Data from: Impact of crop type on biodiversity globally
Data files
Dec 10, 2024 version files 1.41 MB
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README.md
2.15 KB
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Total_dataset_v2.xls
1.41 MB
Abstract
The negative impact of agricultural land on biodiversity is widely recognized. However, there remains a knowledge gap regarding the role of different crop types in maintaining biodiversity within the agricultural landscape. By extracting biodiversity data from global datasets and classifying different crop types, we quantified the contribution of different crop types to biodiversity. Our results indicate that biodiversity levels vary widely among crop types. We found a general loss of biodiversity when natural vegetation is converted to agricultural land, and highest losses in fiber crops, cereals and oil crops, and least in other crops (such as coffee or cocoa) and in mixed crops. In general, perennial crops retain more biodiversity than annual crops. Losses of biodiversity can be mitigated through mixed cropping of multiple crop types, especially by combining annual and perennial crops. The negative impact of converting natural vegetation to agriculture is greater in tropical than in non-tropical areas, and hence, the import of commodities from these biodiversity-rich regions may be particularly detrimental. Given the ongoing increase in biodiversity losses from global intensification and expansion of agricultural land, maintaining or restoring natural vegetation, rating the crop-type specific biodiversity, diversifying crops, and preferring perennial over annual crops, particularly in the tropics, need to be better considered and implemented in global agri-environmental schemes.
README: Impact of crop type on biodiversity globally
https://doi.org/10.5061/dryad.sn02v6xfr
Description of the data and file structure
The data are extracted from PREDICTS dataset (https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1).
Data is extracted from PREDICTS database (https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1). Detail information can be found in method section or reference (Hudson et al., 2014) .
Each row represents data from a site, with each site recording the information of“SS”, "SSB","SSBS","Longitude","Latitude" ,"Predominant_land_use" , "Class" , "Species_richness", "Total_abundance","logAbun" ,"Group2","Group1".
Column "SS": Concatenation of Source_ID (individual published papers or datasets) and Study_number (data within a source that were sampled with the same sampling methods and spanning a contained geographical area).
Column "SSB": Concatenation of Source_ID, Study_number and Block (distinct spatial clusters of sampled locations within a study).
Column "SSBS": Concatenation of Source_ID, Study_number, Block and Site_number (the sampling locations within a block).
Column "Longitude" and "Latitude" : the coordinates of sites.
Column "Predominant_land_use" : Three class variables including Primary vegetation, cropland, planation forest. (It should be noted that this land-use information comes from the classification of PREDICTS database).
Column "Class" : Three class variables including Insecta, Aves, Arachnida.
Column "Species_richness" : the total number of species sampled at a site.
Column "Total_abundance" : total number of individuals across all species sampled at a site.
Column "logAbun" :Transformed from Total_abundance using log(x+1).
Column "Group2" : Crop lifecycle was classified as annual, perennial, or mixed.
Column "Group1" : Crop types were grouped into cereals, fruit, fiber, oil crops, roots & tubers, vegetables & melons, sugar crops, forage, pulses, other, and mixed.