Development of an integrated Knowledge-Based System for Management Support on Dairy Farms

Hogeveen, H.; Noordhuizen-Stassen, E.N.; Schreinemakers, J.F.; Brand, A.

doi:10.3168/jds.s0022-0302(91)78634-7

Cited by 15 publications

(2 citation statements)

References 18 publications

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“…In the livestock farming industry, facilities should be designed in order to fulfill animals' needs and optimize profit [8,9,10,11]. Nowadays, industrial dairy farming facilities have become completely computer-controlled systems as a result of changing conditions such as improvements in the equipment used in the facilities, the importance of animal health, and the biological structure of raw milk ( [14,15,16]. The yield gained from the animals is directly affected by the modernity of the facilities [1,2,17].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Structural Systems for Industrial Dairy Farming Facilities

Arel¹,

Çipoğlu²,

Nouban³

2019

IJITEE

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This paper presents the assessment of conventional structural systems used for industrial dairy farming focusing on the cost and energy consumption. Various structural forms for industrial dairy farming were investigated considering dozen of structural models and materials. Eleven different structural systems were compared in terms of cost and energy consumption. Steel girders, steel columns, glued-laminated timber, reinforced concrete (RC) columns and steel trusses were used in this research as the structural components and materials. The SAP2000 software was used for the analysis of the modeled structures. The obtained results of the analyses demonstrated that the use of RC members or prefabricated concrete components is economically advantageous, while the use of glued-laminated timber was the least economical alternative. The highest cost was observed for a structural model fabricated with a conventional Gable steel frame and the lowest cost was observed for a structural model made of a 2-span twin mono-pitch Portal frames constructed with prefabricated RC beams and columns. However, the lowest energy consumption was observed for a structural model constructed with a single-span twin mono-pitch Portal frames constructed with RC columns and glued-laminated timber beams and the highest energy consumption was observed for the structural model fabricated with a conventional Gable steel frame, which had the highest cost

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Section: Introductionmentioning

confidence: 99%

Evaluation of Structural Systems for Industrial Dairy Farming Facilities

Arel¹,

Çipoğlu²,

Nouban³

2019

IJITEE

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“…The use of decision support systems (DSS) has been proposed to realise the full management benefit of SCC records (ALLORE et al, 1995). The application of Machine Learning (ML) techniques is therefore proposed for the development of a DSS, able to manage all this information (HOGEVEEN et al, 1994). The aim of this work is to highlight the possibilities of using ML techniques to deal with this task.…”

Section: Zusammenfassungmentioning

confidence: 99%

Machine Learning as an aid to management decisions on high somatic cell counts in dairy farms

Díez

López²,

Pajares

et al. 2005

Arch. Anim. Breed.

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High somatic cell counts (SCC) is associated with mastitis infection, in dairy herds, worldwide. This work describes Machine Learning (ML) techniques designed to improve the information offered to farmers on animals producing high SCCs according to particular herd profiles. The analysed population included 71 dairy farms in Asturias (Northern Spain) and a total of 2,407 lactating cows. Four sources of information were available: a) a questionnaire survey describing facilities, milking routines and management practices of the farms studied; b) dairy recording information; c) classification of the cows suspected of being healthy or subclinical mastitic according to farmers' expertise; and d) positive or negative scores with respect to the California Mastitis Test (CMT). The decimal logarithm of the SCC (linear score), lactation number, herd size, lactating cows per milker, milk urea concentration, number of clusters per milker and actual SCC are shown to be the most informative attributes for mimicking both farmers' expertise or CMT performance in order to identify animals producing persistently high SCCs in dairy herds. However, to improve the identification of cows suspected of being non-healthy, the system uses other information related to management and milking routines. Decision rules to predict CMT performance can provide useful, additional information to farmers to improve the management of dairy herds included in milk recording programs.

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Developing knowledge-based system for the diagnosis and treatment of mango pests using data mining techniques

Admass

2022

Int. j. inf. tecnol.

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Development of an integrated Knowledge-Based System for Management Support on Dairy Farms

Cited by 15 publications

References 18 publications

Evaluation of Structural Systems for Industrial Dairy Farming Facilities

Evaluation of Structural Systems for Industrial Dairy Farming Facilities

Machine Learning as an aid to management decisions on high somatic cell counts in dairy farms

Developing knowledge-based system for the diagnosis and treatment of mango pests using data mining techniques

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