Diagnostic Analysis of Technology Adoption and Factors Influencing Adoption Level of Tribal Farmers of Madhya Pradesh

The agricultural technologies developed in the country have generated more income and employment to the cultivators. However, tribal dominated regions of Madhya Pradesh falling under such conditions of agricultural backwardness have not yet been able to benefit from the fruits of technological innovation fully or even partly. Keeping this in view a study on technology adoption and its determinants has been conducted in Santna districts of M.P. Using multistage stratified random sampling techniques total 120 tribal farmers were selected from 10 villages of two block such as majhgawan and shohawal RD block of Stana district of Madya Pradesh. The examination of technology adoption revealed that the area under study was characterized by preponderance of low level of technology adoption and smaller sized farms, the position of which in respect of adoption of new technology was quite disheartening. The increase in the level of adoption was accompanied by increase in the size of holding, use of family workers and maintenance of bullock pairs/machinery. The increase in investment on fixed capital, particularly farm implements and machinery was associated with increase in the technology adoption. Wheat and Soyabean were the predominant crops of the area under study with the highest percentage of area under HYVs compared to other crops. The intensity of cropping, use of bullock labour and hired human labour were found to increase with the increase in the level of adoption. The scores of social determinant particularly literacy index exhibited positive influence on the level of technology adoption. Similarly, the scores of psychological determinants, viz., attitude towards HYVs, risk orientation and credit orientation were observed to increase with the increase in Further, it was observed that increase in the level of adoption was associated with increase in the level of income from the crops grown on sample farms.

Although the technological breakthrough as such in Indian agriculture is no longer a new phenomenon, yet it has got a significant relevance particularly for the regions which are still lying in the embryonic stage of agricultural development. Tribal dominated regions falling under such conditions of agricultural backwardness have not yet been able to benefit from the fruits of technological innovation fully or even partly. Consequently, large number of researchers and economic analysis are concerned about knowing the potentialities created by new technology in the under developed regions and their implications for agricultural development policies and programmes in meeting the socioeconomic goals of these regions. A new agricultural technology is believed to have generated more income to the cultivators. But how this increase in income is distributed among various categories of farms at various levels of technology adoption is a matter of socio-economic significance.
Several available studies on technology adoption have separately addressed either affecting the adoption or its impact on farm economy across the farms and regions. The studies by Rawal (1981), Henery (1983) and Yadava and Gangwar (1987) revealed that economic prosperity, social mobilisation and favourable psychological orientation are instrumented in the adoption of new technology. Frankel (1971) has brought out that the large farmers have benefited more than the small ones from the technology. A comprehensive study incorporating both the aspects as well as interactions between level of adoption and economic gains in tribal agriculture is necessary to get an integrated view. The new agricultural technology is believed to have generated more income to the cultivators. But how this increased income is distributed among various size groups of farms at various levels of technology adoption, is a matter of socio-economic significance. The knowledge to this effect would help the planners, policy makers and administrators to bring about necessary changes in agricultural development policies and programmes in meeting the socio-economic goals of the region. Taking into account the significance of the aforementioned facts, a study on technology adoption and its determinants in Santna region of M.P. was conducted.

Sampling design
For conducting this study, a three-stage stratified random sampling design was used. District Satna is located in the Vindhya hill region and comes under Rewa division of Madhya Pradesh state. This study focuses attention on technology adoption its determinants and impact of technologies on tribals agriculture. Further, two blocks namely, Majhgawan and Sohawal of Satna district were selected purposively on the criterion of relatively higher number of tribals families as compared to other blocks of Satna district. The majhgawan and Sohawal blocks are specially called tribal blocks in Satina district. Thereafter, a list of villages lying in the selected blocks were prepared in respect of tribal population as well as and crop cultivation and agro-economic conditions. Then from the enlisted villages clusters of 5 villages having homogeneous soil crop complexity and ago-economic stratum were selected. Thus 10 villages of the two selected blocks were grouped into 35 clusters. Lastly a sample of 5 per cent clusters from each selected blocks was taken randomly. Thus two clusters were taken in the sample for detailed study. There were 10 villages in the two selected cluster of two blocks. Finally from all ten villages, out of total 600 tribal farmers, 120 farmers were selected randomly for the personal interview and data collection. The study pertaining to the period June 2000 to July 2001.

Analytical tools
Level of technology adoption: In order to work out the level of adoption of new technology and adoption index of individual farmer, following mathematical tool was employed.   The adoption (TAIi) varies from 0 to 100 percent depending upon the degree of adoption of new technology by farmers. On the basis of adoption index, all the 120 sample farmers were classified into three categories viz; low adopter (0 to 20 percent TAI i ) medium adopter (21 to 40 per cent TAI i ) and high adopter (above 40 percent TAI i ).
The present study is primarily based on categories of low, medium and high adopters which will always refer to the aforesaid classification. The above classification is based on the level of adoption on the farm as a whole, taking into account all the major crops (paddy, arhar, soyabean, wheat and gram) grown on the farm. However, there may be some intercrop variation in the level of adoption which will not be reflected when major crops grown on sample farms will be aggregated.

Determinants of technology adoption
For identifying factors that affecting the level of technology adoption, stepwise linear regression analysis was performed taking level of technological adoption i.e. adoption index as a dependent variable and few independent variables which were expected to have effect on the adoption of new technologies. Functional form of cause and effect relationship is written as follows: AI = a + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4 + b 5 X 5 + b 6 X 6 + b 7 X 7 + b 8 X 8 + b 9 X 9 + b 10 X 10 + U

Level of technology adoption and its determinants
Level of technology adoption: The level of technology adoption refers to the extent of application of package of practices recommended for a particular crop. The level of technology adoption across sample farmers of different holding size and its determinants were analysed in detail and the results are presented in the following tables. It is apparent from Table 1 that marginal (<1 ha.) and small (upto 2 ha.) categories of farmers formed the major portion (75 per cent) of the sample size, while the other category (above 2 ha) of farmers accounted for a small proportion of only (25 per cent) of the sample size. This clearly indicates the skewed distribution of land in the study area. of adoption. The foregoing discussion revealed the fact that the adoption of technology had not made an appreciable headway resulting into preponderance of traditional methods of crop cultivation. When the size-group wise level of adoption was examined, the situation of smaller sized farms, which constituted a major fraction of sample farmers, appeared to be more disheartening.

Technology adoption rate
Average adoption index for each categories (holding size) farmers as well for different categories of farmers based on adoption level is also worked out and presented in Table 2.

Economic determinants
Farm size: The size of farm is considered to be one of the most important economic determinants of adoption of new technology. Apart from income generating resource, the importance of size of farm can be visualized in other sense as it is a symbol of greater social status and economic security. Therefore, one may expect that farmers having better command on land resource will have higher level of technology adoption. The magnitudes of level of adoption and size of farm holdings are examined in Table 3.
It is observed from the table that the distribution of farms and cultivated land moved almost proportionately, resulting thereby large acreage of the study area coming under low level of (below 40 per cent) technology adoption. Only a small fraction of (42 per cent) land witnessed high level (above 40 per cent) of adoption. It is interesting to note that the increase in the level of adoption was positively correlated with the farm size. This clearly confirmed the fact that the size of farm had direct bearing on the level of adoption. In nutshell, one may say that the size of farm appeared to be a major determinant of adoption of technology. Since, the area under study was predominated by smaller sized farms, the large proportion of area witnessing low level of technology adoption is obvious.

Capital investment:
The capital investment pattern of farmers is another important determinant of adoption of new technology. The farm building, implements and farm machinery and livestock were the major forms of capital investment, which helps in assessing the farmers' prosperity and their attitude towards these investments and adoption of new technology. The Table 4 gives a picture of per hectare value of fixed assets at different levels of technology adoption, classified under farm building, livestock and farm machinery and implements. Inclusion of land would have made the comparison less sensitive with respect to other assets because it forms a very high proportion of total investment on a farm. For the same reason, residential houses have also been excluded from the fixed investment. The average level of total investment per hectare showed an increasing trend with the level of adoption varying from ` 5355.63 on low adopter group to ` 8319.56 on high adopter group with an average of ` 6103.14 on overall sample farms.
On an average, livestock ranked first in terms of its share in the total investment accounting for about 43 per cent investment on the farms, followed by farm building and implements and machinery. On comparing the variation of total fixed capital investment at different levels of technology adoption, it was observed that with the increase in the levels of technology adoption due to investments on farm building, implements and machinery were also increased. This is symbolic of progressiveness and awareness of farmers towards the importance of farm machinery and implements in adoption of new technology. More so, the higher investment on farm building by higher level of adopters is indicative of their economic prosperity. However, the investment on livestock evoked a mixed trend with the levels of adoption, indicating there by inconsequential role of livestock in adoption of new technology.
Cropping pattern: Cropping pattern within a homogeneous agro-climatic area is considered to be influenced by soil type, size of holding, source of irrigation, level of investment and availability of resources. However, in changing scenario of agriculture, the new agricultural technology plays a vital role. It was found that wheat (local and HYV) is though the adoption of HYVs in case of wheat crop was low, it was still lower and disheartening in case of soyabean, gram, paddy and arhar. It was further noted that with the increase in the level of adoption, there was an increase in the percentage of area under HYVs to total gross cropped area almost in all crops.
Social determinants: Three social factors, which are expected to affect the technology adoption have been taken into account. They are age of the entrepreneur, size of family and literacy index. It can be seen from Table 5 that adoption rate was negatively related with the age of entrepreneur, where as positive relation between adoption rate and education of the farmer was found. However, the average size of family presented a mixed trend with the level of adoption. It was found that largest in family size for high adopter group on account of larger size of holding owned by joint family system. Similarly, the high index of literacy on higher adopter group is symptomatic of their economic prosperity. The higher literacy index may help the farmers to understand the pros and cons of technology in a much better way.  Table 6. Similarly, as the farmers mental orientation towards taking credit from the financial institutions changed in its favour, they went in for higher level of technology adoption. In other words, the attitude towards HYV and risk and credit orientation has direct bearing on technology adoption.
Adoption function: Adoption function equation expresses the level of adoption of new technology as a function of the various factors affecting the technology adoption. The technology adoption index of individual farmer has been taken as the proxy for the level of adoption. As discussed earlier, altogether ten factors coming under the purview of economic, social and psychological determinants were selected on a prior basis, which were expected to affect the level of technology adoption. Taking these factors as the independent variables and the technology adoption index of each farmers as the dependent variable, a linear step wise regression analysis was performed, the result of which is given in Table 7. to influence the resultant. The attitude towards HYV was found to the most consequential factors influencing the technology adoption contributing upto 7.35 per cent with an unit addition in the index of this factor. In the order of merit credit and risk orientation were placed on second and third having a contribution to the resultant by 5.28 and 3.76 per cent respectively with their unitary additional unit. A perusal of the table further reveals that all the significant factors except entrepreneurs 'age (an unalterable factor) were positively associated with the level of adoption and thus as expected, turned out to be commensurate with earlier findings based on simple tabular analysis presented in the preceding tables. This meant that improvement in these major factors would lead to higher level of technology adoption. Similar findings were also reported by many other researchers (Rawal, 1981 andSharma et al. 1987).

SUMMARY AND CONCLUSION
The examination of technology adoption revealed that the area under study was characterized by preponderance of low level of technology adoption and smaller sized farms, the position of which in respect of adoption of new technology was quite disheartening. The increase in the level of adoption was accompanied by increase in the size of holding, use of family workers and maintenance of bullock pairs/machinery. The increase in investment on fixed capital, particularly farm implements and machinery was associated with increase in the technology adoption. Wheat and Soyabean were the predominant crops of the area under study with the highest percentage of area under HYVs compared to other crops. The intensity of cropping, use of bullock labour and hired human labour were found to increase with the increase in the level of adoption. The scores of social determinant particularly literacy index exhibited positive influence on the level of technology adoption. Similarly, the scores of psychological determinants, viz., attitude towards HYVs, risk orientation and credit orientation were observed to increase with the increase in adoption level. Further, it was observed that increase in the level of adoption was associated with increase in the level of income from the crops grown on sample farms.