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spatial analyses annual land use covers  (Agri-Food Laboratories)

 
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    Agri-Food Laboratories spatial analyses annual land use covers
    Spatial Analyses Annual Land Use Covers, supplied by Agri-Food Laboratories, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/kriging/aafc+decadal+land+semi+use/pm41081619-94-0-9
    Average 86 stars, based on 1 article reviews
    spatial analyses annual land use covers - by Bioz Stars, 2026-11
    86/100 stars

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    Article Title: Bringing Together Indigenous Knowledge and Simulation Modelling to Assess Cumulative Impacts to Indigenous Land Use in Northeastern Alberta, Canada.
    Article Snippet: The AAFC Semi-Decadal Land Use (Agriculture and Agri-Food Canada 2021) and Canada Land Use Inventory (Department of Regional Economic Expansion 1970) datasets were used to estimate settlement and agricultural footprints existing as of c. 2000 and c. 1970, respectively.

    Article Title: Patterns in riverine carbon, nutrient and suspended solids export to the Eastern James Bay: links to climate, hydrology and landscape
    Article Snippet: concentrations in these watersheds.. We then combined these to model the contribution of unsampled watersheds to generate a regional budget of riverine freshwater and material export to the Bay.. The peak of water export occurs in spring for free-flowing rivers, whereas for the dammed river La Grande, which accounts for half of the freshwater inputs, the peak discharge was shifted to winter due to high energy demands.

    Article Title: Bringing Together Indigenous Knowledge and Simulation Modelling to Assess Cumulative Impacts to Indigenous Land Use in Northeastern Alberta, Canada
    Article Snippet: The AAFC Semi-Decadal Land Use (Agriculture and Agri-Food Canada ) and Canada Land Use Inventory (Department of Regional Economic Expansion ) datasets were used to estimate settlement and agricultural footprints existing as of c. 2000 and c. 1970, respectively.

    Sampling:

    Article Title: Not-so-simple patterns of neonicotinoids and diamides in small Prairie streams: Implications for assessing risk and understanding pesticide dynamics.
    Article Snippet: .. Spatial analyses Annual land-use covers from the Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory (ACI), at a resolution of 30 m x 30 m (AAFC, 2017, 2018, 2019), were extracted for effective drainage area boundaries for sampling sub-watersheds from the AAFC Watersheds Project (AAFC, 2013) for each year of the study. ..



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    The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary <t>Kriging</t> <t>interpolation</t> method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).
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    The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software

    Kriging interpolation results for different metals. The interpolation results in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: Kriging interpolation results for different metals. The interpolation results in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software

    As completion process. The original heatmap in Fig. 3 are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: As completion process. The original heatmap in Fig. 3 are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software