Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses

Chen, Ru; Hu, Xiaotao; Wang, Wenè; Rong, Hui; Song, Tianyuan; Guo, Yinyin

doi:10.3390/horticulturae6040086

Cited by 46 publications

(45 citation statements)

References 57 publications

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“…In the V (Figure 4A), R (Figure 4B), and M stages (Figure 4C) of maize growth, there were no significant differences in the NWSB slopes of ZD958 and XY335. Because the NWSB slope reflects crop transpiration capacity, this indicated that the transpiration rates of ZD958 and XY335 were similar in each growth stage [50]. However, there were significant differences in the NWSB intercept between genotypes in each growth stage.…”

Section: Significant Differences In Nwsb Are Attributed To the Differ...mentioning

confidence: 95%

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Liao

Gao

et al. 2021

Remote Sensing

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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.

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Section: Significant Differences In Nwsb Are Attributed To the Differ...mentioning

confidence: 95%

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Liao

Gao

et al. 2021

Remote Sensing

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“…Although we cannot guarantee that every measurement was taken on a sunny and windless day, the data were averaged on a large number of measured values (270 data every time), which help to minimize the discrepancy in data and the adverse effect of weather factors. As an index indicating the severity of drought stress on plants, CWSI was adopted and defined as [43]:…”

Section: Crop Water Stress Indexmentioning

confidence: 99%

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

et al. 2021

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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 0.252 for winter wheat, and 0.229 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.

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“…The upper limit was the soil field capacity, and the lower limit was 65% of the upper limit during the shoot growth stage and the veraison and maturity stages and 70% of the upper limit during the fruit expansion stage. The calculation formula for M [31] is expressed as follows:…”

Section: Experimental Designmentioning

confidence: 99%

Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

Peng

Chen

et al. 2021

Water

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Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.

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Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses

Cited by 46 publications

References 57 publications

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

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