Establishment of a model for the occurrence and prediction of bayberry decline disease

doi:10.3785/j.issn.1008-9209.2021.09.021

Journal of Zhejiang University (Agriculture and Life Sciences)

2022, Vol. 48

Issue (2): 163-171 DOI: 10.3785/j.issn.1008-9209.2021.09.021

Plant protection

Establishment of a model for the occurrence and prediction of bayberry decline disease

Jinyan LUO¹(

),Xiliang ZHENG²,Xingjiang QI²,Shuwen ZHANG²,Zheping YU²,Haiying REN²(

)

^1.Shanghai Agricultural Technology Extension & Service Center, Shanghai 201103, China
^2.Institute of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

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Abstract

In order to develop the key prevention and control technologies of bayberry decline disease, and grasp the regularity of the disease, one model of disease prediction and forecasting is needed to be established. In this study, orchards with bayberry decline disease were selected in nine locations of Zhejiang Province from 2018 to 2020, and the diseased trees were randomly selected. This study investigated the disease index, measured the nutrient elements of soil and leaves, vegetative growth parameters and fruit quality parameters. The results showed that the disease index of bayberry decline disease was significantly correlated with the available boron, available phosphorus, and available potassium contents of soil, and zinc, potassium, manganese, and boron contents of leaves, and twig length, twig thickness, leaf length, leaf width of vegetative growth parameters, and the mass of single fruit, titratable acid, soluble solid and vitamin C contents of fruits. The twig length and leaf width, which are easy to measure, were selected to fit with the disease index. SPSS software was used for data analysis, and the prediction model of bayberry decline disease was successfully established. Finally, we get the prediction equation of bayberry decline disease of Y=－0.058X₁－5.255X₂＋165.35 (R²=0.64, P=0.02), then we randomly chose three orchard samples for accuracy detection, and found the prediction precision rates were all more than 90%. The establishment of this model lays a foundation for monitoring forecasting and prevention of bayberry decline disease.

Key words： bayberry decline disease disease index soil physical and chemical properties leaf nutrient element vegetative growth fruit quality

Received: 02 September 2021 Published: 29 April 2022

CLC:

S 431.192

Corresponding Authors: Haiying REN E-mail: toyanzi@126.com;renhy@zaas.ac.cn

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Cite this article:

Jinyan LUO,Xiliang ZHENG,Xingjiang QI,Shuwen ZHANG,Zheping YU,Haiying REN. Establishment of a model for the occurrence and prediction of bayberry decline disease. Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48(2): 163-171.

URL:

https://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2021.09.021 OR https://www.zjujournals.com/agr/Y2022/V48/I2/163

杨梅衰弱病发生测报模型的建立

为开发杨梅衰弱病的关键防控技术，掌握杨梅衰弱病发生规律，迫切需要建立对该病的发生测报模型。本研究于2018—2020年在浙江省9地各选择一个衰弱病发生较轻的杨梅果园，随机选取病树，进行病情级数调查以及土壤和叶片营养元素、营养生长和果实品质参数测定。结果发现：杨梅衰弱病的病情指数与病树周围土壤有效硼、有效磷、速效钾含量，叶片中的锌、钾、锰、硼含量，营养生长的梢长、梢粗、叶长、叶宽，果实中可滴定酸含量、单果质量、可溶性固形物含量、维生素C含量等因子之间具有较显著相关性。选取较容易测量的梢长和叶宽与病情指数进行拟合，利用SPSS软件进行数据分析，成功建立了杨梅衰弱病的发生测报模型，最终得到杨梅衰弱病的预测预报方程Y=－0.058X₁－5.255X₂＋165.35（R²=0.64，P=0.02）。随机选择3个果园样本进行准确率检测，得到预测模型的准确率均大于90%。该测报模型的建立为杨梅衰弱病的测报及防控奠定了一定的基础。

关键词： 杨梅衰弱病, 病情指数, 土壤理化性质, 叶片营养元素, 营养生长, 果实品质

Table 1 Investigation of bayberry decline disease

地点 Address	pH	有机质 Organic matter/%	碱解氮 Alkali-hydrolyzable N/(mg/kg)	有效磷 Available P/ (mg/kg)	速效钾 Available K/ (mg/kg)	交换性钙 Exchangeable Ca/ (mg/kg)	交换性镁 Exchangeable Mg/(mg/kg)
上虞 Shangyu	4.52±0.35c	2.63±0.01e	90.27±4.51c	25.76±1.56a	185.50±9.47a	494.18±25.22c	33.09±1.63c
天台 Tiantai	4.92±0.46b	3.19±0.01d	95.88±5.32b	23.20±1.21a	178.18±8.23a	776.52±36.12b	41.02±2.01b
泰顺 Taishun	5.19±0.52a	5.33±0.02a	150.12±9.82a	11.55±0.68e	122.47±6.46d	780.29±36.56b	46.82±2.34a
定海 Dinghai	4.19±0.21d	2.79±0.01d	90.27±1.65c	17.16±0.98c	163.68±8.36b	303.96±15.47f	28.96±1.23e
临海 Linhai	4.38±0.22d	2.83±0.01d	95.88±4.65b	16.68±0.92c	110.66±6.33e	320.23±16.21f	30.82±1.52d
仙居 Xianju	4.48±0.32c	3.14±0.01d	97.76±8.89b	9.48±0.52e	87.87±4.69f	394.56±20.98e	31.04±1.23d
兰溪 Lanxi	4.20±0.15d	2.93±0.01d	101.95±7.11b	21.19±1.26b	167.45±8.85b	470.33±25.34d	28.68±1.36e
文成 Wencheng	4.23±0.26d	3.81±0.01c	102.91±8.21b	18.84±0.96c	131.33±6.35c	740.52±35.65b	34.97±1.65c
青田 Qingtian	4.91±0.36b	4.23±0.01b	135.17±8.22a	14.18±0.63d	124.06±6.47d	857.79±42.16a	39.72±1.78b
相关系数 Coefficient of correlation	－0.384 941	－0.728 040^*	－0.658 234	0.878 669^*	0.845 196^*	－0.326 363	－0.451 887
地点 Address	有效硫 Available S/ (mg/kg)	有效锌 Available Zn/ (mg/kg)	有效铜 Available Cu/ (mg/kg)	有效铁 Available Fe/ (mg/kg)	有效锰 Available Mn/ (mg/kg)	有效硼 Available B/ (mg/kg)
上虞 Shangyu	25.71±1.21c	3.68±0.01d	0.60±0.01c	37.34±1.12d	57.07±2.32c	0.57±0.01c
天台 Tiantai	25.72±1.06c	6.24±0.03b	0.71±0.01b	37.44±1.15d	79.45±3.67b	0.59±0.01bc
泰顺 Taishun	29.73±1.45ab	8.18±0.04a	1.20±0.01a	47.45±2.34c	100.31±5.89a	0.77±0.02a
定海 Dinghai	24.22±1.20c	3.24±0.01d	0.51±0.01c	38.11±1.56d	44.05±2.29d	0.60±0.01b
临海 Linhai	27.71±1.36b	3.30±0.01d	0.55±0.01c	45.79±2.36c	70.46±3.64bc	0.62±0.01b
仙居 Xianju	28.99±1.52b	4.00±0.02c	0.71±0.03b	45.44±2.59c	98.34±4.78a	0.71±0.02a
兰溪 Lanxi	26.11±1.28bc	4.05±0.02c	0.56±0.10c	41.02±2.01cd	60.64±3.03c	0.60±0.01b
文成 Wencheng	33.65±1.10a	4.10±0.02c	0.67±0.01b	55.08±2.68b	58.98±2.75c	0.63±0.02b
青田 Qingtian	37.29±1.45a	5.72±0.02b	0.80±0.04b	62.11±3.23a	77.56±3.34b	0.66±0.01b
相关系数 Coefficient of correlation	－0.786 618^*	－0.418 414	－0.672 269^*	－0.736 408^*	－0.728 040^*	－0.915 966^**

Table 2 Correlations between soil nutrient contents and disease indexes of the weak bayberry

Table 3 Correlations between leaf nutrient contents and disease indexes of the weak bayberry

Table 4 Correlations between vegetative growth parameters and disease indexes of plant body of the weak bayberry

Table 5 Correlations between fruit quality and disease index of the weak bayberry

Table 6 Accuracy test of the prediction model for bayberry decline disease


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