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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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Mixed application of biochar, maize straw, and nitrogen can improve organic carbon fractions and available nutrients of a sandy soil

TL;DR: In this paper , three rates of biochar applied at 0 (BC), 30 (BC3), and 60 (BC6), two rates of maize straw (S), and two rates (0 and 0.1) of nitrogen (N) fertilizer (ammonium bicarbonate).
Journal ArticleDOI

Effect of manure-based biochar amendment on soil chemical properties, total enzymic activities and tomato growth performance in acidic soil under controlled conditions

TL;DR: In this paper , the authors evaluated the effect of biochar on tomato production under controlled conditions using a complete randomized block design (CRD) with five replications of four treatments: T0 (0), T1, T2, T3 and T4.

Biochar's effect on soil nitrous oxide emissions from a maize field with lime adjusted pH treatment

TL;DR: In this article, the authors present a survey of the literature related to this topic: https://www.saliency.org.au/content/content-related-articles.html
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.

Book

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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