Dataset Information

Global inventory of suitable, cultivable and available cropland under different scenarios and policies.

ABSTRACT: Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs.

SUBMITTER: Schneider JM

PROVIDER: S-EPMC9420104 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Global inventory of suitable, cultivable and available cropland under different scenarios and policies.

Schneider Julia M JM Zabel Florian F Mauser Wolfram W

Scientific data 20220827 1

Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggre ...[more]

PMID: 36030257

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Project description:The pressure on land resources continuously increases not only with the rising demand for agricultural commodities, but also with the growing need for action on global challenges, such as biodiversity loss or climate change, where land plays a crucial role. Land saving as a strategy, where agricultural productivity is increased to allow a reduction of required cropland while sustaining production volumes and meeting demand, could address this trade-off. With our interdisciplinary model-based study, we globally assess regional potentials of land saving and analyze resulting effects on agricultural production, prices and trade. Thereby, different land saving strategies are investigated that (1) minimize required cropland (2) minimize spatial marginalization induced by land saving and (3) maximize the attainable profit. We find that current cropland requirements could be reduced between 37% and 48%, depending on the applied land saving strategy. The generally more efficient use of land would cause crop prices to fall in all regions, but also trigger an increase in global agricultural production of 2.8%. While largest land saving potentials occur in regions with high yield gaps, the impacts on prices and production are strongest in highly populated regions with already high pressure on land. Global crop prices and trade affect regional impacts of land saving on agricultural markets and can displace effects to spatially distant regions. Our results point out the importance of investigating the potentials and effects of land saving in the context of global markets within an integrative, global framework. The resulting land saving potentials can moreover reframe debates on global potentials for afforestation and carbon sequestration, as well as on how to reconcile agricultural production and biodiversity conservation and thus contribute to approaching central goals of the 21st century, addressed for example in the Sustainable Development Goals, the Paris Agreement or the post-2020 global biodiversity framework.

Project description:BackgroundEstimations of causal effects from observational data are subject to various sources of bias. One method for adjusting for the residual biases in the estimation of treatment effects is through the use of negative control outcomes, which are outcomes not believed to be affected by the treatment of interest. The empirical calibration procedure is a technique that uses negative control outcomes to calibrate p-values. An extension of this technique calibrates the coverage of the 95% confidence interval of a treatment effect estimate by using negative control outcomes as well as positive control outcomes, which are outcomes for which the treatment of interest has known effects. Although empirical calibration has been used in several large observational studies, there is no systematic examination of its effect under different bias scenarios.MethodsThe effect of empirical calibration of confidence intervals was analyzed using simulated datasets with known treatment effects. The simulations consisted of binary treatment and binary outcome, with biases resulting from unmeasured confounder, model misspecification, measurement error, and lack of positivity. The performance of the empirical calibration was evaluated by determining the change in the coverage of the confidence interval and the bias in the treatment effect estimate.ResultsEmpirical calibration increased coverage of the 95% confidence interval of the treatment effect estimate under most bias scenarios but was inconsistent in adjusting the bias in the treatment effect estimate. Empirical calibration of confidence intervals was most effective when adjusting for the unmeasured confounding bias. Suitable negative controls had a large impact on the adjustment made by empirical calibration, but small improvements in the coverage of the outcome of interest were also observable when using unsuitable negative controls.ConclusionsThis work adds evidence to the efficacy of empirical calibration of the confidence intervals in observational studies. Calibration of confidence intervals is most effective where there are biases due to unmeasured confounding. Further research is needed on the selection of suitable negative controls.

Dataset Information

Global inventory of suitable, cultivable and available cropland under different scenarios and policies.

Publications

Global inventory of suitable, cultivable and available cropland under different scenarios and policies.

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