The first annually resolved analysis of slash-and-burn practices in the boreal Eurasia suggests their strong climatic and socio-economic controls
Bibliographic record
Abstract
Abstract Slash-and-burn (SAB) was a widespread agricultural practice across large parts of the boreal region until the early 20th century. Despite its paramount importance in the procurement of food and particularly in supporting frontier populations of settlers during the colonization of the Eurasian boreal zone, analyses of spatial and temporal patterns in the use of SAB at annual and sub-annual scales are currently missing. We present the first such analysis of climatic and social controls of SAB practices in a remote region in the northern Ural mountains from 1880 to 1894. We observed a significant positive correlation between the total number of burns and the village population ( p = 0.005, R 2 = 0.26), indicating that the frequency of burns directly reflected the local demand for food. The amount of agricultural land, regarded as a cumulative measure of burning activity over multiple decades, showed a strong positive correlation with the village population ( p < 0.001, R 2 = 0.60). This result supported our interpretation of burns as an important food procurement tool, probably also positively affected by higher labour availability in larger villages. Villages where the number of burns were higher than predicted by the “the population vs. burns” regression tended ( p = 0.15) to have larger areas of arable land than predicted by the “population vs. arable land” regression. This pattern implied that variability in the local environmental and/or socio-economic settings of the villages made some of them more (or otherwise less) favourable for agricultural activities based on SAB. Most reported burns occurred in June and July. The three years with the maximum number of reported burns had a tendency to be wetter during these months when compared to the same period during an “average” year ( p = 0.19). The pattern suggested that farmers preferred conducting burns during years with a below-average climatological fire hazard. An earlier start of the fire season favoured burning activity ( p = 0.10 R 2 = 0.33), while its later ending had no significant effect on the number of burns ( p = 0.53). Our study documented strong climatic controls of SAB practices at the annual scale and their social controls at above-annual scales. These patterns emerged despite the common use of slash-and-burn to mitigate generally limited food availability in the northern Ural mountain region and likely conservative estimates of these practices in available records.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".