Multiyear Assessment of Biofertilizer Application on ‘Gala’ Apple Orchards: Impacts on Soil Fertility, Leaf Mineral Content, and Agronomic Performance
Bibliographic record
Abstract
Biofertilizers are sustainable alternatives to mineral fertilizers in perennial crops, reducing the need for mineral inputs. This five-year field study evaluated three biofertilizers—Mycoshell® (Glomus spp. + humic/fulvic acids), Kiplant iNmass® (Azospirillum brasilense, Bacillus megaterium, and Saccharomyces cerevisiae), and Kiplant All-Grip® (Bacillus megaterium and Pseudomonas spp.)—at different dosages alongside two mineral fertilizer regimes, T100 (full recommended dose) and T70 (70% of T100, alone or combined with biofertilizers), in an apple orchard under Mediterranean conditions. Biofertilizers maintained or increased soil nutrient availability by 5–15% and leaf N, P, K, Mg, and Zn concentrations by 5–12% compared with T100. Trees under biofertilizers, particularly Myc2 and Myc4, exhibited greater shoot growth (up to 30.4 m/year), trunk cross-sectional area (TCSA: 11.9 cm2 in 2022), and canopy volume (2.21 m3), representing 10–20% increases. Selected biofertilizer treatments produced 6–7.5 kg/tree, 130–145 g average fruit weight, 66–74 mm diameter, 13.9–18.7 °Brix, and 13–18% dry matter, maintaining >90% of yield and fruit size relative to T100, with more balanced medium- and large-sized fruit distribution. Principal Component Analysis explained 66–72% of soil and leaf nutrient variance, confirming their multivariate impact. Overall, biofertilizers applied at recommended doses and timings can partially replace mineral fertilizers, sustaining productivity and quality, enhancing nutrient availability, and supporting long-term orchard sustainability. While climate remains the main driver of annual production, these findings provide evidence for integrating biofertilizers into environmentally friendly fertilization strategies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".