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Modelling Pasture-based Automatic Milking System Herds: System Fitness of Grazeable Home-grown Forages, Land Areas and Walking Distances.

ABSTRACT: To maintain a predominantly pasture-based system, the large herd milked by automatic milking rotary would be required to walk significant distances. Walking distances of greater than 1-km are associated with an increased incidence of undesirably long milking intervals and reduced milk yield. Complementary forages can be incorporated into pasture-based systems to lift total home grown feed in a given area, thus potentially 'concentrating' feed closer to the dairy. The aim of this modelling study was to investigate the total land area required and associated walking distance for large automatic milking system (AMS) herds when incorporating complementary forage rotations (CFR) into the system. Thirty-six scenarios consisting of 3 AMS herds (400, 600, 800 cows), 2 levels of pasture utilisation (current AMS utilisation of 15.0 t dry matter [DM]/ha, termed as moderate; optimum pasture utilisation of 19.7 t DM/ha, termed as high) and 6 rates of replacement of each of these pastures by grazeable CFR (0%, 10%, 20%, 30%, 40%, 50%) were investigated. Results showed that AMS cows were required to walk greater than 1-km when the farm area was greater than 86 ha. Insufficient pasture could be produced within a 1 km distance (i.e. 86 ha land) with home-grown feed (HGF) providing 43%, 29%, and 22% of the metabolisable energy (ME) required by 400, 600, and 800 cows, respectively from pastures. Introduction of pasture (moderate): CFR in AMS at a ratio of 80:20 can feed a 400 cow AMS herd, and can supply 42% and 31% of the ME requirements for 600 and 800 cows, respectively with pasture (moderate): CFR at 50:50 levels. In contrast to moderate pasture, 400 cows can be managed on high pasture utilisation (provided 57% of the total ME requirements). However, similar to the scenarios conducted with moderate pasture, there was insufficient feed produced within 1-km distance of the dairy for 600 or 800 cows. An 800 cow herd required 140 and 130 ha on moderate and high pasture-based AMS system, respectively with the introduction of pasture: CFR at a ratio of 50:50. Given the impact of increasing land area past 86 ha on walking distance, cow numbers could be increased by purchasing feed from off the milking platform and/or using the land outside 1-km distance for conserved feed. However, this warrants further investigations into risk analyses of different management options including development of an innovative system to manage large herds in an AMS farming system.

SUBMITTER: Islam MR

PROVIDER: S-EPMC4412988 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Simple Summary Automatic milking systems (AMSs) are revolutionizing dairy farming worldwide. Not only do they control the milking process, but they also bring changes to the whole farm system management. In this review, the examination focused on the study of AMSs using various modeling approaches. Our review primarily encompassed published articles addressing cows’ health, production, and behavior/management. Within this field, Machine Learning (ML) emerged as the prevailing modeling approach. Most of the studies were aimed at detecting cows’ health problems, especially mastitis. However, there is still a lack of a robust methodology for utilizing ML techniques in this domain, and it was also observed that the number of positive and negative cases is often unequal, leading to populations that are not balanced when predicting health issues. Only a small number of studies focused on milk production, even though accurate forecasting of individual cow milk yields could be very useful. Additionally, the study of cow behavior and herd management using AMSs is still not very explored. Abstract Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows’ health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows’ health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows’ behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Project description:BackgroundThe neighborhood environment may play a role in encouraging sedentary patterns, especially for middle-aged and older adults.PurposeThe aim of this study was to examine the associations between walking and neighborhood population density, retail availability, and land-use distribution using data from a cohort of adults aged 45 to 84 years.MethodsData from a multi-ethnic sample of 5529 adult residents of Baltimore MD, Chicago IL, Forsyth County NC, Los Angeles CA, New York NY, and St. Paul MN enrolled in the Multi-Ethnic Study of Atherosclerosis in 2000-2002 were linked to secondary land-use and population data. Participant reports of access to destinations and stores and objective measures of the percentage of land area in parcels devoted to retail land uses, the population divided by land area in parcels, and the mixture of uses for areas within 200 m of each participant's residence were examined. Multinomial logistic regression was used to investigate associations of self-reported and objective neighborhood characteristics with walking. All analyses were conducted in 2008 and 2009.ResultsAfter adjustment for individual-level characteristics and neighborhood connectivity, it was found that higher density, greater land area devoted to retail uses, and self-reported proximity of destinations and ease of walking to places were each related to walking. In models including all land-use measures, population density was positively associated with walking to places and with walking for exercise for more than 90 minutes/week, both relative to no walking. Availability of retail was associated with walking to places relative to not walking, and having a more proportional mix of land uses was associated with walking for exercise for more than 90 minutes/week, while self-reported ease of access to places was related to higher levels of exercise walking, both relative to not walking.ConclusionsResidential density and the presence of retail uses are related to various walking behaviors. Efforts to increase walking may benefit from attention to the intensity and type of land development.

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Modelling Pasture-based Automatic Milking System Herds: System Fitness of Grazeable Home-grown Forages, Land Areas and Walking Distances.

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