Decoding VPD and CWSI for Optimized Crop Water Management

 

1. Introduction to VPD and Its Significance

Recently, I came across a company promoting a new RH and temperature sensor with AI-powered data analytics. The comments section sparked a lively discussion, with many questions raised about VPD measurements, the impact of radiation, and the importance of sensor shielding and ventilation.

While these are valid concerns, I believe a key aspect is often overlooked. Current AgriTech innovations frequently prioritize combining sensors with AI models, potentially neglecting the fundamental biophysical principles and the limitations of the sensors themselves. Furthermore, relying solely on AI in data-limited fields like agriculture may not always be the most effective approach.

Vapor Pressure Deficit (VPD) is a critical environmental factor that profoundly affects plant water status. Let's examine this concept more closely. While standard meteorological VPD provides a general estimate, the canopy-to-air vapor pressure deficit (Dc) more accurately reflects the driving force for water loss through the stomata. Dc is calculated as the difference between the saturation vapor pressure at the canopy temperature (Tc) and the actual vapor pressure of the air (ea).

2. Challenges in Measuring Canopy Temperature

Accurately measuring Tc using infrared thermometers (IRTs) presents several challenges:

• Accuracy Limitations: IRTs require meticulous calibration, yet inherent errors of 5-10% can still occur. Furthermore, the field of view of the IRT significantly impacts accuracy, as readings can be skewed by soil or other objects within the sensor's range.

• Canopy Temperature Variability: Leaf surface temperatures exhibit considerable variation within a single canopy and even between individual plants. This necessitates careful selection of representative leaves or canopies for accurate measurements.

• Stressor Identification: Changes in canopy temperature can be influenced by various stressors, both biotic (e.g., diseases, pests) and abiotic (e.g., nutrient deficiencies, extreme temperatures). Isolating the specific cause of temperature change can be challenging.

• Temporal Considerations: The time of day significantly impacts canopy temperature measurements, particularly in uncontrolled environments.

• Plant-Specific Responses: Different plant species exhibit varying degrees of stomatal sensitivity to VPD changes. Therefore, the relationship between canopy temperature and water stress may not be consistent across all plant species.

These considerations emphasize the importance of a thorough understanding of the target plant species and its specific stomatal behavior before relying heavily on surface temperature measurements for VPD estimation. Additionally, ensuring that the plant's microclimate is closely coupled to the surrounding air is crucial for accurate measurements.

3. Canopy Temperature and Plant Water Status

Assuming accurate canopy temperature data, healthy plants, appropriate measurement timing, and a plant species known to respond to VPD changes, two key questions arise:

1. How should canopy surface temperature readings differ between well-watered and water-stressed plants?

2. How can we effectively utilize canopy temperature and VPD measurements for practical applications?

The canopy-to-air temperature difference (ΔT = Tc - Ta) is fundamental. In healthy, well-watered plants, active transpiration leads to a negative ΔT, indicating that the leaf surface is cooler than the surrounding air. Conversely, water stress induces stomatal closure, reducing transpiration and causing leaf surface temperature to rise.

Furthermore, even with adequate soil moisture, high atmospheric demand (characterized by high wind, solar radiation, temperature, and low relative humidity - leading to high VPD) can trigger stomatal closure to prevent excessive water loss. This results in less negative ΔT values. Conversely, low atmospheric demand (low wind, radiation, temperature, and high relative humidity - leading to low VPD) can result in ΔT values closer to or even exceeding zero.

The intricate interplay between environmental factors and plant responses necessitates the use of sophisticated models. While existing evapotranspiration models like Penman-Monteith lack canopy surface temperature as an input, incorporating this parameter into transpiration models can significantly improve the accuracy of water loss estimations.

4. The Crop Water Stress Index (CWSI)

The Crop Water Stress Index (CWSI) is a valuable tool for assessing plant water stress. CWSI effectively normalizes ΔT values, enabling comparisons against established wet (non-stressed) and dry (stressed) thresholds specific to the plant species.

CWSI is calculated as:

CWSI = (ΔTm - ΔTl) / (ΔTu - ΔTl)

where:

• ΔTm: Measured canopy-to-air temperature difference

• ΔTl: Temperature difference under non-limiting soil water availability (well-watered plant canopy)

• ΔTu: Temperature difference for a non-transpiring canopy (dead plant)

5. Determining Wet and Dry Thresholds

The determination of wet and dry ΔT thresholds is crucial for CWSI calculation.

Wet Threshold (ΔTl)

• Simple Empirical Method: This approach estimates the wet threshold (ΔTl) as a linear function of VPD: ΔTl = a - bVPD.

  • To determine the coefficients 'a' and 'b' in this equation:

    1. Collect temperature and VPD data for your well-watered plants.

    2. Plot the VPD measurements against the corresponding ΔT values.

    3. Perform a linear regression analysis to find the best-fit line, and extract the slope ('b') and y-intercept ('a').

• Complex Modeling Approach: While the simple empirical method provides a basic framework, a more robust and accurate approach involves the use of complex mathematical models.

Dry Threshold (ΔTu)

ΔTu, the temperature difference for a non-transpiring canopy, is typically assumed as a fixed value. Literature suggests values ranging from 2 to 5°C. However, for a more accurate estimate, you can conduct your own field measurements.

6. Linear Regression Analysis

Linear regression is a statistical method used to find the relationship between two variables. Imagine you have a scatter plot of points, where one variable is on the x-axis (e.g., VPD) and the other is on the y-axis (e.g., temperature difference, ΔT). Linear regression helps you draw the "best-fit" straight line through these points. This line shows how one variable changes with respect to the other.

For example, it can help predict the temperature difference (ΔT) based on the VPD value. The slope of this line indicates how much ΔT changes for a given change in VPD. A steeper slope signifies a stronger relationship between VPD and ΔT. The y-intercept represents the value of ΔT when VPD is zero. In essence, linear regression helps us understand the overall trend and make predictions about the relationship between the two variables.

7. Achieving Accuracy with Practical Considerations

While high accuracy is always desirable, you don't necessarily need the most expensive, laboratory-grade equipment to get meaningful CWSI data. Ensure your IRT or thermal camera is properly calibrated using a blackbody calibrator to get reliable CWSI data.

A blackbody calibrator is a device that emits consistent and known temperatures. By regularly calibrating your IRT or thermal camera against a blackbody, you can ensure accurate temperature readings. This is crucial for obtaining reliable ΔT values and, consequently, accurate CWSI calculations.

By understanding these concepts and implementing appropriate measurement and analysis techniques, agricultural professionals can enhance water use efficiency, improve crop yields, and mitigate the impacts of water stress.

For a deeper dive into CWSI, refer to this paper: Daylight crop water stress index for continuous monitoring of water status

Happy Farming!