Das Bundesinformationszentrum Landwirtschaft (BZL) publiziert Daten über die Herstellung, die Verwendung und den Verbrauch von Zucker und Glukose in Deutschland. Im Zeitraum der rund 100 Tage dauernden Kampagne wird in den Monatsmeldungen zusätzlich über die Zuckerrübenverarbeitung berichtet.
Producer price index of industrial products: Germany, Months, Inventory of Goods (GP2019 2-/3-/4-/5-/6-/9-Steller/ Special items)
GENESIS-Online ist eine Datenbank, die tief gegliederte Ergebnisse der amtlichen Statistik enthält. Sie wird kontinuierlich ausgebaut. Der Tabellenabruf erfolgt unentgeltlich und kann variabel auf den individuellen Bedarf angepasst werden. Die Abspeicherung der Ergebnisse ist in verschiedenen Formaten möglich.
With EU trading partners imposing an increasing number of trade measures on imports, there is a risk that goods originally destined for these non-EU markets are redirected to the EU internal market instead.
World Integrated Trade Solution (WITS) | Data on Export, Import, Tariff, NTM
Use WITS to obtain merchandise trade (exports, imports), tariff and non-tariff (NTM) data, perform tariff cut simulation and analyze trade competitiveness of countries.
High-resolution map of sugarcane cultivation in Brazil using a phenology-based method
Abstract. Sugarcane is the most important source of sugar, and its
cultivation area has undergone rapid expansion, replacing other crops,
pastures, and forests. Brazil is the world's largest sugarcane producer and
contributed to approximately 38.6 % of the world's total production in
2019. Sugarcane in Brazil can be harvested from April to December in
the south-central area and from September to April in the northeast area. The
flexible phenology and harvest conditions of sugarcane in Brazil make it
difficult to identify the harvest area at state to country scales. In this
study, we developed a phenology-based method to identify the harvest area of
sugarcane in Brazil by incorporating the multiple phenology conditions into
a time-weighted dynamic time warping method (TWDTW). Then, we produced
annual 30 m spatial resolution sugarcane harvest maps (2016–2019) for 14
states in Brazil (over 98 % of the harvest area) based on the proposed
method using Landsat-7, Landsat-8, and Sentinel-2 optical data. The proposed method
performed well in identifying sugarcane harvest area with limited training
sample data. Validations for the 2018 harvest year displayed high accuracy,
with the user's, producer's, and overall accuracies of 94.35 %, 87.04 %, and
91.47 % in Brazil, respectively. In addition, the identified harvest area
of sugarcane exhibited good correlations with the agricultural statistical
data provided by the Brazilian Institute of Geography and Statistics (IBGE)
at the municipality, microregion, and mesoregion levels. The 30 m Brazil
sugarcane harvest maps can be obtained at
https://doi.org/10.6084/m9.figshare.14213909 (Zheng et al., 2021).
