Ermias Aynekulu

Regeneration Response of Juniperus procera and Olea europaea subsp cuspidata to Exclosure in a Dry Afromontane Forest in Northern Ethiopia

Abstract The Afromontane forests of northern Ethiopia have been degraded and fragmented for centuries. Recently, efforts have been made to restore these forests by protecting them from livestock interference. In this study, the natural regeneration of Juniperus procera Hochst. ex Endl. and Olea europaea L. subsp cuspidata (Wall. ex G. Don) Cif. is investigated under protected conditions after 3 years of enclosure and under open management systems in a dry Afromontane forest in northern Ethiopia. Data on the floristic and structural compositions of the vascular plants were collected using 32 randomly selected plots (20 m × 20 m), while nested plots (10 m × 10 m) were used to investigate the seedling bank at the protected and adjacent open sites. The results reveal that there was a significantly higher regeneration of O. europaea on the protected site than on the open site (P  =  0.01). However, there was no significant difference between the 2 sites for J. procera (P  =  0.16). Thus, protecting the degraded forest in northern Ethiopia seems to be an appropriate management option for the regeneration of O. europaea. The regeneration status of J. procera at both sites is poor, which indicates that protecting the forest from livestock and human disturbance is unlikely to lead to regeneration of this species. Further investigation of other factors that hinder the regeneration of J. procera is therefore recommended.

Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series

With the increasing temporal resolution of medium spatial resolution data, seasonal features are becoming more readily available for land cover characterization. However, in the tropical regions, images can be severely contaminated by clouds during the rainy season and fires during the dry season, with possible effects to seasonal features. In this study, we evaluated the performance of seasonal features based on an annual Landsat time series (LTS) of 35 images for land cover characterization in West Sudanian savanna woodlands. First, the burnt areas were detected and removed. Second, the reflectance seasonality was modelled using a harmonic model, and model parameters were used as inputs for land cover classification and tree crown cover prediction using the random forest algorithm. Furthermore, to study the sensitivity of the approach to the burnt areas, we repeated the analyses without the first step. Our results showed that seasonal features improved classification accuracy significantly from 68.7% and 66.1% to 76.2%, and decreased root mean square error (RMSE) of tree crown cover predictions from 11.7% and 11.4% to 10.4%, in comparison to the dry and rainy season single date images, respectively. The burnt areas biased the seasonal parameters in near-infrared and shortwave infrared bands, and decreased the accuracy of classification and tree crown cover prediction, suggesting that burnt areas should be removed before fitting the harmonic model. We conclude that seasonal features from annual LTS improved land cover characterization performance, and the harmonic model, provided a simple method for computing annual seasonal features with burnt area removal.

Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands

It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance.

Applicability of near‐infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves

There is uncertainty on how generally applicable near-infrared reflectance spectroscopy (NIRS) calibrations are across genotypes and environments, and this study tests how well a single calibration performs across a wide range of conditions. We also address the optimization of NIRS to perform the analysis of crude protein (CP) content in a variety of cowpea accessions (n = 561) representing genotypic variation as well as grown in a wide range of environmental conditions in Tanzania and Uganda. The samples were submitted to NIRS analysis and a predictive calibration model developed. A modified partial least-squares regression with cross-validation was used to evaluate the models and identify possible spectral outliers. Calibration statistics for CP suggests that NIRS can predict this parameter in a wide range of cowpea leaves from different agro-ecological zones of eastern Africa with high accuracy (R2cal = 0.93; standard error of cross-validation = 0.74). NIRS analysis improved when a calibration set was developed from samples selected to represent the range of spectral variability. We conclude from the present results that this technique is a good alternative to chemical analysis for the determination of CP contents in leaf samples from cowpea in the African context, as one of the main advantages of NIRS is the large number of compounds that can be measured at once in the same sample, thus substantially reducing the cost per analysis. The current model is applicable in predicting the CP content of young cowpea leaves for human nutrition from different agro-ecological zones and genetic materials, as cowpea leaves are one of the popular vegetables in the region.