Crop functional diversity drives multiple ecosystem functions during early agroforestry succession
Bibliographic record
Abstract
Abstract We still lack practical guidelines for diversifying cropping systems that consider both yield and regulating functions of agroecosystems. Recent studies have suggested that maximizing functional diversity (FD, the distribution of species with different functional traits in the niche space) may lead to agroecosystems with greater multifunctionality due to niche complementarity. Therefore, scientists are now advocating the use of a trait‐based approach to develop multifunctional sustainable agroecosystems. In order to assess the effects of crop FD on key agroecosystem functions and to consider trade‐offs and synergies among them, we established, in late 2016, permanent experimental field plots of successional agroforestry systems (SAFS), in humid subtropical Southern Brazil. The experiment represents a gradient of plant functional trait diversity (designed FD based on leaf nitrogen concentration), while maintaining crop species richness constant across all treatments. Our hypothesis is that the observed FD of crops (hereafter, crop FD) drives multiple agroecosystem functions. We determined the observed FD by quantifying traits of crops and weeds (maximum plant height, leaf area, specific leaf area) and performed two data collections (March and September 2017) at the end of the summer and winter crops respectively. We used structural equation modelling to test a hypothetical causal model to explain how crop FD affects three functions: weed suppression, soil protection (soil cover by either crops or weeds) and crop yield. Our results support the hypothesis that high crop FD drives agroecosystem processes and contributes to the provision of multiple ecosystem functions. We found that with greater crop FD in SAFS, crop plants occupied a large niche space, thereby increasing the total photosynthetic light intercepted in the agroecosystem, that in turn, increased crop yield. Additionally, greater FD increased soil protection by crops and decreased weed cover. This greater FD also reduced the FD of the weed community. Synthesis and applications. Crop mixtures based on complementary plant traits can increase the multifunctionality of agroecosystems through their sustainable use. A more heterogeneous structure and projection of crop leaf area drives greater resistance to competition with weeds and produces higher crop yields in young diversified crop mixtures.
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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 | 0.001 | 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) | 0.001 | 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".