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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]

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

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]

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]

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]

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]

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]

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]

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

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]

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]

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]

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]

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

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

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]

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

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]

Summary: One of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%. [ABSTRACT FROM AUTHOR]

Under the current water crisis in agriculture, irrigation methods for saving and conserving water are necessary. However, these methods must guarantee an appropriate yield with a concomitant economic benefit and a reduced environmental impact. In this study, two irrigation thresholds for irrigation timing (IT) based on thermal imagery were analyzed with the UNICA potato variety in three trials under drip (DI) and furrow (FI) irrigation during 2017–2018 in Lima, Peru. The control (T1) remained at >70% of soil field capacity. For other treatments, thresholds were defined based on stomatal conductance at light saturation (T2: 0.15 and T3: 0.05 mol H 2 O m − 2 s − 1 ) and crop water stress index (T2: 0.4 and T3: 0.6) based on canopy temperature. An integrated index (IIN) was established for the valuation of treatments using the criteria of high fresh tuber yield (FTY) and a low total amount of irrigated water, production cost (PC), and total C emissions (TE) and using criteria of a score. FI-T2 (0.69–0.72) and DI-T3 (0.19–0.29) showed the highest and lowest IIN value, respectively. FTY in T2 was not significantly reduced under FI, resulting in a lower PC regarding DI–T2 and emphasizing the usefulness of thermal imagery in determining watering schedules in potatoes under furrow irrigation systems. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model's accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model's performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible. [ABSTRACT FROM AUTHOR]

An efficient management of water relies on the correct estimation of tree water requirements and the accurate monitoring of tree water status and canopy growth. This study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment. We used mature olive trees of two cultivars subjected to full irrigation, deficit irrigation (41–44% of full irrigation), or rainfed conditions. Deficit irrigation had limited or no effect on fruit and oil yield. There was a significant relationship between the remotely sensed crop water stress index derived from thermal images and the stem water potential (R2 = 0.83). The RGB images by UAV allowed to estimate tree canopy volume and were able to detect differences in canopy growth across irrigation regimes. A significant relationship between canopy volume and LAI was found for both cultivars (R2 of 0.84 and 0.88 for Frantoio and Leccino, respectively). Our results confirm the positive effects of deficit irrigation strategies to save relevant volumes of water and show that aerial images from UAV can be used to monitor both tree water stress and its effects on canopy growth and yield. [ABSTRACT FROM AUTHOR]

Drought has been identified as a major factor restricting maize productivity worldwide, especially in the rainfed areas. The objective of the present study was to investigate the physiological adaptation strategies and sugar-related gene expression levels in three maize (Zea mays L.) genotypes with different drought tolerance abilities (Suwan4452, drought tolerant as a positive check; S7328, drought susceptible as a negative check; Pac339, drought susceptible) at the seedling stage. Ten-day old seedlings of maize genotypes were subjected to (i) well-watered (WW) or control and (ii) water-deficit (WD) conditions. Leaf osmotic potential of cv. S7328 under WD was significantly decreased by 1.35–1.45 folds compared with cv. Pac339 under WW, whereas it was retained in cv. Suwan4452, which utilized total soluble sugars as the major osmolytes for maintaining leaf greenness, Fv/Fm, ΦPSII, and stomatal function (Pn, net photosynthetic rate; gs, stomatal conductance; and E, transpiration rate). Interestingly, sucrose degradation (65% over the control) in cv. Pac339 under WD was evident in relation to the downregulation of the ZmSPS1 level, whereas glucose enrichment (1.65 folds over the control) was observed in relation to the upregulation of ZmSPS1 and ZmSUS1. Moreover, CWSI (crop water stress index), calculated from leaf temperature of stressed plants, was negatively correlated with E, gs, and Pn. Overall, growth characteristics, aboveground and belowground parts, in the drought-susceptible cv. Pac339 and cv. S7328, were significantly decreased (> 25% over the control), whereas these parameters in the drought-tolerant cv. Suwan4452 were unaffected. The study validates the use of leaf temperature, CWSI, Pn, gs, and E as sensitive parameters and overall growth characters as effective indices for drought tolerance screening in maize genotypes at the seedling stage. However, further experiments are required to validate the results observed in this study under field conditions. [ABSTRACT FROM AUTHOR]

Water productivity of rice is imperative for global water security. Currently, water saving management techniques have been proposed and applied to rice systems. The crop water stress index (CWSI) is a major index for evaluating crop water use. The utility of the CWSI in rice in a humid temperate climate has been given little attention. Previous studies have focused on upland crops and readily available constant reference baselines, primarily the water stressed baseline (WSB), which does not inherently reflect transpiration flux. This study examined the performance of the estimated non-water stressed baseline (NWSB) and WSB for rice in a humid climate and the CWSI sensitivity under variable reference baseline scenarios in a 2-year pot trial under phytotron and field environment conditions with two rice genotypes (IRAT109 and Takanari) in a flooded (FL) and aerobic (AR) water regime. We observed that the dynamics of CWSI is dependent not only on the water regimes but could be strongly influenced by genotype sensitivity to vapor pressure deficit (VPD). A higher slope (pooled data) in the field environment (−5.68 °C kPa−1) compared to the phytotron (−3.04 °C kPa−1) reflected transpiration water loss sensitivity to VPD thresholds. Further studies with diverse rice germplasms to explore generalizability to field conditions and reformulation of reference baselines considering the VPD threshold sensitivity could prove to be significant. [ABSTRACT FROM AUTHOR]

The goal of this study is to determine the crop water stress index (CWSI) and irrigation scheduling based on CWSI values, as well as to examine the correlations between CWSI, physiological parameters and grain yield of hybrid corn P31A34 in semi-arid climate conditions. In 2014 and 2015, the upper limit (UL) temperatures at which plants were entirely exposed to water stress were 1.178°C and 2.38°C, respectively. When the corn grain yield began to decline, the CWSI threshold value was 0.34, indicating the yield limit. Grain yield, crop water consumption, crop water stress index, chlorophyll content, water use efficiency and leaf area index were found to have negative correlations (p = 0.01) with CWSI values in both years of the study. The findings revealed that in semi-arid climate conditions, a maximum of 30% water deficit could be used during the growing period of the corn compared to full irrigation (I100) for water savings and that a water deficit greater than 30% results in considerable grain yield losses. In areas with limited water resources, the moderate water deficit (I70) may be a viable alternative to the I100. [ABSTRACT FROM AUTHOR]

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

Current trends in the intensification of olive cultivation including irrigation have dictated a need to reconsider amendment management practices. The addition of organic wastes to agricultural soils is becoming a common practice as a disposal strategy and to improve the physical and chemical soil properties This study examines physiological varieties of young olive trees (Olea europaea L.) grown on deficit irrigation under four different amended soil. We evaluated the short-term effect of different amended soil under deficit irrigation on soil, plant water status, level of soil, and leaf Nitrogen. Water stress was considered to be insignificant or absent with an irrigation amount of 50% of daily evapotranspiration. Leaf relative water content was between 65 and 88% for the Chemlali cultivar, while that of the Koroneiki was between 60 and 70%. Under conditions of water stress, photosynthesis of trees planted in soil amended with biochar decreased from 8.5 to 1.5 μmol.CO2.m−2s−1, compared to the control for both cultivars.To conclude, amendment addition, used as a tool for sustainable agriculture, could improve plant growth, although soil fertility. The type and amount of amendment should receive special attention to be efficient. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aluminum (Al) toxicity in acidic soils is a major problem in rice crop production, especially in the acid sulfate soil (pH < 4.0). Selecting Al-tolerant varieties of rice with low toxicity is one of the most appropriate strategies to overcome this problem. In the present study, we investigated the Al content in different rice genotypes, IR64 (high yielding), RD35 (local acidic-tolerant), and Azucena (AZU, positive-check Al-tolerant), and their physiological and morphological adaptations under a wide range Al (10, 25, 50 mM [Al2(SO4)3]) treatments in the greenhouse conditions. Under 50-mM Al treatment, Al levels in the root tissues of rice seedlings cvs. AZU and IR64 were increased by 2.74- and 2.10-fold over control. Interestingly, Al contents in the roots of cv. RD35 were also exhibited by 2.04-fold over control. Similarly, Al contents in the leaves trend to increase in relation to a degree of Al treatments, leading to increase leaf temperature, chlorophyll degradation, limited CO2 assimilation, and negative effect on root traits under 50 mM Al were evidently observed. Therefore, leaf temperature was considered a sensitive parameter regulated by high concentration of Al (50 mM), leading to increase in crop water stress index (CWSI > 0.6) and decrease in stomata conductance. Net photosynthetic rate (Pn) and transpiration rate (E) in rice seedlings of cv. RD35 subjected to 50 mM Al were significantly dropped by 74.76% and 47.71% over the control, respectively, resulting in reduced growth performances in terms of root length (26.57% reduction) and shoot fresh weight (46.15% reduction). An enrichment of Al in the root tissues without toxicity in rice cv. AZU may further help in discovering the Al homeostasis. In summary, Al enrichment in rice genotypes grown under Al-treatments was evidently observed in the root, leading to the limited root growth, root length, and root dry weight, especially in cv. RD35. Al restriction in the root tissues of cv. AZU (Al-tolerant) may play a key role as defense mechanisms to avoid translocation to other organs and the stomata closure was an alternative key factor to limit H2O transpiration. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) can help farmers to monitor their crops and provide irrigation and inputs as and when the crops need, reducing risks to yields. This study uses UAV imagery to measure water and plant stress in the winter wheat fields, lying in high, medium and low Desertification Vulnerability Indexed (DVI) zones of South Punjab, a region that has an agrarian economy subject to severe desertification. UAV flights were conducted in nine wheat fields in three districts of Bahawalpur, Rahim Yar Khan and Rajanpur. Flights were operated at 15 m altitude above ground level at midday, February 2019, presenting good resolution images of 30.48ppi, in RGB, with a pixel depth of 16 Bit, from a DJI Phantom 3 Standard quadcopter. Dronedeploy was used for image pre-processing and generating orthomosaics of the nine fields. Orthomosaics were uploaded on the Agremo app, where water stress and plant stress analysis of the sampled fields was performed. Agremo generated maps were reclassified in Arc Map 10.5. Fatehpur Union Council, lying in the High DVI zone, was found to suffer most severe plant stress, potential plant stress, and water stress with 34.83%, 51.16% and 42.35% of the crop affected respectively. The sample fields in high DVI zones in two of the three study districts suffered the highest amounts of plant stress and water stress. The conclusions offer guidance to policy makers on where water redistribution may need to be considered so that exacerbating desertification risk can be avoided, particularly in the most vulnerable zones. [ABSTRACT FROM AUTHOR]

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

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study evaluated the applicability of crop water stress index (CWSI) based on the leaf temperature for diagnosing the grapevine water status. The experiment was conducted at Yuhe Farm (northwest China), with drip-irrigated grapevines under three irrigation treatments. Meteorological factors, soil moisture contents, leaf temperature, growth indicators including canopy coverage and fruit diameter, and physiological indicators including SPAD (relative chlorophyll content), stem water potential (φs), stomatal conductance (φs), and transpiration rate (E) were studied during the growing season. The results show that the relationship between the leaf-air temperature difference (Tc-Ta) and the plant water status indicators (φs, gs, E) were significant (P < 0.05), and the relationship between gs, E and Tc-Ta was the closest, with R² values ranging from 0.530-0.604 and from 0.545-0.623, respectively. CWSI values are more easily observed on sunny days, and it was determined that 14:00 BJS is the best observation time for the CWSI value under different non-water-stressed baselines. There is a reliable linear correlation between the CWSI value and the soil moisture at 0-40 cm (P < 0.05), which could provide a reference when using the CWSI to diagnose the water status of plants. Compared with the Tc-Ta value, the CWSI could more accurately monitor the plant water status, and above the considered indictors, gs has the greatest correlation with the CWSI. [ABSTRACT FROM AUTHOR]

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( rPearson = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha−1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m³ ha−1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings [ABSTRACT FROM AUTHOR]

Low radiation is one of the most important factors which limits potential yield in potato. Under humid conditions, the dominance of diffuse radiation not only imposes challenges for radiation use efficiency in crops but also limits the water status surveillance through non-invasive methods like infrared radiometry. This study was carried out in the humid desert of the Peruvian central coast with the aim to relate maximum stomatal conductance (gs_max, an important water status indicator) with leaf and air temperature (dT) and crop water stress index (CWSI). In a potted trial, gs_max vs. dT were compared along the day in well-irrigated (field capacity) and water restricted (half field capacity) plants. In an additional field experiment, CWSI was validated by testing two irrigation timing treatments with pre-established gs_max threshold (0.15 [T1] and 0.50 [T2] mol H2O m−2 s−1) against a control (frequently irrigated). An acute stomatal closure sensitivity was detected which drove a gs_max fall (gs↓) near the solar noon. The intense stomatal closure caused a dT rise which showed positive higher values (> 2 °C) after gs↓. The significant yield reduction of T1 in relation to the control (− 38.2 ± 10.7%) highlighted that gs_max values > 0.15 must be used to warrant a high potato yield. These findings support the use of CWSI values ≤ 0.3–0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions. [ABSTRACT FROM AUTHOR]

Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function. Abbreviations: CWSI: crop water stress index; DAP: day after planting; Pn: net photosynthetic rate; gs: stomatal conductance; Tr: transpiration rate; VPDair: air vapor pressure; RMSE: root mean square error [ABSTRACT FROM AUTHOR]

The present research was carried out to determine water-stress tolerance of linoleic sunflower genotypes (P64LE119, PR63F73, P64LL62) grown under different water stress conditions [no water-stress (I100); mild water-stress (I70); strong water-stress (I35)] in the years 2015 and 2016. Variance analyses revealed significant differences between the genotypes (P<0.01). As the average of two years, the greatest yield was obtained from no water-stress x genotype interaction (I100xP64LE119) with 4094.66 kg ha-1, the lowest yield was obtained from strong water stress x genotype interaction (I35xPR63F73) with 2487.81 kg ha-1. Again as the average of two years, the greatest chlorophyll content was obtained from no water-stress x genotype interaction (I100xP64LE119) with 49.83 spad, the lowest value was obtained from strong water stress x genotype interaction (I35xPR63F73) with 34.39 spad. The greatest crop water stress index was obtained from strong water stress x genotype interaction (I35xPR63F73) with 0.53, the lowest value was obtained from no water-stress x genotype interaction (I100xP64LE119) with 0.21. The P64LE119 genotype with optimum water use efficiency and prominent with crop water stress index and chlorophyll content both in no water-stress and strong water stress treatments was identified as water stress-resistant and the genotype was considered to have reliable characteristics potentially to be used in further water stress-resistance studies. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of structural, chlorophyll-based, and photochemical indices to detect maize water status and to assess production based on five years of field experiments (2013–2017) during the primary growth stages. We employed three categories of indicators, including water condition and productive and thermal indicators, to quantify the responses of summer maize under continuous water stress from drought to waterlogging conditions. Furthermore, we adopted several spectral indices to assess their sensitivity to three categories of metrics. The results showed the association is the best between the treatment level and Leaf Water Content (LWC). The waterlogging treatment influenced Leaf Water Potential (LWP) in moderate drought stress. Severe drought stress caused the strongest reduction in productivity from both Leaf Area Index (LAI) and chlorophyll content. In terms of sensitivity of various indices, red-edge-position (REP) was sensitive to maize water conditions LWP, LAI and chlorophyll content. Photochemical Reflectance Index (PRI) and Normalized Difference Vegetation Index (NDVI) were the most and second most sensitive indices to productive indicators, respectively. The results also showed that no indices were capable of capturing the information of Crop Water Stress Index (CWSI). [ABSTRACT FROM AUTHOR]

Due to climate change, the application of water saving strategies is of particular interest. The aim of this study was to evaluate the effects of deficit irrigation (DI) and partial root-zone drying (PRD) techniques on the crop water stress index (CWSI), water use efficiency (WUE), and quality parameters in processing tomatoes grown in open field conditions in a Mediterranean climate. Two cultivars were grown for two growing seasons under four irrigation regimes as follows: (i) IR100: full irrigation by restoring 100% of the maximum tomato evapotranspiration (ETc); (ii) IR70DI: 70% of the amount of water given to the IR100; (iii) IR70PRD: 70% of the amount of water given to the IR100 by applying partial root-zone drying and (iv) IR0: irrigation only at transplanting and during fertigation. During the flowering period, the first growing season was characterized by an absence of rainfall and by higher temperatures also showing a higher CWSI. Despite, under IR70PRD, the CWSI was significantly higher than under IR70DI, the marketable yield obtained was significantly higher. Both IR70DI and IR70PRD regimes received approximately 24% less water than IR100, but the yield reduction with relation to the optimum regime was equal to 16.2% under IR70DI, and only 7.6% under IR70PRD. The WUE increment of IR70PRD with respect to IR100 was equal to 27% in the first growing season and to 17% in the second one, showing that the positive effect of PRD on the WUE is more evident in the more stressed year. Finally, the results from the principal component analysis (PCA) showed that the two cultivars had different qualitative responses in the two extreme regimes (IR100 and IR0) but not under PRD and DI regimes. [ABSTRACT FROM AUTHOR]

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called 'speaking plant' and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590-680, 690-830 and 830-1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490-510, 530-560, 660-670 and 730-760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth. [ABSTRACT FROM AUTHOR]

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