Analysis and utilization of farm performance data as a tool in enhancing swine farm productivity and efficiency
Bibliographic record
Abstract
Reproduction performance. Twenty for commercial farms participating in the three year swine performance monitoring of PCARRD and PSIRDFI were utilized in this analysis. The small (n=5; mean=133 SL) medium (n=4, mean=332 SL) and large scale (n=15; mean=1296 SL). Commercial swine farms were grouped according to sow levels (SL), because resources and capital availability differ between these farms sizes. Using General Linear Model, the statistical model partitions the effects of farm sizes (F), year(Y), quarter (Q), quarter (months), F*Y, F*Q, interactions effect on all parameters, such as percent mummified fetus (PMF), percent still births (PSB), percent born alive (PBA), total pigs born (TPB), litter size born alive (LSBA), litter size and weaning (LSW), weaning age (WAD), gestation period in days (GPD), 30-day weight (A30DWT), incidence of abortion in sows (IAS), percent mortality based on total pigs born (PMBT), litter size of weaning (LSW), percent weaning mortality (PWM), farrowing rate (FR), nonproductive days (NPD), weaning to conception interval (WCI), farrowing interval (FInt), farrowing index (FInd) and pigs weaned per sow per year (PWSY). The parameters showing significant differences between months nested within the quarter were TPB, LSBA, LSW, PWSY, PBA, PWM, PMBT, LSW, FR, NPD and FInd. The reproductive syndrome was mostly observed on the 3rd quarter manifested by, significantly higher PMF, PSB, PWM, FInt, IAS, PMBTP and NPD lower LSBA, LSW, PWBT, PBA, FR and PWSY. This 3rd quarter syndrome may be attributed to 1)hot summer breeding months affecting the spermatogenesis and oogenesis, 2) aggravated by lower voluntary feed intake, hence energy reserve; and 3) the lower quality feeds with the onset of rainy season due to possible presence of mycotoxin in feeds. The Farm Sizes × Quarter interactions reveals that the large commercial farms were consistently the best in terms of WCI, FR, FInt and NPD. The incidence of abortion was least in small and worst in medium commercial farms. The farm that is most affected on the third quarter was the medium commercial farms, manifested by lowest FR and highest FInt, IAS and NPD, suggesting the vulnerability of the sows because of lower quality feed. The large-scale commercial farm obtained significantly better reproductive performance indicators, but TPB and LSBA was significantly higher in small farms. The higher TPB in small farms was offset by higher mummified fetuses compared to medium and large commercial farms resulting to comparable PWSY for all commercial farms. Production Performance. The percent suckling and grower mortality, percent pre-starter ration and PPSY showed significant monthly variation, which are the results of the third quarter reproductive syndrome (Vega et al, 2009). The farm efficiency is entrained by FCKLAS [feed cost per kilo of live animal sold] and reaffirms the effect of medium scale feed formulated ration and high temperature in summer, prolonged by high RH at the onset of rainy months. The weight and age of RSHS hogs are low in the months of Apr-Jul, which reflects early sales of hogs due to cultural tradition. This is further supported by the increase in prices of regular slaughter hogs from Jan-June and declines thereafter until Nov. The savings earned by medium scale farms on equipments, housing and space is offset by the feed cost in producing a kg of live animals sold (PhP 5.0/kg) throughout the year. The sixteen years of reproduction parameters trend in swine commercial farms was analyzed using PROC REG of the SAS statistical software.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".