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Record W7106022173 · doi:10.7939/83450

Diversification of annual cropping sequences with perennial forage seed crops

2025· dissertation· en· W7106022173 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCroppingPerennial plantGross marginCropping systemIntercroppingRed CloverForageAgriculture

Abstract

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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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.185
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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