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Journal ArticleDOI

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

TL;DR: 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.
Citations
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Journal Article
TL;DR: In this paper, the authors investigated the fate of BC applied to a savanna Oxisol in Colombia at rates of 0, 11.6, 23.2 and 116.1 t BC ha -1, as well as its effect on non-BC soil organic C.
Abstract: Black carbon (BC) is an important pool of the global C cycle, because it cycles much more slowly than others and may even be managed for C sequestration. Using stable isotope techniques, we investigated the fate of BC applied to a savanna Oxisol in Colombia at rates of 0, 11.6, 23.2 and 116.1 t BC ha -1 , as well as its effect on non-BC soil organic C. During the rainy seasons of 2005 and 2006, soil respiration was measured using soda lime traps, particulate and dissolved organic C (POC and DOC) moving by saturated flow was sampled continuously at 0.15 and 0.3 m, and soil was sampled to 2.0 m. Black C was found below the application depth of 0-0.1 m in the 0.15-0.3 m depth interval, with migration rates of 52.4 ± 14.5, 51.8 ± 18.5 and 378.7 ± 196.9 kg C ha -1 yr -1 (± SE) where 11.6, 23.2 and 116.1 t BC ha -1 , respectively, had been applied. Over 2 years after application, 2.2% of BC applied at 23.2 t BCha -1 was lost by respiration, and an even smaller fraction of 1% was mobilized by percolating water. Carbon from BC moved to a greater extent as DOC than POC. The largest flux of BC from the field (20-53% of applied BC) was not accounted for by our measurements and is assumed to have occurred by surface runoff during intense rain events. Black C caused a 189% increase in aboveground biomass production measured 5 months after application (2.4-4.5 additional dry biomass ha -1 where BC was applied), and this resulted in greater amounts of non-BC being respired, leached and found in soil for the duration of the experiment. These increases can be quantitatively explained by estimates of greater belowground net primary productivity with BC addition.

622 citations

Journal ArticleDOI
TL;DR: In this article, a review of 634 publications on biochar and biochar-compost mixtures as soil amendments is presented to identify the potential gaps in our understanding of the role of these amendments in agriculture.

452 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used a global-scale meta-analysis to show that biochar has, on average, no effect on crop yield in temperate latitudes, yet elicits a 25% average increase in yield in the tropics.
Abstract: Applying biochar to soil is thought to have multiple benefits, from helping mitigate climate change [1, 2], to managing waste [3] to conserving soil [4]. Biochar is also widely assumed to boost crop yield [5, 6], but there is controversy regarding the extent and cause of any yield benefit [7]. Here we use a global-scale meta-analysis to show that biochar has, on average, no effect on crop yield in temperate latitudes, yet elicits a 25% average increase in yield in the tropics. In the tropics, biochar increased yield through liming and fertilization, consistent with the low soil pH, low fertility, and low fertilizer inputs typical of arable tropical soils. We also found that, in tropical soils, high-nutrient biochar inputs stimulated yield substantially more than low-nutrient biochar, further supporting the role of nutrient fertilization in the observed yield stimulation. In contrast, arable soils in temperate regions are moderate in pH, higher in fertility, and generally receive higher fertilizer inputs, leaving little room for additional benefits from biochar. Our findings demonstrate that the yield-stimulating effects of biochar are not universal, but may especially benefit agriculture in low-nutrient, acidic soils in the tropics. Biochar management in temperate zones should focus on potential non-yield benefits such as lime and fertilizer cost savings, greenhouse gas emissions control, and other ecosystem services.

385 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluate the soil biochemical cycling of N and P as influenced by biochars with diverse characteristics, and describe the consequences for plant nutrition with respect to the NUE and P use efficiency of crops grown in biochar-amended soils.
Abstract: There is global interest in understanding the prospects for biochar application to agricultural soils. If biochar enhances the availability of nitrogen (N) and phosphorus (P) to crops, this could be pivotal in reducing N and P fertilizer inputs to agricultural soils. This review evaluates the soil biochemical cycling of N and P as influenced by biochars with diverse characteristics, and describes the consequences for plant nutrition with respect to the N use efficiency (NUE) and P use efficiency (PUE) of crops grown in biochar-amended soils. Fundamentally, biochar can alter microbial-mediated reactions in the soil N and P cycles, i.e. N2 fixation, mineralization of N and P, nitrification, ammonia volatilization and denitrification. As well, biochar provides reactive surfaces where N and P ions are retained in soil microbial biomass and in exchange sites, both of which modulate N and P availability to crops. Distinctions must be made between biochars derived from manure- and crop residue-based feedstocks versus biochars derived from ligno-cellulosic feedstock, as well as biochars produced at a lower production temperature (

330 citations


Additional excerpts

  • ...Improved crop yield in fine-textured soils receiving biochar is counter to the findings of Jeffery et al. (2011) and Crane-Droesch et al. (2013), who reported a weak association between clay content and crop yield gain in biochar-amended soil....

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Journal ArticleDOI
TL;DR: Commercial available biochars derived from waste wood are characterized for physical and chemical properties that can signify their relevant environmental applications, with the highest PAHs observed in biochar produced via fast pyrolysis and lowest among the gasification-produced biochar.

300 citations


Cites background from "Heterogeneous global crop yield res..."

  • ...To date, there is no known correlation between biochar properties and crop yield improvements (Crane-Droesch et al., 2013)....

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References
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Book
01 Jan 1987
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.
Abstract: Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of Imputation Uncertainty.PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.Theory of Inference Based on the Likelihood Function.Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism.Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.Large-Sample Inference Based on Maximum Likelihood Estimates.Bayes and Multiple Imputation.PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.Multivariate Normal Examples, Ignoring the Missing-Data Mechanism.Models for Robust Estimation.Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism.Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism.Nonignorable Missing-Data Models.References.Author Index.Subject Index.

18,201 citations

Journal ArticleDOI

9,941 citations

Book
01 Jan 2006
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.
Abstract: 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. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

9,098 citations

Book
30 May 2017
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.
Abstract: LINEAR MODELS A simple linear model Linear models in general The theory of linear models The geometry of linear modelling Practical linear models Practical modelling with factors General linear model specification in R Further linear modelling theory Exercises GENERALIZED LINEAR MODELS The theory of GLMs Geometry of GLMs GLMs with R Likelihood Exercises INTRODUCING GAMS Introduction Univariate smooth functions Additive models Generalized additive models Summary Exercises SOME GAM THEORY Smoothing bases Setting up GAMs as penalized GLMs Justifying P-IRLS Degrees of freedom and residual variance estimation Smoothing Parameter Estimation Criteria Numerical GCV/UBRE: performance iteration Numerical GCV/UBRE optimization by outer iteration Distributional results Confidence interval performance Further GAM theory Other approaches to GAMs Exercises GAMs IN PRACTICE: mgcv Cherry trees again Brain imaging example Air pollution in Chicago example Mackerel egg survey example Portuguese larks example Other packages Exercises MIXED MODELS and GAMMs Mixed models for balanced data Linear mixed models in general Linear mixed models in R Generalized linear mixed models GLMMs with R Generalized additive mixed models GAMMs with R Exercises APPENDICES A Some matrix algebra B Solutions to exercises Bibliography Index

8,393 citations

Journal ArticleDOI
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.
Abstract: Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. Copyright © 2010 John Wiley & Sons, Ltd.

6,349 citations