Microclimate and Macronutrient In-Season Variations Influence Fruit Crop Growth and Photosynthesis
Bibliographic record
Abstract
Microclimate from small-scale topographic changes in the landscape can cause many environmental variations in uneven solar irradiance, light transmission, air/soil temperatures, and soil water distributions, which can eventually lead to plant abiotic stress. Primary macronutrients (N, P, and K) are very important to ensure healthy plant growth and crop production. This chapter focuses on understanding the impacts of microclimate and limited macronutrient NPK supplies on the physiological growth and fruit productivity of strawberries ( Fragaria×ananassa Duch.), a popular small fruit crop. By summarizing the data of three separate field studies conducted in Nova Scotia, Canada, the results show how strawberry crops respond to in-season variations in solar radiance, soil temperature, soil water, and macronutrient NPK limitation under different crop rotation conditions. The findings indicate that microclimate variations could significantly ( P < 0.05) affect strawberry plant reflectance water index, and fruiting capacity. The results also reveal that limited NK supplies could significantly ( P < 0.05) affect strawberry leaf intercellular CO 2 concentrations, net photosynthesis rates, stomatal conductance, water use efficiency of photosynthesis, and fruit yields and quality (total dissolved solids and N/K ions). Limited P supplies could also lead to significantly higher ( P < 0.05) strawberry plant prolongation and P nutrient uptake. It was concluded that the term microclimate could be useful to describe and explain the differences in strawberry growth and fruit yield in space. The orientation of rows could help plants capture maximum sunlight to reduce stress impacts from microclimate variations. Limited micronutrient NPK supplies at the 25%level could be useful for the physiological development of strawberry crops under different types of crop rotation regimes.
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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 | 0.000 | 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".