berne: Berne - soil mapping case study
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berne R Documentation
Berne – soil mapping case study
Description
The Berne dataset contains soil responses and a large set of explanatory covariates. The study area is located to the Northwest of the city of Berne and covers agricultural area.
Soil responses included are soil pH (4 depth intervals calculated from soil horizon), drainage classes (3 ordered classes) and presence of waterlogging characteristics down to a specified depth (binary response).
Covariates cover environmental conditions by representing climate, topography, parent material and soil.
Usage
data("berne")
Format
A data frame with 1052 observations on the following 238 variables.
site_id_uniqueID of original profile sampling
xeasting, Swiss grid in m, EPSG: 21781 (CH1903/LV03)
ynorthing, Swiss grid in m, EPSG: 21781 (CH1903/LV03)
datasetFactor splitting dataset for calibration and independent validation. validation was assigned at random by using weights to ensure even spatial coverage of the agricultural area.
dclassDrainage class, ordered Factor.
waterlog.30Presence of waterlogging characteristics down to 30 cm (1: presence, 0: absence)
waterlog.50Presence of waterlogging characteristics down to 50 cm (1: presence, 0: absence)
waterlog.100Presence of waterlogging characteristics down to 100 cm (1: presence, 0: absence)
ph.0.10Soil pH in 0-10 cm depth.
ph.10.30Soil pH in 10-30 cm depth.
ph.30.50Soil pH in 30-50 cm depth.
ph.50.100Soil pH in 50-100 cm depth.
timesetFactor with range of sampling year and label for sampling type for soil pH. no label: CaCl_{2} laboratory measurements, field: field estimate by indicator solution, ptf: H_{2}0 laboratory measurements transferred by pedotransfer function (univariate linear regression) to level of CaCl_{2} measures.
cl_mt_etap_pecolumns 14 to 238 contain environmental covariates representing soil forming factors. For more information see Details below.
cl_mt_etap_rocl_mt_gh_1cl_mt_gh_10cl_mt_gh_11cl_mt_gh_12cl_mt_gh_2cl_mt_gh_3cl_mt_gh_4cl_mt_gh_5cl_mt_gh_6cl_mt_gh_7cl_mt_gh_8cl_mt_gh_9cl_mt_gh_ycl_mt_pet_pecl_mt_pet_rocl_mt_rr_1cl_mt_rr_10cl_mt_rr_11cl_mt_rr_12cl_mt_rr_2cl_mt_rr_3cl_mt_rr_4cl_mt_rr_5cl_mt_rr_6cl_mt_rr_7cl_mt_rr_8cl_mt_rr_9cl_mt_rr_ycl_mt_swb_pecl_mt_swb_rocl_mt_td_1cl_mt_td_10cl_mt_td_11cl_mt_td_12cl_mt_td_2cl_mt_tt_1cl_mt_tt_11cl_mt_tt_12cl_mt_tt_3cl_mt_tt_4cl_mt_tt_5cl_mt_tt_6cl_mt_tt_7cl_mt_tt_8cl_mt_tt_9cl_mt_tt_yge_caco3ge_geo500h1idge_geo500h3idge_gt_ch_filge_lgmge_vszonesl_nutr_filsl_physio_neusl_retention_filsl_skelett_r_filsl_wet_filtr_be_gwn25_hdisttr_be_gwn25_vdisttr_be_twi2m_7s_tcilowtr_be_twi2m_s60_tcilowtr_ch_3_80_10tr_ch_3_80_10str_ch_3_80_20str_cindx10_25tr_cindx50_25tr_curv_alltr_curv_plantr_curv_proftr_enessktr_es25tr_flowlength_uptr_global_rad_chtr_lsftr_mrrtf25tr_mrvbf25tr_ndom_veg2m_fmtr_negotr_nnessktr_ns25tr_ns25_145mntr_ns25_145sdtr_ns25_75mntr_ns25_75sdtr_posotr_protindxtr_se_alti10m_ctr_se_alti25m_ctr_se_alti2m_fmean_10ctr_se_alti2m_fmean_25ctr_se_alti2m_fmean_50ctr_se_alti2m_fmean_5ctr_se_alti2m_std_10ctr_se_alti2m_std_25ctr_se_alti2m_std_50ctr_se_alti2m_std_5ctr_se_alti50m_ctr_se_alti6m_ctr_se_conv2mtr_se_curv10mtr_se_curv25mtr_se_curv2mtr_se_curv2m_s15tr_se_curv2m_s30tr_se_curv2m_s60tr_se_curv2m_s7tr_se_curv2m_std_10ctr_se_curv2m_std_25ctr_se_curv2m_std_50ctr_se_curv2m_std_5ctr_se_curv50mtr_se_curv6mtr_se_curvplan10mtr_se_curvplan25mtr_se_curvplan2mtr_se_curvplan2m_grass_17ctr_se_curvplan2m_grass_45ctr_se_curvplan2m_grass_9ctr_se_curvplan2m_s15tr_se_curvplan2m_s30tr_se_curvplan2m_s60tr_se_curvplan2m_s7tr_se_curvplan2m_std_10ctr_se_curvplan2m_std_25ctr_se_curvplan2m_std_50ctr_se_curvplan2m_std_5ctr_se_curvplan50mtr_se_curvplan6mtr_se_curvprof10mtr_se_curvprof25mtr_se_curvprof2mtr_se_curvprof2m_grass_17ctr_se_curvprof2m_grass_45ctr_se_curvprof2m_grass_9ctr_se_curvprof2m_s15tr_se_curvprof2m_s30tr_se_curvprof2m_s60tr_se_curvprof2m_s7tr_se_curvprof2m_std_10ctr_se_curvprof2m_std_25ctr_se_curvprof2m_std_50ctr_se_curvprof2m_std_5ctr_se_curvprof50mtr_se_curvprof6mtr_se_diss2m_10ctr_se_diss2m_25ctr_se_diss2m_50ctr_se_diss2m_5ctr_se_e_aspect10mtr_se_e_aspect25mtr_se_e_aspect2mtr_se_e_aspect2m_10ctr_se_e_aspect2m_25ctr_se_e_aspect2m_50ctr_se_e_aspect2m_5ctr_se_e_aspect2m_grass_17ctr_se_e_aspect2m_grass_45ctr_se_e_aspect2m_grass_9ctr_se_e_aspect50mtr_se_e_aspect6mtr_se_mrrtf2mtr_se_mrvbf2mtr_se_n_aspect10mtr_se_n_aspect25mtr_se_n_aspect2mtr_se_n_aspect2m_10ctr_se_n_aspect2m_25ctr_se_n_aspect2m_50ctr_se_n_aspect2m_5ctr_se_n_aspect2m_grass_17ctr_se_n_aspect2m_grass_45ctr_se_n_aspect2m_grass_9ctr_se_n_aspect50mtr_se_n_aspect6mtr_se_no2m_r500tr_se_po2m_r500tr_se_rough2m_10ctr_se_rough2m_25ctr_se_rough2m_50ctr_se_rough2m_5ctr_se_rough2m_rect3ctr_se_sar2mtr_se_sca2mtr_se_slope10mtr_se_slope25mtr_se_slope2mtr_se_slope2m_grass_17ctr_se_slope2m_grass_45ctr_se_slope2m_grass_9ctr_se_slope2m_s15tr_se_slope2m_s30tr_se_slope2m_s60tr_se_slope2m_s7tr_se_slope2m_std_10ctr_se_slope2m_std_25ctr_se_slope2m_std_50ctr_se_slope2m_std_5ctr_se_slope50mtr_se_slope6mtr_se_toposcale2m_r3_r50_i10str_se_tpi_2m_10ctr_se_tpi_2m_25ctr_se_tpi_2m_50ctr_se_tpi_2m_5ctr_se_tri2m_altern_3ctr_se_tsc10_2mtr_se_twi2mtr_se_twi2m_s15tr_se_twi2m_s30tr_se_twi2m_s60tr_se_twi2m_s7tr_se_vrm2mtr_se_vrm2m_r10ctr_slope25_l2gtr_terrtexturtr_tpi2000ctr_tpi5000ctr_tpi500ctr_tsc25_18tr_tsc25_40tr_twi2tr_twi_normaltr_vdcn25
Details
Soil data
The soil data originates from various soil sampling campaigns since 1968. Most of the data was collected in conventional soil surveys in the 1970ties in the course of amelioration and farm land exchanges. As frequently observed in legacy soil data sampling site allocation followed a purposive sampling strategy identifying typical soils in an area in the course of polygon soil mapping.
dclass contains drainage classes of three levels.
Swiss soil classification differentiates stagnic (I), gleyic (G) and anoxic/reduced (R) soil profile qualifiers with each 4, 6 resp. 5 levels. To reduce complexity the qualifiers I, G and R were aggregated to the degree of hydromorphic
characteristic of a site with the ordered levels well (qualifier labels I1–I2, G1–G3, R1 and no hydromorphic qualifier), moderate well drained (I3–I4, G4) and poor drained (G5–G6, R2–R5).
waterlog indicates the presence or absence of waterlogging characteristics down 30, 50 and 100 cm soil depth. The responses were based on horizon qualifiers ‘gg’ or ‘r’ of the Swiss classification (Brunner et al. 1997) as those were considered to limit plant growth. A horizon was given the qualifier ‘gg’ if it was strongly gleyic predominantly oxidized (rich in Fe^{3+}) and ‘r’ if it was anoxic predominantly reduced (poor in Fe^{3+}).
pH was mostly sampled following genetic soil horizons. To ensure comparability between sites pH was transferred to fixed depth intervals of 0–10, 10–30, 30–50 and 50–100 cm by weighting soil horizons falling into a given interval. The data contains laboratory measurements that solved samples in CaCl_{2} or H_{2}0. The latter were transferred to the level of CaCl_{2} measurements by univariate linear regression (label ptf in timeset). Further, the dataset contains estimates of pH in the field by an indicator solution (Hellige pH, label field in timeset).
The column timeset can be used to partly correct for the long sampling period and the different sampling methods.
Environmental covariates
The numerous covariates were assembled from the available spatial data in the case study area.
Each covariate name was given a prefix:
-
cl_ climate covariates as precipitation, temperature, radiation
-
tr_ terrain attributes, covariates derived from digital elevation models
-
ge_ covariates from geological maps
-
sl_ covariates from an overview soil map
References to the used datasets can be found in Nussbaum et al. 2017b.
References
Brunner, J., Jaeggli, F., Nievergelt, J., and Peyer, K. (1997). Kartieren und Beurteilen von Landwirtschaftsboeden.
FAL Schriftenreihe 24, Eidgenoessische Forschungsanstalt fuer Agraroekologie und Landbau, Zuerich-Reckenholz (FAL).
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A., 2017b. Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL Discuss., https://www.soil-discuss.net/soil-2017-14/, in review.
Examples
data(berne)
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Berne – soil mapping case study
Description
The Berne dataset contains soil responses and a large set of explanatory covariates. The study area is located to the Northwest of the city of Berne and covers agricultural area. Soil responses included are soil pH (4 depth intervals calculated from soil horizon), drainage classes (3 ordered classes) and presence of waterlogging characteristics down to a specified depth (binary response).
Covariates cover environmental conditions by representing climate, topography, parent material and soil.
Usage
data("berne")Format
A data frame with 1052 observations on the following 238 variables.
site_id_uniqueID of original profile sampling
xeasting, Swiss grid in m, EPSG: 21781 (CH1903/LV03)
ynorthing, Swiss grid in m, EPSG: 21781 (CH1903/LV03)
datasetFactor splitting dataset for
calibrationand independentvalidation.validationwas assigned at random by using weights to ensure even spatial coverage of the agricultural area.dclassDrainage class, ordered Factor.
waterlog.30Presence of waterlogging characteristics down to 30 cm (1: presence, 0: absence)
waterlog.50Presence of waterlogging characteristics down to 50 cm (1: presence, 0: absence)
waterlog.100Presence of waterlogging characteristics down to 100 cm (1: presence, 0: absence)
ph.0.10Soil pH in 0-10 cm depth.
ph.10.30Soil pH in 10-30 cm depth.
ph.30.50Soil pH in 30-50 cm depth.
ph.50.100Soil pH in 50-100 cm depth.
timesetFactor with range of sampling year and label for sampling type for soil pH. no label:
CaCl_{2}laboratory measurements,field: field estimate by indicator solution,ptf:H_{2}0laboratory measurements transferred by pedotransfer function (univariate linear regression) to level ofCaCl_{2}measures.cl_mt_etap_pecolumns 14 to 238 contain environmental covariates representing soil forming factors. For more information see Details below.
cl_mt_etap_rocl_mt_gh_1cl_mt_gh_10cl_mt_gh_11cl_mt_gh_12cl_mt_gh_2cl_mt_gh_3cl_mt_gh_4cl_mt_gh_5cl_mt_gh_6cl_mt_gh_7cl_mt_gh_8cl_mt_gh_9cl_mt_gh_ycl_mt_pet_pecl_mt_pet_rocl_mt_rr_1cl_mt_rr_10cl_mt_rr_11cl_mt_rr_12cl_mt_rr_2cl_mt_rr_3cl_mt_rr_4cl_mt_rr_5cl_mt_rr_6cl_mt_rr_7cl_mt_rr_8cl_mt_rr_9cl_mt_rr_ycl_mt_swb_pecl_mt_swb_rocl_mt_td_1cl_mt_td_10cl_mt_td_11cl_mt_td_12cl_mt_td_2cl_mt_tt_1cl_mt_tt_11cl_mt_tt_12cl_mt_tt_3cl_mt_tt_4cl_mt_tt_5cl_mt_tt_6cl_mt_tt_7cl_mt_tt_8cl_mt_tt_9cl_mt_tt_yge_caco3ge_geo500h1idge_geo500h3idge_gt_ch_filge_lgmge_vszonesl_nutr_filsl_physio_neusl_retention_filsl_skelett_r_filsl_wet_filtr_be_gwn25_hdisttr_be_gwn25_vdisttr_be_twi2m_7s_tcilowtr_be_twi2m_s60_tcilowtr_ch_3_80_10tr_ch_3_80_10str_ch_3_80_20str_cindx10_25tr_cindx50_25tr_curv_alltr_curv_plantr_curv_proftr_enessktr_es25tr_flowlength_uptr_global_rad_chtr_lsftr_mrrtf25tr_mrvbf25tr_ndom_veg2m_fmtr_negotr_nnessktr_ns25tr_ns25_145mntr_ns25_145sdtr_ns25_75mntr_ns25_75sdtr_posotr_protindxtr_se_alti10m_ctr_se_alti25m_ctr_se_alti2m_fmean_10ctr_se_alti2m_fmean_25ctr_se_alti2m_fmean_50ctr_se_alti2m_fmean_5ctr_se_alti2m_std_10ctr_se_alti2m_std_25ctr_se_alti2m_std_50ctr_se_alti2m_std_5ctr_se_alti50m_ctr_se_alti6m_ctr_se_conv2mtr_se_curv10mtr_se_curv25mtr_se_curv2mtr_se_curv2m_s15tr_se_curv2m_s30tr_se_curv2m_s60tr_se_curv2m_s7tr_se_curv2m_std_10ctr_se_curv2m_std_25ctr_se_curv2m_std_50ctr_se_curv2m_std_5ctr_se_curv50mtr_se_curv6mtr_se_curvplan10mtr_se_curvplan25mtr_se_curvplan2mtr_se_curvplan2m_grass_17ctr_se_curvplan2m_grass_45ctr_se_curvplan2m_grass_9ctr_se_curvplan2m_s15tr_se_curvplan2m_s30tr_se_curvplan2m_s60tr_se_curvplan2m_s7tr_se_curvplan2m_std_10ctr_se_curvplan2m_std_25ctr_se_curvplan2m_std_50ctr_se_curvplan2m_std_5ctr_se_curvplan50mtr_se_curvplan6mtr_se_curvprof10mtr_se_curvprof25mtr_se_curvprof2mtr_se_curvprof2m_grass_17ctr_se_curvprof2m_grass_45ctr_se_curvprof2m_grass_9ctr_se_curvprof2m_s15tr_se_curvprof2m_s30tr_se_curvprof2m_s60tr_se_curvprof2m_s7tr_se_curvprof2m_std_10ctr_se_curvprof2m_std_25ctr_se_curvprof2m_std_50ctr_se_curvprof2m_std_5ctr_se_curvprof50mtr_se_curvprof6mtr_se_diss2m_10ctr_se_diss2m_25ctr_se_diss2m_50ctr_se_diss2m_5ctr_se_e_aspect10mtr_se_e_aspect25mtr_se_e_aspect2mtr_se_e_aspect2m_10ctr_se_e_aspect2m_25ctr_se_e_aspect2m_50ctr_se_e_aspect2m_5ctr_se_e_aspect2m_grass_17ctr_se_e_aspect2m_grass_45ctr_se_e_aspect2m_grass_9ctr_se_e_aspect50mtr_se_e_aspect6mtr_se_mrrtf2mtr_se_mrvbf2mtr_se_n_aspect10mtr_se_n_aspect25mtr_se_n_aspect2mtr_se_n_aspect2m_10ctr_se_n_aspect2m_25ctr_se_n_aspect2m_50ctr_se_n_aspect2m_5ctr_se_n_aspect2m_grass_17ctr_se_n_aspect2m_grass_45ctr_se_n_aspect2m_grass_9ctr_se_n_aspect50mtr_se_n_aspect6mtr_se_no2m_r500tr_se_po2m_r500tr_se_rough2m_10ctr_se_rough2m_25ctr_se_rough2m_50ctr_se_rough2m_5ctr_se_rough2m_rect3ctr_se_sar2mtr_se_sca2mtr_se_slope10mtr_se_slope25mtr_se_slope2mtr_se_slope2m_grass_17ctr_se_slope2m_grass_45ctr_se_slope2m_grass_9ctr_se_slope2m_s15tr_se_slope2m_s30tr_se_slope2m_s60tr_se_slope2m_s7tr_se_slope2m_std_10ctr_se_slope2m_std_25ctr_se_slope2m_std_50ctr_se_slope2m_std_5ctr_se_slope50mtr_se_slope6mtr_se_toposcale2m_r3_r50_i10str_se_tpi_2m_10ctr_se_tpi_2m_25ctr_se_tpi_2m_50ctr_se_tpi_2m_5ctr_se_tri2m_altern_3ctr_se_tsc10_2mtr_se_twi2mtr_se_twi2m_s15tr_se_twi2m_s30tr_se_twi2m_s60tr_se_twi2m_s7tr_se_vrm2mtr_se_vrm2m_r10ctr_slope25_l2gtr_terrtexturtr_tpi2000ctr_tpi5000ctr_tpi500ctr_tsc25_18tr_tsc25_40tr_twi2tr_twi_normaltr_vdcn25
Details
Soil data
The soil data originates from various soil sampling campaigns since 1968. Most of the data was collected in conventional soil surveys in the 1970ties in the course of amelioration and farm land exchanges. As frequently observed in legacy soil data sampling site allocation followed a purposive sampling strategy identifying typical soils in an area in the course of polygon soil mapping.
dclass contains drainage classes of three levels.
Swiss soil classification differentiates stagnic (I), gleyic (G) and anoxic/reduced (R) soil profile qualifiers with each 4, 6 resp. 5 levels. To reduce complexity the qualifiers I, G and R were aggregated to the degree of hydromorphic
characteristic of a site with the ordered levels well (qualifier labels I1–I2, G1–G3, R1 and no hydromorphic qualifier), moderate well drained (I3–I4, G4) and poor drained (G5–G6, R2–R5).
waterlog indicates the presence or absence of waterlogging characteristics down 30, 50 and 100 cm soil depth. The responses were based on horizon qualifiers ‘gg’ or ‘r’ of the Swiss classification (Brunner et al. 1997) as those were considered to limit plant growth. A horizon was given the qualifier ‘gg’ if it was strongly gleyic predominantly oxidized (rich in Fe^{3+}) and ‘r’ if it was anoxic predominantly reduced (poor in Fe^{3+}).
pH was mostly sampled following genetic soil horizons. To ensure comparability between sites pH was transferred to fixed depth intervals of 0–10, 10–30, 30–50 and 50–100 cm by weighting soil horizons falling into a given interval. The data contains laboratory measurements that solved samples in CaCl_{2} or H_{2}0. The latter were transferred to the level of CaCl_{2} measurements by univariate linear regression (label ptf in timeset). Further, the dataset contains estimates of pH in the field by an indicator solution (Hellige pH, label field in timeset).
The column timeset can be used to partly correct for the long sampling period and the different sampling methods.
Environmental covariates
The numerous covariates were assembled from the available spatial data in the case study area. Each covariate name was given a prefix:
-
cl_climate covariates as precipitation, temperature, radiation -
tr_terrain attributes, covariates derived from digital elevation models -
ge_covariates from geological maps -
sl_covariates from an overview soil map
References to the used datasets can be found in Nussbaum et al. 2017b.
References
Brunner, J., Jaeggli, F., Nievergelt, J., and Peyer, K. (1997). Kartieren und Beurteilen von Landwirtschaftsboeden. FAL Schriftenreihe 24, Eidgenoessische Forschungsanstalt fuer Agraroekologie und Landbau, Zuerich-Reckenholz (FAL).
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A., 2017b. Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL Discuss., https://www.soil-discuss.net/soil-2017-14/, in review.
Examples
data(berne)
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