Predicting the effects of environment and management on cotton fibre growth and quality: a functional-structural plant modelling approach
Author(s) -
Xinbing Wang,
Li Zhang,
Jochem B. Evers,
Li Mao,
Shuai Wei,
Xuebiao Pan,
Xiaohui Zhao,
Wopke Van der Werf,
Ziliang Li
Publication year - 2014
Publication title -
aob plants
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.998
H-Index - 39
ISSN - 2041-2851
DOI - 10.1093/aobpla/plu040
Subject(s) - mulch , sowing , topping , quality (philosophy) , agricultural engineering , biology , agronomy , crop management , crop , environmental science , engineering , philosophy , epistemology
In general, the quality of fruits depends on local conditions experienced by the fruit during its development. In cotton, fruit quality, and more specifically the quality of the fibre in the fruit, depends on interactions between fruit position in the plant architecture, temperature and agronomic practices, such as sowing time, mulching with plastic film and topping of the plant's main stem and branches. To quantify this response of cotton fibre quality to environment and management, we developed a simulation model of cotton growth and development, CottonXL. Simulation of cotton fibre quality (strength, length and micronaire) was implemented at the level of each individual fruit, in relation to thermal time (represented by physiological age of the fruit) and prevailing temperature during development of each fruit. Field experiments were conducted in China in 2007 to determine model parameters, and independent data on cotton fibre quality in three cotton producing regions in China were used for model validation. Simulated values for fibre quality closely corresponded to experimental data. Scenario studies simulating a range of management practices predicted that delaying topping times can significantly decrease fibre quality, while sowing date and film mulching had no significant effect. We conclude that CottonXL may be used to explore options for optimizing cotton fibre quality by matching cotton management to the environment, taking into account responses at the level of individual fruits. The model may be used at plant, crop and regional levels to address climate and land-use change scenarios.
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