Rearing climate resilient livestock for better productivity:a review

Nayak, Vivek; Pathak, P. K.; Adhikary, Sudip

doi:10.5455/ijlr.20170823072522

Cited by 2 publications

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“…Tropical countries have been reported to have over 50% of the world bovine population and Nigeria has been reported to be one of the leading countries in cattle production in Sub-Saharan Africa (Nayak et al, 2018). Over 90% of these populations are traditionally managed in the Northern part of Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Milk Yield and Composition of Dairy Cattle During Dry Season in Selected Dairy Farms in Kano State, Nigeria

Shitu¹,

M.²,

M.³

2023

JASD

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The aim of the study was to assess the milk yield and composition of dairy cattle of different locations during dry season in selected dairy farms in Kano state. One hundred and twenty lactating cows (different stages of lactation) include Bunaji, Bokoloji, Fresian x Bunaji were used, ten from each twelve dairy farms to assess milk yield and composition. Daily milk yield was recorded for two months. Five milk samples from each farm were analysed for its composition. Danbatta farm was the best in average daily milk yield (ADMY) of 3.86l, Dantube farm was the lowest of 2.49l which differ (p<0.001), Imawa arewa, Asibitin tafasa and Lautaye farms didn’t vary other farms differ. Fat(%) did not vary in all the farms, TS(%) Total solid, TA(%) Titrable acidity, CP(%) Crude protein, Ash(%), SNF(%) Solid non fat differ (p<0.001). Tsaunin fulani farm was the best in Fat(%) 4.26, Asibitin tafasa farm was the lowest of 3.84. Lautaye was the best in TS(%) 57.2, Dakatsalle was the lowest of 38.8. Imawa arewa was the best TA(%) of 0.38, Dakatsalle, Kuki and shinkafi farms are the lowest of 0.29. Imawa arewa, Danbatta and Kwanar dumawa farms are the best in CP(%) 4.24, Kuki farm was the lowest of 3.46. Dantube farm was the best in Ash(%) of 0.54, Dakatsalle, Kuki are the lowest of 0.32. Lautaye was the best in SNF(%) 52.96, Dakatsalle farm was the lowest of 34.6. Fat(%) differ (p<0.05), TS(%) (p<0.001) among the breeds while their ADMY, TA(%), CP(%), Ash(%), SNF(%) did not vary. Bunaji was the best in Fat(%) of 4.22, TS(%) 50.2, SNF(%) 45.74 with ADMY of 2.74l. Fresian x Bunaji was superior in CP(%) 4.24, lowest in Fat(%) 3.88, Ash(%) 0.44, TS(%) 39.2, SNF(%) 35.32 with the best ADMY of 4.01l. Bokoloji was superior in Ash(%) of 0.47 lowest in TA(%) 0.31 with ADMY of 2.67l. Therefore, Danbatta farm, Fresian x Bunaji with the best milk yield, Tsaunin fulani farm with the best Fat(%), Lautaye farm TS(%), Imawa arewa TA(%), Imawa arewa, Danbatta and Kwanar dumawa farms CP(%), Dantube farm Ash(%), Lautaye farm SNF(%), Bunaji breed with the best Fat(%), TS(%), TA(%), SNF(%), Bokoloji breed Ash(%), Fresian x Bunaji CP(%) was recommended. Milk yield and composition varied between dairy breeds, herds of the same breed and even between individual cows of a breed.

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Section: Introductionmentioning

confidence: 99%

Milk Yield and Composition of Dairy Cattle During Dry Season in Selected Dairy Farms in Kano State, Nigeria

Shitu¹,

M.²,

M.³

2023

JASD

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“…Milk production from indigenous breeds of cattle in Nigeria represents an important component of the agribusiness sector of the smallholder economy with great economic, nutritional and social implications (Oladapo and Ogunekunn, 2015). According to Nayak et al (2018), tropical regions are home to more than 50% of the world's cattle, and Nigeria is one of the top producers of cattle in Sub-Saharan Africa (Kubkomawa, 2017;Sam and Usoro, 2022). Traditional pastoralists are in charge of more than 80% of this (Kubkomawa, 2017).…”

Section: Introductionmentioning

confidence: 99%

Predicting some Milk Constituents from Linear Body Measurements and Udder Characteristics of Bunaji Cows Utilizing Machine Learning Techniques

Iyiola-Tunji,

Muniru,

Muktar

2023

AKSUJA

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Machine Learning techniques are capable of being used in agriculture since they are powerful, fast and flexible tools for classification and predictions of traits of economic importance, particularly those involving nonlinear systems. This study aimed to show how well machine learning methods could predict milk's crude protein and fat content based on the Bunaji cows' linear body measurements and udder characteristics. Forty (40) lactating Bunaji cows from two dairy farms in Yola South Local Government Area, Adamawa State, were purposefully selected for the study. Milk samples were collected from the cows (one-off measurement). The crude protein and fat content of the milk were determined in percentage. An ensemble model consisting of the random forest (RF), support vector machine (SVM) and neural network (NN) algorithms were created using the Orange Software. The models were cross-validated using five folds to assess their robustness. Mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2 ) are the metrics utilized for the evaluation. Using the udder features, the approach was used to construct demonstrative models for the prediction of milk protein content. The SVM and RF learners had MSE that was lower than that of NN, showing that the two were superior to NN. The lower performance of NN in this study can be explained on the basis of extremely low data that was available for this study. RF learners exhibited a low likelihood of mistakes in predicting crude protein from udder traits despite the study's small data set. Large data sets were needed for machine learning techniques. As a result, these models must be improved with lots of data.

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Rearing climate resilient livestock for better productivity:a review

Cited by 2 publications

References 0 publications

Milk Yield and Composition of Dairy Cattle During Dry Season in Selected Dairy Farms in Kano State, Nigeria

Milk Yield and Composition of Dairy Cattle During Dry Season in Selected Dairy Farms in Kano State, Nigeria

Predicting some Milk Constituents from Linear Body Measurements and Udder Characteristics of Bunaji Cows Utilizing Machine Learning Techniques

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