The study was conducted over a period of 12 months during 2020-2021 in the Guntur district (latitude of 16.39°N and longitude of 80.00°E) of Andhra Pradesh. The farmers are chosen randomly from five Mandals, such as Ponnur, Nagaram, Rentachintala, Vinukond and Piduguralla, in the Guntur district; again, from each Mandal, five villages are selected randomly. The complete work was carried out at Dr. NTR College of Agricultural Engineering, Bapatla. The map of Guntur district with selected Mandals is shown in the following Fig 1.
Method of data collection
Fig 1: Shows a map of Guntur district with the study area highlighted.
The farmers are chosen at random from five Mandals, such as Ponnur, Nagaram, Rentachintala, Vinukond and Piduguralla, in the Guntur district of Andhra Pradesh, with each Mandal selecting five villages randomly and 20 farmers are chosen at random from each village. For the survey, a sample of 500 farmers from 25 villages was selected. The following primary data were collected from the farmer’s investigation; social status, size of landholding and economic status of the farmers. The questionnaire type data was collected to cover aspects like general information of the farmer, name, social status, size of landholding, economic status, number of drought animals, number of farm machines, number of implements, number of tractors, number of power tillers, the average number of human works, number of electric motors and number of the diesel engines, etc
.; these were also collected from each farmer. The data were tabulated and then calculated using standard formulas such as the mechanization index and farm power availability.
The mechanization index was calculated as proportion of machine work in comparison to the sum of human, animal and machine work, mechanization index is expressed in percentage (Shivani et al., 2021; Radhey and Manoj, 2017
MI= Mechanization index, %.
EM= Machine work, kW ha-1
EH= Human work, kW ha-1
EA= Animal work, kW ha-1
Farm power availability
The data obtained from the source of power for farm operations, right from tillage to harvesting, was used to estimate farm power. The information was gathered from farmers who owned or rented land in ha, tractor (HP), power tiller (HP), engine (HP), motor (HP/kW), animal power, human power for farm work. As a result, total landholding and farm power are available for all the farmers surveyed in each community. The following formula was used to calculate farm power availability (Mehta et al., 2014; Singh et al., 2015).
Social status of farmer
Farm machinery and farm equipment availability among specific farmer categories is determined by the social status of the farmers. Farmers in India are classified into four social categories, such as scheduled caste (SC), scheduled tribes (ST), other backward class (OBC) and open category (OC). For evaluation, the number of farmers in each category from each village is considered.
Landholding of farmer
Based on the size of a farmer’s agricultural land, the landholding of a farmer is divided into the three categories below. Farmer’s landholding is also an important component for development of farm mechanization and enhancing farm power availability; the size of the landholding from each farmer in all villages is taken into consideration for evaluation (Prem et al., 2020).
The classification of the farmer based on the size of the landholding is shown in the following Table 1.
Economic status of farmer
Table 1: Classification of the farmer based on the size of the landholding.
The socioeconomic status of the farmer includes measurement of revenue from various sources such as income from agriculture, income from wages and salaries, livestock income, etc
. (Hitesh et al., 2020; Pankaj et al., 2017),
which is a crucial component in improving mechanization and farm power availability. The total annual household income is calculated by adding income from all the sources a farmer has. For evaluation, all the farmer’s data from each village is considered. The farmer’s economic situation is evaluated based on the five economic classifications listed below in Table 2.
Table 2: Economic class of farmers based on annual income.
The data was analyzed using a web-based agricultural statistical software package (WASP 1.0) and a descriptive statistical method was used to determine the standard deviation (SD), standard error mean (SEm) and coefficient of variation (CV) of the mechanization index and farm power availability with the influence of social status, landholding and economic status of farmers.