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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

Plant growth responses to biochar addition: an Australian soils perspective

TL;DR: In this article, the use of biochar as an agricultural amendment has attracted much attention owing to its potential to improve soil condition and plant growth; however, production outcomes are often uncertain.
Journal ArticleDOI

Effect of biochar amendment on compost organic matter composition following aerobic composting of manure.

TL;DR: It is suggested that biochars do not alter the carbon speciation in compost organic matter under conditions optimized for aerobic decomposition of compost feedstock, and might be an attractive strategy to produce a sustainable, slow release fertilizer.
Journal ArticleDOI

Pyrogenic carbon capture and storage

TL;DR: In this paper, the authors show that pyrolytic carbon capture and storage (PyCCS) can achieve carbon sequestration efficiencies of >70%, which is shown to be an important threshold to allow PyCCS to become a relevant negative emission technology.
Journal ArticleDOI

Soil and greenhouse gas responses to biochar additions in a temperate hardwood forest

TL;DR: In this paper, the authors investigated short-term impacts of biochar application on soil nutrient supply and greenhouse gas fluxes as compared to phosphorus fertilization, data were collected over the first year after treatment application; linear mixed models were used to analyze data.
Journal ArticleDOI

Effect of Biochar on Soil Greenhouse Gas Emissions at the Laboratory and Field Scales

TL;DR: In this article, the authors investigated the impact of a mixed wood gasification biochar on CO2 and N2O emissions from loess-derived soils using: (1) controlled laboratory incubations at three moisture (27, 31 and 35%) and three temperature (10, 20 and 30 °C) levels and (2) a field study with four cropping systems (continuous corn, switchgrass, low diversity grass mix and high diversity grass-forb mix).
References
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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

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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

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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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