Evaluating the Accountability of Trunk Size and Canopy Volume Models for Determining Apple Tree Production Potential Across Diverse Management Regimes

Wright, A. Harrison; Nichols, Douglas S.; Embree, C.G.

doi:10.17660/actahortic.2006.707.30

Cited by 19 publications

(14 citation statements)

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“…The TCSA of a given rootstock-scion combination is widely considered a reliable index of tree growth (canopy size) and also yield potential (Robinson and Lakso, 1991;Strong and Azarenko, 2000;Wright et al, 2006). The DRFC and F treatments had the largest TCSAs and thus predictably the highest yields.…”

Section: Discussionmentioning

confidence: 99%

Response of ‘Honeycrisp®’ Apple Trees to Combinations of Pre-plant Fumigation, Deep Ripping, and Hog Manure Compost Incorporation in a Soil with Replant Disease

Braun¹,

Fuller²,

McRae³

et al. 2010

horts

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This study evaluated the effects of pre-plant treatments: deep ripping (DR), fumigation (F), deep ripping plus fumigation (DRF), deep ripping plus hog manure compost (DRC), and deep ripping plus fumigation plus hog manure compost (DRFC) in comparison with a non-treated control (NTC) on shoot and root performance of ‘Honeycrisp’ apple trees on M.4 rootstocks in an old orchard site with apple replant disease (ARD). Cylindrocarpon spp., Pythium spp., and Pratylenchus penetrans Cobb, all potential agents of ARD, were present in the orchard soil. Fine-root numbers (1 to 2.9 mm diameter) were significantly greater in the DRC and DRFC treatments than the DR treatment. After 6 years, trunk cross-sectional area (TCSA) and yield were largest for the DRFC treatment followed closely by F. The DR treatment had no effect on TCSA, yield, or yield efficiency when applied alone compared with the NTC. Contrast analysis demonstrated that F was significantly better than non-F for yield in all years and TCSA and yield efficiency in 2007. Also, there was a significant interaction between DR and F treatment in 2005 that significantly reduced yield in the DRF treatment. Contrast analysis showed that compost had a significant positive effect on yield in all three production years and TCSA and yield efficiency in 2007. Yield efficiency in the third production year was largest for F, DRC, and the DRFC treatments. Nutrient analysis revealed that soil phosphorus concentrations in compost-treated plots were double those in other treatments. High phosphorus content of compost may have contributed to the amelioration of ARD symptoms. This study found that in 2007, soil fumigation alone, as conventionally used for ARD control, and composted hog manure were equally effective in increasing yield and yield efficiency of apple trees planted in an ARD soil. The DRFC treatment was the overall best treatment in all years.

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Section: Discussionmentioning

confidence: 99%

Response of ‘Honeycrisp®’ Apple Trees to Combinations of Pre-plant Fumigation, Deep Ripping, and Hog Manure Compost Incorporation in a Soil with Replant Disease

Braun¹,

Fuller²,

McRae³

et al. 2010

horts

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show abstract

“…Tree growth. Canopy volume (CV) was calculated using a geometrical model that accounts for the specific contours in shape of each tree (Wright et al, 2006b) in which a is the width of the tree parallel to the row at 75% of the canopy height, b is the width of the tree perpendicular to the row at 75% of the canopy height, h is canopy height, m accounts for the shape contour of the tree, x is the width of the tree base parallel to the row, and y is the width of the tree base perpendicular to the row. The TCSA of each tree was calculated from the circumference measured 30 cm above the graft union.…”

Section: Methodsmentioning

confidence: 99%

Effect of Blossom Density and Crop Load on Growth, Fruit Quality, and Return Bloom in ‘Honeycrisp’ Apple

Embree¹,

Myra²,

Nichols³

et al. 2007

horts

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From 2003 to 2006, the blossom level and crop load of ‘Honeycrisp’ apple (Malus × domestica Borkh.) trees on M.26 rootstocks were adjusted to improve fruit quality and return bloom. The treatments consisted of manually removing flower clusters to 50, 100, and 150 per tree, then at ≈50 d after full bloom, the crop load was adjusted to 3, 6, and 9 fruit/cm2 trunk cross-sectional area (TCSA), respectively. All flower and crop load adjustment significantly increased TCSA and canopy volume compared with the control. Classic biennial bearing was observed on the untreated control trees and those thinned to 150 blossom clusters per tree and 9 fruit/cm2 TCSA and was mitigated for trees with 50 and 100 blossom clusters followed by crop load adjustment to 3 and 6 fruit/cm2 TCSA, respectively. Fruit color the “on” year was always lower on the control trees; no difference was found in the “off” year. The treatments increased fruit weight proportional to crop load except for the 2004 “off” year. This study illustrates that for trees with ≈1 m3 canopy volume, the combined effects of blossom and crop load adjustment to 100 blossom clusters/tree followed by fruitlet adjustment to 6 fruit/cm2 TCSA and below will induce consistent annual production for ‘Honeycrisp’.

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“…Numerous researchers also developed tree crop yield estimation model using different crop structural and biophysical parameters, such as, trunk cross sectional area (TCSA; cm 2 ), canopy volume (m 3 ), canopy leaf area (m 2 ) and intercepted photosynthetically active radiation (PAR) [6][7][8][9]. Wright, Nichols and Embree [9] reported tree canopy volume as a better predictor for yield estimation, than the TCSA and canopy leaf area for apple trees. Reginato, De Cortázar, Robinson and Varela [6], while working on pear production, applied PAR to estimate yield and found a better correlation than TCSA.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango

2018

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Pre-harvest yield estimation of mango fruit is important for the optimization of inputs and other resources on the farm. Current industry practice of visual counting the fruit on a small number of trees for yield forecasting can be highly inaccurate due to the spatial variability, especially if the trees selected do not represent the entire crop. Therefore, this study evaluated the potential of high resolution WorldView-3 (WV3) satellite imagery to estimate yield of mango by integrating both geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model. WV3 images were acquired in 2016–2017 and 2017–2018 growing seasons at the early fruit stage from three orchards in Acacia Hills region, Northern Territory, Australia. Stratified sampling technique (SST) was applied to select 18 trees from each orchard and subsequently ground truthed for yield (kg·tree−1) and fruit number per tree. For each sampled tree, spectral reflectance data and tree crown area (TCA) was extracted from WV3 imagery. The TCA was identified as the most important predictor of both fruit yield (kg·tree−1) and fruit number, followed by NDVI red-edge band when all trees from three orchards in two growing seasons were combined. The results of all sampled trees from three orchards in two growing seasons using ANN model produced a strong correlation (R2 = 0.70 and 0.68 for total fruit yield (kg·tree−1) and fruit number respectively), which suggest that the model can be obtained to predict yield on a regional level. On orchard level also the ANN model produced a high correlation when both growing seasons were combined. However, the model developed in one season could not be applied in another season due to the influence of seasonal variation and canopy condition. Using the relationship derived from the measured yield parameters against combined VIs and TCA data, the total fruit yield (t·ha−1) and fruit number were estimated for each orchard, produced 7% under estimation to less than 1% over estimation. The accuracy of the findings showed the potential of WV3 imagery to better predict the yield parameters than the current practice across the mango industry as well as to quantify lost yield as a result of delayed harvest.

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Evaluating the Accountability of Trunk Size and Canopy Volume Models for Determining Apple Tree Production Potential Across Diverse Management Regimes

Cited by 19 publications

References 0 publications

Response of ‘Honeycrisp®’ Apple Trees to Combinations of Pre-plant Fumigation, Deep Ripping, and Hog Manure Compost Incorporation in a Soil with Replant Disease

Response of ‘Honeycrisp®’ Apple Trees to Combinations of Pre-plant Fumigation, Deep Ripping, and Hog Manure Compost Incorporation in a Soil with Replant Disease

Effect of Blossom Density and Crop Load on Growth, Fruit Quality, and Return Bloom in ‘Honeycrisp’ Apple

Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango

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