摘要
以大棚番茄为研究对象,采用正交试验揭示了不同水分、沸石量和埋深条件下番茄生长特性,建立了番茄生长数学模型,对模拟效果进行了分析评价,并进一步阐明了各因素及水平对番茄生长指标的影响。结果表明:不同水分-沸石量-埋深组合条件下番茄株高动态过程均符合S形变化趋势。不同番茄生长数学模型拟合效果表现为Logistic>Gompertz>Mitscherlich>Korf,对番茄生长动态变化过程量化描述宜采用Logistic模型。最大生长量K_(max)和生长速率系数b与水分正相关,且与埋深负相关。沸石量增加对K_(max)和b影响分别表现为先促后抑和先抑后促。三因素对K_(max)和b影响均表现为水分>埋深>沸石量,水分和埋深对K_(max)以及水分对b存在显著影响。70%~90%田间持水率+沸石量6 t/hm^(2)+埋深15 cm为适宜番茄生长的最优处理。
Orthogonal experiment was used to reveal the dynamic characteristics of tomato growth under different moisture contents,zeolite amounts and buried depths.Some mathematical models were established to simulate the growth process of greenhouse tomato,and the simulation accuracy was analyzed and evaluated.The effect of the factors on the growth parameters of tomato was also revealed.The results show that the dynamic process of tomato plant height under different moisture contents,zeolite amounts and burial depths conforms to S-shaped change trend.The accuracy of models is as follows:Logistic>Gompertz>Mitscherlich>Korf.Logistic model is suitable for the quantitative description of the dynamic growth process of tomato.The maximum growth amountK_(max) and the growth rate coefficient b are positively correlated with W,while negatively correlated with H.The effect of increasing zeolite onK_(max) is firstly promoted and then inhibited,while opposite trend on b.The effect of three factors onK_(max) and b is:W>H>Z.W and H have significant effects onK_(max) and W onb(p<0.05).W_(70-90)F_(6)H_(15 )is the optimal treatment for tomato growth.
作者
雷涛
毕远杰
马娟娟
郭向红
吕棚棚
孙西欢
张勇
路明杰
LEI Tao;BI Yuanjie;MA Juanjuan;GUO Xianghong;LYU Pengpeng;SUN Xihuan;ZHANG Yong;LU Mingjie(Shanxi Province Key Laboratory of Organic Dry Farming,Taiyuan 030031,China;College of Water Resource Science and Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《人民黄河》
CAS
北大核心
2022年第2期153-156,160,共5页
Yellow River
基金
山西省重点研发计划重点项目(201703D211002)
山西省自然科学基金资助项目(201701D221193)
有机旱作山西省重点实验室开放基金资助项目(201805D111015-1)。
关键词
番茄
含水量
沸石
埋深
生长模型
tomato
moisture content
zeolite
buried depth
growth model