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

Effect of biochar on soil CO2 production

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

Notice bibliographique

RevueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSoil Carbon and Nitrogen Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiocharAnimal scienceChemistryAtmosphere (unit)NitrogenEnvironmental scienceEnvironmental chemistryAgronomyPyrolysisBiology

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Science ouverte, Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,004
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0000,002
Études des sciences et des technologies0,0020,002
Communication savante0,0010,001
Science ouverte0,0070,003
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,273
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2017
Routes d'admission1
Résumé présentoui

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