Distribution characteristics of soil organic matter and total nitrogen on the Yajiageng vertical belt, Gongga Mountain around the Dadu River banks

2008 ◽  
Vol 13 (3) ◽  
pp. 331-335 ◽  
Author(s):  
Yan Yan ◽  
Xiaodan Wang
2020 ◽  
Author(s):  
Liqun Tang ◽  
Zhijie Shan ◽  
Yang Yu

<p>Re-vegetation has been widely carried out to prevent land degradation, reduce soil erosion, and improve soil quality. In order to investigate the characteristics of soil nutrients content in different land use types of karst gabin basin, soil organic matter, soil total nitrogen, soil total phosphorus, soil total potassium, soil pH, and soil texture in woodland, agricultural land, orchard, and grassland were surveyed in Mengzi Gabin Basin, Southwest of China. The difference of soil indicators between vegetation types was analyzed, and soil fertility quality of four land use types was comprehensively evaluated by the soil quality index (SQI). The results showed that land use significantly affected soil organic matter content. Soil organic matter content was the highest in grassland, followed by agricultural land and forest land, while orchard was lowest. There was a significant difference in soil total nitrogen content between different land uses. The total nitrogen content in farmland soil was the highest, followed by grassland and woodland, and the lowest in the orchard. Woodand had the highest total potassium content and the lowest total phosphorus content. The grassland soil had the highest total phosphorus content and the lowest total potassium content. pH value in the four land use types was acidic, ranged from 5.82 to 6.67. The soil quality index showed that woodland had the highest soil fertility quality. The results of the study could provide the basis of soil nutrients variation and status in Gabin basin, and also provides support for evaluating the soil improvements during vegetation restoration in fragile Karst ecosystems.</p>


2014 ◽  
Vol 34 (18) ◽  
Author(s):  
李燕丽 LI Yanli ◽  
潘贤章 PAN Xianzhang ◽  
王昌昆 WANG Changkun ◽  
刘娅 LIU Ya ◽  
赵其国 ZHAO Qiguo

2016 ◽  
Vol 29 (2) ◽  
pp. 263-273 ◽  
Author(s):  
MARCELO RIBEIRO VILELA PRADO ◽  
FABRICIO TOMAZ RAMOS ◽  
OSCARLINA LÚCIA DOS SANTOS WEBER ◽  
CAIO BATISTA MÜLLER

ABSTRACT: The evaluation of land use and management by the measurement of soil organic matter and its fractions has gained attention since it helps in the understanding of the dynamics of their contribution to soil productivity, especially in tropical environments. This study was conducted in the municipality of Colorado do Oeste, state of Rondônia, Brazil and its aim was to determinethe quantity of organic carbon and total nitrogen in the light and heavy fractions of organic matter in the surface layers of a typic hapludalf under different land use systems: Native Forest: open evergreen forest, reference environment; Agroforestry System 1: teak (Tectona grandis LF) and kudzu (Pueraria montana); Agroforestry System 2: coffee (Coffea canephora), marandu palisade grass (Brachiaria brizantha cv. Marandu), "pinho cuiabano" (Parkia multijuga), teak and kudzu.; Agroforestry System 3: teak and cocoa (Theobroma cacao); Silvopasture System: teak, cocoa and marandu palisade grass; and Extensive Grazing System: marandu palisade grass. The experimental design was a randomized block in split-split plots (use systems versus soil layers of 0-0.05 and 0.05-0.10 m) with three replications. The results showed that relative to Native Forest, the Agroforestry System 2 had equal- and greater amounts of organic carbon and total nitrogen respectively (light and heavy fractions) in the soil organic matter, with the light fraction being responsible for storage of approximately 45% and 70% of the organic carbon and total nitrogen, respectively. Therefore, the light densimetric fraction proved to be useful in the early identification of the general decline of the soil organic matter in the land use systems evaluated.


2007 ◽  
Vol 18 (1) ◽  
pp. 78-80 ◽  
Author(s):  
Bao-hua Zhang ◽  
Zhen-jian Wang ◽  
Zi-ting Liu ◽  
Ai-min Huang ◽  
Qing-xin Tang ◽  
...  

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