- Original article
- Open Access
What are Production Diseases, and How do We Manage Them?
- Oded Nir (Markusfeld)1
© The Author(s); licensee BioMed Central Ltd. 2003
Published: 31 March 2003
The term "Production Diseases" referred traditionally to those diseases induced by management practices, metabolic diseases are typical examples. Recently, the term "Production related diseases" has been enhanced to include other traits, such as infertility, and diseases such as mastitis and lameness that might involve infectious agents but exacerbated by nutritional or managemental factors. The presentation deals with Production Diseases in the context of integrated herd health programs, using periparturient diseases and traits as an example. Studies, based on 9377 lactations of cows calving in the period 1995 through 1998 from the author's practice in 7 Israeli Holstein herds, show that most periparturient diseases and traits are followed by increased culling, lower production associated with late peaks and lower persistency, and impaired fertility. The effects are independent of other diseases, and at times are long lasting. Production Diseases are often multifactorial and appear at the same stage of lactation. Independent relationships among them must be established, so that common cause effects, direct and indirect causal associations, and incidental relationships can be differentiated. Control of Production Diseases often involves various disciplines and therefore calls for a "multivariate approach". Such an approach, centered on the herd, has led to the adaptation of integrated programs for herd health. The programs are characterized by the adaptation of multidisciplinary, multifactorial, and a population approach to clinical entities. Preventive measures and routine examinations are the hard core of programs, but deeper involvement in nutrition, production and economics is called for. A routine monitoring and causal analysis of periparturient traits and diseases, production, fertility and abortions are carried out, relevant data are processed, and monitoring reports are issued routinely. Five different linear regression models evaluate factors responsible for losses of a) peak milk yield; b) economy corrected (ECM) peak milk yield; c) extended 305-d milk yield; d) daily 3.5% FCM in the first 90 days in milk; and e) persistencies. Three different logistic and linear regression models evaluate factors that contribute to a) "non pregnancy to first service"; b) unobserved heat; and c) open days. Narrowing down the field of investigation is essential for an intervention to be efficient. Conclusions are drawn from the epidemiological study and the proposed recommendations are weighed with cost/benefit considerations. Possible losses are quantified and used with expected return value in decision analysis. Production Diseases are at times the outcome of managemental mistakes brought about by the drive for higher yields. Integrated herd health programs help to control the negative effects of management by enhancing production under optimal feeding and management regimens. The estimated contribution of improved management to the Israeli national herd phenotypic increase in yield, and the improved fertility that followed the increase in milk yield presented, show that the goal is within reach.
Summary of multiple logistic regression analyses for ketosis of 590 third or more calvers in 6 herds, 1996a.
Long dry period
Postparturient uterine diseases
BCS ≥ 3.75 at drying off
Milk fat to protein ratio in 1st test dayc
67.6 ± 9.1
Effects of calving diseases and traits on culling variables for 6775 lactations.
1st test day
6th test dayc
1st test day
6th test dayc
Mastitis at calving
Dry for <60 days
Dry for >75 days
Effects of calving diseases and traits on milk yield variables for 5974 lactationsa.
Trait or disease
Month of peakc
Primiparous cows (2315 lactations)a
Multiparous cows (3659 lactations)b
Mastitis at calving
The association of fertility indices with calving diseases and traits (9377 lactations in 7 herds for cows calving 01/95 through 06/98).
Not pregnant to first AIa
Open >150 daysa
Primiparous cows (3620 lactations)c
Induction of calving
Multiparous cows (5757 lactations)d
Induction of calving
Culling and postparturient diseases
Data are from 2415 lactations of primiparous and 4360 lactations of multiparous cows from the author's practice in 6 Israeli Holstein herds, calving in the period 1995 through 1998. Respective lactational incidence rates for primi- and multi-parous cows were 11.9% and 2.3% for induction of calving, 6.8% and 4.8% for stillbirth, 0.5% and 0.6% for prolapsed uteri, 10.0% and 0.8% for edema, 1.3% and 8.9% for mastitis at calving, 18.4% and 18.7% for retained placentae, 35.3% and 16.4% for primary metritis, 1.0% and 1.6% for left displacement of the abomasum, and 1.5% and 9.0% for ketosis. Rates of twins, milk fever, dry-period <60 days, and dry-period >75 days for multiparous cows were 6.9%, 3.6%, 18.3%, and 11.1% respectively. Respective rates of primiparous cows culled before the 1st and before the 6th monthly test days were 5.1% and 11.2% for primiparous cows and 7.0% and 17.2% for multiparous cows respectively. Crude (for the 6th test day) and summary odds ratios being culled after the various calving diseases and traits are presented in Table 2, increased culling follows most calving diseases and traits. The effects are independent of other diseases (ketosis excluded) and at times long lasting.
Yield and postparturient diseases
The effects of the various calving diseases on yield and components of the lactation curve in 5974 lactations of cows that had 6 monthly test days are described in Table 3.
Means and SD of peak milk yields, month of peak yield, persistencies, and extended 305-d milk yields were 36.8 ± 4.8 kg and 48.7 ± 6.9 kg, 3.5 m ± 1.3 months and 2.5 ± 1.1 months, 89.4% ± 4.6% and 86.9% ± 5.6%, and 9511 ± 1341 kg and 11574 ± 1837 kg for 2315 lactations of primiparous and 3659 lactations of multiparous cows respectively. Most calving diseases and traits are associated with lower production, late peak and lower persistency.
Fertility and postparturient diseases
The relative contributions of calving diseases to fertility indices are presented in Table 4. Data are from 3620 and 5757 lactations of primiparous and multiparous cows respectively, calving in 7 herds in the period 01/95–06/98.
Rates of unobserved heat, inactive ovaries, not pregnant to first service, and open >150 days from calving for primiparous and multiparous cows respectively were 35.1% and 42.9%, 8.3% and 8.8%, 55.8% and 66.1%, and 27.7% and 29.7%. Mean rest periods were 84.7 ± 24.7 days SD and 74.1 ± 19.7 days SD for primiparous and multiparous respectively.
Control of Production Diseases often involves various disciplines and therefore calls for a "multivariate approach". Such an approach, centered on the herd had led to the adaptation of integrated programs of herd health. The programs are characterized by multidisciplinary, multifactorial, and with a population approach to clinical entities. Preventive measures and routine examinations are the hard core of programs, but deeper involvement in nutrition, production and economics is called for . Not losing ground in individual cow medicine when going into herd medicine, application of methods to small and large herds alike, and finally the advancement of techniques and methods serving the new approach comprise some of the difficulties encountered.
Integrated herd health program.
1. Early professional treatment.
5. Processing data.
a. Early diagnosis of clinical and sub-clinical disease through routine tests.
a. Establishing targets.
b. A regular presence on the farm.
b. Issuing monitoring reports.
c. Treatment by veterinarian.
2. Prevention of disease.
a. Follow up of feeding plans.
a. Epidemiological evaluation.
b. Advancement of vaccination and prevention plans.
c. Cost/benefit evaluation.
3. Use of "real time" laboratories.
7. Evaluation of results.
4. Recording of data.
Routine diagnostic tests and examinations
Rate of diagnosis of clinical ketosis by a routine urine test compared to diagnosis by herdsmen in 7 herds, from 1982 to 1984.
Routine urinalysis (%)
By herdsman (%)
Routine tests and examinations
Postparturient examination: 5–12 days postpartum (Metritis, ketosis, LDA)
Unobserved heat: 50–90 days postpartum. Repeated weekly if necessary.
Pregnancy check: Service + 40 days and at drying off.
Body condition scoring: 5–12 days and 40–60 days postpartum, 150 days of gestation and at drying off.
CMT and bacteriology of quarters: Annually.
Somatic cell counts: Monthly
Growth chart of heifers: 3 times a year
The detailed routine activity is described elsewhere .
To cross the line from individual to herd medicine, data should be recorded and processed, so that both statistical and epidemiological evaluations can be carried out. These relationships should be further explored by causal studies. Herd health monitoring is done on populations, not on individuals. Individual cow data are yet essential if interactions between factors are to be clarified.
Analysis of calving, reproduction, and production data
Resetting goals for dry periods (15,570 first lactation cows).
Mean length of dry period, days
Combined effect of the length of the dry period and body condition score at drying off on future production (3659 multiparous cows in 7 herds).
90-days milk yield
90-days 3.5% FCM yield
90-days 3.0% PCM yield
BCS at drying off (units)
Dry period (days)
BCS at drying off*Length of dry period
Calving Report 01/09/97–31/08/98.
a. n Calvings
b. % twins
c. % stillbirth
d. % milk fever
e. % prolapsed uterus
f. % displaced abomasum
g. % retained placenta
h. % primary metritis
I. % ketosis
j. % calved with mastitis
k. % daydry >70 d
l. % daydry <60 d
m. % induced calving
n. % calved with edema
o. BCS at calving (n examined)
1. % with body score ≥ 4.25
2. % with body score ≤ 3.00
p. BCS changes in dry period (n examined)
1. % lost ≥ 0.5 units
2. % gained ≥ 0.25 units
Looking for the "local truth" – epidemiological designs
Epidemiological evaluations of factors responsible for falls from targets should become a routine. We evaluate the contribution of various factors to lower fertility and milk yield in the individual herds, presenting the results for first, second and third or more lactations' cows in separate sections.
Five different models evaluate factors affecting a) milk; b) economy corrected milk (ECM) peak yields; c) extended 305-d milk yield; d) daily 3.5% FCM in the first 90 days in milk; and e) persistencies.
Contribution to "Not pregnant to first service" and to "days open"
Incidence or quartile
Contribution to non-pregnancy from 1st AI
Open Days added
Fat/Protein ratio before AIac
Short rest perioda
High yield at drying offad
Long dry perioda
Mean days open (151-d upper limit)
Rate of non-pregnancy (model)
Rate of non-pregnancy (actual)
Cows presented for unobserved heat, those in a negative energy balance when first inseminated, those dried off with high yield (underconditioned) and those inseminated before 75 DIM contributed 7.5%, 11.2%, -4.2% and 10% respectively to the rate of non-pregnancy.
Steps to improve fertility, aiming at those directions, can now be initiated. When intervention is called for, narrowing down the field of investigation often proves essential if results are to be obtained . This selection process enables the clinician to concentrate efforts and resources, in clinical and laboratory investigations, at most promising directions. In the above example evaluation of dry and fresh cows' rations had been called for.
Improving the analysis by introduction of new variables
A negative energy balance (NEB) before first service adversely affects fertility, especially in young cows. When we started the routine causal analysis of infertility in the early eighties we used the highest quarters of 3.5% FCM (average of best 2 out of first 3 milk recordings) as indicators of NEB, assuming that high yielders are in a deeper NEB compared to their lower yielders' counterparts. In 1995 we introduced the loss of BCS from calving to 40 – 60 days in milk, a reflection of fat mobilization , as a preferable indicator.
Because milk fat concentration tends to increase and milk protein concentration tends to decrease during the postpartum negative balance, , suggested that fat to protein ratio could indicate lack of energy supply through feed. , evaluated similar associations in data derived from regular milk control. Data of BCS are not always available; we use now routinely the changing of fat to protein ratios as a measure of NEB in the models. Calculation is done in the following way: a) Fat percentage/Protein percentage is calculated for each test day; b) the changing ratios are then calculated and the value of the upper quartile is obtained for the different lactations.
Fat to protein ratio = (fat/protein in test day following AI/fat/protein in test day preceding AI)
Fertility indices and various measures of negative energy balance (4510 lactations in 6 herds).
107 ± 38
76 ± 19
Lost ≥ 0.75 u BCS from calving to AI
Fat/protein ratio next/preceding AId
Daily FPCM in first 90 DIMd
Using loss of 0.75 units BCS from calving to AI as standard, the sensitivity and specificity of the fat to protein ratio were 22.9% and 73.7% respectively.
Data analysis and quality of the data
Incomplete hypothetical data in second lactations' cows. Estimates of changes in peak milk yield (kg).
Incidence or quartile
Incidence or quartile
Low BCS at calvingc
Short dry periodc
- Bartlett PC, Kaneene JB, Kirk JH, Wilke MA, Martenuik JV: Development of a computerized dairy herd heath database for epidemiological research. Prev Vet Med. 1986, 4: 3-14. 10.1016/0167-5877(86)90003-6.View ArticleGoogle Scholar
- Ezra E: Personal communication. 1998Google Scholar
- Ferguson JD, Chalupa W: Symposium: Interactions of nutrition and reproduction. Impact of protein nutrition on reproduction in dairy cows. J dairy Sci. 1989, 72: 746-766. 10.3168/jds.S0022-0302(89)79168-2.View ArticlePubMedGoogle Scholar
- Fetrow J, Harrington B, Henry ET, Anderson KL: Dairy herd health monitoring. Part I. Description of monitoring systems and sources of data. Comp Food Animal. 1987, 9: F389-F398.Google Scholar
- Grieve DG, Korver S, Rijpkema YS, Hof G: Relationship between milk composition and some nutritional parameters in early lactation. Livestock Production Sci. 1986, 14: 239-254. 10.1016/0301-6226(86)90083-7.View ArticleGoogle Scholar
- Heuer C, Schukken YH, Dobbelaar P: Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield, and culling in commercial dairy herds. J Dairy Sci. 1999, 82: 295-304. 10.3168/jds.S0022-0302(99)75236-7.View ArticlePubMedGoogle Scholar
- Israel Cattle Breeders Association: Israel Holstein Herdbook 1991, 1995, 1999. P.O.Box 3015.Google Scholar
- Markusfeld (Nir) O, Nahari N, Adler H: Evaluation of a routine testing for ketonuria and aciduria in detection of sub and clinical ketosis associated with overfeeding in dairy cattle. The Bovine Practitioner. 1984, 219-222.Google Scholar
- Markusfeld O: Periparturient traits in seven high yielding dairy herds. Incidence rates, association with parity, and interrelationships among traits. J Dairy Sci. 1987, 70: 158-166. 10.3168/jds.S0022-0302(87)79990-1.View ArticlePubMedGoogle Scholar
- Markusfeld-Nir O: Integrated herd health programs – the Israeli experience. Proceedings of the meeting 27th–29th of. Edited by: Thrusfield MV, Goodall EA. 1996, 126-135. March of The Society for Veterinary Epidemiology and Preventive Medicine. GlasgowGoogle Scholar
- Nir (Markusfeld) O, Enevoldsen C, Kroll O: Herd Health – I. 1998, Ruppin Institute for Higher EducationGoogle Scholar
- Radostits OM, Blood DC: Herd Health. A Textbook of Health and Production Management of Agricultural Animals. 1985, W.B. Saunders Company, Philadelphia, 48-Google Scholar
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