




































Ecology, Economy and Society–the INSEE Journal 4 (2): 95-117, July 2021 

RESEARCH PAPER 

Adoption of Soil Conservation Measures: Evidence 
from Rain-fed Watershed Areas of Telangana 

Dayakar Peddi1, Kavi Kumar KS2 

Abstract: Land degradation resulting from soil erosion is a major problem in rain-
fed agricultural areas in India. This study analyses the key determinants of farmers’ 
decisions to adopt on-farm soil and water conservation (SWC) measures in the 
rain-fed watershed areas of Siddipet district in Telangana. Here, SWC measures 
have been undertaken by the government and NGOs at the sub-
watershed/community level and by individual farmers at the farm level. The study 
is based on a primary survey of over 400 farmers conducted in January–March 
2018. In addition to estimating the influence of biophysical and market access 
variables on farmers’ decisions to undertake SWC practices, the study includes a 
logistic model that found a complementarity between community and individual 
plot-level interventions to improve soil health. The findings also highlight the 
influence of conservation measures practised in the neighbourhood on farmers’ 
decisions to implement SWC measures.  

Key words: Land Degradation; Soil and Water Conservation; Sub-watershed; 
Telangana, India. 

JEL Classification: C11, C13, 21, Q2 

 

                                                        
1  Assistant Professor, Centre for Economic and Social Studies, Nizamiah Observatory 
Campus Begumpet, Hyderabad, Telangana State – 500016; dayakar@cess.ac.in.  

2 Professor, Madras School of Economics, Gandhi Mandapam Rd, Behind Government 
Data Center, Surya Nagar, Kotturpuram, Chennai, Tamil Nadu – 600025; kavi@mse.ac.in.  

Copyright © Peddi and Kavi Kumar 2021. Released under Creative Commons Attribution-

NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 

Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v4i2.273  

 

mailto:dayakar@cess.ac.in
mailto:kavi@mse.ac.in
https://doi.org/10.37773/ees.v4i2.273


Ecology, Economy and Society–the INSEE Journal [96] 

1. INTRODUCTION  

The backbone of rural livelihoods in developing countries is the land 
ecosystem. Unsustainable land management practices have led to extensive 
land degradation across the world over the last few decades (Millennium 
Ecosystem Assessment Panel 2005; Brevik et al. 2015). In terms of on-site 
impacts, it is widely acknowledged that soil erosion leads to reduced 
agricultural yield and productivity (Mbaga-Semgalawe and Folmer 2000; 
Bravo-Ureta et al. 2006; Kumar et al. 2019). Moreover, beyond a certain 
threshold, soil erosion can make soil cover regeneration difficult and can 
adversely affect future livelihoods. Therefore, the link between on-farm soil 
erosion and agricultural productivity has both intra-generational and 
intergenerational implications. Soil degradation affects other natural 
resources as well—for instance, a reduction in crop yields may force 
farmers to intensify deforestation (Lopez 2002). Soil erosion also leads to 
significant negative externalities such as water pollution, reduction in soil 
water-carrying capacity, and disturbances in hydrological cycles 
(Somanathan 1991; Mbaga-Semgalawe and Folmer 2000). Most of these 
concerns have also been expressed in the context of Indian agriculture 
(Kerr 2002; Reddy and Syme 2019).  

The issues caused by soil erosion can be minimized or limited through 
adequate on-site and off-site soil conservation practices. Over the past four 
decades, the Indian government has been undertaking soil and water 
conservation (SWC) measures 3  at the community level to prevent land 
degradation. A growing body of literature suggests that farmers are the 
main stakeholders and undertake on-site SWC measures based on the 
perceived level of soil erosion on their plots. These measures include 
terracing, contour practices, fallow practices, land drainage, crop mixture, 
bunding, slope levelling, agroforestry, and crop residue management, which 
are common practices to reduce soil erosion around the world 4 (Scherr 
1999). These SWC measures provide benefits ranging from the local (crop 
yield improvements) and regional (flood control) to the global level (carbon 

                                                        
3 The literature on farm-level conservation measures adopted by farmers to prevent soil 
erosion refers to such measures interchangeably as “soil conservation measures” and “soil 
and water conservation measures” given the close linkages between conservation of soil and 
water resources. Accordingly, this study refers to farm-level conservation measures as SWC 
measures.   

4  Recent literature has argued that small and marginal farmers adopt some of these measures 
to increase their resilience to climate variability and climate change (Tambo and Mockshell 
2018; Makate et al. 2019). Therefore, SWC measures and conservational agricultural measures 
often overlap, and the benefits accruing from such interventions have multiple and 
overlapping benefits.   



[97] Dayakar Peddi and Kavi Kumar KS 

sequestration) and can be both short term and long term in nature (Lal 
Rattan 2001; Bouma, Van Soest, and Bulte 2007; Singha 2019).  

The right combination of on-site SWC measures adopted by individual 
farmers and communities can prevent soil erosion to a great extent 
(Pattanayak 2004). The SWC practices undertaken by farmers are primarily 
based on the costs and benefits realized from avoiding soil erosion losses 
(Lutz, Pagiola, and Reiche 1994). Empirical studies have identified a wide 
range of predominant factors, including plot-level and household 
characteristics, which influence farmers’ implementation of SWC measures 
(Adimassu, Langan, and Johnston 2016). Some predominant socio-
economic factors include membership in farmers’ organizations, formal 
education of the household head (Sidibe 2005), spouse’s education, 
household wealth, labour availability, market accessibility, access to 
extension services (Teklewold et al. 2013), crop mix under cultivation, 
perceived level of soil erosion in the plot, farm size (Feder and Slade 1985; 
Mbaga-Semgalawa and Folmer 2000), and existence of formal credit 
markets (Wossen, Berger, and Di Falco 2015). The literature also suggests 
that farm characteristics such as soil type, depth of soil, and slope of the 
land (Teklewold et al. 2013) significantly influence farmers’ decisions to 
implement SWC measures. There have also been assessments of the role of 
institutional interventions (such as the Integrated Water Management 
Programme [IWMP] in India) in influencing farmers to adopt new 
technologies in agriculture and practise SWC measures (Feder and Slade 
1985; Mbaga-Semgalawa and Folmer 2000).  

Despite the continuous prevention efforts of the government under various 
programmes, the problems of soil erosion and land degradation persist in 
India. The poor uptake of programmes and policy interventions at the field 
level is attributed to the top-down approach often adopted by the 
government and the absence of demonstration of programme benefits at 
the ground level. For instance, Palanisami et al. (2015) argue that only 22% 
of water management technologies developed by research networks and 
promoted by government agencies have been adopted by farmers. A few 
recent studies have explored the influence of neighbourhood conservational 
practices on farmers’ decision-making regarding the same. Battaglini, 
Nunnari, and Palfrey (2012) showed that there can be strategic 
substitutability (free-riding) or strategic complementarities among 
neighbours while investing in public goods like soil conservation. The logic 
is provided in Tobler’s formula—“everything is related to everything else, 
however, closer things are more related than distant things” (Drukker 2009, 
4). There are two main strands of literature on the adoption of technologies 
or SWC measures that attempt to incorporate the interdependence of 



Ecology, Economy and Society–the INSEE Journal [98] 

decision-making at the plot level. The first strand includes models of 
networks and social learning that explicitly account for the influence of 
neighbours (Mbaga-Semgalawa and Folmer 2000; Conley and Udry 2010; 
Bandiera and Rasul 2006; Moser and Barret 2006; Teklewold et al. 2014). 
These models are rooted in Manski’s 1993 law, “the propensity of an 
individual to behave in certain way changes with the behaviour of the 
individual’s social group” (cited in Läpple and Kelley 2015, 3).  

The second strand of literature attempts to capture the role of interactions 
on the decision to adopt a given technology using spatial econometrics 
techniques to model “spatial dependence either in the outcome variable 
(adoption) or in the error term, or both” (Singha 2019, 18). The spatial 
dependence framework is suitable for analysing the determinants of 
adopting SWC measures for many reasons. First, soil conservation practices 
in one farm can assist or constrain the implementation of such practices on 
adjacent farms. The assumption is that farmers with fields located next to 
each other exhibit similar behaviours (Holloway and Lapar 2007). Factors 
such as flow of inter-farm information and neighbourhood competition or 
cooperation can encourage similar adoption behaviours among farmers 
(Abdulai and Hoffman 2005). Second, soil conservation practices can be 
location-specific, with some SWC practices being suitable for particular 
types of land. Agricultural productivity also depends on various localized 
factors including soil type, soil quality, soil moisture, and land topography 
(Colney 1999). Similarities in these factors may lead to similarities in 
farming and conservation practices (Pattanayak and Butry 2005). Empirical 
studies have analysed the influence of practices in the neighbourhood on 
farmers undertaking SWC measures using a spatial probit model (Holloway 
and Lapar 2007; Wang, Iglesias, and Wooldridge 2013; Läpple and Kelly, 
2015; Singha, 2017). The construction of a weight matrix is necessary to 
analyse spatial dependence. The literature suggests that there are six 
possible ways of constructing a matrix including Euclidean distance, 
competition along streets, a combination of the first two, nearest 
neighbours along streets, nearest neighbours in Euclidean distance, and 
neighbours that share a common boundary (Pinkse and Slade 1998). Ideally, 
to construct metrics, a common boundary that minimizes the bias of the 
result is appropriate. However, due to the lack of data availability and 
practical difficulties, empirical studies based on plot-level data use the 
Euclidean distance and nearest neighbours in Euclidean distance criteria to 
construct the spatial weight matrix to account for spatial interaction among 
farmers adopting new technologies or soil conservation measures 
(Holloway and Lapar 2007; Läpple and Kelly 2015). Despite the apparent 



[99] Dayakar Peddi and Kavi Kumar KS 

objectivity in the construction of spatial matrices, the definition of 
“neighbourhood” is based on the researcher’s subjective understanding.  

Taking all the above factors and the limitations posed by the data collection 
process (discussed in the next section) into consideration, the present study 
attempts to account for the association between conservation practices 
followed in a neighbourhood and the farmer’s decision to implement SWC 
measures. Thus, the present study does not undertake a comprehensive 
spatial econometric analysis.  

Taking a cue from the literature discussed above, this study analyses field-
level data collected from Siddipet district in Telangana to understand the 
role of socio-economic variables and farm-level characteristics in prompting 
farmers in Telangana to implement SWC measures. Further, the analysis 
attempts to address two relevant issues concerning the adoption of SWC 
measures: 1) the complementarity between community- and watershed-level 
SWC activities and adoption of individual, plot-level SWC measures; and 2) 
the association between the SWC practices followed in the neighbourhood 
and the conservation practices adopted by farmers. The rest of the paper is 
organized as follows: Section 2 describes the methodology followed in the 
study and discusses insights from the primary survey on conservation 
practices undertaken by the farmers. Section 3 discusses the results 
obtained. Section 4 provides the conclusion. 

2. DATA COLLECTION AND METHODOLOGY 

The analysis presented in this study is based on field-level data collected 
from Siddipet district in Telangana, India.5 Telangana is one of the states 
worst-affected by land degradation, especially water erosion, in the country 
(SAC 2016). The Desertification and Land Degradation Atlas of India (SAC 
2016) reveals that in Telangana, close to 3 million hectares of cropland area 
alone were degraded as of 2011–13. As per the Atlas, Telangana ranks 
fourth among the major Indian states, with 31.34% of the total geographic 
area in the state classified as degraded. Biswas et al. (2015) highlight that in 
Telangana, 37% of the total geographical area has degraded with a moderate 
rate of erosion (5–10 tonnes ha-1 yr-1), while about 20% is susceptible to 
an excessive rate of soil erosion at 10 tonnes ha-1 yr-1. Rain-fed agriculture 
is the dominant mode of cultivation in the state and is considered highly 
vulnerable to soil erosion and subsequently declining agricultural 

                                                        
5 More details on the study area and data collection process are provided in Dayakar and 
Kavi Kumar (2020).  

 



Ecology, Economy and Society–the INSEE Journal [100] 

productivity. Adoption of proper SWC measures can halt top-soil erosion 
and arrest the land degradation process. 

2.1 Study Area and Data Collection 

The study was undertaken in two mandals of Siddipet district in Telangana, 
which lies approximately 130 km north of Hyderabad, the state capital. The 
selected area is located in a region highly vulnerable to drought, with an 
annual average rainfall of 650 mm, over 80% of which is received during 
the monsoon months of June–September (GoT 2015). The area lies in the 
Godavari river basin and has low and moderate levels of soil erosion, as 
assessed through satellite data. A purposive sampling method was followed 
to select the study area and village while accounting for wide variations 
across villages in terms of SWC technology experience, state of soil erosion, 
and socio-economic heterogeneity.  

Twelve villages were selected from the Chinnakodur and Dubbak mandals 
for the field study, of which six are located in the Dubbak and Chinnakodur 
sub-watershed areas and are part of the IWMP. Under the IWMP, 
numerous activities are undertaken to restore the ecological balance by 
harnessing, conserving, and developing degraded natural sources such as 
soil, vegetation cover, and water. The other six villages are also located in 
the same sub-watershed areas but are not covered by the IWMP 
programme. The biophysical, topographical, and hydrological conditions of 
the selected villages are broadly similar. These villages predominantly have 
red loamy, red sand loamy, saline, and black soils. Paddy, maize, cotton, red 
gram, and vegetables are the major crops cultivated in the area. Soil erosion 
leads to nutrient loss, which ultimately reduces agricultural productivity and 
yield. Therefore, farmers traditionally practise SWC measures to control the 
perceived level of soil erosion (Kumar et al. 2015). 

The data used in the study were obtained from a detailed household- and 
plot-level survey of 400 farmers in the two mandals. The survey was 
conducted during January–March 2018. The number of households selected 
for the survey in each village was based on the proportion of households in 
that village to the aggregate number of households across all the selected 
villages. Thus, the total number of households sampled in each village 
ranged between 13 and 67. In each village, the list of households was 
compiled from revenue and agricultural departmental data sets (GoT, 
2015). Once the number of households was finalized, the specific 
households to be surveyed were selected from the complete list of 
households in the village following the simple random sampling approach. 
The final sample consisted of 206 households from IWMP villages and 197 
households from non-IWMP villages. Across the entire sample, about 85% 



[101] Dayakar Peddi and Kavi Kumar KS 

of households had adopted at least one soil conservation measure in their 
plots. 

2.2 Insights on SWC Measures 

Group discussions and the pilot survey revealed that the farmers in the 
study area practise several SWC measures, with the most common ones 
being silt application, construction of grass and stone bunds, slope levelling, 
planting woody perennials, and digging stream trenches and farm ponds. 

Table 1: Description of SWC Measures Practised in the Study Area 

SWC measure Description 

Silt application Farmers apply silt to improve soil quality, especially in 
eroded lands.  

Construction of grass and 
stone bunds 

Farmers construct grass and stone bunds against the 
slope of the farmland to prevent soil erosion. 

Slope levelling Farmers use tools to reduce the slope of the land to 
prevent soil erosion. 

Planting woody perennials Farmers plant woody perennials along the borders to 
control soil erosion.  

Digging stream trenches 
and farm ponds 

Farmers dig stream trenches and farm ponds as SWC 
measures.  

Source: Data based on field study 

Across all the villages surveyed, around 23% of farmers were found to 
practise SWC measures. While several farmers have adopted measures such 
as slope levelling, stream trenching, silt application, planting woody 
perennials, and farm pond construction, a smaller percentage has 
undertaken measures such as constructing grass and stone bunds (Figure 1).  

Around 30% of farmers in IWMP villages have adopted at least one SWC 
measure, whereas only 15% of farmers from the non-IWMP villages have 
adopted one or more SWC measures. Further, the primary data shows that 
farmers from IWMP villages were willing to implement SWC measures that 
are long-lasting, such as constructing stone bunds, slope levelling, planting 
woody perennials, and digging farm ponds, compared to their counterparts 
in the non-IWMP villages. The levels of soil erosion severity and resource 
availability play an important role in determining the SWC measures 
adopted by farmers. 



Ecology, Economy and Society–the INSEE Journal [102] 

 
Figure 1: Category-wise Adoption of Soil Conservation Measures across  
Survey Villages (in Percentages) 
Source: Data based on field study 

Farmers often undertake multiple SWC measures, with a higher number of 
measures being adopted in relatively conducive environments. Figure 2 
shows the percentage of farmers undertaking different numbers of soil 
conservation measures under conditions of “low” and “high” levels of soil 
erosion. Clearly, there is a tendency to implement more SWC measures if 
they perceive the severity of soil erosion to be high. Further, plot-level soil 
conservation measures are often considered supplemental to the measures 
implemented on common/private lands by governmental/non-
governmental agencies under the IWMP. Farmers belonging to IWMP 
villages have undertaken a larger number of SWC measures than their 
counterparts in non-IWMP villages. As shown in Figure 2, a larger 
percentage of farmers in IWMP villages have undertaken more than three 
soil conservation measures compared to the farmers in the non-IWMP 
villages, suggesting that there is a complementarity between farm-level soil 
conservation measures and sub-watershed-level interventions to prevent 
soil erosion. 



[103] Dayakar Peddi and Kavi Kumar KS 

 
Figure 2: Distribution of Soil Conservation Measures across Different Levels of 
Soil Erosion Severity (in Percentages) 
Source: Data based on field study 

2.3 Empirical Specification 

The random utility model provides the basis for adoption choices between 
alternative SWC practices. It has been extensively used in the empirical 
literature to study farmers’ decisions to adopt SWC measures. Both logit 
and probit methods are well-established approaches in analysing the 
adoption of SWC measures. The choice of which models to use is a matter 
of computational convenience (Greene 2008). This study uses the logistic 
regression model to analyse the determinants of farmers’ SWC adoption 
decisions. The parameters estimated using logistic regression can be 
interpreted easily in terms of the odds ratio. The odds ratio shows the 
strength of association between a predictor and the outcome of interest. 
The dependent variable in the model is the logarithm of the odds that a 
given farmer adopts SWC measures. The model is specified as follows 
(Gujarati 2009): 

𝑃i = (𝑌=1|𝑋i) = (1|1+𝑒−Zi) …… (1) 

Thus, the odds ratio in favour of adoption can be specified as: 

(𝑃i|1−𝑃i) = i ……….………. (2) 

where Pi is the probability of adopting SWC measures by the ith farmer; 

1−𝑃I = (1|1+𝑒−Zi) is non-adoption by the ith farmer; Zi is a linear function 
of explanatory variables (X), including socio-economic, institutional, and 
plot-specific variables and village-level factors. In the estimation of factors 
affecting the adoption of SWC measures, the dependent variable is coded as 
1 when the farmer implements one or more than one SWC measure and 0 



Ecology, Economy and Society–the INSEE Journal [104] 

otherwise. 6  The explanatory variables used are as follows: (a) plot-level 
characteristics such as area of the plot, the extent of soil erosion as assessed 
by the farmer, irrigation status, adoption of SWC measures by neighbouring 
farmers, and crop diversity (i.e., the Herfindahl index [HHI)]7); (b) socio-
economic characteristics including age, sex, and educational status of the 
household head, size of the household, and social status of the household; 
(c) connectivity factors including the distance between the plot and the 
dwelling, road connectivity, and the distance between the plot and the 
market; (d) village characteristics such as community-level SWC measures 
implemented through the IWMP and whether the plot has benefited 
directly from IWMP interventions. The parameters of the logit were 
estimated using the maximum likelihood procedure in Stata software using 
the logit user-written command. 

2.4 Summary Statistics  

The definitions of the explanatory variables, the hypothesized direction of 
their influence, and their descriptive statistical properties are presented in 
Table 2. To assess the association of on-site SWC measures undertaken by 
other farmers in the neighbourhood with the soil conservation practices 
adopted by the surveyed farmers, detailed information about SWC activities 
in all the neighbouring plots (for each of the farmers/plots surveyed) was 
collected through the primary survey. A farmer’s neighbours were defined 
as those whose plots share a boundary with their plot. If more than 50% of 
neighbouring plots have adopted a specific SWC measure, then the 
neighbours of the respondent are considered to have adopted that measure 
(coded as 1), and, otherwise, they are considered to have not adopted it 
(coded as 0). The summary statistics suggest that the average area under 

                                                        
6  Since farmers adopt more than one SWC measure, the literature suggests that the 
dependent variable becomes multinomial, and, therefore, the ideal model becomes 
multinomial logit/probit models. However, in this study, the results are estimated using the 
simple logit regression model. Since we have defined the dependent variable as 1 when the 
farmer implements more than one SWC measure and ‘0’ otherwise, the estimated model can 
capture more numbers of SWC measures adopted. This specification is considered relevant 
since most of the conservation measures are determined by similar factors. Thus, the focus 
here is on identifying overall factors that influence the decision to adopt SWC measures in 
general, and not on the factors that influence the adoption of a certain number of SWC 
measures. We have also estimated the multinomial model and report the results in Appendix 
Tables A.1 and A.2.  

7   The Herfindahl index (HHI) represents “crop diversification and is estimated as the 
summation of all squared area shares occupied by crops in total cropped area. The value of 
this index varies from zero to one. It takes the value of one when there is full specialization 
and approaches to zero when there is full diversification” (Datta, 2015). 

 



[105] Dayakar Peddi and Kavi Kumar KS 

cultivation is 3.37 acres per plot, while the average total area of the surveyed 
households is 4.60 acres.  

Table 2: Summary Statistics and Description of the Variables Used in the Analysis 

 

Variable Definition of the variable H1sign Mean Std. Dev. 

Plot-level characteristics 

Area of the 
plot Cultivated area (in acres) +/- 3.37 2.48 

Erosion 
Farmers’ perceived erosion (1 = 
yes; 0 = no) + 0.45 0.50 

Irrigation Irrigation status (1 = yes; 0 = no) +/- 0.81 0.39 

Crop 
diversity Crop diversity index + 0.49 0.23 

Neighbours’ 
adoption 

% of adjacent neighbours having 
adopted SWC + 0.04 0.21 

Socio-economic variables 

Age 
Age of the household head (in 
years) +/- 49.45 14.10 

Sex 
Gender of the household head 
(1 = male; 0 = female) +/- 0.89 0.32 

Caste 

Social status (1 = socially 
forward class; 0 = socially 
backward classes) +/- 0.23 0.42 

Education 
Years of education of the 
household head (in years) +/- 5.29 5.57 

Household size 
Size of the household 
(members) +/- 4.52 1.93 

Market access variables 

Distance Distance to dwelling (in km) +/- 1.46 1.35 

Road 
Road connectivity of the plot (1 = 
yes; 0 = no) + 0.32 0.47 

Distance to 
market 

Distance to the market from the 
plot (in km) - 8.16 9.46 

Village-level characteristics 

IWMP 
intervention 

IWMP intervention covered in 
the village (1 = yes; 0 = no) + 0.51 050 

 
Source: Author’s own calculations based on primary survey data. 

The respondents thus own multiple plots and have a basis for comparison 
across the plots they own while responding to the survey questions. Crop 
diversity, as measured through HHI, had a sample mean value of 0.49, 
suggesting a low level of diversity in the study area. Across the villages, 



Ecology, Economy and Society–the INSEE Journal [106] 

around 23% of farmers practised SWC measures. The average age of the 
respondents was 49 years with about five years of formal education. The 
average household size was close to five members with low variability 
across the sample. The average distance from the dwelling to the plot was 
1.46 km and, on average, the plots had poor road connectivity. Around 
50% of sampled farmers belonged to IWMP villages. 

4. ESTIMATED RESULTS AND DISCUSSION 

A logistic regression model was estimated to identify factors that influence 
farmers’ decisions to adopt SWC measures. The model estimates suggest 
that out of the 14 variables that are hypothesized to influence the adoption 
of SWC measures, 6 are significant—soil erosion, crop diversity index, 
irrigation facility, neighbours’ adoption of conservation measures, distance 
to dwelling, and presence of IWMP interventions. The logistic regression 
can be interpreted in terms of marginal effects and odds ratio.  

The odds ratio shows the strength of association between a predictor and 
the outcome of interest. In the present study, the predicted probabilities of 
the parameters are interpreted in terms of the odds ratio. It may be noted 
that some of the characteristics of a plot, such as the extent of soil erosion, 
are assessed in the present study based on farmers’ perceptions. While a 
more objective measurement of such characteristics is desirable, it is not 
uncommon in the literature to analyse SWC adoption based on farmers’ 
perceptions of several characteristics like slope, soil depth, and soil quality 
(e.g., Tesfaye et al. 2014). To limit potential endogeneity problems that can 
arise due to the inclusion of variables based on farmers’ perceptions, the 
present study incorporates only the extent of soil erosion as assessed by 
farmers as a binary variable. Table 3 reports the coefficients and odds ratios 
estimated based on the logistic regression. In Table 3, the coefficients and 
odds ratios are reported for two separate model specifications that differ in 
terms of the inclusion of conservation measures (Model 2) undertaken by 
farmers in the neighbourhood of surveyed plots. 



[107] Dayakar Peddi and Kavi Kumar KS 

 

 

 

 

 
 

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Ecology, Economy and Society–the INSEE Journal [108] 

If farmers perceive that their plots suffer from soil erosion, they are 2.72 
times more likely to adopt SWC measures compared to those who do not 
perceive soil erosion to be a major issue on their plots. This result is in line 
with similar studies conducted across the world, which have found a 
positive relationship between farmers’ perceptions and adoption of soil 
conservation measures (Shiferaw and Holden 1998; Asrat, Belay, and 
Hamito 2004; Tesfaye et al. 2014). Similarly, the model results show that 
farmers are 2.09 times more likely to undertake SWC measures if they have 
access to an irrigation facility. The possible explanation for this is that 
farmers’ willingness to invest more in sustainable land management 
activities is dependent on their access to irrigation facilities. This result is 
also in line with other similar studies (Knowler and Bradshaw 2007).  

The literature suggests that diversified cultivation improves farmers’ 
incomes and plays a significant role in sustaining agriculture and agriculture-
based livelihoods (Jayne et al. 2010; Bigsten and Tengstam 2011; Birthal, 
Roy, and Negi 2015). The results from the present study suggest that 
farmers are 7.34 times more likely to practise SWC measures if they practise 
diversified cultivation in their plots. Diversified agriculture may yield higher 
profits to farmers, which may encourage them to invest more in SWC 
measures. The literature suggests a strong and negative association between 
the distance from the farmer’s dwelling to the plot and the probability of 
implementing SWC measures (Knowler and Bradshaw 2007). Concurring 
with this, the results from the present study show that farmers are less likely 
to adopt SWC measures if the agricultural plot is far from their dwelling.  

As highlighted above, SWC can be location-specific, with certain SWC 
practices being more suitable for particular types of land. Land is spatially 
contiguous, and the conservation practices suitable for one plot could also 
be suitable for nearby plots. Hence, these factors may lead to similarities in 
conservation and farm practices (Pattanayak and Butry 2005). Empirical 
studies suggest that neighbourhood activities have a positive influence on 
farmers’ decisions to adopt new agricultural technologies as well as SWC 
measures (Holloway and Lapar 2007; Wang et al. 2013; Lapple and Kelly 
2015; Palanisami et al. 2015, Singha 2019). Spatially contiguous information 
is required to rigorously analyse the neighbourhood impact on a farmer’s 
decision to adopt SWC practices. In the present study, a full-fledged spatial 
econometric analysis could not be undertaken due to data constraints. 
Instead, there has been an attempt to capture the influence of conservation 
practices in the neighbourhood on a farmer’s individual SWC practices. 
Model 2 in Table 2 accounts for neighbours’ influence through the 
introduction of a dummy, which takes the value 1 when a majority of the 
neighbouring plots implement SWC and 0 otherwise. The significant and 



[109] Dayakar Peddi and Kavi Kumar KS 

positive coefficient estimated for the neighbourhood dummy suggests an 
association between conservation practices in the farmer’s plot and the 
neighbourhood plots. The estimated coefficient values of most variables are 
lower in Model 2 compared to those in Model 1, suggesting that the 
inclusion of the neighbourhood dummy in the model specification avoids 
omitted variable bias. The study also explored the role of neighbours in 
farmers’ adoption decisions of different SWC measures (results reported in 
Appendices). Farmers are more likely to adopt stream trenches, slope 
levelling, stone bunds, woody perennials, and farm ponds if their 
neighbours adopted these SWC measures, keeping other exogenous 
variables constant. However, there is no significant association between 
farmers’ adoption of grass bunds, silt application, and other measures and 
their implementation by neighbours (see Appendix Table A.3 and A.4). 
Among other things, the relatively low rate of adoption of these SWC 
measures among the farmers in the sample could be one of the reasons for 
the insignificant association.  

As expected, the results show that there is a strong and positive relationship 
between community-level SWC activities and individual farm-level adoption 
of SWC measures. The results suggest that farmers are more likely to 
undertake SWC measures if they belong to IWMP villages compared to 
farmers from non-IWMP villages. The odds ratio suggests that farmers are 
2.20 times more likely to adopt SWC measures if they belong to IWMP 
villages compared to their counterparts in non-IWMP villages, keeping all 
other variables constant. There is mixed evidence in the literature on 
linkages between community-level conservation practices and farm-level 
practices. For instance, Feder and Slade (1985) suggest that there is a 
complementarity between community-level SWC practices and individual 
plot-level SWC measures. Similarly, Palanisami et al. (2015) argue that the 
neighbours’ adoption of water conservation measures positively influences 
farmers’ decisions to adopt similar conservation measures. On the other 
hand, Singha (2017) finds no relationship between community and farm-
level conservation practices given that one of the main objectives of 
programmes like IWMP is to demonstrate the effectiveness of conservation 
measures to farmers; the evidence from the present study provides support 
to such a claim.  

Finally, the literature offers mixed results on the relationship between socio-
economic variables and the adoption of SWC measures (Adimassu, Langan, 
and Johnston 2016). In the present study, variables such as area of the plot, 
sex of the household head, age of the household head, household size, 
formal education of the household head, road connectivity of the plot, and 



Ecology, Economy and Society–the INSEE Journal [110] 

plot distance to the market were found to have no significant relationship 
with the adoption of SWC measures by the farmers. 

5. CONCLUSION  

This study focused on assessing the main factors that influence farmers’ 
decisions to adopt SWC measures in the rain-fed watershed areas of 
Telangana, India. The data were collected through a primary survey of 400 
farmers from two watershed areas and were analysed using a logistic 
regression model. The results suggest that plot-level variables, including the 
level of soil erosion, crop diversity, and availability of irrigation facilities, 
significantly influence farmers’ decisions to undertake SWC measures. 
Further, farmers’ perception of the extent of soil erosion on their plots 
positively and significantly influences this decision. Similarly, farmers are 
more likely to undertake on-site SWC measures if they have access to 
irrigation facilities. The farmers are also more likely to adopt SWC measures 
if they cultivate more diverse crops. The results also indicate that farmers 
are more likely to undertake on-site conservation measures if their 
neighbours undertake SWC measures. As expected, community-level SWC 
activities were found to have a strong influence on the farm-level SWC 
measures undertaken by farmers, thus highlighting their complementarity. 
These findings, particularly that of the associative influence of 
neighbourhood conservation practices on farmers’ decisions to implement 
SWC measures, and the synergetic relationship between community and 
farm-level soil conservation practices, can provide crucial inputs for the 
formulation of effective government policies to promote soil conservation. 
The integration of local knowledge in SWC planning could help in 
overcoming behavioural constraints faced while advocating for 
conservation measures in countries such as India, which is dominated by 
rain-fed cultivation. Future studies could explore such extensions. 

ACKNOWLEDGEMENTS 

The paper is part of the doctoral thesis of Mr Dayakar Peddi submitted to 
Central University of Tamil Nadu. The authors express gratitude to Prof L 
Venkatachalam and Prof Brinda Viswanathan for their valuable comments 
and guidance throughout the work. The assistance provided by Mr Srinivas 
during the data collection process is gratefully acknowledged. The authors 
thank Indian Council of Social Science Research (ICSSR) for their financial 
support. Earlier versions of the paper were presented at the Indian Society 
for Ecological Economics (INSEE) (2019); Australian Agricultural and 
Resource Economics Society (AARES) (2021); and South Asian Economic 
Development (2021) conferences, and the authors would like to thank the 



[111] Dayakar Peddi and Kavi Kumar KS 

participants for their valuable inputs. The authors would like to thank the 
two anonymous reviewers and the editor for their helpful comments. The 
standard disclaimer of assuming responsibility for all errors applies. 

 

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[115] Dayakar Peddi and Kavi Kumar KS 

Appendix 

Table A.1: Factors Influencing Choice of SWC Practices 

Multinomial Logit Model Estimates 

Explanatory 
variables 

Moderate-level 
adoption High-level adoption 

 Coef P>|z| Coef P>|z| 

Base category  Low    

Plot-level characteristics 

Area of the plot 0.052 0.34 0.173* 0.10 

Soil erosion 0.459* 0.07 0.342 0.51 

Crop diversity index 1.960 ***  0.00 4.593*** 0.01 

Irrigation 0.183 0.59 0.962 0.35 

Neighbours’ adoption 0.009*** 0.00 0.023*** 0.00  

Socio-economic variables 

Sex of the household 
head 0.374 0.34 0.986 0.40 

Age of the household 
head -0.003 0.78 -0.011 0.56 

Years of formal 
education of the 
household head -0.039 0.12  0.026 0.62 

Household size 0.086 0.18 0.010 0.95 

Caste of the household -0.326 0.31 -1.186* 0.07 

Market access variables 

Road connectivity of the 
plot -0.080 0.77 -0.481 0.38 

Plot distance to dwelling  0.030 0.75 0.063 0.70 

Plot distance to market -0.005 0.66 -0.031 0.36 

Village-level characteristics 

Village under IWMP 1.723*** 0.00 4.494 ***  0.00 

Diagnostics   

Observations 403 



Ecology, Economy and Society–the INSEE Journal [116] 

LR chi2 184.59 

Prob > chi2 0.0000 

Pseudo R2 0.2544 

***, **, * indicate significance at the 1%, 5%, and 10% probability level, 
respectively. 

Note: If the farmer adopts less than or equal to 1 SWC measure, then she is treated 
as having a “low” level of adoption; if she practices less than or equal to 3 SWC 
measures, then she is treated as having a “moderate” level of adoption, and if she 
practices more than 3 SWC measures a “high” level of adoption.



[117] Dayakar Peddi and Kavi Kumar KS 

Table A.2: Marginal Effects: Multinomial Logit Model 

Explanatory 
variables 

Low-
level 
adoption   

Moderate-
level 
adoption  

High-
level 
adoption  

 
Marginal 
Effects P>|z| 

Marginal 
Effects P>|z| 

Marginal 
Effects P>|z| 

Plot-level characteristics 

Area of the 
plot -0.010 0.23 0.004 0.69  0.006  0.20 

Soil erosion -0.078* 0.07  0.079 *  0.09 -0.001  0.97 

Crop diversity 
index -0.366*** 0.00  0.218* 0.07 0.148** 0.04  

Irrigation -0.040 0.50 -0.000 1.00 0.040 0.40 

Neighbours’ 
adoption -0.002*** 0.00 0.001* 0.07 0.001*** 0.00 

Socio-economic variables 

Sex of the 
household 
head -0.071 0.28 0.037 0.63 0.034  0.52  

Age of the 
household 
head 0.001 0.73 -0.000 0.95  -0.000 0.57 

Years of 
formal 
education of 
the household 
head 0.006 0.16 -0.009** 0.05 0.003 0.13 

Household 
size -0.014 0.16 0.017  0.16 -0.003 0.72  

Caste of the 
household 0.065 0.23  -0.020 0.73  -0.045* 0.09 

Market access variables 

Road 
connectivity 
of the plot 0.018 0.69   0.002  0.96 -0.020 0.40  

Plot distance 
to dwelling  -0.005 0.67 0.004 0.80  0.002 0.72 

Plot distance 
to market 0.001 0.59 0.000 0.96 -0.001 0.19  

Village-level characteristics 

Village under 
IWMP -0.326*** 0.00 0.174*** 0.00 0.152*** 0.00 

Notes: ***, **, * denote significant at 1%, 5%, and 10% respectively. 


