Channel: RESEARCH REPORTS http://journals.caass.org.cn/mhxb EN-US http://journals.caass.org.cn/mhxb/EN/current.shtml http://journals.caass.org.cn/mhxb 5 2SO₄ stress is a major type of salt stress in Xinjiang and has seriously restricted the cotton production in Xinjiang. This study aims to explore the effect of Na₂SO₄ stress on cotton metabolism and to investigate the key metabolites and metabolic pathways in the stress response of cotton. [Method] The metabolic analysis was applied in cotton roots and leaves under two treatment settings, including control (CK) and sulfate stress (Na₂SO₄, SS), to explore the changes of metabolite content and metabolic pathways in cotton under Na₂SO₄ stress. [Result] Sulfate stress significantly inhibited the growth of cotton. Compared with CK, the dry mass of leaves, stems, and roots, and total mass of plant under SS treatment decreased by 46.9%, 50.9%, 43.0% and 47.9%, respectively. Under sulfate stress, there were 42 up-regulated and 10 down-regulated organic acids, 32 up-regulated and 16 down-regulated amino acids and amino acid derivatives, 23 up-regulated and 1 down-regulated carbohydrate in root. There were 37 up-regulated organic acids and 7 down-regulated organic acids, 16 up-regulated and 17 down-regulated amino acids and amino acids derivatives, 16 up-regulated and 4 down-regulated carbohydrate in leaves. A total of 30 differential metabolic pathways were selected in roots, including 9 pathways related to amino acid metabolism, 7 pathways related to organic acid metabolism, and 7 pathways related to carbohydrate metabolism. A total of 17 differential metabolic pathways were selected in leaves, including 7 pathways related to amino acid metabolism, 4 pathways related to organic acid metabolism, and 3 pathways related to carbohydrate metabolism. [Conclusion] Cotton responded to sulfate stress by accumulating organic acids, carbohydrate and alcohols in roots and leaves. The up-regulation of citric acid, succinic acid, pyruvic acid and linoleic acid in leaves suggested the enhanced tricarboxylic acid (TCA) cycle and β-enhanced oxidation. And up-regulation of citric acid, succinic acid and pyruvic acid in roots indicated the enhanced TCA cycle. This study explored the mechanism of Na₂SO₄ stress response in cotton and provided a theoretical basis for cotton cultivation in Xinjiang.]]> -1 1-methylcyclopropene (1-MCP), 0.01, 0.02, 0.05 μmol·L^-1 coronidine (COR) and 0.5, 1.0, 2.0 μmol·L^-1 2, 4-epibrassinolide (EBR) spraying treatments were set at the seedling stage. Water and 400 μmol·L^-1 1-MCP spraying treatment were set at the flowering and boll setting stage. The effects of different plant growth regulators on the dry mass, leaf photosynthetic characteristics, chlorophyll content, antioxidant enzyme activity, yield and fiber quality of cotton under the normal temperature and high temperature conditions were explored. [Result] The results showed that the suitable concentrations of three plant growth regulators could improve the high temperature tolerance of cotton. Under high temperature stress at the seedling stage, compared with water treatment, spraying 400 μmol·L^-1 1-MCP could significantly increase the dry mass of aboveground and underground parts of cotton, and increase the net photosynthetic rate, the contents of chlorophyll a and chlorophyll b, and the activities of antioxidant enzymes (superoxide dismutase, peroxidase, glutathione peroxidase and ascorbate peroxidase), and decrease malondialdehyde content in newly expanded cotton leaves. Principal component analysis proved that 400 μmol·L^-1 1-MCP could effectively improve the high temperature tolerance of cotton. Meanwhile, under high temperature stress at the flowering and boll setting stage, spraying 400 μmol·L^-1 1-MCP could significantly improve the net photosynthetic rate, stomatal conductance and transpiration rate of subtending leaves to cotton bolls, the number of bolls per plant, boll weight, seed cotton yield per plant and fiber quality compared with spraying water. [Conclusion] 400 μmol·L^-1 1-MCP can increase the chlorophyll content, photosynthetic capacity and antioxidant enzyme activity, and reduce the accumulation of malondialdehyde and other harmful substances in cotton leaves, thus enhancing the high temperature tolerance of cotton, and finally improve the cotton yield and fiber quality to a certain extent.]]> 4 plant·hm^-2), medium density (D2, 13.8 × 10⁴ plant·hm^-2) and high density (D3, 24 × 10⁴ plant·hm^-2). The UAV images were obtained on the 25 days old cotton seedlings, and the vegetation indices (VIs) of red, green, and blue (RGB) and target morphological features were extracted from the acquired UAV images. Based on the selected independent variable according to the correlation analysis, the model to estimate the quantity of cotton seedlings was constructed using stepwise multiple regression, followed by the model validation. [Results] (1) Comparing the segmentation effects of extracting cotton targets by triangular greenness index (TGI), excess greenness index (ExG), and green-blue difference + modified excess greenness index (GBDI + MExG), all these three VIs had relatively good segmentation effects, while TGI showed the highest precision of segmentation of cotton targets. (2) Comparing the two cotton plant quantity estimation models constructed with the two feature parameters, the estimation model based on the target morphological features for cotton seedling (R²=0.935 5) is better than the estimation model based on the VI of RGB (R²=0.903 6). (3) The estimation accuracy of the VIs-based seedling quantity estimation model were 96.77%, 99.55%, and 95.95% at D1, D2 and D3 densities respectively, and the overall estimation accuracy was 98.47%; the estimation accuracy of the plant estimation model based on the target morphological features at D1, D2 and D3 densities were 99.98%, 99.21%, and 97.92% respectively, and the overall estimation accuracy was 99.21%. The accuracy of the plant number estimation model based on the target morphological characteristics was slightly higher than that of the plant number estimation model based on VIs, but both models had good estimation outcome under different planting densities. [Conclusion] Using the UAV based low-altitude remote sensing platform with the integration of high-resolution sensors, the quantity estimation models for the drip-irrigated cotton seedlings were constructed by color vegetation indices and morphological features of target plants. Both models can effectively and accurately identify and quantify the drip-irrigated cotton plants under mulching, providing technical support for subsequent precision management in cotton fields.]]>