茶修剪物分解过程中钾释放与酶活性相关性分析
收稿日期: 2025-10-16
修回日期: 2025-12-17
网络出版日期: 2026-08-07
基金资助
农业生态环境保护项目“国家现代生态农业创新示范基地建设”(091821104022292027-4);贵州省科技计划项目“贵州山区生态茶园物种配置关键技术集成与示范”(黔科合支撑〔2020〕1Y011号);2024年度贵州省科技创新人才团队建设项目“贵州大学林学院野生动植物创新团队”(黔科合人才CXTD〔2025〕053)
Correlation analysis of potassium release and enzyme activity during the decomposition of Camellia sinensis prunings
Received date: 2025-10-16
Revised date: 2025-12-17
Online published: 2026-08-07
明确茶(Camellia sinensis)修剪物分解过程中的钾释放规律及其与酶活性的关系,对揭示植物生长和生态系统养分循环规律具有重要意义。2022—2024年在贵州花溪久安生态茶园采用分解袋法开展原位分解实验,在2、4、6、8、10、12、15、18、21、24个月共10个时间点回收样品,测定茶修剪物的干质量损失率、钾残留率、钾释放速率和6种酶活性;采用单因素方差分析比较各指标在不同分解时期之间的差异,并运用Pearson相关和逐步回归分析方法探讨钾释放速率与各酶活性之间的关系。结果表明:1)茶修剪物的钾释放表现为典型的“快速—稳定”双阶段模式,前4个月释放迅速,之后逐渐趋于平缓;2)相关性分析表明,钾释放速率与蔗糖酶、酸性磷酸酶呈显著正相关,而与过氧化氢酶呈显著负相关;3)逐步回归分析结果进一步表明,蔗糖酶、酸性磷酸酶和过氧化氢酶是钾释放的关键预测因子,模型拟合度较高(R2=0.926)。基于酶活性的预测模型能够较好地解释钾释放速率的变异,可为茶园钾肥精准管理和修剪物资源化利用提供理论支撑。
周会 , 杨瑞 , 赵宏玖 , 粟永鑫 , 刘莎茜 . 茶修剪物分解过程中钾释放与酶活性相关性分析[J]. 林草资源研究, 2026 , 0(1) : 67 -74 . DOI: 10.13466/j.cnki.lczyyj.2026.01.007
Understanding the release pattern of potassium during the decomposition of Camellia sinensis pruning and its relationship with enzyme activity is important for understanding plant growth and ecosystem nutrient cycling.An in-situ decomposition experiment was conducted using the litterbag method in the Jiuan ecological tea garden in Huaxi District,Guiyang,Guizhou Province from 2022 to 2024.Samples were collected at 10 time points(2,4,6,8,10,12,15,18,21,and 24 months)to measure the dry mass loss rate,potassium retention rate,potassium release rate,and six enzymes.One-way ANOVA was used to compare differences in these indicators across decomposition stages,while Pearson correlation and stepwise regression analyses were applied to examine the relationship between potassium release rate and enzyme activities.The results indicate that:1)The potassium release from Camellia sinensis prunings exhibits a typical biphasic("fast-stable")pattern,characterized by a rapid release phase during the first four months,followed by a period of gradual stabilization.2)Correlation analysis shows that potassium release rate is significantly positively correlated with sucrase and acid phosphatase activities but significantly negatively correlated with catalase activity.3)Stepwise regression further indicates that sucrase,acid phosphatase,and catalase are key predictors of potassium release,with the model demonstrating a goodness-of-fit(R2=0.926).The prediction model based on enzyme activities can effectively explain the variation in the potassium release rate,providing theoretical support for precise potassium fertilizer management and the resourceful utilization of prunings.
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