Heat stress under different climate conditions for dairy livestock in Benin, West Africa

J S Adjassin, S A Assani, B G C Assogba, Y Idrissou, H S S Worogo, J O Daramola¹ and I T Alkoiret

Laboratory of Ecology, Heath and Animal Productions (LESPA), University of Parakou, P.O Box 123 Parakou, Benin Republic
alassanassani@gmail.com
¹ Department of Animal Physiology, University of Agriculture, Abeokuta, Nigeria

Abstract

Livestock production in the changing climatic condition is influencing by heat stress. The study was aimed to establish a heat stress mapping in Benin by means of Temperature-Humidity Index (THI) from 2009 to 2019. THI combines ambient temperature and relative humidity, and help to predict the effects of thermal environment warmth in dairy animals. Results indicated also that the existence of a thermal stress that manifests all year round with a certain moderation because of the climate zones. Northern Benin has been characterized by three conditions of heat stress (mild, moderate and severe). A combination of moderate and severe stress conditions was observed at beginning dry season (October - November) and during rainy season (June - September), a combination of mild and moderate stress conditions in dry season (December - March) and only severe stress was observed at beginning of rainy season (April - May). In southern Benin, two heat stress conditions were observed. During long dry season (November - March) and long rainy season (April - mid-July) and short rainy season (mid-September - October) a severe stress condition was observed and moderate stress during short dry season (mid-July - mid-September). The trends for the last 10 years show a consistent rise in temperature, a reduction in humidity and an increase in the Temperature- Humidity Index (THI) with the effects being most pronounced in the sub-equatorial region of Benin. Heat stress zone mapping can be used as a management and mitigation guideline for the livestock thermal environment to protect and to improve dairy animal’s welfare.

Keywords: global warming, livestock, temperature humidity index (THI), temperature

Introduction

The sustainability of livestock systems is threatened because of the danger of climate change. In developing countries, livestock is an important sector and contributes to the national economy and to rural employment rate (Thornton et al 2002). However, environmental factors such as temperature, relative humidity, solar radiation, and wind speed directly and indirectly affect animal production (Nienaber et al 1999; Kadzere et al 2002; Bernabucci et al 2010; Sejian et al 2012; Collier et al 2019). Taking into account the importance of the influence of high ambient temperatures and heat waves on the welfare and productivity of livestock, it’s clear that climatic conditions for livestock rearing will deteriorate (Herbut et al 2018) while affecting their thermoregulatory capacity (DeShazer et al 2009). There is a thermal range within which the animal is able to maintain homoeothermic through behavioural and physiological mechanisms (Daramola et al 2012). A decrease in voluntary feed intake, animal weight, fertility, and production in dairy farming caused by high temperatures has been shown by many researchers (Armstrong 1994; Mader et al 2009; Kadzere et al 2002; Amundson et al 2006; Hernandez et al 2011 and Gantner et al 2012). Although, livestock is an important part of Benin’s agricultural sector, little work has been done on the impact of rising temperatures on livestock. Indeed, Benin is a tropical country characterized by an equatorial climate in the south with two dry seasons and two rainy seasons and a tropical climate in the north with a dry season and a rainy season. Thus, estimating the degree of comfort or stress of the environment is complex (Bernabucci et al 2010). The only limits of ambient temperature, as a measure of the thermal environment, have led to efforts to produce an index representing the net effect that environmental conditions may have on heat exchange processes (Thom 1959; Hahn et al 2003; Mader et al 2006). This index combines several environmental components and is very useful for characterizing the thermal environment on the productivity and welfare of animals (Mader et al 2010; Mader and Davis 2004; Amundson et al 2006). In addition, their assessment remains variable according to the geographical location and the type of climate (Bohmanova et al 2007). It is in this context that THI is used to estimate the degree of heat stress in dairy livestock (Armstrong 1994; Mader et al 2006; Bohmanova et al 2007; Dash et al 2015 and Sejian et al 2018). It’s a standard factor for the classification of thermal environments in many animal experiments in order to better manage livestock systems (Ravagnolo et al 2000; Hahn et al 2003; Silva et al 2007; Dikmen and Hansen 2009; Marai and Habeeb 2010) and widely applied from temperate climates to hot and humid climates (NOAA 1976). McDowell et al (1976) suggest that THI is a good indicator of stressful weather conditions. Indeed, many publications have relied on the THI index to delineate areas of animal welfare and heat stress (Bianca 1976; McDowell et al 1976; Fuquay 1981). Different classes of THI have been established in order to qualify the effect of heat stress on production characteristics in dairy farms (Hammani et al 2013). The classifications of THI vary according to the intensity of physiological and productive animal responses to heat stress (Silva et al 2007; Bohmanova et al 2007 and Dikmen and Hansen 2009). The evaluation of these responses made it possible to classify the level of impact (Bouraoui et al 2013). Therefore, it’s important to know the temperature and relative humidity thresholds for which animal comfort is maintained. In spite of extensive research done elsewhere, there is a lack of studies examining THI values for environmental conditions Beninese and relating them to potential heat stress conditions for dairy animals. These species are important for food and nutritional security. However, knowledge of the periods during which environmental conditions remain detrimental to dairy livestock is essential in the context of climate change. The aim of this study was to establish a heat stress map in Benin for a period of time comprising the years between 2009 and 2019. This mapping could be considered as one of the decisions tool before to the implementation of dairy farming units in the country. It can also be used as a basic tool to improve the management of existing dairy herds as long as heat stress management becomes essential for farmers.

Materials and methods

Study area

West Africa country, Benin, is located in the tropical zone between latitudes 6° 30' and 12° 30' North and longitudes 1° and 3° 40’ East. In the southern Benin, the climate is a subequatorial and characterized by a bimodal rainfall regime ruled by two (02) rainy seasons: a large season usually extending from April to July, and a small one covering the period September - November; and two (02) dry seasons: a great season from November to March and a small one from July to August. In the north, the climate is tropical, characterized by the succession in the year of a single rainy season from April to October and a single dry season from November to March, marked by the preponderance of a very dry wind. Over the whole country, the average annual rainfall varies from 700 mm in the extreme north to 1400 mm (mountainous areas in the northwest and southeast). Ambient temperature averages oscillate around 27.2 °C year-round with absolute maxima exceeding 45 °C in the northern. The study environment consists of six (06) synoptic stations (Cotonou, Bohicon, Savè, Parakou, Natitingou and Kandi) spread evenly throughout Benin and representative in three vegetation zones out of a total of four (04) in Benin (Figure 1, Table 2).

Acquisition of meteorological data

The monthly meteorological data of the 6 synoptic stations of Benin spread over three agro-ecological zones a period from 2009 to 2019 were obtained from the National Agency of Meteorology of Benin (ANM-BENIN). Daily values of maximum and minimum temperature, maximum and minimum relative humidity, and rainfall measurements were used to determine the average values of each variable per month and annual (Table 1).

Figure 1. Map of Benin showing the synoptic stations

Calculation and classification of THI values

The temperature humidity index (THI) combines ambient temperature and relative humidity and was calculated from meteorological data obtained according to the formula described by (Mader et al 2006):

THI = (0.8 × T ° C) + [(% RH / 100) × (T ° C - 14.4)] + 46.4

Where: T ° C: Ambient temperature and RH: Relative humidity (%).

The THI values obtained were used to establish seasonal mapping of heat stress in Benin. The Table 2 presents the classification of THI values and the assessment of the intensity of the thermal stress according to the standards recommended according to (Silanikove 2000). This classification shows a better visualization of the effect of heat stress in four (04) levels (Bouraoui et al 2002). Heat stress maps by season of the year were designed using Arc-GIC software version 9.3.1.

Statistical analysis

Historical and current trends in THI values were studied. The variability of THI values obtained from 2009 to 2019 were compared using an analysis of variance (ANOVA) for years and climate zones. Seasonal variations were also highlighted. Seasons considered in this study are: Long dry season (November to March), Long rainy season (April to mid-July), Short dry season (mid-July to mid-September) and Short rainy season (mid-September to October) in Southern; Beginning rainy season (April to May), Full rainy season (July to September), Beginning dry season (October to November) and Full dry season (December to March) in Northern Benin.

Results

Thermal environment for dairy animal in Benin

Data obtained from the six (06) synoptic stations during the period 2009-2019 revealed that there is a thermal difference between northern and southern Benin (Figure 2). All THI average values of the southern synoptic stations are above the critical value 72 at any time of the year. The months of September to March record extreme THI values (THI≥78) and July-August record the most moderate values (75≤THI<78). On the other hand, in the north, the extreme THI values were recorded in March, April, May and June, December-January record the low values of THI and July-September record the most moderate values.

Figure 2. Average values monthly temperature-humidity index (THI), temperature (AT, °C), and relative humidity (RH, %) in a: Northern Benin and b: Southern Benin during a period of 2009-2019. The bars represent standard deviations between the years

Ambient temperature, relative humidity and heat stress index over the time period 2009-2019

There were linear increases in air temperature over the period 2009-2019. In contrast, the relative humidity declined (Figure 3). The overall effect on the temperature-humidity index was for this to increase (Figure 4), but at lower rates than for the increase in temperature.

Figure 3. Trends in ambient temperature and relative humidity for the period 2009-2019 in the sub-equatorial, sub-humid and dry-tropical zones in Benin

Figure 4. Trends in the relative humidity index for the period 2009-2019 in the sub-equatorial, sub-humid and dry-tropical zones in Benin

Seasonal mapping of heat stress in Benin

Seasonal mapping of heat stress in Benin was illustrated in Figure 4. Three levels of heat stress were observed throughout the year. Northern Benin was characterized by three conditions of heat stress (mild, moderate and severe stress) and two conditions of stress (moderate and severe stress) for the southern.

In southern Benin, long dry season (November - March), long rainy season (April - mid-July) and short rainy season (mid-September - October) are characterized by severe heat stress conditions while long dry season (mid-July to mid-September) is characterized by moderate heat stress conditions.

In beginning dry season (October-November), northern country is characterized by two conditions of heat stress. Moderate stress conditions have been observed in major part of the Atacora, northern Donga and western Borgou region, while the others part of northern characterized by severe stress condition. In full dry season (December - March), heat stress is characterized by a mild stress condition throughout northern Benin with the exception of southern Borgou and Donga characterized by moderate stress conditions. In beginning rainy season (April - May), severe stress condition was located over entire northern country. In full rainy season (June-September), severe stress condition was located only of Alibori region and the others part of northern characterized by moderate stress.

Figure 5. Seasonal mapping of heat stress in Benin

Discussion

Relationship between heat stress index and global warming

The recorded increase in air temperature in Benin over the years 2009-19 is in line with global records (IPCC 2019) and was reflected in increases in the temperature-humidity index despite the reduction in relative humidity over the same period.

Higher temperatures lead to increased evaporation and consequently a decrease in relative humidity (Simmons et al 2010; Blunden and Arndt 2016). However, as shown in Figure 4, higher temperatures lead to an increase in heat stress. Similar findings were reported by AghaKouchak et al (2014) and Fischer and Knutti (2015). Thus, this increase in heat stress is a consequence of global warming.

Increasing trends in heat stress index and temperature have also been reported in several studies around the world (Knutson and Ploshay 2016; Sun et al 2017; Ahmadalipour et al 2017). All researchers agree that these are the consequences of global warming. In Benin, it is expected that the intensity and frequency of extreme events such as heat waves, and droughts will increase as well as the associated risks and impacts (Yabi and Afouda 2012). Similarly, the climate projections for Africa show that, whatever the scenarios considered (the moderate scenario, RCP4.5 or the pessimistic scenario, RCP8.5), a temperature increase trend of around 2 ºC to 4 ºC is expected. This implies that the levels of risk of heat stress will be higher and/or more spatially extensive.

Mapping of heat stress

The results of this study showed that Benin is exposed to three (03) heat stress conditions throughout the year: mild, moderate and severe. Different classes of variation of THI have been proposed by several authors taking into account the critical value 72 of THI identified by (Johnson 1985). Average THI values for Benin indicate that dairy animals in ambient field conditions experience long periods of heat stress throughout the year with mild and moderate conditions heat stress at a time of the year. Our results are similar to those obtained by Lallo et al (2018) in Jamaica during the characterization of heat stress on farm animals using the temperature humidity index (THI). These authors obtained very high average THI values, 83 in winter (December-February) and 87 in summer (July-September). Gantner (2012) studied microclimatic conditions in three regions of Croatia, as well as the effect of temperature-humidity index values on dairy cattle production and found that heat stress conditions indicated with average daily values THI> 72 in spring and summer in all regions. This state of affairs could have indirect and direct effects on livestock. An indirect effect is observed through the reduction of food availability. A direct effect by the action of temperature on milk production (notion of heat stress) that has been studied by several researchers (Kadzere et al 2002; West 2003; Jordan 2003; Cook et al 2007; Rhoads et al 2009 and Bernabucci et al 2010). All climate zones in Malaysia are characterized by THI values above 72 during the 12 months of the year indicated (Johnson, 1985). The results of Figure 4a, 4b, 4c and 4d showed that the northern Benin region, except at the beginning of the rainy season (April-May) and dry (October-November) is characterized by low THI values 70≤THI<75. Meat and milk production are favorable in the dry season (December-March) and in the rainy season (June-September) in this region of the country. On the other hand, southern Benin is characterized by a fairly pronounced heat stress due to the exceeding of the critical THI value 72 proposed by (Du Preez et al 1990). For the period 1991-2008, (Hernández et al 2011) in Veracruz, still in Mexico obtained high THI values for ruminants 75-76 in winter increasing from 85 to 86 in summer using maximum monthly temperatures. In the state of Missouri in the United States and in Azizonia 4 and 2 months were respectively obtained as a non-favorable period for milk production and Canada, with the exception of always having THI values which are less than 72 all year (Johnson 1986). However, Benin seems less favourable to milk production than Tunisia, Egypt, South Africa, Mexico, the United States and Canada.

The results obtained in this study differed from those obtained by (Bouraoui et al 2002) in Tunisia where dairy farming is exposed to heat stress over a period of 4 months (June, July, August and September). In South Africa, Du Preez et al (1990) found that during the summer season all milk producing areas are under thermal stress when the critical THI value exceeds 72. In Mexico, 7 to 8 months have been reported obtained as a no favourable period for milk production (Johnson 1985).

In synthesis, the analysis of the THI map in Benin during the 10 last years indicates a north-south gradient, in that the values are always higher in the southern regions of Benin. In general terms, the THI map indicates that livestock on dairy farms will suffer a risk to welfare and performance.

Implication in the development of dairy livestock in Benin

The long-term imbalance of normal weather conditions, such as temperature, wind, and precipitation typical of a given region, is likely to be one of the major challenges facing humanity in this century (Bertocchi et al 2014). Unfortunately, population growth is associated with the food shortage, where about 842 million people are facing food deprivation (FAO 2013). However, livestock will have a major role to play in ensuring global food security (Rashamol et al 2019). The increased demand for meat and milk, mainly from developing countries (Rojas-Downing et al 2017), is expected to double by 2050. These animal products are important to ensuring global food security, current and future. In addition, heat stress affects directly and indirectly feed intake, animal body temperature, metabolic processes, feed efficiency, milk production, reproductive performance, animal behavior and risk of disease (Kadzere et al 2002; West 2003; Jordan 2003; Cook et al 2007; Rhoads et al 2009; Bernabucci et al 2010). The majority of studies on heat stress in livestock are based on temperature and relative humidity (Igono and Johnson 1990; Bouraoui et al 2002; St-Pierre et al 2003; West 2003; Calderon et al 2004), as data on the amount of heat radiation produced by animals; wind speed and precipitation are not publicly available. Temperature-humidity index (THI) is a value representing the combined effect of air temperature and relative humidity, associated with the level of heat stress (Bohmanova et al 2007). THI temperature and humidity index values applicable in Benin were established during this analysis period using computerized mapping. This has important implications for their overall health, production and reproduction of ruminants. Careful plant planning and highly adaptable flock management are needed to protect dairy farms from heat stress.

Conclusions

• Temperature trends for the 10 last years showed an overall warming in all climatic zones of Benin. By contrast, the relative humidity decreased during this time period, The relative humidity index increased but at a slower rate than the increase in temperature.
  
  
• The relative humidity index was lowest in the dry region of Benin but increased with time at a faster rate than the relative humidity index in the equatorial zone.
  
  
• This study provides baseline values for future mapping, based on a bioclimatic index.

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Received 24 March 2020; Accepted 11 April 2020; Published 1 May 2020

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