Parvaneh Hashemi; Leila Taherabadi; Farokh Kafilzadeh
Abstract
Introduction: Ensilage is one of the methods of preserving forage plants for livestock feeding. The use of carbohydrate sources to prepare of silage improve the quality of fermentation by producing high lactic acid concentrations. The aerobic stability of silage after exposure to air is one of the quality ...
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Introduction: Ensilage is one of the methods of preserving forage plants for livestock feeding. The use of carbohydrate sources to prepare of silage improve the quality of fermentation by producing high lactic acid concentrations. The aerobic stability of silage after exposure to air is one of the quality parameters of silages. Heterofermentative lactic acid bacteria are among the additives that have been used to improve the aerobic stability of silages. However, there is no information on the use of the Lactobacillus fermentum on the aerobic stability of Napier grass silage with or without the use of carbohydrate sources.
Aim of study: This research was conducted to study the effect of Lactobacillus fermentum 92069 and molasses on fermentation properties, aerobic stability and in vitro digestibility of Napier grass silage as a new forage source (introduced for the first time) in the country.
Material and methods: Napier grass was cultivated at experimental station of school of Agriculture, Razi University on May 2021. In order to prepare Napier grass silage, the forage was harvested, chopped and treated with 0, 3 or 6 % molasses with or without 0, 1 × 106 cfu or 2 ×106 cfu of Lactobacillus Fermentum 92069 per gram of fresh forage. Forage was ensiled in laboratory silos with four replicates. After 90 days of ensiling, chemical composition and fermentation products of silages such as lactic acid, butyric acid, acetic acid, ammonia nitrogen and in vitro digestibility were determined. The fungal population of silages including yeast and mold were also determined. During the aerobic fermentation process, aerobic stability of the silages and changes in pH and the population of fungal in the silages were determined.
Results and Discussion: Increasing the level of molasses was associated with an increase in dry matter and soluble carbohydrates of silage. The lowest pH values (3.90 to 3.97) and higher production of lactic acid concentration (47.9 to 53.3 g/kg dry matter) were observed in silages containing the high level of molasses with or without bacterial inoculant. The effect of using Lactobacillus fermentum increased the production of acetic acid, but had no effect on the aerobic stability. There was no difference in the fungi population of silages from different treatments. The digestibility of dry matter and organic matter and also metabolize energy were higher in silages containing molasses with or without bacterial inoculant.
Conclusion: The current research regarding the Napier grass silage prepared with no additive had a relatively good quality. However, the use of molasses led to an improvement in the anaerobic fermentation and digestibility. The aerobic stability of Napier grass silages in spite of an increase in acetic acid was not affected by the addition of the Lactobacillus fermentum up to 2 ×106 cfu/ g fresh forage.
Ali Rezazadeh Vishkaei; Alireza Hasani Bafarani; Kian Pahlevan Afshar; Mehran Aboozari
Abstract
Objective: This research aims to systematically identify and analyze the key factors that influence the longevity of Holstein dairy cattle within herds by employing advanced data mining algorithms. Understanding and predicting longevity is vital because it directly impacts dairy farm productivity and ...
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Objective: This research aims to systematically identify and analyze the key factors that influence the longevity of Holstein dairy cattle within herds by employing advanced data mining algorithms. Understanding and predicting longevity is vital because it directly impacts dairy farm productivity and profitability. Longer-lasting cows tend to produce more calves over their lifetime, contribute to higher milk yields, and thus enhance the overall economic efficiency of dairy operations. Furthermore, extended longevity is associated with reduced replacement costs.
Materials and Methods: In recent years, the integration of machine learning techniques into agricultural and livestock management has gained significant momentum. This study utilizes detailed phenotypic data collected from 37,009 daughter animals belonging to 664 sires across 82 distinct herds, representing a comprehensive dataset that spans a decade. Data includes eight milk production records, alongside other relevant variables such as animal age, sire number, shelf life in months, somatic cell count, lactation days, milk production (kg), protein and fat content, calving cycle length, milking frequency, geographic location (province), birth date, calving date, calving interval, herd code, and age at first calving. The data preparation phase involved processing and organizing the dataset using Excel 2016, ensuring data quality and consistency. Subsequent data analyses were conducted using R software (version 4.3.3), employing relevant packages specialized for machine learning and statistical modeling.
Results: The results showed that, the Support Vector Machine has the best accuracy (0.987). The Random Forest was the second most efficient algorithm. The accuracy of the Gradient Boosting Machine was slightly lower than that of the Random Forest but still showed good performance. The Decision Tree provided the least accuracy among these algorithms. The Decision Tree and Support Vector Machine achieved this performance with fewer input variables compared to the Gradient Boosting Machine and Random Forest.
Conclusion: The results showed that none of the algorithms used for survival classification, despite acceptable accuracy, are error-free, but on the other hand, it was shown that the decision tree is simpler and less expensive. The most important features of these methods are the lack of statistical assumptions and requirements that linear regression and interpolation methods require, the lack of normality assumptions, robustness to missing values and values, and the ability to detect complex nonlinear relationships between variables and prediction objectives, which makes them suitable for various applications in the livestock industry. Accurate data recording protocols as well as precise algorithm settings are essential for accurate prediction.