基于气象因子的设施杨梅生育期模拟模型研究

doi:10.11923/j.issn.2095-4050.cjas2025-0118

摘要/Abstract

摘要：

设施栽培杨梅的生育期模拟是研究杨梅生长与环境因子相互作用的关键环节。为探究设施栽培小气候对杨梅生育期的调控效应、预估生育期演变并构建精准模拟模型，利用2022—2024年浙江兰溪设施杨梅生长发育期观测数据与棚内光照、温度等气象资料，采用有效积温法(Growing Degree Day, GDD)、生理发育时间法(Physiological Development Time, PDT)和钟模型3种方法对越冬期、开花期、坐果期、转红期、成熟期进行模拟与验证。结果表明：钟模型在模拟杨梅各生育期时表现出较高的预测精度和适应性，特别是在转红期和成熟期，其预测误差显著小于GDD法和PDT法。钟模型模拟棚内杨梅各生育期的均方根误差(Root Mean Square Error, RMSE)分别为2.83、3.32、4.24、2.38、2.16 d，归一化均方根误差(normalized Root Mean Square Error, nRMSE)分别为0.47、1.11、0.47、0.48、0.54，显著优于GDD与PDT。钟模型能够更精确反映设施杨梅生育期演变规律，可为设施杨梅精准管理、产期调控与高效栽培提供科学依据。后续可优化越冬期参数并拓展至多品种、多区域应用。

关键词: 杨梅, 设施栽培, 生育期模拟模型, 温光效应, 生物学特性

Abstract:

The simulation of the growth stages of Myrica rubra in protected cultivation is a key aspect in understanding the interaction between the growth of the plant and environmental factors. To explore the impact of the microclimate in protected cultivation on the growth stages of Myrica rubra and accurately predict the evolution of these stages, this study utilized growth and development data of Myrica rubra from 2022 to 2024 in Lanxi of Zhejiang, along with concurrent meteorological data on light, temperature, and other factors. Three methods were employed for the growth stage simulation: growing degree day (GDD), physiological development time (PDT), and the clock model. The models were validated based on their performance. The results indicated that the clock model exhibited higher predictive accuracy and adaptability in simulating the various growth stages of Myrica rubra, particularly during the reddening stage and maturity stages, where the prediction errors were significantly smaller compared to GDD and PDT methods. The root mean square errors (RMSE) for the wintering, flowering, fruit setting, reddening stage, and maturity stages in the clock model were 2.83 d, 3.32 d, 4.24 d, 2.38 d, and 2.16 d, respectively. The normalized root mean square errors (nRMSE) were 0.47, 1.11, 0.47, 0.48, and 0.54, respectively. These results demonstrate that the clock model provides a more accurate reflection of the growth stage evolution of Myrica rubra, offering scientific support for optimizing cultivation management strategies. This can effectively promote the implementation of refined management practices, improving both management efficiency and effectiveness. In the future, the parameters of wintering period can be optimized and extended to multi-variety and multi-region applications.

Key words: Myrica rubra, protected cultivation, growth stage simulation model, temperature-light effect, biological characteristics

中图分类号:

S162.5

简单, 吴建欣, 赵芮, 金杨. 基于气象因子的设施杨梅生育期模拟模型研究[J]. 农学学报, 2026, 16(5): 92-100.

JIAN Dan, WU Jianxin, ZHAO Rui, JIN Yang. Simulation Model of Growth Stages of Myrica rubra in Protected Cultivation Based on Meteorological Factors[J]. Journal of Agriculture, 2026, 16(5): 92-100.

图/表 7

参考文献 36

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发育期	系数			模型
发育期	k	p	q	模型
越冬期	-2.04426	-0.05115	0.54237	$\frac{dM}{dt}=\frac{1}{D}={e}^{-0.04426}\times T{E}^{-0.05115}\times P{E}^{0.54237}$
开花期	-2.35149	-0.72681	0.85344	$\frac{dM}{dt}=\frac{1}{D}={e}^{-2.35149}\times T{E}^{-0.72681}\times P{E}^{0.85344}$
坐果期	-2.68462	-0.64178	0.56723	$\frac{dM}{dt}=\frac{1}{D}={e}^{-2.68462}\times T{E}^{-0.64178}\times P{E}^{0.56723}$
转红期	-0.17114	-0.55427	0.92371	$\frac{dM}{dt}=\frac{1}{D}={e}^{-0.17114}\times T{E}^{-0.55427}\times P{E}^{0.92371}$
成熟期	-1.76175	-0.84238	0.80514	$\frac{dM}{dt}=\frac{1}{D}={e}^{-1.76175}\times T{E}^{-0.84238}\times P{E}^{0.80514}$

发育期	系数			模型
发育期	k	p	q	模型
越冬期	-2.04426	-0.05115	0.54237	$\frac{dM}{dt}=\frac{1}{D}={e}^{-0.04426}\times T{E}^{-0.05115}\times P{E}^{0.54237}$
开花期	-2.35149	-0.72681	0.85344	$\frac{dM}{dt}=\frac{1}{D}={e}^{-2.35149}\times T{E}^{-0.72681}\times P{E}^{0.85344}$
坐果期	-2.68462	-0.64178	0.56723	$\frac{dM}{dt}=\frac{1}{D}={e}^{-2.68462}\times T{E}^{-0.64178}\times P{E}^{0.56723}$
转红期	-0.17114	-0.55427	0.92371	$\frac{dM}{dt}=\frac{1}{D}={e}^{-0.17114}\times T{E}^{-0.55427}\times P{E}^{0.92371}$
成熟期	-1.76175	-0.84238	0.80514	$\frac{dM}{dt}=\frac{1}{D}={e}^{-1.76175}\times T{E}^{-0.84238}\times P{E}^{0.80514}$

年份	模拟方法	越冬期/d		开花期/d		坐果期/d		转红期/d		成熟期/d
年份	模拟方法	模拟	误差	模拟	误差	模拟	误差	模拟	误差	模拟	误差
2022	Clock	56.0	4.0	34.0	8.0	44.0	-7.0	19.0	4.0	24.0	-3.0
	PDT	62.0	8.0	23.0	-3.0	58.0	7.0	11.0	-4.0	20.0	-7.0
	GDD	50.0	-2.0	32.0	6.0	56.0	5.0	21.0	6.0	12.0	-15.0
2023	Clock	65.0	2.0	27.0	4.0	43.0	2.0	17.0	0.0	18.0	-2.0
	PDT	60.0	7.0	23.0	3.0	36.0	-5.0	16.0	-1.0	19.0	-1.0
	GDD	58.0	9.0	20.0	-3.0	44.0	3.0	20.0	-3.0	16.0	-4.0
2024	Clock	67.0	-2.0	22.0	1.0	42.0	-1.0	15.0	-1.0	22.0	0.0
	PDT	62.0	-7.0	18.2	-3.0	40.0	-3.0	20.0	-6.0	16.0	-1.0
	GDD	73.0	4.0	24.0	3.0	38.0	-5.0	10.0	-4.0	15.0	-2.0

年份	模拟方法	越冬期/d		开花期/d		坐果期/d		转红期/d		成熟期/d
年份	模拟方法	模拟	误差	模拟	误差	模拟	误差	模拟	误差	模拟	误差
2022	Clock	56.0	4.0	34.0	8.0	44.0	-7.0	19.0	4.0	24.0	-3.0
	PDT	62.0	8.0	23.0	-3.0	58.0	7.0	11.0	-4.0	20.0	-7.0
	GDD	50.0	-2.0	32.0	6.0	56.0	5.0	21.0	6.0	12.0	-15.0
2023	Clock	65.0	2.0	27.0	4.0	43.0	2.0	17.0	0.0	18.0	-2.0
	PDT	60.0	7.0	23.0	3.0	36.0	-5.0	16.0	-1.0	19.0	-1.0
	GDD	58.0	9.0	20.0	-3.0	44.0	3.0	20.0	-3.0	16.0	-4.0
2024	Clock	67.0	-2.0	22.0	1.0	42.0	-1.0	15.0	-1.0	22.0	0.0
	PDT	62.0	-7.0	18.2	-3.0	40.0	-3.0	20.0	-6.0	16.0	-1.0
	GDD	73.0	4.0	24.0	3.0	38.0	-5.0	10.0	-4.0	15.0	-2.0

生育期	模拟方法	观测值标准差	模拟值标准差	a	b	R²	RMSE	nRMSE
越冬期	Clock	9.29	5.86	-3.00	6.00	0.75	2.83	0.47
	PDT		1.15	-8.50	15.67	0.75	8.12	0.48
	GDD		9.29	3.00	-8.33	-0.42	5.80	0.45
开花期	Clock	2.52	4.04	-1.50	6.00	0.75	3.32	1.11
	PDT		2.89	0.00	-2.00	0.00	2.45	0.82
	GDD		2.52	-1.50	5.00	0.11	4.24	0.47
坐果期	Clock	5.29	1.00	3.00	-8.00	0.43	4.24	0.47
	PDT		11.72	-5.00	9.67	0.60	5.26	0.44
	GDD		5.29	-5.00	11.00	0.89	4.40	0.44
转红期	Clock	1.53	3.06	-2.50	6.00	0.89	2.38	0.48
	PDT		2.65	1.00	-4.33	0.43	2.65	0.88
	GDD		1.53	-5.00	11.67	0.85	4.50	0.45
成熟期	Clock	5.13	3.46	2.00	-5.33	0.92	2.16	0.54
	PDT		2.08	3.00	-9.00	0.75	4.12	0.69
	GDD		5.13	6.50	-20.00	0.86	9.04	0.69