影响牛奶生产的因素:以安得拉邦为例

IF 0.6 Q4 DEVELOPMENT STUDIES
M. Rao
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引用次数: 0

摘要

印度的牛奶产量排名第一,占世界产量的18.5%,2014- 2015年的年产量达到1.463亿吨。牛奶的产量取决于各种因素,如饲养、繁殖和对动物的管理。很明显,在农业部门的非农业和相关活动中,奶业提供了有收益的自营职业,并为农村群众创造了收入。杂交品种的水牛被大量饲养;结果,产奶量有了实质性的提高。为了研究安得拉邦Prakasam地区饲养杂交水牛的家庭的产奶量和乳制品方面的问题,数据和信息来自两个选定的mandals,即Martur和Cumbum饲养杂交牛的家庭。为了研究影响产奶量的因素,采用普通最小二乘法建立多元线性回归模型。总样本和子样本的回归结果显示,平均而言,每头动物每天产奶量值的60%至80%的变化可以用每头动物每天使用的饲料、绿饲料和浓缩物以及动物的年龄来解释。在这些解释变量中,每头动物每天使用的饲料、每头动物每天使用的青饲料具有高度显著的正影响,就对因变量的影响程度而言,每头动物每天使用的浓缩物的影响程度最高。然而,解释变量,每天所需的劳动小时数对因变量没有任何显着影响。模型拟合的各种样本是有效的,表明其各自的统计量的p值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Factors Affecting Milk Production:A Case Study in Andhra Pradesh
India ranks first in milk production, accounting for 18.5 per cent of world production, achieving an annual output of 146.3 million tonnes during 2014-15. Production of milk depends on various factors like feeding, breeding and management of the animals. It is evident that among the non-agricultural and allied activities of agricultural sector, dairying is providing gainful self-employment and generating the incomes to the rural masses. The cross breed variety of buffaloes is being reared in large number; as a consequence substantial improvement in milk yield is recorded. To study the milk yield and the dairying aspects of households in Prakasam district of Andhra Pradesh who are rearing the cross breed buffaloes, the data and information is elicited from the households rearing cross breed cattle in the two selected mandals, namely Martur and Cumbum. To study the factors influencing milk yield, the multiple linear regression model is adopted by the method of ordinary least square. The regression results for the total sample and also for sub-sample reveal that on an average 60 to 80 per cent of the variations in the value of milk yield per day per animal could be explained by fodder, green fodder and concentrates used per animal per day, and the age of the animal. Of these explanatory variables, fodder used per animal, green fodder used per animal per day are highly significant with positive impact and in terms of magnitude of impact on dependent variable, the concentrates used per animal per day is the highest. However, the explanatory variable, number of labour hours required per day did not show any significant impact on the dependent variable. The model fitted for various samples are valid as indicated by their respective p-values of the statistic.
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