Animal husbandry – Milkers
Reexamination Certificate
2002-03-07
2004-11-09
Abbott, Yvonne R. (Department: 3644)
Animal husbandry
Milkers
C119S014140, C119S014180
Reexamination Certificate
active
06814025
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to a system and methods for optimizing the production performance of a milk producing animal herd. More specifically, it provides automated or semi-automated means for dynamic real time analysis of milk compounds and parameters to provide quantitative analytical data that are indicative of the overall physiological and nutritional state of the milking animals and which, if required, permit appropriate corrective measures to be taken.
TECHNICAL BACKGROUND AND PRIOR ART
It is known to monitor the physiological and nutritional condition of milking animals, such as cows. It is also known to collect data from individual milking animals including data for milk yield and composition, health condition data, feeding scheme data and breeding data such as genetic data. A currently common procedure is to collect milk samples manually from individual milking animals at regular intervals and subsequently ship the samples to a central laboratory for chemical and biological analysis, thereby deriving information on the milk quality as well as the health condition of each individual milking animal.
In most milk producing countries, dairy herd improvement associations (DHIAs) will collect, evaluate and distribute such data relating to e.g. milk yield, milk quality and mastitis (i.e. Inflammation of the mammary gland). Based on these data that are available from the DHIAs, the dairy farmers can select the best milking animals for breeding, make appropriate adjustments to feeding schemes and control health to thereby optimize the milk production.
However, this current procedure for collecting such data is cumbersome and it requires a substantial amount of manpower as the milk samples are collected manually at the milk production unit and shipped to central laboratories to be analyzed. Consequently, milk from each milking animal is typically only analyzed 6-12 times per year. Using such a procedure it is not possible for the individual farm manager to take immediate corrective actions and, e.g., implement feeding scheme adjustments or initiate prophylactic measures or antibiotic treatments to control disease conditions.
This lack of access to updated information on significant production parameters involves several problems. As an example, the above procedure implies that milking cows may suffer from subclinical mastitis or other diseases for several weeks until detection hereof. An early detection of mastitis is highly desirable, as this condition has an important impact on the overall dairy farm business economy. A further important herd management parameter is that of selecting the optimal point in time for insemination of animals in heat. This is presently done by visual inspection of animals, which evidently is a cumbersome and unreliable procedure. Accordingly, it is economically important for dairy farmers to have instantaneous access to updated data that are indicative of heat and pregnancy in order to determine the optimal insemination time and control of pregnancy. Additionally, aberrant physiological conditions related to feeding such as ketosis, a metabolic disorder, and the overall metabolic balance of the milking animal, such as the protein balance in the rumen, are conditions for which there is a need to institute immediate corrective measures, which, however, is not possible unless up-to-date data that indicate the presence of such conditions are available.
Therefore, a substantial need exists for automated or semi-automated systems and methods that can provide the dairy farmer with instant access to real time data indicating the immediate physiological and nutritional condition of individual milking animals as the basis for taking instantaneous corrective actions to continuously improve the overall production performance of the milk producing herds including productivity of the milking animals, animal welfare and protection of the environment against pollution with animal waste, and with that the overall profitability of the dairy farm.
A review of research objectives for the development of monitoring and sensing systems for controlling the health of dairy cows has been given by Mottram (Livestock Production Science, 1997, 48:209-217).
Several automated systems for monitoring selected compounds and parameters in milk have been developed. Thus U.S. Pat. No. 5,873,323 discloses a method of milking animals automatically while determining whether the milking animal is diseased or in oestrus. Sensors are placed in the milking conduit system leading from the teat cups. The sensors measure simultaneously several parameters including milk flow, milk temperature and electric conductivity of the milk (mastitis detection). The data obtained by the measurements are provided to a computer which compares the new data with similar data from immediately preceding selected periods of time to identify aberrant values that indicate which animals are diseased or in oestrus.
U.S. Pat. No. 5,743,209 discloses a system and method for monitoring and controlling milk production at dairy farms that is capable of qualitative analysis of the composition of milk samples using IR/NIR optical probes. Compounds that are analyzed simultaneously include fat, protein, somatic cells (indicator for mastitis), casein, lactose and urea. EP 896 222 A3 discloses a system for monitoring and controlling protein utilization in animals by automatically analyzing the urea content in milk using a sensor unit containing urease. SE 9902972 discloses a method and a system for analyzing milk during the milking operation, including simultaneous analysis of somatic cells, “clots”, salt ions and progesterone. The analyzing means are generally light emitter means and light detection means, however, there is, in very general terms, referred to the use of a biosensor for measuring, i.e., progesterone, urea and ketone bodies.
The achievement of the highest possible productivity of a milk producing animal herd is an extremely complicated task, as the productivity is highly dependent on a variety of factors including: (i) optimal utilization of feed rations which requires that feeding schemes are designed on an individual animal basis or a group basis and is continuously adjusted according to the milk yield (lactation state); (ii) tight control of subclinical and clinical disease conditions that have an adverse effect on milk yield and composition; (iii) optimal reproduction control including the selection of the most appropriate point in time for insemination to avoid any reduction of overall milk yield due to deferment of new pregnancy, and reliable detection of pregnancy.
Ideally, an automated or semi-automated system for optimizing the production performance of a milk producing animal herd should therefore have the capability to provide, on a real time basis, quantitative measurements of a combination of compounds and parameters in milk samples from individual herd members or a group of herd members that are indicative of all of (i) the health condition, (ii) the physiological condition, (iii) the nutritional and energy state, (iv) the state in the oestrus cycle and (v) pregnancy.
From the above, it is evident that the overall production characteristics of an individual herd member will vary considerably over times, e.g., depending on its state in the lactation cycle and the reproduction cycle which will, e.g., have a significant impact on the feed requirements and utilization of that particular herd member. This variation implies that the range of compounds and parameters that it is required to monitor at any given point in time varies.
In addition to being capable of generating data for all relevant milk compounds and parameters that are required to fully optimize the production performance of a milk producing animal herd, the ideal automated or semi-automated system should be cost-effective. This implies that the individual analytical processes should be based on relatively cheap methods. As it is described in the following, a significant reduction in costs can be achie
Chen Fei
Hansen Henrik
Larsen Flemming
Mathiasen Thomas
Abbott Yvonne R.
Hunton & Williams LLP
Lattec I/S
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