Photosynthesis, yield, energy balance, and water‐use of intercropped maize and soybean
Bibliographic record
Abstract
Abstract By 2050, the U.S. Corn Belt will likely face a 23% increase in leaf‐to‐air vapor pressure deficit (VPDL), the driving force of evapotranspiration (ET), which may restrict maize yield improvements for rainfed agroecosystems. Alternative cropping systems, such as maize and legume intercrops, have previously demonstrated yield and resource‐use advantages over monocultures. In this study, the residual energy balance approach was used to gain insights into how an additive simultaneous maize and soybean intercrop system regulates ET and water‐use efficiency (WUE) compared to standard maize and soybean monoculture systems of the U.S. Corn Belt. Experimental field plots were rain‐fed and arranged in a randomized complete block design in three blocks. Photosynthetic capacity and grain yield of maize were conserved in the intercrop. However, its competitive dominance shaded 80%–90% of incident light for intercropped soybean at canopy closure, leading to a 94% decrease in grain yield compared to soybean monoculture. The total grain yield per unit area of the additive intercrop (land‐use efficiency) increased by 11% ± 6% (1 SE). Compared to maize monoculture, the intercrop had higher latent heat fluxes (λET) at night but lower daytime λET as the intercrop canopy surface temperature was approximately .25°C warmer, partitioning more energy to sensible heat flux. However, the diel differences in λET fluxes were not sufficient to establish a statistically significant or biologically relevant decrease in seasonal water‐use (ΣET). Likewise, the increase in land‐use efficiency by the intercrop was not sufficient to establish an increase in seasonal water‐use efficiency. Intercropping high‐performing maize and soybean cultivars in a dense configuration without negative impact suggests that efforts to increase yield and WUE may lead to improved benefits.
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) | 0.000 | 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".