CHAPTER-9
FACTORS BEHIND THE AGRICULTURAL PRODUCTIVITY AND CROPPING INTENSITY OF
ALL CROPS IN THE EXISTING AGRARIAN STRUCTURE
In the previous chapter, a broad picture has been presented about the
characteristic features of the existing agrarian structure of the sample area covered by
the field survey. The presentations in that chapter have already thrown up some
interesting findings as summarised in its conclusion. The preliminary findings of the
field survey gave an overall picture of the economic and non-economic factors
affecting agricultural production or productivity. The tools of analysis used were simple
and non-rigorous and no attempt was made to assess the importance of the various
factors affecting agricultural productivity and cropping intensity in the existing agrarian
structure of the sample farmers covered by the field survey. The limitation of such a
simplified analysis is obvious as it looked at the individual factors, assuming the
influence of other factors to be constant. Such a simplified analysis was unable to
provide an answer to what factors may have influenced agricultural productivity and
cropping intensity in the existing agrarian structure of Assam. In this chapter, we go in
for a more rigorous analysis of the data and try to test economically the influence of
economic and non-economic or agrarian and technological factors on agricultural
productivity and cropping intensity in the existing agrarian structure of the sample
area. The analysis is primarily aimed at identifying the factors influencing yield of rice
and cropping intensity of all crops per unit area produced by the farmers of the sample
area. The exercise is expected to provide insights, which could be useful for a two-fold
purpose. First it may give explanation for the variation in the extent of yields of rice
per hectare across the farmers. Such explanation in turn is likely to provide policy
suggestion for removing the constraints on the farmers to use improved agricultural
practices more effectively .Obviously ,such policy measures will also serve the cause of
expediting agricultural development of the region.
Since the present study tries to identify the factors affecting significantly the
agricultural production and cropping intensity of all crops existing within the agrarian
structure. Besides this objective, the study proposes to pursue the research question
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that whether the existing agrarian structure in Assam is conducive to achieving higher
productivity in terms of crop productivity per unit area. For obtaining an answer, a
multiple regression model will be used where the productivity i.e. Yield of rice per
hectare (the principal crop of the sample area as well as of the State as a whole), and
the cropping intensity for all crops of the sample farmers will be regressed on several
explanatory variables which are believed to have positive impact on the productivity of
rice and cropping intensity for all crops.
9.1 Variables
Drawing from literature, a number of factors or variables have been identified
as affecting agricultural production and cropping intensity of all crops which are the
dependent variables in the regression model to be used. All the variables and their
respective measures are described below.
Agrarian structure is undoubtedly one of the most important determinants of
farm efficiency. In recent times, two diagonally opposite arguments are put forward
regarding its role in agricultural development. One school of thoughts is of the opinion
that tenancy rights and land distribution would lead to higher land productivity and
employment. Underlying this hypothesis is the ‘stylised fact’ of inverse relationship
between farm size and productivity (Berry and Cline, 1979). Conversely, it is observed
that the expansion of large capitalised farms would result in efficient production
conditions. This argument has its roots in the belief that the advent of green revolution
has weakened the existing size-productivity relationship in favour of large farmers who
are modern and dynamic while the small and marginal farmers are left behind as
backward and inefficient (Bhalla, 1979).
In the light of the findings of the studies and on the basis of preliminary
examination of the data collected in the field survey, the followings have been listed as
important factors, likely to have important bearing on a farmer’s production of crops
as well as cropping intensity across farms.
9.1.1 Adoption and Use of High Yielding Varieties of Seeds
The adoption and use of high yielding varieties of rice by the farmers is
important factor affecting agricultural productivity. The special attention towards rice
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is simply because of the tremendous importance of the crop in the economy and
agriculture of the State. Not only is it the principal food grain for the people of Assam,
but it also comprises even more than 70 percent of the total cropped area of the State.
(In the four circles of our field study, area under rice comprises more than 80 percent
of the total cropped area). The performance of the entire agriculture sector and the
general living conditions of the rural population of the State depends greatly upon the
success of the rice crop. Hence, if anything like a ‘green revolution’ is to come about in
Assam, it has to come through the improvement in rice farming practices in the State
(Bezbaruah, 1994).
The Directorate of Agriculture, Government of Assam, in its estimates of area
under HYV of rice in the State, includes not only the area under the semi-dwarf
modern rice varieties but also those under the varieties of Mohsuri and Manohar Sali.
The latter is in fact a traditional local variety grown during the main kharif season in
Assam, but its yield is higher than other traditional varieties and comparable to that of
some of the modern rice varieties.
In the present study the rice varieties in use in the State are being classified
into only two categories, namely, high yielding varieties and traditional local varieties.
Mahsuri has been included in the category of high yielding varieties along with the
modern semi-dwarf varieties of rice. The reason for including Mahsuri in the same
category with the semi-dwarf modern rice varieties is that it shares a number of
special traits of the latter in spite of its plant being taller than those of the other HYVs
of rice. The variety is capable of giving reasonably high yields of output and is fairly
insensitive to photoperiod. Although it takes somewhat longer time to mature than
most of the semi-dwarf modern varieties, its duration is shorter than those of the
traditional local varieties in general. To the farming community in Assam it is in fact a
new and exotic variety. Monohar Sali, however, has been included in the category of
traditional local varieties to which it actually belong. Farmers being able to use HYVs
during ‘Sali’ season even without any system of irrigation in their farm are in fact not
quite surprising. The monsoon rains are fairly regular and abundant in Assam from July
to September and this ensures adequate supply of water for farmers in the ‘Sali’
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season to cultivate HYVs even without irrigation. In fact, it is excess of water, rather
than the lack of it, which often creates problem in the plain district of the State during
the wet summer months.
Among all new rice varieties Mahsuri has gained overwhelming popularity
among farmers in the ‘Sali’ season. The immense popularity of this variety can be
ascribed to a number of factors e.g. its plants being taller than most new rice varieties.
Due to heavy monsoon rains, it is not uncommon in Assam for paddy fields to be
flooded during the ‘Sali’ season. In our field study a few instances were found to be
cultivating Mahsuri during ‘Sali’ season using very little or no chemical fertilisers at all.
In such cases the variety gave yields which were not very different from those of the
local traditional varieties. Nevertheless, farmers found it convenient to divide their
‘Sali’ acreage between ‘Mahsuri’ and ‘Traditional Local Varieties’ instead of planting
the whole acreage with the latter only. One main factor behind this practice is that the
Mahsuri rice becomes ready for harvest about a month ahead of the usual local
varieties. To the small farmers, cultivation of Mahsuri, therefore, means an early relief
from their usual tight economic situation in the latter half of the ‘Sali’ season.
Moreover division of ‘Sali’ acreage between Mahsuri and the traditional local varieties
also enables farmers to space the hectic operation of harvesting the ‘Sali’ crop over a
longer period of time, which in turn creates considerable managerial convenience for
medium and large farms. Comparatively, early harvesting time of Mahsuri also releases
land in time for various ‘rabi’ crops (Bezbaruah, 1994).
Among the semi-dwarf varieties only Pankaj seems to have gained wide
acceptance among farmers during ‘Sali’ season. The variety was found to be in use
among farmers in all the four area of our field study. Ironically, it is its comparatively
longer duration of maturity which lies at the root of its popularity in the ‘Sali’ season
over the other semi-dwarf variables. The shorter duration semi-dwarf varieties
become ready for harvest much too early even before the end of the rainy season
causing thereby practical problems for harvesting and processing. Such problems do
not arise in case of Pankaj , which has a duration of 145 to 150 days.
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Unlike in the ‘Sali’ season, the use of HYVs in the ‘Ahu’ season is largely
conditioned by availability of irrigation. The ‘Ahu’ season, particularly its early part,
being comparatively dry, use of HYVs in this season becomes difficult unless adequate
watering of the paddy fields is assured through irrigation. The varieties of ‘Mahsuri’
and ‘Pankaj’ which were found to be the most popular of all HYVs in the ‘Sali’ season
were very little in use during the ‘Ahu’ season. There were only three instances of
Mahsuri being used as an ‘Ahu’ rice while there was none for Pankaj. Instead, the short
duration varieties like Pusa, Joya and Cauvery were found to be more popular during
this season. Preference for the short duration varieties in this season is mainly due to
the fact that they become ready for harvest before the onset of the heavy monsoon
rains.
9.1.2 Proportion of HYV rice acreage
The adoption of high yielding paddy variety is almost universal among the
sample farmers. Yet, it has been observed that the sample farmers taken together
have not been able to extend the acreage under HYVs beyond 28.79 percent of the
total area under paddy. Among the different sample farmers, of course, there has
been extreme variation in the percentage of HYV area in the total rice acreage. It is
therefore important to examine the relation between yield of total rice per hectare
and HYV area under total rice acreage. It is expected that HYV proportion in total rice
acreage will have some positive impact on yield of total rice. At best a farmer can be
expected to use HYVs to the extent that his land is suitable for their cultivation. For
example, the HYVs developed so far, cannot be extended to those areas of the farm
which are very low-lying and therefore, prone to flooding or severe water–logging for
the better part of the wet summer season in the State. However, HYVs are capable of
turning large amounts of soil nutrients into grains rather than leaf growth. Secondly,
they are quicker maturing in nature i.e. they take shorter duration to mature than the
traditional local varieties. Thirdly, they are usually photo-period insensitive i.e. their
maturity duration is independent of the length of exposure of daylight. Because of the
first attribute these varieties are able to give higher yields than the traditional
varieties’. So, to get better results out of the HYVs, the soil is to be supplied with
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additional nutrients through application of chemical fertiliser. Thus, it is expected that
the proportion of HYVs area to gross crop area will have a positive impact on the yield
of rice per hectare.
9.1.3 Irrigation and the Proportion of Irrigated Area under the Crop
Water is such a crucial input for plant growth that its importance hardly needs
to be stressed. In India, average annual rainfall is about 1,200 mm but it is seasonal
and highly uneven in its geographical distribution over the country. Nearly 75 percent
of the rainfall in many regions of India is contributed by the southwest monsoon and it
is therefore confined to four months of the rainy season i.e. from June to September.
Apart from its vital importance for healthy crop growth, irrigation has assumed crucial
significance in view of the country’s expanding needs of food production. The task of
providing food to this growing population is stupendous. Regarding the impact of
irrigation on farmers field in general, a comparison of data based on crop cutting
experiment by the National Sample Survey Organisation for the years 1970-71 and
1971-72 showed that compared to unirrigated crops, yields of irrigated crops were
higher by about 80 to 95 percent in the case of paddy and by 10% percent to 11% in
the case of wheat, where as irrigated cotton showed a higher order of increase in
yield, i.e. about three times compared to unirrigated cotton(Government of
India,1976).
Availability of irrigation water not only enhances productivity per hectare by
itself but also promotes adoption of new technology embodied in the use of high
yielding varieties (HYVs), fertilisers and plant protection measures coupled with
improved water management. Thus, irrigation together with a new technology
package raises substantially the productive capacity of land. In fact , it has been argued
by Swaminathan(1981) that introduction of HYVs of wheat and rice in areas covered
with assured irrigation has helped in saving of almost 34.5 million hectares of land by
1979 which would otherwise have been additionally needs to produce the same
quantum of production of wheat and rice. Apart from favourable impact of irrigation
on yield levels of different crops, it is generally expected that introduction of irrigation
may lead to increased cropping intensity. Such an enhancement in multiple cropping
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due to the availability of irrigation is possible when the existing long duration varieties
of the main crop can be replaced by short duration varieties which are adaptive to
local conditions, leaving an increased scope for raising of the second crop and
irrigation water is more economically used both for the main crop and for the
succeeding crop. However, empirical evidence at the aggregate level does not show a
substantial difference between the irrigated and unirrigated lands in terms of the
cropping intensity index. At the all-India level, the average intensity of cropping was
found to be 123 percent for the irrigated lands as against 115 percent on unirrigated
lands in 1970-71. But what was remarkable about the pattern of cropping intensity on
irrigated lands was its stability over the different size-group of holdings. It was 121
percent for large holdings and only slightly higher, i.e. 123 percent for marginal, small
and medium holdings. On the contrary, cropping intensity on unirrigated lands varied
from 108 percent on large holdings to 134 percent on marginal holdings (Government
of India, 1976). This implies that once irrigation water is available, even the large
holdings do not lag much behind the small and medium ones in intensive cultivation of
land. In other words, accelerated development of irrigation may substantially boost
the prospects for raising agricultural production provided it is accompanied by
appropriate technological development and more efficient water management. The
use of HYVs of crops has been extended under well controlled irrigated conditions.
Assured water supply is a pre-requisite for intensive agriculture based on HYVs of
seeds and high levels of fertilisation. About 73 percent of India’s cropped area still
depends on rain fall which is concentrated in a few months of the year. Almost 70
percent of the crop area has undependable and inadequate rainfall during the main
crop season.
Since the HYVs are known to perform better in conditions of controlled
watering, availability of irrigation creates a strong favourable condition for the use of
these varieties. Traditionally irrigation has been the key factor in the use of the HYV
seed-fertiliser package by the farmers. Apart from providing favourable conditions for
exploiting the HYV seed-fertiliser technology, assured irrigation also facilitates multiple
cropping by enabling the farmers to raise crops even in dry season.
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To capture the effect of this factor, the total irrigated area under the crop (e.g.
Rice) and its proportion to the total cropped area has been defined as IRRIG and has
been taken as the explanatory variable where the irrigated area refers to the area
under the facilities owned by the government, but farmers have control over its
management and operation and the irrigation facilities owned and operated by
farmers. A priori, the co-efficient of irrigation is expected to have positive impact on
productivity and cropping intensity of the crop concerned.
9.1.4 Fertiliser consumption per unit of total cropped area
Since chemical fertiliser contains nutrients (N+P+K) in concentrated form, they
in turn have to be applied with adequate supply of water to enable plants to absorb
the nutrients without causing damage to them. The use of chemical fertilisers with HYV
seeds hence necessitates controlled supply of water. In raising crop-yields per unit of
land, and in making it possible to raise cropping intensity of land, HYV seed-fertiliserwater package has the same effect on agricultural production as that of an increase in
the total land resources (Bezbaruah, 1994). Use of chemical fertiliser (N+P+K) has also
been adopted by all except a few of sample farmers. There is, however, great variation
in the dose of fertiliser application. The consumption of nutrients (N+P+K) per hectare
has been recorded to have varied from 22.39 Kg to 174.13 Kg per hectare in case of
rice and from 22.33 Kg to 187.33 Kg per hectare in case of all crops.
In the case of mounting pressure on land for non-agricultural purposes like
construction of houses, factories, roads etc., major reliance has to be placed on
increasing the yield per hectare of cropped land and on increasing the cropping
intensity of land used for cultivation. The net sown area is inelastic and is expected to
increase only marginally from 140 million hectares in 1971 to 150 million hectares in
the year 2000(an increase of only seven percent over a period of three decades)
(Government of India, 1976).
The phenomenal increase in production and productivity in agriculture since
the mid–sixties has been achieved through exploitation of the potential of the HYVs
with the help of increased use of fertilisers. Fertiliser along with better seeds and
irrigation hold the key to the expected achievements. Thus, although the importance
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of increase in the use of fertilisers was known from the beginning of planning, the
major breakthrough in the consumption of fertilisers came with the introduction of
new farm technology, which underlined the need for increased availability and use of
non-conventional scientific inputs (Bezbaruah, 1994). Thus it is expected that fertiliser
consumption per hectare will have positive impact on the yield of a crop per hectare.
9.1.5 Farm size
From the review of literature it may be recalled that during the first few years
under the new agricultural strategy, the large farmers adopted the new farm
technology to a greater extent than the tenants and the small farmers (Bezbaruah,
1994). However, studies referring to subsequent periods found small farmers quickly
catching up with large ones in adoption of HYV seeds. Farm size has often been found
to be an important factor influencing effective use of productivity raising farm
practices with irrigation. However, the direction of the impact has been found to be
varying from situation to situation (Bezbaruah, 1994).
From the earlier discussion about the distribution of sample farms according to
the size group of operational holdings, it is observed that only 0.45 percent of sample
farms are large holders and 8.64 percent of sample farms are marginal holders. The
highest percentage of sample farmers falling in the size group of semi-medium i.e. 40
percent out of 220 farm households occupy 41.61 percent of total size of the
operational holding. In the literature of Indian agricultural economics, the farm-size
productivity relationship debate is one of the most important debates. When the
debate concluded in the mid-seventies, there was a near consensus among scholars
that small farms are more productive as compared to their large counterpart (Sharma
and Sharma, 2000). At the policy level, issues related to land rights of the poor are
gathering momentum in many agrarian economics. This is rationalized by arguing that
small farms are more productive than large farms. However, it is confirmed that farm
size is an important determinant of productivity of crops which is measured in terms of
output per unit of land. Conversely, Farm size may be determined by many
independent factors significantly. Here it is expected that crop productivity i.e. yield
per unit of land is higher for smaller farms as smaller size farms can be expected to use
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their limited land resources more intensively. Here the farm size is measured by the
size of the operational holdings of the farmer in hectare.
9.1.6 Tenancy
The land ownership pattern is altered by tenancy arrangements i.e. leasing-in
and leasing–out. The result is a slightly different size distribution of operational
holdings. Temporary transfer of land takes place through the institution of tenancy
which is one of the most important devices to facilitate the adjustment of resources in
factor markets. The endowment of resources greatly influences the decisions of
cultivators to enter the land-lease market .The cultivators who are surplus or deficit in
family and bullock labour in relation to land owned may adjust resources through
leasing–in or leasing–out the land. The existence of tenancy is explained in terms of
risk and uncertainty, cropping pattern, managerial skills and factor market
imperfections (Birthal and Singh, 1991). The ability of a household to cultivate land
when the factor markets are imperfect or do not exist at all, is partly determined by its
resources endowments. Bliss and Stern’s model specifies that family labour and
bullock capacity available at the start of the season determine the area the household
wish to cultivate (Bliss and Stern, 1982).
Leasing of land is becoming uncommon with the increase in its productivity,
population and tenancy legislations. However, the system is still in practice in certain
places for the various reasons. To some extent, this system of earning can never be
eliminated as land belonging to the disabled persons, widows, service people etc. have
to be rented out. Similarly, with the increasing use of mechanical power in farming,
renting–in land is found to be economically viable on account of the indivisibilities of
certain mechanical resources like tube well and tractor power etc. Here the tenancy
factor is sought to be captured by the variable that refers to the proportion of land
leased-in to the operational holding of the farmer. A priori, the tenancy factor can be
expected to have a negative impact on the productivity of a crop.
9.1.7 The Size of the household
From the point of view of agricultural labour force, household size may be an
important determinant of agricultural operation as well as the size of operational
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holdings. The size of household may also be the cause of fragmentation and number of
agricultural holdings in an agrarian structure. So, it is expected that, size of household
will have a positive and significant relationship with the agricultural production and
productivity.
9.1.8 Mechanisation
Farm mechanisation can give the farmers greater leisure apart from making
work more agreeable. It may even raise the participation rate among those who could
afford to abstain from the drudgery of manual work. Besides this economic
compulsion, the presence of mechanical sources of energy particularly tractors adds to
the prestige of farmers (Singh and Miglani, 1976).
Though farm mechanisation is not an essential component of HYV seedfertiliser-technology package, experience in parts of India shows that farm
mechanisation has followed the adoption of seed-fertiliser technology by farmers
(Sidhu and Singh, 1986).The extent of adoption of mechanised ploughing method and
use of some other machineries and implements by sample farmers is an important
factor in an agrarian structure and agricultural operations. Adoption of mechanised
ploughing, use of pump sets, sprayer, weeder etc. are important for the agricultural
operations in the four circles of the field survey. Thus mechanisation is expected to
have positive impact on productivity as well as cropping intensity of a crop. To capture
the impact of the variable, one dummy variable may be used to the effect.
9.1.9 Access to agricultural extension service
The crucial role of extension service in diffusing new innovations among the
farmers, particularly in relatively backward conditions, has also been emphasized in
several studies (Chauhan, 1980).The importance of extension in bringing growth in
agriculture is widely accepted in all parts of the world. In India, as observed by Sharma
(2005), extension activities played the major role in bringing Green Revolution into the
country.
In the questionnaire used in the field study, a set of questions were used
relating to the extent of farmers’ contact with the extension agencies. The farmers’
response to these queries has been codified and scores were awarded. On the basis of
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the score, the level of contact with extension agencies has been categorised into two
categories i.e. ‘benefitted farmer with good contact’ (with the scores from 5 to 8) and
‘non-benefitted farmer with poor contact’ (with the scores from 0 to 4). Farmers with
good contact with the personnel of the agricultural extension service network are
likely to be benefitted by greater amount of technical guidance regarding the new farm
practices, uses of modern inputs and HYVs etc. This would enable them to use HYVs to
a greater extent than they would otherwise do. To capture the effect of the variable on
productivity as well as on the cropping intensity, dummy variable may be used.
9.1.10 Education
These days, the uses of HYV seeds are not merely a question of replacing some
old seeds by new ones but a process of adoption of a host of new practices. Similarly,
the uses of the nutrients i.e. Nitrogen (N), Phosphorous (P) and Murat of Potash (K) in
right proportion of quantity and uses of irrigation and water control etc. is a matter of
concern for the agricultural practices such as HYV seed-fertiliser-water package. It is
believed in some quarters that a farmer with a fair amount of schooling would be in a
more advantageous position to use them than a less educated farmer. Thus, whether
the yield or productivity of crop tends to increase with the educational or literacy
standard of the farm household is, therefore, a question worth exploring.
9.1.11 Location
Since the field study was carried out in four districts located in four agroclimatic zones of the State, the difference in location of a sample farm can have some
influence on the level of use of the different practices. Since there are four different
locations, so three location dummy variables, namely L1, L2 and L3 have been taken to
capture the effect of the locational difference. The dummy variable L1 takes the value 1
for the sample farms in Bongaigaon district and 0 for sample farms in the other three
districts. Again the dummy variable L2 takes the value 1 for sample farms in Morigaon
district and 0 for others. Similarly, L3 takes the value 1 for sample farm in Jorhat district
and 0 for others.
Thus the co-efficient of L1 captures the differential effect on the dependent
variable for farms in Bongaigaon district over Sonitpur for which both L2 and L3 are
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zero. Similarly, the coefficient of L2 captures the differential effect on dependent
variable (i.e. yield of paddy production or the cropping intensity of the all crops) for
farms in Morigaon district over the farms in Sonitpur district and co-efficient L3
captures the differential effect on dependent variable for the farms in Jorhat district
over the farms in Sonitpur district.
9.2 Factors behind the variation in the yields of rice per hectare of total rice acreage
The crop productivity can be an important determinant of existing land holding
pattern in an agrarian structure. As discussed above, yield of rice in irrigated farms is
said to be high. There is however variation in yields of the total rice crop. The yield of
rice per hectare has been recorded to have varied from 1359.33 Kg to 3864.16 Kg per
hectare. This variation in the yields of rice across farms needs to be explained. To
identify and examine the effect of different explanatory variables on the yield of rice
per hectare of total rice acreage, a multiple regression analysis for the variation of
yields of rice per hectare in sample farms denoted by the variable YR has been carried
out. For this analysis, the following explanatory variables are sorted out from the
review of studies reported in the previous section.
9.2.1 Explanatory variables
i) Farm size i.e. the size of the operational holding of the farm household in hectare
measured by the variable ‘FS’.
ii) House size i.e. the size of the farm house hold measured by ‘HS’.
iii) Tenancy i.e. the percentage of leased-in area in the total operational holding
measured by the variable ‘T ’
iv) Irrigation i.e. the proportion of total irrigated area under rice to total rice acreage
measured by the variable ‘IRRIGR ’
v) HYV i.e. the proportion of HYV rice area in total rice acreage measured by the
variable ‘HYVR ’.
vi) Access to the agriculture extension service network and having close contact with
the personnel of agricultural extension service has been measured on the basis of the
scores as shown in the household schedule (Appendix-I). To capture this effect a
dummy variable D1 has been used as follows
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D1=1, if the ith farmer is a ‘good’ category farmer (i.e. benefitted farmer with
good contact with extension service) with a scoring from 5 to 8.
=0, if the ith farmer is a ‘poor’ category farmer (i.e. non-benefitted farmer
with poor contact with extension service) with a scoring from 0 to 4.
vii) Mechanisation i.e. adoption of mechanisation in agricultural practices measured by
a dummy variable D2 as follows where
D2=1, if the farmer adopts mechanisation in agricultural practices.
=0, otherwise,
viii) Fertiliser consumption i.e. uses of NPK per hectare by the farmer in total rice
acreage and is measured by the variable ‘FC’
ix) Educational standard of the farm family which is represented by the variable ‘EDN’
has been used to capture the effect of literacy or educational standard of the farm
family on the yield of rice. Here, an index of educational standard of the farm family
has been prepared and is based on the scores awarded to the family members on the
basis of their level of formal education. The scores awarded ranged from 0 for
complete illiteracy to 6 for graduation and above. The arithmetic mean of the scores of
the members above the age of 15 years has been taken as the value of index for the
farm family and is expected that it will have a positive impact on the yield of rice.
x) Location factor represented by the dummies L1, L2 and L3 which has been used to
capture the effect of variations in the locations across the four circles. The dummy Li
(i= 1, 2, 3) takes the value 1 for the circle number i and 0 for the other circles.
Thus a semi- logarithmic multiple linear regression model of the following form
has been formulated to see whether the yield of total rice per hectare varies
significantly across different farms and to identify the factors significantly affecting the
yield of rice across the farm households. The model isLnYR=α0+ α1L1i+α2L2i +α3L3i+α4FSi+α5HSi+α6Ti+α7IRRIGRi+α8HYVRi+α9D1i
+α10D2i+α11FCi+α12EDNi+Ui
………………… [9.1]
where Ui is the random disturbance term (Ui=1,2,3,……..,220) and αj’s are the
unknown parameters to be estimated where j=(0,1,2,….12).Now for estimating the
parameters with the Ordinary Least Square Methods(OLS), STATA package with
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White’s robust standard error (VCE) technique has been used and heteroscedasticity
of the disturbance term has been cleared up by the technique (Gujarathi and
Sangeetha, 2007). Thus, the result of the regression model given in the equation
number [9.1] above has been found as presented belowTable-9.1: The Results of the Multiple Regression Analysis of Yield of Rice across Farms
Variables
(1)
Constant
FS
HS
T
IRRIGR
HYVR
D1
D2
FC
EDN
L1
L2
L3
R2
F(n1 ,n2 )
Estimated
co-efficients
(2)
7.603633
0.0023364
0.0014595
0.0002972
0.1770705
0.1065375
-0.0171436
0.2119273
0.002668
0.0525114
-0.0265166
-0.0837784
-0.0727152
0.7292
31.14***
F( n1=12,n2=207)
Standard Error of
co-efficients
(3)
0.0636783
0.0058189
0.0033945
0.0003819
0.032779
0.0549703
0.0210715
0.0270259
0.0004383
0.0189637
0.0314841
0.0313813
0.033409
t-value
(4)
119.41***
0.40
0.43
0.78
5.40***
1.94*
-0.81
7.84***
6.09***
2.77**
-0.84
-2.67**
-2.18*
(*, **, *** indicates level of significance at 10%, 5% and 1% respectively.)
These results consists of the estimates of the regression co-efficient αj (j=0,
1,…..,12) with respective t–values, the values of R2 – the coefficient of determination
for the fitted equation and the values of the F- statistic for testing the overall
significance of the estimated regression equation.
The model gives a good fit to the sample data in terms of R2 value of 0.7292.
The F–statistic for over all significance of the fitted regression is statistically highly
significant. Moreover, seven out of the twelve parameters of the function have come
out statistically significant.
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1) α4, the co-efficient of the FS, the variable representing the size of the operational
holding of farmer ,has been found to be positive and but not significant. Thus, farm
size had little to do with the variation of productivity i.e. yield of rice per hectare
across farms of the four circles. Thus, it can be inferred that farmers with larger farm
size tended to have higher rate of yield of rice per hectare. This finding is in conformity
with the results obtained by Singh and Kalra (2002) in a study related to production of
rice in Punjab. They found that yield of rice had positive association with farm size, the
large farms having yields higher than the smaller ones. Thus the present finding is not
in conformity with the inverse size-productivity relationship.
2) α5, the co-efficient of the HS, the variable representing the size of the household has
been found positive and insignificant. Thus, it can be inferred that house size has little
to do with the yield of rice per hectare.
3) α6 , the co-efficient of T, the variable representing the percentage of leased area
under rice to total rice acreage has been found to be positive but insignificant which
implies that there is no significant impact of tenancy on the yield of rice per hectare.
4) α7 ,the co-efficient of IRRIGR , the variable representing the proportion of irrigated
area under rice to total rice acreage has been found to be positive and highly
significant at 1% level of significance. The positive and highly significant co-efficient of
IRRIGR implies that larger proportion of irrigated area under rice in total acreage tends
to raise the yield of crop per hectare.
5) α8 , the co-efficient of HYVR , representing the proportion of HYV rice area in total
rice acreage has been found to be positive and significant at 10% level of significance.
The positive and significant co-efficient of HYVR implies that larger proportion of rice
acreage under HYV tends to raise the yield of the crop i.e. rice in our case.
6) α9, the co-efficient of D1, i.e. the variable representing the dummy variable to
capture the effect of close contact with official personnels of agricultural extension
service network for the increase of the production of rice per hectare has been found
to be negative but insignificant which implies that the benefit as received by the
farmers from agricultural extension service has not come to the level to have a positive
impact on the yield of rice per hectare. Farmers may adopt the modern agricultural
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practices and use other modern agricultural inputs by seeing their neighbour fellow
farmers or through their own experience and knowledge for the same.
7) α10, the co-efficient of D2 , the variable representing the adoption of mechanisation
in the farming practices has been found positive and highly significant at 1% level of
significance which implies that farmer adopting the mechanised ploughing have higher
productivity as compared to the farmers with traditional practices.
8) α11, the co-efficient of the FC i.e. fertiliser consumption has been found positive and
highly significant at 1% level of significance which implies that fertiliser consumption
had raised the yield of rice per hectare across sample farms.
9) α12 ,the co-efficient of the EDN i.e. the educational standard has been found positive
and significant. Thus, educational standard of farm household had to do with the
extent of variation in the yield of rice.
10) The co-efficients of the location dummy variables that captures the differential
effect on the dependent variable for the farms in Boitamari has been found negative
and insignificant but for the farms in Jaluguti and Dhekorgorha circles over the farms in
Ghahigaon circle have been found negative and significant.
9.3 Factors behind the variation in cropping intensity across sample farms
The extent of multiple cropping is being measured by the cropping intensity i.e.
percentage of the gross cropped area of the farm to its net sown area or net cropped
area. Land being relatively scarce, the growth in agricultural production has to come
primarily from augmentation of productivity per unit of area through more intensive
use of land. To explain the variation in cropping intensity of all crops across farms, a
multiple regression model has been used. For the purpose the following explanatory
variables are included in the model.
9.3.1 Explanatory variables
i) Farm size which is measured by the size of the operational holding of the farmer in
hectare and is denoted by the variable ‘FS’. Cropping intensity is expected to be higher
for smaller farms as smaller farms can be expected to use their limited land resources
more intensively. With smaller farms, land being relatively scarce, it makes economic
sense to go for higher cropping intensity so as to better utilise this scarce resources.
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Hence, the co-efficient of operational holdings, the variable used as a measure of farm,
is expected to be negative.
ii) Irrigation i.e. by ensuring availability of water can facilitate to cultivate crops even in
the dry season.
To capture the effect of irrigation on cropping intensity, the
proportion of total irrigated area in the gross cropped area has been used as an
explanatory variable and is denoted by IRRIGA and is expected to have a positive
impact on the cropping intensity of all the crops across the farms of the four circles.
iii) Tenancy factor that is measured by the percentage of leased in land in operational
holding of the farmer has been denoted by the variable T. A priori, the tenancy factor
can be expected to have a negative impact on cropping intensity of all the crops across
the farms.
iv) House size i.e. the size of the farm household is an important variable in any study
of agrarian structure. It is denoted by the variable ‘HS’ and is expected to have
negative impact on cropping intensity across farms of the study area.
v) Area under HYV seeds is an important explanatory variable which refers to the
proportion of HYV area in a farmer’s total cropped area is generally expected to have
positive relation with cropping intensity. By ensuring availability of water through
irrigation can facilitate in raising crop even in the dry season. On the other hand, the
short duration of HYVs releases land quickly enough for a new crop to be raised after
its harvest for another round of cultivation and can facilitate increased cropping
intensity. Accordingly, proportion of HYV area in total cropped is expected to have
positive impact on cropping intensity and it is measured by the variable HYVA.
vi) It may be expected that an effective extension service besides helping farmer in
adopting improved practices, can also encourage them for more intensive use of the
agricultural land. Accordingly, other things remaining unchanged, the cropping
intensity of a farm may be expected to be higher or lower depending on whether it has
close contact with the extension agencies or not. To capture this effect a dummy
variable has been used which is denoted by D1 as followsD1=1, if the ith farmer is a ‘good’ category farmer (i.e. benefitted farmer with
good contact with extension service) with a scoring from 5 to 8.
209
=0, if the ith farmer is a ‘poor’ category farmer (i.e. non-benefitted farmer
with poor contact with extension service) with a scoring from 0 to 4.
The variable D1 is expected to have a positive impact on cropping intensity across the
farms.
vii) Here, the adoption of mechanisation in agricultural operations is measured by a
dummy variable D2 as followsD2= 1, if the farmer adopts mechanisation in agricultural practices.
= 0, otherwise.
Mechanisation is expected to have a positive impact on cropping intensity of all crops
across the farms.
viii) Education i.e. the educational standard of the farm family which is represented by
the variable ‘EDN’ has been used to capture the effect of literacy or educational
standard on cropping intensity. Here, an index of educational standard of the farm
family has been prepared as discussed in the previous section and thus has been taken
as the value of index for the farm family which is expected to have a positive impact on
cropping intensity.
ix) Location i.e there are four different locations, so three location dummy variables,
namely L1, L2 and L3 have been taken as discussed in the previous section to capture the
effect of the locational difference.
The semi-logarithmic multiple linear regression model for explaining the
variation in cropping intensity across sample farms has been formulated as follows Ln (CI) = β0+β1L1i+β2L2i+β3L3i+β4FSi+β5IRRIGAi+β6Ti+β7HSi+β8HYVAi
+β9D1i+β10D2i+β11EDNi+Ui
………………… [9.2]
Where Ui (i=1,2,3,…..,220) is the random disturbance term and the βj’s are the
unknown parameters to be estimated where j=(0,1,2…..11)
The dummies Li (i=1, 2, 3) are used to capture the differential effect of location on the
variation in cropping intensity across sample farms. As usual Li takes the value 1 for the
circle number 1 and 0 for the other circles. As discussed above, the variables IRRIGA,
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HYVA, D1 and D2 are expected to have positive impact and the others, namely, FS, T and
HS are expected to have negative impact on the variation in cropping intensity.
The results of the regression model estimated by using the method of Ordinary
Least Square (OLS) with the help of STATA package with White’s robust standard error
(VCE) technique ( Gujarati and Sangeetha,2007) are presented below in the Table-9.2
The model gives a moderate fit to the sample data in terms of the R2- value
(R2=0.38.15) .It is not too small in the context of a cross sectional detailed study as the
present one. However, the F-statistics for the overall significance of the fitted
regression has been found to be highly significant and affirms the credibility of the
results. Further the co-efficients of as many as four variables besides those of one
locational dummies and the constant term have come statistically significant.
Table-9.2: The Results of the Multiple Regression Analysis on Cropping Intensity of
Sample Farms
Variables
(1)
Constant
FS
IRRIGA
T
HS
HYVA
D1
D2
EDN
L1
L2
L3
R2
F(n1=10,n2=209)
Estimated
co-efficients
(2)
5.237668
-0.0557882
0.0839162
-0.0010915
-0.0055941
0.0476161
0.020084
-0.007455
0.0041209
-0.0081378
0.1433055
-0.0043688
0.3815
18.40***
F(n1=11,n2=208)
Standard Error of
co-efficients
(3)
0.0676558
0.0072738
0.0415585
0.000484
0.0036459
0.0654229
0.0267654
0.0332437
0.0244237
0.0327641
0.0299192
0.0327036
t-value
(4)
77.42***
-7.67***
2.02*
-2.25*
-1.53
0.73
0.75
-0.22
0.17
-0.25
4.79***
-0.13
(*, **, *** indicates the level significance at 10%, 5% and 1% respectively.)
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The coefficient of FS, the variable representing the farm size is negative and
highly significant at 1% level of significance implying that cropping intensity tends to be
higher for smaller farms. The negative co-efficient of the farm size indicates that
smaller farms use land with greater intensity. The obvious explanation is that
availability of land being less, this resource is more intensively utilised.
The coefficient of IRRIRA, i.e. the variable representing the proportion of
irrigated area in total cropped area has been found positive and significant at 10% level
of significance. As expected, the obvious explanation is that availability of irrigation in
the cropping area tends to increase the cropping intensity even in the dry season. The
results can be rationalised on the reason that the water control and flexibility of
irrigation facility can give the farmer enough freedom for multiple cropping.
The co-efficient of T, the variable representing the tenancy i.e. the percentage
of leased–in area in the total operational holdings has been found to be negative and
highly significant at 10% level of significance which indicates that tenant farmers are
less inclined towards multiple cropping. Thus it can be inferred that land ownership
can give enough freedom to the farmers to go for multiple cropping.
The co-efficient of HS, i.e. the variable representing the size of the household
has been found to be negative and insignificant indicating that cropping intensity is
higher for the smaller size of the household than their counterparts of large size of
household. Similarly, the co-efficient of HYVA, the variable representing the proportion
of area under HYV in gross cropped area has been found to be positive but not
significant. The coefficient of D1, i.e. the variable representing agricultural extension
service has been found to be positive but not significant. The support of extension
services tends to encourage the farmers to go for multiple cropping through their
effective interaction with the farmers and different departmental schemes of the
government. Thus, the co-efficient of extension is expectedly positive showing that
access to extension service induces farmers to adopt multiple cropping but it is not
significant which indicates the poor level of extension service in their areas.
The co-efficient of the variable D2 i.e. the adoption of mechanisation in the
agricultural practices is negative and insignificant. The adoption of mechanisation
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process only may not encourage the farmers to go for multiple cropping. Farmers in
dry season may go for vegetable cultivation or for the ‘rabi’ cultivation where they may
not be able to adopt mechanisation in the farming practices. The location factor L2, i.e.
the variable representing the farms of the Jaluguti ADO Circle in the district of
Morigaon is positive and highly significant at 1% level of significance. It shows the
differential impact on the cropping intensity over the farms of the Ghahigaon ADO
circle in the district of Sonitpur which is positive and significant indicating that the
farmers of the Jaluguti Circle go for multiple cropping as compared to the other farms
very intensively. This may be due to that farmers go for cultivation of both the ‘Boro’
and the ‘Ahu’ paddy apart from the usual ‘Sali’ crops more intensively and extensively
which was observed during the field survey also. But other location dummy variables
are found to be negative and insignificant in case of their differential impact over the
farms of Sonitpur district.
9.4 Summing Up
The principal findings emerging from the discussions in this chapter can be
summarised in the following points1. The crop productivity and the cropping intensity can be an important determinant of
existing land holding pattern in an agrarian structure. The farm size has been found to
have positive impact, though not significant, on the productivity of rice crop i.e. the
yield of rice per hectare. Thus it can be inferred that farmers with large farm size
tended to have higher rate of yield of rice per hectare. It may refer to the belief that
the advent of Green Revolution has weakened the existing size-productivity
relationship and it was in favour of large farmers who are modern and dynamic. Thus
farm size has often been found to be an important factor influencing effective use of
productivity raising farm practices with irrigation. In the case of cropping intensity,
farm size had negative and significant impact which may imply that cropping intensity
tends to be higher for smaller farms. They may use land with greater intensity.
2. The size of the household and the tenancy i.e. percentage of leased-in area under
rice were found to have positive and insignificant impact on the productivity of rice.
But tenancy has been found to have negative and highly significant impact on the
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cropping intensity which may indicate that tenant farmers are less inclined towards
multiple cropping. Thus, it can be inferred that land ownership can give enough
freedom to the farmers to go for multiple cropping. Similarly the size of the household
has been found to have negative and insignificant impact on the cropping intensity
which may indicate that cropping intensity is higher for the smaller size household
than their counterpart of large size household.
3. Availability of irrigation has been found to have a significant and positive impact on
the productivity of rice and the cropping intensity of all crops. Larger proportion of
irrigated area under rice in the total rice acreage tends to raise the yield of rice crop
per hectare. Moreover, availability of irrigation in the cropping area tends to increase
the cropping intensity even in dry season. Unlike in the ‘Sali’ season, the use of HYVs in
the ‘Ahu’ season is largely conditioned by availability of irrigation. However, it can be
said that water control and flexibility of irrigation facility can give the farmer enough
freedom for multiple cropping.
4. The adoption of HYVs and the proportion of HYV rice area in the total rice acreage
have been found to have positive and significant impact on the productivity of rice i.e.
larger proportion of rice acreage under HYV tends to raise the yield of the crop.
5. The contact with extension agencies has become less influential as per adoption and
uses of HYVs are concerned. The farmers’ contact with extension agencies has been
found to be negatively associated with the productivity of rice, though not significant.
But the agricultural extension services having positive and insignificant impact on the
cropping intensity of all crops may imply that the support of extension services tends
to encourage the farmers to go for multiple cropping through their effective
interaction with the farmers. The adoption of mechanised ploughing which have
positive and significant impact on the productivity of rice implies that farmers adopting
the mechanised ploughing have higher productivity as compared to the farmers with
traditional manual practices. Similarly, adoption of mechanised ploughing may not
encourage farmers to go for multiple cropping. Farmers in dry season may go for
vegetable cultivation or for other ‘rabi’ crop cultivation where they may not be able to
adopt mechanisation in the agricultural operations.
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6. Uses and consumption of soil nutrients i.e. fertiliser consumption has a positive and
highly significant impact on the yield of rice. Thus, higher the fertiliser consumption,
greater is the yield of rice per hectare.
7. Educational attainment and standard of the farm household has positive and
significant impact on the productivity of crop. Thus, educational attainment had to do
with the extent of variation in the yield of rice crop per hectare of cropped area.
Similarly educational attainment has also positive impact on the cropping intensity of
all-crops across farmers of the circles though not significant.
215