烟草光合-蒸腾速率日变化估算模型研究
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摘要
为了快速、准确估测自然条件下烟草生长状况,提升作物精细管理水平,建立了烟草光合蒸腾速率日变化标准模型;利用主成分分析与神经网络分析方法拟合旺长期烟草的光合蒸腾速率日变化的过程,并对该模型的通用性进行了验证。研究表明,对豫烟10号光合蒸腾速率的调控因子叶片截获的光合有效辐射(PAR)、叶片表面温度(T)、气孔导度(gs)和胞间CO_2浓度(Ci)建立的多元回归模型的相关系数均在0.87**以上,模型的拟合精度较高。利用该模型拟合秦烟96的光合蒸腾生理参数,实测值与预测值的相关程度也达到0.91**以上。与主成分分析建立的拟合模型相比,用神经网络进行演算的拟合模型精确度更高。
In order to rapidly and accurately estimate the growth status of tobacco plants under natural conditions,and to improve the precision management level of tobacco production,the author set up standard models to simulate the daily variation in photosynthesis-transpiration rate of tobacco at vigorous growth stage by using principal component analysis and neural network analysis,and verified the universality of these models. The results showed that the established multiple regression models about the factors( photosynthetic active radiation,leaf surface temperature,stomatal conductance,and intercellular carbon dioxide concentration) regulating the leaf photosynthesis-transpiration rate of tobacco variety Yuyan No. 10 had high fitting precisions,and their correlation coefficients were more than 0.87**. These models were used to simulate the photosynthesis-transpiration parameters of Qinyan 96,and the correlation coefficient between actually-measured values and fitted values was above 0.91**. The constructed simulation model by using neural network analysis had a higher fitting precision than that by using principal component analysis.
In order to rapidly and accurately estimate the growth status of tobacco plants under natural conditions,and to improve the precision management level of tobacco production,the author set up standard models to simulate the daily variation in photosynthesis-transpiration rate of tobacco at vigorous growth stage by using principal component analysis and neural network analysis,and verified the universality of these models. The results showed that the established multiple regression models about the factors( photosynthetic active radiation,leaf surface temperature,stomatal conductance,and intercellular carbon dioxide concentration) regulating the leaf photosynthesis-transpiration rate of tobacco variety Yuyan No. 10 had high fitting precisions,and their correlation coefficients were more than 0.87**. These models were used to simulate the photosynthesis-transpiration parameters of Qinyan 96,and the correlation coefficient between actually-measured values and fitted values was above 0.91**. The constructed simulation model by using neural network analysis had a higher fitting precision than that by using principal component analysis.
引文
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[24]陈云浩,蒋金豹,黄文江,等.主成分分析法与植被指数经验方法估测冬小麦条锈病严重度的对比研究[J].光谱学与光谱分析,2009,29(8):2161-2165.
[25]宋开山,张柏,王宗,等.基于人工神经网络的大豆叶面积高光谱反演研究[J].中国农业科学,2006,39(6):1138-1145.
[26]杨飞,张柏,宋开山,等.大豆叶面积指数的高光谱估算方法比较[J].光谱学与光谱分析,2008,28(12):2951-2955.
[2]常水晶,左兵,王晓炜,等.光照、温度及盐分对钠猪毛菜种子萌发的影响[J].干旱区地理,2008,31(6):897-903.
[3]Shaft M,Bakht J,Hassan M J,et a1.Effect of cadmium and salinity stresses on growth and antioxidant enzyme activities of wheat(Trticum aestivum L.)[J].Bull Environ Contam Toxico1,2009,82:772-776.
[4]Berry J A,Beer ling D J,Franks P J.Stomata:key players in the earth system,past and present[J].Current Opinion in Plant Biology,2010,13(3):233-239.
[5]Yu G R,Song X,Wang Q F,et al.Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables[J].New Phytologist,2008,177(4):927-937.
[6]赵风华,于贵瑞.陆地生态系统碳-水耦合机制初探[J].地理科学进展,2008,27(1):32-38.
[7]江力,曹树青,戴新宾,等.光强对烟草光合作用的影响[J].中国烟草学报,2000,6(4):17-20.
[8]王润元,杨兴国,赵鸿,等.半干旱雨养区小麦叶片光合生理生态特征及其对环境的响应[J].生态学杂志,2006,25(10):1161-1166.
[9]林忠辉,项月琴,莫兴国,等.夏玉米叶面积指数增长模型的研究[J].中国生态农业学报,2003,11(4):69-72.
[10]李建明,邹志荣.温度、光辐射及水分对温室甜瓜幼苗干物质积累与分配的影响及其模拟模型[J].应用生态学报,2007,18(12):2715-2721.
[11]Adams S R,Pearson S,Hadley P.Modeling growing and development of pansy cv Universal Violet in response to photothermal environment:application for decision support and scheduling[J].Acta Horticulturae,1996,417:23-32.
[12]Lee T,Raja R K,Sassenrath-Cole G F.Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration[J].Crop Science,2000,40:1814-1819.
[13]Wang X J,Xu H L,Wang J H,et al.Effects of organic and chemical fertilizations and microbe inoculation on physiology and growth of sweet corn plants[J].Pedosphere,2000,10(3):229-236.
[14]孙忠富,陈人杰.温室园艺作物生长发育模型研究现状与发展趋势[J].园艺学报,2001,28(增刊):700-704.
[15]李建明.温室厚皮甜瓜幼苗生长模型与生态生理变化规律研究[D].杨凌:西北农林科技大学,2006.
[16]王大成,王纪华,靳宁,等.用神经网络和高光谱植被指数估算小麦生物量[J].农业工程学报,2008,24(2):196-201.
[17]汪海波,罗莉,汪海玲,等.SAS统计分析与应用[M].北京:人民邮电出版社,2011:292-306.
[18]高飞翔,淡建斌,孙向伟,等.白草光合蒸腾日变化及其与气象因子的关系[J].安徽农业科学,2010(11):5733-5735,5873.
[19]于贵瑞,王秋凤.植物光合、蒸腾与水分利用的生理生态学[M].北京:科学出版社,2010:119-122.
[20]赵风华,王秋凤,王建林,等.小麦和玉米叶片光合-蒸腾日变化耦合机理[J].生态学报,2011,31(24):7526-7532.
[21]丁永军,李民赞,安登奎,等.基于光谱特征参数的温室番茄叶片叶绿素含量预测[J].农业工程学报,2011,27(5):244-247.
[22]黄宁.关于主成分分析的应用与思考[J].数理统计与管理,1999,18(5):44-52.
[23]Chaurasia S,Dadhwal V K.Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data[J].International Journal of Remote Sensing,2004,25(14):2881-2887.
[24]陈云浩,蒋金豹,黄文江,等.主成分分析法与植被指数经验方法估测冬小麦条锈病严重度的对比研究[J].光谱学与光谱分析,2009,29(8):2161-2165.
[25]宋开山,张柏,王宗,等.基于人工神经网络的大豆叶面积高光谱反演研究[J].中国农业科学,2006,39(6):1138-1145.
[26]杨飞,张柏,宋开山,等.大豆叶面积指数的高光谱估算方法比较[J].光谱学与光谱分析,2008,28(12):2951-2955.