Full Length Research Paper
An Analysis of the Factors Responsible for the Cultivation of Sugarcane
Crop in the Baghpat District, Uttar Pradesh
Dushyant Kumar1[1] and Dr
Arun Solanki2
1-Department of
Agricultural Economics, U P College, Varanasi, Uttar Pradesh, India.
2-Professor and
Head, Department Of Agricultural Economics, J V
College, Baraut, Baghpat,
Uttar Pradesh, India.
ARTICLE DETAILS ABSTRACT
1. Introduction
The
cultivation of sugarcane is a critical component of agricultural activities in
the Baghpat District of Uttar Pradesh, playing a
significant role in the region's economy and socio-cultural fabric. Sugarcane,
being a high-value cash crop, provides substantial income to farmers and
contributes to the agro-based industries in the area, including sugar mills and
jaggery production units. The choice of cultivating
sugarcane over other crops is influenced by multiple factors, including
climatic conditions, soil fertility, water availability, and government
policies. Understanding these factors is essential for enhancing productivity,
ensuring sustainable agricultural practices, and supporting the livelihoods of
the local farming communities (Kumar et al., 2017). Sugarcane,
scientifically known as Saccharum officinarum L.,
is a member of the Gramineae family of plants and is thought to have
originated in the tropical regions of
south and southeast Asia. Sugarcane, in addition to
producing biofuel, fibre, and fertiliser, and a plethora
of other by-products that are ecologically sustainable, is an agricultural resource that is renewable and
naturally occurring. White sugar, brown sugar (Khandhasari), jaggery (Gur), and ethanol
are all products that can be made from sugarcane juice. Sugarcane juice
includes 111.13 kilocalories (26.56 kilojoules) of energy per serving (28.35 grammes), 27.51 grammes of carbs, 0.27 grammes of
protein, 11.23 milligrammes of calcium, 0.37 milligrammes of
iron, 41.96 milligrammes of potassium, and 1.01 milligrammes of sodium
(Nutrient Information from ESHA Research). Several studies have
highlighted the influence of environmental and socio-economic factors on
sugarcane cultivation. Climatic conditions, such as temperature and rainfall,
are pivotal, with sugarcane requiring a warm, humid climate for optimal growth
(Singh et al., 2019). The soil type in Baghpat,
predominantly alluvial, is well-suited for sugarcane, provided there is
adequate irrigation. The availability of water resources, particularly through
the extensive canal systems in Uttar Pradesh, plays a crucial role in the
decision to grow sugarcane. Furthermore, government support in the form of
subsidies, minimum support prices, and policies aimed at promoting sugarcane
farming also significantly impacts farmers' choices (Sharma & Kaur, 2018). Despite the favorable conditions, the
cultivation of sugarcane in Baghpat faces several
challenges. Issues such as water scarcity, fluctuating market prices, and the
high cost of inputs can affect productivity and profitability. Additionally,
the environmental impact of intensive sugarcane farming, including soil
degradation and water resource depletion, raises concerns about the
sustainability of these practices. This study aims to identify and analyze the
various factors responsible for the cultivation of sugarcane in Baghpat District, providing insights that could help in
formulating strategies for improving crop management and ensuring the long-term
viability of sugarcane farming in the region (Pandey
& Tripathi, 2020). Sugarcane production in India has seen significant
growth, with the highest area under cultivation reaching 50.66 lakh hectares in
2014-2015 and the lowest at 41.7 lakh hectares in 2009-2010. The highest
production was 4003.69 million tonnes in 2018-19,
while the lowest was 2923 million tonnes in 2009-10.
The selection and growth of specific crops in a region are influenced by
factors such as land tenancy, land ownership, number of holdings, and field
size. In Uttar Pradesh, the largest area under sugarcane cultivation was 21.80
lakh hectares in 2020-21, with a record high of 1776.72 million tonnes in 2020-21. The region's major crops include wheat,
rice, and sugarcane, which compete for space and generate the highest profit
for a specific year. The decision-making process influences farmers' crop
selection preferences, promoting the creation of new types and species,
technological elements, policies, tangible progress, and educational programs.
Uttar Pradesh's leading sugarcane-producing districts include Bareilly, Muzaffarnagar, Bulandshahar,
Meerut, Baghpat, and Saharanpur.
This study aims to explore the factors responsible for
sugarcane crop cultivation in Baghpat district, Uttar
Pradesh, a critical area for the state's output. Understanding these factors is
crucial for developing strategies to improve productivity and profitability in
the agricultural sector.
- Materials and Methods
The sampling design takes representativeness into
account as one of these things. Representativeness can be thought of as a
measure of how well data show a process state, an environmental factor, a
change in a parameter at a sampling point, or a trait of a community. Making a
sampling plan is an important first step in getting data that is valid, can be
defended, and is representative of the problem being studied. The production
area for the study is based on a lot of facts.
i)
There are tools for farming sugarcane.
ii)
A road network to make it easier for people to
connect and move both inputs and outputs.
iii)
More than half of the sugarcane grown in India is
in the Baghpat district.
A new system was put in place that ranks the six
blocks of the district by the amount of sugarcane growers in each one. To learn
more, the blocks of Baraut, Chhaprauli,
and Binauli were picked at random based on the number
of sugarcane farmers, going from least to most. Out of all the villages in the
designated blocks, six were picked at random based on the percentage of
sugarcane growers, from least to most: Bijrol, Malakpur, Basauli, Ramala, Pusar, and Jiwana. This was done because more than 70% of the land in
these villages was used for sugarcane farming. We chose people from each
village in the Baghpat district who grow more than
80% of the sugarcane. For this study, 120 farmers were picked at random from a
list, ranging from those who grew the least amount of sugarcane to those who
grew the most.
3. Principal component analysis
The
main goal of PCA is to explain the maximum variance through a few number of principal components. PCA has many applications in
agriculture, social science, marketplace research and other industries, where
experiments are based on a multitude of variables. We can use PCA to examine
the factor more responsible for the preference of sugarcane cultivation and
avoid multi- colinearity as stated by Jolliffe (2002). Shlens (2014)
has proposed the computation of PCA in the following steps:
Arrange
data as an m×n matrix, where m represents the number
of measurement types and n symbolizes number of samples.
·
Subtract off the mean
for each measurement type.
·
Compute first the
correlation matrix and then eigen
values of the correlation matrix.
3.1 Deriving
principal components:
Derivation of principal components
prescribed by Jolliffe (2002) is given by;
Var{α1
x} = α1 ∑ α1
The
Variable used in the derivation is α1.α1 = 1, that is, the sum of
squares of elements of α1 equals one. In general, the kth
PC of x is a'kx and Var {αk x}. Data were subjected to SPSS software and
principal component analysis was performed.
Eigen
values, variance percentage and cumulative percentage were found. Scree plot
and score plot were also obtained in order to decide how many principal
components are sufficient to describe the relationship.
- Results and Discussion
Table 1. Eigen analysis of the Marginal farmers.
|
Component |
Initial Eigenvalues |
|||||
|
Total |
% of Variance |
Cumulative % |
|
|||
|
Cultivation
Easy |
3.1751 |
19.8445 |
19.8445 |
|
||
|
Profitable
crop |
2.3296 |
14.5601 |
34.4047 |
|
||
|
Minimum
Risk |
2.1082 |
13.1763 |
47.5809 |
|
||
|
Availability
of Credit |
1.7238 |
10.7736 |
58.3545 |
|
||
|
Technical
Assistant by government |
1.5194 |
9.4964 |
67.8509 |
|
||
|
Loss
due to natural Hazard in less |
0.9720 |
6.0749 |
73.9258 |
|
||
|
Loss
due to wild animal in less |
0.7667 |
4.7921 |
78.7179 |
|
||
|
More
Productivity of crop |
0.7156 |
4.4723 |
83.1902 |
|
||
|
join
product are equally useful |
0.6348 |
3.9673 |
87.1575 |
|
||
|
Easy
availability of input resource |
0.6012 |
3.7573 |
90.9148 |
|
||
|
Minimum
rquired labour |
0.3815 |
2.3846 |
93.2994 |
|
||
|
Local
marketing facility available in the local market |
0.3148 |
1.9675 |
95.2669 |
|
||
|
Assured
Pricing |
0.2759 |
1.7243 |
96.9912 |
|
||
|
More
Profitable in comparison of other crop in this area |
0.2224 |
1.3900 |
98.3811 |
|
||
|
Availability
of Good processing in the area |
0.1710 |
1.0685 |
99.4496 |
|
||
|
Option
of mixed cropping |
0.0881 |
0.5504 |
100.0000 |
|
||
The analysis of the initial eigenvalues and variance
explained by each component reveals that the first five components account for
a substantial portion of the total variance. "Cultivation Easy" has
the highest eigenvalue of 3.1751, explaining 19.84% of the variance, followed
by "Profitable crop" with 2.3296, explaining 14.56%, and
"Minimum Risk" with 2.1082, explaining 13.18%. Together, these three
components explain nearly half (47.58%) of the total variance. The fourth and
fifth components, "Availability of Credit" and "Technical
Assistance by Government," contribute an additional 10.77% and 9.50%,
respectively, bringing the cumulative variance explained to 67.85%. The
remaining components each contribute less than 7% individually, with the
cumulative variance reaching 100% by the sixteenth component, "Option of
mixed cropping," which explains only 0.55%. This suggests that the first
few components capture most of the variability in the data, indicating that
these factors are the most significant in explaining differences in crop
cultivation and profitability.
Table 2. Eigen analysis of the Small farmers
|
Component |
Initial
Eigen value |
||
|
|
Total |
% of Variance |
Cumulative % |
|
Cultivation
Easy |
3.1342 |
19.5886 |
19.5886 |
|
Profitable
crop |
2.3749 |
14.8431 |
34.4318 |
|
Minimum
Risk |
2.0726 |
12.9537 |
47.3855 |
|
Availability
of Credit |
1.7015 |
10.6341 |
58.0196 |
|
Technical
Assistant by government |
1.5213 |
9.5082 |
67.5277 |
|
Loss
due to natural Hazard in less |
1.0097 |
6.3108 |
73.8386 |
|
Loss
due to wild animal in less |
0.7906 |
4.9411 |
78.7797 |
|
More
Productivity of crop |
0.7003 |
4.3771 |
83.1568 |
|
join
product are equally useful |
0.6440 |
4.0253 |
87.1821 |
|
Easy
availability of input resource |
0.5803 |
3.6271 |
90.8093 |
|
Minimum
required labour |
0.3836 |
2.3975 |
93.2068 |
|
Local
marketing facility available in the local market |
0.3179 |
1.9870 |
95.1938 |
|
Assured
Pricing |
0.2849 |
1.7809 |
96.9747 |
|
More
Profitable in comparison of other crop in this area |
0.2254 |
1.4086 |
98.3833 |
|
Availability
of Good processing in the area |
0.1696 |
1.0602 |
99.4435 |
|
Option
of mixed cropping |
0.0890 |
0.5565 |
100.0000 |
The eigen
value analysis indicates that the first five components capture the majority of
the variance in the data. "Cultivation Easy" has the highest initial
eigenvalue of 3.1342, accounting for 19.59% of the total variance. The second
component, "Profitable crop," follows with an eigenvalue of 2.3749,
explaining 14.84% of the variance. "Minimum Risk" and
"Availability of Credit" have eigenvalues of 2.0726 and 1.7015,
respectively, explaining an additional 12.95% and 10.63% of the variance. The
fifth component, "Technical Assistance by Government," contributes
9.51%, bringing the cumulative variance explained by these five components to
67.53%. The sixth component, "Loss due to Natural Hazard," adds
6.31%, resulting in a cumulative variance of 73.84%. Subsequent components each
contribute less than 5%, with the cumulative variance reaching 99.44% by the
fifteenth component, "Availability of Good Processing in the Area."
This distribution highlights that the first few components are the most significant
in explaining the variation in the data, with diminishing contributions from
the remaining components.
Table
3. Eigen analysis of the Medium farmers
|
Component |
Initial Eigen values |
|||
|
Total |
% of Variance |
Cumulative % |
||
|
Cultivation
Easy |
5.5356 |
34.5977 |
34.5977 |
|
|
Profitable
crop |
3.2372 |
20.2326 |
54.8302 |
|
|
Minimum
Risk |
2.0774 |
12.9839 |
67.8142 |
|
|
Availability
of Credit |
1.4769 |
9.2309 |
77.0451 |
|
|
Technical
Assistant by government |
1.3381 |
8.3629 |
85.4080 |
|
|
Loss
due to natural Hazard in less |
0.7570 |
4.7316 |
90.1396 |
|
|
Loss
due to wild animal in less |
0.6015 |
3.7592 |
93.8988 |
|
|
More
Productivity of crop |
0.4584 |
2.8650 |
96.7638 |
|
|
join
product are equally useful |
0.3030 |
1.8939 |
98.6577 |
|
|
Easy
availability of input resource |
0.2148 |
1.3423 |
100.0000 |
|
|
Minimum
required labour |
0.0000 |
0.0000 |
100.0000 |
|
|
Local
marketing facility available in the local market |
0.0000 |
0.0000 |
100.0000 |
|
|
Assured
Pricing |
0.0000 |
0.0000 |
100.0000 |
|
|
More
Profitable in comparison of other crop in this area |
0.0000 |
0.0000 |
100.0000 |
|
|
Availability
of Good processing in the area |
0.0000 |
0.0000 |
100.0000 |
|
|
Option
of mixed cropping |
0.0000 |
0.0000 |
100.0000 |
|
The
eigen value analysis reveals
that the first five components account for the bulk of the variance in the
data. "Cultivation Easy" is the most significant factor with an
eigenvalue of 5.5356, explaining 34.60% of the total variance. The second
component, "Profitable crop," follows with an eigenvalue of 3.2372,
explaining 20.23% of the variance. Together, these two components account for
more than half (54.83%) of the variance. "Minimum Risk," with an
eigenvalue of 2.0774, contributes an additional 12.98%, bringing the cumulative
variance explained to 67.81%. The fourth component, "Availability of
Credit," adds 9.23%, and "Technical Assistance by Government"
contributes 8.36%, resulting in a cumulative variance of 85.41%. The remaining
components explain progressively smaller portions of the variance, with the
sixth component, "Loss due to Natural Hazard," adding 4.73%, and
subsequent components contributing less than 4% each. Notably, the last six
components (from "Minimum required labour"
to "Option of mixed cropping") do not contribute any additional
variance. This suggests that the first few components are the most influential
in explaining the variation in the data, while the latter components have
negligible impact.
Table 4. Structure of first
five principal components in Marginal farmers.
|
Component |
PC 1 |
PC 2 |
PC 3 |
PC 4 |
PC 5 |
|
Cultivation
Easy |
-0.3934 |
-0.4814 |
-0.5557 |
-0.2259 |
0.1730 |
|
Profitable
crop |
-0.0275 |
-0.4545 |
-0.3750 |
0.2261 |
0.1220 |
|
Minimum
Risk |
0.3289 |
-0.1157 |
-0.2584 |
-0.1218 |
-0.6480 |
|
Availability
of Credit |
0.0255 |
0.7372 |
0.0405 |
-0.3645 |
-0.2360 |
|
Technical
Assistant by government |
-0.4234 |
0.7580 |
0.0711 |
-0.1901 |
-0.0230 |
|
Loss
due to natural Hazard in less |
-0.7903 |
0.1787 |
-0.0309 |
0.0791 |
-0.1550 |
|
Loss
due to wild animal in less |
0.2910 |
-0.3837 |
0.6926 |
0.0468 |
-0.0560 |
|
More
Productivity of crop |
0.3666 |
0.3906 |
-0.4629 |
0.1634 |
0.2020 |
|
join
product are equally useful |
0.3557 |
-0.2701 |
0.4630 |
-0.5256 |
0.2660 |
|
availability
of input resource |
-0.0672 |
0.4208 |
0.2676 |
0.0686 |
0.6580 |
|
Minimum
required labour |
-0.6351 |
-0.1727 |
-0.2371 |
0.2816 |
0.3800 |
|
Local
marketing facility available in the local market |
0.5710 |
0.1654 |
0.0903 |
0.1649 |
0.4040 |
|
Assured
Pricing |
-0.5445 |
0.1203 |
0.2388 |
0.4991 |
-1.9500 |
|
More
Profitable in comparison of other crop in this area |
0.5208 |
0.2294 |
-0.2187 |
0.5171 |
0.0660 |
|
Availability
of Good processing in the area |
0.6663 |
0.2091 |
-0.3007 |
0.3215 |
-0.1780 |
|
Option
of mixed cropping |
-0.1254 |
-0.0997 |
0.5872 |
0.6288 |
-0.2140 |
The principal component
analysis (PCA) loadings for the first five principal components (PC1 to
PC5) highlight the key contributing factors for each component. PC1, which
explains the largest variance, is heavily influenced by "Loss due to
natural Hazard in less" (-0.7903), "Assured Pricing"
(-0.5445), and "Minimum required labour"
(-0.6351), indicating that these factors contribute significantly to the
overall variance captured by PC1. PC2, the second most significant
component, is strongly associated with "Technical Assistance by
Government" (0.7580) and "Availability of Credit" (0.7372),
emphasizing the importance of these factors in explaining the variance. PC3
shows high loadings for "Cultivation Easy" (-0.5557) and
"Loss due to wild animal in less" (0.6926), highlighting
different aspects of cultivation challenges and risks. PC4 is influenced by
"Assured Pricing" (0.4991) and "More Profitable in
comparison of other crop in this area" (0.5171), reflecting economic
factors, while PC5 has notable contributions from "Minimum Risk"
(-0.6480) and "availability of input resource" (0.6580), focusing
on risk and resource availability.
The principal component
analysis (PCA) loadings for the first five principal components (PC1 to
PC5) highlight the key contributing factors for each component. PC1, which
explains the largest variance, is heavily influenced by "Loss due to
natural Hazard in less" (-0.7903), "Assured Pricing"
(-0.5445), and "Minimum required labour"
(-0.6351), indicating that these factors contribute significantly to the
overall variance captured by PC1. PC2, the second most significant
component, is strongly associated with "Technical Assistance by
Government" (0.7580) and "Availability of Credit" (0.7372),
emphasizing the importance of these factors in explaining the variance. PC3
shows high loadings for "Cultivation Easy" (-0.5557) and
"Loss due to wild animal in less" (0.6926), highlighting
different aspects of cultivation challenges and risks. PC4 is influenced by
"Assured Pricing" (0.4991) and "More Profitable in
comparison of other crop in this area" (0.5171), reflecting economic
factors, while PC5 has notable contributions from "Minimum Risk"
(-0.6480) and "availability of input resource" (0.6580), focusing
on risk and resource availability.
|
This analysis indicates that various aspects of crop
cultivation and profitability are multidimensional, with different components
capturing distinct sets of factors.
Table
5. Structure of first six principal components in small
farmers.
|
Component |
PC 1 |
PC 2 |
PC 3 |
PC 4 |
PC 5 |
PC 6 |
|
Cultivation
Easy |
-0.4230 |
-0.4676 |
-0.5672 |
-0.1665 |
0.1877 |
-0.0283 |
|
Profitable
crop |
-0.1172 |
-0.4703 |
-0.3056 |
0.2110 |
0.1618 |
0.5564 |
|
Minimum
Risk |
0.3493 |
-0.1264 |
-0.3634 |
0.0686 |
-0.5927 |
0.4352 |
|
Availability
of Credit |
0.0246 |
0.7346 |
-0.0318 |
-0.3171 |
-0.2872 |
0.2041 |
|
Technical
Assistant by government |
-0.3875 |
0.7678 |
0.0701 |
-0.2112 |
-0.0694 |
0.0940 |
|
Loss
due to natural Hazard in less |
-0.7887 |
0.1992 |
0.0306 |
0.0644 |
-0.1706 |
-0.0994 |
|
Loss
due to wild animal in less |
0.3387 |
-0.3962 |
0.6602 |
-0.0124 |
-0.0851 |
-0.0583 |
|
More
Productivity of crop |
0.3162 |
0.4013 |
-0.4375 |
0.1748 |
0.2611 |
0.1359 |
|
join
product are equally useful |
0.4083 |
-0.2865 |
0.3609 |
-0.5918 |
0.1680 |
0.0999 |
|
Easy
availability of input resource |
-0.0279 |
0.4242 |
0.2878 |
-0.0464 |
0.6698 |
0.2176 |
|
Minimum
required labour |
-0.6555 |
-0.1508 |
-0.1488 |
0.2394 |
0.4377 |
-0.0157 |
|
Local
marketing facility available in the local market |
0.5531 |
0.1549 |
0.1331 |
0.0675 |
0.4231 |
0.3253 |
|
Assured
Pricing |
-0.5300 |
0.1343 |
0.3237 |
0.4737 |
-0.1366 |
0.2548 |
|
More
Profitable in comparison of other crop in this area |
0.4786 |
0.2342 |
-0.1440 |
0.5351 |
0.1487 |
-0.4096 |
|
Availability
of Good processing in the area |
0.6412 |
0.2018 |
-0.3021 |
0.4118 |
-0.0877 |
-0.0755 |
|
Option
of mixed cropping |
-0.0898 |
-0.0998 |
0.6493 |
0.5915 |
-0.1441 |
0.1609 |
The principal component analysis (PCA) loadings
for the first six principal components (PC1 to PC6) indicate the significant
factors influencing each component. PC1, which accounts for the largest
variance, is highly influenced by "Loss due to natural Hazard in
less" (-0.7887), "Minimum required labour"
(-0.6555), and "Cultivation Easy" (-0.4230), suggesting that these
factors are key to the overall variance captured by PC1. PC2 is dominated by
"Technical Assistance by Government" (0.7678) and "Availability
of Credit" (0.7346), highlighting the importance of these factors in
explaining the variance. PC3 shows strong loadings for "Cultivation
Easy" (-0.5672) and "Loss due to wild animal in less" (0.6602),
reflecting cultivation challenges and risks. PC4 is significantly influenced by
"More Profitable in comparison of other crop in this area" (0.5351)
and "Assured Pricing" (0.4737), indicating economic considerations. PC5
has notable contributions from "Easy availability of input resource"
(0.6698) and "Minimum Risk" (-0.5927), emphasizing resource
availability and risk. Lastly, PC6 is characterized by high loadings on
"Profitable crop" (0.5564) and "Minimum Risk" (0.4352),
indicating profitability and risk factors. This analysis reveals that different
components capture various aspects of crop cultivation and profitability, with
distinct factors contributing to each principal component.
Table 6. Structure of first
five principal components in medium farmers.
|
Component |
PC 1 |
PC 2 |
PC 3 |
PC 4 |
PC 5 |
|
Cultivation
Easy |
0.4906 |
-0.7634 |
-0.3359 |
0.1176 |
-0.0710 |
|
Profitable
crop |
0.1364 |
-0.6926 |
0.5922 |
-0.0365 |
0.1430 |
|
Minimum
Risk |
-0.6364 |
-0.0873 |
-0.5441 |
0.2261 |
-0.0940 |
|
Availability
of Credit |
0.2819 |
0.7501 |
0.1968 |
-0.2197 |
-0.4710 |
|
Technical
Assistant by government |
0.7244 |
0.5067 |
0.0159 |
0.1313 |
-0.1220 |
|
Loss
due to natural Hazard in less |
0.8971 |
0.2445 |
-0.0392 |
-0.1078 |
0.1040 |
|
Loss
due to wild animal in less |
-0.7515 |
0.0689 |
0.1536 |
0.0858 |
0.5070 |
|
More
Productivity of crop |
-0.4004 |
-0.4415 |
0.6638 |
0.1039 |
0.0280 |
|
join
product are equally useful |
-0.7761 |
0.1501 |
0.2110 |
-0.1775 |
0.1060 |
|
Easy
availability of input resource |
0.2769 |
0.1109 |
0.7854 |
0.2724 |
-0.3540 |
|
Minimum
required labour |
0.7420 |
-0.4359 |
0.1114 |
-0.0518 |
-0.1470 |
|
Local
marketing facility available in the local market |
-0.5887 |
0.7319 |
0.0108 |
0.2621 |
-0.0530 |
|
Assured
Pricing |
-0.6259 |
-0.2226 |
0.0335 |
-0.4892 |
-0.4570 |
|
More
Profitable in comparison of other crop in this area |
0.3108 |
0.0756 |
0.0171 |
0.8495 |
0.0790 |
|
Availability
of Good processing in the area |
0.3172 |
0.5219 |
0.3280 |
-0.2708 |
0.5630 |
|
Option
of mixed cropping |
0.7563 |
0.0030 |
-0.1736 |
-0.3226 |
0.3240 |
The principal components (PCs) reveal distinct
dimensions of agricultural cultivation factors: PC 1 emphasizes favorable
conditions like low natural hazard losses and government technical assistance;
PC 2 highlights economic support through credit availability and local
marketing facilities; PC 3 focuses on resource availability and crop
productivity; PC 4 deals with marketing and pricing challenges; and PC 5
reflects a mix of lesser-impact factors including wild animal losses and
resource availability. Each component captures a different aspect of the
cultivation environment, from support and risk management to economic and
productivity factors.
5. Conclusion
The Principal
Component Analysis (PCA) conducted on factors influencing sugarcane cultivation
in Baghpat district, Uttar Pradesh, highlights the
multifaceted nature of agricultural productivity. The analysis identifies key
components that explain the majority of variance in the data, with significant
emphasis on environmental conditions, economic support, risk management, and
resource availability. The distinct factors vary across different farmer
groups—marginal, small, and medium—each with unique challenges and
opportunities.
6. References
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estimates for sugar season 2016-17. March Issued by Department of Agriculture
& farmers Welfare. 2017; 49:7.
2. Department
of Economics & Statistics, Baghpat (Govt. of
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