Research on Growth Model for Hydroponic Lettuce in Facility Plastic Greenhouse in Southern China

doi:10.11924/j.issn.1000-6850.casb2023-0488

Abstract

Abstract:

In order to optimize the digital production management measures of hydroponic lettuce in facility plastic greenhouse in southern China, the hydroponic planting experiments on ‘Italian lettuce’ were conducted in facility plastic greenhouse of Guangdong Agro-meteorological Experimental Station from November 2021 to February 2022. Models for predicting hydroponic lettuce growth index based on two agro-meteorological indicators of effective accumulated temperature (EAT) and product of thermal effectiveness and PAR (TEP) were used to determine greenhouse environmental elements and lettuce growth data, and the models were validated by independent experimental sampling data. The results showed that, (1) the determination coefficients (R²) of the predicted and measured values of leaf number, leaf area, leaf fresh and dry weight based on the EAT model were as follows: 0.911, 0.963, 0.948 and 0.934, the root mean square errors were 3.194 pieces, 398.298 cm², 26.488 g and 1.934 g. (2) The R² of the predicted and measured values of the model based on the TEP indicator were 0.940, 0.972, 0.965 and 0.956, the root mean square errors were 1.260 pieces, 170.672 cm², 9.261 g and 0.464 g, respectively. This study suggests that the TEP model is superior to EAT model in general, and the fitting accuracy of TEP model between the predicted value and the measured value is better than EAT model in facility plastic greenhouse of southern China.

Key words: hydroponic lettuce, plastic greenhouse, TEP, EAT, model

ZHU Huaiwei, HE Bohan, ZHENG Zehua, WANG Guanglun, LIANG Huanqing. Research on Growth Model for Hydroponic Lettuce in Facility Plastic Greenhouse in Southern China[J]. Chinese Agricultural Science Bulletin, 2024, 40(16): 49-56.

Figures/Tables 6

References 34

[1]	李燕, 王丹丹, 齐连芬, 等. 基于主成分分析和聚类分析不同施氮量对黄瓜产量、品质影响的综合评价[J]. 东北农业科学, 2022, 47(2):110-114,149.
[2]	王晓森, 刘祖贵, 刘浩, 等. 番茄茎直径MDS的通径分析与数值模拟[J]. 农业机械学报, 2012, 43(8):187-192.
[3]	彭丽英, 麦宗鉴, 王华, 等. 基于积分回归的花生产量气候影响因子研究[J]. 气象科技进展, 2021, 11(6):49-51,75.
[4]	周丽涛, 孙爽, 郭尔静, 等. 干旱条件下APSIM模型修正及模拟华北冬小麦产量效果[J]. 农业工程学报, 2023, 39(4):1-12.
[5]	杨雪宁, 张永强, 张选泽, 等. 基于留一交叉验证法的APSIM-Maize产量模拟[J]. 作物学报, 2023, 49(10):2854-2860. doi: 10.3724/SP.J.1006.2023.23064
[6]	赵子皓. 高温胁迫下ORYZA2000模型对水稻生殖生长的模拟与改进[D]. 南京: 南京信息工程大学, 2022.
[7]	郑昌玲, 张蕾, 侯英雨, 等. 基于WOFOST模型的冬小麦产量动态预报方法[J]. 干旱地区农业研究, 2022, 40(6):242-250,267.
[8]	宋春亚. 基于AquaCrop模型的冬小麦作物模型构建与应用研究[D]. 天津: 天津大学, 2019.
[9]	李剑萍, 杨侃, 曹宁, 等. 气候变化情景下宁夏马铃薯单产变化模拟[J]. 中国农业气象, 2009, 30(3):407-412.
[10]	陈杰. 中国温室作物模型研究状况[J]. 中国农机化, 2012(2):29-32.
[11]	刘志刚, 徐勤超. 基于辐热积的温室微灌基质栽培生菜生长模拟[J]. 江苏农业学报, 2016, 32(6):1315-1319.
[12]	黄语燕, 王涛, 廖水兰, 等. 基于有效积温的NFT栽培生菜生长模型[J]. 北方园艺, 2021(14):39-45.
[13]	石小虎, 李超. 基于辐热积的灌水量处理对日光温室青椒干物质生产与分配影响的模型分析[J]. 灌溉排水学报, 2021, 40(10):10-17.
[14]	黄健熙, 王佳丽, 黄然, 等. 基于积温-辐射与LAI积分面积模型的玉米成熟期预测[J]. 农业机械学报, 2019, 50(12):133-143.
[15]	吴芳, 邹学智, 李静, 等. 基于辐热积的油菜花始花期预报模型[J]. 江苏农业科学, 2021, 49(11):153-157.
[16]	蔡淑芳, 吴宝意, 雷锦桂. 基于光温效应的大白菜生理特性及营养品质动态模拟效果[J]. 中国农业气象, 2021, 42(1):34-43.
[17]	刘世哲. 现代实用无土栽培技术[M]. 北京: 中国农业出版社, 2001:18-20.
[18]	李蕊, 郭建平. 东北春玉米积温模型的改进与比较[J]. 应用气象学报, 2017, 28(6):678-689.
[19]	HEINRICH J L, PATRICIA C. Modeling elongation and leaf unfolding of easter lily during greenhouse forcing[J]. Scientia horticulturae, 1990(44):149-162.
[20]	NASH J E, SUTCLIFFE J V. River flow forecasting through conceptual models, part I- A discussion of principles[J]. Journals of hydrology, 1970, 10:282-290.
[21]	李永秀, 罗卫红, 倪纪恒, 等. 基于辐射和温度热效应的温室水果黄瓜叶面积模型[J]. 植物生态学报, 2006(5):861-867. doi: 10.17521/cjpe.2006.0109
[22]	倪纪恒, 罗卫红, 李永秀, 等. 温室番茄叶面积与干物质生产的模拟[J]. 中国农业科学, 2005, 38(8):1629-1635.
[23]	潘瑞炽. 植物生理学(4版)[M]. 北京: 高等教育出版社, 2006:54-99.
[24]	曹卫星, 罗卫红. 作物系统模拟及智能管理[M]. 北京: 高等教育出版社, 2000:27-28.
[25]	严妍. 温度和光周期对水培生菜生长的影响及生长模型初探[D]. 武汉: 华中农业大学, 2008.
[26]	KATHARINE B P, SANDERS D C, GRANBERRY D M, et al. Heat units, solar radiation and day length as pepper harvest predictors[J]. Agricultural and forest meteorology, 1993, 65:197-205.
[27]	ORTEGA-FARÍAS S O, LEÓN L. Models for predicting apple diameter by using growing-degree days, cultivar Royal Gala[J]. Acta horticulturae, 2002(584):163-167.
[28]	明村豪, 蒋芳玲, 王广龙, 等. 黄瓜壮苗指标与辐热积关系的模拟模型[J]. 农业工程学报, 2012, 28(9):109-113.
[29]	张伟娟, 郭文忠, 王晓晶, 等. 营养液供液高度对水培生菜生长及矿质元素吸收的影响[J]. 中国农业气象, 2018, 39(9):594-600.
[30]	梁飞虹, 崔秋芳, 涂特, 等. 基于水培技术的沼液净化及生菜品质提升[J]. 农业环境科学学报, 2018, 37(4):788-795.
[31]	沈家洛. 营养液管理方式和氮素形态对NFT栽培生菜生长和烧边发生的影响[D]. 南京: 南京农业大学, 2012.
[32]	袁俊强. 植物工厂中光照、吹风模式对生菜产量与品质的影响[D]. 广州: 华南农业大学, 2018.
[33]	胡玥, 崔雯, 金敏凤, 等. 不同栽培方式对生菜生长和营养品质的影响[J]. 上海师范大学学报(自然科学版), 2019, 48(5):566-573.
[34]	李琨. 植物工厂根域通风技术对生菜微环境与生长的影响[D]. 杨凌: 西北农林科技大学, 2020.

生长指标	模型拟合方法	a	b	R²	P
叶片数	EAT	8.072	0.003	0.985	<0.001
叶片数	TEP	8.316	0.006	0.995	<0.001
叶面积	EAT	69.267	0.009	0.987	<0.001
叶面积	TEP	80.637	0.019	0.976	<0.001
叶片鲜重	EAT	1.806	0.011	0.981	<0.001
叶片鲜重	TEP	2.143	0.023	0.956	<0.001
叶片干重	EAT	0.166	0.01	0.956	<0.001
叶片干重	TEP	0.194	0.02	0.930	<0.001

生长指标	模型拟合方法	a	b	R²	P
叶片数	EAT	8.072	0.003	0.985	<0.001
叶片数	TEP	8.316	0.006	0.995	<0.001
叶面积	EAT	69.267	0.009	0.987	<0.001
叶面积	TEP	80.637	0.019	0.976	<0.001
叶片鲜重	EAT	1.806	0.011	0.981	<0.001
叶片鲜重	TEP	2.143	0.023	0.956	<0.001
叶片干重	EAT	0.166	0.01	0.956	<0.001
叶片干重	TEP	0.194	0.02	0.930	<0.001