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Predicting adoption of agricultural technologies in Indo-Gangetic Region
Article in Indian Journal of Agricultural Sciences · June 2022
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19 authors, including:
Some of the authors of this publication are also working on these related projects:
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Purushothaman Venkatesan
National Academy of Agricultural Research Management
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National Academy of Agricultural Research Management
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Indian Agricultural Research Institute
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Predicting adoption of agricultural technologies in Indo-Gangetic Region
P VENKATESAN
1
, N SIVARAMANE
1
, B S SONTAKKI
1
, R ROY BURMAN
2
*, C H SRINIVASA RAO
1
,
V P CHAHAL
3
, A K SINGH
3
, P SETHURAMAN
4
, J P SHARMA
5
, R N PADARIA
2
, S CHAKRAVORTY
2
,
NISHI SHARMA
2
, NEELAM PATEL
2
, HARSHWARDHAN CHOUDHARY
2
, GAUTAM MONDAL
6
,
RAHUL SINGH
2
, B KALYANI
1
, SHAILENDRA SHARMA
2
and RAJESH KUMAR
2
Received: 17 December 2021; Accepted: 15 March 2022
ABSTRACT
Present study aims to find out how the technological interventions performed under the Farmer FIRST programme
by assessing the peak adoption level and time taken to attain it. ADOPT tool was used to assess the impact of the
technological interventions. Thirty farmers who have participated in the programme implemented at Haryana, India,
were interviewed during 2021 to elicit data pertaining to the year 2016–21 and the modal value of their responses
were used as input in the ADOPT model to estimate the parameters of interest. The results showed that the extent
of peak adoption level is high for interventions related to cereal crops and animal components while the time taken
to reach peak adoption level is also low indicating that the advisory system for these commodities have borne good
results and this calls for streamlining the advisory system for horticultural crops to achieve the desired output from
these enterprises as well.
Keywords: Adoption, ADOPT, Learnability, Scalability, Technologies, Trialability
1
ICAR-National Academy of Agricultural Research
Management (NAARM), Rajendranagar, Hyderabad, Telangana;
2
ICAR-Indian Agricultural Research Institute (IARI), Pusa, New
Delhi;
3
Indian Council of Agricultural Research (ICAR), Krishi
Anusandhan Bhawan-1, Pusa, New Delhi; ICAR- Central Tuber
Crops Research Institute, Thiruvananthapuram, Kerala;
4
Sher-
e-Kashmir University of Agricultural Sciences and Technology,
Jammu;
5
ICAR-National Dairy Research Institute (NDRI), Karnal.
*Corresponding author email: burman_extn@hotmail.com
97
Application of improved technologies in agriculture
played significant role in eradicating poverty, reducing
production costs and hunger, and enhancing rural household
income (Kassie et al. 2011). Farmer FIRST (Farm Innovation
Resources Science and Technology) (FFP) is an ICAR
frontline extension programme, being implemented in many
states of India. Present study was carried out at Haryana
which is one of the FFP centres representing Indo Gangetic
plains (IGP) where rice-wheat cropping system dominates.
There are serious problems in agriculture (IGP) like lack of
farm diversification, food insecurity, declining soil fertility,
development hard soil pan, adverse soil structure problems
and low crop productivity, monocropping, and crop yield
instability (Sekar and Pal 2012, Chandra et al. 2020). FFP
aims towards creating and sustaining a dynamic farmers-
scientists interface for developing system-specific livelihood
interventions through technology assemblage, application
and feedback, which is achieved through partnership and
institution building along with content mobilization (Kokate
and Singh 2013) to alleviate the problems.
Scaling up is a proven approach for distribution of
benefits of agricultural technology over a wider geographic
area more quickly, more equitably and more lastingly
(Menter et al. 2004, Hartmann and Linn 2008). A number
of frameworks and approaches has been developed for
scaling up of agricultural technologies, focusing on the
issues in the adoption of technologies, viz. conventional
top-down technology-dissemination approach (Biggs 1990),
innovation platforms (Posthumus and Wongtschowski
2014), planned comparisons (Coe et al. 2017), contextually-
appropriate interventions (Sola et al. 2017) and insurance
approach (Sulaiman et al. 2018), and still there is a need
for simpler approaches.
Adoption and Diffusion Outcome Prediction Tool
(ADOPT) model developed by Commonwealth Scientific
and Industrial Research Organization (CSIRO) enables the
researchers to predict the peak adoption level and time taken
to attain peak adoption level (Kuehne et al. 2017). This
research paper highlights on the use of ADOPT to assess the
impact of the “technology assemblage” interventions made
under FFP in Haryana. We examined the level of adoption
of technology modules under FFP interventions along with
the issues that control their adoption at the farmer’s level
over a period of time.
ICAR-National Academy of Agricultural Research Management, Hyderabad, Telangana 500 030, India 770[Indian Journal of Agricultural Sciences 92 (6)
98
VENKATESAN ET AL.
resulting in increased milk production (Gupta et al. 2017)
was included.
ADOPT Model: The model was used to estimate the
extent and time taken to attain peak adoption level helps
in evaluating and predicting the likely level of adoption
and diffusion of specific agricultural technologies, with a
particular target population. The framework and model is
based upon the work of Kuehne et al. (2017). The ADOPT
framework is built on two key factors influencing the
adoption process i.e. the relative advantage of the technology
(Rogers 2003) and effectiveness of the process through
which the farmers learn about the technology (Ghadim and
Pannell 1999). The variables related to relative advantage
and learning process are plotted across innovation and
population dimensions to generate an adoption matrix
(Fig 1).
The adoption-matrix (Fig 1) indicates how various
population and innovation factors interact with relative
advantage and learning process elements in four dimensions
– (i) population-specific influences that determine the ability
to understand the technology; (ii) learnability characteristics
of the technology; (iii) relative advantage derived from
application of technology for a specific population and (iv)
relative advantage of the technology per se. Past studies
on technology adoption indicated that first two learning
dimensions of the matrix had positive influence on the time
taken to reach the peak of adoption (Marsh et al. 2000, Leung
et al. 2009, Straub 2009, Munguia and Llewellyn 2020).
A research conducted in a high risk business-to-business
environment, the trialability of the technology was found to
MATERIALS AND METHODS
Locale of study and technology modules: Present
study was carried out in the three Farmer FIRST villages -
Amarpur, Dadhota and Katesra of Palwal district in Haryana
state, to assess the scalability of six proven technology
module interventions involving vegetables and legumes.
The first technology module was introduction of high
yielding variety of Bottle Gourd, Pusa Santushti (Behera
et al. 2015), which fetched an average yield of 171.50 q/ha
with a net returns of `1.00 lakh/ha. The second technology
intervention module was high yielding variety of Carrot Pusa
Rudhira, released by IARI, New Delhi, which outperformed
existing varieties in terms of about 8 q/ha higher yield
and 11% more net return (Singh et al. 2018). The third
intervention taken into account was the introduction of
high yielding and black rot disease resistant vegetable leafy
Mustard variety (Pusa Sag-1), which yielded significantly
higher than popular varieties (Rathaur et al. 2016). Another
intervention was the selection of high yielding paddy variety,
PB-1637, which recorded the yield of 40.7 q/ha, with a net
returns 0.87 lakh `/ha (Sharma et al. 2020). Likewise, the
interventions on wheat with the introduction of varieties
namely HD-3086, which recorded yield of 52.65N q/ha
(Kirandeep et al. 2020) was also considered for prediction.
In the animal husbandry component one of the promising
technological interventions namely, supplementation of
mineral mixture in cows and buffaloes, which is proven
to be an immunity booster, improving cellular and other
productive and reproductive functions in animal system,
Fig 1 Adoption Matrix. 771June 2022]
99
ADOPTION OF AGRICULTURAL TECHNOLOGIES
to adoption of specific technology module implemented
under FFP in Haryana. The collected data were analysed
using online version of ADOPT tool inputting the modal
values of the responses.
RESULTS AND DISCUSSION
The respondents rating of six technology modules across
22 ADOPT variable, which were found to perform well at
various centres of FFP, given in Table 1. While the top five
technologies were pertaining to crop based system, the last
intervention is pertaining to livestock. Higher weightage was
assigned for variables like local village/community costs
and benefits; income/productivity benefit in years that it is
used and future income/productivity benefit.
The table also reveals low level of variations in the
responses which indicates that all these technologies were
preferred by the farmers. The average respondent scores
exceeded over 60% for 18 aspects of ADOPT model,
be a necessary condition for translation of adaptor’s intent
to actual adoption and a strong determinant of time taken
for adoption (Banerjee et al. 2012).
The third and fourth “relative advantage” dimensions-
relative advantage for the population as well as the relative
advantage of the practice, directly influences the peak
level of adoption through other factors (Marsh et al. 2000,
Baumgart-Getz et al. 2012, Munguia and Llewellyn,
2020). While the “relative advantage to the practice” is
determined by four factors, i.e. profit advantage (Griliches
1957), environmental advantage (Munguia and Llewellyn
2020), ease of convenience (Piggott and Marra 2008),
risks involved and all of them together influence relative
advantage of the technology.
The data were collected through a structured survey
schedule from 30 farmer-partners of FFP selected randomly.
These respondents were provided with twenty-two questions
measuring the variables under ADOPT dimension pertaining
Table 1 Responses in numeric value on the six practices
ADOPT variableRange
of scale
Responses (ADOPT model inputs)*
P1 P2 P3 P4 P5 P6MeanCV%
Relative advantage for the population
Income/ productivity orientation 1-5 4 4 4 5 5 4 4.3312
Local community benefit orientation 1-5 3 2 3 4 4 5 3.5030
Risk orientation 1-5 3 3 5 4 4 4 3.8320
Enterprise scale 1-5 2 2 3 4 4 5 3.3336
Management horizon 1-5 4 4 3 4 4 3 3.6714
Short term constraints 1-5 3 3 3 4 4 3 3.3315
Learnability characteristics of the intervention
Trialable 1-5 4 4 4 5 5 4 4.3312
Innovation complexity 1-5 3 3 3 4 4 3 3.3315
Observability 1-5 2 3 2 4 4 2 2.8335
Learnability of the population
Advisory support 1-5 4 4 4 5 5 4 4.3312
Group involvement 1-5 3 3 3 4 4 3 3.3315
Relevant existing skills and knowledge 1-5 4 3 3 4 4 4 3.6714
Innovation awareness 1-5 2 2 4 3 3 5 3.1737
Relative advantage of the innovation
Relative upfront cost of innovation 1-5 3 4 4 4 3 4 3.6714
Reversibility of innovation 1-5 1 2 2 3 3 4 2.5042
Income/productivity benefit in years that it is used 1-8 7 7 6 8 7 7 7.009
Future income/ productivity benefit 1-8 6 7 7 7 6 6 6.508
Time until any future income/ productivity benefits are
likely to be realised
1-6 5 3 5 4 4 4 4.1718
Local village/ community costs and benefits 1-8 8 5 6 6 8 7 6.6718
Time to local village/community benefit 1-6 3 3 3 4 4 2 3.1724
Risk exposure 1-8 2 4 3 3 5 4 3.5030
Ease and convenience 1-8 3 7 4 6 6 7 5.5030
*P1, Bottle Gourd variety Pusa Santushti; P2, Carrot variety Pusa Rudhira; P3, Leafy Mustard variety Pusa Sag-1; P4, Paddy variety
PB-1637; P5, Wheat variety-HD-3086; P6, Supplementation of Mineral Mixture for Cow and Buffalo; CV, Coefficient of Variation. 772[Indian Journal of Agricultural Sciences 92 (6)
100
VENKATESAN ET AL.
carrot and leafy mustard. This may be due to low level of
knowledge and skills as indicated by poor scores of these
variables for non-cereal crops. Considering the dominance
of Rice-Wheat systems in the Indo-Gangetic plains and
low level of knowledge and innovation awareness for
commercial crops like vegetables, there is a concern which
calls for intensification of extension and advisory efforts
in this region.
Under relative advantage of innovation, respondent
weighed high on local village or community cost and
benefits followed by income/productivity benefit revealed
and expected in the future. The reversibility of innovation
was considered as the least preferred by the respondents
as the cost of adoption of technologies does not involve
any initial investment. The income/productivity benefit
was similar for all interventions. However, the respondents
rated that community benefits derived from these vegetable
crops were lower than the cereals.
Predicting adoption of technologies: Inputting the
information collected in ADOPT tool, it is predicted that
the time taken for all technology modules to reach peak
adoption level ranging from 8 to 13 years (Table 2). In
view of the shorter variety or technology lifecycle (5–6
years), the longer time to reach the peak adoption may lead
to replacement of those technologies with their improved
versions. Considering the huge investment made in
development and transfer of these technologies, it is essential
to accelerate extension and advisory efforts to promote these
and only four attributes - reversibility of innovation, time
to local village/community benefit, risk exposure and
ease and convenience scored poorly (<50% of maximum
score). Among the six practices implemented, the wheat
variety - HD-3086 (Mean=4.55), paddy variety PB-1637
(Mean=4.5) and supplementation of mineral mixture for
cow & buffalo (Mean=5.50) scored high. While the wheat
variety obtained highest scores in all the four dimensions,
the paddy variety received top scores in three dimensions
(except relative advantage of the innovation dimension).
The mineral nutrient supplementation had high scores
in three dimensions and rated poorly on the learnability
characteristics of the intervention. Precisely, the respondents
were sceptical about its observability. When compared to
cereal crops, non-cereals scored poorly in all dimensions
indicating high relative advantage for the cereal crops. The
Bottle Gourd variety Pusa Santushti and Carrot variety Pusa
Rudhira scored poorly in all dimensions and obtained low
scores (<50% of mean) in the local community benefit
orientation, enterprise scale, innovation awareness, time
until any future income/productivity benefits are likely to
be realised, risk exposure and ease and convenience to use
aspects. The results indicate that, though these varieties
performed well under the technology module intervention,
their scalability is relatively low.
Under learnability characteristics of the intervention
dimension, the farmers preferred technology related
to cereal crop varieties for their higher degree of
trialability, innovation complexity
and observability. The level of
observability is abysmally low for
technology related to livestock.
It indicates that the technology
related to crop system generate
convincing evidence of benefits
than that of livestock, i.e. adoption
of mineral mixture, which does
not show instant results. Among
all the factors under learnability,
the critical factor is innovation
awareness as it reflects high
variance. Innovation awareness was
high with technologies related to
livestock and low for technology
related to horticultural crops like
Table 2 Estimation of the predictions and actual adoption of the selected technologies
Code Practice/TechnologiesPeak adoption level (%)Time to peak adoption (yrs.)
Predicted Actual Predicted Actual
P1 Bottle Gourd Pusa Santushti72 88 13.0 13.3
P2 Carrot-Pusa Rudhira96 97 13.0 13.2
P3 Sarson Sag-Pusa Sag-170 86 13.1 11.8
P4 Paddy-PB-163798 98 7.7 6.5
P5 Wheat-HD-308698 98 7.6 6.3
P6 Supplementation of Mineral Mixture98 98 10.8 9.7
Fig 2 Yearly Adoption Level. 773June 2022]
101
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and Ouzman J. 2013. ADOPT: the Adoption and Diffusion
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directly affect utility: A derived demand approach. AgBioForum
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technologies. Further, the predicted peak adoption level for
all the technologies in the study area was estimated in the
range of 70–97 which is very high and desirable. A critical
analysis of peak adoption level and duration indicates that
cereal crop technologies reach the peak of 98% in shorter
period (6–7 years) compared to the vegetables. Though
supplemental mineral mixture tends to reach 98% adoption
level, it takes 9–10 years (Table 2, Fig 2).
The longer duration and lower proportion of peak
level of adoption of vegetables are explained by their lower
scores in the relative advantage and learnability dimensions.
With a potential to increase the household income at
lower cost and to offer community benefits, it is essential
to intensify extension efforts to promote vegetables. The
yearly adoption level chart shows the technologies which
were predicted to show rapid diffusion at the field level (Fig
2). Among the technologies, those related to cereal crops
attains peak adoption level in a shorter span of time while
that of horticultural crops takes about 13 years. Among the
horticultural crops, leafy mustard variety has potential to
reach closer to 100 percent indicating huge potential of this
technology. By promoting adoption of this crop through
effective advisory system and other promotional measures
like mini kit and demos, the time for attaining peak can be
considerably reduced.
For development of effective extension strategies in
dissemination of various technologies, it is important to
understand the effectiveness of the technology along with
how long it will take for the technology to diffuse in a
community and attain the peak level given the factors
intrinsic to the technology and community. Six technologies
including three horticulture crops, two field crops and one
livestock based, identified to have greater benefits, were
taken up for the study. The result shows that all these
technologies were preferred by the farmers in the study
area. Among the technologies, the technologies related to
field crops (paddy and wheat) have peaked earlier while the
extent of peak adoption level was higher for field crops and
livestock interventions. The time to peak adoption was higher
to the extent of 13 years for horticulture crops indicating
that proper advisory services to speed up the process will
result in higher benefits to the stakeholders.
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Vegetable varieties with multiple attributes spread at faster
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VENKATESAN ET AL.
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Predicting adoption of agricultural technologies in Indo-Gangetic Region
Article in Indian Journal of Agricultural Sciences · June 2022
CITATIONS
0
READS
108
19 authors, including:
Some of the authors of this publication are also working on these related projects:
MSc Project: An Analytical Study on Changing Livelihood Systems in Coral Islands of Lakshadweep View project
Inspire, Innovate and Change: Field Innovations of Farmer FIRST Project View project
Purushothaman Venkatesan
National Academy of Agricultural Research Management
46 PUBLICATIONS 43 CITATIONS
SEE PROFILE
N. Sivaramane
National Academy of Agricultural Research Management
66 PUBLICATIONS 248 CITATIONS
SEE PROFILE
Bharat Sontakki
58 PUBLICATIONS 138 CITATIONS
SEE PROFILE
Rajarshi Roy Burman
Indian Agricultural Research Institute
159 PUBLICATIONS 660 CITATIONS
SEE PROFILE
All content following this page was uploaded by Purushothaman Venkatesan on 06 July 2022.
The user has requested enhancement of the downloaded file. Indian Journal of Agricultural Sciences 92 (6): 769–74, June 2022/Article
Predicting adoption of agricultural technologies in Indo-Gangetic Region
P VENKATESAN
1
, N SIVARAMANE
1
, B S SONTAKKI
1
, R ROY BURMAN
2
*, C H SRINIVASA RAO
1
,
V P CHAHAL
3
, A K SINGH
3
, P SETHURAMAN
4
, J P SHARMA
5
, R N PADARIA
2
, S CHAKRAVORTY
2
,
NISHI SHARMA
2
, NEELAM PATEL
2
, HARSHWARDHAN CHOUDHARY
2
, GAUTAM MONDAL
6
,
RAHUL SINGH
2
, B KALYANI
1
, SHAILENDRA SHARMA
2
and RAJESH KUMAR
2
Received: 17 December 2021; Accepted: 15 March 2022
ABSTRACT
Present study aims to find out how the technological interventions performed under the Farmer FIRST programme
by assessing the peak adoption level and time taken to attain it. ADOPT tool was used to assess the impact of the
technological interventions. Thirty farmers who have participated in the programme implemented at Haryana, India,
were interviewed during 2021 to elicit data pertaining to the year 2016–21 and the modal value of their responses
were used as input in the ADOPT model to estimate the parameters of interest. The results showed that the extent
of peak adoption level is high for interventions related to cereal crops and animal components while the time taken
to reach peak adoption level is also low indicating that the advisory system for these commodities have borne good
results and this calls for streamlining the advisory system for horticultural crops to achieve the desired output from
these enterprises as well.
Keywords: Adoption, ADOPT, Learnability, Scalability, Technologies, Trialability
1
ICAR-National Academy of Agricultural Research
Management (NAARM), Rajendranagar, Hyderabad, Telangana;
2
ICAR-Indian Agricultural Research Institute (IARI), Pusa, New
Delhi;
3
Indian Council of Agricultural Research (ICAR), Krishi
Anusandhan Bhawan-1, Pusa, New Delhi; ICAR- Central Tuber
Crops Research Institute, Thiruvananthapuram, Kerala;
4
Sher-
e-Kashmir University of Agricultural Sciences and Technology,
Jammu;
5
ICAR-National Dairy Research Institute (NDRI), Karnal.
*Corresponding author email: burman_extn@hotmail.com
97
Application of improved technologies in agriculture
played significant role in eradicating poverty, reducing
production costs and hunger, and enhancing rural household
income (Kassie et al. 2011). Farmer FIRST (Farm Innovation
Resources Science and Technology) (FFP) is an ICAR
frontline extension programme, being implemented in many
states of India. Present study was carried out at Haryana
which is one of the FFP centres representing Indo Gangetic
plains (IGP) where rice-wheat cropping system dominates.
There are serious problems in agriculture (IGP) like lack of
farm diversification, food insecurity, declining soil fertility,
development hard soil pan, adverse soil structure problems
and low crop productivity, monocropping, and crop yield
instability (Sekar and Pal 2012, Chandra et al. 2020). FFP
aims towards creating and sustaining a dynamic farmers-
scientists interface for developing system-specific livelihood
interventions through technology assemblage, application
and feedback, which is achieved through partnership and
institution building along with content mobilization (Kokate
and Singh 2013) to alleviate the problems.
Scaling up is a proven approach for distribution of
benefits of agricultural technology over a wider geographic
area more quickly, more equitably and more lastingly
(Menter et al. 2004, Hartmann and Linn 2008). A number
of frameworks and approaches has been developed for
scaling up of agricultural technologies, focusing on the
issues in the adoption of technologies, viz. conventional
top-down technology-dissemination approach (Biggs 1990),
innovation platforms (Posthumus and Wongtschowski
2014), planned comparisons (Coe et al. 2017), contextually-
appropriate interventions (Sola et al. 2017) and insurance
approach (Sulaiman et al. 2018), and still there is a need
for simpler approaches.
Adoption and Diffusion Outcome Prediction Tool
(ADOPT) model developed by Commonwealth Scientific
and Industrial Research Organization (CSIRO) enables the
researchers to predict the peak adoption level and time taken
to attain peak adoption level (Kuehne et al. 2017). This
research paper highlights on the use of ADOPT to assess the
impact of the “technology assemblage” interventions made
under FFP in Haryana. We examined the level of adoption
of technology modules under FFP interventions along with
the issues that control their adoption at the farmer’s level
over a period of time.
ICAR-National Academy of Agricultural Research Management, Hyderabad, Telangana 500 030, India 770[Indian Journal of Agricultural Sciences 92 (6)
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VENKATESAN ET AL.
resulting in increased milk production (Gupta et al. 2017)
was included.
ADOPT Model: The model was used to estimate the
extent and time taken to attain peak adoption level helps
in evaluating and predicting the likely level of adoption
and diffusion of specific agricultural technologies, with a
particular target population. The framework and model is
based upon the work of Kuehne et al. (2017). The ADOPT
framework is built on two key factors influencing the
adoption process i.e. the relative advantage of the technology
(Rogers 2003) and effectiveness of the process through
which the farmers learn about the technology (Ghadim and
Pannell 1999). The variables related to relative advantage
and learning process are plotted across innovation and
population dimensions to generate an adoption matrix
(Fig 1).
The adoption-matrix (Fig 1) indicates how various
population and innovation factors interact with relative
advantage and learning process elements in four dimensions
– (i) population-specific influences that determine the ability
to understand the technology; (ii) learnability characteristics
of the technology; (iii) relative advantage derived from
application of technology for a specific population and (iv)
relative advantage of the technology per se. Past studies
on technology adoption indicated that first two learning
dimensions of the matrix had positive influence on the time
taken to reach the peak of adoption (Marsh et al. 2000, Leung
et al. 2009, Straub 2009, Munguia and Llewellyn 2020).
A research conducted in a high risk business-to-business
environment, the trialability of the technology was found to
MATERIALS AND METHODS
Locale of study and technology modules: Present
study was carried out in the three Farmer FIRST villages -
Amarpur, Dadhota and Katesra of Palwal district in Haryana
state, to assess the scalability of six proven technology
module interventions involving vegetables and legumes.
The first technology module was introduction of high
yielding variety of Bottle Gourd, Pusa Santushti (Behera
et al. 2015), which fetched an average yield of 171.50 q/ha
with a net returns of `1.00 lakh/ha. The second technology
intervention module was high yielding variety of Carrot Pusa
Rudhira, released by IARI, New Delhi, which outperformed
existing varieties in terms of about 8 q/ha higher yield
and 11% more net return (Singh et al. 2018). The third
intervention taken into account was the introduction of
high yielding and black rot disease resistant vegetable leafy
Mustard variety (Pusa Sag-1), which yielded significantly
higher than popular varieties (Rathaur et al. 2016). Another
intervention was the selection of high yielding paddy variety,
PB-1637, which recorded the yield of 40.7 q/ha, with a net
returns 0.87 lakh `/ha (Sharma et al. 2020). Likewise, the
interventions on wheat with the introduction of varieties
namely HD-3086, which recorded yield of 52.65N q/ha
(Kirandeep et al. 2020) was also considered for prediction.
In the animal husbandry component one of the promising
technological interventions namely, supplementation of
mineral mixture in cows and buffaloes, which is proven
to be an immunity booster, improving cellular and other
productive and reproductive functions in animal system,
Fig 1 Adoption Matrix. 771June 2022]
99
ADOPTION OF AGRICULTURAL TECHNOLOGIES
to adoption of specific technology module implemented
under FFP in Haryana. The collected data were analysed
using online version of ADOPT tool inputting the modal
values of the responses.
RESULTS AND DISCUSSION
The respondents rating of six technology modules across
22 ADOPT variable, which were found to perform well at
various centres of FFP, given in Table 1. While the top five
technologies were pertaining to crop based system, the last
intervention is pertaining to livestock. Higher weightage was
assigned for variables like local village/community costs
and benefits; income/productivity benefit in years that it is
used and future income/productivity benefit.
The table also reveals low level of variations in the
responses which indicates that all these technologies were
preferred by the farmers. The average respondent scores
exceeded over 60% for 18 aspects of ADOPT model,
be a necessary condition for translation of adaptor’s intent
to actual adoption and a strong determinant of time taken
for adoption (Banerjee et al. 2012).
The third and fourth “relative advantage” dimensions-
relative advantage for the population as well as the relative
advantage of the practice, directly influences the peak
level of adoption through other factors (Marsh et al. 2000,
Baumgart-Getz et al. 2012, Munguia and Llewellyn,
2020). While the “relative advantage to the practice” is
determined by four factors, i.e. profit advantage (Griliches
1957), environmental advantage (Munguia and Llewellyn
2020), ease of convenience (Piggott and Marra 2008),
risks involved and all of them together influence relative
advantage of the technology.
The data were collected through a structured survey
schedule from 30 farmer-partners of FFP selected randomly.
These respondents were provided with twenty-two questions
measuring the variables under ADOPT dimension pertaining
Table 1 Responses in numeric value on the six practices
ADOPT variableRange
of scale
Responses (ADOPT model inputs)*
P1 P2 P3 P4 P5 P6MeanCV%
Relative advantage for the population
Income/ productivity orientation 1-5 4 4 4 5 5 4 4.3312
Local community benefit orientation 1-5 3 2 3 4 4 5 3.5030
Risk orientation 1-5 3 3 5 4 4 4 3.8320
Enterprise scale 1-5 2 2 3 4 4 5 3.3336
Management horizon 1-5 4 4 3 4 4 3 3.6714
Short term constraints 1-5 3 3 3 4 4 3 3.3315
Learnability characteristics of the intervention
Trialable 1-5 4 4 4 5 5 4 4.3312
Innovation complexity 1-5 3 3 3 4 4 3 3.3315
Observability 1-5 2 3 2 4 4 2 2.8335
Learnability of the population
Advisory support 1-5 4 4 4 5 5 4 4.3312
Group involvement 1-5 3 3 3 4 4 3 3.3315
Relevant existing skills and knowledge 1-5 4 3 3 4 4 4 3.6714
Innovation awareness 1-5 2 2 4 3 3 5 3.1737
Relative advantage of the innovation
Relative upfront cost of innovation 1-5 3 4 4 4 3 4 3.6714
Reversibility of innovation 1-5 1 2 2 3 3 4 2.5042
Income/productivity benefit in years that it is used 1-8 7 7 6 8 7 7 7.009
Future income/ productivity benefit 1-8 6 7 7 7 6 6 6.508
Time until any future income/ productivity benefits are
likely to be realised
1-6 5 3 5 4 4 4 4.1718
Local village/ community costs and benefits 1-8 8 5 6 6 8 7 6.6718
Time to local village/community benefit 1-6 3 3 3 4 4 2 3.1724
Risk exposure 1-8 2 4 3 3 5 4 3.5030
Ease and convenience 1-8 3 7 4 6 6 7 5.5030
*P1, Bottle Gourd variety Pusa Santushti; P2, Carrot variety Pusa Rudhira; P3, Leafy Mustard variety Pusa Sag-1; P4, Paddy variety
PB-1637; P5, Wheat variety-HD-3086; P6, Supplementation of Mineral Mixture for Cow and Buffalo; CV, Coefficient of Variation. 772[Indian Journal of Agricultural Sciences 92 (6)
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VENKATESAN ET AL.
carrot and leafy mustard. This may be due to low level of
knowledge and skills as indicated by poor scores of these
variables for non-cereal crops. Considering the dominance
of Rice-Wheat systems in the Indo-Gangetic plains and
low level of knowledge and innovation awareness for
commercial crops like vegetables, there is a concern which
calls for intensification of extension and advisory efforts
in this region.
Under relative advantage of innovation, respondent
weighed high on local village or community cost and
benefits followed by income/productivity benefit revealed
and expected in the future. The reversibility of innovation
was considered as the least preferred by the respondents
as the cost of adoption of technologies does not involve
any initial investment. The income/productivity benefit
was similar for all interventions. However, the respondents
rated that community benefits derived from these vegetable
crops were lower than the cereals.
Predicting adoption of technologies: Inputting the
information collected in ADOPT tool, it is predicted that
the time taken for all technology modules to reach peak
adoption level ranging from 8 to 13 years (Table 2). In
view of the shorter variety or technology lifecycle (5–6
years), the longer time to reach the peak adoption may lead
to replacement of those technologies with their improved
versions. Considering the huge investment made in
development and transfer of these technologies, it is essential
to accelerate extension and advisory efforts to promote these
and only four attributes - reversibility of innovation, time
to local village/community benefit, risk exposure and
ease and convenience scored poorly (<50% of maximum
score). Among the six practices implemented, the wheat
variety - HD-3086 (Mean=4.55), paddy variety PB-1637
(Mean=4.5) and supplementation of mineral mixture for
cow & buffalo (Mean=5.50) scored high. While the wheat
variety obtained highest scores in all the four dimensions,
the paddy variety received top scores in three dimensions
(except relative advantage of the innovation dimension).
The mineral nutrient supplementation had high scores
in three dimensions and rated poorly on the learnability
characteristics of the intervention. Precisely, the respondents
were sceptical about its observability. When compared to
cereal crops, non-cereals scored poorly in all dimensions
indicating high relative advantage for the cereal crops. The
Bottle Gourd variety Pusa Santushti and Carrot variety Pusa
Rudhira scored poorly in all dimensions and obtained low
scores (<50% of mean) in the local community benefit
orientation, enterprise scale, innovation awareness, time
until any future income/productivity benefits are likely to
be realised, risk exposure and ease and convenience to use
aspects. The results indicate that, though these varieties
performed well under the technology module intervention,
their scalability is relatively low.
Under learnability characteristics of the intervention
dimension, the farmers preferred technology related
to cereal crop varieties for their higher degree of
trialability, innovation complexity
and observability. The level of
observability is abysmally low for
technology related to livestock.
It indicates that the technology
related to crop system generate
convincing evidence of benefits
than that of livestock, i.e. adoption
of mineral mixture, which does
not show instant results. Among
all the factors under learnability,
the critical factor is innovation
awareness as it reflects high
variance. Innovation awareness was
high with technologies related to
livestock and low for technology
related to horticultural crops like
Table 2 Estimation of the predictions and actual adoption of the selected technologies
Code Practice/TechnologiesPeak adoption level (%)Time to peak adoption (yrs.)
Predicted Actual Predicted Actual
P1 Bottle Gourd Pusa Santushti72 88 13.0 13.3
P2 Carrot-Pusa Rudhira96 97 13.0 13.2
P3 Sarson Sag-Pusa Sag-170 86 13.1 11.8
P4 Paddy-PB-163798 98 7.7 6.5
P5 Wheat-HD-308698 98 7.6 6.3
P6 Supplementation of Mineral Mixture98 98 10.8 9.7
Fig 2 Yearly Adoption Level. 773June 2022]
101
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technologies. Further, the predicted peak adoption level for
all the technologies in the study area was estimated in the
range of 70–97 which is very high and desirable. A critical
analysis of peak adoption level and duration indicates that
cereal crop technologies reach the peak of 98% in shorter
period (6–7 years) compared to the vegetables. Though
supplemental mineral mixture tends to reach 98% adoption
level, it takes 9–10 years (Table 2, Fig 2).
The longer duration and lower proportion of peak
level of adoption of vegetables are explained by their lower
scores in the relative advantage and learnability dimensions.
With a potential to increase the household income at
lower cost and to offer community benefits, it is essential
to intensify extension efforts to promote vegetables. The
yearly adoption level chart shows the technologies which
were predicted to show rapid diffusion at the field level (Fig
2). Among the technologies, those related to cereal crops
attains peak adoption level in a shorter span of time while
that of horticultural crops takes about 13 years. Among the
horticultural crops, leafy mustard variety has potential to
reach closer to 100 percent indicating huge potential of this
technology. By promoting adoption of this crop through
effective advisory system and other promotional measures
like mini kit and demos, the time for attaining peak can be
considerably reduced.
For development of effective extension strategies in
dissemination of various technologies, it is important to
understand the effectiveness of the technology along with
how long it will take for the technology to diffuse in a
community and attain the peak level given the factors
intrinsic to the technology and community. Six technologies
including three horticulture crops, two field crops and one
livestock based, identified to have greater benefits, were
taken up for the study. The result shows that all these
technologies were preferred by the farmers in the study
area. Among the technologies, the technologies related to
field crops (paddy and wheat) have peaked earlier while the
extent of peak adoption level was higher for field crops and
livestock interventions. The time to peak adoption was higher
to the extent of 13 years for horticulture crops indicating
that proper advisory services to speed up the process will
result in higher benefits to the stakeholders.
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