Farm Production Growth in the Upper and Middle Parts of the Yellow River Basin,China,During 1980-1999

The impact of inputs on farm production growth was evaluated by analyzing the economic data of the upper and middle parts of the Yellow River basin, China for the period of 1980-1999. Descriptive statistics were employed to characterize the temporal trends and spatial patterns in farm production and five pertinent inputs of cultivated cropland, irrigation ratio, agricultural labor, machinery power and chemical fertilizer. Stochastic frontier production function was applied to quantify the dependence of the farm production on these inputs. The growth of farm production was decomposed to reflect the contributions by input growths and change in total factor productivity.. The change in total factor productivity was further decomposed into the changes in technology and in technical efficiency. The gross value of farm production in the region of study increased by 1.6 fold during 1980-1999. Among the five selected farm inputs, machinery power and chemical fertilizer increased by 1.8 and 2.8 fold, respectively. The increases in cultivated cropland, irrigated cropland, and agricultural labor were all less than 0.16 fold. The growth in the farm production was primarily contributed by the increase in the total factor productivity during 1980-1985, and by input growths after 1985. More than 80% of the contributions by input growths were attributed to the increased application of fertilizer and machinery. In the change of total factor productivity, the technology change dominated over the technical efficiency change in the study period except in the period of 1985-1990, implying that institution and investment played important roles in farm production growth. There was a decreasing trend in the technical efficiency in the region of study, indicating a potential to increase farm production by improving the technical efficiency in farm activities. Given the limited natural resources in the basin, the results of this study suggested that, for a sustainable growth of farm production in the area, efforts should be directed to technology progress and improvement in technical efficiency in the use of available resources.

Technical efficiency and its determinants in China's hog production

China＇s hog production is undergoing a great transformation due to the soaring demand and changing raising system. Regarding the essential role of pork in Chinese diet, a systematic analysis on the productivity and efficiency of hog production can provide significant implications for policy makers. This paper investigates the productivity and efficiency of hog production and the determinants of technical efficiency in China using a household level panel data（2004–2010）. A stochastic frontier translog production function with scaling property in inefficiency term is adopted for hog production analysis, and the determinants of technical efficiency are incorporated in a one-step estimation using maximum likelihood estimation. Our results show that the average technical efficiency of hog production in China is 0.5914. More importantly, we find that specialized farmers have higher technical efficiency than others, and technical efficiency in the eastern region is higher than that in Central and West China.

Jinzhai County Household Technical Change and Efficiency Change Analysis: Stochastic Frontier Production Function Approach

Stochastic frontier production function approach is adopted, 93 farmer samples have been collected, pure efficiency, technical efficiency, technical change and scale efficiency and the institutional contribution have been calculated. The results indicated that increasing productivity is the sole measurement to reduce poverty, institution and technical change are the two key factors. Therefore, stable institution, improving technical changes are required. At present, it is urgent to make technical progre...

The Econometric Approach to Technical Efficiency of Public and Private Textile Enterprises in Iran

This study considers measurement of technical efficiency of 250 public and privately owned textile companies in Iran.Two stochastic frontier production functions are used for this purpose.One assumes that firm characteristics directly influence the degree of technical inefficiency while the other assumes that the technology plays a key role.Maximum likelihood method is used to estimate parameters of the models and predict technical efficiency for each enterprise.The results obtained when using these two models are compared.The sensitivity of efficiency measures with respect to different model specifications is also analyzed.Empirical results show that most of the enterprises are operating at high level of efficiencies.The overall mean efficiency is 86%,indicating that,on average,there is a potential for an increase of output by 14%.The result also shows that the public firms are operating more inefficiently than the private ones.

Agricultural Policy, Climate Factors and Grain Output: Evidence From Household Survey Data in Rural China

This paper estimates a stochastic frontier function using a panel data set that includes 4 961 farmer households for the period of 2005-2009 to decompose the growth of grain production and the total factor productivity （TFP） growth at the farmer level. The empirical results show that the major contributor to the grain output growth for farmers is input growth and that its average contribution accounts for 60.92% of farmer’s grain production growth in the period of 2006-2009, whereas the average contributions sourced from TFP growth and residuals are only 17.30 and 21.78%, respectively. The growth of intermediate inputs is a top contributor with an average contribution of 44.46%, followed by the planted area （18.16%）, investment in fixed assets （1.05%）, and labor input （-2.75%）, indicating that the contribution from the farmer’s input growth is mainly due to the growth of intermediate inputs and that the decline in labor inputs has become an obstacle for farmers in seeking grain output growth. Among the elements consisting of TFP growth, the contribution of technical progress is the largest （32.04%）, followed by grain subsidies （8.55%）, the average monthly temperature （4.26%）, the average monthly precipitation （-0.88%）, the adjusted scale effect （-5.66%）, and growth in technical efficiency （-21.01%）. In general, the contribution of climate factors and agricultural policy factor are positive and significant.