meth_2.27 In silico analysis of Australian Soil Classification

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Soil classifications were derived using the Atlas of Australian Soils spatial dataset ESRI shapefile using a samples latitude and longitude. Output values in Atlas of Australian Soils mapping units were converted to Australian Soil Classification (Isbell et al.,1997) terms. Digital resources to enable soil classification and conversion were obtained from the Australian Soil Resource Information System website: https://www.asris.csiro.au/themes/Atlas.html#Atlas_Digital (last accessed 15/07/2022)

Classification was performed using python 3 with the following steps:

import pandas as pd

import geopandas as gpd

#import file containing AM sample_id, and sample latitude and longitude

df = pd.read_csv(‘<filename>’)

#read in the files describing soil types

soiltype_lookup = pd.read_csv(‘asclut.txt’, names=[‘MAP_UNIT’, ‘unknown’, ‘short_type’, ‘local_class’])

polygons_df_ASRIS = gpd.read_file(‘soilAtlas2M.shp’)

# replace none values.

polygons_df_ASRIS = polygons_df_ASRIS.fillna(‘None’)

#get the CRS of the ASRIS file we will use it to define geopandas dataframe crs

soilType_CRS = polygons_df_ASRIS.crs

#Convert the AM data to a geopandas dataframe with same coordinate reference system to the ASRIS gdf

df = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.longitude, df.latitude),crs=soilType_CRS)

df = gpd.sjoin(df, polygons_df_ASRIS, how=’left’)

df = pd.merge(df,soiltype_lookup,left_on=’MAP_UNIT’,right_on=’MAP_UNIT’,how=’left’)

References

Isbell, R.F., McDonald, W.S., Ashton, L.J (1997) Concepts and rationale of the Australian Soil Classification. ACLEP, CSIRO Land and Water, Canberra.