中国农业气象 ›› 2023, Vol. 44 ›› Issue (07): 611-623.doi: 10.3969/j.issn.1000-6362.2023.07.006

• 农业生物气象栏目 • 上一篇    下一篇

基于微气候适宜度指数构建番茄生长速率模拟模型

郭申伯,刘福昊,王 笛,黄 博,曹晏飞   

  1. 西北农林科技大学园艺学院/农业农村部西北设施园艺工程重点实验室,杨凌 712100
  • 收稿日期:2022-08-20 出版日期:2023-07-20 发布日期:2023-07-17
  • 通讯作者: 曹晏飞,副教授,主要从事设施结构优化及环境调控,E-mail:caoyanfei@nwsuaf.edu.cn E-mail:caoyanfei@nwsuaf.edu.cn
  • 作者简介:郭申伯,E-mail:guoshenbo01@163.com
  • 基金资助:
    陕西省技术创新引导专项(基金)区域创新能力引导计划(2021QFY08−02);陕西省科技创新团队(2021TD-34);陕西省重点研发计划项目(2022ZDLNY03−02)

Construction of A Tomato Growth Rate Simulation Model Based on Climate Suitability Index

GUO Shen-bo,LIU Fu-hao,WANG Di,HUANG Bo,CAO Yan-fei   

  1. College of Horticulture, Northwest A&F University/Key Laboratory of Northwest Facility Horticulture Engineering, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
  • Received:2022-08-20 Online:2023-07-20 Published:2023-07-17

摘要: 设施微气候是番茄生长的重要影响因素,具有参数复杂、变化快的特点。为了科学掌握微气候特征,探明微气候适宜度指数与番茄生长速率的关系,本研究于2021年以番茄为试材,开展春、秋茬实验,对设施内微气候包括气温、相对湿度、太阳辐射、CO2浓度和饱和蒸汽压差(Vapor pressure deficit,VPD)进行监测,每7d进行一次番茄形态指标和生长速率测定。运用因子分析法提出设施微气候适宜度指数计算方法,利用多元线性回归方法构建基于微气候适宜度指数的番茄生长速率模拟模型,对秋茬番茄生长速率进行模拟验证。结果表明:基于计算方法辨别微气候适宜度与基于人工经验判断相符率为75%,微气候适宜度与番茄鲜重(r=0.690)、干重(r=0.623)、株高(r=0.748)的增长量相关性均达到显著水平(P<0.05)。秋茬番茄生长速率模拟结果中,生长速率模拟的拟合度和精度较好,苗期相对生长速率模拟值与实测值R2=0.875,RMSE=0.048d−1,开花坐果期绝对生长速率模拟值与实测值R2=0.785,RMSE=0.877g·d−1。综上所述,本研究为设施微气候定量分析提供了一种新的方法,比温度判断更为全面,同时也为番茄生长速率模型构建提供了新思路。

关键词: 番茄, 因子分析法, 微气候适宜度, 生长速率

Abstract: The microclimate of the facility is an important influencing factor for tomato growth and is characterized by complex parameters and rapid changes. In order to scientifically grasp the microclimate characteristics and explore the relationship between microclimate suitability index and tomato growth rate, two experiments were conducted in 2021 using tomatoes as the test material in insulated plastic greenhouses, with ‘Provence’ as the test tomato variety for the spring crop (January 18-May 24, 2021) and ‘Baolufuqiang’as the test tomato variety for the autumn crop (August 27-December 31, 2021), both in substrate bags. Authors adopt conventional field management methods. Microclimate including temperature, relative humidity, solar radiation, CO2 concentration and vapor pressure deficit (VPD) were monitored in the facility, and tomato morphological indicators and growth rate were measured every 7 days. Authors proposed a method for calculating the microclimate suitability index for facilities using factor analysis, and constructed a tomato growth rate simulation model based on the microclimate suitability index using multiple linear regression method to simulate and verify tomato growth rate in autumn crop. The results showed that the microclimate suitability identified based on the computational method matched 75% with that based on manual empirical judgment, and the correlation between microclimate suitability and the growth amounts of fresh tomato weight (r=0.690), dry weight (r=0.623), and plant height (r=0.748) reached significant levels (P<0.05). In the simulation results of the growth rate of autumn crop, the fitting degree and accuracy of the growth rate simulation were better. The results of autumn crop tomato growth rate simulations showed good fit and accuracy, with simulated relative growth rate at seedling stage with measured values R2=0.875 and RMSE=0.048d−1, and simulated absolute growth rate at flowering and fruiting stage with measured values R2=0.785 and RMSE=0.877g·d−1. In summary, this study provides a new method for quantitative facility microclimate analysis, which is more comprehensive than temperature determination, and also provides a new way of thinking for the construction of tomato growth rate model.

Key words: Tomato, Factor analysis, Microclimate suitability, Growth rate