# packages used in this toolchain walkthrough
library(sysrevdata)
library(tidyverse)
library(kableExtra)
library(janitor)


# this avoids tidyverse conflicts with the base function filter
conflicted::conflict_prefer("filter", "dplyr")

creating a narrative synthesis table

The objective of a is to summarise each study included in a systematic review or systematic map, often using a table:

Rows | One study per row.

Columns | Extracted meta-data and descriptive information about each study.

For some syntheses, particularly , the number of variables considered can be substantial, and this poses a challenge for fitting the table into a succinct readable format.

Here we consider ways we can condense and format data for presentation for humans to read, as opposed to machine-readable data for analysis and complex visualisation.

We may begin with sparsely-filled wide data or long-format machine-structured data, neither of which are good for inclusion in the text of a research manuscript or interpretation by human readers. So, we will consider how to condense these structures in turn, before demonstrating how to format the output.

condensing from wide data

We’ll use bufferstrips (from this published systematic map), which can be loaded from sysrevdata, to investigate this. These data are in a format, useful for creating systematic maps. We’ll restrict ourselves to the first 5 rows of studies with head(5) as we work through these examples, so this document doesn’t run too long.

buffer_example <-
  bufferstrips %>% 
  head(5)

To see how these data are sparse and wide, we will extract the abbreviated title, year, and the columns that contain observations relating to spatial scale.

buffer_example %>% 
  select(short_title, contains("spatial"))
#> # A tibble: 5 x 7
#>   short_title spatialscale_pl~ spatialscale_fi~ spatialscale_fa~
#>   <chr>       <chr>            <chr>            <chr>           
#> 1 Aaron (200~ <NA>             <NA>             <NA>            
#> 2 Aavik (200~ <NA>             <NA>             <NA>            
#> 3 Aavik (201~ <NA>             <NA>             <NA>            
#> 4 Abu-Zreig ~ Plot scale       <NA>             <NA>            
#> 5 Abu-Zreig ~ Plot scale       <NA>             <NA>            
#> # ... with 3 more variables: spatialscale_catchment <chr>,
#> #   spatialscale_regional <chr>, spatialscale_notdescribed <chr>

When we format these data (which we’ll provide toolchains for at the end), the wideness becomes an issue. And this is with only the spatial variables (columns) selected. The table is unwieldy, large, and filled with white space that together make it very difficult for a reader to comprehend.

short_title spatialscale_plot spatialscale_field spatialscale_farm spatialscale_catchment spatialscale_regional spatialscale_notdescribed
Aaron (2005) Catchment scale Regional scale
Aavik (2008) Regional scale
Aavik (2010) Regional scale
Abu-Zreig (2004) Plot scale
Abu-Zreig (2003) Plot scale

To solve the wideness issue, we’ll the variables, specifying the character that should be used to separate the values where multiple values exist in one cell. Here’s a way of doing this for the spatial variable.

buffer_example %>%
  # choose the desired columns
  select(short_title, contains("spatialscale")) %>%
  # condense variable
  unite(# name condensed column
    col = "spatialscale",
    # columns to condense
    contains("spatialscale"),
    # set separator
    sep = "; ",
    # remove NAs
    na.rm = TRUE)
#> # A tibble: 5 x 2
#>   short_title      spatialscale                   
#>   <chr>            <chr>                          
#> 1 Aaron (2005)     Catchment scale; Regional scale
#> 2 Aavik (2008)     Regional scale                 
#> 3 Aavik (2010)     Regional scale                 
#> 4 Abu-Zreig (2004) Plot scale                     
#> 5 Abu-Zreig (2003) Plot scale

You can see that spatial scale is now a single column, with some cells containing multiple values, separated with a semi-colon and a space for readability.

But, of course, we wish to do this for all of the variables. The table contains columns that don’t need condensing, such as title, year, and so forth.

buffer_example %>% 
  names() %>% cat()
#> item_id short_title title year period google_scholar_link vegetated_strip_description nation study_country study_location latitute longitude studydesign_observational studydesign_manipulative Study length (years) Time since intervention (years) spatialscale_plot spatialscale_field spatialscale_farm spatialscale_catchment spatialscale_regional spatialscale_notdescribed measurementquarter_Q1 measurementquarter_Q2 measurementquarter_Q3 measurementquarter_Q4 measurementquarter_notdescribed farmingsystem_conventional farmingsystem_organic farmingsystem_notdescribed farmingproductionsystem_livestock farmingproductionsystem_livestockinfo farmingproductionsystem_croppedfields farmingproductionsystem_croppedfieldsinfo farmingproductionsystem_horticulture farmingproductionsystem_horticultureinfo farmingproductionsystem_viticulture farmingproductionsystem_viticultureinfo farmingproductionsystem_fruit farmingproductionsystem_fruitinfo farmingproductionsystem_grassland farmingproductionsystem_grasslandinfo farmingproductionsystem_other farmingproductionsystem_otherinfo farmingproductionsystem_notdescribed farmingproductionsystem_notdescribedinfo vegetationtype_grasses vegetationtype_grassesinfo vegetationtype_wildflowers vegetationtype_wildflowersinfo vegetationtype_shrubs vegetationtype_shrubsinfo vegetationtype_hedgerow vegetationtype_hedgerowinfo vegetationtype_trees vegetationtype_treesinfo vegetationtype_other vegetationtype_otherinfo vegetationtype_notdescribed vegetationtype_notdescribedinfo striplocation_withinfield striplocation_fieldedge striplocation_riparian striplocation_notdescribed stripmanagement_unmanaged stripmanagement_unmanagedinfo stripmanagement_soilamendment stripmanagement_soilamendmentinfo stripmanagement_soildisturbed stripmanagement_soildisturbedinfo stripmanagement_cut stripmanagement_cutinfo stripmanagement_grazed stripmanagement_grazedinfo stripmanagement_harvested stripmanagement_harvestedinfo stripmanagement_sownplanted stripmanagement_sownplantedinfo stripmanagement_pesticide stripmanagement_pesticideinfo stripmanagement_managedother stripmanagement_managedotherinfo stripmanagement_notdescribed stripmanagement_notdescribedinfo intervention_presence intervention_presenceinfo intervention_location intervention_locationinfo intervention_dimension intervention_dimensioninfo intervention_structure itervention_structureinfo intervention_management intervention_managementinfo intervention_vegetationtype intervention_vegetationtypeinfo intervention_management_1 intervention_managementinfo_1 intervention_other intervention_otherinfo intervention_transect intervention_transectinfo intervention_notdescribed intervention_notdescribedinfo outcome_biodiversityaquatic outcome_biodiversityaquaticinfo outcome_biodiversitysemiaquatic outcome_biodiversitysemiaquaticinfo outcome_biodiversityterrestrial outcome_biodiversityterrestrialinfo outcome_pollination outcome_pollinationinfo outcome_gamespecies outcome_gamespeciesinfo outcome_pestcontrol outcome_pestcontrolinfo outcome_nutrientsn outcome_nutrientsninfo outcome_nutrientsp outcome_nutrientsninfo_1 outcome_soillossretention outcome_soillossretentioninfo outcome_soilsedimentchem outcome_soilsedimentcheminfo outcome_waterchem outcome_watercheminfo outcome_soilsedimentphys outcome_soilsedimentphysinfo outcome_waterlossretention outcome_waterlossretentioninfo outcome_economic outcome_economicinfo outcome_ghgs outcome_ghgsinfo outcome_pesticides outcome_pesticidesinfo outcome_toxins outcome_toxinsinfo outcome_wind outcome_windinfo outcome_gmpollen outcome_gmpolleninfo outcome_yield outcome_yieldinfo outcome_social outcome_socialinfo outcome_recreation outcome_recreationinfo outcome_other outcome_otherinfo outcome_pathogens outcome_pathogensinfo outcome_noncropyield outcome_noncropyieldinfo outcome_light outcome_lightinfo outcome_habitat outcome_habitatinfo outcome_climate outcome_climateinfo outcome_microbes outcome_microbesinfo outcome_physiology outcome_physiologyinfo es_provisioning_food es_provisioning_freshwater es_provisioning_fibrefuel es_provisioning_biochemicalproducts es_provisioning_geneticmaterials es_regulating_climateregulation es_regulating_hydrologicalregimes es_regulating_pollutioncontrol es_regulating_erosionprotection es_regulating_naturalhazards es_regulating_pestregulation es_regulating_nutrientcycling es_cultural_piritualinspirational es_cultural_recreational es_cultural_aesthetic es_cultural_educational es_supporting_biodiversity es_supporting_pollination es_supporting_soilformation

Our first task is to identify the columns that have common prefixes, that is, columns that begin with the same text string (e.g. ‘spatialscale_plot’, ‘spacialscale_field’, and ‘spacialscale_farm’).

(
  buffer_variables <-
    # extract the prefix of each column
    # i.e., the string before the first _
    buffer_example %>%
    # we can exclude the column names that don't contain _
    select(contains("_"), -study_country, -study_location) %>%
    # get column names
    names() %>%
    # extract the word before the first _
    str_extract("[a-z]*") %>% {
      # convert to a tibble so we can use count
      tibble(variables = .)
    } %>%
    # count how many instances
    count(variables) %>%
    # filter out the unique variables we won't condense
    filter(n > 1) %>%
    # convert to a vector
    pull(variables)
)
#>  [1] "es"                      "farmingproductionsystem"
#>  [3] "farmingsystem"           "intervention"           
#>  [5] "measurementquarter"      "outcome"                
#>  [7] "spatialscale"            "striplocation"          
#>  [9] "stripmanagement"         "studydesign"            
#> [11] "vegetationtype"

Now we adapt the code we used to condense the variables to a .

condense_variable <-
function(variable_to_condense, df) {
  buffer_example %>%
    # choose the desired columns
    select(contains(variable_to_condense)) %>%
    # condense variable
    unite(# name condensed column
      col = !!variable_to_condense,
      # columns to condense
      contains(variable_to_condense),
      # set separator
      sep = "; ",
      # remove NAs
      na.rm = TRUE)
}

condense_variable("spatialscale")
#> # A tibble: 5 x 1
#>   spatialscale                   
#>   <chr>                          
#> 1 Catchment scale; Regional scale
#> 2 Regional scale                 
#> 3 Regional scale                 
#> 4 Plot scale                     
#> 5 Plot scale

Next we apply our function to every variable we wish to condense and combine the results into a new table of condensed variables.


condensed_buffer_example <-
buffer_variables %>%
  # apply the function to each variable we wish to condense, producing a list of dataframes
  map(condense_variable) %>% 
  # bind the columns together, with the short title as the first column
  bind_cols(
    buffer_example %>% select(-contains(buffer_variables)), .
  )

# just showing the condensed variables
condensed_buffer_example %>% 
  select(short_title, contains(buffer_variables))
#> # A tibble: 5 x 12
#>   short_title es    studydesign farmingproducti~ farmingsystem intervention
#>   <chr>       <chr> <chr>       <chr>            <chr>         <chr>       
#> 1 Aaron (200~ Ripa~ Observatio~ Not described    Not described Not stated;~
#> 2 Aavik (200~ Fiel~ Observatio~ Livestock; Crop~ Not described Not stated;~
#> 3 Aavik (201~ Fiel~ Observatio~ Other (please s~ Conventional~ Not stated;~
#> 4 Abu-Zreig ~ Vege~ Manipulati~ Not described    Not described Not stated;~
#> 5 Abu-Zreig ~ Vege~ Manipulati~ Not described    Not described Not stated;~
#> # ... with 6 more variables: measurementquarter <chr>, outcome <chr>,
#> #   spatialscale <chr>, striplocation <chr>, stripmanagement <chr>,
#> #   vegetationtype <chr>

The output is a condensed datatable suitable for inclusion in a manuscript, with a minimum number of columns, no unnecessary white space, designed specifically to be easily digestible by the reader. In the interests of , review authors should also make the underlying data freely accessible in a widely usable and easily understandable format, not solely in text form.

condensing from long data

Suppose we start with long-format data in a format, where each row is an independent observation for a specific study: in the case of systematic maps, this could be each outcome measured using a different method, for example. In the interests of space, we’ll show the title, category, and subcategory: the columns that don’t require condensing remain the same.

For this section, we’ll use the long format of the bufferstrips dataset, using the dataframe created in the vignette on creating long-form datasets.

buffer_example_long %>% 
  # in the interests of space, we'll just show the relevant columns
  select(short_title, category_type, subcategory_type)
#> # A tibble: 97 x 3
#>    short_title  category_type                   subcategory_type       
#>    <chr>        <chr>                           <chr>                  
#>  1 Aaron (2005) vegetated                       strip_description      
#>  2 Aaron (2005) studydesign                     observational          
#>  3 Aaron (2005) farmingsystem                   notdescribed           
#>  4 Aaron (2005) farmingproductionsystem         notdescribed           
#>  5 Aaron (2005) vegetationtype                  notdescribed           
#>  6 Aaron (2005) stripmanagement                 notdescribed           
#>  7 Aaron (2005) intervention                    presence               
#>  8 Aaron (2005) intervention                    presenceinfo           
#>  9 Aaron (2005) es                              supporting_biodiversity
#> 10 Aaron (2005) Time since intervention (years) <NA>                   
#> # ... with 87 more rows

Now we’ll wide so we can condense.

# this function creates a string from the vector of multiple values

mash_subcategories <- function(x) {
  paste0(x, collapse = "; ")
}

# go from long to wide
buffer_example_long %>% 
  pivot_wider(
    names_from = category_type,
    values_from = subcategory_value,
    values_fn = mash_subcategories
  ) %>% 
  # we'll just show a few examples because space
  select(short_title, vegetated, farmingproductionsystem)
#> # A tibble: 81 x 3
#>    short_title  vegetated       farmingproductionsystem
#>    <chr>        <chr>           <chr>                  
#>  1 Aaron (2005) Riparian buffer <NA>                   
#>  2 Aaron (2005) <NA>            <NA>                   
#>  3 Aaron (2005) <NA>            Not described          
#>  4 Aaron (2005) <NA>            <NA>                   
#>  5 Aaron (2005) <NA>            <NA>                   
#>  6 Aaron (2005) <NA>            <NA>                   
#>  7 Aaron (2005) <NA>            <NA>                   
#>  8 Aaron (2005) <NA>            <NA>                   
#>  9 Aaron (2005) <NA>            <NA>                   
#> 10 Aaron (2005) <NA>            <NA>                   
#> # ... with 71 more rows

Now we have the long data in the same condensed format as the wide data in the section above, and we are ready to consider formatting functions for publication.

formatting for output

We can format the database into a nice, readable format by converting it into an html output designed for human readability.

condensed_buffer_example %>% 
  # converts dataframe to an html output table
  kable() %>% 
  # useful function for customising table formatting
  kable_styling(
    bootstrap_options = "striped", 
    font_size = 9)
item_id short_title title year period google_scholar_link nation study_country study_location latitute longitude Study length (years) itervention_structureinfo es farmingproductionsystem farmingsystem intervention measurementquarter outcome spatialscale striplocation stripmanagement studydesign vegetationtype
20641367 Aaron (2005) Invertebrate Biodiversity in Agricultural and Urban Headwater Streams: Implications for Conservation and Management 2005 2005-2009 http://scholar.google.co.uk/scholar?q=Invertebrate+Biodiversity+in+Agricultural+and+Urban+Headwater+Streams:+Implications+for+Conservation&btnG=&hl=en&as_sdt=0%2C5 USA Maryland, USA Cattail Creek, Hawling’s River, Northwest Branch, Paint Branch of Chesapeake Bay Watershed, Maryland 39.045755 -76.641271 2 NA Riparian buffer; Observational; Not described; Not described; Not described; Not described; Strip presence; Percentage riparian cover; Biodiversity Not described Not described Not stated; Strip presence; Percentage riparian cover Q1; Q2 Biodiversity (aquatic); Macroinvertebrate species/functional group richness, density, Shannon-Wiener diversity, evenness Catchment scale; Regional scale Riparian Not described Observational Not described
20641374 Aavik (2008) What is the role of local landscape structure in the vegetation composition of field boundaries? 2008 2005-2009 http://scholar.google.co.uk/scholar?q=What+is+the+role+of+local+landscape+structure+in+the+vegetation+composition+of+field+boundaries?&btnG=&hl=en&as_sdt=0%2C5 Estonia Estonia Vaike-Maarja, Are, Vihtra, Virratsi, Abja-Paluoja and Ilmatsalu 58.595272 25.013607 Not stated NA Field boundary; Observational; Not described; Not described; Livestock; Not described; Not described; Biodiversity (terrestrial); Vascular plant abundance; Biodiversity Livestock; Cropped fields (arable) Not described Not stated; Vegetation type; Grassy boundary, woody ditch verge, ditch verge, woody field boundary, road verge; Transect into field/across vegetative strip Not described Biodiversity (terrestrial); Vascular plant abundance Regional scale Field edge Not described Observational Not described; Vegetation type; Grassy boundary, woody ditch verge, ditch verge, woody field boundary, road verge
20641375 Aavik (2010) Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries 2010 2010-2014 http://scholar.google.co.uk/scholar?q=Quantifying+the+effect+of+organic+farming,+field+boundary+type+and+landscape+structure+on+the+vegeta&btnG=&hl=en&as_sdt=0%2C5 Estonia Estonia Tartu County 58.405713 26.801576 <1 NA Field boundary; Observational; Not described; regional study with limited details on single vegetated strips; Biodiversity (terrestrial); plant species richness and composition; Biodiversity Other (please specify); Mixed conventional and organic, multiple farms (not described) Conventional; Organic Not stated; Strip type (dimension); Strip type (structure) Q2; Q3 Biodiversity (terrestrial); plant species richness and composition Regional scale Field edge Not described; regional study with limited details on single vegetated strips Observational Hedgerow; Other (please specify); road boundaris; grassland-adjacent boundaries; ditch verges; ditch verges with trees and bushes; woody boundaries, forest edges, tree lines and hedgerows; road verges with trees and shrubs
20641382 Abu-Zreig (2004) Experimental investigation of runoff reduction and sediment removal by vegetated filter strips 2004 2000-2004 http://scholar.google.co.uk/scholar?q=Experimental+investigation+of+runoff+reduction+and+sediment+removal+by+vegetated+filter+strips&btnG=&hl=en&as_sdt=0%2C5 Not stated Not stated Not stated not stated not stated <1 NA Vegetated filter strip; Manipulative; Not described; Not described; Not described; Grasses; Perennial rye grass; Not described; Not described; Erosion protection Not described Not described Not stated; Strip type (dimension); Strip width (described as length); Vegetation type Not described Soil loss/retention; Sediment trapping efficiency Plot scale Not described Not described Manipulative Grasses; Perennial rye grass; Other (please specify); Creeping red fescue, birdsfoot trefoil, native riparian vegetation (wild oat, quack, fescue, dandelions, etc.); Vegetation type
20641384 Abu-Zreig (2003) Phosphorus removal in vegetated filter strips 2003 2000-2004 http://scholar.google.co.uk/scholar?q=Phosphorus+removal+in+vegetated+filter+strips&btnG=&hl=en&as_sdt=0%2C5 Canada Ontario, Canada Elora, Ontario 43.683715 -80.430543 <1 NA Vegetated filter strip; Manipulative; Not described; Not described; Not described; Grasses; Not described; Not described; Hydrological regimes; Pollution control; Nutrient cycling Not described Not described Not stated; Strip type (dimension); Filter width (described as length); Vegetation type; Perennial ryegrass (Lolium perenne), legume and creeping red fescue (Festuca rubra), bare filters, native grass species Not described Nutrients P; Phosphorus trapping efficiency; Water loss/retention; Water retention Plot scale Not described Not described Manipulative Grasses; Other (please specify); Perennial ryegrass, legume and creeping red fescue, native grass species; Vegetation type; Perennial ryegrass (Lolium perenne), legume and creeping red fescue (Festuca rubra), bare filters, native grass species

The formatting still isn’t perfect because the cell values are long, and the URLs that we’ve included to Google Scholar are unwieldy. We can present this with a separate table of metadata, using the short title as the reference. We’ll combine the Google Scholar link with the title.

study_descriptions <-
buffer_example %>% 
  # combine the google scholar link in a way that will be rendered as a clickable link 
  mutate(title = str_c("[", title, "](",google_scholar_link,")")) %>% 
  select(-contains(buffer_variables),
         -google_scholar_link) 

study_descriptions %>% 
  kable() %>% 
  kable_styling("striped",
                font_size = 9)
item_id short_title title year period nation study_country study_location latitute longitude Study length (years) itervention_structureinfo
20641367 Aaron (2005) Invertebrate Biodiversity in Agricultural and Urban Headwater Streams: Implications for Conservation and Management 2005 2005-2009 USA Maryland, USA Cattail Creek, Hawling’s River, Northwest Branch, Paint Branch of Chesapeake Bay Watershed, Maryland 39.045755 -76.641271 2 NA
20641374 Aavik (2008) What is the role of local landscape structure in the vegetation composition of field boundaries? 2008 2005-2009 Estonia Estonia Vaike-Maarja, Are, Vihtra, Virratsi, Abja-Paluoja and Ilmatsalu 58.595272 25.013607 Not stated NA
20641375 Aavik (2010) Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries 2010 2010-2014 Estonia Estonia Tartu County 58.405713 26.801576 <1 NA
20641382 Abu-Zreig (2004) Experimental investigation of runoff reduction and sediment removal by vegetated filter strips 2004 2000-2004 Not stated Not stated Not stated not stated not stated <1 NA
20641384 Abu-Zreig (2003) Phosphorus removal in vegetated filter strips 2003 2000-2004 Canada Ontario, Canada Elora, Ontario 43.683715 -80.430543 <1 NA

And, if we are happy to have a wider table, we can combine both tables together to form one narrative synthesis table with all variables condensed. Although wide, it is not as wide or sparse as where we started, and is much easier to read.

full_join(study_descriptions, condensed_buffer_example,
          by = "short_title") %>% 
  kable() %>% 
   kable_styling("striped",
                font_size = 9)
item_id.x short_title title.x year.x period.x nation.x study_country.x study_location.x latitute.x longitude.x Study length (years).x itervention_structureinfo.x item_id.y title.y year.y period.y google_scholar_link nation.y study_country.y study_location.y latitute.y longitude.y Study length (years).y itervention_structureinfo.y es farmingproductionsystem farmingsystem intervention measurementquarter outcome spatialscale striplocation stripmanagement studydesign vegetationtype
20641367 Aaron (2005) Invertebrate Biodiversity in Agricultural and Urban Headwater Streams: Implications for Conservation and Management 2005 2005-2009 USA Maryland, USA Cattail Creek, Hawling’s River, Northwest Branch, Paint Branch of Chesapeake Bay Watershed, Maryland 39.045755 -76.641271 2 NA 20641367 Invertebrate Biodiversity in Agricultural and Urban Headwater Streams: Implications for Conservation and Management 2005 2005-2009 http://scholar.google.co.uk/scholar?q=Invertebrate+Biodiversity+in+Agricultural+and+Urban+Headwater+Streams:+Implications+for+Conservation&btnG=&hl=en&as_sdt=0%2C5 USA Maryland, USA Cattail Creek, Hawling’s River, Northwest Branch, Paint Branch of Chesapeake Bay Watershed, Maryland 39.045755 -76.641271 2 NA Riparian buffer; Observational; Not described; Not described; Not described; Not described; Strip presence; Percentage riparian cover; Biodiversity Not described Not described Not stated; Strip presence; Percentage riparian cover Q1; Q2 Biodiversity (aquatic); Macroinvertebrate species/functional group richness, density, Shannon-Wiener diversity, evenness Catchment scale; Regional scale Riparian Not described Observational Not described
20641374 Aavik (2008) What is the role of local landscape structure in the vegetation composition of field boundaries? 2008 2005-2009 Estonia Estonia Vaike-Maarja, Are, Vihtra, Virratsi, Abja-Paluoja and Ilmatsalu 58.595272 25.013607 Not stated NA 20641374 What is the role of local landscape structure in the vegetation composition of field boundaries? 2008 2005-2009 http://scholar.google.co.uk/scholar?q=What+is+the+role+of+local+landscape+structure+in+the+vegetation+composition+of+field+boundaries?&btnG=&hl=en&as_sdt=0%2C5 Estonia Estonia Vaike-Maarja, Are, Vihtra, Virratsi, Abja-Paluoja and Ilmatsalu 58.595272 25.013607 Not stated NA Field boundary; Observational; Not described; Not described; Livestock; Not described; Not described; Biodiversity (terrestrial); Vascular plant abundance; Biodiversity Livestock; Cropped fields (arable) Not described Not stated; Vegetation type; Grassy boundary, woody ditch verge, ditch verge, woody field boundary, road verge; Transect into field/across vegetative strip Not described Biodiversity (terrestrial); Vascular plant abundance Regional scale Field edge Not described Observational Not described; Vegetation type; Grassy boundary, woody ditch verge, ditch verge, woody field boundary, road verge
20641375 Aavik (2010) Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries 2010 2010-2014 Estonia Estonia Tartu County 58.405713 26.801576 <1 NA 20641375 Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries 2010 2010-2014 http://scholar.google.co.uk/scholar?q=Quantifying+the+effect+of+organic+farming,+field+boundary+type+and+landscape+structure+on+the+vegeta&btnG=&hl=en&as_sdt=0%2C5 Estonia Estonia Tartu County 58.405713 26.801576 <1 NA Field boundary; Observational; Not described; regional study with limited details on single vegetated strips; Biodiversity (terrestrial); plant species richness and composition; Biodiversity Other (please specify); Mixed conventional and organic, multiple farms (not described) Conventional; Organic Not stated; Strip type (dimension); Strip type (structure) Q2; Q3 Biodiversity (terrestrial); plant species richness and composition Regional scale Field edge Not described; regional study with limited details on single vegetated strips Observational Hedgerow; Other (please specify); road boundaris; grassland-adjacent boundaries; ditch verges; ditch verges with trees and bushes; woody boundaries, forest edges, tree lines and hedgerows; road verges with trees and shrubs
20641382 Abu-Zreig (2004) Experimental investigation of runoff reduction and sediment removal by vegetated filter strips 2004 2000-2004 Not stated Not stated Not stated not stated not stated <1 NA 20641382 Experimental investigation of runoff reduction and sediment removal by vegetated filter strips 2004 2000-2004 http://scholar.google.co.uk/scholar?q=Experimental+investigation+of+runoff+reduction+and+sediment+removal+by+vegetated+filter+strips&btnG=&hl=en&as_sdt=0%2C5 Not stated Not stated Not stated not stated not stated <1 NA Vegetated filter strip; Manipulative; Not described; Not described; Not described; Grasses; Perennial rye grass; Not described; Not described; Erosion protection Not described Not described Not stated; Strip type (dimension); Strip width (described as length); Vegetation type Not described Soil loss/retention; Sediment trapping efficiency Plot scale Not described Not described Manipulative Grasses; Perennial rye grass; Other (please specify); Creeping red fescue, birdsfoot trefoil, native riparian vegetation (wild oat, quack, fescue, dandelions, etc.); Vegetation type
20641384 Abu-Zreig (2003) Phosphorus removal in vegetated filter strips 2003 2000-2004 Canada Ontario, Canada Elora, Ontario 43.683715 -80.430543 <1 NA 20641384 Phosphorus removal in vegetated filter strips 2003 2000-2004 http://scholar.google.co.uk/scholar?q=Phosphorus+removal+in+vegetated+filter+strips&btnG=&hl=en&as_sdt=0%2C5 Canada Ontario, Canada Elora, Ontario 43.683715 -80.430543 <1 NA Vegetated filter strip; Manipulative; Not described; Not described; Not described; Grasses; Not described; Not described; Hydrological regimes; Pollution control; Nutrient cycling Not described Not described Not stated; Strip type (dimension); Filter width (described as length); Vegetation type; Perennial ryegrass (Lolium perenne), legume and creeping red fescue (Festuca rubra), bare filters, native grass species Not described Nutrients P; Phosphorus trapping efficiency; Water loss/retention; Water retention Plot scale Not described Not described Manipulative Grasses; Other (please specify); Perennial ryegrass, legume and creeping red fescue, native grass species; Vegetation type; Perennial ryegrass (Lolium perenne), legume and creeping red fescue (Festuca rubra), bare filters, native grass species