Study on Light-temperature Model of Rice in Yutai and Yield Forecast

doi:10.11923/j.issn.2095-4050.cjas2024-0035

Abstract

Abstract:

The aims were to study the growth and development law of Yutai rice under different accumulated temperature conditions, and to explore the influence of sunshine hours and temperature in different growth stages on the yield factors of Yutai rice, and to provide agricultural meteorological service basis for optimal planting of rice in Yutai area. A Logistic growth model was constructed based on the growth index data of Yutai rice from 2017 to 2022 and meteorological factors such as accumulated temperature during growth period. By using statistical methods such as correlation analysis and regression analysis, the influence of light and temperature in different growth stages on rice yield factors was analyzed, and the prediction model of yield factors was established accordingly. The results showed that the overall accuracy of Logistic model was high in the simulation of rice growth and development in Yutai, and the Root Mean Square Error (RMSE) between the simulated value and the measured value was between 0.591 and 5.100, the Normalized Root Mean Squared Error (nRMSE) was between 0.087 and 0.107, and the R² between the simulated value and the measured value was between 0.970 and 0.996. The number of sunshine hours in tillering, jointing, booting and grain filling maturity of rice was significantly correlated with yield, and the accumulated temperature in heading and grain filling maturity was significantly correlated with yield. The prediction model of rice yield and grain number per ear was established by multiple linear regression method, which was verified by historical band and histogram. The prediction model has high accuracy.

Key words: Yutai rice, accumulated temperature, growth model, correlation analysis, regression analysis, historical generation, modelling verification, yield forecasting model, yield factor

ZHU Yuqing, LI Huazhao. Study on Light-temperature Model of Rice in Yutai and Yield Forecast[J]. Journal of Agriculture, 2025, 15(4): 83-91.

Figures/Tables 8

References 24

[1]	刘延刚, 刘丽娟. 鱼台大米的地理标志特征、独特生产方式及品牌建设策略[J]. 北方水稻, 2021, 51(2):54-58.
[2]	田虎. 鱼台县农业优势特色产业发展存在的问题及建议[J]. 现代农业科技, 2015(24):293-294.
[3]	陆佳岚, 马成, 陶明煊, 等. 不同光温条件对水稻9311产量及品质的影响[J]. 江苏农业学报, 2020, 36(3):535-543.
[4]	李秀芬, 贾燕, 黄元才, 等. 播栽期对水稻产量和产量构成因素及生育期的影响[J]. 生态学杂志, 2004, 23(5):98-100.
[5]	王萌萌, 杨沈斌, 江晓东, 等. 光温要素对水稻群体茎蘖增长动态影响的分析及模拟[J]. 作物学报, 2016, 42(1):82-92.
[6]	陈杨. 有效积温与夏玉米生长发育和氮磷钾积累定量化研究[D]. 北京: 中国农业科学院, 2021.
[7]	段光俊, 赵家松, 刘振洋, 等. 基于有效积温的生菜生长模型构建[J]. 北方园艺, 2024(6):9-16.
[8]	MCMASTER G S, WILHELM W W. Growing degree-days: one equation, two interpretations[J]. Agricultural & forest meteorology, 1997, 87(4):291-300.
[9]	郑大玮, 孙忠富. 关于积温一词及其度量单位科学性问题的讨论[J]. 中国农业气象, 2010, 31(2):165-169.
[10]	YANG Q H, CHEN X X, LI Y H, et al. Logistic growth model for crop growth simulation: a review[J]. Pedosphere, 2016, 26(6):776-795.
[11]	石楠, 高志强, 陈崇怡, 等. 基于Logistic模型水稻地上部干物质与叶面积指数模拟与分析[J]. 东北农业大学学报, 2022, 53(3):10-18.
[12]	苏李君, 刘云鹤, 王全九. 基于有效积温的中国水稻生长模型的构建[J]. 农业工程学报, 2020, 36(1):162-174.
[13]	毛留喜, 魏丽. 大宗作物气象服务手册[M]. 北京: 气象出版社,2015:1-278.
[14]	罗丕. 气象因子对小麦生长发育及品质性状的影响[D]. 长沙: 湖南农业大学, 2009.
[15]	彭菊, 张上都, 伍祥, 等. 温光条件对不同类型水稻品种叶龄和生育期的影响[J]. 现代农业科技, 2016(16):14-20.
[16]	中国气象局. 农业气象观测规范(上卷)[M]. 北京: 气象出版社,1993:1-212.
[17]	薛晓萍. 棉花临界氮浓度稀释模型确定及其应用研究[D]. 南京: 南京农业大学, 2007.
[18]	张翠英, 吕令华, 樊献政, 等. 油用牡丹种子采摘始期预报模型研究[J]. 农学学报, 2021, 11(9):85-91. doi: 10.11923/j.issn.2095-4050.cjas2020-0184
[19]	摆虹霞. 基于不同水分条件下的日光温室黄瓜生长模型研究[D]. 银川: 宁夏大学, 2021.
[20]	ASSADULLAH U, ISLAM M M. Fitting a logistic model for yield estimation of aus rice[J]. Journal of the Bangladesh Agricultural University, 2017, 15(1):141-146.
[21]	NGUYEN T T H, NGUYEN T V. Using Logistic regression models for predicting rice yield in the Mekong Delta, Vietnam[J]. Journal of the science of food and agriculture, 2019, 99(14): 6469-6474.
[22]	杨东, 段留生, 谢华安, 等. 花前光照亏缺对水稻物质积累及生理特性的影响[J]. 中国生态农业学报, 2011, 19(2):347-352.
[23]	DENG X, KIM W Y, TAO J. Light sensing and signaling in rice during the early stage of plant development[J]. International journal of molecular sciences, 2019, 20(13):3253-3264.
[24]	唐卷, 汤永禄, 李朝苏, 等. 基于分位数回归模型分析小麦千粒重与气候因子的关系[J]. 西南农业学报, 2014, 27(3):943-949.

生育期	产量因素
生育期	有效穗数/（穗/m²）	穗粒数/（粒/穗）	结实率/%	千粒重/g	产量/(g/m²)
返青分蘖	0.701*	-0.091	-0.263	-0.559	0.113
拔节	0.022	-0.361	-0.701	-0.370	-0.772
孕穗	-0.387	-0.172	0.289	0.563	0.142
抽穗	-0.436	0.871**	0.030	-0.461	0.018
灌浆成熟	0.405	0.173	0.314	-0.191	0.774**
全生育期	-0.013	-0.246	0.203	0.819**	0.272

生育期	产量因素
生育期	有效穗数/（穗/m²）	穗粒数/（粒/穗）	结实率/%	千粒重/g	产量/(g/m²)
返青分蘖	0.701*	-0.091	-0.263	-0.559	0.113
拔节	0.022	-0.361	-0.701	-0.370	-0.772
孕穗	-0.387	-0.172	0.289	0.563	0.142
抽穗	-0.436	0.871**	0.030	-0.461	0.018
灌浆成熟	0.405	0.173	0.314	-0.191	0.774**
全生育期	-0.013	-0.246	0.203	0.819**	0.272

生育期	产量因素
生育期	有效穗数/（穗/m²）	穗粒数/（粒/穗）		结实率/%		千粒重/g					产量/(g/m²)
返青分蘖	-0.565		0.671*		0.633*		0.459		0.685*
拔节	-0.195		0.585		0.499		-0.067		0.775**
孕穗	-0.453		0.539		0.942**			0.256		0.734*
抽穗	-0.296		0.571		-0.150			-0.510		0.031
灌浆成熟	-0.197		0.676*		0.297			0.008		0.551
全生育期	-0.330		0.720*		0.500			0.022		0.714*

生育期	产量因素
生育期	有效穗数/（穗/m²）	穗粒数/（粒/穗）		结实率/%		千粒重/g					产量/(g/m²)
返青分蘖	-0.565		0.671*		0.633*		0.459		0.685*
拔节	-0.195		0.585		0.499		-0.067		0.775**
孕穗	-0.453		0.539		0.942**			0.256		0.734*
抽穗	-0.296		0.571		-0.150			-0.510		0.031
灌浆成熟	-0.197		0.676*		0.297			0.008		0.551
全生育期	-0.330		0.720*		0.500			0.022		0.714*

穗粒数/（粒/穗）				产量/(g/m²)
年份	模拟值	实测值	误差	年份	模拟值	实测值	误差
2017	112.8	112.2	0.6	2017	985.4	988.7	-3.3
2018	112.7	114.0	-1.3	2018	998.6	1001.7	-3.1
2019	113.4	112.0	1.4	2019	978.4	978.0	0.4
2020	123.6	124.2	-0.6	2020	998.7	995.5	3.2
2021	110.9	112.0	-1.1	2021	981.6	978.1	3.5
2022	118.6	118.6	0.0	2022	974.3	975.5	-1.2