Evaluation of the Concord Crop Load Response for Current Commercial Production in New York

Bates, Terry; Jakubowski, Rhiann; Taylor, James A.

doi:10.5344/ajev.2020.20026

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…Several different types of VIs were selected across the three years; however, the DifVI index was the most common higher-order predictor in the data set. This is in accordance with an industry wide survey of Taylor et al (2021) 4 and 5).…”

Section: Discussionsupporting

confidence: 92%

“…doi: 10.5344/ajev.2022.22050 vines in any production system. Concord (Vitis labruscana Bailey) juice grape production in the Lake Erie American Viticulture Area (https://www.ecfr.gov/current/title-27/chapter-I/subchapter-A/part-9/subpart-C/section-9.83 (accessed June 2022)), a cool climate region, operates under such a temperature limitation and the importance of managing crop load to achieve a sustainable and profitable annual level of production is well understood (Bates et al 2021). If the fruit load set is too high for the vine size (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size

et al. 2023

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Background and goals: Lake Erie Concord growers have access to high-resolution spatial soil and production data but lack protocols and information on the optimum time to collect these data.This study intends to provide clearer information regarding the type and timing of sensor information to support in-season management. Methods and key findings:A three-year study in a 2.6 ha vineyard collected yield, pruning mass, canopy vigor and soil data, including yield and pruning mass from the previous year, at 321 sites.Stepwise linear regression and random forest regression approaches were used to model sitespecific yield and pruning mass using spatial historical production data, multi-temporal in-season canopy vigor and soil data. The more complex yield elaboration process was best modelled with

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size

et al. 2023

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“…Moreover, the GDD units' requirements have been specified for a wide variety of cultivations, considering plants' physiological characteristics, phenological observations, and growth stages [19][20][21][22]. The GDD can be characterized as a classic bioclimatic index since it is constantly used for more than three decades, because it is easy to calculate, and there is no need of unobtainable parameters, but it is applicable for a wide variety of cultivations [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Estimation of the Olive and Vine Cultivations’ Growing Degree Days in the Balkans Region

et al. 2021

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Olive and vine cultivations are two of the most important crops in Europe, yielding high quality and value food products. The climate change over the Balkans may elevate the agroecological pressure for the established crops and shift their cultivations areas. One of the widely-used agroclimatic indices is the growing degree days (GDD) which accumulates the necessary thermal units for the selected crops. Despite the advances on the agroclimatic research, there are few available methods for spatiotemporal estimation of this useful index. So, this research is focused on the construction of simple and reliable equations for the calculation and projection of olive and vine cultivations’ GDD over the Balkans. The models’ input parameters are the time, the altitude, the distance from the seashore, and the latitude. Its assembly is made by the extracted spatial data, combined with the Agri4Cast dataset for the period of 1980 to 2018 incorporating the regional climate change trend. The results indicate that the most influential parameter is the time, followed by the latitude, for both cultivations. According to the projections, as quantified by GDD, a vast sprawl of olive and vine cultivation areas will have been formed to the northern parts of the studied area. To be more precise, the viticulture could expand spatially by 28.8% (of the Balkans area) by 2040, and by 15.1% to 2060, when the olive cultivations’ area could sprawl 23.9% by 2040 and 20.3% by 2060.

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“…The hypothesis that B+Com or cover crop or their combination in the vineyard's alleys will positively affect vine growth, grape yield, fruit quality and vine N status was rejected except for the positive effect of cover crop/B+Com on yield in 2020. The effect of B+Com on increasing the number of clusters and reducing berries soluble solids in 2018, was aligned with a trend toward higher yield values in this treatment (Bates et al 2021;Sivilotti et al 2020). The mechanism of this effect is not clear, but could be due to a more balanced vine vigour due to immobilization of soil N in this inherently vigorous vineyard (Bruun et al 2012;Nelissen et al 2015;Zhang et al 2012).…”

Section: Yield Fruit Quality Pruning Weights and Petiole N Concentrationmentioning

confidence: 68%

“…The effect of fresh application of B+Com on soil N immobilization was confirmed by small average cluster size and greater soluble solids compared to other treatments in 2020. The reverse relationship between yield and soluble solids was reported in several studies and was associated with the dilution of photosynthates (Bates et al 2021;Sivilotti et al 2020). The reduction in pruning weight in 2020, and petiole N concentration in 2018 and 2020 by cover crop treatment can be related to competition for resources between cover crop and vine.…”

Section: Yield Fruit Quality Pruning Weights and Petiole N Concentrationmentioning

confidence: 80%

Biochar–compost mixture and cover crop effects on soil carbon and nitrogen dynamics, yield, and fruit quality in an irrigated vineyard

Sharifi

Kamrani

2023

Can. J. Soil. Sci.

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Effects of biochar-compost mixture (B+Com) and cover crop were assessed on soil and grapevine productivity in an irrigated Merlot (Vitis vinifera L.) vineyard in Okanagan Valley, British Columbia (BC), Canada from 2017 to 2020. The experimental design was a factorial arrangement of control, B+Com, cover crop, and combination of cover crop and B+Com (cover crop/B+Com) treatments in alleys with four replications. The B+Com comprised a 1:1 ratio of biochar and compost and was applied at a rate of 22 Mg ha-1 dry-weight basis in May 2017 and 2019. The cover crop consisted of a dryland forage mixture and birds foot trefoil (Lotus corniculatus L.). Biochar-compost treatment did not affect cover crop biomass or tissue C and N concentrations except for a 12% reduction in 2019 biomass. Biochar-compost and cover crop/B+Com increased soil C content averaged across sampling dates by 11 and 17% (P<0.05) only at the 0-15 cm soil depth compared to the control. Cover crop treatment did not affect (P<0.05) soil C content at two soil depths in all sampling dates. The soil N content was not affected by B+Com, decreased by cover crop by average of 12.5% in both soil depths, and increased with cover crop/B+Com by 4% only at the 0-15 cm soil depth averaged across sampling dates (P<0.05). Grape yield was increased by 32% by cover crop/B+Com relative to control only in 2020. The cover crop reduced petiole N and pruning weights in one or two years out of three.

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Evaluation of the Concord Crop Load Response for Current Commercial Production in New York

Cited by 5 publications

References 16 publications

Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size

Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size

Spatiotemporal Estimation of the Olive and Vine Cultivations’ Growing Degree Days in the Balkans Region

Biochar–compost mixture and cover crop effects on soil carbon and nitrogen dynamics, yield, and fruit quality in an irrigated vineyard

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