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Abstract

Grapevines in cold regions are prone to frost damage in winter. Due to its adverse effects on soil structure, plant damage, high operational costs, and limited mechanization feasibility, buried soil overwintering has been gradually replaced by no-burial overwintering techniques, which are now the primary focus for mitigating frost damage in wine grapes. While current research focuses on the selection of thermal insulation materials, less attention has been paid to the insulation mechanism of covering materials and covering methods. In this study, we investigated the insulation performance of two covering materials (tarpaulin and insulation blanket) combined with six height treatments (5–30 cm) to analyze the effect of insulation space volume on no-buried-soil overwintering. The results show that the thermal insulation performance of the insulation blanket is significantly better than that of the tarpaulin. The 5 cm height treatment under the tarpaulin cover and the 25 cm height treatment under the insulation blanket cover exhibited the best thermal insulation performance. Using a neural network machine learning approach, we constructed a model related to the height of the insulation material and facilitate the model’s accurate predictions, in which tarpaulin R2branches = 0.92, R220 cm = 0.99, and R240 cm = 0.99 and insulation blanket R2branches = 0.89, R220 cm = 0.98, and R240 cm = 0.99. The model predicted optimal insulation heights of 6 cm for the tarpaulin and 22 cm for the insulation blanket. Factors like solar radiation within the insulation space, ground radiation, airflow, and material thermal conductivity affect the optimal insulation height for different materials. This study used a neural network model to predict the optimal insulation heights for different materials, providing systematic theoretical guidance for the overwintering cultivation of wine grapes and aiding the safe development of the wine grape industry in cold regions.

Details

1009240
Business indexing term
Taxonomic term
Title
Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes
Author
Ma, Yunlong 1 ; Yang, Jinyue 1 ; Chen, Yibo 1 ; Wang, Ping 2 ; Sun Qinming 1 

 Agricultural College, Shihezi University, Shihezi 832003, China; [email protected] (Y.M.); [email protected] (Y.C.), Key Laboratory of Special Fruits & Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps, Shihezi 832003, China 
 Food College, Shihezi University, Shihezi 832000, China 
Publication title
Agronomy; Basel
Volume
15
Issue
5
First page
1060
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20734395
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-04-27
Milestone dates
2025-03-20 (Received); 2025-04-25 (Accepted)
Publication history
 
 
   First posting date
27 Apr 2025
ProQuest document ID
3211847027
Document URL
https://www.proquest.com/scholarly-journals/neural-network-based-prediction-non-burial/docview/3211847027/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-05-27
Database
ProQuest One Academic