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Agricultural Administration 12 (1983) 141-153
Production Efficiency, Returns to Scale and Farm Size in Rice Production: Evidence from Sri Lanka
H. M. G. Herath
Department of Agricultural Economics, Faculty of Agriculture, University of Peradeniya, Sri Lanka
(Received: 5 May, 1982)
SUMMARY
The incestigation of the effects of new technology embodied in the Green Retlolution on resource producticity provides concincing evidence that landproductiuity has increased. The returns to scale parameters indicate no increasing returns to scale either before or after the Green Recolution. In general, the use oftraditional ef$ciency criteria indicates inef$cient use of most resources. This study also shows that there are no economies of scale relative to farm size. The size productivity relationships in general show an inverse relationship between labour use andfarm size anda direct relationship between projit and farm size.
INTRODUCTION
The Green Revolution package provided farmers in most rice growing countries with a basis for speeding up development. The post-Green Revolution evidence indicates that the diffusion and adoption of the new technology are highly uneven both amongst and within countries.’ The adoption of new technology requires a concomitant change in their cultivation techniques; in particular, high dosages of fertiliser and more labour are required. Because of this dynamic nature of technical change, allocative efficiencies may be different in regions using new and tradi- tional technologies.2
The uneven adoption of new technology can also exacerbate existing income differentials. This would be further aggravated if new technology shows increasing returns to scale. Many previous studies have assumed
141 Agricultural Administration 0309-586xj83/0012-0141/$03+)0 0 Applied Science Publishers Ltd, England, 1983. Printed in Great Britain
142 H. M. G. Herath
constant returns to scale.3 Some studies also suggest that smallholder agriculture is characterised by an inverse relationship between output and farm size.4-6 They contend, however, that net profit per acre is positively related to farm size. It is therefore useful to study how new technology has affected the above relationships.
The specific objectives of this study are:
(1) To determine the productivity of resources under modern and traditional technologies.
(2) To test the hypothesis of constant returns to scale with data for the post-Green Revolution period.
(3) To study the types of allocative inefficiencies present under modern and traditional technologies.
(4) To test the hypothesis that farm size is both inversely related to output per acre and positively related to net profits per acre.
METHOD OF ANALYSIS
Data
The data for this study were collected from two rice growing areas in Sri Lanka-the Kandy district and the Anuradhapura district. The village selected from the Kandy district is called Galaha (hereafter referred to as the Galaha sample) and the sample from the Anuradhapura district was selected from an area close to the city (hereafter referred to as the Anuradhapura sample). The data pertain to the two seasons, Yala, 1980 (dry season), and Maha, 1980/81 (wet season). The two areas have contrasting economic and agricultural features. The most important observation in the two areas is that the rice farmers in the Anuradhapura sample use improved varieties of rice and the farmers in the Galaha sample use traditional varieties. The two areas would therefore reflect the differential impact of technical change in rice production. The sample selection was based on a stratified random sampling procedure. Stratification was done according to farm size. From each stratum a random sample was selected. The farms in the Galaha sample are small relative to those of Anuradhapura. Three size groups were distinguished in each area (see Tables 1 and 2). The nature of the returns to scale and efficiency of resource use were tested for each size class separately. The size-productivity relationships were examined for the total sample.
TABL
E I
Prod
uctio
n Fu
nctio
ns
and
Retu
rns
to S
cale
in R
ice
Prod
uctio
n in
Sri
Lank
a:
Gal
aha
Sam
ple
Seas
on
Farm
siz
e (a
cres
) Sa
mpl
e Pa
ram
eter
s si
ze
Cons
tant
La
bour
us
e O
pera
ting
Farm
siz
e co
st
Yala
, 19
80
<0.5
22
7,
099
- 0,
223
26
0.14
2 86
(0
.320
99)
(0
.116
14)
M
aha,
19
80/8
1 <0
.5
33
5.84
2 02
-0
.158
29
0.40
9 73
**
(0.2
13 7
1)
(0.1
3438
) Ya
la,
1980
0.
5-l
.o
42
4.49
3 28
0.
1189
2 0,
374
33*
(0.1
6495
) (0
.140
66)
Mah
a,
1980
/8 I
0.55
1 .o
47
5.32
448
0.14
453
0.25
9 05
**
(0.1
46 5
1)
(0.0
89 4
6)
Mah
a,
1980
/81
> 1.
0 34
4.
739
18
0.02
8 54
0,
299
32*
(0.2
1622
) (0
.153
23)
Note
s:
Figu
res
with
in
pare
nthe
ses
indi
cate
th
e st
anda
rd
erro
rs.
* Si
gnifi
cant
ly
diffe
rent
fro
m
zero
at
the
1 y0
leve
l. **
Sig
nific
antly
di
ffere
nt
from
ze
ro a
t th
e 5
“A le
vel.
***
Sign
ifica
ntly
di
ffere
nt
from
un
ity
at t
he 5
“/, l
evel
.
Buffa
lo
cost
R2
Re
turn
s to
sca
le
1.00
846*
* (0
.232
42)
0.
666
39**
(0
.192
71)
0.
148
35
(0.2
09 5
5)
0.37
2 78
**
(0.1
6668
) 0.
501
73*
(0.2
57 9
6)
0.09
3 54
(0
,082
56)
0,
013
48
(0.0
53 5
6)
- 0.
009
21
(0,0
61 9
3)
-0.0
25
44
(0.0
59 4
5)
0.15
3 83
* (0
,078
92)
-
0.79
7 1.
0216
0.78
9 0.
931
3
0.32
2 0.
632*
**
0.50
5 0.
751*
**
0.66
0.
983
TABL
E 2
Prod
uctio
n Fu
nctio
ns
and
Retu
rns
to S
cale
in R
ice
Prod
uctio
n in
Sri
Lank
a:
Anur
adha
pura
Sa
mpl
e
Seas
on
Farm
siz
e Sa
mpl
e (a
cres
) si
ze
Cons
tant
La
bour
us
e Bu
ffdo
cost
Yala
, 19
80
< 1.
0 27
5.
000
19
0.26
7 24
* -0
.014
33
(0,1
44 7
8)
(0.0
09 4
6)
Mah
a,
1980
/81
<I.0
31
8.
404
31
-0.1
1674
-0
.014
14
* (0
.143
17)
(0
,007
49)
Ya
la,
1980
1.
0-3.
0 28
8,
494
87
0.02
35 2
4*
0.02
5 75
(0
.128
42)
(0
.019
93)
Mah
a,
1980
/81
1 ,o-
3.0
41
8.58
2 83
-0
,005
23
0,
003
25
(0.1
46 3
6)
(0.0
06 3
9)
Mah
a, 1
980/
8 1
> 3.
0 38
7.
253
12
0.06
3 34
-
0.00
9 99
(0
.130
55)
(0
.007
64)
Ya
la,
1980
>
3.0
11
7.84
2 18
-
0.02
7 79
-0
.010
26
(0.3
05 4
7)
(0.0
1687
)
Note
s: F
igur
es w
ithin
pa
rent
hese
s in
dica
te
the
stan
dard
er
rors
. *
Sign
ifica
ntly
di
ffere
nt
from
ze
ro a
t th
e 1 7
” le
vel.
** S
igni
fican
tly
diffe
rent
fro
m
zero
at
the
5 y0
leve
l. *?
;* Si
gnifi
cant
ly
diffe
rent
fro
m
unity
at
the
5 “A
leve
l.
Para
met
ers
Mac
hine
ry
Farm
siz
e O
pera
ting
cost
-0.0
1041
0,
534
78*
(0,0
09 6
5)
(0.2
64 2
6)
-0.0
07
06
1.19
8 32
**
(0.0
07 6
3)
(0.2
2192
) -0
.041
35
0.34
243
(0.0
3497
) (0
,583
38)
0,
0178
0*
1,00
48**
(0
.009
00)
(0
.209
02)
-0
.003
42
1.04
450*
* (0
.008
20)
(0
,293
07)
-0
,020
10
1,
264
15**
(0
.172
40)
(0.5
59 9
0)
0,26
5 17
(0
.176
20)
0,07
5 57
(0
.122
31)
- 0.
057
78
(0.3
02 2
0)
-0.0
5666
(0
.103
22)
0.
088
29
(0.1
3790
) 0.
055
02
(0,2
41 1
5)
R2
Retu
rns
to s
cale
0,67
5 60
0.
777
28**
*
0.74
1 18
3 I.1
3595
0.39
2 71
0.
5042
9***
0.57
2 64
0 2
0.96
3 96
0.36
1 71
8 1.
1827
2
0,70
5 39
84
1.26
102*
**
Production efJiciency and farm size in rice production 145
Technological change and resource productivity
In order to proceed on the main objectives the simple production function model of the CobbDouglas type given below was fitted:
Y=AX;‘X,b’,...,X> (1)
where Y is the dependent variable, the X,s represent the different indepen- dent variables and the bp are the partial elasticities of the different variables. In this study the dependent variable is gross farm income and the independent variables are farm size, human labour, bullock power, machinery use and operating cost.
The output was measured in terms of the value of output per farm. In cases where domestic consumption, as well as payments in kind, is reported, the valuation of such items was based on the existing market prices. Land input was measured as a stock variable by the physical amount of land measured in acres. The human labour variable combines family labour, hired labour and also, in some cases, exchange labour. The labour variable was incorporated in terms of man-day equivalents by taking 8 hours of work as a day. In computing equivalent man-days of work per farm, a woman-day and a child-day were considered to be 0.75 and 0.5 of a man-day, respectively. The operating cost variable included inputs such as fertiliser, herbicides, pesticides and all other incidental expenses measured in rupees. The bullock labour variable was measured in terms of the number of pairdays. The variable representing mechanical services is an important variable in the context of technical change. This was measured in terms of the cash expenditure on mechanical services. The above functions, fitted to the three different sizes in each area for the two seasons, are presented in Tables 1 and 2.
Results
The results in Tables 1 and 2 indicate that one of the most important variables in both areas and for all sizes-is farm size. It was found to be significant in four of five cases examined in the Galaha sample and in five of six for the Anuradhapura sample. A striking point is the larger partial elasticity coefficient for land in the Anuradhapura sample where high yielding varieties are widely used. Similar findings have been reported by others. The elasticities were above 1 in most cases in the Anuradhapura sample atid were lower than 1 in the Galaha sample. With regard to
146 H. M. G. Herath
labour, it was noted that in the Galaha sample where farm sizes were generally very small and no technical change was observed, the elasticity coefficients were not statistically significant at traditional levels. Also, in some cases, the elasticity was found to be negative. In contrast to the Galaha sample, however, the labour variable was found to be significant in two of six cases in the Anuradhapura sample. In three cases it was found to be negative but not significant. Another important feature of the results relates to the operating cost variable in the Galaha sample. The operating cost variable comprises mainly fertiliser use, herbicides and pesticides use, etc. The coefficient was found to be significant in four out of the five cases in the Galaha sample. The operating cost variable was not found to be significant at any level in the Anuradhapura sample. The other inputs common for both samples is the buffalo use variable which was found to be negative in four cases in the Anuradhapura sample. In the Galaha sample also this variable was found to be negative in two cases although the coefficients were found to be insignificant. The mechanical services variable, which was reported only for the Anuradhapura sample, was found to be negative in five of the six cases.
Returns to scale iu paddy production
The returns to scale parameter is determined by the summation of the partial elasticities of the CobbDouglas production function. This is then statistically tested for deviation from unity, and if it is not significantly different from unity, then constant returns to scale are said to prevail. The returns to scale parameters of the function discussed above are also given in Tables 1 and 2. It is noted that the scale parameters are not statistically different from unity in three cases in the Galaha sample. In two cases they are statistically different from unity and lower than 1. In the Anuradhapura sample the scale parameters are not significantly different from unity in three cases. In two cases the parameter is significantly different from unity and lower than 1. In the other case it is significantly different from one and greater than 1.
Allocative efficiency
The ratio between marginal value productivities obtained from the production functions to opportunity cost (market prices) for the inputs gives an indication of allocative efficiency. If the ratio is greater (less) than
Production efjciency and farm size in rice production 147
unity, more (less) of that input should be used. The marginal value products are determined using the elasticities and the geometric means of the inputs and output. The market price of renting an acre of land was taken as the average value of land per acre. The average value of the landlord’s crop share was assumed to reflect the value of land. This was computed to be Rs.800.00 and Rs.1600.00 per acre for the Galaha and Anuradhapura samples, respectively. The average wage rate for a man- day of labour was Rs.1500 and Rs.2500 in the Galaha and Anuradhapura samples, respectively. This was taken to reflect the opportunity cost of labour. The opportunity cost of a rupee spent on production was treated as 1.2, taking an average interest rate of 20 % for short-term loans. The marginal value products and the ratios of the marginal value products to the factor costs are presented in Tables 3 and 4 for the two samples.
It is seen that most of the ratios are different from 1 and hence most variables are not used efficiently. Land appears to be under-utilised in the ~0.5 acre group in the Galaha sample whereas, in the other two size groups, it appears to be more intensively used. Under-utilisation of land appears to be even more prevalent in the Anuradhapura sample except for the l-3 acre group in the Yala season. An over-utilisation of labour is evident in both samples for all size groups and also for both seasons. This may seem difficult to reconcile at first in the Anuradhapura sample where
TABLE 3 Marginal Value Products and the Ratios of Marginal Value Products to Factor Cost:
Galaha Sample
Yala, 1980 < 0.5 AC. 0.5-1.0 AC.
Maha, 1980/81 <O.SAc. 0.fl.O AC. >l.OAc.
Marginal Value Products (Rs.) Land (Acres) 1260.65 Labour (Man-days) 1.95 Buffalo Use (Rs.) 0.83 Operating Cost (Rs.) 1.26
175.34 1005.74 555.11 642.97 1.50 1.60 2.23 0.58 0.05 0.12 0.19 1.03 2.78 3.68 2.33 2.60
Ratios of Marginal Value Products to Factor Costs Land (Acres) 1.58 0.22 1.26 0.69 0.80 Labour (Man-days) 0.13 0.10 0.12 0.13 0.04 Buffalo (Rs.) 0.69 0.13 0.10 0.16 0.86 Operating Cost (Rs.) 1.05 2.32 3.07 1.94 1.33
148 H. M. G. Herath
TABLE 4 Marginal Value Products and the Ratios of Marginal Value Products to Factor Costs:
Anuradhapura Sample
Yala, 1980 Maha, 1980181 <I.OAc. 1-3 AC. >3Ac. <I.OAc. 1-3 AC. >3Ac.
Marginal Value Products (Rs.) Land (Acres) 2 280.44 1440.65 5 280.32 4979.75 4 169.99 4 166.37 Labour (Man-days) 21.19 13.80 2.66 8.75 0.47 6.36 Buffaloes (Rs.) 0.22 0.55 0.23 0.19 0.06 0.13 Machinery (Rs.) 0.10 0.41 0.18 0.08 0.20 0.03 Operating cost (Rs.) 3.75 0.87 69.22 0.91 0.87 1.07
Ratio of Marginal Value Products to Factor Cost Land (Acres) 1.43 0.90 3.30 3.11 2.60 2.60 Labour (Man-days) 0.84 0.55 0.11 0.35 0.02 0.25 Buffaloes (Rs.) 0.18 0.46 0.19 0.16 0.04 0.11 Machinery (Rs.) 0.08 0.34 0.15 0.07 0.16 0.02 Operating cost (Rs.) 3.12 0.72 57.68 0.76 0.72 0.89
labour bottlenecks are generally experienced. However, the adoption of mechanisation may have broken the labour bottleneck and may have made some labour redundant. It is necessary to caution, however, that the use of the daily wage rate for both hired and family labour may perhaps overstate the opportunity cost of family labour and the indicated inefficiencies may be illusory.
An over-utilisation of buffaloes in both samples and of machinery in the Anuradhapura sample is evident in all three size groups. The indivisibility of some of these inputs such as tractors may account for the over-utilisation of such inputs.
The operating cost variable which embodies expenditure on cash inputs such as fertiliser and herbicides did indicate some interesting features. In the Anuradhapura sample these inputs appear to be used efficiently in the Maha season. An under-application of these inputs is evident in the Yala season. The under-utilisation of this variable in Yala could be due to shortages of water. This may also have been influenced by other factors, such as credit, but appears to be more related to the risk of applying fertiliser and other inputs in the face of uncertainty about water availability.‘j The operating cost variable is also less optimal in the Galaha sample. This may be caused more by capital bottlenecks since, in this area, institutional sources of borrowing are less well organised.
Production efjciency and farm size in rice production 149
RETURNS TO SCALE RELATIVE TO FARM SIZE
The finding that physical returns to scale are constant both prior to and after the Green Revolution does not imply that constant returns to scale will hold relative to any one factor, e.g. land, labour or capital. By and large, technological change in Sri Lanka has been oriented towards modern inputs raising productivity per acre. Since there is evidence of differential adoption and level of application of new technology among different farm size groups, it can be postulated that the size-productivity relationship may have changed to a positive one in recent years.
In this study the size-productivity relationship was investigated by fitting the function Y = aXb where Y is output per farm and Xis farm size. The statistical results of this estimation are given in Table 5. In all cases examined the coefficient for land was not significantly different from unity. Thus, both for the Green Revolution and the pre-Green Revolution period the scale parameter for land did not change. Thus it is concluded that in general there are no decreasing or increasing economies of scale relative to farm size.
TABLE 5 The Relationship Between Output Per Farm and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Anuradhapura 1980/81 Maha 118 4.2102 0.966 5 0.885 8956 (0.021 1)
Anuradhapura 1980 Yala 61 4.2418 0.979 4 0.855 383.52 (0.029 4)
Galaha 1980/81 Maha 113 3.421 3 0.9130 0.875 783.82 (0.022 5)
Galaha 1980 Yala 101 3.260 8 0.905 3 0.818 447.75 (0.028)
Notes: Figures within parentheses indicate standard error.
FARM SIZE AND PRODUCTIVITY RELATIONSHIPS
The studies in the pre-Green Revolution period indicated that higher output per unit of land on small farms was a function of higher application of inputs of labour and non-labour per unit of land.7 The
150 H. M. G. Herath
TABLE 6 Relationship Between Labour Cost and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Anuradhapura 1980 Yala 66 5402 0.154 2 0.06 442 (0.235 7)
Anuradhapura 1980/81 Maha 118 6.946 2 (0.777 6* 0.69 258.38 (0.032)
Galaha 1980 Yala 101 6.941 0.432 8* 0.57 135.84 (0.025 3)
Galaha 1980/81 Maha 113 7.076 0.5819* 0.49 107.33 (0.038)
* Significantly different from unity at 5 y0 level.
relationship between input use, size and productivity in rice production in Sri Lanka was tested by fitting three equations where farm size is the independent variable in all three models. In model 1, labour cost (family, hired and exchange) was used as a dependent variable. In model 2, all non-labour input cost was used as a dependent variable. In model 3, total cost (labour and non-labour imputed and actually incurred) was used as a dependent variable.
The results of this analysis are presented in Tables 6,7 and 8. The data in Table 6 indicate that all coefficients are significantly different and lower than unity except for the Yala, 1980 for the Anuradhapura sample. All in
TABLE 7 Relationship Between Non-labour Cost and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Anuradhapura 1980 Yala 66 4.944 7 0.999 9 0.098 I 7.1247 (0.246)
Anuradhapura 1980/81 Maha 118 6.676 1 1.044 7* 0.9014 1060.94 (0.02 1 2)
Galaha 1980 Yala 101 5.6819 0.590 8* 0.398 6 65.62 (0.062 4)
Galaha 1980/81 Maha 113 5.838 5 0.925 6* 0.676 7 232.42 (0.041)
* Significantly different from unity at the 5 y0 level.
Production efjciency andfarm size in rice production 151
TABLE 8 Relationship Between Total Cost and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Anuradhapura 1980 Yala 66 5.646 7 1.064 6 0.094 2 6.662 (0.251)
Anuradhapura 1980/8 1 Maha 118 7.467 6 0.893 7* 0.475 I 105.252 (0.058)
Galaha 1980 Yala 101 1,216 6 0.560 9* 0.7515 299.453 (0.021 5)
Galaha 1980/8 1 Maha 113 7.3174 0.610 I* 0.766 7 364.849
* Significantly different from unity at the 5 y0 level.
all, this therefore indicates that there is an inverse relationship between labour inputs and farm size. The total labour inputs per acre decline as the farm size increases. Table 7 presents the results for the relationships between non-labour inputs and farm size. The results indicate that the coefficients are significantly different from unity in three cases. In two of these cases, the coefficients are less than unity and in one case it is greater than unity. The results in general support the inverse relationship between non-labour inputs and farm size. Table 8 presents the data with respect to total costs and farm size and here, again, an inverse relationship is observed.
PROFIT PER ACRE AND FARM SIZE
It is contended that there is a direct relationship between profit per acre and farm size. This was tested with the data in the present study by fitting a function of the form Y = aXb where Y is profit and Xis the farm size. In the Galaha sample it was noted that most farmers obtained negative profits. Thus, in fitting the function, the sample was divided into those who sustained losses and those who obtained profits and separate functions were fitted for the two groups. The negative profits were treated as losses (which are positive to avoid logs of negative numbers) and regressed against farm size. The results are presented in Tables 9 and 10. The results in Table 9 indicate that, as far as profits are concerned, in the Galaha sample in two cases it was significantly different from unity and
152 H. M. G. Herath
TABLE 9 Relationship Between Profit and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Anuradhapura 1980 Yala 65 1.1512 0.7284* 0.47 56.512 (0.063)
Anuradhapura 1980/81 Maha 118 8.3449 -0.0003 0.000 1 0.149 (0.142)
Galaha 1980 Yala 9 3.6682 3.5265* 0.422 5.115 (0.368)
Galaha 1980/81 Maha 19 5.0303 1.645 3* 0.271 6.35 (0.294)
* Significantly different from unity at the 5 o/0 level.
TABLE 10 Relationship Between Loss and Farm Size
Sample Year and Sample Parameters season size a b R2 F ratio
Galaha 1980/81 Maha 92 5.585 08 -0.104 324* OX)04 2 0.386 5 (0.110 1)
Galaha 1980 Yala 94 5.236 72 - 0.059 08* 0.0015 0.138997 (0.107 11)
* Significantly different from unity at the 5 y0 level.
greater than 1 and in the Anuradhapura sample it is significantly different from unity and lower than 1. Table 10 indicates that in the case of losses the coefficient is significantly different from unity and less than 1. Although these results are mixed, there is strong evidence about a positive relationship between profit and farm size both prior to and after the Green Revolution.
SUMMARY AND CONCLUSIONS
The analysis conducted herein using the Cobb-Douglas framework provided evidence that the productivity of the land resource has increased after the Green Revolution, confirming that the new rice varieties belong
Production efficiency and farm size in rice production 153
to a land-saving type of technology. There is also evidence that the returns to scale characteristics have not significantly altered after the Green Revolution and in general constant returns to scale prevail. The exploration made on allocative efficiency indicates an under-utilisation of land in both samples. This has been even more crucial after the Green Revolution. In respect to operating cost, under-utilisation was observed in the Galaha sample. The study of size-productivity relationships indicated that there are no economies of scale. It was noted that there was an inverse relationship between farm size, labour use, non-labour inputs and total costs. A direct relationship between profitability and farm size was observed. One could surmise that the institutional factors, such as lack of capital, or irrigation facilities, prevent farmers benefitting from new technology. In general the study indicates that institutions are biased against small farmers. Undesirable impacts, such as exacerbation of income differentials, could be obviated by creating a more propitious institutional set up. Within the limits of this study technology by itself cannot be considered to have contributed significantly to disparities in incomes in rural communities.
REFERENCES
1. Dalrymple, D. G., Development and spread of high yielding varieties of wheat and rice in less developed nations, US Department of Agriculture, Foreign Agricultural Economics Report No. 95, Washington, DC, 1978.
2. Huang, Y., Allocative efficiency in a developing agricultural economy in Malaya, American Journal of Agricultural Economics, 53 (August, 1971) pp. 514-16.
3. Srivastava, U. K., Nagadevara, V. and Heady, E. O., Resource productivity, returns to scale and farm size in Indian agriculture: Some recent evidence, Australian Journal of Agricultural Economics, 17 (April, 1973), pp. 43-55.
4. Sen, A. K., Peasants and dualism with or without surplus labour, Journal of Political Economy, 74 (1966), pp. 425-50.
5. Mazumdar, D., Size of farm and productivity, a problem of Indian agriculture, Economica, 32 (1965), pp. 163-73.
6. Khusro, A. M., Returns to scale in Indian agriculture, Indian Journal of Agricultural Economics, 19 (1964), pp. 51-80.
7. Ahmed, I., Technological change and agrarian structure: A study of Bangladesh, Geneva, International Labour Office, 1980.
8. Wharton, N. C. R., Risk, uncertainty and the subsistence farmer, Sri Lanka Journal of Agrarian Studies, 1 (June, 1980), pp. 12-19.
