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BioEnergy Research

, Volume 8, Issue 2, pp 832–842 | Cite as

Predicting Specific Biogas Yield of Maize-Validation of Different Model Approaches

  • Jürgen RathEmail author
  • Hauke Heuwinkel
  • Friedhelm Taube
  • Antje Herrmann
Article

Abstract

A reliable tool for predicting the specific biogas yield (SBY) of maize is required for breeding purposes to support a more efficient biomass production and for a quality-based payment of maize-substrate supply. The objective of the current study was to validate the recently published prediction model by Rath et al. (Bioenergy Research 6:939–952) with an independent data set and to compare its predictive ability to the approaches of four previously reported models (Baserga, Keymer and Schilcher, Kaiser, Weißbach). The validation data set was based on a multisite field experiment, providing a large genotypic variation in maize chemical composition and SBY, which ranged between 612 and 826 lN kg−1 OM. Predicted and measured SBY were positively correlated (r = 0.15 to r = 0.48) for all approaches tested. The model by Rath et al. revealed the highest predictive ability and was the only approach that allowed the variability of SBY within the data set to be reflected. This could be attributed to an extensive and reliable calibration database covering the genotype × environment interaction, which allowed a thorough examination of the assumptions before conducting a multiple linear regression analysis. Even with this provision, sufficient samples per genotype are obviously required to predict satisfactorily the ranking of maize genotypes with respect to SBY. Using genotype averages over sites, instead of single-location values, is recommended to reduce the strong impact of weather conditions on the chemical composition, thereby enabling a more reliable estimate of the genotype SBY potential.

Keywords

Anaerobic fermentation Maize Batch test Chemical composition Multiple linear regression Validation 

Abbreviations

ADF

Acid detergent fiber

ADL

Acid detergent lignin

BIAS

Mean systematic deviation

CEL

Cellulose

CH4

Methane

CV

Coefficient of variation

DM

Dry matter

EIOM

Enzyme-insoluble organic matter

ESOM

Enzyme-soluble organic matter

FOM

Fermentable organic matter

ha

Hectare

HBT

Hohenheim Biogas Yield Test

HCEL

Hemicelluloses

lN

Liters adjusted to norm conditions (273.15 K, 1013.25 mbar)

ME

Metabolizable energy

MLR

Multiple linear regression

NDF

Neutral detergent fiber

NDFom

Ash-free neutral detergent fiber

NIRS

Near-infrared spectroscopy

OM

Organic matter

OR

Organic residue

SBY

Specific biogas yield

SG

Reducing sugars

RMSECV

Root mean square error of cross-validation

RMSEP

Root mean square error of prediction

RMSEP(c)

Root mean square error of prediction (RMSEP) corrected for mean systematic deviation (BIAS)

SMY

Specific methane yield

XA

Crude ash

XC

Carbohydrates

XF

Crude fiber

XL

Crude fat

XP

Crude protein

XS

Starch

XX

Nitrogen-free extracts

Notes

Acknowledgments

This work was largely supported by enterprises represented in the Breeding Working Group of the German Maize Committee (DMK), both through the performance of the practical experimental work as well as part of the funding for the fermentation experiments. The authors are grateful to M. Hasler for assistance with statistical questions. Special thanks go to Alan Hopkins for linguistic editing of the manuscript.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jürgen Rath
    • 1
    Email author
  • Hauke Heuwinkel
    • 2
  • Friedhelm Taube
    • 3
  • Antje Herrmann
    • 3
  1. 1.German Maize Committee e. V.BonnGermany
  2. 2.Faculty of Agriculture and Nutritional SciencesUniversity of Applied Sciences Weihenstephan-TriesdorfFreisingGermany
  3. 3.Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic AgricultureKiel UniversityKielGermany

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