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A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050

TLDR
In this paper, the authors conducted a systematic literature review and meta-analysis to assess the range of future global food security projections to 2050 and found that the total global food demand was expected to increase by 35% to 56% between 2010 and 2050, while population at risk of hunger is expected to change by −91% to +8%.
Abstract
Quantified global scenarios and projections are used to assess long-term future global food security under a range of socio-economic and climate change scenarios. Here, we conducted a systematic literature review and meta-analysis to assess the range of future global food security projections to 2050. We reviewed 57 global food security projection and quantitative scenario studies that have been published in the past two decades and discussed the methods, underlying drivers, indicators and projections. Across five representative scenarios that span divergent but plausible socio-economic futures, the total global food demand is expected to increase by 35% to 56% between 2010 and 2050, while population at risk of hunger is expected to change by −91% to +8% over the same period. If climate change is taken into account, the ranges change slightly (+30% to +62% for total food demand and −91% to +30% for population at risk of hunger) but with no statistical differences overall. The results of our review can be used to benchmark new global food security projections and quantitative scenario studies and inform policy analysis and the public debate on the future of food.

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Chitosan Nanoparticles-Based Ionic Gelation Method: A Promising Candidate for Plant Disease Management

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Optimizing nitrogen fertilizer use for more grain and less pollution

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Agronomic Practices to Increase the Yield and Quality of Common Bean (Phaseolus vulgaris L.): A Systematic Review

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References
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Journal ArticleDOI

Fitting Linear Mixed-Effects Models Using lme4

TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
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Mixed Effects Models and Extensions in Ecology with R

TL;DR: In this paper, the authors apply additive mixed modelling on phyoplankton time series data and show that the additive model can be used to estimate the age distribution of small cetaceans.
Journal ArticleDOI

lmerTest Package: Tests in Linear Mixed Effects Models

TL;DR: The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects, and implementing the Satterthwaite's method for approximating degrees of freedom for the t and F tests.
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