Calculated variograms and fitted models for the standardized bud burst percentage data in 2012 and 2013, respectively.

<p>Calculated variograms and fitted models for the standardized bud burst percentage data in 2012 and 2013, respectively.</p

Pearson’s correlation coefficients between bud burst percentage of 2012 and 2013 with soil properties for low, medium and high groups respectively (numbers significant at 0.05 level only).

<p>Pearson’s correlation coefficients between bud burst percentage of 2012 and 2013 with soil properties for low, medium and high groups respectively (numbers significant at 0.05 level only).</p

Partition of the overall spatial variation into nonspatial soil variation (NSV), spatially structured variation shared by soil variables (SV_S), spatial variation not shared by soil variables (SV_NS) and unexplained variation (UV).

<p>The calculations were based on the partial least square regressions of bud burst percentage with soil, trend surface, and combination of the two sets of variables.</p

The location of the experimental site in the country, along with layout of the sampling location and elevation map of the field.

<p>The location of the experimental site in the country, along with layout of the sampling location and elevation map of the field.</p

Pearson’s correlation coefficients between the extracted factors from partial least square regressions (PLSRs) of bud burst percentage with soil properties for the low and high groups of 2012 and 2013 respectively.

<p>Two factors were selected for each PLSR.</p

Description statistics of grapevine bud burst percentage in 2012 and 2013, as well as of the statistics of soil properties in the vineyard.

<p>Description statistics of grapevine bud burst percentage in 2012 and 2013, as well as of the statistics of soil properties in the vineyard.</p

Parameters of the fitted variogram models for the standardized bud burst percentage data in 2012 and 2013, respectively.

<p>Parameters of the fitted variogram models for the standardized bud burst percentage data in 2012 and 2013, respectively.</p

Daily maximum, mean and minimum temperatures and irrigation water amount during the bud burst period in 2012 and 2013.

<p>Daily maximum, mean and minimum temperatures and irrigation water amount during the bud burst period in 2012 and 2013.</p

Determination coefficient (R²) from partial least square regressions of bud burst percentage with soil properties, trend surface model, and combination of the two sets of variables for the low, medium and high groups as well as for the entire field of 2012 and 2013 seasons.

<p>Determination coefficient (R²) from partial least square regressions of bud burst percentage with soil properties, trend surface model, and combination of the two sets of variables for the low, medium and high groups as well as for the entire field of 2012 and 2013 seasons.</p

Scaling Up Stomatal Conductance from Leaf to Canopy Using a Dual-Leaf Model for Estimating Crop Evapotranspiration

<div><p>The dual-source Shuttleworth-Wallace model has been widely used to estimate and partition crop evapotranspiration (<i>λET</i>). Canopy stomatal conductance (<i>G_sc</i>), an essential parameter of the model, is often calculated by scaling up leaf stomatal conductance, considering the canopy as one single leaf in a so-called “big-leaf” model. However, <i>G_sc</i> can be overestimated or underestimated depending on leaf area index level in the big-leaf model, due to a non-linear stomatal response to light. A dual-leaf model, scaling up <i>G_sc</i> from leaf to canopy, was developed in this study. The non-linear stomata-light relationship was incorporated by dividing the canopy into sunlit and shaded fractions and calculating each fraction separately according to absorbed irradiances. The model includes: (1) the absorbed irradiance, determined by separately integrating the sunlit and shaded leaves with consideration of both beam and diffuse radiation; (2) leaf area for the sunlit and shaded fractions; and (3) a leaf conductance model that accounts for the response of stomata to PAR, vapor pressure deficit and available soil water. In contrast to the significant errors of <i>G_sc</i> in the big-leaf model, the predicted <i>G_sc</i> using the dual-leaf model had a high degree of data-model agreement; the slope of the linear regression between daytime predictions and measurements was 1.01 (R² = 0.98), with RMSE of 0.6120 mm s⁻¹ for four clear-sky days in different growth stages. The estimates of half-hourly <i>λET</i> using the dual-source dual-leaf model (DSDL) agreed well with measurements and the error was within 5% during two growing seasons of maize with differing hydrometeorological and management strategies. Moreover, the estimates of soil evaporation using the DSDL model closely matched actual measurements. Our results indicate that the DSDL model can produce more accurate estimation of <i>G_sc</i> and <i>λET</i>, compared to the big-leaf model, and thus is an effective alternative approach for estimating and partitioning <i>λET</i>.</p></div