Microclimate, soil moisture and forage yield vary spatially within a temperate tree-based intercropping system: From competition to facilitation
Bibliographic record
Abstract
Tree-based intercropping (TBI) can improve water conservation in agroecosystems and yield stability in the face of climate change. Greater understanding is required regarding mechanisms determining spatial and seasonal variation in moisture availability and agricultural yields in TBI systems. Our two-year study assessed spatiotemporal dynamics of soil hydraulic properties, microclimatic conditions, soil moisture and forage performance (yield, nutritive value) with increasing distances from tree rows (0, 4, 12, 20 m) within a TBI system (50 trees ha −1 ; 10- to 11-years-old) and in agricultural controls. TBI rows included hybrid poplars that were inter-planted with high-value hardwoods. In each block (n = 3), the TBI system and control were divided in two: with vs without root-pruning (0.75 m depth using a sub-soiler). Increased soil organic matter and infiltration rate, and decreased bulk density were measured in the TBI compared to the control, but only under trees (0 m). We observed increased soil moisture near the centre of the alleys (12 and 20 m) during both extremes of water availability, likely due to measured wind speed reductions. Under high potential evaporation, atmospheric evaporative demand near the alley centres was lower than in the control. Moisture, light availability and forage yield close to tree rows (4 m) were lower than those near alley centres and in the control. Tree root pruning increased soil moisture and yield at 4 m from the tree row, but not completely to control levels, indicating that forage was limited by light and water availability at the tree-crop interface. Unlike alfalfa, yield of grasses was not negatively influenced by tree competition. Our study suggests that TBI systems can potentially improve hydrological responses of agroecosystems to extreme weather events. This potential of TBI systems appears highly useful given expected increases in the frequency and intensity of moisture deficits and extreme rainfall events.
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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 | 0.000 | 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 | 0.000 |
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".