Modelling the evolution of regional carbon stocks in Belgian cropland soils
Bibliographic record
Abstract
In long-term experiments, it has been demonstrated that management of cropland influences the evolution of soil organic carbon (SOC ) stocks. National inventories of SOC stocks in Belgium have recently been compiled, and show an evolution of SOC stocks of arable land from 1960 to 1990 and 2000. In order to analyse the driving forces of these changes, we concentrate on the SOC evolution in the soil associations of three of the 13 Belgian agricultural regions (Dunes-Polders, Loam belt and Condroz). The small confidence limits around the mean SOC values within some soil associations (0.412.7 t C ha-1) allow us to compare the observed values with the results of the RothC soil carbon model and hence quantify the most important driving forces. After estimating the local parameters by fitting the model to SOC values from a longterm experiment in central Belgium, the model was run from 1960 to 2000 for typical soil profiles of soil associations in the three agricultural regions. The main factors inducing changes in SOC stocks are the increase in plough depth as a result of continued mechanisation in the 1960s and the sustained input of organic amendments in the form of farmyard manure and slurry. In contrast to earlier publications on CO2 emissions from agricultural soils, the model did not predict a decrease in SOC stocks for the period 1990-2000. A slight increase was observed, although this increase is not significant for most soil associations. The comparison between modelled and observed SOC data at two time slices allows the uncertainty of the model results to be estimated. This uncertainty ranges from 7.5 to 14.4% of the SOC stock and is in the same order of magnitude as the uncertainty around SOC modelling for the long-term experiments both in Belgium and elsewhere in Europe. The organic matter concentration in the topsoil, an indicator for soil quality, was in the range of 1.5 to 3.3%. Organic matter content increased in the Dunes-Polders and decreased in the Loam belt and the Condroz from 1960 to 2000. Many soils in the Loam belt are now close to the critical level of 2% under which the soils are vulnerable to compaction and erosion. Key words: Soil organic carbon, regional modelling, soil-land-unit, Belgium
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".