Precision Pollination Mapping for Agriculture (Version 3)

Spatio-temporal workflow for mapping pollination climate suitability and crop-weighted exposure across Germany, Belgium, and the Netherlands.

Research Questions

• Q1. Where is climate suitability for pollination highest and most stable during crop-specific bloom windows across DE-BE-NL?
• Q2. Where do crop distributions overlap with unsuitable or variable pollination climate, and which NUTS-2 regions carry the greatest crop-weighted deficit exposure?
• Q3. Are crop-weighted suitability and deficit exposure spatially clustered at NUTS-2 level, and where are significant hotspots?

Data Sources

• E-OBS daily gridded climate (TG, RR), v27.0e: https://doi.org/10.1029/2017JD028200
• SPAM 2020 crop harvested area (RAPE, TEMF, SUNF): https://doi.org/10.7910/DVN/SWPENT
• NUTS 2024 boundaries (Eurostat GISCO): https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics
• SPAM methodology reference: https://doi.org/10.7910/DVN/DHXBJX

Data Setup

Download the data_raw/ folder from:

https://drive.google.com/file/d/1TLU2tMSJ6exEXXrR_FwvIL6va9WXZlz6/view?usp=drive_link

How To Run

quarto render main.qmd

Output Structure

• outputs/maps/: generated raster and LISA maps
• outputs/plots/: generated time-series plots
• outputs/tables/: generated ranking and comparison tables

R Package Dependencies

• terra
• sf
• exactextractr
• sfdep
• spdep
• dplyr
• tidyr
• tmap
• ggplot2
• here
• tibble