Adjustment of the photothermic model to estimate soybean development and leaf area index

被引:3
|
作者
Toledo, Neila T. [1 ]
Muller, Artur G. [2 ]
Berto, Jorge L. [3 ]
Mallmann, Carise E. S. [1 ]
机构
[1] Univ Reg Noroeste Estado Rio Grande do Sul, BR-98700000 Ijui, RS, Brazil
[2] Embrapa Cerrado, BR-73310970 Planaltina, DF, Brazil
[3] Univ Reg Noroeste Estado Rio Grande do Sul, Dept Ciencias Agr, BR-98700000 Ijui, RS, Brazil
关键词
mathematical modeling; photothermic constant; meteorological variables;
D O I
10.1590/S1415-43662010000300008
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
The present study was carried out to adjust the soy population development model and the leaf area index model for the IAS 5 cultivar. Different sowing dates were used in two experiments as source of development variation during 2004/2005 (three dates) and 2005/2006 (four dates) in the IRDeR (Instituto Regional de Desenvolvimento Rural) located in Augusto Pestana, in the State of Rio Grande do Sul (28 degrees 27' 17 '' S and 53 degrees 54' 50 '' W). The main crop development phases were identified according to the Feher & Caviness phenological scale, and the leaf area index was determined in four occasions: at the final period of plant population installation (V6); at the beginning of flowering (R1); at the beginning of seed filling (R5), and at the beginning of maturation (R7). The minimum and maximum temperatures were obtained daily. After the adjustment of the genetic coefficients, the model was formatted using the program Stella 5.0. The soy development model presented a suitable performance, precise estimates from the original data. The leaf area index model also presented satisfactory estimation.
引用
收藏
页码:288 / 295
页数:8
相关论文
共 50 条
  • [1] Model to estimate the leaf area of manicoba
    de Caldas Pinto, Maria do Socorro
    Pereira de Andrade, Albericio
    Esfrain Pereira, Walter
    Ponciano de Arruda, Francineuma
    Veronica Meira de Andrade, Maria
    REVISTA CIENCIA AGRONOMICA, 2007, 38 (04): : 391 - 395
  • [2] Comparison of Methods for Estimating Soybean Leaf Area Index
    Yang Fei
    Zhang Bai
    Song Kai-shan
    Wang Zong-ming
    Liu Dian-wei
    Liu Huan-jun
    Li Fang
    Li Feng-xu
    Guo Zhi-xing
    Jin Hua-an
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28 (12) : 2951 - 2955
  • [3] Training a neural network with a canopy reflectance model to estimate crop leaf area index
    Danson, FM
    Rowland, CS
    Baret, F
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2003, 24 (23) : 4891 - 4905
  • [4] Allometric Method to Estimate Leaf Area Index for Row Crops
    Colaizzi, Paul D.
    Evett, Steven R.
    Brauer, David K.
    Howell, Terry A.
    Tolk, Judy A.
    Copeland, Karen S.
    AGRONOMY JOURNAL, 2017, 109 (03) : 883 - 894
  • [5] Efficiency of the leaf disc method for estimating the leaf area index of soybean plants
    Pierozan Junior, Clovis
    Kawakami, Jackson
    ACTA SCIENTIARUM-AGRONOMY, 2013, 35 (04) : 487 - 493
  • [6] Sentinel Image to Estimate Industrial Tomato Leaf Area Index
    Dorneles, Mylena Marques
    Brito, Gustavo Henrique Mendes
    Rocha, Ivandro Jose de Freitas
    Alves, Sueli Martins de Freitas
    BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY, 2023, 66
  • [7] Application of Digital Image for Leaf Area Index Estimation of Soybean
    Sermsak, Raksak
    Boonjung, Hatsachai
    SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY, 2007, 14 (02): : 163 - 171
  • [8] Reliability of yield models of defoliated soybean based on leaf area index versus leaf area removed
    Klubertanz, TH
    Pedigo, LP
    Carlson, RE
    JOURNAL OF ECONOMIC ENTOMOLOGY, 1996, 89 (03) : 751 - 756
  • [9] Using multiple radiometric correction images to estimate leaf area index
    Gu, Zhujun
    Shi, Xuezheng
    Li, Lin
    Yu, Dongsheng
    Liu, Liusong
    Zhang, Wentai
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (24) : 9441 - 9454
  • [10] A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images
    Kinane, Stephen M.
    Montes, Cristian R.
    Albaugh, Timothy J.
    Mishra, Deepak R.
    REMOTE SENSING, 2021, 13 (06)