پديد آورندگان :
سيدشريفي، رضا دانشگاه محقق اردبيلي - گروه علوم دامي , اميري، صونا دانشگاه محقق اردبيلي - دانشكده كشاورزي و منابع طبيعي - گروه علوم دامي , هدايت ايوريق، نعمت دانشگاه محقق اردبيلي - گروه علوم دامي , سيف دواتي، جمال دانشگاه محقق اردبيلي - گروه علوم دامي , يلچي، طاهر دانشگاه محقق اردبيلي - گروه علوم دامي
چكيده لاتين :
Introduction:To maximize the profitability of livestock industry decisions are based on individual animal yield and other factors such as overall health of animal as well as production and reproduction performance. To increase the revenue changes in replacement policies and breeding are essential (Nasr Esfahani 2018). Accordingly, in Iran issues such as feed price fluctuations and lack of coherent policies increase the economic risk of the dairy cow industry. To better make a decision on elimination or replacement of a cow, the producer shall compare the expected benefits of both keeping or replacing an individual animal. The most important objective of a livestock unit is to maximize the profit of the herd; one of the issues affecting this profit is the criteria and level of elimination (Rogers et al,1988). If elimination and replacement is not optimal, namely the cows are eliminated sooner or later than the optimal time, the profitability of the herd will decreases. For determining the optimal elimination time point several biological and economic variables shall be concurrently taken into consideration (De Vries,2006). The elimination decision must be based on predicted future incomes of the cow. Generally, keeping an animal in the herd for a longer time leads to more profit. The proper decision of optimal and reasonable elimination is made by comparing the present value of future liquidity flow (income and expenses) of the current livestock in the herd with the present value of the future liquidity flow of heifer as its replacement. Ultimately livestock with most current value will occupy the position (Groenendall et al. 2005). The application of dynamic programming in the animal sciences mostly is about animal replacement issues. The optimal policy at each stage represents best decision from that stage till the final stage. In this method, an expected value will be calculated for each of the situations that will happened. Furterhmore, the decision maker selects the best decision based on the calculated expected value and upcoming situations. For optimal replacement decision making in dairy herds, several dynamic programming models have been proposed by De Vries et al. (2006) and Van Arendonk et al. (1985). Cardoso et al. (1999) reported optimizing replacement and insemination policies in dairy cows by calculating income, costs and monthly probability of elimination.
Material and methods: In this study, we estimated the biological parameters of the herd, including risk of forced elimination and the possibility of pregnancy in different lactation periods and different months after calving based on the data collected from industrial dairy farms (Ardabil city, Iran) between 2015 and 2018. Also, financial data of the herd was also obtained in the form of an economic questionnaire from the studied units and was developed by importing biological parameters and financial data into a bio economic model in Dairy Vip software. This software simulates the livestock over time and calculates the performance of herd based on optimal and non-optimal modes. The basis for optimal elimination decision is to minimize the cost of missed opportunity (cost of rejecting the best alternative when deciding). So that by negating the value of keeping livestock (obtained from the difference between the net present value of existing livestock and alternative heifers), optional removal will be done. The criteria of optional elimination in the studied herds were determined to reach the milk production of non-pregnant cows to less than 18 kg/day. Milk production was evaluated using daily milk production records and also fitting the incomplete gamma curve (Wood). The 21-day mean insemination rate and success insemination rate of cow were 49.3% and 37%, respectively. By default, in Dairy Vip software, a livestock can be in the herd for 24 months maximum after calving. The risk of abortion from the second to the eighth month was 6.24, 4.16, 2.08, 1.11, 0.45, 0.19, and 0.19%, respectively. Dynamic programming model was developed to determine the optimal replacement policy. The objective function in this study was to maximize the present value of net income from current cows and alternative heifers. In order to estimate the expected statistics under optimal policy, Markov chain simulation was used.
Results and discussion: With the implementation of optimal policies, the annual elimination rate increased from 30.11% to 43.80%. However, the forced elimination rate slightly decreased )2.3%(. The optimal removal rate can vary depending on the economic and biological conditions in the herd. With the implementation of optimal policies, pregnancy rate increased from 15.31% to 18.20% and increased by 2.89%. Correspondingly, it can be concluded that the economic importance of increasing the pregnancy rate is more urgent in herds with weaker reproductive performance. Also, success rate of insemination dropped from 33.9% in non-optimal mode to 37%, indicating higher likelihood of livestock pregnancy in different months. The 21-day insemination rate increased from 47.6% to 49.3%, indicating appropriate cow inoculation. milk production in dairy cows could be increased by reducing the pregnancy days. In this study pregnancy days decreased from 139 to 132 days by optimal policies leading to significantly higher milk production. Furthermore, open days (the interval from calving to the next gestation period) decreased from 167 to 161 days reducing the pregnancy days and subsequently increasing the pregnancy rate. Also, reducing the calving distance from 13.6 to 13.3 (0.3 per month) resulted in higher annual milk production. Optimal policies compared with the non-optimal policy resulted shorter lactation days thus significantly higher milk production (daily and annual) and performance (Table 3). Daily milk performance increased from 41.4 to 44.2 equaling to 3 Liters more milk per cow (annually 935 kg more milk). According to our observations, annual milk yield increased from 12,548 to 13,483 kg per cow via the implementation of optimal policies. Moreover, reducing the average lactation days resulted in increase in the daily and annual production of cows by 3 and, respectively.
Conclusion: The most important goal of a livestock unit is maximizing the profit of the herd. One of the factors affecting profitability, is criteria and elimination rate. Implementing optimal policies is associated with increased livestock elimination rates and replacement costs and also increased feed costs. However, higher revenue from these policies can compensate the increased costs thus increasing the net profit per cow. One of the basic criteria in estimating the expected present value is to sort the cows in the herd based on future income and expenses, and according to these values, the decision is made to keep or eliminate the cows. So, regardless of these values, cows may be eliminated sooner or later than the optimal time, which reduces the profitability of the herd.
