SDM current and future projections for alternate CAM-based cultivation in SSA
Data files
Aug 30, 2023 version files 131.46 KB
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CAM_SDM_example.R
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README.md
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species_response_curve_data.csv
Abstract
Globally we are facing an emerging climate crisis, with impacts to be notably felt in semi-arid regions across the world. Cultivation of drought-adapted succulent plants has been suggested as a nature-based solution that could: (i) reduce land degradation, (ii) increase agricultural diversification and provide both economic and environmentally sustainable income through derived bioproducts and bioenergy, (iii) help mitigate atmospheric CO2 emissions, and (iv) increase soil sequestration of CO2. Identifying where succulents can grow and thrive is an important pre-requisite for the advent of a sustainable alternative ‘bio-economy’. Here we first explore the viability of succulent cultivation in Africa under future climate projections to 2100 using species distribution modelling to identify climatic parameters of greatest importance and regions of environmental suitability. Minimum temperatures and temperature variability are shown to be key controls in defining the theoretical distribution of three succulent species explored, and under both current and future SSP5 8.5 projections, the conditions required for growth of at least one of the species is met in most parts of sub-Saharan Africa. These results are supplemented with an analysis of potentially available land for alternative succulent crop cultivation. In total, up to 1.5 billion hectares could be considered ecophysiologically suitable and available for succulent cultivation due to projected declines in rangeland biomass and yields of traditional crops. These findings may serve to highlight new opportunities for farmers, governments, and key stakeholders in the agriculture and energy sectors to invest in sustainable bioeconomic alternatives that deliver on environmental, social and economic goals.
README
Title: SDM current and future projections for alternate CAM-based cultivation in SSA
Description:
(1) The uploaded R script allows the user to recreate the species distribution models and projections for the species studied in the article: Buckland et al. (2023) Drought-tolerant succulent plants as an alternative crop under future global warming scenarios in sub-Saharan Africa. GCB Bioenergy.
(2) The uploaded csv file provides the 2D response curve data associated with each of the three species (Opuntia ficus-indica, Euphorbia tirucalli, Portulacaria afra) and each explanatory variable. Data is organised by species, with 'expl.nam' referring to bioclim variable, 'expl.var' refers to corresponding value for each bioclim variable, and 'pred.val' gives the projected likelihood of species occurrence according to the respective explanatory variable.
Instructions:
(1) Instructions for usage of the R script are included in the code and ask the user to insert directory information and species choice as desired by the user. Models are trained on near historical (1970 – 2000 AD) environmental data downloaded from the WorldClim catalogue via script in the code, allowing the user to download the bioclim variables of interest directly. Future bioclim datasets that can be used to project the trained SDMs forward into future climatic conditions should be downloaded from the WorldClim catalogue https://www.worldclim.org/data/bioclim.html (Fick & Hijmans, 2017).
Further data:
(1) Supplementary data associated with the manuscript is available open access online: http://doi.org/10.1111/gcbb.13095
Supplementary data provides: (i) details on all the SDM datasets and parameters used in the R script; (ii) results from the 3D response curve analysis; (iii) available land analysis results presented in million hectares for each SSP scenario.
(2) Additional data used in the manuscript was derived from the following authors and groups: Dr. Cecile Godde (CSIRO), Dr. Vassilis Daioglou (PBL Netherlands Environmental Assessment Agency) and the AgMIP research group.
Usage notes
The uploaded R script is to be used in R Studio and the libraries required are detailed within the script.