Little or no maize (<i>Zea mays</i>) grain yield loss occurred in intercrop with mid‐maturity lablab (<i>Lablab purpureus</i>) in northeastern Tanzania
Bibliographic record
Abstract
Abstract Intercropping is among sustainable intensification tactics farmers may use to increase or maintain productivity and environmental quality. Effects of planting lablab [ Lablab purpureus (L.) Sweet] 1 or 2 weeks after planting maize (WAPM) are not well characterized, especially when mid‐maturity lablab cultivars are used. Greenhouse (Springfield, MO) and field studies (Arusha, Tanzania) were conducted in 2018 and 2019 to assess early (≤60 days) and full‐season effects on growth and grain production. The greenhouse study characterized early growth characteristics when lablab was sown 0, 1, or 2 WAPM for each of four cultivars of lablab. “Echo Cream” lablab produced greater vine length and more nodes than other cultivars in the greenhouse. Greenhouse lablab biomass and node number were greatest when sown ≤1 WAPM. The field study compared monoculture and intercrop grain yields. Highest maize yields occurred when lablab was sown ≥1 WAPM, but highest lablab yields occurred when planted at the same time as maize. The maize/lablab intercrop always resulted in favorable land equivalent ratios (LERs) ranging from 1.5 to 2.1. The partial LER of maize yield was not reduced by more than 7% relative to monoculture either year. Lablab yield was negatively affected by planting delay, decreasing about 60% in 2018 and 31% in 2019 in the 2 WAPM planting date treatment. Although twining lablab vines complicate grain harvest, our results demonstrate increased grain production per unit area with maize/lablab intercropping, with a potential for increased farmer income and household nutrition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".