Effect of Cover Crops on Yield and Leaf Nutrient Concentrations in an Organic Honeycrisp Apple (Malus domestica ‘Honeycrisp’) Orchard in Nova Scotia, Canada
Bibliographic record
Abstract
There is an increasing interest for use of cover crops in orchards; however, the species that are most likely to be successfully implemented and their impact on yield and soil productivity have not been fully explored under Maritimes climate. This study investigated the effect of various cover crops treatments on organic apple ( Malus domestica Borkh cv. Honeycrisp) yield and leaf nutrient concentrations in Nova Scotia over 3 years. Various cover crop mixtures including legumes, cereals, and grasses were planted using a modified Swiss Sandwich System (SSS). The cover crops treatments did not affect apple yield. In 2012, the input of biomass to the soil was 89% and 144% greater for alfalfa (ALF) and other cover crop treatments than unseeded (CON) treatment, respectively. The pea, oats, vetch mixture (POVM) contributed 24% higher biomass N to soil compared with average of other cover crops in 2012. Soil available K concentration in the tilled strip was increased in the 3rd year of the study compared with the initial values across cover crop treatments. The red clover oats mixture (RCOM), POVM, and Triple Mix (TM) treatments appeared to add the greatest amount of available K to the soil among treatments. The CON, TM, and ALF treatments resulted in higher leaf Mn concentration in only 2012 and CON, sweet clover and oats mixture (SCOM), and ALF resulted in higher leaf P concentration in 2014, compared with other treatments. Cover crops did not compete with apple trees and their most beneficial and consistent contribution was to total C, total N, and K input to the soil.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".