Diverse approaches to crop diversification in agricultural research. A review
Bibliographic record
Abstract
Abstract Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. Here, we review the use of ‘crop diversification’ measures in agricultural research. We (i) analyse changes in crop diversification studies over time; (ii) identify diversification practices based on empirical studies; (iii) differentiate their use by country, crop species and experimental setup and (iv) identify target parameters to assess the success of diversification. Our main findings are that (1) less than 5% of the selected studies on crop diversification refer to our search term ‘diversification’; (2) more than half of the studies focused on rice, corn or wheat; (3) 76% of the experiments were conducted in India, USA, Canada, Brazil or China; (4) almost any arable crop was tested on its suitability for diversification; (5) in 72% of the studies on crop diversification, at least one additional agronomic measure was tested and (6) only 45% of the studies analysed agronomic, economic and ecological target variables. Our findings show the high variability of approaches to crop diversification and the lack of a consistent theoretical concept. For better comparability and ability to generalise the results of the different primary studies, we suggest a novel conceptual framework. It consists of five elements, (i) definition of the problem of existing farming practices and the potential need for diversification, (ii) characterisation of the baseline system to be diversified, (iii) definition of the scale and target area, (iv) description of the experimental design and target variables and (v) definition of the expected impacts. Applying this framework will contribute to utilizing the benefits of crop diversification more efficiently.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".