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Open AccessJournal ArticleDOI

Heterogeneous global crop yield response to biochar: a meta-regression analysis

TLDR
In this article, the authors employ meta-analytical, missing data, and semiparametric statistical methods to explain heterogeneity in crop yield responses across different soils, biochars, and agricultural management factors, and then estimate potential changes in yield across different soil environments globally.
Abstract
Biochar may contribute to climate change mitigation at negative cost by sequestering photosynthetically fixed carbon in soil while increasing crop yields. The magnitude of biochar's potential in this regard will depend on crop yield benefits, which have not been well-characterized across different soils and biochars. Using data from 84 studies, we employ meta-analytical, missing data, and semiparametric statistical methods to explain heterogeneity in crop yield responses across different soils, biochars, and agricultural management factors, and then estimate potential changes in yield across different soil environments globally. We find that soil cation exchange capacity and organic carbon were strong predictors of yield response, with low cation exchange and low carbon associated with positive response. We also find that yield response increases over time since initial application, compared to non-biochar controls. High reported soil clay content and low soil pH were weaker predictors of higher yield response. No biochar parameters in our dataset—biochar pH, percentage carbon content, or temperature of pyrolysis—were significant predictors of yield impacts. Projecting our fitted model onto a global soil database, we find the largest potential increases in areas with highly weathered soils, such as those characterizing much of the humid tropics. Richer soils characterizing much of the world's important agricultural areas appear to be less likely to benefit from biochar.

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Citations
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Journal ArticleDOI

Phytolith‐rich biochar: a potential Si fertilizer in desilicated soils

TL;DR: In this article, the authors evaluated the global potential of biochar produced from major crop residues and manures in terms of phytogenic Si (PhSi) supply and concluded that using phytolithic biochar as a Si fertilizer offers undeniable potential to mitigate desilication and to enhance Si ecological services.
Journal ArticleDOI

Biochar by design

TL;DR: In this article, environmental and social circumstances should both be considered for biochar's application, in order to get the most benefit from its application, environmental conditions should be taken into account.
Journal ArticleDOI

Influence of biochars on the accessibility of organochlorine pesticides and microbial community in contaminated soils.

TL;DR: The results indicated that biochar-amendments had strong effects upon OCP accessibility over time and can act as super sorbent, and the findings from total phospholipid acid (PLFA) and Illumina next-generation sequencing revealed that the incorporation of biochar have altered the soil microbial community structure over time.
Journal ArticleDOI

Root development of non-accumulating and hyperaccumulating plants in metal-contaminated soils amended with biochar

TL;DR: Biochar can have antagonist effects on plant metal uptake by decreasing metal availability, on one hand, and by increasing root surface and inducing root proliferation, on the other hand.
Journal ArticleDOI

Biochar enhances nut quality of Torreya grandis and soil fertility under simulated nitrogen deposition

TL;DR: It is suggested, for the first time, that the ‘win-win’ can be achieved, both nut quality of T. grandis and soil fertility can be improved by biochar application to soils suffering from N deposition.
References
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Book

Statistical Analysis with Missing Data

TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI

Generalized Additive Models.

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Data Analysis Using Regression and Multilevel/Hierarchical Models

TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.
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Generalized Additive Models: An Introduction with R, Second Edition

Simon N Wood
TL;DR: In this article, a simple linear model is proposed to describe the geometry of linear models, and a general linear model specification in R is presented. But the theory of linear model theory is not discussed.
Journal ArticleDOI

Multiple imputation using chained equations: Issues and guidance for practice

TL;DR: The principles of the method and how to impute categorical and quantitative variables, including skewed variables, are described and shown and the practical analysis of multiply imputed data is described, including model building and model checking.
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