What Drives Decline Productivity in Ageing Tea Plantation - Soil Physical Properties or Soil Nutrient Status?
Bibliographic record
Abstract
Over the years, the tea plantations in the Ribeira Valley, Brazil had been recording declining productivity and reduced tea quality. This had been associated with several factors including the age of the plantation, decling fertility, soil degradation among others factor. In this study, our objective was to identify the main driver of declining productivity in tea yield in the Ribeira Valley tea plantation in Brazil and to evaluate the effects of long-term tea cultivation on the physico-chemical changes and nutrient dynamics in the soil at 2 profile depths. Therefore, we evaluated the effects of long-term cultivation on changes in the physical and chemical properties of Acrisol Haplic planted to Tea in the Ribeira Valley region, Brazil. The soil samples were collected at two depths 0-10 cm and 10-20 cm in two representative plantations and analyzed for chemical, physical and mechanical soil properties. The selected sites; Thea Hills – TH1987 and Braço Preto – BP1972 presents different plantation ages on a similar cultivation practices. The harvested quantities of tea from the sites were monitored and their quality classified following international standards. We observed declining productivity in terms of harvested tea from both sites and the quality of harvested tea were better in TH1987 than BP1972. The soil nutrient study indicated an improvement in fertility parameters in the older plantation (BP1972). We observed degradation in the soil physical quality parament and all possible factors indicated that degradation of the soil structure through compaction was the main factor resulting in the observed declined productivity (quantitatively and qualitatively). Soil structure degradation adversely affects the soil functions including aeration, crop water use efficiency and soil nutrient conversion. We recommend appropriate soil tillage management in tea plantations too improve the productive cycle and capacities.
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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 | 0.001 |
| 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 | 0.001 | 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".