Search

The aim of the study is to describe the main trends and possibilities in Russian forestry. Based on statistical data, the authors analyze general trends in the Russian forest industry like woodworking, roundwood and wood pellets production. Russia's output of roundwood, sawnwood, wood pellets, wood-based panels and veneer sheets has been growing steadily over the past 5 years. The production of only two wood-based panel types – hardboard and other fiberboard – is either absent or in decline. The results include decline in roundwood export and the growth rates in wood pellets, sawnwood, wood-based panels, and veneer sheets exports. The Russian forest industry has room to grow its export of forest products. This requires technology upgrading, which could be facilitated by proactive government policies. Keywords: forest industry, woodworking industry, bioenergy, Russia

A comprehensive in situ dataset of chlorophyll a (Chl a; N = 18,001), net primary production (NPP; N = 165) and net community production (NCP; N = 95), were used to evaluate the performance of Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A) algorithms for these parameters, in the South Atlantic Ocean, to facilitate the accurate generation of satellite NCP time series. For Chl a, five algorithms were tested using MODIS-A data, and OC3-CI performed best, which was subsequently used to compute NPP. Of three NPP algorithms tested, a Wavelength Resolved Model (WRM) was the most accurate, and was therefore used to estimate NCP with an empirical relationship between NCP with NPP and sea surface temperature (SST). A perturbation analysis was deployed to quantify the range of uncertainties introduced in satellite NCP from input parameters. The largest reductions in the uncertainty of satellite NCP came from MODIS-A derived NPP using the WRM (40%) and MODIS-A Chl a using OC3-CI (22%). The most accurate NCP algorithm, was used to generate a 16 year time series (2002 to 2018) from MODIS-A to assess climate and environmental drivers of NCP across the South Atlantic basin. Positive correlations between wind speed anomalies and NCP anomalies were observed in the central South Atlantic Gyre (SATL), and the Benguela Upwelling (BENG), indicating that autotrophic conditions may be fuelled by local wind-induced nutrient inputs to the mixed layer. Sea Level Height Anomalies (SLHA), used as an indicator of mesoscale eddies, were negatively correlated with NCP anomalies offshore of the BENG upwelling fronts into the SATL, suggesting autotrophic conditions are driven by mesoscale features. The Agulhas bank and Brazil-Malvinas confluence regions also had a strong negative correlation between SLHA and NCP anomalies, similarly indicating that NCP is forced by mesoscale eddy generation in this region. Positive correlations between SST anomalies and the Multivariate ENSO Index (MEI) in the SATL, indicated the influence of El Niño events on the South Atlantic Ocean, however the plankton community response was less clear. UK Natural Environment Research Council | Ref. NERC; NE / L002434 / 1 European Space Agency | Ref. AMT4SentinelFRM (ESRIN / RFQ / 3-14457 / 16 / I-BG) European Space Agency | Ref. AMT4OceanSatFlux (4000125730/18 / NL / FF / gp) NERC International Opportunity Fund Grant Satellite estimates of marine net community production in the South Atlantic from Sentinel-3 | Ref. SemSAS; NE / P00878X / 1 P&D ANP / BRASOIL | Ref. 48610.011013 / 2014-66 Oceanographic Institute of the University of São Paulo | Ref. FAPESP 2015 / 01373-0 Oceanographic Institute of the University of São Paulo | Ref. CNPq 442926 / 2015-4 Oceanographic Institute of the University of São Paulo | Ref. FAPESP 2014 / 50820-7 Oceanographic Institute of the University of São Paulo | Ref. CNPq 565060 / 2010-4 NERC | Ref. NE / R015953 / 1

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate Change Initiative (OC-CCI) version 3.0 data. The objectives of the study were to determine which is the most accurate PP model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (weekly) temporal resolution OC-CCI data, and whether the performance of the models is improved by implementing a photoinhibition function? The Platt-Sathyendranath primary production model (PPPSM) was the most accurate over all NEA provinces and, specifically, in the Atlantic Arctic province (ARCT) and North Atlantic Drift (NADR) provinces. The implementation of a photoinhibition function in the PPPSM reduced its accuracy, especially at lower range PP. The Vertical Generalized Production Model-VGPM (PPVGPM) tended to over-estimate PP, especially in summer and in the NADR. The accuracy of PPVGPM improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PPAph), with and without photoinhibition, was the least accurate model for the NEA. Mapped images of each model showed that the PPVGPM was 150% higher in the NADR compared to PPPSM. In the North Atlantic Subtropical Gyre (NAST) province, PPAph was 355% higher than PPPSM, whereas PPVGPM was 215% higher. A sensitivity analysis indicated that chlorophyll-a (Chl a), or the absorption of phytoplankton, at 443 nm (aph (443)) caused the largest error in the estimation of PP, followed by the photosynthetic rate terms and then the irradiance functions used for each model.