基于源库关系的黄花植株三维动态生长及产量模拟

doi:10.12133/j.smartag.SA202310011

Smart Agriculture

• •

基于源库关系的黄花植株三维动态生长及产量模拟

张悦¹(), 李伟佳², 韩志平¹, 张琨¹, 刘佳雯¹, HENKE Michael³()

^1. 山西大同大学农学与生命科学学院/设施农业技术研发中心，山西大同 037005，中国
^2. 山西大同大学煤基生态碳汇技术教育部工程研究中心，山西大同 037005，中国
^3. 湖南农业大学农学院，湖南长沙 410128，中国

收稿日期:2023-10-12 出版日期:2024-02-06
作者简介:
张悦，研究方向为植物功能-结构-环境互作的系统仿真与数字孪生。E-mail：zhangyue@sxdtdx.edu.cn

ZHANG Yue, E-mail: zhangyue@sxdtdx.edu.cn
通信作者:
HENKE Michael，博士，教授，研究方向为植物（三维）表型研究与应用研发。E-mail：mhenke@hunau.edu.cn

Three-dimensional Dynamic Growth and Yield Simulation of Daylily Plants Based on Source-Sink Relationships

ZHANG Yue¹(), LI Weijia², HAN Zhiping¹, ZHANG Kun¹, LIU Jiawen¹, HENKE Michael³()

^1. College of Agronomy and Life Sciences/Facility of Agriculture Technology Research and Development Center, Shanxi Datong University, Datong 037005, China
^2. Engineering Research Center of Coal-based Ecological Carbon Sequestration Technology of the Ministry of Education, Shanxi Datong University, Datong 037005, China
^3. College of Agronomy, Hunan Agricultural University, Changsha 410128, China

Received:2023-10-12 Online:2024-02-06
corresponding author:
HENKE Michael, E-mail: mhenke@hunau.edu.cn
Supported by:
Science and Technology Innovation Project of Higher Education Institutions in Shanxi Province(2021L398); Applied Basic Research Programs of Shanxi Province(202203021222295); Applied Basic Research Programs of Datong City(2023063); Basic Research Programs of Shanxi Datong University(2020CXZ8); Doctoral Research Project of Shanxi Datong University(2022-B-03); Shanxi Datong University Students Innovation and Entrepreneurship Project(XDC2022176)

摘要/Abstract

摘要：

目的/意义 为探究并表达环境因素对黄花各器官生长发育、形态结构和产量的影响，提出一种基于源库关系的黄花植株三维动态生长及产量模拟模型。方法以大同地区黄花主要栽培种植品种大同黄花为研究材料，采集黄花叶片、花葶、花蕾等形态数据和叶片光合生理参数，利用功能-结构植物模型（Functional-Structural Plant Model，FSPM）平台的三维建模技术，建立基于云量的室外地表太阳辐射模型及适配黄花的光合作用模型，同时基于黄花源库关系建立黄花光合产物碳分配模型，利用β生长函数构建黄花各器官生长模拟模型，计算黄花生长期内逐日形态数据，最终实现黄花植株三维动态生长及产量模拟。 结果和讨论 采用实测数据对模型进行检验。结果显示，室外地表太阳辐射实测值和模拟值R²为0.87；剩余标准差（Root Mean Squared Error，RMSE）为28.52 W/m²，黄花各器官模拟模型实测值和预测值R²为0.896~0.984，RMSE为1.4~17.7 cm；平均花蕾产量模拟R²为0.880，RMSE为0.5 g；整体F值为82.244~1 168.533，Sig.值均小于显著水平0.05，表明上述模型拟合度和显著性较好。结论模型能够准确地表现黄花植株在3个主要生长时期的生长规律和形态特征，模拟结果与实际情况相符合，表现出较高的可信度。因此，本模型具有理想的模拟效果，足以满足精细农业领域的研究需求。

关键词: 黄花, 源库关系, 光合作用模型, 形态模型, 三维动态生长模拟, 产量模拟

Abstract:

Objective The daylily, a resilient perennial belonging to the lily family, is widely cultivated across diverse regions of China. This plant boasts a rich nutritional profile, containing significant amounts of protein, carbohydrates, fats, vitamins, and a variety of amino acids, making it a vegetable that is both nourishing and visually appealing. It is often affectionately termed the 'wealth flower' by farmers, due to its high market value and profitability as a crop. Given its economic importance, boosting its yield is a crucial objective. However, current research into daylily cultivation is limited, particularly in the realm of three-dimensional dynamic growth simulation of daylily plants. This study presents an innovative three-dimensional dynamic growth and yield simulation model for daylily plants, grounded in the understanding of the plant's source-sink relationship and harnessing the power of three-dimensional modeling techniques from the functional-structural plant model (FSPM) platform. The simulation model provides a technological foundation for improved cultivation management, growth dynamics prediction, and the design of plant variety types in daylily crops, thereby enhancing agricultural practices and economic returns. Methods This study utilized the open-source GroIMP software platform to simulate and visualize three-dimensional scenes. With Datong daylily, the primary cultivated variety of daylily in the Datong area, as the research subject, a field experiment of daylily was conducted from March to September 2022, which covered the growth season of daylily. Through actual cultivation experiment measurements, morphological data and leaf photosynthetic physiological parameters of daylily leaves, flower stems, flower buds, and other organs were collected. The FSPM platform's three-dimensional modeling technology was employed to establish the Cloud Cover-based solar radiation models (CSRMs) and the Farquhar, von Camerer, and Berry model (FvCB model) suitable for daylily. Moreover, based on the source-sink relationship of daylily, the carbon allocation model of daylily photosynthetic products was developed. By using the β growth function, the growth simulation model of daylily organs was constructed, and the daily morphological data of daylily during the growth period were calculated, achieving the three-dimensional dynamic growth and yield simulation of daylily plants. Finally, the model was validated with measured data. Results and Discussions The coefficient of determination (R²) between the measured and simulated outdoor surface solar radiation was found to be 0.87, accompanied by a Root Mean Squared Error (RMSE) of 28.52 W/m². For the simulated model of each organ of the daylily plant, the R² of the measured against the predicted values ranged from 0.896 to 0.984, with an RMSE varying between 1.4 and 17.7 cm. The R² of the average flower bud yield simulation was 0.880, accompanied by an RMSE of 0.5 g. The overall F-value spanned from 82.244 to 1 168.533, while the Sig. value was consistently below the 0.05 significance level, suggesting a robust fit and statistical significance for the aforementioned models. Subsequently, a thorough examination of the light interaction, temperature influences, and photosynthetic attributes of daylily leaves throughout their growth cycle was carried out. The findings revealed that leaf nutrition growth played a pivotal role in the early phase of daylily's growth, followed by the contribution of leaf and flower stem nutrition in the middle stage, and finally the growth of daylily flower buds, which is the crucial period for yield formation, in the later stages. Analyzing the photosynthetic traits of daylily leaves comprehensively, it was observed that the photosynthetic rate was relatively low in the early spring as the new leaves were initially emerging and reached a plateau during the summer. Considering real-world climate conditions, the actual net photosynthetic rate was marginally lower than the rate verified under optimal conditions, with the simulated net assimilation rate typically ranging from 2 to 4 μmol CO₂/m²·s. Conclusions The appropriate timing, mode of planting, and plant type structure are vital to securing high and consistent yields of daylily. A three-dimensional dynamic growth simulation of daylily plants offers an inclusive representation of their growth and development, morphological structures, and yield formation, spanning from their response to the external environment through germination and growth to the final plant type structure. The three-dimensional dynamic growth model of daylily plants developed in this study can faithfully articulate the growth laws and morphological traits of daylily plants across the three primary growth stages. This model not only illustrates the three-dimensional dynamic growth of daylily plants but also effectively mimics the yield data of daylily flower buds. The simulation outcomes concur with actual conditions, demonstrating a high level of reliability. Consequently, the model exhibits an exemplary simulation effect, providing a comprehensive depiction of the development and transformation of daylily crops. This model serves as an invaluable tool for visualizing plant type design, cultivation management, growth prediction, and other facets of daylily crop research, effectively addressing the demands of precision agriculture.

Key words: Daylily citrina Baroni, source-sink relationship, photosynthesis model, morphological model, three-dimensional dynamic growth simulation, yield simulation

张悦, 李伟佳, 韩志平, 张琨, 刘佳雯, HENKE Michael. 基于源库关系的黄花植株三维动态生长及产量模拟[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202310011.

ZHANG Yue, LI Weijia, HAN Zhiping, ZHANG Kun, LIU Jiawen, HENKE Michael. Three-dimensional Dynamic Growth and Yield Simulation of Daylily Plants Based on Source-Sink Relationships[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202310011.

图/表 14

图1

表1

图2

表2

黄花植物功能-结构模型中的能量平衡方程、光合作用模型（FvCB模型［31］）、气孔导度（BWB模型［32］）

模型类型	公式	描述
叶片能量平衡模型	$T L = T a + R a b s - ε σ T a 4 - λ g v D / P a c p g h + g r + λ [d e s (T a) / d T P a] g v$ （6）	叶片温度 $T L$ 的能量平衡方程
	$g r = 4 ε σ T L 3 C p$ （7）	叶片辐射电导
	$g h = g b × 0.135 / 0.147$ （8）	用于叶片传热计算的叶边界层电导
	$g v = 0.5 × g s g b g s + g b$ （9）	叶片边界层电导
	$e s T = 0.611 e x p (17.502 T 240.97 + T)$ （10）	在T ℃下的叶饱和蒸气压
	$e = 2 g V e s (T L) - e a P a$ （11）	叶片蒸腾速率
光合作用模型（FvCB模型）	$A = V c - 0.5 V 0 - R d = m i n A c, A j, A p - R d$ （12）	净光合速率
	$A c = V c m a x C c - Γ * C c + K c (1 + O / K o)$ （13）	Rubisco限制条件下光合速率
	$A j = J (C i - Γ ) 4 (C i + 2 Γ )$ （14）	RuBP再生限制条件下光合速率
	$θ J 2 - I 2 + J m a x J + I 2 J m a x = 0$ （15）	电子传输速率的光依赖性
	$I 2 = I (1 - f) (1 - δ) / 2$ （16）	光系统II对PAR的吸收
	$A p = 3 P u$ （17）	TPU限制条件下光合速率
	$K T = k 25 e x p E a (T L - 25) 298 R T L + 273$ （18）	Arrhenius函数； $K c, K o, R d, V c m a x$ 的温度依赖性
	$J m a x =$ $J m 25 e x p T p - 25 E a R T p + 273 298 1 + e x p S 298 - H R 298 1 + e x p S T p + 273 - H R (T p + 273)$ （19）	$J m a x$ 的温度依赖性
	$Γ * = 36.9 + 1.88 T p - 25 + 0.036 (T p - 25) 2$ （20）	$Γ *$ 的温度依赖性
	$f ξ = d 0 1 - e x p - d 1 ξ e x p (- d 2 ξ)$ （21）	$J m a x, V c m a x$ 和 $P u$ 的叶龄依赖性
	$C i = C a - A (1 g b + 1 g s) P a$ （22）	胞间CO₂浓度
	$C c = C i - A g m P a$ （23）	叶绿体内CO₂浓度
	$g m = e x p (c - ∆ H a R T k) 1 + e x p (∆ S × T k - ∆ H d R T)$ （24）	叶肉细胞对于CO₂的电导
气孔导度模型（BWB模型）	$g s = b + m A h s (C s / P a)$ （25）	气孔导度
	$Γ = R d K c (1 + O / K o) + V c m a x Γ * V c m a x - R d$ （26）	CO₂补偿点，考虑呼吸作用（R_d ）
	$C s = C a - A g b P a$ （27）	叶片表面CO₂分压
	$g b = 0.147 u / d$ （Gr/Re² >1）（28） $g b = 0.055 ∙ (T p - T a d) 14$ （Gr/Re² <1）（29） $d = 0.72 w$ （30）	水蒸气的边界层电导
	$a h h s 2 + b h h s + c h = 0$ 其中 $a h = (g 1 A) / C s b h = g 0 + g b - (g 1 A / C s) c h = - R H g b - g 0$ （31）	$将 h s 和 g s$ 与扩散方程相结合得到的二次方程

表2

表3

黄花植物功能-结构模型中的能量平衡方程、光合作用模型和气孔导度符号说明

符号类型	符号	含义及单位
光合作用	$Γ$	存在R_d 时的CO₂补偿点［μbar］
	$Γ *$	无R_d 时的CO₂补偿点［μbar］
	$ξ$	以展开后的天数计算的叶龄［day］
	$θ$	电子传递对PAR的响应曲率［0.7，-］
	$δ$	叶反射率加上透射率［0.15，-］
	$A$	叶片净光合速率［μmol/m²·s］
	$A c$	Rubisco限制的CO₂同化率［μmol/m²·s］
	$A j$	电子传输限制的CO₂同化率［μmol/m²·s ］
	$A m a x$	环境条件下光饱和CO₂同化速率［CO₂］［μmol/m²·s］
	$A p$	磷酸丙糖再利用受限的CO₂同化率［μmol/m²·s］
	$C a$	空气CO₂分压
	$C i$	细胞间CO₂分压［μbar］
	$d 0$	叶龄效应的比例因子［1.296，-］
	$d 1$	叶龄效应生长的经验系数［0.146 8，-］
	$d 2$	叶龄效应下降斜率的经验系数［0.010 3，-］
	$E a$	活化能［kJ mol^-1］
	$f$	光谱校正因子［0.15，-］
	$H$	$J m a x$ 温度依赖性的曲率参数［219.4 kJ mol^-1］
	$I$	入射PAR ［μmol photons/m²·s］
	$J$	光合电子传递速率［μmol electrons/m²·s］
	$J m 25$	25 °C 时电子传输的潜在速率［36.8 μmol/m²·s］
	$K c 25$	CO₂的Rubisco-Michaelis-Menten常数［267 μbar］
	$K o 25$	25 °C时O₂的Rubisco-Michaelis-Menten常数［164 mbar］
	O	氧分压［205 mbar ］
	$P u 25$	25 °C时磷酸丙糖利用率［10.35 μmol/m²·s］
	R	通用气体常数［8.314 J/mol·K ^［31］］
	$R d 25$	25 °C时光照条件下的线粒体呼吸作用［0.0089V_cmax25 ］
	S	电子传输温度响应参数［704.2 J/mol·K］
	$V c$	羧化率［μmol/m²·s］
	$V c m 25$	25 °C时的光合Rubisco潜力［92.07 μmol/m²·s］
	$V o$	氧合率［μmol/m²·s］
温度相关的变量	E	每个过程的活化能
	$J m a x$	最大电子传输速率［μmol/m²·s］
	$K c$	CO₂的Rubisco-Michaelis-Menten常数［μmol/m²·s］ E_Kc 80 990
	$K o$	O₂的Rubisco-Michaelis-Menten常数［μmol/m²·s］ E_Ko 36 000
	$P u$	磷酸丙糖利用率［μmol/m²·s］ E_Pu 47 140.1
	$R d$	光下的线粒体呼吸速率［μmol/m²·s］ E_Rd 46 390
	$V c m a x$	Rubisco羧化的最大速率［μmol/m²·s］ E_Vcmax 65 330
气孔导度模型	b	光补偿点时BWB模型对水汽的最小气孔导度［0.096 0 mol/m²·s］
	$C a$	环境CO₂分压［μbar］
	$C s$	叶面CO₂分压［μbar］
	$g b$	边界层对水蒸气的电导［mol/m²·s］
	$g s$	气孔对水蒸气的导度［mol/m²·s］
	$g h$	用于传热的叶片边界层传导率［mol/m²·s］
	$g v$	叶边界层电导［］
	$h s$	叶面相对湿度［］
	m	BWB模型中g_s 对A、C_s 和h_s 敏感度的经验系数［10.055，-］
能量平衡方程	$ε$	叶片热发射率［0.97］
	$σ$	斯特藩-玻尔兹曼常数［ $5.67 × 10 - 8 W / m 2 · K 4$ ］
	$λ$	25 ℃时的潜热［44.0 kJ/mol］
	$c p$	空气的比热容［29.3 J/mol·℃］
	D	环境空气的水蒸气压差［kPa］
	$D s$	叶片表面的水蒸气压差［kPa］
	E	单位叶面积的蒸腾速率［mol/m²·s］
	$e a$	单位投影叶面积的蒸腾速率［kPa］
	$e s$	叶片表面的水蒸气压力［kPa］
	$g h$	边界层单位表面叶面积的热传导率［mol/m²·s］
	$g r$	单位表面叶面积的辐射传导率［mol/m²·s］
	$g v$	单位表面叶面积的总水蒸气传导率［mol/m²·s］
	$P a$	大气压力［kPa］
	$R a b s$	单位表面叶面积吸收的长波和短波辐射能量［W/m²］
	$T a$	周围空气温度［℃］

表3

图3

图4

图5

图6

图7

图8

表3

图9

图10

参考文献 33

1	段九菊, 宋卓琴, 贾民隆, 等. 大同市黄花菜产业发展历程、现状及对策[J]. 中国种业, 2021(1): 17-19.
	DUAN J J, SONG Z Q, JIA M L, et al. Development course, present situation and countermeasures of daylily industry in Datong city[J]. China seed industry, 2021(1): 17-19.
2	田宇. 基于多边形建模和动画关键帧技术的小麦生长三维可视化[D]. 太谷: 山西农业大学, 2020.
	TIAN Y. 3D visualization of wheat growth based on polygonal modeling and animation key frame technique[D]. Taigu: Shanxi Agricultural University, 2020.
3	王勇健. 基于节单位的玉米形态结构三维数字化可视化研究[D]. 南京: 南京农业大学, 2020.
	WANG Y J. Research on 3D digitization and visualization of maize morphological architecture based on phytomer[D]. Nanjing: Nanjing Agricultural University, 2020.
4	李玉超. 基于多视角图像的苗期玉米植株三维重建和表型测量研究[D]. 保定: 河北农业大学, 2022.
	LI Y C. Three-dimensional reconstruction and phenotype measurement of maize plants at seedling stage based on multi-view images[D]. Baoding: Hebei Agricultural University, 2022.
5	淮永建, 杨丹琦, 蔡东娜. 基于三维点云数据的花瓣形态及生长过程模拟[J]. 农业工程学报, 2019, 35(15): 155-164.
	HUAI Y J, YANG D Q, CAI D N. Simulation of plant petal shape change and growth based on 3D point cloud data[J]. Transactions of the Chinese society of agricultural engineering, 2019, 35(15): 155-164.
6	陆健强, 兰玉彬, 毋志云, 等. 植物三维建模ICP点云配准优化[J]. 农业工程学报, 2022, 38(2): 183-191.
	LU J Q, LAN Y B, WU Z Y, et al. Optimization of ICP point cloud registration in plants 3D modeling[J]. Transactions of the Chinese society of agricultural engineering, 2022, 38(2): 183-191.
7	徐浩, 张小虎, 邱小雷, 等. 格网化小麦生长模拟预测系统设计与实现[J]. 农业工程学报, 2020, 36(15): 167-172.
	XU H, ZHANG X H, QIU X L, et al. Design and implementation of gridded simulation and prediction system for wheat growth[J]. Transactions of the Chinese society of agricultural engineering, 2020, 36(15): 167-172.
8	李晓, 吴亚鹏, 贺利, 等. 基于近地高光谱遥感的小麦叶片生长参数动态模型研究[J]. 河南农业大学学报, 2022, 56(2): 199-208, 227.
	LI X, WU Y P, HE L, et al. Dynamic model for wheat leaf growth parameters based on in situ hyperspectral remote sensing[J]. Journal of Henan agricultural university, 2022, 56(2): 199-208, 227.
9	李书钦, 诸叶平, 刘海龙, 等. 基于有效积温的冬小麦返青后植株三维形态模拟[J]. 中国农业科学, 2017, 50(9): 1594-1605.
	LI S Q, ZHU Y P, LIU H L, et al. 3D shape simulation of winter wheat after turning green stage based on effective accumulated temperature[J]. Scientia agricultura Sinica, 2017, 50(9): 1594-1605.
10	杨乐, 彭军, 龙兰, 等. 基于三维动态生长模型的水稻根系模拟[J]. 湖南农业大学学报(自然科学版), 2022, 48(5): 613-618.
	YANG L, PENG J, LONG L, et al. Rice root system simulation research based on three dimensional dynamic growth model[J]. Journal of Hunan agricultural university (natural sciences), 2022, 48(5): 613-618.
11	SOUALIOU S, WANG Z W, SUN W W, et al. Functional-structural plant models mission in advancing crop science: Opportunities and prospects[J]. Frontiers in plant science, 2021, 12: ID 747142.
12	PAO Y C, KAHLEN K, CHEN T W, et al. How does structure matter? Comparison of canopy photosynthesis using one- and three-dimensional light models:A case study using greenhouse cucumber canopies[J]. In silico plants, 2021, 3(2): ID diab031.
13	ZHU X G, WANG Y, ORT D R, et al. E-photosynthesis: A comprehensive dynamic mechanistic model of C3 photosynthesis: From light capture to sucrose synthesis[J]. Plant, cell & environment, 2013, 36(9): 1711-1727.
14	SONNEWALD U, FERNIE A R. Next-generation strategies for understanding and influencing source-sink relations in crop plants[J]. Current opinion in plant biology, 2018, 43: 63-70.
15	LACOINTE A, MINCHIN P E H. Amechanistic model to predict distribution of carbon among multiple sinks[M]// LIESCHE J, Ed. Phloem. New York, NY: Springer New York, 2019: 371-386.
16	SONG Q F, SRINIVASAN V, LONG S P, et al. Decomposition analysis on soybean productivity increase under elevated CO₂ using 3-D canopy model reveals synergestic effects of CO₂ and light in photosynthesis[J]. Annals of botany, 2020, 126(4): 601-614.
17	徐利锋. 整合水稻形态,生理及数量遗传因子的功能与结构模型构建[D]. 杭州: 浙江大学, 2011.
	XU L F. A functional-structural model of rice, integrating morphology, physiology and quantitative genetics[D]. Hangzhou: Zhejiang University, 2011.
18	REYES F, PALLAS B, PRADAL C, et al. MuSCA: A multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An application to static apple tree structures[J]. Annals of botany, 2020, 126(4): 571-585.
19	AUZMENDI I, HANAN J S. Investigating tree and fruit growth through functional-structural modelling: Implications of carbon autonomy at different scales[J]. Annals of botany, 2020, 126(4): 775-788.
20	李进, 韩志平, 李艳清, 等. 大同黄花菜生物学特征及其高产栽培技术[J]. 园艺与种苗, 2019, 39(5): 5-10.
	LI J, HAN Z P, LI Y Q, et al. Review on biological characteristics and higher production culture technique of Datong daylily[J]. Horticulture & seed, 2019, 39(5): 5-10.
21	耿晓东, 王菊秋, 周英, 等. 不同光照强度下3种萱草属植物的光合特性与叶绿素荧光特性[J]. 分子植物育种, 2023, 21(4): 1322-1329.
	GENG X D, WANG J Q, ZHOU Y, et al. Characteristics of photosynthetic and chlorophyll fluorescence of three Hemerocallis species under different light intensities[J]. Molecular plant breeding, 2023, 21(4): 1322-1329.
22	KNIEMEYER O. Design and Implementation of a graph grammar based language for functional-structural plant modelling[D]. Cottbus: Brandenburgische Technische Universität Cottbus, 2008.
23	HEMMERLING R, KNIEMEYER O, LANWERT D, et al. The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition[J]. Functional plant biology, 2008, 35(10): 739-750.
24	ZHANG Y, HENKE M, LI Y M, et al. High resolution 3D simulation of light climate and thermal performance of a solar greenhouse model under tomato canopy structure[J]. Renewable energy, 2020, 160: 730-745.
25	AHAMED M S, GUO H, TANINO K. Cloud cover-based models for estimation of global solar radiation: A review and case study[J]. International journal of green energy, 2022, 19(2): 175-189.
26	HENKE M, KURTH W, BUCK-SORLIN G H. FSPM-P:Towards a general functional-structural plant model for robust and comprehensive model development[J]. Frontiers of computer science, 2016, 10(6): 1103-1117.
27	YIN X Y, GOUDRIAAN J, LANTINGA E A, et al. A flexible sigmoid function of determinate growth[J]. Annals of botany, 2003, 91(3): 361-371.
28	YIN X Y, VAN LAAR H H. Crop systems dynamics an ecophysiological simulation model for genotype-by-environment interactions[M]. Wageningen, the Netherlands: Wageningen Academic Publishers, 2005.
29	XU L F, HENKE M, ZHU J, et al. A rule-based functional-structural model of rice considering source and sink functions[C]// Proceedings of the 2009 Plant Growth Modeling, Simulation, Visualization, and Applications. New York: ACM, 2009: 245-252.
30	GOUDRIAAN J, VAN LAAR H H. Modelling potential crop growth processes: Textbook with exercises[M]. Dordrecht: Kluwer Academic Publishers, 1994.
31	FARQUHAR G D, CAEMMERER S, BERRY J A. A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species[J]. Planta, 1980, 149(1): 78-90.
32	BALL J T, WOODROW I E, BERRY J A. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions[M]// Progress in photosynthesis research. Dordrecht: Springer Netherlands, 1987: 221-224.
33	诸叶平, 李世娟, 李书钦. 作物生长过程模拟模型与形态三维可视化关键技术研究[J]. 智慧农业, 2019, 1(1): 53-66.
	ZHU Y P, LI S J, LI S Q. Research on key technologies of crop growth process simulation model and morphological 3D visualization[J]. Smart agriculture, 2019, 1(1): 53-66.

形态指标（生长最终值）	最大值	最小值	极差	平均值	标准差
叶片长度（1~13叶位）/cm	70.30、84.10、98.20、102.40、109.80、112.70、120.80、122.20、124.10、130.90、132.30、134.20、136.40	45.80、54.20、66.30、71.70、77.90、80.20、81.80、88.00、92.20、94.50、99.30、101.40、102.90	24.50、29.90、31.90、30.70、31.90、32.50、39.00、34.20、31.90、36.40、33.00、32.80、33.50	55.35、64.45、76.85、82.25、90.25、94.63、98.20、104.83、106.28、115.43、120.20、126.38、127.66	8.90、11.53、12.46、10.26、11.90、10.84、14.01、10.20、10.52、12.20、9.83、10.38
叶片宽度/mm	29.44	16.32	13.12	24.61	2.19
花蕾长度/mm	153	91	62	127	1.27
花蕾直径/mm	13.07	8.11	4.96	10.42	1.08
花蕾鲜重/g	6.17	1.92	4.25	3.79	0.84
薹蕾数/（蕾/薹）	59	18	41	40.1	11.48
花葶高度/cm	133.90	86.50	47.40	118.57	16.12
花葶鲜重/g	45.47	25.02	20.45	38.48	7.14

类别	R ²	RMSE	F	Sig.
叶位1	0.984	0.028	304.046	1.14×10^-5
叶位2	0.943	0.064	82.244	2.73×10^-4
叶位3	0.980	0.057	344.191	3.28×10^-7
叶位4	0.938	0.083	106.812	1.72×10^-5
叶位5	0.916	0.142	109.306	1.06×10^-6
叶位6	0.938	0.111	166.137	5.56×10^-8
叶位7	0.919	0.177	158.659	5.01×10^-9
叶位8	0.896	0.144	138.523	2.72×10^-9
叶位9	0.934	0.111	270.099	1.09×10^-12
叶位10	0.920	0.123	230.845	1.9×10^-12
叶位11	0.951	0.123	508.713	1.35×10^-18
叶位12	0.909	0.147	278.480	4.41×10^-16
叶位13	0.926	0.129	426.891	7.99×10^-21
花葶长	0.972	0.088	1 168.533	6.49×10^-28
平均花蕾长	0.914	0.014	211.946	9.72×10^-12
平均花蕾鲜重	0.880	0.511	146.685	1.15×10^-10

基于源库关系的黄花植株三维动态生长及产量模拟

Three-dimensional Dynamic Growth and Yield Simulation of Daylily Plants Based on Source-Sink Relationships

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