Tracing the complementary and competitive water use patterns in a Theobroma cacao (cocoa) agroforestry system: A stable isotope approach
Bibliographic record
Abstract
In cocoa agroforesty systems, shade trees are used to create climate conditions that benefit cocoa growth and survival. However, the benefits may be offset by competition between shade trees and cocoa for water, especially in a changing climate. Here we use stable isotope tracers to quantify the patterns and depths of water uptake among shade timber trees, cocoa, and banana for a tropical agroforestry system in Trinidad. Rainfall was collected from August 2021 to September 2023. Three field campaigns were carried out at an upslope and downslope location representing different hydro-climatological conditions and at different times in the crop cycle. During each campaign and at each slope position, soil was collected from three pits at depths of 5, 15, 25, 50 and 75 cm below the surface, while up to 10 xylem cores were collected from the different plant species. Additional soil texture and soil moisture data were also collected. Cryogenic vacuum extraction was used to extract water from the soil and vegetation samples, while an Elementar Isoprime isotope ratio mass spectrometer was used to determine the δ2H and δ18O of the extracted water. These data were subsequently used for MixSIAR endmember mixing modelling. Our results suggest that cocoa and banana plants primarily use shallow soil water (0 – 10 cm below the surface), while shade and timber trees like Immortelle (Erythrina poeppigiana) and Cedar (Cedrela odorata) use water from deeper sources (20 – 50 cm below the surface). Spatially, plants located in upslope areas appear to use water from slightly deeper soil depths than downslope locations. Soils in the valley bottom were also wetter and had relatively higher clay content. This study indicates that shade trees do not compete with cocoa for water; however, bananas likely compete with cocoa making managing that co-cropping important.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".