Increased avian bioacoustic diversity without lost profit after planting perennial vegetation in marginal cropland
Bibliographic record
Abstract
Expansion of arable cropland and intensification of agriculture has driven substantial losses of habitat, biodiversity, and ecosystem services. Balancing biodiversity conservation and environmental priorities with farm economics and food production is particularly challenging. However, many areas of crop fields contain marginal areas (e.g., wet or saline soils) that produce inconsistent and low crop yields. These suboptimal growing areas may be ideal targets for perennial restoration to address biodiversity conservation goals without reducing crop yield and profitability. We tested the value of restoring marginal areas within crop fields growing primarily canola, cereal, and legume crops in Saskatchewan, Canada. The objective was to identify changes in acoustic soundscapes of biodiversity and associated crop yields and profitability over three years following the conversion. Using prior-year yield maps and knowledge of the field topography, participating producers converted an average of 17.6 % (range 3–48 %) of cropland to perennial vegetation near marginal low yielding wetlands and/or saline areas, and these were compared to matched nearby reference fields that were cropped as usual. From 2019–2022, autonomous recording units (ARUs) recorded over 2450 hours of environmental soundscapes in treatment (n = 20) and reference (n = 30) fields. After controlling for crop type, time of day, year, and the amount of non-crop land, four bioacoustic diversity indices — (bioacoustic index (BIO), acoustic complexity index (ACI), acoustic diversity index (ADI), and normalized difference soundscape index (NDSI)) — all significantly increased in the treatment fields relative to reference fields, with the most substantial increases from the first to second year after planting. Total field level crop yields were, on average, 14 % lower in treatment fields; however, profitability did not significantly differ from reference fields. This suggests that restoring marginal areas within cropland adds landscape and habitat complexity to support biodiversity and is a promising solution to provide environmental, economic, and agronomic benefits in agriculture. • Planting perennial forage in marginal cropland rapidly increased biodiversity. • All four bioacoustic indices showed positive responses to marginal land restoration. • Field yield was reduced due to conversion but not proportionate to cropland removed. • Profit was not significantly reduced from perennial forage treatments.
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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 | 0.000 | 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 | 0.001 |
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".