Intercropping Wheat and Beans: Effects on Agronomic Performance and Land Productivity
Bibliographic record
Abstract
ABSTRACT Declining land productivity associated with soil degradation is a significant issue for intensive wheat production. An intercropping system combining wheat and grain legumes may provide a farmwide production system that fulfills both economic and environmental concerns. We grew spring wheat (Triticum aestivum L. ‘Scarlet’) as a monoculture and intercropped with either a common bean cultivar (Phaseolus vulgaris L. ‘Red Kidney’ or ‘Black Turtle’) or a fava bean cultivar (Vicia faba L. ‘Bell’) without fertilizer in rows of wheat/bean 1:1 and 2:1 as well as broadcast arrangements during 2011 and 2012 to assess the impact of different genera (Vicia and Phaseolus) and cultivars (Red kidney or Black Turtle) on the wheat performance, land productivity, N and C accumulation in aboveground biomass, and soil mineral N balance. As baseline, the monoculture wheat plots yielded 3.2 t ha−1. However, wheat–fava bean plots displayed higher land equivalent ratio (LER) and total land outputs (TLO) with increased land productivity of 50% in the 1:1 and 32% in the 2:1 arrangements. Intercropped plots in row arrangements also improved wheat biomass nitrogen and grain protein content compared with monoculture plots. Wheat–fava bean in the 1:1 arrangement accumulated the highest N (34 kg ha−1, i.e., 176% higher) and organic C (2138 kg ha−1, i.e., 26% higher) in shoot biomass compared with monocultured wheat. Both NH4+ and NO3− pools were higher in intercrop plots with the highest mineral N balance in wheat‐fava bean in the 1:1 arrangement (+0.2 mg NH4+ and +1.1 mg NO3−kg‐1 dry soil). This study demonstrates that intercropping wheat with fava bean is an efficient strategy to increase land productivity while also increasing forage and soil quality.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".