| چکیده انگلیسی مقاله |
Extended Abstract
Introduction and Objective: The main goal of the animal husbandry industry as a production-economic system is to increase efficiency and profit; Therefore, in the design of the genetic improvement program of domestic animals, attention should be paid to the identification of the production system and the factors affecting its performance, as well as the amount of profit of the system, i.e., the amount of income and expenses. An important issue in a breeding program is deciding which individuals will transmit the greatest biological economic value to the next generation. The economic coefficient of a trait determines the importance of that trait in the selection decision. Considering the importance of this issue, estimating the economic coefficients of important traits in Lori Bakhtiari sheep will play an important role in determining the breeding goals of this livestock. Various methods have been considered to determine economic coefficients, including profit function methods and bio-economic models, among which bio-economic models are more useful in estimating economic coefficients due to their higher accuracy and flexibility. According to the fact that the largest population of sheep are raised in rural systems, studies that can determine the improvement goals and selection index of sheep in rural breeding conditions are important and decisive. The purpose of this study is to identify the sources of cost and income and determine the economic coefficients and improvement goals of Lori Bakhtiari sheep in the conditions of rural breeding of this breed.
Materials and methods: In this study, production, reproduction, management and economic parameters obtained from the recording of 4 sheep flocks of Lori Bakhtiari with 979 productive ewes from Naghan and Aliabad regions of Chaharmahal Bakhtiari province under the rural system (smallholder production circumstances) were used. The information of the herds was collected during an annual production cycle from the beginning of September 2022 to the beginning of September 2023 and was used to determine breeding goals and absolute and relative economic coefficients with different trends of maximum profit. In order to check these parameters, first the target herds were identified and then briefing sessions were held with the relevant ranchers, and then records and information were collected from the herds under investigation. In order to record and collect information, the target herds were divided into 7 different groups based on age group. In the following, the income of the system, which was obtained from Selling excess lambs, selling of ewes and rams, selling of milk and wool, as well as the costs of the system, including the two parts of fixed costs and management costs, were investigated and then for Each age group was estimated separately. Management costs included labor costs, health costs and maintenance costs. After estimating the cost and income for each age group, the equation of income and cost per ewe in the year of formation and the annual profit of the flock was estimated. the equation of income and cost per ewe in the year of formation and the annual profit of the flock was estimated after estimating the cost and income for each age group. To calculate the economic value of traits, the level of each trait was increased by one unit, while other traits were kept constant at the average level. the difference in profit from the increase of one unit in the trait was considered as the absolute economic coefficient. In order to study the sensitivity test in estimating the economic values of traits, the effects of a 20% increase and decrease in the selling price of each kilogram of six-month-old live lamb, the price of each kilogram of wool and the total cost of each kilogram of ewe were calculated, and finally, based on the economic value of the traits, Breeding was determined as a linear function of economically important traits.
Results: According to the results of this research, the largest part(portion) of income (91.08 percent) was related to the selling weight of lambs, and the next largest part(portion) of income was related to milk production. Amount of income from wool was insignificant. Also, the results showed that the highest cost (78.84%) is related to feed costs, followed by personnel, maintenance and health costs, respectively. The lowest cost was related to fixed costs. The highest contribution of revenue (91.08%) and cost (78.84%) of the production system was related to six month old lambs and feeding. Absolute economic coefficients for ewe survival traits, pregnancy rate, lambing frequency, litter size, survival to weaning, sales weight, milk and wool produced were obtained, 592078.23, 675665.75,470750.72, 584396.92, 686031.16, 1305807.22, 115677.32 and 12500 rails respectively. The range of economic coefficients relative to wool weight based on the tendency of maximum profit was between -18.46 and 104.46 for ewe weight and six-month lamb weight, respectively. Trait ranking for two trends of revenue to cost and cost to revenue has been the same. The sensitivity of the economic coefficients of the traits to the price/kg of lamb was high and to the change in the price of wool and the cost of each kg of ewes was low. The ranking of traits in all three trends: income to cost, cost to income and maximum profit showed the same results, so that in all three trends, the highest economic coefficient is related to six-month weight (selling weight of lambs) and Survival of lambs to weaning weight. The results of the sensitivity test of the economic value of the examined traits showed the highest sensitivity to the change in the price of lamb, and this sensitivity level was low compared to the change in the price of wool and the cost of each kilo of ewe.
Conclusion: Based on the estimated economic coefficients, the breeding goals of Lori Bakhtiari sheep were, six-month weight, survival to weaning, pregnancy rate, ewe survival, number of lambs weaned, little size, wool production, weight of the ram, the weight of the replacement, milk production and the weight of the ewe respectively, which shows the importance and impact of these traits in the profitability of the system.
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