Influence of Phosphorus Fertilizer on “Ware” Potato Production in Acid Soils in Kenya
Bibliographic record
Abstract
One of the major challenges facing potato (Solanum Tuberosum L) production in Kenya is low and declining yield. This trend is caused by several factors which include low quality and quantity of seed, climate change, inadequate extension services, pests and diseases and more importantly low and declining soil fertility, particularly phosphorus (KEPHIS, 2016 and Karanja et al., 2014). Unfortunately, the current phosphorus fertilizer rate recommendation available for “ware” potato production in Kenya is “blanket” or general (90 kg phosphorus ha-1) and has not been updated for a long time to address the declining soil fertility. This prevents proper utilization of phosphorus fertilizers in achieving optimal production of “ware” potatoes. Therefore, this study investigated influence of different rates of phosphorus (TSP) fertilizer on “ware” potato yield and quality in three acidic (pH ≤ 5.8) test sites: Lari, Ainabkoi and Saboti sub Counties. Two varieties, Unica and Shangi, were tested. The field experiment was a split plot arrangement in Randomised Complete Block Design (RCBD) with six treatments (0 N & 0 P), 0, 30, 60, 90 and 120 kg ha-1 phosphorus, replicated three times. Data collected included weight, quantity and quality of tubers. The data was analysed using analysis of variance (ANOVA) at 5 % confidence levels with General Statistics (GENSTAT) and excel soft wares. Results indicated that phosphorus fertilizer influenced “ware” potato yield. At Saboti application of 120 and 90 kg phosphorus ha-1 for Shangi and Unica resulted in highest “ware” yield of 19.6 and 40.2 t ha-1, respectively. At Ainabkoi application of 120 kg ha-1 phosphorus produced highest “ware” potato yields of 10.7 t ha-1 and 26.8 t ha-1 of Shangi and Unica, respectively. At Lari, application of 90 and 120 kg ha-1 phosphorus produced highest “ware” potato yield of 7.0 t ha-1 and 17.5 t ha-1 for Shangi and Unica, respectively. During the season, there was a build-up of soil available phosphorus. Thus, there is need for farmers to test their soil at the beginning of every potato growing season.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".