A new afforestation method can accelerate nutrient return from litterfall compared with natural regeneration— a 17-year observation in subalpine region on the eastern Tibetan Plateau

Author:

Zhou Zhiqiong1,Chen Xiaoxia2,He Qihua2,Li Xiaoming2,Ding Jianlin2,Bao Weikai2,Shi Fusun2

Affiliation:

1. Chengdu Institute of Biology

2. Maoxian Mountain Ecosystem Research Station, Chinese Academy of Sciences

Abstract

Abstract Restoring degraded ecosystems to be mixed-species plantations is an important strategy to address the land degradation problem worldwide, for its advantage in recovering ecosystem functions over monoculture plantations. However, few studies have evaluated the ecological functions of mixed-species plantations, especially mixtures with indigenous trees. The general objective of our study was to assess carbon and nutrient return from litterfall production in the high indigenous-species diversity plantation (HISD plantation) with a new strip-cutting shrubs method compared with which in the secondary forest with natural regeneration over 17 years. The results clearly showed that the annual litterfall production was 47% higher, resulting in higher C (50%), P (31%), and N (8%) return from litterfall in the HISD plantation over the secondary forest. Specifically, leaves (52%), twigs (36%) and reproductive (28%) litter production were also higher in the HISD plantation than which in the secondary forest. Annual litterfall production was collectively regulated by tree community parameters (basal area), soil fertility (N, P and available P), and climatic variables (annual mean temperature and precipitation). Higher basal area of canopy trees and soil fertility (C, N, P and available P) resulted in higher litterfall production in the HISD plantation relation to the secondary forest. The results suggest that strip-cutting shrubs restoration can be a useful technique to accelerate litterfall production, and carbon and nutrient return from litter compared with natural regeneration by recovering soil fertility in this region.

Publisher

Research Square Platform LLC

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