Determination of factors affecting dairy cattle: a case study of Ardahan province using data mining algorithms

Karadaş, Köksal; Birinci, Avni

doi:10.1590/rbz4820170263

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Feed cost represents the largest contribution to the total cost of rearing (Boulton et al 2017). The feed cost depends on the feed price (Haryo et al 2017), type of feed, and amount of feed given (Karadas & Birinci, 2019). In addition, the delay of first calving age (FCA) showed a higher cost of rearing as compared to the suggested FCA (24-25 months) (López-Paredes et al 2018).…”

Section: Resultsmentioning

confidence: 99%

The Growth Performance and Costs of Rearing Friesian Crossbreed Dairy Young Stock in Malaysian Commercial Farm

Tong

Nor

Sithambaram

et al. 2021

jma

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The important elements in rearing dairy young stock are good farm management, proper growth and optimal costs of rearing. A survey on these important elements was conducted at two commercial farms in Johor and Sabah in 2019. The farm herd size is 214 heads and 2,221 heads with 163,682 litres and 4.2 mil. litres of milk production, in Johor and Sabah respectively. In addition, the body weight data of 188 dairy young stock was collected and analysed to determine the growth performance using polynomial growth function. The results showed the two farms have youngstock with different Friesian blood levels (60% and 70% in Johor, and 87.5% in Sabah) with different growth performance. The average weight of dairy young stock with 60%, 70% and 87.5% Friesian blood levels at birth were 21.31±3.70kg, 22.33±2.23kg and 26.55±2.68kg, respectively, while average weight at 3 months of age were 45.00±7.07kg, 55.57±8.36kg and 75.84±12.54kg, respectively. Heifers with 87.5% Friesian blood levels was bred at 15 months of age (444kg) while heifers with lower Friesian blood levels was bred 6 months later (250kg). The average rearing (feed) cost was RM4,932 (USD1,194)/heifer. The findings of this study can give awareness and insights in the performance and costs of rearing crossbreed dairy young stock in tropics.

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Section: Resultsmentioning

confidence: 99%

The Growth Performance and Costs of Rearing Friesian Crossbreed Dairy Young Stock in Malaysian Commercial Farm

Tong

Nor

Sithambaram

et al. 2021

jma

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“…Data Mining Decision Tree models are used recently in evaluating agricultural production and yield data, determining, and evaluating product, input, and yield relationships. Decision Tree Model has been mostly used in livestock farming (Aytekin et al, 2018;Celik et al, 2018;Karadas and Birinci, 2019;Zaborski et al, 2019), while studies on crop production are very limited (Küçükönder et al, 2015 Irmak and Ercan, 2017;Bostancı and Eren Atay, 2018). The aim of this study is to determine the factors affecting watermelon yield in Diyarbakır province by using Data Mining Regression Tree Method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of The Factors Affecting Watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) Yield Using Data Mining

Karadaş

Kadirhanoğulları

KADİRHANOĞULLARI

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production.

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“…In the view of Karadas and Birinci (2019) the breed is the most important factor of milk yield as demonstrated by all scientific hypotheses and applied algorithms, followed by calving month, control of mastitis before milking, provision of feed and duration of lactation.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

Int.J.Curr.Microbiol.App.Sci

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The aim of the present study was to establish the age of productive life and milk yields of different lactations in Holstein cows, born in the period from 2010 to 2017 using the Test day approach. The object of the study are cows of the Holstein dairy breed, bred on 18 farms in different regions of the country. An analysis of 333,721 records of milk productivity data for the control day was performed for 22,464 animals born in the period 2010-2017. The data for the records of origin and productivity are provided by ARCHSHPB -Dobrich. To analyze the factors influencing the assessed traits -milk yield for the control day, fat and protein content, a mathematical model was formulated based on the following hypothesis: variation in milk yield and quality of milk for the control day is caused by genetic and environmental factorsindividual, consecutive lactation, farm, breeder, month of production, effect of the genetic value of the individual and random, not studied and described in the model, factors. A control day model was used in which each daily milk control was considered as a separate observation. The data processing was performed using the software products Pest (Groeneveld) and SYSTAT 13. The number of animals that attained 4 th and subsequent lactation was low. The influence of the environmental factor farm on the three studied traits was very significant. The sire, test-day month and parity has statistically significant influence on milk yields. The highest average daily milk yield in cows was observed during the third lactation. Neither milk fat nor milk protein contents demonstrated consistent variations.

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Determination of factors affecting dairy cattle: a case study of Ardahan province using data mining algorithms

Cited by 6 publications

References 8 publications

The Growth Performance and Costs of Rearing Friesian Crossbreed Dairy Young Stock in Malaysian Commercial Farm

The Growth Performance and Costs of Rearing Friesian Crossbreed Dairy Young Stock in Malaysian Commercial Farm

Prediction of The Factors Affecting Watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) Yield Using Data Mining

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