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高产“Bolaina”(Guazuma crinita)树的生物计量特征和养分含量的土壤特性和比例模型。

Soil characteristics and allometric models for biometric characteristics and nutrient amounts for high yielding "Bolaina" (Guazuma crinita) trees.

机构信息

Department of Soils, Instituto de Cultivos Tropicales (ICT), Tarapoto, Peru.

Professional School of Agronomic Engineering, Universidad Nacional Autonoma de Alto Amazonas (UNAAA), Yurimaguas, Peru.

出版信息

Sci Rep. 2024 Jan 30;14(1):2444. doi: 10.1038/s41598-024-52790-1.

Abstract

The Peruvian amazon is very diverse in native forestry species, the Guazuma crinita "Bolaina" being one of the most planted species in the country; however, little or no information about soil requirements and nutrient demands is known. The objective of this work was to assess the general conditions of soil fertility, biomass and macro- and micronutrient amounts in high-productivity Guazuma crinita plantations. Fields of high yielding Bolaina of different ages (1-10 years) were sampled in two regions. Soil and plant samples were collected in each field and biometric measurements of fresh weight, diameter at breast height and height were performed. For soil and plant analysis, both macro- (N, P, K, Ca, Mg, S) and micronutrients (B, Cu, Fe, Mn, Zn) were determined. Finally, allometric equations were constructed for biometric and nutrient amounts. This study is the first to assess and model macro- and micronutrient amounts in the productive cycle in this species, which grows in fertile soils. In the case of biometric equations, the logarithmic and logistic models performed better. For nutrient amounts, this species followed a pattern of Ca > N > K > P > S > Mg for macronutrients and Fe > B > Mn > Zn > Cu for micronutrients. The best prediction models for nutrients were the square root and logistic models.

摘要

秘鲁亚马逊地区的本土森林物种非常多样化,“Bolaina”番石榴树是该国种植最多的物种之一;然而,对于其土壤需求和养分需求的信息却知之甚少。本研究的目的是评估高生产力番石榴种植园的土壤肥力、生物量以及大量和微量养分的一般状况。在两个地区对不同年龄(1-10 年)的高产 Bolaina 番石榴进行了采样。在每个田间采集土壤和植物样本,并进行了生物量测量,包括新鲜重量、胸径和高度。对土壤和植物分析,确定了大量元素(N、P、K、Ca、Mg、S)和微量元素(B、Cu、Fe、Mn、Zn)。最后,构建了生物量和养分含量的异速方程。这是首次在该物种的肥沃土壤中评估和模拟其在生产周期中的大量和微量养分含量。在生物量方程中,对数和逻辑模型表现更好。对于养分含量,该物种的大量元素模式为 Ca>N>K>P>S>Mg,微量元素模式为 Fe>B>Mn>Zn>Cu。养分的最佳预测模型是平方根和逻辑模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71fa/10825134/71d4c37c07d9/41598_2024_52790_Fig1_HTML.jpg

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