Assessment of Technical Manpower Requirements in Agriculture
Sector in India
Balagopal G Menon’, Biswajit Mahanty’, D Rama Rao*
ae Department of Industrial Engineering and Management
Indian Institute of Technology, Kharagpur 721 302, India
5 Principal Scientist, ICM Division
National Academy of Agricultural Research Management, Hyderabad, India
‘Corresponding author. Tel.: +91 3222 282271
> Tel.: +91 3222 283736
*Tel.: +91 40 24581334
E-mail addresses: balagopalgmenon@yahoo.com (B G Menon),
bm@hijli.iitkgp.ernet.in (B Mahanty),
ramarao@naarm.ermet.in (D Rama Rao)
Abstract
The aim of this paper is to develop an integrated demand-supply model to forecast
the requirements of technical manpower in the Indian agriculture sector. Technical
manpower has played an important role in achieving self sufficiency in food grain
production in India. At the current levels of operations itself, there is shortage of
technical manpower at various levels of the agriculture sector. Drawing on system
dynamics methodology, a causal model is initially developed which is subsequently
transformed to a dynamic simulation model that captures the dynamics of manpower
demand-supply interactions. The simulation results show technical manpower
shortages in agriculture sector in India. A policy of 50 percent increase in the intake
capacity of the technical institutions is proposed which is thereafter evaluated with
anticipated sectoral growth rates of 3 per cent and 4 per cent respectively. The
policy was found to alleviate the shortage of technical manpower substantially for
both the scenarios. However, the policy makers need to make further interventions in
balancing the technical manpower supply and demand in the long run.
Keywords Agriculture sector, Technical manpower, Demand-supply gap, System
dynamics
Introduction
Agriculture is and continues to be one of the cornerstones of the Indian economy. It
accounts for about 24 percent of India’s gross domestic product (GDP) and accounts
for about 52 percent of the employment in the country. It is estimated that if the
country’s GDP rate of over 8 percent has to be maintained, the agricultural sector
has to grow at the rate of at least 4 percent (Golait, 2007; Viswanadham, 2005). In
India, growth of other sectors and overall economy depends on performance of
agriculture to a considerable extent. Not only it is a source of livelihood and food
security for a large population of India but also has a special significance for low
income, poor and vulnerable sections in the country.
Technical manpower in agriculture sector has played an important role in achieving
self sufficiency in food grain production. At the current levels of operations itself,
there is shortage of technical manpower at various levels of the agriculture sector. A
survey by Federation of Indian Chambers of Commerce and Industry estimated a
shortage of agricultural scientists to exist to the tune of 60 percent, and shortage of
food safety professionals to exist to the tune of 70 percent (FCCI, 2007). There is a
pressing need to address the demand-supply gap of technical manpower in
agriculture sector if the country has to raise its level of processing and also to gain a
sizeable share of the international trade in agricultural products and processed foods.
The purpose of this paper is to assess the requirements of technical manpower in
agriculture sector in India through the development of an integrated model. A
dynamic simulation model based on system dynamics methodology is developed for
this purpose. The model is utilised for projecting the demand and supply of technical
manpower forward to a target of twenty years in the future with anticipated additions
of new degree graduates along with the subtractions out of migration. Finally, the
projected supply is compared with the projected demand, and a policy is analysed to
effect a balance.
The rest of the article is organized as follows. Section 2 is on the literature review.
Section 3 is on developing a dynamic manpower forecasting model utilising system
dynamics and discuss the base run and policy results. Finally the conclusions are laid
out.
Literature Review
Manpower forecasting studies
The manpower planning process includes forecasting the future demand and supply
of manpower and then developing action plans to reconcile the discrepancies
between demand and supply (Kwak and Garrett, 1980; Milkovich and Boudreau,
1994). Reliable manpower demand and supply forecasts can provide a basis for
making better decisions for avoiding redundant investments, achieving efficient and
balanced growth of an industry, and in developing an economy (Chan et al., 2006;
Kao and Lee, 1998; Kwak et al., 1977). Kao and Lee (1998) list the various
forecasting techniques utilised in manpower modeling. Much of the literature on
demand analysis is devoted to manpower forecasting at the micro level, while
manpower forecasting at the macro level is equally important, when it comes to the
economic development of a country (Kao and Lee, 1998).
Many of the previous studies focused mainly on either supply or demand forecasting
of manpower. Bechet and Maki (1987), and O’Brien-Pallas et al. (2001) give the
forecasting techniques used for modeling either supply or demand of manpower.
Hence there is a paucity of studies that deal with a combination of the two which is
known as an integrated model (Park et al., 2008). Moreover, many researchers call
for the need of demand-supply integrated models. Lomas et al. (1985), O’Brien-
Pallas et al. (2001), and Prescott (1991) observers that in modeling for manpower
requirements, there is a need to account for all the factors that would influence the
manpower supply, demand and utilisation in an industry. In short, industry level
manpower forecasting is required to make decisions on alternate policies. For the
same, an integrated model, considering demand and supply simultaneously is
required to be developed. Previous studies on manpower forecasting at industry level
includes that for health care industry (O’Brien-Pallas et al., 2001), manufacturing
industry (Kao and Lee, 1998), construction industry (Chan et al., 2006) and
information security industry (Park et al., 2008) to name a few.
System dynamics and manpower forecasting
System dynamics (SD), a methodology of system enquiry (Wolstenholme and Coyle
1983) is a theory of structure and behavior of systems that helps in analyzing and
representing the interactions governing the dynamic behavior of complex socio-
economic systems (Forrester, 1961). It can handle complex feedbacks and delays
present in the system in predicting the system’s behaviour over time. To develop a
system dynamics model, a causal loop model (or CLD) is developed initially. Causal
loop diagrams depict the causal relations that exist between the variables in a system
through the use of text, arrows and symbols (Stepp et al., 2009). A causal
relationship between two variables is positive if they move in the same direction and
negative if they vary in the opposite direction. A causal loop is reinforcing if it have
zero or an even number of negative causal relations and which result in reinforcing
the behaviour of the system. A causal loop is balancing if it has odd number of
negative causal relations and which stabilizes the system behaviour over time.
System dynamics aids in analysing the effects of alternate policies on the system’s
behaviour before implementing them.
System dynamics have been used in manpower planning and forecasting studies all
over the globe. Suitability of SD in manpower forecasting is cited by Jantsch (1972;
1973), Khoong (1996), and Martino (1980). Parker and Caine (1996) bring out the
advantages of utilising system dynamics methodology over sophisticated
mathematical modeling techniques in human resource forecasting. System dynamics
was used for manpower planning and forecasting in steel plants (Roy and Koul,
2009), health services (Chung et al., 2010), enterprises (Wu et al., 2003), and
information security industry (Park et al., 2008). Sterman (2002) suggested that
complex systems require a mastery of concepts such as stocks and flows, feedback,
delays, and non-linearity. The present study is aimed at developing a forecasting
model of this kind which can incorporate the feedbacks present in the system.
Developing a dynamic manpower forecasting model
Causal loop model
Here we develop a dynamic integrated demand-supply model for manpower
forecasting in agriculture sector in India. The model is built on a system dynamics
framework. Initially a causal loop model (or casual loop diagram) is presented to
capture the structural relationships existing between manpower supply and demand
and its determinants (Figure 1). Details of causal loop model on feedback loops, loop
polarity, loop name and loop components are depicted in Table I.
> Total Supply.
Intake for agricultural
descipline
+
)
+
Attractiveness of
sector
Total demand
is,
YN fo Growth and
Demand supply
attrition rate of
Bap. -_ sector
» Activity of.
sector
Figure 1. Causal loop diagram for the manpower forecasting model
Loop ID | Balancing (-) Loop name Loop components
or reinforcing
(+) loop
B1 Balancing Supply Total supply, Demand-supply gap, Attractiveness
enhancement | of sector, Intake for technical courses
loop
B2 Balancing Demand Total demand, Demand-supply gap, Activity of
fulfillment sector, Growth and attrition rate of agriculture
loop sector
Table I. Details of causal model on feedback loops, loop polarity, loop name and
loop components
The causal loop diagram in Figure 1 shows two negative feedback loops — one
related to supply enhancement (loop B1) and the other related to demand fulfillment
(loop B2). The ultimate goal is to create a situation where the demand and supply
equilibrates. However, dynamicity of the loops creates imbalances all the time.
Agriculture manpower in India comes mainly from educational institutions
(undergraduate and graduate colleges). The supply constitutes the graduating
students from these colleges and universities in three categories — degree graduates,
post-graduates and doctorates. The demand is generated from the attrition of
currently employed stock and additional requirements due to the growth of the
agriculture sector employing the graduates. Demand-supply gap affects the
attractiveness of the sector which subsequently affects the intake in the technical
institutes in the country and also negatively affects the central activity of the sector.
Development of the system dynamics model
Based on the causal loop model for the agriculture sector manpower supply and
demand, a system dynamics model is developed for manpower forecasting needs of
the sector. The dynamic model was designed and run using STELLA software,
chosen for its graphic performance and the ease of comparing results. The data was
collected from the reports of authorized institution of National Academy of
Agricultural Research Management (NAARM). The data for supply side and
demand side modeling are summarized in Table III and Table IV respectively. The
growth and attrition rates were arrived on discussion with the experts. The system
dynamics model is developed for degree holders, and has two major divisions. These
are as follows:
1) Demand for degree holders
2) Supply of degree holders
Level Definition
Primary Under-graduates students.
Middle Post-graduate students.
High Doctoral students.
Table II. Manpower levels defined
Courses Variable Value
Current stock 38632
Doctorates Intake capacity | 2320
Migration rate 2 percent
Current stock 84482
Post-Graduate Intake capacity | 7999
Migration rate 2 percent
Current stock 317837
Under-Graduate | Intake capacity | 18769
Migration rate | 2 percent
Table III. Data of degree holders for supply side modeling for base year 2009-2010
Source: NAARM
Variable Value
Degree employed 442716
Agriculture sector growth rate 4 percent
Attrition rate 2 percent
Table IV. Data for demand side modeling for base year 2009-2010
Source: NAARM
1. Demand for degree holders
The stock and flow diagram for the demand of degree holders is shown below in
Figure 2.
Subsec growth rate
Degree Employed
Oma aw
Att Adj Time
OX tes Rate Attrition oe)
Hiring Time
Perceived New Jobs
Total Demand
Actual New Jobs
Total Supply
Demand Supply Gap
Figure 2. Demand for degree holders
As is evident from the Figure 2, the ‘Hiring Rate’ of the degree holders depends on
“Actual New Jobs’ that are available. The ‘Attrition Rate’ is assumed to 2 percent
per year. The ‘Degree Employed’ is the level variable showing the accumulations at
a point of time. ‘Total Demand’ for the degree holders depend on the ‘Degree
Employed’ multiplied by the (1 + ‘Growth Rate’). If the ‘Total Demand’ is less than
the ‘Total Supply’ for the sector, then there exists a ‘Demand-supply Gap’.
2. Supply of degree holders
The stock and flow diagram for the supply of degree holders is shown below in
Figure 3.
Total Supply
Current stock PG Pursuing PhD urrent stock PhD.
Pursuing UG Current stock UG.
Intake UG Pass rate PG Pass Rate | Transfer
PhD PhD Pasgout Rate
Pq Migration PhD Years PifD Migration
UG Years UG Migration 85 ae
PG Olttum PhD Outturn
UG in Cap PhD In Cap
PG In Cap
Demand Supply Gap
Figure 3. Supply of degree holders
The model shows that there are Intake Capacities specified for under-graduate (UG),
post-graduate (PG) and doctoral (PhD) students. Out of these capacities, only a
percentage of students actually pass out. The years of study is considered to be 4
years for under-graduate, 2 years for post-graduate and 3 years for doctorates. Not
all UG students passing out will join for PG as some migrates to other disciplines.
The migration rates are all assumed to be 2 percent per year. In case of PG and PhD,
the pass-out rate depends on Transfer rate of students from other related disciplines
as well. For simplicity, it is assumed that 50% seats are filled by such transfers. The
students who pass out join the accumulations of ‘Current Stock UG’, ‘Current Stock
PG’ and ‘Current Stock PhD’ respectively. When these are added together, we get
the ‘Total Supply’ for a sector.
Base run results
The base run was realized using the data from Table III and IV. The time horizon for
which the forecast was carried out is 20 years spanning from 2010 to 2030. The
bases run result for degree holders are tabulated in Table V and depicted in Figure 4.
Course | Variables 2010 2015 2020 2025 2030
Supply 440951 460964 483128 503208 521673
Degree | Demand 442716 475159 498028 519297 538829
Demand-
supply 1765 14195 14900 16089 17156
Jap
Table V. Base run results of key variables
The simulation results show an excess of demand over supply for degree holders,
indicating a shortage of technical manpower in the sector. The demand and supply of
degree holders are increasing over time and both the demand and supply curves are
moving parallel. Still the increasing supply cannot keep pace with the increasing
demand creating a demand-supply gap. Moreover, the demand-supply gap shows an
increased widening from 2020 to 2030 compared to that of period 2010-2020.
Hence, if the present scenario continues, it is to be expected that the demand for the
degree holders will outrun their supply during the period 2020-2030 as evident from
Figure 4.
550000 + 7 18500
+ 16500@
‘5 @ 530000 +
28 + 145002
aa 2
2£ 510000 + + 125002
az
cae + 105005
2 5 490000 + >
sz + 8500 &
ES 2
a> 470000 + + 6500 &
a? z
$5 + 4500 Ss
FQ 450000 + 5
+ 2500 4
430000 | | | | 500
2010 2015 2020 2025 2030
Years
—#- Total Supply —@— Total Demand —&— Demand-Supply Gap
Figure 4. Agriculture sector manpower demand and supply of degree holders
The agriculture sector manpower forecast base run results for degree holders show a
demand-supply gap to exist. This in turn is a clear indication of creation of more
employment opportunity for degree holders in the future.
Policy analysis and outcomes
To alleviate the shortage for technical manpower, agriculture educational
institutions’ efforts are necessary. The efforts should be directed towards increasing
the intake capacities for the degree courses, thereby increasing the supply of
additional manpower to meet the high future demands. Presently the policy of a 50
percent increase in intake capacity for degree courses for two scenarios of 4 percent
and 3 percent sectoral growth was analysed for its effects to balance the manpower
supply and demand in the sector.
10
The SD model was rerun with the policy for two growth scenarios, and the results
are tabulated in Table VI and depicted in Figures 5 and 6 respectively.
Policy Variables | 2010 2015 2020 2025 2030
Supply 440951 | 460703 | 516571 | 564268 | 608804
50% Demand | 442716 | 467708 | 526821 | 577131 | 624187
increase In Demand-
intakes 4%, ee 1765 | 7005 | 10250 | 12864 | 15384
sectoral iP
growth
Supply 440951 | 461102 | 515305 | 561544 | 604537
50% Demand 442716 | 463633 | 520764 | 569088 | 614111
increase in Demand-
intake, 3% | supply 1765 | 2531 | 5459 | 7544 | 9575
Gap
sectoral
growth
Table VI. Simulation results of key variables for the adopted policy with two
sectoral growth scenarios
The policy of an increase in intake capacity by 50 percent for degree holders with an
anticipated sectoral growth rate of 4 percent (Table VI, Figure 5) shows some
improvement over the base run results. The supply curve has moved closer to
demand curve thereby reducing the demand-supply gap compared to the base run.
However, the demand-supply gap continues to widen towards 2030 showing the
need for further intervention in the future. The same policy of an increase in intake
capacity by 50 percent for degree holders with an anticipated sectoral growth rate of
3 percent (Table VI, Figure 6) also show improvements over the base run results.
Here also the supply curve has moved closer to demand curve compared to the base
run. Hence, the policy of a 50 percent increase in intake capacity for the degree
courses can alleviate the manpower shortages to a considerable extent in the future
by reducing the demand-supply gap in both the scenarios of the sector registering
either a 4 percent growth or a 3 percent growth.
11
Total Demand & Supply of
Degree Holders (Numbers)
630000 —-
580000 --
530000 --
480000 --
+ 14500
+ 12500
+ 10500
+ 8500
+ 6500
+ 4500
+ 2500
430000
2010
2015
2020
Years
2025
2030
500
—#- Total Supply —@— Total Demand —&— Demand-Supply Gap
Demand-Supply Gap (Numbers)
Figure 5. Overall agriculture demand-supply plot for degree holders
for 50% increase in the intakes for 4% growth rate
Total Demand & Supply of
Degree Holders (Numbers)
630000 —-
580000 +
530000 +
480000 +
+ 9500
+ 8500
+ 7500
+ 6500
+ 5500
+ 4500
+ 3500
+> 2500
+ 1500
430000
2010
2015
2020
Years
2025
2030
500
—- Total Supply —@— Total Demand —&— Demand-Supply Gap
Demand-Supply Gap (Numbers)
Figure 6. Overall agriculture demand-supply plot for degree holders
for 50% increase in the intakes for 3% growth rate
Conclusion
This study has presented an integrated demand-supply model for manpower
forecasting in agriculture sector in India. The results of the base run showed that the
demand for agriculture sector manpower of degree holders in India would exceed the
supply during the forecasting period (2010-2030) and hence a demand-supply gap
exists. Thus, the technical manpower expected to be required in a greater number,
will continue to be insufficient during the time horizon. The base run results in turn
points towards the generation of more employment opportunities in the future for
degree holders in agriculture sector in India.
After applying a policy of 50 percent increase in intake capacities at two different
targeted sectoral growth rates of 4 percent and 3 percent, the demand-supply gaps
were found to be reduced compared to the base run thereby alleviating the
manpower shortages. However, the demand outpaces the supply and the manpower
shortage continues unabated in future also. Hence, policy makers must think of
appropriate intervention in the future such as extending the retirement period of the
current stock employed etc. in order to balance the manpower supply and demand
needs of the sector in future.
The current paper has some limitations. The Indian agriculture sector has sub-sectors
in itself which are not discussed separately in this paper. Moreover, only technical
manpower in agriculture sector in India is considered. Future studies can also be
directed towards including the non-technical manpower requirements into the model.
References
Bechet, T.P. and Maki, W.R. (1987), “Modeling and forecasting focusing on people
as a strategic resource”, Human Resource Planning, Vol. 10, No. 4, pp. 209-17.
Chan, A.P.C., Chiang, Y.H., Mak, S.W.K., Choy, L.H.T. and Wong, J.M.W. (2006),
“Forecasting the demand for construction skills in Hong Kong”, Construction
Innovation, Vol. 6, No. 1, pp. 3-19
13
Chung, S.H., Jung, D.C., Yoon, S.N. and Lee, D.H. (2010), “A dynamic forecasting
model for nursing manpower requirements in the medical service industry”, Service
Business, Vol. 4, pp. 225-236.
FCCI (2007), “Emerging skill shortages in the Indian industry”, Federation of
Indian Chambers of Commerce and Industry, July, pp. 1-12.
Forrester J.W. (1961), Industrial Dynamics, MIT Press, Cambridge, MA.
Golait, R. (2007), “Current issues in agriculture credit in India: An assessment”,
Reserve Bank of India Occasional Papers, Vol. 28, No. 1, pp. 79-99.
Jantsch, E. (1972), “Forecasting and the systems approach: A critical survey”, Policy
Sciences, Vol. 3, pp. 475-498.
Jantsch, E. (1973), “Forecasting and the systems approach: A frame of reference”,
Management Science, Vol. 19, No. 12, pp. 1355-1367.
Kao, C. and Lee, H.T. (1998), “Demand for industrial management manpower in
Taiwan”, International Journal of Manpower, Vol. 19, No. 8, pp. 592-602.
Khoong, C.M. (1996), “An integrated system framework and analysis methodology
for manpower planning”, International Journal of Manpower, Vol. 17, No. 1, pp.
26-46.
Kwak, N.K. and Garrett, W.A. (1980), “A micro-model of internal supply
forecasting for technical manpower planning”, Computers & Operations Research,
Vo. 7, Issue 3, pp. 169-176.
Kwak, N.K., Garrett, W.A. and Barone, S. (1977), “A stochastic model of demand
forecasting for technical manpower planning”, Management Science, Vol. 23, No.
10, pp. 1089-1098.
Lomas, J., Stoddart, G.L. and Barer, M.L. (1985), “Supply projections as planning: a
critical review of forecasting net physician requirements in Canada”, Social Science
& Medicine, Vol. 20, No. 4, pp. 411-24.
Martino, J.P. (1980), “Technological forecasting-an overview”, Management
Science, Vol. 26, No. 1, pp. 28-33.
Milkovich, G.T. and Boudreau, J.W. (1994), Human Resource Management, 7th ed.,
Irwin, Burr Ridge, IL.
O’Brien-Pallas, L., Baumann, A., Donner, G., Murphy, G.T., Lochhaas-Gerlach, J.
and Luba, M. (2001), “Forecasting models for human resources in health care”,
Journal of Advanced Nursing, Vol. 33 No. 1, pp. 120-9.
Park, S.H., Lee, S.M., Yoon, S.N. and Yeon, S. J. (2008), “A dynamic manpower
forecasting model for the information security industry”, Industrial Management
and Data Systems, Vol.108, No.3, pp. 368-384.
14
Parker, B. and Caine, D. (1996), “Holonic modelling: human resource planning and
the two face of janus”, International Journal of Manpower, Vol. 17, No. 8, pp. 30-
45.
Prescott, P.A. (1991), “Forecasting requirements for health care personnel”, Nursing
Economics, Vol. 9, No. 1, pp. 18-24.
Richardson, G.P. and Pugh, A.L. (1981), Introduction to System Dynamics
Modeling with Dynamo, MIT Press, Cambridge, MA.
Roy, D. and Koul, S. (2009), “Human resource planning in a shore-based integrated
steel plant: A system dynamics model”, Proceedings of the 27" International
Conference of the System Dynamics Society, Albuquerque, USA.
Stepp, M.D., Winebrake, J.J., Hawker, J.S. and Skerlos, S.J. (2009), “Greenhouse
gas mitigation policies and the transportation sector: The role of feedback effects on
policy effectiveness”, Energy Policy, Vol. 37, pp. 2774-2787.
Sterman, J.D. (2000), Business dynamics: systems thinking and modeling for a
complex world. Irwin, Chicago
Sterman, J.D. (2002), “All models are wrong: reflections on becoming a systems
scientist”, System Dynamics Review, Vol. 18, No. 4, pp. 501-531.
Viswanadham, N. (2005), “Can India be the food basket for the world?”, Working
Paper Series of Indian School of Business, December, pp. 1-16.
Wolstenholme, E.F. and Coyle, R.G. (1983), “The development of system dynamics
as a methodology for system description and qualitative analysis”, Journal of
Operational Research Society, Vol. 34, pp. 569-581.
Wu, M., Xu, Q., Gui, B., Zhu, L. and Chen, J. (2003), “System dynamics modeling
of manpower forecasts and programming”, Proceedings of the 21" International
Conference of the System Dynamics Society, New York, USA.
15
