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As an important physiological indicator, wheat canopy temperature (CT) can be observed after flowering in an attempt to predict wheat yield and quality. However, the relationship between CT and wheat yield and quality is not clear. In this study, the CT, photosynthetic rate (Pn), filling rate, wheat yield, and wheat quality of 68 wheat lines were measured, in an attempt to establish a connection between CT and yield and quality and accelerate the selection of new varieties. This experiment used an infrared imaging camera to measure the CT of wheat materials planted in the field in 2022. Twenty materials with significant temperature differences were selected for planting in 2023. By comparing the temperature trends in 2022 and 2023, it is believed that materials 4 and 13 were cold-type materials, while materials 3 and 11 were warm-type materials. The main grain filling period of cold-type materials occurs in the middle and late stages of the grain filling period and the Pn and the thousand-grain weights of cold-type materials were higher than those of warm-type materials. Similarly, under continuous rainy conditions, cold-type materials had a higher protein and wet gluten contents, while warm-type materials had higher sedimentation values and shorter formation times. [ABSTRACT FROM AUTHOR]

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley's seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI. [ABSTRACT FROM AUTHOR]

This study aimed to determine whether canopy and air temperature difference (ΔT) as an existing simple normalizing index can be used to detect an increase in canopy temperature induced by soil drought in urban parks, regardless of the unique energy balance and three-dimensional (3D) structure of urban trees. Specifically, we used a thermal infrared camera to measure the canopy temperature of Zelkova serrata trees and compared the temporal variation of ΔT to that of environmental factors, including solar radiation, wind speed, vapor pressure deficit, and soil water content. Normalization based on a 3D energy-balance model was also performed and used for comparison with ΔT. To represent the 3D structure, a terrestrial light detection and ranging-derived 3D tree model was used as the input spatial data. The temporal variation in ΔT was similar to that of the index derived using the energy-balance model, which considered the 3D structure of trees and 3D radiative transfer, with a correlation coefficient of 0.85. In conclusion, the thermal-image-based ΔT performed comparably to an index based on the 3D energy-balance model and detected the increase in canopy temperature because of the reduction in soil water content for Z. serrata trees in an urban environment. [ABSTRACT FROM AUTHOR]

Studying the physiological mechanisms underlying the traits associated with salt tolerance in genotypes could lead to the discovery of new genetic resources for salt tolerance. In this study, the mechanisms of salt tolerance were evaluated, based on ionic, physiological, and agronomic traits in four varieties that differ in their salt tolerance and in 18 F8 recombinant inbred lines (RILs) grown in real field conditions. The salt tolerance of plant materials was assessed under both normal (3.5 mM NaCl) and high salinity stress (150 mM NaCl) conditions for two consecutive years. Different growth and physiological traits were assessed 75 days after sowing, while ion contents in the shoots, grain yield, and its components were determined at the maturity stage. Multivariate analysis was used to conduct a comprehensive evaluation of salt tolerance across various genotypes and traits. The ANOVA results showed significant differences (p ≤ 0.05 and 0.001) among salinity, genotypes, and their interactions for all ionic and agro-physiological traits, with a few exceptions. Salinity stress resulted in a considerable increase in Na+ content and canopy temperature (CT), with a simultaneous decrease of 11.3% to 94.5% in other ionic and agro-physiological traits compared to the control treatment. However, the salt-tolerant genotypes showed minimal increases in Na+ content and CT, as well as decreases in other ionic and agro-physiological traits when compared to salt-sensitive genotypes under salinity stress. All ionic and agro-physiological traits exhibited strong correlations with each other under salinity stress, but these correlations were weak or insignificant under control conditions. The principal component analysis identified Na+ and CT as negative indicators and other ionic and agro-physiological traits as positive indicators for salt tolerance under salinity stress. The negative indicators were strongly linked to salt-sensitive genotypes, while the positive indicators were closely associated with salt-tolerant genotypes. Heatmap clustering, using multiple traits, successfully differentiated the salt-tolerant genotypes from the salt-sensitive ones. The salt-tolerant group showed a significant reduction in Na+ content by 36.9%, in CT by 10.0%, and in HI by 16.7%, along with an increase of 6.3–51.4% in other ionic and agro-physiological traits compared to the salt-sensitive group. In conclusion, the mechanisms associated with Na+ exclusion and high K+/Na+ and Ca2+/Na+ ratios, as well as chlorophyll and relative water content, along with low CT, resulted in significant improvements in growth and yield under salinity stress conditions. Given that the effectiveness of various ionic and agro-physiological traits in evaluating salt tolerance in wheat has been proven in real field conditions, these traits will play a key role in the development of salt-tolerant wheat genotypes. [ABSTRACT FROM AUTHOR]

Drought is the most important factor limiting winter wheat yield in the North China Plain (NCP). Choosing high-yielding cultivars is an important measure to minimize the negative effects of drought stress. Field studies were conducted with 10 cultivars in the 2020–2022 seasons under three irrigation treatments (I0, without irrigation; I1, irrigated at jointing stage; I2, irrigated at jointing and anthesis stages) in the NCP to examine the water use strategies and root and shoot traits of high-yielding cultivars under different water supply conditions. The results showed that yield variation among cultivars was 21.2–24.6%, 23.7–25.9% and 11.6–15.3% for the I0, I1 and I2 treatments, respectively. Under water deficit conditions (I0 and I1), high-yielding cultivars reduced water use during vegetative stages and increased soil water use during reproductive stages, especially water use from deeper soil layers. Those cultivars with higher root length density (RLD) in deep soil layers exhibited higher water uptake. Each additional millimeter of water used after anthesis from the 100–200 cm soil layers increased grain yield by 23.6–29.6 kg/ha and 16.4–28.5 kg/ha under I0 and I1, respectively. This water use strategy enhanced dry matter accumulation after anthesis, decreased canopy temperature (CT) and increased relative leaf water contents (RLWC), which ultimately improved grain yield. For winter wheat grown under I2, cultivars that decreased water use after anthesis had higher water productivity (WP). Root length (RL), root weight (RW) and root:shoot ratio were each negatively correlated with grain yield, while above-ground biomass was positively correlated with grain yield. Therefore, higher dry matter accumulation and smaller root systems are two important traits of high-yielding cultivars under sufficient water supply conditions (I2) in the NCP. [ABSTRACT FROM AUTHOR]

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface ( AWD 5 ,   AWD 10 , and AWD 20 ) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD 20.   In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels. [ABSTRACT FROM AUTHOR]

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused. [ABSTRACT FROM AUTHOR]

Knowledge of the response of growth parameters of almond (Badam) trees to different water supply conditions is important for Badam production. To identify growth parameters that are sensitive to water deficit in almond trees, field experiments were conducted during a median water year to monitor the major growth characteristics of almond trees under different irrigation conditions in Shache County, Xinjiang, China. A field (in-situ) monitoring system was also constructed using various sensors for the continuous and non-destructive monitoring of the growth parameters, such as soil water in the root zone, canopy temperature depression, trunk diameter, and fruit diameter of almond trees. The results confirmed the reliability of the monitoring system. Both canopy temperature depression and the diameter shrinkage of the trunk and fruit were significantly negatively correlated (r values ranging from −0.996 to −0.823) with the irrigation water quantity. This correlation was observed from the young fruit stage to the maturation stage of almond trees, under irrigation conditions representing 50–100% of crop evapotranspiration (ETc). These parameters were sensitive (|r| ≥ 0.778) to the water deficit status of almond trees from 14:00 to 18:00 in sunny weather. These results can provide both technical and theoretical support for real-time non-destructive assessment of the water deficit status of almond trees. [ABSTRACT FROM AUTHOR]

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants. [ABSTRACT FROM AUTHOR]

Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management. [ABSTRACT FROM AUTHOR]

This field experiment focuses on the effects of a heavy rainfall event (DANA, depresión aislada en niveles altos) that occurred on 12–14 September 2019 (DOY, Day of the year, 255–257), in southern Spain on plant water status and the thermal response of nectarine trees. Two irrigation treatments were applied during the summer–autumn postharvest period (DOY 158–329): full-irrigated (CTL) and non-irrigated (DRY). Volumetric soil water content (θv), air temperature (Ta) and canopy temperature (Tc) were monitored in real-time and the crop water stress index (CWSI) was calculated. The difference in Tc between the DRY and CTL treatments (Tc' − Tc) is proposed as a new thermal indicator. Stem water potential (Ψstem) and leaf gas exchange measurements were recorded on representative days. During the DANA event, only the Tc measured by the infrared radiometer sensors could be monitored. Therefore, the effects of the DANA forced the soil water content sensors to be switched off, which prevented Ψstem and leaf gas exchange determinations from DOY 255 to 275. Before the DANA event, withholding irrigation caused a gradual decrease in the soil and plant water status in the DRY treatment. Significant differences appeared between treatments in the studied thermal indexes. Moreover, Tc' − Tc was more sensitive than Tc − Ta in assessing nectarine water stress. The effects of the DANA reduced these differences, suggesting different baselines for the calculation of CWSI. In this respect, the relationship Tc − Ta vs. VPD improved the coefficient of determination after the DANA event in full-irrigated trees. Similar values of Ψstem and leaf gas exchange were found in both treatments after the DANA event, even though thermal indexes showed some significant differences. In addition, the strong relationship found between Tc − Ta and CWSI vs. Ψstem worsened after DANA occurred, revealing a lower sensitivity of Ψstem compared to canopy temperature to accurately assess nectarine water status in these saturated soil conditions. This research underlined the robustness of infrared thermography to continuously monitor plant water status under these extreme weather conditions. [ABSTRACT FROM AUTHOR]

The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D0%, D20%, and D40% from the field capacity (FC) and D100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D0%, D20%, D40%, and D100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region. [ABSTRACT FROM AUTHOR]

Fluctuations in precipitation and higher evapotranspiration due to rising temperatures are reflected in reduced wheat yields, even in areas with a low historical incidence of drought. In this study, the effects of drought (S) and irrigation (IR) on spelt, einkorn wheat, and two common wheat cultivars were assessed in a field experiment in the years 2018–2021. Water availability was differentiated from the flowering stage using a mobile cover and drip irrigation. Grain yield, canopy temperature, and discrimination of 13C in grain (Δ 13C) were monitored. Drought reduced the average grain yield of common wheat to 5.24 t.ha−1, which was 67.00% of the rain-fed control (C) yield, and 62.09% of the irrigated wheat yield. For spelt and einkorn wheat, the average grain yield from stressed plants was 2.02 t.ha−1; this was 79.97% of the C-variant yield, and 70.82% of the IR-variant yield. Higher stand temperatures were an excellent indicator of water deficit in the stressed crops. The relationship between temperature and final grain yield in the monitored variants was always negative. In all years, discrimination of 13C in grain corresponded to water availability; in its effect on yields, the correlation was always positive. Between 2018 and 2020, spelt and einkorn exhibited lower Δ 13C in comparison with common wheat in all variants, suggesting a greater impact of differentiated water supply. The results of the experiment conclusively demonstrated systematic effects of drought after flowering upon yields and other studied characteristics. [ABSTRACT FROM AUTHOR]

Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress. [ABSTRACT FROM AUTHOR]

Deficit irrigation is a common approach in water-scarce regions to balance productivity and water use, whereas drought stress still occurs to various extents, leading to reduced physiological performance and a decrease in yield. Therefore, seeking a rapid and reliable method to identify wheat varieties with drought resistance can help reduce yield loss under water deficit. In this study, we compared ten wheat varieties under three deficit irrigation systems (W0, no irrigation during the growing season; W1, irrigation at jointing; W2, irrigation at jointing and anthesis). UAV thermal imagery, plant physiological traits [leaf area index (LAI), SPAD, photosynthesis (Pn), transpiration (Tr), stomatal conductance (Cn)], biomass and yield were acquired at different growth stages. Wheat drought resistance performance was evaluated through using the canopy temperature extracted from UAV thermal imagery (CT-UAV), in combination with hierarchical cluster analysis (HCA). The CT-UAV of W0 and W1 treatments was significantly higher than in the W2 treatment, with the ranges of 24.8–33.3 °C, 24.3–31.6 °C, and 24.1–28.9 °C in W0, W1 and W2, respectively. We found negative correlations between CT-UAV and LAI, SPAD, Pn, Tr, Cn and biomass under the W0 (R2 = 0.41–0.79) and W1 treatments (R2 = 0.22–0.72), but little relevance for W2 treatment. Under the deficit irrigation treatments (W0 and W1), UAV thermal imagery was less effective before the grain-filling stage in evaluating drought resistance. This study demonstrates the potential of ensuring yield and saving irrigation water by identifying suitable wheat varieties for different water-scarce irrigation scenarios. [ABSTRACT FROM AUTHOR]

Capsicum (Capsicum annuum L.) belongs to the Solanaceae family and is an economically important vegetable crop. However, the crop is very sensitive to adverse weather conditions such as high temperatures and excessive sunlight, which cause flower and young fruit to drop and sunscald to mature fruits. Using protected cultivation such as shade covers or net houses is a feasible agronomic approach to protect the crop from high light intensity, which increases plant growth, reduces fruit damage, and increases marketable fruit yield and quality. Low-cost protected cropping options such as fixed-roof net houses have proved cost-effective and suitable for fruiting vegetable production in semi-arid climatic regions. However, this structure type is unable to protect the crops from rainfall, is prone to cyclone damage and is inflexible to accommodate various vegetable crops which have different requirements for healthy and productive growth. This study was conducted in Carnarvon, which has semi-arid climatic conditions and is a key horticultural district of Western Australia, to compare the Retractable Roof Production System (RRPS) and open field (OF) conditions in the production of capsicum. The data showed that the RRPS modified the internal light, temperature and humidity in favour of the capsicum crop. The RRPS-grown capsicum had higher plant height and lower canopy temperature on hot days than those in the OF. The mean marketable fruit yield of capsicum varieties grown in the RRPS was significantly higher than those in the OF with fruit yields of 97 t ha−1 and 39.1 t ha−1, respectively, but the fruit quality remained unchanged. Overall, the data suggest that the RRPS altered the internal microenvironment and enhanced capsicum crop growth, physiology and fruit yield by setting climatic parameters to automatically control the opening and closing of the roof, the insect net and side curtains, and activation of the fogging system. The future perspective of the deployment of RRPS for capsicum production under climatic conditions in Carnarvon was also discussed. [ABSTRACT FROM AUTHOR]

Accurate classification of multilayered plants is vital to understanding the interaction of each canopy in a greenhouse environment and designing plant models based on the irradiation, canopy temperature, transpiration, and heat flux by the leaf area index (LAI). Based on the measurements from a greenhouse in operation, plant models for each LAI are discussed in this study. If the heat flux between plants and air can be accurately predicted through plant models using LAI, the heating and cooling load in various virtual greenhouses with densely planted crops can be predicted. To enhance the measurement accuracy, a temperature and humidity sensor with an aspirated shield, an infrared canopy sensor, and CO2 sensor were installed. The plant environment was measured with a portable pyranometer, porometer, ceptometer, and anemometer. The measurements were inputted to the plant models, and the canopy temperature was calculated. The canopy temperature from the models was evaluated for reliability by comparing it with field measurements (R2 = 0.98 and RMSE = 0.46). The results indicated that the big leaf model is suitable when the air circulation layer is larger than the canopy size, but when physical properties of the plant change band affect the LAI, as in a greenhouse, a multi-layer model should be considered. [ABSTRACT FROM AUTHOR]

Nitrogen (N) deficiency and heat stress (HS) are major abiotic stresses that affect the quantity and quality of wheat grains. This study was conducted to examine wheat varietal response to RSI and RNDVI at the anthesis stage and their relationship to yield and yield-related traits under variable N supply and terminal heat stress. Twelve wheat varieties were evaluated in 2016–2017 and 2017–2018 at the National Agricultural Research Centre (NARC), Islamabad, Pakistan. The experiment was divided into three sets, i.e., N120 (120 kg N/ha), N60 (60 kg N/ha) and N0 (0 kg N/ha), based on the nitrogen fertilizer application. The physiological and yield-related parameters were recorded. Mean grain yield for all twelve varieties, averaged from two years of data, ranged between 1655.0 and 3890.1 kg/ha. Maximum RSI (0.99), RNDVI (1.03) and GY (3890.9 kg/ha) were recorded for FSD-08, while AARI-11 showed minimum RSI (0.50), RNDVI (0.56) and GY (1396.40 kg/ha). In the present study, mean CTD was lower, at N0 (3.57 °C), followed by N60 (5.07 °C) and N120 (5.47 °C) on average for the two years of data. The strong positive correlation of RSI and RNDVI with grain yield at R2 = 0.73 and R2 = 0.49 suggest that these parameters can be used as efficient and precise selection criteria for identifying nitrogen-use-efficient wheat varieties under terminal heat-stress conditions. This work will help the researchers to identify and develop nitrogen-use-efficient and thermos-tolerant wheat cultivars by minimizing the negative impacts of heat stress at the anthesis stage. [ABSTRACT FROM AUTHOR]

Drought-tolerant plant growth-promoting rhizobacteria (PGPR) may promote plant development under limited water supply conditions, when plant's water demand is not completely satisfied under rain-fed conditions or when irrigation water availability is limited. The aim of this study was to examine the effects of two inoculation treatments (B2: Alcaligenes sp. 3573, Bacillus sp. BAR16, and Bacillus sp. PAR11 strains and B3: Pseudomonas sp. MUS04, Rhodococcus sp. BAR03, and Variovorax sp. BAR04 strains) and compare those to a control (B0) without artificial inoculation on chlorophyll fluorescence, leaf chlorophyll content (SPAD value), canopy temperature, and the yield of the processing tomato cultivar H-1015 F1 grown under field conditions. The young seedlings of the hybrid tomato variety H-1015 F1 were immersed in 1% of B2 or B3 products (BAY-BIO, Szeged Hungary) for 5 min. Inoculated and untreated seedlings were grown under three irrigation treatments [regular irrigation (RI), deficit irrigation (DI), and no irrigation (I0)], to reveal the effect of PGPR under different levels of water stress. In the dry year (2018), higher canopy temperature and chlorophyll fluorescence (Fv/Fm) were measured during flowering in plants treated with bacteria than in untreated plants. In the stage of flowering and fruit setting, the B3 treatment led to a significant decrease in the Fv/Fm value, canopy temperature remained high, and the SPAD value was statistically the same in all treatments. Under limited water supply, in most cases, PGPR led to a significantly greater total yield but more unripe green berries compared to untreated plants. Under moderate water shortage (dry year + deficit irrigation), the B3 treatment resulted in 26% more ripe, marketable fruit and 49% less unripe fruit compared to the B2 treatment. On the other hand, in the wet year (2020), the bacterial treatments generally did not affect physiological properties, though the B2 treatment produced a higher marketable yield while the amount of green and diseased fruits did not differ statistically, compared to the B3 treatment under deficit irrigation. Based on our study, we recommend the application of the B3 PGPR product as it positively affected key physiological processes, leading to a higher marketable yield particularly under water shortage. [ABSTRACT FROM AUTHOR]

Late detection of fungal infection is the main cause of inadequate disease control, affecting fruit quality and reducing yield of grapevine. Therefore, infrared imagery as a remote sensing technique was investigated in this study as a potential tool for early disease detection. Experiments were conducted under field conditions, and the effects of temporal and spatial variability in the leaf temperature of grapevine infected by Plasmopara viticola were studied. Evidence of the grapevine's thermal response is a 3.2 °C increase in leaf temperature that occurred long before visible symptoms appeared. In our study, a correlation of R2 = 0.76 at high significance level (p ≤ 0.001) was found between disease severity and MTD. Since the pathogen attack alters plant metabolic activities and stomatal conductance, the sensitivity of leaf temperature to leaf transpiration is high and can be used to monitor irregularities in temperature at an early stage of pathogen development. [ABSTRACT FROM AUTHOR]

Thermal infrared imaging technology was used to understand the effects of arbuscular mycorrhizal fungi (AMF) and dark septate endophytic (DSE) fungi, both separately and together, on plant growth and physiological status, and to screen and develop efficient microbial agents in a pot experiment design. Eight treatments comprised the control (CK), AMF inoculation alone, DSE fungal treatments (DSE20%, DSE40% and DSE80%; 2, 4, 8 × 105 CFU mL−1) and combined inoculation treatments (DSE20% + AMF, DSE40% + AMF, and DSE80% + AMF). Canopy temperature (Tcanopy) and stomatal conductance (gs) were monitored at different growth stages, and plant biomass-related indicators were obtained at harvest. These indicators were used to assess plant growth and the physiological status resulting from the different inoculation treatments. During plant growth, the plant Tcanopy decreased following inoculation. Differences in Tcanopy between control and inoculated plants were detected by thermal infrared imaging technology and were −3.8 to + 9.3 °C (control–inoculation treatment). Growth index and Tcanopy monitoring indicate that the growth-promoting effect of combined inoculation was higher than that of either fungal type alone, with DSE80% + AMF producing the highest growth promotion. During the growth process of inoculated maize, the effect of inoculated AMF on the physiological condition of maize growth can be better monitored by thermal infrared at 10 a.m., 12 p.m., 2 p.m. and 4 p.m. on the 31st–57th days of the growth period. The method and results of this experiment are conducive to the rapid and efficient monitoring of the effects of microorganisms on plant growth and physiological status and can be applied to the screening, application, and promotion of microbial agents. [ABSTRACT FROM AUTHOR]

The direct examination of plant canopy temperature can assist in optimizing citrus irrigation management in greenhouses. This study aimed to develop a method to measure canopy temperature using thermal imaging in one-year-old citrus plants in a greenhouse to identify plants with water stress and verify its potential to be used as a tool to assess citrus water status. The experiment was conducted for 48 days (27 November 2019 to 13 January 2020). We evaluated the influence of five levels of irrigation on two citrus species ('Red Ruby' grapefruit (Citrus paradisi) and 'Valencia' sweet orange (Citrus sinensis (L.) Osbeck)). Images were taken using a portable thermal camera and analyzed using open-source software. We determined canopy temperature, leaf photosynthesis and transpiration, and plant biomass. The results indicated a positive relationship between the amount of water applied and the temperature response of plants exposed to different water levels. Grapefruit and sweet orange plants that received less water and were submitted to water restrictions showed higher canopy temperatures than the air (up to 6 °C). The thermal images easily identified waterstressed plants. Our proof-of-concept study allowed quickly obtaining the canopy temperature using readily available equipment and can be used as a tool to assess citrus water status in one-year-old citrus plants in greenhouses and perhaps in commercial operations with mature trees in the field after specific experimentation. This technique, coupled with an automated system, can be used for irrigation scheduling. Thus, setting up a limit temperature is necessary to start the irrigation system and set the irrigation time based on the soil water content. To use this process on a large scale, it is necessary to apply an automation routine to process the thermal images in real time and remove the weeds from the background to determine the canopy temperature. [ABSTRACT FROM AUTHOR]

Infrared thermography has been introduced as an affordable tool for plant water status monitoring, especially in regions where water availability is the main limiting factor in agricultural production. This paper outlines the potential applications of low-cost thermal imaging devices to evaluate the water status of young and mature sweet cherry trees (Prunus avium L.) submitted to water stress. Two treatments per plot were assayed: (i) a control treatment irrigated to ensure non-limiting soil water conditions; and (ii) a water-stress treatment. The seasonal evolution of the temperature of the canopy (Tc) and the difference between Tc and air temperature (ΔT) were compared and three thermal indices were calculated: crop water stress index (CWSI), degrees above control treatment (DAC) and degrees above non-water-stressed baseline (DANS). Midday stem water potential (Ψstem) was used as the reference indicator of water stress and linear relationships of Tc, ΔT, CWSI, DAC and DANS with Ψstem were discussed in order to assess their sensitivity to quantify water stress. CWSI and DANS exhibited strong relationships with Ψstem and two regression lines to young and mature trees were found. The promising results obtained highlight that using low-cost infrared thermal devices can be used to determine the plant water status in sweet cherry trees. [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]

Many late-season physiological traits affect grain yield in wheat, either directly or indirectly. However, information on the genetic control of yield-related traits is still limited. In this study, we aimed to identify quantitative trait loci (QTL) for canopy temperature and chlorophyll content index during anthesis (CTa and CCIa, respectively), the mid grain-filling stage (CTg1 and CCIg1, respectively), and the late grain-filling stage (CTg2 and CCIg2, respectively) as well as for plant height (PH), thousand kernels weight (TKW), and grain yield (GY) using genome-wide linkage mapping. To this end, a double haploid population derived from a cross between two high yielding wheat cultivars, UI Platinum and SY Capstone, was phenotyped in four irrigated environments and genotyped using the wheat 90K iSelect platform and simple sequence repeats. The genotypic data were used to construct a high-density genetic map of 43 linkage groups (LGs) with a total length of 3594.0 cm and a marker density of 0.37 cm. A total of 116 QTL for all nine traits was detected on 33 LGs, spreading to all wheat chromosomes, except for Chr. 7D. Of these, six QTL (CTa.ui-4B.1, Q.CTg1.ui-5B-2.1, Q.CTg2.ui-6B.1, Q.PH.ui-6A-2.1, Q.TKW.ui-2D-1, and Q.GY.ui-6B) were consistently detected in more than three irrigated environments, called as stable QTL. Additionally, we identified 26 QTL clusters for more than two traits, of which the top four were located on Chromosomes 4A-1, 1B-1, 5B-2, and 2D-1. Overall, the stable QTL significantly related with grain yield, QTL clusters, and linked molecular markers identified in this study, may be useful in marker-assisted selection in early generation and early growth stage for grain yield improvement. [ABSTRACT FROM AUTHOR]

Precision irrigation management is based on the accuracy and feasibility of sensor data assessing the plant water status. Multispectral and thermal infrared images acquired from an unmanned aerial vehicle (UAV) were analyzed to evaluate the applicability of the data in the assessment of variants of subsurface irrigation configurations. The study was carried out in a Cabernet Sauvignon orchard located near Benton City, Washington. Plants were subsurface irrigated at a 30, 60, and 90 cm depth, with 15%, 30%, and 60% irrigation of the standard irrigation level as determined by the grower in commercial production management. Half of the plots were irrigated using pulse irrigation and the other half using continuous irrigation techniques. The treatments were compared to the control plots that received standard surface irrigation at a continuous rate. The results showed differences in fruit yield when the control was compared to deficit irrigated treatments (15%, 30%, 60% of standard irrigation), while no differences were found for comparisons of the techniques (pulse, continuous) or depths of irrigation (30, 60, 90 cm). Leaf stomatal conductance of control and 60% irrigation treatments were statistically different compared to treatments receiving 30% and 15% irrigation. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and canopy temperature were correlated to fruit yield and leaf stomatal conductance. Significant correlations (p < 0.01) were observed between NDVI, GNDVI, and canopy temperature with fruit yield (Pearson's correlation coefficient, r = 0.68, 0.73, and -0.83, respectively), and with leaf stomatal conductance (r = 0.56, 0.65, and -0.63, respectively) at 44 days before harvest. This study demonstrates the potential of using low-altitude multispectral and thermal imagery data in the assessment of irrigation techniques and relative degree of plant water stress. In addition, results provide a feasibility analysis of our hypothesis that thermal infrared images can be used as a rapid tool to estimate leaf stomatal conductance, indicative of the spatial variation in the vineyard. This is critically important, as such data will provide a near real-time crop stress assessment for better irrigation management/scheduling in wine grape production. [ABSTRACT FROM AUTHOR]

Tree growth and survival predominantly depends on edaphic and climatic conditions, thus climate change will inevitably influence forest health and growth. It will affect forests directly, for example, through extended periods of drought, and indirectly, such as by affecting the distribution and abundance of forest pathogens and pests. Developing ways of early detection and monitoring of tree stress is crucial for effective protection of forest stands. Thermography is one of the techniques that can be used for monitoring changes in the physiological state of plants; however, in forestry, it has not been widely tested or utilized. The main challenge rises from the need for high spatial resolution data. Newly emerging technologies, such as unmanned aerial vehicles (UAVs) could aid in provision of the required data. However, their main constraint is the limited payload, requiring the use of miniature sensors. This paper investigates whether a miniature microbolometer thermal camera, designed for a UAV platform, can provide reliable canopy temperature measurements of conifers. Furthermore, it explores whether there is a distinction in whole canopy temperature between the control and the stressed trees, assessing the potential of low-cost thermography for investigating stress in conifers. Two experiments on young larch trees, with induced drought stress, were performed. The plants were imaged in a greenhouse setting, and readings from a set of thermocouples attached to the canopy were used as a method of validation. Following calibration and a basic normalization for background radiation, both the spatial and temporal variation of canopy temperature was well characterized. Very mild stress did not exhibit itself, as the temperature readings for both stressed and control plants were similar. However, with a higher stress level, there was a clear distinction (temperature difference of 1.5 °C) between the plants, showing potential for using low-cost sensors to investigate tree stress. [ABSTRACT FROM AUTHOR]

The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can be avoided. This study proposes a robust strategy to assess crop water status in grapevines. Experiments were performed on Riesling grapevines (Vitis vinfera L.) planted in rows oriented north–south and subjected to three irrigation regimes in a vineyard maintained at an experimental farm in southeastern Washington, USA. Thermal and red–green–blue (RGB) images were acquired during the growing season, using a thermal imaging sensor and digital camera installed on a ground-based platform such that both cameras were oriented orthogonally to the crop canopy. A custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images. The relationship between leaf water potential (Ψleaf) and CWSI was investigated. The results revealed that the proposed algorithm combining thermal and RGB images to determine CWSI can be used for assessing crop water status of grapevines. There was a correlation between CWSI and Ψleaf with an R-squared value of 0.67 for the measurements in the growing season. It was also found that CWSI from the shaded (east) side of the canopy achieved a better correlation with Ψleaf compared to that from the sunlit (west) side around solar noon. The created algorithm allowed real-time assessment of crop water status in commercial vineyards and may be used in decision support systems for grapevine irrigation management. [ABSTRACT FROM AUTHOR]

High temperature and water deficit are among the major limitations reducing lentil (Lens culinaris Medik.) yield in many growing regions. In addition, increasing atmospheric vapor pressure deficit (VPD) due to global warming causes a severe challenge by influencing the water balance of the plants, thus also affecting growth and yield. In the present study, we evaluated 20 lentil genotypes under field conditions and controlled environments with the following objectives: (i) to investigate the impact of temperature stress and combined temperature-drought stress on traits related to phenology, grain yield, nutritional quality, and canopy temperature under field conditions, and (ii) to examine the genotypic variability for limited transpiration (TRlim) trait in response to increased VPD under controlled conditions. The field experiment results revealed that high-temperature stress significantly affected all parameters compared to normal conditions. The protein content ranged from 23.4 to 31.9%, while the range of grain zinc and iron content varied from 33.1 to 64.4 and 62.3 to 99.3 mg kg−1, respectively, under normal conditions. The grain protein content, zinc and iron decreased significantly by 15, 14 and 15% under high-temperature stress, respectively. However, the impact was more severe under combined temperature-drought stress with a reduction of 53% in protein content, 18% in zinc and 20% in iron. Grain yield declined significantly by 43% in temperature stress and by 49% in the combined temperature-drought stress. The results from the controlled conditions showed a wide variation in TR among studied lentil genotypes. Nine genotypes displayed TRlim at 2.76 to 3.51 kPa, with the genotypes ILL 7833 and ILL 7835 exhibiting the lowest breakpoint. Genotypes with low breakpoints had the ability to conserve water, allowing it to be used at later stages for increased yield. Our results identified promising genotypes including ILL 7835, ILL 7814 and ILL 4605 (Bakria) that could be of great interest in breeding for high yields, protein and micronutrient contents under high-temperature and drought stress. In addition, it was found that the TRlim trait has the potential to select for increased lentil yields under field water-deficit environments. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI), based on canopy temperature (Tc), has been widely used in evaluating plant water status and planning irrigation scheduling, but whether CWSI can diagnose the stress status of crops and predict the physiological traits and growth under combined water and salt stress remains to be further studied. Here, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB). The relationships between physiology, growth, and yield of maize with CWSI were analyzed. There were significant differences in NWSB between the two maize genotypes at the same and different growth stages; thus, growth stage-specific NWSBs were used. The difference in NWSB was due to the difference and change in effective leaf width. CWSI was closely related to leaf water potential, stomatal conductance, and net photosynthetic rate under different water and salt stress, and also explained the variations in leaf area index, biomass, water use, and yield. Collectively, CWSI can be used as a proxy indicator of high-throughput phenotyping maize performance under combined water and salt stress, which will be valuable for predicting yield and improving water use efficiency. [ABSTRACT FROM AUTHOR]

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline ( T w e t ) and upper baseline ( T d r y ) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast T w e t and T d r y . The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both T w e t and T d r y , which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI. [ABSTRACT FROM AUTHOR]

Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress. [ABSTRACT FROM AUTHOR]

The temperature-based crop water stress index (CWSI) can accurately reflect the extent of crop water deficit. As an ideal carrier of onboard thermometers to monitor canopy temperature (Tc), center pivot irrigation systems (CPIS) have been widely used in precision irrigation. However, the determination of reliable CWSI thresholds for initiating the CPIS is still a challenge for a winter wheat–summer maize cropping system in the North China Plain (NCP). To address this problem, field experiments were carried out to investigate the effects of CWSI thresholds on grain yield (GY) and water use efficiency (WUE) of winter wheat and summer maize in the NCP. The results show that positive linear functions were fitted to the relationships between CWSI and canopy minus air temperature (Tc − Ta) (r2 > 0.695), and between crop evapotranspiration (ETc) and Tc (r2 > 0.548) for both crops. To make analysis comparable, GY and WUE data were normalized to a range of 0.0 to 1.0, corresponding the range of CWSI. With the increase in CWSI, a positive linear relationship was observed for WUE (r2 = 0.873), while a significant inverse relationship was found for the GY (r2 = 0.915) of winter wheat. Quadratic functions were fitted for both the GY (r2 = 0.856) and WUE (r2 = 0.629) of summer maize. By solving the cross values of the two GY and WUE functions for each crop, CWSI thresholds were proposed as being 0.322 for winter wheat, and 0.299 for summer maize, corresponding to a Tc − Ta threshold value of 0.925 and 0.498 °C, respectively. We conclude that farmers can achieve the dual goals of high GY and high WUE using the optimal thresholds proposed for a winter wheat–summer maize cropping system in the NCP. [ABSTRACT FROM AUTHOR]

Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R 2 ) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes. [ABSTRACT FROM AUTHOR]

There is unprecedented increase in low-temperature stress (LTS) during post-heading stages in rice as a consequence of the recent climate changes. Quantifying the effect of LTS on yields is key to unraveling the impact of climatic changes on crop production, and therefore developing corresponding mitigation strategies. The present research was conducted to analyze and quantify the effect of post-heading LTS on rice yields as well as yield and grain filling related parameters. A two-year experiment was conducted during rice growing season of 2018 and 2019 using two Japonica cultivars (Huaidao 5 and Nanjing 46) with different low-temperature sensitivities, at four daily minimum/maximum temperature regimes of 21/27 °C (T1), 17/23 °C (T2), 13/19 °C (T3) and 9/15 °C (T4). These temperature treatments were performed for 3 (D1), 6 (D2) or 9 days (D3), at both flowering and grain filling stages. We found LTS for 3 days had no significant effect on grain yield, even when the daily mean temperature was as low as 12 °C. However, LTS of between 6 and 9 days at flowering but not at filling stage significantly reduced grain yield of both cultivars. Comparatively, Huaidao 5 was more cold tolerant than Nanjing 46. LTS at flowering and grain filling stages significantly reduced both maximum and mean grain filling rates. Moreover, LTS prolonged the grain filling duration of both cultivars. Additionally, there was a strong correlation between yield loss and spikelet fertility, spikelet weight at maturity, grain filling duration as well as mean and maximum grain filling rates under post-heading LTS (p < 0.001). Moreover, the effect of post-heading LTS on rice yield can be well quantified by integrating the canopy temperature (CT) based accumulated cold degree days (ACDDCT) with the response surface model. The findings of this research are useful in modeling rice productivity under LTS and for predicting rice productivity under future climates. [ABSTRACT FROM AUTHOR]

Drought and high temperature are the major abiotic stresses for wheat production. The present study investigated the effect of drought and chronic heat stress on physiological parameters of durum wheat lines derived from interspecific crosses and their association with yield. Seventy-seven durum wheat lines were evaluated during two seasons (2016–2017 and 2017–2018) for drought tolerance at Tessaout (Morocco) using irrigated and rainfed treatments and for heat tolerance at Wad Medani (Sudan). Five drought screening indices (alone or combined) and physiological parameters were used to assess drought and heat tolerance. Among the physiological parameters used, canopy temperature (CT) had moderate heritability and was significantly affected by both severe and moderate drought stresses. CT at early heading showed a stronger correlation with grain yield (GY) and total biomass (BY) under heat stress. The use of maximum quantum yield of PSII (Fv/Fm) for drought/heat screening was limited by the low genetic variation despite its significant correlation with yield under drought (r2 = 0.22) and heat (r2 = 0.4). The normalized difference vegetation index (NDVI) at vegetative stage was highly correlated with GY and BY and it showed high genotypic variation that can allow for efficient selection. The grain filling rate (GFR) was found to be highly correlated with GY and BY under heat stress. The modified stress tolerance index (MsSTI) had the highest association with GY under drought (R2 = 0.82) while the mean productivity (MP) was adapted to both optimal conditions (R2 = 0.77) and drought stress (R2 = 0.73). The computation of a mean score index (MSI) improved the selection efficiency under drought (R2 = 0.92). The results showed good potential for lines derived from wide crosses to increase variability for heat and drought adaptive physiological traits. [ABSTRACT FROM AUTHOR]

The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities. [ABSTRACT FROM AUTHOR]

Wheat crop experiences high temperature stress during flowering and grain-filling stages, which is termed as "terminal heat stress". Characterizing genotypes for adaptive traits could increase their selection for better performance under terminal heat stress. The present study evaluated the morpho-physiological traits of two spring wheat cultivars (Millet-11, Punjab-11) and two advanced lines (V-07096, V-10110) exposed to terminal heat stress under late sowing. Early maturing Millet-11 was used as heat-tolerant control. Late sowing reduced spike length (13%), number of grains per spike (10%), 1000-grain weight (13%) and biological yield (15–20%) compared to timely sowing. Nonetheless, higher number of productive tillers per plant (19–20%) and grain yield (9%) were recorded under late sowing. Advanced lines and genotype Punjab-11 had delayed maturity and better agronomic performance than early maturing heat-tolerant Millet-11. Advanced lines expressed reduced canopy temperature during grain filling and high leaf chlorophyll a (20%) and b (71–125%) contents during anthesis under late sowing. All wheat genotypes expressed improved stem water-soluble carbohydrates under terminal heat stress that were highest for heat-tolerant Millet-11 genotype during anthesis. Improved grain yield was associated with the highest chlorophyll contents showing stay green characteristics with maintenance of high photosynthetic rates and cooler canopies under late sowing. The results revealed that advanced lines and Punjab-11 with heat adaptive traits could be promising source for further use in the selection of heat-tolerant wheat genotypes. [ABSTRACT FROM AUTHOR]

Canopy temperatures are important for understanding tree physiology, ecology, and their cooling potential, which provides a valuable ecosystem service, especially in urban environments. Linkages between tree species composition in forest stands and air temperatures remain challenging to quantify, as the establishment and maintenance of onsite sensor networks is time-consuming and costly. Remotely-sensed land surface temperature (LST) observations can potentially acquire spatially distributed crown temperature data more efficiently. We analyzed how tree species modify canopy air temperature at an urban floodplain forest (Leipzig, Germany) site equipped with a detailed onsite sensor network, and explored whether mono-temporal thermal remote sensing observations (August, 2016) at different spatial scales could be used to model air temperatures at the tree crown level. Based on the sensor-network data, we found interspecific differences in summer air temperature to vary temporally and spatially, with mean differences between coldest and warmest tree species of 1 °C, and reaching maxima of up to 4 °C for the upper and lower canopy region. The detectability of species-specific differences in canopy surface temperature was found to be similarly feasible when comparing high-resolution airborne LST data to the airborne LST data aggregated to 30 m pixel size. To realize a spatial resolution of 30 m with regularly acquired data, we found the downscaling of Landsat 8 thermal data to be a valid alternative to airborne data, although detected between-species differences in surface temperature were less expressed. For the modeling of canopy air temperatures, all LST data up to the 30 m level were similarly appropriate. We thus conclude that satellite-derived LST products could be recommended for operational use to detect and monitor tree species effects on temperature regulation at the crown scale. [ABSTRACT FROM AUTHOR]

The influence of leaf temperature on transpiration, photosynthesis, respiration, and other metabolic activities is critical to plant growth, development, production and distribution. However, traditional measurement of canopy temperature by thermocouples or thermal infrared thermometers is laborious and difficult, especially for tall trees. The recent development of a handheld thermal infrared imager has made it possible to perform high temporal and spatial canopy temperature measurements efficiently. However, the signal recorded by the sensor is often a mixture of radiation from the target and the atmosphere, which must be corrected to get the true temperature. In this study, we propose a ground-based indirect measurement method of canopy temperature by a handheld thermal infrared imager through upward observation. Visible and thermal images are combined to distinguish the canopy pixels and sky pixels. To remove the atmospheric radiation from the sky, an empirical atmospheric model is established, which can perform atmospheric correction accurately and efficiently. To validate the proposed method, we collected canopy temperatures of 36 species of trees with a FLIR T420 thermal infrared imager and compared the estimated temperatures with those directly measured by thermocouples. The accuracy of the corrected canopy temperature has been significantly improved with mean absolute error reduced from 3.73 °C to 0.64 °C. This proposed canopy temperature measurement method can be used to various applications in remote sensing product validation, and ecosystem and forestry studies. [ABSTRACT FROM AUTHOR]

Newly improved formulations of the bare soil evaporation and the surface temperature are presented, using the multilayer land surface scheme TERRA of the Consortium for Small-scale Modeling (COSMO) atmospheric model. The simulations were carried out in offline mode with atmospheric forcing data from the Meteorological Observatory Lindenberg–Richard-Aßmann-Observatory of the German Meteorological Service. The results show that the bare soil evaporation simulated by the reference version of TERRA is substantially overestimated under wet conditions, and underestimated under dry conditions. Furthermore, the amplitude of the diurnal cycle of the surface temperature is systematically underestimated. In contrast, the diurnal cycles of the temperatures in the soil are overestimated instead. The new description of the bare soil evaporation in TERRA is based on a resistance formulation analogue to Ohm's law, while the surface temperature is now based on the skin temperature formulation by Viterbo and Beljaars. The new formulation improves the simulated bare soil evaporation substantially. In particular, the overestimation under wet conditions is reduced, also acting against an extensive drying of the soil during the annual cycle. Additionally, the underestimation under dry conditions is reduced as well. Furthermore, the simulated amplitude of the diurnal cycle of the surface temperature is substantially increased. In particular, a nocturnal warm bias is systematically reduced. In addition to this, the new formulations were also applied in coupled mode in the COSMO model, resulting in improved diurnal cycles of near-surface temperature and dew point. [ABSTRACT FROM AUTHOR]

Physiological plant water status indicators are useful for managing precision irrigation in regions with limited water resources. The aim of this work was to evaluate the effect of shade netting on the diurnal and seasonal variations of several plant water status indicators in young lime trees (Citrus latifolia Tan., cv. Bearss), grown at the CEBAS-CSIC experimental station in Murcia, Spain. Stem water potential (Ψstem), leaf gas exchange (net photosynthesis (Pn) and stomatal conductance (gs)), and canopy temperature (Tc) were measured on representative days of winter and summer. The Ψstem daily pattern was quite similar in both seasons under both conditions. However, the circadian rhythm of leaf gas exchange was affected by shade conditions, especially in summer, when shaded leaves showed maximum gs values for a longer time, allowing higher net photosynthesis (37%). Canopy temperature behaved similarly in both conditions, nevertheless, lower values were recorded in open-air than in shaded trees in the two seasons. The canopy-to-air temperature difference (Tc − Ta), however, was lower in shaded trees during the daylight hours, indicating the higher degree of leaf cooling that was facilitated by high gs values. The possibility of continuously recording Tc makes it (or the proposed canopy thermal index, CTI) a promising index for precise irrigation scheduling. Shade netting was seen to favour gas exchange, suggesting that it may be considered alternative to open-air for use in semi-arid areas threatened by climate change. [ABSTRACT FROM AUTHOR]

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (ΨPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R2 ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R2 ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard's water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management. [ABSTRACT FROM AUTHOR]

One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding. [ABSTRACT FROM AUTHOR]

Monitoring plant water status is relevant for the sustainable management of irrigation under water deficit conditions. Two treatments were applied to an early-maturing nectarine orchard: control (well irrigated) and precise deficit irrigation (PDI, based on soil water content thresholds). Moderate water deficits generated by PDI were assessed by comparing terrestrial: stem water potential (Ψstem) and gas exchange parameters, with remote: canopy temperature, normalized difference vegetation (NDVI), and soil adjusted vegetation index (SAVI), plant water status indicators. The Ψstem was the only indicator that showed significant differences between treatments. NDVI and SAVI at the postharvest period were appropriate indexes for estimating winter pruning, although they did not serve well as plant stress indicator. Vapor pressure deficit along with Ψstem values were able to predict remote sensing data. Ψstem and canopy to air temperature difference values registered the highest signal intensity and NDVI the highest sensitivity for detecting water deficit situations. The results suggest that care should be taken when using instantaneous remote indicators to evaluate moderate water deficits in deciduous fruit trees; more severe/longer water stress conditions are probably needed. The proposed PDI strategy promoted water saving while maintaining yield, and could be considered a promising tool for semi-arid agrosystems. [ABSTRACT FROM AUTHOR]

Canopy temperature (Tc), one of the most important plant ecophysiological parameters, has been known to respond rapidly to environmental change. However, how environmental factors—especially the temperature and precipitation pattern—impact Tc has been less discussed for forest stands. In this study, we investigated seasonal variations and responses of the Tc and canopy-to-air temperature difference (ΔT) associated with environmental conditions in two subtropical forests with contrasting temperature and precipitation patterns—Dinghushan (DHS) (temperature and precipitation synchronous site: hot and wet in the summer) and Qianyanzhou (QYZ) (temperature and precipitation asynchronous site: hot and arid in the summer). The results showed that Tc exhibits clear diurnal and seasonal variations above air temperature throughout the day and year, suggesting that the canopy of both DHS and QYZ is typically warmer than ambient air. However, the canopy-warming effect was substantially intensified in QYZ, and the difference of ΔT between dry and wet seasons was small (−0.07 °C) in DHS, while it was up to 0.9 °C in QYZ. Regression analysis revealed that this resulted from the combined effects of the increased solar radiation and vapor pressure deficit (VPD), but reduced canopy conductance (gc) caused by drought in the summer in QYZ. Sensitivity analysis further indicated that the responses of ΔT to VPD and gc changes were quite divergent, presenting negative responses to the enhanced VPD and gc in QYZ, while there were positive responses in DHS. The high productivity coupled with low transpiration cooling that occurs in a temperature and precipitation synchronous condition mainly contributes to the positive responses of ΔT in DHS. This study reveals the seasonal variations, environmental responses, and underlying causes of Tc under different temperature and precipitation patterns, providing useful information for the regional assessment of plant responses to future climate change. [ABSTRACT FROM AUTHOR]