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94 The Management Accountant - April 2021 www.icmai.in
AGRICULTURE
Introduction:
T
he expansion, productivity and efficient
management of an agricultural activities, are
extremely important for the globe at large, including
the Indian Continental. As the demand creation by
societal member at large, depends on the agricultural output.
Food, Shelter, Cloth, Health and Education services are needed
Abstract
Agriculture has been the locomotive for economic growth in India. The agriculture growth and
development are depended on the proper utilization of resources.
Researcher has tried to study the data in quantitative form (not in monetary term) available from
dierent government’s reports. Researcher has considered the data for the period of 2000-2001 to
2014-2015, and used SPSS 22 for statistical analysis. The dependent variables considered are the
yield per hectare (kgs/hectare), and production of food gains (thousand tonnes). Independent variables
are: 1. Population density per square kilometer, 2. Cropping intensity in terms of percentage, 3.
Poverty rates – persons per million, and 4. Gross irrigated area in thousand hectares. The conclusion
supports that the yield per hectare is 64% dependent on gross irrigated land area and crop density.
The conclusions have supported the null hypothesis between yield per hectare, and crop density;
while not accepted the null hypothesis between yield per hectare and gross irrigation land area.
CRITICAL STUDY OF
IMPACT OF QUANTIFIABLE
VARIABLES
ON THE YIELD PER HECTARE
IN THE
INDIAN AGRICULTURAL SECTOR
CMA (Dr.) Paresh Shah
Principal and Professor
Rai Business School, Rai University
Delhi
paresh@profparesh.in
for the maintaining and sustaining livelihood for society at
large. The agricultural output and its movements across the
globe, gave rise to the Global village, and export-import
activities, etc. Agriculture will continue to be an important
component of Indian Economy because she needs to feed
1369.56 million (1.37 billion) plus population. The agriculture
is the single largest livelihood provider. It contributes around
16 percent of the national GDP as per the data for the year
2018-19.
To achieve the ambition of reaching a level of ve trillion
economy, will demand the agricultural contribution towards
the GDP should be increased. At best, the India’s policy makers
need to treat the farmers as ‘Agripreneurs’ and farming as an
enterprise activity, and in turn agricultural activities has been
the locomotive for economic growth in India.
Signicance of Agricultural Sector:
Researcher has tried to correlate the micro level of demand
by population at large, with the role of government agencies,
and in turn drive towards the overall growth, productivity
management of agricultural sector.
Researcher felt the need to revisit the concept of disposable
www.icmai.in April 2021 - The Management Accountant 95
income, as part of academical studies and activities. Researcher
felt that, eective disposable income would be more important
than the disposable income concept as per macro economical
variable. The eective disposable income is the residual
after providing for societal need, superannuated needs,
and cautionary funds on account of changes in the policies
of state and central government. Researcher also tried to
establish the linkages between the parameters, like demand
and consumption, governmental support (both nancial and
non-nancial), agricultural growth, and its impact on industrial
output; through the consideration of savings of society diverted
in productive way to maintain their own need of future.
Research methodology:
Researcher has focused on the Empiricism, as the heart
of scientic analysis. Researcher has used the model that
is best suited in given thought process; concentrating only
on quantitative variables and data, neglecting the monetary
and nancial terms and values; as a simplied description or
representation. The changing values in monetary and nancial
terms on account of socio-economic-commercial parameters,
are omitted, to undertake the meaningful study.
The researcher has felt that, considering the above, the
essential focus is on increasing productivity (including
factor level productivity) with thrust on optimal utilization
of resources, supplied through the Government Agencies,
directly and indirectly.
Objectives of the study:
1.
To analyse the impact of selected agricultural input on
yield per hectare.
2.
To analyse the impact of selected agricultural resource
available on yield per hectare.
3. To give suggestions for improvement in the impact of
agricultural yield.
Hypotheses:
To conduct empirical analysis, researcher refers to a model’s
predictions as hypotheses. Such hypotheses are supported
and/or contradicted by the available data. These are the
proposal intended to describe certain facts and observations.
The following ve hypotheses are formed to conclude the
research study.
H01
No signicant dierence between percentage of
changes in population density and cropping density.
H11
Signicant dierence between percentage of
changes in population density and cropping density.
H02
No signicant dierence between percentage of
changes in population density and food grains
production.
H12
Signicant dierence between percentage of
changes in population density and food grains
production.
H03
No signicant dierence between percentage of
changes in population density and yield per hectare.
H13
Signicant dierence between percentage of
changes in population density and yield per hectare.
H04
No signicant dierence between percentage of
changes in yield per hectare and gross irrigated
land.
H14
Signicant dierence between percentage of
changes in yield per hectare and gross irrigated
land.
H05
Change in Yield per hectare is not dependent on
change in crop density and change in gross irrigated
land.
H15
Change in Yield per hectare is dependent on change
in crop density and change in gross irrigated land.
Data Collection:
The proposed research study based on secondary data related
to variables taken in to consideration. Researcher has tried
to study the data in quantitative form (not in monetary term)
available from dierent government’s reports, etc. Researcher
has considered the data from the period of 2000-2001 to 2014-
15, and used SPSS 22 for statistical analysis. The dependent
variables considered are the yield per hectare (kgs/hectare),
and production of food gains (thousand tonnes). Independent
variables are: 1. Population Density per square kilometer,
2. Cropping intensity in terms of percentage, and 3. Gross
irrigated area in thousand hectares. The four hypotheses are
being developed to establish the relationship between two
variables. To frame the conclusive result, multi-linear based
hypothesis is developed.
Tools and Techniques:
For the analysis, researcher has used the descriptive statistics,
T test (SE of two sample means), F test (Ratio of Variations),
Correlation coefficient, Co-efficient of determination,
Kolmogorov-Smirnov test, and Durbin-Watson test.
Researcher has used the interpolation, and extrapolations
techniques of statistics, to ll the gap of few information, as
that information are not available.
Data Analysis:
The study is based on the impact of population density,
cropping density and gross irrigated land on the yield per
hectare and food grains production. To achieve the scientic
resolutions and study of data, the absolute form of data has
been converted in to the percentage of changes.
Interpretation:
The interpretation for each ve hypotheses is presented in the
table 1, as given hereunder entitled as Testing of Hypotheses.
AGRICULTURE
96 The Management Accountant - April 2021 www.icmai.in
Table 1: Testing of Hypotheses
Values
Percentage
of Change in
Population Density
Percentage
of Change in
Cropping Density
Percentage of
change in Food
Grains Production
Percentage of
change in Yield
per hectare
Percentage of
change in Gross
Irrigation Land
Arithmetic Mean 1.6300 0.5607 2.2093 1.9236 1.7525
Standard deviation 0.0000 1.3457 9.6102 6.3124 3.8395
Co-ecient of
Standard Deviation 0.00 2.4000 4.3499 3.2816 2.1909
Hypothesis One
H01
No signicant dierence between percentage of changes in
population density and cropping density.
H11
Signicant dierence between percentage of changes in
population density and cropping density.
Correlation -4.89691E-17
T Test (SE of Two Sample Mean)
Signicance Level 0.05
SE (calculated t value) -2.973
Table value 3.012
Compare SE<Table Value, H01 Accepted, H11 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
F test (Ratio of Variations)
Signicance Level 0.05
SE (calculated t value) -2.431
Table value 3.14
Compare SE<Table Value, H01 Accepted, H11 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
Kolmogorov-Smirnov Test (non-parametric test of equality)
0.2001,2
Retain Null (H01)
1 Lilliefors Corrected
2 This is lower bound of the true signicance
Hypothesis Two
H02
No signicant dierence between percentage of changes in
population density and food grains production.
H12
Signicant dierence between percentage of changes in
population density and food grains production.
Correlation -3.65347E-17
T Test (SE of Two Sample Mean)
Signicance Level 0.05
SE (calculated t value) -0.226
Table value 3.012
Compare SE<Table Value, H02 Accepted, H12 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
F test (Ratio of Variations)
Signicance Level 0.05
SE (calculated t value) 0.740
Table value 3.14
Compare SE<Table Value, H02 Accepted, H12 Rejected
AGRICULTURE
www.icmai.in April 2021 - The Management Accountant 97
AGRICULTURE
Conclusion It is proved that there is no signicant dierence between
two sample mean.
Kolmogorov-Smirnov Test (non-parametric test of equality)
0.2001,2
Retain Null (H02)
1 Lilliefors Corrected
2 This is lower bound of the true signicance
Hypothesis Three
H03
No signicant dierence between percentage of changes in
population density and yield per hectare.
H13
Signicant dierence between percentage of changes in
population density and yield per hectare.
Correlation -9.73465E-17
T Test (SE of Two Sample Mean)
Signicance Level 0.05
SE (calculated t value) -0.174
Table value 3.012
Compare SE<Table Value, H03 Accepted, H13 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
F test (Ratio of Variations)
Signicance Level 0.05
SE (calculated t value) 1.300
Table value 3.14
Compare SE<Table Value, H03 Accepted, H13 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
Kolmogorov-Smirnov Test (non-parametric test of equality)
0.2001,2
Retain Null (H03)
1 Lilliefors Corrected
2 This is lower bound of the true signicance
Hypothesis Four
H04
No signicant dierence between percentage of changes in
yield per hectare and gross irrigated land.
H14
Signicant dierence between percentage of changes in
yield per hectare and gross irrigated land.
Correlation 0.787117604
T Test (SE of Two Sample Mean)
Signicance Level 0.05
SE (calculated t value) 0.049
Table value 3.012
Compare SE<Table Value, H04 Accepted, H14 Rejected
Conclusion It is proved that there is no signicant dierence between
two sample mean.
F test (Ratio of Variations)
Signicance Level 0.05
SE (calculated t value) 0.898
Table value 3.14
Compare SE<Table Value, H04 Accepted, H14 Rejected
98 The Management Accountant - April 2021 www.icmai.in
AGRICULTURE
Conclusion It is proved that there is no signicant dierence between
two sample mean.
Kolmogorov-Smirnov Test (non-parametric test of equality)
0.2001,2
Retain Null (H04)
1 Lilliefors Corrected
2 This is lower bound of the true signicance
Hypothesis Five (Multi Linear Model)
H05
Change in Yield per hectare is not dependent on change in
crop density and change in gross irrigated land.
H15
Change in Yield per hectare is dependent on change in
crop density and change in gross irrigated land.
Intercept -0.548
Regression coecient of Changes in Crop density -1.137
Regression coecient of Changes in Gross Irrigated Land 1.688
Multi-correlation 0.800
Co-ecient of determination 0.639
Durbin-Watson (auto-correlation) 2.101 (lower than the critical value)
Conclusion
It is proved that the change in yield per Hectare is
negatively impacted by changes in crop density while
positively impacted by changes in gross irrigated land. The
model has supported for the 64% causal relationships.
Suggestions and Recommendations:
There are following suggestions and
recommendations for improvement in
yield per hectare and enhancing the food
grains production.
1.
The population density in
agricultural land by farmer to
be reduced. It means that number
of members involved in farming
activities are to be reduced.
2.
Government should provide
the alternative employment
to the rural persons involved
in farming activities, so that
quasi-employment level can be
reduced.
3.
The cropping density should
be reduced, considering the
seasonality, environmental
factors, etc.
4.
The gross irrigated land
proportion should be increased
among the agricultural land.
5. Government should implement
the systems and processes so that
more land can cover under the
irrigation facilities.
6.
In general, inspire and impart
knowledge and information to
the farmers about reducing the
crop density, population density,
rain water storage and uses of
irrigation facilities.
Conclusions:
This research study establishes
that the impact of increasing the
gross irrigation land and reducing the
cropping density on yield per hectare
is apparent. Present scenario of gross
irrigation land and cropping density
is satisfactory, providing 64% impact
on the yield per hectare. It can also
be concluded that, farmers with small
land holding, are employing more
manpower than the needed. This has
created the quasi-employment in the
farming sector; in turn inecient use
of labour as a resource. Application
of various suggestions given in this
research paper can help in improving
the food grains production and yield per
hectare, through proper knowledge, skill
sharing and governmental support.
References:
1. Reddi Sankara G. H. &
Reddy Yellamanda T. (2012),
“Ecient use of irrigation
water”, Kalyani Publisher
Ludhiana.
2. Bhende M. J., “cropping
pattern and Resources use
Eciency for major crop- A
case study of Kamataka,
GLIMPSES of Indian
Agriculture-macro & micro
aspects Vol. No.2
3. Singh, Alok Kumar. and
Singh, Narendra., “Impact of
Diversication of Cropping
Patterns on Sustainable
Development of Farm Sector
of Uttar Pradesh: Theoretical
Underpinnings”, International
journal of management
studies, July 2018
4. Majhi, B. and Kumar,
Awadhesh., “Changing
Cropping Pattern in Indian
Agriculture”, Journal of
Economics and Social
Development, Jan, 2018.
5. National Sample Survey
Organisation (1995-2019):
Situation Assessment of
Farmer’s Household”,
6. National Sample Survey
Organisation (1995-2019):
Situation Assessment of
Farmer’s Household”,
7. Governmental Reports,
including working papers
Websites:
1. www.economicsdiscussion.net
2. www.micronancegateway.org
3. https://www.statista.
com/statistics/263766/
totalp-population-of-india/
