Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.info:eu-repo/semantics/publishedVersio

potential for soil health improvement anwd plant growth promotion

Funding text: This work was supported by funds from Camões, Instituto da Cooperação e da Língua and Fundação para a Ciência e a Tecnologia through the research unit UIDB/00239/2020 (CEF), the PhD grant SFRH/BD/113951/2015 (Ivete Sandra Maquia), and the contribution to the International Rice Research Institute.(1) Aims: Assessing bacterial diversity and plant-growth-promoting functions in the rhizosphere of the native African trees Colophospermum mopane and Combretum apiculatum in three landscapes of the Limpopo National Park (Mozambique), subjected to two fire regimes. (2) Methods: Bacterial communities were identified through Illumina Miseq sequencing of the 16S rRNA gene amplicons, followed by culture dependent methods to isolate plant growth-promoting bacteria (PGPB). Plant growth-promoting traits of the cultivable bacterial fraction were further analyzed. To screen for the presence of nitrogen-fixing bacteria, the promiscuous tropical legume Vigna unguiculata was used as a trap host. The taxonomy of all purified isolates was genetically verified by 16S rRNA gene Sanger sequencing. (3) Results: Bacterial community results indicated that fire did not drive major changes in bacterial abundance. However, culture-dependent methods allowed the differentiation of bacterial communities between the sampled sites, which were particularly enriched in Proteobacteria with a wide range of plant-beneficial traits, such as plant protection, plant nutrition, and plant growth. Bradyrhizobium was the most frequent symbiotic bacteria trapped in cowpea nodules coexisting with other endophytic bacteria. (4) Conclusion: Although the global analysis did not show significant differences between landscapes or sites with different fire regimes, probably due to the fast recovery of bacterial communities, the isolation of PGPB suggests that the rhizosphere bacteria are driven by the plant species, soil type, and fire regime, and are potentially associated with a wide range of agricultural, environmental, and industrial applications. Thus, the rhizosphere of African savannah ecosystems seems to be an untapped source of bacterial species and strains that should be further exploited for bio-based solutions.publishersversionpublishe