Transpiration-Based CWSI: A More Accurate Approach to Crop Water Stress Assessment

 

This article builds upon my previous work on canopy temperature measurements and the Crop Water Stress Index (CWSI), and I highly recommend reading "Decoding VPD and CWSI for Optimized Crop Water Management" before continuing. My research has focused on two key challenges in the conventional CWSI approach: the calculation of CWSI itself, and how it's applied to irrigation scheduling (the CWSI algorithm). I've developed improvements in both areas, specifically by using a transpiration-based CWSI calculation and a dynamic threshold algorithm. These enhancements are detailed below.

 

CWSI Definitions

Traditional CWSI Definition 

As discussed previously, CWSI is traditionally defined as:

CWSI = (ΔTm - ΔTl) / (ΔTu - ΔTl)

where ΔTm is the measured canopy-to-air temperature difference, ΔTl is the temperature difference under non-limiting soil water availability (well-watered plant canopy), and ΔTu is the temperature difference for a non-transpiring canopy (dead plant).

Transpiration-Based CWSI Definition

However, a less common expression for CWSI exists, based on plant evapotranspiration or transpiration:

CWSI = 1 - (ETa / ETp)

or

CWSI = 1 - (Ta / Tp)

where ETa and ETp are the actual and potential plant canopy evapotranspiration (ET), respectively, and Ta and Tp are the actual and potential plant canopy transpiration, respectively.

The latter definition, based on transpiration, is often more accurate because it isolates the water loss through the plant and doesn't include the evaporation component of ET – which is unrelated to crop water status. While there are fewer established models for calculating transpiration directly, focusing on this aspect offers valuable insights.

 

The transpiration-based CWSI definition is often more accurate because it isolates the water loss through the plant and doesn't include the evaporation component of ET

 

Both ETa and Ta require microclimate data and canopy temperature as inputs, while ETp and Tp require only microclimate data. Potential ET is often calculated as ETp = Kc x ETr, where Kc is the crop coefficient and ETr the reference crop ET.

The calculation of ETp is well-documented, particularly in relation to the Penman-Monteith equation. While widely used, the Penman-Monteith equation has limitations, which I have addressed in my previous post (“Why the Penman-Monteith Evapotranspiration Model Does Not Work in Apple Trees?”).

I personally prefer the transpiration-based CWSI definition. It aligns more naturally with plant physiology, and calculating actual and potential transpiration rates provides valuable information on its own. The resulting CWSI then indicates whether plants are experiencing water stress. Combined with soil moisture sensors, this approach forms a robust crop water management strategy.

Python and C++ Libraries for CWSI and Transpiration

To facilitate the calculation of CWSI and transpiration rates, I've developed Python and C++ libraries, available on GitHub: https://github.com/envitronicslab/CWSI_Function. This repository includes functions for estimating theoretical CWSI, actual transpiration (Ta), and potential transpiration (Tp), based on microclimate data and canopy surface temperatures. Although initially calibrated for apple trees, the code can be adapted for other fruit trees and crops with appropriate adjustments for plant physiology.

 

Model Sensitivity Analysis

My research has shown that air and canopy temperatures are the most influential parameters in CWSI calculation. Therefore, inaccuracies in these measurements have the largest impact on CWSI. For reliable CWSI, air and canopy temperature measurements should ideally be within ±0.5°C. Achieving this level of accuracy can be challenging in practice, but it's the target to strive for.

 

Sources of Error in CWSI

• Sensor and Equipment Accuracy: Accurate microclimate parameters and canopy measurements are essential. Remember, proper calibration and maintenance are more important than simply having expensive equipment.

• Model Accuracy: Physics-based models are generally preferred over empirical or machine learning-based models, as they are more transferable across different locations and conditions.

• Microclimate Measurement Location: Microclimate parameters should be measured as close as possible to the target plant canopy. Measurements taken too far away may not accurately represent the canopy's immediate environment.

• High Humidity: CWSI can be less reliable in high humidity, as transpiration rates decrease and the difference between potential and actual transpiration narrows, reducing the signal-to-noise ratio.

   

   

Accurate microclimate parameters and canopy measurements are essential. Proper calibration and maintenance are more important than simply having expensive equipment.

 

Relative CWSI and Practical Application

Inaccurate measurements can lead to incorrect irrigation decisions. For example, a canopy temperature reading 1°C higher than the actual value could indicate severe water stress when the plant is well-hydrated, leading to unnecessary irrigation. Recognizing the inherent challenges in achieving perfect accuracy, my research has focused on using relative CWSI values, rather than absolute values, and integrating soil moisture data into decision-making.

My research suggests that using relative CWSI—comparing the current CWSI to a baseline established for well-watered conditions—can be more informative than relying on absolute values. This approach, combined with monitoring soil moisture levels, allows for more nuanced irrigation management (see "Unlocking the Secrets of Soil Moisture: A Plant's Perspective" and "Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold").

 

Even "well-watered" plants will have slight variations in their CWSI depending on environmental conditions. By using a dynamic baseline, we account for these natural variations.

 

Summary

CWSI, particularly when based on transpiration rates, is a valuable tool for assessing crop water stress. Accurate proximal measurements of air and canopy temperature are crucial for reliable CWSI values. Factors such as sensor accuracy, model limitations, measurement location, and high humidity can introduce errors.

Integrating relative CWSI and soil moisture data offers a more robust approach to irrigation management, mitigating some of the challenges associated with absolute CWSI values. Future posts will explore these techniques in greater detail.

References

Osroosh, Y., Peters, R.T., Campbell, C., Zhang, Q., 2016. Comparison of irrigation automation algorithms for drip-irrigated apple trees. Computers and Electronics in Agriculture, 128: 87–99.

 Osroosh, Y., Peters, R.T., Campbell, C., 2016. Daylight crop water stress index for continuous monitoring of water status in apple trees. Irrigation Science, 34(3): 209–219.

 Osroosh, Y., Peters, R.T., Campbell, C., Zhang, Q., 2015. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Computers and Electronics in Agriculture, 118: 193–203.

 Osroosh, Y., Peters, R.T., Campbell, C., 2015. Estimating potential transpiration of apple trees using theoretical non-water-stressed baselines. Journal of Irrigation and Drainage Engineering, 141(9): 04015009.

 Osroosh, Y., Peters, R.T., Campbell, C., 2015. Estimating actual transpiration of apple trees based on infrared thermometry. Journal of Irrigation and Drainage Engineering, 141(8): 04014084.

Mohamed, A.Z., Osroosh, Y., Peters, R.T., Bates, T., Campbell, C., Ferrer-Alegre, F., 2020. Monitoring water status in apple trees using a sensitive morning crop water stress index. Irrig. and Drain. 1–15.