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Environment, Development and Sustainability (2021) 23:2973–3008
https://doi.org/10.1007/s10668-020-00762-6
1 3
REVIEW
Reflections onfarmers’ social networks: ameans
forsustainable agricultural development?
SriroopChaudhuri1· MimiRoy1· LouisM.McDonald2· YvesEmendack3
Received: 7 November 2018 / Accepted: 29 April 2020 / Published online: 15 May 2020
© Springer Nature B.V. 2020
Abstract
Sustainable agrarian development has emerged as a key agenda in many recent global
development dialogues, owing to its intimate links with rural development. Agrarian devel-
opment paradigms, however, mostly root for technocratic solutions (agro-systems’ mod-
ernization), overlooking the social dimension (social networking/learning) of agricultural
innovation. In view of the above, this reflective article summarizes existing worldviews
on the role of farmers’ social networking/learning on agrarian development, with special
emphasis on India. Cyclic interactions between water (irrigation), food (agriculture) and
energy have led to dire socioenvironmental crises (e.g., groundwater depletion, energy
shortage, irrigation systems’ failures, food insecurity, livelihood loss, etc.) in India that
demands focused policy interventions. Under the circumstances, participatory action via
farmers’ social networks provides an effective tool to harnesses resilience. With illustra-
tive examples from India and the world, the study demonstrates that social learning is key
to adoption of new paradigms (new technology/crop/cropping methods, etc.). Dissemina-
tion of new knowledge/idea is fundamentally keyed to extent of farmer-to-farmer interac-
tion (friendship-/peer-advising network). In the process, the study highlights key barriers
to establish functional networks among farming communities. Particular emphasis is laid
upon the Water Users’ Association in India, to enumerate growing concerns around farm-
ers’ involvement in Participatory Irrigation Management schemes. Pitfalls in existing net-
work literature are highlighted, ranging from sampling issues to unaccounted effects of
“unobservable” variables. The final section attempts to outline certain strategic interven-
tions that might be pursued at the policy level to harness social capital. Overall, the study
was a plea to the concerned authorities, research bodies and stakeholders in India, to forge
substantive collaborations for new knowledge creation in the theory and practice of social
networking/learning and identify contextualized means to integrate them in the develop-
ment matrix.
Keywords Sustainable agrarian development· Social networking· Participatory action·
Water–food–energy nexus· Capacity building· Institutional governance
* Sriroop Chaudhuri
schaudhuri@jgu.edu.in
Extended author information available on the last page of the article
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1 Introduction
In recent times, issues of sustainable agrarian development have received critical policy
attention in many global development dialogues (Saravanan and Suchiradipta 2017). Other
than obvious reasons involving food security, a major cause for heightened interests is its
connections with rural development—in most of the developing nations, farming and allied
sectors support majority of rural households. However, in view of recent global environ-
mental degradations, farming communities are facing grave sustainability crises as their
livelihood primarily depends on ecosystem services (Vermeulen etal. 2012). It is in this
context that the need for social capital development becomes a key need to ensure sustain-
able development (Moulaert etal. 2013; Caulier-Grice etal. 2012; Murray etal. 2010; Har-
risson etal. 2009).
Agricultural development paradigms, however, are mostly inclined to a business-cen-
tric model (output maximization or input cost reduction) that typically adopts the tech-
nocratic approach—agro-systems’ modernization (new technology/farming routines/crops,
etc.) without paying heed to the sociocognitive aspects of farming (Kolade and Harpham
2014) that influence technology adoption decisions (Bock 2012). This school of thought
views the social factor (de Bruin and Ensor 2018; Bullock etal. 2017; Jiggins etal. 2016)
as the key determinant of technology adoption. In general, social innovation encompasses
a set of ideas that aim to meet social needs (Murray etal. 2010) by strengthening rela-
tional ties among social actors (Caulier-Grice etal. 2012) for judicious mobilization of
assets and resources around common goal—economic, social and environmental resilience
(Horgan and Dimitrijevic 2018). A social network is a particular form of social innovation
that builds upon interpersonal ties among a group of like-minded individuals, connected
through flow of information, goods, services or participatory action around a common goal
(Poudel etal. 2015). Studies have shown that social networking can improve chances of
meeting multiple social, economic and environmental demands by facilitating participatory
action (Murray etal. 2010).
Potential role of farmers’ social networks in agrarian development, however, has come
to light only very recently (de Bruin and Ensor 2018; Jenson and Harrisson 2013). It is
particularly beneficial in developing countries where rural education, extension and agri-
cultural information services are yet underprovided (Ma etal. 2014). This reflective arti-
cle attempts to bring out dominant worldviews on farmers’ social networks as a potential
strategy to plan sustainable agricultural development. In the process, India is taken a test
bed for farmers’ social networks to assess opportunities and challenges. In India, agricul-
ture and allied sectors (forestry and fisheries) contribute to about 14% of the nation’s GDP,
engaging about 53% of the population (Mohapatra etal. 2018). However rampant farmers’
suicides, over crop damage and livelihood losses in recent times (Merriott 2016), have cor-
roded the foundation of rural societies, which, in turn, is throttling sustainable agrarian
development initiatives. However, the idea of social capital development (social network-
ing/learning between farmers) is yet poorly acknowledged in Indian policy framework.
In view of the above, the study apportions itself into six main Sects.3–8: Sect.3
introduces key organizational facets of farmers’ networks and broad typology; Sect.4
highlights the ramifications of irrigated agriculture in India—both environmental and
social—arguing for social capital development; Sect. 5 reflects on the present Indian
scenario, to present illustrative examples from various states; Sect.6 discusses the pit-
falls in the Participatory Irrigation Management (PIM) system associated with the Water
Users’ Association (WUA); Sect.7 highlights research shortfalls in existing network
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literature; and final Sect.8 ponders on potential policy interventions. A major impetus
was to evaluate the relevance of farmers’ networks in India in the backdrop of water
(irrigation), food (agriculture) and energy (pumping) nexus around irrigated agricultural
practices, which has menacing societal dimensions (Kaur and Vatta 2015). Interestingly,
impacts of the water–food–energy (WFE) nexus on development paradigms are realized
in agrarian societies in most South Asian nations, demanding focused policy interven-
tions (Rasul 2016; Rasul and Sharma 2016). Under the circumstances, the present study
is a modest attempt to convene an interdisciplinary dialogue between practitioners, aca-
demics, policy-makers, NGOs and stakeholders in India to organize more synchronized
efforts to institutionalize reforms to develop agrarian social capital.
2 Methodology
A comprehensive literature search was conducted using different databases and search
engines including SCOPUS, PubMed, Web of Science (WoS), EconLit, JSTOR, Google
Scholar, etc., using a variety of search phrases (detailed below). In addition to peer-
reviewed journal articles, the search also included working papers, white papers, dis-
sertations, newspaper articles, book chapters, gray literature and any other technical
reports/notes from government/nongovernment organizations published between Janu-
ary 2000 and June 2018. The search was conducted using different categories of search
phrases including:
• Category I: “social innovation,” “social capital,” “farmers network *,” “farmers’
* network,” “farmers * networking,” “farmers’ networking,” “farmers’ social *,”
“agrarian network*” agricultural network*,” “technology adoption” (and various
permutation–combinations)
• Category II: “agr* innov*.” “irrigation,” “food,” “energy,” “WFE *,” “WEF *” (and
various permutation–combinations therein)
• Category III: WUA, PIM, Participatory Management
For each category above, India was used a preceding and/or succeeding word. Cat-
egories were mixed within and between each other in various permutation–combination
to expand search horizons. The “*” symbol in the search phrase was used as “wildcard,”
to include any extension of word/phrase that it precedes or succeeds to maximize hits
(Abbas etal. 2016). Documents were subjected to further screening using following a
definite “eligibility criteria,” as presented below. Aim at each stage was to remove arti-
cles that were not relevant or did not contain appropriate data with a three-tier screening
process assessing: (1) titles of articles; (2) abstracts; and (3) full text of the articles. The
eligibility criteria essentially comprised of careful scrutiny of five main aspects of the
respective article:
• Objective(s) aimed at assessing farmers’ network, and/or in a way, evaluating col-
lective behavior established by some sort of “bonding” (formal/informal) between
farmers
• Reasonable appraisal of bonding characteristics (network attributes)
• Clear enumeration of impacts of networking/learning on returns
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• Brief discussion of future implications (e.g., policy recommendation)
Based on the above, extraction of the literature took a sequential rout to “include” and/
or “exclude” out documents. After the initial database search by SC, three researchers
(MR, YE and LM) independently scrutinized the documents. The latter also helped in the
re-evaluation of the search criteria and setting up rules for “eligibility” and “exclusion.”
Independent double screening was carried out by MR and YE. Any discrepancies were
discussed, and if a decision could not be reached, feasibility of inclusion discussed by all
members of the team. As native English speakers, YE and LM independently reviewed the
completed manuscript to check for grammatical errors and typos.
Initial search yielded a total of 231 articles in total. However, following article “types”
were excluded due to: (a) duplication of results, (b) full text not found, (c) too many con-
founding factors, (d) foreign language, (d) published before 2000, (e) questionable review
design (Fig.1). The reference lists of identified reports and reviews were searched (reverse-
citation chasing), along with the list of papers that had cited included studies (forward-cita-
tion chasing) to identify further relevant papers (Leonard etal. 2018). However, a persistent
pitfall in relevant literature, realized during the course of the study, was lack of appraisal of
Fig. 1 Sequential steps adopted in document extraction
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network sampling design/technique, type of data (panel or cross-sectional) or time period
of sampling, etc. In that regard, certain authors were contacted individually to obtain more
details about their studies. Those did not respond in time were excluded.
3 Farmers’ social networks: key organizational facets
A way to approach the present agrarian crises, and yet largely under-explored in India, is to
harness local social capital—facilitating local social networks to engage farmers in co-pro-
duction and co-management of common pool resources (water and energy) for equitable
access. Numerous studies have demonstrated the importance of social networks to harness
local adaptive capacity among farmers (Table1).
For farmers’ social network to be an integral part of agrarian development, first and
foremost, there is the need to have clear understanding of the (1) network organizational
facets and (2) types of networks. The first one deals with the structural attributes of a net-
work involving two fundamental facets (a) centrality and (b) density (Table2). Apparently,
the higher the measure of each attribute, stronger is the relational ties, and higher the like-
lihood of participatory action among farmers (Pratiwi and Suzuki 2017; Ramirez 2013;
Gamboa etal. 2010; Bodin etal. 2006; Crona and Bodin 2006; Ghimire etal. 2004). How-
ever, higher values may also lead to certain undesirable outcomes such as internal power
struggles, lack of diversity in worldview/solution or stringent social norms. the latter dis-
courages interactions with outside a given population and thus reduces potentials of adopt-
ing new paradigms (new technology/crop/cropping methods, etc.).
However, the dimensions of network structural attributes may change over time in
response to changes in external conditions (environmental factors, political dynamics,
workforce, market conditions, social structure, etc.). The transient nature of structural
attributes demands systematic research into the theory and practice of social networking/
learning using more contextual evidences. Unfortunately, to best of our knowledge, there is
yet any nationwide appraisal of such kind in India. There is yet any empirical study to gen-
erate long-term data (or data suitable for multi-site comparison) that could be incorporated
in relevant policy measures.
The other aspect of the network that demand careful scrutiny is the variety. Global stud-
ies have identified three main generic forms of farmers’ social networks that help in har-
nessing local social capital to withstand negative externalities (Pratiwi and Suzuki 2017):
• Friendship network—farmer-to-farmer interactions/exchanges about new knowledge/
idea/technology applications within own community
• Peer-advising network—farmer-to-farmer networking within own community, but
mostly involving individuals who are more experienced/knowledgeable to provide
farm-related consultations
• Official-advising network—extends outside the community; involving experts (e.g.,
extension agents, engineers, etc.) from whom the farmers acquire new knowledge/infor-
mation or paradigms (technology/farming routine/crop, etc.)
From development perspective, enunciation of network types, by recognizing their spe-
cific roles in social capital development, is a key requirement. Under traditional farming
methods (farmers are already experienced with existing practices), facilitating friendship-/
peer-advising networks may suffice the need. Peer-advising networks are more effective in
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Table 1 Examples of social networking in agrarian sector
Citation Region Method of analysis Data type Salient feature
Wossen etal. (2013) Ethiopia Regression analysis 2901 farm households in Farmers
Innovation Fund (FIF); World Bank
Network structure and size influenced
social learning about natural resources
management and farming practices
External source of information (exten-
sion services) significantly improved
imbibing new concepts
Dessie etal. (2012) Ethiopia Semi-structured interviews, group
discussion; literature review; quali-
tative data analysis
Not specified Interactions among network actors
helped understanding about soil
conservation techniques
Networking created opportunities for
application of scientific as well as
tacit knowledge
Lower adaptabilities for famers staying
outside the network
Pratiwi and Suzuki (2017) Indonesia Spatial autoregressive (SAR) mode-
ling; computation of Mora’s I; nodal
analysis for degree centrality
120 farmers participating in training
workshop
Informal associations among farm-
ers’ to devise and harness climate
resilience, resource management and
settling of internal disputes, etc.
Networking aided in collective risk
management
Ombogoh etal. (2018) Kenya and Uganda Focused group discussion;
Qualitative data analysis
Simple random survey; 240 house-
holds in Kenya; 200 in Uganda
Participatory action raised climate
adaptability
Elevated internal farmers’ group capaci-
ties for resource/asset mobilization,
risk spreading, collective marketing
Participation bolstered collective finan-
cial capacity
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Table 1 (continued)
Citation Region Method of analysis Data type Salient feature
Erikson and Selboe (2012)Norway Not specified Not specified Informal associations aided in labor,
resource, and equipment sharing, even
though industrial farming promised
better yield
Reduction in workforce reduced net-
working opportunities
Fefchamps and Gubert (2007) Philippines Reduced-form regression (dyadic
model); econometric analysis
939 network members in 189 house-
holds
Social and geographic proximity are
major determinants of networking and
mutual insurance links among rural
population
Gifts/loans among network actors
helped individuals to cope with nega-
tive externalities
Hoang etal. (2006) Vietnam Network analysis with UCINET IV
software suit
Semi-structured interviews were
conducted with one respondent in
82 households
Social networking increased access to
latest agro-information
Well-formed networks helped extension
agents to better target rural population
and facilitate knowledge/information/
technology transfer
Morgan (2011) Wales 3D CoP model (community of
practice)
3 farmers groups: > 20 individuals;
10–20 and < 10
Farmers associate and engage in social
learning more readily with peers
determined by similar attitudes to
farm business, farming styles and
understanding of what organic agri-
culture entails
Poudel etal. (2015) Nepal Network analysis with UCINET and
NetDraw software suits
24 farmers using sociometric survey
model
Social seed systems helped to maintain/
enhance crop genetic diversity
Farmers with high centrality appeared
useful to regulate seed flow through
the network
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Table 2 Structural attributes of social networks and implication for network functionality
Structural attributes Measure Implication
Centrality (two main variants) Degree Centrality total number of “direct” ties an individual farmer
shares with other farmers in the network
C
D
n
i
=d
n
i
=
X
(G−
1
)
where CD(ni) = degree centrality, d(ni) = number of farmers con-
nected, X(G−1) = all other farmers connected to it, G = total number
of farmers in network
Betweenness Centrality tie between two farmers “indirectly” through
a third party within a network
C
Bni=
i
≠
j
≠
k
G
jk
n
i
Gjk
where CD(ni) = betweenness centrality, d(ni) = number of farmers
connected, Gjk = number of shortest ties between farmer j and k
(without a third party), Gjkni = number of possible ties between
farmer j and k via farmer ni, G = total number of farmers in network
High degree centrality
Better coordination among the farmers’ network
Ease in decision making
Potential power struggle
May give nurture homophili (similar perceptions of a problem and thus
lack in diversity in solutions)
High betweenness centrality
Productive exchange of farming-related knowledge/information
between farmers
Development of modularity within a network (partly distinct perspec-
tives than the network as a whole)
Density Ratio of number of ties to number of actors involved in the group/
network
Higher density:
Stronger relations of trust between farmers
Higher participatory action
Easier to establish agreeable network norms, operational methods,
social learning
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information gathering than friendship networks. However, when new paradigms (new tech-
nology/farming routines/crop, etc.) are necessary, the official-advising network is preferred
(farmer-to-expert liaison). Friendship and peer-advising networks draw from an earlier
concept—bonding network (Newman and Dale 2005; Bodin etal. 2006), while the official-
advising network equates to bridging network. For development purpose, there should be
a dynamic balance—bridging network to bring in new paradigms, while the bonding net-
work is to harness internal ties to make the network conducive to adopt and institutionalize
them (Dale and Onyx 2005).
4 Irrigated agriculture inIndia: theWFE Nexus
Rise of irrigated agriculture in India has helped advancing toward national food security
goals. However, as flipside, it has strained water and energy resources, cumulatively trans-
lating into severe ecosystem services losses. Water is an essential input for food produc-
tion (agriculture), while energy is required to distribute both water and food—pump water,
power irrigation equipment, process/store/market agricultural products. Such interweaved
fashion demands a nexus approach (Water–food–energy; WEF) (Fig.2), to appraise the
concerns (e.g., groundwater depletion, energy shortage, etc.) within an Integrated Natural
Resource Management framework (INRM) (Simpson and Jewitt 2019). The need grows
more compelling as the nexus is influenced by myriad externalities ranging from popula-
tion growth to injudicious policy-making, political entrenchment of agrarian and climate
aberrations (Barik et al. 2017) to undermine sustainable development paradigms. How-
ever, in the absence of INRM approach in agrarian policies, issues around WFE nexus have
amplified to aggravate livelihood crises among farming communities (Kumar and Meena
2017; Kaur and Vatta 2015; Sarkar and Das 2014).
4.1 Irrigated practices: groundwater depletion
A major impact of WFE nexus is realized in the freshwater reserves, groundwater in par-
ticular. The latter provides the mainstay to the irrigation sector and presently faces grave
sustainability crises owing to years of sustained over-exploitation (Zaveri et al. 2016;
Rodell etal. 2009). India is the current leader of global groundwater users (Seibert etal.
2010) with about 85% of groundwater serving the irrigation sector alone (Tyagi et al.
2012). Over the years, groundwater-based irrigation has skyrocketed in India (Srivastava
etal. 2017). Share of groundwater-based irrigation in net irrigated area has soared from
28.7 to 62.4% between 1950–51 and 23.6% in 2012–13. As a consequence, several states,
where stage of groundwater development (rate of abstraction vs. natural recharge) exceeds
sustainable rate, faces dire water shortage (Fig.3a). In at least 10 of 29 Indian states, over
half the administrative blocks are denoted unsafe by the Central Ground Water Board. In
several states, over half the groundwater reserve is already depleted, implicating irriga-
tion crises in future (Fig.3b). Lack of yet any nationwide robust groundwater pricing sys-
tem (Narayanmoorthy 2018; Chaudhuri and Roy 2018), coupled with rampant agro-power
subsidies (discounted farm power supply services) is undermining the national irrigation
sector. Policy of providing farm power subsidies has given farmers free reign to pump and
deplete groundwater at will. Such unwarranted extraction has aggravated the WFE nexus,
leading to multiple eco-environmental hazards including water resources depletion (Hum-
phreys etal. 2010; Chaudhuri and Ale 2014a), degradation (Chaudhuri and Ale 2014b, c),
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Fig. 2 Mutual interplay of water, food and energy resources (WFE nexus) around irrigated agriculture in
India and overarching factor that potentially aggravates it
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land subsidence (Chaudhuri and Ale 2013), air pollution (Bijay-Singh etal. 2008), and
overall ecosystem services loss (Wada etal. 2010), to name a few.
4.2 WFE Nexus: socioeconomic ramifications
The net upshot of the above is increased cost of water production for irrigation and mount-
ing competition among farming communities, with inevitable consequences for production,
income and livelihood opportunities. Among others, it is manifested by burgeoning socio-
economic inequity among agrarian communities on account of sustainable and equitable
Fig. 3 State-wise a percentage groundwater development (groundwater drafted as percent of availabil-
ity) and endangered administrative blocks and b groundwater availability. Discontinuity in the line graphs
results due to (1) none to extremely low groundwater development and/or (2) low use of groundwater
resources for irrigation. Negative values in panel “b” indicate scarcity in groundwater for irrigation due to
abstraction rate > natural recharge (bcm: billion cubic meters) Source: Groundwater Yearbook, 2016–17;
Central Ground Water Board
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access to irrigation resources (Srivastava etal. 2017; Kaur and Vatta 2015; Mukherjee
etal. 2014; Sarkar 2011). Large/wealthier farmers, with their superior economic capacity,
infrastructural support and political lobbies, largely dictate the proceedings of the irriga-
tion sector. It primarily ensues from the fact that the marginal/smallholding communities
(landholding < 2 hectares) are compelled to seek assistance from the larger/wealthier farm-
ers to maintain production and stay in business. It gives larger/wealthier communities the
command of rural sociopolitical processes, which they use to distort agrarian policies for
individual benefits. In many rural areas this disparate economic endowment has given rise
to groundwater markets—a highly corrupt informal water trading system in many rural
areas dictated by large/wealthier farmers who sell surplus irrigation water to the marginal/
smallholders in exchange of cheap labor and/or high-interest rate credit. In the process, it
entraps the marginal/smallholding communities in intergenerational cycle of debt and pov-
erty, eventually compelling them to “quit” farming or committing suicide.
4.3 WFE Nexus: implications forsocial capital development
Under present circumstances, pondering on pure technocratic solutions (e.g., solar-power
irrigation, high yielding seeds, precision agriculture, etc.) to resolve the irrigation crises
is naïve and reductionist approach. Unfortunately, this is still the model of agrarian devel-
opment/innovation pursued in India. However, growing burdens of the WFE nexus has
made the need of devising social solutions and incorporating them in development para-
digms, ever more apparent. It is a difficult proposition, given the dilapidating conditions
rural social system. Atomistic individualism—tendency of individual profit maximization
by hogging water and/or energy supplies—has compounded the social processes more than
ever (Badiani and Jessoe 2011; Shah 2010). By the same token, it also offers an opportu-
nity to the authorities to convince the farmers to get connected with each other (friend-
ship-/peer-advising network) and the field/extension agents (official-advising network), to
give into more collective interventions.
Current recommendation for alleviation of WFE-induced socioeconomic transforma-
tions primarily center on a set of inter-weaved policy interventions (Ringler etal. 2013):
• Implementing stringent irrigation water pricing framework—Pay for what you reap
• Revise/forfeit agro-power subsidy schemes—No free ride
• Promoting participatory irrigation management (PIM)—Strength in collective action
The above demands an INRM approach. However, INRM-based interventions have
inherent challenges due to myriad externalities (Wichelns 2017). First and foremost,
this demands regular and structured awareness campaigns among agrarian communities
(Official-advising network) to explain the merits of the integrated approach (Table3). For
example, paying for sustainable irrigation services raises the input cost of production. It is
a new concept and contradictory to traditional practices—free rides. Proposition of “paying
up front” makes them apprehensive of returns. Under the circumstances, it is the responsi-
bility of the field/extension agents to explain the trade-offs between short-term losses ver-
sus long-term gains. More importantly, the rates of payments have to be contextualized
keeping in mind the capacity of farmers, especially the marginal/smallholders who have
limited means to afford irrigation services. This demands consultation with the farmers. It
may take forms of workshops, focused group discussions, field days, advertorials on elec-
tronic and social media, etc.
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Table 3 Development of farmers’ networks to address irrigation-induced WFE nexus in India
Need Official-advising network (farmer-to-expert) Friendship-/peer-advising network (farmer-to-farmer)
Implementing irrigation water pricing Focused group discussions (FGD): dispelling myths/misconcep-
tion about irrigation water pricing
Consultation with farmers/farmers’ communities to determine
viable pricing rates
Introducing new technology: micro-irrigation (MI), laser land
leveling (LLL), etc.
Introducing new ideas/knowledge: groundwater conservation
(e.g., protection of recharge areas); rainwater harvesting; esti-
mation of soil–water potentials; less water-intensive varieties
Internal exchange of ideas about pros-n-cons of water pricing,
new crops, cropping techniques
Increase coordination between marginal/smallholders and
wealthier sections for equitable resource sharing
Cooperate with government officials for accurate billing and bill
collection
Growing community awareness against illegal groundwater
marketing
Pool up resources to devise local water conservation structures
Revision of agro-power subsidy system Brief farmers on economic losses and its negative implications
on irrigation power supply services on long-run
Introducing new technology: Develop ICT-based smart metering
system
Introducing new ideas/knowledge: consult with farmers for most
effective implementation of metering system
Advise farmers against political entrenchment of farm power
policies
Increase coordination between marginal/smallholders and
wealthier sections
Internal discussions on benefits of paying up for power supply
Protect meters and other electronic equipment from damage
Determine internal norms to operate pumps, and other electrical
equipment, only for stipulated period daily
Cooperate with government officials for accurate billing and bill
collection
Growing community awareness against illegal power piracy
Promoting participatory irrigation
management (PIM)
Establishment of water users’ associations (WUAs) for equitable
access to irrigation supply services
Train farmers on operation and maintenance of WUAs
Make farmers aware of roles and responsibilities in WUAs
Exchange views/experience about participatory irrigation manage-
ment (PIM)
Participate from planning to implementation stage of irrigation
infrastructure operation and maintenance
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However, wider acceptance new paradigms (especially ones which raise short-term eco-
nomic challenges) can only occur when there is information flow between farmers (friend-
ship-/peer-advising network) to discuss pros and cons. Farmers are known to be more
receptive to new paradigms when they receive positive feedbacks from co-farmers within
a network who have benefitted from them (Shaijumon 2018; Mwangi and Kariuki 2015;
Mignouna etal. 2011). Not until a neighbor has adopted and benefited from it, that they are
convinced about the gains. For example, Conley and Udry (2010) found that fertilizer use
in pineapple farming in Ghana is an outcome of social learning—farmers “adjust” applica-
tions rates based on information obtained from other network actors who have achieved
improved results. Under the circumstances, social learning via networks provides farmers
a tool of “risk aversion” (Ma etal. 2014; Feder etal. 2010). It is as Bock (2012) comments
“in order to be adopted, new products, technologies need to fit into a specific social context
with a specific organization of social relations and specific norms and values and accepted
behavioral patterns”.
Under the circumstances, irrigation authorities in India have to devise means to foster
bonds between farmers to expedite flow of information. Identify viable networks within
farming communities, and individuals who have high degree of centrality. Convince them
to adopt the new paradigm and influence others in the network to try them. It was time the
authorities realized that decision making is a collective phenomenon that demands partici-
patory action from a group of progressive farmers. Any new paradigm (new technology/
crop/cropping method) is almost always met with apprehension initially as it destabilizes
the age-old practices. Farmers are even skeptic about demonstration plots set up by the
field agents, doubting if the results could be replicated in their plots as well (Weber 2012).
Under the circumstances, the need is for judicious amalgamation of friendship-/peer-advis-
ing (farmer-to-farmer) with official-advising (farmer-to-extension specialists) network to
realize best results.
Networking is particularly beneficial in the marginal/smallholding circumstances due to
their: (1) inability to create scale economies, (2) low bargaining power because of low quanti-
ties of marketable surplus, (3) scarcity of capital, (3) lack of market access, (4) shortage of
knowledge and information, (5) market imperfections and fluctuations and (6) poor infrastruc-
ture and communications (Barham and Chitemi 2009; Bienabe and Sautier 2005). Informal
social networks among smallholders drive technology adoption by augmenting extension ser-
vices (Ma etal. 2014). It is centrality in agrarian development paradigms soars with rapid
land fragmentation and rise of marginal/small landholdings. Since the 1970s, proportion of
marginal/smallholding land in India has increased significantly, while that of the large hold-
ing declined (Table4). Presently, about 85% agricultural plots in India are of marginal/small-
holder type (< 2 hectares) (Singh 2012). Maintaining production, and income, off such small
landholdings demands more networking among farmers for dissemination of ideas/knowl-
edge/technology and build up a participatory framework of risk aversion (e.g., groundwater
Table 4 Decadal changes in agricultural handholding (million hectare) between 1970–1971 and 2010–2011
in India (value in the parentheses indicates relative percentage)
1970–71 1980–81 1990–91 2000–01 2010–11
Marginal (< 1ha) 36 (50.70) 50 (56.18) 63 (58.88) 75 (63.50) 93 (66.91)
Small(< 2ha) 13 (18.31) 16 (22.54) 20 (18.69) 23 (19.17) 25 (17.99)
Medium 19 (26.76) 21 (29.58) 22 (20.56) 21 (17.50) 20 (14.39)
Large 3 (4.23) 2 (2.82) 2 (1.87) 1 (0.83) 1 (0.72)
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depletion, energy shortage, etc.). This laid the foundations of the Water Users’ Association
(WUA) in the early 1990s, to encourage farmers to manage irrigation systems themselves (dis-
cussed in details in Sect.5.2).
4.4 Contemplating farmers’ social network: ground facts
Contingent to network development is policy amendments to meld technocratic solutions—
systems’ modernization via new technology adoption (e.g., solar-powered irrigation pumps)—
with social innovations within agrarian development paradigms (Bock 2012; Pol and Ville
2009; Moors etal. 2004). However, experiences around the world suggest that the adoption
decisions are keyed to information flow (farmer-to-farmer as well as farmer-to-field/exten-
sion agents) within a network (Table5). For inclusion in relevant policy mainframe, there is
need for the authorities to understand the synergies and trade-offs between a set of exogenous
and endogenous factors they influence technology adoption decisions, and for that matter, any
newly introduced paradigm (Fig.4). However, diffusion of knowledge is deterred by several
externalities, mutually reinforcing on multiple levels rural lives and livelihoods (Table6):
• Differences in viewpoints among participant groups/individuals
• Lack of trust (between farmers; and between farmers and experts)
• Poor economic and/or intellectual capacity (Hammers etal. 2015; Beers etal. 2014)
• Diminishing workforce (Erikson and Selboe 2012)
• Corrupt sociopolitical influences
• Lack of focused financing
What dents policy initiatives further is gendered dimension of agrarian societies that keeps
a vast rural workforce—women—out of participatory initiatives. Establishment of mothers’
groups, women’s self-help groups (SHGs), food security groups are effective means to foster
liaisons among farmers/farmers’ communities (Chhetri etal. 2011). In India, rural women are
engaged in various farm activities including land preparation, fertilizer application, transplant-
ing, harvesting, grading, threshing and a great deal of other activities (Pal 2015). However,
they are contractual labors, underpaid, and kept from decision making. Their role in agrar-
ian social networks is acknowledged in most rural societies. A vast fraction of women farm
workers are yet uneducated (unaware of legal rights, environment, farming innovations, etc.),
unskilled, lack rights to productive resources such as land and livestock and constrained by
myriad social norms/taboos (Banerjee etal. 2016; Lal and Khurana 2011). Under the circum-
stances, a prime requirement is to give into women empowerment, as studies around the world
show that women farmers have greater sensibility and ease of communication and can mediate
between conflicting parties (Vasylaki and Kenneth 2016; Guo and Marchand 2013; Thuo etal.
2013). All of the above are prerequisites of viable networks. Unfortunately, such informed
decisions at policy level are yet gravely lacking.
5 Farmers’ networks andcollectives: examples fromIndia
The earliest example of network literature in India probably comes from Munshi (2004),
evaluating on rice and wheat growers during the Green Revolution. The study observed
that social learning aided in marketing and labor management, expedited flow of informa-
tion, and influenced technology adoption (Munshi 2004). However, network studies are yet
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Table 5 Empirical studies demonstrating influence of social learning on adoption decision making (new technology/crop/cropping technique)
Study Country Method of analysis Data type Salient feature
Bandiera and Rasul (2006) Mozambique Regression analysis Random sampling survey; 9 villages;
204 households
Adoption of new crop of farmers hav-
ing better information about the new
crop are less sensitive to the adoption
choices of others
Adoption decisions are more correlated
within family and friends than reli-
gion‐based networks
Cai etal. (2015) China Qualitative data analysis Random sampling survey; 185 vil-
lages; 5332 households
Weather insurance is dependent on
network-driven social information
diffusion influenced by farmers with
better knowledge/experience about
insurance policies
Guo and Marchand (2013) China Spatial autoregressive model (SAR)
fitting
Panel data; 8 farmers’ groups For conventional farming, smallholder
farmers mostly rely on their own
knowledge/skills
Adoption of a new farming practice
(“organic farming”) necessitates
farmer-to-farmer and farmer-to-peer
learning
Social learning substantially improves
farmers’ capacities to adapt new para-
digms to local conditions
Conley and Udry (2010) Ghana Regression analysis Two year survey identifying 180 of
550 households; 132 farmers
Farmers tend to adjust their fertilizer
inputs based on information/advice
from “successful” neighbors while
ignore that from those who failed
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Table 5 (continued)
Study Country Method of analysis Data type Salient feature
McNiven and Gillian (2012) Uganda Qualitative data analysis 48 farmers’ associations of variable
sizes
With expanding dimensions of a social
networks and increasing ties among
the actors involved, adoption likeli-
hood of a new crop (“orange-fleshed
sweet potato”—high yielding and rich
in vitamin A) increases
The increase is proportional to number
of “informed” actors (trained with
cropping skills) within the network
Thuo etal. (2013) Uganda and Kenya 2 econometric models: seemingly
unrelated bivariate probit (SUBP);
recursive bivariate probit (RBP)
Random sampling; 461 farmers: 232 in
Uganda, 229 in Kenya
Ties with external support (researchers,
extension agents) influence adoption
decision for new crops (“groundnut”)
Mekonnen etal. (2016) Ethiopia Random matching within sample
technique
379 households Identifying right network as key to
adoption of new cropping routines
Networks that exclusively facilitate
agro-information needs to be identi-
fied and trained
Gamboa etal. (2010) Ecuador Network analysis with NetDraw
software suit
Semi-structured survey of 135 farming
households
Ethnicity and differences in sociocul-
tural structures (with regards to ease
of information access) may cause
differences in technology adoption
(agroforestry species)
Ramirez (2013)USA Social network analysis (SNA) with 37 farmers in Texas Participation in network as key to irriga-
tion technology adoption—reducing
water usage via improved water use
efficiency
Ownership types (the farmer is a
landowner or share-cropper) is a deter-
minant of participation
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Table 5 (continued)
Study Country Method of analysis Data type Salient feature
Poudel etal. (2015) Nepal Network analysis with UCINET and
NetDraw software suits
24 farmers using sociometric survey
model
Development of social seed systems to
maintain/enhance genetic diversity
Farmers with high centrality are useful
to regulate seed flow through the
network
Ma etal. (2014) Pakistan Correlation analysis —PROBIT model
and multinomial model
Panel data; structured survey of 728
cotton farmers from 52 villages
Information flow within farmers’ net-
works positively influences adoption
decision of Bt cotton cultivation
Weber (2012)Peru Correlation analysis Panel data; structured survey of 315
farmers
Technology diffusion depends more
on farmer-to-farmer social learning
than field/extension agent-to-farmer
networking
Adoption rate improves with second/
third-party diffusion via word of
mouth, and information bulletins
through electronic media
Incentivizing farmers helps expedited
adoption of new paradigms
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scanty and unorganized in India. Most agrarian studies yet do not approach farmers’ col-
lectives to establish network structures and/or explore network traits per se (e.g., measures
of centrality, density, etc.). Following are examples where farmers’ networks of sorts have
been noticed.
Kerala Social networking plays a vital role in dissemination of new knowledge and
information. With an empirical analysis involving a group of farmers attending workshops/
training from village resources center (VRC) in Kerala, Shaijumon (2018) showed that liai-
son between the experts and farmers (Official-advising network) impacted likelihood of
adoption of new paradigms. Interestingly, VRC nonattendees linked up with the attendees,
and benefitted as well (Peer-advising network). Among others, the study underscored the
need of a robust institutional system (e.g., VRC) to promote networking among farmers.
Fig. 4 Interactions between various exogenous and endogenous factors that influence technology adoption
decision making in agrarian sector
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Table 6 Ground challenges to establishing farmers’ social networks
Barrier Key concerns Impacts on social networking
Differences in opinions Political rivalry, socioeconomic inequity, cultural beliefs
Atomistic attitude—personal prosperity over commonwealth
Divergent viewpoints/objectives (Tisenkopfs etal. 2014)—conflicting
“values” about natural resources ownership and management
Rise of opportunistic tendencies
Over-exploitation of natural resources (resource wars and political
entrenchment of policies)
Restricted opportunities of participatory action and co-production (low
resource use efficiency, inequity in resource accessibility)
Relation of Trust Farmer-to-farmer relations of trust (fundamental to social collabora-
tion/learning)
Relations of trust among farmers and with peers (e.g., extension
agents, private service providers, NGOs, etc.) (Herman etal. 2015)
Restrict opportunities for collective learning about resource use optimi-
zation
Reduced opportunities of skill/experience sharing
Growing distrust on public policies/officials (Oreszczyn etal. 2010)
Apprehensions about new paradigms (new technology/farming routine/
crop)
Resources and capacity Lack of desired mindset to imbibe new solutions (Hammers etal.
2015)
Lack of aptitude/experience to recognize the urgency of need and seek
assistance (knowledge/information/technology)
Lack of training about changes in external environment (Beers etal.
2014)
Reduced potential to try/adopt new paradigms
Reduced knowledge about mitigation–adaptation techniques
Reduced understanding of need to networking and/or collective learning
Reduced potential to participate and/or contribute to social networks
Workforce Declining population of farmers
Seeking alternate professions
Rural to urban migration
Restricted dimensions and flexibility of social networks
Limited scope and impact
Growing apathy for public policies/officials
Political dynamics Corruption and biased decision making
Favored policy-making for individuals/communities who are critical
for electoral dynamics
Repealing economic policy reforms to bolster natural resources man-
agement framework
Distortion of agrarian policies in favor of large/wealthier communities
Inequitable access to irrigation resources
Growing split between large/wealthier and smallholder communities
Reduced extension services or public support in regions which are not
the vote banks
Distrust/discontent on public policies/officials, new paradigms, etc.
Public–private partnership Lack of focused funding in social innovations
Limited development of farm-related knowledge/information database
Lack of capacity building
Lack of research in social innovation
Reduced understanding of environmental processes
Reduced access to latest developments in natural resources management
Reduced knowledge about networking techniques
Reduced
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Manipur Jyothi and Devarani (2019) conducted and empirical analysis involving 64
farmers pooled from eight villages in the Imphal district to understand the role of farm-
ers’ networking in adoption of the CAU-RI variety of rice by computing different forms of
“centrality” measures within sociometric network analysis (SNA). Outcomes revealed that
social participation and trainings were positively correlated while the farming experience.
However, the authors stressed on the need for a more concerted effort by the farmers and
stakeholders to sensitize farmers about the variety through exposure visits, trainings, incen-
tives and timely input supply.
West Bengal Goswami and Basu (2010) analyzed a group of farmers in Nadia Dis-
trict, West Bengal to evaluate impact of networking on adoption of horticulture crop types
(banana and guava) employing SNA techniques. It was found that both in the spread of
banana and guava cultivation, most of the farmers who had higher network scores were
earlier adopters and vice versa. This indicates that farmers’ adoption decision regarding
new crops is an outcome of their relative position in social networks.
Uttar Pradesh Magnan et al. (2014) carried out a field experiment to understand
the impact of social learning on technology adoption—Laser Land Leveling (LLL) in
rice–wheat cropping system- in Uttar Pradesh. Results showed that adoption is heavily
influenced by farmer-to-farmer interactions (Friendship-/Peer-advising network). It rises
from the fact that farmers are more akin to adopting new paradigms only after receiving
positive feedback from their own kin. However, there is also need of Official-advising net-
works (experts-to-farmers) for acquiring information and training with modern equipment.
Most importantly, the study found that, impact of networking was strongest between poor
farmers. The study particularly demonstrated the growing impact of WFE nexus on agrar-
ian livelihoods. Under uneven field conditions (undulating/sloping/rutted, etc.), much of
the irrigation water is wasted, and energy alongside, aggravating the WFE nexus. Under
the circumstances, LLL
Telengana and Jharkhand In a study Agarwal (2018) demonstrated that “group farm-
ing” could substantially outscore “individualistic” family farming, yet the most common
practice in India. Conducting surveys in the state of Kerala and Telengana, the study dem-
onstrated that group farming potentially provides the participant farmers a variety of ben-
efits that include (a) economies of scale, (b) a sizable and dependable labor force, access
to more investible funds and diverse skill/tool-sets, and overall, elevates bargaining power
with governments and markets. However, level of profitability (yield and income) is tied to
several factors including the technical support the groups receive from peers, institutional
arrangements, composition, land accessibility, selection of crops and cropping methods,
etc.
Gujarat A variant of farmers’ network is the Farm Producers Organization (FPO). In
a study in Avirat, Gujarat, Bikkina etal. (2015) found that the FPOs help the participant
communities lower their farm input costs by availing of government subsidies on prices
of seeds and farm equipment. It ensued from better bargaining power that benefits all par-
ticipating groups instead of individuals. In addition, the FPO provides crop-based train-
ing to participants and encourages farmer-to-farmer learning (“friendship” network type).
The study indicated that the FPO facilitated in technology adoption as well, introducing
microirrigation (drip and Sprinkler) to participants. It also facilitates “official” network-
ing—between participating farmers and universities—on best management practices so as
to transfer knowledge from laboratory to the farm.
Maharashtra Maharashtra showcases another form of networking in form of Producer
Companies can significantly improve farmers, and especially the smallholding communi-
ties, opportunities to participate in emerging high-value markets, such as the export market
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and increase accessibility to modern retail sector (Trebbin and Hassler 2012). Apart from
providing marketing benefits (e.g., direct interface with buyers), it leads to better trans-
portation, storage facilities, production planning, and above all, access to extension ser-
vices and new technology. The latter is demonstrated by stronger liaisons between farmers
(friendship and/or peer-advising network) as well as between the farmers and extension
agents (official-advising network).
However, farmers’ networks/collectives of any sorts, face a variety of challenges that
include (a) lack of operational credit; (b) high resource sharing for small/marginal commu-
nities with no or little capital support from the government; (c) lack of clear policy direc-
tives in government department; (d) lack of recognition by central/state government, etc.
(Venkattakumar and Sontakki 2012; Bikkina etal. 2018). In view of the above, Agarwal
(2010) provided a set of “thumb rules” for networks: (1) small size, (e.g., groups of 10–12
or 15–20 individuals); (2) socioeconomic homogeneity or marked social affinities among
members; (3) participatory decision making in production, management and distribution;
(4) checks and penalties for containing free riding and ensuring accountability; and (5)
group control over the returns and a fair distribution of the benefits, as decided transpar-
ently by the members.
6 Water Users’ Association (WUA): participatory action framework
amongfarmers/farmers’ communities inIndia
Water users’ association (WUA) in India is a particular example where different network
types potentially intermingle. It was fundamentally conceived to encourage farmers to
come together in Participatory Irrigation Management (PIM) framework (Phadnis etal.
2010), under the aegis of the Ministry of Water Resources as part of National Water Policy
1987. During the early 90s, participatory irrigation management (PIM), through Irrigation
Management Transfer (IMT) to farmers, became an appropriate mechanism to raise irriga-
tion water efficiency (Swain and Das 2008). However, the effort dates back to early 80s,
when the idea of including farmers’ in the mainframe of irrigation decision making began
to take shape. By late 80s it was realized that such arrangement is not viable without giv-
ing it a formal structure (farmers’ organization). With time the idea evolved into one that
should motivate the farmers to own and manage their irrigation details themselves. This
laid the foundation of PIM—creation of farmers’ organization and turning over the sys-
tem to the farmers. By late 90s, most state governments have made policy decisions to
implement PIM. The governing idea was to engage farmers’ organization (bonding net-
work) in planning, operation and maintenance (O&M) of their own irrigation infrastruc-
tures under the supervision of experts (bridging network). Envisioned that way, benefits
of PIM include effective and economic maintenance of irrigation infrastructure, improved
distribution equity of irrigation water, increase in irrigated area, freedom of crop planning,
effective use of surface and groundwater, increased cost recovery, and higher income to
farmers (Kulkarni etal. 2011).
Sixteen of 29 Indian states have so far established their PIM acts including Andhra
Pradesh and Goa (1997), Madhya Pradesh (1999); Rajasthan, Tamil Nadu and Karnataka
(2000); Orissa and Sikkim (2002); Bihar and Kerala (2003); Assam (2004); Maharashtra
(2005); Chhattisgarh (2006); Gujarat (2007); Uttar Pradesh (2009); and Nagaland (2013).
However, out of about 67.5 million hectares of net irrigated area in the country, only about
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14.5 million hectares are currently under WUA coverage, with stark heterogeneity among
states regarding level of implementation (Fig.5).
The above owes to multidimensional concerns that limit effectiveness. First and fore-
most, the famers are apprehensive and reluctant to accept responsibilities of irrigation man-
agement (Bhatt 2015). Farmers’ have little trust in the ideal of WUA—it was envisioned to
benefit them collectively. Farming communities around the world, including India, view
new reforms with suspicion as they disturb age-old practice, those tried and tested through
generations. Reforms like PIM demand explicit users’ involvement at every stage of water
management decision making, which is new paradigm. Farmers view it “impositions” by
the authorities and government privatization process of sorts to divest the problem of dis-
tribution and operation by “dumping” responsibility to farmers (Swain and Das 2006).
Farmers feel that the government is merely trying to shift responsibility to them, without
providing any additional benefits (Playan etal. 2018). A way to address it is to provide tan-
gible economic returns, benefits of governance and management exceeding input cost (Ul
Hassan 2011), to boost up participation (Hamada and Samad 2011; Ounvichit etal. 2008).
However, it is yet far from reality.
Bardhan (2002) stressed on revamping the ICT scheme—workshops, interviews,
focused group discussions with local farming communities (Official-advising network) to
elicit its organizational structure, functional procedures, provision of assets, infrastructural
design, and stakeholders’ rights and responsibilities. However, caution must be taken to
ensure that such methods do not end up in a top-down model of systems’ governance. It
is, however, mostly the case yet in India, Poddar etal. (2011) found out while conducting
studies of WUA in the Krishna Basin. Similar observations are also reported from China
(Hu etal. 2014), Turkey (Aydogdu etal. 2015), Ethiopia (Yami 2013) as well. It means that
the proportion of official-advising network (field/extension agents-to-farmers) still domi-
nates the systems’ mix, while as discussed in earlier sections, there should be a more bal-
anced approach to blend official-advising with friendship-/peer-advising network for wider
diffusion of ideas/knowledge and make farmers’ feel comfortable with the ideas. This can
only happen when famer’s get the opportunity to discuss advantages and drawbacks of the
system among themselves within a network structure.
Fig. 5 State-wise percentages of net irrigated area covered by Water Users’ Association under the participa-
tory irrigation management (PIM) initiative
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Overall, negative externalities associated with WUAs include (1) socioeconomic het-
erogeneity, (2) internal power struggle and lack of leadership, (3) lack of appropriate ICT
(Information, Education and Training) for capacity building, (4) inherent systems’ ineffi-
ciency, (5) lack of legal back-ups, (6) financial constraints, (7) lack of external support,
(8) lack of regular monitoring and evaluation, (9) lack of flexibility in reform programs,
and (10) lack of political commitment (Playan etal. 2018; Kulkarni etal. 2011; Swain and
Das 2008). The first three are the fundamental pillars PIM-based initiatives, and by the
same token, the main impediments. For example, socioeconomic heterogeneity is under-
pinned by differences in opinions and aspirations—political, social, cultural, economic,
etc. It causes power struggles within/among communities while electing committee mem-
bers. Elite farmers view it as means to advance on their political ambitions and/or rise in
rural social hierarchy. The net upshot is, opportunities of farmers networking/social learn-
ing is throttled. It is, as Ananda and Crase (2006) suggested, there should be a “learning by
doing’ approach to determine the organizational structure of WUAs, accounting for con-
textual challenges, such as sense of inequity, psychology, and cultural heritage of the farm-
ers, to realize full potentials of a participatory system as WUA.
In this regard, studies around the world advocate on harnessing “sense of ownership”
among the farmers about the system, and cultivating strong community feeling about com-
mon pool resources (e.g., water and energy) (Ostrom 2011). However, it demands certain
key design principles associate with WUAs (Cox etal. 2010; Naiga (2018). The key idea
is to establish a robust set of rules for appropriation and provision among the local water
users and ensuring high a level of compliance to the rules (Table7). Certain factors that
deserve special consideration while giving into such community-based initiatives include
(1) size of the water users’ community, (2) types of socioeconomic/cultural heterogeneity
within and between communities, and (3) type of governmental regime within which the
water users’ communities operate.
7 Network literature: research shortfalls
Maertens and Barrett (2013) provided a detailed notion on challenges faced by researchers,
and policy-makers, in ascertaining accurate impacts of networks on social learning among
agrarian communities. First and foremost, the authors caution about data collection at the
fundament to “establish” a network structure. To that end, they cautioned about issues
involved in common sampling techniques:
• Snowball—the survey subjects recruit further respondents for sampling. It is beneficial
to study the network characteristics itself but results in nonrepresentative sampling of
households.
• Network Within Sample—asking each farmer about his link to every other person in
the sample. However, a limitation is that it artificially truncates the network and might
result in biased estimates of behavior in the presence of structured networks as “un-
observables” influence both the probability of a link and, independently, the behavior
of interest (Santos and Barrett 2008). A way to deal with that is probably to ask the
farmer to list a certain number of people (typically 5 to 10, or in some cases unlimited)
from whom he learns (Bandiera and Rasual 2006).
• Random Matching Within Sample—Each farmer is matched with a certain number (typ-
ically 5 to 10) of randomly drawn individuals from the sample and, for each match, one
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elicits the details of the relationship between the farmer and the match, including possi-
bly the farmer’s knowledge about the match’s farming activities and outcomes (Conley
and Udry 2010; Maertens 2017). This method is better suited for network analysis than
the Network Within Sample Method (Santos and Barrett 2008). it can be integrated
in a time efficient manner within an existing sample, and one can use predicted links
as generated regressors in subsequent regression analysis, one needs to be wary in the
presence of certain network structures. If the sample omits a key network node, i.e.,
someone with many links compared to others, the resulting omitted variable bias can be
substantial.
Second, data should be able to distinguish between confounding variables that may
lead to spurious correlation among agents’ behaviors. In this regard, georeferencing data
points is essential, to link to biophysical information available in soils, climate and agro-
nomic databases; behavioral experiments to elicit otherwise—unobservable parameters
for risk and time preferences, trust, etc.; elicitation of agents’ subjective beliefs about dif-
ferent technologies and their traits, market prices, etc. Third, researchers should test for
different learning and social interaction models against one another, instead of imposing
a particular model on the data. This is necessary to understand the sequencing of inter-
actions and the nature of information flows. Fourth, there is need for the economists to
Table 7 Fundamental design principles to harness sense of ownership about the system
Principle Salient features
Establishing well-defined boundaries Users’: Demarcation between legitimate water users and non-
users, or illegal users (users who illegally divert water from
main sources to deprive legal users)
Resources: clear characterization of the resource system
(water) to isolate it from the larger biophysical environ-
ment
Ensuring congruity Applying appropriation and provision rules (extent benefits
reaped by users should be proportional to their individual
input costs in form of labor, and/or material and/or money)
in congruence with local social and environmental condi-
tions/challenges
Arrangement of collective choices Most individuals affected by the operational rules can par-
ticipate in modifying them
Conduct timely monitoring Users’: monitoring of level of compliance to appropriation
and provision rules
Resources: physical conditions of resources
Graduated sanctions Violators of the appropriation and provision rules should
receive graduated sanctions by a common (and impartial)
committee formed by either local water users or govern-
ment officials
Transparency in conflict resolution protocols Local water users and their appropriators should be equipped
with adequate and sustainable means to gain rapid access
to low-cost local arenas to resolve disputes
Minimal recognition of rights to organize Independent institutions developed by appropriators are not
challenged by external public officials/system
Nested enterprises The above facets are arranged in logically meaningful and
mutually reinforcing layers of systems’ governance
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relax the widespread assumption that social interactions’ effects on technology adoption
reflect “learning” as distinct from nonlearning social interaction effects. Moreover, defi-
nition in different studies lack clear demarcation of types of interactions (e.g., by infor-
mation, goods, services, or participatory action around a common goal) among farmers
and thus may give rise to different network, weighted or un-weighted, leading to varying
structures and features of the network. In addition, most studies talk about “structured” or
“semi-structured” interviews to collect information. However, they rarely provide detailed
accounts of the questionnaire or parameters that determine structuredness.
Another confounding issue, frequently encountered in network studies, is the effect of
“unobserved” variables (Ma etal. 2014). True representation of the network demands care-
ful isolation of the endogenous social network effect (community choices on individual
behavior) from the effect of exogenous shocks on farmers’ technology adoption decisions.
The reason being, farmers making the same adoption choice as their neighbors may simply
do it because they share the same characteristics and conditions with their neighbors and
not because they learn about the technology from these neighbors. Presently, there are two
schools of thought to approach the problem (Weber 2012).
• Relating lagged aggregates to the adoption decisions of an individual farmer (Munshi
2004; Moser and Barrett 2006); however, the challenge is to deal with serially corre-
lated unobservable variables that would lead to correlation between lagged aggregate
measures and individual behavior.
• Identification of an individual’s reference group and links information from the refer-
ence group to the individual’s behavior (Bandiera and Rasual 2006; Conley and Udry
2010).
To that end, the literature suggests that network studies should consider incorporat-
ing panel data as it allows the researcher to difference out the unobserved time-invariant
factors that affect farmers’ adoption behavior (Ma etal. 2014; Conley and Udry 2010).
Another option to deal with the above is to use randomized control trials (RCTs) to identify
the network effects by comparing adoption choices between the control group and the treat-
ment group (Duflo etal. 2007; Duflo and Saez 2003; Magnan etal. 2014).
Moreover, there is yet comprehensive discussions on different interactions (e.g., by
information, goods, services, or participatory action around a common goal) among farm-
ers, that may lead to different network, weighted or un-weighted, leading to varying struc-
tures and network attributes. This demands careful deliberation from the global research
community to develop suitable sampling design questionnaire for interviewing. However, a
hitch therein is, on many occasions it is observed that farmers are unaware of each other’s
networks, i.e., with whom their learning contacts themselves link, which limits what the
farmer can learn about his contacts’ beliefs. Defining a network, under such circumstances,
demand informed survey by individuals/agencies who are aware of the region and sociode-
mographic texture.
8 Harnessing social capital: strategic interventions
A prime hindrance to plan social capital is stark social heterogeneity among rural agrarian
communities in India—underpinned by differences in opinions/ambitions and lack of com-
munity feeling. However, it was time the ideas of sustainable development converged on a
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Reflections onfarmers’ social networks: ameans forsustainable…
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key question: what does it take to achieve the social transformation that bridges between
competing aspirations? It is an appeal as much to the national scholarship, as it is to the
policy gurus, which will decide the course agrarian development trajectory in days ahead.
Literature suggests that increasing sociocultural endowment could be a viable means to
address it (Chhetri etal. 2011). Though there is no one-size-fits-all solution, certain strate-
gies could be considered on ground:
• Train future trainers (TTT ): identifying certain nodal persons (“broker”) within a net-
work, usually with a high degree of centrality, and train them so that they can train
the others in the network in turn (Bodin etal. 2006; Burt 2003). The trainer should
be well aware of strength and weaknesses of all network actors and assign roles and
responsibilities accordingly. However, a challenge therein is to select trainers with cer-
tain qualities: demonstrated expertise in farming (different crops, cropping routines);
tacit knowledge of environment, sociodemographic traits, political dynamics, etc.; good
communication skill; reachability; honesty; impartiality, etc. (Lukuyu etal. 2012).
• Strategic Management and Development of Human Talents (SMDHT)—In collabora-
tion to the above, farmers in a network should be considered as assets and cultivated for
their skills and talents (Fig.6) (Mbabu and Hall 2012). The main idea is that, contribu-
tions of every actor in the network should be valued and each should be encouraged
(incentivized) to perform better to meet network objective. However, it would require
strategic integration of organizations and farmers on multiple levels to make the lat-
ter aware of their individual roles and responsibilities within a network, understand
broader objective, and work collaboratively to improve resource use efficiency.
• Incentivizing “Progressive” Farmers—Providing incentives to “progressive” who are
willing to test new paradigms (technology/crop/cropping techniques) can significantly
improve the likelihood of information flow through a network. Demonstrating the case
of coffee farmers in Peru, Weber (2012) found that rewarding farmers, who hold central
position in the network, for experimenting new cropping technique (pruning) expedited
diffusion of idea. However, he warned against cash incentives and rather advocates
“constant presence” among participating farmers, which builds farmers’ confidence and
credibility of government agents.
The above is keyed to sound capacity building program (CBP) to work with farmers at
the grass-root level to establish ground rules of networking. A word of caution therein is,
CBP has to be carefully contextualized—not copycats of foreign paradigms/examples—to
local conditions. In the least, an effective CBP: (a) earns lasting commitment of farmers
to participatory action, (b) help farmers envision future needs and prospects, (c) conduct
capacity need assessment, and (d) strategize implementation (Saravanan and Suchiradipta
2017). Concurrently, arrangements must be set up for continuous reflection and re-scaling
of CBP, periodic monitoring and evaluation of outcomes. Such multifaceted action plan
builds upon on an enabling institutional arrangement that formulates and regulates network
dimensions, and develops contextualized solutions for information flow.
The above locate at the intersection of the sphere of development and practice on mul-
tiple levels of agrarian lives and livelihoods. Both CBP and institutional arrangement, by
definition, exemplify the Official-advising network (field/extension agents-to-farmer).
Unfortunately, there is yet little conscious effort/awareness in India at the policy level to
appraise the need of a truly multi-party governance system that could bridge between vari-
ous organizational domains, and facilitate participatory actions (Table8) (Saravanan and
Suchiradipta 2017; OECD 2005).
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9 Conclusion
A systematic review of global literature reveals that means to incorporate of social
capital within the policy mainframe should be an integral part of sustainable agrarian
development initiatives. The sociocognitive aspect of human behavioral traits suggests
that famers are generally apprehensive of new paradigms when there is a top-down
approach. Farmers are only willing to indulge in such when they receive positive feed-
back from their neighbors/peers who have benefitted from the same. What is means is
that adoption of new agricultural paradigms (new technology/crop/cropping method) in
agrarian societies is a collective process and keyed to the level of information flow:
Fig. 6 Organizational levels of a human talents management and development system (HTMDS)
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Table 8 Institutional domains and their potential contributions to facilitate social networking/learning
Domains Organizations Functionalities
Research (Producing codified knowledge) Universities
Private centers/companies
Foundations, NGOs
International Organizations
Assessment of network organization
Monitoring (long-term data generation)
Knowledge creation and data dissemination
Developing social networking/learning methods
Provide extension services
Formulate policy guidelines
Enterprise (users of codified knowledge, producers of tacit
knowledge)
Farmers
Farmers’ associations
Agro-industry and processors
Input supply agents
Transporters
Traders
Core module of social networking/learning:
Promotion of knowledge/information exchange
Skill development
Support Banking and financial systems
Marketing infrastructure
Professional networks
Education systems
Financing for social networking/learning
Providing credits and insurance
Adding value to goods and services,
Ensuring infrastructural development,
Maintaining food value-chains productive and sustainable
Demand Consumers (food and food products)
Consumers (industrial raw materials)
International commodity markets
Policy-makers (public agencies)
Facilitating institutional innovation (legal and regulatory
framework) for networking
Removing agricultural policies that distort market trends
Intermediary Extension and advisory services
NGOs and development agents
Trade associations
Consultants
Donors
Facilitating of social learning
Technology development and penetration
Facilitating capacity building
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farmer-to-farmer (friendship-/peer-advising network) and field/extension agents-to-
farmer (official-advising network).
The main idea perpetrated through this study was need of social capital development to
alleviate the growing burden of water–food–energy (WFE) in the irrigation sector. It evalu-
ates feasibility of different recommendation (groundwater pricing, revision of agro-power
subsidies, etc.), to argue that without social networking/learning among agrarian commu-
nities, information flow is deterred, and thus, likelihood of wider implementation of pro-
posed interventions. In the process, the study presents numerous case studies from different
parts of India, to reflect on ground challenges. A disturbing fact, as revealed by the study
is, lack of yet any clear policy directive and institutional arrangement, to develop social
capital. The idea of getting connected within a participatory framework is yet a foreign
concept to Indian farmers and viewed with apprehension. The above is a fundamental chal-
lenge to implementing WUAs. Through a comprehensive review of national and interna-
tional literature, the study shows that top-down model of system’s governance is the major
stumbling block of implementing Participatory Irrigation Management (PIM) in India.
In the process, the study discusses the types of networking: apparently, farmer-to-farmer
bonding (friendship-/peer-advising network) is as essential as field/extension agent-to-
farmer (official-advising network), for wider dissemination of new paradigms (e.g., PIM).
However, there is yet any concerted effort at policy level to facilitate such a careful mix of
bonding types. Farmers believe the government is avoiding their responsibility and merely
trying to “dump” the same on farmers, without providing tangible benefits. In the process,
the study reflects on prime factors that undermine social networking opportunities in India
at the fundament.
But the above is largely keyed to the fact that formal network studies are yet scanty and
unorganized in India to help authorities capture the full spectrum of challenges involved.
Fundamentally, Efforts to establish network structures (e.g., measures of centrality, den-
sity, etc.) is yet sparse. A word of caution therein is, choice of sampling design. Relevant
literature suggests that network studies are frequently confounded by “unobservable” vari-
ables that often hinder drawing accurate conclusions about new technology adoption. It
leads from the fact that true representation of the network demands careful isolation of the
endogenous social network effect (community choices on individual behavior) from the
effect of exogenous shocks on farmers’ technology adoption decisions. In the process, the
literature advocates the use of panel data sets. However, collection of such data is cumber-
some and demands experts’ choices. Overall, the present study was a modest attempt to
urge research communities and practitioners to conduct more in-depth research into the
theory and practice of farmers’ social networks to be able to make policy-relevant recom-
mendations for sustainable development.
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Aliations
SriroopChaudhuri1· MimiRoy1· LouisM.McDonald2· YvesEmendack3
Mimi Roy
mroy@jgu.edu.in
Louis M. McDonald
LMMcdonald@mail.wvu.edu
Yves Emendack
Yves.Emendack@ars.usda.gov
1 co-Director, Center forEnvironment, Sustainability andHuman Development (CESH), Jindal
School ofLiberal Arts andHumanities, O.P. Jindal Global University, Sonipat, Haryana131001,
India
2 Davis College ofAgriculture, Natural Resources andDesign, West Virginia University,
Morgantown, WV26505-3740, USA
3 USDA-ARS, Lubbock, TX79415, USA
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6.
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