Study on the influence of different production factors on PSY and its correlation

Abstract

Background Finding out the key reproductive performance factors, affecting piglets weaned per sow per year (PSY) can improve the production efficiency and profitability of pig farms. The objective was to understand the actual distribution of different production factors and PSY of breeding pig farms, analyze the correlation to find the main production factors affecting PSY, and formulating a Production Efficiency Improvement Plan in practice. Data included 603 breeding pig farms from September 28, 2020 to September 26, 2021. Regression analysis was used to evaluate the relationship between PSY and key production factors, and the characteristics of total pig farms versus high performance (HP) pig farms (the production performance was in the top 10%) or top 5% pig farms were compared. Spearman’s rank correlation coefficient was used to analyze the correlation between production factors and find the factors related to PSY. Non-linear support vector regression (NL-SVR) was used to analyze the personalized PSY improvement through a various change of the four key factors. Results The median distribution of 15 production factors and PSY in total pig farms were different from those of HP farms. All of data were distributed nonlinearly. Mating rate within 7 days after weaning (MR7DW), farrowing rate (FR), number of piglets born alive per litter (PBAL) and number of weaned piglets per litter (WPL) were moderately correlated with PSY, and the correlation coefficients were 0.5058, 0.4427, 0.3929 and 0.3839, respectively. When the four factors in NL-SVR changed in medium (0.5 piglet or 5%) or high level (1.0 piglet or 10%), PSY can be increased by more than 0.5. Conclusion NL-SVR model can be used to analyze the impact of changes in key production factors on PSY. By taking measures to improve MR7DW, FR, PBAL and WPL, it may effectively improve the current PSY and fully develop the reproductive potential of sows.

Comparative analysis of productive performance and fattening efficiency of commercial pigs in China for two consecutive years

“…In our study, we found five productive factors with the highest correlation with FEI (Table 2). Targeted improvement of these factors may help improve the productivity of commercial pigs, including appropriate nutrition, reasonably supplemented additives, good care at the early nursing stage, special attention to slow-growing pigs, pig health management (control and prevention of viral infections and secondary bacterial infections), correct housing conditions (environmental temperature and ventilation), available bio-security measures, appropriate immunization procedures and regular staff training 14,20,[28][29][30][31][32][33][34] .…”

Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms

“…The automatic feeding system, mechanical ventilation system, formula of standardized feed and mating schedule of these farms were described in a previous study [ 8 ]. The farms were from 20 provinces, located in seven regions of the country, namely, East China (40.5%), North China (3.8%), South China (15.2%), Central China (8.9%), Northwest (15.2%), Northeast (5.1%) and Southwest (11.4%) regions.…”

“…The collection and authorization of these production data in this study were described before [ 8 ]. This study analyzed 291 large-scale (750–5,800 sows) pig farms for the whole year of 2021.…”

“…However, there is a lot of room for improvement in this regard in China. In production practice, if you can choose the factor that has the greatest impact on PSY among the related factors of PSY, improving this factor will achieve a multiplier effect with half the effort [ 8 ]. Algorithm models (including linear and nonlinear models) may provide an effective technical method to solve this problem.…”