生产函数在农业生产分析中的应用

农业生产函数是表明农业投入资源与产出成果之间的技术性关系。目前,世界上最广泛应用的农业生产函数形式,被认为具有普遍意义的是 Cobb—Douglas(柯布-道格拉斯)生产函数。农业生产与工业或其他产业不同,它受外界自然条件的影响,因此,研究农业生产函数也必须注意农业生产的这一特点。在对农业生产函数定式化时,如果将各种自然条件因素一一都包含在模型中,解释变量的数目就会大大增加,这样,在模型推断时往往很难获得较理想的参数估计结果。因此,本文通过利用主成份分析的方法将多项气候条件因素归结为两个综合特性值,导入生产函数中,以替代多项自然条件因素。通过1981年农业横断面实际资料对农业生产函数的估计试验,表明在生产函数中导入综合气候条件特性值是处理气候条件因素的一种可靠的方法,并能够使农业生产函数模型获得更理想的统计推断结果。

THE APPLICATION OF PRODUCTION FUCTION IN THE AGRICULTURAL PRODUCTION ANALYSIS

Agricultural production function indicates the technical relations bet- ween agricultural input resources and output returns.For the present time, the agricultural production function form is most widely used in the world, and among them Cobb-Douglas production function has been recognized as the one with universal significance.Agricultural production is quite dif- ferent from that of the industry or others since it is distinctly influenced by the external natural conditions.So the characteristics of agricultural pro- duction should also be taken into consideration and attention during the study of agricultural production function. External natural conditions include factors such as elevation,air tem- perature,rainfall,duration of daily sunshine,and frost-free season,etc.In the transformation of agricultural production function,if each of these me- teorological factors could be included in the model,the amount of explana- tory variables will be greatly increased.But it is rather difficult to obtain more ideal parameter estimate in the model inference.In this paper,the method of principal component analysis is developed so that the multinomial meteorological factors are grouped into two comprehensive climatic charac- teristic values.It has the advantage of transforming the production function to replace the multinimial meteorological factors.With the actual data of agricultural crosssection in 1981,the test of production function estimate showed that the translation of comprehensive climatic characteristic values into the production function is a reliable method in the management of meteorological factors.It gives a better ideal statistic inference for the model of agricultural production function.