Field’s Spatial Variation Influenced Outcomes more so than N-fertiliser, FYM, Cover Crops or Their Legacy Effects Following Conversion to a No-till Arable System
Bibliographic record
Abstract
No-till in agricultural arable systems is a practice that offers benefits to soil health. Combined with methods such as the incorporation of crop residues and manures, no-till can influence the dynamics of soil organic carbon (SOC) and organic matter (SOM), crop productivity and nutrient status. These turnovers are shaped by spatial and temporal factors and associated microbial mineralisation processes. There is a lack of diachronic large-scale field studies that include baseline data and seasonal variations in arable systems. This study aimed to investigate the interaction between microbial and soil physicochemical properties as they evolved over time during the transition from full-inversion to no-till. It utilised a combination of soil microbial assays (microbial biomass carbon (MBC) and nitrogen (MBN) with bio-physico-chemical analyses (SOC and SOM quantification, textural class, pH, gravimetric water content (GWC), and macronutrients to assess soil over a period of two-years. Two experiments were established within the same four-hectare field, one on a relatively level area (Experiment-1) and another on a slope (Experiment-2). Experiment-1 treatments consisted of Farmyard Manure (FYM), Green Manure (GM) and Standard Practice (SP = Control). Experiment-2 was a repeat of Experiment-1, but without the FYM treatment. Soil was sampled twice per crop season, in Spring and Autumn, in Expriment-1, and in Autumn only in Experiment-2. Diachronic analyses were applied to quantify changes in SOC and SOM, MBC and MBN, and associated physicochemical properties. The results from shifting to a no-till system were influenced by spatial and temporal variations that were not always linked to management practices. This two-year study demonstrated that the quantification of SOC and SOM were poor predictors of change in management practices over the timeframe of the study, but that microbial biomass responded quickly to the incorporation of FYM. SOC and SOM were affected by texture class, but not significantly by inputs, and associated with extractable Ca2+ and %tN. This study demonstrated that diachronic studies increase our understanding of SOC, SOM, MBC and MBN dynamics and the impacts of short-term impacts of change in soil management practices. Furthermore, spatial variation within one field was found to lead to different outcomes and a better predictor in response to those management practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".