmental conditions. The sensitivity analyses indicated that all four CWSIs performed poorly in shaded conditions, as they had relatively low sensitivity to stomatal conductance and were sensitive to all environmental parameters. Two CWSIs had high sensitivity to stomatal conductance, and low sensitivity to all environmental parameters except wind speed. None of CWSIs could remove sensitivity to all environmental parameters while retaining sensitivity to stomatal conductance.

In order to promote the sustainable development of agriculture and ecological environment in the Loess Plateau, based on MODIS evapotranspiration products and meteorological station data from 2001 to 2020, the characteristics of drought change and its influencing factors in the Loess Plateau were discussed by using crop water stress index ( CWSI), Theil-Sen Median trend analysis, Mann-Kendall test and partial correlation analysis. The results show these as follows. Interannually, the CWSI of the Loess Plateau showed a significant linear decline process from 2001 to 2020, and the decline rate was 0. 0058 ·a -1 (P < 0. 01). The degree of drought decreased first (2001—2018) and then increased (2018—2020). Spatially, the CWSI was dominated by a decreasing trend, with a significant decreasing trend accounting for 91. 24% of the total area of the region. Among them, the CWSI in Qinghai, Gansu, Ningxia and southern Shaanxi decreased faster. The negative partial correlation between CWSI and temperature was dominant in the Loess Plateau, and the significant negative partial correlation accounted for 7. 78% of the regional area, which was concentrated in the eastern Qinghai, southern Shanxi and northern Henan. There is a negative partial correlation with precipitation, which accounts for 60. 88% of the total area and is concentrated in the southwest and east of the Loess Plateau. Under the background of rising temperature, the increase of precipitation promoted the significant increase of ET(evapotranspiration) and insignificant decrease of PET(potential evapotranspiration) in the Loess Plateau, resulting in a significant decrease of CWSI. It is concluded that precipitation is the main factor affecting the decrease of CWSI in the Loess Plateau, and the significant increase of annual precipitation slows down the drought trend in the Loess Plateau. [ABSTRACT FROM AUTHOR]

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Identifying the irrigation-induced cooling effects from a particular plant species used for urban groundcovers while optimizing the rates of irrigation applications is important in regions with hot and dry summers. A 2-year (2020–21) study was conducted in Riverside, CA, USA, to evaluate the effect of irrigation rates on the canopy temperature dynamics of 10 urban groundcovers. Four reference evapotranspiration (ETo)-based irrigation treatments (20%, 40%, 60%, and 80% ETo) and 10 groundcovers were laid in a randomized complete block design and replicated three times. The effect of irrigation rates on the difference between canopy–air temperature (ΔT), leaf area index (LAI), and stomatal conductance (gs) were evaluated. All response variables were collected between May and October 2020 and 2021. The crop water stress index for five groundcovers was also computed. The ΔT was affected (P < 0.05) by irrigation rates, and groundcovers, including Rhagodia spinescens and Baccharis × ‘Starn Thompson’, maintained the canopy temperature less than the ambient air temperature for all irrigation rates imposed. For most of the groundcovers, the ΔT yielded a strong relationship with LAI (r = –0.41 to –0.73), and gs (r = –0.35 to –0.60). Crop water stress index also showed a strong correlation to normalized difference vegetation index (r = 0.42 to –0.72) and gs (r = –0.57 to –0.64). Irrigation-included cooling was evident in most groundcovers irrigated at higher rates; however, Rhagodia spinescens and Baccharis × ‘Starn Thompson’ were found to perform well in cooling ability and maintaining the canopy growth as evidenced by LAI. Our study showed that proper plant selection and irrigation management could help maintain green spaces and mitigate the urban heat island effect while conserving irrigation water.

The production of fruits and vegetables in a protected environment (e.g., greenhouse) and open field mainly depends on a substantial water supply. Water stress is the most common plant stress, significantly affecting the quality and yield of crops, especially in organic plant cultivation and arid regions. An automated and well-scheduled irrigation system that corresponds to the actual water demand helps reduce the consumption of this limited resource without negatively affecting crop productivity. This study outlines the design, construction, and functionality of an autonomous multi-sensor monitoring system for real-time water stress detection and precisely targeted irrigation called PLANTSENS. The developed prototype combines the absorption-based and the temperature-based measuring method by using three optical sensors operating in the near-infrared, shortwave, and longwave (thermal) infrared spectrum. The camera modules are mounted on a moving rail-based platform with an integrated positioning system. Appropriate networking and cloud environment with a central server for storing, analyzing, and visualizing the acquired data was created. PLANTSENS monitoring system has been successfully implemented and trained on tomato plants in a greenhouse. The algorithm for scheduling irrigation time has been developed by merging two water stress detection algorithms, namely the Water Index (WI) and the Crop Water Stress Index (CWSI), thereby improving the system's capability and accuracy. WI determines the plant stress from the reflection differences at 1450 and 1300 nm, where water absorbs strongly and weakly, respectively. CWSI, on the other hand, uses the leaf temperature, which tends to increase during water stress due to the lower transpiration rate caused by closed stomata. This work presents the performance of investigated measuring methods and developed algorithms based on the system approach from July 2020 through August 2020. The obtained irrigation time has been verified in accordance with observed changes in the plant's physiological response, such as vapor conductivity, validating this approach as promising and confirming water stress as a significant indicator in irrigation scheduling.

The goal of irrigation for wine grape grown in arid or semiarid regions is to sustain vine survival and to optimize berry attributes for quality wine production. Precision irrigation of wine grape is impeded by the lack of a smart, decision support system (DSS) to remotely monitor vine water status. The objectives of this study were to: develop and field test an Internet of Things (IoT) DSS system for precision irrigation of wine grape. The IoT system was comprised of a suite of in situ sensors used to monitor real-time weather conditions, grapevine canopy temperature, soil moisture, and irrigation amount. Sensor data were collected and stored on a field deployed data logger that calculated a daily thermal Crop Water Stress Index (CWSI) for grapevine using a neural network model with real-time sensor data model inputs. The data logger also hosted, via a cellular modem, webpages showing a running, 12-day history of daily CWSI, fraction of available soil moisture (fASW), irrigation amount, and other sensor data. The webpages were accessible to vineyard managers via cell phone or computer. The CWSI based IoT DDS system was installed at two small acreage, commercial estate vineyards in southwestern Idaho USA over four growing seasons. At each vineyard site, the DSS was used daily by the vineyard irrigation manager to schedule irrigation events. Neither vineyard manager used any other quantitative vine water status monitoring tool for irrigation management decisions. The midday leaf water potential (LWP) of grapevines was routinely measured by research project personnel. Data collected over the study period at each vineyard showed a significant (p < 0.001) correlation with LWP and fASW, providing evidence that, under the conditions of this study, the daily CWSI based IoT provided automated, remote monitoring of vine water status. Both vineyard managers reported daily use of the DSS for irrigation scheduling decisions. Over the four-year study, each vineyard manager was able to maintain consistent seasonal average CWSI daily values and irrigation application amounts, despite yearly differences in climatic conditions. The results of this study demonstrate that a CWSI based IoT DSS can be used for precision irrigation of wine grape in a commercial vineyard under semiarid growing conditions. The CWSI based IoT DSS used a unique combination of neural network modeling, edge computing, and IoT for real-time vine water stress monitoring for precision irrigation.

Field experiments were conducted in 2019 and 2021 growing seasons to evaluate the chlorophyll readings and crop water stress index (CWSI) response to full and deficit irrigation for drip-irrigated sugar beet (Beta vulgaris L.) under sub-humid climate of Bursa, Turkey. In addition, the changes of soil water content under different irrigation treatments and statistical relationships between chlorophyll and CWSI values and ETc, root yield and sugar yield were investigated. Experiments were carried out in a completely randomized blocks design with three replications. Irrigations were scheduled based on the replenishment of 100 (S1), 66 (S2), 33 (S3), and 0% (S4) of soil water depletion within the soil profile of 0–90 cm using 7 day irrigation intervals. Lower and upper baselines obtained by measurements based on the canopy temperature from the treatments full irrigated and non-irrigated were used to calculate CWSI. The variations in CWSI values were consistent with the variations of seasonal soil water contents induced by the different irrigation practices. CWSI values generally varied between 0 and 1 throughout the experimental periods. In 2019, seasonal mean chlorophyll readings varied between 203.3 and 249.1, and mean CWSI values varied between 0.12 and 0.85. In 2021, seasonal mean chlorophyll readings varied between 232.7 and 259.3 and mean CWSI values between 0.19 and 0.89. Unlike chlorophyll values, CWSI decreased with increased irrigation water amount. In both years, statistically significant relationships were determined between chlorophyll readings and CWSI and ETc, root yield and sugar yield. The greatest root yield was achieved with a seasonal mean CWSI value of 0.12. An exponential equation determined as "Root Yield = 10.804e−1,55CWSI" between seasonal average CWSI values and root yield can be used for estimation of root yield in sugar beet farming. The mean CWSI values determined by infrared thermometer technique can be used in determination of crop water stress and irrigation scheduling of sugar beet cultivation under sub-humid climatic conditions. [ABSTRACT FROM AUTHOR]

Canopy temperature is one of the most important parameters to calculate the crop water stress index（CWSI）.Among them,the unmanned aerial vehicles（UAVs） with thermal infrared remote sensing function have been widely used to extract the crop canopy temperature in recent years.The monitoring accuracy of crop water can be improved to accurately remove the soil background in the thermal infrared images.In this study,an improved method was proposed to monitor the CWSI using UVA remote sensing.The research object was firstly selected as the summer maize at the jointing stage.Four water treatments were then carried out:T1（50% of the field capacity）,T2（65% of the field capacity）,T3（80% of the field capacity）,and T4（95%-100% of the field capacity）.The spatial distribution of surface maize canopy temperature in the study area was extracted by the red green ratio index（RGRI） with the visible and thermal infrared images from UAVs.Specifically,the UAV images were selected as 5 days in 2020 and 7 days in 2021.The cumulative frequency was then analyzed for the canopy temperature on each thermal infrared image.Soil water content data at a depth of 40cm was also collected at the same time.Subsequently,two methods were proposed to improve the performance of CWSI.The accuracy was evaluated using the correlation between the CWSI and crop physiological indexes（Stomatal conductance,Gs;Net photosynthetic rate,Pn;and Transpiration rate,Tr） with the soil moisture content.Method 1,the canopy temperature was segmented using different statistical quantiles in the normal distribution.Some unreasonable values were then eliminated for the low and high temperature in the histogram of canopy temperature frequency.The commonly-used statistical quantiles were 93.32%,84.13%,69.15%,61.79%,50%,38.21%,30.85%,and15.87%,respectively.The intervals 0.62% and 2.28% were used to eliminate the high temperature values on the right side of the frequency histogram,and then the CWSI(denoted as CWSITcF%) was calculated from the average canopy temperature over different statistical quantiles.The statistical quantiles with the best correlation with the crop physiological indexes were selected as the most sensitive canopy temperature interval.In Method 2,maize canopy data was divided into four parts:Interval Ⅰ,Tc ＜40,Var＜10;Interval Ⅱ,Tc ＜40,10＜Var＜20;Interval Ⅲ,35＜Tc ＜45,Var＞20;and Interval Ⅳ,40＜Tc＜50,0＜Var＜20,according to the variance of canopy temperature.The most sensitive statistical quantile on the respective interval was then selected to calculate the CWSI（denoted as CWSIn）The results showed that:1) The coefficients R² of CWSIn and crop physiological indexes（Gs,Pn,Tr） calculated from 2020 and 2021 were 0.72,0.52 and 0.62,and normalized root mean square error（nRMSE） were 23.96%,24.06% and 25.60%.The accuracy of model fitting was higher than that of original CWSI（R2 were 0.73,0.34,0.46,nRMSE were 23.69%,28.27%,30.21%）,but little different from CWSITcF%（R² were 0.74,0.54,0.61,nRMSE were 22.87%,23.74% and 25.61%2) The most sensitive canopy temperature range varied greatly between years（2020,61.17%;2021,49.38%;and Two-year data,83.51%）,although the CWSITcF% was provided a higher accuracy of crop water stress.There was also some influence on the application of CWSITcF% in practice.The CWSIn presented a higher stability for the crop physiological indexes（The nRMSE between physiological indicators were 16.60%,27.37% and 28.49% in 2020 and 21.60%,18.95%,22.64% in2021）Therefore,the improved CWSIn can be expected to more accurately monitor the crop water stress under UAVs remote sensing,compared with the original. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes. [ABSTRACT FROM AUTHOR]

Water management is becoming a critical issue for sustainable agriculture, especially in the semi-arid region, where problems with water scarcity are rising. More accurate water status recovery in crops is required for precise irrigation through remote sensing technologies. These technologies have a lot of potential in intelligent irrigation because they allow for real-time environmental data collection. Nowadays, digital practices have been used, such as unmanned aerial vehicle (UAV), which plays an essential role in various applications related to crop management. Drones offer an exciting opportunity to track crop fields with high spatial and temporal resolution remote sensing to enhance water stress management in irrigation. Farmers have historically depended on soil moisture measurements and weather conditions to detect crop water status for irrigation scheduling. This review paper summarizes the use of UAV remote sensing data in crops for estimating the water status and gives a detailed summary of the potential capacity of UAV remote sensing for water stress application. The remote sensing techniques help modify agricultural practices to meet this significant challenge by providing repeated information on crop status at different scales and various performances during the season. UAVs successful implementation in water stress estimations depends on UAV features, such as flexibility of use in flight planning, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. UAV with a thermal sensor is considered the most effective technique for detecting water stress using specific indices. Thermal imaging can identify water status variations and crop water stress index (CWSI). This CWSI acquired through UAV thermal sensors imagery can be acceptable for managing real-time irrigation to achieve optimum crop water efficiency. [ABSTRACT FROM AUTHOR]

Using thermal infrared remote sensing from unmanned aerial vehicles (UAVs) to obtain crop canopy temperature and calculate the crop water stress index (CWSI) is a promising method for monitoring field water conditions. However, such endeavors are often constrained to instantaneous scales due to the diurnal variability of thermal infrared data. To address this limitation, we developed a daily-scale CWSI suitable for UAV remote sensing, enhancing the temporal representativeness of crop water stress diagnostics. We focused on spring maize in the Hetao Irrigation District of Inner Mongolia and investigated four key growth stages. UAV thermal infrared was used to obtain multiple instantaneous statistical CWSI (CWSI s) values during the day. UAV multispectral data and the Penman–Monteith model were combined to obtain the actual evapotranspiration and daily-scale CWSI (CWSI t_day). A temporal upscaling model from instantaneous CSWI to daily-scale CWSI was established by comparing the relationships between the CWSI s and CWSI t_day at different times. Results show that compared to the fluctuations of the CWSI s values throughout the day, those of the CWSI t_day values were smaller, with values of 0.13, 0.09, 0.03, and 0.03 during the ninth leaf (V9), tasseling (VT), silking (R1), and milk (R3) stages, respectively. The CWSI t_day demonstrated a higher correlation with the measured stomatal conductance (g s) at different time periods, thereby being more stable and temporally representative. However, both indices may incorrectly interpret the decline in leaf physiological activity due to aging as water stress at the end of maize growth, leading to overestimated CWSI values. The temporal upscaling model, which was developed by combining CWSI s values observed at 12:00, 14:00, and 16:00 with the random forest regression algorithm, achieved coefficient of determination of 0.794 and root mean square error of 0.04. Hence, multiple instantaneous observations can be used effectively instead of daily-scale observations, providing key insights into the popularization and application of the CWSI t_day. Overall, this study presents a new method for obtaining continuous CWSI values with high temporal and spatial resolutions based on a UAV platform. [Display omitted] • A daily scale CWSI (CWSI t_day) suitable for UAV remote sensing was developed. • CWSI t_day offers greater stability and temporal representativeness for assessing crop water stress. • Multiple instantaneous-scale CWSI (CWSI s) observations can effectively replace CWSI t_day observations. • CWSI s and CWSI t_day obtained based on UAV can misdiagnose the moisture state of maize at the end of its growth. [ABSTRACT FROM AUTHOR]

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat (Triticum aestivum L.) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling. • Accuracy enhanced by integrating CWSI with soil moisture stress; irrigate at CWSI ≥0.35 for optimal wheat yields. • The indigenous sensing device enables efficient and intelligent irrigation scheduling. • Yield and water productivity can be predicted using the CWSI approach across different irrigation regimes. [ABSTRACT FROM AUTHOR]

The Scholander-type pressure chamber to measure midday stem water potential (MSWP) has been widely used to schedule irrigation in commercial vineyards. However, the limited number of sites that can be evaluated using the pressure chamber makes it difficult to evaluate the spatial variability of vineyard water status. As an alternative, several authors have suggested using the crop water stress index (CWSI) based on low-cost thermal infrared (TIR) sensors to estimate the MSWP. Therefore, this study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs in a drip-irrigated Cabernet Sauvignon vineyard under two levels of water stress. For this study, the MLX90614 sensor was used to measure canopy temperature (Tc), and thus compute the CWSI. The results indicated that good performance of the MLX90614 infrared thermometers was observed under laboratory and vineyard conditions with root mean square error (RMSE) and mean absolute error (MAE) values being less than 1.0 °C. Finally, a good nonlinear correlation between the MSWP and CWSI (R2 = 0.72) was observed, allowing the development of intra-vineyard spatial variability maps of MSWP using the low-cost wireless infrared sensor network.