The present study investigated cereal crop farmers’ acreage response to price and non-price factors in Rajasthan State of India using time series data spanning from 1981 to 2014. The cereal crops considered for the study were jowar, maize, bajra, wheat and barley. Furthermore, the Nerlovian model was used for data synthesis. From the results, it was observed that farmers in the state were not price responsive except for maize. The growers of these crops considered the lagged area and lagged price of competing crops to be the major factors for area allocation decision. The lagged price and lagged yield emerged as an important factor in deciding area allocation to maize and barley crops respectively. Therefore, findings showed that farmers’ decision on cereal crops acreage allocation were governed by both price and non-price factors. Hence price incentive alone was not sufficient in bringing desirable change in cropping pattern as well as the production of these crops. Therefore, the creation of other infrastructural facilities like irrigation is important to increase acreage and production with stability in the studied area.

1. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
AJAERD
Supply Response of Cereal Crop Farmers’ to Price and
Non-Price Factors in Rajasthan State of India
Sadiq MS1,2*, Singh IP2, Karunakaran N3
1
Department of Agricultural Economics and Extension Technology, FUT, Minna, Nigeria
2
Department of Agricultural Economics, SKRAU, Bikaner, India
3
Department of Economics, EKNM Government College Elerithattu, Kerela, India
The present study investigated cereal crop farmers’ acreage response to price and non-price
factors in Rajasthan State of India using time series data spanning from 1981 to 2014. The cereal
crops considered for the study were jowar, maize, bajra, wheat and barley. Furthermore, the
Nerlovian model was used for data synthesis. From the results, it was observed that farmers in
the state were not price responsive except for maize. The growers of these crops considered the
lagged area and lagged price of competing crops to be the major factors for area allocation
decision. The lagged price and lagged yield emerged as an important factor in deciding area
allocation to maize and barley crops respectively. Therefore, findings showed that farmers’
decision on cereal crops acreage allocation were governed by both price and non-price factors.
Hence price incentive alone was not sufficient in bringing desirable change in cropping pattern
as well as the production of these crops. Therefore, the creation of other infrastructural facilities
like irrigation is important to increase acreage and production with stability in the studied area.
Keywords: Acreage; response; Nerlovian model; cereal crops; Rajasthan state; India
INTRODUCTION
A disquieting feature of Indian agricultural economy is the
uncertain trend in the movement of prices of agricultural
products. In a developing country like India where the
emphasis is being laid on planned development of the
economy, the role of price mechanism cannot be
undermined in effecting desired changes in production.
Supply response function is based on generally accepted
notion that current experience relating to the past
decisions influences decisions. Farmers are rational in
allocating the land to a particular crop, hence, it is
important to identify and examine important price and non-
price factors which affect this rational decision.
Nerlove (1958) as cited by Maddala and Lahiri (2009) and
Subba et al. (2016) developed two distributed lag models,
and estimated the supply elasticity of corn, cotton and
wheat in U.S.A. In the distributed lag model based on price
expectations, he assumed past price experience
influences formation of expected price, which in turn,
influenced acreage allocation decision. Since then, the
Nerlovian lagged adjustment model has been used by
researchers for analysis, for example, Subba et al. (2016);
Abujam et al. (2015), because Nerlove introduced the
element of dynamism by incorporating the concept of
distributed lag in the analysis of crop acreage response.
Another advantage for the choice of this particular model
is that it facilitates separate estimation of the long-run and
short-run elasticities. Following this suite, this research
aimed at investigating empirically factors beyond price that
influence farmers’ acreage response in Rajasthan State of
India.
*
Corresponding author: Sadiq MS, Department of
Agricultural Economics and Extension Technology, FUT,
Minna, Nigeria. Department of Agricultural Economics,
SKRAU, Bikaner, India. Email: sadiqsanusi30@gmail.com;
Tel:+2347037690123
Journal of Agricultural Economics and Rural Development
Vol. 3(2), pp. 230-235, August, 2017. © www.premierpublishers.org. ISSN: XXXX-XXXX
Review Article

2. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
Sadiq et al. 231
RESEARCH METHODOLOGY
The study made use of time series data spanning from
1981-2014, covering the area, production, productivity,
farm harvest prices and wholesale prices of selected
crops. The criteria used for the identification of major
cereal crops was that the crop should have an average
area of at least 2-3 lakh hectares during the last five years,
while district which accounts for 50 percent share in the
gross area under a particular crop in Rajasthan was
chosen. Data sources were the statistical abstract of
Rajasthan State, Directorate of Economics and Statistics,
AGMARKNET etc. Nerlovian model was used for data
analysis.
Table 1: Selected districts for each crop
Crops Districts
Jowar Nagaur and Tonk
Maize Chittore and Udaipur
Bajra Nagaur, Churu and Sri-Ganganagar
Wheat Jaipur, Alwar and Sri-Ganganagar
Barley Jaipur, Alwar and Sri-Ganganagar
Empirical model specification
The basic model which has come to be called as Nerlovian
price expectation model is as follows:
At = α + βiP*t + U ………….……………………………… (1)
(Pt* - P*t-1) = β(Pt-1 – P*t-1) 0<β<1 ……………… (2)
Where;
At = Actual acreage under the crop in year t
P*t = expected price of the crop in year t
P*t-1 = expected price of the crop in year t-1
Pt-1 = actual price of the crop in year t-1
Ut = Stochastic term
β= the coefficient of price expectation, and; α and β are
parameters to be estimated.
The hypothesis described through equation (2) is price
expectation hypothesis. The expression on the left-hand
side of this equation is the revision in price expectation
from year to year. On the right-hand side, the expression
is the error made by the farmers in predicting the price
during t-1. The coefficient of price expectation (β) indicates
that only a fraction of last year’s error in price prediction is
translated into revision in expected price during the current
year. Nerlovian model depicting farmer’s behavior in its
simplest form is given below:
At* = β0 + β1Pt-1 + β2Yt-1 + β3RF_IRR + β4YRt + β5PRt +
β6CYt-1 + β7CPt-1 + DD + Ut… (3)
At-At-1 =B (At*-At-1) (Nerlovian adjustment equation) … (4)
As expected variables are not observable, for estimation
purpose, a reduced form containing only observable
variables may be written after substituting the value of At*
from equation (4) into equation (3), as follows:
At = β0 + β1Pt-1 + β2Yt-1 + β3RF_IRR + β4YRt + β5PRt +
β6CYt-1 + β7CPt-1+ β8At-1 + DD + Ut ….. (5)
The “step-up/ backward” regression technique was used,
as this technique allows the variable explaining maximum
variability in the dependent variable to enter first in order
of their explanatory power. The first equation is a
behavioral equation, stating that desired acreage (At*)
depend upon following independent variables.
Where,
At = current year under study crop;
Pt-1 = one year lagged price of study crop;
Yt-1= one lagged yield of study crop;
RF_IRR = seasonal rainfall and/ or irrigated area under
study crop;
YRt = yield risk of study crop measured by standard
deviation of three preceding years;
PRt = price risk of studied crop measured by standard
deviation of three preceding years;
CYt-1= lagged yield of competing crop;
CPt-1 = lagged price of competing crop;
At-1 = lagged area of study crop;
DD = District Dummy;
β0 = intercept;
β1-n = parameter coefficients; and,
Ut = stochastic term.
The extent of adjustment to changes in the price and/or
non-price factors is measured in terms of “coefficient of
adjustment”. The adjustment takes place in accordance
with the actual planted area in the preceding year. If the
coefficient of adjustment is one, farmers fully adjust area
under the crop in the current year itself and there were ‘no
lags’ in adjustment. But if the coefficient of adjustment is
less than one, the adjustment goes on and gives rise to the
lags, which are distributed over time. The number of years
required for 95 percent of the effect of the price to
materialize is given as follows:
(1-r)n = 0.05
Where;
r = coefficient of adjustment (1-coefficient of lagged area);
and,
n = number of year.
The proportionate change in area under the crop (At) with
respect to a proportionate change in any of the factors
which cause variation in At is called ‘elasticity’ of At with
respect to that variables. In the present study, both short
run (SRE) and long run (LRE) elasticities of the area under
the crop with respect to price were estimated to examine

3. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
J. Agric. Econ. Rural Devel. 232
and compare the effect of price on the responsiveness of
area in the short-run as well as in the long-run. The price
elasticities for linear regression model are given below:
Short-run elasticity (SRE) = Price coefficient X
Mean of price
Mean of area
Long-run elasticity (LRE) =
SRE
Coefficient of adjustment
RESULTS AND DISCUSSION
This part empirically investigated the supply response of
farmers’ with the motive to tentatively study the relevance
of policy in the process of decision–making by the farmers.
Two folds exist in which a farmer‘s supply response can
manifest itself: either to make adjustments in crop
cultivated acreage, or vary the pattern of input use and try
to adjust the output of crop to market conditions. Farmers
allocate land to different crops depending on expected
revenue from different crops: assume input costs are the
same or move uniformly overtime for different crops,
expected revenue would depend on expected prices and
expected yields. If yield levels remain constant overtime
due to inadequate significant technological changes,
output response equals the acreage response. The
acreage response functions using Nerlovian adjustment
lag model were fitted through step-wise regression
technique, in order to allow the variable explaining
maximum variability in explained variable to enter first in
order of their explanatory power. The regression results
with respect to the cereal crops studied are presented
below:
Jowar (Sorghum) crop
The R2 of acreage under Jowar was 0.67, implying that 67
percent of the variation in acreage under Jowar was
explained jointly by the explanatory variables included in
the model (Table 2). In the allocation of acreage for Jowar
crop, the significant influencing factors were lagged area
under Jowar and lagged yield of competing crop. Current
area under Jowar was positively influenced by lagged area
of the crop which is statistically significant at 1 percent
probability level. The estimated adjustment coefficient
(0.44) was moderate, indicating moderate adjustment of
the area under jowar crop by the farmers. The coefficient
of lagged yield of competing crop (maize) was negative
and statistically significant at 5 percent probability level,
meaning acreage under Jowar cultivation decreased over
the years mainly due to better yield performance of
competing crops. As the productivity of competing crop
(Maize) increased, acreage under Jowar cultivation
decreased significantly. However, the dummy variable for
districts indicated that there were some un-measureable
attributes in Nagaur and Tonk districts which steered area
allocation to Jowar crop.
Table 2: Acreage response function of Jowar crop
Parameters Coefficient
Intercept 0.65 (0.11)***
Lagged price of jowar 0.23 (0.16)NS
Lagged area of jowar 0.56 (0.21)***
Lagged yield of maize -0.11 (0.05)**
Lagged price of maize 0.13 (0.11)NS
Nagaur -0.33 (0.182)*
Tonk 0.08 (0.045)*
R2
0.67
Note: ***, **,* denotes significance at 1, 5 and 10% level of probability
respectively.
NS: Non-significant
Maize crop
Four variables significantly influenced the current acreage
under maize cultivation viz. lagged price of maize, lagged
area of maize, rainfall and dummy district-Chittore were
statistically significant at 1 percent probability level (Table
3). The acreage allocation of maize crop was significantly
affected by lagged price of maize, and the relationship
turned out to be negative, due to subsistence farming
system associated with maize producing areas, thus, a
certain amount of production irrespective of the prevailing
price in the market. Also, the negative relationship is
attributed to non-availability of substitute crops to cultivate,
as substitute crops require extra capital for cultivation, well
known that capital is the major constraint affecting small
and marginal farmers, they tend to glue themselves to the
cultivation of maize crop. The adjustment coefficient was
0.81 which was very high, thus, indicating very rapid
adjustments of the area under maize crop by the farmers.
Other significant variables observed are lagged area and
rainfall that exert positive significant influence on acreage
allocation under the crop. The coefficient of determination
of acreage under the crop was satisfactory as exogenous
variables included in the model explained 65 percent
variation in current acreage under maize cultivation.
District dummy variable: Chittore explaining the existence
of un-measureable characteristics influencing area
allocation under the crop. The negative sign of the district
dummy implied less area allocation to maize as compared
to another district.
Table 3: Acreage response function of maize crop
Parameters Coefficient
Intercept 0.98 (0.732)NS
Lagged price of maize -0.51(0.13)***
Lagged area of maize 0.19(0.05)***
Lagged yield of maize 0.44 (0.41)NS
Price risk of maize 0.11(0.08)NS
Rainfall 0.09 (0.031)***
Chittore -0.521 (0.112)***
Udaipur 0.545 (0.521)NS
R2
0.65
Note: ***, **, * denotes significance at 1, 5 and 10% level of probability
respectively. NS: Non-significant

4. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
Sadiq et al. 233
Bajra (Millet) crop
All variables included in the model to determine acreage
response of Bajra were negative (Table 4) due to decline
over the years in the area under Bajra cultivation. Only
intervening variables were significant factors influencing
area allocation to Bajra crop, and the possible reason was,
there were no substitute crops to cultivate if the price of
Bajra decline and the alternative crops requires extra
capital which these paucity poor resource farmers are
capacity-wise bereft. Furthermore, the adjustment
coefficient was high (0.87), implying rapid adjustments of
the area under Bajra crop in the current year. As Jodhpur
district was assumed as the base line, all the district
dummy variables were significant, expressing un-
measurable district characteristics influencing acreage
under Bajra. However, the coefficient of multiple
determination was good as it explained 79 percent
variation in the current area under allocation of Bajra crop.
Table 4: Acreage response function of Bajra crop
Parameters Coefficient
Intercept 1.23 (0.99)NS
Lagged price of bajra -0.71 (0.591)NS
Lagged area of bajra -0.13 (0.11)NS
Lagged yield of maize -0.65 (0.631)NS
Lagged price of maize -0.04 (0.032)NS
Nagaur -0.10 (0.06)*
Churu -0.24 (0.113)**
Sri-Ganganagar 0.19 (0.101)*
R2
0.79
Note: ***, **, * denotes significance at 1, 5 and 10% level of probability
respectively.
NS: Non-significant
Wheat crop
The R2 of the acreage response function was 0.83;
meaning that 83 percent variation in acreage under wheat
crop cultivation was explained by the exogenous variables
included in the model. The lagged area under wheat, yield
risk and availability of irrigation facilities with the farmers
were the identified significant parameters influencing area
under wheat crop. The lagged acreage of wheat and
availability of irrigation facilities exert a positive influence
on current acreage under wheat production, implying that
with assured irrigation facilities to farmers, sustainable
wheat production could be achieved. The acreage
adjustment coefficient was low (0.39), meaning that the
adjustment rate of area under wheat crop was very low.
Yield risk had a negative impact on the current acreage
devoted to wheat cultivation, meaning farmers react to
yield risk involved. Districts dummy explained the non-
importance of Jaipur, Alwar and Sri-Ganganagar districts,
respectively, in wheat production, as their estimated
coefficients were not significant (Table 5).
Table 5: Acreage response function of Wheat crop
Parameters Coefficient
Intercept 0.432 (0.212)**
Lagged price of wheat 0.311 (0.235NS
Lagged area of wheat 0.612 (0.321)*
Yield risk of wheat -0.821 (0.482)*
Irrigated area under wheat 0.10 (0.052)*
Lagged yield of rice 0.321 (0.298)NS
Lagged price of rice 0.09 (0.07)NS
Jaipur 0.271 (0.191)NS
Alwar 0.111 (0.103)NS
Sri-Ganganagar 0.218 9 (0.189)NS
R2
0.83
Note: ***, **, * denotes significance at 1, 5 and 10% level of probability
respectively.
NS: Non-significant
Barley crop
The acreage response function of barley explained 84
percent variation in the area under Barley cultivation. A
perusal of Table 6 revealed that farmers considered
lagged acreage, lagged yield and availability of irrigation
water as significant parameters in area allocation decision
to Barley crop. The area under barley was declining, as
depicted by the negative and significant coefficient of
lagged yield of barley, and maybe due to the fact that
competing crops are more remunerative in term of price
and yield as reflected by the non-significant coefficient of
lagged price of barley (non-remunerative price). The
estimated adjustment coefficient (0.32) was very low,
indicating a very low rate of adjustment of the area under
barley by the farmers. Assuming Sikar district as the base
for dummy variable, all the district dummy variables were
significant in expressing un-measureable district
characteristics.
Table 6: Acreage response function of Barley crop
Parameters Coefficient
Intercept -0.09 (0.32)**
Lagged price of barley 0.073 (0.0617)NS
Lagged area of barley 0.677 (0.393)*
Lagged yield of barley -0.83 (0.491)*
Irrigated area under barley 0.24 (0.125)*
Lagged yield of rice 0.17(0.154)NS
Lagged price of rice 0.19 (0.134)NS
Jaipur 0.23 (0.129)*
Alwar 0.09 (0.051)*
Sri-Ganganagar 0.32 (0.171)*
R2
0.84
Note: ***, **, * denotes significance at 1, 5 and 10% level of probability
respectively.
NS: Non-significant
Short-run and Long-run elasticities
Owned lagged price variables were found to be positive for
three crops viz., jowar, wheat and barley, while negative

5. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
J. Agric. Econ. Rural Devel. 234
Table 7: Short-run and Long-run price elasticity
Crops Short-run elasticity Long-run elasticity Year(s) required for price effect to
materialize
Jowar 0.23 0.52 3.61
Maize -0.51 -0.63 14.13
Bajra -0.71 -0.82 -
Wheat 0.311 0.79 3.18
Barley 0.073 0.23 2.65
Source: Authors computation, 2017
for the remaining cereal crops under consideration viz.,
maize and bajra (Table 7). Out of the aforementioned
crops, only maize crop coefficient of lagged price of itself
was significant (negative signed). The short run elasticity
revealed acreage responsiveness of a crop to price
changes in preceding crop period, and the elasticity for
these crops ranged from -0.21 to 0.094: negative price
response was observed in maize and bajra (non-
commercial crops). However, it should be noted that
negative supply response is not an uncommon feature on
supply response as seen in earlier studies: Sud and
Kahlon (1969) observed negative price coefficients in
nearly six gram cultivating districts in Punjab; Cumming
(1975) also observed negative price coefficient in nearly
half of the 100 wheat cultivating districts in India; Jhala
(1979) also observed negative price response in six out of
fourteen cases he studied on groundnut crop. In studies of
Rao and Krishna (1965); Krishna and Rao (1967) and
Bhowmick and Goswami (1998), this kind of conflicting
estimates were reported. The long run elasticity reflects
the acreage responsiveness of a crop to price change
given sufficient time for adjustment. None of the crop
under consideration showed very high long-run elasticity
as such the impact of price policy on these crops would be
mild/light in the long-run. The number of years required for
price effect to materialize depends on the technological
and institutional constraints faced by the farmers for a
particular crop. The higher the constraints, the more is the
time required for adjustment. It was observed that Jowar
and maize crops, respectively, took medium time for
adjustment, while barley and wheat crops, respectively;
take very small time for adjustment. The smaller the time
for adjustment, the more effective is the price policy
instruments in bringing desired change in the supply of a
crop. In the case of bajra, the number of years required for
the price to materialize was indeterminate.
CONCLUSION AND RECOMMENDATION
The study empirically investigated farmers supply
response with the aim of studying the relevance of policy
in the process of decision-making by farmers, and other
relevant stakeholders involved in the business of
agriculture directly and indirectly. Empirical investigation
showed that the acreage response of cereal crop in
Rajasthan state was governed by price and non-price
factors; hence, price incentive alone was not sufficient in
bringing desirable change in cropping pattern as well
production of the cereal crop. Non-price factors like yield
risk, the yield of own and competing crops, rainfall and
irrigation facilities, investigated were relevant explanatory
variables. Therefore, policy for better implementation of
price support system, development of consistently
performing varieties and further enhancement of irrigation
facilities will go a long way in ensuring agricultural stability
in the state.
REFERENCES
Abujam AD, Chahal SS (2015). Acreage response of
sugarcane to price and non-price factors in Punjab.
Indian Journal of Economics and Development.11
(3):623-630
Bhowmick BC, Goswami J (1998). Supply response of
some important crops in Assam-An inter-district
analysis. Agricultural Situation in India. 55(6):349-356
Cummings JT (1975). The supply responsiveness of
Indian farmers in the post-independence period: Major
cereal and cash crops. Indian Journal of Agricultural
Economics, 30(1):25
Jhala ML (1979). Farmers' response to economic
incentives: An analysis of interregional groundnut supply
response in India. Indian Journal of Agricultural
Economics, 34(1):55
Krishna J, Rao MS (1967). Dynamics of acreage allocation
for wheat in uttar pradesh-a study in supply response.
Indian Journal of Agricultural Economics, 22(1):37- 43
Maddala GS, Lahiri K (2009). Introduction to
Econometrics-Fourth edition. John Wiley and Sons
Nerlove M (1958). The dynamics of supply: Estimation of
farmers response to price. John Hopkins, Baltimore,
U.S.A
Rao MS, Krishna J. (1965). Price expectation and acreage
response for wheat in Uttar Pradesh. Indian Journal of
Agricultural Economics, 20(1):22-27
Subba RS, Raghu RP, Neelakanta STV, Bhavani DI
(2016). Agricultural Economics-Second Edition. Oxford
and IBH Publishing Company PVT. LTD.
Sud L, Kahlon AS (1969). Estimation of acreage response
to price of selected crops in Punjab state. Indian Journal
of Agricultural Economics, 24(3):46

6. Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Rajasthan State of India
Sadiq et al. 235
Accepted 24 July 2017
Citation: Sadiq MS, Singh IP, Karunakaran N (2017).
Supply Response of Cereal Crop Farmers’ to Price and
Non-Price Factors in Rajasthan State of India. Journal of
Agricultural Economics and Rural Development, 3(2): 230-
235.
Copyright: © 2017 Sadiq et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.