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Record W3139481808 · doi:10.5194/soil-7-71-2021

Complex soil food web enhances the association between N mineralization and soybean yield – a model study from long-term application of a conservation tillage system in a black soil of Northeast China

2021· article· en· W3139481808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSOIL · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesPeople's Government of Jilin ProvinceYouth Innovation Promotion AssociationChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsTillageAgronomyEnvironmental scienceMineralization (soil science)Soil food webConventional tillageAgroecosystemSoil managementSoil biologyEcologyBiologySoil scienceSoil waterAgriculture

Abstract

fetched live from OpenAlex

Abstract. Long-term (10 years) application of conservation tillage following conversion from conventional tillage (CT) can achieve a new equilibrium in the soil environment, which is vital to reverse soil biodiversity declines and fulfil the goal of maintaining agroecosystem sustainability. However, in such a situation, how the soil community regulates nutrient cycling impacting crop yield is not well documented. Therefore, the relations between mineralized nitrogen (N) delivered by soil food web and soybean (Glycine max Merr.) yield were investigated after 14 years application of CT, reduced tillage (RT) and no tillage (NT) in a black soil (Typic Hapludoll) of Northeast China. We hypothesized that soil mineralizable N would increase with the complexity of the soil food web, and that the trophic groups involved in associating N mineralization with crop yield will vary with soil depth in the conservation tillage practice. During the soybean growing season, soil organisms, including bacteria, fungi, nematodes, mites and collembolans, were extracted and identified monthly from 0–5 and 5–15 cm soil depths to estimate the complexity of the food web indicated by the species richness and connectance indices, and to simulate the mineralized N using energetic food web modelling. The species richness and connectance of the food web at both soil depths were significantly affected by tillage practices, and their values decreased of the order of NT > RT > CT. A similar trend was also revealed for the simulated N mineralization, that is, the mineralized N released either from the functional feeding guilds or from the energy pathways of the food web were greater in RT and NT than in CT at both soil depths. Multiple linear regression analysis showed that soil organisms involved in coupling the mineralized N with soybean yield were different at different soil depths, in which fungal and root pathways at 0–5 cm and bacterial pathway at 5–15 cm were the driving factors for the supply of mineralized N to soybean in NT and RT soils. These results support our hypothesis and highlight the essential role of soil food web complexity in coupling N mineralization and crop yield after long-term application of conservation tillage. Additionally, the current modelling work provides basic hypotheses for future studies to test the impact of soil biodiversity or specific functional guilds on the fate of N in agro-ecosystems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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