Effects of Short-term Tillage of a Long-term No-Till Land on Quantity and Quality of Organic C and N in Two Contrasting Soil Types
Notice bibliographique
Résumé
Pre-seeding tillage of long-term no-till soil may alter soil quality by changing some properties, but the magnitude of change depends on soil type and climatic conditions. Effects of short-term (2 or 3 years) tillage (hereafter called reverse tillage [RT]) of land previously under long-term no-till (NT, 29 or 30 years), with straw management (straw removed [SRem] and straw retained [SRet]) and N fertilizer rate (0, 50 and 100 kg N ha-1 in SRet, and 0 kg N ha-1 in SRem plots) were determined in autumn 2011 on total organic C (TOC) and N (TON), light fraction organic C (LFOC) and N (LFON), and mineralizable N (Nmin) in the 0-7.5, 7.5-15, or 15-20 cm soil layers at Breton (Gray Luvisol [Typic Cryoboralf] loam) and Ellerslie (Black Chernozem [Albic Argicryoll] loam), Alberta, Canada. Short-term RT following long-term NT had no significant negative effect on TOC and TON in soil at both sites, although these parameters tended to be slightly lower in the 0-7.5 cm soil layer with RT compared to NT. For the zero-N treatment, SRet had greater TOC and TON compared to SRem in both soil layers at both sites. On average, over both sites, TOC and TON in the 0-15 cm soil increased by 2.08 Mg C ha-1 and 0.216 Mg N ha-1, respectively. Application of N fertilizer increased TOC and TON in both soil layers, up to the 50 kg N ha-1 rate at Breton (by 7.96 Mg C ha-1 and 0.702 Mg N ha-1 in the 0-15 cm soil) and up to the 100 kg N ha-1 rate at Ellerslie (by 5.11 Mg C ha-1 and 0.439 Mg N ha-1 in the 0-15 cm soil). In both RT and NT treatments, the effects of N rate on TOC and TON were similar for SRet and SRem. There was greater LFOC and LFON in the 7.5-15 cm soil layer with RT than NT at both sites. In the 0-15 cm soil layer, averaged over both sites, RT increased LFOC by 66 kg C ha-1 and LFON by 4.0 kg N ha-1. In both 0-7.5 and 7.5-15 cm soil layers, LFOC and LFON increased with SRet compared to SRem. Averaged over both sites, the increase in LFOC and LFON in the 0-15 cm soil was 97 kg C ha-1 and 3.5 kg N ha-1, respectively. Mass of LFOC and LFON increased dramatically in both soil layers with application of N fertilizer up to the 100 kg N ha-1 rate at both sites, with an average increase of 866 kg C ha-1 and 45.5 kg N ha-1. In the zero-N treatment, LFOC and LFON increased with SRet compared to SRem under RT at Breton and under NT at Ellerslie. On average, tillage had no effect on Nmin in soil, but SRet increased Nmin in soil in both RT and NT, with an average increase of 4.8 kg N ha-1. Application of N fertilizer increased Nmin in the 0-20 cm soil up to 50 kg N ha-1 rate at Breton (by 13.7 kg N ha-1) and up to 100 kg N ha-1 rate at Ellerslie (by 18.6 kg N ha-1). In conclusion, RT had no effect on TOC, TON and Nmin in soil, but LFOC and LFON increased with RT compared to NT in the 7.5-15 cm layer at one site. SRet and N fertilization usually had dramatic positive effects on TOC, TON, LFOC, LFON and Nmin in soil compared to the corresponding treatments.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».