ArticlePDF Available

Awareness about Minimum Support Price and Its Impact on Diversification Decision of Farmers in India: MSP Awareness and Crop Diversification

Authors:

Abstract and Figures

In this article, we have analysed farmers' awareness about Minimum Support Price (MSP) and its impact on diversification of crops grown in India. We used nationally representative data collected by National Sample Survey Office, 70th round data. The data revealed that only 23.72 and 20.04 per cent of farmers in the rural agricultural households in India are aware of MSP of crops grown by them in kharif and rabi season, respectively. From the results of probit model, it is inferred that MSP needs to be backed up by effective procurement coupled with awareness creation by extension system to enable more number of farmers to take benefit of this safety net. We have also explored the relationship between farmers' awareness about MSP and decision to go for crop specialization using Heckman selection model. The study shows that farmers' knowledge of MSP had not lead to specialization.
Content may be subject to copyright.
Original Article
Awareness about Minimum Support Price and Its Impact on
Diversication Decision of Farmers in India
K.S. Aditya, S.P. Subash, K.V. Praveen, M.L. Nithyashree, N. Bhuvana and
Akriti Sharma*
Abstract
In this article, we have analysed farmers
awareness about Minimum Support Price
(MSP) and its impact on diversication of crops
grown in India. We used nationally representa-
tive data collected by National Sample Survey
Ofce, 70th round data. The data revealed that
only 23.72 and 20.04 per cent of farmers in the
rural agricultural households in India are
aware of MSP of crops grown by them in kharif
and rabi season, respectively. From the results
of probit model, it is inferred that MSP needs
to be backed up by effective procurement
coupled with awareness creation by extension
system to enable more number of farmers to
take benetofthissafetynet.Wehavealsoex-
plored the relationship between farmers
awareness about MSP and decision to go for
crop specialization using Heckman selection
model. The study shows that farmersknowl-
edge of MSP had not lead to specialization.
Key words: agricultural policy, crop
diversication, Minimum Support Price, crop
specialization
1. Introduction
Minimum Support Price (MSP) is an integral
component of Agriculture Price Policy of
India. It targets to ensure support price to
farmers and affordable price to consumers
through public distribution system (PDS)
(Parikh & Singh 2007). The price support sys-
tem was conceptualized during pre-green revo-
lution period as an institutional mechanism for
incentivizing farmers to adapt new technolo-
gies (Planning Commission 2005; Deshpande
2008). Later, Agriculture Price Commission
was established in the year 1965, based on
Jha committee recommendations to suggest
support prices for crops after considering the
cost of cultivation to account (Kadasiddappa
et al. 2013). Broad objectives of the commis-
sionaretoensureremunerativepricesto
farmers and reasonable prices to consumers
and promote sustainable use of resources
towards socially desirable crop mix (Parikh &
Singh 2007).
Price incentives in the form of support prices
helped India to increase food production dur-
ing green revolution period. MSP also aims at
procuring food grains from food surplus states
for distribution through PDS and maintaining
buffer stockand thus bridge the demand supply
gap (Jha & Srinivasan 2006; Chand 2008).
Price incentives in form of MSP are credited
for the increase in area under rice and wheat
in the green revolution states like Punjab and
Haryana.
Agricultural situation in India has under-
gone sea change after the green revolution
* Aditya, Praveen and Nithyashree: Division of Ag-
ricultural Economics, Indian Agricultural Research
Institute (IARI), New Delhi, India; Subash: National
Institute of Agricultural Economics and Policy
Research (NIAP), New Delhi 110012, India;
Bhuvana: Agricultural Extension, PJTSAU, Hydera-
bad; Sharma: Zonal Technology Management and
Business Planning and Development Unit, IARI.
Corresponding Author: Praveen, email <veenkv@
gmail.com>.
Received: 5 May 2017 | Revised: 2 August 2 017 | Accepted: 7 August 2017
Asia & the Pacic Policy Studies, vol. ••,no.••,pp.••–••
doi: 10.1002/app5.197
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
This is an open access article under theterms of the Creative Commons Attribution-NonCommercial License, which permits
use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial
purposes.
bs_bs_banner
period, but the agriculture price policy has
more or less remained same (Chand 2003).
Food surplus is available in many states and
not just Haryana, Punjab and Andhra Pradesh;
however, the procurement has largely conned
to these regions (Planning Commission 2005).
MSP is viewed as a safety net to ensure price
security for a long-term investment decision
to farmers.
There have been many concerns off late
regarding operation and effectiveness of
MSP. Many studies have pointed out that
MSP is leading to regional imparity in incomes
as it is effective only in few states where it is
backed by procurement (Ali et al. 2012;
Tripathi 2013; Schiff et al. 1992). MSP is also
said to have favoured crop specialization in
with rice and wheat at the cost of pulses and
oil seeds (Chand 2003; Jha & Srinivasan
2006; Jha 2009; Mittal & Hariharan 2016).
With demandsupply situation undergoing
sea change over the last couple of decades or
so, the agriculture price policy needs a relook.
Treating MSP as a safety net, in this study,
we explore the farmersawareness of MSP of
crops grown by them, across crops and states,
with the hypothesis that awareness is the bare
minimum requirement for policy interventions
to have any impact. We also explore the
reasons for the apathy of farmers to sell their
produce to procurement agencies. The corre-
lates of awareness about MSP have also been
examined. The study also tries to reconnoitre
the possible relationship between knowledge
of MSP and farmerschoice of crop
specialization/diversication. The key objec-
tives of the study are to understand the status
of farmersawareness of MSP of crops grown
by them and its correlates and to explore the
nature of the relationship between farmers
awareness of MSP and decision to diversify
the crops.
2. Theoretical Framework
When viewed from a safety net perspective,
MSP helps farmers by setting oor price if
procurement agency purchases the product at
MSP when the open market price falls below
the oor price. In absence of procurement, a
farmer can refuse to settle for a price below
MSP if he is aware of the support price for
the crops. If he is not even aware of MSP of
crops, traders and middlemen can turn exploit-
ative and offer price less than MSP (Economic
Survey 2016). So, in this study, awareness
about MSP is considered as a proxy for the
impact of support prices.
The underlying theoretical framework for
the relationship between crop diversication
and MSP awareness is drawn from livelihood
diversication theory. Livelihood diversica-
tion is dened as the process by which rural
families construct a diverse portfolio of activi-
ties and social support capabilities in their
struggle for survival in order to improve their
standards of living (Ellis 1998). The theory
states that farmers respond to policy, environ-
mental and resource constraints with measures
like changing crop choices. This study is based
on the assumption that if there is no MSP, the
farmers would rationally diversify his cropping
pattern. In India, a signicant amount of area
under coarse cereal crop has been replaced by
ne cereals like rice and wheat, which clearly
suggest the biasness of MSP towards the later
mentioned crops. Farmers who are aware of
MSP will be inclined to allocate major area
share to that crop as there is little price risk
involved. This assumption is valid in our case
as most of the farmers who were aware of
MSP of crops are either rice or wheat growers.
Our aim here is to see whether knowledge of
MSP makes them to allocate more area to that
crop leading to crop specialization (or less or
no diversication) or not.
3. Data and Methods
We have used the data from Situational
Assessment Survey of Farmers(National
Sample Survey Ofce 70th round). The sample
consists of data regarding 35,200 rural agricul-
tural households spread across 4,529 villages
of India collected using stratied multistage
random sampling (NSSO 2015). The data were
collected for kharif and rabi (the two major
agricultural seasons of India) in two separate
visits and pertain to the year 201213. Only
the households that grow at least one crop for
2 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
which MSPs are announced were considered
for analysis as MSP is announced only for 24
crops at present (Table A1). A household,
which is aware of MSP of at least one crop, is
treated as being awareabout MSP and vice
versa.
We have also used agro-climatic region
dummies at the district level to account for
the difference in agro-climatic parameters that
affects the crop diversication. Theoretically,
amongst the climatic variables, rainfall plays
an important role in farmerschoice of crop
diversication. Hence, district-wise rainfall
data for the corresponding year was collected
from Indian Meteorological Department and
used explicitly in the regression equation.
3.1. Simpson Diversity Index for Measuring
Crop Specialization
There are different methods to measure
crop diversity. Diversity indices are a common
measure for assessing plant diversity
(Magurran 2004). Simpson index is more
responsive to dominant species (Aguilar et al.
2015). Crop diversication was quantied
using Simpson index in this study. The index
is based on the share of the ith crop in gross
cropped area for the season (Mittal &
Hariharan 2016). The value of the index
depends on a number of crops grown and
their share in gross cropped area. Assuming
all the crops grown have the same share of
gross cropped area, as the number of crops
grown approaches innity, the value of index
approach unity. Theoretically, Simpson index
ranges between zero and one, zero being the
case of complete specialization.
SI ¼1
n
i¼1
P2
i
where P
i
represents the share of area under
each crop to gross cropped area.
3.2. Heckman Model
Heckman selection model could be used to
access whether there is an underlying regres-
sion relationship between awareness about
MSP (regression equation) and cropping
diversity (selection equation). The underlying
assumption is that awareness about MSP is
non-random contingent upon a set of ob-
served and unobserved characteristics. Gen-
erally, estimated impact of MSP awareness
may be biased and inconsistent because of
selection biases. Source of this bias is
endogeneity of awarenessvariable (corre-
lated with the error term consequential upon
non-randomness). We need to account for
unobservable factors affecting both probabil-
ity of being aware of MSP and choice of
crop diversication.
The rst step in our model is the selection
equation that captures the factors that affect
probability of farmer being aware of MSP
of crops that he grows. Then we estimate
inverse lambdato account for selection bias
and include it as an explanatory variable in
the outcome equation that establishes the rela-
tionship between crop diversication and
MSP awareness (following Briggs 2004). As
suggested by Heckman, this approach will
account for the unobserved selection bias and
produce efcient estimates. And also, the rst
equation helps us to identify the drivers of
awareness about MSP awareness.
Regression equation used in the study
is expressed mathematically as outcome
equation:
Yj¼Xjβþu1j(1)
where Y
j
is the Simpson index of diversica-
tion and X
j
is the vector of independent
variables.
The corresponding selection equation is
M¼zjγþμ2jand observed only if zjγ
þμ2j>0(2)
where Mis equal to awareness about MSP of
crops
μ1
eN0;σðÞ
μ2
eN0;σðÞ
corr u1;u2
ðÞ¼ρ
3Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
When ρ0, standard regression techniques
applied to the rst equation yield biased results.
Heckman provides consistent, asymptotically
efcient estimates for all the parameters in such
models. The variables used in the model are
given in Table 1.
3.3. Limitations
Farmerschoice of crop diversication also
depends on the scope for diversication in
the region and socio-economic criteria. We
have tried to account for most of these
factors within the constraints imposed by the
availability of data and methods. We admit that
research question can be better addressed with
panel data.
4. Results and Discussion
Minimum Support Prices are an important
component of agriculture price policy in
India. The scheme provides the oor price for
farm produceand also makes food grains avail-
able for buffer stock and PDS. It provides secu-
rity for long-term investment decisions of the
farmers. Another important objective of MSP
is to incentivize the farmer to allocate resources
in socially desired cropping patterns. MSP is
expected to provide a sense of price security
to the farmer and motivate them to diversity
the crops. MSP as an incentive for diversica-
tion is superior to other incentives (Planning
Commision 2005).
Policy decisions by the Government in
terms of MSP for the past 14 years are
Table 1 Variables Used in the Analysis and Their Description
Name Type
Outcome equation: Dependent variable is Simpson index of diversication
Aware about MSP of crops Dummy = 1 if aware and 0 otherwise
Inverse mills Variable to account for selection bias
Annual rain Mean annual rainfall of the district (for 201213)
Annual drought Dummy = 1 if actual rainfall is decient by more than 20%
SC Dummy = 1 if belongs to scheduled caste and 0 otherwise
ST Dummy = 1 if belongs to scheduled tribe and 0 otherwise
OBC Dummy = 1 if belongs to other backward caste and 0 otherwise
Age Age of the head of the household
Age
2
Squared term of age of head of household
Household size Number of members is family
Literate Dummy = 1 if literate and 0 otherwise
Received training in agriculture Dummy = 1 if received training in agriculture and 0 otherwise
Public extension contact Dummy = 1 if accessed public extension service and 0 otherwise
Progressive farmer contact Dummy = 1 if accessed progressive farmer as source of information and 0 otherwise
Mass media contact Dummy = 1 if accessed progressive farmer as source of information and 0 otherwise
Agriculture as primary income
source
Dummy = 1 if agriculture is the main source of income and 0 otherwise
Marginal farmer Dummy = 1 if size of operational holding is less than 1 ha and 0 otherwise
Small farmer Dummy = 1 if size of operational holding is between 1 and 2 ha and 0 otherwise
Semi-medium farmer Dummy = 1 if size of operational holding is between 2 and 5 ha and 0 otherwise
Medium farmer Dummy = 1 if size of operational holding is between 5 and 10 ha and 0 otherwise
BPL Dummy = 1 if farmer belongs to below poverty line category
AEZ_dummy Dummies for 14 different ACZ, =1 if the farmer belongs to that particular ACZ.
Selection equation: Dependent variable n= awareness about MSPDummy = 1 if farmer is aware
Male headed Farm household where head of the family is male
Effective states Dummy = 1 if the farmer belongs to states where there is the active procurement of
food grains, that is, Haryana or Punjab or Chhattisgarh or Andhra Pradesh or Karnataka
and 0 otherwise (classication based on literature survey)
Notes: Squared term of age is used to improve the t of the model as the reviewed literature suggests thatthe variable follows
quadratic form. ACZ, agro-climatic zones; MSP, Minimum Support Price.
4 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
presented in Figure 1. MSP for all crops is
continuously rising and conscious effort by
the policy to favour pluses, oilseeds and minor
cereals can be seen particularly after 200708.
During this period, MSP for major pulses was
raised by Rs. 526 (Tur), 955 (each for Moong
and Urad), 624 (Groundnut) and 156 (Gram)
over the previous years when compared with
Rs. 240, 367 and 240 hikes in MSP for Jowar,
Ragi and Bajra respectively in real terms.
Further, in 201112, MSP for these was hiked
by Rs. 615 (Tur), 860 (Moong), 961 (Urad),
967 (Groundnut), 588 (Gram), 506 (Jowar),
436 (Ragi) and 184 (Bajra) over the previous
years in real terms. But is this a sufcient
incentive for the farmer to diversify the crops
with the inclusion of pulses and other minor
crops? Does the announcement of MSP instil
a sense of price security in a farmer?
For MSP to function as safety net, there
must be a system of procurement, which
should buy the produce at MSP whenever
market prices fall below support price for the
crop, and farmer must be aware of the MSP
for the crops grown by him so that he can
refuse to sell his produce at price below MSP.
Table 2 presents the percentage of farmers
who are aware of MSP of crops grown by
them. At crop level (each farmer may be grow-
ing more than one crop), the awareness stands
at around 17 per cent for both kharif and rabi.
But MSP is announced only for selected crops
in each season and calculating the share to a
total number of crops may be erroneous. We
have considered subsample of farmers who
grow at least one crop for which MSP is
announced, and data of such farmers are used
in the analysis (except summary statistics).
Any household having knowledge of MSP
for at least one crop is considered as aware,
and the share of such households is 28.30 and
23.13 per cent in rabi and kharif, respectively
(Table 2). We can draw inference for India as
Figure 1 Trends in Minimum Support Price for Major Pulses, Oilseeds and Cereal Crops (in Rs. at 201112 Prices)
Table 2 Share of FarmersAware of MSP of Crops
Grown by Them (%)
Awareness of MSP Rabi Kharif
Awareness at crop level 17.51 17.16
Awareness at household level 28.30 23.13
Awareness at household level
with sampling weight
23.72 20.04
Note: MSP, Minimum Support Price.
Source: NSSO data.
5Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
a whole only after accounting for sampling
weights. So we can say that only 23.72 and
20.04 per cent of Indian households are aware
of MSP of crops grown by them (Figure 2).
Even after more than 40 years after its
implementation, less than 25 per cent of
farmers knows the MSP of crops they grow.
Although MSP is announced for the whole of
India, the operation is limited only to few states
where the designated government agencies
procure the produce from farmers. Statewise
gures on farmersknowledge of MSP of
crops support our proposition. In states where
procurement of food grains through designated
agencies is more active, like Punjab, Haryana,
Chhattisgarh, Uttar Pradesh and Telangana,
the awareness of MSP is also high (Table 3).
Further, 27.83 and 30.48 per cent of farmers
are reported of being unaware about the agency
that procures the food grains at MSP (Table 4).
There is a need for creating a good network of
procurement agencies and also for awareness
amongst farmers about the operation of MSP.
Except for crops like rice and wheat, quantity
procured is very limited leading low level of
awareness. Even for rice and wheat, procure-
ment takes place only in few states, and more
farmers are aware of MSP in those states
(Figure 3).
Minimum Support Price of rice and wheat is
known to the majority of the farmers in
Figure 2 Statewise Awareness of Farmers about Minimum Support Price (MSP) of Crops in Major
Crop Seasons (%)
Table 3 StatewiseAwareness of Farmers about MSP of
Crops (%)
States Rabi Kharif
Punjab 52.94 48.93
Chhattisgarh 37.09 47.20
Delhi 64.29 41.18
Odisha 9.85 36.23
Haryana 32.10 27.80
Uttar Pradesh 22.43 27.59
Bihar 22.84 27.49
West Bengal 19.29 26.23
Telangana 30.82 25.32
Kerala 19.47 22.09
Rajasthan 20.90 15.06
Andhra Pradesh 14.35 14.60
Karnataka 14.61 13.97
Jharkhand 4.96 13.25
Himachal 10.24 13.24
Madhya Pradesh 30.47 12.19
Gujarat 9.97 12.02
Uttaranchal 9.14 9.81
Jammu and K 6.03 8.21
Maharashtra 8.00 8.19
Tamil Nadu 15.12 7.71
Arunachal 7.39 6.49
Tripura 21.50 5.9 9
Mizoram 0.30 4.24
Assam 3.88 4.09
Nagaland 1.96 3.87
Chandigarh 6.67 3.13
Meghalaya 12.52 1.33
Manipur 0.14 0.48
Sikkim 0.00 0.00
Note: MSP, Minimum Support Price.
Source: NSSO data.
6 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
Haryana, Punjab and Chhattisgarh; major
states from where food grains are procured
for buffer stock or PDS. Knowledge of MSP
of rice and wheat is less in other growing
regions like Andhra Pradesh, Telangana,
Karnataka, Uttar Pradesh and Bihar. For
important pulse crops like Tur and Gram, the
share of farmers knowing MSP of crops is very
less across all states. Less than 10 per cent
awareness for most the pulses is another cause
of concern (Tables 5, 6). With India trying to
increase the pulse production, support prices
can act as an incentive if the government starts
procurement through a good network of chan-
nels. Then only the benet of support prices
will reach farmers and be able to provide price
security that it intends to.
Out of meagre proportion of farmers who
were aware of MSP, 75.09 and 75.58 per cent
of farmers (in rabi and kharif, respectively)
have not sold the produce to procurement
agencies. The reasons for not selling to pro-
curement agency are given in Table 7. The role
of MSP is to set the oor price, and if farmers
have received a better price than MSP, it is ne.
Only 7.97 and 8.77 per cent of farmers reported
that they did not sell to procurement agency as
they received a better price in the market. But
nearly 25 per cent of farmers reported that there
is no procurement agency/local purchaser
available to procure the produce at MSP.
Ideally speaking, the benetofMSPshould
reach all farmers across all states and for which
a good network of procurement agencies with
required infrastructure is also must. But from
a practical point of view, this may not be
feasible. Government agencies are already
facing difculty in storage and maintenance
Table 4 Farmers Awareness of Procurement Agencies
(%)
Procurement agency Rabi Kharif
Food Corporation of I ndia 18.8 0 19.02
Jute Corporation of India 1.02 1.35
Cotton Corporation of India 0.62 2.13
National Agricultural Cooperative
Marketing Federation of India Ltd
5.24 3.30
State Food Corporation 17.74 13.23
State Civil Supplies 7.73 9.13
Others 21.03 21.36
Did not know 27.83 30.48
Total 100 100
Source: NSSO data.
Figure 3 Cropwise and Statewise Awareness of Farmers about Minimum Support Price of Crops
Table 5 Farmers Awareness about MSP by Crop
Groups (%)
Crop group Rabi Kharif
Cereals 29.02 22.04
Pulses 10.1 8.67
Oilseeds 17.67 15.88
Sugarcane, cotton and jute 24.24 21.04
Total 17.51 17.16
Note: MSP, Minimum Support Price.
Source: NSSO data.
7Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
of food grains procured. The infrastructure
available may not allow us to expand procure-
ment to the whole country. Instead, MSP can
be backed by the method of deciency pay-
mentsas suggested by many earlier studies
and reports. If the market prices fall below
MSP, 50 per cent of the difference between
price and MSP will be paid to a farmer under
this system. MSP needs to be backed by either
effective procurement or system of deciency
payment if it were to help the farmer by setting
oor prices.
Another thing to note is 66.68 and 63.22 per
cent of farmers in rabi and kharif reported that
they have not sold to procurement agency
because of other reasons. The other reason
might include a delay in payments of money
by procurement agencies. A study by
(Deshpande 2008) reported lag of nearly
1 month between procurement and payment.
The method of payment using checks rather
than cash might also add to farmersindifferent
attitude to procurement agencies.
We have used a probit model to identify the
correlates of farmersknowledge of MSP of
crops grown by them. The same model is used
as selection equation (rst step) in Heckman
two-step selection model. Households aware-
ness of MSP is the dependent variable for the
regression. State dummies were used to nullify
the effect of unobserved heterogeneity across
the states. Standard errors were clustered at
the region to enhance the precision. The results
indicate that literacy and extension contact
(both public extension and progressive farmer
contact) are positively associated with aware-
ness about MSP. Marginal farmers have less
probability of knowing MSP of crops com-
pared with their counterparts. Households
below the poverty line
1
were also found to
have lesser awareness about MSP (Table 8).
If farm household belongs to states where
MSP is coupled with active procurement
1. In this study, the poverty line is measured using posses-
sion of below povertyline cards. These cards are distributed
amongst rural households that make them eligible under
PDS.
Tabl e 6 Fa r me r sKnowledge of MSP by Crops
Crop
Rabi Kharif
Aware Not aware Aware Not aware
Number Percentage Number Percentage Number Percentage Number Percentage
Paddy 1,183 28.15 3,020 71.85 4,987 27.43 13,192 72.57
Jowar 46 8.65 486 91.35 128 7.8 1,514 92.2
Bajra 7 5.22 127 94.78 320 16.15 1,661 83.85
Maize 200 15.55 1,086 84.45 466 11.29 3,663 88.71
Wheat 3,621 33.2 7,285 66.8 2 11.11 16 88.89
Gram 256 11.67 1,937 88.33 7 5.38 123 94.62
Tur 38 7.72 454 92.28 97 7.87 1,135 92.13
Urad 48 13.83 299 86.17 121 10.07 1,080 89.93
Sugarcane 458 55.18 372 44.82 702 46.34 813 53.66
Groundnut 37 9.44 355 90.56 67 7.88 783 92.12
Cotton 111 24.24 347 75.76 491 21.04 1,843 78.96
Note: MSP, Minimum Support Price.
Source: NSSO data.
Table 7 Reasons quoted by farmers for not selling to
procurement agency
Rabi Kharif
Percentage of farmers not selling to procurement agencies
75.09 75. 58
Reason for not selling to procurement agencies
Procurement agency not available 16.53 17.53
No local purchaser 6.63 6.94
Poor quality of crop 1.25 2.31
Crop pre-pledged 0.75 1.20
Received better prices 7.97 8.77
Other 66.88 63.26
Total 100.00 100.00
Source: NSSO data.
8 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
(variable-effective states), the probability of
such household knowing MSP of the crop is
high. But, probit coefcients cannot be
compared based on magnitude. So, we calcu-
lated marginal effect of each variable that sig-
nies magnitude of increase in the probability
Table 8 Correlates of MSP Awareness: Results of Probit Analysis (First Stage of Heckman Model = Selection
Equation)
Variables
Kharif Rabi
Coefcient P>|z|Coefcient P>|z|
Male headed 0.144 0.004 0.158 0.246
Literate 0.266 0.000 0.261 0.000
Received training in agriculture 0.153 0.030 0.085 0.553
Household size 0.007 0.079 0.015 0.042
ST 0.157 0.059 0.128 0.274
SC 0.118 0.023 0.014 0.863
OBC 0.068 0.061 0.009 0.917
Age 0.011 0.010 0.008 0.572
Age
2
0.000 0.116 0.000 0.726
Marginal farmer 0.566 0.000 0.637 0.002
Small farmer 0.280 0.011 0.286 0.149
Semi-medium farmer 0.146 0.163 0.236 0.194
Medium farmer 0.026 0.802 0.255 0 .109
BPL 0.152 0.000 0.145 0.059
Land leased in 0.035 0.099 0.039 0.224
Agriculture as primary income source 0.040 0.304 0.068 0.162
Public extension contact 0.347 0.000 0.336 0.000
Progressive farmer contact 0.386 0.000 0.412 0.000
Mass media contact 0.089 0.137 0.090 0.202
Effective states 1.169 0.000 1.156 0.001
Constant 1.763 0.000 1.918 0.000
Notes: Dependent variable: Aware about MSP. Log pseudolikelihood = 30,483,404, number of observations = 27,715,
pseudo-R
2
= 0.1699, standard error adjusted for 84 clusters in region and state dummies were also used (for kharif). Log
pseudolikelihood = 26,819,397, number of observations = 20,816, pseudo-R
2
= 0.1453,standard error adjusted for 85 clus-
ters in region and state dummies were also used (for rabi). BPL, below poverty line; MSP, Minimum Support Price; OBC,
other backward caste; SC, scheduled caste; ST, scheduled tribe.
Figure 4 Marginal Effects of Variables on Probability of Farmer Being Aware about Minimum Support Price. BPL,
Below Poverty Line; OBC, Other Backward Caste; SC, Scheduled Caste; ST, Scheduled Tribe
9Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
of being aware of MSP for a unit increase
in a respective variable. The result is depicted
in Figure 4. Most important variables that
increase the awareness about MSP are active
procurement, awareness creation (public ex-
tension contact and progressive farmer contact)
and literacy. This reinstates that announcing of
MSP does not work in isolation unless coupled
with procurement. Recent hikes in MSP of
pulses and oilseeds to increase in production
may not work unless it is backed by procure-
ment or deciency payment.
In the next step, using Heckman selection
model, we tried to establish the relationship
between farmersdecision to diversify (or to
specialize) and MSP awareness (Table 9). For
kharif season, variable for awareness was
statistically insignicant, and for rabi, it was
positive as well signicant. So we do not
accept the null hypothesis that awareness about
Table 9 Relationship between Knowledge of MSP and Crop Diversication: Result of Heckman Outcome Equation
Simpson index
Kharif Rabi
Coefcient P>|t| Coefcient P>|t|
Aware about MSP of crops 0.007 0.379 0.023 0.009
Inverse mills 0.055 0.305 0.045 0.603
Annual rain 0.000 0.047 0.000 0.944
Annual drought 0.010 0.515 0.000 0.976
SC 0.006 0.676 0.022 0.085
ST 0.059 0.001 0.014 0.356
OBC 0.004 0.699 0.006 0.457
Age 0.001 0.490 0.002 0.048
Age
2
0.000 0.575 0.000 0.077
Household size 0.003 0.000 0.003 0.000
literate 0.023 0.166 0.015 0.458
Received training in Agriculture 0.014 0.274 0.008 0.559
Public extension contact 0.017 0.190 0.019 0.427
Progressive farmer contact 0.008 0.322 0.012 0.274
Mass media contact 0.019 0.014 0.020 0.034
Agriculture as primary income source 0.070 0.125 0.074 0.122
Marginal farmer 0.029 0.437 0.017 0.608
Small farmer 0.004 0.912 0.005 0.845
Semi-medium farmer 0.019 0.442 0.003 0.873
Medium farmer 0.008 0.611 0.008 0.749
BPL 0.003 0.784 0.000 0.974
AEZ15_dummy 0.060 0.187 0.113 0.024
AEZ2_dummy 0.051 0.172 0.015 0.822
AEZ3_dummy 0.016 0.743 0.101 0.402
AEZ4_dummy 0.009 0.922 0.005 0.970
AEZ5_dummy 0.058 0.542 0.017 0.891
AEZ6_dummy 0.059 0.151 0.000
AEZ7_dummy 0.135 0.001 0.052 0.665
AEZ8_dummy 0.025 0.761 0.059 0.606
AEZ9_dummy 0.051 0.522 0.036 0.750
AEZ10_dummy 0.061 0.270 0.097 0.314
AEZ11_dummy 0.121 0.000 0.055 0.607
AEZ12_dummy 0.011 0.865 0.044 0.694
AEZ13_dummy 0.014 0.813 0.003 0.950
AEZ14_dummy 0.036 0.706 0.053 0.653
Constant 0.444 0.000 0.207 0.298
Notes: Number of observations = 26,330, R
2
= 0.1700, standard error adjusted for 84 clusters in the region and state dummies
were also used in kharif season. Number of observations = 19,901, R
2
= 0.1350, standard error adjustedfor 83 clusters in the
region and state dummies were alsoused in rabi season. BPL, below poverty line;MSP, Minimum Support Price; OBC, other
backward caste; SC, scheduled caste; ST, scheduled tribe.
10 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
MSP leads to crop specialization. Although the
sign of awareness variable is positive, the mag-
nitude of the coefcient is very small indicat-
ing the weak relationship it has with crop
diversication. Most of the variability is
absorbed by state dummies, indicating a deci-
sion to diversify mostly depends on the poten-
tial of the region more than the other factors
(Figure 5).
5. Concluding Remarks
Minimum Support Prices are considered as an
important pillar of Indian Agricultural price
policy rolled out with an intention of providing
price security to farmers. Theoretically, the
support prices are to benet farmers of most
of the crops in the entire nation. In this article,
we tried to analyse the level of awareness of
farmers about MSP of crops they grow and
its correlating factors using a comprehensive
dataset of National Sample Survey Ofce,
70th round. We found that more than 75 per
cent of Indian households is not aware of
MSP of crops grown by them. Awareness
was high only in case of rice and wheat that
too only in few states like Punjab, Haryana
and Chhattisgarh, from where food grains are
heavily procured by designated agencies for
maintaining buffer stock or PDS. Awareness
of MSP of pulse crops was even less (<10
per cent for most of the crops), which is a cause
of concern. If the farmers are awareof the MSP
of crops, they can bargain price and refuse to
settle for less. Their ignorance would make it
easy for middlemen and other traders to exploit
the farmers by quoting less price.
Out of few who were aware of MSP, nearly
25 per cent of farmers reported not selling the
produce to procurement agencies. Unavailabil-
ity of procurement agencies and local pur-
chasers were reported as the major reason.
From probit regression, we conclude that to
make more farmers aware about MSP of crops
and to enable them to take benet of it, better
network of procurement agencies should be
developed. Decentralized procurement agen-
cies with local presence coupled with increased
storagecapacityorsystemofdeciency pay-
ments to bypass the need for procurement can
extend the benets of support prices to a larger
segment of the farming community. Public
extension machinery will also play a vital role.
We found no empirical evidence to prove that
awareness of MSP leads to crop specialization
as procurement is biased towards rice and
wheat.
References
Aguilar J, Gramig GG, Hendrickson JR,
Archer DW, Forcella F, Liebig MA (2015)
Figure 5 Results of Outcome Equation of Heckman Selection Model. BPL, Below Poverty Line; OBC, Other
Backward Caste; SC, Scheduled Caste; ST, Scheduled Tribe
11Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
Crop Species Diversity Changes in the
United States: 19782012. Plos One 10(8),
14.
Ali SZ, Sidhu RS, Vatta K (2012) The Effec-
tiveness of Minimum Support Price Policy
for Paddy in India with a Case Study of
Punjab. Agricultural Economics Research
Review 25(2), 23142.
Briggs DC (2004) Causal Inference and the
Heckman Model. Journal of Educational
and Behavioral Statistics 29(4), 397420.
Chand R (2003) Government Intervention in
Foodgrain Markets in the New Context.
Policy paper 19.2003. ICAR-National Insti-
tute for Agricultural Economics and Policy
Research. Erstwhile National Centre for Ag-
ricultural Economics and Policy Research.
New Delhi.
Chand R (2008) MSP and Other Interventions
in Wheat Market: Are They Contributing
to the Buffer Stock Cycles and Market
Destabilization? http://citeseerx.ist.psu.edu/
viewdoc/download?doi=10.1.1.620.3112&
rep=rep1&type=pdf
Deshpande RS (Ed) (2008) Impact of Mini-
mum Support Prices on the Agricultural
Economy. In Glimpses of Indian Agricul-
ture. Ministry of Agriculture & Academic
Foundation, Government of India. New
Delhi.
Economic Survey (2016) Agriculture: More
from Less. http://indiabudget.nic.in/es2015-
16/echapvol1-04.pdf
Ellis F (1998) Household Strategies and Rural
Livelihood Diversication. Journal of
Development Studies 35(1), 138. http://
doi.org/10.1080/00220389808422553.
Jha, Brajesh (2009) Drivers of Agricultural
Diversication in India, Haryana, and the
Greenbelt Farms of India. Working Paper
Series No. 303. Institute of Economic
Growth. New Delhi.
Jha S, Srinivasan PV (2006) India
Reforming Farm Support Policies for Grains
Report Prepared for IGIDRERS/USDA
Project: Indian Agricultural Markets and
Policy. Mumbai.
Kadasiddappa M, Soumya B, Prashanth P,
Sachin HM (2013) A Historical Prospective
for Minimum Support Price of Agricultural
Crops. Kisan World 40(12).
Magurran AE (2004) Measuring Biological
Diversity. Blackwells publication, UK.
Mittal S, Hariharan VK (2016) Crop
Diversication by Agro-climatic Zones of
IndiaTrends and Drivers. Indian Journal
of Economics and Development 12(1),
12332.
NSSO (National Sample Survey Ofce) (2015)
Situation Assessment Survey of Agricultural
Households: JanuaryDecember 2013. NSS
70
th
Round (unit level data). Ministry of
Statistics and Programme Implementation
(MOSPI). Government of India.
Parikh J, Singh C (2007) Extension of MSP:
Fiscal and Welfare Implications. http://
planningcommission.nic.in/reports/sereport/
ser/ser_msp.pdf.
Planning Commission (2005) Report of the
Inter-ministry Task Group on Comprehen-
sive Medium-Term Strategy for Food and
Nutrition Security. http://planningcommis-
sion .nic.in/aboutus/taskforce/inter/inter_
nutrn.pdf
Schiff MW, Alberto V, Anne OK (eds) (1992)
The Political Economy of Agricultural
Pricing Policy: A Synthesis of the Political
Economy in Developing Countries.John
Hopkins, Baltimore, MD, USA.
Tripathi AK (2013) Agricultural Price Policy,
Output, and Farm ProtabilityExamining
Linkages during Post-reform Period in
India. Asian Journal of Agriculture and
Development 10(1), 91111.
12 Asia & the PacicPolicyStudies •• 2017
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
Appendix A
Table A1 List of Crops for Which MSP Is Declared
Serial number. Crop Common/other name Type/variety
Kharif crops
1Paddy Common/gradeA
2 Sorgum Jowar Hybrid/maldandi
3 Pearl millet Bajra
4MaizeCorn
5 Finger millet Ragi
6 Pigeon pea Arhar (Tur)
7 Green gram Moong
8BlackgramUrad
9 Cotton Medium staple/long staple
10 Groundnut In shell
11 Sunower Seed
12 Soyabeen Yellow and black
13 Sesamum
14 Niger Seed
Rabi crops
15 Wheat
16 Barley
17 Gram
18 Lentil Masur
19 Rapeseed/mustard
20 Safower
Other crops
21 Copra Milling/ball
22 De-husked coconut
23 Raw jute
24 Sugarcane
Notes: MSP of the crops are declared by the government based on recommendations of CACP. CACP is under Ministry of
Agriculture and Farmers Welfare, Government of India. They provide price policy reports to government for crops grown in
kharif and rabi and for other cops such as sugarcane, raw jute and copra. Although cost of production is a key factor in de-
termination of MSP, other factors such as demand and supply, price trends (domestic and international), intercrop price par-
ity, terms of trade between agriculture and non-agriculture and implication of MSP on consumers are considered. For more
details on MSP, see CACP website http://cacp.dacnet.nic.in/. CACP, Commission for Agricultural Costs and Prices; MSP,
Minimum Support Price.
13Aditya et al.: MSP Awareness and Crop Diversication
© 2017 The Authors. Asia and the Pacic Policy Studies
published by John Wiley & Sons Australia, Ltd and Crawford School of Public Policy at The Australian National University
... The price for wheat grain in the mandis is fixed by the government before every cropping season as an institutional mechanism for incentivizing farmers to adopt new technologies. The MSP is uniform for a crop across markets and varieties [81]. We did not find Lok-1 or Sujata growers getting a premium price from the private markets either. ...
Article
Full-text available
The research on crop genetic enhancement has created a continuous flow of new, improved germplasm for the benefit of farmers and consumers of the Global South during and after the Green Revolution. Understanding farmers’ heterogeneous preferences for varietal traits in different market segments and incorporating the prominent ones in crop breeding programs are expected to facilitate a faster diffusion of these new varieties. Albeit knowing little about farmers’ trait preferences in South Asia, public-sector breeding programs prioritize yield enhancement and risk reduction over other varietal traits. Against this backdrop, we examined wheat farmers’ preferences for varietal traits in Central India, where the prevailing varietal turnover rate has been meager. We conducted a ranking exercise among 120 individuals, followed by a sex-disaggregated survey with a choice experiment among 420 farm-households in 2019. The lowest varietal turnover rate was observed for the socially marginalized castes. Most women respondents were not actively involved in making decisions related to wheat cultivation, including varietal selection. However, the results indicate that marginalized caste and women farmers are open to experimentation with new varieties, as shown by their positive willingness to pay for improved varietal traits. Across the gender and caste groups, grain quality attributes (especially chapati quality) were ranked high, above the yield-enhancing and risk-ameliorating traits. From the observed patterns, one could deduce that developing and disseminating improved varieties with better grain quality and targeting women and marginalized social groups in varietal dissemination programs could enhance farmer adoption of new, improved germplasm and wheat productivity in Central India.
... We chose to abstract from this as there is evidence that these MSPs are not fully effective. For instance, Aditya et al. (2017) show that less than 25 percent of farmers in their data are even aware of what the MSP is for their crops. 8 Even when the output of the home crop is not fixed, lower P H will lower farm income as long as the demand for the home crop is price inelastic. ...
Article
Full-text available
A large fraction of the world’s poor rely on rain-fed agriculture, which makes them vulnerable to changes in rainfall patterns. In this paper, we examine whether spatial correlation in rainfall results in these households also being vulnerable to an adverse spatial-spillover effect. In particular, we use household-level panel data from India along with high-resolution meteorological data to show how rural household consumption varies with own-region rainfall as well as rainfall in neighboring areas. We find that while greater own rainfall has a positive effect on rural household consumption, greater rainfall in neighboring regions has an adverse spatial-spillover effect. Our results suggest that when this spillover effect is taken into account, the positive impact of own-region rainfall on household consumption falls by 38 percent.
... With regard to the impact of the target price policy on agricultural production (cotton), according to the economic analysis of how policies function, the implementation of the target price policy will increase the expected income of farmers and reduce the expected production risk, thus forming a positive incentive effect on production and promoting farmers to expand the scale of production [36,37], but farmers' decisions are also affected by factors such as their wealth level and whether the policy implementation in the previous period has reached the expected income [38,39]. Specifically, from the perspective of farmers' decision-making behaviors, the effect of policy implementation also depends on farmers' confidence in policies, the stability they felt, comparative benefits of soybeans and corn, and subsidy methods [40,41]. ...
Article
Full-text available
China is an important cotton production area in the world. Since 2014, China has implemented a cotton target price subsidy policy in Xinjiang for 7 years. As the policy implementation time has lengthened, some deep-seated problems have started to emerge. Therefore, it is necessary to summarize and evaluate to clarify the future policy direction of the cotton target price subsidy policy. Based on county-level panel data of Xinjiang and Shandong from 2011 to 2018, this paper used the Propensity Score Matching-Difference in Difference method to analyze the impact of the implementation of cotton target price subsidy policy on cotton planting in Xinjiang. The results showed that: (1) after the implementation of the cotton target price subsidy policy, cotton production was stimulated by the transition, cotton producers' enthusiasm for cotton production was higher, cotton production increased rapidly, and the yield per unit area decreased, indicating that there was a 'bubble' in cotton cultivation. (2) The target price subsidy policy mainly achieves the expansion of the cotton planting scale by reducing the area of competitive crops. In view of the above research conclusions, this paper further explains its policy implications. It is proposed that the future cotton target price level should be formulated to fully consider the comparative benefits between different crops, to restrict the subjects that enjoy subsidies and the upper limit of subsidies, and strictly implement the concept of green development; it is necessary to guide cotton production out of ecologically vulnerable areas.
... According to standard economic theory, minimum support price is expected to raise the grain price (see e.g., Li et al. [7], Kozicka et al. [9], Qian et al. [12], Kim and Chavas [13], Lyu and Li [14], Qian et al. [15], and Tripathi [16]), and thereby stimulate the use of agrochemicals and other variable inputs [5,[17][18][19]. However, only a limited number of studies, such as Li et al. [7], Qian et al. [12], Ali et al. [20], Aditya et al. [21], Krishnaswamy [22], Chintapalli and Tang [23], and Ritu et al. [24], provided empirical evidence of this. Most of these studies focus on minimum support price influencing land area in India. ...
Article
Full-text available
China’s minimum grain procurement price program aims to boost grain production and ensure food self-sufficiency. It may also affect the already very high levels of chemical fertilizer and pesticides consumption, but little is known about these potential side-effects. In this paper, we apply panel data regression techniques to a large rural household-level data set for the period 1997–2010 to examine whether and how the minimum grain procurement price program affected households’ agrochemical use. We find that the minimum grain procurement price program negatively affected both chemical fertilizer and pesticides use, with pesticides use being more responsive than the use of fertilizer. The higher wheat and rice prices that resulted from the program stimulated the use of agrochemicals, but they also stimulated area expansion which contributed to lower agrochemical use per unit of land. These counteracting indirect effects were overshadowed by the large negative direct effect of the minimum procurement price of rice on the use of fertilizer and pesticides.
... Further, 27.83 and 30.48 per cent of reported farmers were unaware of the agency that procures the MSP food grains. In Karnataka, 14.61 per cent and 13.97 per cent of the farmers know about the MSP announced during kharif and rabi, respectively (Aditya et al., 2017). In case of wheat, much difference was not seen between the rate of growth in MSP (5.13) and cost of production (6.09) indicating that profit margin of farmers remained nearly stable during the study period (2010-11 to 2016-17). ...
Article
Full-text available
Paddy is an important staple food crop of southern India. In the recent past, farmers exhibited a transition in cropping pattern from paddy to commercial crops due to non-remunerative and labour intensive nature of the field crop. It is a cause of concern from the viewpoint of food and fodder security. Hence, retention of farmer's interest in continuing paddy cultivation is indispensable. To retain the interest, both the central and state government intervenes in the form of an announcement of minimum support price. Minimum Support Price (MSP) safeguards, encourages and instils confidence among paddy growers. In this context, the present study was taken up to assess the perception of paddy growers about MSP. For the study, four taluks with the highest area under paddy cultivation in the Shivamogga district of Karnataka were selected, and from each taluk,31 respondents were chosen randomly totalling to124. The result indicated that majority of the respondents had favorable perception about Risk Mitigation (79.03%), Institutional Frontier (66.94%), Decision Making (68.55%), Procedural Hitches (75.00%) and State Intervention (54.03%). More than half of the respondents have agreed thatMSP ensures minimum profit to the farmers (55.65%), MSP will be announced for kharif and rabi season (53.23%) and procurement of the product often gets delayed (54.03%). Majority of the respondents strongly agreed that MSP is announced prior to the sowing season (55.65%) and irregular and untimely payment is the usual practice (50.81). It is interesting to know that 50.83 per cent of the respondents disagreed that MSP comprises of additional benefit from concerned state government. Farmers though knew the MSP, but they could not able to utilize. If we could resolve these shortcomings of the system, it might help farmers in deciding profitable cropping pattern to sustain their livelihood.
Chapter
Full-text available
Pulses are important in global agriculture not only for their nutritive value as a major source of protein but also play an important role in making agriculture sustainable. On the sustainability front, pulses need less water, less input-intensive and also fix atmospheric nitrogen (N). Globally, the area under pulses has increased from 64.14 Mha (Triennium Ending (TE) 1970) to 93.54 Mha (TE 2019) and correspondingly, the production also increased from 42.1 Mt to 92.13 Mt. The productivity of pulses during the same period has increased from 664 kg ha⁻¹ to 994 kg ha⁻¹. India has the maximum area under pulses with 33.35 Mha, which accounts for 35% of the global area producing 26% of the global production. Taking the country with the highest productivity as a benchmark, we estimate the efficiency gap for 171 pulses producing countries, which ranges from 15 to 98% indicating the vast potential to increase the pulses production. Only 19.01 Mt of pulses is traded in the world market, which is dominated by Canada (5.70 Mt), followed by Australia (2.07 Mt). The chapter also presents the results of bibliometric analysis using the peer-reviewed publications between 2000 and 2021 and provides an overview of the global research networks and trends. India has been identified as a major contributor to global research output. Apparently, it is the largest producer (26%), the consumer (27%), and importer (15%) of the pulses. To increase the pulses production and bridge the gap between demand and supply, India has implemented several policy initiatives and research priorities for the overall pulses sector development. We hypothesise that such initiatives have led the countries to witness an increase in production leading to a decline in imports implying self-sufficiency. In this chapter, we provide an overview of the global pulses production, trade and policy implications with a special focus on the Indian scenario.
Chapter
Predictive analytics is an interesting field of research aimed at discovering future trends/patterns from the past data. With proliferation of big data over the past decade, analytics has been gaining more significance in almost all domains with tremendous contribution to the insightful knowledge. In this paper, we present the initial walkthrough for novel predictive analytics in the field of agriculture, aimed at enhancing and strengthening the current feeble agriculture ecosystem. Important challenges we faced include vast heterogeneity of data and highly scattered data with differences of timelines/structure/area. Hence, integration of data is also an important contribution of current work. Our fundamental motivation has been to develop a novel efficient platform for bridging the gap between the numerous geographically dispersed producers and consumers, thereby not only increasing business value for the producers, but also providing suitable recommendations based on predictive analytics, that can be pivotal in market decisions in the future. In current work, we have experimented with the data provided by government sources and performed in-depth data analysis and visualization to infer precious insights that can be communicated to the producers, thereby directly impacting the markets and society. Our main contribution is development of a novel predictive analytics-based platform for real-time agriculture supply chain management.
Article
Full-text available
Protected cultivation requires high initial investment and intensive use of inputs for crop production but offers better yield which in turn increases the profitability of the farm. The study attempts to explore the economics of protected cultivation with different interest rates regime and subsidy in Pune and Nasik districts of Maharashtra conducted during 2018-19. About 95 to 97 per cent of the farmers availed subsidy from the government and the rest of the farmers constructed their polyhouse and shade net house without subsidy. About 47 to 50 per cent of the total cost was given as subsidy. Heckman selection model showed that the factors such as years of education, farm size, farm income, membership and occupation were the major determinants of access to credit. The study also indicated that household age, farm size, farm income, distance from the market and access to subsidies were important drivers of technology adoption. Among all, the access to subsidy reflected the availability of external capital support as one of the determining factors for adoption of technology. The factors responsible for non-repayment of loans were increase in farm income, family size and years of schooling.
Article
Indian agricultural practices in most instances are nature dependent; notwithstanding, it contributes a huge amount to the nation’s gross domestic product and in providing food security to the masses. The government’s announcements of minimum support prices (MSPs) for the crops work as insurance for the farmers from distress sales in the times of bumper crops as well as from nature’s shocks. In the recent past, a new agricultural bill has been passed by both houses of the Indian Parliament which constitutes a major source of concern on the MSP. Under this backdrop, the present study investigates through an endogenous growth model incorporating MSP as the public policy variable and empirical analysis whether MSP has long-run relations with yield rates and total quantity of production of different food and non-food crops for 1983–2019. Using a unit root test, cointegration test and causality test under structural break, it concludes that MSP and yield rates, and MSP and outputs of the selected crops have long-run relations under different break points with temporary deviations from the equilibrium path. Further, there are certain crops like pulses and groundnuts where MSP makes a cause to yield rates and, on the other hand, there are the crops like jute and cotton where MSP makes a cause to quantity of production. Thus, it is recommended that the Government of India should revisit the new agricultural bill and make necessary amendments for the overall benefits of the farmers and the economy as a whole. JEL Classification: Q180; O4; C22
Article
Full-text available
Enhancing and maintaining on-farm diversity is a potential strategy to improve farming systems’ sustainability and resilience. However, diversification is driven or constrained by different factors and dynamics that vary across environmental, socio-economic and political contexts. Identifying drivers and constraints of diversification can help to support the adoption of on-farm diversification strategies, where doing so is beneficial. For the first time, we systematically review and summarise recent peer-reviewed studies assessing drivers and constraints of on-farm diversity from 42 different countries. From 2312 studies, we selected a total of 97, reporting 239 drivers and constraints, which we categorised using the Sustainable Rural Livelihood Framework. We extracted the number of times they were assessed as having a positive, negative or neutral relationship with on-farm diversity. Some factors mainly have a positive relationship, such as the need to adapt to risks or belonging to indigenous ethnicities, but for most of the others the results are mixed. Our major conclusions are as follows: (1) The adoption of diversification strategies is affected by both production and demand dynamics, with differences depending on farms and contexts; (2) small subsistence-oriented farms tend to adopt on-farm diversification strategies to cope with environmental characteristics and risks and satisfy their subsistence needs; (3) farmers may shift towards specialisation strategies if the comparative advantage of diversification and its natural insurance effect gets displaced by market opportunities, financial capital, technologies and the availability of alternative and more profitable sources of income; (4) the availability of technologies enabling farm diversification and the access to alternative market options are crucial to stimulate the implementation and maintenance of on-farm diversity; (5) future policies and research promoting the adoption of on-farm diversification strategies need to design mechanisms and incentives that consider the opportunity-cost of alternative livelihood opportunities, and that are suitable for the local context and for farmers’ objectives.
Article
Full-text available
Crop diversification is seen as a means to improve income and also as a risk mitigating strategy. Using district level data of 318 districts from 16 states of India, and classifying the districts according to 12 agro-ecological zones and irrigation types, this paper examines the pattern of crop diversification and computes the Simpson Index and Herfindahl index to present agricultural diversification between the periods 1966-67 to 2007-08. The paper shows that the demand side-push and urbanization are some of the major factors that explain the crop diversification across agro-climatic zones in India. The paper poses questions for need of future analysis to check if this pattern of diversification can be an adaptive measure to manage climate risk and what should be carefully thought over the broader policy scenario to make the farmers more responsive to manage climate risk through diversification as an adaptive strategy.
Article
Full-text available
Anecdotal accounts regarding reduced US cropping system diversity have raised concerns about negative impacts of increasingly homogeneous cropping systems. However, formal analyses to document such changes are lacking. Using US Agriculture Census data, which are collected every five years, we quantified crop species diversity from 1978 to 2012, for the contiguous US on a county level basis. We used Shannon diversity indices expressed as effective number of crop species (ENCS) to quantify crop diversity. We then evaluated changes in county-level crop diversity both nationally and for each of the eight Farm Resource Regions developed by the National Agriculture Statistics Service. During the 34 years we considered in our analyses, both national and regional ENCS changed. Nationally, crop diversity was lower in 2012 than in 1978. However, our analyses also revealed interesting trends between and within different Resource Regions. Overall, the Heartland Resource Region had the lowest crop diversity whereas the Fruitful Rim and Northern Crescent had the highest. In contrast to the other Resource Regions, the Mississippi Portal had significantly higher crop diversity in 2012 than in 1978. Also, within regions there were differences between counties in crop diversity. Spatial autocorrelation revealed clustering of low and high ENCS and this trend became stronger over time. These results show that, nationally counties have been clustering into areas of either low diversity or high diversity. Moreover, a significant trend of more counties shifting to lower rather than to higher crop diversity was detected. The clustering and shifting demonstrates a trend toward crop diversity loss and attendant homogenization of agricultural production systems, which could have far-reaching consequences for provision of ecosystem system services associated with agricultural systems as well as food system sustainability.
Article
Full-text available
The formulation of agricultural price policy is complicated by the multiplicity of functions that price performs. The objectives, thrust, and instruments of agricultural price policy in India have undergone conspicuous shifts during the past 50 years and so has the role and effectiveness of price policy as a tool to influence the agricultural economy. The country's post-reform period witnessed higher emphasis and dependence on price policy compared with previous decades, where price policy aimed only at maintaining a balance between the interests of consumers and producers. It is in this context that the paper examines the effectiveness of procurement prices in getting sufficient income to the farmers. An in-depth analysis of costs and returns was conducted for wheat and paddy, the crops offered the highest protection by the state, to get idea of the profitability of Indian agriculture and gain insights into the workings of the price policy.
Research
Full-text available
The present study analyze the impacts of a hypothetically extended procurement system (bringing new states and more districts of existing states under procurement net) of food grains at both macro and micro levels, for example i.e. on the level of procurement, the consequent changes in fiscal outlay, impact on local mandi price (Retail price) and consequently change in producers’ income and consumers’’ expenditure on food grains, under ceteris paribus.
Article
Full-text available
Also available at:http://www.igidr.ac.in/pdf/publication/PP-052.pdf The objective of this study is to analyze some of the recent reforms proposed in the operation of government buffer stocks and provision of price support to wheat and rice farmers in India. Based on the Indian grain market scenario and the recent policy initiatives this study estimates the potential impacts of reforms in India's farm support policies on producers, consumers and traders in various regions of the country. The results are based on a multi commodity partial equilibrium simulation model of regional supplies and demands of grains by different economic classes. In particular, the study focuses on the decentralization of procurement of grains by the individual states where the latter are free to fix their minimum support prices for wheat and rice and the purchase of grain for the public distribution system (PDS) is from the open market. The results show that a switch to decentralized PDS and procurement and removal of rice levy leads to a fall in both procurement and buffer stocks of grains. We also consider implications of reducing minimum support prices (MSP) from their current exorbitant levels. Since in a decentralized scenario the PDS requirements are purchased from the open market, costs of operating the PDS tend to go up. But, when the states reduce the MSP from its current high level, these costs go down. This, in fact, results in a fall in market prices leading to higher consumption by all income classes with consequent rise in consumer welfare. Adding across all agents, total surplus from rice and wheat policy reform is positive in net consuming states and negative in major surplus states. But at the aggregate national level, there are net gains. Price support to farmers could also be offered to farmers in the form of cash subsidy or 'deficiency payment'. That is, farmers are compensated through deficiency payments when market prices fall below an insured price floor. This results in great cost savings to the government (as it no longer needs to undertake storage and physical handling of grains) while at same time benefiting consumers of all economic classes.
Article
Full-text available
Historical Prospective of Minimum Support price of Agriculture crops Kadasiddappa Malamasuri, Soumya B, Prasanth P and Sachin Himmatrao Malve. Ph.D. Scholars, Acharya N.G.Ranga Agricultural University Hyderabad-500030 During mid sixties the technological change was a step towards meeting the food crisis that threatened food security of the country during those years. So, it was suggested that the technological change alone might not bring the required dynamism in the growth of agricultural sector and it needed to be supported with proper institutional backup. Therefore, a series of institutional reforms were undertaken in order to supplement and induce growth. As a first step, land reforms were revamped to herald its second phase in the early seventies. Agricultural administration and extension formed the second step in the process of institutional change. This was accompanied by strengthening the system of agricultural education. As a next crucial step the banking sector underwent the metamorphosis through nationalization with a renewed thrust on priority sector lending. The most important step followed by this was the initiative taken to evolve an agricultural price policy encouraging the envisaged growth through price incentive. In order to understand and construct a proper price policy framework, the Government of India appointed a committee under the Chairmanship of Late Shri L K Jha to suggest the required steps towards organising the agricultural price policy of the country. Following the Jha Committee report, a series of measures were taken and as a result the Agricultural Prices Commission came into being in January 1965. The first report was submitted in August 1965 covering the Kharif Season. The preface of this report makes clear the focus of the then emerging price policy. It is stated in the preamble of the report that “The Agricultural Prices Commission was set up in January 1965 to advise the Government on price policy for agricultural commodities, with a view to evolving a balanced and integrated price structure in the perspective of the overall needs of the economy (emphasis added) and with due regard to the interests of the producer and the consumer” (GOI, 1965).Since March 1985, the “Commission has been known as Commission for Agricultural Costs and Prices”(CACP). Assurance of a remunerative and stable price environment is considered very important for increasing agricultural production and productivity since the market place for agricultural produce tends to be inherently unstable, which often inflict undue losses on the growers, even when they adopt the best available technology package and produce efficiently. Towards this end, minimum support prices (MSP) for major agricultural products are fixed by the government, each year, after taking into account the recommendations of the Commission for Agricultural Costs and Prices (CACP) which is directed to provide insurance to agricultural producers against sharp fall in farm prices. The minimum guaranteed prices are fixed to set a floor below which market price cannot fall. Government of India has been announcing Minimum Support Price (MSP) keeping in view of the interest of the farmers presently for 24 major crops (Table 1&2). Commission on Agricultural Cost and Prices (CACP) recommends MSP for these agricultural products. Farmers are free to sell their products in the open market or to the government at the MSP depending on what is more profitable to them. This price support policy of the government is to provide insurance to agricultural producers against any sharp fall in farm price. This MSP is announce each year after taking into account the recommendations of CACP , which in turn while recommendation takes into account all important factors viz; Cost of production, changes in input prices, input/output price parity, trends in market prices, inter crop price parity, demand and supply situation and parity between price paid to price received by farmers etc. The CACP analysis the cost of production data for various states in respect of different commodities in consultation and meeting with the state chief ministers and declares the appropriate prices. Cost of calculations are calculated based on both per quintal and per hectare basis, since cost variations are large over states. In fixing the support price, CACP relies on the cost concept which includes input costs including inputs owned by farmers like rental value of the owned land and interest on the fixed capital. There are two concepts of costs used by CACP are C2 cost and C3 Cost concepts. C2 cost includes any expenses of cultivation in the form of cash and kind incurred for production including rent paid for leased land and input value of the family labour plus interest on the fixed capital. C3 cost is C2 cost+ 10% of cost of C2 to account for managerial remuneration to the farmer. Determination of Minimum Support Price: In formulating the recommendations in respect of the level of minimum support prices and other non-price measures, the Commission takes into account, apart from a comprehensive view of the entire structure of the economy of a particular commodity or group of commodities, the following factors: 1. Cost of production 2. Changes in input prices 3. Input-output price parity 4. Trends in market prices 5. Demand and supply 6. Inter-crop price parity 7. Effect on industrial cost structure 8. Effect on cost of living 9. Effect on general price level 10. International price situation 11. Parity between prices paid and prices received by the farmers. 12. Effect on issue prices and implications for subsidy The Commission makes use of both micro-level data and aggregates at the level of district, state and the country. The information/data used by the Commission, inter-alia include the following: • Cost of cultivation per hectare and structure of costs in various regions of the country and changes there in; • Cost of production per quintal in various regions of the country and changes therein; • Prices of various inputs and changes therein; • Market prices of products and changes therein; • Prices of commodities sold by the farmers and of those purchased by them and changes therein; • Supply related information - area, yield and production, imports, exports and domestic availability and stocks with the Government/public agencies or industry; • Demand related information - total and per capita consumption, trends and capacity of the processing industry; • Prices in the international market and changes therein, demand and supply situation in the world market; • Prices of the derivatives of the farm products such as sugar, jaggery, jute goods, edible/non-edible oils and cotton yarn and changes therein; • Cost of processing of agricultural products and changes therein; • Cost of marketing - storage, transportation, processing, marketing services, taxes/fees and margins retained by market functionaries; and • Macro-economic variables such as general level of prices, consumer price indices and those reflecting monetary and fiscal factors. As already mentioned, 24 agricultural commodities (Table 1&2) are currently covered under the mandate given to the CACP for advising the government in respect of the price policy. The Commission is required to convey its recommendations to the government well before the sowing season of the crop. With a view to interacting with various interest groups, the Commission follows the sequence of steps indicated below: • The Commission identifies the main issues of relevance for the ensuing season (short, medium or long turn). • The Commission sends a questionnaire to Central Ministries, State Governments and other organisations related to trade, industry, processors, and farmers both in the cooperative and the private sector and seeks their views on certain issues and factual information on related variables. • Subsequent to step (2), the Commission holds separate discussions with the State Governments, Central Ministries/Departments and other organisations. The Commission also interacts with research and academic institutions and keeps track of relevant studies and their findings. The Commission visits certain areas for on-the-spot observations and feedback from local level organisations and farmers. Refferences: • Commission for Agricultural Costs and Prices, Ministry of Agriculture, Government of India. • Government of India (GoI) (1965). Government Resolution on the Terms of Reference of the Agricultural Prices. New Delhi: Agricultural Prices Commission. Ministry of Food and Agriculture. • WWW.indiabudget.nic.in Table1. Minimum support prices (Rs/qt) of agricultural commodities (1980-81 to 1999-2000) Sl.No Crops 1980-81 1985-86 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 KHARIF CROPS 1 Paddy - - 170 195 215 240 280 330 360 375 395 445 470 520 2 Jowar 105 130 145 165 180 205 240 260 280 300 310 360 390 415 3 Bajra 105 130 145 165 180 210 245 265 290 310 320 360 390 415 4 Maize 105 130 145 165 180 210 245 265 290 310 320 360 390 415 5 Ragi 105 130 145 165 185 205 240 260 280 300 310 360 390 415 6 Redgram 190 300 360 425 480 545 640 700 760 800 840 900 960 1105 7 Moong 200 300 360 425 480 545 640 700 760 800 840 900 960 1105 8 Urad 200 300 360 425 480 545 640 700 760 800 840 900 960 1105 9 Cotton 304 480 550 630 685 767.5 875 975 1100 1250 1280 1430 1545 1575 10 Groundnut 205 350 430 500 580 645 750 800 860 900 920 980 1040 1155 11 Sunflower 183 335 450 530 600 670 800 850 900 950 960 1000 1060 1155 12 Soybean black 183 250 275 325 350 395 475 525 570 600 620 670 705 755 13 Soybean yellow 198 275 320 370 400 445 525 530 650 680 700 750 795 845 RABI CROPS 14 Wheat 130 162 183 215 225 250 330 350 360 380 475 510 550 580 15 Gram - 260 325 421 450 500 600 640 670 700 740 815 895 1015 16 Rapeseed/Mustard - 400 460 575 600 670 760 810 830 860 890 1000 1060 1100 17 Safflower - 400 440 550 575 640 720 760 780 800 830 910 990 1100 OTHER CROPS 18 Sugarcane 13 16.5 19.5 22 23 26 31 34.5 39 42.5 45.9 48.45 52.7 56.10 19 Tobacco (VFC-black soil) 8.25 12 12.8 13.5 14.75 16 18 18.5 19 19 20.5 22.5 25 25 Tobacco (VFC-light soil) 8.25 12 12.8 13.5 14.25 16 17.5 20 21 21.5 22 23.5 25.5 27 20 Copra (Milling) - - - - - 1850 - 2350 2375 2725 2725 2925 3125 3325 Copra (Ball) - - - 1500 1600 1700 - 2150 2350 2500 2600 2700 2900 3100 Table2. Minimum support prices (Rs/qt) of agricultural commodities (2000-01 to 2013-14) Sl.No Crop Variety 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 KHARIF CROPS 1 Paddy Common 510 530 550 550 560 570 580 645 850 950 1000 1080 1250 1310 Grade A 540 560 580 580 590 600 610 675 880 980 1030 1110 1280 1345 2 Jowar Hybrid - - - 505 515 525 540 600 840 840 880 980 1500 1500 Maldandi 445 485 490 505 515 - 555 620 860 860 900 1000 1520 3 Bajra 445 485 490 505 515 525 540 600 840 840 880 980 1175 1310 4 Maize 445 485 490 505 525 540 540 620 840 840 880 980 1175 1310 5 Ragi 445 485 490 505 515 525 540 600 915 915 965 1050 1500 1500 6 Redgram 1200 1320 1325 1360 1390 1400 1410 1550 2000 2300 3000 3200 3850 4300 7 Moong 1200 1320 1335 1370 1410 1520 1520 1700 2520 2760 3170 3500 4400 4500 8 Urad 1200 1320 1335 1370 1410 1520 1520 1700 2520 2520 2900 3300 4300 4300 9 Cotton Medium staple 1625 1675 1695 1725 1760 1760 1770 1800 2500 2500 2500 2800 3600 3700 Long staple 1825 1875 1895 1925 1960 1980 1990 2030 3000 3000 3000 3300 3900 4000 10 Groundnut 1220 1340 1375 1400 1500 1520 1520 1550 2100 2100 2300 2700 3700 4000 11 Sunflower 1170 1185 1210 1250 1340 1500 1500 1510 2215 2215 2350 2800 3700 3700 12 Soybean Black 775 795 805 840 900 900 900 910 1350 1350 1400 1650 2200 2500 Yellow 865 885 895 930 1000 1010 1020 1050 1390 1390 1440 1690 2240 2560 13 Sesamum 1300 1400 1455 1485 1500 1550 1560 1580 2750 2850 2900 3400 4200 4500 14 Niger 1025 1100 1120 1155 1180 1200 1220 1240 2405 2405 2450 2900 3500 3500 RABI CROPS 15 Wheat 580 610 620 630 630 640 650 750 1000 1080 1100 1120 1285 1350 16 Barley 430 500 500 505 525 540 550 565 650 680 750 780 980 980 17 Gram 1015 1100 1200 1225 1400 1425 1435 1445 1600 1730 1760 2100 2800 3000 18 Lentil - 1200 1300 1325 1500 1525 1535 1545 1700 1870 1870 2250 2800 2900 19 Rapeseed/Mustard 1100 1200 1300 1340 1600 1700 1715 1715 1800 1830 1830 1850 2500 3000 20 Saflower 1100 1200 1300 1305 1500 1550 1565 1565 1650 1650 1680 1800 2500 2800 21 Toria 1065 1165 1265 1305 1565 1665 1680 1680 1735 1735 1735 1780 2425 OTHER CROPS 22 Jute 750 785 810 850 860 890 910 1000 1055 1250 1375 1575 1675 2300 23 Sugarcane 56.10 59.50 62.05 69.50 73 74.50 79.50 80.25 81.18 81.18 129.84 139.12 145 210 24 Copra (Calendar year) Milling 3100 3250 3300 3300 3320 3500 3570 3590 3620 3660 4450 4450 4525 5250 Ball 3325 3500 3350 3350 3570 3570 3820 3840 3870 3910 4700 4700 4775 5500
Article
Full-text available
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the purpose of estimating an unbiased causal effect and shows how the Heckman Model can be used to correct for the problem of selection bias. It discusses in detail the assumptions necessary before the approach can be used to make causal inferences. The Heckman Model makes assumptions about the relationship between two equations in an underlying behavioral model: a response schedule and a selection function. This article shows that the Heckman Model is particularly sensitive to the choice of variables included in the selection function. This is demonstrated empirically in the context of estimating the effect of commercial coaching programs on the SAT performance of high school students. Coaching effects for both sections of the SAT are estimated using data from the National Education Longitudinal Study of 1988. Small changes in the selection function are shown to have a big impact on estimated coaching effects under the Heckman Model.
Article
Full-text available
The effectiveness of minimum support price (MSP) for paddy has been examined in different regions of India and its role and contribution towards production in surplus states like Punjab have been studied. Based on the secondary data spanning from 1980-81 to 2006-07, the deviations of farm harvest prices from the MSP have been used as a measure of ineffectiveness and the impact of prices and technology on rice productivity has been examined by using the simultaneous equation model. While the MSP policy has been very effective in surplus producing states like Punjab and Andhra Pradesh, it has not been so effective in the deficit states. In Punjab, the effective implementation of the price policy has helped in improving the production and productivity of rice. Non-price factors such as use of improved varieties, availability of assured irrigation at subsidized rates and high fertilizer-use have been found to be significant determinants of growth in rice production. The study has suggested that without losing sight of the environmental concerns, the Punjab model can be used for increasing the production of rice in other potential areas of the country.
Article
The present study discusses factors responsible for agricultural diversification at different levels : country (India), state (Haryana) and farms of Kurukshetra district in Haryana. The study regressed alternate measures of diversification namely, the Simpson index and concentration of non-food crops, on several possible factors such as income, land distribution, irrigation intensity, institutional credit, road density, urbanization and market penetration. The regression analysis suggests that increased road density, urbanization encourages commercialization of agriculture and with commercialization, farms in a region are increasingly specialized under certain crops and crop-groups as per the resource, infrastructure and institutions of the region.
Article
Adoption of the green revolution technology helped India accelerate the growth in wheat production which in turn helped in reducing the heavy dependence on wheat imports to meet the domestic demand. However, the growth in production was never large enough to meet the domestic demand on a sustained basis. While most of the times the wheat production fell short of the demand, in some years the production exceeded the effective demand. Because of this, imbalances in domestic demand and supply of wheat are a recurrent phenomenon. Since these imbalances create serious problems for producers and consumers, and also for the market stability, the government has been intervening actively to stabilize the wheat market. Such interventions have been in various forms like - public procurement, guaranteed price to the producers, open market sales, maintenance of buffer stock, public distribution system, tariffs, export, import and trade regulations. However, these interventions typically have several other goals besides market stabilization. These demand and supply imbalances in the case of wheat have become more serious in the recent period, which is a cause of concern. This is indicated by the increase in fluctuations in the net trade of wheat, and from the stock held by public agencies (Fig.1 and 2). After 1998, India was caught in a spiral of accumulation of large stock of wheat, followed by large exports, and the subsequent depletion of stock followed by large imports. Again the country started building stock of wheat beginning with July 2008 and it seems to be at a threshold of accumulating the large stock. Why is this cycle repetitious and its magnitude getting aggravated? How do public interventions in the wheat market affect these cycles and the imbalances? Is there a need to change the policy of intervention in food market to improve market stability? What are the alternative options for maintaining the demand and supply balance in a better way? This paper makes an attempt to address these questions.