NDLI: Modeling Branching Effects on Source-Sink Relationships of the Cotton Plant

Content Provider	IEEE Xplore Digital Library
Author	Dong Li Letort, V. Yan Guo de Reffye, P. Zhigang Zhan
Copyright Year	2009
Abstract	Compared with classical process-based models, the functional-structural plant models provide more efficient tools to explore the impact of changes in plant structures on plant functioning. In this paper we investigated the effects of branches on the source-sink interaction for the cotton plant (Gossypium hirsutum L.) based on a two-treatment experiment conducted on cotton grown in the field: the single-stem plants and the plants with only two vegetative branches. It was observed that the branched cotton had more organs for the whole plant but the organs on the trunk were smaller than those on the single-stem cotton. The phytomer production of the branches was four or five growth cycles delayed compared with the main stem. The organs on the trunk had similar dynamics of expansion for both treatments. Effects of branches were evaluated by using the functional-structural model GREENLAB. It allowed estimating the coefficients of sink strength to differentiate the biomass acquisition abilities of organs between different physiological ages. We found that the presence of the two vegetative branches increased the ground projection area of plant leaves and had led to slight changes on the directly measured parameters; the potential relative sink strengths of organs were found similar for the two treatments.
Starting Page	293
Ending Page	300
File Size	588964
Page Count	8
File Format	PDF
ISBN	9781769539881
e-ISBN	9781424463305
DOI	10.1109/PMA.2009.58
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Area measurement Process control Functional-structural model Source-sink relationship Predictive models Educational institutions Biomass Cotton Parameter optimization Delay Plants (biology) Simulation Production
Content Type	Text
Resource Type	Article

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