ORIGINAL ARTICLE

Assessing the impact of policy-driven agricultural practicesin Karnataka, India

Seema Purushothaman • Sheetal Patil •

Ierene Francis

Received: 9 April 2012 / Accepted: 24 August 2012 / Published online: 28 September 2012

� Springer 2012

Abstract The classical approach of assessing sustainability

with respect to its three underlying pillars, ecological, eco-

nomic, and social, is adopted in this paper, with an added

emphasis on estimating the simultaneous effects of each pillar

on the other two. The paper assesses the impact of policy-

driven changes in cultivation practices in five districts in the

south-western Indian state of Karnataka. A comparative

statics analysis using a simultaneous equations model is

developed to capture the stability of each pillar into the future

and their concurrent interactive impacts and trade-offs. Eco-

logical and economic impacts of policies favoring organic

farming are estimated to be uniformly significant and positive

in the study districts. However, the impact on socio-cultural

criteria is subjective to the eco-regional context. Cost savings,

through producing organic inputs on-farm, maximizes syn-

chrony among the three pillars vis-a-vis sourcing these inputs

from the market. With more reliance on organic inputs, better

prospects are estimated for small and rain-fed farms compared

to large and irrigated farms.

Keywords Sustainability impact assessment � Small-

scale farming � Farmers’ distress � Comparative statics

analysis � Simultaneous equations model � Ex-ante

assessment

Introduction

With growing expectations from agriculture and its allied

sectors towards poverty reduction and overall economic

growth, the Green Revolution1 was set in motion in India

during the 1960s. The spread of high-yielding variety seeds

and subsidized synthetic fertilizers increased the produc-

tivity of certain crops in certain regions. Between

1950–1951 and 2010–2011, the production of food grains

increased five-fold from 51 to 241.6 mt (Bhattacharyya and

Chakraborty 2005, Economic Survey 2011–20122), making

the nation self-sufficient in major food grains. However, an

increasingly liberal and capitalist approach to agricultural

development (Satyasai and Vishwanathan 1997; Bhalla and

Singh 2009) also became partly responsible for precipi-

tating a crisis, often culminating in farmers’ suicides

(Reddy and Galab 2006; Deshpande and Prabhu 2005;

Vasavi 2009). The manifestation of agrarian crisis in India

in general can be traced back to the farmers’ movements in

different regions during the 1980s. The spread of this crisis

is often attributed, among others, to adverse terms of trade

(Balagopal 1988; Bose 1981), pro-urban policies (Lipton

1980), and increased rural–urban migration (Kalamkar

2009; Saha et al. 2009), along with multiple risks related to

production, price, inputs, technology, and credit (Mishra

2008).

The agricultural situation in the state of Karnataka is

similar to that of the country, with high growth in the Gross

Domestic Product (GDP) (8 and 10 % in India and Kar-

nataka, respectively, for 2009–2010 [Government of India,

Handled by Vinod Tewari, The Energy and Resources Institute

(TERI) University, New Delhi, India.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11625-012-0188-y) contains supplementarymaterial, which is available to authorized users.

S. Purushothaman (&) � S. Patil � I. Francis

Ashoka Trust for Research in Ecology and the Environment,

Bangalore, Karnataka, India

e-mail: seemap@atree.org

1 For the nature and impacts of the Green Revolution, please see

Evenson and Gollin (2003) and Hazell (2003), respectively.2 Economic Survey 2011–12 is available online at: http://indiabudget.

nic.in/es2011-12/estat1.pdf.

123

Sustain Sci (2013) 8:173–185

DOI 10.1007/s11625-012-0188-y

GoI 2011]), falling share of agriculture in GDP (decline of

3 and 4.5 % for India and Karnataka, respectively, for

2006–2010), and a large population which is dependent

upon agriculture (66 and 70 % for India and Karnataka,

respectively, in 2010). In the last two decades, the state of

Karnataka witnessed a significant increase in acreage under

commercial non-food crops (cotton, areca, sugarcane, oil-

seeds, and tobacco) and horticultural crops (grapes, coco-

nut, pomegranate) (Purushothaman and Kashyap 2010).

This change in cropping pattern has had implications on the

type and quantity of inputs applied. Consequently, the

average fertilizer consumption in Karnataka has increased

by 83 %3 between 1983–1984 and 2006–2007 (Chand and

Pandey 2008). Manifestations of agrarian problems are

similar in most parts of the country, including stagnated

agricultural growth, declining public investment in agri-

culture, shrinking farm size, and persisting farmers’ sui-

cides. Various measures have been implemented at both the

state and the center for tackling the distress amidst spiral-

ing food prices. These measures include loan waivers,

input subsidies, and price support. An initiative in a

direction different from such usual mitigation strategies is

the Karnataka State Policy on Organic Farming4 (KSPOF)

introduced in 2004 and implemented since 2006.

The policy statement of the KSPOF proclaims its goals

of reducing indebtedness of small farmers (although large

farmers are not totally excluded from the policy support)

by encouraging the use of inputs generated on-farm (hence,

organic) without compromising on economic and social

sustainability. The policy has been implemented in all

taluks5 of the state since 2006 by the state department of

agriculture, covering 100 ha of contiguous land area

(usually in one village) in each taluk. The process of the

selection of villages for policy implementation followed

criteria such as willingness among villagers, availability of

livestock, and presence of local non-governmental orga-

nizations (NGOs) with whose support the policy was to be

implemented. The KSPOF provided support for planting

materials, vermi-compost pits, azolla culture, bio-pesti-

cides, and livestock. The significant involvement of NGOs

was accomplished in guiding farmers through gradual

conversion to organic practices, producing and applying

vermi-compost, azolla culture, and bio-pesticides, and also

in organizing farm visits and training programs.

Organic farming, as defined in this policy, requires less

external inputs, relying more on the natural and human

resources available on the farm. By reducing financial

burden and increasing on-farm activities, the KSPOF

aimed at curbing migration to urban areas and extending

the benefits of sustainable agriculture to both farmers and

consumers. Since 2006, the number of farmers registered

under the policy has grown five-fold and budgetary support

allocated for subsidies for organic inputs in the state

increased by nearly 20 %.6 The policy also triggered other

similar investments by the state government towards sus-

tainable agriculture now coming to more than INR 1 billion

per year. Thus, the recent policy trend in Karnataka aims at

making agriculture both profitable and sustainable. This

could be the beginning of a potential agricultural reform in

the making, albeit at the state level. This, in turn, calls for a

detailed assessment of its prospects for small farms in order

to strategize effective and continued support to this sector.

The changes in the social and ecological aspects of

Karnataka’s agrarian economy in tune with policy changes

form the main subject of this paper. ‘‘Study areas’’ starts

with the selection and characterization of the study dis-

tricts, followed by a description of the process of identifi-

cation and selection of indicators and their aggregation into

three indices. ‘‘Methodology’’ narrates how we foresee the

scenarios for assessment prior to specifying the model for

impact assessment. The subsequent section discusses the

performance of and trade-offs among ecological, eco-

nomic, and socio-cultural criteria in the two policy sce-

narios. The paper concludes in ‘‘Results and discussion’’,

eliciting policy implications for sustaining small-scale

agriculture in Karnataka.

Study areas

Assuming that change in agricultural practices and input

use will be greater in districts with notable change in

acreage towards commercial crops, we focus on such dis-

tricts in Karnataka where area under cultivation has chan-

ged significantly. Thus, the process of choosing study

districts involved a three-stage, temporal (from 1976 to

2006) analysis, followed by a stakeholder workshop. The

first stage consisted of analyzing the change in land use in

all districts of the state within the prevalent land use

classification, followed by an analysis of change in crop-

ping pattern from 1995 to 2005. The districts showing

significant change in land use and cropping pattern were

then examined for incidence of social distress (e.g., farmer

suicides). This was followed by a workshop to discuss

these results and objectives of the study with a range

of stakeholders from the landscape (from academic,

3 At 122.5 kg/ha, the NPK application in Karnataka is still less than

the national average of 131.1 kg/ha for 2005–2006.4 http://raitamitra.kar.nic.in/kda_booklet.pdf.5 A taluk is a subdistrict administrative division that entails local

government for exercising certain fiscal and administrative power

over the villages and municipalities within its jurisdiction.

6 http://www.kar.nic.in/finance/bud2010/budhig10e.pdf and Karna-

taka State Annual Budget 2011–2012.

174 Sustain Sci (2013) 8:173–185

123

voluntary, and government sectors). Thus, based on the

workshop deliberations on temporal changes observed in

agricultural land use and farmers’ distress in Karnataka,

four districts from different agro-climatic zones (Bijapur,

Chitradurga, Chikballapur, and Mysore) were selected, as

well as a control district (Udupi) with relatively insignifi-

cant changes in these selected criteria (Fig. 1).

Selected districts located in different agro-climatic

zones possess different ecological, economic, and social

characteristics. Table 1 highlights the major differences in

the selected districts, such as extent of cultivation,

irrigation, fertilizer distribution, population density, and

social distress in terms of incidences of suicides. Two

districts located in northern and central dry zones (Bijapur

and Chitradurga) seem to have more area under cultivation,

and moderate irrigation and fertilizer use, but higher sui-

cide incidences compared to the other three districts in

eastern dry, transition, and coastal zones.

Three taluks in each selected district were chosen

(except in Udupi, a smaller district, where only two taluks

were chosen), following the same procedure that was car-

ried out for district selection. The village where the KSPOF

Fig. 1 Selected study districts

in the state of Karnataka, India

Table 1 Characteristics of the selected districts

Bijapur Chitradurga Chikballapur Mysore Udupi

Geographical areaa (ha) 1053471 770702 404501 676382 356446

Mean operational landholdinga (2005–2006) (ha) 3.03 2.05 1.15e 0.97 0.78

Net sown areab (2008–2009) (%) 71 51 26 49 26

Net irrigated areab (2008–2009) (%) 35 19 25 47 33

Fertilizers distributedb (N ? P ? K, kg/acre of net sown area) (2009–2010) 92 72 221 229 69

Population densityc (2011) (per km2) 207 197 298 437 304

Farmers’ suicidesd (2003–2007) 408 1058 241e 383 192

a Directorate of Economics and Statistics (DES 2006)b DES (2011)c Census of India 2011d State Crime Records Bureau, Government of Karnatakae Chikballapur was formed as a new district from the larger Kolar district on 23 August 2007; this data is available only for Chikballapur and

Kolar combined

Sustain Sci (2013) 8:173–185 175

123

was being implemented was identified in each taluk. Thus,

14 villages that adopted organic practices since 2006 were

chosen for the primary data collection.

Methodology

Sustainability impact assessment of any target system

generally refers to the three pillars/dimensions7—ecologi-

cal, economic, and social. In that sense, agricultural sus-

tainability could imply production activities that are

environmentally non-damaging, financially viable, and

socially desirable at a given point of time and scale.

Although differences exist on the concept of sustainable

agriculture, there is consensus on these basic dimensions of

sustainability (Cai and Smith 1994; Hansen 1996; Pretty

1995; Rigby and Caceres 2001). This is summarized by

Zhen et al. 2005 as: (1) ‘ecological soundness’, referring to

the preservation and improvement of the natural environ-

ment; (2) ‘economic viability’, denoting the maintenance

of yields and productivity of crops and livestock; and (3)

‘social acceptability’, referring to self-reliance, equality,

and improved quality of life. Nevertheless, sustainability as

depicted here is difficult to be condensed into a single

simple definition, though changes in its multi-faceted nat-

ure are considered to be amenable to monitoring by a range

of diverse indicators (Pannell and Glenn 2000). Thus, the

assessment of sustainability is understood as feasible and

meaningful at the level of individual indicators rather than

a singular index of overall sustainability of any target

system (Walter and Stutzel 2009). While pursuing this, we

also opt to investigate the interactions between and across

the criteria of sustainability.

Assessing both these changes (in indicators as well as in

the interactions across criteria) as policy impacts could

employ different approaches. These assessments generally

make use of agricultural household models (Taylor and

Adelman 2003; Bouman et al. 1999; Bhende and Kalirajan

2007; Ray 2007; Vashisth et al. 2007; Singh et al. 1986),

rural system change models (Ali 2007), multi-market

limited general equilibrium models (Croppenstedt et al.

2007), or bio-physical models (Thomas et al. 2006).

Agricultural household models require extensive data to

generate equations for output, consumption, input demand,

and prices. Built on cross-sectional data, these models are

poorly equipped for modeling changes in agricultural land

use and cropping pattern. Induced rural system models

need panel data on indicators of impacts as well as on

drivers of land uses. Bio-physical models may not suit an

integrated assessment (in addition to the problem of

incommensurability of different measurements) and may

be better suited for generating input–output coefficients for

sustainable production levels.

Impact assessment of farm practices (unlike that of

policies) generally pertains to environmental components

(biotic and abiotic). In the context of farm practices, Sattler

et al. (2010) assessed environmental components using

fuzzy logic and financial components with individual

interviews, followed by categorizing the output of alternate

production practices into ‘strong’ and ‘weak’ sustainability

groups. Fan et al. (2008) used a multi-equation system for

assessing the effects of government investment and subsi-

dies on agricultural growth and poverty reduction in India.

When it comes to linking the policy, social, and ecological

aspects related to agriculture, suitable models are rarer,

especially at smaller scales. Learning from the above, we

adopt a fresh approach to analyze impacts of policy-driven

changes in agricultural practices in the villages of

Karnataka.

The attempt here is to link policy drivers of farm

practices with impact in terms of the three criteria men-

tioned earlier. Projecting the change in sustainability

dimensions or indicators for a target year using multiple

linear regressions is typically attempted in the literature (Li

et al. 2010; Black et al. 2002). We attempt to comprehend

the effect of change in criteria as a result of change in

practices, using a comparative statics simultaneous equa-

tions model. The latter reflects reality better, though it fails

to capture the temporal dynamics of sustainability criteria,

as and when they affect each other across time and space.

We pursue the status of change in individual sustainability

criteria and their effects on each other given plausible

policy and intervention scenarios. A target year of policy

impact assessment is, thus, irrelevant, as the question is not

‘when’ will each criterion or the system be sustainable, but

‘how’ the criteria will behave in the long run under dif-

ferent scenarios. Thus, through a sustainability lens, we ask

how or in what situation the change in ecological, socio-

cultural, and economic criteria would stabilize or become

asymptotic in the due course of time. In a nutshell, the

purpose of this exercise was not to estimate the level of

sustainability of each criterion at a point of time in the

future, but to pinpoint the interactions that make the cri-

teria stabilize over time.

Indicators of the assessment criteria

The three criteria, ecological (E), economic or financial

(F), and socio-cultural (S), are represented by eight selec-

ted indicators that capture changes in the farms under

assessment (Table 2). These indicators were selected fol-

lowing related literature and interactions with experts,

including farmers.

7 The words ‘dimension’ and ‘criteria’ in the context of sustainability

are synonymously used in this paper.

176 Sustain Sci (2013) 8:173–185

123

Indicator quantification

This section describes how each specific indicator is

quantified.

Soil organic carbon (SOC), an important indicator of

soil fertility and productivity, was measured in terms of the

proportion of active carbon content in the collected soil

samples. Soil and water samples were collected for eco-

logical assessment during the primary survey with farmer

respondents. These samples were analyzed in the labora-

tories of Krishi Vigyan Kendra (KVK) in the respective

study districts. Electrical conductivity (EC), indicating the

salinity of water, was taken as an indicator of its suitability

for irrigation. Data on agro-biodiversity (BD) per acre of

farm land was collected during the primary survey as the

number of species [crops, trees (multi-purpose trees), and

animals (domesticated)].

Among the economic indicators, profit is calculated in

two ways as mentioned in Table 2. Profit (IN1) is calcu-

lated by subtracting cash expense on chemical inputs from

the gross income. Profit (IN2) is calculated by subtracting

the sum of cash expenses on chemical inputs as well as

expenses on organic inputs purchased (that is, in excess of

that produced on-farm). The purchased quantity of organic

manure was computed by subtracting the manure produced

on-farm from the quantity of manure that is actually

applied. For IN2, the quantity of organic manure generated

on-farm was computed based on the number of livestock in

each farm household. The quantity of organic manure

currently being applied in excess of what is produced on-

farm is considered to be procured from the market. The

prevailing prices of all inputs at the time of data collection

in each study district was collected from the local markets.

We have not assumed any price premium for organic

products while calculating the income from agriculture for

each household.

Home-grown food consumption (FF, Table 2) is mea-

sured as the quantity of food (grain and vegetables) grown

on the farm that is consumed by members of the farm

family. Indebtedness (NDT) is measured as the probability

of possessing outstanding loans. This indicator is assumed

to be reflecting the possibility of social distress or unrest

among the households interviewed. NDT is estimated using

a logistic function for each village, as in Eq. (1):

NDTi = Probability of ith farm household having any

outstanding overdue loan

NDTi ¼ f tið Þ ¼1

1þ e�ti½ � ð1Þ

where ti ¼ aþ b1 � x1i þ b2 � x2i. . .þ bn � xni

where x1…xn determine the probability of having out-

standing loans such as landholding size, irrigated area,

extent of commercial crops, cropping intensity, etc.,

introduced separately for each study district. Migration and

non-farm income are not excluded here both because the

related data was hard to collect and also because the inputs

and farming practice were not directly dependent on this.

For differentiating between rich and poor farm households,

we rely on the indicator profit from farming.

Of the socio-cultural indicators, collective activities

(CA) is captured by the number of social activities the

family gets involved in. AD is an indicator of the distri-

butional status of the households in a village in terms of the

aggregation of three variables—land area, vehicles, and

gadgets. The distribution of assets rather than income

across households was chosen to represent distributional

status as assets reflect long-term equity dynamics better

Table 2 Indicator framework for impact assessment

Assessment criteria Broad indicators Specific indicators Measurement

Ecological (E) Soil quality Soil organic carbon (SOC) % carbon in soil

Water quality (Electrical conductivity)-1 (WQ) (dS/m)-1

Agro-biodiversity Species of crops, multi-purpose trees,

and domesticated animals on-farm (BD)

Number/acre

Economic (F) Profit Net annual agricultural income withoutshadow price for organic inputs (IN1)

INR/acre

Net annual agricultural income withshadow price for organic inputs (IN2)

INR/acre

Consumption of

home-grown food

Quantity consumed in the household out

of produced food grains (FF)

kg/person/year

Indebtedness Probability of having outstanding loans (NDT) Probability from logistic function

Socio-cultural (S) Collective activitiesa Involvement in collective activities (CA) Number

Asset distribution Status of asset distribution (AD) Range between indices of asset groups

above and below the village mean index

a Collective activities include watershed development, afforestation, festival celebrations, and participation in microfinance and self-help groups

Sustain Sci (2013) 8:173–185 177

123

than current incomes. A composite index of assets (CIA)

was constructed for each household using three variables:

land size, number of vehicles, and gadgets, using Eq. (2):

CIAi ¼X3

j¼1

Sj �Wj

� �ð2Þ

where CIAi, composite index of assets for the ith household;

Sj, value of variable j (j1 = landholding size, j2 = number

of vehicles, j3, number of gadgets); Wj = weightage for

variable j8

Using these computed values of the CIA for each

household, we calculated the mean asset index (CIAmean).

We categorized households into two classes, one Dhigh with

asset index higher than or equal to CIAmean and the other

Dlow with asset index lower than CIAmean for the village.

The variables Dhigh and Dlow are defined below:

Dhigh ¼Pn

i¼1 CIAi

nhigh

8 CIAi�CIAmeanð Þ

an

Dlow ¼Pn

i¼1 CIAi

nlow

8 CIAi\CIAmeanð Þ

where CIAmean, mean asset index for a village;

nhigh, number of households with CIAi�CIAmean;

nlow, number of households with CIAi\CIAmean:

Then, D is defined as D ¼ Dhigh � Dlow: Thus, the range

of D would be 0 \ D \ CIAhigh, where CIAhigh is the

highest asset index in a village.

The variable AD representing the distribution status is

the reciprocal of D (i.e., 1D), as the lower the D value, the

better the asset distribution in a village.

Data

The primary data for the study districts consist of bench-

mark data collected by the state department of agriculture

and the collaborating local NGO in 2006 before the organic

farming policy was initiated and the data that we collected

through the primary survey in 2009–2010 after more than

3 years of organic farming policy implementation in these

villages. Data from the survey included all the indicators of

assessment (Table 3), as well as other key variables in the

policy context.

Aggregating indicators

Although it may be ideal for any impact assessment to have

a wide range of indicators and variables, when it comes to

analysis, it amounts to large data sets for assessment across

multiple study sites. Such situations may involve double

counting or may lead to multi-collinearity. At the same

time, reducing the number of indicators might cause loss of

vital information regarding the system. Aggregating indi-

cators into indices could reduce these shortcomings in

indicator selection. Hence, we condense the indicator sets

for each criterion into an appropriate composite index for

that criterion, using the weights obtained from the Partic-

ipatory Impact Assessment (PIA)9 workshops. Indices,

corresponding to each criterion, are constructed from the

products of indicator values for each household and the

corresponding weightage attributed to the indicators in

PIA. These composite indices are calculated for each cri-

terion for two points in time (using the same weights for

indicators in both periods)—2006 and 2009 (see Eq. 3).

If E, S, and F denote ecological, financial, and socio-

cultural criteria, then the composite index of ecological

criterion in 2009, for example, is computed as:

Ei 2009ð Þ ¼X3

JE¼1

Si jð ÞE �Wi jð ÞE� �

ð3Þ

where Ei 2009ð Þ, composite index of ecological criteria for

the ith household in 2009; SiðjÞE, value of indicator jE(jE1 = SOC, jE2 = WQ, jE3 = BD) for the ith household in

2009; Wi jð ÞE , weightage for indicator jE (jE1 = SOC,

jE2 = WQ, jE3 = BD) for the ith household from the PIA.

Similarly, composite indices for financial (Fið2009Þ) and

socio-cultural (Sið2009Þ) criteria using their respective indi-

cators (as in Table 2) and the three indices for 2006

(Eið2006Þ; Fið2006Þ; Sið2006Þ) as well, were computed.10

Developing policy–practice scenarios

Evolving realistic policy scenarios is crucial for any

assessment. In this paper, scenarios are visualized in the

context of agricultural policies as possible future potential

changes towards reducing agrarian distress. In a8 Weightage for each variable constituting the CIA for any household

is calculated as

Wj ¼1

D� SD Sj

� �

where D ¼P3

j¼1

1

SD Sjð Þ

� �, SD(Sj) = standard deviation of Sj1 to Sj3.

This ensures that a lower weightage is assigned for the variable that is

highly dispersed and diverse, indicating the unequal distribution of

assets within a village.

9 PIA workshops were held in all study sites involving farmers,

researchers, as well as government officers and NGO representatives

implementing the policy. Details of PIA can be found in Purushoth-

aman et al. (2012b).10 While computing the composite index of financial criteria (F), the

indicator NDT (indebtedness) is used as its reciprocal, as the absence

of indebtedness contributes to financial sustainability.

178 Sustain Sci (2013) 8:173–185

123

sustainability science perspective, scenario-building con-

siders both qualitative and quantitative expectations (Swart

et al. 2004) and foresights on different criteria. Scenarios of

impacts were developed after discussions with key stake-

holders in the area, analysis of past trends in key variables,

and after reviewing the agricultural action plan of the state

government.11

Until 2006, in the study villages, farming, as it was

commonly practiced, mainly involved the application of

chemical fertilizers and pesticides and a small amount of

organic manure. After the introduction of the KSPOF in

2006, farmers in the selected villages were given assistance

and guidance to change production practices towards

making agriculture sustainable. The focus of the policy was

on change in agricultural practices or the ways in which

farming is managed; hence, the important scenario vari-

ables were with respect to cropping pattern and intensity,

irrigation, input use, etc. With the increase in public and

private investment in irrigation facilities and demand for

export-oriented agricultural commodities, the current sce-

narios reflect the increasing use of chemical inputs for

commercial crops in the state. On the other hand, with the

implementation of the KSPOF and similar policies towards

sustainable agriculture, some farmers are making an effort

to reduce their dependence on chemical inputs. If this

policy trend continues into the future, these farmers are

willing to increase the application of livestock manure, leaf

manure, farm waste, etc., which requires more livestock

and biomass than that which is available. The scenarios

also draw from (apart from the examination of trends and

expert consultation) trends in other data variables signifi-

cant for model development (‘‘Estimating the change in

variables in the two scenarios’’). The two scenarios that

emerged include one with continuation of the conventional

that is intensive in mineral fertilizers and market-oriented,

referred to as ‘Business as usual’ (BAU), and the alterna-

tive with more sustainable farming practices, in line with

new policies like the KSPOF, referred to as ‘With policy’

(WP). The major distinguishing difference between WP

and BAU scenarios is the relative quantity of organic

(IORG) and chemical inputs (IINORG) applied. Some

variables such as landholding size remain the same, as very

little impact is expected on this variable from the two

scenarios. The specific changes anticipated in any indicator

for either scenario are discussed later in ‘‘Estimating the

change in variables in the two scenarios’’ (Annexure 1

provides the changes in the variables in the two scenarios)

Ta

ble

3M

ean

val

ues

of

sele

cted

ind

icat

ors

fro

mth

ep

rim

ary

dat

a(h

igh

erv

alu

esar

eit

ali

cize

d)

Indic

ators

Unit

Bij

apur

(n=

25)

Chit

radurg

a(n

=25)

Chik

bal

lapur

(n=

31)

Myso

re(n

=20)

Udupi

(n=

15)

2009

2006

2009

2006

2009

2006

2009

2006

2009

2006

Soil

org

anic

carb

on

(SO

C)

%1.1

9(0

.47)

0.9

9(0

.23)

0.4

6(0

.07)

0.4

7(0

.19)

0.4

1(0

.25)

0.3

4(0

.11)

0.7

9(0

.35)

0.6

0(0

.27)

0.3

9(0

.19)

0.7

3(0

.14)

Wat

erqual

ity

(WQ

)(d

S/m

)-1

1.2

7(0

.37)

0.4

0(0

.05)

0.7

5(0

.22)

0.6

1(0

.11)

3.1

9(0

.10)

0.6

0(0

.04)

1.7

9(0

.24)

0.5

2(0

.25)

2.3

9(0

.02)

1.3

3(0

.02)

Agro

-bio

div

ersi

ty(B

D)

Num

ber

/acr

e3.8

1(1

.26)

2.8

0(1

.72)

1.1

0(0

.06)

1.9

9(0

.34)

1.8

4(0

.79)

1.2

3(0

.29)

2.1

3(0

.78)

2.2

0(1

.14)

3.0

4(1

.06)

1.1

3(0

.49)

Net

inco

me

(IN

2)

INR

/acr

e-

3314

(26148)

2999

(19084)

3799

(23571)

3505

(5462)

7696

(22058)

142

(6393)

12729

(21423)

7985

(29413)

18120

(31905)

18150

(20123)

No

over

due

loan

s(N

DT

-1)

Pro

bab

ilit

y0.9

4(0

.03)

0.0

4(0

.00)

0.4

2(0

.12)

0.4

9(0

.1)

0.2

2(0

.02)

0.3

3(0

.02)

0.4

9(0

.29)

0.5

7(0

.40)

0.4

6(0

.2)

0.7

9(0

.23)

Food

from

farm

(FF

)kg/p

erso

n/y

ear

376

(237)

762

(209)

1151

(171)

626

(114)

531

(70)

618

(258)

806

(108)

506

(41)

203

(91)

275

(18)

Coll

ecti

ve

acti

vit

ies

(CA

)N

um

ber

0.5

2(0

.05)

0.1

2(0

.04)

0.9

0(0

.3)

0.9

7(0

.45)

0.9

4(0

)0.5

8(0

.05)

0.5

6(0

.05)

0.4

4(0

.07)

1.4

1(0

.62)

0.6

3(0

.07)

Ass

etdis

trib

uti

on

(AD

)R

ange

bet

wee

nin

dic

esof

asse

tgro

ups

above

and

bel

ow

the

vil

lage

mea

nin

dex

1.4

9(0

.02)

0.8

8(0

.01)

1.2

0(0

.09)

1.0

5(0

.08)

1.5

6(0

.05)

0.9

6(0

.06)

1.4

9(0

.84)

0.9

4(0

.03)

1.5

6(0

.08)

1.0

6(0

.1)

Sta

ndar

ddev

iati

ons

are

show

nin

par

enth

eses

nnum

ber

of

house

hold

ssu

rvey

ed

11 Karnataka state agricultural action plans can be accessed at:

http://kappec.kar.gov.in/future.html, http://empri.kar.nic.in/Directives

%20and%20actions%20taken%202011-02-02%20CFN%20RMNS.pdf,

and http://ces.iisc.ernet.in/envis/sdev/etr15.pdf.

Sustain Sci (2013) 8:173–185 179

123

Tracing the change in criteria over time

Change in indices from 2006 to 2009

We intend to assess changes in the three composite indices

(that represent the three criteria) due to alterations in cul-

tivation practices in the villages where the organic farming

policy was implemented. In order to trace this change,

spatially and temporally, we used panel data (for 2006

from the state department of agriculture and for 2009

through primary surveys) for the study sites.

Denoting sustainability criteria as E, F, and S, the crops

grown as ‘C’, inputs used as ‘I’, and policy(-ies) prominent in

driving the change (in C and I) over time as ‘P’, we concep-

tualize the relation impacting E, S, and F as in Eq. (4):

DPð Þ ! DCð Þ ! DIð Þ !DEDFDS

0

@

1

A

2

4

3

5 ð4Þ

where the change in E, F, and S from 2006 to 2009

DE;DF;DSð Þ, with selected indicators for each of them (as

in Table 2), is calculated as:

DE ¼ E2009 � E2006 ð5ÞDF ¼ F2009 � F2006 ð6ÞDS ¼ S2009 � S2006 ð7Þ

Determinants of change in sustainability indices

We estimate the determinants of DE, DF, and DS using the

difference in values of independent variables between 2006

and 2009. Separate multiple regression equations were

used to determine DE, DF, and DS for each study district,

with corresponding sets of independent variables

Da1. . .Dan; Db1. . .Dbm; and Dc1. . .Dcrð Þ that significantly

influence DE, DF, and DS in each district [Eqs. (8)–(10)]:

DE ¼ aþ bE1� Da1 þ bE2

� Da2 þ � � � þ bEn� Dan ð8Þ

DS ¼ aþ bS1� Db1 þ bS2

� Db2 þ � � � þ bSm� Dbm ð9Þ

DF ¼ aþ bF1� Dc1 þ bF2

� Dc2 þ � � � þ bFr� Dcr ð10Þ

where bE1 � � �En; bS1 � � � Sm and bF1 � � �Fr are the slope

coefficients of variables (Da1 to Dan, Db1 to Dbm, and Dc1

to Dcr) determining DE, DF, and DS, respectively. An-

nexures 2 and 3 give the details of the variables used and

the regression equations that significantly explain DE, DF,

and DS in the study districts.

The set of three equations above [Eqs. (8)–(10)] were

estimated using 3SLS regression in order to capture the

simultaneous effects of DE, DF, or DS on each other in

each district (see Annexure 4 for the set of equations). The

interactive and simultaneous impact on each criterion is

represented in a set of three equations [Eqs. (11)–(13)]:

DE ¼/ þbE1� Da1 þ bE2

� Da2 þ � � � þ bEn� Dan

þ bDS � DSþ bDF � DF ð11Þ

DS ¼/ þbS1� Db1 þ bS2

� Db2 þ � � � þ bSm� Dbm

þ bDE � DE þ bDF � DF ð12Þ

DF ¼/ þbF1� Dc1 þ bF2

� Dc2 þ � � � þ bFr� Dcr

þ bDE � DE þ bDS � DS ð13Þ

Estimating the indices for the WP and BAU scenarios

To estimate the stabilized levels of the three indices in both

the scenarios (WP and BAU), we use a comparative statics

approach. Vector ‘Z’ represents the current change in indices

E, S, and F from 2006 to 2009 (from ‘‘Determinants of change

in sustainability indices’’) and ‘DY’ (for each scenario) is a

vector formed by average anticipated changes in the values of

exogenous variables determining DE, DF, and DS:

Z ¼DEDFDS

24

35 and DYs ¼

Da1

Db1

Dc1

. . .Dan

Dbm

Dcr

2666666664

3777777775

where s is scenario WP or BAU.

Then, Z, which is the estimated change in indices E, S,

and F beyond 2009, can be expressed in terms of matrices

of coefficients of endogenous (A) and exogenous (B)12

variables in the two scenarios. Equations (11)–(13) can also

be written in a matrix form as:

Z ¼DEDFDS

24

35 ¼ A � Z þ B � DYs ¼ I � Að Þ�1�B � DYs ð14Þ

where I is the identity matrix of A (3 9 3) and s is either

scenario WP or BAU.

12 Matrices of coefficients of endogenous and exogenous variables

for comparative statics analysis: the matrices of coefficients of

endogenous (of DE, DF, and DS as in matrix A in the first

expression below) and exogenous (a1 to an, b1 to bm, and c1 to cr as in

matrix B in the second expression below) were formed in order to

estimate the future status of DE, DF, and DS.

A ¼0 b DEDSð Þ b DEDFð Þ

b DsDeð Þ 0 b DSDFð Þb DFDEð Þ b DFDSð Þ 0

B ¼b DEa1ð Þ b DEa2ð Þ. . . b DEanð Þb DSb1ð Þ b DSb2ð Þ. . . b DSbmð Þb DFc1ð Þ b DFc2ð Þ. . . b DFcrð Þ

:

From matrices A and B, we find how DE, DF, and DS further become

constant or stable in two different scenarios: WP and BAU. The

scenarios capture the change in values of exogenous variables that

influence DE, DF, and DS to reach relatively stable levels of

DE;DF and DS:

180 Sustain Sci (2013) 8:173–185

123

Estimating the change in variables in the two scenarios

We solved the Eqs. (11)–(13) for estimating Z using sce-

nario assumptions on DYs based on feedback from expert

consultation (including farmers) and examination of trends.

We used the estimated regression Eqs. (8)–(10) to validate

the assumptions made for scenario development. If

DE;DF and DS were not found to be significantly deter-

mined by the change in independent variables

(Da1;Db1;Dc1 to Dan;Dbm;Dcr), then further change was

considered in the combination and values of independent

variables till they significantly determined the estimated

values of (DE;DF;DS).

The values of Da1;Db1;Dc1 to Dan;Dbm;Dcr in the

vector ‘DYs’ change based on the iterative process of val-

idation tests carried out separately for each district.13 Thus,

a combination of such changes in all the exogenous vari-

ables constituting WP and BAU scenario options (see

Annexure 1) would yield the projected values for changes

in criteria: DE;DF and DS. This is followed by the esti-

mation of the index for each sustainability criterion in the

future. For instance, E is calculated from E2009 [refer to

Eq. (3)], as in [Eq. (15)]:

E ¼ E2009 þ DE ð15Þ

where E estimated ecological index for future WP or

BAU

E2009 mean actual ecological index in 2009 of the sur-

veyed households

DE estimate change in ecological index

As mentioned in ‘‘Aggregating indicators’’, two variants

of net income (IN1 and IN2) were used in the financial

criteria in order to capture the relative difference between

producing organic inputs on own farm vis-a-vis procuring

it from outside. Hence, at every step of analysis starting

from computing the composite index through estimation of

change in the two scenarios [from Eq. (3) through 15],

these two variants of financial criterion were included. The

next section discusses the results of applying the above

discussed model to the data gathered in the study.

Results and discussion

Agricultural crisis reflected in low productivity, high

indebtedness, and farmers’ suicides is assumed to be dri-

ven, among others, also by unsustainable farming practices.

Mitigation strategies for such a situation in the state of

Karnataka include state support for alternate farming

practices. In order to compare the potential and limitations

of this trend in state policy with those that support con-

ventional farming practices, this paper relies on the com-

parative statics approach built across two scenarios, WP

and BAU. Differences between the estimated indices of

assessment criteria [e.g., E in Eq. (15) and corresponding

calculations for F and S] in the WP and BAU scenarios for

each district is presented in Tables 4 and 5. Such projected

sustainability status is compared among small and large

landholders, as well as for rain-fed and irrigated farms

across the study districts (Figs. 2 and 3).

Impact of policy-driven farming practices in the study

areas

With prevailing scarcity of livestock and biomass in most

farm holdings, farmers will need to purchase at least part of

their organic input requirement. However, with emerging

support from government policies, it might be possible for

farmers to be self-reliant in terms of organic inputs.

Table 4 presents the results for interactive impacts on the

indices of each criterion when organic manure is produced

on-farm. Table 5 gives the results when farmers purchase

manure, when the quantity needed is in excess of what is

produced on-farm.

From Tables 4 and 5, it is apparent that, most often, the

WP scenario performs better than BAU, with the exception

of socio-cultural criteria in Bijapur, Chitradurga, and

Udupi. If farmers have to purchase organic inputs from the

market, a dampening effect is observed in the potential

performance of WP in Bijapur (socio-cultural criterion)

and Chikballapur (ecological criterion).

Higher values of the ecological index in the WP sce-

nario across the districts resonate findings from other en-

quiries about impacts of similar farm practices on agro-

ecological factors (e.g., Bengtsson et al. 2005; Rasul and

Thapa 2004; Singh 2000) or on stabilizing the productivity

of small farms (Altieri 2002).

Literature on non-ecological impacts of farm practices

generally touches upon debt situations and food security

rather than direct profit, though there are some assessments

on the latter also from field experiments. Degraded natural

resources on-farm are shown to result in yield stagnation,

escalated costs, and reduced profits (Singh 2009), leading

to a continued downfall in farm economy that makes

farmers indebted to such a level that the non-repayment of

loan becomes a major cause of distress (Mishra 2008). The

advantages of sustainable farms for food security in small

farms have also been recorded (Rao 1994). The findings of

this paper on the financial performance of small farms in

WP with uncertified organic cultivation practices differ

13 For instance, in the case of the WP scenario in Udupi district, the

change in variable IINORG (inorganic input use, a determinant of

DE) is expected to decrease by 30 %, whereas for the BAU scenario,

it is expected to increase by 30 %, both with respect to 2009 values.

Sustain Sci (2013) 8:173–185 181

123

from the general perception on economic disadvantages of

sustainable cultivation practices.

Our results, despite not imputing a premium price for

organically grown farm produce, show that financial

response (both when organic inputs are generated on-farm

or purchased) is better in WP in the study districts, devi-

ating from the general perception (Reganold et al. 2001;

Ramesh et al. 2005, 2010) on a lackluster economic impact

of organic farming on small farms. The dampened eco-

logical advantage in Chikballapur for the WP scenario

when organic inputs are purchased from outside implies

that, in poorer districts, application would be impacted

adversely if organic inputs have to be purchased.

The response of socio-cultural criteria (computed by

aggregating asset distribution and collective activities)

emerges better in WP than BAU in three districts with on-

farm generation of organic inputs. This could indicate a

possible unexpected implication of sourcing organic inputs

from markets, at least in situations where farm practices act

as drivers of social interactions among farmers.

Based on ecological, financial, and distributional

impacts, our results favor strengthening local institutions

and collective activities around the production and usage of

organic inputs on-farm than public investment in producing

and marketing organic inputs at industrial scales. This is

despite providing price incentives (premium) for organic

produce. Such a policy also ensures distributional equity

among village households in terms of assets, helping WP to

be socially sustainable.

Generally, studies mention a necessary trade-off

between sustainability and productivity if one has to

choose between farm-based inputs and chemically inten-

sive farming practices (Azadi et al. 2011). Our results show

that productivity may not be a cause for concern, as eco-

nomic dimension performs well with policy (in spite of not

attributing a price premium for organic produce). The

performance of economic criteria is linked to non-declining

yield and diversifying crops, along with the avoidance of

unaffordable credit needs.

Impact of policy-driven farming practice on different

farm holdings

Categorizing the interviewed farm households into groups

of small and large, as well as rain-fed and irrigated, reveals

relative and disaggregated impacts of changes in farm

practices. Figures 2 and 3 depict the results with pooled

data across the districts segregated into categories: small

(B2 ha) and large ([2 ha), rain-fed (B20 % of the total

land under irrigation) and irrigated ([20 % of the total land

under irrigation).

In brief, the three sustainability criteria improve in WP

for small farms compared to large farms (Fig. 2). However,

even for large farms, WP emerges to be beneficial com-

pared to BAU, though in the case of small farms, the dif-

ference between estimated values of E, S, and F in WP

becomes significantly higher than BAU.

The relative advantage of WP is noted in rain-fed farms

over irrigated farms with both on-farm generated inputs as

well as purchased, without any trade-off between the three

sustainability criteria. Irrigated farms also exhibit more

benefits from on-farm input generation without trade-off

among the three criteria in WP than when sourced from

outside the farm. Thus, Figs. 2 and 3 convey that the

greatest benefits of WP in the three assessment criteria

(E, S, and F) were for small and rain-fed farms compared to

large and irrigated farms.

While the results from disaggregated district level

analysis (from Tables 4 and 5) revealed the need to build

new and strengthened institutions for promoting and

Table 4 Impact on assessment criteria in the two scenarios across study sites when organic manure is produced on-farm

Assessment criteria WP better than BAU WP same as BAU WP worse than BAU

Ecological (E) Chitradurga, Udupi, Mysore, Bijapur, Chikballapur None None

Financial (F using IN1) Chitradurga, Udupi, Mysore, Bijapur, Chikballapur None None

Socio-cultural (S) Mysore, Chikballapur, Bijapur None Udupi, Chitradurga

Actual values of estimated E, S, and F are in Annexure 5

Table 5 Impact on assessment criteria in the two scenarios across study sites when organic manure is procured from outside

Assessment criteria WP better than BAU WP same as BAU WP worse than BAU

Ecological (E) Udupi, Chitradurga, Mysore, Bijapur, Chikballapur Chikballapur None

Financial (F using IN2) Bijapur, Udupi, Mysore, Chitradurga, Chikballapur None None

Socio-cultural (S) Mysore, Chikballapur None Bijapur, Chitradurga, Udupi

Actual values of estimated E, S, and F are in Annexure 5

182 Sustain Sci (2013) 8:173–185

123

propagating the in situ generation and application of

required organic inputs, subsequently, it is also recognized

that small and rain-fed farms gain more in WP (Figs. 2 and

3). These results question the arguments (e.g., Gulati et al.

2008) for enhancing policy support towards the industri-

alization of farms as the panacea for viability.

Conclusions

This paper discusses the impact of change in policies

towards organic practices, a trend that is becoming

increasingly popular in several states in India, on the three-

dimensional sustainability of farms in selected districts.

Fig. 2 Impact of farm practices

on different holdings [when

organic inputs are generated on-

farm (F with IN1) and when

purchased from the market

(F with IN2)]

Fig. 3 Impact of farm practices

on irrigated and rain-fed farms

[when organic inputs are

generated on-farm (F with IN1)

and when purchased from the

market (F with IN2)]

Sustain Sci (2013) 8:173–185 183

123

The methods employed provided space for involving

stakeholders in the selection and weighing of indicators,

though the need for panel data could be a constraint. While

acknowledging that sustainability as a whole cannot be

measured, the paper takes into account its three pillars,

namely, ecological, economic, and social, with relevant

indicator sets, without trying to bifurcate ‘weak’ and

‘strong’ sustainability, but allowing stakeholders to decide

on the acceptable trade-offs. The paper makes use of locally

relevant functions from agricultural land use and corre-

sponding indicators to trace the future impact on the occu-

pational mainstay of rural Karnataka. It brings out the

interplay of sustainability dimensions at grass roots, revealing

location-specific trade-offs in implementing agricultural pol-

icies oriented towards sustainability (Tables 4 and 5).

The paper uses actual farm-level information on a

flexible set of indicators, tracing their collective and

interactive impact on the criteria with the help of a

simultaneous equations model and comparative statics

scenario analysis. The results reveal the levels of stabil-

ization of the chosen indices under scenarios in WP (pol-

icies favoring organic practices) and BAU scenarios in

order to identify the sustainability trade-offs with different

policy options.

The results are noteworthy for the fact that no financial

trade-offs were found in WP for any of the five selected

districts. Though the absence of ecological trade-offs in

WP was intuitive, a lack of financial trade-off is not backed

by the literature. The socio-cultural trade-offs in WP found

in Bijapur, Chitradurga, and Udupi districts remain unex-

plainable. Nevertheless, these results resonate the findings

of a multi-criteria analysis for short-term policy impact on

indicators (Purushothaman et al. 2012a). The WP scenario

performs better with respect to profits, if organic inputs are

generated on-farm. The study also shows that small and

rain-fed farms vis-a-vis large and irrigated farms gain lar-

ger benefits from organic farming practices.

These findings help strategize the micro level planning

of public investment in agriculture. Two emerging rec-

ommendations from our analysis are; effective and targeted

support to produce organic inputs on-farm or in the village

commons and strengthening collective actions around

organic farming practices. Since public investment towards

this is justifiable, as the ecological and economic outcomes

are of interest to the whole society, the study strengthens

the argument for sustenance of small holders on economic

and ecological grounds through policies promoting sus-

tainable agricultural practices with self-reliance in inputs as

the key. It also advocates varying and contextual emphasis

on different dimensions of farm sustainability in imple-

menting such policies aimed at reducing rural poverty and

agrarian distress, while making explicit the trade-offs, so as

to design location-specific mitigation strategies.

Acknowledgments Authors gratefully acknowledge the support

from EU FP6 project ‘‘Land use policies and sustainable development

in developing countries (LUPIS)‘‘, as also valuable comments from

Dr Gopal Kadekodi and Rosa Abraham

References

Ali AMS (2007) Population pressure, agricultural intensification and

changes in rural systems in Bangladesh. Geoforum 38(4):720–

738

Altieri MA (2002) Agroecology: the science of natural resource

management for poor farmers in marginal environments. Agric

Ecosyst Environ 93:1–24

Azadi H, Schoonbeek S, Mahmoudi H, Derudder B, De Maeyer P,

Witlox F (2011) Organic agriculture and sustainable food

production system: main potentials. Agric Ecosyst Environ 144(1):

92–94

Balagopal K (1988) Rich peasant, poor peasant. Seminar 352:19–23

Bengtsson J, Ahnstrom J, Weibull A-C (2005) The effects of organic

agriculture on biodiversity and abundance: a meta-analysis.

J Appl Ecol 42:261–269

Bhalla GS, Singh G (2009) Economic liberalisation and Indian

agriculture: a statewise analysis. Econ Political Weekly 44(52):

34–44

Bhattacharyya P, Chakraborty G (2005) Current status of organic

farming in India and other countries. Indian J Fertilizers 1(9):

111–123

Bhende MJ, Kalirajan KP (2007) Technical efficiency of major food

and cash crops in Karnataka (India). Indian J Agric Econ 62(2):

176–192

Black JA, Paez A, Suthanaya PA (2002) Sustainable urban transpor-

tation: performance indicators and some analytical approaches.

J Urban Plan Dev 128(4):184–209

Bose P (1981) Farmer’s agitation. Seminar 257:58–62

Bouman BAM, Jansen HGP, Schipper RA, Nieuwenhuyse A,

Hengsdijk H, Bouma J (1999) A framework for integrated

biophysical and economic land use analysis at different scales.

Agric Ecosyst Environ 75:55–73

Cai YL, Smith B (1994) Sustainability in agriculture: a general

review. Agric Ecosyst Environ 49:299–307

Chand R, Pandey LM (2008) Fertiliser growth, imbalances and

subsidies: trends and implications. National Centre for Agricul-

tural Economics and Policy Research. Discussion Paper: NPP

02/2008. Available online at: http://www.ncap.res.in/upload_

files/others/oth_13.pdf

Croppenstedt A, Bellu LG, Bresciani F, DiGiuseppe S (2007)

Agricultural policy impact analysis with multi-market models:

a primer. Agricultural Development Economics Division, The

Food and Agriculture Organization of the United Nations, Italy.

ESA Working Paper No. 07-26. Available online at: ftp://ftp.

fao.org/docrep/fao/010/ai253e/ai253e00.pdf

Deshpande RS, Prabhu N (2005) Farmers’ distress: proof beyond

question. Econ Political Weekly 40(44–45):4663–4665

Directorate of Economics and Statistics (DES) (2006) Agricultural

Census 2005–06. DEC

Directorate of Economics and Statistics (DES) (2011) Karnataka at a

Glance 2009–2010. DEC

Evenson RE, Gollin D (2003) Assessing the impact of the Green

Revolution, 1960 to 2000. Science 300(5620):758–762

Fan S, Gulati A, Thorat S (2008) Investment, subsidies, and pro-poor

growth in rural India. Agric Econ 39:163–170

Government of India (GoI) (2011) Economic survey of India, 2011.

Ministry of Finance, Government of India

184 Sustain Sci (2013) 8:173–185

123

Gulati A, Joshi PK, Landes M (eds) (2008) Contract farming in India:

a resource book. Available online at: http://www.ncap.res.in/

contract_%20farming/Resources/2.1%20Ashok%20Gulati.pdf.

Accessed 22 Sep 2011

Hansen JW (1996) Is agriculture sustainability a useful concept?

Agric Syst 50:117–143

Hazell PBR (2003) Is there a future for small farms? In: Proceedings

of the 25th International Conference of Agricultural Economists

(IAAE), Durban, South Africa, August 2003. International Food

Policy Research Institute, Washington DC. ISBN 0-958-46098-

1. Available online at: http://ecsocman.hse.ru/data/846/660/

1219/151.pdf

Kalamkar SS (2009) Urbanisation and agricultural growth in India.

Indian J Agric Econ 64(3):442–461

Li XM, Xiao RB, Yuan SH, Chen AJ, Zhou JX (2010) Urban total

ecological footprint forecasting by using radial basis function

neural network: a case study of Wuhan city, China. Ecol Ind

10:241–248

Lipton M (1980) Why poor people stay poor: a study of urban bias in

world development. Heritage, New Delhi

Mishra S (2008) Risks, farmers’ suicides and Agrarian crisis in India:

is there a way out? Indian J Agric Econ 63(1):38–54

Pannell DJ, Glenn NA (2000) A framework for the economic

evaluation and selection of sustainability indicators in agricul-

ture. Ecol Econ 33:135–149

Pretty JN (1995) Regenerating agriculture: policies and practice for

sustainability and self-reliance. Earthscan, London

Purushothaman S, Kashyap S (2010) Trends in land use and crop

acreages in Karnataka and their repercussions. Karnataka J Agric

Sci 23(2):330–333

Purushothaman S, Patil S, Francis I (2012a) Impact of policies

favouring organic inputs on small farms in Karnataka, India: a

multicriteria approach. Environ Dev Sustain 14:507–527

Purushothaman S, Patil S, Francis I, Konig HJ, Reidsma P, Hegde S

(2012b) Participatory impact assessment of agricultural practices

using the land use functions framework: case study from India.

Int J Biodivers Sci Ecosyst Serv Manag. doi:10.1080/21513732.

2012.721005

Ramesh P, Singh M, Subba Rao A (2005) Organic farming: its

relevance to the Indian context. Curr Sci 88(4):561–568

Ramesh P, Panwar NR, Singh AB, Ramana S, Yadav SK, Shrivastava

R, Subba Rao A (2010) Status of organic farming in India. Curr

Sci 98(9):1190–1994

Rao CHH (1994) Agricultural growth, rural poverty and environ-

mental degradation in India. Oxford University Press, New Delhi

Rasul G, Thapa GB (2004) Sustainability of ecological and conven-

tional agricultural systems in Bangladesh: an assessment based

on environmental, economic and social perspectives. Agric Syst

79:327–351

Ray SK (2007) Economics of change in cropping pattern in relation to

credit: a micro level study in West Bengal. Indian J Agric Econ

62(2):216–231

Reddy VR, Galab S (2006) Looking beyond the Debt Trap. Econ

Political Weekly 41(19):1838–1841

Reganold JP, Glover JD, Andrews PK, Hinman HR (2001) Sustain-

ability of three apple production systems. Nature 410:926–930

Rigby D, Caceres D (2001) Organic farming and the sustainability of

agricultural systems. Agric Syst 68:21–40

Saha DK, Singh YV, Gajja BL, Singh R, Chand K, Amtul-Waris,

Singh B, Jangid BL (2009) Livelihood opportunities and socio-

economic issues. In: Kar A, Garg BK, Singh MP, Kathju S (eds)

Trends in arid zone research in India. Central Arid Zone

Research Institute, Jodhpur

Sattler C, Nagel UJ, Werner A, Zander P (2010) Integrated

assessment of agricultural production practices to enhance

sustainable development in agricultural landscapes. Ecol Ind

10(1):49–61

Satyasai KJS, Vishwanathan KU (1997) Commercialisation and

diversification of Indian agriculture. National Bank for Agricul-

ture and Rural Development (NABARD). Occasional Paper, 5

Singh RB (2000) Environmental consequences of agricultural devel-

opment: a case study from the Green Revolution state of

Haryana, India. Agric Ecosyst Environ 82:97–103

Singh K (2009) Environmental degradation and measures for its

mitigation with special reference to India’s agricultural sector.

Indian J Agric Econ 64(1):40–61

Singh I, Squire L, Strauss J (eds) (1986) Agricultural household

models: extensions, applications and policy. Johns Hopkins

University Press, Baltimore

Swart RJ, Raskin P, Robinson J (2004) The problem of the future:

sustainability science and scenario analysis. Global Environ

Change 14:137–146

Taylor JE, Adelman I (2003) Agricultural household models: genesis,

evolution, and extensions. Rev Econ Household 1(1):33–58

Thomas CP, Hengsdijk H, Wolf J, van Ittersum MK, Reimund PR,

Son TT, Laborte AG (2006) TechnoGIN, a tool for exploring and

evaluating resource use efficiency of cropping systems in East

and Southeast Asia. Agric Syst 87:80–100

Vasavi AR (2009) Suicides and the making of India’s agrarian

distress. South African Rev Soc 40(1):94–108

Vashisth AK, Sujith Kumar S, Aggarwal PK, Pathak H, Joshi HC,

Choudhary RC (2007) An integrated evaluation of trade-offs

between environmental risk factors and food production using

interactive multiple goal linear programming—a case study of

Haryana. Indian J Agric Econ 62(3):511–523

Walter C, Stutzel H (2009) A new method for assessing the

sustainability of land-use systems (II): evaluating impact indi-

cators. Ecol Econ 68:1288–1300

Zhen L, Routray JK, Zoebisch MA, Chen G, Xie G, Cheng S (2005)

Three dimensions of sustainability of farming practices in the

North China Plain. A case study from Ningjin County of

Shandong Province, PR China. Agric Ecosyst Environ 105:

507–522

Sustain Sci (2013) 8:173–185 185

123