中国农业科学 ›› 2016, Vol. 49 ›› Issue (23): 4520-4530.doi: 10.3864/j.issn.0578-1752.2016.23.005

• 耕作栽培·生理生化·农业信息技术 • 上一篇    下一篇

夏玉米叶片形状系数的时间和空间变异

周元刚1,2,李华龙1,2,蒋腾聪1,2,窦子荷1,2,刘 健1,2,吴淑芳1,2,冯 浩2,3,张体彬3,何建强1,2

 
  

  1. 1西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌 712100
    2西北农林科技大学中国旱区节水农业研究院,陕西杨凌 712100
    3中国科学院水利部水土保持研究所,陕西杨凌 712100
  • 收稿日期:2016-04-26 出版日期:2016-12-01 发布日期:2016-12-01
  • 通讯作者: 何建强,E-mail:jianqiang_he @nwsuaf.edu.cn
  • 作者简介:周元刚,E-mail:yuangang_zhou@nwsuaf.edu.cn
  • 基金资助:
    国家高技术研究发展计划(863计划)(2013AA102904)、2016年度陕西省科技统筹创新工程计划(S2016TNNY0011)、国家自然科学基金(51209176)、高等学校学科创新引智计划(B12007)

Temporal and Spatial Variations of Leaf Shape Coefficients of Summer Maize

ZHOU Yuan-gang1,2, LI Hua-long1,2, JIANG Teng-cong1,2, DOU Zi-he1,2, LIU Jian1,2, WU Shu-fang1,2, FENG Hao2,3, ZHANG Ti-bin3, HE Jian-qiang1,2   

  1. 1 Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi
    2 Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi
    3 Institute of Water and Soil Conservation, Chinese Academy of Science and Ministry of Water Resource, Yangling 712100, Shaanxi 3Institute of Water and Soil Conservation, Chinese Academy of Science and Ministry of Water Resource, Yangling 712100, Shaanxi
  • Received:2016-04-26 Online:2016-12-01 Published:2016-12-01

摘要: 【目的】叶片形状系数(α)为测量作物的叶面积和叶面积指数提供了简单快捷的方法。然而,以往研究表明对作物叶片形状系数的选取存在很大的随意性,缺乏统一标准,且通常将其视为常数,不考虑它的时间和空间变异性。为解决这一问题,文章对陕西关中地区夏玉米不同生长阶段和不同叶位叶片形状系数的时间和空间变异性进行了深入研究。【方法】选取20156—10月生长季6个夏玉米品种,将玉米生育期划分为三叶、拔节、抽雄、开花、吐丝、成熟6个不同生长阶段,每6天采样一次,测量叶片面积(LA)、叶片长度(L)和宽度(W),计算各个阶段的α值,同时对比α值在单个玉米植株不同叶位之间的差异。然后分别建立线性、二次、对数等3类共5个叶面积估算模型,以RMSERRMSEARE 3个统计量作为评价指标,对各叶片面积估算模型的精度进行评价。【结果】对全生育期6个夏玉米品种的760个叶片的面积和长宽乘积进行线性回归分析,夏玉米叶片形状系数均值约为0.78在被验证的5种叶面积估算模型中,叶面积模型LA=α×L×W,其中α=0.78精度最高,其相对均方根误差RRMSE)约9.50%绝对相对误差ARE6.96%α值范围为0.720.87,并随玉米生育期的变化而变化,自三叶期到开花期逐渐增大到全生育期最大值0.87,开花后缓慢下降至0.78,其中开花期叶片的α值与开花前各阶段的α值存在显著差异,而与开花后各阶段的α值不存在显著差异。不同熟性的夏玉米品种之间叶片α值也只在开花、吐丝期表现显著差异。不同叶型叶片α值表现出不同的变化规律,三叶期到拔节前,短宽型叶片的α值大于细长型叶片,此后一直到成熟期,细长型叶片的α值则大于短宽型叶片。在单个植株不同叶位叶片之间,α变异性明显,开花期、吐丝期、成熟期均呈现出两头大中间小的规律,其中植株中部棒三叶位置α值最为稳定,为0.78,对应的标准差在0.05以内,而植株上部和下部α值约为0.84,对应的标准差在0.030.10。其中拔节、抽雄期不同叶位叶片的α值不存在显著差异,而在开花、吐丝、成熟期则表现出显著差异。【结论】叶面积模型LA=0.78×L×W更适于估算田间夏玉米叶片面积,较一般采用叶片性状系数0.75时提高模拟精度ARE3.86%。应在不同的生长阶段和不同叶位分别采用不同的叶片形状系数,这样才能进一步提高玉米叶面积估算的精度。

关键词: 夏玉米, 叶面积, 形状系数, 叶长, 叶宽, 变异性

Abstract: 【Objective】Leaf shape coefficient (α) provides a simple and fast way for the measurement of crop leaf area and LAI (leaf area index) in field. However, the selection of values of this coefficient was very arbitrary and there was no unique standard to follow. In addition, this coefficient was usually considered as a constant regardless of its variations through the whole lifetime of a given crop. 【Method】 In this study, the temporal and spatial variations of α values of summer maize were investigated through a field experiment conducted from June to October in 2015. A total of six maize cultivars with different properties of ripening were involved. The whole growth season of maize was divided into six different stages, i.e. trefoil, jointing, heading, flowering, silking, and maturity. Maize plants were randomly sampled every six days and all leaves were cut off and measured for their length, width, and area with a digital leaf area scanner. Then, α value was calculated for each leaf. The variations of α values were analyzed for different growth stages and among different leaf positions within a single maize plant. Finally, five different models of leave area estimation, which belong to the linear, quadratic, and logarithmic types, were established to estimate the area of each maize leaf. Three different statistics of RMSE (root mean square error), RRMSE (relative root mean square error), and ARE (absolute relative error) were used to represent the estimation accuracy. 【Result】 Based on linear regression analysis between leaf areas and products of leaf length and width of 760 leaf samples of six different maize cultivars, the general average value of &alpha was about 0.78. Then, when estimating maize leaf areas with the model of LA=0.78×L×W, the relative root mean square error (RRMSE) and absolute relative error (ARE) were 9.50% and 6.96%, respectively. The accuracy was the highest among the five different models investigated for the estimation of maize leaf area. The results showed that α value ranged from 0.72 to 0.87 and varied at different growth stages. It increased with fluctuations from trefoil to flowering stage, and then decreased. At flowering stage, the α value was significantly different from other before-flowering stages, while showed no significant difference at silking and maturity stages. For different ripening cultivars, α value only showed a significant difference at flowering and silking stages The α value varied for different leaf shapes in the whole growth season. From the trefoil to before-jointing stage, α value of wide-short leaves was higher than that of thin-long leaves. From then on, α value of wide-short leaves became lower than that of thin-long ones. Within a single maize plant, α values varied remarkably for leaves at different positions. At the flowering, silking, and maturity stages, α values were higher at top and bottom than in the middle. The average value of three-ear-leaves was 0.78 and the standard deviation was less than 0.05. However, α value was about 0.87 for leaves at the top and bottom of maize canopy, with standard deviations from 0.03 to 0.10. The α values at different leaf positions only showed significant difference at flowering, silking, and maturity stages. 【Conclusion】 When estimating maize leaf areas with the model of LA=0.78×L×W, the accuracy was the highest among the five different models investigated. The leaf shape coefficient of 0.78 improved the estimation accuracy of maize leaf area by about 3.86%, when compared with the estimation resulted from a coefficient of 0.75. In general, it is better to adopt various α values at different growth stages and different leaf positions so as to improve the accuracy of simulation and prediction of leaf area of summer maize.

Key words: summer maize, leaf area, leaf shape coefficient, leaf length, leaf width, variation