Tree biomass and soil carbon stocks in indigenous forests in comparison to plantations of exotic species in the Taita Hills of Kenya

Carbon (C) densities of the tree biomass and soil (0-50 cm) in indigenous forest and plantations of eucalyptus, cypress and pine in the Taita Hills, Kenya were determined and compared. The cypress and pine plantations were about 30-years-old and eucalyptus plantations about 50-years-old. Biomass C densities were estimated from breast height diameter and wood density using allometric functions developed for tropical species and an assumed C content of 50 %. Belowground biomass C densities were estimated using root:shoot biomass ratios. Soil organic C (SOC) densities were calculated from measured organic carbon contents (0-20 and 20-50 cm layers) and modelled bulk density values. Mean total biomass C and SOC densities for indigenous forest were greater than those of the plantations, and the difference was significant (p<0.05) in the cases of cypress and pine biomass and pine SOC. The correlation between biomass C and SOC densities was nearly significant in the case of indigenous forest, but negative. Biomass C densities were not significantly correlated with mean annual precipitation, mean annual temperature or potential evapotranspiration, but pine biomass C densities were significantly correlated to actual evapotranspiration. SOC densities were more strongly correlated to mean annual precipitation than biomass C densities, but only significantly so in the case of pine. Neither biomass C nor SOC densities were correlated to plant available water capacity of the soil. Indigenous forest SOC densities were significantly correlated to soil clay contents, but negatively. Indigenous forests sequester more C in biomass and soil than do 30 to 50-year-old plantations of exotics, but it remains unclear if this is an intrinsic difference between indigenous forest and plantations of exotics or because of insufficient time for SOC levels in plantations to recover after clearance of original indigenous forest.Peer reviewe

Microbial carbon use efficiency along an altitudinal gradient

Soil microbial carbon-use efficiency (CUE), described as the ratio of growth over total carbon (C) uptake, i.e. the sum of growth and respiration, is a key variable in all soil organic matter (SOM) models and critical to ecosystem C cycling. However, there is still a lack of consensus on microbial CUE when estimated using different methods. Furthermore, the significance of many fundamental drivers of CUE remains largely unknown and inconclusive, especially for tropical ecosystems. For these reasons, we determined CUE and microbial indicators of soil nutrient availability in seven tropical forest soils along an altitudinal gradient (circa 900-2200 m a.s.l) occurring at Taita Hills, Kenya. We used this gradient to study the soil nutrient (N and P) availability and its relation to microbial CUE estimates. For assessing the soil nutrient availability, we determined both the soil bulk stoichiometric nutrient ratios (soil C:N, C:P and N:P), as well as SOM degradation related enzyme activities. We estimated soil microbial CUE using two methods: substrate independent O-18-water tracing and C-13-glucose tracing method. Based on these two approaches, we estimated the microbial uptake efficiency of added glucose versus native SOM, with the latter defined by 18O-water tracing method. Based on the bulk soil C:N stoichiometry, the studied soils did not reveal N limitation. However, soil bulk P limitation increased slightly with elevation. Additionally, based on extracellular enzyme activities, the SOM nutrient availability decreased with elevation. The C-13-CUE did not change with altitude indicating that glucose was efficiently taken up and used by the microbes. On the other hand, 18O-CUE, which reflects the growth efficiency of microbes growing on native SOM, clearly declined with increasing altitude and was associated with SOM nutrient availability indicators. Based on our results, microbes at higher elevations invested more energy to scavenge for nutrients and energy from complex SOM whereas at lower elevations the soil nutrients may have been more readily available.Peer reviewe

Microbial carbon use efficiency along an altitudinal gradient

Soil microbial carbon-use efficiency (CUE), described as the ratio of growth over total carbon (C) uptake, i.e. the sum of growth and respiration, is a key variable in all soil organic matter (SOM) models and critical to ecosystem C cycling. However, there is still a lack of consensus on microbial CUE when estimated using different methods. Furthermore, the significance of many fundamental drivers of CUE remains largely unknown and inconclusive, especially for tropical ecosystems. For these reasons, we determined CUE and microbial indicators of soil nutrient availability in seven tropical forest soils along an altitudinal gradient (circa 900–2200 m a.s.l) occurring at Taita Hills, Kenya. We used this gradient to study the soil nutrient (N and P) availability and its relation to microbial CUE estimates. For assessing the soil nutrient availability, we determined both the soil bulk stoichiometric nutrient ratios (soil C:N, C:P and N:P), as well as SOM degradation related enzyme activities. We estimated soil microbial CUE using two methods: substrate independent 18O-water tracing and 13C-glucose tracing method. Based on these two approaches, we estimated the microbial uptake efficiency of added glucose versus native SOM, with the latter defined by 18O-water tracing method. Based on the bulk soil C:N stoichiometry, the studied soils did not reveal N limitation. However, soil bulk P limitation increased slightly with elevation. Additionally, based on extracellular enzyme activities, the SOM nutrient availability decreased with elevation. The 13C-CUE did not change with altitude indicating that glucose was efficiently taken up and used by the microbes. On the other hand, 18O-CUE, which reflects the growth efficiency of microbes growing on native SOM, clearly declined with increasing altitude and was associated with SOM nutrient availability indicators. Based on our results, microbes at higher elevations invested more energy to scavenge for nutrients and energy from complex SOM whereas at lower elevations the soil nutrients may have been more readily available

Allometric models for estimating leaf biomass of sisal in a semi-arid environment in Kenya

Publisher Copyright: © 2021 The AuthorsBiomass is a key variable for crop monitoring and for assessing carbon stocks and bioenergy potential. This study aimed to develop an allometric model for predicting the dry leaf biomass of sisal, an agave plant with crassulacean acid metabolism grown for fibre production in the tropics and subtropics and whose biomass can be utilised as a feedstock to produce biogas through anaerobic digestion. The allometric model was used to estimate leaf biomass and productivity across different stand ages in a sisal plantation in semi-arid region in south-east Kenya (annual rainfall 611 mm and temperature 24.9 °C). Based on a sample of 38 leaves, the best predictor for biomass was leaf maximum width and plant height used as a combined variable in a log-log regression model (cross-validated R2 = 0.96 and root-mean-square error = 7.69 g). The mean productivity in nine 26- to 36-month-old plots was 11.1 Mg ha−1 yr−1, which could potentially yield approximately 3000 m3 CH4 ha−1 yr−1. The leaf biomass in 55 field plots (400 m2 in area) ranged from 2.7 to 42.7 Mg ha−1, with mean at 13.5 Mg ha−1, which equals to 6.3 Mg C ha−1. The yielded allometric equations can be utilised for predicting the leaf biomass of sisal in similar agro-ecological zones. The estimates on plantation biomass can be used in assessing the role of sisal plantations as a regional carbon storage. In addition, the results provide reference on the productivity of agave and crassulacean acid metabolism in semi-arid regions of East Africa, where such reports are few.Peer reviewe

Potential impacts of agricultural expansion and climate change on soil erosion in the Eastern Arc Mountains of Kenya

The Taita Hills form the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, is one of the world's most important regions for biological conservation. Due to the expansion of agricultural activities during the last centuries, currently only 1% of the original vegetation remains preserved in the Taita Hills. These landscape changes, together with potential increases in rainfall volumes caused by climate change,offer a great risk for soil conservation. The present research aims to evaluate how future changes in climate and land use can alter, in time and space, the variables inherent to a widely used soil erosion model, and to assess the impacts of these changes for soil conservation. A modelling framework was assembled by integrating a landscape dynamic model, a soil erosion model and synthetic precipitation datasets generated through a Monte Carlo simulation. The results indicate that, if the current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. Although these land use changes will certainly increase soil erosion figures, new croplands will likely take place predominantly in the lowlands, which comprises areas with lower soil erosion potential. By the year 2030, rainfall erosivity is likely to increase during April and November, while a slight decrease tendency is observed during March and May. An integrated assessment of these environmental changes, performed using the modelling framework, allows a clear distinction of priority regions for soil conservation policies during the next 20 years.The document is available at www.elsevier.com/locate/geomorphStudy was funded by the Academy of Finland, the Centre for International Mobility (CIMO) and the University of Helsinki Research Foundation

Season-dependence of remote sensing indicators of tree species diversity

<div><p>During recent years, many studies have been undertaken to investigate how spectral characteristics of forests can provide information on spatial patterns of tree species diversity (TSD). Important advances have been made, and significant relationships between TSD and remotely sensed indicators of net primary productivity and environmental heterogeneity have been reported. However, the season-dependence of these relationships has not yet been fully investigated, and the influence of phenology remains poorly understood. In this study, we aim to assess how the relationships between remote sensing indicators and TSD depend on the season of the year. TSD measures, including species richness, Shannon’s diversity and Simpson’s diversity, were determined for 82 field plots in the Afromontane cloud forests of Taita Hills, Kenya. A time series of 15 Landsat images were used to calculate a set of spectral and heterogeneity metrics. The relationship between remote-sensing metrics and TSD measures was analysed by simple and multivariate regression analysis. We conclude that the relationships between remote-sensing metrics and TSD are season-dependent. Hence, it is demonstrated the date of image acquisition is an important aspect to be considered in biodiversity studies. Given that the dependence of the relationships is closely linked to climate seasonality defining vegetation phenology, the relationships may also vary according to geographical conditions.</p></div

Delimiting tropical mountain ecoregions for conservation

Ecological regions aggregate habitats with similar biophysical characteristics within well-defined boundaries, providing spatially consistent platforms for monitoring, managing and forecasting the health of interrelated ecosystems. A major obstacle to the implementation of this approach is imprecise and inconsistent boundary placement. For globally important mountain regions such as the Eastern Arc (Tanzania and Kenya), where qualitative definitions of biophysical affinity are well established, rule-based methods for landform classification provide a straightforward solution to ambiguities in region extent. The method presented in this paper encompasses the majority of both contemporary and estimated preclearance forest cover within strict topographical limits. Many of the species here tentatively considered 'near-endemic' could be reclassified as strictly endemic according to the derived boundaries. LandScan and census data show population density inside the ecoregion to be higher than in rural lowlands, and lowland settlement to be most probable within 30 km. This definition should help to align landscape scale conservation strategies in the Eastern Arc and promote new research in areas of predicted, but as yet undocumented, biological importance. Similar methods could work well in other regions where mountain extent is poorly resolved. Spatial data accompany the online version of this article