diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5b8d5408..8f4c05d1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -38,6 +38,13 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   - Collection-only properties are kept when merging collections
   - Default GeoParquet compression is now zstd (level 15), configurable via `--compression_level`
 - DE-SH: make the 2023, 2025 and 2026 editions convert — glob the GeoPackage inside the archive (2023 was written with user_version = 0, so the archive alone matches no driver), parse fachguelti as DD.MM.YYYY, and map the 2023 and upper-case 2025/2026 column spellings that silently dropped determination:datetime and metrics:area (their area is text with a decimal comma)
+- Add DuckDB BaseConverter for efficiently transforming large datasets
+- Update vecorel-cli to v0.2.18:
+  - The DuckDB converter base and the GeoParquet post-processing live there now
+  - Converters record the collection id in the collection metadata instead of a constant `collection` column
+  - Converters drop rows that can never validate (missing required values, empty or missing geometries), bounded by `max_dropped_share`
+  - Converters fail when both `sources` and `variants` are declared (fixed in the HR converter), and warn when no column is mapped to `id` or the id column is not unique
+  - Converters load all schemas upfront with retries
 
 ## [v0.21.0] - 2026-02-16
 
@@ -49,6 +56,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Fix the column additions of the determination fields in the AI4SF converter
 - Add HCAT to datasets where possible
 - Updated years & variants for at_crop, be_vlg, es_an, es_cl, es_pv, ie, pt, se
+- Add DuckDB BaseConverter for efficiently transforming large datasets
 - Extend create_stac, include include fiboa data
 - Publish command; skip hidden files, generate better texts
 - Fix to vecorel: converter.license and provider should be string
diff --git a/fiboa_cli/conversion/duckdb.py b/fiboa_cli/conversion/duckdb.py
new file mode 100644
index 00000000..f23c8da2
--- /dev/null
+++ b/fiboa_cli/conversion/duckdb.py
@@ -0,0 +1,10 @@
+from vecorel_cli.conversion.duckdb import DuckDBBaseConverter
+
+from .fiboa_converter import FiboaBaseConverter
+
+
+# This converter is experimental, use with caution.
+# Results may not be fully fiboa compliant yet.
+# Use this primarily for datasets that are too large to be processed by the default converter
+class FiboaDuckDBBaseConverter(DuckDBBaseConverter, FiboaBaseConverter):
+    pass
diff --git a/fiboa_cli/datasets/commons/euro_land.py b/fiboa_cli/datasets/commons/euro_land.py
index f33bee51..5294c22e 100644
--- a/fiboa_cli/datasets/commons/euro_land.py
+++ b/fiboa_cli/datasets/commons/euro_land.py
@@ -47,3 +47,11 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         provider = "Europe-LAND HE Project <https://doi.org/10.5281/zenodo.14230620>"
         self.provider = (f"{self.provider}, {provider}") if self.provider else provider
+
+    def migrate(self, gdf):
+        # Some Europe-LAND files (e.g. LT 2024) ship an empty crop_code column next to
+        # a populated crop_name; the name is then the best available crop code.
+        if "crop_code" in gdf.columns and gdf["crop_code"].isna().all():
+            self.warning("crop_code is empty, using crop_name as crop:code")
+            gdf["crop_code"] = gdf["crop_name"]
+        return super().migrate(gdf)
diff --git a/fiboa_cli/datasets/hr.py b/fiboa_cli/datasets/hr.py
index 834deb5b..d0c923dc 100644
--- a/fiboa_cli/datasets/hr.py
+++ b/fiboa_cli/datasets/hr.py
@@ -7,7 +7,6 @@
 
 
 class Converter(AdminConverterMixin, AddHCATMixin, FiboaBaseConverter):
-    sources = "https://www.apprrr.hr/wp-content/uploads/nipp/land_parcels.gpkg"
     variants = {
         "2024": f"{base}/land_parcels.gpkg",
         **{str(y): f"{base}/arkod_31_12_{y}.gpkg" for y in range(2023, 2010, -1)},
diff --git a/fiboa_cli/datasets/jp.py b/fiboa_cli/datasets/jp.py
index 57c31a24..1799c7f4 100644
--- a/fiboa_cli/datasets/jp.py
+++ b/fiboa_cli/datasets/jp.py
@@ -1,10 +1,9 @@
-import pandas as pd
+from fiboa_cli.conversion.duckdb import FiboaDuckDBBaseConverter
 
-from ..conversion.fiboa_converter import FiboaBaseConverter
 
-
-class JPConverter(FiboaBaseConverter):
+class JPConverter(FiboaDuckDBBaseConverter):
     variants = {
+        "test": "./tests/data-files/convert/jp/jp_field_polygons_2024.parquet",
         "2024": "https://data.source.coop/pacificspatial/field-polygon-jp/parquet/jp_field_polygons_2024.parquet",
         "2023": "https://data.source.coop/pacificspatial/field-polygon-jp/parquet/jp_field_polygons_2023.parquet",
         "2022": "https://data.source.coop/pacificspatial/field-polygon-jp/parquet/jp_field_polygons_2022.parquet",
@@ -30,23 +29,11 @@ class JPConverter(FiboaBaseConverter):
         "polygon_uuid": "id",
         "land_type_en": "land_type_en",
         "local_government_cd": "admin_local_code",
-        "issue_year": "determination:datetime",
-    }
-    column_migrations = {
-        "issue_year": lambda col: pd.to_datetime(col, format="%Y"),
     }
-
+    column_additions = {"determination:datetime": "2024-01-01T00:00:00Z"}
     missing_schemas = {
         "properties": {
             "land_type_en": {"type": "string"},
             "admin_local_code": {"type": "string"},
         }
     }
-
-    def convert(self, *args, **kwargs):
-        # Open only these columns to limit memory usage
-        super().convert(
-            *args,
-            columns=["GEOM", "polygon_uuid", "land_type_en", "local_government_cd", "issue_year"],
-            **kwargs,
-        )
diff --git a/fiboa_cli/datasets/lt.py b/fiboa_cli/datasets/lt.py
index 42a00b3a..e174aa85 100644
--- a/fiboa_cli/datasets/lt.py
+++ b/fiboa_cli/datasets/lt.py
@@ -1,5 +1,7 @@
 from fiboa_cli.datasets.commons.euro_land import EuroLandBaseConverter
 
+BASE = "https://zenodo.org/records/18670815/files"
+
 
 class LTConverter(EuroLandBaseConverter):
     id = "lt"
@@ -10,4 +12,12 @@ class LTConverter(EuroLandBaseConverter):
     provider = "Nacionalinė mokėjimo agentūra prie Žemės ūkio ministerijos <https://www.nma.lt>"
     attribution = "Nacionalinė mokėjimo agentūra prie Žemės ūkio ministerijos"
     ec_mapping_csv = "lt_2021.csv"
-    sources = {"https://zenodo.org/records/14384070/files/LT_2024.zip": ["GSA-LT-2024.geoparquet"]}
+    # Europe-LAND v1.3 (record 18670815, February 2026) bundles both editions in
+    # one archive; v1.1 (record 14384070), which this converter used to read,
+    # carried 2024 alone.
+    variants = {
+        str(year): {f"{BASE}/LT_years_2024-2025.zip": [f"GSA-LT-{year}.geoparquet"]}
+        for year in (2025, 2024)
+    }
+    # The inventory carries no per-feature date, so the edition is the year.
+    use_variant_as_determination = True
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+      - pypi: https://files.pythonhosted.org/packages/82/ab/e04a8f97865251b544aee9501088d4f0cb8e8b37339bd465c0d33857d411/duckdb-1.4.2-cp314-cp314-macosx_10_15_x86_64.whl
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+      - pypi: https://files.pythonhosted.org/packages/da/b6/ba9cfde9eb83094a6642a42f036b90f74dcff9abdd307cbdd21dc996e562/vecorel_cli-0.2.15-py3-none-any.whl
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       osx-arm64:
@@ -1989,6 +2003,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl
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+      - pypi: https://files.pythonhosted.org/packages/47/ec/b8229517c2f9fe88a38bb1a172a2da4d0ff34996d319d74554fda80b6358/duckdb-1.4.2-cp314-cp314-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/54/e4/fac19dc34cb686c96011388b813ff7b858a70681e5ce6ce7698e5021b0f4/geopandas-1.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/70/b8/073a79716e093db973b8823bdfb02e10fbdf65642dbe1fa3cda24832aeb2/inflate64-1.0.4-cp314-cp314-macosx_11_0_arm64.whl
@@ -2021,7 +2036,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/ee/c8/f2e3096528f7f2dbf4069ab998137e001da38576b2755da2d2ee9289a5eb/vecorel_cli-0.2.17-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/da/b6/ba9cfde9eb83094a6642a42f036b90f74dcff9abdd307cbdd21dc996e562/vecorel_cli-0.2.15-py3-none-any.whl
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       - pypi: ./
       win-64:
@@ -2112,6 +2127,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
       - pypi: https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl
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+      - pypi: https://files.pythonhosted.org/packages/25/5e/6f5ebaabc12c6db62f471f86b5c9c8debd57f11aa1b2acbbcc4c68683238/duckdb-1.4.2-cp314-cp314-win_amd64.whl
       - pypi: https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl
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@@ -2144,7 +2160,7 @@ environments:
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-      - pypi: https://files.pythonhosted.org/packages/ee/c8/f2e3096528f7f2dbf4069ab998137e001da38576b2755da2d2ee9289a5eb/vecorel_cli-0.2.17-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/da/b6/ba9cfde9eb83094a6642a42f036b90f74dcff9abdd307cbdd21dc996e562/vecorel_cli-0.2.15-py3-none-any.whl
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       - pypi: ./
@@ -2789,6 +2805,54 @@ packages:
   - pkg:pypi/distlib?source=hash-mapping
   size: 275642
   timestamp: 1752823081585
+- pypi: https://files.pythonhosted.org/packages/23/35/b1fae4c5245697837f6f63e407fa81e7ccc7948f6ef2b124cd38736f4d1d/duckdb-1.4.2-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl
+  name: duckdb
+  version: 1.4.2
+  sha256: 128c97dab574a438d7c8d020670b21c68792267d88e65a7773667b556541fa9b
+  requires_dist:
+  - ipython ; extra == 'all'
+  - fsspec ; extra == 'all'
+  - numpy ; extra == 'all'
+  - pandas ; extra == 'all'
+  - pyarrow ; extra == 'all'
+  - adbc-driver-manager ; extra == 'all'
+  requires_python: '>=3.9.0'
+- pypi: https://files.pythonhosted.org/packages/25/5e/6f5ebaabc12c6db62f471f86b5c9c8debd57f11aa1b2acbbcc4c68683238/duckdb-1.4.2-cp314-cp314-win_amd64.whl
+  name: duckdb
+  version: 1.4.2
+  sha256: dfcc56a83420c0dec0b83e97a6b33addac1b7554b8828894f9d203955591218c
+  requires_dist:
+  - ipython ; extra == 'all'
+  - fsspec ; extra == 'all'
+  - numpy ; extra == 'all'
+  - pandas ; extra == 'all'
+  - pyarrow ; extra == 'all'
+  - adbc-driver-manager ; extra == 'all'
+  requires_python: '>=3.9.0'
+- pypi: https://files.pythonhosted.org/packages/47/ec/b8229517c2f9fe88a38bb1a172a2da4d0ff34996d319d74554fda80b6358/duckdb-1.4.2-cp314-cp314-macosx_11_0_arm64.whl
+  name: duckdb
+  version: 1.4.2
+  sha256: 20c45b4ead1ea4d23a1be1cd4f1dfc635e58b55f0dd11e38781369be6c549903
+  requires_dist:
+  - ipython ; extra == 'all'
+  - fsspec ; extra == 'all'
+  - numpy ; extra == 'all'
+  - pandas ; extra == 'all'
+  - pyarrow ; extra == 'all'
+  - adbc-driver-manager ; extra == 'all'
+  requires_python: '>=3.9.0'
+- pypi: https://files.pythonhosted.org/packages/82/ab/e04a8f97865251b544aee9501088d4f0cb8e8b37339bd465c0d33857d411/duckdb-1.4.2-cp314-cp314-macosx_10_15_x86_64.whl
+  name: duckdb
+  version: 1.4.2
+  sha256: 459b1855bd06a226a2838da4f14c8863fd87a62e63d414a7f7f682a7c616511a
+  requires_dist:
+  - ipython ; extra == 'all'
+  - fsspec ; extra == 'all'
+  - numpy ; extra == 'all'
+  - pandas ; extra == 'all'
+  - pyarrow ; extra == 'all'
+  - adbc-driver-manager ; extra == 'all'
+  requires_python: '>=3.9.0'
 - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
   sha256: ee6cf346d017d954255bbcbdb424cddea4d14e4ed7e9813e429db1d795d01144
   md5: 8e662bd460bda79b1ea39194e3c4c9ab
@@ -2803,10 +2867,11 @@ packages:
 - pypi: ./
   name: fiboa-cli
   version: 0.21.0
-  sha256: a97f8b680673e3f70718b7081f3783d28ffe6e8040e4dd85a9cbac167a7ac574
+  sha256: 544a959421501ea1dcf28cede8c6b5c41b70ce5ddeb55f0c4ed1a54fc32c2d64
   requires_dist:
-  - vecorel-cli==0.2.17
+  - vecorel-cli==0.2.15
   - spdx-license-list==3.27.0
+  - duckdb==1.4.2
   requires_python: '>=3.11'
   editable: true
 - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.21.2-pyhd8ed1ab_0.conda
@@ -7075,10 +7140,10 @@ packages:
   purls: []
   size: 115235
   timestamp: 1767320173250
-- pypi: https://files.pythonhosted.org/packages/ee/c8/f2e3096528f7f2dbf4069ab998137e001da38576b2755da2d2ee9289a5eb/vecorel_cli-0.2.17-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/da/b6/ba9cfde9eb83094a6642a42f036b90f74dcff9abdd307cbdd21dc996e562/vecorel_cli-0.2.15-py3-none-any.whl
   name: vecorel-cli
-  version: 0.2.17
-  sha256: b62387569ededb111cffd7339fede015c747176b59cfdd77bd4f2259f5ce60c4
+  version: 0.2.15
+  sha256: b8e297b5967521b994dc7342aa72cf43cc0a0326609d8e7c4afb32d09c9f786c
   requires_dist:
   - pyyaml>=6.0,<7.0
   - click>=8.1,<9.0
@@ -7088,7 +7153,7 @@ packages:
   - numpy>=2.0,<3.0
   - pyarrow>=21.0,<24.0
   - py7zr>=1.0,<2.0
-  - fsspec>=2025.7.0
+  - fsspec==2025.7.0
   - jsonschema[format]>=4.20,<5.0
   - aiohttp>=3.9,<4.0
   - yarl>=1.20,<2.0
@@ -7096,10 +7161,6 @@ packages:
   - semantic-version>=2.10.0,<3.0
   - json-stream>=2.3.0,<3.0
   - loguru==0.7.3
-  - s3fs>=2025.7.0 ; extra == 's3'
-  - gcsfs>=2025.7.0 ; extra == 'gcs'
-  - s3fs>=2025.7.0 ; extra == 'cloud'
-  - gcsfs>=2025.7.0 ; extra == 'cloud'
   requires_python: '>=3.11'
 - conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.36.1-pyhd8ed1ab_0.conda
   sha256: fa0a21fdcd0a8e6cf64cc8cd349ed6ceb373f09854fd3c4365f0bc4586dccf9a
diff --git a/pyproject.toml b/pyproject.toml
index de306340..20c352ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ classifiers = [
 ]
 requires-python = ">=3.11"
 dependencies = [
-    "vecorel-cli==0.2.17",
+    "vecorel-cli==0.2.18",
     "spdx-license-list==3.27.0",
 ]
 
@@ -64,8 +64,8 @@ ruff = "==0.12.8"
 build = ">=1.0.0,<2.0.0"
 
 [tool.pixi.feature.cloud.dependencies]
-s3fs = ">=2025.7.0"
-gcsfs = ">=2025.7.0"
+s3fs = "==2025.7.0"
+gcsfs = "==2025.7.0"
 
 [tool.pixi.environments]
 default = {solve-group = "default"}
@@ -107,7 +107,7 @@ exclude = [
 line-length = 100
 indent-width = 4
 
-target-version = "py311"
+target-version = "py310"
 
 [tool.ruff.lint]
 select = ["E4", "E7", "E9", "F", "W", "I", "NPY201"]  # "C901"
diff --git a/tests/data-files/convert/lt/LT_2024.zip b/tests/data-files/convert/lt/LT_years_2024-2025.zip
similarity index 100%
rename from tests/data-files/convert/lt/LT_2024.zip
rename to tests/data-files/convert/lt/LT_years_2024-2025.zip
diff --git a/tests/test_convert.py b/tests/test_convert.py
index 3ac24a11..d838f81c 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -71,6 +71,8 @@ def _input_files(converter, *names):
 
 extra_convert_parameters = {
     "ai4sf": _input_files("ai4sf", "1_vietnam_areas.gpkg", "4_cambodia_areas.gpkg"),
+    # the fixture archive holds the 2024 edition only
+    "lt": {"variant": "2024"},
     "nl": {"variant": "2023"},
     "se": {"variant": "2023"},
     "si": {"variant": "2023"},
diff --git a/tests/test_convert_parity.py b/tests/test_convert_parity.py
new file mode 100644
index 00000000..a889f284
--- /dev/null
+++ b/tests/test_convert_parity.py
@@ -0,0 +1,100 @@
+import json
+
+import pyarrow.parquet as pq
+import shapely
+
+from fiboa_cli.conversion.fiboa_converter import FiboaBaseConverter
+from fiboa_cli.datasets.jp import JPConverter
+from fiboa_cli.validate import ValidateData
+
+# A GeoDataFrame-based twin of the DuckDB-based jp converter, built from the
+# same configuration so that the two codepaths cannot drift apart.
+SHARED_ATTRIBUTES = (
+    "id",
+    "short_name",
+    "title",
+    "description",
+    "provider",
+    "attribution",
+    "license",
+    "variants",
+    "columns",
+    "column_additions",
+    "missing_schemas",
+)
+PandasJPConverter = type(
+    "PandasJPConverter",
+    (FiboaBaseConverter,),
+    {key: getattr(JPConverter, key) for key in SHARED_ATTRIBUTES},
+)
+
+
+def test_jp_codepath_parity(tmp_folder):
+    """The DuckDB-based jp converter and its GeoDataFrame-based twin must
+    produce comparable files: same schema, same rows in the same order,
+    same key metadata, same packaging."""
+    kwargs = {"variant": "test", "compression": "zstd", "geoparquet_version": "1.1.0"}
+    pandas_dest = tmp_folder / "pandas.parquet"
+    duckdb_dest = tmp_folder / "duckdb.parquet"
+    PandasJPConverter().convert(pandas_dest, **kwargs)
+    JPConverter().convert(duckdb_dest, **kwargs)
+
+    with pq.ParquetFile(pandas_dest) as pf:
+        pandas_schema = pf.schema_arrow
+        pandas_table = pf.read()
+        pandas_groups = pf.metadata.num_row_groups
+        pandas_compression = pf.metadata.row_group(0).column(0).compression
+    with pq.ParquetFile(duckdb_dest) as pf:
+        duckdb_schema = pf.schema_arrow
+        duckdb_table = pf.read()
+        duckdb_groups = pf.metadata.num_row_groups
+        duckdb_compression = pf.metadata.row_group(0).column(0).compression
+
+    # Same columns with the same types and nullability (the column order is
+    # allowed to differ)
+    assert sorted(pandas_schema.names) == sorted(duckdb_schema.names)
+    for name in pandas_schema.names:
+        f1, f2 = pandas_schema.field(name), duckdb_schema.field(name)
+        assert f1.type == f2.type, f"{name}: {f1.type} != {f2.type}"
+        assert f1.nullable == f2.nullable, f"{name}: nullability differs"
+
+    # Same rows in the same order: both codepaths sort against the same Hilbert
+    # grid and resolve ties by source order. The geometries must describe the
+    # same shapes, but the WKB may differ in vertex order (different GEOS builds)
+    assert pandas_table.num_rows == duckdb_table.num_rows
+    for column in pandas_schema.names:
+        if column in ("geometry", "bbox"):
+            continue
+        assert pandas_table[column].to_pylist() == duckdb_table[column].to_pylist(), (
+            f"{column}: values differ"
+        )
+    pandas_geoms = shapely.from_wkb(pandas_table["geometry"].to_pylist())
+    duckdb_geoms = shapely.from_wkb(duckdb_table["geometry"].to_pylist())
+    for g1, g2 in zip(pandas_geoms, duckdb_geoms):
+        assert shapely.equals(g1, g2)
+
+    # Same collection metadata and the same key GeoParquet metadata
+    pandas_collection = json.loads(pandas_schema.metadata[b"collection"])
+    duckdb_collection = json.loads(duckdb_schema.metadata[b"collection"])
+    assert pandas_collection == duckdb_collection
+
+    pandas_geo = json.loads(pandas_schema.metadata[b"geo"])
+    duckdb_geo = json.loads(duckdb_schema.metadata[b"geo"])
+    for key in ("version", "primary_column"):
+        assert pandas_geo[key] == duckdb_geo[key]
+    pandas_column = pandas_geo["columns"]["geometry"]
+    duckdb_column = duckdb_geo["columns"]["geometry"]
+    for key in ("encoding", "covering", "crs", "bbox"):
+        assert pandas_column.get(key) == duckdb_column.get(key), f"geo {key} differs"
+    assert sorted(pandas_column.get("geometry_types", [])) == sorted(
+        duckdb_column.get("geometry_types", [])
+    )
+
+    # Same packaging
+    assert pandas_compression == duckdb_compression
+    assert pandas_groups == duckdb_groups
+
+    # Both validate
+    for dest in (pandas_dest, duckdb_dest):
+        validation = ValidateData().validate(dest, num=100, schema_map={})
+        assert validation.errors == []