Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees

Piwczyński, Dariusz; Sitkowska, Beata; Kolenda, Magdalena; Brzozowski, Marcin; Aerts, J.; Schork, Pamela M.

doi:10.1111/asj.13414

Cited by 19 publications

(13 citation statements)

References 60 publications

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“…A large number of factors relating to cows and the dairy production environment provide inspiration for forecasting the milk yield of cows, taking into account the herds milked via robotic systems. As a result of using appropriate statistical tools, including the decision tree technique [85], the most important factors responsible for the milk yield of cows can be identified. In the case of cows milked by AMSs, these factors were milking frequency, lactation number, and DIM (days in milk).…”

Section: Discussionmentioning

confidence: 99%

Implementation of Technical and Technological Progress in Dairy Production

Gaworski

2021

Processes

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The involvement of people and technical devices is a characteristic feature of technological processes in agriculture. Human access to modernized and more efficient technical equipment determines the differentiation of the proportions of the contributions of human labor and technical equipment to the implementation of production technology on farms. Taking into account the data on manual and machine work inputs, the methodology of determining the technological index level (TL) was presented. The aim of the present study was to present the scope of use of the technological index level to assess the effects of technological progress in the dairy production system, with particular emphasis on cow milking. For the value range of the technological index level (0–100%), changes in the milkman’s work efficiency were presented based on research carried out on farms equipped with milking equipment at different levels of technical advancement. Moreover, the course of changes in electricity and water consumption per liter of milk was determined in association with the technological index level. The issue of simultaneous implementation of various forms of progress was developed based on the example of milking cows with a milking robot. Five categories (ranges) of cows’ milk yield were distinguished and compared with the current yields of cows in the European Union. On this basis, a discussion was initiated on the factors that facilitate and limit the implementation of technical and technological progress in dairy production.

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Section: Discussionmentioning

confidence: 99%

Implementation of Technical and Technological Progress in Dairy Production

Gaworski

2021

Processes

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“…In fact, heat load accumulation and individual cow-related factors proved to be significant factors for prediction models based on the individual susceptibility of animals to heat stress [ 14 , 15 ]. Applied statistical methods used in the literature [ 16 ] showed that milking frequency, lactation number (parity number), month of milking, and type of lying stall represent important factors responsible for the monthly milk yield of dairy cows in farms with AMSs. In this context, Machine Learning (ML) algorithms have been already applied in some areas of dairy research, particularly to predict data, and they represent a promising tool, useful to develop and improve decision support for farmers [ 17 ] in order to increase both milk yield and animal welfare and, on the other hand, to reduce the resources needed, hence increasing the sustainability of the sector [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Bovo

Agrusti

Benni

et al. 2021

Animals

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Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

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“…Compared to the colostral period, milking speed slightly increased (up to 2.81 kg/min), milk electrical conductivity decreased (to 68.77 µS/cm), and milk temperature, like in the study of King et al [21], remained similar at 39.03 • C. Similar values (2-2.5 kg/min) for milking speed were reported by Gäde et al [29] and Bogucki et al [25], and higher values (3-4 kg/min) were observed by Carlström et al [30], who concluded that milking time and milk flow rate determine the cow's milkability. During 5-28 days of lactation, cows yielded over 35 kg milk/day, which is more than daily yield of AMS-milked cows in the EU countries and the USA in the years 2014-2017 reported by Piwczyński et al [11], who showed the highest value in the US population (33.5 kg/day) and the lowest value in Lithuania (22.7 kg/day). Milk fat content averaged 4.00% and average protein content was 3.54%.…”

Section: Resultsmentioning

confidence: 72%

“…Decision tree techniques have found application in dairy cow breeding to study mastitis [10], predict milk yield [11], parturition process [12] and reproduction in cows [13]. The advantages of decision trees (composed of the root, trunk, branches and leaves) [14] are that they are intuitive and it is easy to interpret the data shown as simple graphical models for analyzing the effect of single factors in the model but also their interactions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period

Kliś¹,

Piwczyński

Sawa

et al. 2021

Animals

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Early prediction of lactation milk yield enables more efficient herd management. Therefore, this study attempted to predict lactation milk yield (LMY) in 524 Polish Holstein–Friesian cows, based on information recorded by the automatic milking system (AMS) in the periparturient period. The cows calved in 2016 and/or 2017 and were used in 3 herds equipped with milking robots. In the first stage of data analysis, calculations were made of the coefficients of simple correlation between rumination time (expressed as mean time per cow during the periparturient period: second (14–8 days) and first (7–1 days) week before calving, 1–4, 5–7, 8–14, 15–21 and 22–28 days of lactation), electrical conductivity and temperature of milk (expressed as means per cow on days 1–4, 5–7, 8–14, 15–21 and 22–28), amount of concentrate intake, number of milkings/day, milking time/visit, milk speed and lactation milk yield. In the next step of the statistical analysis, a decision tree technique was employed to determine factors responsible for LMY. The study showed that the correlation coefficients between LMY and AMS traits recorded during the periparturient period were low or moderate, ranging from 0.002 to 0.312. Prediction of LMY from the constructed decision tree model was found to be possible. The employed Classification and Regression Trees (CART) algorithm demonstrated that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation. We proved that the application of the decision tree method could allow breeders to select, already in the postparturient period, appropriate levels of AMS milking variables, which will ensure high milk yield per lactation.

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Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees

Cited by 19 publications

References 60 publications

Implementation of Technical and Technological Progress in Dairy Production

Implementation of Technical and Technological Progress in Dairy Production

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period

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