Factors influencing crop diversification as a tool to twofold farmers’ earnings in Uttarakhand

Abstract

Crop diversification is an important strategy for overall agriculture development in the country. The present study was an attempt to identify the factors affecting crop diversification in Uttarakhand state of India. The present study is based on primary data. Multistage stratified random sampling technique was followed to select 45 farmers from hill region and 30 farmers from plain region. Multiple linear regression was used to examine the factor affecting crop diversification. The major factors responsible for the change in crop diversification were fertilizer consumption, gross irrigated area, road length, mechanization and certified seeds, at districts, divisions and state level. In case of farm households age of household head, size of farm households, distance to market, off-farm/non-farm income and fertilizer which effects the crop diversification. The creation of basic infrastructural facilities is an essential prerequisite for crop diversification and fostering the process of agricultural development.

Full text

www.IndianJournals.com
Members Copy, Not for Commercial Sale
Downloaded From IP - 220.227.97.99 on dated 17-Jun-2017
www.soed.in
Factors Influencing Crop Diversification as a Tool to Twofold Farmers'
Earnings in Uttarakhand
ABSTRACT
Crop diversification is an important strategy for overall agriculture development in the country. The present study was an attempt to
identify the factors affecting crop diversification in Uttarakhand state of India. The present study is based on primary data.
Multistage stratified random sampling technique was followed to select 45 farmers from hill region and 30 farmers from plain
region. Multiple linear regression was used to examine the factor affecting crop diversification. The major factors responsible for
the change in crop diversification were fertilizer consumption, gross irrigated area, road length, mechanization and certified seeds,
at districts, divisions and state level. In case of farm households age of household head, size of farm households, distance to market,
off- farm/non- farm income and fertilizer which effects the crop diversification. The creation of basic infrastructural facilities is an
essential prerequisite for crop diversification and fostering the process of agricultural development.
Keywords
Crop diversification, farmers' income, regression
JEL Codes
Q10, Q11, C21, L25
1 2 1
Swati Goswami, Harshika Choudhary*, Asha Bisht
1Department of agricultural economics, G.B.P.U.A&T, Pantnagar, U.S.Nagar, Uttarakhand-263145
2Department of agricultural economics, I.A.S, Banaras Hindu University, U.P.-221005
*Corresponding author’s email: chaudharyharshika@gmail.com
Received: January 10, 2017 Accepted: March 15, 2017
228
INTRODUCTION
India is a country of about 1.24 billion people
(Population Census, 2011). More than 70 per cent of
India's population lives in rural areas where the main
occupation is agriculture. Indian agriculture is
characterized by marginal and small farm holdings.
Agriculture is a critical sector of the Indian economy.
Though its contribution to the overall Gross Domestic
Product (GDP) of the country has fallen from about 30per
cent in 1990-91 to less than 13.9 per cent in 2011-12
(Economic Survey, 2011-12), this d ec re as e in
agriculture's contribution to GDP has not been
accompanied by a matching reduction in the share of
agriculture in employment, hence agriculture yet forms
the backbone of development. An average Indian still
spends almost half of his/her total expenditure on food.
About 52 % of the total workforce is still employed by the
farm sector which makes more than half of the Indian
population dependant on agriculture for nourishment
(Birthal et al., 2006). The economy of Uttarakhand is
predominantly agrarian, as more than three -fourth of its
population is directly engaged in agriculture for their
livelihood. Uttarakhand has only 14 per cent of the total
land under cultivation and about 65 per cent of population
depends on agriculture for their livelihood. The land
holdings are mostly marginal and sub-marginal. Around
71 per cent of the operational holdings of the hill region
are less than 1 ha and nearly 29 per cent are up to 4.0 ha
(Sankhiyiki Diary Uttarakhand, 2010-11).
In the hills of the Uttarakhand, over 40 crop species
and numerous varieties are grown, a diversity which is
maintained through diverse cropping patterns, and which
has evolved in the context of wide variations in edaphic
conditions, coupled with careful selections by farmers.
The practice of Barahnaja is the name of a sophisticated
intercropping system of rain-fed hill farming, the
Barahnaja-literally meaning '12grains'. This practice
involves the sowing of a mixture of crops into a single plot
of land such as Rajma (beans, Phaseolus vulgaris), urd
(black gram, Vigna mungo), mung (green gram,
Vignaradiata), kulath, gahat (horse gram, Macrotyloma
uniflorum), ramdana (Amaranthus frumentaceous),
Indian Journal of Economics and Development
Volume 13 No. 2a: 228-231
April, 2017
DOI: 10.5958/2322-0430.2017.00070.1

www.IndianJournals.com
Members Copy, Not for Commercial Sale
Downloaded From IP - 220.227.97.99 on dated 17-Jun-2017
mandua (finger millet, Eleusinecoracana), jhangora
(barnyard millet, Echinochloa frumentacea), bhatt
(soybean, Glycine soja), lobia (Vignacatiang), kheera
(cucumber, Cucumissativus), bhang (cannabis, Cannabis
sativus), naurangi (ricebean,) and ogal (Buckwheat,
Fagopyrum esculentum) are grown together in a mix
which is finely balanced to optimise productivity,
maintenance of soil fertility, conservation of crop-
diversity, and is geared towards meeting diverse
household requirements (Sati, 2012). Hence crop
diversification is certainly an important component of the
overall strategy for a farm development and is usually
viewed as a risk management strategy.
METHODOLOGY
To identify the factors affecting crop diversification
(Acharya & Basavaraja, 2011).
The diversity index can be expressed as a function of,
DI = f (Xi)
Or
Y = a + b X + b X + ………..+ bnXn+ U
1 1 2 2
Where, Y or DI= Diversity Index (%)
Xi = Explanatory variables (i =1, 2….n)
At districts, division and state level the explanatory
variables taken were,
X = Fertilizer consumption (kg/ha).
1
X = Percentage of gross irrigated to gross cultivated
2
area.
X = Road length (square km)
3
X = Mechanization (includes numbers of manually
4
operated, animal operated and power operated
implements)
X = Quantity of distributed certified seed (quintals)
5
At farm household level the explanatory variables were,
X = Farm size (hectares)
1
X = Age of the household head (years)
2
X = Households size (numbers)
3
X = Distance to market
4
X = Off farm/ non farm income
5
X = Fertilizer use (kg/ha)
6
In case of time series data and cross sectional data, to
check the multicollinearity among different variable, zero
order correlation matrix had been prepared for each
districts as well as for farm households level. At districts,
division and state level, five variables i.e. fertilizer
consumption (kg/ha) X , percentage of gross irrigated to
1
gross cultivated area X , road length(square kilometer)X ,
2 3
mechanization (numbers of manually operated, animal
operated and power operated implements)X , quantity of
4
distributed certified seeds (quintals) X , were taken and
5
for each district zero order correlation matrix has been
prepared to check multicollinearity problem. Districts-
wise only Dehradun, Haridwar, and Nainital showed the
multicollinearity problem among the variables. In
multiple linear regression analysis was carried out to
id ent ify the important f act ors affecting cro p
diversification at district as well as in farm household
level
Dehradun, road length was found multicollinear with
mechanization and percentage of gross irrigated to gross
cultivated area, hence one of the remedy to remove this
multicollinearity problem is to drop the variable so that all
exaplanatory variable could show their effect on
dependent variable. Same nature of multicollinearity was
found in case of Nainital. In Haridwar district,
multicollinearity problem also occurred among the
variables, hence one of the variable i.e. mechanization,
was d r o p p e d t o rem o v e t h i s p r oblem . N o
multicollinearity problem arises in case of farm
households level.
RESULTS AND DISCUSSION
To identify the various factors underlying crop
diversification in Uttarakhand,
Road length shows the significant positive
relationship with crop diversification in Uttarkashi
district indicates that better road inducing diversification
in crops due to easy accessibility to markets and low
transport cost. In some districts the road shows the
insignificant negative relation with crop diversification
indicating the poor road connection and condition in the
districts. Garhwal division and state as whole shows
multiple linear regression
analysis as has been fitted separately at district, division,
state and farm households levels.
Factors affecting crop diversification at district,
division and state level.
At districts, divisions and state level, the Simpson
diversification index was regressed on several causing
factors such as fertilizer consumption (kg/ha) (X ),
1
percentage of gross irrigated area to gross cultivated area
(X ), road length (sq. Km)(X ), mechanization (manually
2 3
operated, animal operated and power operated
implements)(numbers) (X ), and seed distribution
4
(quintals) (X ). To capture the effect of technology
5
adoption, three important variables namely fertilizer use
(kg/ha), percentage of gross irrigated to gross cultivated
area and seed distribution have been included and for
infrastructural development, road connectivity has been
taken.
From the Table 1 it can be revealed that fertilizer
consumption has positive effect on diversification of
Dehradun and Pithoragarh districts, while in other
districts its relationship with the crop diversification is
negative which indicates the constraint of imbalanced
fertilizer use in these districts, while at division and state
levels, fertilizer consumption has no any significant effect
on crop diversification. The regression coefficient of the
percentage of gross irrigated area to gross cultivated area
shows the negative relationship with diversification in
Pithoragarh and Nainital districts, indicating that the crop
diversification is declining with increasing irrigated area
and due to irrigation facilities the farmers were moving
toward specialization, whereas, contrast to this Uttarkashi
district shows the positive relation with diversification
index, indicating that in hilly region availability of
irrigation increasing diversification of crops.
229
Goswami et al.: Factors influencing crop diversification as a tool to twofold farmers' earnings

www.IndianJournals.com
Members Copy, Not for Commercial Sale
Downloaded From IP - 220.227.97.99 on dated 17-Jun-2017
insignificant negative effect on crop diversification.
Among the districts, Almora, Chamoli, Champawat,
Nainital, Rudrapayag and Tehri shows the significant
posi ti ve effect of m echanizatio n on the c rop
diversification indicating the more the number of
manually, animal and power operated machine the more
diversification in crop can be observed, whereas contrast
Districts/
Divisions/ State
Intercept Fertilizer
consumption
(X )
1
Per cent of
gross
irrigated
to
gross
cultivated
area (X )
2
Road
length
(X )
3
Mechanization
(X )
4
Certified
seed
(X )
5
2
R
Almora -4.19 -0.001 0.003 0.0004 0.0006* -0.0001 0.81
Bageshwar 0.47 0.006 -0.001 0.0002 0.0004 0.00006 0.32
Chamoli -0.52 0.003 0.0005 0.00001 0.00001** 0.00005 0.87
Champawat -1.46 -0.0005 0.005 -0.0001 0.0008*** 0.00006 0.69
Dehradun 0.49 0.006*** -0.05 - 0.00007* -0.00008** 0.91
haridwar 0.37 -0.0001 0.003 0.0008 - -0.0001 0.65
Nainital -2.05 0.0002 -0.013* - - 0.0007*** -0.00002 0.93
Pauri 1.22 0.004 -0.0008 -0.005 -0.00001 -0.0008 0.39
Pithoragarh 1.28 0.02** -0.02* -0.0007 -0.00006** 0.00003*** 0.95
Rudrprayag 0.26 0.002 -0.0008 -0.00004 0.00001* -0.00002 0.85
Tehri -0.144 -0.001 -0.005 0.0005 0.0006* 0.00002 0.87
U S Nagar 1.10 -0.005 -0.002 0.0001 -0.0004 0.0001 0.35
Uttarkashi 0.43** -0.0001 0.009* 0.0002** -0.0001* -0.0002 0.95
Kumaun 1.25 0.0006 -0.01 0.0001 0.0001 0.0003 0.54
Garhwal 3.28 0.002 -0.03 -0.0005 -0.0007** 0.000002 0.85
Uttarakhand 1.81 0.001 -0.006 -0.0002 -0.0005 0.00001 0.70
Table 1: Estimated regression function for the determinants of crop diversification
*, **, *** indicate significant at 1 per cent, 5 per cent and 10 per cent probability levels, respectively.
Figures in parentheses denote standard error
Variables Valley Mid hills High hills Overall hills Plains
Intercept 0.07
(0.40)
1.46
(0.36)
0.12
(0.42)
0.55
(0.39)
0.29
(0.28)
Farm size (ha) X10.03
(0.06)
0.03
(0.04)
-0.02
(0.11)
0.013
(0.07)
-0.003
(0.005)
Age of the head (years) X20.01***
(0.008)
0.001
(0.007)
-0.01
(0.06)
0.0003*
(0.02)
0.006*
(0.001)
Household size (numbers) X3-0.04
(0.02)
-0.003
(0.01)
0.03***
(0.01)
0.02**
(0.01)
0.01***
(0.006)
Distance to markets (km) X40.08
(0.09)
-0.03***
(0.01)
0.03
(0.02)
-0.03
(0.04)
0.01
(0.02)
Off farm/non farm income X5-0.009
(0.0001)
0.001
(0.008)
0.0004
(0.0006)
-0.0002
(0.0001)
-0.008***
(0.009)
Fertilizer (kg/ha) X6-0.001
(0.0009)
-0.07**
(0.03)
0.001
(0.0007)
-0.02
(0.001)
0.002
(0.004)
2
R 0.46 0.59 0.56 0.58 0.47
Table 2: Factors affecting crop diversification at farm household level
*, **, *** indicate significant at 1 per cent, 5 per cent and 10 per cent probability levels, respectively.
Figures in parentheses denote standard error
to this some districts like Pithoragarh and uttarkashi show
negative relation with crop diversification. Distribution
of seed has significant dampening effect on crop
diversification in Dehradun. While Pithoragarh district
registered as a significant positive effect on crop
diversification. Division and state as whole show no
significant effect on crop diversification.
230
Indian J Econ Dev 13(2a): 2017 (April)

www.IndianJournals.com
Members Copy, Not for Commercial Sale
Downloaded From IP - 220.227.97.99 on dated 17-Jun-2017
Factors affecting crop diversification at farm
household level
A t f a r m h o us e h o ld s l e v e l t h e S i mp s o n
Diversification indices was regressed on several causing
factors such as farm size (ha) (X ), age of the households
1
heads (years) (X ), households size (numbers)(X ),
2 3
Distance from market (km) (X ), off farm/non-farm
4
income (rupees) (X ), and Fertilizer use (kg/ha) (X )
5 6
(Patil & Taillie,
Table 2 indicates that farm size has no significant
effect on crop diversification.
1982).
Age of the household head
shows the significant positive relation in valley, and
overall hills and also on plains indicating that older farm
operators are more likely to diversify. The size of the farm
households exerts a significant positive effect on crop
diversification in high hills, overall hills condition and
plain region. Significant negative impact of distance to
market on the crop diversification was noticed only in mid
hills indicating the more the proximity of main road and
market, the more is the importance of diversification of
crops. Effect of off farm/ non-farm income on crop
diversification show significant positive effect in plain
region, while in valley and overall hills no any significant
relation has been observed. Fertilizer use show the
negative effect on crop diversification only in mid hills
and no any significant effect on diversification in other
hilly altitudes as well as in plains has been found. So it can
be inferred that age of the household head and household
size has positive effect on crop diversification, while
distance to market and fertilizer use show negative effect
on crop diversification.
CONCLUSIONS
The major factors responsible for the change in crop
diversification were fertilizer consumption, gross
irrigated area, road length, mechanization and certified
seeds, at districts, divisions and state level. In case of farm
households age of household head, size of farm
households, distance to market, off farm/non- farm
in come a nd fer tilizer which effects the crop
diversification. The creation of basic infrastructural
facilities is an essential prerequisite for crop
diversification and fostering the process of agricultural
development. Crop varietal diversification at farm level
has been found less in hilly region in case of paddy
(kharif), so emphasis should be given on proper
development and dissemination of high yielding varieties
(HYV) suitable to hilly environment. The two factors i.e.
age of household head and household sizes had positive
effect on crop diversification.
REFERENCES
Acharya, S.P., & Basavaraja, H. (2011). Crop diversification in
Karnataka: An economic analysis. Agricultural Economics
Research Review, 24, 351-357.
Birthal, P.S., Jha, A.K., Joshi, P.K., & Singh, D.K. (2006).
Agricultural diversification in North-Eastern Region of
India: Implications for growth and equity. Indian Journal
of Agricultural Economics, 61(3), 328-340.
Economic Survey 2011-12. (2012). Government of India, New
Delhi.
Patil, G.P., &Taillie, C. (1982). Diversity as a concept and its
measurement. Journal of American Statistical Association,
77, 548-568.
Population Census. (2011). Government of India, New Delhi.
Sankhiyika Dairy Uttarakhand. (2010-11). Directorate of
Economics and Statistics, Government of Uttarakhand,
Dehradun.
Sati,V.S. (2012). Agricultural diversification in the Garhwal
Himalaya: A spatio-temporal analysis. Sustainable
Agricultural Research, 1(1), 77-86.
Webliography
Censusindia.gov.in/Tables_Published/Tables_published.html
231
Goswami et al.: Factors influencing crop diversification as a tool to twofold farmers' earnings