15 July 2018, Volume 30 Issue 4
    

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  • Feng Guoyi, Wang Shulin, Qi Hong, Zhang Qian, Lei Xiaopeng, Wang Yan, Liang Qinglong, Lin Yongzeng
    Cotton Science. 2018, 30(4): 291-299. https://doi.org/10.11963/1002-7807.fgylyz.20180717
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    [Objective] The effects of sowing date on the yield and quality formation of two different cotton cultivars were studied under machine-picked conditions in the coastal saline-alkali area of Eastern Hebei Province, China to identify the optimal sowing date and harvest method to improve cotton yield and quality consistency. [Method] The experiments were conducted in the state-run Haixing farm in Hebei Province during 2017 with a cotton crop seeded in 2016. The hybrid cotton Jiza 2 and conventional cotton Shikang 126 were used in this experiment. Varieties were tested using three sowing dates: April 15 (B1), April 25 (B2), and May 5 (B3) as the main plot. Six harvest dates were also used as subplots in this experiment: September 10, September 20, September 30, October 10, October 20, and October 30. [Results] The lint yield of Jiza 2 was nearly 1 400 kg·hm-2 when the sowing date was May 5. Conversely, the highest lint yield for Shikang 126 was obtained when the cotton was sown on April 25. The yield associated with different sowing times was mainly formed in September, with this month accounting for more than 75% of yield. The fiber quality was better in September than October. The fiber quality of Jiza 2 was improved with a delay in the sowing date. The best fiber quality for Shikang 126 was obtained with the April 25 sowing date. There was a small difference between the micronaire, fiber length, and length uniformity with different September harvest times. The fiber quality index was optimized and the consistency of the micronaire, upper half mean length, and length uniformity were higher with Shikang 126 before September 20 when sown on April 25. [Conclusion] Comprehensive consideration of yield and quality index indicated that hybrid cotton Jiza 2 performed best when sown on May 5 and harvested for the first time at the end of September. The conventional cotton Shikang 126 should be sown around April 25 and harvested for the first time on the last ten-day of September.
  • Wang Xianfeng, Ding Jun, Zhu Yihai
    Cotton Science. 2018, 30(4): 300-307. https://doi.org/10.11963/1002-7807.wxfwxf.20180709
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    [Objective] The occurrence and development of cotton diseases and insect pests are mainly related to environmental information. Because this environmental information is various, complex and unstable, the study on the  prediction methods of cotton diseases and insect pests is a certain challenge. This study aims to establish a forecasting model for the timely and accurate prediction of cotton diseases and insect pests. [Method] A forecasting model of cotton diseases and insect pests is proposed based on environmental information and a modified Deep Belief Network (DBN) that is constructed by a three-layer restricted Boltzmann machine (RBM) and a supervised back-propagation (BP) network. In the method, the RBM is used to transform the original environmental information vectors into a new feature space related to the diseases and pests; the BP network is trained to classify and forecast the features generated by the last RBM layer and two rules of dynamic learning and comparison and dispersion are adopted to accelerate the training process of RBM. The proposed model was validated on a dataset of cotton bollworm, aphids, spider, cotton Verticillium wilt, and Fusarium wilt in a recent six-year period. [Result] Compared with the traditional prediction models of cotton diseases and insect pests, the proposed model can deeply explore the extensive correlation between the occurrence of cotton diseases and pests and environmental information. The results show that the proposed model has a higher accuracy compared with the classical predictive models, and the average forecasting accuracy is above 83%. [Conclusion] The proposed method is an effective crop disease and pest forecasting method that can provide a technical support for preventing cotton disease and insect pests.
  • Wang Xinyi, Li Xueyuan, Gong Zhaolong, Wang Junduo, Fan Liping, Zheng Juyun, Liang Yajun, Guo Jiangping, Mamat Moming, Ai Xiantao
    Cotton Science. 2018, 30(4): 308-315. https://doi.org/10.11963/1002-7807.wxyaxt.20180723
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    [Objective] The aim of this study was to construct a DNA fingerprinting database of 120 upland cotton cultivars from Xinjiang and to analyze their genetic diversity based on SSR markers. [Methods] Seventy-eight evenly distributed SSR primer pairs with high polymorphism and good repeatability were successfully screened out from 586 candidates to construct the fingerprinting database. [Result] A total of 392 alleles from 120 varieties were screened using 78 pairs of core primers, 324 of which were polymorphic loci with a polymorphism rate of 82.7%. Seventeen cultivars had specific genotypes determined using 24 primer pairs and 120 upland cotton cultivars could be identified by only 12 primer combinations. Cluster analysis indicated that genetic similarity coefficient for the 120 upland cotton cultivars ranged from 0.50 to 0.96, with an average of 0.73, indicating that upland cotton resources possess high genetic similarity and have an accordingly narrow genetic basis. [Conclusion] The primer combination method is one of the most effective methods for constructing DNA fingerprinting. The 120 upland cotton varieties were divided into three types with the genetic similarity coefficient matrix; these groups were strongly consistent with their pedigrees.
  • Liu Pengcheng, Sun Hongchun, Liu Liantao, Zhang Yongjiang, Liu Yuchun, Bai Zhiying, Li Meng, Li Cundong
    Cotton Science. 2018, 30(4): 316-325. https://doi.org/10.11963/1002-7807.lpclcd.20180503
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    [Objective]We aimed to prove the effects of limited irrigation on the regulation of soil moisture, growth, and yield in different mature cotton varieties in the Yellow River region. [Method] Using the early maturing cotton variety CCRI 50, earlier middle maturing variety Nongdamian 601 (ND 601), and medium maturing variety Jimian 958 (JM 958) as test materials, field tests were carried out using both conventional irrigation (W1) and limited irrigation (proper irrigation before sowing, no irrigation during growing period; W2). The traits of interest were measured, including leaf area index, water use efficiency, and yield. [Result] The dry matter quality of the three varieties was significantly lower with the W2 treatment, with the greatest reduction observed in CCRI 50 (P<0.05). Compared with the W1 treatment, the yield of the three varieties under the W2 treatment decreased significantly, with the greatest reduction observed in the early maturing variety CCRI 50. The water use efficiency of CCRI 50 decreased significantly while that of ND 601 and JM 958 improved significantly during the two study years. [Conclusion] ND 601 and JM 958 have good adaptability to moderate drought stress and W2 treatment can improve the water use effi ciency of these varieties to obtain the ideal output level. CCRI 50 is sensitive to moisture; it is difficult to form a high yield population for this variety under W2 treatment.
  • Gui Huiping, Dong Qiang, Zhang Hengheng, Wang Xiangru, Pang Nianchang, Wang Zhun, Liu Ji, Zheng Cangsong, Fu Xiaoqiong, Zhang Xiling, Song Meizhen
    Cotton Science. 2018, 30(4): 326-337. https://doi.org/10.11963/1002-7807.ghpsmz.20180720
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    [Objective] The aim of this study was to screen cotton varieties (lines) able to tolerate low nitrogen conditions, thereby tapping the potential of cotton to absorb and utilize nitrogen. [Method] This study analyzed the differences and correlations between the main cotton agronomic traits under two nitrogen levels using the sand culture method and 270 cotton varieties from different generations in three major cotton regions as materials. Principal component analysis and the membership function method were used to screen for low nitrogen-tolerant cotton varieties. [Result] The seven agronomic traits of 270 varieties varied greatly with different nitrogen levels. The coefficients of variation were all above 10 except for the SPAD value in four batches, and root biomass and cotton plant biomass in the first batch under low nitrogen level. The variation coefficient of nitrogen accumulation reached 60.02. The maximum relative value of SPAD, leaf area, and root biomass was greater than 80% in the first batch. The maximum relative values of SPAD, plant height, root biomass, shoot biomass, and cotton plant biomass were greater than 80% in the third and fourth batches, indicating that there are low nitrogen-tolerant genotypes in the selected materials. [Conclusion] Preliminary screening identified 32 low nitrogen-tolerant cultivars, including CCRI 35, CCRI 69, Yumian 12, Xinluzao 12, and Xinluzao 23. Furthermore, 32 varieties sensitive to nitrogen stress, including CCRI 64, CCRI 662, Xinluzhong 15, and Xinluzao 53, were identified.
  • Zheng Cangsong, Li Pengcheng, Sun Miao, Pang Chaoyou, Zhao Xinhua, Gui Huiping, Liu Shuai, Qin Yukun, Dong Helin, Yu Xueke
    Cotton Science. 2018, 30(4): 338-343. https://doi.org/10.11963/1002-7807.zcsdhl.20180703
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    [Objective] The purpose of this study was to explore the effects of foliar nitrogen application on nitrate nitrogen uptake and cotton growth. [Method] The experiment was carried out using the 15N isotopic tracer technique in a greenhouse hydroponic culture experiment. Four foliar treatments were applied; ammonium nitrogen, nitrate nitrogen, amide nitrogen treatments (all with the same concentration of nitrogen applied), and a water control treatment. [Result] Compared to the water control treatment, the nitrogen contents of the cotton shoots and the whole plant were significantly higher in plants with foliar nitrogen treatments 6 d after application. The nitrogen accumulation in the shoots, roots, and total plants was higher with the ammonium nitrogen treatment, but there was no significant difference among treatments. The isotopic tracer results showed that 15N accumulation in the shoot and root was 0.794 mg·plant-1 and 0.318 mg·plant-1 with the ammonium nitrogen treatment, respectively. These values were higher than the 15N accumulation with the water control treatment and the amide nitrogen treatment and significantly higher than the nitrate treatment. After foliar application, the plant accumulation of nitrogen via root uptake was approximately 11.35 mg with the ammonium nitrogen treatment. Compared with the water control treatment, the nitrogen uptake efficiency increased by 28.0% with the ammonium nitrogen treatment and reduced by 9.5% and 20.5% with the amide nitrogen treatment and the nitrate treatment, respectively. The proportion of nitrogen from root uptake was about 7:3 between the shoots and the roots with each treatment, indicating that the form of foliar-applied nitrogen did not affect the distribution of nitrate nitrogen via root uptake. [Conclusion] Foliar application of ammonium nitrogen could, therefore, promote nitrate nitrogen uptake by cotton seedling roots.
  • Liu Shuai, Wu Jie, Li Yabing, Han Yingchun, Dong Helin, Li Pengcheng, Zheng Cangsong, Sun Miao
    Cotton Science. 2018, 30(4): 344-352. https://doi.org/10.11963/1002-7807.lslyb.20180711
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    [Objective] We examined the effects of spraying AFD (Agent of flower bud differentiation), a new plant growth regulator, on cotton yield formation and fiber quality. This study was designed to provide a scientific basis for chemical regulation. [Method] Six treatments of different AFD concentrations were used for this experiment. Cotton plant height, boll number, boll weight, seed cotton yield, and fiber quality were assessed to clarify the effects of chemical regulation on cotton at different concentrations. [Result] The results showed that cotton plant height was associated with AFD concentration while AFD was effective in inhibiting the cotton boll shedding ratio and significantly increasing the boll opening ratio. Furthermore, the boll number per plant and boll weight were significantly higher in plants sprayed with AFD at concentrations of 1 350-1 800 mL·hm-2 than in the control group and higher seed cotton yields were obtained. Different concentrations of AFD had no significant effect on cotton breaking strength, micronaire value, and breaking elongation. However, the highest AFD concentration had certain inhibiting effects on fiber length and uniformity index. [Conclusion] The use of chemical control via a suitable concentration of AFD had a significant effect on increasing cotton yield and little effect on fiber quality. The study of the mechanism by which AFD affects cotton traits is of great significance for cotton production.