Factors influencing the adoption of organic farming: a case of Middle Ganga River basin, India

11 Pages·2022·0.555 MB·English

by  S. P. Singh, Priya, Komal Sajwan

Org. Agr.
https://doi.org/10.1007/s13165-022-00421-2
RESEARCH
Factors influencing the adoption of organic farming: a case
of Middle Ganga River basin, India
S. P. Singh · Priya · Komal Sajwan
Received: 11 September 2022 / Accepted: 28 December 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract The sustainability of the agricultural adoption. Moreover, lack of financial support, lower
system has become a global concern. Although the yield levels, unavailability of markets, and expected
growth driven by green revolution technology has low profits in organic farming are significant reasons
significantly contributed to making India self-suf- that discourage farmers from adopting it. Therefore,
ficient in food production, the sustainability of the by identifying significant variables associated with
agricultural system has become debatable due to its its adoption, the current study’s findings can provide
adverse impact on the environment. Organic farming better information for policymakers, which may help
has become an alternative farming system to improve them make policies related to increasing the adoption
agricultural sustainability, yet farmers hesitate to rate among farmers.
adopt it. Therefore, this study aims to (i) identify the
factors that affect the adoption of organic farming and Keywords Organic farming · Conventional
(ii) investigate farmers’ perceptions towards its adop- farming · Productivity · Determining factors ·
tion. A total of 600 farmers (i.e., 300 organic and Logistic regression
300 conventional farmers) were randomly selected
to conduct a field survey from two districts of the
Middle Ganga River basin, India. A binary logistic Introduction
regression was used to identify the factors that could
affect the adoption of organic farming in the region. Agriculture plays a significant role in the develop-
The results show that region, education, social cat- ment of an economy like India. It provides a liveli-
egory, training, farming experience, and monthly hood for more than one-third of the population and
household income significantly affect organic farming contributes about 20% to the national GDP (GoI
2022). Further, with the continuously growing popu-
lation in India, the demand for food increased, which
S. P. Singh · Priya (*) · K. Sajwan  put pressure on agricultural land (Chatterjee 2011).
Department of Humanities and Social Sciences, The technical change promoted through the green
Indian Institute of Technology (IIT) Roorkee,
revolution in the 1960s played a significant role in
Uttarakhand 247667, India
enhancing agricultural productivity and helped India
e-mail: [email protected]
become self-sufficient in food grains. But its impact
S. P. Singh 
did not last for a longer period, and the sustainability
e-mail: [email protected]
of the Indian agricultural system has become ques-
K. Sajwan 
tionable. The intensive use of agrochemicals such as
e-mail: [email protected]
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fertilizers and pesticides/insecticides has decreased corridors along the Ganga River in five states through
soil fertility, resulting in stagnation of agricultural which it passes (starting from Gangotri in Uttarakhand
productivity in recent years and environmental deg- to Ganga Sagar in West Bengal). The main aim of the
radation (Narayanan 2005; Priya and Singh 2022). campaign is to promote organic farming clusters in a
Hence, the adoption of organic farming is consid- 5 km stretch on both sides of the Ganga River. Moreo-
ered one of the strategies for sustainable agricultural ver, the regional governments of Uttarakhand and Uttar
growth. It has been proven to be one of the sustaina- Pradesh are also encouraging the practice of organic
ble methods to address environmental challenges and farming in the Ganga River basin with a scheme called
enhance productivity (Aryal et al. 2018; Rigby and the Organic Agriculture Development Scheme or Jaivik
Cáceres 2001; Tey et al. 2014). Krishi Vikas Yojan (2020). However, the rate of adop-
“Organic farming is a holistic production man- tion is slow in the area. There are various factors studied
agement system which promotes and enhances agro- in the literature that affect the adoption of organic farm-
ecosystem health, including biodiversity, biological ing; however, it differs in the geographic and biophysi-
cycles, and soil biological activity” (FAO 2015). It cal characteristics of the area. Hence, there is a need to
is considered an alternative to chemical farming that conduct study for a better understanding of the adoption
largely avoids synthetic chemicals and depends on rate among farmers in the Indo-Gangetic Plain.
crop rotation, crop residues, green manure, bio-fer- In the existing literature, the significant factors that
tilizers, and bio-pesticides (Azam & Shaheen 2019; affect the adoption of sustainable agricultural prac-
Narayanan 2005; Ramesh et al. 2005). National and tices, including organic farming, have been divided
international research institutions have taken several mainly into six categories, viz. socio-economic, agro-
initiatives to promote organic farming. Similarly, ecological, institutional, technological, financial, and
in India, central and state governments and various psychological (Ashari et al. 2017; Knowler & Brad-
organizations and NGOs (e.g., APEDA, ICAR) are shaw 2007; Lesch & Wachenheim 2014; Melisse 2018;
promoting organic farming. Moreover, Pramparagat Mozzato et al. 2018; Priya & Singh 2022). The com-
Krishi Vikas Yojana (PMKVY), Mission Organic mon factors in the socio-economic dimension are gen-
Value Change Development for North Eastern Region der, age, education, ethnicity, experience, household
(MOVCDNER), National Mission on Oilseeds size, and household income. It has been observed in
and Oil Palms (NMOOP), National Food Security the studies done by Kassie et al. (2020) and Okon &
Mission (NFSM), Rashtriya Krishi Vikas Yojana Idiong (2016) that aged farmers are less likely to adopt
(RKVY), and National Project on Organic Farm- new technologies due to their risk aversion behavior.
ing (NPOF) are some of the initiatives taken by the Further, education plays an essential role in adoption;
Indian government for its promotion. higher education often introduces farmers to new ideas
Because of several policy initiatives, the cultivated and enhances their environmental concerns (Digal &
area under organic farming in India increased from Placencia 2019; Joshi et al. 2019; Xie et al. 2015).
0.78 million ha in 2010–2011 to 2.66 million ha in Since organic farming is labor-intensive, farm-
2021 (Manik & Tanwar, 2021). As per the FIBL & ers with larger household sizes are expected to adopt
IFOAM Year Book 2022, India ranked 4th in terms of organic farming earlier than those with smaller house-
area under organic cultivation. Although India’s share hold sizes (Tey et al. 2014). Further, more investment
of organic agricultural land has increased from 0.1% in improved technologies is significantly affected by
in 2005 to 1.08% in 2018, the growth rate is still low, higher farming experience (Ganpat et al. 2014; Lemeil-
and farmers hesitate to convert conventional farming leur 2013). However, Kunzekweguta et al. (2017) and
to organic farming (Chadha & Srivastava 2020). Srisopaporn et al. (2015) find the farming experience
Additionally, the Indo-Gangetic Plain is agricultur- to be an insignificant variable in the adoption. Addi-
ally the most fertile region of the country (Aryal et al. tionally, farmers with larger farms are more willing to
2018). However, the toxic chemicals used in intensive invest in new technologies due to their greater capacity
cultivation in the Gangetic plains eventually end up for investment and risk undertaking (Rajendran et al.
in the water bodies and pollute the river system (Shah 2016; Tey et al. 2014). However, literature also shows
& Parveen 2021). Hence, to curb it, the Indian gov- farm size as an insignificant adoption variable (Chi-
ernment runs a campaign to develop organic farming chongue et al. 2020; Laosutsan et al. 2019).
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The extension services by various agencies such as Further, in the “Discussion” section, the discussion of
the government, NGOs and farmers provide training empirical results has been done. Finally, concluding
to the farmers and motivate them to adopt sustain- remarks are provided in the “Conclusion” section.
able agricultural practices (Chichongue et al. 2020;
Eliyas & Sumathi 2019; Okon & Idiong 2016). Fur-
Materials and methods
ther, farmers generally avoid taking risks by shifting
from one farming system to another. Hence, financial
A cross-sectional study was conducted in the Middle
factors like crop insurance and access to credit play
Ganga River basin India. The Middle Ganga River basin
a significant role in the adoption (Laxmi & Mishra
lies from Haridwar in Uttarakhand to Varanasi in Uttar
2007; Tey et al. 2014). Moreover, the literature shows
Pradesh. Two districts, Haridwar and Bulandshahr, have
that farmers’ perceptions and attitudes toward sustain-
been chosen for the primary data collection from the
able farming influence their adoption. They are more
Middle Ganga River basin. Out of these two districts, 20
likely to adopt sustainable farming if they perceive
villages were selected for the final data collection of 600
that adoption would reduce the input cost and benefit
farmers (organic = 300, conventional = 300). The data
human health and the environment (Joshi et al. 2019;
were collected through a semi-structured questionnaire.
Sarker et al. 2005; Sriwichailamphan et al. 2008).
The questionnaire was divided into two sections: the first
Although several studies examine the factors
section includes the socio-economic, bio-physical and
affecting the adoption of organic farming globally,
demographic characteristics of the farmers, whereas, in
we hardly find any study on factors in the Middle
the second section, qualitative data regarding their per-
Ganga River basin in India. Further, farmers’ opin-
ception towards organic farming were asked.
ion towards organic farming also plays an essen-
To study the factors affecting the adoption of organic
tial role in the adoption. Therefore, it is important
farming, a binary logistic regression model has been
to understand the adoption behavior of the farmers.
applied for data analysis, which helps to predict the
Thus, this study aims to (i) identify the factors that
probability of occurrence of the events with specific
affect the adoption of organic farming in the Middle
sets of independent variables. This model is commonly
Ganga River basin, India, and (ii) investigate farm-
used in the literature to assess the relationship between
ers’ perceptions towards adopting organic farming in
adoption and the associated factors (Digal & Placencia
the study area. Therefore, by identifying significant
2019; Mlenga 2015; Okon & Idiong 2016; Xie et al.
factors associated with the adoption of organic farm-
2015). The estimated model’s results will help to iden-
ing, the findings of the current study can provide bet-
tify the factors that show a statistically significant rela-
ter information to the government and policymakers
tionship with the dependent variable, i.e., adoption. The
for designing effective plans for promoting organic
standard functional form of a logit model is given by:
farming in India. This study was motivated to under-
stand the reasons for the non-adoption and adoption Logit=𝛽0+𝛽1X1+𝛽2X2+𝛽3X3+𝛽4X5+……⋯+𝛽nXn+𝜀
of organic farming among farmers in India, irrespec-
tive of significant efforts taken by the central and where β0 is a constant, β’s are the parameters, X’s are
the independent variables, logit is the log of the odds
state governments. Such an understanding will help
ratio and can be shown as:
to show the different prospects available to enhance
the adoption of organic farming for sustainable agri- P
Logit=Log i
cultural development in the study area. Further, this 1−P
i
study is also important in accumulating the efforts to
encourage sustainable agricultural practices that are where Pi is the probability of the dependent variable
environmentally sound and economically viable. taking the value 1 and Pi/1-Pi is the odds ratio. The
The sections of the current paper are designed as higher the odds ratio, the higher the chances of the
follows: in the “Materials and methods” section, we dependent variable taking the value 1. In the model
describe the materials and methods used in the study. applied here, the dependent variable was the adoption
The “Results” section discusses the results regarding of organic farming; adopter is represented by 1 and
the descriptive statistics of variables used in the model non-adopter by 0. The functional form of the logistic
and the estimation of factors affecting the adoption. regression model is given below:
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Logit(adopter)=𝛽 +𝛽 region+𝛽 gender+𝛽 maritalstatus+𝛽 socialcategory+𝛽
0 1 2 3 4 5
education+𝛽 HHsize+𝛽 farmsize+𝛽 livestock+𝛽 training+𝛽 logexp+𝛽
6 7 8 9 10 11
loginc+𝛽 logdismandi+𝜀
12
Additionally, to summarize the impact of selected primary and secondary level of education, approxi-
independent variables on the adoption of organic mately 62% and 58%, respectively. The mean edu-
farming, marginal effects were also calculated. Mar- cation level of the total sampled farmers was middle
ginal effects in logistic regression measure the prob- school, followed by secondary and senior secondary.
ability of a change in the dependent variable due to In contrast, it is secondary level among adopters and
a change in an independent variable. In contrast, senior secondary level among non-adopters. But the
regression coefficients show only directional change adoption rate among farmers with primary education
(Serebrennikov et al. 2020). Further, post-estimation is the highest (61.70%). Among all, 67.17%of farmers
tests were also used to validate the model. A vari- have taken training, of which 63.77 were adopters of
ance inflation factor (VIF) was estimated to check the organic farming.
multicollinearity among the independent variables. Moreover, the adopter farmers have a high average
Further, Hosmer–Lemeshow test was also used to test farming experience compared to non-adopter farmers,
the model’s goodness of fit. Previous literature about i.e., 35 and 23 years, respectively. Similarly, adop-
the adoption of organic farming includes various ter farmers’ average monthly household income was
variables, viz. age, education, gender, extension ser- more than two times higher than non-adopter farm-
vices, training, farm size, perception towards organic ers. Further, there is not much difference between the
farming, etc. (Knowler & Bradshaw 2007; Lesch & average household size among both categories; it is 5
Wachenheim 2014; Mutyasira et al. 2018; Priya & for adopters and 4 for non-adopters.
Singh 2022). Therefore, initially, we included mul-
tiple variables for data analysis, but due to the high Logistic regression
correlation among the variables, we narrowed down
only those variables which were not significantly cor- The results of the empirical model in Table 3 indi-
related. For example, age was highly correlated with cate that variables like region, social category,
years of farming experience; hence we deleted the training, log of experience, and log of income play
age variable from the analysis. Finally, this study uses a statistically significant role in adopting organic
the following variables shown in Table 1. farming in the study area. The positive signifi-
cance (at a 5% level of significance) results of the
region show that farmers belonging to the Harid-
Results war region are more likely to adopt organic farming
than Bulandshahr. Further, the social category also
Descriptive statistics plays a vital role in adoption. The negative value of
coefficients and marginal effects (dy/dx) indicate
Table 2 describes the overall descriptive statistics of that farmers belonging to OBC and SC categories
variables used in estimating the model. It shows that, have approximately 16 and 21% fewer chances of
out of the total sample of 600 farmers, 569 were male adopting organic farming than the general category.
and only 31 were female. Moreover, most of the farm- Interestingly, the higher likelihood of adoption is
ers belonged to OBC social category (66%), followed associated with those with primary and second-
by the general (28.33%) and SC (5.68%) categories. ary education levels; however, the adoption rate is
The adoption level among general social category 13 and 21%, respectively, in these two education
farmers was higher than other categories since 70% of categories.
total general category farmers are adopters. Table 2 On the other hand, the insignificant values of gen-
further demonstrates that compared to non-adopters, der, marital status, household size, farm size, live-
a more significant percentage of adopters have a stock, and distance from mandi (local market) show
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Table 1 Variables and description of variables used in logistic regression
Variables Source Description
Dependent variable
Adopter = 1 If the farmer is an adopter of organic farming; = 0 otherwise
Independent variables
Region (Aryal et al. 2018; Habanyati et al. 2020; Best 2009) = 1 If the farmer is an adopter of organic farming;
= 0 otherwise
Gender (Raghu et al. 2014; Joshi et al. 2019; Senanayake & = 1 Male
Rathnayaka 2015; Digal & Placencia 2019) = 0 Female
Marital status (Koesling et al. 2008; Laosutsan et al. 2019) = 1 married
= 0 Unmarried
Social category (Aryal et al. 2018; Singh & Sharma 2019; Kafle 2011) = 0 General
= 1 OBC
= 2 SC
Education (Xie et al. 2015; Laxmi & Mishra 2007; Best 2009) = 0 illiterate
= 1 Primary (1st to 5 th standard)
= 2 Middle (6th to 8 th)
= 3 Secondary (9th to 10th)
= 4 Senior Secondary ( 11th to 12th)
= 5 Higher (graduate or above)
Household size (Mutyasira et al. 2018; Xie et al. 2015; Aryal et al. = total no. of the members in the family
2018)
Farm size (Laxmi & Mishra 2007; Kafle 2011; Raghu et al. 2014) = Cultivated area (in hectares)
Livestock (Mlenga 2015; Kafle 2011) = No. of livestock
Training (Sriwichailamphan et al. 2008; Singh & Sharma 2019) = 1 Yes
= 0 otherwise
Logexp (Mutyasira et al. 2018; Joshi et al. 2019; Ganpat et al. = log of farming experience (in years)
2014)
loginc (Laosutsan et al. 2019; Chichongue et al. 2020; Pong- = log of monthly household income (in rupees)
vinyoo et al. 2014)
Logdismandi (Kunzekweguta et al. 2017; Lemeilleur 2013) = log of distance from farm to nearest mandi
(local market) (in km)
that adoption decision is not much affected by these nominal value of HL test shows no problem with the
variables in the study area. Further, farmers who have model or there is no risk in prediction. Further, the
received any training on organic farming have a 22% variables have no multicollinearity since all variables’
greater likelihood of adopting organic farming. Addi- variance inflation factor (VIF) values are less than 10.
tionally, farmers with more experience in farming
and a high monthly household income have higher Perceptions of the farmers regarding adoption and
chances of adoption since their coefficient values non-adoption
are positively significant at a 1% significance level.
The marginal effects show that farmers with higher Besides the above-discussed factors that affect the
income and experience have approximately 60 and adoption, the perception of the farmers regarding
35% higher likelihood of adoption. organic farming also plays a significant role in adop-
Moreover, the p-value of the model shows that tion. Table 4 explains the reasons for conventional
model is statistically significant, and 81.83% is cor- farmers’ non-adoption of organic farming. The pri-
rectly classified. The Hosmer–Lemeshow (HL) good- mary reason for the non-adoption of organic farm-
ness of fit test can be utilized to get an equivalent ing is a lack of financial support, followed by lower
summary of the test statistic for the sample authen- yields. Previous study done by Digal & Placencia
tication and risk prediction (Midi et al. 2010). The (2019) also proves low productivity as a primary
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Table 2 Descriptive
Variables Frequency Frequency Total frequency
statistics of variables
(adopter) (non-adopter)
used in logistic regression
(N = 600) Region
    Haridwar 150 (50) 150 (50) 300 (50)
   Bulandshahr 150 (50) 150 (50) 300 (50)
Gender
   Male 286 (50.26) 283 (49.74) 569 (94.83)
   Female 14 (45.17) 17 (54.83) 31 (5.17)
Social category
   General 119 (70) 51 (30) 170 (28.33)
   OBC 169 (42.68) 227 (57.32) 396 (66)
   SC 12 (35.29) 22 (64.71) 34 (5.67)
Marital status
   Married 292 (51.68) 273 (48.32) 565 (94.17)
   Unmarried 8 (22.86) 27 (77.14) 35 (5.83)
Education level
   Illiterate 28 (38.36) 45 (61.64) 73 (12.17)
   Primary 29 (61.70) 18 (38.29) 47 (7.83)
   Middle 75 (53.57) 65 (46.43) 140 (23.33)
   Secondary 79 (57.67) 58 (42.33) 137 (22.83)
   Senior Secondary 53 (39.85) 80 (60.15) 133 (22.67)
   Higher 36 (51.43) 34 (48.57) 70 (11.67)
Training
   Yes 257 (63.77) 146 (36.23) 403 (67.17)
   No 43 (21.83) 154 (78.17) 197 (32.83)
Average HH size (in numbers) 5 4 4.68
Average farm size (in hectares) 1.43 0.88 1.16
Average livestock (in numbers) 3 2 2.6
Average experience (in years) 35 23 29.54
Average monthly HH income (in Rupees) 31,062 12,994 22,028.17
Figures in parentheses are
The average distance from mandi (in km) 13 16 14.69
the percentage
reason for non-adoption. Difficulties in finding a However, 91% of organic farmers believed that
market for their organic produce further demotivates organic farming is not profitable, but they adopted it
farmers to shift from conventional to organic farming. only because of government incentives (90%). Nev-
Additionally, 85% of conventional farmers give ertheless, 87% of farmers also accepted that soil and
the reason for not adopting organic farming as low water conditions are deteriorating due to the over-
profit. However, only twelve percent of farmers use of agrochemicals.
perceived the organic input cost as higher than the
chemical inputs. Still, they are not adopting it due
to their perception of lower yield and unavailability Discussion
of markets.
On the other hand, the common reasons for the The current study determines factors affecting the
adoption of organic farming by adopters are shown adoption of organic farming in the Middle Ganga
in Table 5. Ninety-five percent of organic farm- Region in India. The estimated results of regression
ers agreed that they adopted organic farming due analysis show that region, social category, educa-
to its potential positive impact on human health. tion, training, experience, and household income are
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Table 3 Estimates of logistic regression for the adoption of organic farming
Variables Coefficients Std. error p-value dy/dx Significance
Region (base category = Bulandshahr) 0.721 0.318 0.024 0.099 **
Gender (base category = female) 0.066 0.578 0.908 0.009
Marital status (base category = unmarried) 0.127 0.549 0.816 0.017
Social category (base category = general)
OBC − 1.145 0.323 0.000 − 0.164 ***
SC − 1.480 0.549 0.014 − 0.213 **
Education (base category = illiterate)
Primary (up to 5th standard) 0.949 0.515 0.065 0.131 *
Middle (6th to 8 th) 0.258 0.406 0.525 0.035 **
Secondary (9th to 10th) 0.927 0.421 0.028 0.128
Senior Secondary ( 11th to 12th) 0.397 0.423 0.347 0.055
Higher (higher than 12th) 0.831 0.511 0.104 0.115
Household size − 0.018 0.070 0.794 − 0.002
Farm size 0.022 0.170 0.896 0.003
Livestock − 0.045 0.075 0.543 − 0.006
Training 1.668 0.259 0.000 0.228 ***
logexp 4.366 0.633 0.000 0.598 ***
loginc 2.589 0.488 0.000 0.354 ***
logdismandi − 0.107 0.486 0.825 − 0.014
Constant − 18.094 2.207 0.000 ***
Number of observations 600
LR chi2 (17) 319.81
Prob > chi2 0.000
Pseudo R2 0.384
Log-likelihood − 255.98
Correctly classified 81.83%
Hosmer–Lemeshow goodness of fit (p-value) 0.530
*, **, *** indicate significance at 10%, 5% and 1% levels respectively
Table 4 Reasons for non-
Statements Yes No
adoption of organic farming
by non-adopters (n = 300) Low profit in organic farming 255 (85%) 44 (15%)
High risk in organic farming 233 (77.67%) 67 (22.33%)
The complexity of organic production system 216 (72%) 84 (28%)
Difficulty in finding markets for organic products 286 (95.33%) 14 (4.67%)
Lack of extension services in OF system 285 (95%) 15 (5%)
A lower level of yields 286 (95.33%) 14 (4.67%)
Higher costs of inputs for OF 36 (12%) 264 (88%)
Lack of financial support 292 (97.33%) 8 (2.67%)
among the factors that significantly influenced farm- adoption. For example, in Sikkim, the state govern-
ers’ decision to convert to organic farming. The active ment opted Organic Mission plan in 2003 as an initial
involvement of the governments through various poli- step to becoming the first organic state by banning the
cies can significantly increase the chance of greater import of chemical inputs, which resulted in Sikkim
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Table 5 Reasons for
Statements Yes No
adoption of organic farming
by adopters (n = 300) Deterioration of soil and water conditions 261 (87%) 39 (13%)
More profitable as compared to CF 26 (8.67%) 274 (91.33%)
Government incentives/government support 270 (90%) 30 (10%)
OF Adopted by neighbor 146 (48.67%) 154 (51.33%)
Its potential impact on human health 287 (95.67%) 13 (4.33%)
becoming the first fully organic state in India (Govt. Moreover, farmers with longer years of experience
of Sikkim 2022). tend to be more receptive to adopting organic farm-
Similarly, the results of the study show that farmers ing (Digal & Placencia 2019; Giannakis 2014). Pos-
in the Haridwar region are more likely to adopt organic sibly, due to the high farming experience, farmers
farming; this might be because the region belongs to could observe the adverse effects of input-intensive
the state of Uttarakhand, where central and various state farming on high input costs and environmental deg-
agencies are focusing majorly on promoting organic radation. The findings of the current study also indi-
farming to make the state fully organic. For example, cate that farmers with more experience in farming are
the state has established a dedicated nodal agency, Utta- more likely to adopt organic farming. Similar results
rakhand Organic Commodity Board (UOCB) (2003), to have been found by Kumar et al. (2010) and Lemeil-
promote sustainable agricultural development through leur (2013) in their study done in India and Peru,
organic farming (Meena & Sharma 2015). The UOCB respectively.
aims to provide training and organize seminars/exhibi- Similarly, training in agriculture plays a positively
tions to promote organic products in the state. It also significant role in adopting organic farming. Because
provides marketing and certification for organic prod- the training provided by various governmental and
ucts, resulting in 12 villages in Uttarakhand being non-governmental agencies disseminates informa-
declared bio-villages (Meena & Sharma 2015). Hence, tion and enhances farmers’ technical skills, which
we can say that government plays a significant role in helps to increase the adoption rate (Joshi et al. 2019;
promoting and adopting organic farming with a strong Kafle 2011; Raghu et al. 2014). Further, the results
policy framework. of descriptive statistics also justify that 63.77% of
Further, our findings indicate that farmers belong- farmers have shifted farming from non-organic to
ing to SC and OBC social categories are less likely organic farming after training and 78.17% of farmers
to adopt organic farming since coefficient values are who have not received training are non-adopters (see
negatively significant. These results are similar to the Table 2).
studies conducted by Aryal et al. (2018) and Singh & Additionally, farmers’ decision to convert to
Sharma (2019) in the Indo-Gangetic plains of Bihar organic farming is an economic decision. Profitabil-
& Haryana and Rajasthan, respectively. Moreover, it ity is the most crucial factor for a farmer in making
has been observed in previous literature that educa- a farming decision (Koesling et al. 2008). The risk
tion plays a significant role in adoption as it enhances of having poor financial prospects demotivates farm-
farmers’ knowledge and influences them to adopt ers to adopt it due to initial yield loss in the transi-
(Aryal et al. 2018; Digal & Placencia 2019; Singh & tion period (Uematsu & Mishra 2012). And farmers
Sharma 2019). But the estimated results of the current with a high level of monthly household income have
study indicate (Table 3) that farmers with only pri- the capacity to bear that risk. The results of this study
mary and secondary education levels are more likely analyze that the monthly household income of the
to adopt organic farming. Farmers who are illiterate farmers significantly affects the adoption rate in the
or have higher education are not interested in organic Middle Ganga River basin in India. Similar results
farming; it might be because 90% of farmers have have also been observed in the studies conducted by
opted for organic farming in the study area because of Laosutsan et al. (2019) and Senanayake & Rathnay-
government incentives and support (Table 5). aka (2015) in Thailand and Sri Lanka, respectively.
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Moreover, farmers’ perceived attitude towards factors affecting farmers’ adoption rates. Therefore,
organic farming also plays an essential role in its adop- policies must be focused on educating farmers by
tion. The non-adopters perceived that lack of financial organizing various training programs and extension
support, low level of yield and difficulties in finding services. It will help spread awareness of environ-
the markets were the major hurdles to adopting. Hence, mental pollution due to conventional farming.
more focus should be given to the establishment of the Moreover, although farmers believe that the
organic market and providing them premium prices input cost under organic farming is lower, they
at least during the transition period (initials 2–3 years did not adopt it for various reasons. Lack of finan-
when production declines). Our results are consistent cial support, lower yield levels and unavailability
with the literature where production and marketing bar- of markets under organic farming are significant
riers are discovered as significant constraints to adopt- reasons that discourage farmers from adopting it.
ing organic farming (Nandi et al. 2015; Panneerselvam Hence, the policy must be focused on establish-
et al. 2012). Further, adopter farmers were more aware ing markets, raising yield through reorientation
of the environmental degradation and adverse impact of research and development (R&D), and provid-
of chemical farming on their health. Hence, more sem- ing premium during the initial years of conver-
inars and extension services regarding organic farming sion. Further, another reason for less adoption is
can make farmers aware and motivate about its poten- their belief in low profitability in organic farming.
tial benefits on environment and human health. Therefore, various seminars and programs should
The above discussion concludes that there are mul- be organized to motivate and guide them on how it
tiple socio-economic factors (ranging from region becomes profitable. Moreover, the adopter showed
to training and family income) that affect the rate of attention to continuing it because of its potential
adoption of organic farming. However, there might impact on human health, followed by government
be some other important factors, for example socio- incentives and support. The study suggests that
political, socio-cultural and psychological which can government incentives can play a significant role
also affect the adoption rate, which have not studied in adopting organic farm practices in the Middle
in the current study. Thus, this study can open the Ganga River basin. Further, this research also has
scope of future research for the parallel study. certain limitations such as the limited number of
variables due to the limited resources, hence more
comprehensive research including more variables
Conclusion can be conducted for a more holistic picture. More-
over, this study limits the area to two districts in
In view of improving the sustainability of agricul- Middle Ganga river basin, hence in order to under-
tural systems in India, this study aimed to identify stand the impact of various factors on the adoption
the factors that could affect the adoption of organic of organic farming in the entire Ganga river basin,
farming in the Middle Ganga River basin, India. the study area can be expanded to include upper
The results of binary logistic shows that the sig- and lower Ganga river basin for further research.
nificant factors that affect the adoption of organic
farming were region, education, social category, Author contribution Professor S.P. Singh has developed
the idea for the current research and received the fund for the
farming experience, training and monthly house-
study. He assisted other authors in each and every step and
hold income. This analysis has important policy
finalized the draft of the manuscript. Priya has reviewed the
implications as it highlights the areas where the relevant literature, analyzed the data and wrote the manuscript.
government can intervene in order to promote Komal has helped in data collection and data analysis.
organic farming among conventional farmers. For
Funding Professor S.P. Singh (first author) has received
example, farmers in the Haridwar region with pri-
the funding from Indian Council of Social Science Research
mary and secondary education levels and the gen-
(ICSSR), New Delhi, India.
eral social category are more likely to adopt organic
farming. Further, farming experience, training, and Declarations
monthly household income are other significant
Conflict of interest The authors declare no competing interests.
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Org. Agr.
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