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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

Wheat growth and yield responses to biochar addition under Mediterranean climate conditions

TL;DR: In this article, the effects of the addition of a slow pyrolysis biochar (produced from olive-tree pruning) to a vertisol were studied in a field experiment during one wheat (Triticum durum L.) growing season.
Journal ArticleDOI

Emissions and Char Quality of Flame-Curtain "Kon Tiki" Kilns for Farmer-Scale Charcoal/Biochar Production

TL;DR: The flame curtain kilns represent a promising possibility for sustainable rural biochar production with benefits such as high quality biochar, low emission, no need for start-up fuel, fast pyrolysis time and, importantly, easy and cheap construction and operation.
Journal ArticleDOI

Optimal bioenergy power generation for climate change mitigation with or without carbon sequestration.

TL;DR: It is shown that despite BECCS offering twice the carbon sequestration and bioenergy per unit biomass, BEBCS may allow earlier deployment of CDR at lower carbon prices when long-term improvements in soil fertility offset biochar production costs.
Journal ArticleDOI

Plant growth-promoting effects of Hartmannibacter diazotrophicus on summer barley (Hordeum vulgare L.) under salt stress

TL;DR: The capability of strain E19T to colonize barley roots under salt stress conditions was revealed with a specific designed fluorescence in situ hybridization (FISH) probe, and indicates that the mode of action is based on ACC-deaminase production.
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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