Association between disease occurrence and fertility of dairy cows in three geographic regions of Chile

Theriogenology xxx (2016) 1–7

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Association between disease occurrence and fertility of dairy cows in three geographic regions of Chile P.J. Pinedo a, b, *,1, P. Melendez c, S. Paudyal d, R. Krauss e, F. Arias e, H. Lopez f, A. Luco e, C.F. Vergara e, f a

Texas A&M AgriLife Research, Amarillo, Texas, USA Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University System, College Station, Texas, USA c Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, Univesity of Missouri–Columbia, Missouri, USA d Department of Agricultural Sciences, West Texas A&M University, Canyon, Texas, USA e ABS Chile Ltd., Santiago, Chile f ABS Global Inc., DeForest, Wisconsin, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 November 2015 Received in revised form 18 February 2016 Accepted 2 March 2016

The objective was to analyze the association between disease occurrence during early lactation and reproductive performance and survival of dairy cows in high-producing herds, under different management practices in three geographic regions of Chile. Data included 30,757 lactation records of cows calving from January 2013 to June 2014 in three different locations: Central (C) area (n ¼ 6198 cows in eight herds), Central-South (CS) area(n ¼ 17,234 cows in 12 herds), and South (S) area (n ¼ 7325 cows in six herds). Data were analyzed using logistic regression and ANOVA, considering cow as the experimental unit. Covariables offered to the models included parity number, season of calving, cow and herd relative milk yield, geographic location, and management system. Average milk yield (305 ME) per cow were 12,091, 11,783, and 6852 kg for C, CS, and S regions, respectively. Time from calving to first service and time to conception were consistently greater for cows with at least one disease event within 50 days in milk (DIM), for cows that were reported lame, or for cows that had mastitis or metritis. The odds (95% confidence interval) of pregnancy at 150 DIM (P150) and the odds of survival until 150 DIM (S150) for cows that had at least one disease event within 50 DIM were 0.84 (0.79–0.91) times the odds of pregnancy and 0.25 (0.22–0.28) times the odds of survival for healthy cows. The odds of P150 for cows located in the C and CS areas were 1.56 (1.36–1.80) and 1.16 (1.04–1.30) times the odds of P150 for cows in the S area. The odds of S150 for cows located in the C and CS areas were 0.48 (0.37–0.62) and 0.54 (0.42–0.67) times the odds of S150 for cows in the S area. These data suggested that cow health status and geographic location are significantly associated with reproductive performance and survival in this population of Chilean dairy cows. Ó 2016 Elsevier Inc. All rights reserved.

Keywords: Dairy Health Fertility Survival

1. Introduction The transition period is a critical time for cow health and survival and for the profitability of the lactation. Significant * Corresponding author. Tel.: 1 970 491 8300; fax: 1 970 491 5326. E-mail address: [email protected] (P.J. Pinedo). 1 Present address: Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA. 0093-691X/$ – see front matter Ó 2016 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.theriogenology.2016.03.001

endocrine changes occur with calving [1], and highproducing dairy cows require severe metabolic adjustments to allow nutrient partitioning to support milk synthesis. The abrupt increase in nutrient requirements that occurs at the onset of lactation, when feed intake is usually depressed, causes extensive mobilization of body fat and results in a shift from a positive energy balance to a negative one. These events are concurrent with substantial immune suppression [2–4], resulting in suboptimal health

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and performance in the affected cows. Notably, about 30% to 50% of high-producing cows may be affected by some disease around parturition and 75% of health disorders will occur in the first 30 DIM [5–7], which is also a critical time for survival with close to 8% of cows leaving the herd during the first 2 months of lactation [8,9]. Reproductive efficiency has a major impact on the profitability of dairy farms. Improvement in reproductive performance of dairy cattle depends on factors associated with resumption of ovarian function, estrus expression, and establishment and maintenance of pregnancy [10]. The effect of disease on reproductive performance has been well characterized; factors such as extensive loss of body condition and postpartum health problems have been implicated with impaired resumption of postpartum ovulation, compromised fertilization and pre- and peri-implantation conceptus development, altered conceptus gene expression, increased pregnancy loss, and ultimately reduced pregnancy per insemination [10–13]. Contrasting with the availability of studies on disease prevalence during the transition period and their effects on reproductive performance in North America [14], there is a paucity of information on this topic from dairy farms in other countries, where similar genetics are used in a variety of environments and management systems. Chile has a dairy cattle population of 480,000 lactating cows [15], with the Holstein breed being the most common because genetics from Canada and the United States have been introduced gradually during the last 30 years [16,17]. Chilean dairy farms are represented by two typical groups: The small family agriculturist group, holding 50% of the country’s livestock and characterized by a low-technology level and extensive management and the commercial group, characterized by more advanced technology and intensive management [18]. Only, 18% of Chilean producers contribute 86.4% of the industrial volume of milk [15]; 70% of Chilean dairy cattle is distributed in the South (S) area of the country being managed under grazing conditions and the remaining 30% is distributed in the Central (C) and Central-South (CS) areas being managed under confinement conditions [15]. The prevalence of clinical and subclinical diseases in Chilean dairy herds and their impact on reproductive outcomes have not been extensively characterized, but, as reported by some authors, it may be expected that disease occurrence varies depending on production level and whether the cows are under confinement or grazing conditions [14,19]. We hypothesize that the occurrence of clinical and subclinical diseases in dairy farms located in multiple geographic areas of Chile differs from values observed in high-producing dairy cows in North America and that diseases compromise reproductive performance and survival of this population of cows. Therefore, the objective of this study was to analyze the association between disease occurrence during early lactation and reproductive performance and survival of dairy cows in high-producing herds under different management practices in three geographic regions of Chile.

2. Materials and methods 2.1. Study population The present study analyzed lactation records from cows maintained in high-producing herds in three regions of Chile (Fig. 1). A convenience sample of farms was selected from the technical service platform offered by ABS Global to dairy producers in Chile. Herds were visited monthly by one of the authors in a routine fashion to collect and record data. Data from cows calving between January 2013 and June 2014 were included in the analysis, resulting in a total of 30,757 complete lactation records. This time frame was defined to include the most recent lactation of the participant cows and to provide enough time for reproductive outcomes to be completed (breedings and diagnosis of pregnancy). Data were provided by 26 herds, ranging from 126 to 9803 cows, located in the C area (n ¼ 6198 cows in eight herds), CS area (n ¼ 17,234 cows in 12 herds), and S area (n ¼ 7325 cows in six herds). Housing systems consisted of open dry lot (n ¼ 3881 cows in six herds), freestall (n ¼ 21,421 cows in 12 herds), grazing (n ¼ 1211 cows in three herds), and freestall/grazing (n ¼ 4244 cows in five herds). Reproductive programs in the participant farms were on the basis of insemination from visual estrus detection, with varying levels of application of estrus or ovulation synchronization procedures and timed artificial insemination. Information was extracted from on-farm software (DairyComp 305; Valley Agricultural Software, Tulare, CA, USA) and consisted of calving date, parity, health events and date, reproductive information (date of breedings and diagnosis of pregnancy), 305-day mature equivalent (305 ME) milk yield, herd code, and recorded culling date or death. Lactation records with missing calving date were removed from the data set. 2.2. Geographic regions The C area of Chile is an agricultural region located between 32.0 S and 35.3 S latitude and 71.1 W and 71.5 W longitude. It has a Mediterranean-type climate, with a minimum and maximum ambient temperature of 3  C and 32  C, respectively, and a mean rainfall of 400 mm/y (0 mm in January and 80 mm in July). The S area holds the largest concentration of the country’s dairy cattle. It extends from about 39 S to about 42 S and has a temperate oceanic climate. The summer months of January and February are the driest, with a monthly average precipitation of 67 mm. The winter months average 410.6 mm of rainfall. Annual rainfall ranges from 1500 to 2500 mm. Temperatures in the area are moderate. The CS area represents a transition zone between central and south Chile [20]. 2.3. Events of interest and independent variables Outcome variables were calving to first service interval (days), defined as the number of days between parturition and the subsequent first breeding; calving to conception interval (days), defined as the number of days between parturition and the breeding that resulted in a pregnancy;

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Fig. 1. Map of Chile, indicating the three geographic regions and the number of participant farms by area.

reported pregnancy at 150 DIM (P150); and survival at 150 DIM (S150). Heat detection rates, conception rates, and pregnancy rates (defined as the number of cows that became pregnant divided by the number eligible to be bred during a 21-day interval) were calculated by herd and geographic area for the following periods of lactation (50–70, 71–92, 93–113, 114–124, and 50–124 days after calving). Cow-related independent variables included occurrence of clinical disease within 50 days after calving and comprised the following specific disorders: Lameness, mastitis, and metritis/retained fetal membranes. Although participant farms had available information on other health problems, analyses were restricted to the aforementioned conditions because symptoms are very specific, making their diagnosis simple and resulting in a relatively low potential for misclassification and underreporting.

Control variables included parity (categorized as 1, 2, and 3), season of calving, cow-relative 305 ME milk yield, and herd. Herd-related variables considered were herdrelative 305 ME milk yield, geographic location, and management system (open dry lot, freestall, grazing, and freestall/grazing). To facilitate analyses, all variables were categorized. The distribution of the noncategorical variables was examined using histograms and descriptive statistics to assist in categorization. Independent variables were categorized as follows. Cow-relative 305 ME milk yield was calculated as the difference from the herd mates year-season average milk yield: less than 1792 kg (low), 1792 to 2065 kg (intermediate), and greater than 2064 kg (high). Herdrelative 305 ME milk yield was calculated as the difference from the herd year-season average: less than 1354 kg (low), 1354 to 1722 kg (intermediate), and greater than 1722 kg (high).

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2.4. Statistical analyses The association between clinical diseases and binary outcomes for fertility and survival (pregnancy and survival by 150 DIM) was analyzed by multivariable logistic regression using the PROC GLIMMIX procedure from SAS (SAS Institute Inc., Cary, NC, USA; release 9.4), whereas continuous variables (calving to first service interval, calving to conception interval, heat detection rate, conception rate, and pregnancy rate) were evaluated by ANOVA (PROC GLM from SAS). The significance of differences for continuous variables was tested using Tukey test (least square means statement). Cows were classified as healthy (no diseases diagnosed within 50 DIM) or as having clinical disease (any health disorder, lameness, mastitis, and metritis/retained fetal membranes). Univariate analyses were used to test the inclusion of each of the explanatory variables (health status, parity, season of calving, cow and herd’s relative 305 ME milk yield, geographic location, and management system) in the final models. Subsequently, effects with a P value 0.20 were included in the multivariable analysis, and potential interactions between the effect of disease and other variables were tested in each analysis. The final models were tested through a backward elimination procedure with herd included as the only random variable. The significance level for inclusion in the final model was set at P  0.10 to account for potential confounders affecting both the response and the predictors. 3. Results 3.1. Descriptive statistics Data included 30,757 lactation records of cows calving between January 2013 and June 2014, in three different geographic locations: C area (n ¼ 6198 cows), CS area (n ¼ 17,234 cows), and S area (n ¼ 7325 cows). Average milk yields (305 ME) per cow were 12,091, 11,783, and 6852 kg for C, CS, and S regions, respectively. Proportions of cows with at least one reported disease event within 50 DIM were 28.9%, 51.3%, and 26.6% for C, CS, and S regions, respectively. 3.2. Reproductive performance Average heat detection rates were greatest for C (51.9%) and CS (52.0%) compared with S region (46.0%). Conception rates were 37.5%, 32.7%, and 36.7% for C, CS, and S regions, respectively. No significant differences were determined in pregnancy rates by region, with 19.5%, 17.4%, and 17.2% for C, CS, and S regions, respectively (Fig. 2). Days from calving to first breeding were lowest for CS (69.8 days) compared with C (74.6 days) and S (74.6 days). However, no significant difference was found among geographic locations for days to conception (Table 1). Interestingly, a smaller proportion of cows was pregnant at 150 DIM in S (58.6%) compared with C (62.3%) and CS (62.9%). Averages for time from calving to first service and time to conception were consistently greater for cows with at least one disease event, cows that were reported lame, or cows that had

mastitis or metritis within 50 DIM (Table 2). The odds (95% confidence interval [CI]) of P150 for cows located in the C and CS areas were 1.56 (1.36–1.80) and 1.16 (1.04–1.30) times the odds of P150 for cows in the S area (Table 3). When health status was analyzed, the odds of P150 were significantly lower for cows with at least one disease event (odds ratio [OR] ¼ 0.84; 95% CI ¼ 0.79–0.91) or cows that had metritis within 50 DIM (OR ¼ 0.54; 95% CI ¼ 0.47– 0.61). 3.3. Survival Cows located in the C and CS areas had lower odds of S150 (OR ¼ 0.48 and OR ¼ 0.54, respectively) compared with cows in the S region (Table 4). Regarding health status, the odds of S150 were significantly lower for cows with at least one disease event (OR ¼ 0.25; 95% CI ¼ 0.22–0.28), cows that were reported lame (OR ¼ 0.43; 95% CI ¼ 0.33– 0.58), or cows that had mastitis (OR ¼ 0.58; 95% CI ¼ 0.50– 0.68) within 50 DIM. Cows with metritis had greater odds of S150 compared with unaffected cows (OR ¼ 1.85; 95% CI ¼ 1.63–2.12). 4. Discussion This study was a multi-region analysis exploring the association between disease occurrence during early lactation and the reproductive performance and survival of dairy cows in herds under different management practices in three geographic regions of Chile. Although the study herds were a convenience sample, they represent the most common management conditions found in commercial dairy operations in the regions under analysis. Results for heat detection rate, conception rate, and pregnancy rate within different ranges of days in milk (Fig. 2) are similar to results reported in a previous study conducted in the C area of Chile [18]. In addition, results for days from calving to first service and days from calving to conception are similar to those reported in a large-scale study, including more than 150,000 lactations from the CS area of Chile [21]. Numerous factors influence fertility, including climate and environment, herd characteristics, herd management, and cow characteristics [22], so the effects of disease on reproduction may vary depending on the interaction with these multiple variables. Moreover, some cow factors impairing reproduction may also constitute risk factors for some diseases, and unfavorable genetic correlations between yield and fertility are well known [23]; therefore, our models included cow and herd relative milk yield. Parity number, management system, and geographic region were also integrated in the analysis where appropriate. Consequently, the present study estimated the pure association between disease occurrence and fertility in lactating dairy cows. In the present study, milk yield differed among regions, with the highest production in the C area, where more intensive systems (freestall and open dry lot) are predominant [18]. Milk yield levels were intermediate in the CS area where the most common systems are freestall and a combination of freestall and grazing. The lowest average

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Fig. 2. Heat detection rate (A), conception rate (B), and pregnancy rate (C) by days after calving and geographic location. Different letters within a period indicate significant differences (P < 0.05).

milk yields were found in the S area where grazing is predominant. Compared with high-producing dairy cows in confinement, grazing dairy cows produce less milk but

are generally considered more fertile and healthier [19,24]. Disease occurrence within 50 DIM was lowest in the S area and S150 was the highest, compared with the other

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Table 1 Reproductive and productive parameters by geographic location. Geographic location

Calving to first service interval (days) Calving to conception interval (days) Pregnancy at 150 days after calving (%) Milk yield (305 ME)

Central area

Central-South area

South area

74.6b 152.7a 62.3b 12,091c

69.8a 154.0a 62.9b 11,783b

74.6b 146.8a 58.6a 6,852a

Different superscripts in the same row indicate significant differences (P < 0.05). Abbreviation: ME, mature equivalent.

regions. However, in our study, cows in the S area had a lower proportion of P150 (58.6%) compared with cows in the C (62.3%) and CS areas (62.9%). A possible explanation is that nutrient intake in grazing farms may be limited during certain periods of the year, especially affecting cows that are genetically selected for high production [25,26]. Consequently, loss of body condition score in early lactation can be extensive and compromise reproduction [14]. Interestingly, the proportion of cows with at least one disease event reported within 50 DIM was highest for the CS area (51.3%) where the intensity level of management is intermediate. As expected, a limitation of the present study is that disease occurrence was based on data reported by the farmers each with their own definitions for the diseases analyzed. To reduce this bias, we only analyzed diseases that can be easily detected by objective criteria and are characterized by symptoms that are very specific, resulting in a relatively low potential for misclassification and underreporting. In addition, two of the authors provided regular assistance in managing data collection entry by the participant farms. Overall, 59.1% of cows in the study did not experience any type of health problem within 50 DIM. This subgroup of cows had better fertility compared with affected cows: The odds of P150 were significantly lower for cows with at least one disease event (OR ¼ 0.84; 95% CI ¼ 0.79–0.91). Interestingly, in the analysis of specific diseases, only cows that had metritis within 50 DIM evidenced a lower probability of P150 (OR ¼ 0.54; 95% CI ¼ 0.47–0.61). Contrary to our hypothesis, we found no relationship between P150 and Table 2 Reproductive parameters by health event within 50 days after calving.

Sickc Yes No Lameness Yes No Mastitis Yes No Metritis/RFM Yes No

Calving to first service (days)

Calving to conception (days)

74.9b 72.2a

166.3b 158.1a

77.7b 73.4a

154a 162a

79.2b 73.2a

154.7b 162.7a

73.7a 73.5a

171.0b 159.1a

Different superscripts in the same column (within type of health event) indicate significant differences (P < 0.05). Abbreviation: RFM, retained fetal membranes. c At least one disease event within 50 days after calving.

the occurrence of lameness and mastitis during early lactation. Similar to our findings, Santos et al. [11] reported that cows with clinical diseases had reduced fertility because they were less likely to be cyclic and become pregnant and more likely to lose a pregnancy in the first 60 days of gestation. In agreement, Ribeiro et al. [14] reported that fertility was further reduced in cows diagnosed with multiple health problems, clinical or subclinical, compared with those that experienced a single health problem. In addition to low pregnancy per artificial insemination, multiple clinical diseases also increased the incidence of pregnancy losses by gestation day 65. Concordant with these reports, a study in the CS area of Chile [27] stated that subclinical mastitis, measured as linear somatic cell score 4.5, had a significant effect on reproductive performance, manifested by increased calving to first service and calving to conception intervals, a greater number of services per conception, and greater odds of abortion occurrence. Our study indicated an association between geographic location and S150. The odds (95% CI) of S150 for cows located in the C and CS areas were 0.48 (0.37–0.62) and 0.54 (0.42–0.67) times the odds of S150 for cows in the S area. As expected, survival was also associated with disease occurrence: The odds of S150 were significantly lower for cows that with at least one disease event (OR ¼ 0.25; 95% Table 3 Multivariate logistic regression for the risk of pregnancy at 150 DIM.

Geographic location Central area Central-South area South area Sickb 1 0 Lamec 1 0 Mastitisc 1 0 Metritis/RFMc 1 0

OR

95% CIa

1.56 1.16 d

1.36–1.80 1.04–1.30 d

0.84 d

0.79–0.91 d

0.92 d

0.75–1.15 d

1.11 d

0.98–1.25 d

0.54 d

0.47–0.61 d

Model included lactation number and cow and herd milk yield. In addition, the model for disease included geographic location. Abbreviations: CI, confidence interval; OR, odds ratio; RFM, retained fetal membranes. a 95% Confidence intervals not including 1 indicate P < 0.05. b At least one disease event within 50 days after calving. c Health event within 50 days after calving.

P.J. Pinedo et al. / Theriogenology xxx (2016) 1–7 Table 4 Multivariate logistic regression for survival at 150 DIM. 95% CIa

[3]

0.48 0.54 d

0.37–0.62 0.42–0.67 d

[4]

0.25 d

0.22–0.28 d

[6]

0.43 d

0.33–0.58 d

[7]

0.58 d

0.50–0.68 d

[8]

1.85 d

1.63–2.12 d

[9]

OR Geographic location Central area Central-South area South area Sickb Yes No Lamec Yes No Mastitisc Yes No Metritis/RFMc Yes No

Model included lactation number and cow and herd milk yield. Abbreviations: CI, confidence interval; OR, odds ratio; RFM, retained fetal membranes. a 95% Confidence intervals not including 1 indicate P < 0.05. b At least one disease event within 50 days after calving. c Health event within 50 days after calving.

[5]

[10]

[11]

[12]

CI ¼ 0.22–0.28), cows that were reported lame (OR ¼ 0.43; 95% CI ¼ 0.33–0.58), or cows that had mastitis (OR ¼ 0.58; 95% CI ¼ 0.50–0.68) within 50 DIM. Surprisingly, cows with metritis had greater odds of S150 compared with unaffected cows (OR ¼ 1.85; 95% CI ¼ 1.63–2.12). A possible explanation for this finding is that cows maintained in herds with greater levels of confinements, allowing for more intensive health monitoring programs, have greater probabilities of being detected as sick. The affected cows that were detected and reported with metritis will likely receive appropriate treatment and will have a greater recovery rate, compared with cows that remain undetected. The result would be that metritis had a protective effect for survival. 4.1. Conclusions Occurrence of diseases significantly compromised reproductive performance and survival of this population of dairy cows in Chile. These findings highlight the importance of adequate health and nutritional programs during the transition period aimed at prevention, early detection, and treatment of disease. Acknowledgments The authors thank Chilean dairy farmers and ABS Chile for providing the data for this study. References [1] Grummer RR, Mashek DG, Hayirli A. Dry matter intake and energy balance in the transition period. Vet Clin North Am Food Anim Pract 2004;20:447–70. [2] Burton JL, Madsen SA, Chang LC, Weber PS, Buckham KR, van Dorp R, et al. Gene expression signatures in neutrophils exposed to

[13]

[14]

[15] [16]

[17]

[18]

[19]

[20] [21]

[22]

[23]

[24] [25]

[26] [27]

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