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Adoption and Abandonment of Conservation Technologies
in Developing Economies: The Case of South Asia
Alwin Dsouza,
Graduate Student and Graduate Research Assistant,
Arizona State University,
Ashok K. Mishra,
Professor and Marley Foundation Chair,
Arizona State University,
Selected Paper prepared for presentation at the 2016 Agricultural & Applied Economics Association
Annual Meeting, Boston, Massachusetts, July 31-August 2
Copyright 2016 by Alwin Dsouza and Ashok K. Mishra. All rights reserved. Readers may make verbatim
copies of this document for non-commercial purposes by any means, provided that this copyright notice
appears on all such copies.
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Introduction
Malthus, a 19th century pastor and scholar claimed in his “An Essay on the Principle of Population”
that if population growth was not checked, it would grow exponentially while the resources would
grow arithmetically leading to food insecurity and social problems. This seemed to be true during
the 1960s in South Asia when high population growth and food scarcity became a serious issue,
particularly, during which India was at the brink of famine (Khush, 2001). But the claim stood
falsified when “miracle seeds” also known as high yielding varieties (HYV) of maize, rice and
wheat crops were introduced in South Asia (Jacoby, 1972). As the adoption of HYV spread across
South and East Asian countries, productivity of rice, wheat and maize rose significantly, along
with increased use of labor and land. In addition, the growing periods for these crops became
shorter resulting in increased scope for multiple cropping (Pinstrup-Anderson & Hazell, 1985).
This phenomenon led to increased yields and profits of farmers, in South Asian countries,
especially in states of Punjab, Haryana and Uttar Pradesh, India.
A requirement for HYV is that farmers have to use higher amounts of fertilizer and water.
Over four decade use of HYV seeds has led to an over exploitation of water resources and soil—
drastically lowering ground water table and degrading soil fertility, especially in areas of rice-
wheat cropping system (Byerlee & Siddiq, 1994; Hobbs & Morris, 1996; Rahman, 2003; Morris,
Dubin, & Pokhrel, 1994). Increased fertilizer use, however, did not compensate the over-use of
soil. HYV rapidly deplete micronutrients from soils and chemical fertilizers, unlike organic
manures which contain a wide range of trace elements, did not compensate for the losses of
micronutrients. Five decades later farmers in South Asia and India in particular, are facing several
problems and the need for resource conservation is paramount. With falling yields, increasing
energy, fertilizer and input costs, agriculture in South Asia is becoming an unprofitable
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proposition. This would have an adverse impact not only on the income of farmers but also poses
a greater threat to food security of smallholder and marginal farmers. Food security for a growing
population in most South Asian countries, while sustaining agricultural systems under the current
scenario of depleting natural resources, increasing costs of inputs, and climate variability calls for
a paradigm shift in farming practices. This requires eliminating unsustainable parts of conventional
agriculture (ploughing/tilling the soil, removing all organic material, monoculture) and adopting
agricultural systems that conserves resources for productive agriculture.
Conservation Agriculture (CA) is one such agricultural system which has been claimed to
be sustainable (Hobbs, Sayre, & Gupta, 2008). CA is defined as resource saving food production
system aiming at intensification of production with high yields. It also entails an enhancement of
the natural resource base through compliance with three interrelated principles which is (1)
permanent or semi-permanent soil cover through crop residues or crop covers; (2) minimal soil
disturbance; (3) crop rotations (Hobbs, Sayre, & Gupta, (2008), Abrol & Sangar, (2006)).
Technologies pertaining to CA not only have the potential to lower input usage but also preserve
the environment (Stevenson et.al, 2014; Pannel et. al., 2013). While the literature on CA is
extensive (mostly adoption of CA technology), it does not offer insights in three key issues. First,
the “partial” adoption and abandonment of CA technologies, especially in developing economies.
We defined “partial” as adopting one or more technologies or practices under CA rather than the
full package. Second, the role of social networks in the “partial” adoption and abandonment of CA
technologies/practices. Third, the role of spouses with regard to the “partial” adoption and
abandonment of CA technologies.
Herein lies the objective of the study. First, to assess the role of social networks and (2)
role of spouses in “partial” adoption and abandonment of CA technologies/practices. CA has
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evolved overtime with the joint efforts of farmers, farm advisors and farm supply representatives
through constant feedback and knowledge sharing. This has led researchers to believe that CA
system is not “an individual creation” but a “network product” Coughenour, (2003). We also argue
that the success of CA needs testing through trials, successive assessment and tentative
conclusions. This is because there exist no prescribed steps which is required to be followed. Any
alternative can be adopted which suits the local conditions but satisfying the theme of resource
conservation. And therefore CA has been claimed to be a loosely coupled system in which social
networks play a significant role. Therefore, ignoring the social heterogeneity in CA may lead to
biased results (Coughenour, 2003).
On the other hand, spouses have been taking up more responsibilities related to the decision
making of major agricultural activities. For example, spouses are making decisions when it comes
to marketing of agricultural products, selection of crop varieties, purchase of machinery, adoption
of new technologies, purchasing, leasing and selling of land (Lu, 2011). Trends also suggest that
they are replacing men as principal operators of farms, not only in the United States but in the
developing economies as well (Lu, 2011; Jiggins, 1998; Lastarria-Cornhiel, 2006). High migration
of male household heads to other countries or urban areas for off-farm work, such as construction
and manufacturing jobs, may have led to this change. Conventional gender models focus mainly
on the gender of the head of the household. This approach does not reveal spouse’s role in the
decision making of farming operation and welfare (Doss and Morris, 2001). Recently, Campos,
Covarrubias, & Patron, (2016) concluded that for better understanding of the contribution of
spouse’s in agriculture, one must closely study the dynamics of household decision making in
relation to major agricultural activities (Udry, 1995 ;Campos, A, & Patron, 2016). We study the
spouses’ role in “partial” adoption or abandonment of CA technologies/practices.
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The paper is divided into four sections. Section 1 presents literature review of conservation
agriculture, contribution of social networks and its importance in CA followed by studies focusing
on the importance of spouse’s role in agriculture. Section 2 presents the methodology used while
Section 3 specifies the model. Descriptive statistics differentiating adopters/non-adopters and
abandoners/non-abandoners of “partial” CA is presented in Section 4. Econometric analysis of the
factors influencing the adoption and abandonment of CA is discussed in Section 5. The last section
concludes the study and provides some policy implications.
Literature Review
Adoption of conservation agriculture in developing countries
There is an extensive literature on the adoption of agricultural technologies particularly in the
context of developing countries (Feder & Umali, 1993; Feder, Just, & Zilberman, 1985; Batte,
Jones and Schnitkey, 1990). Farmer’s decision to adopt new technologies are generally derived
from the maximization of expected utility subject to the land, credit, labor and other relevant
constraints in a given period. Similarly farmers abandon technologies if the cost of using
technoloiges is greater than the benefits. But adoption of appropriate technologies are especially
relevant to countries of Asia, Africa and Latin America as this is expected to shift their production
frontier outward for a given level of inputs. This results in the increase in yield and therefore have
a positive impact on their income (Pretty, Morison, & Hine, 2003).
A survey study by Feder, Just, & Zilberman, (1985) on the adoption of agricutural
technologies conjectured that factors such as marginal farm holdings, limited access to
information, averseness to risk, inadequate human capital, labor shortages, credit constraint, land
tenure arrangement, supply constraints are integral to the adoption of any agricultural technologies
in developing countries. However, factors such as size of the farm, tenure status, education,
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extension access and credit was in fact relevant only during the first phase of diffusion and
becomes insignificant in the later stages (Feder & Umali, 1993). During later stages, small farmers
become adopters by observing large farmers and therefore land size and related factors became
insignificant. For example, adoption of modern varieties of rice in Bangladesh (Alauddin &
Tisdell, 1988).
However, in this study we focus on technologies pertaining to CA only. CA features
minimum soil disturbance and permanent soil cover also known as mulching along with crop
rotation and is considered to be most sustainable cultivation system (Hobbs, 2006). CA has been
claimed to have the benefits of allowing water intrusion, reducing soil erosion, promoting
biological tillage and reducing the growth of weeds. Additionally, CA reduces production costs,
saves time and augments yield through timely planting, decreases incidents of pest attack and
reduces carbon emissions (Hobbs 2006). Finally, CA has been promoted as a sustainable
agricultural system which can increase food production with more economic use of resources and
having negligible impact on environment. Given these benefits, adopting CA has increased the
Indo-Gangetic Plains of South Asia and Northwest Mexico (Hobbs, Sayre, & Gupta, 2008).
However, the benefits of CA has long been a contentious issue. There are studies both
confirming and refuting these the benefits of CA. For exaple Pretty, et. al. (2006) found that CA
leads to increased yield gains in water use effeciency, decline in pesticide usage and increased
gains in carbon sequestering especially for small farmers in developing countries, refutes the claim
to solving food security. Gathala et. al. (2015) found that ZT direct seeded rice with residue
retention resuted in similar yield as puddled and transplanted rice. However, the authors show that
direct seeded rice lowered production costs, reduced water used and subsequently higher incomes.
Similar results were observed under ZT maize and ZT wheat. But in case of ZT wheat though it
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resulted in increased productivity and profitability but it depended on crop management along with
residue retention.
Several studies in a recent issue of Agricultural, Ecosystem and Environment (2015)
concluded that the benefits of CA are context specific and varies from region to region. Moreover
farmers are not expected to reap significant benefits in the short run (Corbeels et. al. , 2015).
Additionally, most studies have considered different components of CA such as effects of zero
tillage (Erestein & Laxmi, 2008), (Krishna & Veettil, 2014); direct seeding (Mazid et. al. , 2002),
(Pandey, Velasco, & Suphanchaimat, 2002); practices such as crop rotation and minimum soil
distrubance (Arslan et. al. , 2014) in isolation which actually underestimates the actual effect of
CA rather than considering CA as a package of techologies and practices. In a recent study,
Pannell, Llewellyn and Corbeels (2014) concluded that CA should be considered as a package,
partial or full, as various components of CA tend to complement each other. Additionally, the
authors note that adopting CA as a “full” package is not practically feasible for smallholder farmers
in developing economies like India and Bangladesh, given the financial, land, and risk related
constraints (Pannell, Llewellyn, & Corbeels, 2014). Similar conclusion can be drawn for Sub-
Saharan Africa and South Asia (Kirkegaarda, Conyersb, Hunta, Kirkbya, & al., 2014). In this study
we will be considering “partial” adoption of the technology/practices.
In this study we also investigate the abandonment of CA technologies. Though there has
been a comprehensive literature on adoption of CA technologies/practices but there exists a
significant gap specific to the abandonment of CA technologies/practices. However, it should be
noted that it is not just pertaining to CA but literature on any abandonment of
technologies/practices. With the exception of few papers (Walton, et al., 2008; Dinar & Yaron,
1992; Walton J. C., et al., 2009) the issue of abandonment of CA technologies/practices have been
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overlooked by researchers. In order to avoid wastage of scare resources and duplication of efforts,
the study of abandonment of any technology is needed. Abandonment can happen due to many
reasons, such as financial constraint, lack of sufficient information, and unsuitable environment.
According to Rogers (1983), abandonment of technologies happens due to “disenchantment
discontinuance” or when the benefits accruing from using the technology is less than the costs of
using it.
Abandonment may also happen when farmers replace current technology with a more
efficient technology or “replacement discontinuance”. For example, Walton J. C., et al. (2008)
concluded that farmers were likely to abandon grid soil sampling as they have been using for
longer duration and planting cotton crops in relatively smaller . This is because greater efforts
would be required to manage the detailed soil sample information. In another study, Dinar &
Yaron, (1992) estimated technology cycles for different irrigation technologies for citrus groves
in Israel and found that it is the technology related factors and not the prevailing physical
conditions that determined the length of the technology cycle. Authors concluded that in order to
avoid the binding constraint faced by rice cultivators, policies pertaining to improvements in
irrigation efficiencies needs to be implemented.
Adoption and abandonemnt of technologies are mostly driven by economic and structural
considerations Lapple & Donnellan (2009). For example, respect to the organic farming, full-time
farmers managing a more intensive farming system are less likely to abandon. The authors also
notes that better access to market outlets and quality of information are important factors than
subsidies in encouraging farmers to adopt organic farming. While Barham et. al. (2004) concluded
that abandonemnt is less likely to happen in technologies which involve significant sunk costs.
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Nevertheless, it should be noted that the authors did not address the issue of abandonment when it
comes to “partial” abandonment of CA technology/practices.
Another important factor which influences the diffusion of adoption of any technology
especially in the presence of information failures, is social network. In agriculture, farmers
interested in using new technology may not possess sufficient information about the technologies
(Bardhan & Udry, 1999; Evenson & Westphal, 1995; Feder, Just, & Zilberman, 1985). This
problem exists in developing countries because of limited reach of the agricultural extention
services and low education attainment of farmers. In such situation farmer’s own social network
of family, friends and neighbours becomes a major source of information (Stephens E. , 2008).
Specifically, farmers adopt new technologies by closely observing their neighbours through a
process of social learning (Coonley & Udry, 2001). Yishay et. al. (2013) experimented with three
different groups of people in order to test the most efficient information source of information.
The group included government employed extension workers, lead farmers who are educated and
peer farmers representing the general population with respect to land sizes. The authors concluded
that peer farmers were most efficient in convincing other farmers to adopt new technologies. This
is because peer farmers and the other recepient farmers were similar with respect to farm size and
input usage.
However, farmers were motivated to persuade others only if they were provided with small
incentives such as small bags of seeds. To this end Bandiera & Rasul (2006) stress that social
network influences decision making both endogenously and exogenousy. Endogenously when a
member has an influence on other member’s decision making and exogenously through gender
and education. (Hobbs, Sayre, & Gupta, 2008). Additionally, a study by Stephens E. C. (2008) and
Deorian (2002) further confirms the existence of positive relationship between profits earned by
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farmers and strength of social networks (Matuschke & Qaim, 2009). These studies recommend
that policymakers should invest in policies that encourage network formation in order to improve
the spread of information on new technologies and practices. The contribution of social networks
to CA is significantly different than any other technologies/practices. CA entails a qualitative
reform in the agricultural system engaging multiple actors, institutions and farming environment.
CA involves a complete overhaul of the agricultural practices. CA or no tillage systems
entails “loosely-coupled1” farming systems with “tightly coupled components2” (Coughenour,
2003). More importance is given to the management skills and the shared knowledge among
various stakeholders. In other words, the model termed as “network model of innovation” is
different from the traditional models of innovation-diffusion. Under this the focus shifts from
farmers learning new techniques to farmers learning from collaboration of agricultural scientists,
advisors and professional soil conservationists and agribusiness technicians, through the exchange
of their experiences. In this context, networks play an important role in promoting CA (Warriner
& Moul ,1992 and Pannell et. al. ,2006). On the other hand, when a farmer is planning to abandon
CA, social networks can also prove to be useful channel of better quality of information, and
therefore may prevent farmers from abandoning CA technologies/practices.
Involvement of women or spouses may also play an important role both in the adoption
and abandonment of technologies. For example, Ogunlana (2004) found that female farmers were
more likely to adopt alley farming. In a current study, Koirala, Mishra, & Mohanty (2016) found
that female headed households relative to male headed households performed better in restraining
1 Conservation tillage system involves selection of crops and planting methods based on the choice of the farmer and
his fields which may vary across different years. The objective is to produce sufficient quantities of residue and
including practices of no tilling (Coughenour, 2003). 2 One of the components of CA ie. No Till wheat drill involves activities which needs to be followed in exact terms
without any ambiguity (Coughenour, 2003).
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farming costs and were more likely to adopt improved varieties of rice in Phillipines. This issue
was also confirmed by a study on rice farmres in Cote d’Ivoire wherein women farmers were
considered of having relatively better absolute allocative efficiency compared to men, even though
the overall economic efficiency was almost similar (Adesina & Djato,1997). A recent World Bank
study (Larson, Savastano, & Murray, 2015) argued that women farmers with better information
and better access to markets were more likely to influence productivity in farming community.
Recall that, better access to markets provides information on input prices, technologies and also
act as a channel for surplus production (Larson, Savastano, & Murray, 2015).
Along with these studies on women and their contribution in agriculture, there have been
studies on whether women have concerns for environment. In a study, Mohai, (1992) examined
gender differences in the matters related to environmental concern and activism. He concluded
that women were more concerned about protecting the environment than men. With all issues
related to women’s contribution to agriculture and concern for environment, there have been
computational concerns related to the correct evaluation of women farmers’ contribution.
Women farmers’ contribution may vary significantly under roles of household heads, a plot
managers or joint owners of plots. Under the role of household heads, females are the worse off as
they face severe constraints with respect to the access of land and inputs. But if these constraints
are removed, then women farmers as household heads could become technological innovators
(Kumar, 1994). Whereas spouses in the role plot managers or joint owners are better endowed
compared to those heading households. But there still exists heterogeneity among them with
respect to their roles in the decision making of various major agricultural activities. Kumar (1994)
in his study on the intra-household decision making in Zambia with respect to hybrid maize and
local maize cultivation concluded that women farmers were less likely to involve in activities
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related to the hybrid maize cutlivation. They were also more likely to involve themselves in labour
hiring decisions than decisions on the use of inputs since they lacked easy access to extesion.
Majority of the proceeds from the crop sales would go to men rather than women even if they were
jointly involved in the cultivation of that particular crop. Other than this study, most of the
literature studying the gender models considered only the household head’s gender for the analysis.
They assumed that the allocation of resources within the household would be Pareto efficient, but
this could be flawed (Udry, 1995 ; Campos, A, & Patron, 2016; Quisumbing, 1996).
Additionally, in the case of developing economies, the decision making power among
women as spouses in recent years have changed due to high rates of migration of males to other
countries or urban areas in search of work. As a result, spouses are increasingly undertaking more
farm related activities such as seeding, weeding, application of pesticides, harvesting, and
marketing of produce (Lu, 2011). This phenomenon has been taking place at a faster rate as noticed
by Lastarria-Cornhiel, (2006); Binswanger-Mkhize & Dsouza, (2012). Equal access to land along
with open access to credits and efficient markets would go a long way in improving agricultural
productivity (Brauw, et. al., 2008; Kumar, 1994).
Our study incorporates this dimension in the analysis by including the involvement of
spouses in the decision making of major agriultural activities in the analysis.
Conceptual Framework
We considered two stages of decision-making specifically, adoption of “partial” CA
technologies/practices and abandonment of “partial” CA technologies/practices. We assume the
decision maker is rational and therefore maximize utility through adoption/abandonment
decisions. With respect to “partial” CA technologies/practices, the decision to adopt happens when
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the expected profits from adoption is greater than non-adoption and the opposite is true for the
decisions to abandon. Specifically, the selection equation is:
𝑦𝑖𝐴𝐷= 𝑓(𝑍𝐴𝐷)
𝑦𝑖∗
𝐴𝐷= 𝑍𝐴𝐷𝐴 + ∈𝐴𝐷
Where 𝑦𝑖∗
𝐴𝐷 is the unobservable latent variable which is a function of observable exogenous
variables 𝑍𝐴𝐷. A is a vector of unknown parameters and ∈𝐴𝐷 is a random disturbance term for the
adoption stage and follows standard normal distribution. Specifically, the decision to adopt
“partial” CA technologies/practices observable is given by:
𝑌𝑖𝐴𝐷 = 1 𝑖𝑓 𝑦𝑖∗
𝐴𝐷> 0 (0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒),
Now let us consider the outcome equation the decision on abandonment of “partial” CA
conditional on for those households who have adopted “partial” CA. However, since abandonment
of CA can happen only when it has been adopted, the estimation process should account for the
potential biases originating from the self-selection process. This may happen due to some
unobservable factors that might influence both adoption and abandonment decisions. Also, the
second stage (abandonment) is observed for a subset of individuals which were non-randomly
selected and with unobserved factors unevenly distributed among underrepresented groups (Gaeta,
2015). Therefore, Heckman two stage sample selection (Heckman 1979; Ven and Pragg, 1981)
was considered in our study. Specifically,
𝑌𝑖𝑠𝑒𝑙𝑒𝑐𝑡
𝐴𝐵 = 𝑍𝐴𝐵𝐵 + ∈𝐴𝐵,
Where 𝑌𝑖𝑠𝑒𝑙𝑒𝑐𝑡
𝐴𝐵 is dichotomous variable on abandonment; is a function of observable exogenous
variables 𝑍𝐴𝐵. B is a vector of unknown parameters and ∈𝐴𝐵 is random disturbance term for the
abandonment stage and has a standard normal distribution. The estimates of the outcome equation
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(abandonment in our case) depicts probability of abandoning, conditional on the probability of
having “partially” adopted CA.
If the correlation between ∈𝐴𝐷 and ∈𝐴𝐵 is not equal to zero, then there exists unobserved
factors jointly affecting both stages of adoption and abandonment. In such a case, Heckman two
stage sample selection method needs to be applied to obtain unbiased estimates. If the correlation
is equal to zero then independent probit models for the two stages would provide consistent
estimates.
Data and Model Specification:
The data for our study was obtained from a baseline farm household survey, conducted under the
aegis of Cereal Systems Initiative for South Asia (CSISA)3 in late 2010 and early 2011. CSISA
was launched in 2009 with the support from Bill and Melinda Gates Foundation and USAID. The
main purpose of the project was to promote sustainable and efficient technologies in the rice-wheat
belt of Indo-Gangetic Plain with a view to improve food supply and livelihood of smallholder farm
households. The survey was conducted across CSISA hub centers located in Haryana, Punjab,
eastern Uttar Pradesh, Bihar and Tamil Nadu in India; Dinajpur and Gazipur in Bangladesh and
the Terai region of central Nepal. The survey collected information on the existing production
farming practices, livelihood of farmers along with their socio-economic status. The dataset
covered 2567 smallholder farm households across different regions and countries. The novelty of
this dataset is that it had information on adoption and abandonment of each of the CA
technologies/practices.
With regard to CA, only 936 farm household members were considered for this study; 432
were “partial” adopters of CA and 117 had abandoned CA technologies/practices. The “partial”
3 http://csisa.org/resources/csisa-phase-i-baseline-data/ accessed on 6/1/2016
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adoption of CA technologies/practices were identified as those using one or more technologies/
practices strictly following the principles of CA. In the case of abandonment, we considered
smallholder farm households who were not eager to continue using any technologies/practices that
they had adopted. CA technologies/practices considered here are direct seeded rice, zero tillage,
lazer land leveling, bed planting, double no till, leaf color, nutrient management, relay cropping,
seed treatment and turbo seeder. These “partial” CA technologies/practices are consistent with
other studies in the literature (FAO July, 2010, Pannell et. al., 2006; Kassam et. al. , 2009).
𝑌𝑖𝐴𝐷𝑎𝑛𝑑 𝑌𝑖𝐴𝐵 represent adoption and abandonment decisions of the ith farmer specifically,
𝑌𝑖𝐴𝐷 = 1 , 𝑖𝑓 𝑜𝑛𝑒 𝑜𝑟 𝑚𝑜𝑟𝑒 𝐶𝐴 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑖𝑒𝑠/𝑝𝑟𝑎𝑐𝑡𝑖𝑐𝑒𝑠 ℎ𝑎𝑣𝑒 𝑏𝑒𝑒𝑛 𝑎𝑑𝑜𝑝𝑡𝑒𝑑, 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑌𝑖𝐴𝐵 = 1 , 𝑖𝑓 𝑎𝑙𝑙 𝑡ℎ𝑒 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑖𝑒𝑠/𝑝𝑟𝑎𝑐𝑡𝑖𝑐𝑒𝑠 𝑎𝑑𝑜𝑝𝑡𝑒𝑑 ℎ𝑎𝑣𝑒 𝑏𝑒𝑒𝑛 𝑎𝑏𝑎𝑛𝑑𝑜𝑛𝑒𝑑, 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Based on the literature, following variables are expected to have a positive effect on the “partial”
adoption of CA. (1) agricultural experience of head of household head, education, farm size,
quality of information on technologies/practices pertaining to CA, farm diversification (number of
crops grown in a year), labor constraint, social networks, cost of irrigation (unit either through
electricity or diesel), amount borrowed as loans and involvement of spouses in agricultural
activities. On other hand the age of household head is expected to have a negative impact on
adoption of “partial” CA technologies/practices. However, the opposite is true for the
abandonment stage. For the abandonment stage, labor constraint was excluded as this was not
expected to affect abandonment. This was also done for the identification of the outcome equation.
Additionally, average years of use (Walton J. C., et al., 2008) and ownership of CA
technologies/practices were considered as explanatory variables in the abandonment stage. Both
the factors are expected to affect abandonment of CA technologies/practices negatively. Finally,
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crop residues left as mulch in the fields, is an important component of CA (Hobbs, Sayre, & Gupta,
2008) and was therefore included in our model.
Descriptive Statistics
(a) Attributes of “partial” CA adopters and non-adopters
Overall the adoption rate of CA was around 46%. The average age of CA adopters was about 51
years and owned relatively larger cultivated area compared to the non-adopters (Table 1). About
14% of CA adopters had education higher than 13 years. For measuring quality of information
regarding CA technologies/practices, Likert scale of 0 (not useful at all), 1 (low), 2 (medium) and
3 (high) was used. According to the data, on an average adopters were better informed about CA
as depicted by value of 2.14 representing medium quality of information as compared to the non-
adopters who received low quality of information. Therefore, suggesting that poor quality of
information may be an impeding factor in the adoption of CA technologies/practices. Adopters
also seem to diversify more by planting more crops than non-adopters. It was also observed that
almost 82% of the adopters faced labor constraints especially during rice/wheat/maize cultivation
while the proportion was about 63% for non-adopters (p > 0.001). Findings here may suggest that
labor shortages may be one of the reasons for adopting CA technologies/practices, as use of Zero
tillage and laser land leveling reduces the demand for labor. Table 1 also shows that higher
proportion of adopters of CA have membership in farmer’s unions or in other groups compared to
the non-adopters (p > 0.001). Group membership may act as a source of valuable information
especially in case of new technologies/practices. Recall that adoption of CA technologies/practices
needs to be customized so as to adapt to local conditions, and in this respect peer farmers or co-
members of farm unions may act as a valuable source of experiences. Table 1 also reported that
the cost of irrigation is significantly higher for adopters than for non-adopters (p > 0.001). Note
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that CA technologies/practices require less water for irrigation and therefore may be one of the
reasons for adoption. Adopters of CA technologies/practices are also most likely to borrow more
money (both from formal and informal sources) compared to those not adopting farmers. This may
be because purchasing CA technologies/practices may be expensive. Also it is seen that higher
proportion of adopter’s spouses get involved with major agricultural activities. Table 1 shows that
on an average 96% of adopter’s spouses gets involved in farming activities. These activities
include selecting crop varieties, purchasing of machinery, adopting new crop technologies,
employing laborers in farm, selling of grains, and sale of livestock, sale and leasing of land. The
difference was statistically different from the non-adopters (p > 0.1). Now we study the
characteristics of those abandoning CA.
(b) Attributes of “partial” CA4 abandoners and non-abandoners
Turning our attention to abandoners and non-abandoners, table 1 reveals that on average, 27% of
smallholder farmers in the sample abandoned it. Table 1 reveals that abandoners and non-
abandoners are significantly different with respect to age, farm size (cultivated acreage), and
educational attainment. Specifically, on average abandoners are likely to be older, own small
farms, and have low educational attainment (primary school or less) compared to non-abandoners
of “partial” CA technologies/practices. Additionally, note that 18% of non-abandoners of CA have
13 or more years of education. When it comes to quality of information among abandoners and
non-abandoners, abandoners had received relatively poor quality of information, about CA
technologies/practices, compared to non-abandoners (p > 0.01). Interestingly, most abandoners
did not own CA technologies while about 50% of non-abandoners owned CA technologies (Table
1). However, table 1 reports that there was no significant difference between abandoners and non-
4 Here CA adoption and abandonment follows similar partial definition.
18
abandoners with respect to crop residues being left as mulch on fields. This is an important
component of CA practices—presumably it increases soil fertility.
Both abandoners and non-abandoners cultivated around three to four crops, on an average,
in a year, however, the difference between them is not statistically significant. Finally, when it
comes to group membership which we assume here social network, table 1 reports that abandoners
were less likely to be interacting with other farmers—be a member of farmer groups. In addition,
abandoners faced low cost of irrigation and borrowed less money, compared to the non-abandoners
of “partial” CA technologies/practices. Lastly, there was not much difference between the
proportions of spouses of these two groups being involved in the major agricultural activities.
In the next section, we report the findings from econometric analysis of the factors explaining both
“partial” adoption and abandonment of CA technologies/practice in Bangladesh, India and Nepal.
Results and Discussion
Table 2 presents the parameter estimates of factors affecting the decision of “partial” adoption of
CA (stage 1) and abandonment (stage 2), respectively. Although explanatory power of a regression
is not directly provided by the Heckman Probit method, but using simple probit analysis indicates
that almost 63% of the cases of adoption of “partial” CA technologies/practice and 75% cases of
the non-adopters of “partial” CA technologies/practice were correctly predicted. Overall, the
predictive power of successful cases was about 69%, suggesting a high explanatory power.5
Additionally, we used delayed labor (labor shortage) as an instrumental variable in the second
stage (Abandonment). Recall that one of the advantages of CA technologies/practices was to
5 Additionally, Variance Inflation Factor (VIF) for both stages was 8.73 and 7.81, respectively, suggesting no significant signs of multicollinearity among the explanatory variables (Meyers, 1990).
19
reduce labor requirement. Therefore, labor shortages (delayed labor)6 may affect the adoption
process but is it is less likely to affect the abandonment of “partial” CA technologies/practices.
The Wald test statistics was significant (p > 0.01) suggesting the presence of correlation
(ρ=-0.71) between the two-stages of adoption and abandonment of “partial” CA
technologies/practices. The findings here are as expected and suggests potential sample selection
bias. Therefore, a comparison of parameters was made using a Probit model for the abandonment
stage (Table 4). The signs of all significant variables in the Heckman Probit model were consistent
with those of the Probit model in table 4. This justifies the use of two-stage Heckman selection
model. Additionally, factors explaining both adoption and abandonment of “partial” CA
technologies/practices may not satisfy the assumptons of separability. Therefore, bivariate probit
modeling approach may not be sutiable in our case. For robustness check (Table 3) , logged cost
of irrigation was dropped from the model, there was no changes in the sign of the significant
varibles, suggesting a stable model.
Factors affecting adoption and abandonment of “partial” CA technologies/abandonment
We begin by reporting the findings of our variables of interest. These include social networking
and role of spouses in decision making in farming. Results in table 2 show that the coefficient of
social network is positive and statistically significant at the 1% level of significance in the adoption
of “partial” CA technologies/practices. An additional membership in clubs and unions increases
adoption of “partial” CA technologies/practices by 0.15 (marginal effect). Findings indicate that
household heads who belong to clubs or unions were more likely adopt to “partial” adoption of
CA technologies/practices. A plausible explanation is that farmers belonging to social networks
get better and reliable information about new technologies/practices and therefore were more
6 Delayed variable (or labor shortage) variable was also included for full identification of the model (Ven and Pragg,
1981). However, this labor shortage (delayed labor) variable was insignificant in the abandonment stage.
20
likely to adopt “partial” adoption of CA technologies/practices. Recall that the flow of information
is integral to CA, as there is no single procedures that can fit to all local conditions. Experence
with different procedures and methods is often shared by members of clubs and unions—social
networking.7
Results in table 2 show that the coefficient of spouses is positive and statistically significant
at the 5% level of significance in the “partial” adoption of CA technologies/practices. Resuts
indicate that presence of spouses and their active role in farmign activities (such as in selecting
crop varieties, purchasing of machinery, adopting new crop technologies, employing laborers in
farm, selling of grains, sale of livestock, sale and leasing of land) increases the likelihood of
adoption of “partial” CA teachnologies/practices. Recall that the role of spouses (women) is in
agriculture have been increasing in developing countries. This phenomena is seen to have an
increasing trend in developing countries and therefore has been termed as feminization of
agriculture.8 A possible explanation is that spouses may show more concerned for the environment
and reducing operational costs. Our finding is consistent with Teklewold, Kassie, and Shiferaw
(2013) who found that spouses were more likely to adopt conservation tillage. On the other hand,
the coefficient of spouses is negative but insignificant in the “partial” abandonment of CA
technologies/practices. The results underscore the important role spouses (women) play in
technology adoption decisions in developing countries.
Results in table 2 show that although farm size (cultivated acerage) has no significant
impact on the adoption of “partial” CA technologies/practices, it has a negative and statistically
significant imapct on the abandonment of “partial” CA technologies/practices. Findings suggest
7 Anecdotal evidence suggests that farmers often discuss their experiences with technology with others and they are
likely to get more and better information with larger number of peers. 8 Zepeda and Castillo (1997) note that farm technology adoption decisions may not only be made by the head of the
household, but can be part of an overall household strategy.
21
that farmers with large land holding were less likely to abandon “partial” CA
technologies/practices. Recall that CA is a relatively new technology/practice and is generally
adopted by wealthy farmers with large land holdings. Our finding is consisient with previous study
(Feder, Just, & Zilberman, 1985). We classifed educational attainemnt of head of hosueholds into
primary (0-5 years of schooling), secondary (6–12 years of schooling) and tertiary (13 or more
years of schooling) categories. Results in table 2 reveal that, compared to head of households with
primary education, head of households with secondary and tertiary education were more likely to
adopt “partial” CA technologies/practices. The coefficients are highly significant—at the 1% level
of significance. Interestingly, the marginal effect (=2.4) of head of households with tertiary
education have most imapct on the adoption of “partial” CA technologies/practice. Findings are
consistent with technology adoption literature (Feder, Just, & Zilberman, 1985). On the other hand,
the coefficients of secondary and tertiary education are negative and statistically significant at the
1% level of significance. Findings suggest that more educated heads of hoseuholds are less likely
to abondon “partial” CA technologies/practices. Additionally, the margianl effect for secondary is
slightly smaller (0.19) and slighlty higher for tertiary education (0.27). Our finding is consisient
with previous studies (Feder, Just, & Zilberman, 1985; Walton J. C., et al., 2008). Again findings
undersocres the importance of education in technology adoiption and abandonment decsions,
espacially in the “partial” CA teachnologies/practices.
Table 2 shows that the coefficient of quality of information is positive and statistically
significant at the 5% level of significance. The marginal effect is about (0.082). Findings suggest
that smallholder farm households with better quality of information9 were more likely to adopt
9 Recall that quality of information is perception based indicator. Farmers were asked to rank the quality of
information they received from their most trusted source (e.g., extension agents, cooperative union, farmer
associations, etc.) regarding CA technology/practices. The rank as from 0 to 4; 4 being the best.
22
“partial” CA technologies/practices. It is likely that better quality of information keeps farmers
interested and engaged in the technology as noted by Adesina & Forson-Baidu (1995). Quality of
information is integral to the farmers’ perception formation regarding “partial” CA
technologies/practices. On the other hand, results in table 2 (column 5) reveals that the coefficient
of quality of information is negative and statistically significant at the 1% level of significance.
Findings suggest that farmers with better quality of information are less likely to abandon “partial”
CA technologies/practices; the marginal effect is higher (=0.11) than the one obtained in the
adoption equation.
The coefficients of both diversification (total number of crops grown) and delayed labor
(or labor shortage) is positive and statsitically significant at the 1% level of significance. Findings
confirm anecdotal evidence that labor costs may be one of the drivers of adotpion of “partial” CA
tachnologies/practices. Shortage of agricultural labor and high agricutural wages have forced
farmers to either depend upon family members to perform farm operations or seek technologies
that reduced labor requirement; including “partial” CA technologies/practices. With regard to
credit constraint (defined as total amount money borrowed from formal and informal sources)
coefficient is positive and statistically significnat at the 5% level of signficance (table 2, column
5). Results suggest that the likelihood of abandoning “partial” CA technologies/practice increases
with total amount of borrowed money. Perhaps, farmer may not be able to service the debt and
would like to get out of the debt trap. This is highly likely as informal sources charge exhorbitantly
high interest rates. Our finding is consistent with previous studies (Feder, Just, & Zilberman, 1985;
Feder & Umali, 1993).
Finally, the coefficient of residue as mulch is negative and statistically significant at the
1% level of significance. The marginal effect is about (0.26). Results suggest that leaving residue
23
on the field as mulch decreases the likelihood of abandonment of “partial” CA
technologies/practice. Recall that leaving the residues on the field as mulch is an important practice
under CA. One explanation is that residue mulch increases soil fertility by promoting biological
activity and reducings weed infestation (Hobbs, Sayre, & Gupta, 2008); presence of mulch
decreases water requirement (Gathala, et al., 2015) and increases yield (Giller, et al., 2009).
Conclusion and policy recommendation
With falling yield and increasing energy and fertilizer costs, agriculture in South Asia is becoming
an unprofitable proposition. This would have an adverse impact not only on the incomes of farmers
but is likely to pose a threat to the food security of marginal farmers who are highly dependent on
agriculture for their livelihood. Therefore, attaining food security for a growing population in most
South Asia countries, while sustaining agricultural systems under the current scenario of depleting
natural resources, increasing costs of inputs, and climate variability, calls for a paradigm shift in
farming practices. This includes eliminating unsustainable parts of conventional agriculture
(ploughing/tilling the soil, removing all organic material, monoculture) for future productivity
gains, while sustaining natural resources.
CA is a resource saving food production system that aims for production intensification
and high yields while enhancing the natural resource base through the compliance with three
interrelated principles (Abrol and Sangar, 2006). Technologies pertaining to CA not only have the
potential to lower input usage but also preserve the environment (Stevenson et.al, 2014; Pannel et.
al., 2013). While the literature on CA is extensive (mostly adoption of CA technology), it does not
offer insights in three key issues. First, the partial adoption and abandonment of CA technologies,
especially in developing economies. Second, the role of social networks in the adoption and
abandonment of CA technologies. Third, the role of spouses with regard to the adoption and
24
abandonment of CA technologies. In our study, we considered partial adoption and abandonment
of CA technologies/practices as due to labor, land, credit constraints, farmers in developing
countries are not able to adopt CA as a full package.
Our study found that across the Indo-Gangetic plains which includes the states of Punjab,
Haryana, Bihar of India, Tamil Nadu, Bangladesh and Nepal, 46% of the households had adopted
CA while 27% of those adopted had abandoned it. Our results further suggest that social networks
play an influential role in both the adoption and the abandonment of CA technologies/practices.
While involvement of spouses just influenced the adoption stage.
Social networks was found to positively influence adoption of CA and negatively in the
case of abandonment of CA. Social networks may provide better quality and trustworthy
information from peer-groups and co-members on network. Moreover, developing social network
is integral to conservation agriculture. It is claimed to be a “network model of innovation” as
farmers needs to collaborate with agricultural scientists, advisors and professional soil
conservationists and agribusiness technicians in successfully implementing CA. This is done as
CA needs to be tailored to fit to local conditions. Therefore, in this context, relationships play an
important role in promoting CA. On similar intuition, social networks may discourage farmers
from abandoning CA technologies/practices. Therefore, farmers with memberships at the farmers’
union needs to be targeted.
Our results also show that if spouses are involved in the decision making—with regard to
marketing of agricultural products, selection of crop varieties, purchase of machinery, adoption of
new technologies, purchasing, leasing and selling of land—then it is more likely that farmers adopt
CA technologies/practices. However, the influence of spouses on the abandonment stage was
negative but statistically insignificant. The findings suggest that spouses may show more concern
25
for the conserving the environment and in reducing the operational costs of major agricultural
activities. The involvement of spouses in agriculture have recently increased due to the increasing
feminization of agriculture. They are involving themselves either partially or fully in the major
decision making of agricultural activities in the absence of male household heads. Therefore,
policy makers should be cognizant of this transformation and promote equal access of land along
with open access of credits and efficient markets.
Results also reveal that household heads with better education, better quality of information
and facing labor constraints are more likely to adopt CA technologies/practices. Whereas, those
facing credit constraints, poor quality of information, owning relatively less cultivated area were
likely to abandon CA technologies/practices. We also found empirical evidence that those who left
residue as mulch on fields were less likely to abandon. Mulching is an integral part of CA and has
been claimed that without mulching a significant fall in yield may be expected.
Therefore, for the purpose of targeting, educated households owning large cultivable land and
having memberships in farmer’s unions may be chosen. Policies favoring equal access of land
along with open access to credit and markets may be promoted. More awareness campaigns and
demonstration may be organized in villages to enhance the existing information about CA. During
these awareness drives, special attention may be provided to inform farmers about the benefits of
mulching. These may help in increasing the uptake of CA and reduce instances of abandonment.
26
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31
Table 1. Definitions and summary statistics of variables, CSISA Data 2010-11
Variable Definition
Stage 1:
CA Adoption (N= 936)
Stage 2:
CA Abandonment (N=432)
Overall
Mean
Adopters
(N=432)
Non-
Adopters
(N=504)
t-test1 Overall
Mean
Abandoners
(N=117)
Non-
Abandoners
(N=316)
t-test1
Dependent variables
CA adoption =1 if HH2 head adopted any
technology/practice3 related to CA, 0
otherwise
0.46 1 0 - - - -
CA abandon
=1 if HH head abandoned any
technology/practice3 related to CA, 0
otherwise
- - - - 0.27 1 0 -
Farm Attribute
Area owned Total cultivated area (acres)4 2.53 3.31 1.86 *** 3.28 1.57 3.91 ***
Human capital
Age Age of HH head (years) 48.59 50.65 46.82 *** 50.63 54.53 49.19 ***
Primary
education
=1 if years of schooling between 0 to 5
years
0.69 0.57 0.79 *** 0.58 0.86 0.47 ***
Secondary
education
=1 if years of schooling between 6 to
12 years
0.22 0.29 0.17 *** 0.29 0.12 0.35 ***
Tertiary
education =1 if years of schooling >= 13 years
0.09 0.14 0.05 *** 0.14 0.02 0.18 ***
Factors specific to CA
Quality
information
Average quality of information
regarding technologies/practices
1.97 2.14 1.83 *** 2.14 2.02 2.18 **
Average tech
usage
Average years of technologies or
practices used
- - - - 1.25 1.28 1.24 n.s
Ownership =1 if HH head owned technologies
pertaining to CA
- - - - 0.44 0.27 0.50 ***
Diversification Total number of crops cultivated in a
year
3.41 3.60 3.26 ** 3.60 3.75 3.55 n.s
32
Delayed labour
=1 if HH head faced labour constraint
in farming operations during rice or
wheat crop cultivation
0.71 0.82 0.63 *** - - -
Residue mulch 1 if HH head left residue on fields after
rice, wheat and maize cultivation
- - - - 0.97 0.98 0.97 n.s
Other Factors
Social network 1 if HH head is member of farmer’s
union or other social groups
0.35 0.47 0.24 *** 0.46 0.21 0.56 ***
Cost of
irrigation
Total cost of irrigating from own tube-
well using electricity or diesel per unit5
(Rupees)
106 202 25 *** 201 71 249 n.s
Credit Total amount borrowed from formal
and informal sources (Rupees)3
30,318 48,117 15061 *** 48,135 28,400 55,441 **
Gender
(Women)
=1 if spouse/woman actively involved
in agricultural activities6
0.87 0.90 0.84 *** 0.90 0.88 0.91 ns
1 *, **, *** asterisks indicate significant at 10%, 5% and 1% level of significance. 2 HH = Household. 3 Conservation technologies/practices considered include: direct seeded rice, zero tillage, laser land leveling, bed planting, double no till, leaf color, nutrient
management, relay cropping, seed treatment, turbo seeder. 4 For ease of interpretation, summary statistics are provided for the unlogged variable. 5 Exchange rates: 1 BDT = 0.84780 INR, 1 NPR = 0.62501 (http://www.exchange-rates.org/) accessed on 5/1/2016 6 Activities include: selecting crop varieties, purchasing of machinery, adopting new crop technologies, employing laborers in farm, selling of grains, sale of
livestock, sale and leasing of land.
33
Table 2. Maximum Likelihood estimates of Heckman Probit selection model explaining adoption of CA (1st stage) and
abandonment of CA conditional on adoption (2nd stage) in Nepal, India and Bangladesh
Stage 1:Dependent Variable: CA Adoption Stage 2:Dependent Variable: CA Abandonment
Variable Coefficient Marginal
effects z-value2 Coefficient Marginal
effects z-value1
Ln owned
cultivated area
0.002 0.001 0.18 -0.031 -0.008 -1.76*
Age 0.035 0.012 1.15 -0.031 -0.008 -0.60
Age squared -0.0001 -0.00004 -0.49 0.0003 0.0001 0.69
Secondary education d 0.590 0.196 3.93*** -0.732 -0.191 -4.28***
Tertiary education d 0.708 0.236 3.36*** -1.042 -0.271 -3.83***
Quality information 0.245 0.082 2.46** -0.414 -0.108 -2.66**
Average tech usage - - - -0.062 -0.016 -1.19
Ownership d - - - -0.010 -0.003 -0.06
Diversification 0.070 0.023 1.88* -0.056 -0.014 -1.03
Delayed labor d 0.439 0.146 3.37*** - - -
Residue as mulch d - - - -0.980 -0.255 -4.04***
Social network d 0.455 0.152 3.07*** -0.582 -0.152 -3.26***
Ln cost of irrigation 0.011 0.004 1.03 -0.020 -0.005 -1.45
Ln credit 0.002 0.0005 0.37 0.013 0.003 2.17**
Gender (Women) d 0.371 0.124 2.76*** -0.312 -0.081 -1.28
Number of observations = 936
Number uncensored obs. (2nd stage) = 432
Wald chi-square (14) = 93.61***
Wald test of independent equations: chi-square (1) = 6.59**
1st stage explanatory power:
Cases of CA adopters correctly predicted = 62.04%
Cases of CA non-adopters correctly predicted = 74.60%
Overall cases correctly predicted = 68.80%
2nd stage explanatory power:
Cases of CA abandoners correctly predicted = 58.97%
Cases of CA non-abandoners correctly predicted =
90.82%
Overall cases correctly predicted = 82.22% *,**,*** asterisk indicate significant at the 10%, 5%, 1% level of significance 1Based on robust standard errors adjusted for 113 village-level clusters. dDummy variable.
34
Table 3. Robustness check for the Heckman Probit selection model
Stage 1
Dependent Variable: CA Adoption
Stage 2
Dependent Variable: CA Abandonment
Variable Coefficient Marginal
effects z-value2 Coefficient Marginal
effects z-value1
Ln owned
cultivated area
0.005 0.002 -0.033 -0.009 -1.85*
Age 0.033 0.011 1.06 -0.026 -0.007 -0.49
Age squared -0.0001 -0.00004 -0.42 0.0003 0.0001 0.59
Secondary education d 0.624 0.209 4.20*** -0.753 -0.197 -4.32***
Tertiary education d 0.731 0.245 3.51*** -1.085 -0.284 -3.81***
Quality information 0.251 0.084 2.54** -0.421 -0.110 -2.68***
Average tech usage - - - -0.072 -0.019 -1.34
Ownership d - - - -0.135 -0.035 -0.84
Diversification 0.807 0.027 2.14** -0.067 -0.018 -1.25
Delayed labor d 0.439 0.147 3.36*** - - -
Residue as mulch d - - - -1.072 -0.275 4.09***
Social network d 0.448 0.150 3.03*** -0.565 -0.148 -3.10***
Ln cost of irrigation2 - - - - - -
Ln credit 0.002 0.0008 0.54 0.013 0.003 2.03*
Gender (Women) d 0.368 0.123 2.77*** -0.288 -0.075 -1.19
Number of observations = 936
Number uncensored obs. (2nd stage) = 432
Wald chi-square (13) = 77.62***
Wald test of independent equations: chi-square (1) = 5.61** *,**,*** asterisk indicate significant at the 10%, 5%, 1% level of significance 1Based on robust standard errors adjusted for 113 village-level clusters. dDummy variable. 2 for robustness check logged cost of irrigation was dropped from the model
35
Table 4: Estimates of Probit model of the abandonment stage without correcting for sample selection bias
Factors Dependent Variable: CA Abandonment
Coefficient Marginal effects z-value1
Ln owned area -0.016 -0.003 -1.20
Age 0.052 0.008 1.22
Age squared -0.0003 -0.0001 -0.77
Secondary education d -0.108 -0.016 -0.65
Tertiary education d -0.444 -0.051 -2.01**
Quality information -0.087 -0.013 -0.75
Average tech usage 0.339 0.051 3.28***
Ownership d -0.141 -0.021 -0.79
Diversification 0.015 0.002 0.37
Delayed labor d - - -
Residue as mulch d -0.545 -0.072 -2.71***
Social network d -0.294 -0.041 -1.79*
Ln cost of irrigation -0.023 -0.004 -1.63*
Ln credit 0.0163 0.002 2.84***
Gender (Women) d 0.079 0.011 0.52
*,**,*** asterisk indicate significant at the 10%, 5%, 1% level of significance 1Based on robust standard errors adjusted for 113 village-level clusters. dDummy variable.