PREDICTING THE DECOMPOSITION PATTERNS OF TREE BIOMASS IN TROPICAL HIGHLAND MICROREGIONS OF KENYA

Citation
Dn. Mugendi et Pkr. Nair, PREDICTING THE DECOMPOSITION PATTERNS OF TREE BIOMASS IN TROPICAL HIGHLAND MICROREGIONS OF KENYA, Agroforestry systems, 35(2), 1997, pp. 187-201
Citations number
36
Categorie Soggetti
Forestry,Agriculture
Journal title
ISSN journal
01674366
Volume
35
Issue
2
Year of publication
1997
Pages
187 - 201
Database
ISI
SICI code
0167-4366(1997)35:2<187:PTDPOT>2.0.ZU;2-N
Abstract
Decomposition- and nitrogen-release patterns of biomass from three agr oforestry multipurpose trees (Calliandra calothyrsus, Cordia africana and Grevillea robusta) were investigated in four contrasting environme nts (microregions) in the Kenyan tropical highlands during two croppin g seasons. Dried leafy biomass was placed in 2-mm litter bags, buried at 15-cm depth and recovered after 2, 4, 7, 10, 15 and 20 weeks. Decom position patterns were best described by first-order exponential decli ne curves. The decomposition rate constants ranged from 2.1 to 8.2 yr( -1), and the rates of decomposition among the species were in the orde r: calliandra greater than or equal to cordia > grevillea. There was a species-by-environment interaction during both seasons, but the nitro gen released did not follow such a pattern. Among the three tree speci es, calliandra released the highest amount of cumulative N, followed b y cordia and greviIlea. Using multiple regression techniques, decompos ition pattern was described as a function of three groups of factors: biomass quality (N, C, lignin and polyphenol), climate (soil temperatu re and rainfall), and soil conditions (pH, soil organic C, total N and P). For all the species and factors combined, the adjusted R-2 values were 0.88 and 0.91 for seasons 1 and 2, respectively. Among the three groups of factors, climate and biomass quality had the most influence on decomposition rates. Climatic factors accounted for 75% of the tot al rate of decomposition in season 1 ('irregular' season with less rai nfall and more soil temperature fluctuations), whereas biomass quality factors were more influential in season 2 ('regular' season), account ing for 65% of the total variability.