Abstract
Leaf area index (LAI) and canopy structure are important parameters affecting grape quality and yield of grapevines. Two different experimental protocols as well as the average LAI value of the different protocols for indirect estimation of LAI by gap fraction analysis in VSP-trained grapevines (Vitis vinifera L. cv. Riesling) were tested in this study using plant area index (PAI). Measurements were performed using a plant canopy analyzer. Directly measured LAI and estimated PAI were compared. Protocol SFC (sensor facing the canopy) gave accurate estimates of LAI by measuring PAI along a diagonal transect including eight vines on each side. The correlation between directly measured LAI and estimated PAI was very high (R2 = 0.93) and the root mean square error was lowest of the methods tested here (RMSE = 0.21). Eight measurements below the canopy were enough to accurately estimate LAI. By applying the empirical calibration equation, the measurements provide accurate LAI estimates. Nevertheless, local calibration is required. The method presented provides a useful tool for rapid and precise LAI estimation in VSP training systems and for supporting canopy or management decisions based on LAI.
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Claims 7
Claim · model-written summary
The sensor-facing-canopy protocol gave accurate LAI estimates.
VitisNot reportedComparatorDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Protocol SFC (sensor facing the canopy) gave accurate estimates of LAI by measuring PAI along a diagonal transect including eight vines on each side.”
t0“Grapevines”
Claim · model-written summary
Direct LAI and estimated PAI were highly correlated, with R2 = 0.93.
- Outcome
- correlation between directly measured LAI and estimated PAIe0
- Direction
- very highe0
- Value
- R2 = 0.93e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The correlation between directly measured LAI and estimated PAI was very high (R2 = 0.93)”
t0“Grapevines”
Claim · model-written summary
The tested protocol had the lowest RMSE, 0.21.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the root mean square error was lowest of the methods tested here (RMSE = 0.21)”
t0“Grapevines”
Claim · model-written summary
Eight measurements below the canopy were sufficient for accurate LAI estimation.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Eight measurements below the canopy were enough to accurately estimate LAI.”
t0“Grapevines”
Claim · model-written summary
Applying the empirical calibration equation yielded accurate LAI estimates.
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“By applying the empirical calibration equation, the measurements provide accurate LAI estimates.”
t0“Grapevines”
Claim · model-written summary
Local calibration is required for the estimation method.
- Outcome
- local calibration is requirede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“local calibration is required”
t0“Grapevines”
Claim · model-written summary
The method is recommended for rapid, precise LAI estimation and canopy management decisions.
- Outcome
- rapid and precise LAI estimation in VSP training systems and for supporting canopy or management decisions based on LAIe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The method presented provides a useful tool for rapid and precise LAI estimation in VSP training systems and for supporting canopy or management decisions based on LAI.”
t0“Grapevines”