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APPLICATIONS OF TECHNOLOGY FORECASTING METHODS

IN AGRICULTURE

Ramasubramanian V.

Indian Agricultural Statistics Research Institute

Library Avenue, New Delhi – 110012

[email protected]

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

M IV: 5: Application of Technology forecasting methods in Agriculture

M IV-33

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


M IV-34

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


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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


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Figure (iv): Indian Cotton scenario over time (with curb on imports)

Figure (v): Indian Cotton scenario over time (with curb on imports)


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References

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Martino, J.P. (1983). Technological forecasting for decision making, 2nd

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applications of technology forecasting methods in …cabgrid.res.in/cabin/publication/smfa/module...

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