Research on World Agricultural Economy | Vol.3,Iss.3 September 2022

Editor-in-Chief
Associate Editor
Cheng Sun
Jesus Simal-Gandara
Editorial Board Members
China branch of world productivity Federation of science and technology; Beijing world science and technology re-
search and Development Center for productivity, China
University of Vigo, Spain
Alberto J. Nunez-Selles Universidad Nacional Evangelica (UNEV), Dominican Republic
Jiban Shrestha National Plant Breeding and Genetics Research Centre, Nepal
Zhiguo Wang China Association for Science and Technology, China
Xiaoyong Huang International Energy Security Research Center, Chinese Academy of Social Sciences, China
Geeth Gayesha Hewavitharana University of Sri Jayewardenepura, Sri Lanka
Alamgir Ahmad Dar Sher-e-Kashmir University of Agricultural Sciences & Technology, India
Xiuju Zhang Hunan Academy of Agricultural Sciences, China
Keshav D Singh Agriculture and Agri-Food Canada (AAFC), Canada
K. Nirmal Ravi Kumar Acharya NG Ranga Agricultural University, India
Lijian Zhang Chinese Academy of Agricultural Sciences, China
Zhengbin Zhang Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, China
Ruhong Mei China Agricultural University, China
Mingzao Liang Institute of Agricultural Resources and Agricultural Regional Planning, Chinese Academy of
Agricultural Sciences, China
Rishi Ram Kattel Agriculture and Forestry University, Nepal
Yunbiao Li Jilin University, China
Zhizhong Huang Shandong High-end Technology Engineering Research Institute, China
Jianping Zhang Chinese Academy of International Trade and Economic Cooperation, China
Lin Shen China Agricultural University, China
Juan Sebastián Castillo Valero Universidad de Castilla-La Mancha, Spain
Kassa Tarekegn Southern Agricultural Research Institute, Ethiopia
Shahbaz Khan National Agricultural Research Centre, Pakistan

Volume 3 Issue 3 • September 2022 • ISSN 2737-4777 (Print) 2737-4785 (Online)
Research on World
Agricultural Economy
Editor-in-Chief
Cheng Sun

Volume 3 ｜ Issue 3 ｜ September 2022 ｜ Page1-67
Research on World Agricultural Economy
Contents
Research Articles
1 Technical Efficiency of Rice Farmers in Telangana, India: Data Envelopment Analysis (DEA)
K. Nirmal Ravi Kumar
13 Economics of Pulse Production in Bundelkhand Region of Uttar Pradesh, India: An Empirical Analysis
Prabhakar Kumar Ankhila R Handral Biswajit Mondal R.K. Yadav P. Anbukkani
22 Value Chain Analysis of Korarima (Aframomum Corrorima) in South Omo Zone, SNNPR Ethiopia
Asmera Adicha Yidnekachew Alemayehu Gedion Ermias Dawit Darcho
38 Determinants of Barley Output Supply Response in Ethiopia: Application of Ardl Bound Cointegration
Approach
Abera Gayesa Tirfi
52 Assessing the Short-term Effect of Exchange Rate Liberalisation on Food Import Prices: The Regression
Discontinuity in Time Employed for Russian Food Markets in 2014
Daria Loginova

1
Research on World Agricultural Economy | Volume 03 | Issue 03 | September 2022
https://ojs.nassg.org/index.php/rwae
Copyright © 2022 by the author(s). Published by NanYang Academy of Sciences Pte. Ltd. This is an open access article under the Creative
Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/).
DOI: http://dx.doi.org/10.36956/rwae.v3i3.559
Received: 17 June 2022; Received in revised form: 11 July 2022; Accepted: 19 July 2022; Published: 5 August 2022
Citation: Kumar, K.N.R., 2022. Technical Efficiency of Rice Farmers in Telangana, India: Data Envelopment
Analysis (DEA). Research on World Agricultural Economy. 3(3), 559. http://dx.doi.org/10.36956/rwae.v3i3.559
*Corresponding Author:
K. Nirmal Ravi Kumar,
Department of Agricultural Economics, Agricultural College, Bapatla, Acharya NG Ranga Agricultural University(ANGRAU),
Andhra Pradesh, India;
Email: drknrk@gmail.com
RESEARCH ARTICLE
Technical Efficiency of Rice Farmers in Telangana, India: Data
Envelopment Analysis (DEA)
K. Nirmal Ravi Kumar*
Department of Agricultural Economics, Agricultural College, Bapatla, Acharya NG Ranga Agricultural University
(ANGRAU), Andhra Pradesh, India
Abstract: It is known that the inability of the farmers to exploit the available production technologies results in lower
efficiencies of production. So, the measurement of technical efficiency in agricultural crops in developing countries like
India gained renewed attention in the late 1980s from an increasing number of researchers. Accordingly, the present
study has employed Data Envelopment Analysis (DEA) and Malmquist Total Factor Productivity Index to ascertain
the Technical Efficiency of rice productivity (2021-2022) and its changes over the study period (2019-2020 to 2021-
2022) respectively in Telangana, India. This study was based on secondary data pertaining to rice productivity (output
variable), fertilizer doses (NPK), seed rate, water applied and organic manure (input variables). The findings of Data
Envelopment Analysis revealed that the overall mean technical efficiency score across all the Decision-Making Units
was 0.860 ranging between 0.592 to 1.000. So, the Decision-Making Units, on average, could reduce their input usage
by 14 percent and still could produce the same level of rice output. Further, fertilizers (60.54 kg/ha); seed (5.63 kg/
ha); water (234.48 mm) and organic manure (3.76 t/ha) use can be reduced without affecting the current level of rice
productivity. Malmquist Total Factor Productivity indices (2019-2020 to 2021-2022) revealed that the mean scores of
technical efficiency change, pure technical efficiency change and scale efficiency change are more than one (1.153,
1.042 and 1.009 respectively), unlike technological change (0.983). All the Decision-Making Units showed impressive
progress with reference to technical efficiency change (1.112) and it is the sole contributor to Total Factor Productivity
change in rice cultivation. The DEA results suggest that farmers should be informed about the use of inputs as per the
scientific recommendations to boost the technical efficiency of rice productivity in Telangana. It also calls for policy
initiatives for the distribution of quality inputs to the farmers to boost technical efficiency in rice production.
Keywords: Constant returns to scale; Malmquist total factor productivity index; Decision Making Units; Telangana
1. Introduction
FAO during the International Year of Rice of 2004
stated that “Rice contributes to many aspects of soci-
ety and therefore can be considered a crystal or prism
through which the complexities of sustainable agriculture
and food systems can be viewed. The issues related to

2
rice production should not be viewed in isolation but in
the framework of agricultural production systems through
ecological and integrated systems” [1]
. This statement
highlights rice not only as one of the most important food
crops world-wide but also an intricate part of socio-cultural
influencer of many people’s lives. Rice is grown in about
120 countries and China leads other countries in the world
with a production of 214 million tonnes followed by India
with 116 million tonnes and these two countries together
contribute over 50 percent of the world’s output in 2019.
Nine out of the top ten and 13 out of the top twenty rice-
producing countries are in Southeast-Asia [2]
.
Rice contributed more than 40 percent of the total food
grains production in India in 2019 and accounted for 21
percent of global rice production. West Bengal, Uttar
Pradesh, Punjab, Andhra Pradesh, Odisha and Telangana
are the leading rice producing States in India [3]
. Boosting
the yields of rice is very much critical for the well-being
of millions of rice producers and consumers in India, as
around 22 percent of the population still lie Below Poverty
Line (BPL) in 2018 [4]
. Further, the demand for rice is pro-
jected at 137.3 million tonnes by 2050 [5]
. To accomplish
these goals, the rice yields must be increased by around
42 percent i.e., from the present level of 2393 kg/ha
(in 2011-2012) to 3400 kg/ha.
Telangana State is emerging as the ‘Rice Bowl of
India’ because, in a short span of five years, the area un-
der rice cultivation has doubled from 0.91 million hec-
tares in 2014-2015 to 1.93 million hectares in the 2018-
2019. Recently, with the completion of Kaleshwaram
Lift Irrigation Scheme, the extent of rice cultivation in
Telangana has increased in just one year from 1.93 million
hectares in 2018-2019 to 2.88 million hectares in 2019-
2020 and accordingly, production shot up from 6.6 mil-
lion tonnes to 10.5 million tonnes during this reference
period 2022 [6]
. So, the adequate water resources and other
inputs like seed, fertilizers subsidy, free power etc., being
provided by the State Government enabled the farmers
to take up rice cultivation. However, the statistical data
available in the offices of Joint Director of Agriculture in
Telangana has revealed drastic variations in rice produc-
tivity and resources usage. These variations in resources
usage contributed to low productivity of rice (compared
to potential) and this may arise owing to lower Technical
Efficiency (TE). This is an indicator of presence of techni-
cal inefficiency in rice productivity across the districts in
Telangana. Considering the socio-economic importance
of rice farming in this state, there seems to be a research
need for investigating the extent of such inefficiencies. It,
therefore, calls for a scientific inquiry on TE of rice pro-
duction in Telangana, which would be of much relevance
for farmers, researchers, policymakers and other stake-
holders to take appropriate measures for enhancing TE in
rice productivity, efficient management practices and con-
sequent, sustainable agricultural planning. In this context,
this study formulated the following three research ques-
tions viz., what is the TE of rice productivity across all
the districts in Telangana? What is the trend in TEs of rice
productivity over a period of time? What input quantities
are required to produce at the technically efficient point on
the production frontier? [4]
So, this study gives an impor-
tant direction to farmers for employing right combination
of productive resources in the rice production programme.
Further, the lack of empirical studies in Telangana on this
pertinent issue has prompted the researcher to conduct sci-
entific enquiry across the 32 rice producing districts with
the following specific objectives:
● To estimate TEs in rice productivity across the dis-
tricts or Decision-Making Units (DMUs) in Telan-
gana
● To find out the potentials for reduction in the levels
of critical inputs across the DMUs.
● To analyze the trends in TE and sources of TFP of
rice over the study period.
2. Review of Literature
There have been a sizeable number of studies on ef-
ficiency measure in the field of agriculture through apply-
ing DEA approach because of its non-parametric nature.
A review of literature on application of DEA in measuring
efficiency in crop productivity is presented here under.
Tolga et al. (2009) [7]
measured TE and determinants of
TE of rice farms in Marmara region, Turkey. Their study
revealed that mean TE score of sample rice farms was 0.92
and ranged between 0.75 to 1.00 implying that they can
reduce the inputs usage by eight per cent without affecting
the level of output.
Fabio (2015) [8]
studied both technical and scale ef-
ficiency in the Italian citrus farming through employing
both DEA and Stochastic Frontier Analysis (SFA). The
findings revealed that though the estimated TE from SFA
is on par with the DEA, the scale efficiency realized from
SFA is found higher compared to DEA. Both the models
revealed that TE and scale efficiency were positively in-
fluenced by farm size, unlike number of plots of land and
location of farm in a less-favoured area.
Sivasankari et al. (2017) [9]
employed DEA to analyze
the TE of rice farms in Cauvery delta zone of Tamil Nadu.
The findings revealed that TE index ranged from 0.41
to 1.00 under both Constant Returns to Scale (CRS) and
0.48 to 1.00 under Variable Returns to Scale (VRS) speci-
fications with mean TEs of 0.76 and 0.81 respectively.

3
Regarding scale efficiency, majority of the farms (81%)
exhibited showed Increasing Return to Scale (IRS). The
study also inferred that there is excess use for all inputs
especially for fertilizers like potash, phosphorus and farm
yard manure among the sample farms.
Bingjun and Xiaoxiao (2018) [10]
analyzed rice produc-
tion efficiency based on DEA-Malmquist Indices in Henan
Province of China. The results showed that from the time
dimension (2006-2016), the comprehensive TE change,
the technological progress change, the pure TE change,
the scale efficiency change and the TFP change have not
shown much improvement. However, from the perspec-
tive of spatial dimension, the TFP of rice in all provinces
is less than one, mainly because the production technol-
ogy was not fully utilized in each area. So, they suggested
strengthening of research and development, dissemination
of advanced production technology, proper allocation of
production factors etc., should deserve special attention to
ensure efficiency improvement and thereby, food security
of the country.
Joseph et al. (2018) [11]
employed DEA to measure TE
of rice production in the Centre region of Cameroon con-
sidering both CRS and VRS assumptions. The findings re-
vealed that the mean TE score is 0.67 and 0.95 at the CRS
and VRS respectively and with a mean scale efficiency of
0.70.
Shamsudeen et al. (2018) [4]
employed input-oriented
DEA to analyze the TE of rice production in northern
Ghana for the 2011-2012 cropping season. The mean
TE score was 77 percent implying the farmers employed
higher doses of inputs viz., chemical fertilizer, seed,
weedicides and hired labour than their prescribed opti-
mum. Around 84.4 of the sample farms experienced IRS,
while 5.6 per cent experienced Decreasing Returns to
Scale (DRS).
Nazir and Abdur (2022) [12]
analysed the TFP of cash
crops viz., sugarcane, cotton, and rice in Pakistan by em-
ploying Malmquist productivity index. The study decom-
posed the TFP of cash crops into technical change and TE
change. The findings showed an increase in the TFP of
selected cash crops in Pakistan by 2.2 percent and this is
mainly attributed to technical change. So, the researchers
emphasized on increasing both research and extension
investments to provide better seed varieties, better infra-
structure, and timely credit facilities.
3. Analytical Framework and Methodology
This study uses a two-step approach. In the first step,
the DEA model was employed to measure TE of DMUs as
an explicit function of discretionary variables pertaining to
Kharif season, 2021-2022. In the second step, DEA-based
Malmquist Index was used to analyze the trends in TE of
rice productivity during Kharif season across the DMUs
over the reference period, 2019-2020 to 2021-2022. This
study considered all the 32 DMUs in Telangana consider-
ing output variable (rice productivity) and input variables
(seed rate, fertilizer doses (NPK), water applied during
crop growth period and organic manure). The secondary
data on these variables are collected from respective Joint
Director of Agriculture Offices at DMU level.
3.1 DEA
This linear programming tool was employed to meas-
ure the TE of rice productivity in Telangana considering
input-oriented-CRS model [13-15]
. In this model, there are
32 DMUs and each DMU uses four inputs (K) and pro-
duces one output (M). For the ith
DMU, these are repre-
sented by the vectors xi and yi, respectively. The selected
inputs and output are represented by a K × N input matrix
denoted by X, and M × N output matrix denoted by Y
respectively. For the ith
DMU, the efficiency score θ is ob-
tained by solving the linear programming as follows:
minθλ θ
st
-yi + Yλ > 0
θxi - Xλ > 0
λ > 0
Here, θ indicates the TE score of input-oriented CRS of
the DMU under evaluation. If the value of θ = 1, it implies
the DMU is functioning on the production frontier with
100 per cent of efficiency and hence, there is no need for
changing the level of resources employed in the produc-
tion. On the contrary, if θ < 1, it implies the DMU under
consideration is relatively inefficient and thus, it could
reduce the level of inputs usage without affecting the out-
put [9]
.
3.2 Malmquist TFP Index: Input Oriented, CRS
This index based on DEA is employed to study the
trends in TE, technological change, Pure TE change, scale
efficiency change and changes in TFP of rice productiv-
ity during 2019-2020 to 2021-2022 across the selected 32
DMUs. So, the average values of the selected output and
input variables during this reference period are subjected
to DEA-based Malmquist Index analysis. The change in
productivity from the period t to t + 1 is calculated using
the following formula [9,16]
:
M y x y x
D y x
D y x
D y
t t t
t t t
t t t
t
1
1 1
1 1
1
1
1
+
+ + +
( , , , )= X
t+1 t+1 ( , )
( , )
( t
t t
t t t
t t t
x
D y x
M y x y x
D
+ +
+
+






1 1
1
1
1
1
1 2
, )
( , )
/
( , , , )=
t+1 t+1 1
1
1 1 1
1
1
1 1
1
1 1 1
t t t
t t t
t t t
t t t
y x
D y x
D y x
D y x
+ + + + +
+ + +
( , )
( , )
( , )
( , )
*
*
D
D y x
D y x
D y x
t t t
t t t
I
t
t t
1
1
1
1
1 2
( , )
( , )
/
( , )
+
−















 
 = mi
in
min
min
θλ
θλ
θ
θ
θ
D y x
D y x
I
t
t t
I
t
t t
+
+ +
−
+ −

 
 =

 
 =
1
1 1
1
1 1
( , )
( , ) λ
λθ
(1)
where, M1 = Malmquist Productivity Change Index

4
D1 = Input distance functions [15]
y = the level of output(s)
x = the level of input(s); and
t = time
Equation (1) is decomposed as:
M y x y x
D y x
D y x
D y
t t t
t t t
t t t
t
1
1 1
1 1
1
1
1
+
+ + +
( , , , )= X
t+1 t+1 ( , )
( , )
( t
t t
t t t
t t t
x
D y x
M y x y x
D
+ +
+
+






1 1
1
1
1
1
1 2
, )
( , )
/
( , , , )=
t+1 t+1 1
1
1 1 1
1
1
1 1
1
1 1 1
t t t
t t t
t t t
t t t
y x
D y x
D y x
D y x
+ + + + +
+ + +
( , )
( , )
( , )
( , )
*
*
D
D y x
D y x
D y x
t t t
t t t
I
t
t t
1
1
1
1
1 2
( , )
( , )
/
( , )
+
−















 
 = mi
in
min
min
θλ
θλ
θ
θ
θ
D y x
D y x
I
t
t t
I
t
t t
+
+ +
−
+ −

 
 =

 
 =
1
1 1
1
1 1
( , )
( , ) λ
λ
θλ
θ
θ
D y x
I
t
t t
( , )
+ +
−

 
 =
1 1
1
min
(2)
The first term on the RHS of the above equation in-
dicates the change in input-based TE between the years
t and t + 1, while the second term indicate the change in
technology between the selected periods. From the above
Equation (2), it can be inferred that the product of change
in TE and technological change gives a measure of change
in TFP. If the TFP is 1, it implies the TFP is increasing
during the selected periods (t and t + 1) and vice versa
and if the TFP = 1, it implies no change [15]
. To obtain the
change in Malmquist Indices, the following series of Lin-
ear Programing Problems (LPPs) are to be solved [16]
:
1
( , )
t
I t t
D y x minθλθ
−
  =
  (3)
st
-yit + Yt λ 0
θxit - Xtλ 0
λ 0
1
1
1 1
( , )
t
I t t
D y x minθλθ
−
+
+ +
  =
  (4)
st
-yi,t+1 + Yt+1 λ 0
θxi,t+1 - Xt+1λ 0
λ 0
1
1
( , )
t
I t t
D y x minθλθ
−
+
  =
  (5)
st
-yit + Yt+1 λ 0
θxit - Xt+1λ 0
λ 0
1
1 1
( , )
t
I t t
D y x minθλθ
−
+ +
  =
  (6)
st
-yi,t+1 + Yt λ 0
θxi,t+1 - Xtλ 0
λ 0
These LPPs are solved for each firm in the sample.
Therefore, given the number of periods (T) and number of
observations (N), [N × (3T - 2)] problems are to be solved.
This study considered all the 32 districts (as the DMUs)
in Telangana and the relevant secondary data are obtained
from respective Joint Director of Agriculture Offices. Rice
yield (kg/ha) is considered as the output, whereas seed
rate, fertilizer doses (NPK), annual rainfall received (mm)
and organic manure are considered as inputs. The aver-
age values of the output and input variables (2019-2020
to 2021-2022) are collected for the DMUs and subjected
to DEA and DEA-based Malmquist TFP Index analysis
for estimating the TE and change in TE respectively. The
efficiency analysis and Malmquist Index for efficiency
change over time has been done using the DEAP version
2.1 program developed by Coelli, 1996 [15]
.
3.3 Sample Adequacy Test
According to Cooper et al., 2007 [17]
, the thumb rules
for sample size acceptable for conducting DEA should be
either greater than or equal to the product of inputs (X)
and outputs (Y) or the sample size should be at least three
times the sum of the number of X and Y variables. So,
considering X = 4 and Y = 1, the sample size of 32 DMUs
in Telangana confirms the sample adequacy for conduct-
ing DEA.
4. Results and Discussion
4.1 Summary Statistics of Output and Input Vari-
ables
Table 1 shows that the average productivity of rice in Tel-
angana was estimated as 3288.28 kg/ha with maximum and
minimum productivity levels of 3705 kg/ha and 2720 kg/ha
respectively with the estimated Coefficient of Variation
(CV) of 59.928 percent. There exist larger variations
across the DMUs in terms of inputs usage viz., fertilizer
doses, seed rate, water applied and organic manure. Re-
garding the quantity of fertilizers (NPK) applied, it ranged
from 110 kg/ha to 350 kg/ha with an average value of
263.37 kg/ha and CV of 55.798 percent. The application
of chemical fertilizers is on the higher side among all the
DMUs compared to the recommended dosages (NPK
@ 120:40:40 kg ha-1
for short duration varieties; NPK
@ 150:50:60 kg ha-1
for medium duration varieties and
NPK @ 150:50:80 kg ha-1
for long duration varieties).
Similarly, average quantity of water applied was 1190.01
mm with minimum and maximum values of 780 mm and
1670 mm respectively and with a CV of 41.579 percent.
For majority of the DMUs (87%), the actual quantity of
water applied is higher than the scientific recommenda-
tion of 1200 mm to 1250 mm. The quantity of seed used
pitches between 17 kg/ha and 28 kg/ha with a mean value
of 23.47 kg/ha and with a CV of 38.508 percent. A close
examination of the data collected, the actual seed used
by all the DMUs is considerably higher compared to the
recommended level of 20 kg/ha. However, the CV is

5
slightly lower with respect to organic manure applied for
rice cultivation (24.617%) and across the DMUs it varied
between 2 t/ha to 12 t/ha with an average of 8.37 t/ha.
The higher CVs of inputs is an indicative of presence of
technical inefficiency in contributing to the productivity
of rice across the DMUs in Telangana. Again for major-
ity of the DMUs, the quantity of organic manure applied
is higher compared to the recommended dosage of 8 t/ha
to 10 t/ha. Though the application of this input is on the
higher side, it is heartening that the farmers realized the
importance of organic farming in producing both cost-
effective and quality output.
4.2 DEA-Input-oriented CRS
The results of CRS TE scores (θ) along with bench-
marking DMUs and peer lambda weights (λj) for the
DMUs under evaluation are presented through Table 2.
The findings revealed that only nine out of 32 DMUs
namely, Karimnagar, Jogulamba Gadwal, Kamareddy,
Khammam, Mahabubnagar, Medak, Medchal-Malkajgiri,
Narayanpet and Suryapet received TE score of ‘1’. This
implies they are the best performing DMUs in Telangana,
as they are operating on the efficiency frontier in the peer
group. For the remaining 23 DMUs, the TE scores are less
than one ranging between 0.592 (Warangal-Rural) to 0.931
(Jagtial) with a mean TE score of 0.806. This implies pres-
ence of relative technical inefficiency in rice productivity,
as these 23 DMUs are operating below the efficiency fron-
tier. So, these 23 DMUs could reduce current level inputs
to the tune of 19.4 per cent without affecting the rice pro-
ductivity. The overall mean TE score for all the 32 DMUs
was estimated as 0.860 indicating relative technical inef-
ficiency is to the extent of 14 percent. This means that, on
an average, the DMUs can check over-use of current level
input resources to the tune of 14 percent without affecting
the rice productivity in the State. The DMU, Warangal-
Rural is with the lowest TE score of 0.592 followed by
Vikarabad (0.611), Mulugu (0.661), Mancherial (0.717)
etc., and all are lying at the bottom of the performance
ladder (Table 3). So, these DMUs could reduce the cur-
rent level of input usage by 40.80, 38.90, 33.90 and 28.30
percents respectively without affecting their correspond-
ing rice productivity levels. For the inefficient DMUs
(θ 1), the benchmarking DMUs are given in Column
4 and it will guide the former to reduce their inputs us-
age corresponding to the benchmarking DMUs [9,10]
. For
example, Suryapet and Kamareddy are the benchmarking
DMUs for Adilabad with respective lambda (λj) weights
of 0.903 and 0.023. With the λj weights, the benchmark-
ing DMUs form linear combinations with the inefficient
DMUs in terms of efficiency perspective. For the efficient
DMUs (with TE score of 1.000), the benchmarking DMUs
are peer of themselves with λj weights of ‘one’.
The comparative picture of efficient and inefficient
DMUs in terms of TE scores (Figure 1) indicate that the
dark color bars represent the DMUs (9) operating on the
efficiency frontier (with TE scores of ‘1’) and the light
color bars denote the DMUs (23) lying below the efficien-
cy frontier (with TE scores of ‘1’). So, the vertical gap
between efficient and inefficient DMUs indicate the extent
of technical inefficiencies of 23 DMUs.
4.3 Determining Optimal Level of Inputs Utiliza-
tion from the CRS Model
From Table 2, it was inferred that there are nine techni-
cally efficient DMUs and 23 technically inefficient DMUs.
Accordingly, DMU-wise projected input quantities and
possible reductions across inefficient DMUs was comput-
ed [14,15]
to realize higher TE scores without affecting their
current level of rice productivity (Table 4). The projected
input quantities indicate the minimum quantities of select-
ed inputs required across the DMUs to produce technical-
ly efficient output on the production frontier. So, the dif-
ference between actual and projected quantities of inputs
(obtained from the one-stage DEA) indicate the possible
input quantity reductions. For example, the actual use of
fertilizers, seed rate, water applied and organic manure
for the DMU, Adilabad are 205.935 kg/ha, 32.67 kg/ha,
Table 1. Summary Statistics of output and input variables (2021-2022)
Item Minimum Maximum Mean Std. Deviation CV
Rice productivity (kg/ha) 2720 3705 3288.28 1970.60 59.928
Fertilizer Use (NPK) (kg/ha) 110 350 263.37 146.96 55.798
Seed rate (kg/ha) 17 28 23.47 9.04 38.508
Water applied (mm) 780 1670 1190.01 494.79 41.579
Organic manure (t/ha) 2 12 8.37 2.06 24.617

6
Table 2. Results of Input-oriented CRS
Sl.
No.
Districts
CRS Technical
Efficiency (θ)
Benchmarking Districts
Peer Weights (λj) in Order of
Benchmarking Districts
1 Adilabad 0.828 Suryapet, Kamareddy 0.903, 0.023
2 Bhadradri Kothagudem 0.875 Medak, Karimnagar, Khammam 0.179, 0.631, 0.214
3 Karimnagar 1.000 Karimnagar 1.000
4 Jagtial 0.931 Kamareddy 0.920
5 Jangaon 0.803
Karimnagar, Medchal-Malkajgiri,
Narayanpet
0.668, 0.028, 0.288
6 Jayashankar Bhupalpally 0.858 Suryapet, Kamareddy 0.334, 0.566
7 Jogulamba Gadwal 1.000 Jogulamba Gadwal 1.000
8 Kamareddy 1.000 Kamareddy 1.000
9 Khammam 1.000 Khammam 1.000
10 Kumuram Bheem 0.812 Khammam, Karimnagar, Suryapet 0.558, 0.403, 0.009
11 Mahabubabad 0.868
Kamareddy Karimnagar, Mahabubnagar,
Bhadradri Kothagudem
0.357, 0.371, 0.137, 0.214
12 Mahabubnagar 1.000 Mahabubnagar 1.000
13 Mancherial 0.717 Karimnagar, Kamareddy, Suryapet 0.343, 0.505, 0.035
14 Medak 1.000 Medak 1.000
15 Medchal-Malkajgiri 1.000 Medchal-Malkajgiri 1.000
16 Mulugu 0.661 Khammam, Karimnagar, Suryapet 0.255, 0.469, 0.183
17 Nagarkurnool 0.889 Narayanpet, Mahabubnagar 0.604, 0.365
18 Nalgonda 0.834 Narayanpet, Jogulamba Gadwal, Suryapet 0.631, 0.120, 0.196
19 Narayanpet 1.000 Narayanpet 1.000
20 Nirmal 0.724
Suryapet, Narayanpet, Mahabubnagar,
Kamareddy
0.594, 0.036, 0.094, 0.245
21 Nizamabad 0.848 Suryapet, Karimnagar, Kamareddy 0.077, 0.523, 0.356
22 Peddapalli 0.838
Karimnagar, Narayanpet, Kamareddy
Suryapet
0.028, 0.319, 0.488, 0.226
23 Rajanna Sircilla 0.836
Karimnagar, Mahabubnagar, Kamareddy,
Narayanpet
0.583, 0.115, 0.161, 0.136
24 Rangareddy 0.869
Karimnagar, Medchal-Malkajgiri,
Narayanpet
0.174, 0.089, 0.694
25 Sangareddy 0.775 Karimnagar, Narayanpet, Mahabubnagar 0.396, 0.456, 0.205
26 Siddipet 0.819 Karimnagar, Medak, Narayanpet, Suryapet 0.323 0.01,1 0.059, 0.408
27 Suryapet 1.000 Suryapet 1.000
28 Vikarabad 0.611 Suryapet, Narayanpet, Jogulamba Gadwal 0.101, 0.524, 0.211
29 Wanaparthy 0.917 Narayanpet 0.947
30 Warangal (Rural) 0.592
Suryapet, Narayanpet, Kamareddy,
Mahabubnagar
0.021, 0.602, 0.224, 0.030
31 Warangal (Urban) 0.804 Kamareddy, Mahabubnagar, Suryapet 0.195, 0.533, 0.201
32 Yadadri Bhuvanagiri 0.819 Suryapet, Narayanpet, Jogulamba Gadwal 0.017, 0.895, 0.173
Average of all districts 0.860
Source: Authors’ estimation from DEAP version 2.1 [15]

7
Table 3. Frequency distribution and summary statistics on overall TE, pure TE and Scale efficiency measures of select-
ed DMUs
Efficiency
level
No. of DMUs Per cent DMUs
0.501-0.600 1 3.12 Warangal (rural)
0.601-0.700 2 6.25 Mulugu, Vikarabad
0.701-0.800 3 9.38 Mancherial, Niirmal, Sangareddy
0.801-0.900 15 46.88
Adilabad, Bhadradri Kothagudem, Jangaon, Jayashankar
Bhupalpally, Kumuram Bheem, Mahabubabad, Nagarkurnool,
Nalgonda, Nizamabad, Peddapalli, Rajanna Siricilla, Rangareddy,
Siddipet, Warangal (urban), Yadadri Bhuvanagiri
0.901-0.999 2 6.25 Jagtial, Wanaparthy
1.000 9 28.13
Karimnagar, Jogulamba Gadwal, Kamareddy, Khammam,
Mahbubnagar, Medak, Medchal-Malkajgiri, Narayanpet, Suryapet
Total 32 100.00
Minimum 0.592
Maximum 1.000
Mean 0.860
Figure 1. Position of the DMUs in relation to TE scores

8
1511.301 mm and 10.215 t/ha respectively, whereas the
projected input values obtained from the model for main-
taining the same productivity (3124.73 kg/ha) are 145.395
kg/ha, 27.04 kg/ha, 1276.821 mm and 6.455 t/ha respec-
tively. So, the estimated differences between the actual
and projected input values (fertilizers 60.54 kg/ha; seed
use 5.63 kg/ha; water applied 234.48 mm and organic
manure 3.76 t/ha) indicate their excess use in rice produc-
tion. Hence, this excess use of inputs should be reduced
for Adilabad without affecting rice productivity. The same
explanation can be offered for other technically inefficient
DMUs. However, for the efficient DMUs with TE score
1.000, the gap between actual and projected input usage is
around zero, as they are already operating on the produc-
tion frontier (the best performing DMUs) and hence, there
is no scope for reduction in the existing level of inputs
usage. At the pooled (State) level i.e., considering the
average of all the DMUs, there is overuse of fertilizers,
seed use, water applied and organic manure to the tune of
53.998 kg/ha, 6.528 kg/ha, 86.436 mm and 2.249 t/ha re-
spectively, as the production scenario of rice in dominated
by technically inefficient DMUs (23) compared to only
nine technical efficient DMUs.
So, it is felt appropriate to compare the extent of inputs
usage between technically efficient DMUs and technically
inefficient DMUs in terms of rice productivity in Telan-
gana. As shown through Table 5, the efficient DMUs (n =
9) employed on an average of 170.184 kg/ha of fertilizer,
21.667 kg/ha of seed, 1275.986 mm of water applied and
5.000 t/ha of organic manure to produce a yield of 3317 kg/ha
of rice. However, for the inefficient DMUs (n = 23), to
move up to the production level of the efficient DMUs,
they should check excess application of fertilizers by
40.105 kg/ha, seed by 3.724 kg/ha, water use by 36.100
mm and organic manure by 2.870 t/ha in order to boost
rice productivity by 778 kg/ha [4]
.
4.4 Trends in TE of DMUs - Malmquist TFP In-
dex
Table 6 portrayed the Malmquist indices for each DMU
during the period 2019-2020 to 2021-2022 [18]
. The find-
ings revealed that with reference to TE change index,
78 percent of the DMUs have made progress (TE change
value 1.000) and remaining 22 percent of DMUs have
regressed (TE change value 1.000). The top three DMUs
that showed progress with reference to TE change in-
clude: Nizamabad (48.3%), Nagarkurnool (45.5%) and
Sangareddy (43.4%) and the top three DMUs that are
regressed in terms of TE change are Kumuram Bheem
(30.3%), Jagtial (22.2%) and Khammam (19.5%). It is
heartening that the mean score for TE change in Telan-
gana is more than 1 (i.e. 1.153) and this shows that the
DMUs as a whole have witnessed impressive performance
in TE change of rice productivity during the reference pe-
riod [9,10,16]
.
However, it is disappointing that 56% of the DMUs
have regressed with reference to technological change
during the above reference period and hence, the mean
score of technological index in Telangana is less than one
(0.983). The top three DMUs that are regressed include:
Mulugu, Medak and Narayanpet with 13.6 percent, 12.9
percent and 12.8 percent respectively. It is found interest-
ing that majority of the DMUs have showed progress with
reference to pure TE change (53%) and scale efficiency
change (59%). Further, 75 percent of the DMUs showed
progress with reference to TFP change and remaining 25
percent of DMUs have regressed. The top three DMUs
viz., Nizamabad, Karimnagar and Sangareddy have
enjoyed TFP growth of 42.1 percent, 40.1 percent and
35.2 percent respectively. At the state level, the results are
found encouraging with reference to TE change (15.3%),
pure TE change (4.2%), Scale efficiency change (0.9%)
and TFP change (11.2%). So, on comparing the TE change
and technological change, it can be inferred that the pro-
gress in TFP change is purely from TE change during the
reference period.
The break-up of Malmquist indices across the selected
periods viz., 2019-2020 to 2021-2022 (Table 7) revealed
that TE change has showed increasing trend during from
1.139 (2019-2020) to 1.179 (2021-2022) with mean TE
change of 1.153. This shows that there is a gradual pro-
gress in terms of TE change for enhancing rice productiv-
ity in the State during the overall reference period. On the
contrary, the mean technological change was regressed
during the reference period with 0.983. Though techno-
logical change was marginally progressed (2.7%) during
2021-2022 compared to 2020-2021, the mean technologi-
cal change is regressed during the overall reference pe-
riod. It is also interesting that the DMUs have marginally
progressed in terms of pure TE change (4.2%) and Scale
Efficiency change (0.9%) during the reference period. The
TFP change has witnessed progress in the State with an
average value of 1.112. Considering these trends, it can be
inferred that at State level, pure TE change and scale ef-
ficiency change have almost remained stagnant and hence,
the gain in TFP of rice in Telangana is solely due to TE
change of inputs over time.

9
Table 4. Results of Input-oriented CRS: Single Stage Calculation
S.No Districts
Projected Input Quantities
Possible Inputs Reduction
(Actual - Projected)
Fertilizer
Use (NPK)
(kg/ha)
Seed rate
(kg/ha)
Water
applied
(mm)
Organic
manure
applied (t/ha)
Fertilizer
Use (NPK)
(kg/ha)
Seed rate
(kg/ha)
Water
applied
(mm)
Organic
manure
applied (t/ha)
1 Adilabad 145.395 27.040 1276.821 6.455 60.540 5.630 234.480 3.760
2 Bhadradri Kothagudem 181.191 36.177 1399.781 3.501 51.620 5.160 130.776 1.000
3 Karimnagar 145.670 38.000 1232.784 3.000 0.000 0.000 0.000 0.000
4 Jagtial 100.580 34.959 1562.051 5.520 14.840 5.370 42.354 4.960
5 Jangaon 142.383 36.562 1122.035 3.212 69.900 14.100 91.800 0.900
6 Jayashankar Bhupalpally 114.741 31.185 1418.841 5.733 37.860 5.150 129.900 2.540
7 Jogulamba Gadwal 201.000 28.000 871.146 7.000 0.000 0.000 0.000 0.660
8 Kamareddy 109.330 38.000 1697.940 6.000 0.000 0.000 0.000 0.660
9 Khammam 205.670 29.330 1649.358 5.000 0.000 0.000 0.000 0.660
10 Kumuram Bheem 174.908 31.947 1429.609 4.061 80.860 7.390 185.790 1.220
11 Mahabubabad 137.995 40.209 1400.504 5.207 42.020 6.120 71.058 0.920
12 Mahabubnagar 115.000 39.000 1025.550 8.000 0.000 0.000 0.000 -0.660
13 Mancherial 110.722 33.239 1328.369 4.305 87.220 13.090 194.862 2.720
14 Medak 252.330 33.000 1499.022 3.000 0.000 0.000 0.000 -0.660
15 Medchal-Malkajgiri 208.000 53.000 1217.412 2.000 0.000 0.000 0.000 0.000
16 Mulugu 149.843 30.627 1250.676 3.966 153.640 15.710 290.424 3.400
17 Nagarkurnool 124.410 34.599 930.028 5.341 31.180 7.070 38.850 3.320
18 Nalgonda 141.268 30.309 953.896 4.739 56.140 6.020 63.162 5.860
19 Narayanpet 136.330 33.670 918.846 4.000 0.000 0.000 0.000 -0.660
20 Nirmal 136.427 31.388 1359.256 6.520 103.820 11.950 172.374 4.300
21 Nizamabad 127.272 35.636 1354.761 4.242 45.460 6.360 120.462 1.520
22 Peddapalli 136.674 36.894 1466.075 5.869 52.660 7.110 94.134 2.260
23 Rajanna Sircilla 134.357 37.354 1235.797 4.181 52.620 7.310 80.676 1.640
24 Rangareddy 138.404 34.680 960.221 3.475 41.860 6.650 48.396 1.060
25 Sangareddy 143.316 38.366 1116.504 4.648 83.360 12.970 108.246 2.700
26 Siddipet 122.589 26.480 1029.404 4.095 54.160 5.850 75.798 1.820
27 Suryapet 158.330 29.000 1371.816 7.000 0.000 0.000 0.000 0.000
28 Vikarabad 129.762 26.471 803.320 4.276 187.140 16.860 170.538 6.120
29 Wanaparthy 129.065 31.876 869.879 3.787 70.540 5.790 26.154 7.760
30 Warangal (Rural) 113.414 30.574 993.618 4.142 156.500 21.090 228.534 6.380
31 Warangal (Urban) 114.463 34.042 1153.809 6.844 55.740 8.290 93.636 4.320
32 Yadadri Bhuvanagiri 159.532 35.477 996.773 4.913 138.260 7.860 73.548 1.500
Average of all Districts 145.012 33.972 1215.497 4.814 53.998 6.528 86.436 2.249
Table 5. Comparison of average input use between inefficient and efficient farmers in Telangana
Input use
Number of
DMUs
Mean TE
score
Fertilizer Use
(NPK) (kg/ha)
Seed rate
(kg/ha)
Water applied
(mm)
Organic manure
applied (t/ha)
Yield
(kg/ha)
Average of efficient
DMUs
9 1.000 170.184 21.667 1275.986 5.000 3317
Average of inefficient
DMUs
23 0.806 210.289 25.391 1312.086 7.870 2539
Source: Authors’ estimation from DEAP version 2.1 (Coelli et al., 1996 [15]
)

10
Table 6. Malmquist Index Summary for District Means
Districts TE Change Technological Change Pure TE Change Scale Efficiency Change TFP Change
Adilabad 0.879 0.979 0.867 1.013 0.861
Bhadradri Kothagudem 1.217 0.961 0.950 1.070 1.209
Karimnagar 1.410 1.092 1.000 1.010 1.401
Jagtial 0.778 1.042 0.855 0.910 0.811
Jangaon 1.161 0.957 1.115 1.042 1.112
Jayashankar Bhupalpally 1.117 1.048 0.863 0.970 1.108
Jogulamba Gadwal 1.113 0.996 1.000 0.941 1.108
Kamareddy 1.084 1.044 1.055 1.027 1.132
Khammam 0.805 0.979 0.853 0.944 0.788
Kumuram Bheem 0.697 0.918 0.726 0.960 0.640
Mahabubabad 0.826 1.015 1.000 0.826 0.838
Mahabubnagar 1.254 0.972 1.044 1.010 1.211
Mancherial 1.290 0.964 1.417 0.910 1.317
Medak 1.340 0.871 1.280 1.047 1.303
Medchal-Malkajgiri 1.390 1.014 1.044 1.044 1.284
Mulugu 1.113 0.864 1.084 1.026 1.064
Nagarkurnool 1.455 0.968 1.074 0.964 1.002
Nalgonda 1.061 1.010 1.012 1.049 1.072
Narayanpet 0.862 0.872 1.000 0.862 0.752
Nirmal 1.170 0.924 1.000 1.170 1.162
Nizamabad 1.483 1.000 1.265 1.123 1.421
Peddapalli 1.333 0.996 1.186 1.124 1.328
Rajanna Sircilla 1.123 0.952 0.953 0.992 1.048
Rangareddy 1.343 1.002 1.100 1.039 1.345
Sangareddy 1.434 1.015 1.250 0.987 1.352
Siddipet 1.165 1.046 1.068 1.090 1.089
Suryapet 1.026 0.970 1.000 1.026 0.995
Vikarabad 1.043 1.017 0.958 1.088 1.060
Wanaparthy 1.275 1.009 1.036 1.133 1.211
Warangal (Rural) 1.356 0.966 1.202 0.961 1.316
Warangal (Urban) 1.331 1.006 1.151 0.896 1.298
Yadadri Bhuvanagiri 0.954 0.983 0.922 1.035 0.938
Average of all Districts 1.153 0.983 1.042 1.009 1.112
Note: All Malmquist index averages are geometric means

11
5. Summary and Conclusions
Input-oriented DEA Model with CRS was employed in
this study to analyze the TE in rice productivity in Telan-
gana. Out of 32 DMUs considered, only nine DMUs are
found technically efficient. The overall TE score for Tel-
angana is 0.860 implying that the DMUs, on an average,
could reduce their inputs usage by 14 per cent without af-
fecting their current level of rice productivity. Compared
to technically efficient DMUs, inefficient DMUs has to
check the use of inputs viz, fertilizer use by 40.105 kg/
ha, seed use by 3.724 kg/ha, water use by 36.100 mm and
organic manure use by 2.870 t/ha in order to boost yield
by 778 kg/ha and to reach on the production frontier.
Malmquist index analysis concluded that the progress in
TFP change during 2019-2020 to 2021-2022 was purely
due to TE change only. During this period, on an aver-
age, the technological change has regressed and pure TE
change and scale efficiency change have almost remained
stagnant.
6. Policy Recommendations
Policy suggestions from this study include: dissemina-
tion of modern production technologies to the farmers,
capacity building of farmers on Good Agricultural Prac-
tices, supply of quality inputs to farmers at affordable
prices etc., should deserve special attention. The poor and
marginalized farmers cultivating rice in the State must be
encouraged to join Farmer-Producer Organizations (FPOs)
for availing need-based assistance, participation in various
training programs and benefit from strengthened back-
ward linkages to enhance TE of inputs usage. Further, to
boost the technological change, the Government should
enhance investments both in research and extension. The
enabling environment in the State should be conducive to
promoting private sector agricultural investments [19]
. The
coordination between demand-driven research and tech-
nology dissemination should also be given priority.
Conflict of Interest
There is no conflict of interest.
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13
Biswajit Mondal,
ICAR-National Rice Research Institute (NRRI), Cuttack, Odisha, India;
Email: bisumondal@rediffmail.com
Received: 19 June 2022; Received in revised form: 11 July 2022; Accepted: 19 July 2022; Published: 5 August 2022
Citation: Kumar, P., Handral, A.R., Mondal, B., Yadav, R.K., Anbukkani, P., 2022. Economics of Pulse Production in
Bundelkhand Region of Uttar Pradesh, India: An Empirical Analysis. Research on World Agricultural Economy. 3(3),
560. http://dx.doi.org/10.36956/rwae.v3i3.560
RESEARCH ARTICLE
Economics of Pulse Production in Bundelkhand Region of Uttar
Pradesh, India: An Empirical Analysis
Prabhakar Kumar1
Ankhila R Handral1
Biswajit Mondal2*
R.K. Yadav3
P. Anbukkani1
1. Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, New Delhi, India
2. ICAR-National Rice Research Institute (NRRI), Cuttack, Odisha, India
3. College of Agriculture, Lakhimpur Kheri Campus, C.S. Azad University of Agriculture Technology, Kanpur, India
Abstract: The Bundelkhand region contributes more than half of the total pulse area of the Uttar Pradesh state
but the productivity is below the state average, which calls for various technological interventions, development
of infrastructure and marketing strategies. This study assessed the profitability of pulse cultivation, identified the
constraints and suggested policy measures using the data collected during 2016-2017 from 100 pulse growers selected
from two backward districts of the Bundelkhand region, namely Jalaun and Hamirpur. Growth in area, production
and yield was estimated using data for 1980-2015 through compound annual growth rate and the highest growth was
observed during the 1980-1990 period. Modern cost concepts were used to assess the profitability of pulse cultivation
and results revealed that the cost of cultivation per hectare was significantly higher in pigeon peas in comparison to
gram, pea and lentil crops. The marketing charges paid by the village trader, wholesaler and retailer ranged between
INR 20 to INR 40 per quintal for different crops. It was also observed that the quantum of marketable surplus and its
percentage share to total production in pigeon peas, gram and lentils increased with the increase in the size of land
holding. The pulse production in the region is faced with constraints related to production, processing and marketing.
Hence, technologies and infrastructure need to be embraced through suitable policies to favor farmers, so as to maintain
balance and keep the interest of both producers and consumers.
Keywords: Bundelkhand; Cost of cultivation; Marketable surplus; Pulse production
1. Introduction
Among the total agricultural crops grown in India,
pulses are most important being a major source of protein
to the majority of the people in the country, especially
those lives on a vegetarian diet and remains a very impor-
tant crop group from the perspective of nutrition as well as
environmental sustainability [1,2]
. They are rich in complex
carbohydrates, micronutrients, protein and B vitamins;
low in fat and rich in fibre, therefore excellent for manag-

14
ing cholesterol, digestive health and regulating energy
levels [3]
. Pulses not only have nutritional value for human
beings but also contribute fertility to the soil. In spite of
huge nutritive value, per capita availability and consump-
tion is very low, which has been reduced almost half from
about 60.7 g/day in 1950-1951 to 48 g/day during 2018-
2019 [4]
.
The production of total pulses in India is about 23.40
million tonnes, covering an area of about 29.03 million
hectares (ha) during 2018-2019 [4]
, the majority of which
fall under rainfed, resource-poor and harsh environment,
frequently prone to drought and other abiotic stress condi-
tion. The 3rd
estimates for 2020-2021 indicate that the total
pulse production is 25.58 million tonnes from 29.51 mil-
lion ha area [5]
. To meet the demand of pulses, India is at
present importing about 3 million tonnes chickpea, which
continues to be the largest consumed and comprising of
45%-50% of the total pulse production of India. Major
producers of pulses in the country are Madhya Pradesh
(24%), Uttar Pradesh (16%), Maharashtra (14%), Ra-
jasthan (6%), Andhra Pradesh (10%), followed by Karna-
taka (7%), which together share about 77% of total pulses
production, while remaining 23% is contributed by Guja-
rat, Chhattisgarh, Bihar, Odisha and Jharkhand. India was
the world’s largest pulses importer and Myanmar, Canada
and Australia are major suppliers of dry peas and Kabuli
chickpeas to the Indian market.
Uttar Pradesh is the second-largest producer of pulses
with about 2.8 million tonnes, which accounts for 21.4%
of the national production. It continued to record the high-
est pulses productivity among the major pulses growing
states in the country. Pigeon pea, mung bean (green gram)
and urad bean (black gram) during kharif season and
chickpea, lentil and field pea, during rabi season are the
important crops with its share of 31.4% of the total area
under pulse in the state followed by lentil (21.5%), urad
bean/mung bean (16.5%), pigeon pea (14.1%) and field
pea (10.1%) [6]
. During the year 2018-2019, the area under
pulse was 2.30 million ha, production was 2.40 million
tonnes and productivity recorded at 1044 kg/ha [7]
.
Agro-climate zone wise information indicated that the
Bundelkhand zone shares maximum area under major
pulses (44.5%) followed by central plain zone (20.5%).
These two zones together share 65% area under pulses in
the state [8]
. The northeastern plain zones also share con-
siderable acreage under pigeon pea and lentil. Looking at
the productivity of individual pulse crop, it reveals that
in the case of urad bean and mung bean, the mid-western
plain and western plain zones have the highest produc-
tivity of 5.5 q/ha and 5.8 q/ha, respectively, however,
the Bundelkhand zone with considerable area possesses
lower average yield (1.3 q/ha and 2.6 q/ha). For pulse
crop against the state average of 5.3 q/ha and 5.5 q/ha in
the case of lentil, Bundelkhand zone possesses the high-
est acreage as well as productivity (10.1 q/ha) [6]
. Bun-
delkhand region is the central semi-arid plateau of India
that spans over about 7.1 million ha area. The region cov-
ers 14 districts comprising Jhansi, Jalaun, Lalitpur, Hamir-
pur, Mahoba, Banda and Chitrakoot of Uttar Pradesh and,
Newari, Datia, Tikamgarh, Chattarpur Damoh, Sagar and
Panna district in Madhya Pradesh state. The region is
complex, rainfed, risky, under invested, vulnerable, socio-
economical heterogeneous, ethically unique, agrarian and
backward [9,10]
. Among all the nine agro-climatic zones of
Uttar Pradesh state, Bundelkhand region of Uttar Pradesh
has the lowest average annual household income [11]
and
lowest livelihood security [12]
. Bundelkhand region suf-
fers from water scarcity, natural resource degradation, low
crop productivity (1 q/ha ~ 1.5 q/ha), low rainwater use
efficiency (35%–45%), high erosion, poor soil fertility,
frequent droughts, poor irrigation facilities, inadequate
vegetation cover and frequent crop failure resulting in
scarcity of food, fodder and fuel [13,14]
. The region experi-
ences extremes of temperature, varying from more than
45 °C during summers to about one degree centigrade in
winters and receives average 800 mm-900 mm annual
rainfall. The occurrence and distribution of rains however
have no definite pattern rendering farmers unprepared for
timely crop sowing and almost every year they faced the
problem of drought even during good rainfall year [15]
. A
declining and irregular trend of annual rainfall and a grad-
ual drying up of the region has emerged as a challenge to
sustain crop yield in the region [16]
. Droughts, short-term
rain and flooding in fields add to the uncertainties. Based
on the composite drought hazard analysis, eight districts
of Bundelkhand region are under severe to moderate
drought vulnerability [17]
. Bundelkhand region contrib-
utes 8.4% (1377 tonnes) of total pulse production in the
country. The contribution of the region to total area and
production of crops like field pea, lentil and urad bean is
highly significant as it contributes about 43%, 16% and
11.5% of total national production of field pea, urad bean
and lentil in the country. The overall productivity level of
pulses in the region (677 kg/ha) was slightly higher than
national average (655 kg/ha), the yield levels of field pea,
chickpea and lentil crops were also higher as compared to
the national average (2015-2016). Among the major pulse
crop growing in the Bundelkhand region are pigeon pea,
mung bean urad bean in kharif season and gram, pea,
lentil in rabi season. Gram is the most important pulse
crop in the Bundelkhand region followed by urad, lentil,
pea and mung bean.

15
Keeping in view the importance of pulse production in
the Bundelkhand region of Uttar Pradesh, this study has
been conducted to estimate pulse production status and
growth rates and make an objective assessment in terms
of cropping pattern, cost returns, market intermediaries
and marketed surplus as well as identify the constraints in
production and marketing of major pulses in the region.
2. Material and Methods
2.1 Area and Data
The study used both secondary and primary data to
achieve the objectives. Secondary data were collected
from published sources of Government Departments. For
collection of primary data, a multi-stage sampling tech-
nique was adopted to choose the study units, i.e. farmer
respondents. Bundelkhand region was selected purposive-
ly as cropping pattern in the region is dominated by pulse
crops. Bundelkhand region comprised of two-divisions,
viz. Jhansi and Chitrakoot Dham. At the first stage, one
district has been selected from each division, namely Ja-
laun from Jhansi division and Hamirpur from Chitrakoot
Dham division on the basis of higher area and production
of pulses. At second stage, one block from each district
has been selected randomly, in which Kadoura block from
Jalaun district and Kurara block from Hamirpur district
got selected. Third stage of sampling comprised of selec-
tion of 5 villages from each block and a total of 10 vil-
lages from the selected blocks were chosen randomly for
the study. From the universe of selected 10 villages, a list
of all those farmers i.e. pulses growers have been pre-
pared and thereafter a total of 100 respondent/pulse grow-
ers have been selected randomly. Again these respondents
have been categorized in four groups based on land hold-
ing size i.e. marginal (0 ha ~ 1 ha), small (1 ha ~ 2 ha),
medium (2 ha ~ 4 ha) and large (4 ha and above). Primary
data were collected from each respondent by personal
interview using a structured interview schedule regarding
farmer and farm details, cultivation practices, input used,
output marketed and returns received. The number of se-
lected cultivators from selected villages under each size
groups has been presented in Table 1.
2.2 Analytical Techniques
2.2.1 Estimation of Growth Rates
Data on area, production and yield collected for the
period of 1980 to 2015 were grouped into 3 periods, viz.
1980-1990, 1991-2000 and 2001-2015 and compound an-
nual growth rate (CAGR) was calculated separately for
each period.
2.2.2 Estimation of Costs and Returns
Costs of cultivation were also estimated using other
cost concepts [18]
that are widely adopted in farm manage-
ment research [19]
. The concepts used were: (i) Cost A= All
variable expenses incurred to procure the material inputs
and expenditure on hired labour, all types of machine la-
bour and including land revenue, depreciation and interest
on operational expenses, land (leased in) rent paid, (iii)
Cost B= Cost A + interest on value of permanent assets
and imputed rent of owned land, (v) Cost C= Cost B + im-
puted value of family labour. On the similar line, income
Table 1. Description of selected villages and number of farmers in different size group
S. No. Name of the district Name of the blocks
Name of the
Selected villages
Number of cultivators selected in different size groups
Total
Size-groups (ha)
0-1 1-2 2-4 4 above
A.
1.
Hamirpur Kurara
Deviganj 5 3 2 1 11
2. Jalla 6 2 2 1 11
3. Para 4 3 1 2 10
4. Jakhela 3 2 3 2 10
5. Beri 4 3 2 1 10
B.
1.
Jalaun Kadoura
Udanpur 4 2 2 2 10
2. Chatela 3 3 2 1 09
3. Bugi 5 3 1 1 10
4. Babina 3 2 2 2 09
5. Sujanpur 4 4 1 1 10
Total 41 27 18 14 100

16
concepts used were as: (i) Gross return = Total value of the
produce (main product and by product), (ii) Net income =
Gross return - Cost C, (iii) Family labour income =
Gross return – Cost B, and (v) Farm business income =
Gross return – Cost A.
Cost C includes all the possible costs and is consid-
ered as the real cost of production in a farm situation.
But rental value of owned land and managerial costs for
the farmer can be excluded in a marginal profit situation
and Cost A can be taken as the standard cost of produc-
tion which includes all actual expenses expressed in cash
and kind, the depreciation and interest on value of owned
capital assets (excluding land) [19]
. Similarly, if we want to
calculate the income over family labour, we can consider
Cost B or subtract the value of family labour from Cost C.
2.2.3 Estimation of Marketable Surplus
Marketable surplus refers to the quantity of produce
available for disposal through markets after fulfilling all
consumption requirements. In this study, the marketable
surplus was estimated by subtracting requirements for
consumptions, seeds from the total production of pulses.
3. Result and Discussion
3.1 Production and Growth Rates
India is the largest producer of chickpea, sharing 65%
of global production [20]
. The production of pulses in India
during 2015-2016 is shown in Table 2. It is evident that
chickpea occupies a major share (46.68%) in total pulse
production in India followed by red gram, mung (green
gram), urad (black gram) and others.
Table 2. Share of different pulses production in India [21]
Pulse crop
Production (2015-2016)
(’000 tonnes)
Share in total
production (%)
Red gram 2550 14.71
Chickpea 8090 46.68
Urad (black gram) 1740 8.94
Mung (green gram) 1550 10.04
Other pulses 3400 19.62
Total pulses 17330 100
The estimated growth rates of red gram, chickpea, kha-
rif and rabi pulses with respect to area, production and
yield for the periods 1980-1990, 1991-2000 and 2001-
2015 are given in Table 3. The period-wise analysis re-
vealed that the maximum growth rate in the area for all
pulses, except chickpea was observed during 1980-1990
in comparison to other periods. The overall growth rate in
the area for all pulses was also highest (6.12%) during the
period 1980-1990 and there was a negative growth rate
for the next decade (1991-2000) and a positive growth
rate to the extent of 1.12% during the period 2001-2015.
Further crop-wise analysis of the growth rate in area of
kharif pulses was observed to be high in 1980-1990 and it
increased at the rate of nearly 8.08% per annum. Against
this, the area under the same crop during the period 1991-
2000 declined at a maximum rate of 8.26% per annum and
a negative growth rate was observed during 2001-2015
(–0.25% per annum). In the case of chickpea, growth rate
Table 3. Compound annual growth rate of pulses - All India
Crop Items 1980-1990 1991-2000 2001-2015
Red gram
Area 2.3 2.3 –2.5
Production 2.80 5.40 –1.73
Yield 0.55 1.60 1.04
Chickpea
Area –1.5 17.42 5.36
Production –0.8 10.01 5.82
Yield 0.74 1.68 1.77
Kharif pulses
Area 8.08 –8.26 –0.25
Production 8.67 –6.55 2.05
Yield 0.55 1.87 2.30
Rabi pulses
Area 4.32 –4.75 2.32
Production 5.50 –3.15 4.22
Yield 1.13 1.68 1.86
Total pulses
Area 6.12 –6.49 1.12
Production 6.74 –4.48 3.45
Yield 0.58 2.15 2.30
Source: Author’s calculations

17
in area was observed to be high (17.42%) during 1991-
2000 and the growth rate declined to one-third (5.36%)
during 2001-2015, while the negative growth rate was
observed for the same crop during 1980-1990. The growth
rate in the area in case of red gram was observed to be the
same (2.3%) in the period 1980-1990 1991-2000 and it
declined at a rate of nearly 2.5% per annum during 2001-
2015. The growth rate in the production of different pulses
in different periods shows that maximum growth has been
exhibited by chickpea which was 10.01% per annum fol-
lowed by red gram (6.3%) during 1991-2000. During
the period 1980-1990, the growth rate of production of
all pulses was positive except chickpea. The growth rate
in production of total pulses was 3.4% during the period
2001-2015.
The Table 4 showed that the average size of farms,
which was 2.18 ha. The number of farmers in the marginal
size category (0 ha ~ 1 ha) accounted for 41% of the total
number of sample farms, commanding only 11.11% of the
total cultivated area, whereas, the farmers of the largest
size group (4 ha and above) accounted for only 14% of
the total number of holdings but commanded as much as
42.50% of the total cultivated area. This indicated the un-
even distribution of cultivated land among the farmers of
different size groups.
3.2 Cropping Pattern
In Uttar Pradesh, the Chitrakoot Dham region is fa-
mous for pulse production, where production takes place
under rainfed condition due to lack of irrigation facilities
and typical physiography. Chitrakoot Dham accounts for
18.11% of the total area and 25.67% of the total produc-
tion of the state. The productivity of pulses in this region
was higher in the state being 8.76 q/ha as against 8.08 q/ha
of the state average during 2012-2013. However, the pulse
production in the state as well as in the area did not show
any appreciable increase for the last fifty years, rather it
has been declined. The growth of pulse production in the
state was (–) 0.11% per annum, while it was 0.62% per
annum in Chitrakoot Dham and (+) 2.71% per annum in
Banda district.
Table 5 indicates that on an average, gram occupied
the highest area (20.70%) to the total cropped area fol-
lowed by wheat (20.18%), jowar + pigeonpea (16.92%),
lentil (12.15%), pea (10.14%), pigeon pea (9.93%), jowar
(6.99%), mung (4.88%), urad (4.19%), linseed and mus-
tard (6.30%) and others (4.50%). With regard to the size
groups for individual pulse crops, it is to be noted that
large farmers put higher proportion of cropped area to
gram, pea and urad, whereas for linseed-mustard, pigeon
pea and other crops, area decreased with increase in hold-
ing size. For the crops like lentil, wheat and jowar, no
such trend was observed.
Table 6 presented the production, costs and returns of
pulse crops from per unit area in the region. It is observed
that the cost of cultivation was highest for pigeon pea to
the extent of Rs. 20675 and the lowest was for lentil (Rs.
18161). However, due to higher yield level, per quintal
production expenses were lower in case of gram and pea
in comparison to pigeon pea and lentil. Due to higher sell-
ing price of pigeon pea, gross return was sufficiently high
than other pulses. On estimation of various categories
of costs, it was observed that though Cost C per ha was
highest for pigeon pea, Cost A B per ha was highest for
gram followed by pigeon pea, pea and lentil. With regard
to various types of income per ha, again pigeon pea re-
corded the highest income and highest benefit-cost ration
in comparison to other pulses.
From the above results, it can be concluded that pigeon
pea crop is the most economical and profitable pulse crop
having a higher benefit-cost ratio followed by gram than
that of lentil and pea crops and recommendation can be
made to put more emphasis toward their cultivation in the
study region.
Table 4. Distribution of farms under different size groups
Sl. No.
Size group
(ha)
No. of
farmer
Cultivated
area (ha)
% age of total cultivated
area
Average size of holdings (ha)
1. 0-1 41 24.19 11.11 0.59
2. 1-2 27 36.18 16.61 1.34
3. 2-4 18 64.80 29.78 3.60
4. 4 above 14 92.55 42.50 6.61
Total 100 217.77 100.00 2.18

18
Table 5. Cropping pattern on the sample farms of different sizes (area in ha)
Sl. No. Crops
Size groups (in ha)
Total area
0-1 1-2 2-4 4 above
Rabi
1. Gram 4.79 (16.67) 7.53 (16.91) 17.99 (21.89) 26.62 (22.42) 56.75 (20.70)
2. Lentil 3.57 (12.43) 6.39 (14.35) 8.46 (10.29) 14.71 (12.39) 33.31 (12.15)
3. Pea 2.33 (8.11) 4.34 (9.72) 8.73 (9.83) 12.42 (10.46) 27.81 (10.14)
4. Linseed Mustard 2.14 (7.48) 3.26 (7.32) 5.59 (6.80) 6.28 (5.28) 17.28 (6.30)
5. Wheat 5.81 (20.22) 8.19 (18.40) 17.66 (21.48) 23.67 (19.94) 55.33 (20.18)
6. Pigeon pea 3.53 (12.29) 5.24 (11.77) 8.05 (9.79) 10.41 (8.76) 27.23 (9.93)
Kharif
7. Mung bean 1.89 (6.58) 2.18 (4.89) 3.67 (4.46) 5.65 (4.75) 13.39 (4.88)
8. Urad bean 0.96 (3.34)
1.69
(3.79)
3.17 (3.85) 5.67 (4.77) 11.49 (4.19)
9. Jowar 1.26 (4.38) 3.17 (7.12) 6.18 (7.52) 8.57 (7.21) 19.18 (6.99)
10. Others 1.98 (6.89)
2.53
(5.68)
3.33 (4.05) 4.52 (3.41) 12.36 (4.50)
Total cropped area 28.72 44.51 82.18 118.72 274.13
Note: Figure in parenthesis show the percentage to their respective total
Table 6. Costs and returns of pulse crops in Bundelkhand region
Particulars
Crops
Gram Pigeon pea Lentil Pea
Cost of cultivation (’000 rupees/ha) 20.55 20.68 18.16 20.28
Yield per hectare (q/ha) 12.70 9.71 9.66 12.47
Price per quintal (’000 rupees) 2.34 3.61 2.51 2.23
Total value of output (’000 rupees/ha) 33.41 38.03 27.15 30.09
Cost of production (’000 rupees/q) 1.62 2.13 1.68 1.50
Various categories of costs (’000 rupees/ha)
(a) Cost A 11.14 10.45 9.86 10.18
(b) Cost B 14.58 13.89 13.30 13.62
(c) Cost C 20.55 20.68 18.16 20.28
The measure of farm profit (’000 rupees/ha)
Farm business income (over Cost A) 22.28 27.58 17.29 19.91
Family labour income (over Cost B) 18.83 24.14 13.85 16.47
Net income (over Cost C) 12.87 17.35 8.99 9.81
Benefit-cost ratio 1.62:1 1.83:1 1.49:1 1.48:1

19
3.3 Marketing Charges
The marketing charges paid by the village trader, wholesal-
er and retailer in the marketing of gram, pigeon pea, lentil and
pea were worked out at Rs. 40, Rs. 26 and Rs. 20 per quintals,
respectively. Total marketing charges paid by different
marketing middlemen were observed to be Rs. 86, spread
over the consumer’s price for the crops gram, pigeon pea,
lentil and pea has been shown in Table 7. The sale price
received by the producer was the highest being Rs. 3610
per quintal for pigeon pea, Rs. 2510 per quintal for lentil,
Rs. 2335 per quintal for gram and Rs. 2230 per quintal for
pea. The purchase price of consumers came to Rs. 2440,
Rs.3763, Rs.2629 and Rs. 2324 per quintal of gram, pi-
geon pea, lentil and pea, respectively. There were different
intermediaries, viz. village traders, wholesalers and retail-
ers who incurred market expenses to the extent of Rs. 40,
Rs. 26 and Rs. 20, respectively. Among the pulses, price
spread and market margins were highest in case of pigeon
pea followed by lentil, gram and pea. Producer’s share in
consumer’s rupee was calculated, which was almost simi-
lar for all the pulse crops indicating similar margin for the
farmers.
Table 7. Marketing charges, producer’s share and margins
of intermediaries
S. No. Particulars Gram Pigeon pea Lentil Pea
1.
Sale price by
producer (Rs.)
2335 3610 2510 2230
2.
Consumer’s price
(Rs.)
2440 3763 2629 2324
3. Price spread (Rs.) 105 153 119 94
4
Market charges
(Rs.)# $ 86 86 86 86
5
Market margins
(Rs.)# 19 67 33 8
6
Producer’s share
in consumer’s
rupee (%)
95.70 95.93 95.47 95.96
#
Total for all intermediaries; $
Market charges for village
traders, wholesalers and retailers were Rs.40, Rs.26 and Rs.20,
respectively.
3.4 Marketable Surplus
In rural areas, family sizes remain almost similar, hence,
lower production owing from less cropped area led to low
quantum of marketable surplus of gram, pigeon pea, lentil and
pea and their percentage to the total production on the farms
of lower size group as compared to the large sized farms (Table
8). It is observed that the quantum of pulses consumed was
highest is gram, as it is a good source of energy, protein,
minerals, vitamins, fiber, and also contains potentially
health-beneficial phytochemicals [22]
. The quantity utilized
per household for all purposes comprising seed, consump-
tion, wages and others were also highest in case of gram
followed by pea, pigeon pea and lentil. The amount of
marketable surplus was highest in case of pea followed by
gram, lentil and pigeon pea. However, when we calculated
marketed surplus as percentage of quantity produced, again it
was observed to be highest for pea followed by lentil, pigeon
pea and gram.
Table 8. Marketable surplus of pulse grains (per house-
hold)
Sl.
No.
Particular Gram
Pigeon
pea
Lentil Pea
1
Total quantity
produced (q)
12.70
(100.00)
9.71
(100.00)
9.66
(100.00)
12.47
(100.00)
2
Quantity retained
for seed (q)
0.87
(6.85)
0.55
(5.66)
0.56
(5.79)
0.96
(7.69)
3
Quantity consumed
by family (q)
0.98
(7.71)
0.87
(8.95)
0.76
(7.86)
0.57
(4.57)
4
The quantity given
as wages (q)
0.70
(5.51)
0.74
(0.74)
0.65
(6.72)
0.83
(6.65)
5 Others (q)
0.66
(5.19)
0.37
(3.81)
0.45
(4.65)
0.51
(4.08)
6
Total quantity
utilized (q)
3.22
(25.35)
2.54
(26.15)
2.41
(24.94)
2.88
(23.09)
7
Marketable surplus
(1-6)
9.49
(74.72)
7.17
(73.84)
7.24
(74.94)
9.59
(76.90)
Note: Figures in brackets indicates per cent of total quantity
produced
4. Constraints in Cultivation of Pulses in
Bundelkhand Region
4.1 Constraints in Production
Non-availability of high yielding pulse varieties, in
general, have poor harvest index (HI). Improvement in the
HI in cereal crops in recent years has resulted in very high
yields. In pulses, the HI ranges from 10 to 20 as compared
to 40 and above in wheat. Mixed cropping of pulses with
other crops is an important agronomic practice in the
Bundelkhand area of the state. Here we could have two

20
situations (i) the pulse crop completes its life-cycle before
the second crop enters the active growth phase, or (ii) the
pulse crop enters the active phase of growth only after the
subsidiary crop has completed its life-cycle. Although,
a number of improved varieties of different pulse crops
have been recommended, yet they have not become popu-
lar among the farmers in the study area mainly due to lack
of a systematic seed multiplication and distribution pro-
gram. Adequate plant population makes a big difference
in yield. Farmers in the study area generally do not follow
the recommended seed rate, which causes low yields.
4.2 Constraints in Marketing
During the course of the investigation, the following
market problems were ascertained in different regulated
mandis in the study area. When the farmer reached in the
market, they had to arrange with the kaccha arhatia (com-
mission agents) for the sale of produce. Kaccha arhatias
though employed by the producer, but they remained
more inclined towards the buyer and favored them at
the expense of producers. More number of intermediar-
ies in marketing channel reduces the producers’ share in
consumers’ price. There was common practice that after
settlement of price and during the time of weighing, the
buyer complained of the quality of product and levied
some refraction charges in spite of the price was settled on
the basis of a sample.
4.3 Constraints in Processing
The present-day processing technologies use direct
solar energy for drying in large open yards. In order to
loosen the husk, prolonged sun drying is essential for all
pulses, pigeon pea, black gram and green gram. The con-
version of grains into dal become difficult to mill mainly
during the summer months, whereas pulses that are easy
to dehusk are processed in other seasons. This limitation
restricts milling and production schedules. The cost and
time taken for processing of pulses in these units were
about 2-3 times higher when compared to the traditional
units. The time interval between each step and natural
splitting of grains produces good quality dal and improves
dal recovery and increases keeping quality of dal, which
fetches them a better price for their products.
5. Conclusions
It can be concluded that pigeon pea crop is the most
economical and profitable crop having a higher benefit-
cost ratio and contributing higher return than that of gram,
lentil and pea crops under study. In fact, pulses can be
profitably cultivated in rice fallows in the post rainy sea-
son, which also contributes in saving N fertilizer and in-
creased the yield of subsequent cereal crops, thus decline
the cost of production. Therefore, it is recommended that
more emphasis should be given towards the cultivation of
pigeon pea and gram than other pulse crops. Moreover,
it was observed that gross income, net income per ha and
the benefit-cost ratio was significantly higher on pigeon
pea as compared to gram, lentil and pea crops. Further,
lower or higher producer’s share cannot be considered as
a true indicator of an efficient marketing system. Efficient
marketing system is one in which both the producers and
consumers are well satisfied, benefited and protected from
the clutches of the marketing functionaries and middle-
men on the one hand and the consumers are in position to
get the product according to their preference and quality.
The government should take necessary steps in the regula-
tion of laws of regulated markets, control on the proces-
sor, wholesaler and retailer in the interest of both produc-
ers and consumers.
Author Contributions
All authors contributed equally.
Funding
This research received no external funding.
Data Availability
Data based on which this research was conducted
can be accessed by contacting the corresponding author
through sending emails at the address provided on the title
page.
Conflicts of Interest
The author declares that there is no conflict of interest
regarding the publication of this paper.
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22
Asmera Adicha,
Southern Agricultural Research Institute, Jinka Agricultural Research Center, SNNPR, Jinka, Ethiopia;
Email: asmera05@gmail.com
Received: 27 June 2022; Received in revised form: 27 July 2022; Accepted: 8 August 2022; Published: 1 September 2022
Citation: Adicha, A., Alemayehu, Y., Ermias, G., Darcho, D., 2022. Value Chain Analysis of Korarima (Aframomum
Corrorima) in South Omo Zone, SNNPR Ethiopia. Research on World Agricultural Economy. 3(3), 568. http://dx.doi.
org/10.36956/rwae.v3i3.568
RESEARCH ARTICLE
Value Chain Analysis of Korarima (Aframomum Corrorima) in South
Omo Zone, SNNPR Ethiopia
Asmera Adicha1*
Yidnekachew Alemayehu2
Gedion Ermias1
Dawit Darcho1
1. Southern Agricultural Research Institute, Jinka Agricultural Research Center, SNNPR, Jinka, Ethiopia
2. Southern Agricultural Research Institute, Areka Agricultural Research Center, SNNPR, Areka, Ethiopia
Abstract: Korarima is a known cash crop in the South Omo zone and provides a wide range of economic and
sociocultural benefits. Even though its economic and socio-cultural importance, the development of the Korarima sector
along with the value chain is hampered by several constraints. Hence, the study aimed to analyze the Korarima value
chain in the South Omo zone. Using a two-stage sampling technique, 120 Kororima producers were selected to collect
primary data through structured questionnaires. Descriptive statistics and an econometrics model (multivariate probit
model) were used for data analysis. The study identified three major Korarima market outlet choices as collectors,
retailers, and wholesalers as alternatives to Korarima producers to sell the majority of their products. Thus, collectors
accounted for 82.2%, wholesalers (73.6%), and retailers (35.5%) of the total sold. The results of a multivariate
probit model indicated that the sex of the household, credit access, family size, price information, market distance,
and extension contact of farmers significantly affected the market outlet choice decisions in one or another way.
Furthermore, no brand indicating this crop, inadequate infrastructural development, and market accessibility, and weak
extension services regarding improved varieties were major problems identified. Therefore, it is better to work on the
brand name of this particular crop to trace up to the end market, infrastructural development and market accessibility,
extension services provided regarding the improved Korarima variety, and accessing formal market information from
the concerned body are essential to enhance Korarima producers’ benefit and bargaining power through avoiding
information asymmetry.
Keywords: Value chain; Market outlet; Multivariate; Korarima; South Ari

23
1. Introduction
Agriculture remains the main activity in the Ethiopian
economy. Agricultural growth is not only required to feed
the country but is also the driving force to generate for-
eign exchange. About 80% of Ethiopia’s foreign exchange
is derived from agricultural exports [1]
. Enhancing agricul-
tural production and export trade is the current strategy
followed by the country to curtail the critical capital short-
age and enhance economic growth.
Spices have a major stake in the production system
and the foreign earnings of the country. It has a great role
in transforming farmers into producers for the market in-
stead of producing merely for subsistence [2]
. Ethiopia has
become one of the largest consumers of spices in Africa.
People use spices to flavour bread, butter, meat, soups,
and vegetables. They also use spices to make medicines
and perfumes [3]
. Ethiopia is a homeland for many spices,
such as Ethiopian Korarima (Korarima/Aframomum Cor-
rorima), long red pepper, black cumin, white cumin/bish-
ops weed, coriander, fenugreek, turmeric, sage, cinnamon,
and ginger [3]
. Out of the 109 spices listed by International
Organization for Standardization (ISO), 50 spices are
cultivated or grown in Ethiopia. Apart from this, there are
several other spices and herbs available in small quantities [4]
.
The average land covered by spices is approximately
222,700 ha and the production is 244,000 tons per annum.
However, the supply has dwindled considerably in recent
years and the Ethiopian Korarima export was less than
100 MT in 2012. The production of Ethiopian Korarima
during the 2014/2015 crop season was 9.56 thousand tons
with a productivity of 5.1 Q/ha.
In Ethiopia, Southern National and Nationalities Peo-
ples Regional States (SNNP) is one of the regions which
produce the maximum quantity of spices in the country.
The major Ethiopian Korarima production areas are the
forest ecology of South and South West mid-altitude and
highland Korarima areas such as the Kaffa zone (center of
origin of Korarima), Bench-Maji zone, Sheka zone, Ma-
jang zone, Dawuro zone, Wolayita zone, and Gamo Gofa
zone, Kembata-Tembaro zone in SNNPR and Jimma zone
(Oromiya). The price of a kilo of dry Korarima capsule in
the domestic market ranges from 80 Birr to 100 Birr (One
US$ = 21 Birr) in the villages. Ethiopia exports about 200
MT of Ethiopian Korarima per year [4]
.
In South Omo Zone Korarima is also abundantly found
and potentially grown/produced by smallholder farmers
of South Ari, Semen Ari, and Salamago districts. In the
Zone, for the past five consecutive years about 16,843.96
ha, Korarima has grown with a production of around
70,744.63 Quintal with average productivity of 4.2 quin-
tal/ha [5]
. Korarima has a contribution to income genera-
tion and also has value in reducing/minimizing poverty
for smallholder farmers.
Despite, its availability, huge potential, and the role it
plays, limited attention has been given to its production,
value, value addition activities, and marketing outlets
choice. As result, the unregulated price of Korarima (black
market), South Omo zone Korarima is transported to Gofa
by the black market, and recognition and benefit from it
are given to the former Gamo Gofa zone. And also small
farm gate prices and less market access are disadvanta-
geous for producers. Therefore, this study focused on
identifying major value chain and marketing actors, value
additive activities in production, outlet choice in the mar-
keting of Korarima and its products, and identifying the
major value chain and marketing opportunities and con-
straints.
2. Research Methodology
2.1 Type and Sources of Data
Qualitative and quantitative data were collected from
primary and secondary data sources. The primary data on
the value chain and marketing of Korarima, value chain,
and marketing channels, direct and indirect benefits of
Korarima, supply and market price of Korarima, transac-
tion cost in marketing Korarima, main actors and their
role, margin share and distribution among market actors,
marketing infrastructure and information, market partici-
pants and concentration at each market chain, opportuni-
ties and threats of Korarima production and marketing,
farmers perception will be collected from key value chain
actors and stakeholders. Value chain actors and marketing
stakeholder includes sample producers, collectors, traders,
exporters, consumers, enterprise operators engaged in the
value chain and marketing of Korarima, end-users of the
products, formal and informal institutions involved in Ko-
rarima value chain and marketing, supporters of Korarima
value chain and marketing, as well as representatives from
government organizations and others working in Korarima
production. Secondary data were collected from literature,
reports, and documents both published and unpublished
data sources.
2.2 Methods of Data Collection
To collect the primary data both participatory rural ap-
praisal (PRA) tools of informal methods and formal sur-
vey methods of data collection were employed. Informal
survey methods such as focus group discussions (FGDs),
in-depth interviews with key informants (KII), and di-
rect observation with transacting walk will be employed,

24
whereas for the formal survey method, structured survey
questionnaires were administered to sample respondents.
Informal survey such as focus group discussion with
known social strata groups (e.g. women, youths, elders,
others) was conducted before the formal survey. A ques-
tionnaire was pre-tested to indorse new information and
to modify the structured questionnaire. Open discussion
with producers, traders, consumers, and exporters were
interviewed according to their activities or function (as
Value Chain Analysis starts from production up to final
consumption).
2.3 Sampling Technique
Two-stage sampling technique was employed to draw
the sample from a given population of Korarima produc-
ers and traders. In the first stage, potential Korarima pro-
ducing and marketing Kebeles were identified purposive-
ly. In the second stage, sample households were identified
by random selection. Yemane [6]
sample size determination
formula was used to determine the number of respondents.
2
(1 )
N
n
N e
=
+ ∗
(1)
where, n=the sample size, N=total number of Korarima
producers, e=acceptable sampling error, and the value of
‘e’ is 95% confidence level and it’s assumed to be e=0.05.
After determination of sample size, the sample respondent
from smallholder household was selected randomly from
sample Kebeles.
2.4 Data Analysis
Both simple statistics and econometric models were
chosen for the analysis. The econometric analysis was
employed to analyze factors affecting the level of market
outlet choice and value addition. Software called Statisti-
cal Package for Social Science (SPSS) and STATA were
used for the analysis.
2.4.1 Econometric Model Specification
This study used a multivariate probit model as it cap-
tures the household variation in the choice of market
outlets and estimates several correlated binary outcomes
jointly. A multivariate probit model would be appropri-
ate for jointly predicting these three choices (collector,
retailer, and wholesaler) on an individual-specific basis.
A multivariate probit model simultaneously set out the
influence of a set of explanatory variables on the choice
of market outlets, while allowing for the potential correla-
tions between unobserved disturbances as well as the rela-
tionship between the choices of different market outlets [7]
.
In this case, three-outlet choices are collector, retailer,
and wholesaler and the model enables Korarima produc-
ers to choose more than one outlets that are not mutually
exclusive to get a better price. The selection of appropri-
ate market outlet i by farmer j is
C
ij
Y defined as the choice
of farmer j to transact market channel i (
C
ij
Y =1) or not
(
C
ij
Y =0) is expressed as follows;
Yij
C
=
1 if Yij
C
= Xij
C
αij
+ εc
≥ 0 ⇔ Xij
C
≥− εc
0 if Yij
C
= Xij
C
αij
+ εc
0 ⇔ Xij
C
− εc
'
(2)
where v C
ij
α aector of estimators,
Yij
C
=
1 if Yij
C
= Xij
C
αij
+ εc
≥ 0 ⇔ Xij
C
≥− εc
0 if Yij
C
= Xij
C
αij
+ εc
0 ⇔ Xij
C
− εc
'
is a vector of error
terms under the assumption of normal distribution,
C
ij
Y de-
pendent variable for market outlet choices simultaneously
and C
ij
X combined effect of the explanatory variables.
The selection of one type of market outlet choice
would be dependent on the selection of the other, since
smallholder farmers’ choice decisions are interdependent,
suggesting the need to estimate them simultaneously. To
solve this problem many scholars suggested and used a
multivariate probit simulation model [8,9]
. Since smallhold-
er farmers’ market outlet choice decisions were expected
to be affected by the same set of explanatory variables.
Collectorj = x'1β1 + εA
Retailerj = x'2β2 + εB
Wℎolesalerj = x'3β3 + εC
(3)
where collector j, wholesaler j, and retailer j are binary
variables taking values 1 when farmer j selects collector,
wholesaler, and retailer respectively, and 0 otherwise; X1
to X4 is a vector of variables; β1 to β3 a vector of param-
eters to be estimated and ε disturbance term.
In a multivariate model, the use of several market out-
lets simultaneously is possible and the error terms jointly
follow a multivariate normal distribution (MVN) with
zero conditional mean and variance normalized to unity,
and ρij represents the correlation between endogenous
variables, given by



…..N
0
0
0
1 12 13
21 1 23
31 32 1
(4)
E (/) = 0
Var (/) = 1
Cov (/) = 
(5)
2.4.2 Description of Variables and Expected Sign
The likely variables, which were supposed to affect
producers’ market outlet choice decisions, are explained
in Table 1.

25
3. Results and Discussion
3.1 Socio-Economic and Demographic Charac-
teristics of the Respondents
This sub-section explains the profile of sampled re-
spondents regarding their age, sex, family size, experi-
ence, level of education, access to extension services, ac-
cess to market information, and distance from the nearest
market (Table 2). Gender was analyzed by checking the
number of male and female-headed households. Out of
the total households interviewed 95.8% were male-headed
households while 4.2% were female-headed households.
In both theoretical and practical situations, education level
plays an important role in ensuring household access to
basic needs such as food, shelter, and clothing. Skills and
education enhance working efficiency resulting in more
income and food security. In the study area, the mean
grade level achieved by respondents was about grade 6.
The minimum grade was 0 for those who were illiterate
and the maximum was grade (10+3). The age of sample
respondents was measured in years and provided a clue
on the working ages of households. The mean age of the
sample household was 37 years with the minimum and
maximum age of 18 and 65 years, respectively.
The mean family size of the total sample households
was nearly 7 persons with a minimum of 2 and a maxi-
mum of 12 persons and a standard deviation of 2.67.
Therefore, this might help them for a better market outlet
choice of households during Korarima marketing because
of labor availability. The respondents have an average of
17 years of farming experience in Korarima production
and marketing with a standard deviation of 11 years. The
total land size of sampled farmers varies from 0.13 to 3
hectares and the average farm size for sampled farmers is
found to be 0.78 hectares with a standard deviation of 0.53.
From the total land size, the land allotted to Korarima was
on average 0.29 ha with a minimum of 0.03 and a maxi-
mum of 1.5 ha with a standard deviation of 0.24.
According to the sample respondents, the major sourc-
es of income were crop, livestock, and livestock product
selling, and also there is some practice of getting off-farm
and non-farm sources. The total estimated average annual
income that the respondents obtained from those sources
was 12,192 Birr. Distance to market is an important vari-
able that affects the marketing of Korarima. The mean
distance to the market center for sample households was
18 minutes with a minimum of 10 and a maximum of 50
minutes of walking on their barefoot and a standard de-
viation of 2.67. Farmers who are located distant from the
market center might be weakly accessible to the market
outlet and have less transportation cost and time spent.
3.2 Korarima (Aframomum Corrorima) Cultiva-
tion Practice in the Study Area
Korarima is a known cash crop in the South Omo zone
and cultivation of it is mainly practiced in the agro for-
estry and river banks of South and Semen Ari areas of the
zone. According to Getasetegn and Tefera [10]
, the cultiva-
Table 1. Summary of hypothesized explanatory variable that determines Korarima producers’ market outlet choices
Explanatory variables Measurement Expected sign
Sex 1 if a male farmer, 0 if a female farmer -/+
Age Years +
Education level(formal) Years of schooling (grade) +
Family size Family members in a household living for more than 6 months (number) +
Land size The total area of land managed by a household (hectare) +
Annual income An annual income of a household (Ethiopian Birr) +
Price information 1 if a household has price information of Korarima, 0 otherwise -/+
Extension contact Contact with extension agents in a month (Frequency) +
Access to credit 1 if farmer has access to credit service, 0 otherwise +
Distance to a market center Distance to the nearest market center by foot walk (minute) -
Quantity produced The quantity of Korarima produced in a year (kilogram) +
Experience Experience of farmers producing Korarima (years) +

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