Tropical Animal Health and Production

, Volume 43, Issue 3, pp 597–601

Effect of climate factors on conception rate of lactating dairy cows in Mexico

Authors

    • Laboratorio de Genética y Reproducción, Facultad de Medicina Veterinaria y ZootecniaBenemérita Universidad Autónoma de Puebla
    • Departamento de Biología de la ReproducciónUniversidad Autónoma Metropolitana Unidad Iztapalapa
  • Maximino Méndez-Mendoza
    • Laboratorio de Genética y Reproducción, Facultad de Medicina Veterinaria y ZootecniaBenemérita Universidad Autónoma de Puebla
  • Rubén Huerta-Crispín
    • Laboratorio de Genética y Reproducción, Facultad de Medicina Veterinaria y ZootecniaBenemérita Universidad Autónoma de Puebla
  • Felicitas Vázquez-Flores
    • Laboratorio de Genética y Reproducción, Facultad de Medicina Veterinaria y ZootecniaBenemérita Universidad Autónoma de Puebla
  • Alejandro Córdova-Izquierdo
    • Departamento de Producción Agrícola y AnimalUniversidad Autónoma Metropolitana Unidad Xochimilco
Original Research

DOI: 10.1007/s11250-010-9737-5

Cite this article as:
Villa-Mancera, A., Méndez-Mendoza, M., Huerta-Crispín, R. et al. Trop Anim Health Prod (2011) 43: 597. doi:10.1007/s11250-010-9737-5

Abstract

The purpose of this study was to investigate the relationship between conception rate (CR) and climate variables. Data consisted of 24,380 inseminations of Holstein dairy herd in Hidalgo, Mexico. Weather records, including daily temperature (T), relative humidity (RH), rainfall, wind speed, and solar radiation, were obtained from a nearby weather station. Means for each climatic variable from 2 days before artificial insemination (AI) to the AI day were calculated for each conception date represented in the study. A significant negative correlation was observed between the CR and mean and minimum T, mean and minimum RH, mean and minimum temperature–humidity index (THI), and rainfall. The overall mean CR was 34.3%. The CR in lactating dairy cows followed a seasonal pattern, lower CRs were observed in summer months than during winter (32.1% vs. 36.9%; P < 0.01). The variables that had the greatest influence on CR were minimum and maximum T, minimum RH, minimum THI, wind speed, and rainfall.

Keywords

Dairy cowsReproductionConception rateClimate factors

Abbreviations

AI

Artificial insemination

CR

Conception rate

Max

Maximum

Min

Minimum

RH

Relative humidity

T

Temperature

THI

Temperature–humidity index

WS

Wind speed

Introduction

Heat stress causes important annual economic losses in the US dairy industry and is estimated at $900 million (Collier et al. 2006). Decades of rapid progress in genetics and management in dairy cattle have been based almost entirely for increased milk production. Therefore, the reproductive efficiency has suffered a dramatic decrease since the 1950s (Lucy 2001). First-service conception rates (CR) in dairy cattle decreased from approximately 65% in 1951 to 40% in 1996 (Butler 1998). Using records from dairy herds in Kentucky, services per conception increased from 1.62 in 1972 to 2.91 in 1996 (Silvia 1998). A similar trend was found in Holstein and Jersey herds, where the services per conception increased from 1.91 in 1976 to 2.94 in 1999 (Washburn et al. 2002). Deterioration in first-service conception in dairy cattle has been reported in Ireland, the UK, and Australia (Lucy 2001). The decline in pregnancy rates of the Holstein–Friesian cattle over time has been reported in the scientific literature (López-Gatius 2003; De Vries and Risco 2005), which is further exacerbated in season or months with higher temperature–humidity index (THI). The negative impact of heat stress or season on CR has been a topic of many studies (De Rensis and Scaramuzzi 2003; Huang et al. 2008; Flamenbaumand and Galon 2010).

The detrimental effects of high ambient temperatures include impaired oocyte quality and embryo, reduced dominance of the selected follicle and steroidogenic capacity, diminished uterine blood flow, and endometrial dysfunction (Collier et al. 2006; Roth 2008). Climate is a combination of elements that include temperature (T), relative humidity (RH), rainfall, solar radiation, and wind speed (WS) and contributes to the degree of heat stress or cooling that occurs for the cow. Estimating impact of ambient conditions around animals on their performance has been done using the THI that represents the combined effects of air temperature and humidity associated with the level of thermal stress. Climatic conditions before or after the day of service was consistently associated with reduced CRs, and the effects on these days may be equal to or more important than conditions on the day of service (Ingraham et al. 1974; Ravagnolo and Misztal 2002; García-Ispierto et al. 2007). The purpose of this study was to investigate the relationship between CR of several climate variables such as T, RH, THI, rainfall, solar radiation, and WS from 2 days before artificial insemination (AI) to the AI day.

Materials and methods

Management of cows and data collection

This study used data from 24,380 inseminations of Holstein dairy cows collected over 10 years in Hidalgo, Mexico (20°14′14″N, 98°57′00″W). The herd size and mean annual milk production range from 800 to 1,000 lactating cows and from 8,000 to 12,000 kg per cow, respectively. The dairy cattle were kept in open stalls. These cows did not have access to pasture. Rations were balanced with NRC recommendations (1981) and adjusted throughout the year as season and stage of lactation changed. AI records with birth date, calving date, service dates, cow identification, and lactation number were considered as valid data. The estrus was detected utilizing the Heat Watch system and confirmed by examination of the genital tract and vaginal fluid. Subsequently, pregnancy detection was performed by rectal palpation 30–45 days post-insemination. Conception dates that resulted in abortion were excluded. The CR was calculated per calendar years, seasons, and months using all records. Seasons were defined in the following way: spring (April to June), summer (July to September), autumn (October to December), and winter (January to March). Meteorological data were obtained from a nearby weather station. Weather records included daily T (degrees Celsius), RH (percent), rainfall (millimeters), WS (kilometers per hour), and solar radiation (watts per square meter). Means for each climatic variable from 2 days before AI to the AI day were calculated for each conception date represented in the study. The mean THI (THImean), minimum THI (THImin), and maximum THI (THImax) were calculated using the following equation (National Research Council 1971):
$$ {\hbox{THI}} = \left( {1.8 \times T + 32} \right) - \left( {0.55 - 0.0055 \times {\hbox{RH}}} \right) \times \left( {1.8 \times T - 26} \right) $$
where T is the temperature in degrees Celsius, and RH is a percentage.

The THI is designed to indicate level of heat stress of outdoor cattle. This data set was augmented with THImean, THImin, and THImax from 2 days before AI to the AI day (3 days).

Statistical analysis

Data were analyzed using the GLM procedure (Statistical Analysis System (SAS) 2002). The first analysis was done using CR as a dependent variable. Climate variables such as mean T (Tmean), minimum T (Tmin), maximum T (Tmax), mean RH (RHmean), minimum RH (RHmin), maximum RH (RHmax), THImean, THImin, and THImax, mean WS (WSmean), maximum WS (WSmax), rainfall, and season were independent variables. Pearson's correlation coefficients between the CR and climate variables were performed using the CORR procedure (SAS 2002). The differences were considered to be significant at P < 0.05, unless otherwise indicated.

Results and discussion

Average monthly CR from the study period was 34.3%. The greatest CR was observed during winter months (36.9%) and the smallest during summer (32.1%, P < 0.01), whereas CR during winter was greater than that during spring (36.9% vs 33.0%, P < 0.01). The difference in CR between dairy cows inseminated during spring and winter was 3.9%. That difference was the strongest during summer and winter (4.8%). The CR during autumn was 35%. Similar results were reported by Badinga et al. (1985). The absolute difference between pregnancy rates during winter and summer remained similar over time (from 1976 to 2002) at 11 percentage units in Georgia and Florida (De Vries and Risco 2005). Declines in first-service CRs are not only occurring in the USA (65% in 1951 to 40% in 1996 (Butler 1998)), whereas in the UK, between 1975–1982 and 1995–1998, the rates to first service declined from 55.6% to 39.7% (Royal et al. 2000). The CR in Holstein and Jersey herds decreased from about 52% to 53% in the late 1970s to about 33% to 35% in the late 1990s (Washburn et al. 2002). In northeastern Spain, the average monthly CR of four herds from 2002 to 2004 was 32% (García-Ispierto et al. 2007).

Monthly average of climate variables and CRs for the study period is summarized in Table 1. Mean annual rainfall was 380.7 mm, while the dry season (November through March) and rainfall season (April through October) were 53.5 and 327.2 mm, respectively. The CR decreased with onset of the rainy season in April through June with its increased Tmean and THImean. However, Ingraham et al. (1974) found that CR decreased with increased Tmin and humidity, and onset of the rainy season. The CR decreased significantly on the day after AI was associated with the amount of rainfall (Badinga et al. 1985). The CR was higher in Jersey dairy cows inseminated during dry season than in rainy season (Soydan et al. 2009).
Table 1

Monthly average of climate variables and conception rates

Month

Temperature (°C)

Relative humidity (%)

Temperature–humidity index

Rainfall (mm)

Wind speed (km/h)

Solar radiation (W/m2)

Conception rate (%)

Mean

Max

Min

Mean

Max

Min

Mean

Max

Min

Mean

Max

Jan

12.1

23.0

1.5

61.4

94.1

23.6

54.6

72.9

44.5

9.7

3.1

12.5

249.9

37.3

Feb

13.2

24.7

1.5

59.0

92.9

27.5

56.2

75.7

44.0

8.7

2.8

11.9

259.5

36.0

Mar

15.1

26.8

3.6

54.2

91.6

20.9

58.9

79.3

47.0

12.7

3.2

12.1

280.9

37.5

Apr

16.4

27.0

5.2

55.1

88.4

23.7

60.7

79.2

48.4

22.5

3.2

12.3

247.6

34.9

May

16.9

26.9

7.4

62.0

89.2

26.6

61.5

79.0

50.5

33.3

3.2

11.5

272.0

32.6

Jun

16.3

25.8

8.8

68.4

94.2

29.0

60.7

77.8

51.8

50.3

3.7

12.2

226.4

31.4

Jul

15.1

23.3

9.0

70.9

95.2

39.6

59.0

73.5

51.4

56.7

4.3

10.9

234.5

32.4

Aug

15.0

23.2

9.0

72.3

94.2

43.4

58.9

73.4

51.3

40.7

4.6

12.5

227.7

33.1

Sep

14.6

23.1

7.1

75.4

95.6

37.8

58.3

73.2

49.5

61.4

4.2

12.8

185.4

30.9

Oct

13.6

23.6

4.5

73.0

95.8

32.6

56.7

74.1

46.8

62.0

3.4

11.1

212.5

34.1

Nov

12.9

23.0

3.7

69.3

94.5

31.2

55.7

73.0

46.0

12.5

3.0

11.9

242.2

36.1

Dec

12.3

22.5

2.1

66.3

96.1

29.4

54.9

72.2

44.4

9.7

2.8

11.4

250.2

34.8

Max maximum, Min minimum

In our study, the highest monthly average of climatic variables such as Tmean, Tmax, and THImean was observed during spring months (April through June), and RHmin and WSmean during summer months (July through September). The CR of lactating cows decreased during spring and summer months. THImean, THImax, and THImin for the study period were 58.1, 75.3, and 48.0, respectively. The THI that represents the combined effects of air temperature and humidity is usually classified into classes; definitions of those indices vary between authors (Bohmanova et al. 2007). Temperature–humidity indices differ in their ability to detect heat stress. Indices with larger weights on humidity seem to be more suitable for humid climates, and indices with the most emphasis on ambient temperature are more suitable for semiarid climates (Bohmanova et al. 2007). However, Ravagnolo and Misztal (2002) report that the reproduction was negatively influenced by average daily THI ≥70 for Georgia and Florida. Huang et al. (2008) found that the CR was similar in New York and Georgia, at approximately 55% from December to April. In New York, the CR decreased approximately 10% in May and June and mostly recovered by July; whereas in Georgia, the CR started declining in May, bottomed at 31% in September, and did not recover until December. Georgia and New York never reached an average THI of 70 and 80 in any month of the year, respectively. A similar trend was found in this study between CR and THI from 2 days before AI to the AI day. The maximum THI on the AI day and maximum temperature on day 1 after AI were related to low CR (García-Ispierto et al. 2007).

Table 2 shows the analysis of variance for the effects of climate variables on CR. As can be observed, Tmin, Tmax, RHmin, rainfall, WSmax, and THImin from 2 days before AI to the AI day all significantly (P < 0.05) affected CR. Tmean, RHmean, RHmax, WSmean, THImean, and THImax from 2 days before AI to the AI day had no significant effects. However, the analysis of variance for average THI 2 days before breeding reveals a significant effect on CR (Ingraham et al. 1974). Significant variations of CR were associated with Tmax and rainfall on the day after AI (Badinga et al. 1985). Cavestany et al. (1985) found a negative relationship between CR and Tmax on the day of breeding. The THI on the day of AI showed the highest effect on reproduction, followed by 2 days prior, 5 days prior, and 5 days after AI, but no relationship was found with THI at 10, 20, and 30 days after AI (Ravagnolo and Misztal 2002).
Table 2

Results from the analysis of variance for climate variables and conception rates from 2 days before AI to the AI day

Variable

 

SD

P

Temperature (°C)

   

 Mean

14.5

0.9

0.22

 Maximum

24.4

1.4

0.04

 Minimum

5.3

1.6

0.02

Relative humidity (%)

   

 Mean

65.6

5.2

0.21

 Maximum

30.4

7.6

0.20

 Minimum

93.5

4.05

0.04

Temperature–humidity index

   

 Mean

58.1

1.6

0.35

 Maximum

75.3

2.7

0.37

 Minimum

48.0

1.8

<0.01

Rainfall (mm)

   

 Mean

31.7

26.6

<0.01

Wind speed (km/h)

   

 Mean

3.5

0.9

0.65

 Maximum

11.9

3.0

0.04

Solar radiation (W/m2)

   

 Mean

240.7

40.8

0.20

SD standard deviation

As indicated in Table 3, CR was significantly correlated with Tmean, Tmin, RHmean, RHmin, rainfall, THImean, and THImin, but correlations were negative. Indeed, most associated coefficients of correlation were RHmin (−0.388) and Tmin (−0.334) from 2 days before AI to the AI day. A significant positive correlation was observed between the CR and solar radiation (P = 0.002). There were no significant correlations between CR and Tmax, RHmax, THImax, WSmean, and WSmax. Ingraham et al. (1974) also reported that the CR was correlated negatively with Tmin, Tmax, RHmean, and THImean. The highest correlation between CR and THImean was 2 days before breeding, followed by the day after breeding and the day before conception, and the least correlated was the day of breeding (Ingraham et al. 1974). Numerous studies have documented the detrimental effects of high ambient temperatures on fertility in lactating dairy cows, including diminished expression of estrus and compromised CR (Collier et al. 2006; Huang et al. 2008; Roth 2008; Flamenbaumand and Galon 2010). The effects of exposure to elevated temperatures after AI in the uterus or oviduct could compromise sperm survival, fertilizing capacity, or both (López-Gatius 2003).
Table 3

Correlations between climate variables and conception rates from 2 days before AI to the AI day

Variable

R

P

Temperature (°C)

  

 Mean

−0.213

0.021

 Maximum

0.132

0.160

 Minimum

−0.334

<0.001

Relative humidity (%)

  

 Mean

−0.245

0.008

 Maximum

0.019

0.842

 Minimum

−0.388

<0.001

Temperature–humidity index

  

 Mean

−0.209

0.024

 Maximum

0.144

0.126

 Minimum

−0.250

0.007

Rainfall (mm)

  

 Mean

−0.271

0.003

Wind speed (km/h)

  

 Mean

−0.110

0.240

 Maximum

0.065

0.485

Solar radiation (W/m2)

  

 Mean

0.291

0.002

In conclusion, CR in lactating dairy cows followed a seasonal pattern; lower CR was observed in summer months than during winter. Likewise, results from the present study indicate that the effect of climate variables such as Tmin, Tmax, RHmin, THImin, WS, and rainfall 2 days before AI to the AI day had the greatest influence on CR. Further studies are needed to determine which climate variable is more appropriate to predict the CR of lactating dairy cows and trends in CR over time.

Acknowledgement

Special thanks to the Consejo Nacional de Ciencia y Tecnología (CONACyT), Mexico for scholarships and financial support to carry out this study.

Copyright information

© Springer Science+Business Media B.V. 2010