Precision dairy farming with automated heat detection
Dairy farming in India is undergoing significant changes in the past few years. Being number one position in milk production at global level, India produces around 165.4 million liter in year 2016-17 with per capita availability of 355 grams per day (Source: Department of Animal Husbandry, Dairying & Fisheries, Ministry of Agriculture, GoI) with expected levels of milk production around 189 million MT with an assumed growth rate of 5% in 12th year plan.
Due to increased demand for milk and unavoidable production cost pressure, farmers are bound with intensified production through high producing animals in large scale facilities, which lead to shorter product life, impaired health and decline reproductive efficiency. Reproductive performance on dairy farm animals is one of the major key drivers to ensure the farm’s profitability. But the overall reproductive efficiency in terms of fertility status is constantly decreasing among the crossbreed animals. It has been reported that a drop of 1-2 % in the conception rate per year in high-performance dairy herds (Sheldon et al. 2006; Norman et al. 2009).
Heat detection is one of the major factors associated with better reproductive efficiency among dairy herds. The pregnancy rate (PR) on the farm is decided by the product of the heat detection rate (HDR) and conception rate (CR). More heat detection rate ensures a higher pregnancy rate and fewer numbers of dry days on farms but due to changes in animal performance, management, and environmental conditions, the estrus expression has decreased at a significant level. Estrus duration and intensity have decreased and less pronounced, which complicates heat detection, the situation becomes more pronounced during summer stress conditions due to high environmental temperatures and humidity with an increase in temperatures humidity index.
There are several other challenges which limit HDR significantly are due to low duration of estrous behavior during increased production of milk near the time of estrus which limits efficiency of visual methods of estrus detection behavior (Lopez et al., 2004), less number of cows with standing estrus (Lyimo et al., 2000; Roelofs et al., 2005; Palmer et al., 2010), silent ovulations (Thatcher and Wilcox, 1973; Palmer et al., 2010; Rana, Singh et al., 2010), and reduced estrus expression of due to confinement (Palmer et al., 2010). Another related issue of low HDR may be due to the cows often tend to show typical signs of being in heat like mounting and standing during the night at times when the herdsman is not observing the animals (Peralta et al. 2005). It has been proved that low HDR with whatsoever reason will increase the time from calving to first artificial insemination (AI), increases the average interval of consecutive AI services (Stevenson and Call, 1983), thereby limiting the pregnancy rate of the dairy herd. Low pregnancy rate steals profit, tolls heavy losses due to extended calving period, fewer wet days with the high number of dry days, less milk per lactation with more expense on feeding and management of dry cows leads to a less productive life of the dairy animal.
Efficiency and accuracy of heat detection are increased by noticing an animal in heat, before elapse of 50 days of parturition. Factors that affect the expression of estrus should be thoroughly monitored. The herd is critically monitored using a heat expectancy chart. The number and percentage of breedable heat should be observed carefully. The efficiency of detection is expressed as the percentage of possible estrus periods that are observed in a given period of time. The accuracy of detection is the percentage of the estrous period observed that is true estrus.
Technology is key to success:
Activity monitoring systems can improve reproductive performance, reduce labor, and reduce the cost of production. The principle of the system is based on behavioral change occurring in the cow in the heat is the increase in activity (Roelofs et al. 2010). Among estrous behaviors, this sign is difficult to detect without technical aids and thus has elicited the invention of automated activity monitoring devices currently used in dairy herds. Activity meters attached around the neck, which record neck movements in all three dimensions. The data on activity is registered continuously and transmitted to a receiver by radio telemetry at regular time intervals. From this receiver, the data are automatically forwarded to a database in a central computer or to a cell phone via the GSM network.
The software supplied with each device compares the activity of each animal with that of a previous reference period (of a various number of days depending on the algorithm) and, in some cases, with the average activity of the herd. The relationships between increased activity, time of ovulation and fertility have been investigated with the help of activity monitoring devices (Lopez-Gatius et al. 2005; Roelofs et al. 2005b; Hockey et al. 2010).
Ovulation takes place on average 29–33 h after the onset of increased activity and 17–19 h after the end of increased activity in lactating Holstein cows (Roelofs et al. 2005b; Hockey et al. 2010). The analysis of more than 5800 heat periods followed by AI in high-producing dairy cows demonstrated that the likelihood of pregnancy at 38–45 days post-AI increased with enhanced physical activity, suggesting a positive correlation between increased activity during oestrus and fertility (Lopez-Gatius et al. 2005). The efficiency of oestrus detection using activity-meters is generally in excess of 80% but varies with the threshold set and the reference period of previous activity that are used to define the increase in activity, as well as with the time interval between two successive recordings (De Mol et al. 1997; Nebel et al. 2000; At-Taras and Spahr 2001; Firk et al. 2002; Cavalieri et al. 2003a; Roelofs et al. 2005b; Sakaguchi et al. 2007; Galon 2010; Hockey et al. 2010; Lovendahl and Chagunda 2010).
The specificity of activity-meters is generally between 90% and 100% but their accuracy may vary considerably depending on the devices and on the algorithm used by the software (De Mol et al. 1997; Firk et al. 2002; Cavalieri et al. 2003a; Roelofs et al. 2005b; Sakaguchi et al. 2007; Hockey et al. 2010; Lovendahl and Chagunda 2010). So far no clear difference in sensitivity, specificity, and accuracy of a neck activity-meter for oestrus detection could be observed between two dairy herds differing markedly in terms of animal environment and calving systems (Hockey et al. 2010).
Investment in these automation systems may become a source of profit provided the recorded data are properly analyzed and managed by breeders and veterinarians