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Krista Shellie - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of neural network modeling to predict non-water-stressed Leaf Temperature in wine grape for calculation of crop water stress index
Agricultural Water Management, 2016Co-Authors: Bradley A. King, Krista ShellieAbstract:Precision irrigation management of wine grape requires a reliable method to easily quantify and monitor vine water status to allow effective manipulation of plant water stress in response to water demand, cultivar management and producer objective. Mild to moderate water stress is desirable in wine grape in determined phenological periods for controlling vine vigor and optimizing fruit yield and quality according to producer preferences and objectives. The traditional Leaf Temperature based crop water stress index (CWSI) for monitoring plant water status has not been widely used for irrigated crops in general partly because of the need to know well-watered and non-transpiring Leaf Temperatures under identical environmental conditions. In this study, Leaf Temperature of vines irrigated at rates of 35, 70 or 100% of estimated evapotranspiration demand (ETc) under warm, semiarid field conditions in southwestern Idaho USA was monitored from berry development through fruit harvest in 2013 and 2014. Neural network (NN) models were developed based on meteorological measurements to predict well-watered Leaf Temperature of wine grape cultivars ‘Syrah’ and ‘Malbec’ (Vitis vinifera L.). Input variables for the cultivar specific NN models with lowest mean squared error were 15-min average values for air Temperature, relative humidity, solar radiation and wind speed collected within ±90min of solar noon (13:00 and 15:00 MDT). Correlation coefficients between NN predicted and measured well-watered Leaf Temperature were 0.93 and 0.89 for ‘Syrah’ and ‘Malbec’, respectively. Mean squared error and mean average error for the NN models were 1.07 and 0.82°C for ‘Syrah’ and 1.30, and 0.98°C for ‘Malbec’, respectively. The NN models predicted well-watered Leaf Temperature with significantly less variability than traditional multiple linear regression using the same input variables. Non-transpiring Leaf Temperature was estimated as air Temperature plus 15°C based on maximum Temperatures measured for vines irrigated at 35% (ETc). Daily mean CWSI calculated using NN estimated well-watered Leaf Temperatures between 13:00 and 15:00 MDT and air Temperature plus 15°C for non-transpiring Leaf Temperature consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall. The methodology used to calculate a daily CWSI for wine grape in this study provided a daily indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system.
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Evaluation of neural network modeing to calculate well-watered Leaf Temperature of wine grape
2014Co-Authors: Bradley A. King, Krista ShellieAbstract:Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality, but precision irrigation management is hindered by the lack of a reliable method to easily quantify and monitor vine water status. The crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring Leaf Temperature under identical environmental conditions. In this study, a daily CWSI for the wine grape cultivar Syrah was calculated by estimating well-watered Leaf Temperature with an artificial neural network (NN) model and non-transpiring Leaf Temperature based on the cumulative probability of the measured difference between ambient air and deficit-irrigated grapevine Leaf Temperature. The reliability of this methodology was evaluated by comparing the calculated CWSI with irrigation amounts in replicated plots of vines provided with 30, 70 or 100% of their estimated evapotranspiration demand. The input variables for the NN model were 15-minute average values for air Temperature, relative humidity, solar radiation and wind speed collected between 13:00 and 15:00 MDT. Model efficiency of predicted well-watered Leaf Temperature was 0.91 in 2013 and 0.78 in 2014. Daily CWSI consistently differentiated between deficit irrigation amounts and irrigation events. The methodology used to calculate a daily CWSI for wine grape in this study provided a real-time indicator of vine water status that could potentially be automated for use as a decision-support tool in a precision irrigation system.
Bradley A. King - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of neural network modeling to predict non-water-stressed Leaf Temperature in wine grape for calculation of crop water stress index
Agricultural Water Management, 2016Co-Authors: Bradley A. King, Krista ShellieAbstract:Precision irrigation management of wine grape requires a reliable method to easily quantify and monitor vine water status to allow effective manipulation of plant water stress in response to water demand, cultivar management and producer objective. Mild to moderate water stress is desirable in wine grape in determined phenological periods for controlling vine vigor and optimizing fruit yield and quality according to producer preferences and objectives. The traditional Leaf Temperature based crop water stress index (CWSI) for monitoring plant water status has not been widely used for irrigated crops in general partly because of the need to know well-watered and non-transpiring Leaf Temperatures under identical environmental conditions. In this study, Leaf Temperature of vines irrigated at rates of 35, 70 or 100% of estimated evapotranspiration demand (ETc) under warm, semiarid field conditions in southwestern Idaho USA was monitored from berry development through fruit harvest in 2013 and 2014. Neural network (NN) models were developed based on meteorological measurements to predict well-watered Leaf Temperature of wine grape cultivars ‘Syrah’ and ‘Malbec’ (Vitis vinifera L.). Input variables for the cultivar specific NN models with lowest mean squared error were 15-min average values for air Temperature, relative humidity, solar radiation and wind speed collected within ±90min of solar noon (13:00 and 15:00 MDT). Correlation coefficients between NN predicted and measured well-watered Leaf Temperature were 0.93 and 0.89 for ‘Syrah’ and ‘Malbec’, respectively. Mean squared error and mean average error for the NN models were 1.07 and 0.82°C for ‘Syrah’ and 1.30, and 0.98°C for ‘Malbec’, respectively. The NN models predicted well-watered Leaf Temperature with significantly less variability than traditional multiple linear regression using the same input variables. Non-transpiring Leaf Temperature was estimated as air Temperature plus 15°C based on maximum Temperatures measured for vines irrigated at 35% (ETc). Daily mean CWSI calculated using NN estimated well-watered Leaf Temperatures between 13:00 and 15:00 MDT and air Temperature plus 15°C for non-transpiring Leaf Temperature consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall. The methodology used to calculate a daily CWSI for wine grape in this study provided a daily indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system.
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Evaluation of neural network modeing to calculate well-watered Leaf Temperature of wine grape
2014Co-Authors: Bradley A. King, Krista ShellieAbstract:Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality, but precision irrigation management is hindered by the lack of a reliable method to easily quantify and monitor vine water status. The crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring Leaf Temperature under identical environmental conditions. In this study, a daily CWSI for the wine grape cultivar Syrah was calculated by estimating well-watered Leaf Temperature with an artificial neural network (NN) model and non-transpiring Leaf Temperature based on the cumulative probability of the measured difference between ambient air and deficit-irrigated grapevine Leaf Temperature. The reliability of this methodology was evaluated by comparing the calculated CWSI with irrigation amounts in replicated plots of vines provided with 30, 70 or 100% of their estimated evapotranspiration demand. The input variables for the NN model were 15-minute average values for air Temperature, relative humidity, solar radiation and wind speed collected between 13:00 and 15:00 MDT. Model efficiency of predicted well-watered Leaf Temperature was 0.91 in 2013 and 0.78 in 2014. Daily CWSI consistently differentiated between deficit irrigation amounts and irrigation events. The methodology used to calculate a daily CWSI for wine grape in this study provided a real-time indicator of vine water status that could potentially be automated for use as a decision-support tool in a precision irrigation system.
Kendall C. Dejonge - One of the best experts on this subject based on the ideXlab platform.
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Leaf Temperature of maize and Crop Water Stress Index with variable irrigation and nitrogen supply
Irrigation Science, 2017Co-Authors: David A. Carroll, Neil C. Hansen, Bryan G. Hopkins, Kendall C. DejongeAbstract:Crop canopy Temperature and Crop Water Stress Index (CWSI) are used for assessing plant water status and irrigation scheduling, but understanding management interactions is necessary. This study evaluated whether nutrient deficiencies would confound interpretation of plant water status from Leaf Temperature. Leaf Temperature and CWSI in maize (Zea mays L.) were evaluated with different irrigation strategies and varying nitrogen (N) supply for replicated glasshouse and field studies. Glasshouse treatments consisted of well-watered or simulated drought and sufficient, intermediate, or deficient N. Field study treatments consisted of well-watered, controlled deficit irrigation, or simulated drought and sufficient, sufficient delayed, or deficient N. Average CWSI values varied across irrigation treatments, with 0.37 and 0.54 for glasshouse well-watered and drought and 0.34, 0.47, and 0.51 for field well-watered, drought, and controlled deficit treatments, respectively. Nitrogen levels created widely different Leaf chlorophyll contents without affecting Leaf Temperature or CWSI. Canopy water stress measurements were robust across varying N levels, but CWSI did not correlate well with Leaf area due to confounding effects of irrigation timing and nitrogen levels. Leaf Temperature and CWSI are useful for evaluating crop water status, but nutrient status and timing of water stress must also be considered for crop growth prediction.
Thomas Udelhoven - One of the best experts on this subject based on the ideXlab platform.
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Water stress detection in potato plants using Leaf Temperature, emissivity, and reflectance
International Journal of Applied Earth Observation and Geoinformation, 2016Co-Authors: Max Gerhards, Gilles Rock, Martin Schlerf, Thomas UdelhovenAbstract:Water stress is one of the most critical abiotic stressors limiting crop development. The main imaging and non-imaging remote sensing based techniques for the detection of plant stress (water stress and other types of stress) are thermography, visible (VIS), near- and shortwave infrared (NIR/SWIR) reflectance, and fluorescence. Just very recently, in addition to broadband thermography, narrowband (hyperspectral) thermal imaging has become available, which even facilitates the retrieval of spectral emissivity as an additional measure of plant stress. It is, however, still unclear at what stage plant stress is detectable with the various techniques. During summer 2014 a water treatment experiment was run on 60 potato plants (Solanum tuberosum L. Cilena) with one half of the plants watered and the other half stressed. Crop response was measured using broadband and hyperspectral thermal cameras and a VNIR/SWIR spectrometer. Stomatal conductance was measured using a Leaf porometer. Various measures and indices were computed and analysed for their sensitivity towards water stress (Crop Water Stress Index (CWSI), Moisture Stress Index (MSI), Photochemical Reflectance Index (PRI), and spectral emissivity, amongst others). The results show that water stress as measured through stomatal conductance started on day 2 after watering was stopped. The fastest reacting, i.e., starting on day 7, indices were Temperature based measures (e.g., CWSI) and NIR/SWIR reflectance based indices related to plant water content (e.g., MSI). Spectral emissivity reacted equally fast. Contrarily, visual indices (e.g., PRI) either did not respond at all or responded in an inconsistent manner. This experiment shows that pre-visual water stress detection is feasible using indices depicting Leaf Temperature, Leaf water content and spectral emissivity.
Marc Saudreau - One of the best experts on this subject based on the ideXlab platform.
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Intra-crown spatial variability of Leaf Temperature and stomatal conductance enhanced by drought in apple tree as assessed by the RATP model
Agricultural and Forest Meteorology, 2017Co-Authors: Jérome Ngao, Boris Adam, Marc SaudreauAbstract:The influence of drought intensity on spatial variability of Leaf Temperature was explored by the RATP model. We aimed at specifically determining (i) whether the spatial variability of Leaf Temperature was similar for the whole crown and for the top-viewed crown parts, and (ii) if the spatial variability at these two levels was comparable in a drought stress situation. During the pre-drought period, the Temperature gradient along the Leaf crown evolved diurnally under the major influence of incoming radiation for the whole crown, with the warmer leaves generally distributed in the top part of the crowns, leading to a significant spatial autocorrelation. In the upper part of the crown, the Leaf Temperature gradient was smaller than for the whole crown, and the Leaf Temperature values were more randomly distributed. As drought developed, the model reproduced realistic differences between Leaf Temperature and air Temperature dynamics. Drought increased Leaf spatial variability (in terms of stomatal conductance and Temperature), regardless of the tree position considered. Moreover, for the whole crown, drought did not modify spatial autocorrelation with respect to the pre-drought period, whereas for the upper crown, the spatial autocorrelation felt under the significance level for the lowest relative soil water content levels (i.e., Leaf Temperatures were randomly distributed). When direct radiation reached low values, both the difference between Leaf and air Temperature and Temperature gradient dramatically decreased compared to higher direct radiation levels, regardless of the air Temperature and drought level. The geometrical and functional assumptions of the model are discussed. This study provided an example of the use of functional-structural plant models for assessing multiple interactions between climate, tree architecture and plant physiology, as well as the influence of drought on the within-crown microclimate
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Intra-crown spatial variability of Leaf Temperature and stomatal conductance enhanced by drought in apple trees as assessed by the RATP model
Agricultural and Forest Meteorology, 2017Co-Authors: Jérome Ngao, Boris Adam, Marc SaudreauAbstract:The influence of drought intensity on spatial variability of Leaf Temperature was explored by the RATP model. We aimed at specifically determining (i) whether the spatial variability of Leaf Temperature was similar for the whole crown and for the top-viewed crown parts, and (ii) if the spatial variability at these two levels was comparable in a drought stress situation. During the pre-drought period, the Temperature gradient along the Leaf crown evolved diurnally under the major influence of incoming radiation for the whole crown, with the warmer leaves generally distributed in the top part of the crowns, leading to a significant spatial autocorrelation. In the upper part of the crown, the Leaf Temperature gradient was smaller than for the whole crown, and the Leaf Temperature values were more randomly distributed. As drought developed, the model reproduced realistic differences between Leaf Temperature and air Temperature dynamics. Drought increased Leaf spatial variability (in terms of stomatal conductance and Temperature), regardless of the tree position considered. Moreover, for the whole crown, drought did not modify spatial autocorrelation with respect to the pre-drought period, whereas for the upper crown, the spatial autocorrelation felt under the significance level for the lowest relative soil water content levels (i.e., Leaf Temperatures were randomly distributed). When direct radiation reached low values, both the difference between Leaf and air Temperature and Temperature gradient dramatically decreased compared to higher direct radiation levels, regardless of the air Temperature and drought level. The geometrical and functional assumptions of the model are discussed. This study provided an example of the use of functional-structural plant models for assessing multiple interactions between climate, tree architecture and plant physiology, as well as the influence of drought on the within-crown microclimate.
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Modelling Spatial and Temporal Leaf Temperature Dynamics - A focus on the Leaf boundary layer
2013Co-Authors: Marc Saudreau, Boris Adam, Amélie Ezanic, Sylvain PincebourdeAbstract:A 3D Leaf Temperature model is proposed to estimate dynamics of Temperature gradients at Leaf surface. The 3D Leaf shape, the Leaf physiology, and the microclimate are accounted for. CFD was used to prescribe realistic spatial evolution of the sensible heat flux at the Leaf surface.