Enhancing the Precision and Spatial Acuity of Point Quadrat Analyses via Calibrated Exposure Mapping

Sample data set.

Sample data to demonstrate the proposed new PQA metrics were obtained from a 16-row block of Vignoles (Vitis sp.) at a commercial vineyard in Hector, New York (Finger Lakes region, east side of Seneca Lake). Vines were planted in north-south row orientation, trained to high-wire umbrella, and managed according to standard viticultural practices for hybrid canopies in the Finger Lakes region. Half of the vines in the block were shoot thinned to a target of 20 shoots per linear canopy row meter in a replicated fashion, while the remaining (control) vines averaged 24 shoots per linear meter of canopy.

Canopy biomass characterization.

Point quadrat analysis was performed preveraison in mid-July by inserting a thin metal rod into the fruiting zone along the transverse axis of the canopy row (Smart and Robinson 1991). A tape measure was used as a guide for insertions, which were made at 20-cm intervals along the length of the four-vine panel at the height of the fruiting wire, resulting in a total of 36 insertions per panel.

Photon flux measurements.

PPF was measured with an AccuPAR LP-80 photosynthetically active radiation sensor (Decagon Devices, Pullman, WA). Ambient flux was measured above each data panel by averaging 10 flux samples collected over ~10 seconds. For the ambient measurements, the ceptometer sensor bar was oriented parallel to the ground, with the sensors facing directly upward toward the sky. Intracanopy photon flux was measured by placing the ceptometer inside the canopy with the sensor bar aligned with the longitudinal axis of the row and the sensors facing directly upward toward the sky. Sensor height was the same height used for PQA measurements, and sensor depth was the transverse axis (center) of the trellising system. In practice, this depth equated to the location of the cordon wire. The in-canopy flux of each vine was measured by averaging 10 flux samples, from a single location, collected over ~10 seconds. %PPF for the center of each vine was determined by dividing the average vine canopy flux measurement by the ambient flux measurement for the panel and multiplying by 100. Measurements were recorded with the ceptometer bar set to sensor averaging mode.

Continuous functions for cluster and leaf exposure.

The standard PQA metrics for sunlight exposure, PIC and PIL, are binary functions through which clusters and leaves are categorized as either interior or not interior to the canopy. Three new metrics were developed to provide a continuous analog measure of exposure. The first, occlusion layer number (OLN), is the total number of leaf and cluster contacts for an insertion sample. The purpose of OLN is to formalize the idea that all canopy contacts contribute to canopy density (Reynolds et al. 1994, 1996), and thus, create shade in the canopy. OLN is a measure of the overall shade-producing biomass density of the canopy, and was calculated as in equation 1:

Clusters and leaves at the second position in a PQA insertion are partially exposed to sunlight (Reynolds et al. 1994). We developed new metrics that determine the distance, in occlusion layers, of a leaf or cluster to the nearest canopy boundary. For a given set of PQA data, the new functions, cluster exposure layer (CEL) and leaf exposure layer (LEL), were calculated as follows:

The ‘Min’ expression in equations 2 and 3 denotes that the smaller of the two values was used in the calculation. The first parameter in the expression computes the distance of the leaf or cluster to the PQA insertion side of the canopy, while the second parameter calculates the distance to the PQA exit side of the canopy. In determining the distance to the canopy boundary, both cluster and leaf contacts are counted. In computing CEL for a data set, the distance from the canopy boundary for each contact was individually computed, added to a running total, and divided by the total number of cluster contacts. In computing LEL for a data set, the distance from the canopy boundary for each contact was individually computed, added to a running total, and divided by the total number of leaf contacts. A leaf or cluster at either the insertion or exit canopy boundary was considered to be at exposure layer zero (i.e., on the exterior of the canopy). Canopy gaps were not included in either CEL or LEL calculations. Gaps are often localized (i.e., not evenly distributed) in the canopy, and thus, would inappropriately skew the CEL and LEL values of the denser canopy portions.

Canopy biomass symmetry.

Cluster canopy symmetry (CCS) was developed to further enhance the precision of the exposure analysis by computing the positional bias of clusters within the canopy. CCS was expressed as a number between -1 and 1, with a value of 0 for a set of PQA insertion data indicating that clusters were equally balanced between their distance, in canopy layers, to insertion side of the canopy and the exit side of the canopy. A hypothetical CCS value of 1 would indicate that all clusters are located exactly at the insertion side boundary of the canopy, while a value of -1 would indicate that all clusters were located exactly at the exit side boundary of the canopy. The CCS metric was designed to characterize the distribution of biomass along the transverse axis of the canopy. By quantifying this symmetry, CCS enables researchers to integrate any available temporal flux data related to the local solar zenith angle and row orientation. CCS was calculated as in equation 4:

The expression, OLN – CEL – 1, computes the number of shading layers between a cluster and the farthest canopy boundary.

Calculating the positional bias of leaves required a different approach, because there are many more leaves and they have more influence, versus clusters, on the light environment in the canopy. Because leaves account for most of the contacts in a PQA data set, they are inherently symmetric within the set. We determined that computing leaf symmetry using an LEL-based variant of equation 4 would generally produce numbers very close to zero because the midpoint of the average leaf-dominated PQA insertion sample has an equal number of leaves on either side. That does not necessarily mean the leaves are symmetrically arranged around the centerline of the canopy.

As an alternative to calculating a self-referential symmetry for leaf contacts, we developed a metric to calculate biomass symmetry with respect to the intended centerline of the trellising system. By including the trellising system centerline in PQA insertion data (using ‘W’ to record the location of the wire), we calculated the trellis contact symmetry (TCS). TCS, also expressed as a number between -1 and 1, was developed to provide a measurement of trellis consistency and the efficacy of cultural practices intended to maintain a symmetric vine row. TCS was also intended to reveal thigmomorphogenetic responses to local weather phenomena or other environmental stressors (Tarara et al. 2005). Designed as a measure of consistency, TCS is intended to be used in standard deviation calculations. For example, a vineyard could have a mean TCS of zero, but still have high variability from panel to panel or row to row. This variability would be revealed in the standard deviation of the TCS values. TCS was calculated as follows:

The use of traditional PQA calculations (Smart and Robinson 1991) and the new spatial calculations on three example insertions are shown in Table 2⇓.

Calibration of light attenuation.

Light attenuation in grapevine canopies has been shown to have an exponential relationship to canopy depth when depth is expressed as either absolute distance (Dokoozlian and Kliewer 1995a, 1995b) or as a function of LLN (Smart 1985). This implies that the general shape of the light attenuation curve for any given canopy is exponential with respect to PQA exposure layer. With the goal of maximally leveraging the spatial precision of CEL and LEL, we developed a field method for calibrating the light attenuation curve of a canopy. By assuming that the acceleration of attenuation across occlusion layers is approximately constant, we determined that the PPF exposure at a given insertion position can be calculated as:

where Ep_{Exposure layer} represents the percentage of above-canopy PPF (%PPF) that has reached a given exposure layer, and where Ep₁, or the canopy calibration coefficient, represents the percentage of light that is transmitted across each canopy occlusion layer. For example, an Ep₁ value of 0.34 indicates that each occlusion layer in the canopy blocks 66% of sunlight, while allowing the remaining 34% (a combination of sun flecks and light transmitted through leaves) to reach the next layer. Each canopy will possess a unique rate of attenuation because of its particular canopy architecture and Ep₁ value, which is influenced by innumerable variables including cultivar, nutritional status, and cultural practices.

Since Ep₁ represents a constant acceleration of attenuation, a canopy can be calibrated by fitting the n^th root curve with only two known points. The first point, 100% transmittance at occlusion layer zero, is fixed for all canopies. To locate a second point, %PPF was measured directly in the canopy. Rearranging equation 6 yielded:

Although Ep₁ is equal to the transmittance at occlusion layer one, we determined that it was not practical to attempt to measure it directly because occlusion layer 1, or any other fixed integer occlusion layer, cannot be reliably located for flux sampling. The need to locate a specific canopy layer was avoided by sampling %PPF at the longitudinal midline of the canopy. On average, the longitudinal midline of the canopy is half the distance between the PQA insertion side and exit side of the canopy. Thus, the longitudinal midline can be said to be at OLN/2. Applying the %PPF measurements at OLN/2 to equation 7 yielded:

Photon flux measured at the center of the canopy is the sum of sunlight penetrating from both sides of the row. To incorporate this bilateral approach, equation 6 was updated to independently calculate the PPF from either side of the canopy, as follows:

Biomass exposure mapping.

Following from OLN, CEL, LEL, and Ep₁, additional metrics were developed to determine %PPF for any given leaf or cluster in a canopy. Cluster exposure flux availability (CEFA) and leaf exposure flux availability (LEFA) express %PPF of a given PQA data set. CEFA and LEFA follow from equation 9 and were computed as follows:

Exposure maps, depicting distribution of %PPF values among cluster and leaf contacts, were created by calculating CEFA and LEFA values for each contact in the PQA data set (Figure 1⇓).

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Figure 1

Cluster exposure map (A) and leaf exposure map (B) for sample data set. Total cluster count for control and shoot-thinned treatments was 592 and 580, respectively. Total leaf count for control and shoot-thinned treatments was 2107 and 1751, respectively.

Cluster exposure flux symmetry (CEFS) and leaf exposure flux symmetry (LEFS) were developed to provide calibrated PPF symmetry metrics that are analogous to CCS. CEFS and LEFS represent the symmetry of PPF received by clusters and leaves from either side of the canopy row. Like CCS, CEFS and LEFS were expressed as a number between -1 and 1, where a CEFS value of 0 for a set of PQA insertion data indicated that clusters were receiving an equal amount of photon f lux from both sides of the canopy. A hypothetical CEFS value of 1 would indicate that all clusters in the data set received all of their PPF from the insertion side of the canopy, while a value of -1 would indicate that all clusters received all of their photon flux from sunlight from the exit side of the canopy.

CEFS and LEFS were designed to enable the integration of temporal flux data related to the local solar zenith angle and row orientation. For example, if mesoclimatic data indicated that a block received 10% more sunlight on the canopy exit side, a grower would probably assume that clusters were receiving more light from that canopy side. By calculating CEFS, true bias in cluster exposure symmetry can be calculated. CEFS was defined as follows,

Continuing the example, assume that the grower calculated a CEFS value for the block of 0.08, indicating that biomass asymmetry in the canopy caused an 8% bias in %PPF toward the insertion side of the canopy. This bias acts to offset the imbalance caused by sun tracking asymmetry, suggesting that the clusters actually received an approximately equal amount of light from either side of the canopy, despite the sun tracking bias to the exit side. Similarly, LEFS calculations were defined by substituting LEFA for CEFA in equation 12. LEFS is expected to generally be very close to zero for most canopies because of the dominance of leaf contacts in PQA data sets.

Canopy characterization and calibration of flux attenuation.

The new metrics, when applied to the sample data set, indicated there was an effect of shoot thinning in reducing canopy density and increasing sunlight exposure of both leaves and fruit. By traditional PQA metrics, shoot thinning decreased canopy LLN, PIC, PIL, and increased PG (Table 3⇓). Using the new spatial metrics and calibrated flux metrics (Table 4⇓), shoot thinning decreased canopy OLN, CEL, LEL, CEFS and increased CCS, Ep₁, CEFA, LEFA, and LEFS.

Biomass exposure gradients.

Cluster exposure mapping indicated that the clusters in the shoot-thinned canopies had higher %PPF values versus the control (Figure 1A⇑). The exposure map indicated that largest reduction in cluster counts for shoot-thinned vines occurred in the %PPF range of 35–39.9%, followed by the ranges of 50–54.9% and 30–34.9%. The largest increase in cluster counts for shoot-thinned vines occurred in the %PPF range 45–49.9%, followed by the ranges 60–64.9%, 65–69.9%, and 80–84.9%.

Similarly, LEFA values were calculated for each leaf contact in the sample data set. The resulting exposure map indicated that the leaves in the shoot-thinned canopies had higher %PPF values versus the control and that the largest reductions in leaf counts for shoot-thinned vines occurred in the %PPF range of 50–54.9%, followed by the ranges 55–59.9%, 75–79.9%, and 35–39.9% (Figure 1B⇑). The largest increase in leaf counts for shoot-thinned vines occurred in the %PPF range of 65–69.9%, followed by the ranges 80–84.9% and 45–49.9%.

Scope of metabolic effect.

Cluster exposure mapping indicated that the effects of shoot thinning on cluster exposure were concentrated in the lower %PPF ranges, with most improvements occurring for clusters originally below 35% ambient PPF (Figure 1A⇑). However, leaf exposure mapping revealed a concentration of efficacy in %PPF ranges above 50% (Figure 1B⇑). These differences suggest that shoot-thinning had broader metabolic effect beyond the increase in cluster exposure.

Improved utility.

The limited spatial precision of PQA has narrowed its use to diagnosing simple canopy problems and providing coarse measurements of treatment efficacy. Spatially-aware cluster and leaf exposure metrics expand the usefulness of PQA by enabling the measurement of subtle differences in light environment, canopy density, and biomass distribution. Canopy calibration further increases the utility of PQA by providing researchers with the tools needed to make direct quantitative comparisons among dissimilar canopies and across multiple studies. The simplicity and precision of calibrated PQA makes it an effective alternative to more time-consuming coordinate-based canopy measurement techniques and to less robust methods such as photographic sunfleck analysis.

Integration of mesoclimatic data.

When temporal mesoclimatic f lux data is available, it can be overlaid onto a %PPF exposure map to produce a map of absolute photon flux. These maps could be used to evaluate flux-dependent physiological responses, such as leaf light compensation point and light saturation. In this way, exposure maps could be used to estimate a variety of physiological responses, such as a canopy’s potential net photosynthetic carbon production over a specific timeframe or the development of light-sensitive metabolites.

Considerations for training systems.

The new spatial metrics (OLN, CEL, LEL, and CCS) were intended to be equally appropriate for any type of canopy, as they do not make any assumptions regarding light attenuation—they merely improve on the spatial precision of standard PQA data sets. The metrics that rely on attenuation calibration (CEFA, LEFA, CEFS, and LEFS) are also intended to be used with any type of training system, but may be more revealing when applied to nonvertical shoot-positioned systems, as they are more likely to have higher OLN and more spatial distribution.

Additional applications.

The light attenuation curve for a calibrated canopy correlates with its distribution of leaf area density (Gladstone and Dokoozlian 2003). As such, the exposure maps created by the methods presented here could be used as leaf area density maps in the study of pests and disease densities or for the calibration of spray equipment for optimal canopy deposition. Canopy biomass symmetry and cluster exposure symmetry metrics could guide the severity and timing of alternate side leaf pulling or other leaf-density management practices. The relevance of these mapping methods could be applied to other row crops.

Use of model.

Calculation of the new spatial metrics can be performed using traditional PQA data sets. Spatial exposure mapping, which requires a minimal number of ceptometer measurements to calibrate the canopy, adds only a few minutes of data collection time per panel. The optional trellising symmetry calculation requires only that the center of the trellis, or “wire,” be recorded along with leaf and cluster contacts as a ‘W’ in the PQA data set. A library of Excel spreadsheet functions that automates the data processing required to compute the new metrics is available from the corresponding author.