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APPLICATIONS OF TECHNOLOGY FORECASTING METHODS
IN AGRICULTURE
Ramasubramanian V.
Indian Agricultural Statistics Research Institute
Library Avenue, New Delhi – 110012
1. Introduction
Indian agriculture in future is likely to be much different than what it is now. Increasing
urbanization and income growth will cause significant changes in the food consumption
basket and hence shifts in dietary pattern. Moreover, the size of land holding/ cropped
area is declining, resources for agriculture are dwindling, globalization is unfolding and
new forms of markets are emerging. Recent trends suggest a disproportionate increase in
demand for high value horticultural and animal food products as compared to staples.
The production environment and demand scenario has entirely changed. Of late, there
have arisen specialized preferences (e.g. organic foods) and attitudes among consumers
owing to environmental and ethical concerns. However, majority of the people live-off
the farms but have heavy dependency on agricultural produce. In addition, food system
will be governed by stringent food safety and quality regulations in the days to come.
These changes give clear signals for development of a more science-based, demand-
driven agriculture. Nevertheless, rapid developments in scientific fields like space,
telecommunications, nanotechnology, computer science, molecular biology,
biotechnology, etc. undoubtedly had profound applications in agricultural sciences and
technologies. In this computer age, perhaps, only a fraction of the effort of the past is
needed to usher in a second revolution in Indian agriculture.
New upcoming technologies are expected to be different than in the past, which would
reconcile conflicting socio-economic and environmental objectives with minimum trade-
offs among them. It is therefore imperative to articulate technological needs of different
segments of agriculture, and contemplate how developments in science can help address
these needs. Thus the future of Indian agriculture is very much affected by the emerging
scenario of higher economic growth, population explosion, shifts in dietary pattern,
declining size of land holdings, globalization etc. Moreover, impact of technologies such
as use of HYVs, adoption policies for improved implements etc. will cause significant
changes in the whole gamut of demand and supply of agricultural produce.
Technology is nothing but the totality of the means employed to provide objects
necessary for human sustenance and comfort. Here the word „objects‟ would mean not
only goods but also services. In other words, they include not only „hardware‟ like
machines but also „software‟ such as procedures and techniques. Forecasting is the
process of computation/ prediction or giving a statement of what is expected to happen in
the future in relation to a particular event or situation. In order that these forecasts are
reliable they should be necessarily based on the results of rational study, statistics-based-
forward looking methods and analysis of available and/or collected pertinent data (rather
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information) in a specific domain by means of scientific and systematic methodology.
There are many definitions of TF; some of them are given below.
M.J. Cetron: TF is the prediction with a stated level of confidence, of the anticipated
occurrence of a technological achievement within a given time frame with a specified
level of support (Cetron, 1969).
J.P. Martino: A technological forecast is a prediction of the future characteristics of
useful machines, procedures or techniques (Martino, 1983).
W.E. Lanford: TF is the prediction or determination of the feasible or desirable
characteristics of performance parameters in future technologies (Rohatgi et al., 1979).
Accordingly it can be stated that TF is the qualitative and/or quantitative prediction with
stated level of confidence of feasible and/or desirable characteristics of performance
parameters of future technologies given a specific time frame also with specified level of
support (policy, capital, human resource and infrastructural needs). Here the term
qualitative means that the nature of technology will be in narrative form as regards to the
technical approaches and technology; quantitative means the scale of technological
activity will be given in numerical form i.e. specification of the functional capabilities
being forecast and numerical measures of their levels; time is the period in which the
forecast level of technology is expected to occur and probability is the likelihood of the
technological event at a given level by a certain time. TF is needed for better planning
and future preparedness, enlarging the choice of opportunities, setting priorities and
assessing impact and chances, focusing selectively on economic, technological and social
areas for further research, for strategic advantage and global competitiveness in R&D etc.
Reliable and long-range technological forecasts provide important and useful inputs for
proper, foresighted and informed planning, more so, in agriculture which is full of
uncertainties. Of late agriculture has become highly input and cost intensive. Without
judicious use of fertilizers and plant protection measures, agriculture no longer remains
as profitable as before. New pests and diseases are emerging as an added threat to the
production. Under the changed scenario today, forecasting of various technological
aspects relating to agriculture is becoming essential.
TF can aid in mapping technological scenarios of agriculture in future in order to
facilitate decision making providing ground for dedicated policies for priority setting of
key agricultural components. Thus TF can make us ready for meeting emerging trends
based on market pull and technology push. Assessment of food requirement in the long-
term as per the future demand is of utmost importance. TF may pave way for corrective
measures for population growth versus production growth imbalance. Foresight and
forecasts of the technological needs as per the emerging scenarios will enhance the
sustainability and growth of agricultural systems. The technological needs required to
maintain sustainable agriculture in turn is compelling that a stock assessment about the
human and physical capital requirements‟ situation is warranted. There is also a need for
assessing their impacts on the agricultural R&D system and the flow of technologies. For
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instance, patterns can be found as to whether improved varieties at a particular location is
stagnant over years or is there any shift in area under the same, changes in crop statistics
viz. area/ production/ yield in different agro-climatic zones may give indication about
required technologies in future etc. TF may give us information about which
commodities are going to be major in particular location and about what are the problem
areas. Future technological forecasts can be made in terms of future demand of
agriculture and contemplate how developments in science can help address these needs
Forecasting technological needs in the domain of agriculture will be thus quite useful in
framing policies in advance for properly planning and managing the resources and
produce. This paper discussed technology forecasting in agriculture in retrospect, its
prospects and methodological aspects.
Few studies have been conducted for TF in the domain of agriculture in India and mostly
Delphi method has been used. Some other methods used are scenario writing, trend
extrapolation and relevance trees.
Rohatgi et al. (1979)
Food and Agriculture,
Education, Energy
Resources, Health
services etc.
Delphi, Trend extrapolation,
Substitution techniques,
Relevance tree
Rama Rao and Kiresur
(1994)
Sorghum Delphi
Rama Rao et al. (2000) Oilseeds Delphi, Brainstorming, trend
extrapolation and growth rates
Rohatgi et al. (1979) have concluded that by 2000, intensive farming techniques and
mechanization should given priority to bridge the gap between total demand and total
food output. Rohatgi et al. (1982) have forecasted energy scenarios and their outputs for
the year 2000. Rama Rao and Kiresur (1994) forecasted that in Sorghum by 2000, the
following will be achieved:
Area – 12 m ha
Production – 12.09 m t,
Yield – 949 Kg/ha,
% area under HYVs – 60%,
No. of years for sorghum to become attractive for alternate uses -10 years
Rama Rao et al. (2000) have conduced studies in TF on entities of Oilseeds such as area,
yield, consumption, competing crops, research breakthroughs, processing improvement
etc. They concluded that by 2010,
Area under total oilseeds -32.0 m Ha
Average Yield – 1150 kg/ha
Major competing crops – Paddy / millets
Research breakthrough – drought tolerance varieties
Processing improvement – pesticide free by products
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Bhatia et al. (2012) have done Technology Forecasting in agriculture for commodities
Rice and Cotton and for the domains viz., Plant Breeding and Genetics, Rainfed
Agriculture and Fisheries and forecasted relevant technological trends and / or needs. In
addition, they have done impact studies of other frontier fields of science on the domain
of agriculture by considering two fields viz., Information & Communication Technology
(ICT) and Remote Sensing (RS)
2. Some TF tools and techniques
2.1 Analytic Hierarchy Process
Analytic Hierarchy Process (AHP) is a multi-criteria decision technique proposed in the
area of Operations Research. It consists of studying complex system of interrelated
components by successive grouping of components within levels of hierarchy leading to
distinguishing among levels of complexity between these hierarchies. Thus an AHP tree
is built by development of a hierarchy of “decision criteria” leading to “alternative
courses of actions/ factors”. The comparative judgments are done by a pairwise
approach. Thus it synthesizes the information by finding relations through experts‟
opinions in order to infer how strongly components at various levels of the hierarchy
influence the top or goal by finding intensities (priorities) at various levels. AHP
algorithm is basically composed of two steps i.e. determination of relative weights of the
“decision criteria” and determination of relative rankings (priorities) of “alternatives”.
Qualitative information using informed judgments are utilized to derive these weights
and rankings and prioritization of the alternatives is done based on the rankings obtained.
2.2 Bass diffusion model
The Bass model shows how a new product or idea spreads through the user community
by quantifying the introduction of new technologies depending on the take up by
innovators and imitators.
Basic formula can be described as follows:
N(t) is the total or cumulative number of consumers that have already adopted the new
product through period t.
N(t - 1) is the cumulative number of adopters for the new product through the previous
time period.
S(t) is the number of new adopters for the product during the time period t.
The Bass model has three key parameters: the total market size (m), coefficient of
innovation (p), and coefficient of imitation (q). The Bass model asserts that the likelihood
of an initial purchase being made at time (t) is a linear function of the number of the
previous adopters, as shown in the following:
p+(q/m) N(t-1) is the likelihood of purchase by a new adopter in time period t.
m-N(t-1) is the number of consumers that have not previously adopted by the start of
time period t.
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S(t) = [p+q/mN(t-1)] [m-N(t-1)] is the number of new adopters during time t.
2.3 Brainstorming
Brainstorming is a free and fair unconventional intuitive technique in which in principle
hierarchy is not maintained in expressing opinion on expert topics. It is reasonable to
expect that a rough picture of the future is already formed in the minds of these experts
and thus they will have the ability to assess the future in their respective areas of
specialization. Since most discoveries and innovations are deliberately engineered by
sustained inputs of funds and manpower for R&D activities, it is felt that probing the
minds of the experts involved in these developments can give an idea of likely future
events. Moreover, the experts chosen are well-informed individuals who can use their
insights and experience and are better equipped to predict the future than theoretical
approaches or extrapolation of trends. In addition, in situations where evaluation of
unconventional „alternatives‟ is needed, Brainstorming is resorted to as a frank and free
search technique, which when properly conducted, minimises the effects of bureaucracy
and bandwagon (Martino, 1983). It is exploratory in nature as it starts from today‟s
assured basis of knowledge and is oriented towards the future and thus extending the
present into the future.
2.4 Cross impact analysis
Realistic problems involve a multiplicity of competing variables, presenting a complexity
of behavior that usually dwarfs human capacity for comprehension. Consequently
decisions are usually made in truncated spaces by sharply reducing the variables that will
be considered. It has been the consistent endeavor of systems scientists to develop models
which have the capacity of enlarging the scope of human comprehension. Cross impact
analysis method is one of the expert opinion based techniques (like the most popular
Delphi technique) that can be utilized for technological forecasting. This method was
developed to overcome some of the demerits of other existing methods on experts‟
opinions. A potential shortcoming of Delphi (as well as many other forecasting
techniques) is that interrelationships among events shaping the future are difficult to
consider explicitly. Here these interrelationships can be referred to as cross impacts, and
the purpose of this lecture is to describe and demonstrate a forecasting technique that is
capable of dealing with cross impacts in preparing forecasts. Cross-impact method
utilises certain subjective information (in terms of probabilities) as part of the procedure
in order to obtain forecasts. When dependencies are suspected among future events, the
probability that a potential development will actually occur is influenced by the
occurrence or non-occurrence of related developments. The cross-impact method
estimates each development‟s probability of occurrence based on interrelationships that
exist between events included in the analysis.
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2.5 Fisher Pry/ Pearl, Gompertz and Lotka-Volterra substitution models
Pearl/ Fisher-Pry/Logistic and the Gompertz models are two chief substitution models
prevalent in technology forecasting literature. These models are all non-linear and
sigmoidal in nature with the pattern taking an elongated S-shape. It can be
mathematically shown that Fisher Pry model is equivalent to the Pearl model which is
nothing but the usual logistic growth model. Frequently, one is interested in forecasting
the rate at which a new technology will be substituted for an older technology in a given
application. Substitution of new technology for an older one often exhibits a growth
curve. Initially, the older technology has the advantage. Initial rate of substitution is low.
The older technology is well understood, its reliability is probably high, users have
confidence in it, and both spare parts and technicians are readily available. The new
technology is unknown and its reliability is uncertain; spare parts are hard to obtain and
skilled technicians are scarce. As the initial problems are solved, the rate of substitution
increases. As the substitution becomes complete, however, there will remain a few
applications for which the old technology is well suited. The rate of substitution slows, as
the older technology becomes more and more difficult to replace.
Fisher Pry model is given by
0tanh12
1ttg
L
yf
Here t0 is time for 50% substitutions.
y – study variable at time t; L – max. attainable value of y.
The Pearl or the logistic model is given by
trea
Ly
1
Here
y – study variable at time t
L - upper limit to the growth of the variable of y
a – function of value of y at time t=0
r – growth rate
For fixed L, the values of r and a are usually estimated by non-linear fitting under TF
domain.
The Gompertz model is given by
Properties of the Pearl curve are that the study variable takes an initial value of zero at
time - and a value of L at time = +. The inflection point occurs at t=ln(a)/r, when
y=L/2. Also, the curve is symmetrical about this point, with the upper half being a
reflection of the lower half.
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Properties of the Gompertz curve is that like the Pearl curve, the initial value is zero at
time = - and a value of L at time = +. But the curve is not symmetrical. The inflection
point occurs at t = (ln a)/r, when y = L/e
The slope of the Pearl curve involves y and (L-y), i.e., distance already come and
distance yet to go to the upper limit. For large values of y, the slope of the Gompertz
curve involves only (L-y), i.e., the Gompertz curve is a function only of distance to go to
the upper limit.
2.6 Linear Combination Weighted Scoring method
The mathematically elegant and computationally simple method i.e. the linear
combination scoring approach consists of weighted total method for each factor under
consideration. Each factor can be scored by experts individually on a comparable
linguistic scale: “most important” through “least important”, and frequency counts
determined. Thereafter a weighted total score (weights assigned appropriately with
gradation) is defined as a linear combination of these individual counts against factors.
2.7 Questionnaire approach
Also by questionnaire approach, information from experts was obtained for identification
of specific technologies with greater utility in the years to come and for prioritizing
factors affecting various aspects such as agricultural productivity, yield gap, new varietal
development etc. The questionnaire has been designed in consultation with the experts.
As far as possible, the questions were made self explanatory, avoiding ambiguity,
answers easy to select with sufficient space left for remarks. It was made simple but not
compromising on the essentials for prioritizing factors / technological forecast of the
events chosen. Each question focussed on one event only. Some questions were open-
ended leaving space for the experts to pen down their ideas without influencing their
thought process while some were having a list of possible options which they need to
prioritize on a five point linguistic scale ranging from „Extremely important‟ (EI) to „least
important‟ (LI), the intermediate scales being „very important‟ (VI), „moderately
important‟ (MI) and „somewhat important‟ (SI), with few additional rows left blank for
the experts to fill in factors left out, if any, in the list provided.
2.8 Scientometrics
To stay aware of changing scenarios, scientometrics is a tool that helps in monitoring
early signals of new technological developments by searching literature (journals,
patents, reports etc.). Such technology monitoring methods assume that some future
technologies are in the process of development and current research areas are the
embryos where action is taking place. Thus scientometric studies apply quantitative
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methods to journals wherein analysis of science is viewed as an information process with
outputs of scientific and technological activities as publications.
2.9 Delphi method
Delphi is the most commonly used method. It uses a panel of individuals who make
anonymous subjective judgments about the probable time when a specific technological
capability will be available. The results of these estimates are statistically aggregated fed
back to the group, which then uses the feedback to generate another round of judgments.
After several iterations, the process is stopped and areas of agreement or disagreement
are noted and documented. The features of Delphi technique are anonymity, iteration
with controlled feedback and statistical group response.
Anonymity: The group members are not made known to each other. The interaction of
the group members is handled in a completely anonymous fashion, through the use of
questionnaires. This feature avoids the possibility of identifying a specific opinion with a
particular person. The originator of an opinion can change his mind without publicly
admitting that he has done so, and thereby possibly losing face. It also means that an idea
can be considered on its merits, without regard to whether the originator is held in high or
low esteem by individual members of the group.
Iteration with Controlled Feedback: The individual or agency carrying on the sequence
extracts from the questionnaires only those pieces of information as are relevant to the
issue, and presents these to the group. The individual serving as a forecaster thus is
informed only of the current status of the collective opinion of the group, and the
arguments for and against each point of view. It prevents the group from taking on its
own goals and objectives and concentrate on its original objectives, without being
distracted by self-chosen goals such as winning an argument or reaching agreement for
the sake of agreement.
Statistical Group Response: Typically, a group will produce a forecast which contains
only a majority viewpoint. This feature presents a statistical response which includes the
opinions of the entire group. On a single question, for instance, the group response may
be presented in terms of a median i.e. a number such that half the group were above it,
and half below and the inter-quartile range i.e. between the two numbers that separate the
inner half of the group from the outer quarters.
2.10 Relevance trees
The most appropriate path of the tree is determined by arranging in a hierarchical order,
the objectives, sub-objectives and tasks in order to ensure that all possible ways of
achieving the objectives have been found. The relevance of individual tasks and sub-
objectives to the overall objective is then evaluated. Thus relevance trees help in priority
setting. As an illustration the one of the many relevance trees given for achieving a goal
of eradicating „hunger and malnutrition‟ is given in Fig (i).
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Fig (i) An example of a relevance tree (reproduced from Rohatgi et al., 1979)
2.11 Scenario writing
The main purpose of scenario writing is to provide a composite picture of compatible
future events. It considers the interrelationships between predicted events and develops a
collective impact of a group of forecasts. Alternate scenarios provide the decision-maker
with an overall view of alternate futures before selecting the most desirable and feasible
one. This can be followed by planning efforts to convert it into reality by taking action
for implementation of ideas.
SOLUTION FOR HUNGER AND MALNUTRITION
MAXIMIZE UTILIZATION OF AVAILABLE RESOURCES
Increase yield by more efficient production
Increase farm
mechanization
Reduce animal
grazing
Develop
agriculture as
organized sector
Maintain a gene bank for varieties
which are
1. High yielding
2. Resistant to diseases
3. Have better nutritive value
4. High protein content
5. Fast developing and
growing (super-cereals)
1. by putting a plastic or polymer
layer 3 ft below the soil
2. by closing leaf pores to avoid
evaporation
3. by sowing crops requiring less
water
4. by using drip water irrigation
5. by new methods
Breed better
animals for milk,
eggs and meat
Reduce water
requirement
Increase water
resources
Achieve faster animal
growth cycles, using
chemical, genetic or
radiation techniques
Produce
high-yield
varieties of
food plants
Change policy
and attitude
Increase
irrigation
Increase protein
conversion efficiency
in animals
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3. Case studies
3.1 Forecasting technological needs and prioritizing factors in agriculture from a .
Plant Genetics and Breeding domain perspective
As a TF exercise, exploratory and intuitive TF tools viz., Brainstorming and
Questionnaire approaches were employed to envision future technological needs for one
of the subdomains of agriculture, i.e. Plant Genetics and Breeding (PG&B). Information
from experts obtained through questionnaire for identification of specific factors enabling
promising technologies was subjected to linear combination weighted scoring method for
prioritizing key factors leading to future technological needs. The data were also
analyzed using multi-dimensional scaling for identifying key dimensions encompassing
these factors in agriculture. Also an attempt has been made to indicate the time frame for
agricultural technologies in the PG&B domain in general and also in particular to evolve
technologies in crop varieties for specific end uses.
One of the subdomains of agriculture, i.e. Plant Genetics and Breeding (PG&B) has
continued to evolve with a much broader scope and potential than in the past more so
with incorporation of new technologies and new knowledge from other fields of science.
The PG&B programs will reduce the time frame for evolving new technologies if it takes
full advantage of emerging fields like biotechnology, nanotechnology etc. Nevertheless,
the classical methods of PG&B will continue to flourish as the new sciences will be
useful only when they are built upon such established and time tested technologies.
Improved understanding of plant metabolic pathways can pay enormous dividends in
terms of ultimate economic yield in crops. Agriculture has just scratched the genetic
surface of plants. Research into plant genomics can help boost crop yields with much less
exposure to biotic and abiotic stresses by harnessing the untapped genetic diversity in
addition to reducing the environmental impact on agriculture. Concerted efforts are
hence needed to perform TF and also technology assessment in PG&B.
Techniques used
In this study, two chief TF tools, viz., Brainstorming and Questionnaire approaches have
been used for forecasting technological needs and prioritizing factors in agriculture from
a PG&B domain perspective. The collected data were also analyzed using multi-
dimensional scaling for identifying key dimensions in agriculture.
As a TF exercise, a one day Brainstorming session was organized at Division of Genetics,
Indian Agricultural Research Institute (IARI), New Delhi which provided a platform to
experts which included plant breeders, geneticists etc. in scripting the future
technological needs of agriculture pertaining to the PG&B domain for envisioning
conversion of crop varieties/commodities into viable products for productivity
improvement and effective utilization of modern tools for value addition and genetic
enhancement
Methodological steps and results
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(i) Brainstorming
In the Brainstorming session conducted, to start with, the group of experts were first
sensitized about the need and approach of technology forecasting and the objectives of
the initiative undertaken. Thereafter, every expert was given opportunity to air their
views. The information obtained from Brainstorming were utilised to envision future
technological needs in the subdomain under consideration. On synthesizing the opinions
floated, future technological needs in PG&B domain for sustainable agriculture such as
exploitation of heterosis for developing hybrids based on Cytoplasmic Male Sterility
(CMS) system, biotechnological interventions like gene pyramiding, Marker Assisted
Selection (MAS), transgenics, structural and functional genomics, association mapping,
QTL mapping etc. for crop improvement emerged out. (For brevity, all of them are not
listed here)
(ii) Questionnaire approach
Information from 35 completely filled-in questionnaires obtained from experts was then
analyzed for prioritizing factors leading to envisioning future technological needs in
PG&B using linear combination scoring method. It is noted here that while around 80
experts participated in the Brainstorming session, only 35 of them handed back the
questionnaires either at the end of the session or after follow-ups. Even with the
approach that was followed, the non-response is as high as more than 50%. Hence the
usually popular Delphi technique which requires several rounds of eliciting information
through questionnaire was not resorted to and the present practical way of inviting
experts at one place was done. Each factor has been scored by experts individually on a
comparable linguistic scale: “most important” through “least important”, as was
discussed in the preceding section and frequency counts were determined. Thereafter a
weighted total score (weights being 1, 0.75, 0.50, 0.25, 0 respectively) is defined as a
linear combination of these individual counts against factors.
For example, consider Table 1 for prioritizing major factors that may enhance
agricultural productivity. In this, against each factor, the row total is approximately (and
need not be exactly) 35 as some of the factors may not have been ranked at all by some
experts. The score of the first factor “Quality seed availability‟ has been obtained by
simply adding the weighted counts i.e. (25x1.00+6x0.75+2x0.50+0x0.25+0x0) = 30.5
and likewise for other factors. Note also that the factors have been written here in the
descending order of the scores obtained for prioritization. In the same way, factors
responsible for achieving other technological needs have been prioritized and are stated
subsequently in brief.
In general, while mathematically elegant and computationally simple, the linear
combination scoring approach has shortcomings. An increase in linguistic scale
(observed variable) from scale 1 (EI) to scale 3 (MI), for instance, may be an
insignificant change in „importance of the factor‟ (latent variable), while an increase from
scale 3 (MI) to scale 5 (LI) may mean that the same is highly significant. Hence the
multivariate approach viz., MDS, which has sound statistical basis has been employed in
order handle such ordinal data and is discussed in the next section.
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Table 1: Major factors for enhancing agricultural productivity: Scoring method
Factors EI
(1.00)
VI
(0.75)
MI
(0.50)
SI
(0.25)
LI
(0.00)
Score
Quality seed availability 25 6 2 0 0 30.5
Better varieties 22 8 3 0 0 29.5
Timely availability of inputs 11 20 2 0 0 27.0
Proper research infrastructure 16 11 4 2 0 26.8
Better agronomic practices 11 15 7 0 0 25.8
Adaptation to climatic
change
12 12 7 2 0 25.0
Marketing facilities 11 11 8 3 0 25.0
Minimum Support Price 11 10 10 2 0 24.0
Development of location
specific technologies
9 13 8 3 0 23.5
Better extension services 11 8 12 2 0 23.5
GM crops 3 18 10 2 0 22.0
Technology to fill nutrient
depletion- sup
ply to soil gap
3 18 10 2 0 22.0
Nutrient management 5 15 10 3 0 22.0
Plant Protection measures 4 14 13 1 1 21.3
Use of ICT 4 13 14 2 0 21.3
Post harvest management 3 15 11 4 0 20.8
Domestic / International
trade
5 11 13 4 0 20.8
Farm mechanization 5 9 15 4 0 20.3
3.2 Technological trends of adoption of Bt Cotton in India
Two quantitative approaches viz., substitution models and one conceptual approach viz.,
framework forecasting were applied in the context of inferring about technological trends
of adoption of Bt Cotton in India.
Substitution models
The substitution models viz., Fisher-Pry/Pearl/Logistic, Gompertz and Lotka Volterra
models were fitted for data on area under adoption of Bt Cotton in India. Relevant
computer program was written in R for fitting the models in R software. The Solver
utility in MSExcel was also tried for non-linear model fitting. Among the two models,
Pearl and Gompertz, Gompertz model came out to be the best for the data under
consideration both in terms of minimum residual sum of squares and plot of actual versus
fitted data. (Fig. (ii) and (iii)) It was found that by 2013, if the same trend continues, all
of area under Indian Cotton will be substituted by Bt Cotton. It was also shown
mathematically that Fisher Pry model is equivalent to the Pearl model which is nothing
but the usual logistic growth model.
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Table 2 gives area under adoption of Bt Cotton in India (thousand hectares) over years.
Table 2: Area under adoption of Bt Cotton in India (thousand hectares)
Year Overall BtCotton % adoption
2002 9130 50 0.55
2003 7670 100 1.30
2004 7600 500 6.58
2005 8790 1300 14.79
2006 8680 3800 43.78
2007 9140 6200 67.83
2008 9410 7605 80.82
2009 9407 8381 89.09
Source: GoI ISAAA
GoI: Government of India; Directorate of Economics and Statistics; ISAAA:
International Service for the Acquisition of Agri-biotech Applications
Note: L=100%;
Model a r ResidualSS
Pearl 258.18 1.03 76.04
Gompertz 29.86 0.71 30.56
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Fig (ii) : % Area under adoption of Bt Cotton in India: Pearl curve (the smooth
curve) along with actual data points over years
Fig (iii): % Area under adoption of Bt Cotton in India: Gompertz curve (the smooth
curve) along with actual data points over years
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Cross impact analysis
Kane‟s KSIM cross impact simulation model was utilised for inferring about the future
behaviour of variables of Indian cotton viz., Production, Export, Import and Supply over
time. For this, time series data of these variables for the years 1960-2011 were collected
from secondary sources. The initial values of these variables were determined as a
fraction (a number between 0 and 1) by dividing the latest figures with the corresponding
maximum assumed that can be attained by these variables. Thereafter, the impact of each
variable on another (on a pairwise basis) was determined by finding the regression
coefficient of the simple linear regressions of each of the variable upon each one of the
other variables. These coefficients were converted into a -3 to +3 scale by
transformation and judgement. Also an „outside world‟ variable was also considered
which would impact these variables (at the same time will not be impacted upon by these
variables). Thus it was inferred from the study that if no curb on imports was done then
it may increase over time in the long run. (Fig. (iv) and (v))
Kane simulation model (KSIM) is given by (if xt is taken as the study variable)
where
Table 3 (i): Indian Cotton statistics (1000 480 lb. bales)- Source: FAO
Year Exports Imports Production Supply
2001 60 2388 12300 18461
2002 56 1216 10600 16942
2003 700 800 14000 18386
2004 660 1038 19000 24224
2005 3675 400 19050 28214
2006 4875 465 21800 30104
2007 7500 600 24000 31729
M IV: 5: Application of Technology forecasting methods in Agriculture
M IV-47
2008 2360 800 22600 29029
2009 6550 480 23000 32399
2010 5100 450 25400 31849
2011 5250 450 27500 34199
Max.
(assump.) 20000 4000 50000 60000
2011
upon max. 0.26 0.11 0.54 0.57
Initial
value 0.3 0.1 0.5 0.6
Table 3 (ii): OLS regression coeffs. (1960-2011 cotton - India)
Cotton Initial value Production Export Supply Import
Production 0.5 - 0.68 0.90 0.12
Export 0.3 1.02 - 0.93 -0.02
Supply 0.6 1.09 0.75 - 0.18
Import 0.1 0.22 -0.02 0.27 -
Note: 0.68 is reg. coeff. of production on export and so on
Table 3 (iii): Kane’s Cross impact analysis –Cotton scenario in India
Production Export Supply Import Outside
world
Production 0.5 0 2 3 1 2
Export 0.3 3 0 3 0 1
Supply 0.6 3 2 0 1 0
Import 0.1 1 0 1 0 -2
M IV: 5: Application of Technology forecasting methods in Agriculture
M IV-48
Figure (iv): Indian Cotton scenario over time (with curb on imports)
Figure (v): Indian Cotton scenario over time (with curb on imports)
M IV: 5: Application of Technology forecasting methods in Agriculture
M IV-49
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