A new Global Agro-Environmental Stratification (GAES)

2016 
The GAES database (Version 01a) is a newly developed Global Agro-Environmental Stratification within the EU SIGMA (Stimulating Innovation for Global Monitoring of Agriculture) project. GAES will serve as a new agro-environmental stratification for better global monitoring of the agricultural production on the basis of Earth Observation data and crop growth models. It is anticipated that GAES will be exploited for a wider range of applications, some within SIGMA, towards data gap analysis that identifies agro-environmental strata with limited capacity and monitoring data on agricultural production. GAES was produced by applying segmentation techniques to newly available global agroenvironmental data with a high spatial resolution re-sampled to 1 km spatial resolution. The datasets were able to stratify the agricultural production zones according to the region’s agro-environmental characteristics, including climatic regimes, soil, terrain, elevation conditions, water availability and land cover proprieties. The GAES strata obtained by segmentation at four different spatial levels (with Level 4 as the most detailed) have been further characterised and described in terms of phenology (e.g. start and peak of the growing season), agricultural (water) management practices, field size, biotic constraints, national and sub-national crop production statistics, GDP, transport infrastructure conditions or market accessibility. The GAES database has four hierarchical layers, with 92 attributes. GAES Level 1 has 194 agro-environmental (AE) types (818 strata); GAES Level 2 has 300 AE types (1,688 strata); GAES Level 3 has 374 AE types (2,087 strata); GAES Level 4 has 516 AE types (3,208 strata). GAES typology is a combination of temperature, altitude, parent material and land cover characteristics. GAES Version 01 has become freely available.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    4
    Citations
    NaN
    KQI
    []