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Factors Affecting Agricultural Mechanization-A Case Study from Aligarh Division of Uttar Pradesh

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Factors Affecting Agricultural Mechanization-A Case Study from Aligarh Division of Uttar Pradesh Vol. 43(4), 2019 Factors Affecting Agricultural Mechanization-A Case Study from Aligarh Division of Uttar Pradesh Roaf Ahmad Parray*, Tapan Kumar Khura and Satish Devram Lande Division of Agricultural Engineering ICAR-Indian Agricultural Research Institute, New Delhi-110012 *Corresponding author's e-mail: [email protected] Date of submission: October 7, 2019 Date of acceptance: November 29, 2019 ABSTRACT Mechanization of agriculture is an essential input to the modern agriculture. It enhances productivity, besides reducing human drudgery and cost of cultivation and improves utilization efficiencies of other inputs. The effect of different factors on mechanization was assessed through survey study in Aligarh, Etah and Hathras districts of Uttar Pradesh. A total of 240 respondents across different categories of land holding from twelve blocks of Aligarh division were selected through stratified simple random sampling without replacement (SRSWOR). Custom hiring service (CHS) availability and education of farmers positively influenced mechanization index with a Pearson’s correlation coefficient of 0.84 and 0.81 for land holding and annual income, respectively and Kandal’s tau of 0.63 and 0.54 for CHS availability and education, respectively. Family labour availability was observed to negatively influence the mechanization with Pearson’s correlation coefficient of (-) 0.44. Land holding was alone responsible for 47% variation in mechanization index. The factors land holding (LH), annual income (AI) and family labour (FL) had fairly high explanatory power and explained nearly 80 % of variation in the Mechanization Index. Key words: Mechanization index, SRSWOR, Custom hiring INTRODUCTION power judiciously for production purposes. Besides Increased use of improved technologies in Indian its paramount contribution to the multiple cropping agriculture since the mid-sixties has brought about and diversification of agriculture, mechanization also multi-fold increase in agricultural production. Modern enables timeliness of operations, a very important agriculture with increasing use of commercial inputs aspect of agricultural production system. Farm has necessitated enhanced use efficiency through mechanization is key for agricultural productivity timely and precise application of inputs using efficient as it has positive correlation with level of farm and precise machines, which in turn have raised mechanization. Cropping intensity also increased demand of increased level of mechanization. There with an increase in per unit power availability (Mehta are seven vital inputs to agricultural production system et al, 2014). It was 120% with power availability of - seed, fertilizer, irrigation water, plant protection 0.48 kW/ha-1 during 1975-76 and increased to 139% chemicals, agro machinery, transfer of knowledge with increase in power availability to 1.71 kW/ha-1 in and credit. The efficiency of all non-engineering 2009-10 (ICAR, 2013). The available farm power inputs depends on efficiency, accuracy and precision and productivity in India are expected to reach 2.2 of engineering input i.e. agricultural machines. In kW/ha-1 and 2.3 t/ha, respectively by the year 2020 fact, mechanization alone enhances productivity of (Mehta et al., 2014). During last 53 years the average crop by 15% and reduces cost of crop production by farm power availability in India has increased from 20% (Parray et al., 2018). Agricultural implements about 0.30 kW/ha in 1960-61 to about 2.02 kW/ha-1 and machines enable the farmers to employ the in 2013-14 (Singh et al., 2015). 1 Agricultural Engineering Today With increased cropping intensity, farmers have were selected by simple random sampling. Based supplemented or largely replaced animate power on land holdings, the blocks were divided into five with mechanical power sources( tractors, power stratum i.e marginal, small, semi-medium, medium tillers, diesel engines and electric motors). This and large category. From each of the twelve selected is an indicative of the fact that slowly and steadily blocks, a sample of 20 farmer respondents was India has been progressing towards higher level selected through stratified simple random sampling of farm mechanization. However this increased without replacement (SRSWOR). A total of 240 level of mechanization has been found skewed respondents were selected for detailed survey. A towards some specific operations as evident pre-tested schedule was used for personal interview from the fact that some operations are highly of respondents. The schedule covered a wide range mechanized whereas other and in some operations of information related to farm machinery, farm power, has a wide mechanization gap exists. There are custom hiring of different operations and cultural number of challenges in achieving appropriate practices followed in rice-wheat cropping system. level of mechanization in different parts of the The secondary data for the present investigation country and in different crop production operations. was collected from published sources as well as FFTC (2005) in its annual report on Small farm block offices and district headquarters. The data mechanization systems development, adoption collected through personal interview method during and utilization in Asia concluded that the barriers survey was tabulated and compiled systematically, that impede the growth and sustainability of farm commensurate with the objectives of the study. The mechanization industry and programs in the region variation in different parameters of mechanization can be classified into technological constraints, across different land holdings was analyzed though socio-cultural and behavioral barriers, financial test for equality of means (Duncan’s t-test) using and economic problems, and environmental statistical package for social sciences (SPSS) issues. Farm machines are likewise beyond the software. reach of most farmers owing to high acquisition and maintenance costs. Small-size farm is a big Mechanization involves use of agricultural machines issue when it comes to mechanization because for different operations. In fact, with progress of it is against the economies of scale. Poor rural mechanization, animate power is replaced by infrastructures such as roads, bridges, canals, and inanimate power in addition to improved implements power network also pose as a major obstacle to farm and machines. Mechanization index (MI), a ratio mechanization. Also, in developing countries, farm of work of the tractors in total of human work labour is abundant; hence, the need for machinery and that of the machinery was calculated for is seldom recognized. The present study focused randomly selected 240 respondents, to account for on understanding the role of different factors in mechanization extent. Nowacki (1974), developed mechanization of agriculture. The main aim of the mechanization Index with the grading of the level of study was to understand the major issues that need mechanization as hand tools (M1) = 1, animal drawn to be addressed in improving level mechanization (M2) =2, Tractorized (M3) = 3. For the purpose of in particular region. this research study, the index of mechanization was determined based on the prominent available power MATERIALS AND METHODS sources in the study area M1 and M3. The degrees The study was carried out in Aligarh division of mechanization at the two available power sources situated in western part of Uttar Pradesh. Aligarh were defined as follows: division forms a part of the Upper Indo-Gangetic plain region and lies between 27° 30’ and 37° 36’ Degree of Mechanization M1 is the average energy N latitude and 78° 06’ and 79° 40’ E longitude. The input of work provided exclusively by human power sampling design used for the survey was stratified (labour) per hectare, as described by Nowacki four stage sampling. Aligarh division consisted of (1974): four districts-Aligarh, Hathras, Etah and Kasganj and from each of the selected districts four blocks LH = 0.1 x kWh/ha ...(1) 2 Vol. 43(4), 2019 Where, Where, LH : Average energy input or work provided per MI = mechanization index, % hectare by human labour (kWh/ha) LM = average sum of all mechanical operation work NH : Average number of labour employed of the machine, kWh/ha TH : Average working time devoted to manual LH = total sum of operation work done by man operation kWh/ha 0.1 : Theoretical average power of an average LT = Sum of all average work outlays by human man working optimally (kW), and and tractor powered machines, kWh/ha A : area of land cultivated (ha) LT = LM + LH Degree of Mechanization M3 represents the first Initially several variables, were considered for degree of mechanization, motorized machinery inclusion in the regression model related to coexisting with a high participation of operators agricultural Mechanization index. Following (Nowacki, 1974). It is indicated as careful consideration of all variables, five relevant independent variables (education, land holding, LM = 0.2 x kWh/ha …(2) family income, availability of custom hiring service, family labour) were considered for the correlation Where, and regression analysis. Each of the independent variables was individually correlated with the L : Average energy input or work per hectare M dependent variable (Mechanization index) so as to by tractor powered machines (kWh/ha) identify significant variables. 0.2 : Correction coefficient of the tractor-powered machine Accordingly, it was hypothesized that mechanization index in the study area
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