DIMENSIONS AND DETERMINANTS OF DIVERSIFICATION ON KANGRA FARMS OF HIMACHAL PRADESH: AN EMPIRICAL ANALYSIS

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1 Bangladesh J. Agric. Econs XXVI, 1& 2(2003) 1-22 DIMENSIONS AND DETERMINANTS OF DIVERSIFICATION ON KANGRA FARMS OF HIMACHAL PRADESH: AN EMPIRICAL ANALYSIS Mahajan Girish ABSTRACT Diversification of agriculture is considered essential to minimise the risk of crop failure, earn foreign exchange, alleviate poverty, and to make optimum use of slack resources. This study was based on primary as well as secondary data collected from a sample of 120 farmers. A three stage stratified random sampling was followed in drawing the sample cultivators. To study the magnitude of crop, income and employment diversification, Herfindhal and Entropy indices were calculated. The multiple linear regression models were estimated to study the factors affecting crop, income, and employment diversification. The results revealed that for crop acreage and income diversification, large farms had more diversified cropping pattern and income structure than small farms of both irrigated as well as unirrigated areas. In the overall farms analysis, un-irrigated areas were found to be more diversified in terms of number of crops than irrigated farms, whereas reverse was observed for income diversification. For employment diversification, large farms had more diversified employment pattern than small farms of irrigated areas, while reverse was observed for un-irrigated areas. In the overall farms situation, the pattern of employment was found to be more diversified, in un-irrigated areas than in case of irrigated areas. The regression results for the determinants of income diversification inferred that both the economic factors such as size of operational holding, number of fragments of holdings, dummy variable for bullock and the extent of tenancy as well as social factors like family size and age of the head of family were found to be significant factors, irrespective of farms categories, for explaining the process of income diversification, in both irrigated as well as un-irrigated agriculture. While family size, age and education level of the head of the family, size of operational holding, gross farm income and dummy variable for bullock were found to be the important determinants of off-farm income diversification in both irrigated as well as un-irrigated agriculture, irrespective of the farm categories. I. INTRODUCTION In rural economies, diversification of crop enterprises and sources of off-farm income and employment are the most important strategies adopted by the rural households to combat the crop risk and stabilise their income and consumption. Farming household that are risk averse will diversify their sectoral income to reduce overall risk. Diversification of rural economies is also considered essential to lesson the burden on agriculture in the face of mounting population pressure to reap the scale economies arising out of complementary and supplementary enterprises or inter-enterprises growth linkages boost the employment This paper is written while the author was working for short term fellowship at Noragric (Center for International Environment and Development Studies), Norway (2003)and is based on his M.Sc. dissertation, Department of Agricultural Economics, Himachal Pradesh Agricultural University, Palampur.

2 2 The Bangladesh Journal of Agricultural Economics opportunities for rural masses and so on. It is, therefore, no wonder that, in recent times, rural diversification has the focus of attention of policy makers. More precisely, the objectives of the study were as under 1. To examine the extent of diversification in terms of crops and other enterprises, income and employment ; 2. To examine the empirical relationship between diversification and important socio economic variables like farm-size, wealth, education, family-size etc. II. METHODOLOGY For the purpose of this study, Kangra district of Himachal Pradesh was selected purposively as this district has the highest number of development blocks and is highly advanced from agricultural development point of view. In this district, there are 12 development blocks. These are Baijnath, Panchrukhi, Bhawarna, Lamba-gaon, Nagrota bagwan, Kangra, Nurpur, Indora, Nagrota-surian, Dhera, Rait and Garli pragpur. Out of these, Panchrukhi is one of the highly developed blocks from agricultural as well as from otherr infra-structural point of view. Due to these reasons, Panchrukhi block was selected for attaining the objectives of the study. For the selected block, a list of all the villages was prepared. These villages were classified into three groups, one, completely irrigated, second, completely un-irrigated, and third, partially irrigated. The first and the second groups of villages were purposively taken for in-depth analysis. The number of villages in the first group was 127; while the second group consisted of 65 villages. Sampling Plan For the purpose of selection of farming households, two stages stratified random sampling with stratification at the third stage, villages as first stage and households as second stage unit, was used for both the groups of villages separately. From the list of villages of group I and group II, about five per cent of the villages ( 6 from group I and 3 from group II such that the total is 9 in all ) were selected, using simple random sampling without replacement technique. In the second stage of sampling, a complete list of cultivators from each selected villages was obtained along with their land and operational holding size separately for the groups. A sample of 85 and 35 households were selected using simple random sampling technique from the villages, in approximate proportion of the number of households of group I and group II respectively. The selected farmers from each villages were stratified into two strata as: -Small farmers having land less than 15 Kanals (0.6 ha.) - Large farms having land more than 15 Kanals (0.6 ha.) Land holding which is standardized one is not used because the total land holding of the

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7 Dimensions and determinants of diversification on kangra farms of himachal pradesh 7 From economic point of view the study of cropping pattern is important to know the point at which the different crops may be grown to fetch the farmers a good stock of food grains of commercial crops and raw materials for agro-based industries. Besides, the nature of crops grown in an area is a correct index of the agricultural economy and economy standard of the farmers who are striving to eke-out their living. It is, therefore, necessary to examine the cropping pattern of sample farms. The same has been examined for irrigated (IR) and un - irrigated farms (UIR) according to size of holding and is presented in Table 2. The Table-2 revealed that total area under maize in irrigated farms was 3.41 per cent of the total cropped area. A very small portion (0.87 per cent) of area is put under maize (local) and 1.23 per cent was put under maize (L) plus other crops and 0.50 per cent to maize (HYV) plus other crops The mixed crops varied from one area to the other. The mixture comprised number of different crops such as mash, rajmash, soybean the area under each was very low and thus maize plus other crops has been taken as one enterprises. On the other hand, total area under maize in case of un -irrigated agriculture was found to be per cent of the total cropped area. Out of this percentage, per cent of the area was devoted to maize (L) and 2.61 per cent was under maize (HYV), 6.40 per cent was found under maize (L) plus other crops and only 1.52 per cent was observed under maize (HYV) plus other crops. Paddy was found to be an important crop of irrigated farms which was raised on 100 per cent irrigated area. Total area under paddy was found to be per cent. Out of which per cent was devoted to paddy (L) and 4.17 per cent to paddy (HYV). It is important to note that paddy (L) occupied the highest area on small farms of irrigated agriculture (43.68 per cent) as against per cent on large farms. During rabi season, farmers of irrigated agriculture devoted per cent of the area to wheat (L) as against per cent was found in un -irrigated agriculture. Total area under wheat (HYV) was revealed to be 1.86 per cent in irrigated farms as against 4.21 per cent in un -irrigated farms. Mustard was an important intercrop with wheat in both the areas. Another important commercial crop of irrigated agriculture was potato occupying 3.94 per cent of the total cropped area. Total area devoted to linseed was found to be per cent in irrigated farms and was completely absent in un -irrigated farms. Barley was the staple crop of un - irrigated agriculture and is grown in about 4 per cent area. The farmers of irrigated agriculture devoted 0.53, 0.42 and 0.36 per cent of the total cropped area to berseem, barley and teosinte respectively as feed for animals. The analysis further revealed that maize and wheat was preferred under un -irrigated conditions. In general, irrigated land was put to paddy, wheat, linseed and potato. Average yield of selected crops The average yield of selected crops was worked out for two different farm size groups and also for average situation taking the farms together. These crop yields have been presented in Table-3. It was found that the average yield of wheat on irrigated farms was

8 8 The Bangladesh Journal of Agricultural Economics quintals per hectare as against quintals per hectare in un -irrigated farms. The yield level of paddy in irrigated farms was quintals per hectare. It is interesting to observe that average yield of paddy on small farms was higher than large farms of irrigated agriculture. Productivity of maize in irrigated farms was quintal per hectare compared to quintals per hectare in un -irrigated farms. In general, the yield levels of wheat differ Table 3. Average yield of selected crops on sample farms (quintal / hectare ) Crop enterprises Farm category Small Large All farms IR UIR IR UIR IR UIR Wheat Paddy Maize Linseed Potato Barley Source: Primary Survey. on small and large farms of irrigated areas. Potato was found to be more productive on large farms than on small farms of irrigated agriculture. The average yield of potato on overall farm situation of irrigated agriculture was found to be quintals per hectare. The yield level of barley was 8.25 quintals per hectare in un-irrigated farms. Household Income The source-wise distribution of annual household income is shown in Table-4. The major sources of income were service, pension, trade / business, daily paid labourer wage, and rental income from non -farm sources and farm income derived from agriculture and dairy activities. Total income in Rs./farm was 38,64,687 in irrigated areas as against 12,56,816 in un-irrigated areas. The corresponding figure for small farms of irrigated areas was 27, 69,267 as against 10,95,420 in case of large farms. These values for un-irrigated areas were 4, 91,012 for small farms and 7, 65,804 for large farms. The table showed non-farm income from sources such as service, pension, trade / business, daily paid labourer wage contributed more than fifty per cent of the total household income in both the areas. On large farms, the percentage contribution of off-farm income was comparatively lower compared to small farms of irrigated agriculture while, reverse was observed for un -irrigated agriculture. Among nonfarm sources, service sector played a crucial role in the household economy of sampled households followed by farm income derived from livestock in both irrigated as well as in un -irrigated areas. This may be probably because of small size of holding with the cultivators. The income contribution of raising livestock was per cent on irrigated farms as against percent on un -irrigated farms.

9 Dimensions and determinants of diversification on kangra farms of himachal pradesh 9 Table 4. Source wise distribution of household income on sample farms (per cent) Source of income (I) Farm income : Agricultural activities Livestock (dairy+cattle) (II) Off-farm income : Farm category Small Large All farms IR UIR IR UIR IR UIR Service , Pension , Trade / business Daily paid wage earner Others (rental income etc.) Total income in Rs. / farm Source: Primary Survey ,69,267 4,91,012 10,95,420 7,65,804 38,64,687 12,56,816 Employment Structure Table-5 depicted the pattern of employment of sample farms. The table revealed that per cent of the person s was employed in kharif agriculture during a year followed by service (26.00 per cent ) in irrigated areas. In case of un -irrigated areas, the percentage of persons engaged in kharif agriculture was per cent followed by service around per cent. Table 5. Employment pattern of sample farms (per cent) Particulars 1.Agriculture Farm category Small Large All farms IR UIR IR UIR IR UIR i. Rabi season ii. Kharif season Livestock Non -farm enterprises Daily Paid Labourer Service Total employment in, man days / farm Source: Primary Survey Note : One man day = 8 hours (considered standard day )

10 10 The Bangladesh Journal of Agricultural Economics On small farms, persons employed in kharif agriculture was higher (29.03 per cent) followed by service (25.00 percent), while on large farms employment was higher in service sector (29.98 per cent) followed by kharif agriculture (27.81 per cent) in irrigated areas. In comparison, in un - irrigated areas, the percentage of person employed in rabi agriculture was higher (21.55 per cent) on small farms while, on large farms employment was observed to be higher in service (24.71 per cent) followed by kharif agriculture (20.34 per cent). The employment pattern in non-farm activities like shops, DPL's, etc was found to be more skewed in un -irrigated agriculture (about per cent) compared to irrigated agriculture (around per cent). Total employment in man days per farm in all farms case was 79,156 in irrigated areas as against 30,325 in un-irrigated areas. Diversification Indices of Crop Acreage, Income and Employment Across Farm- size Categories Herfindhal and Entropy indices for crop acreage, income and employment diversification were computed from acreage of crops, sources of income and employment, respectively for two different farm-size categories on an average for irrigated and un -irrigated agriculture (Table-6 ). Crop Diversification It was observed from Table-6 that Entropy index for crop diversification in overall farms situation increased from in irrigated areas to in un -irrigated areas. It means that un - irrigated farms were more diversified than irrigated farms due to the fact that higher proportion of area was put under two major crops viz.; maize and wheat in un -irrigated areas. Between farm categories, large farms were more diversified in terms of number of crops than small farms of both the categories. Income Diversification Table-6 depicted the values of these indices for income diversification also. Both the indices were constructed from farm as well as from off-farm income sources. Farm income included income from agriculture and dairy activities, whereas off-farm income consisted of service, pension, trade; daily paid labourer wage and rental income. The analysis revealed that the magnitude of Herfindhal index varied from in small farms to in large farms, thereby showing increase in diversification in large farms over small farms of irrigated areas. The corresponding values for Entropy index was in small farms and in large farms thereby depicting the same trend for irrigated areas because of direct relationship. In the case of un -irrigated areas, the value of Herfindhal index was for small farms and for large farms. Also, the Entropy values was for small farms and for large farms. This showed income diversification was less on small farms than on large farms. For the overall situation, irrigated agriculture had more diversified income pattern than un -irrigated agriculture. The value for Entropy index was for irrigated areas and for un - irrigated areas.

11 Dimensions and determinants of diversification on kangra farms of himachal pradesh 11 Table 6. Indices of diversification on sample farms (average) Indices of diversification Particulars Herfindhal index Entropy index IR UIR IR UIR 1. Crop acreage Small Large All farms Income Small G Large All farms Employment Small Large All farm Source: Primary Survey Employment Diversification For employment diversification, indices were also worked out and presented in Table-6. In a family, family members were employed in various occupation such as service, trade, daily paid wage earners, agriculture livestock and so on. Using the number of days in a year, members of family were found engaged in various occupations, as the basis for employment diversification, Herfindhal and Entropy indices wer e calculated. As is evident from Table-7, in un - irrigated areas, small households were found to have more diversified occupational structure than large households. This is reflected in the low Herfindhal and high Entropy values for the farmers. For the overall farms situation in these areas, the magnitude of Herfindhal and Entropy indices was and respectively. In case of un -irrigated areas, large farms had more diversified employment pattern than small farms. For all farm case of this areas had a value of for Herfindhal index and o.536 for Entropy measure. This showed employment structure was more diversified in un -irrigated areas than in irrigated areas. Empirical Relationship Between Crop Acreage, Income and Employment Diversification with Selected Socio-economic Variables To examine the empirical relationship between crop acreage, income and employment diversification with socio-economic variables in Panchrukhi block, regression analysis was done. The level of diversification as measured through alternative diversification measures was regressed upon selected socio-economic variables. Eight independent variables were considered for regression analysis. These were family size, age of the head of the family, education, number of fragments of holdings, size of operational holding, proxy variables for bullocks and for measuring the extent of tenancy.

12 The Bangladesh Journal of Agricultural Economics 12 Income Diversification: Results of Regression Analysis The regression results between income diversification and selected socio-economic variables for irrigated and un -irrigated farms is presented in Table-7 and Table-8 respectively. Table-7 shows that for small farms of irrigated areas, neither the family size nor the age and educational status of the head of family had any significant effect on income diversification. Though the regression coefficients were positive for family size and age of the head of family and negative for educational status of the head of family. Another important factor was found to be the number of fragments of holding which had a significant negative effect on income diversification suggesting that land holdings with higher number of fragments is expected to have less income from diversification. Size of operational holding is yet another important factor which had a significant positive effect on income diversification indicating that farms with larger holding size are expected to promote more income from diversification. Dummy variable for bullock and the extent of tenancy were not found to be significant. The regression equation on the whole explained 20 per cent variation by Herfindhal index and 27 per cent variation by Entropy measure. On the other hand in large farms of irrigated areas, the extent of income diversification was affected positively and significantly by family size, and number of fragments of holding and negatively by bullock Table 7. Results of regression analysis for income diversification on irrigated farms Variables Farm category Small Large All farms HI El HI El HI El Constant Family size ** 2.32** (0.91) (0.73) (1.37) (1.19) (1.03) (1.60) Age ** -0.61** (0.21) (0.17) (0.51) (0.45) (0.30) (0.31) Education (1.60) (1.29) (4.82) (4.20) (2,56) (2.96) No. of fragments of 2.54** -2.77*** -3.92** 3.20** holdings (1.32) (1.06) (1.75) (1.53) (1.06) (1.10) Operational holding -2.58*** 2.49*** (0.66) (0.53) (0.48) (0.41) (0.60) (0.60) Dummy for bullock ** * ** (3.95) (3.18) (12.99) (11.31) (6.16) (6.33) Dummy for leasing-in *** *** (4.65) (3.74) (8.81) (7.67) (7.11) (7.39) R Source: Primary Survey Note: The original coefficients have been multiplied by a constant figure of 100 to improve the readability. Interpretations have to be, therefore, made accordingly. Figures in paren - theses indicate standard error of regression coefficients. significant at 10, 5, & I per cent level of significance.

13 Dimensions and determinants of diversification on kangra farms of hintachal pradesh 13 density. All other factors like age and educational status of the head of family, operational holding size and dummy variable for the extent of tenancy were not found to be significant. Though the regression coefficients were positive for education, size of operational holding and the extent of tenancy and negative for age of the head of family. All these variables put together explained 33 per cent variation by Herfindhal index and 19 per cent variation by Entropy measure. For the overall farms analysis of irrigated areas, the age of the head of family had significant negative effect on income diversification. Likewise, extent of tenancy had significant negative effect on income diversification. Another important factor was bullock which had significant positive effect on income diversification. All other factors such as family size, educational status of the head of family, number of fragments of holding, and size of holding were not found to be significant. The value of adjusted coefficient of multiple determinations was 0.24 by Herfindhal index and 0.20 by Entropy measure. In comparison, Table-8 shows that in small farms of un-irrigated areas, family size had significant positive effect on income diversification suggesting that family with higher size is expected to generate more income from diversification, Age of the head of family had significant negative effect on income diversification. All other factors were not significant. The value of R Z was 0.37 by Herfindhal index and 0.19 by Entropy measure. For large farms of this areas all the factors were found to be non significant. Herfindhal and Entropy values for adjusted coefficient of multiple determinations were 0.36 and 0,29 respectively. In the overall farms analysis of un - irrigated areas, family size, operational holding size and dummy Table 8. Results of regression analysis for income diversification on unirrigated farms Variables Farm category Small Large All farms HI El HI El HI EI Constant Family size (1.60) 1.71* (1.44) 0.09 (1.65) 0.35 (1.38) -1.57* (0.99) 1.51** (0.81) Age 1.38*** (0.46) -0.90*** (1.44) (0.52) (00.44) 0.68*** (0.28) -0.42* (0.23) Education 6.00 (5.02) (4.51) (3.87) 3.88 (3.23) (2.51) 2.22 (2.06) No. of fragments of holdin s (1.39) 0.70 (1.25) (0.85) 0.21 (0.71) (0.45) 0.41 (0.37) Operational holding (1.26) 1.55 (1.13) 1.22 (3.87) (3.23) -1.68* (1.04) 1.41* (0.85) Dummy for bullock (9.56) 2.87 (8.58) (13.97) 2.79 (11.67) ** (5.90) 8.98** (4.85) Dummy for leasing-in 11.0 (8.43) (7.57) (16.87) (14.09) 18.29*** (6.86) *** (5.63) R Source: Primary Survey

14 The Bangladesh Journal of Agricultural Economics 14 variable for bullocks had significant positive effect on income diversification. This indicates that these factors would lead to income diversification if any one or all these factors are increased. While, age of the head of family and extent of tenancy had significant negative effect on income diversification. This reveals that farms with higher tenancy extent and age of the head of family is expectod to have less income from diversification. The other variables like educational status of the head of family and number of fragments of holding were found to be positive but not significant. The regression equation on the whole explained 25 per cent of the total variation in Herfindhal and Entropy indices. Crop Acreage and Employment Diversification: Results of Regression Analysis Regression analysis on crop acreage and employment diversification were also run. But, the results were not expected one, regarding signs of regression coefficients. Therefore, both these determinants of crop acreage and employment diversification were not discussed here. However, the attained results are presented in Appendix -1, 2, 3 & 4. Table 9. Results of regression analysis for off-income diversification on irrigated farms. Farm category Variables Small Large All farms Constant Family size ( ) ( ) (960.06) Age * (271.29) ** (904.72) *** (274.52) Education *** ( ) ( ) *** ( ) No. of fragments of holdings * (904.51) ( ) (523.53) Operational holding ( ) ( ) ( ) Gross farm income *** (0.6770) (0.7124) * (0.4404) Dummy for bullock *** ( ) ( ) ** ( ) Dummy for leasing-in ( ) ( ) ( ) R Source: Primary Survey Note: Figures in parentheses indicate standard error of regression coefficients, *, **, *** significant at 10, 5,& I per cent level of significance. Non-farm Income Diversification: Results of Regression Analysis Alternatively, the proportion of off-farm income of the total household income was also taken as a measure of rural diversification. This proportion of household non-farm income

15 Dimensions and determinants of diversification on kangra farms of himachal pradesh 15 was regressed upon several socio-economic variables and the results of irrigated and un - irrigated agriculture is presented in Table-9 and Table-10 respectively. The results showed that in small farms of irrigated areas( Table-9), age of the head of the family, educational status of the head of the family, and gross farm income had a significant positive effect on non-farm income diversification, while size of holding and proxy variable for bullock had a significant negative effect on diversification of non-farm income sources. The adjusted coefficient of multiple determinations was found out to be 0.52 which shows 52 per cent of the total variation in off-farm income was explained by all the factors discussed above. In large farms of irrigated areas, only age of the head of the family which was significant at 5 per cent level had positive effect on diversification. All other variables were not significant. This caused a low value in the coefficient of multiple determination.for the overall farms of irrigated areas, the age and the educational status of the head of the family and binary variable for bullocks were found to be important variables for explaining the process of income diversification. The positive relationship between the age and educational status of the head with off-farm income showed with higher age and educational status of the head, diversification of non -farm income was expected to increase. Bullock was yet another important factor which had inverse relationship with the level of off-farm income diversification suggesting that greater the bullock density lower will be the level of non-farm income diversification. Other variables were not significant because of wide variation in the total sample of 85. The value of R Z was In comparison, in small farms of un -irrigated areas (Table -10), out of eight explanatory variables taken into consideration only family size had significant effect on non-farm income diversification. This reveals that higher the family size higher will be the level of off -farm income diversification by means of sources such as service, trade, daily paid labourer, small scale industry, etc. All other factors like age and educational status of the head of the family, operational holdings, number of fragments of holding, gross farm income, proxy variables for bullock and the extent of tenancy were not found to be significant. This may be because of wide variation in all these factors. The adjusted coefficient of multiple determination was found to be about On the other hand in large farms and in overall farms of un -irrigated areas only three viz.; family size and education of the head and dummy variable for bullock were found to be significant. This suggests that these factors would lead to diversification if any one or both of these factors are increased. All other factors were not significant. The regression equation on the whole explained about 78 per cent variation in large farms and around 68 per cent variation in overall farms in the level of off-farm income. For the overall farms of un-irrigated areas, family size, age and education of the head of the family and size of operational holding were found to be significant. The value of the adjusted coefficient of multiple determination was 0.67.

16 The Bangladesh Journal of Agricultural Economics 16 Table 10. Results of regression analysis for off-income diversification on un-irrigated farms Farm category Variables Small Large All farms Constant Family size *** ( ) *** ( ) *** ( ) Age (686.69) (939.69) * (428.94) Education ( ) *** ( ) *** ( ) No. of fragments of holdings ( ) ( ) (860.92) Operational holding ( ) ( ) * ( ) Gross farm income (6.1987) (4.1245) (2.4757) Dummy for bullock ( ) ** ( ) ( ) Dummy for leasing-in ( ) ( ) ( ) R Source: Primary Survey Note: Figures in parentheses indicate standard error of regression coefficients. *, **,*** significant at 10, 5, & 1 per cent level of significance. IV. CONCLUSION To conclude for crop diversification, large farms had more diversified cropping pattern than small farms of both irrigated as well as un -irrigated areas. In the overall farms situation, unirrigated farms were more diversified in terms of number of crops than irrigated farms. The results for income diversification revealed that large households were found to have more diversified income structure than small households in both the areas. In the overall farms analysis, irrigated agriculture had more diversified income pattern than un-irrigated agriculture. For employment diversification, the value of Herfindhal and Entropy indices showed that for irrigated areas, large farms had more diversified employment pattern than small farms, while small farms were found to have more diversified occupational structure than large farms of un-irrigated areas. In the overall farms situation, employment diversification was found to be more in un-irrigated arras than in case of irrigated areas. The regression results for the determinants of income diversification revealed that irrespective of the farm categories, the variables like family size, age of the head of the family, size of holdings, number of fragments of holdings, dummy variables for bullock and the extent of

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19 Dimensions and determinants of diversification on kangra farms of himachal pradesh 19 APPENDICES Appendixl: Results of regression analysis for crop acreage diversification on irrigated farms. Farm category Variables Small Large All farms HI El HI El HI El Constant Family size 1.02*** *** 0.07 Age (0.60) -0.33*** (0.90) 0.19 (0.77) (0.92) 0.04 (0.44) -0.39*** (0.64) 0.14 Education (0.15) -0.13* (0.22) -2.94*** (0.29) (0.35) 0.10 (0.13) 0.53 (0.18) No. of fragments of (1.10) (1.63) 0.81 (3.17) (3.79) -0.86** (1.00) -0.42*** (1.43) holdings (0.49) (0.72) (0.42) (0.50) (0.24) (0.35) Operational holding (0.88) 0.87 (1.31) (1.07) (1.28) (0.60) 2.26*** (0.87) Gross farm income ** *** *** Dummy for bullock (0.0004) (0.0054) (0.0002) (0.0003) (0.0002) (0.0003) 0.15 Dummy for leasing-in (2.60) 1.29 (3.87) (7.43) (8.89) 5.30 (1.38) (3.43) (3.10) (4.61) (5.23) (6.26) (2.61) (3.76) R Source: Primary Survey. Note: The original coefficients have been multiplied by a constant figure of 100 to improve the readability. Interpretations have to be, therefore, made accordingly. Figures in paren - theses indicate standard error of regression coefficients.*,**,*** significant at 10, 5, & 1 per cent level of significance.

20 The Bangladesh Journal of Agricultural Economics 20.Appendix 2: Results of regression analysis for crop acreage diversification on un - irrigated farms. Farm category Variables Small Large All farms HI El HI El HI El Constant Family size (1.29) 0.57 (1.13) 0.93 (2.18) (1.31) 1.40 (1.04) 0.02 (0.07) Age (0.40) (0.35) (0.77) (0.47) (0.30) (0,20) Education (4.22) (3.70) (4.76) 1.49 (2.86) -4.88*** (2.58) (1.74) No. of fragments of holdings (1.76) 0.35 (1.54) 1.87 (1.44) (0.87) 1.39*** (0.60) (0.41) Operational holding 0.50 (1.06) 1.34 (0.93) 3.49 (4.72) (2.84) 1.30 (1.07) 0.59 (0.72) Gross farm income (0.003) (0.003) (0.003) 0.004*** (0.002) *** (0.002) 0.002*** (0.001) Dummy for bullock 20.70*** (7.71) 0.84 (6.76) 6.54 (18.11) (10.89) 6.59 (6.21) 4.03 (4.20) Dummy for leasing-in 11.79*** (6.74) (5.91) (27.06) (16.27) *** (7.17) (4.85) R Source: Primary Survey. Note: The original coefficients have been multiplied by a constant figure of 100 to improve the readability. Interpretations have to be, therefore, made accordingly. Figures in parentheses indicate standard error of regression coefficients. *, **, *** Significant at 10, 5, & 1 per cent level of significance.

21 Dimensions and determinants of diversification on kangra farms of himachal pradesh 21 Appendix 3. Results of regression analysis for employment diversification on irrigated farms. Farm category Variables Small Large All farms HI El HI El HI El Constant Family size (0.57) 0.84 (0.55) (0.80) 0.82 (0.82) (0.57) 0.84 (0.55) Age (0.14) 0.04 (0.13) 0.07 (0.30) (0.31) (0.14) 0.04 (0.13) Education 1.45 (1.03) (1.00) (3.27) 0.96 (3.35) 1.45 (1.03) (1.00) No. of fragments of holdings -1.33*** (0.46) 1.40*** (0.45) 0.11 (0.43) 0.02 (0.44) -1.33*** (0.46) 1.40 (0.45) Operational holding (0.83) (0.81) (0.10) 1.04 (1.13) (0.83) (0.80) Gross farm income *** (0.0003) *** (0.003) (0.0002) (0.0002) *** (0.0003) *** (0.0003) Dummy for bullock (2.45) 0.81 (2.39) (7.66) 5.59 (7.86) 2.07 (2.45) 0.81 (2.39) Dummy for leasing-in (2.45) 2.45 (2.84) (5.40) 4.72 (5.54) (2.91) 2.45 (2.84) R Source: Primary Survey. Note: The original coefficients have been multiplied by a constant figure of 100 to improve the readability. Interpretations have to be, therefore, made accordingly. Figures in parentheses indicate standard error of regression coefficients significant at 10, 5, & 1 per cent level of significance.

22 The Bangladesh Journal of Agricultural Economics 22 Appeadix 4: Results of regression analysis for employment diversification on un irrigated farms. Farm category Variables Small Large All farms HI El HI El HI El Constant Family size 0.49 (0.46) 0.08 (0.46) 0.71 (0.68) (0.57) 0.66*** (0.34) (0.31) Age 0.05 (0.14) (0.14) (0.24) 0.16 (0.20) (0.09) 0.07 (0.09) Education 1.27 (1.49) (1.50) (1.48) 0.75 (1.25) (0.83) 0.14 (0.78) No. of fragments of holdings (0.62) 0.72 (0.62) (0.45) 0.19 (0.38) 0.29 (0.19) (0.18) Operational holding (0.37) 0.08 (0.38) 0.48 (1.47) (1.24) (0.35) (0.32) Gross farm income (0.012) (0.0013) (0.0011) (0.0089) ** (0.0005) (0.0005) Dummy for bullock (2.72) Dummy for leasing-in (2.38) (2.74) 0.79 (2.40) (5.64) 2.66 (8.43) (4.75) (7.10) (2.00) (2.31) (1.87) 0.89 (2.16) R Source: Primary Survey. Note: The original coefficients have been multiplied by a constant figure of 100 to improve the readability. Interpretations have to be, therefore, made accordingly. Figures in paren - theses indicate standard error of regression coefficients.*,**,*** significant at 10, 5, & 1 per cent level of significance.