EU OSI introduction
Gerard H Ros
2025-08-12
eu_osi_introduction.RmdIntroduction
The crucial role of healthy soil in achieving sustainable food production and environment is increasingly recognized, as is the importance of proper assessment of soil quality. Healthy soils are not just a growing medium for crops, but they regulate and support essential ecosystem services, such as water purification, carbon sequestration, and nutrient cycling, and they provide habitats for biodiversity. Improving and sustaining soil health are therefore key to sustainable crop production. To date, many attempts have been made to develop indices for assessing soil health, but an operational and reproducible methodology to assess soil health has not been developed so far. There is a broad consensus that multiple aspects of soils (e.g., chemistry, structure, and biology) and their interactions need to be considered. Further, various approaches have been proposed to translate soil attributes into indicators and aggregate them into an index, including (advanced) statistical methods and refined expert knowledge systems. These approaches however are often highly dependent on costly soil measures, are difficult to interpret, and not easily applicable across spatial scales, ranging from field to regions. To overcome these challenges, Ros et al. (2002) introduced a new soil assessment framework, being the Open Soil Index.
The OSI builds on extensive soil and agronomic research to maintain a direct link between soil health indicators and the objective to achieve a sustainable crop production, leveraging routine laboratory data and public databases to make its large-scale application affordable. The OSI framework has a modular design to allow for easy adjustment and expansion, is developed in an open-source environment (OBIC R package) to assure transparency, and provides advice for field-level farming practices. In this way, OSI strives to provide an operational soil assessment that valorizes soil health and therewith promote sustainable soil management.
From 2021 onwards, soil scientists, agronomy researchers, farm advisors and extension service providers in the Netherlands collaborated to expand the OSI approach to include multiple ecosystem services. Where the OSI strongly focuses on the interpretion of soil health in view of crop production, many other ecosystem services are recognized as crucial societal objectives that soil can contribute. Relevant soil ecosystem services from the Sustainable Development Goals (SDGs) include not only delivery of healthy food (SDG2 and SDG3) but also clean and sufficient water (SDG6), the mitigation of climate (SDG13), and the support for biodiversity and protection of land degradation (SDG15). Engaging farmers to consider these SDGs requires extension of the framework to include more environmental soil functions. The newly developed framework follows the conceptual approach of the OSI and expands the soil health assessment in view of these ecosystem services. The soil health indicator functions can be found in the BLN R package. This tutorial gives a short explanation of the package focussing on the main functionalities of the R package.
In 2023, Gerard Ros (WU and NMI) and Elise van den Eynde (From JRC) discussed the possibility to expand the OSI framework to the European scale given the need for scientific sound and standardized soil health assessments for monitoring, policy support, and for integrative soil recommendation systems used by farmers and farm advisors. This ultimately led to the development of the euosi R package.
Importing dataset for research farm
We start with some (virtual) data being collected for a farms with
fields in the Netherlands, Belgium and Finland. The preparation of this
file is described in dev/osi_farm.R.
The dataset contains soil properties from 60 agricultural fields and
is documented in ?osi_farm. An overview of all variables is
given below. When interested, look to the summary of the
osi_farm object.
More information on the input and ouput variables are documented in a
separate vignette vignette("eu_osi_variables").
# select the data for de euosi farm
dt.of <- copy(euosi::osi_farm)
# show the first line with all properties for a single field and latest year
print(colnames(dt.of[1]))## [1] "ID" "B_COUNTRY" "B_LU" "B_SOILTYPE_AGR"
## [5] "B_PREC_SUM" "B_PREC_WIN" "B_PET_SUM" "B_PET_WIN"
## [9] "B_TEMP_SUM" "B_TEMP_WIN" "A_SOM_LOI" "A_CLAY_MI"
## [13] "A_SAND_MI" "A_PH_CC" "A_CACO3_IF" "A_CEC_CO"
## [17] "A_C_OF" "A_N_RT" "A_N_PMN" "A_P_OL"
## [21] "A_K_AAA" "A_B_HW" "A_ZN_CC" "A_MG_AAA"Calculate soil health index for a single field
Lets see how to assess the soil quality for a single field. First
select the first field given a crop rotation scheme for 4 years (the
Dutch fields). This is basically a subset of the farm and contains all
soil and field properties for the last 4 years. We subsequently use the
function osi_field() to calculate the different soil
indicators and soil scores. Note that the osi_farm has only the generic
input parameters. Country specific soil properties can also be used (see
?osi_field). These specific parameters are by default set
to NA.
We start with a request for the soil indicators.
# select the data for the first field of osi farm
dt <- dt.of[ID=='F1']
# calculate the OSI indicator values for that field
out <- euosi::osi_field(B_LU = dt$B_LU,
B_SOILTYPE_AGR = dt$B_SOILTYPE_AGR,
B_COUNTRY = dt$B_COUNTRY,
B_BGZ = NA_character_,
B_PREC_SUM = dt$B_PREC_SUM,
B_PREC_WIN = dt$B_PREC_WIN,
B_PET_SUM = dt$B_PET_SUM,
B_PET_WIN = dt$B_PET_WIN,
B_TEMP_SUM = dt$B_TEMP_SUM,
B_TEMP_WIN = dt$B_TEMP_WIN,
A_CLAY_MI = dt$A_CLAY_MI,
A_SAND_MI = dt$A_SAND_MI,
A_SOM_LOI = dt$A_SOM_LOI,
A_C_OF= dt$A_C_OF,
A_CEC_CO = dt$A_CEC_CO,
A_PH_CC = dt$A_PH_CC,
A_CACO3_IF = dt$A_CACO3_IF,
A_N_RT = dt$A_N_RT,
A_N_PMN = dt$A_N_PMN,
A_P_OL = dt$A_P_OL,
A_K_AAA = dt$A_K_AAA,
A_MG_AAA = dt$A_MG_AAA,
A_B_HW = dt$A_B_HW,
A_ZN_CC = dt$A_ZN_CC,
A_ZN_EDTA = NA_real_,
ID = dt$ID,
output = 'indicators')
# show the first line with all properties for a single field and latest year
# format in a long table, and subset the numeric columns only
dt2 <- copy(dt)[1]
# select numeric values and round to two digits
cols <- colnames(dt2)[grepl('A_|B_PREC|B_PET|B_TEMP',colnames(dt2))]
dt2[,c(cols) := lapply(.SD,function(x) round(x,2)),.SDcols = cols]
# melt the data.table
dt.melt1 <- melt(dt2[,mget(c(cols,'ID'))],
id.vars = c('ID'),
variable.name = 'parameter', value.name = 'value')
# print the table
# knitr::kable(dt.melt1,caption='numerical input variables for euosi')
# print the table in nicer format
knitr::kable(
list(dt.melt1[1:10,2:3],dt.melt1[11:20,2:3]),
caption = 'numerical input variables for euosi.',
booktabs = TRUE
)
|
|
# do the same for the categorial values
# format in a long table, and subset the numeric columns only
dt3 <- copy(dt)[1]
# select categorial variables
cols2 <- colnames(dt3)[!colnames(dt3) %in% cols]
# convert to chracter
dt3 <- dt3[,lapply(.SD,as.character),.SDcols = cols2]
# melt the data.table
dt.melt2 <- melt(dt3[,mget(cols2)],
id.vars = c('ID'),
variable.name = 'parameter', value.name = 'value')
# print the table
knitr::kable(dt.melt2,caption='categorial input variables for euosi')| ID | parameter | value |
|---|---|---|
| F1 | B_COUNTRY | NL |
| F1 | B_LU | 172 |
| F1 | B_SOILTYPE_AGR | dekzand |
To simplify, one can also use a data.table as input. In that case use
the function soi_field_dt(). The function checks whether
all desired input variables are present. Again, the illustration below
shows the use of osi_field_dt to retrieve the euosi
indicators. For the interpretation of each of the soil indicators, one
can have a look at the vignette("eu_osi_variables")
# select the data for the first field of osi farm
dt <- dt.of[ID=='F1']
# calculate the osi indicator values
out <- osi_field_dt(dt, output = 'indicators')
# print the names of the output object
print(colnames(out))## [1] "ID" "i_b_biodiv" "i_b_pmn" "i_c_b"
## [5] "i_c_k" "i_c_mg" "i_c_n" "i_c_p"
## [9] "i_c_ph" "i_c_zn" "i_e_carbon" "i_e_kexcess"
## [13] "i_e_nleach" "i_e_pexcess" "i_e_watererosie" "i_p_cr"
## [17] "i_p_dens" "i_p_ksat" "i_p_paw" "i_p_wef"
## [21] "i_p_whc"To ask for the euosi soil health scores, summarizing the contribution
of the soil to various ecosystem services, one can ask for the scores as
output. Just replace the argument output with scores rather
than indicators. It is also possible to ask for both: just
set the argument output equal to all, being the default
option.
Below we summarize the osi score for a single field. Given the hierarchical structure of the euosi soil quality assesment, the following scores are calculated:
- OSI total score (
s_euosi_total) represents the overall integrated soil quality assessment, where the score varies from zero (serious bottlenecks being present) up to one (the quality is optimal for the crop rotation plan on this field) - ESD crop production (
s_euosi_ess_prod): the quality score reflecting the ability of the soil to sustain crop production - ESD environment (
s_euosi_ess_env): the quality score reflecting the ability of the soil to contribute to a healthy environment for air, water and biodiversity - ESD climate mitigation (
s_euosi_clim): the quality score reflecting the ability of the soil to store carbon in soil and mitigate climate change - ESD nutrient cycling (
s_euosi_nutcycle): the quality score reflecting the ability of the soil to recycle (regional) nutrients efficiently - ESD water (
s_euosi_water): the quality score reflecting the ability of the soil to retain and purify water leaching to ground and surface water
Besides these four ecoystem services provided by the soil, the ESS
crop production can be shown in more detail, thereby reflecting
underlying processes controlling the ESS. The ESS crop production can be
separated into the soil quality assessment in view of chemical
(s_euosi_prod_c), physical (s_euosi_prod_p)
and biological (s_euosi_prod_b) soil functions contributing
to the ability of soils to sustain crop production.
# calculate the OSI scoring values for ecosystem services
out <- osi_field_dt(dt, output = 'scores')
# reformat the output in a long table
out.melt <- melt(out,id.vars='ID',variable.name='soil_ESD',value.name='shi')
# set levels for the soil_ESD with a new label for the figure
plevel = c('s_euosi_ess_env','s_euosi_ess_prod',
's_euosi_prod_c','s_euosi_prod_p','s_euosi_prod_b',
's_euosi_clim','s_euosi_water','s_euosi_nutcycle',
's_euosi_total')
tlabel = c('ESS environment', 'ESS crop production',
'crop production chemistry','crop production physics','crop production biology',
'ESS climate mitigation','ESS water','ESS nutrient cycling',
'EUOSI total score')
out.melt[,tsoil_ESD := factor(soil_ESD,levels = plevel,labels = tlabel)]
# print the output object
knitr::kable(out.melt[,.(ID,ESS = soil_ESD,description=tsoil_ESD,score = shi)],
caption='OSI scores for field 1')| ID | ESS | description | score |
|---|---|---|---|
| F1 | s_euosi_ess_env | ESS environment | 0.92 |
| F1 | s_euosi_ess_prod | ESS crop production | 0.64 |
| F1 | s_euosi_prod_b | crop production biology | 0.99 |
| F1 | s_euosi_prod_c | crop production chemistry | 0.42 |
| F1 | s_euosi_clim | ESS climate mitigation | 0.88 |
| F1 | s_euosi_nutcycle | ESS nutrient cycling | 0.96 |
| F1 | s_euosi_prod_p | crop production physics | 0.67 |
| F1 | s_euosi_water | ESS water | 0.90 |
| F1 | s_euosi_total | EUOSI total score | 0.74 |
# to show the output in a lollipop figure
plabel = c('ESS environment','ESS crop\nproduction',
'crop production\nchemistry','crop production\nphysics','crop production\nbiology',
'ESS climate\nmitigation','ESS water','ESS nutrient\ncycling',
'OSI total score')
out.melt[,psoil_ESD := factor(soil_ESD,levels = plevel,labels = plabel)]
p1 <- ggplot(out.melt, aes(x= soil_ESD, y= shi*10, group = soil_ESD, color = shi)) +
geom_col(fill = 'grey', color = NA, width = 0.05) + geom_point(size = 5) +
ylab('OSI-score') + xlab('') +
theme_bw(12) + theme(legend.position = 'none',
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linewidth = 0.5),
panel.grid.minor.x = element_blank()) +
coord_flip() +
scale_fill_viridis_d()+ scale_color_viridis_c() +
ggtitle('OSI soil quality assessment field 1')
p1
Reading more?
More vignettes will be made available regarding the derivation of all soil health indicators, the aggregation methods applied, the derivation of optimum land use given the current soil quality assessments, and the best management practices to be applied to improve the soil quality. Are you interested to contribute, please contact the author(s) of this R package. Enjoy!