Wang Sen, Wang Xuejiao, Ji Chunrong, Jiang Yuanan, Yang Mingfeng, Ji Fen. Impact of delayed-type chilling damage on cotton fiber quality based on CottonXL[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(20): 171-177. DOI: 10.11975/j.issn.1002-6819.2019.20.021
    Citation: Wang Sen, Wang Xuejiao, Ji Chunrong, Jiang Yuanan, Yang Mingfeng, Ji Fen. Impact of delayed-type chilling damage on cotton fiber quality based on CottonXL[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(20): 171-177. DOI: 10.11975/j.issn.1002-6819.2019.20.021

    Impact of delayed-type chilling damage on cotton fiber quality based on CottonXL

    • Cotton (Gossypium hirsutum) fiber quality is the key factor that determines the price of cotton and its textiles. Fiber length, strength, strength and micronaire are the main indicators of cotton fiber quality. Temperature is the most important meteorological factor that determines the fiber quality. Delayed-type chilling damage is the main meteorological disaster in cotton production in Xinjiang. And it is also the meteorological disaster that has the greatest impact on fiber quality. Quantitative evaluation of the effect of chilling damage on cotton fiber quality is of great significance for disaster assessment and formulating countermeasures. Functional-structural model of cotton (CottonXL) is a visual model that developed on GroIMP platform. It can vividly simulate the three-dimensional growth process and spatial distribution of fiber quality of cotton under different scenarios. So, the model is a powerful tool for studying the effect of climate on fiber quality. In this study, the parameters of the model were calibrated and validated by the experimental data of staged sowing. Because the quality data of stage sowing experiment were the cotton fiber on fruit branches 3 and 4, the simulated value of fiber quality of bolls on fruit braches 3 and 4 were averaged to compared with the observed data. The root mean square error (RMSE) and normalized root mean square error (NRMSE) of observed and simulated fiber length, strength and micronaire were 0.7 mm, 0.9 cN/tex, 0.1 and 2.6%, 3.2%, 3.0%, respectively. The results showed that CottonXL can accurately simulate the effects of different heat conditions on fiber quality. In order to study the effect of delayed chilling injury on cotton fiber quality in Shihezi area, the model were validated by the sample survey data of cotton fiber quality in Shihezi area from 2006 to 2011. Because the sampled data were the average value of Shihezi area, the simulated value of fiber quality of all cotton bolls in the whole plant were averaged to compared with sampled data. The RMSE and NRMSE of observed and simulated fiber length, strength and micronaire were 0.4 mm, 0.9 cN/tex, 0.1 and 1.5%, 3.1%, 1.3%, respectively. The results showed that CottonXL can accurately simulate the average value of fiber quality in Shihezi area. The typical years of delayed-type chilling damage in different degrees were selected from 1961 to 2017 by using the index of delayed chilling damage and disaster data. Based on calibration and validation of functional-structural plant model CottonXL, we simulated the effects of different degrees of chilling damage on fiber quality. The results showed chilling damage had the greatest effect on micronaire, followed by fiber strength, and had the least effect on fiber length. With the aggravation of chilling damage, the number of cotton bolls with longer fiber length, lager fiber strength and moderate micronaire decreased significantly, and the fiber quality decreased as a whole. The fiber length, strength and micronaire decreased by 0.8, 1.4 and 1.5 mm, 3.9, 4.5 and 5.1 cN/tex, 1.0, 1.2 and 1.4, respectively when mild, moderate and severe chilling damage occurred. Under the same degree of delayed chilling damage, the effect of chilling damage in summer and autumn on fiber quality was greater than that in spring. Under condition with insufficient heat, changing sowing time, cut top time and planting density could reduce losses.
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