Assessing temperature and humidity conditions for dairy cattle in Córdoba, Argentina

Assessing temperature and humidity conditions for dairy cattle in Córdoba, Argentina A.C. de la Casa and A. C. Ravelo Antonio C. de la Casa (*) Facultad de Ciencias Agropecuarias Universidad Nacional de Córdoba, C.C. 509 Córdoba, Argentina Andrés C. Ravelo CONICET / Facultad de Ciencias Agropecuarias Universidad Nacional de Córdoba, C.C. 509 Córdoba, Argentina

Abstract Temperature and humidity conditions affect livestock production in Central Argentina. This study evaluates the risk of thermal stress affecting dairy production. The Temperature- Humidity Index (THI) was used to analyze the regional and seasonal effects of temperature and humidity. Statistically, the THI was found to be normally distributed. The probability of occurrence of daily THI higher than 72 was 40% for Río Cuarto during January. Regional variability of THI indicates small harmful extreme thermal stress conditions. The THI 78 probability of occurrence ranges between 4% and 10% for the main dairy region of Córdoba during January. Also, in January and February, dairy production losses between 3 and 4 liters/cow/day could be expected with a frequency of 5% in Río Cuarto and 15% in Villa de María de Río Seco. Key words: Temperature-humidity index, dairy cattle, probability distribution, risk index.

Introduction Environmental conditions such as high temperature and humidity cause animal stress and impact negatively the animals production, behavior, health and well-being. Hahn (1985) identified critical atmospheric temperatures corresponding to different species, ages and levels of production and then contrasted zones or temperature ranges where performance is optimum and those where productivity losses were only nominal, meaning that performance reduction was lower than 3%. A rational selection of livestock management practices can be based on meteorological and climatological information, therefore enabling an objective consideration of different management alternatives (Hahn, 1981). Burgos (1977) used an average air temperature of 26º C for the hottest month to establish a thermal threshold for excessive heat. Using the thermal threshold, the South American territory was divided in regions suitable for tropical and temperate livestock. Fuquay (1981) indicates that in studies conducted in controlled environments, temperatures between 24º C and 27º C are, in general, the critical upper limit for animals not adapted to hot climates. By combining environmental variables affecting animal comfort, some indices have been proposed to evaluate the impact of those variables on physiological and productive responses of animals. The Temperature-Humidity Index (THI) (Thom, 1958) has been used to estimate production loss in dairy livestock in the United States (Hahn, 1969) and in Argentina (Valtorta, 1999). It is also used to make decisions in livestock management during the summer months (Armstrong, 1994) and to assess the feasibility of summer

environmental control for dairy cattle based on expected production losses (Hahn and Osburn, 1969; Hahn and Osburn, 1970). The objective of this study was to assess the temperature and humidity conditions in Córdoba province of Argentina and the thermal stress risk affecting the dairy production during the warm season. Materials and Methods Daily maximum temperature (TMAX (ºC)), minimum temperature (TMIN (ºC)) and vapor pressure (EA (HPa)) data for 17 weather stations from Córdoba and neighboring provinces for the 1971-1990 period (INTA-CIRN, 1995) were used to calculate the THI. The mean daily temperature (TMED (ºC)) was calculated using daily TMAX and TMIN. The THI was calculated as follows: THI = TMED + 0.36 TPR + 41.2 TMED: Mean daily temperature. TPR: Dew point temperature (ºC), calculated using the vapor pressure (EA) daily average as follows (Torres Ruiz, 1995): TPR = 237,3*LN(EA/6,11)/(17,27-LN(EA/6,11)) The regional variability of the THI for Córdoba was analyzed using isoline maps of THI for 10-day and monthly periods during the warm season (October-March). These maps were obtained by interpolation using a krigging technique, which considers the distance between stations using an experimental semi-variogram (Tabios and Salas, 1985). The THI isolines were drawn by the computer program SURFER (Golden Software, 1994), through an isotropic search by quadrants.

Descriptive statistics for daily THI for Cordoba s meteorological stations were calculated for the warm season. Also a frequency distribution was obtained for daily THI and the Kolmogoroff-Smirnoff goodness of fit test (Sachs, 1978) was applied for a normal probability distribution. A map of spatial variation of THI was made for different levels of probability using a normal probability distribution. Dairy production losses caused by thermal stress were estimated by a productivity function obtained by Valtorta et al (1999). The dairy production (PL) function used here was: PL = -0,2524*THI+40,51 (l/c/d). Daily low and high thermal stress conditions were analyzed using hourly THI for morning (8:00 AM) and early afternoon (2:00 PM). The occurrence of heat waves were established for periods when the THI was equal or higher than 72 for 48 hours. Results and Discussion Figure 1 shows a frequency histogram of daily THI for Río Cuarto for the warm season and the fitted curve for a normal distribution. The Kolmogoroff-Smirnoff test showed that there is not a significant difference between the empirical and theoretical normal probability distribution. Therefore, this distribution will be applied to analyze the THI probability of reaching certain levels. Figure 1. The probability of having a THI greater than 72 for Río Cuarto is 21% during October-March warm season. A THI above 72 represents adverse effects for high producing dairy cows (Armstrong, 1994).

The unifactorial ANOVA shows significant differences for the monthly THI. In January the probability of occurrence of THI higher than 72 in Río Cuarto reaches 40%. In October, the probability decreases to 3.7 %. The monsoon rainfall regime increases the risk of harmful THI until March, when there is a high dew point temperature. Figure 2 shows the mean THI regional variability during January indicating a 71-72 index range for the NE region where the main dairy producing area is located. Figure 2. In terms of probability, the occurrence of different levels of stress were established adopting the ranges used by dairy farmers in the USA (Armstrong, 1994). Considering a THI of 78 to establish the beginning of harmful extreme conditions, probability isolines are shown in Figure 3. Figure 3. The probability of having a THI higher than 78 varies between 10 % in the north and 4% in the south of the main dairy region of the province (INTA, 1986). THI higher than 88 (emergency situation) has a very low probability of occurrence. The intensity of thermal stress affects the most important region of dairy production of the province. Valtorta et al. (1999) determined for the region of Rafaela (Santa Fe, Argentina) and for dairy cows of 22.0 ± 2.5 liters/cow/day mean production, a rate of decrease of approximately 0.25 liters/cow/day per unit of increase of THI between 65 and 80. The mean dairy production losses due to heat stress conditions in Córdoba locations during January and February are shown in Figure 4. The mean losses vary between 1.35

liters/cow/day in Río Cuarto (6% of mean production) and 2.06 liters/cow/day in Villa María de Río Seco (9% of mean production). Similar losses were reported by Hahn and McQuigg (1967) for Columbia, Missouri. Figure 4. Dairy production losses between 2 and 3 liters/cow/day are expected with frequency of 26% in Río Cuarto and 37% in V. M. Río Seco, while losses between 3 and 4 liters/cow/day have a frequency less than 5% in Río Cuarto and up to 15% in V. M. Río Seco during January and February. A daily mean THI constitutes a useful index to represent the effect of heat stress on dairy cattle. However, certain meteorological conditions can mask the index effectiveness by attenuating or intensifying the heat stress. For example, some locations with large daily temperature range may allow for night cooling and animal recovery from heat stress. In Figure 5, the THI (8:00 AM and 2:00 PM) for the warmest month (January) and two distinct locations are shown. Considering the morning temperature, Río Cuarto has 84 % of nights with THI < 72 or only 16 % of nights above that THI level. Assuming that the 2:00 pm temperature will be higher than the morning temperature then the THI will also be higher causing a difficult stress recovery in only five days (16% of 31 days) during January. On the other hand, in V.M. Río Seco the number of nights with THI > 72 reaches 34 %. This means that during January in V.M. Río Seco the animal will be under 24-hour heat stress for 10 days which is twice as large as in Río Cuarto. Figure 5.

On the other hand, heat waves (consecutive days of extreme heat) can intensify the impact of heat stress and increase its penalties. Figure 6 shows the occurrence of heat waves between 1968 and 1987. V. M. Río Seco shows 133 cases with an average THI during a heat wave between 75 and 81, and a maximum duration of 264 continuous hours (11 days). In Río Cuarto, heat wave impact is less significant as indicated by a smaller frequency (45 cases) and a decrease in the duration of the heat waves, with a maximum of 126 continuous hours (5 days, approximately). Figure 6 Conclusions The results showed that heat stress in the warmer months produces production losses representing a mean reduction of 6 % in Rio Cuarto and 9 % in V.M. Rio Seco, in relation to normal production level of 22 l/c/d. However, losses can be affected as consequence of factors that attenuate or intensify the effect of heat stress. Rio Cuarto shows a milder stress as consequence of larger nocturnal cooling with a THI below 72 in 82 % of the nights. In V.M. Rio Seco, THI nightly values less than 72 occurred in 63 % of the nights. Also the occurrence of heat waves are felt with greater intensity and duration in V.M. Rio Seco, as expected by its subtropical climate. Acknowledgements We want to thank Dr. LeRoy Hahn for helpful comments and suggestions to the manuscript.

References Armstrong, D.V.; 1994. Heat stress interaction with shade and cooling. J. Dairy Science, 77: 2044-2050. Burgos, J.J.; 1977. El clima en la produccion de ganado tropical. Ganadería Tropical (M.B. Helman), Cap.1: 3-32. Fuquay, J.W.; 1981. Heat stress as it affects animal production. J. Anim. Sci., 52:164-174. Golden Software, 1994. SURFER for Windows V5.00. Golden Software, Golden, CO. Hahn, G.L and J.D. McQuigg ; 1967. Expected production losses for lactating Holstein dairy cows as basis for rational planning of shelters. ASAE Paper MC-67-107. Hahn, G.L.; 1969. Predicted vs measured production differences ussing summer air conditioning for lactating cows. J. Dairy Sci., 52:800. Hahn, G.L. and D.D. Osburn; 1969. Feasibility of summer environmental control for dairy cattle based on expected production losses. Trans. ASAE, 7(3):329-331. Hahn, G.L. and D.D. Osburn; 1970. Feasibility of evaporative control for dairy cattle based on expected production losses.trans. ASAE, 7(3):329-331. Hahn, G.L.; 1981. Housing and management to reduce climatic impacts on livestock. J. Ani. Sci., 52(1): 175-186. Hahn, G.L.; 1985. Management and housing of farm animals in hot environments. Chap.11 pp 151-174 in Stress Physiology in Livestock, Vol. 2, edited by M. Yousef. CRC Press, Boca Raton, Fl. Hahn, G.L.; 1995. Environmental management for improved livestock performance, health and well-being. Jpn. J. Livest. Management, 30(3): 113-127. INTA-Centro Regional Córdoba; 1986. Análisis de la evolución, situación actual y problemática del sector agropecuario del Centro Regional Córdoba. INTA CRC y SMAGyRR de la Pcia. de Córdoba. Area Homogénea III: Región Lechera del Centro Este. INTA-CIRN; 1995. Estadística agroclimática decadial serie 1971-1990. Centro de Investigaciones de Recursos Naturales, Instituto de Clima y Agua. Sachs, L.; 1978. Estadística Aplicada. Ed. Labor, S.A. 567 pp.

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900 800 Normal Expected 700 Nº of observations 600 500 400 300 200 100 0 45 48 51 54 57 60 63 66 69 72 75 78 81 84 Upper limit of range Figure 1. THI frequency histogram and the normal probability distribution curve (Kolmogoroff-Smirnoff test: d=0.05565, p<0.01) corresponding to the October-March warm season in Río Cuarto.

Figure 2. Regional variability of mean THI during January in Córdoba province and the meteorological stations used in the regional analysis. Figure 3. Probability isolines for THI values above 78 during January. 3.4 3.0 ±Std. Dev. Mean Dairy Production Losses (l/c/d) 2.6 2.2 1.8 1.4 1.0 0.6 0.2 RS VD MJ PI LB RC Locations Figure 4. Mean and standard deviation of dairy milk production losses due to heat stress during January and February for selected locations of Córdoba province (RS: V.M. Río Seco; VD: V. Dolores; MJ: M. Juarez; PI: Pilar; LB: Laboulaye; RC: Río Cuarto).

Cumulative relative frequency (%) 100% 80% 60% 40% 20% (a) Río Cuarto 08:00 a.m. 02:00 p.m. 0% <=63 63.1-66 66.1-69 69.1-72 72.1-75 75.1-78 78.1-81 >=81.1 Ranges of THI 100% (b) V.M. Río Seco 08:00 a.m. 02:00 p.m. Cumulative relative frequency (%) 80% 60% 40% 20% 0% <=63 63.1-66 66.1-69 69.1-72 72.1-75 75.1-78 78.1-81 >=81.1 Ranges of THI Figure 5 a & b. Accumulated relative frequency of morning (8:00 AM) and early afternoon (2:00 PM) THI for (a) Río Cuarto and (b) V. M. Río Seco during January.

300 250 Villa de María Río Cuarto Continuous hours with THI>72 200 150 100 50 0 30/12/67 29/12/68 29/12/69 29/12/70 29/12/71 28/12/72 28/12/73 28/12/74 28/12/75 27/12/76 27/12/77 27/12/78 27/12/79 26/12/80 26/12/81 26/12/82 26/12/83 25/12/84 25/12/85 25/12/86 25/12/87 Beginning dates of heat waves Figure 6. Accumulated relative frequency of morning (8:00 AM) and early afternoon (2:00 PM) THI for (a) Río Cuarto and (b) V. M. Río Seco during January.