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Farm investments relationships this chapter is a step further to the analysis of farm investments


FARM INVESTMENTS : RELATIONSHIPS

This Chapter is a step further to the analysis of farm investments delt in the preceding Chapter. It deals with the third and fourth objectives of the study talcing cross-sectional examina­ tion of data from the sample farmers of flood prone and non-flood prone area separately. This Chapter consists of two parts. The first examines the factor influencing the level of farm investments in flood prone and non-flood prone areas, and in the second part -the crop production estimates on the sample farms were taken up inview to verify the consistancy of investment pattern with the economic rationality and allocative efficiency of capital on different inputs in the study area.

I

FARM INVESTMENTS : Influencing Factors

Usually, investment is the subject of savings, which is determined primarily through income and consumption levels,Some factors which are directly responsible to influence savings may be taken as indirect influencing factors to investment.

In this study a functional formulation was workedout taking farm investments a function "‘of some indirect influencing factors i.e., size of family,size of operational holdings,average borrowings and average non-farm income on the sample farms. The relationship was examined through the application of a regression model of multiple nature as under *

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Y = a + J2. h± where/

The dependent factor 'Y' was .taken for farm investment including investments for the purpose of (i) purchase of livestock;/ (ii) purchase of implements and machinery/ (iii) purchase of land/ (iv) land development/ (v) irrigation structure and resources/ (vi) farm house, barn and cattleshed and (vii) orchards on the sarplc farms of flood prone and non-flood prone areas separately.

The causal factors were chosen for the analysis provided the equations are not collinear being confirmed that the correlations between independent variables are zero. The included regressors in the model were s

= size.of family ( no./ farm) X2 = size of operational holdings (ha./ farm) X3 = average borrowings (Rs./ farm) X^ *= average non-farm income (Rs./farm)

The estimates and statistical measures are presented in Table 6.1.

The coefficient of multiple correlation (R) was signifi­ cant statistically on both the sample farms of flood prone and non-flood prone areas(Table 6.1). The coefficient of multiple determination (R ) was observed higher (0.7624) in the non-flood prone area than (0.6041) in flood prone area,indicating thereby, that the variations in farm investment (Y) is e:xplained by the

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Table 6.1 Regression Estimates

Le Farms Intercept Coefficients of Regression

RX, X„

3 Prone 67.9062 (-) 97.5216** 81.7764X* 0.4749** 0.2847** 0.6041 (31.6180) (23.8982) (0.1969) (0.1098) X b± = (->14.9856, R = 0.7772 S.b (b2,b3/b4) = 82.536

X JL ^ x JLSlood Prone 211.4287 (->102.7304 113.4593 0.6227 0.3182 0.7624 (20.2151) (39.6057) (0.2003) (0.1166) 'Z.bi = 11.6698, R = 0.8732

** : Significant at 0.01 level of significance, independent factors (X^) considered in the analysis jointly upto 76.24 per cent in non-flood prone area which is greater than 60.41 per cent e^slanation in flood prone area. Resultantly, 24 and 40 per cent respective variations in farm investment (Y) in non-flood prone area and flood prone area are still to be esqplained. it leads to the fact that further examination of the situation in both the conditions with reformulated variables including some other direct and indirect factors may be taken in future for intensive pursuit in the subject.

Observing the equation fittedin both the conditions,it is seen that all the regression coefficients but for X^ i.e. size of family were positive and significant statistically at 1 per cent level of significance. The extent of positive coefficients pertains to 81.7764 and 113.4593 for X2 , 0.4749 and 0.6227 for X3 and 0.2847

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and 0.3182 for X^. in the two respective conditions, which indicate thereby that an enhancement in the level of X^ by 1 hectare per farm would result into an increase in the farm-investment level by Rs.81.77 and 113.46 on flood prone and non-flood prone farms respectively. It stands for X3 at an enhancement by Rs.0.48 and 0.62 in the level of farm investment due to an increase of Re.l in the level of average borrowings on the respective farm conditions. In case of X^, the contribution of an increase of Re.l per farm in the farm income to the farm investment will be of Re.0.28 and 0.32 per farm respectively on the two farms. Whereas, negative but statistically significant coefficients of family size (X-^) pertaining to (-) 97.5216 and (-) 102.7304 on the respective farms indicate that an increase of one person in the size of farm family, will result into a decrease in the level of farm investment by Rs,97.52 in flood prone and Rs.102.73 in non-flood prone area. Collating the coefficients of the two conditions we found that the extent of estimates are greater in non-flood prone area than flood prone area in both the directions of positive and negative relationships. It reflects the fact that the response of farm invest­ ment to the regressors under consideration are greater in non-flood prone area than flood prone in both the ways of increase and decrease of farm investment.. It provides the ground to prove better prospects of farm investment in non-flood prone area than flood prone. Looking forward, the summation of b^s was found to be > 1 In non-flood prone area indicating thereby the prevalance of I RTS in the function. It explains tlf3 fact that with the fitted equation in non-

X1 , v<?

^ . 164

flood prone area the decisions of production are allowed by the estimates in the I stage of production i.e., the region of increa­ sing returns to scale. Thus, chances of increase in the levels of X. to achieve the maxima: "of farm investment in non-flood prone is --- area are bright. But an absurd situation arose in floods prone area due to negative summation value of b^ which indicates apparantlv the prevalance of downward trend in the function fitted in the data. One can argue the case in the way that it is i.e.,size of family which plays an adverse role in the whole picture. Due to this only factor (X^) the situation of returns- goes absurd.Because, by exclusion of this factor the summation of arK^ ^4 foun<5 to be > 1, reflecting the prevalence of IRTS and the coinciding situation of non-flood prone area.

Summarising the whole story we can note the concluding facts that the prospect of achieving the maxima in farm investment is bright with an enahanced level of X2/X3 and X4 on both the farms. Where a control on the increase in size of farm family will prove beneficial to the farms of both the conditions.Ultimately investment prospect was proved brighter in non-flood prone area than flood prone.

II

Crop Production Estimates

This part of the Chapter enquires consistancy of farm investment pattern with economic rationality and allocative efficiency of capital on the sample farms of flood prone and non­ flood prone areas. Thi:— part firstly analyses the rationality of

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Crop production on the sample farms and in the second it looks forward for entrepreneurial ability tested by allocative efficiency parameters.

A,Rationality of Crop Production.

In this section crop production rationality was enquired applying production function model of Cobb-Dauglas type in the data of farm crops taken from the sample farms of the two areas i.e. flood prone and non-flood prone separately.The function applied was as under J

Y = a. X, *2 X. .4

where# The total income from crops in terms of Rs./faxm vas

taken as dependent factor (Y), The response of crop income was workedout against the independent factors chosen for trie study verifying their collinearity using correlation matrix-of ir.put factors. The selected causal variables were

Xx = expenditure on purchased inputs (Rs./fam) X2 = expenditure on hired human labour (Rs./faxm) X^ operational area (ha./farm) X^ = expenditure on power ( Rs./farm)

The input factor (X^) i.e.# expenditure on purchased inputs was taken as joint input factor inclusive of fertilizer# irrigation and purchased seed costs.The input factor(X/,) i.e,# expenditure on power was also taken as joint factor including cost on bullock and tractor power on the sample farms.

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Table 6.2 presents the elasticities of crop production on the sample farms. Table 6.2 Crop Production Estimates Sample Farms Intercept Elasticities of Crop Production R2 X1 • X2 X3 X4 Flood Prcxne ^.rea 263.4081 ** ** 0.1521 0.3974 (0.0404) (0.1997) ** 0,2724 (0.0546) ** 0.1812 (0.0869) 0.7162 Jon-Flood 5rone Area 1027.7154 Z‘bA « 1.0031, R = 0.8463 0.2256 0.4745 0.3284 0.1079 (0.0549) (0.1817) (0.0667) (0.0359) 0.S351 lb. = 1.1364, r R = 0. 9138 ** '• Significant at 0.01 level of significance. Coefficients of multiple correlation (R) in both the conditions of flood prone and non-flood prone were found to be signi­ ficant statistically(Table.6<2). The coefficient of multiple determina- tion (R ) was found to be greater ( 0.8351) on the farms of non-flood prone area than (0.7162) of flood prone area. It indicates the fact that the variations in crop income level are; explained by the considered independent factors jointly upto 83.51 per cent on the sample farms of non-flood prone area and 71.62 per cent of flood prone area. Thus, a variation in the crop Income upto 16 and 28 per cent on the respective non-flood prone and, flood prone farms remained unaccounted. It leads to the necessity of further studies in detailed pursuits of the subject.

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Scrutinizing the fitted equation we noted that the elasticities workedout in both the conditions of non-flood prone and flood prone were found to be positive and statistically signi­ ficant at 1 per cent level of significance in case of all the variables taken for the analysis. The extent of elasticities is seen to be 0.15 21, 0.3974,0.2724 and 0.1812 for and X4 respectively on the sample farms of flood prone area. Where, it pertains to 0.2256,0.4745,0.3284 and 0.1079 for respective variables of non-flood prone area. It indicates thereby that an increase by I. per cent in the applied level of and X^ would result into an enhaced level.of crop Income ranging between 0.1521 and 0.3974 per cent on the farms of flood prone area and 0.1049 and 0.4745 per cent on non-flood prone farms. Thus, all the four input factors are under utilised for crop production on the farms of both the conditions. Prospects of further increase in crop income by an enhanced level of input application on the farms of both the condi­ tions are bright. Further it explains that farmers of both the areas are lacking entrepreneurial ability. Comparing the elasticities in the two conditions we noted that the extent of estimates in non-flood prone area was greater significantly than the estimates in flood prone area for all the variables but for X4 in which a reverse was noted with a greater estimate of 0.1812 in flood prone area than 0.1079 in non-flood prone area. It proves^ by and large^p revalance of better farming conditions for crops in non-flood prone area than flood prone area. -f- 43^7 , ■ .O Sx7 -■'V c*e_

168

Taking the summation of elasticities s we observed that the sum of b^ was slightly greater than 1, which indicates that the production function just passes from increasing returns to constant returns as the entrepreneure uses up all his entrepreneurial ability. Considering the picture as a whole and summarising the findings we noted conclusive facts that all the four input factors considered in the function are under utilized for crop production on the farms of both the conditions. Thus, prospect of further increase in crop income by an enhanced level of input application in the area is bright. Further, it may be noted that farmers of both the conditions are inefficiently cultivating their farms for crop production.Guidance and training for application of technical inputs in crop production may convert them into efficient farmers improving their entrepreneurial ability. E, Allocative Efficiency. "Output will vary over the cross-section with the extent of entrepreneurial ability. Eut,from the marginal productivity conditions, we see that input will vary in proportionately the same way. There will be the appearance of constant returns to scale. The attempt of any firm to esq?and its output will give rise to decreasing returns, and, if the firm reduced output, increasing returns would appear." 1 " The most difficult model to deal with is when the coefficients of the production function vary from one entrepreneur 1« Walter?,A.A.,1968 * An Introduction to Econometrics. London. Macmillan & Co. Ltd., P.293.

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to another. ............... .This may be rationalised by suggesting that scsne entrepreneurs are better, on the average, at more caoital intensive production than others. Certain other entrepreneurs are more adept at organising labour intensive methods. This is 3quivale.1t to saying that there is no unique production function;there are as

2many production functions as there are entrepreneurs."

" A less complicated way of elaborating the entrepreneurship assumptions is to suppose that the coefficients are the same for each firm, but that in addition to the entrepreneurial effect in the production function, there are also similar entrepreneurial effect?: appearing in the marginal productivity conditions. In other words the efficiency of an entrepreneur is reflected not merely in his production function, it is also reflected in the precision with which he achieves the best employment of factors. An efficient entrepre - neur , in this sense, will employ factors upto the amount when their marginal productivity is exactly equal to the price ratio., A relatjvel inefficient entrepreneur will miss by a mile - either overshoot or

3underenrploy."

To measure entrepreneurial ability in an occurate way one can workout allocative efficiency taking output and input price s with Cobb - Dauglas estimates in formula as under *

2. Ibid, P.295. 3. Ibid, P.296.

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The Formula, where, MVPi j MVP. 1j b. l

Q 3

P b±. (Q/ *i.) 3 J j P ij Marginal value productivity of ith input Output elasticity of i th input Geometric mean of output Geometric mean of i th input Geometric mean of output prices Allocative efficiency parameter Geometric mean of prices of i th input The resultant factor of the study i.e., gross income from crops was measured in value term in rupees per farm. The causal factors were considered in varied units i.e., expenditures on purchased inputs, hired human labour and power in terms of rupees per farm and operational area in hectare per farm.Since the input and output prices prevailing in the whole'study area were assumed to be governed from the same market conditions, therefore, the allocative efficiency parameters for causal variables were workedout with the form of formula; b <Q./ X. ) j

Allocative efficiency parameters refer to the extent of resource use in crop production activities. The absolute alloca­ tive efficiency requires the parameter equivalent to unity. While the parameter less than unity(l) indicates over utilisation of resources and the value more than unity (1) reflects under utilisa­ tion of input factors in the activity. , The estimates of allocative efficiency are presented in Table 6.3 Table 6.3 Allocative Efficiency Parameters. Allocative Efficiency Parameters Variables Flood Prone Non-flood prone Area Area Expenditure on purchased inputs (xx)

2.01 2.42 Expenditure on hired human labour 1.08 2.03 (x2>

Cpe rational area (X3) 32.05 42.76 Ejqpenditure on power (X^) 3.54 2.02 It is observed from the Table that values of parameters of allocative efficiency were found to be more than unity in case of all the four causal factors in both the conditions of flood prone area and non-flood prone area. Thus, prima-facie it is clearly seen that all the four input factors considered in the analysis are under utilised in crop activities in both the conditions.

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Scruitlnising the extent of parameter values it is revealed that the extent varies in the range from 1.08 to 32.05 in flood prone condition and from 2.02 to 42.76 in non-flood prone condition.The highest value stands for operational area in both the conditions explaining the fact that area under operation is highly under utilised. Thus, with the expansion of utilisation of area under operation for crop cultivation the production and income fran craps may be enhanced on both the farms of flood prone and non-flood prone areas. So far as the values next to the highest and its following values are concerned, a varied trend is in observation. In flood prone area the next to the highest is expenditure on power with 3.54 followed by expenditure on purchased inputs and hired human labour for respective values of 2.01 and 1,08. While, in non-flood prone area the reverse trend was seen with expenditure on purchased irputs placed on second having parameter value 2.42 followed by 2.03 and 2.02 of expenditure on hired human labour and power respectively. The extent of under utilisation of particular variables is in the same trend as they pertain in the order of their extent of values of allocative parameters.

Comparing the two conditions of flood prone area and non­ flood prone area for the parameters and the extent of under utilisation of variable resources in crop activities,we noted the fact that level of under utilised resources is greater in non-flood prone area than flood prone for all the considered variables but for power expendi­ ture (X4) in which under utilisation is greater in flood prone area. Thus, farming conditions in non-flood prone area are prosperous for crop cultivation than flood prone. This finding is consistent with our fitTaing in the previous part of this Cnapter (F.167).

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As concluding remarks it can be noted that the input factors of crop production considered in the analysis are under utilised in both the conditions of flood prone and non-flood prone. This finding too is consistant with our findings in this Chapter(P.168). The empirical examination of rationality of crop production and allocative efficiency in this Chapter conclusively note that J 1, Most of the input factors i.e. purchased inputs (fertilizer/ irrigational water and seed)/ hired human labour, operational area and power are under utilised in crop activities on the farms of flood prone and non-flood prone areas both* 2, Farming conditions in non-flood prone area are better than flood prone area. 3, The farmers of both the conditions are inefficient in utilisation of technical inputs in crop activities on their farms. Thus, farm investments in both the conditions of flood prone and non-flood prone are consistant with the rationality of crop production economy and allocative efficiency of resources in the area of study. Further, prospects of resource use for improving crop income is bright in both the conditions. Resultantly,corro­ borative to the findings of part I in this Chapter a bright prospect of farm investment with an enhanced level of farm income was found to be existing in both the flood prone and non-flood prone areas.

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On the basis of above findings it can be suggested that guidance and training for application of technical and non technical inputs in crop activities would help the farmers in converting them into efficient farmers improving their entrepre neurial ability. Further, incentives and facilities of availabi ty of technical inputs for crop activities may improve the situation of resource use and the level of crop income on the farms of both the conditions. Resultantly, the farm investment situation may also improve with an enhancement in crop income levels on the farms.

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pronefloodareafarmcropfarmsproductionconditionsfactorslevel






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