不同农业景观下植物有机质分解动态

Pub Date : 2023-03-01 DOI:10.1590/1983-21252023v36n115rc
J. H. C. S. Silva, A. Barbosa, D. Gomes, I. S. Aquino, Janaína R. da Silva
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引用次数: 1

摘要

摘要生态系统或农业生态系统的功能主要取决于土壤-凋落物的相互作用。因此,本工作的目的是评估不同的土壤利用和管理系统对印楝(印楝)和海泡桐(Gliricidia sepium)植物残留物分解动力学的影响。这项研究在四个土地占用系统中进行,即:剩余森林、农林业、农业曼陀罗和牧场。使用尼龙袋(垃圾袋)估计分解率,尼龙袋含有20克印度洋和印度洋的叶子,将它们放置在每个区域的土壤表面18、36、54、72、90和108天。对每个物种采用完全随机设计,将每个区域视为一个处理,每个集合进行四次重复。对数据进行回归分析,并通过Tukey检验对平均值进行比较(p<0.05)。无论评估的系统如何,A.indica和G.sepium植物物质在108天后分别有85%和90%以上被分解,这表明这些物种具有较高的分解率。根据土地利用系统的不同,海泡桐的植物群半衰期为16-23天,印度洋泡桐的为25-37天。叶温、土壤含水量、叶片理化特性是减肥的预测因子。研究结果为森林管理实践提供了重要信息。
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Dynamics of plant organic matter decomposition in different agricultural landscapes
ABSTRACT The functioning of ecosystems or agroecosystems is mainly dependent on the soil-litterfall interaction. Thus, the objective of this work was to evaluate the effect of different soil use and management systems on the dynamics of decomposition of plant residues of Azadirachta indica (neem) and Gliricidia sepium (gliricidia). The study was conducted in four land occupation systems, namely: remaining forest, agroforestry, agricultural mandala, and pasture. The decomposition rate was estimated using nylon bags (litter bags), containing 20 g of leaves of A. indica and G. sepium, which were arranged on the soil surface of each area for 18, 36, 54, 72, 90, and 108 days. A completely randomized design was used for each species, considering each area as one treatment, with four replications for each collection. The data were subjected to regression analysis and the means were compared by the Tukey's test (p<0.05). More than 85% and 90% of A. indica and G. sepium plant matters, respectively, had been decomposed after 108 days, regardless of the system evaluated, denoting that these species present high decomposition rates. The phytomass half-life time varied from 16 to 23 days for G. sepium and from 25 to 37 days for A. indica, depending on the land use system. Edaphic temperature, soil water content, and leaf physical and chemical characteristics are weight loss predictors. The results provide important information to enable forest management practices.
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