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Record W4416621349 · doi:10.1002/agj2.70229

Off‐season crops as a strategy for renovating degraded pastures and improving maize yield in a low‐altitude tropical region

2025· article· en· W4416621349 on OpenAlex
Sarah V. Pedrão, Job Teixeira de Oliveira, Otávio M. Correa, Aline Oliveira Silva, Aline O. Matoso, Marco Aurélio Carbone Carneiro, Eric Haydt Castello Branco van Cleef, Flávio Hiroshi Kaneko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgronomy Journal · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsUniversity of Saskatchewan
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsIntercroppingPastureYield (engineering)TropicsProductivityMonocroppingGrain yieldCrop yield

Abstract

fetched live from OpenAlex

Abstract Brazil can expand cultivated areas without deforestation by restoring degraded pastures and optimizing grain production through off‐season crops. This study evaluated eight off‐season treatments, including fallow, monocrops, and intercropping combinations, and their effects on maize ( Zea mays L.) intercropped with guinea grass ( Megathyrsus maximus (Jacq.) B.K. Simon & S.W.L. Jacobs ‘Massai’) in a tropical low‐altitude region. The millet ( Pennisetum glaucum (L.) R. Br.) + guinea grass treatment produced high aboveground biomass, efficient macronutrient accumulation, reduced soil temperature, and increased maize grain yield and guinea grass productivity compared to other treatments. Overall, the use of off‐season crops improves degraded pasture renovation, enhances subsequent summer intercropped maize productivity, and represents a promising and sustainable agricultural practice.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.243
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it