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Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.

It is expected that Brazil could play an important role in biojet fuel (BJF) production in the future due to the long experience in biofuel production and the good agro‐ecological conditions. However, it is difficult to quantify the techno‐economic potential of BJF because of the high spatiotemporal variability of available land, biomass yield, and infrastructure as well as the technological developments in BJF production pathways. The objective of this research is to assess the recent and future techno‐economic potential of BJF production in Brazil and to identify location‐specific optimal combinations of biomass crops and technological conversion pathways. In total, 13 production routes (supply chains) are assessed through the combination of various biomass crops and BJF technologies. We consider temporal land use data to identify potential land availability for biomass production. With the spatial distribution of the land availability and potential yield of biomass crops, biomass production potential and costs are calculated. The BJF production cost is calculated by taking into account the development in the technological pathways and in plant scales. We estimate the techno‐economic potential by determining the minimum BJF total costs and comparing this with the range of fossil jet fuel prices. The techno‐economic potential of BJF production ranges from 0 to 6.4 EJ in 2015 and between 1.2 and 7.8 EJ in 2030, depending on the reference fossil jet fuel price, which varies from 19 to 65 US$/GJ across the airports. The techno‐economic potential consists of a diverse set of production routes. The Northeast and Southeast region of Brazil present the highest potentials with several viable production routes, whereas the remaining regions only have a few promising production routes. The maximum techno‐economic potential of BJF in Brazil could meet almost half of the projected global jet fuel demand toward 2030. We quantify the techno‐economic potential of biojet fuels (BJF) from energy crops through various biochemical and thermochemical conversion routes in Brazil between 2015 and 2030. Depending on local fossil jet fuel prices, up to 7.2 EJ of techno‐economic viable BJF could be supplied toward 2030, mainly sourced from the Southeast and Northeast regions, where land availability is high, and agro‐ecological conditions and existing infrastructure are adequate. These drivers are presented spatially explicitly, which is a key information for decentralizing energy policies and supporting the regional development of BJF supply chains.

The analysis and monitoring of the bioeconomy at the regional level is of interest for policy design and evaluation, and it aligns with the territorial approach called for by the Bioeconomy Strategy (2018) of the European Union (EU). Although some initiatives provided estimates of the size and/or regional distribution of the bioeconomy in some countries, there are no homogeneous data allowing the analysis of the regional dimension of the EU’s bioeconomy. This report describes a methodology to estimate employment and value added of the bioeconomy sectors at the NUTS2 level in the EU. It consists of a systematic combination of Eurostat regional statistics with national bio-based shares from the public JRC-Bioeconomics database for allocating employment and value added in the bioeconomy sectors amongst regions. National bio-based shares are calculated following Ronzon et al. (2020)’s approach. When missing from Eurostat data sources, regional series are estimated by applying various criteria to regionalise national statistics. Finally, some missing data estimation algorithms are applied to complete the dataset. Preliminary results evidence that this approach manages to fill in the majority of missing series and data in the initial datasets. We extract some key figures and trends for the regional bioeconomies in the EU. We discuss our results through the comparison with available official statistics, other previous estimates and expert feedback, and propose potential improvements.