Organic farming and seminatural habitats for multifunctional agriculture: a case study in hedgerow landscapes of Brittany
Data files
Oct 24, 2024 version files 37.11 KB
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COORD.csv
939 B
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ENV.csv
6.26 KB
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INDICATORS.csv
3.63 KB
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MULTIFUNC.csv
21.07 KB
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README.md
5.21 KB
Abstract
Finding more sustainable ways to produce food is a major challenge for humanity in the face of biodiversity extinction and climate change. Consequently, research on the ability of agroecosystems to provide multiple functions is growing. In this regard, the relative importance of organic farming and landscape-scale measures for improving multifunctionality has recently been debated. We investigated the effects of the farming system (conventional vs organic) at the field scale, the total length of hedgerows in the landscape, and their interaction with the multifunctionality of 40 winter cereal fields in Brittany (France). Our multifunctionality assessment integrated 21 indicators of biodiversity conservation, nutrient cycling, soil structure, pest and disease regulation, food production, and socio-economic performance. Many indicators of biodiversity conservation, pest and disease regulation, and socio-economic performance were higher in organic than in conventional systems. However, indicators of nutrient cycling and soil structure did not improve and food production was much lower in organic systems. Total hedgerow length in the landscape had less influence than organic farming on indicators, although we observed positive interactions. Granivorous carabid abundance and semi-net margin were highest in organic fields located in well-preserved hedgerow landscapes. Our study suggests that field-scale organic farming is necessary to promote biodiversity conservation and associated ecological functioning in crop fields, whereas landscape-scale preservation of semi-natural habitats alone is likely insufficient. Preservation of hedgerows in the landscape brings additional ecological and socio-economic benefits for organic systems without compromising agricultural production. More broadly, our results call for more ambitious research into the myriad possible combinations of farming practices and agri-environmental measures at both field and landscape scales, to improve both belowground and aboveground functioning.
INDICATOR DATASET
This dataset provides the indicators collected in crop fields (see Fig. 1). Sampling was performed between April and July 2019 in winter cereal fields. Samples were collected in crop fields at least 5 m away and up to 50 m from field margins. Plant and invertebrate samples were summed per field before computing diversities and abundances. Microorganism samples were averaged per field (relative abundances) before computing diversities and proportion of fungi.
field = field ID
bacterial_diversity = sequence cluster richness of bacteria
fungal_diversity = sequence cluster richness of fungi
earthworm_diversity = species richness of earthworms
weed_diversity = species richness of weeds
carabid_diversity = species richness of carabids
soil_enzyme_activities = aggregated soil enzyme activities (PHOS, PAK, GLU, GAL, ARN, NAG, ARS) based on the qMef index (Byrnes et al, 2023)
symbio_sapro_fungi = proportion of symbio- and saprotrophic fungi (%)
earthworm_abundance = total abundance of earthworms
SOC_clay_ratio = soil organic carbon:clay ratio
C_N_ratio = soil organic carbon:nitrogen ratio
graniv_carabid_abundance = activity-density of granivorous carabids
carniv_carabid_abundance = activity-density of carnivorous carabids
staphylinid_abundance = activity-density of staphylinids
spiders_abundance = activity-density of spiders
aphid_parasitism_rate = aphid parasitism rate (number of aphid mummies / total number of aphids) (%)
weed_abundance = total coverage of weeds (%.m²-1)
aphid_abundance = total number of aphids
septoria_abundance = percentage of the leaf attacked by Septoria tritici (%)
grain_yield = field-scale grain yield (q.ha-1)
duration_interventions = cumulative duration of interventions (h.ha-1) between the harvest of the previous and current crops
semi_net_margin = semi-net margin (€.ha-1)
MULTIFUNC DATASET
This dataset provides the z-standardized indicators and AES goods, along with (unstandardized) multifunctionality indices giving equal weight to indicators or AES goods (see Fig. 1). For indicators whose lower values indicate higher levels of functionality or benefits, we inverted variables using the formula −xi + max(xi) where xi are the measures of variable i. All indicators and AES goods are z-standardized before computing multifunctionality indices.
We used a recent approach developed by Byrnes et al. 2023 based on Hill numbers (doi: 10.1111/oik.09402) to estimate AES goods from collected indicators and compute multifunctionality. This approach is equivalent to the effective number of species (e.g., Hill-Shannon index) that accounts for both the number of species and their relative abundances to quantify species diversity. Similarly, multifunctionality indices consider not only the mean of indicators or AES goods but also the relative contribution of each indicator or AES good to the total level of functioning.
field = field ID
biodiv_conservation = aggregation of diversities of bacteria, fungi, earthworms, weeds, and carabids
nutrient_cycling = aggregation of soil enzyme activities, % symbio- and saprotrophic fungi, earthworm abundance, SOC:clay ratio, C:N ratio
pest_disease_regulation = aggregation of natural enemy activity-densities, aphid parasitism rate, and (inverted) abundances of weeds, aphids, septoria
food_production = field-scale grain yield
socioeco_perf = aggregation of cumulative duration of interventions (inverted) and semi-net margin
MF_indicators = multifunctionality giving equal weight to the 21 indicators
MF_goods = multifunctionality giving equal weight to the five AES goods
ENV DATASET
This dataset provides the farming practices and landscape variables measured for each sampled field.
field = field ID
landscape = landscape ID
farming = conventional farming (CF) or organic farming (OF)
sowing_density = sowing density (kg/ha)
nb_tillage = number of tillage events between the previous and current crops
nb_ploughing = number of ploughing events between the previous and current crops
fertilization = nitrogen amount per hectare (including both mineral and organic fertilization)
herbicide = herbicide Treatment Frequency Index between the previous and current crops
TFI_tot = total Treatment Frequency Index (including herbicides, fungicides, insecticides) between the previous and current crops
field_size = focal field size (ha)
hedge_length.250 to 1000 = total hedgerow length in the landscape (m) within buffer radii of 250m to 1000m
SNH_cover.250 to 1000 = cover of seminatural habitats in the landscape (%, excluding hedgerows) within buffer radii of 250m to 1000m
grassland_cover.250 to 1000 = cover of permanent grasslands in the landscape (%) within buffer radii of 250m to 1000m
crop_div.250 to 1000 = Shannon crop diversity in the landscape within buffer radii of 250m to 1000m
OF_cover.250 to 1000 = cover of organic farming in the landscape (%) within buffer radii of 250m to 1000m
COORD DATASET
This dataset provides the spatial coordinates (decimal degrees) of each sampled field
field = field ID
x = longitude
y = latitude
We conducted the study in the southern part of the Zone Atelier Armorique, a Long-Term Socio-Ecological Research (LTSER) site in Brittany, France (47°59′35 N, 1°45′12 W). This region is characterized by dense hedgerow networks and crop-livestock farming systems. We selected 40 cereal fields, half under long-term organic farming (generally >20 years) and half under conventional farming. Crop fields were located along a gradient of total hedgerow length in the landscape. Field sampling was performed between April and July 2019 in winter cereal fields. Samples were collected in crop fields at least 5 m away and up to 50 m from field margins. In the manuscript, we describe the protocols for the measurement of soil enzyme activities and physicochemical properties, sampling of soil microorganisms, earthworms, weeds, crop disease severity, aphids and mummies, and natural enemies.