Influence of dietary manipulations and milking frequency on production of dairy cows

Khaskheli, Asad Ali

doi:10.51227/ojafr.2020.25

Cited by 3 publications

(14 citation statements)

References 26 publications

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“…Nutritional consumption, milking frequency, high-quality feed, and ad libitum water intake are essential factors enhancing dairy cows' performance and productivity [ 22 ]. The higher the milking frequency, the better the feeding efficiency, the more frequent the feeding, and the more milk produced [ 22 ]. Dairy farmers have to examine various methods and techniques and their potential impact as they attempt to maximize herd profitability through outstanding herd performance [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…The feed and feeding method used influences dairy cattle productivity and reproduction. Suitable feeding is a basic need of dairy cows, and changing feeding frequency, despite appearing to be a simple idea, has been documented as a significant contributor to dairy cow production reduction [ 22 ]. Throughout lactation, dairy cows require a lot of nutrients [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…e following 10 production features were selected for this study: feeding frequency, feed type, watering frequency, milking frequency, vaccination frequency, frequency of extension officer visit, e Scientific World Journal milk peak value, total land, number of milking cows, and litres of milk sold. Nutritional consumption, milking frequency, high-quality feed, and ad libitum water intake are essential factors enhancing dairy cows' performance and productivity [22]. e higher the milking frequency, the better the feeding efficiency, the more frequent the feeding, and the more milk produced [22].…”

Section: Production Features Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Mavura

Pandhare

Mkoba

et al. 2022

The Scientific World Journal

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Smallholder dairy producers account for around half of all African livestock ventures; nevertheless, they face challenges in producing more milk due to an insufficient framework and infrastructure to maximize their output. Smallholder dairy producers in this scenario use a variety of tactics to boost milk output. However, the attempts need multiple heuristics, time, and financial investment. Furthermore, because of a lack of extension officers, smallholder dairy producers become trapped in failure cycles, unsuccessful attempts, and a diminished motivation to continue farming. Therefore, the interventions were more straightforward as smallholder dairy producers with comparable characteristics grouped. This research aimed to create a rule-based engine that automatically assigns smallholder dairy producers to predefined clusters. About 78 stakeholders were interviewed, including 69 smallholder dairy producers and 9 extension officers from Meru-Arusha, Tanzania. The 10 production features and 6 predefined clusters were adopted from the previous study. Therefore, a rule-based engine used the selected 10 production features. As a result, the rule-based engine automatically assigns the smallholder dairy producers to their respective clusters. Therefore, smallholder dairy producers share their farming skills and experience to increase milk output through these clusters. Furthermore, extension officers in the system provide timely assistance to smallholder dairy producers with farming concerns.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Production Features Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Mavura

Pandhare

Mkoba

et al. 2022

The Scientific World Journal

View full text Add to dashboard Cite

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“…The dry matter content in the diet has a significant effect on the total water intake [5]. The cows in the study group received a complete feed mixture with a dry matter content of 27.0 ± 0.4 kg•cow -1 .…”

Section: Methodsmentioning

confidence: 99%

“…Research on the water demand of dairy cows was conducted by quite many scientists for different housing conditions in different climatic zones. The season, milk yield, animal weight, diet dry matter content, barn climate, and precipitation are distinguished as the factors to a greater extent affecting the water intake [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Effect of water temperature in drinkers and their location on water intake by cows

Vtoryi

Gordeev

et al. 2021

20th International Scientific Conference Engineering for Rural Development Proceedings

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Water temperature in the drinkers and humid environment combined with low or high air barn temperature are equally important factors for cow health, water intake and milk productivity. The study purpose was to determine the water intake by lactating cows from heated drinkers with different location and water temperature and the barn climate in North-West Russia. A group of Holstein dairy cows with loose housing in boxes was selected for the study. Two automatic group drinkers with heating elements were installed in different places in the barn. The experiments had two variants of the drinkers' operation mode. In Variant 1, the heating element in Drinker 1 was switched off, in Drinker 2switched on. In Variant 2, the heating elements were switched off in both drinkers. In Variant 1, the difference in the average daily water temperature between Drinker 1 and Drinker 2 was from 4.8 °С to 6.2 °С, with the average daily air temperature in the barn being from +11.7 °С to +14.8 °С. In this case, the water intake from Drinker 2 with higher, the water temperature was 2.5-5 times bigger than that from Drinker 1. In Variant 2, the difference in water intake between Drinker 1 and Drinker 2 was 35.1-72.3%. At that time, the average daily air temperature in the barn ranged between +10.7 °С and +14.9 °С. The average daily water temperature in the drinkers was from +6.3 С to +8.7 С, with the difference between the drinkers not exceeding 0.8 С. During the study, the milk yield was from 36.3 to 41.4 kg•cow -1•day -1, with the water intake being from 1.96 l•kg -1 to 2.48 l•kg -1 . During the experiments, the cow diets did not change. The graphical models based on the experimental data allowed estimating the effect of water temperature on water intake by cows. The study also showed that drinkers' location influenced the water intake. So, when designing the modern drinking systems, more attention is needed to their optimal placement and provision of free access to freshwater for animals at all times.

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Mobile-based peer-to-peer learning prototype for smallholder dairy producers

Mavura

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About half of Africa's animal production comes from smallholder dairy farmers, who employ various strategies to maximize milk output. Some time-consuming and expensive heuristics are used by smallholder dairy farmers to increase milk yield, trapping them in a cycle of failure and lowering their incentive to continue making agricultural investments. Grouping smallholder dairy producers with comparable characteristics makes information sharing and interventions easier, increasing milk output. This study aimed at developing a mobile-based peer-to-peer learning prototype which considers farmers’ homogeneity with respect to husbandry practices and auto-allocates them to their respective production clusters. The developed prototype's rule-based engine handles the auto-allocation procedure by grouping farmers with similar farming characteristics into the proper production clusters. Smallholder dairy producers exchange knowledge and expertise through these groups to increase milk output. In Tanzania's Arusha Region, 69 smallholder dairy farmers and nine extension workers responded to a questionnaire to provide information, which was then analyzed using R programming. The important findings are; smallholder dairy producers were automatically allocated to their clusters based on their milk output. Cluster position regarding milk yields was determined using cluster performance for overall production attributes. Consequently, high yielding smallholder dairy producers are assigned to the high-yielding cluster, and vice versa, and extension officers provide timely support. This study is unique since smallholder dairy producers may use it to share dairy farming expertise and boost milk output. Mobile-based peer-to-peer should be integrated with the market by engaging enterprises that process milk for other milk products

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Influence of dietary manipulations and milking frequency on production of dairy cows

Cited by 3 publications

References 26 publications

Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Effect of water temperature in drinkers and their location on water intake by cows

Mobile-based peer-to-peer learning prototype for smallholder dairy producers

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