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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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The promise of biochar: From lab experiment to national scale impacts

TL;DR: In this paper, the authors evaluated the effect of biochar presence on soil and crop in various spatial scales ranging from lab experiments to regional scale simulations, and they used an incubated experiment with three biochar application rates (0, 3% and 6%), two application methods and three replications.
Dissertation

Effects of Biochar Application on Soil Fertility and Pearl Millet (Pennisetum glaucum L.) Yield

TL;DR: Evaluated biochars made from peanut shell and mixed pine wood on soil nutrients and pearl millet yields in Blacksburg (VA, USA) for two growing seasons and in Ndoff (Senegal), application of biochar did not reduce salinity nor improve soil fertility.
Journal ArticleDOI

Biochar and Fertilization Effects on Weed Incidence in Winter Wheat

TL;DR: In this article, the influence of biochar and mineral fertilizer on weed infestation and winter wheat yield was investigated in a split-plot where biochar (B) was the main factor and fertilization was the sub factor.
Journal ArticleDOI

The impact of corncob biochar and poultry litter on pepper (Capsicum annuum L.) growth and chemical properties of a silty-clay soil

TL;DR: In this article , the authors evaluated the effects of biochar derived from corncob and poultry litter on growth of red pepper (Capsicum annuum L.) and some chemical properties of a silty clay soil.
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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