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Record W2790418656 · doi:10.15414/afz.2017.20.04.72-77

Effect of biochar on soil CO2 production

2017· article· en· W2790418656 on OpenAlexaboutno aff
Jan Hořák, Vladimí­r Šimanský

Bibliographic record

VenueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBiocharAnimal scienceChemistryAtmosphere (unit)NitrogenEnvironmental scienceEnvironmental chemistryAgronomyPyrolysisBiology

Abstract

fetched live from OpenAlex

Received: 2016-06-13 | Accepted: 2016-12-13 | Available online: 2017-12-31 http://dx.doi.org/10.15414/afz.2017.20.04.72-77 The study focuses on looking for answers to the following questions: 1. Is biochar application a suitable solution for reducing CO 2 emissions? 2. What application rate significantly reduces CO 2 production to the atmosphere? 3. Does have the application of enriched biochar a justification in relation to reducing CO 2 production? The experiment was established on Haplic Luvisol at the experimental site of SUA in Nitra (Dolná Malanta), where we measured CO 2 emissions from the soil to the atmosphere under the following treatments: different rates (0, 10, 20 t ha -1 ) of pure biochar (B0, B10 a B20) and enriched biochar (EB10, EB20) combined with different levels of mineral nitrogen at doses of 0, 40 and 80 kg ha -1 (N0, N40, N80). Overall, the average values of CO 2 emissions were lower by 19.8 %, 13.3 %, 12.9 %, 9.4 % and 8.7 % in B10N0, B20N40, B20N0, B20N80 and B10N40 treatments as compared to B0N0 (control) during the studied period. On the other hand, the average values of CO 2 were higher by 20% in B10N80 treatments as compared to control (B0N0). Application of enriched biochar whether individually (EB10N0, EB20N0) or with additional N (EB10N40, EB20N40, EB10N80, EB20N80) increased average CO 2 by 29.7 %, 34.6 %, 36.0 %, 44.9 %, 45.8 % and 53.6 % as compared to control (B0N0). The cumulative CO 2 emissions for the whole studied period (2014) were in the following order from the lowest one B10N0 < B20N0 < B20N40 < B20N80 < B10N40 < B0N0 (control) < B10N80 < EB20N40 < EB20N80 < EB10N80 < EB20N0 < EB10N0 < EB10N40. Keywords: biochar, enriched biochar, N-fertilization, CO 2 emission References ALVAREZ, R. et al. (1995) Soil respiration and carbon inputs from crops in a wheat-soybean rotation under different tillage systems. In Soil Use Mamagment, Vol. 11, pp. 45–50 doi: http://dx.doi.org/10.1111/j.1475-2743.1995.tb00495.x BIELEK, P. 2001. Carbon sequestration by soil effets. In Humic substances in ecosystems 4 . Bratislava : VÚPOP, 2001, pp. 11–14. Duiker , S.W. and Lal, R. (1999) Crop residue and tillage effects on carbon sequestration in a Luvisol in central Ohio. In Soil Tillage Res., vol. 52, pp. 73–81. doi: http://dx.doi.org/ 10.1016/S0167-1987(99)00059-8 Dukes , J.S. and Hungate, B.A. (2002) Elevated carbon dioxide and litter decomposition in California annual grasslands: which mechanisms matter? In Ecosystems , vol. 5, pp. 171–183. doi: http://dx.doi.org/10.1007/s10021-001-0063-7 Fischer, D. and Glaser, B. (2012) Synergisms between Compost and Biochar for Sustainable Soil Amelioration. In Kumar, S. (ed.) Management of Organic Waste . Earthscan, Rijeka, pp. 167–198. GREGORICH, E.G. et al. (1998) Carbon distribution and losses: erosion and deposition effects. In Soil and Tillage Research , vol. 47, pp. 291–302. doi: http://dx.doi.org/ 10.1016/S0167-1987(98)00117-2 HAN, F. et al. (2016) Effect of biochar on the soil nutrients about different grasslands in the Loess Plateau. In Catena , vol. 137, pp. 554–562. doi: http://dx.doi.org/ 10.1016/j.catena.2015.11.002 Heitkötter, J. and Marschner, B. (2015) Interactive effects of biochar ageing in soils related to feedstock, pyrolysis temperature, and historic charcoal production. In Geoderma , vol. 245–246, pp. 56–64. doi: http://dx.doi.org/ 10.1016/j.geoderma.2015.01.012 IPCC, (2014): Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. Jacinthe , P.A. et al. (2002) Carbon budget and seasonal carbon dioxide emission from a central Ohio Luvisol as influenced by wheat residue amendment. In Soil Tillage Res., vol. 67, pp. 147–157. doi: http://dx.doi.org/ 10.1016/S0167-1987(02)00058-2 Jeffery, S. et al. (2011) A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis. In Agric. Ecosyst. Environ ., vol. 144, pp. 175–187. doi: http://dx.doi.org/ 10.1016/j.agee.2011.08.015 Jien, S.H. and Wang, Ch.S. (2013) Effects of biochar on soil properties and erosion potential in a highly weathered soil. In Catena, vol. 110, pp. 225–233. doi: http://dx.doi.org/10.1016/j.catena.2013.06.021 JUMA, N.G. (1994) A conceptual framework to link carbon and nitrogen cycling to soil structure formation. In Agric. Ecosyst. Environ. , vol 51, pp. 257–267. JUMA, N.G. (1999) Pedosphere and its dynamics . 1 vyd. Edmonton (Canada) : Salman Productions Ins., 1999. 335 s. ISBN 1-896263-10-0. Kammann, C. et al. (2011) Influence of biochar on drought tolerance of Chenopodium quinoa: Willd and on soil–plant relations. In Plant Soil , vol. 345, pp. 195–210. doi: http://dx.doi.org/ 10.1016/j.catena.2013.06.021 Lal, R. (2008) Carbon sequestration. In Philos. Trans. R. Soc ., vol. 363, pp. 815–830. doi: http://dx.doi.org/ 10.1098/rstb.2007.2185 Laird, D.A. et al. (2010) Impact of biochar amendments on the quality of a typical Midwestern agricultural soil. In Geoderma , vol. 158, pp. 443–449. doi: http://dx.doi.org/ 10.1016/j.geoderma.2010.05.012 Lopez-Capel, E. et al. (2016) Biochar properties, In: Shackley, S. et al. (eds.): Biochar in European soils and agriculture, Routledge, London, New Your, pp. 41–72. MONREAL, C.M. et al.  (1995) Soil organic structures in macro and microaggregates of a cultivated brown chernozem. In Soil Biol. Biochem ., vol. 27, pp. 845–853. doi: http://dx.doi.org/ 10.1016/0038-0717(94)00220-U PASCUAL, J.A. et al. (1998) Carbon Mineralization in an Arid Soil Amended with Organic Wastes of Varying Degerees of Stability. In Commun. Soil. Sci. Plant Anal. , vol. 29, pp. 835–846. doi: http://dx.doi.org/ 10.1080/00103629809369989 POPELÁROVÁ, E. et al. (2002) Mineralization activity in soils for the development of the precision farming system. In Arch. Acker Pfl. Boden , vol. 48, pp. 147–153. REICOSKY, D.C. and LINDSTROM, M.J. (1995) Impact of fall tillage on short-term carbon dioxide flux. In Soil and global change , pp. 177–187. Å IMANSKÝ, V. (2016) Effects of biochar and biochar with nitrogen on soil organic matter and soil structure in Haplic Luvisol. In Acta fytotechnica et zootechnica , vol. 19, pp. 129–138. doi: http://dx.doi.org/10.15414/afz.2016.19.04.129-138 Å IMANSKÝ, V. et al. (2017) Carbon sequestration in water-stable aggregates under biochar and biochar with nitrogen fertilization. In Bulgrian Journal of Agricultural Research, vol. 23 (2) – in print SINGH, B.P. and COWIE, A.L. (2014) Long-term influence of biochar on native organic carbon mineralisation in a low-carbon clayey soil. In Sci. Report. , vol. 4, pp. 1–9. doi: http://dx.doi.org/10.1038/srep03687 YUAN, J.H. and XU, R.K. (2012) Effects of biochars generated from crop residues on chemical properties of acid soils from tropical and subtropical China. In Soil Res., vol. 50, pp. 570–578. doi: http://dx.doi.org/10.1071/SR12118 Â

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0070.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.273
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2017
Admission routes1
Has abstractyes

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