Farm Machinery Service Survey in Rural Shanxi Province and Agricultural Mechanization

Transcription

1 Farm Machinery Service Survey in Rural Shanxi Province and Agricultural Mechanization XI Aihua CHEN Baofeng School of Economics and Management, China Agricultural University Abstract Farmers demand for and use of farm machinery largely determine China's agricultural mechanization development rate and level. Based on the survey of Shanxi farmers, this paper analyzed the factors that influenced farmers hiring machinery service, and used multivariate regression model for the empirical data. The results show that farmers machinery service utilization are influenced by multi-factors, such as farmers land size, the degree of specialization on planting, the availability of agricultural machinery services. From these empirical findings, this paper presents several corresponding recommendations. Key words: farm machinery service, factors, variable descriptive statistics 1 Introduction Agricultural modernization is an inevitable process of social development, and agricultural mechanization is an important phase of agricultural modernization. At present, farmers are the main economic organization unit in China's agricultural production. Farmers demand for agricultural machinery and their use to a great extent determine China's agricultural mechanization development rate and level. Wande, Berger, and Wittmer (2003), based on the transaction cost economy, find that the small-scale farmers can reduce the cost to hire machinery service than to purchase agricultural machinery by themselves. Huang, Huang, and Fu (2002), using probit equation, analyze the factors impacting the Taiwan farmers rice cultivation either by means of self-plowing or contract-plowing. Some of the Chinese scholars have also showed concern over the farmers demand for and use of farm machinery. Yi, Li, and Jiao (2006) analyze farmers agricultural equipment requirements from the perspective of the quantity and type but without analyzing the underlying reasons for the different ways of using agricultural machinery. Yang and Yang (2006) make a division of farmers demand for farm machinery and suggest that the farmers demand for large and performance farm machinery services has not been properly met currently. But this view is short of empirical data. However, many Chinese studies in the field of farm machinery services are based on the macro level (e.g. Guo and Yang, 2004, Lu and Zhang, 2006), and a few from farmer s perspective (e.g. Yi, Li, and Jiao, 2006, Yang and Yang, 2006). The empirical analysis on the micro level has not yet been found in this area. Although not many studies on the farm machinery services from abroad take the perspective of farmer yet their theoretical and empirical approach is more mature, and available for reference. What s the reality of China's farmer use of machinery service? Which factors on earth affect Chinese farmers in the use of farm machinery service? These issues need to be answered by means of empirical research. To answer these questions, this paper, based on the farmers household sample survey data in Shanxi Province, reveals the major factors affecting farmers use farm machinery services. It is believed to help to orient the farm machinery service development and promote agricultural mechanization and agricultural modernization. 2 Sampling Information From July to September 2006, a sample survey was conducted concerning the farmers in Shanxi Province. Altogether 821 questionnaires were collected and considered valid, involving 75 counties of 11 districts. 725

2 The average households possess 13 Chinese mu (1 hectare=15 mu) of field area and their average income is 15,468 RMB, of which nearly 40% is cultivating income, corresponding to 6,159 RMB. The households who plant the crops of yearly produce occupy about 70% of the total. 66% of the sampled farmers do not own farm machinery and 68% have no intention to buy farm machinery in one or two years due to insufficient funds and low cost to hire farm machinery services. The farm machinery service expenditure was included in the questionnaire to judge whether or not they have the experience of hiring the farm machinery service and if yes, how much. Results showed that 95% of the sampled farmers in the year of 2005 had machinery service expenditures. Among the farmers with no machinery service expenditure, 78% of them owned machinery (34% of the overall samples owned agricultural machinery). 3 Factors Analysis concerning Farmer s Use of Farm Machinery Service The extent to which farmers used farm machinery service can be expressed in terms of the machinery service expenditure according to the survey data available. The more expenditure the farmers devoted, the greater extent they utilized the machinery service. The influencing factors on machinery service expenditure will be analyzed below. 3.1 The Choice of Influencing Factors With reference to the previous studies on farmer behavior (Wande, Berger, and Wittmer, 2003; Qian and Zhang, 2007; Guo and Jiang, 2004) and with the consideration of the characteristics of the machinery service, the factors impacting the farm household machinery service expenditure can be divided into three categories: farmer household features, farmer management features and farm machinery service availability. Each category has a number of variables, whose inferential influence on the dependents will be discussed as follows. The farmer household features include the number of family labor forces and the family size. Generally speaking, the more the labor forces, the more likelihood the machinery service to be replaced. However if the labor forces can be transferred to other high-rewarding industries, the possibilities will increase of hiring machinery service. The number of family members on one hand represents the family size and the corresponding field scale. On the other, the family size can also indicate the cost of living burdened on the family. In view of this family size is not regarded as an influencing factor on the farm machinery service. Farmer management features refer to the cropping system, the ratio of planting earnings in the total household income, farmer s land scale of each laborer, the land types (land suitable for plowing by agricultural machinery), the availability of agricultural machinery, etc. Cropping system means yearly produce, or three produces every two years, or two produces each year. The higher the planting frequency, the more inclined to use farm machinery service to improve planting efficiency with the time requirement. The proportion of planting earnings in the total household income suggests the farmer s specialization of cropping. The higher the ratio, the more likely to hire farm machinery services expenditures. The type of farmland here refers to the appropriateness of farm machinery planting. This data can be approximately expressed by the ratio of land to be planted by machinery to all arable land from the surveyed area. The higher the ratio, the more appropriate to adopt the machinery service. Whether a family owns farm machinery has a certain impact on the machinery service expenditure. The farmers owning farm machinery may have less demand for service. Availability of machinery service can be expressed by the power of tractors and combines in the unit area in the surveyed counties. Considering tractors and combine harvesters are the main machineries in service, their motive power can be represented by the number of farm machineries available. The more power in the unit area, the more likely and convenient for the farmer to obtain machinery service, and the farm machinery service expenditure will increase accordingly. 3.2 Model Select and Variables Note The multivariate regression model is considered suitable and selected for the analysis. The form of the 726

3 model is as follows: y = b + b x + b x + L + b x k k Among them, xi (i = 1, 2,..., k) are explanatory variables to farmer machinery service expenditure. b0 is constant; bi (i = 1, 2,..., k) are the estimated parameters. The variable names and descriptions are as follows. Table 1 Model variables note Variable Variable Definition EXP (y) Farm machinery services expenditure in a year by one household(unit: yuan) HSIZE Household size(persons) LABNUM The number of laborers in a farm household PRP The proportion of farmer s planting earnings in the total family income of the year 2005 PLANT1 1 = yearly produce; Otherwise = 0 PLANT 2 1 = two produces each year; Otherwise = 0 PLANT 3 1 = three produces every two years; Otherwise = 0 LANDAV Cultivated area for each family laborer(unit: mu) PROM The proportion of the land that can be cultivated by machinery to the total cultivated land, representing the local land types or land adaptability to use farm machinery MACH 1 =have; 0 = no POWER The power (kw) of tractors and combines in the unit area (1,000 hectares) in the local counties (cities) Variable descriptive statistics are summarized in Table 2: Table 2 Variable descriptive statistics Minimum Maximum Mean Standard deviation EXP (y)) , HSIZE LABNUM PRP PLANT PLANT PLANT LANDAV PROM MACH POWER , , , Using SPSS software, the variables are introduced into the multiple linear regression procedure. And stepwise regression method is employed to select variables and achieve optimal equation ultimately. Final estimates of the results are in Table 3 as follows: Table 3 regression coefficients, significant test Non-standardized regression coefficients standardized regression coefficient standard error Beta t value Sig. (Constant) LANDAV LABNUM POWER PLANT PRP PROM

4 HSIZE F In Table 3, the significant probability of F value is 0.000, far less than 0.05, the given significant level. This shows that the farmer machinery service and explanatory variable selected have the significant linear relationship overall, which can be described by linear models and reflect the relationship between them. After sifting through the explanatory variable, LANDAV, LABNUM, POWER are significant below the level of 0.01, and PRP, PROM, HSIZE below the level of As for the dummy variable, PLANT1 is significant under the level of 0.1. This reflects that explanatory variables have a significant linear relationship with the variables after the screening of each explanatory variable, and can effectively explain the expenditure volume of the farm machinery service. Through the above test, the final simulation equation is as follows : EXP (y) = LANDAV LABNUM POWER PLANT PRP PROM HSIZE 3.3 Discussion According to the model estimation results, the main factors influencing the machinery service expenditures can be found. Specific analysis is as follows: (1) The average laborer cultivated area and farm machinery service expenditure are positively correlated. This shows that the greater the average laborer cultivated area, the greater degree of farmer machinery service utilization. The survey also reveals that the larger of the land scale, the lower cost the farmers use machinery service while with the higher efficiency. (2) The number of the laborers in each household and the number of family members in each household are both positively correlated with machinery service expenditure. This finding does not agree with the hypothesis that laborers can replace machinery. This is due to the reality that China possesses scarce farmland in the rural area with the result of laborer surplus. With one household centralizing farming land for operation, the surplus labor force can then transfer to be engaged in the off-farm work. Farmer household size here can indicate the number of the laborers and the whole area of farmland supply for a farmer. With the increase of household members, labor forces and the area of cultivated land will also increase, which will in turn bring increase to machinery service expenditure. (3) The ratio of the planting earning to the family income is also an important variable affecting farmers machinery service expenditure. It reflects the degree of the farmer s cultivation specialization. The higher the proportion, the higher the degree of specialization and the greater demand for farm machinery service. (4) The average farm machinery powers of the arable land are positively correlated with machinery service expenditure. The more power of the agricultural machinery in the unit area, the greater availability of agricultural machinery service, and the machinery service expenditure will increase accordingly. (5) The proportion of the land that can be cultivated by machinery to the total cultivated land is positively correlated with machinery service expenditure. With other conditions unchanged, the more flat the farmland, the more suitable for farming the agricultural machinery. The higher level of utilization of farm machinery service, the more spent on agricultural machinery service. (6) The yearly produce is notable in that the system of cultivation has certain effect on machinery service expense. Due to the yearly produce, the farming and harvesting consequently have a longer period with a lower requirement of farming timeliness and efficiency. Therefore the field operations can be extended by labor forces to reduce the demand for efficient machinery. It should be explained that the influencing factors which are not reflected in micro-sample data, include the maturity of agricultural machinery technology, the price and quality of machinery service. All these will also affect farmers use of machinery service. However, such factors are beyond the control of farmers for the micro cross-section data analysis. Farmers are faced with the same level of agricultural machinery technology development. As for the price, farmers have to the market price, rather than 728

5 influence or decide. In the long run, with the development of agricultural machinery such as maize harvest machine, the demand for machinery operation and service will definitely increase. 4 Recommendations Expanding the scale of farmland and improving the degree of cultivation specialization are conducive to the efficient use of farmers machinery service, which can be achieved through the centralized farmland operation by means of two channels. One is to transfer the current surplus labor force in rural areas to provide them with more employment opportunities in other industries and to create conditions for the farmland centralization; the other is to develop reasonable policies concerning the farmland transfer to entitle the farmers to freedom of land transfer and gains. To increase the number of farm machinery can bring the farmers convenience and efficiency of utilizing the farm machinery services. For this proposal, government can increase the amount of purchase subsidy and expand the coverage of preferential policies to the main bodies of farm machinery service to promote their enthusiasm. On the other hand, with appropriate and reasonable guide of government, a rational and orderly machinery service market is thus established, which is helpful for the main body of machinery service to increase the operational efficiency and income, and their ability to purchase agricultural machinery. At the same time, it can also guarantee the farmers to obtain quality service. To promote the research and production of agricultural machinery technology and provide efficient agricultural machinery to meet farmers needs will be helpful for farmers to improve the utilization degree of agricultural machinery services, and also conducive to the development of agricultural mechanization. References [1] Yi Dandan, Li Xiaohong, and Jiao Changfeng, Research on famer s Actuality and Demand of Agricultural Equipments in China. Chinese Agricultural Mechanization P64-67 (in Chinese) [2] Yang Futang andyang Zhongzhi. Game Analyses on the Agricultural Mechanization of China on the Basis of the Demand Chain Model. Chinese Agricultural Mechanization p44-48 (in Chinese) [3] Alcido Elenor Wander, Regina Berger, and Heidi Wittmer. Transaction Cost Economics Can Explain the Different Contractual Arrangements for the Provision of Agricultural Machinery Services? A Case Study of Brazilian State of Rio Grande do Sul Teoriaepráticadaliber e Evdiência Economica, Passo Fundo, V11, N20, Mario 2003 p10-25 [4] Mei-Ying Huang, Cliff J. Huang, and Tsu-Tan Fu. Cultivation Arrangement and the Cost Efficiency of Rice Farming. Taiwan Journal of Productivity Analysis, Vol. 18, No. 3 ( ) p [5] Guo Hongpeng, and Yang Yinsheng. The Exploratory Study of the Agricultural Machinery Operation Form of the Contract Hire System in China. Agricultural Machinery Journal p (in Chinese) [6] Lu Bingfu, Zhang Zuli. Tactics and Restrictive Factors of Developing Agricultural Mechanization in China. Agricultural Mechanization Research p9~ 11 (in Chinese) [7] Guo Hongdong, Jiang Wenhua. The Influencing Factors Analysis of Farmers Participation Behavior in Specialized Cooperative Economic Organizations ---- Based on the Empirical Study of Zhejiang Farmers. Economic Research Reference (63) p10-16 (in Chinese) [8] Qian Wenrong, Zhang Zhongming. An Empirical Study of the Influencing Factors of Farmers willingness to the Management of the Land Scale. Agricultural Economic Problems P28~ 33 (in Chinese) 729