DOI: 10.5061/dryad.hmgqnk9nf
title: Food production and biodiversity are not incompatible in temperate heterogeneous agricultural landscapes

description: We need landscape-scale approaches to design and manage agro-ecosystems
 that can sustain both agricultural production and biodiversity
 conservation. In this study, yield figures provided by 299 farmers served
 to quantify the energy-equivalents of food production across different
 crops in 49 1-km2 landscapes. Our results show that the relationship
 between bird diversity and food energy production depends on the
 proportion of farmland within the landscape, with a negative correlation
 observed in agriculture dominated landscapes (≥ 64–74% farmland). In
 contrast, neither typical farmland birds nor butterflies showed any
 significant relationship with total food energy production. We conclude
 that in European temperate regions consisting of small-scale, mixed
 farming systems (arable and livestock production), productivity and
 biodiversity conservation may not be purely antagonistic, particularly
 when (semi-)natural habitats make up a large fraction of the landscape (≥
 20%).
description: The yield data was collected during farmer interviews and
 information on crop area per landscape was obtained from cantonal
 agricultural surveys and crop mapping. The data on bird and butterflies
 was collected on the field by the Swiss Biodiversity Monitoring. The yield
 dataset was completed using multiple imputation. After post processing the
 completed data set was used to calculate the (mean) yield per crop per
 landscape, which was consecutively converted into food energy (given as
 metabolizable energy for human consumption), summed up and scaled to
 landscape level. Generalized linear models (GLM) were used to describe the
 relationship between biodiversity, total produced food energy and farmland
 area per landscape. Species richness, abundance and Pielou’s evenness
 index of birds and butterflies were used as response variables. Food
 energy production per landscape in gigajoule (GJ) and the amount of
 farmland in hectare (ha) were included as explanatory variables.
description: Excell and R
description: # Food production and biodiversity are not incompatible in temperate
 heterogeneous agricultural landscapes # Authors: Silvia Zingg (a), Jan
 Grenz (a), Jean-Yves Humbert (b) --- ## Summary In this study, yield
 figures provided by 299 farmers served to quantify the energy-equivalents
 of food production across different crops in 49 1-km2 landscapes. The
 results show that the relationship between bird diversity and food energy
 production depends on the proportion of farmland within the landscape,
 with a negative correlation observed in agriculture dominated landscapes
 (≥ 64–74% farmland). In contrast, neither typical farmland birds nor
 butterflies showed any significant relationship with total food energy
 production. ## Description of the data and file structure We have
 submitted our R Script (Zingg_2023_R_Script_Imputation_Analyse_Figures.R),
 the raw data (Zingg_2023_RawData.csv), the post-processed imputed dataset
 (Zingg_2023_Completed_data_post_processed.csv), the dataset containing the
 crop areas from the agricultural survey and crop mapping
 (Zingg_2023_Scaled_crop_area.csv), the dataset which was used for
 generalized linear models (GLM), combining he estimated food energy values
 and the biodiversity indicators per landscape
 (Zingg_2023_Data_for_modelling.csv). And the dataset that was used for the
 single species analysis
 (Zingg_2023_Data_for_modelling_Mean_Energy_Single_Species.csv).  #
 Zingg\_2023\_R\_Script\_Imputation\_Analyse\_Figures.R The R Script
 contains the code for the statistical analysis including the multiple
 imputation, the modelling and the main figures from the manuscript. All
 data sets needed are available as csv from DRYAD (see description below).
 # Zingg\_2023\_RawData.csv The Raw data contains information on all
 agricultural fields intersecting the 49 landscape squares. Information was
 obtained from farmer interviews (= Farmer) and from cantonal agricultural
 surveys, or crop mapping (= Admin). * *KoordID* = Landscape ID * *Kanton*
 = Name of the Swiss canton: AG = Aargau, BE = Bern, LU = Luzern, FR =
 Fribourg, VD = Vaud, SG = St. Gallen, TG = Thurgau * *Year* = Year of
 observation (2012 to 2016). Data from farmers is available for three
 consecutive years, information from the cantonal agricultural survey or
 crop mapping is available for one year. * *Crop* = Crop name (42
 categories). Abbreviations: Ext. = Extensively managed, Int. = Intensively
 managed, BPA = Biodiversity promotion areas, Orn. = Ornamental *
 *Crop_Category* = Crops were grouped into 12 categories: 1) Cereals =
 Wheat, Barley, Oat, Rye, Sorghum, Spelt, Triticale and other cereals 2)
 Oilseed crops = Rapeseed, Soja and Sunflower 3) Leguminous_crops = Field
 bean, Protein pea and other Leguminous 4) Vegetables = Vegetables,
 Asparagus, other crops in greenhouses 5) Ext_grassland = Extensively
 managed meadows and pastures 6) Int_grassland = Intensively managed
 meadows and pastures and temporary grasslands (ley) 7) Non_edible =
 Wildflower strips or other non productive Biodiversity promotion areas
 (BPA) on arable land, Tobacco and other non-productive areas (e.g.
 christmas trees) 8) Fruits_Berries = Berries, Apple, Stone fruit, Pear and
 other orchards, Vine 9) Grain_maize = Grain maize, 10) Silage_maize =
 Silage maize, 11) Potato = Potatoes 12) Sugar_beet = Sugar and Fodder beet
 * *Label* = Form of production: Conv = Conventional production, Extenso =
 Extenso production (reduced pesticide application) * *Altitude* =
 Elevation in meters above sea level (m) * *Area_ha* = Field size in
 hectares (ha) * *Yield_dt* = Crop yield in decitons (dt), NA indicates the
 yield was not known. * *Use* = Number of cuts or grazing events per year
 (only given for grasslands). NA indicates the use was not known. *
 *Source* = Source of information: Farmer = information on agricultural
 fields and yields collected during farmer interviews (available for three
 years), Admin = information on agricultural fields from the cantonal
 agricultural survey or crop mapping (available for one year). #
 Zingg\_2023\_Completed\_data\_post\_processed.csv We used Multiple
 Imputation to impute missing yield values. Multiple imputation creates
 several (here n = 50) “complete” datasets by replacing missing values with
 plausible estimates. Each imputation represents a different set of imputed
 values. This file contains all 50 imputed and post-processed datasets.
 Post processing:  Because Multiple Imputation can generate implausible
 values (e.g. 200 dt/ha for wheat), we restricted the yield values after
 the imputation, to the 1st and 3rd quartile of real yield values given by
 farmers. In addition non-edible crops, such as ornamental plants (e.g.
 Christmas trees) or non-productive Biodiversity promotion areas such as
 wildflower strips, or hedgerows were attributed a yield value of 0 and the
 yield figures for vegetables have been doubled (to account for two
 harvests per year). *Reproducibility: Multiple imputation was used to
 create the dataset
 "Zingg_2023_Completed_data_post_processed.csv ". The imputation
 process involves randomness (e.g., random draws from distributions) and
 variability. As a result, even if the same analysis is repeated, the
 imputed values may differ slightly due to this inherent variability.* *
 *.imp* = Number of imputation (n = 50). The rawdata set was imputed 50
 times. Every imputation contains 7727 entries (number of rows from the
 rawdata set) * *.id* = Row ID from the imputation (arable crops n = 4792
 and grassland crops n=2034). Arable and grassland crops were were
 separately imputed and the datasets merged afterwards. *
 *KoordID* = Landscape ID * *Kanton* = Name of the Swiss canton. AG =
 Aargau, BE = Bern, LU = Luzern, FR = Fribourg, VD = Vaud, SG = St. Gallen,
 TG = Thurgau * *Year* = Year of observation (2012 to 2016). Data from
 farmers is available for three consecutive years, information from the
 cantonal agricultural survey or crop mapping is available for one year. *
 *Crop* = Crop name (42 categories). Abbreviations: Ext. = Extensively
 managed, Int. = Intensively managed, BPA = Biodiversity promotion areas,
 Orn. = Ornamental * *Crop_Category* = Crops were grouped into 12
 categories: 1) Cereals = Wheat, Barley, Oat, Rye, Sorghum, Spelt,
 Triticale and other cereals 2) Oilseed crops = Rapeseed, Soja and
 Sunflower 3) Leguminous_crops = Field bean, Protein pea and other
 Leguminous 4) Vegetables = Vegetables, Asparagus, other crops in
 greenhouses 5) Ext_grassland = Extensively managed meadows and pastures 6)
 Int_grassland = Intensively managed meadows and pastures and temporary
 grasslands (ley) 7) Non_edible = Wildflower strips on arable land (BPA),
 Tobacco and other non-productive areas (e.g. christmas trees) 8)
 Fruits_Berries = Berries, Apple, Stone fruit, Pear and other orchards,
 Vine 9) Grain_maize = Grain maize, 10) Silage_maize = Silage maize, 11)
 Potato = Potatoes 12) Sugar_beet = Sugar and Fodder beet * *Label* = Form
 of production: Conv = Conventional production, Extenso = Extenso
 production (reduced pesticide application) * *Altitude* = Elevation in
 meters above sea level (m) * *Area_ha* = Field size in hectares (ha) *
 *Yield_dt* = Crop yield in decitons (dt), NA indicates the yield was not
 known. * *Use* = Number of cuts or grazing events per year (only given for
 grasslands). NA indicates the use was not known. * *Source* = Source of
 information: Farmer = information on agricultural fields and yields
 collected during farmer interviews (available for three years), Admin =
 information on agricultural fields from the cantonal agricultural survey
 or crop mapping (available for one year). #
 Zingg\_2023\_Scaled\_crop\_area.csv To calculate the total produced food
 energy, the crop area per landscape (obtained from either the agricultural
 survey or crop mapping) must be adjusted according to the total amount of
 farmland within the landscape square. Notably, that the sum of all crop
 areas corresponds to the overall farmland area within that same landscape
 square. Therefore the scaled crop area (*Scaled_Area_ha*) was used to
 calculate the total produced food energy (see R Script). * *KoordID* =
 Landscape ID * *Crop_Category* = Crops were grouped into 12 categories: 1)
 Cereals = Wheat, Barley, Oat, Rye, Sorghum, Spelt, Triticale and other
 cereals 2) Oilseed crops = Rapeseed, Soja and Sunflower 3)
 Leguminous_crops = Field bean, Protein pea and other Leguminous 4)
 Vegetables = Vegetables, Asparagus, other crops in greenhouses 5)
 Ext_grassland = Extensively managed meadows and pastures 6) Int_grassland
 = Intensively managed meadows and pastures and temporary grasslands (ley)
 7) Non_edible = Wildflower strips on arable land (BPA), Tobacco and other
 non-productive areas (e.g. christmas trees) 8) Fruits_Berries = Berries,
 Apple, Stone fruit, Pear and other orchards, Vine 9) Grain_maize = Grain
 maize, 10) Silage_maize = Silage maize, 11) Potato = Potatoes 12)
 Sugar_beet = Sugar and Fodder beet * *Area_ha* = Crop area per landscape
 (in hectares). The total area of all crops per landscape can exceed the
 total area of the farmland within the landscape square (because all
 intersecting agricultural fields were taken into account, also if
 partially outside the landscape square). * *Scale_fac* = Factor to scale
 the crop area to the total amount of farmland within the landscape square.
 * *Scaled_Area_ha* = Crop area per landscape (in hectares) scaled to the
 amount of farmland within the landscape square. The sum of the crop area
 equals the amount of farmland within the landscape square. #
 Zingg\_2023\_Data\_for\_modelling.csv For each of the 50 completed
 datasets, we calculated the mean crop yield per ha, averaging over all
 three sampling years and fields, within each landscape. Using this, and
 the scaled crop areas from the agricultural survey or crop mapping we
 calculated the total food energy production (in GJ of metabolizable energy
 ME per year), in each landscape for each imputed dataset. This dataset set
 contains the values for the the total produced food energy and the
 biodiversity indicators: Species Richness (SP), Abundance (Abun) and
 Pielous's evenness (Even) for birds and butterflies. *KoordID* =
 Landscape ID *.imp* = Number of imputation (n = 50).
 *Scenario_1_Total_ME_GJ* = Total produced food energy per landscape (given
 as metabolizable energy for human consumption in Gigajoule) for scenario 1
 (all food energy production directly consumed by humans)
 *Scenario_2_Total_ME_GJ* = Total produced food energy per landscape (given
 as metabolizable energy for human consumption in Gigajoule) for scenario 2
 (part of the production used as animal feed to produce meat) *UAA* = Area
 of farmland in hectares (ha) *Bird_Sp_All* = Total bird species richness
 *Bird_Sp_K* = Farmland bird species richness *Bird_Abun_All* = Total bird
 abundance *Bird_Abun_K* = Farmland bird abundance *Even_bird* = Bird
 evenness *Butt_Sp_All* = Butterfly species richness *Butt_Abun_All* =
 Butterfly abundance *Even_butt* = Butterfly evenness #
 Zingg\_2023\_Data\_for\_modelling\_Mean\_Energy\_Single\_Species.csv The
 dataset contains information on the abundance (number of individuals) of
 different bird and butterfly species per landscape.
 "Zingg_2023_Data_for_modelling_Mean_Energy_Single_Species.csv"
 was used to generate the single species curves in the Supp. Mat. *KoordID*
 = Landscape ID *Mean_Scenario_1* = Total produced food energy per
 landscape (given as metabolizable energy for human consumption in
 Gigajoule) for scenario 1 (all food energy production directly consumed by
 humans) - Mean value from all imputed datasets *Mean_Scenario_2* = Total
 produced food energy per landscape (given as metabolizable energy for
 human consumption in Gigajoule) for scenario 2 (part of the production
 used as animal feed to produce meat - Mean value from all imputed datasets
 *UAA* = Area of farmland in hectares (ha) Defined by the Latin names, the
 columns contain data on the abundance of 159 different bird and butterfly
 species. ## Sharing/Access information Links to other publicly accessible
 locations of the data: * NA Data was derived from the following sources: *
 See manuscript ## Code/Software We used R Version 4.2.3. All packages and
 functions are given in the R Script.