Diversification of annual cropping sequences with perennial forage seed crops
Bibliographic record
Abstract
Identifying profitable cropping systems from a sustainable perspective is crucial for addressing the challenges faced in modern farming. A truly sustainable cropping system can ensure farm profitability, promote environmental stewardship, and advance social equity. In this study, we explored the potential of cropping system diversification as a strategy to achieve the sustainability goals. We hypothesized that integrating perennial forage seed crops into cropping systems would increase productivity and profitability while enhancing soil and environmental health. To test this, a field study was initiated in 2013 at Beaverlodge Research Farm, Agriculture and Agri-Food Canada, Alberta to identify beneficial cropping systems. The experiment was laid out in a split plot design randomized complete block design with four replicates. Eight different cropping sequences were considered main-factor treatments and three nitrogen levels (0, 45, 90 kg ha-1) were assigned in sub-plots. The cropping sequences included four annual crops (canola [Brassica napus L.], wheat [Triticum aestivum L.], pea [Pisum sativum L.], and barley [Hordeum vulgare L.]), three perennial grasses (creeping red fescue [Festuca rubra L.], meadow bromegrass [Bromus riparius Rehm.], and timothy [Phleum pratense L.]), and two perennial legumes (alsike clover [Trifolium hybridum L.], and red clover [Trifolium pratense L.]). Among the eight cropping sequences treatments, six were diversified with both perennial forage legumes and grasses to compare with annual-based traditional sequences. The productivity of the cropping system was evaluated based on seed yield and expressed as canola equivalent yield (CEY), while gross revenue and gross margin were used as profitability metrics for uniform comparison among the tested sequences. Soil samples were collected from 0-15 cm depth over short- and long-term periods to assess soil physical, chemical, and biological properties that are sensitive to change with cropping system diversification. The CEY, gross revenue, and gross margin were notably higher in the legume to vernalizing grass rotation, regardless of nitrogen fertility level. The higher seed price of the perennial legume (red clover) and the higher seed yield and price of the vernalizing grass (meadow bromegrass) during their production phases provided an opportunity to capitalize on favorable seasonal weather and local market demands. However, the aboveground biomass yield was significantly higher in annual cropping sequences and improved with increasing nitrogen rates as compared to perennial forage-based sequences. Soil health indicators such as soil organic carbon, microbial biomass carbon, active carbon, and the activities of carbon and nitrogen cycling enzymes were significantly improved with the inclusion of perennial forages in annual cropping systems. Specifically, the creeping red fescue dominated cropping sequence exhibited higher soil aggregate stability and structural resilience as compared to other sequences. However, soil compaction, bulk density, pH, water infiltration rates, and water content at field capacity showed no significant differences among cropping sequences tested in this study. In summary, the perennial forage seed crops-based cropping system can be adopted to optimize farm profits while improving soil health under soil-climatic conditions similar to those of the Peace River region in western Canada.
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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 | 0.000 | 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".