Automated Measurement of Crop Water Status Using Canopy Temperature and Biophysical Principles.

Methods that directly determine plant physiological responses to water availability have potential to be significantly more sensitive and accurate than indirect approaches like soil moisture measurement. Stomatal conductance is a rapid physiological response to leaf water potential. Stomatal conductance in single leaves has long been calculated using energy balance principles.  This same biophysical approach can be extended to plant communities using: 1) standard meteorological measurements, 2) accurate measurement of average canopy temperature, and 3) knowledge of canopy architecture.  Here we use a two-source energy balance model designed for the calculation of stomatal conductance (g_C) in row crops with random spatial distribution within rows. The two source model separates soil and canopy heat sources and accounts for the unique characteristic of vegetation clumped in rows.  The distribution of plants in rows affects not only the wind and radiation penetration in the canopy but also the separation of soil and canopy heat sources. Using the necessary environmental measurements, aerodynamic parameters and model modifications, g_C was continuously determined for 7 corn and 8 cotton crops throughout the Midwest and Southern United States. This g_C value was then compared to a calculated reference g_C for a well-watered crop.  This ratio is an indicator of crop water status, which is called the stomatal conductance ratio (SCR).  The SCR was close to one after each irrigation or significant precipitation, and steadily declined until the next irrigation event.  Significant drought stress occurred in several of the fields.  Leaf rolling in corn occurred at an SCR of about 0.5. Daily SCR values were weighted to correspond with growth stage sensitivity to drought stress.  These weighted values were highly correlated with yield (r² values above 0.9).  This biophysical approach has the potential to provide a powerful tool for precision irrigation management.