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Record W4415979424 · doi:10.5539/jas.v17n12p33

Determination of the Water Requirements of Two Legume Species (Pueraria phaseloides L. (Kudzu) and Desmodium heterocarpon subsp. ovalifolium cv. Maquenque) Used as Cover Crops in Oil Palm Cultivation

2025· article· W4415979424 on OpenAlexvenueno aff
T. Rodríguez Delgado, Nólver Atanacio Arias Arias

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Language
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
FundersFederación Nacional de Cultivadores de Palma de Aceite
KeywordsCover cropEvapotranspirationLysimeterLegumeSoil waterWater balanceWater contentGreen manureWater useAgriculture

Abstract

fetched live from OpenAlex

The use of legume cover crops has become an established sustainable practice in agriculture due to their numerous benefits for soil conservation and their role in nutrient cycling and recycling. Additionally, it is essential to comprehend their role in crop water management, particularly in irrigated regions. This research was conducted at the Palmar de la Sierra Experimental Field in the Magdalena department of Colombia. The study aimed to determine the water requirements of the legumes Pueraria phaseloides L. and Desmodium heterocarpon subsp. ovalifolium cv. Maquenque and the evaluation of their development under three different soil moisture conditions: field capacity, 50% depletion, and 95% depletion. To achieve this, drainage lysimeters were set up using a completely randomized experimental design, and the water balance method was employed. The results indicate that both legume species are adversely affected by water deficit conditions, particularly P. phaseloides, which experiences a reduction in leaf area index of up to 12%. Under optimal soil moisture conditions, achieving more than 70% soil coverage, both legumes exhibited a similar average evapotranspiration rate of 3.2 mm day-1, with a range spanning from 1.2 to 4.8 mm day-1.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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