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Journal of Animal Science - Animal Growth, Physiology, and Reproduction

Effect of daily environmental temperature on farrowing rate and total born in dam line sows1

 

This article in JAS

  1. Vol. 91 No. 6, p. 2667-2679
     
    Received: Sept 26, 2012
    Accepted: Feb 25, 2013
    Published: November 25, 2014


    2 Corresponding author(s): Egbert.Knol@topigs.com
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doi:10.2527/jas.2012-5902
  1. S. Bloemhof*†,
  2. P. K. Mathur*,
  3. E. F. Knol 2 and
  4. E. H. van der Waaij
  1. TOPIGS Research Center IPG, P.O. Box 43, 6640 AA Beuningen, The Netherlands
    Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH Wageningen, The Netherlands

Abstract

Heat stress is known to adversely affect reproductive performance of sows. However, it is important to know on which days or periods during the reproduction cycle heat stress has the greatest effects for designing appropriate genetic or management strategies. Therefore, this study was conducted to identify days and periods that have greatest effects on farrowing rate and total born of sows using 5 different measures of heat stress. The data consisted of 22,750 records on 5024 Dutch Yorkshire dam line sows from 16 farms in Spain and Portugal. Heat stress on a given day was measured in terms of maximum temperature, diurnal temperature range and heat load. The heat load was estimated using 3 definitions considering different upper critical temperatures. Identification of days during the reproduction cycle that had maximum effect was based on the Pearson correlation between the heat stress variable and the reproduction trait, estimated for each day during the reproduction cycle. Polynomial functions were fitted to describe the trends of these correlations and the days with greatest negative correlation were considered as days with maximum effect. Correlations were greatest for maximum temperature, followed by those for heat load and diurnal temperature range. Correlations for both farrowing rate and total born were stronger in gilts than in sows. This implies that heat stress has a stronger effect on reproductive performance of gilts than of sows. Heat stress during the third week (21 to 14 d) before first insemination had largest effect on farrowing rate. Heat stress during the period between 7 d before successful insemination until 12 d after that had largest effect on total born. Correlations between temperatures on consecutive days during these periods were extremely high ( > 0.9). Therefore, for farrowing rate the maximum temperature on 21 d before first insemination and for total born the maximum temperature at day of successful insemination can be used as predictive measures of heat stress in commercial sow farms. Additionally, differences between daughter groups of sires were identified in response to high temperatures. This might indicate possibilities for genetic selection on heat tolerance.



INTRODUCTION

Undisturbed piglet production is of great value in the pork supply chain. Farrowing rate is a key factor for consistent piglet production as it affects the number of non-productive sow days per year and the number of litters per sow per year (Bloemhof et al., 2012). In addition, total born has been considered as 1 of the most important traits in pig breeding programs (Hanenberg et al., 2001).

Seasonal variation has been shown to result in a decreased farrowing rate and an increased weaning to estrus interval (Love et al., 1993; Prunier et al., 1996). Pigs are especially sensitive to increased ambient temperatures because they have problems with self-thermo regulation. Increased environmental temperatures can lead to reductions in feed intake, milk yield, and overall reproductive performance of sows (Black et al., 1993; Love et al., 1995; Lewis and Bunter, 2011). Economic losses to the United States swine industry from heat stress were estimated to be $299 million per year (St-Pierre et al., 2003).

In a previous study on the interaction between reproductive performance and heat stress (Bloemhof et al., 2008), animals from a Dutch Yorkshire dam line were more sensitive to high temperatures than animals from an International Large White dam line. Bloemhof et al. (2008) only considered the maximum temperature on the actual day of insemination as measure of heat stress. However, it is reasonable to hypothesize that heat stress before or after day of insemination will also have a significant impact on reproductive performance. Only when the most heat stress sensitive period(s) during the reproduction cycle are identified suitable management and breeding strategies can be developed. Therefore, the objectives of this study are 1) to investigate what the effect of heat stress during each day of the reproduction cycle of the sow is on farrowing rate and total born and 2) to identify when this effect on farrowing rate and total born is largest.


MATERIAL AND METHODS

Animal Care and Use Committee approval was not needed for this study because regularly collected data for the TOPIGS breeding program (Vught, The Netherlands) were used. The TOPIGS breeding program operates according to the EFABAR code of conduct (Neeteson et al., 2006).

Reproduction Records

Data were sourced from nucleus farms in Spain and Portugal that were breeding purebred Dutch line sows. The Dutch line is a Yorkshire dam line and part of the TOPIGS breeding program (Vught, the Netherlands). Insemination and subsequent farrowing data was available for 32,059 first mating records performed from January 2003 until December 2010 on 23 farms. Each record included sow identification number, birth date of the sow, parity, farm, date of first insemination in that specific cycle, date of successful insemination, service sire, farrowing date, gestation length, farrowing rate, total born, number of piglets born alive, number of stillborn piglets, weaning date and number of weaned piglets. Farrowing rate on animal level was defined as a binary trait: 1 if the first insemination within a parity resulted in a gestation length longer than 108 d, or if total born from first insemination was at least 1 piglet. Otherwise farrowing rate within a parity was considered as 0. For sows culled after first insemination within a parity because of reproductive failure, farrowing rate was also recorded as 0. Sows culled for other reasons after first insemination, such as leg problems, were removed from the dataset. Total born was recorded immediately after farrowing and was defined as the sum of number of piglets born alive and number of stillborn piglets. Total born ranged from 1 to 30. A gilt in this dataset was defined as a sow with first insemination in parity 1 that never farrowed before. Sows in this dataset refer to sows from second parity up to seventh parity. Observations were removed from the dataset when 1 of these traits was larger/smaller than its mean ± 3 × SD: 1) farrowing interval; 2) lactation length; 3) gestation length; 4) number born alive; and 5) number stillborn. Seven farms had fewer than 250 observations in the dataset and were therefore removed. Observations from sows that originated from a different line than the Dutch Yorkshire dam line were removed from the dataset. After these data editing steps the dataset used for the analysis consisted of 22,750 records from 5024 sows on 16 farms.

Meteorological Data Used

Meteorological data were available from the European Climate Assessment Dataset (Klein Tank et al., 2002) and included daily summaries for the maximum, minimum and average outside temperature for 6 Spanish and 3 Portuguese weather stations and were available from January 2003 until June 2011. The closest available weather station was assigned to each farm. Most of the farms had a weather station within 122 km radius of the farm, with the closest being 45 km and the farthest being 209 km. Figure 1 shows a map of Spain and Portugal indicating the approximate location of farms and weather stations. Freitas et al. (2006) estimated a correlation of 0.9 between on-farm temperature data and weather station temperature in the USA, even for weather stations more than 300 km from the farm. Therefore, the meteorological data used in the current study should fairly represent the temperature at these farms. Climate in Spain and Portugal is characterized by hot summers and mild winters; humidity is relatively low ( < 70%, Klein Tank et al., 2002) and was therefore not considered in the analysis. Gilts and sows were housed in automatically controlled natural ventilated sheds (using wind breaking curtains), without any cooling. Daylength in Spain/Portugal on June 21 and December 21 was 13 h 59 min and 8 h 34 min, respectively. Day light in the sheds was admitted through the wind breaking curtains; however, artificial light was also available.

Figure 1.
Figure 1.

Map of Spain and Portugal with the approximate location of the 9 weather stations (identified with W) and the 16 farms (identified with F).

 

Estimation of Heat Stress

For farrowing rate, maximum and minimum temperature per day was matched to each day of the cycle of the sow from 28 d before day of first insemination until 108 d after first insemination. For total born maximum and minimum temperature per day was matched to each day of the cycle of the sow from 28 d before day of successful ( = insemination which resulted in a farrowing) insemination until day of farrowing. Diurnal temperature range was calculated for each day as the difference between maximum temperature and minimum temperature. Heat load for each day was calculated separately for both farrowing rate and total born as:where hl = heat load, MT = maximum temperature on day used for analysis (for example the day of insemination), and UCT = the upper critical temperature. The UCT used here relates to the thermo-neutral zone for pig production as described by Bloemhof et al. (2008). When temperature rises above this UCT, the pig has to reduce performance to avoid extra heat production (Bloemhof et al., 2008). To validate the UCT definition from Bloemhof et al. (2008), 2 additional UCT definitions were studied. Three different UCT definitions were used to calculate heat load for farrowing rate: 1) an UCT of 19.2°C based on the study from Bloemhof et al. (2008); 2) an UCT of 15°C; 3) an UCT of 25°C. Three different UCT definitions were used to calculate heat load for total born: 1) an UCT of 21.7°C based on the study from Bloemhof et al. (2008); 2) an UCT of 15°C; 3) an UCT of 25°C.

Statistical Analysis

A descriptive analysis was performed for gilts and sows separately using the MEANS procedure in (SAS Inst. Inc., Cary, NC). For the descriptive analysis maximum outside temperature was rounded to whole numbers and divided into 26 temperature classes (10°C, 11°C, …, 35°C), and means were only calculated when temperature class had at least 25 observations. Figure 2 shows number of observation for each temperature class for gilts and sows separately. Differences in means between gilts and sows were tested for significance using the TTEST procedure in SAS.

Figure 2.
Figure 2.

Number of observations for farrowing rate and total born in relation to maximum temperature for gilts (sows from parity 1) and sows (parity 2 to 7).

 

To identify on which day during the reproduction cycle of the sow heat stress had largest impact on farrowing rate and total born, data were analyzed separately for gilts and sows using a 3-step approach. First, data were corrected for systematic effects using the GLM procedure in SAS with the model:where yijkl is the value of farrowing rate or total born, herdi is the effect of herd i (16 classes), yearj is the effect of year of insemination j (8 classes) and herdi × yearj is the interaction between herd and year, service sirek is the effect of service sire k (411 classes), and eijkl is a random residual term. Corrected observations (y*) for farrowing rate or total born were calculated for each insemination record as .

Second, to identify on which day(s) during the reproduction cycle of the sow heat stress had maximum effect on farrowing rate, pairwise Pearson correlations were estimated between the corrected observation (y*) for farrowing rate and maximum temperature, diurnal temperature range and the 3 heat load variables (based on UCT of 15°C, 19.2°C, and 25°C) at each day of the cycle of the sow from 28 d before day of first insemination until 108 d after first insemination. As temperature might have a different effect on gilts vs. sows the Pearson correlations and standard errors were estimated separately for gilts and sows using the CORRELATION procedure in SAS. Third, to identify on which day(s) during the reproduction cycle of the sow heat stress had maximum effect on total born pair wise Pearson correlations were estimated between the corrected observation (y*) for total born and maximum temperature, diurnal temperature range and the 3 heat load variables (based on UCT of 15°C, 21.7°C, and 25°C) for each day of the sow’s cycle from 28 d before day of successful insemination (insemination which resulted in a farrowing) until day of farrowing. Pearson correlation and SE were estimated for gilts and sows using the CORRELATION procedure (SAS).

Exploration of Opportunities for Genetic Selection

To explore opportunities for genetic selection, sires of the sows were added to the dataset with only daughters from sires with more than 50 daughters remaining in the dataset. This resulted in a dataset of 12,849 observations. Then for each sire, the performance of his daughters for farrowing rate and total born was regressed to the heat load (as in Bloemhof et al., 2008) on the day during the reproduction cycle of the sow with maximum effect as:where yijklmno is the value of farrowing rate or total born, parityi is the general level of parity i (7 classes), herdj is the effect of herd j (16 classes), yeark is the effect of year of insemination k (8 classes) and herdj × yeark is the interaction between herd and year, service sirel is the effect of service sire l (361 classes), sirem is the general level of sire m (33 classes), is the slope of the performance of the daughters from the m-th sire when x increases by 1°C, xn is the heat load (as in Bloemhof et al., 2008) on the day with maximum effect on farrowing rate or total born and eijklmno is a random residual term. All effects fitted in the model were significant. Based on the b-values resulting from model, 3 the sires with the 25% strongest slopes were defined as sensitive and the sires with the 25% lowest slopes were defined as robust. This sire group was then added to each insemination record in the dataset.

To study if the differences in response to high temperatures between the daughters from robust and sensitive sires were significant, the model described by Bloemhof et al. (2008) was applied. First, data were corrected for systematic effects using the GLM procedure (SAS) with the model:where yijklm is the value of farrowing rate or total born, parityi is the general level of parity i (7 classes), herdj is the effect of herd j (16 classes), yeark is the effect of year of insemination k (8 classes) and herdj × yeark is the interaction between herd and year, service sirel is the effect of service sire l (361 classes) and eijklm is a random residual term. Corrected observations (y*) for farrowing rate or total born were calculated for each insemination record as . For the sensitive and robust sire daughter group an average of the corrected observations for farrowing rate and total born was calculated per degree Celsius of maximum outside temperature at the day with maximum effect, resulting from the correlation estimates, using the MEANS procedure (SAS) and plotted.

To test if daughters from sensitive sires and robust sires respond differently to high temperatures, corrected observations were analyzed by sire group using linear regression models and plateau-linear models. The plateau-linear model was based on the approach by Bloemhof et al. (2008). Corrected observations, resulting from model 5, for farrowing rate or total born were included in both models as dependent variable, and regressed against maximum temperature at the day with maximum effect. The linear model was defined as yi* = int + b × xi+ ei [6]. The plateau-linear model [7] was defined as yi* = c + ei when xiUCT and yi* = int + b × xi + ei when x > UCT. Where yi* is the dependent corrected observation for farrowing rate or total born; int is the intercept; b is the change of yi* when xi increases with 1°C; xi is the maximum temperature on the day with maximum effect; c is the constant value of yi* when farrowing rate or total born of the sow is unaffected by temperature; ei is the residual; and UCT is the upper critical temperature where temperature starts to affect farrowing rate or total born: . The linear regression and plateau-linear models were compared for goodness-of fit using an F-test.


RESULTS

Meteorological Data

For the period of time considered, temperature on day of first insemination within a parity ranged from an average daily minimum temperature of 10.5°C to an average daily maximum temperature of 20.6°C (Table 1). Diurnal temperature range on day of first insemination within a parity was on average 10°C, and the maximum difference between maximum and minimum temperature was 36.5°C. Temperature on day of successful insemination ranged from an average daily minimum temperature of 10.4°C to an average daily maximum temperature of 20.4°C (Table 1). Diurnal temperature range on day of successful insemination was exactly the same to diurnal temperature range on day of first insemination within a parity. Approximately 50% of the inseminations were performed on days with a maximum temperature above 20°C (Fig. 2).


View Full Table | Close Full ViewTable 1.

Descriptive statistics of meteorological data at day of first insemination and day of successful insemination

 
Day of first insemination
Day of successful insemination
Characteristic Mean SD Minimum Maximum Mean SD Minimum Maximum
Minimum temperature, °C 10.5 6.3 -16.9 26.0 10.4 6.2 -16.9 26
Maximum temperature, °C 20.6 8.1 -2.4 44.1 20.4 8.1 -2.4 43.2
Diurnal temperature range, °C 10.0 4.1 0.0 36.5 10.0 4.1 0.0 36.5

Effect of Temperature on Farrowing Rate and Total Born in Gilts and Sows

Differences between gilts and sows with respect to reproduction traits are shown in Table 2. As expected, there were hardly any differences in gestation lengths between gilts and sows, suggesting similar reproduction cycles. Average farrowing rate of sows was significantly (P < 0.01) greater than farrowing rate of gilts; in sows, 87% of the first inseminations resulted in a farrowing, whereas this was 79% for gilts. Average total born of sows (12.2 piglets) was almost 1 piglet greater than total born of gilts (11.3 piglets; P < 0.01). Gilts and sows differed in their responses to increasing temperatures. These differences were observed in both farrowing rate and in total born. Means for farrowing rates plotted against maximum temperatures on days of first insemination and means for total born plotted against maximum temperatures on days of successful insemination are shown in Fig. 3. Farrowing rate of gilts was significantly (P < 0.05) less than farrowing rate of sows at almost all temperatures with a slightly greater decrease in farrowing rate of gilts at higher temperatures than for sows. More variation in farrowing rate was shown in gilts than in sows. Figure 2 shows the number of records used for the estimates. There were more than 25 insemination records per temperature class, covering a wide range of temperatures from 5°C to 35°C. This suggests that the greater variability in reproductive performance in gilts than in sows was not due to a lower number of observations per temperature class for gilts than for sows. Total born of sows was also significantly (P < 0.05) greater than total born of gilts at all temperatures and difference in total born was even greater at higher temperatures (Fig. 3). In view of these differences further analysis was done separately for gilts and sows.


View Full Table | Close Full ViewTable 2.

Descriptive statistics of reproduction data

 
Gilts (parity 1)
Sows (parity 2 to 7)
Gilts vs. Sows
Characteristic n Mean SD n Mean SD Difference P-value
Parity 4331 1 ± 0 16,556 4.1 ± 1.6
Gestation length, days 4200 113.7 ± 9.1 16,223 113.9 ± 6.7 -0.25 0.09
Farrowing rate 4244 0.79 ± 0.41 16,298 0.87 ± 0.34 -0.07 < 0.01
Total born 4111 11.3 ± 2.7 16,053 12.2 ± 2.7 -0.84 < 0.01
Figure 3.
Figure 3.

Means for farrowing rate and total born per °C maximum outside temperature for gilts (sows from parity 1) and sows (parity 2 to 7). An asterisk (*) indicates significance of difference between gilts and sows within each temperature class.

 

Relationship between Farrowing Rate and Daily Temperature Variables

The relationship between farrowing rate and daily temperature variables was evaluated in terms of a Pearson correlation between temperature on a given day of the reproduction cycle and farrowing rate. The main purpose of this analysis was to identify days and periods that had maximum effect on farrowing rate. Estimates of Pearson correlations with respect to 3 temperature variables a) maximum daily temperature; b) diurnal temperature range and c) heat load for gilts are shown in Fig. 4a, 4b, and 4c, respectively. Estimates ranged from -0.11 ± 0.02 on d 16 before first insemination to 0.01 ± 0.02 on d 107 after first insemination for daily maximum temperature (Fig. 4a). It is important to note that the correlation of -0.11 indicates an unfavorable effect of increase in temperature on farrowing rate. The increase in correlations with respect to days of reproduction cycle was not linear, suggesting distinct periods of greater effects. A sixth degree polynomial function was the best fit to the correlation estimates according to R-squares. According to this function, high temperatures on the days during the third week (21 to 14 d) before first insemination had maximum effect on farrowing rate. A second period between 38 d and 55 d after first insemination also had an increased effect but less than the period before insemination. The Pearson correlations with respect to daily diurnal temperature range were weaker than those for maximum temperature (Fig. 4b). They followed similar pattern but periods of strongest effects were less distinct. The effect of heat load was evaluated using 3 different upper critical temperatures (Fig. 4c). The estimates of Pearson correlations followed a similar pattern as for daily maximum temperature. Correlations were strongest when UCT was as low as 25°C and were weaker with decreasing UCT. However differences between correlation estimates with different UCT were not significant.

Figure 4.
Figure 4.

Pearson correlations between farrowing rate of gilts (sows from parity 1) and daily temperature variables. Farrowing rate was corrected for the systematic effects of herd, year of insemination, the interaction between herd and year of insemination and service sire. Day zero was the day of first insemination in a parity. Vertical bars in a) and b) show SE of the correlations. Heat load in c) was estimated as deviation from the upper critical temperature (UCT) considering 3 levels of UCT (i.e., 15°C, 19.2°C, and 25°C). A sixth degree polynomial function was fitted to the correlations.

 

Estimates of Pearson correlations for sows with respect to farrowing rate and 3 temperature variables a) maximum daily temperature; b) diurnal temperature range and c) heat load) are shown in Fig. 5a, 5b, and 5c, respectively. Considering daily maximum temperature as the effect variable, the correlation estimates ranged from -0.08 ± 0.01 on d 18 before first insemination to 0.02 ± 0.01 on d 63 after first insemination (Fig. 5a). The negative but stronger correlation indicates that increasing temperatures in the period before first insemination have largest effect on farrowing rate. A majority of the correlations were negative but some of them were positive especially after the 56th day after first insemination. A third degree polynomial function was the best fit to the relationship between correlation estimates and days of the reproduction cycle according to its R-square. According to this polynomial function, heat stress during 21 and 14 d (third week) before first insemination had largest impact on farrowing rate of sows. Estimates of Pearson correlations between daily diurnal temperature range and farrowing rate were weaker than those between maximum temperature and farrowing rate but followed similar pattern (Fig. 5b). For sows also the effect of heat load was evaluated using 3 different upper critical temperatures (Fig. 5c). Correlations were weakest between farrowing rate and UCT 25°C and strongest between farrowing rate and UCT 15°C but correlations between UCT 19.2°C and farrowing rate were quite similar to correlations between UCT 15°C and farrowing rate.

Figure 5.
Figure 5.

Pearson correlations between farrowing rate of sows (parity 2 to 7) and daily temperature variables. Farrowing rate was corrected for the systematic effects of herd, year of insemination, the interaction between herd and year of insemination and service sire. Day zero was the day of first insemination in a parity. Vertical bars in a) and b) show SE of the correlations. Heat load in c) was estimated as deviation from the upper critical temperature (UCT) considering 3 levels of UCT (i.e., 15°C, 19.2°C, and 25°C). A third degree polynomial function was fitted to the correlations.

 

Relationship between Total Born and Daily Temperature Variables

The analysis to identify days and periods of heat stress that had maximum effect on total born of gilts was performed using Pearson correlations. Again, the changes in these correlations during different days of reproduction cycle were examined with respect to maximum daily temperature and diurnal temperature range as well as heat load. The results are given in Fig. 6a, 6b, and 6c, respectively. In this analysis, d 0 of the reproduction cycle was the day of successful insemination that led to the total born rather than the day of first insemination as in case of farrowing rate. In general, correlations were smaller in magnitude than those for farrowing rate. Estimates of correlations ranged from -0.08 ± 0.02 on 10th day after insemination to 0.05 ± 0.02 on 109 d after day of successful insemination (Fig. 6a). A third degree polynomial function was found to be the best fit to the correlation estimates. According to this function high temperatures during the period between day of successful insemination and 14 d after successful insemination had the strongest effect on total born of gilts. The Pearson correlations with respect to daily diurnal temperature range were more variable than those for maximum temperature (Fig. 6b). The effect of heat load (Fig. 6c) followed similar patterns as for daily maximum temperature; however, correlations between heat load and total born were stronger. Strongest correlations between total born of gilts and daily heat load were estimated with heat load based on an UCT of 21.7°C, which was according to Bloemhof et al. (2008) the UCT for total born.

Figure 6.
Figure 6.

Pearson correlations between total born of gilts (sows from parity 1) and daily temperature variables. Total born was corrected for the systematic effects of herd, year of insemination, the interaction between herd and year of insemination and service sire. Day zero was the day of successful insemination in a cycle. Vertical bars in a) and b) show SE of the correlations. Heat load in c) was estimated as deviation from the upper critical temperature (UCT) considering 3 levels of UCT (i.e., 15°C, 21.7°C, and 25°C). A third degree polynomial function was fitted to the correlations.

 

Correlations with respect to total born and maximum daily temperature, diurnal temperature range and heat load for sows are given in Fig. 7a, 7b, and 7c, respectively. Estimates of Pearson correlations ranged from -0.05 ± 0.01 on d 3 before day of successful insemination to 0.02 ± 0.01 on d 111 after day of successful insemination (Fig. 7a). According to the third degree polynomial function, high temperatures in the period from 7 d before successful insemination until 12 d after successful insemination have largest effect on total born. The correlations with respect to total born and diurnal temperature range followed the same pattern as for maximum temperature however estimates were somewhat weaker than with maximum temperature (Fig. 7b). Correlations between total born and daily heat load followed similar pattern as for daily diurnal temperature range but the periods of high effects were more distinct (Fig. 7c).

Figure 7.
Figure 7.

Pearson correlations between total born of sows (parity 2 to 7) and daily temperature variables. Total born was corrected for the systematic effects of herd, year of insemination, the interaction between herd and year of insemination and service sire. Day zero was the day of successful insemination in a cycle. Vertical bars in a) and b) show SE of the correlations. Heat load in c) was estimated as deviation from the upper critical temperature (UCT) considering 3 levels of UCT (i.e., 15°C, 21.7°C, and 25°C). A third degree polynomial function was fitted to the correlations.

 

Exploration of Opportunities for Genetic Selection

In addition to the analysis to identify days during the reproduction cycle that had maximum effect on farrowing rate or total born, a preliminary analysis was conducted to evaluate the expected effect of genetic selection of sires based on the heat load on a given day. The purpose was to explore if there are any opportunities for selection of sires and dams to improve the farrowing rate or total born in the next generation based on heat tolerance. In case of farrowing rate, the temperature on the 21st day before first insemination was considered to classify the sires into sensitive, and robust categories using model 4. The average farrowing rates of the daughters of the sensitive or robust sires with respect to the maximum temperature on this day are given in Fig. 8 as well as the significant model of best fit, either the plateau-linear or linear model. Differences in response to high temperatures were found when applying the plateau-linear model to farrowing rate of daughters of sensitive or robust sires. For daughters of robust sires no effect of temperature on farrowing rate could be estimated (Fig. 8). A plateau-linear relationship was observed for farrowing rate of daughters of sensitive sires (Fig. 8), the estimated UCT was 20.9°C and farrowing rate dropped 1% per °C above UCT. Resulting at 30°C in an estimated average farrowing rate of 78% for daughters of the sensitive sires compared with an average estimated farrowing rate of 85% for daughters of robust sires. In case of total born, maximum temperature at day of successful insemination was considered for classification of the sires into sensitive and robust categories using model 4, the results are shown in Fig. 9. Again, no effect of temperature on total born of daughters from robust sires could be estimated. A plateau-linear relationship was significantly the best fit for total born of daughters of sensitive sires (Fig. 9), the estimated UCT was 20°C and total born dropped with 0.05 piglet per °C above UCT. Even though total born of daughters of robust sires was lower than total born of daughters of sensitive sires when temperature was below 25°C, above 25°C total born of daughters of robust sires was greater than total born of daughters of sensitive sires.

Figure 8.
Figure 8.

Means for farrowing rate (corrected for the systematic effects of parity, herd, year of insemination, the interaction between herd and year of insemination and service sire) in relation to maximum temperature on the 21st day before first insemination for daughters from sires grouped according to their sensitivity or robustness to high temperatures. The symbols show the corrected daughter averages for each temperature class and the lines show the estimated effect of maximum temperature on the 21st day before first insemination on farrowing rate for daughters from robust and sensitive sires.

 
Figure 9.
Figure 9.

Means for total born (corrected for the systematic effects of parity, herd, year of insemination, the interaction between herd and year of insemination and service sire) in relation to maximum temperature at day of successful insemination for daughters from sires grouped according to their sensitivity or robustness to high temperatures. The symbols show the corrected daughter averages for each temperature class and the lines show the estimated effect of maximum temperature at day of successful insemination on total born for daughters from robust and sensitive sires.

 


DISCUSSION

This study shows that heat stress during 21 to 14 d before first insemination has largest effect on farrowing rate of gilts and sows. Heat stress in the period between 7 d before successful insemination until 12 d after had largest impact on total born. Differences between daughter groups of sires were identified in response to high temperatures. This might indicate possibilities for genetic selection on heat tolerance.

Differences between Gilts and Sows in their Responses to Heat Stress

Farrowing rate of sows was on average 8% greater than farrowing rate of gilts, 87 and 79%, respectively. Gilts are typically in the growth phase and not completely mature. Therefore, low farrowing rate in gilts might be caused by immaturity of the endocrine system of gilts compared with the endocrine system of older sows. Koketsu et al. (1997) suggested that the concentration of circulating progesterone produced by the corpora lutea of the gilts is lower than in sows. The concentration of progesterone directly influences pregnancy maintenance and embryonic survival (Jindal et al., 1996; Athorn et al., 2011). Immaturity of the endocrine system in gilts might have resulted in pregnancy disruption (Koketsu et al., 1997) and lower farrowing rate.

Total born in sows was almost 1 piglet greater than in gilts, which could be expected because many studies have observed the lowest total born in gilts and a maximum total born in parity 3 to 5 sows (e.g., Quesnel et al.,2008). Basically, sows are a selected subset of farrowed gilts that have not been culled due to reproductive failure and other problems after the first parity. Approximately 15 to 20% of sows are culled after producing only 1 litter (Lopéz-Serrano et al., 2000; Engblom et al., 2007; Koketsu, 2007). This partly explains the differences in farrowing rate and total born between gilts and sows, as the main culling reason of gilts is reproductive failure or low productivity (Engblom et al., 2007).

Correlations between temperature and reproductive outcomes (farrowing rate or total born) were stronger in gilts than in sows. This implies that heat stress has a stronger effect on reproductive performance of gilts than of sows. According to Tummaruk et al. (2010), farrowing rate in gilts is more affected by high temperatures than farrowing rate in sows. The main difference between gilts and sows is that sows have been lactating before being inseminated, which is not the case for gilts. This means that the physiological status is very different between gilts and sows. Additionally, gilts are still growing to their mature size. Their growth increases metabolic rate (Bastianelli and Sauvant, 1997), and this makes gilts more sensitive to heat stress than sows. Next to growth, it can be hypothesized that gilts differ from older parity sows in the fact that producing a first litter (the act of gestation) might be a stressor in itself. Pigs have been shown to be sensitive to novel environments, situations and novel changes within themselves that cause disruption to their internal environment, and this could have significant production outcomes (Lewis et al., 2008). Therefore it can be hypothesized that producing a first litter and the internal changes within the gilt associated with that, might make the gilt even more sensitive to heat stress.

Usefulness of Different Measures of Heat Stress

Maximum temperature, diurnal temperature range and heat load were compared for their usefulness as measure of heat stress. The correlations were greatest for maximum temperature, followed by those for heat load and diurnal temperature range. Therefore, maximum temperature can be considered as the most important heat stress descriptor for as well farrowing rate as total born. This also suggests that gilts and sows are more sensitive to peaks in temperature than to differences between temperatures during day and night or chronic periods of high temperatures.

The correlations between the 3 heat load definitions and farrowing rate or total born followed similar pattern as for daily maximum temperature. This was expected as heat load is the difference between maximum temperature and UCT. Among the 3 definitions of heat load, correlations were stronger with lower values for UCT. However, differences in correlations between a UCT of 15°C and the UCT of 19.2°C or 21.7°C, as estimated by Bloemhof et al. (2008), were not very large.

Pigs encounter heat stress when temperature exceeds the UCT of the thermo-neutral zone. This zone is based on the body temperature of the pig and is the range of ambient temperatures between the lower and upper critical temperature (Bianca, 1976). However, one could argue that there is a lower and upper critical temperature for pig performance (Bloemhof et al., 2008). Below the lower critical temperature, the performance is reduced due to greater requirement for maintaining body temperature. When temperature rises above the UCT, the pig has to reduce performance to avoid extra heat production (Bloemhof et al., 2008). As pig production is expanding more and more in regions with hot climates such as Latin America and South and East Asia (FAO, 2006) and temperature is expected to increase globally as a result of climate change (Hoffmann, 2010), heat stress will become even more important and could be a limiting factor for pig production. Bloemhof et al. (2008) used a plateau-linear model to estimate upper critical temperatures for farrowing rate and total born for an International Large White Line and a Dutch Yorkshire line. The data used for the current study is an extension of the data used by Bloemhof et al. (2008). According to the results shown in the current study the upper critical temperature of 19.2°C for farrowing rate and 21.7°C for total born seem to be still valid in the Dutch Yorkshire population.

There has been limited success in identifying the relation between reproductive traits and humidity. However, Lewis and Bunter (2011) found no effect of humidity on reproduction in a climate zone which was characterized by low relative humidity. Therefore, in the current study humidity was not considered as humidity in Spain and Portugal is in general not high.

Effects of Heat Stress in Different Periods during Reproduction on Farrowing Rate

High temperatures 21 to 14 d before first insemination show largest effect on farrowing rate of sows and gilts (Fig. 4 and Fig. 5). This period coincides with the previous estrous and start of the new estrous cycle for gilts and the start of the lactation period of sows. The greater effect of heat stress during the lactation could be partly related to reduced feed intake. Several studies have reported that high temperatures during lactation period result in a decreased feed intake (Black et al., 1993; Lewis and Bunter, 2011; Bergsma and Hermesch, 2012). At the same time, there is an increase in follicle development during the lactation phase (Kunavongkrit et al., 1982). Decreased feed intake could lead to reduction in the release of LH during lactation, resulting in reduced follicle development during and after lactation (Quesnel et al., 1998). Reduced follicle development results in decreased ovulation rates and reduced quality of oocytes and follicular fluid which results in increased embryonic mortality (Kemp et al., 2006). Low ovulation rate, poor oocyte quality and high embryonic mortality may result in low number of embryos (Bertoldo et al., 2012). For maternal recognition at d 12 of gestation, at least 2 viable embryos in each uterine horn are needed (Senger, 1999). When this threshold is not reached, the sow returns to estrus resulting in decreased farrowing rate.

For gilts a second period with strong effect on farrowing rate was observed (Fig. 4). High temperatures during 38 to 55 d after first insemination affect farrowing rate of gilts negatively. However it is hard to relate this period to any physiological process. To allow pregnancy after fertilization, pregnancy recognition by the sow is required. Pregnancy recognition in the gilt and sow happens as a biphasic embryonic signal on d 12 and 18 of gestation (Bertoldo et al., 2012). The second embryonic signal at d 18 is needed for maintenance of the gestation after d 30 (Peltoniemi et al., 2000). Hence, this second period of greater correlation did not coincide with these physiological expectations. On the other hand the period between 21 and 14 d before first insemination has a stronger effect and could be used as a precaution before insemination.

Effects of Heat Stress in Different Periods during Reproduction on Total Born

Total born of sows and gilts was affected most by high temperatures during the period of 7 d before successful insemination until 12 d after. The greatest effect was that of the 10th day after insemination. The temperature on the day of insemination is also highly correlated with the 10th day after and other days in this period. Hence, temperature on the day of successful insemination could also be considered as a single day with greatest effect on total born.

The results are in agreement with the study of Omtvedt et al. (1971) which reported that heat stress during the first 2 wk after mating decreases conception rate and total number of viable embryos. Also in the present study, there was a strong effect of temperature on the 10th day after mating. Lewis and Bunter (2011) also reported from commercial data that high temperatures on 10th day after mating affected total born and number born alive negatively. Day 10 after mating coincides with the interval of pregnancy recognition and embryonic implantation which occurs around d 12 after insemination (Senger, 1999). Magnitude of correlations between farrowing rate and heat stress were larger than correlations between total born and heat stress. This implies that heat stress affects farrowing rate more than total born. Seasonal disruption of gestation has in generally been related to reduced farrowing rate (Peltoniemi et al., 2000) and less to reduced total born.

Opportunities for Optimizing Farrowing Rate and Total Born under Heat Stress

One of the intentions of the current study was to optimize models for estimation of genetic parameters and genetic evaluations using heat stress (e.g., as used by Bloemhof et al., 2012). In that study genetic variation in heat stress tolerance was identified for farrowing rate of sows, using maximum temperature at day of insemination as heat stress factor. However, it is reasonable to hypothesize that heat stress before or after day of insemination could also have a significant effect on farrowing rate and total born. Identification of the most effective heat stress factor should result in more accurate estimation of genetic variation for heat stress tolerance in farrowing rate and total born.

Previously differences between dam lines were found in the relationship between temperature and reproductive performance traits (Bloemhof et al., 2008). It was observed that the Dutch line was sensitive to increasing temperatures, and the International line was robust and had very little effect of increasing temperatures beyond the thermo neutral zone on farrowing rate and total born. Genetic selection within a line might be possible as well. In that case differences among families might be an indication for genetic variation between animals and provide an indication of the opportunities for genetic selection of more robust animals. Therefore, sires were ranked as being sensitive or robust based on regression of reproductive performance of their daughters on heat load.

Farrowing rate of daughters of robust sires was not affected by temperature. Conversely, farrowing rate of daughters of sensitive sires decreased by 1% point per degree Celsius when temperature exceeded 21°C. Their farrowing rate was more than 10% less than farrowing rate of daughters of robust sires when the temperature reached 32°C. These results suggest that genetic differences with respect to the response to heat stress do exist in the dam line investigated. The pattern is also similar to the one observed by Bloemhof et al. (2008) regarding line differences beyond the upper critical temperature.

Total born of daughters of sensitive sires decreased when temperature exceeded 20°C. In contrast total born of daughters from robust sires was constant regardless of temperature at day of successful insemination. Therefore even though daughters of sensitive sires had larger total born under temperate conditions, daughters of robust sires were superior to daughters of sensitive sires when outside temperatures at day of successful insemination exceeded 25°C.

The rather undisturbed performance of daughters of robust sires regardless of temperature is consistent with the results of Bloemhof et al. (2008) for an International Large White dam line and the results of Lewis and Bunter (2011) for an Australian Large White dam line as well as a Landrace dam line. Bloemhof et al. (2008) and Lewis and Bunter (2011) suggested that the absence of a relationship between temperature and reproduction reflected the history of selection in these dam lines in hotter climates. The current study is based on a Dutch Yorkshire dam line, which has been selected on reproductive performance mainly in the Netherlands that has a temperate climate and the temperatures remain therefore below UCT during most part of the year. The differences between daughter-groups described here might be an indication that even though the Dutch Yorkshire dam line was selected in a temperate climate, there is still genetic variation for heat tolerance expressed in reproductive performance.

The above results suggest that selection of sires and dams based on their reaction to heat load could be used to produce progeny with minimum disturbance in farrowing rate and total born under high temperatures. Proper genetic selection will require estimation of heritability under heat load, genetic correlations with other traits and genetic evaluations possibly using individual animal models. The present study has identified the days and periods in which heat stress has maximum effects on farrowing rate and total born for use in the models for these estimates. The periods identified in the present study can also be used to optimize management under heat stress. For example, one could consider cooling the barn during the third week before insemination for better farrowing rate and during the week of insemination and 1 wk after that to increase total born if the temperatures rise above 20°C.

Conclusion

The key period during the reproduction cycle of the sow is 21 to 14 d before first insemination for farrowing rate. Heat stress during this period has largest impact on farrowing rate. For total born, heat stress in the period between 7 d before successful insemination until 12 d after had largest impact on total born. Correlations between temperatures on consecutive days were extremely high ( > 0.9). Therefore, for ease of interpretation for farrowing rate the maximum temperature on 21 d before first insemination and for total born the maximum temperature at day of successful insemination can be used as predictive measures of heat stress in commercial farms.

 

References

Footnotes


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