Shade tree functional traits drive critical ecosystem services in cocoa agroforestry systems
Bibliographic record
Abstract
The inclusion of shade trees into cocoa (Theobroma cacao L.) systems can generate livelihood opportunities for smallholder farmers. Yet, there is the need to examine the ecological context within which shade trees, and their functional traits, have a positive impact on ecosystem services in cocoa systems. Here, we used a network of farms of similar aged hybrid cocoa, in a nested design consisting of agroforestry or monoculture management, on three initial soil quality levels (poor, moderate or good) in two agroecological zones (humid or sub-humid) to investigate whether shade tree functional traits are linked with soil-based and cocoa-based ecosystem services. Initial soil quality level was the main driver of differences in soil organic matter, soil N, soil C:N, soil total C, soil permanganate-oxidizable C, while agroecological zone largely explained differences in cocoa yield and aboveground C. The inclusion of shade trees increased soil macrofauna abundance and mass but decreased cocoa aboveground C compared to cocoa monoculture plots. Importantly, within agroforestry systems, shade tree leaf traits expressed as community weighted means of SLA, leaf N, and leaf dry matter content explained differences in soil-based and cocoa-based ecosystem services. These results show that agroforestry systems have the potential to enhance soil-based ecosystem services without notably decreasing cocoa yield. And a trait-based approach to describe shade tree diversity can advance our understanding and management of shade tree-ecosystem service relationships in cocoa agroforestry systems.
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 | 0.001 |
| 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 | 0.001 | 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".