DOI: 10.25349/d9ss44
title: Projections of Future Cropland Abandonment: Impacts to Biodiversity and Carbon Sequestration

description: Cropland abandonment occurs when ecological and economic shifts force
 farmers to retire lands formerly used to grow food. Without proper
 management, cropland abandonment can lead to soil erosion, inhibit
 nutrient cycling, increase wildfire risk, and threaten food security.
 However, if managed strategically, restoration of these abandoned lands
 can safely store climate-warming greenhouse gasses and enhance
 biodiversity. While historical cropland abandonment is well documented,
 patterns of future abandonment are not well-understood. The first stage of
 our project examines where croplands are projected to be abandoned
 globally under several future climate change scenarios, and where
 abandonment overlaps with important areas for biodiversity and climate
 change mitigation. We find that cropland abandonment will be widespread in
 2050, but the amount and location of abandoned croplands vary by climate
 scenario. Projected abandoned croplands consistently overlap with areas
 important to biodiversity and carbon sequestration, highlighting valuable
 conservation opportunities.  The next stage of our analysis
 focuses on Brazil, an essential region for global climate change
 mitigation and biodiversity preservation. Given a conservation budget, our
 analysis identified regions of Brazil where projected abandoned lands can
 be managed to maximize benefits for biodiversity and carbon sequestration.
 Our findings indicate that while patterns of cropland abandonment vary by
 region, conservation goals can be met by leveraging abandoned lands with
 existing policy mechanisms. Through our research and analysis, we hope to
 inform effective policies and management strategies that balance the need
 for agricultural production with climate and biodiversity goals.
description: Rasters of abandoned cropland (globally and in Brazil) were
 generated by processing raw LULC data from Chen et al., 2021 between the
 years 2015 and 2050. Abandoned cropland was defined as any area currently
 classified as cropland that has a different land use classification in
 future projections, with the exception of land that was urbanized.
 Urbanized cropland was excluded from the analysis as these areas could not
 support our project goal of evaluating potential areas for natural
 restoration. Areas of global cropland abandonment were found using a
 series of raster calculations between future (2050) and current (2015)
 LULC data. Within Brazil, any area of cropland projected to be abandoned
 between 2020-2050 (and not urbanized) served as the input to our spatial
 prioritization.  After determining where Brazilian
 cropland is projected to be abandoned across different climate scenarios
 (outlined above), we determined which should be prioritized for
 restoration using the prioritizr package (version 7.2.2) in R. This
 software uses mixed integer linear programming to flexibily build and
 solve spatial planning problems. Locations of abandoned cropland served as
 available planning units for restoration efforts. Along with these
 planning units, the prioritization software requires the input of the
 features (biodiversity and carbon) and cost (restoration estimates) to be
 evaluated for each planning unit, specific targets for how much of each
 feature should be represented in the solution, and a primary objective for
 solving the problem. A separate problem was formulated for each SSP
 scenario and their associated planning units and features
 data.   Each problem
 utilized a minimize shortfall objective, in which the solution minimizes
 the overall target shortfall across all features while not exceeding a
 specified budget. Biodiversity and carbon were weighted equally, and
 relative targets were set to 0.5 each (50% feature conservation). Each SSP
 problem was run under both low and high-budget scenarios. After
 formulation, each problem was solved using Gurobi Optimizer (version
 9.5.2) with a 0.05 “gap to optimality”; this value represents a 5%
 accepted deviance from the optimal solution, reducing computational
 requirements. For greater user flexibility in the Shiny app, additional
 problems and solutions were created weighting biodiversity and carbon
 differently. These solutions were not evaluated in the final
 report. Code for running these analyses are publicly
 available on the linked GitHub repositories.