Crop Domestication, Root Trait Syndromes, and Soil Nutrient Acquisition in Organic Agroecosystems: A Systematic Review
Bibliographic record
Abstract
Selecting crops that express certain reproductive, leaf, and root traits has formed detectable, albeit diverse, crop domestication syndromes. However, scientific and informal on-farm research has primarily focused on understanding and managing linkages between only certain domestication traits and yield. There is strong evidence suggesting that functional traits can be used to hypothesize and detect trade-offs, constraints, and synergies among crop yield and other aspects of crop biology and agroecosystem function. Comparisons in the functional traits of crops vs. wild plants has emerged as a critical avenue that has helped inform a better understanding of how plant domestication has reshaped relationships among yield and traits. For instance, recent research has shown domestication has led important economic crops to express extreme functional trait values among plants globally, with potentially major implications for yield stability, nutrient acquisition strategies, and the success of ecological nutrient management. Here, we present an evidence synthesis of domestication effects on crop root functional traits, and their hypothesized impact on nutrient acquisition strategies in organic and low input agroecosystems. Drawing on global trait databases and published datasets, we show detectable shifts in root trait strategies with domestication. Relationships between domestication syndromes in root traits and nutrient acquisition strategies in low input systems underscores the need for a shift in breeding paradigms for organic agriculture. This is increasingly important given efforts to achieve Sustainable Development Goal (SDG) targets of Zero Hunger via resilient agriculture practices such as ecological nutrient management and maintenance of genetic diversity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".