PSXII-4 Intercropping perennial cereal grain crops in alternate or same row seeding method for improved forage yield and quality
Bibliographic record
Abstract
Abstract Intercropping perennial cereal crops with legume species can offer multiple ecosystem functions and agronomic benefits. This study evaluated the forage dry matter yield (FDMY) and the nutritive value of two perennial cereal grain crop species [ACE 1-rye and Kenza, intermediate wheatgrass (IWG)] intercropped with three perennial legume species (alfalfa, white clover, and sainfoin) using alternate- and same-row seeding methods at five different sites in Alberta. The experimental sites were established in June 2022 using a factorial experimental model with eight treatment factors, including perennial monocultures or intercrop with a legume species. One year after establishment in 2023, the forage dry matter yield (FDMY) varied significantly (P < 0.05) across experimental sites, ranging from 228 kgּ ha-1ּ yr-1 for the IWG/sainfoin intercrop to 6,833 kgּ ha-1ּ yr-1 for the IWG and alfalfa intercrop treatment. Seeding methods (Same or Alternate row) and site location had a significant effect on the FDMY (P = 0.00126), indicating consistent performance in the two cropping systems across diverse environments and site locations. The study assessed eight treatments (cereal-legume combinations) for forage quality performance, of which seven treatments, including Rye-Clover, Rye-Sainfoin, Wheat-Alfalfa, Wheat-Clover, Wheat-Sainfoin, Wheat mono, and Rye mono, showed significant effects on forage quality factors of crude protein (CP), NDF, NDFD-48, and the Relative Feed Value (RFV). The nutritive value of forage varied across different treatments, with CP content ranging from 6.28% to 18.94% and acid detergent fiber (ADF) content ranging from 10.28% to 40.27%. This variation suggests that the protein and digestibility requirements for livestock consumption are met at different stages. Additionally, the study suggests that intercropping perennial cereal grain crops with legume species using either alternate or the same row seeding methods can lead to consistent forage productivity across different sites. The forages from these intercropping systems exhibit adequate concentrations of CP and total digestible nutrient (TDN), with an average TDN concentration across all sites of approximately 59.3%. This makes them suitable for fulfilling the dietary requirements of beef or dairy cows at various developmental stages. Further research is ongoing to investigate ecosystem functions such as biological nitrogen fixation and water-use efficiency to optimize management practices for sustainable forage production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".