Content uploaded by Christian Andres
Author content
All content in this area was uploaded by Christian Andres on Dec 16, 2013
Content may be subject to copyright.
Yield and Economic Performance of Organic and
Conventional Cotton-Based Farming Systems – Results
from a Field Trial in India
Dionys Forster
1
, Christian Andres
1
*, Rajeev Verma
2
, Christine Zundel
1,3
, Monika M. Messmer
4
,
Paul Ma
¨der
4
1International Division, Research Institute of Organic Agriculture (FiBL), Frick, Switzerland, 2Research Division, bioRe Association, Kasrawad, Madhya Pradesh, India,
3Ecology Group, Federal Office for Agriculture (FOAG), Bern, Switzerland, 4Soil Sciences Division, Research Institute of Organic Agriculture (FiBL), Frick, Switzerland
Abstract
The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant
interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural
resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost
importance. However, information concerning the performance of farming systems under organic and conventional
management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the
conversion phase (2007–2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A
cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton)
management was investigated. We observed a significant yield gap between organic and conventional farming systems
in the 1
st
crop cycle (cycle 1: 2007–2008) for cotton (229%) and wheat (227%), whereas in the 2
nd
crop cycle (cycle 2: 2009–
2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In
contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (21%
in cycle 1, 211% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross
margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due
to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system
(+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean
production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research
needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact
of the different farming systems.
Citation: Forster D, Andres C, Verma R, Zundel C, Messmer MM, et al. (2013) Yield and Economic Performance of Organic and Conventional Cotton-Based
Farming Systems – Results from a Field Trial in India. PLoS ONE 8(12): e81039. doi:10.1371/journal.pone.0081039
Editor: Jean-Marc Lacape, CIRAD, France
Received May 13, 2013; Accepted October 18, 2013; Published December 4, 2013
Copyright: ß2013 Forster et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Biovision Foundation for Ecological Development, http://www.biovision.ch/; Coop Sustainability Fund, http://www.coop.ch/pb/site/nachhaltigkeit/
node/64228018/Len/index.html; Liechtenstein Development Service (LED), http://www.led.li/en/home.html; Swiss Agency for Development and Cooperation
(SDC), http://www.sdc.admin.ch/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: christian.andres@fibl.org
Introduction
The green revolution has brought about a series of technological
achievements in agricultural production, particularly in Asia.
Worldwide cereal harvests tripled between 1950 and 2000, making
it possible to provide enough dietary calories for a world
population of six billion by the end of the 20th century [1]. So
far, global agricultural development has rather focused on
increased productivity than on a more holistic natural resource
management for food security and sovereignty. The increase in
food production has been accompanied by a multitude of
challenges and problems such as the exploitation and deterioration
of natural resources, i.e. loss of soil fertility, strong decline of agro-
biodiversity, pollution of water [2,3], and health problems
associated with the use of synthetic plant protection products
[4]. At present, more comprehensive system-oriented approaches
are gaining momentum and are expected to better address the
difficult issues associated with the complexity of farming systems in
different locations and cultures [5].
The concept of organic agriculture builds on the idea of the
efficient use of locally available resources as well as the usage of
adapted technologies (e.g. soil fertility management, closing of
nutrient cycles as far as possible, control of pests and diseases
through management and natural antagonists). It is based on a
system-oriented approach and can be a promising option for
sustainable agricultural intensification in the tropics, as it may offer
several potential benefits [6–11] such as: (i) A greater yield
stability, especially in risk-prone tropical ecosystems, (ii) higher
yields and incomes in traditional farming systems, once they are
improved and the adapted technologies are introduced, (iii) an
improved soil fertility and long-term sustainability of farming
systems, (iv) a reduced dependence of farmers on external inputs,
(v) the restoration of degraded or abandoned land, (vi) the access to
attractive markets through certified products, and (vii) new
PLOS ONE | www.plosone.org 1 December 2013 | Volume 8 | Issue 12 | e81039
partnerships within the whole value chain, as well as a
strengthened self-confidence and autonomy of farmers. Critics
contend that organic agriculture is associated with low labor
productivity and high production risks [1,12–14], as well as high
certification costs for smallholders [15]. However, the main
criticism reflected in the scientific literature is the claim that
organic agriculture is not able to meet the world’s growing food
demand, as yields are on average 20% to 25% lower than in
conventional agriculture [16,17]. It should however be taken into
account, that yield deviations among different crops and regions
can be substantial depending on system and site characteristics
[16,17]. In a meta-analysis by Seufert et al. [17] it is shown that
yields in organic farming systems with good management practices
can nearly match conventional yields, whereas under less favorable
conditions they cannot. However, Reganold [18] pointed out that
productivity is not the only goal that must be met in order for
agriculture to be considered sustainable: The maintenance or
enhancement of soil fertility and biodiversity, while minimizing
detrimental effects on the environment and the contribution to the
well-being of farmers and their communities are equally important
as the above mentioned productivity goals. Farming systems
comparison trials should thus - besides agronomic determinants -
also consider ecological and economic factors over a longer period.
These trials are inherently difficult due to the many elements the
farming systems are comprised of, thus necessitating holistic
research approaches in order to make comparisons possible [19].
Results from various farming systems comparison trials between
organic and conventional management have shown, that even
though yields may be slightly lower, organic farming systems
exhibit several ecological and economic advantages, particularly
long-term improvement of soil fertility [20–25]. However, most of
the data has been obtained from trials in the temperate zones [20–
26]. The little data available under tropical and subtropical
conditions [9,27–29] calls for more long-term farming systems
comparison trials to provide a better basis for decision making in
these regions [17]. To address this issue, the Research Institute of
Organic Agriculture (FiBL) has set up three farming systems
comparison trials in Kenya, India and Bolivia, thereby encom-
passing different cropping systems and ethnologies. The main
objective of these trials is to collect solid agronomic and socio-
economic data on major organic and conventional agricultural
production systems in the selected project regions. These trials will
contribute to close the existing knowledge gap regarding the
estimation of profitability of organic agriculture in developing
countries (http://www.systems-comparison.fibl.org/). This paper
presents results from cotton-based farming systems in India.
India is the second largest producer (after China) of cotton lint
worldwide [30]. Cotton is a very important cash crop for
smallholder farmers, but also one of the most exigent crops in
terms of agrochemical inputs which are responsible for adverse
effects on human health and the environment [27]. Genetically
modified (GM) cotton hybrids carrying a gene of Bacillus
thuringiensis (Bt) for protection against bollworm (Helicoverpa spp.)
attack, have spread rapidly after their official introduction to India
in 2002 [31,32]. By 2012, 7 million farmers cultivating 93% of
India’s total cotton area had adopted Bt cotton technology
[32,33]. This high adoption rate might be attributed to the high
pressure caused by cotton bollworms, and associated reductions in
pesticide use upon the introduction of Bt cotton technology in
India [34,35]. However, the discussion about the impacts of Bt
cotton adoption remains highly controversial [36,37]. Giving focus
to yields, advocates of Bt cotton claim that the technology has led
to an increase in productivity of up to 60% [38–40] and in some
cases even ‘‘near 100%’’ [41]. Opponents of Bt cotton on the other
hand attributed the yield gains, compared to the pre-Bt period, to
other factors. These include (i) the increase of the area under
cotton cultivation, (ii) the shift from traditional diploid cotton (G.
arboreum, G. herbaceum which accounted for 28% of total cotton area
in 2000) to tetraploid G. hirsutum species [42] and the widespread
adoption of hybrid seeds, (iii) the increased use of irrigation
facilities, (iv) the introduction of new pesticides with novel action
(e.g. Imidacloprid seed treatment), and (v) the increased use of
fertilizers in Bt cotton cultivation [43,44]. Critics of Bt crops also
stress uncertainties concerning the impact of the technology on
human health [45] and on non-target organisms [46], as well as
the higher costs of Bt seeds [34,47].
While some argue that GM crops in general can contribute
significantly to sustainable development at the global level [33,48],
others state that there is no scientific support for this claim [49].
Considering economic benefits of Bt cotton, the same controversy
prevails: Advocates claim sustainable socio-economic benefits and
associated social development [33,36], while opponents claim Bt
cotton to be responsible for farmer debt [50], thereby contributing
to India’s notoriety for farmers’ suicides [37,51], a linkage which
has been criticized as reductionist and invalid [52]. However,
comparisons are mainly drawn between Bt and non-Bt cotton
under conventional management in high-input farming systems.
Organic cotton production systems - holding a minor percentage
of the cotton growing area in India - are often neglected, and little
information exists on the productivity and profitability of organic
farming in India [53]. However, organic cotton production is
slowly gaining momentum in the global cotton market [27]. GM
cultivars are not compatible with the guidelines of organic
agriculture [54]. Therefore, organic cotton producers have to
refrain from Bt cotton hybrids. In addition, organic producers and
processors have to take all possible measures to avoid contami-
nation with Bt cotton in order not to lose organic certification.
While organic farming systems have attracted considerable
interest of the scientific community [16,17,21,26], biodynamic
farming systems are less common and little investigated. The
biodynamic agricultural movement started in the early 1920s in
Europe [55] and developed the international certification organi-
zation and label DEMETER. In India, the biodynamic movement
started in the early 1990s (www.biodynamics.in). Preparations
made from manure, minerals and herbs are used in very small
quantities to activate and harmonize soil processes, to strengthen
plant health and to stimulate processes of organic matter
decomposition. Most biodynamic farms encompass ecological,
social and economic sustainability and many of them work in
cooperatives. One of the first initiatives in India was bioRe India
Ltd. in Madhya Pradesh state (formerly called Maikaal cotton
project), where several thousand farmers (2007–2010 between
4’700 and 8’800) produce organic cotton mainly for the European
market (www.bioreindia.com). Although the farmers in this
cooperative are trained in biodynamic farming, and follow the
taught practices to a certain extent, the system is not certified as
biodynamic. Nonetheless the products are declared as organic.
The farming systems comparison trial presented here was set up in
2007 in Madhya Pradesh state, central India, and is embedded at
the training and education center of bioRe Association (www.
bioreassociation.org/bioresearch.html). The main aim of the trial
is to assess the agronomic, economic and ecological performance
of cotton-based farming systems under organic, biodynamic and
conventional management including non-Bt and Bt cotton. In this
paper, we present the yield and gross margin of cotton, soybean
and wheat of the four different farming systems within the first four
years after inception of the trial (considered as conversion period
in this paper).
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 2 December 2013 | Volume 8 | Issue 12 | e81039
Materials and Methods
1 Site description and socioeconomic context
The trial site is located in the plains of the Narmada river belt in
the Nimar Valley, Khargone district, Madhya Pradesh state, India
(22u8930.30N, 75u4949.00E), at an altitude of 250 meters above sea
level. The climate is subtropical (semi-arid), with an average
annual precipitation of 800 mm, which occurs in a single peak
monsoon season usually lasting from mid-June to September.
Temperatures range from 15uCto49uC with a yearly average of
25uC, and are highest in May/June and lowest in December/
January. Climatic data from 2007–2010 obtained near the trial are
shown in Figure 1. The trial is located on a fertile Vertisol soil
characterized by an average clay content of 600 g kg
21
soil, pH
(H
2
O) of 8.7, organic C content of 5.0 g kg
21
soil, and available P
content (Olsen) of 7.0 mg kg
21
soil at the start of the trial.
Vertisols have shrink-swell characteristics; they cover about 73
million ha of the subtropical (semi-arid) regions of India and are
the predominant soil type in Madhya Pradesh [56].
Agriculture is the main livelihood activity in the project area.
Farm sizes range from less than 1 ha to more than 10 ha, and soil
fertility as well as access to irrigation water vary greatly throughout
the region. The major crops in the region are cotton, soybean and
wheat. Since 2002, Bt cotton has become very popular and is
currently grown on more than 90% of the total area under cotton
cultivation in Madhya Pradesh [57,58]. About 50% of India’s
organic cotton is produced in Madhya Pradesh [59]. The year
consists of three seasons with distinctly different climatic charac-
teristics: The Kharif (monsoon) season is characterized by the
monsoon and lasts from June to October. Crops which require
humid and warm condition are grown, for example cotton, or
soybean. The Rabi (winter) season is characterized by lower
temperatures and less rainfall; it lasts from November to March.
Crops which require cool temperatures for vegetative growth are
grown, for example wheat or chick pea. Finally, the Zaid (summer)
season is characterized by hot temperatures and an extensive dry
spell; it lasts from March to June. Only farmers with access to
irrigation facilities or near river banks grow crops such as melons,
gourds or cucumbers in this season. Longer duration crops such as
cotton are cultivated during both Kharif and Rabi seasons.
2 Trial description
The farming systems comparison trial was established in 2007,
and is expected to run for a period of 20 years. Before trial setup,
the site was under conventional management by a local farmer.
The homogeneity of the terrain was assessed before the
implementation of the different farming systems with a test crop
of unfertilized wheat (HA (0)) grown from December 2006 to
Figure 1. Temperature and precipitation recorded near the trial, Madhya Pradesh, India, 2007–2010, and irrigation practices in the
farming systems comparison trial. Vertical arrows (Q) indicate flood irrigation prior to sowing of cotton (C), wheat (W) and sunn hemp (SH).
Sunn hemp (green manure) was only grown in 2009 and 2010 on BIODYN and BIOORG plots before cotton sowing. Single closed undulating lines
indicate period of drip and flood irrigation in cotton, multiple open undulating lines indicate period of flood irrigation in wheat (wheat received four
to five flood irrigations).
doi:10.1371/journal.pone.0081039.g001
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 3 December 2013 | Volume 8 | Issue 12 | e81039
March 2007 (Figure 2). The test crop was harvested using a
565 m grid. Data of wheat grain yield, organic C and pH of the
soil were used for allocation of strips, blocks and plots (Figure S1).
The trial comprises two organic farming systems (biodynamic
(BIODYN), organic (BIOORG)) and two conventional farming
systems (conventional (CON), conventional including Bt cotton
(CONBtC)). Details of the farming systems are shown in Tables 1
and S1. Organic and biodynamic farming were carried out
according to the standards defined by the International Federation
of Organic Agriculture Movements (IFOAM) [60] and DEME-
TER-International [61], respectively. Conventional farming sys-
tems followed the recommendations of the Indian Council of
Agricultural Research (ICAR) [62] with a slight adjustment to
represent local conventional farming systems: farmyard manure
(FYM) was applied to account for the integrated nutrient
management of local conventional farmers. BIODYN represented
the predominant local organic practices, as farmers associated to
bioRe India Ltd. (see above) are provided with the respective
inputs and trained in biodynamic farming as practiced in the field
trial. BIOORG represented general organic practices as practiced
in various regions of India where organic cotton is grown (mainly
Madhya Pradesh, Maharashtra and Gujarat [59]). CON repre-
sented the local conventional practices in Madhya Pradesh before
the introduction of Bt cotton in 2002, and CONBtC represented
the current local conventional practices.
The four farming systems mainly differed in the following
aspects: Genetic material (cotton only), type and amounts of
fertilizer inputs, green manures, plant protection, the use of
biodynamic preparations (Table 1, Table S1), and crop sequence
(Figure 2). Farming systems are extremely complex, whereby
individual management practices are closely linked and interde-
pendent. For instance, it is well known that chemical plant
protection is in most cases only economically feasible under
conditions of optimal fertilization. That means that we mirror to a
certain extent the complexity of a system rather than analyzing
effects of single factors, and we intended to mimic common
regional practices for the respective farming systems with respect
to all management practices, as specified above. This approach is
quite common in farming systems research and reflects effects of
the system as a whole [20,21], but does not allow to trace potential
differences to individual practices. As a basis for the design of the
organic and conventional farming systems served a farm survey of
Eyhorn et al. [9] in the same region.
The two-year crop rotation consisted of cotton (Gossypium
hirsutum L.), soybean (Glycine max (L.) Merr.) and wheat (Triticum
aestivum L.) (Figure 2). While in organic farming systems green
gram (Vigna radiata) was grown between cotton rows in all four
years and sunn hemp (Crotalaria juncea) was used as a preceding
green manure crop for cotton in 2009 and 2010 (crop cycle 2),
none of these practices were followed in conventional farming
systems. Both green manure crops were cut at flowering and
incorporated to the soil. In order to compare the CON and
CONBtC farming systems as a whole (rather than the effect of the
Bt gene), both the fertilizer dose and crop sequence was adapted
(Figure 2).
Fertilizer inputs relied mainly on synthetic products in
conventional farming systems (depending on crop between 68
and 96% of total nitrogen (N
total
) applied (Table S1)), whereas
organic farming systems received nutrients from organic sources
only (Table 1). Organic fertilizers were compost, castor cake, and
FYM. Compost was prepared using crop residues, weeds, FYM,
and slurry from biogas plants (fed with fresh FYM) as raw
materials. FYM was also applied in both conventional farming
systems. The relatively high levels of organic fertilizer inputs
(Table 1) reflect practices of local smallholder farmers who usually
apply some 18.5 t ha
21
fresh matter of compost to cotton. On
average, compost and FYM contained 0.8-0.6-1.5% and 0.8-0.6-
1.6% of N
total
-P
2
O
5
-K
2
O, respectively, whereas castor cake
contained 3.3-0.9-0.9% of N
total
-P
2
O
5
-K
2
O. Compost and FYM
were broadcasted on the field after land preparation and
subsequently incorporated to the soil by bullock-drawn harrows
in all farming systems; However, in the organic farming systems in
cotton, only 50% of the compost was applied as basal fertilizer
input, and the remaining 50% were applied in two equal split
applications as top dressings, at square formation and flowering,
respectively. Castor cake was applied plant to plant. Nutrient
inputs by N-fixing green manure crops were not considered, but
will be assessed in future studies (Table S1). Synthetic fertilizers
applied in both conventional farming systems were Diammonium
phosphate (DAP), Muriate of Potash (MOP), Single Super
Phosphate (SSP) and Urea. MOP, SSP and Urea/DAP were
applied as basal fertilizer input at sowing time, except in cotton
where only 50% of Urea/DAP was applied as basal fertilizer
input, and the remaining 50% as a single top dressing at flowering.
Across all crops and years, input of N
total
was 65 kg ha
21
in
organic farming systems (BIODYN, BIOORG), 105 kg ha
21
in
CON and 113 kg ha
21
in CONBtC (Table S1). The lower inputs
of N
total
in organic compared to conventional farming systems
represent local organic practice. The difference in inputs of
N
total
between CON and CONBtC arises from adhering to
Figure 2. Sequence of crops in different farming systems of the farming systems comparison trial 2007–2010. Seasons: Zaid (summer):
March to June, Kharif (monsoon): June to October, Rabi (winter): November to March. HA (0) indicates the homogeneity assessment performed with
unfertilized wheat before the implementation of the different farming systems. In 2009 and 2010 Bt cotton was uprooted 2 months earlier to grow a
second wheat crop (wheat 2) to reflect common practice of local Bt cotton farmers.
doi:10.1371/journal.pone.0081039.g002
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 4 December 2013 | Volume 8 | Issue 12 | e81039
recommendations by ICAR [62] who advocate systems with Bt
cotton to be managed more intensively than systems with non-Bt
cotton.
Pest management - including seed treatment - was done with
organic (natural) pesticides in organic farming systems, while in
conventional farming systems synthetic pesticides were used
(Table 1). The type and number of pesticide applications in
CON and CONBtC was the same to reflect local farmers’
practices [63]. This practice was also confirmed in the survey of
Kathage and Qaim [36] comparing conventional Bt and non-Bt
cotton in the period 2006–2008 conducted in the four states
Maharashtra, Karnataka, Andhra Pradesh, and Tamil Nadu.
The BIODYN system received small amounts of biodynamic
preparations (Table 1) consisting of organic ingredients (cow
manure, medicinal plants), and mineral compounds (quartz,
basalt) which are intended to activate the soil and increase plant
health [64]. No significant amounts of nutrients were added by
these applications. For further details of biodynamic practices see
Carpenter-Boggs et al. [64].
With cotton, soybean and wheat the trial represents a cash crop-
based farming system in a two-year crop rotation, which is typical
for the Nimar Valley in the plains of the Narmada river belt, were
the trial is located. Cotton was grown from May to February,
except in 2009 and 2010 (crop cycle 2) in CONBtC; in these two
years Bt cotton was uprooted two months earlier than in the other
three farming systems in order to grow an additional wheat crop
(wheat 2) in the Rabi (winter) season (Figure 2). This was done to
account for local practices; local conventional farmers noticed that
Bt cotton matures earlier than non-Bt cotton, and produces the
majority of the yield during the first three months of the harvesting
period. Therefore, they started between 2007 and 2010 to grow
another wheat crop before the start of the Zaid (summer) season, a
practice which was also confirmed by Brookes & Barfoot [65].
Soybean was grown from July to October and followed by
wheat from December to March. The land was prepared with
Table 1. Management of the different farming systems compared in a two-year rotation in central India (2007–2010).
Practices Organic farming systems
1
Conventional farming systems
2
BIODYN (biodynamic) BIOORG (Organic) CON (conventional)
CONBtC (conventional
including Bt cotton)
Genetic material (difference in cotton only)
Non-Bt cotton Non-Bt cotton Non-Bt cotton Bt cotton
Fertilizer input
Type and level (for nutrient
inputs see Table S1)
aerobically composted crop
residues, weeds, farmyard
manure (FYM), and slurry;
19.5-7.7-12.0 t ha
21
to
cotton-soybean-wheat
aerobically composted crop
residues, weeds, farmyard
manure (FYM), and slurry;
19.5-7.7-12.0 t ha
21
to
cotton-soybean-wheat
mineral fertilizers (MOP, SSP,
Urea, DAP (wheat only))
mineral fertilizers (MOP, SSP,
Urea, DAP (wheat only))
stacked FYM; 2.8-1.6-2.2 t
ha
21
to cotton-soybean-
wheat
stacked FYM; 2.8-1.6-2.2 t
ha
21
to cotton-soybean-
wheat
stacked FYM; 8.1-3.9-1.6 t ha
21
to cotton-soybean-wheat
stacked FYM; 8.1-3.9-1.6 t ha
21
to cotton-soybean-wheat
castor cake; 0.1 t ha
21
to
cotton (2007 & 2008 only)
castor cake; 0.1 t ha
21
to
cotton (2007 & 2008 only)
Green manure
Type and timing of green
manure
broadcasted sunn hemp
(Crotalaria juncea) before
cotton in 2009 and 2010
only
broadcasted sunn hemp
(Crotalaria juncea)before
cotton in 2009 and 2010
only
None None
hand sown green gram
(Vigna radiata, 9’070 plants
ha
21
) between cotton rows
in all years
hand sown green gram
(Vigna radiata, 9’070 plants
ha
21
) between cotton rows
in all years
None None
Plant protection
Weed control bullock-drawn blade or tine
harrows
bullock-drawn blade or tine
harrows
bullock-drawn blade or tine
harrows
bullock-drawn blade or tine
harrows
Hand weeding in cotton Hand weeding in cotton Hand weeding in cotton Hand weeding in cotton
None None Herbicide (2009 and 2010 in
soybean and wheat only)
Herbicide (2009 and 2010 in
soybean and wheat only)
Insect control and average
number of applications per
crop rotation (detailed
product list, see Table S1)
organic (natural) pesticides
12.5
organic (natural) pesticides
12.25
synthetic pesticides 11.5 synthetic pesticides 11.0
Disease control None None None None
Special treatments biodynamic preparations
3
None None None
1
in the text, BIODYN and BIOORG are referred to consistently as organic farming systems,
2
in the text, CON and CONBtC are referred to consistently as conventional
farming systems, average dry matter content of organic fertilizers: 70%, DAP: Diammonium phosphate, MOP: muriate of potash, SSP: single super phosphate,
3
biodynamic preparations entailed cow dung (BD-500) and silica powder (BD-501) both stored for six months, and a mixture of cow dung, chicken egg shell powder,
basalt rock powder, and plant materials (yarrow, chamomile, stinging nettle, oak bark, dandelion, valerian) stored for 6 months in an open pit (cow pat pit = CPP).
doi:10.1371/journal.pone.0081039.t001
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 5 December 2013 | Volume 8 | Issue 12 | e81039
bullock-drawn ploughs, harrows and levelers. Cotton was sown by
hand at a rate of 0.91 plants m
22
(9’070 plants ha
21
). Soybean
and wheat were sown with bullock-drawn seed drills. The inter
row and intra row spacing were 30 cm and 4 cm, respectively for
both soybean and wheat. In 2007, heavy monsoon rains led to
severe waterlogging in the plots which stunted soybean growth and
necessitated re-sowing the whole trial. Cultivars were selected
according to local practice and availability. In cotton, these were
Maruti 9632 (2007), Ankur 651 (2008), Ankur AKKA (2009) and
JK Durga (2010) in all farming systems, except in CONBtC where
isogenic Bt lines of the same hybrids were used. Non-GM soybean,
variety JS-335, and non-GM wheat, variety LOK-1 were
cultivated in all farming systems and years. The whole trial was
irrigated and all plots received similar amounts of irrigation water;
prior to sowing, flood irrigation was carried out on sunn hemp
(green manure), cotton and wheat plots (Figure 1). After the
monsoon, cotton received additional drip irrigation and two to
three flood irrigations to ensure continuous water supply
throughout the cropping season. Sunn hemp and wheat received
three to four and four to five flood irrigations, respectively.
Soybean was grown purely rainfed during the Kharif (monsoon)
season. Weeding was done mechanically at 20 (cotton) and 45
(soybean, wheat) days after sowing, using bullock-drawn blade or
tine harrows in all farming systems. In cotton, additional hand
weeding was carried out. No hand weeding was carried out in
soybean and wheat. No synthetic herbicides were applied in
conventional farming systems except in soybean and wheat in
2009 and 2010, which reflects the situation of most smallholder
cotton farmers in India [66]. Cotton was harvested by several
manual hand pickings. Soybean and wheat were harvested
manually with sickles, and bound to bundles which were removed
from the field and subsequently threshed with a threshing
machine.
In order to obtain data from each crop during each year, the
layout was doubled with shifted crop rotation in two strips,
resulting in a total of 32 plots, and 16 plots per strip (Figure S1).
Each farming system was replicated four times in a randomized
block design in each of the two strips. Plots are sized 16 m616 m
( = gross plot) and time measurements of activities were recorded
for gross plots. The outermost 2 m of each plot served as a border,
and yield data were only obtained in the inner sampling plot sized
12 m612 m ( = net plot) in order to avoid border effects. The
distance between two plots within a strip and between the two
strips is 6 m and 2 m, respectively. Data was obtained from 2007
to 2010. Data from 2007–2008 belongs to the complete crop
rotation of the 1
st
crop cycle (cycle 1), and data from 2009–2010 to
the 2
nd
crop cycle (cycle 2).
As Bt cotton was commercially released in India in 2002, no
official approval of the study was required. The land needed for
the farming systems comparison trial was purchased and belongs
to bioRe Association. No protected species were sampled.
3 Data consolidation and economic calculations
Calculations of gross margins required consolidation of
production costs. We only considered variable (operational)
production costs in our study, excluding interest rates for credits.
We included input costs, labor costs for field activities (including
e.g. compost preparation), and costs associated with the purchase
of inputs from the local market. Time measurements on gross plots
and farmers’ fields were complemented with data obtained in
expert meetings with experienced farmers and local extension
officers. Variable production costs for cotton (Table S2), soybean
(Table S3), and wheat (Table S4) were cross-checked with the
values reported by the Ministry of Agriculture, Government of
India [67]. Gross margins were obtained by subtracting the
variable production costs from the gross return ( = yield * price
per unit). Prices (products, inputs, labor) corresponded to local
market conditions and were adapted each year (Table 2, Table
S2). A premium price for organic cotton was considered in 2010
only (after three years conversion period according to IFOAM
standards).
4 Statistical analysis
Data exploration revealed four outliers which were removed
from the dataset. The reason was heavy monsoon rains and
subsequent water-logging in four plots in 2009 (Plots 11 and 27
(both BIOORG), and plots 12 and 28 (both BIODYN), Figure S1).
Yield and gross margin data of each crop, and of the complete
crop rotation (cotton+wheat 2+soybean+wheat) were analyzed
separately with linear mixed effect models using the function lme
from the package nlme [68] of the statistical software R version
2.15.2 [69]. We checked our data for model assumptions
graphically (normal Q-Q of fixed and random effects, Tukey-
Anscombe and Jitter plots) and no violation was encountered. We
used a model with System,Cycle, the interaction of System6Cycle and
Strip as fixed effects, and Year (n = 4), Block (n = 4) and Pair (n = 16)
as random intercepts.
The fixed effect Cycle was included in the model to account for
repeated measures on the same plot (e.g. cotton on plot 1 in 2007
and in 2009) and allows a partial separation of Cycle and Year
effects due to the shifted crop rotation in the two strips as proposed
by Loughin [70] for long-term field trials. Cycle effects give an
indication how the situation changes across the timeframe of the
trial. However, as we only have two levels of Cycle (thus Df = 1 for
Cycle in the ANOVA) at this stage of the trial, we have little
statistical power to detect Cycle effects. The same applies to the
fixed effect Strip. We nevertheless included Cycle and Strip into our
model to separate the System effect from possible Cycle and Strip
effects. To account for similar conditions of neighboring plots (e.g.
Plots 1 and 17, 2 and 18, etc., Figure S1) we included the random
intercept Pair with 16 levels.
For yield and gross margin data of the complete crop rotation,
the random intercept Year was removed from the model, as data
from two years were compiled. Significant System6Cycle interac-
tions suggested that the main effects of System and Cycle have to be
interpreted with caution; As the effects of the different systems
were not consistent across cycles, we split the datasets and
performed post-hoc multiple comparisons for the fixed effect System
separately for each cycle (method: Tukey, superscript letters after
cycle-wise values in Tables 3 and 4). In the case of gross margin
data of soybean, no significant System6Cycle interaction was
encountered. Therefore, we performed post-hoc multiple com-
parisons on the whole dataset of cycle 1 and cycle 2 together
(superscript letters after average values in Table 4). We defined a
difference to be significant if P,0.05 (a= 0.05).
Results and Discussion
1 Yield
Cotton yields (seed cotton, picked bolls containing seed and
fiber) were, averaged across the four years, 14% lower in organic
(BIOORG, BIODYN) compared to conventional farming systems
(CON, CONBtC). This is in the same range as the findings of a
study conducted in Kyrgyzstan [27]. The System6Cycle interaction
had a significant effect (P,0.001) on cotton yields (Table 3). The
difference in yield was very pronounced in cycle 1 (2007–2008,
+42% yield increase in conventional farming systems), while yields
were similar among all farming systems in cycle 2 (2009–2010)
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 6 December 2013 | Volume 8 | Issue 12 | e81039
(Figure 3). CONBtC consistently showed higher yields than the
three other farming systems, except in 2010. This is in line with the
findings of several international meta-studies, which also reported
generally higher yields and increased profitability in Bt cotton
compared to non-Bt cotton production [34,65,71]. However,
cotton yield increases through the use of Bt seeds may vary greatly
among regions (from zero in Australia, up to 30% in Argentina)
due to e.g. varieties used in Bt and non-Bt production, and
effectiveness of chemical plant protection in non-Bt production
[65]. Glover [72] also points out that the performance and impacts
of Bt crops have been highly variable, socio-economically
differentiated and contingent on a range of agronomic, socio-
economic and institutional factors, thus underlining that the
contextual interpretation of results is of paramount importance.
The cotton yields per hectare of CONBtC in cycle 1 in our study
were in the same range reported by Konduru et al. [73]. The
severe decline in yield observed for CONBtC in cycle 2 when
compared to cycle 1 (Figure 3) can be partly explained by the fact
that Bt cotton plants were uprooted two months earlier than plants
in other farming systems in cycle 2 (see chapter 2.2). However, this
does not explain the decline in yield observed from 2007 to 2010
in the CON farming system, in which cotton plants were not
uprooted. In cycle 1 the cotton yields in CONBtC were 16%
higher than in CON (Table 3) which could be due to both the
effect of the Bt gene products on pests (as isogenic hybrids were
used) as well as the higher input of fertilizer (166 kg N ha
21
vs. 146
kg N ha
21
) recommended for Bt cotton. The difference in yield
between Bt and non-Bt cotton in our study was much smaller than
the differences in yield reported by others for India [33,36,38–
40,74], indicating that the chemical plant protection applied to the
CON system in our experiment was relatively effective. In contrast
to the conventional systems, both of the organic farming systems
showed rather stable cotton yield throughout the entire experi-
mental period (Figure 3). As cycle effects and the System6Cycle
interaction are confounded by year effects, we have to consider
that in 2009 and 2010 the cotton yield was generally lower than in
2007 and 2008, as was confirmed by statistical yield data of the
state Madhya Pradesh [67]. Apparently, the conventional farming
systems could not realize their yield potential due to the less
advantageous growing conditions in cycle 2 (rainfall and water
logging in the harvest period October – December, Figure 1). The
organic farming systems however, were not affected by the
disadvantageous conditions in cycle 2 (Figure 3). An additional
nitrogen fixing green manure pre-crop, planted before cotton in
the organic systems in cycle 2 (Figure 2), may have contributed to
the observed stability in yield in these systems through the
consistent provision of nitrogen to the plants. Cotton yields of
future crop cycles will thus determine whether productivity in
conventional systems will reach their initial high level as well as
determine whether yields of organic farming systems will start to
increase. A yield depression is usually observed during the
conversion to organic farming in India [75]. However, in our
trial no such trend was oberserved between cycle 1 and cycle 2.
Non-GM soybean and wheat varieties were cultivated in both
CON and CONBtC systems. In 2007, average soybean yields
across all farming systems were 45% lower compared to the other
three years (Figure 3), as the whole trial had to be re-sown due to
severe water logging. Soybean yields were, averaged across the
four years, 7% lower in organic compared to conventional farming
systems. The System6Cycle interaction had a significant effect
(P,0.05) on soybean yields (Table 3). No significant difference in
yield could be identified between farming systems in cycle 1.
However, in cycle 2 CON and CONBtC showed significantly
higher yields than BIOORG (Table 3). This is likely due to higher
pest incidences and thus lower yields in organic systems in 2009.
BIODYN produced similar soybean yields as both conventional
systems throughout the experimental period (P.0.05). The 1%
and 11% lower yields in organic farming systems in cycle 1 and 2,
respectively, are considerably lower than the 18% lower yields
reported for organic soybean in the Karnataka region [76]. These
results indicate similar productivity of conventional and organic
soybean production systems under subtropical (semi-arid) condi-
tions and suggest that in similar settings no further yield gains can
be achieved through the provision of synthetic inputs compared to
organic management practices. The smaller difference in yield
between conventional and organic soybean - when compared to
cotton and wheat (see below) - could be explained by considering
the plant type. Soybean is the only legume crop in the crop
rotation, possessing the ability to fix atmospheric N, thereby
avoiding potential nitrogen shortage for optimal plant growth.
These results confirm the findings of Seufert et al. [17] whose meta-
analysis showed a lower yield gap between conventional and
organic legume crops when compared to non-legume crops, and
indicate that cotton and wheat yields in organic farming systems in
our trial may be restricted by nitrogen limitation in the soil.
Wheat yields were, averaged across the four years, 15% lower in
organic compared to conventional farming systems, which is
similar to the 20% yield gap reported for Uttarakhand [76]. The
System6Cycle interaction had a significant effect (P,0.001) on
wheat grain yield. Similar to cotton, there was a significant yield
gap between conventional and organic farming systems in cycle 1
(+37% yield increase in conventional farming systems), but not in
cycle 2 due to both slightly lower yields in the conventional systems
and slightly higher yields in the organic systems compared to cycle
Table 2. Domestic market prices of cotton, soybean and wheat, premium prices on organic cotton and prices per working hour
2007–2010 in Khargone district, Madhya Pradesh, India.
Year Commodity
Cotton [INR kg
21
]
Cotton premium price
[INR kg
21
] Soybean [INR kg
21
] Wheat [INR kg
21
] Labor [INR h
21
]
2007 23.3 4.7 (n.c.) 15.5 10.4 7.5
2008 26.8 3.3 (n.c.) 20.0 11.0 9.0
2009 31.5 3.3 (n.c.) 22.5 12.0 11.3
2010 49.0 4.0 (c.) 22.5 12.0 12.5
n.c.: not considered in economic calculations (conversion = first three years, according to IFOAM standards), c.: considered in economic calculations; No premium exists
for organic soybean and wheat due to local market structures; Exchange rate INR: USD = 50:1 (source: http://eands.dacnet.nic.in/AWIS.htm, stand October 2012).
doi:10.1371/journal.pone.0081039.t002
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 7 December 2013 | Volume 8 | Issue 12 | e81039
Table 3. Mean yields [kg ha
21
] of cotton, soybean and wheat, and total productivity per cycle and across four years (2007–2010) in the farming systems compared in central India.
Farming system Crop Total productivity of crop rotation
Seed cotton SEM Wheat 2 grains SEM Soybean grains SEM Wheat grains SEM
Seed cotton
+
Wheat 2 grains
+
Soybean grains
+
Wheat
grains SEM
Cycle 1 (2007–2008)
BIODYN 2’047
c
68 - - 1’399
a
158 2’997
c
153 6’443
b
104
BIOORG 2’072
c
49 - - 1’536
a
192 2’831
c
121 6’440
b
187
CON 2’700
b
141 - - 1’483
a
155 4’262
a
221 8’444
a
146
CONBtC 3’133
a
176 - - 1’473
a
195 3’730
b
272 8’336
a
254
Cycle 2 (2009–2010)
BIODYN 1’894
a
108 - - 1’807
ab
87 3’338
a
207 7’039
b
268
BIOORG 1’942
a
103 - - 1’739
b
117 3’303
a
191 6’984
b
239
CON 1’614
a
43 - - 1’993
a
108 3’273
a
175 6’880
b
119
CONBtC* 1’834
(a)
179 1’573 169 1’997
a
161 3’481
a
182 8’885
a
390
Average (2007–2010)
BIODYN 1’971 64 - - 1’603 104 3’167 132 6’741 270
BIOORG 2’007 56 - - 1’638 114 3’067 125 6’712 257
CON 2’157 157 - - 1’738 113 3’767 187 7’662 455
CONBtC 2’484 207 (787) - 1’735 140 3’605 161 8’610 376
ANOVAs of linear mixed effect models
Source of variation Pvalue Df - - Pvalue Df Pvalue Df Pvalue Df
System (S) ,0.001 3 - - 0.102 3 ,0.001 3 ,0.001 3
Cycle (C) ,0.001 1 - - 0.066 1 0.686 1 0.912 1
Strip 0.141 1 - - 0.472 1 0.960 1 0.002 1
S6C,0.001 3 - - 0.039 3 ,0.001 3 ,0.001 3
SEM: standard error of the mean, BIODYN: biodynamic, BIOORG: organic, CON: conventional, CONBtC: conventional with Bt cotton, different superscript letters indicate significant difference between farming systems within one
Cycle (Tukey test, P,0.05), * in 2009 and 2010 Bt cotton was uprooted 2 months earlier to grow a second wheat crop (wheat 2) to reflect common practice of local Bt cotton farmers (for the sequence of crops in different farming
systems see Figure 2), Pvalue and degrees of freedom (Df) of fixed effects in linear mixed effect models, random factors in the model: Year (n = 4), Block (n = 4), Pair (n = 16), for total productivity random factor Year was excluded as
data from two years were compiled.
doi:10.1371/journal.pone.0081039.t003
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 8 December 2013 | Volume 8 | Issue 12 | e81039
Table 4. Mean gross margins [INR ha
21
] of cotton, soybean and wheat, and total gross margin per cycle and across four years (2007–2010) in the farming systems compared in
central India.
Farming system Crop Total gross margin of crop rotation
Seed cotton SEM Wheat 2 grains SEM Soybean grains SEM Wheat grains SEM
Seed cotton
+
Wheat 2
grains
+
Soybean grains
+
Wheat grains SEM
Cycle 1 (2007–2008)
BIODYN 38’243
c
2’226 - - 19’211 4’210 26’044
c
1’584 83’498
b
8’021
BIOORG 38’676
c
1’203 - - 21’830 4’858 24’420
c
1’291 84’926
b
8’268
CON 51’792
b
2’779 - - 18’401 4’093 37’099
a
2’221 107’292
a
4’546
CONBtC 60’811
a
4’851 - - 18’147 4’719 31’361
b
2’832 110’319
a
8’308
Cycle 2 (2009–2010)
BIODYN 62’786
a
7’217 - - 32’176 1’683 30’764
a
2’374 125’726
a
6’721
BIOORG 64’490
a
6’714 - - 30’812 2’278 30’443
a
2’181 125’745
a
9’354
CON 42’962
b
4’653 - - 28’949 3’724 24’773
b
2’028 96’683
b
6’701
CONBtC* 43’810
(b)
2’995 4’837 2’190 29’399 4’644 27’037
b
2’117 105’082
b
5’232
Average (2007–2010)
BIODYN 50’514 4’918 - - 25’694
ab
2’758 28’404 1’507 104’612 7’461
BIOORG 51’583 4’789 - - 26’321
a
2’865 27’432 1’450 105’335 7’008
CON 47’377 2’852 - - 23’675
b
2’780 30’936 2’155 101’988 3’089
CONBtC 52’310 3’165 (2’418) - 23’773
b
3’311 29’199 1’797 107’701 3’849
ANOVAs of linear mixed effect models
Source of variation Pvalue Df - - Pvalue Df Pvalue Df Pvalue Df
System (S) 0.115 3 - - 0.006 3 0.022 3 0.298 3
Cycle (C) 0.046 1 - - 0.158 1 0.606 1 ,0.001 1
Strip 0.001 1 - - 0.469 1 0.805 1 ,0.001 1
S6C,0.001 3 - - 0.150 3 ,0.001 3 ,0.001 3
SEM: standard error of the mean, BIODYN: biodynamic, BIOORG: organic, CON: conventional, CONBtC: conventional with Bt cotton, different superscript letters indicate significant difference between farming systems within one
Cycle (Tukey test, P,0.05), * in 2009 and 2010 Bt cotton was uprooted 2 months earlier to grow a second wheat crop (wheat 2) to reflect common practice of local Bt cotton farmers (for the sequence of crops in different farming
systems see Figure 2), Pvalue and degrees of freedom (Df) of fixed effects in linear mixed effect models, random factors in the model: Year (n = 4), Block (n = 4), Pair (n = 16), for total gross margin random factor Year was excluded
as data from two years were compiled.
doi:10.1371/journal.pone.0081039.t004
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 9 December 2013 | Volume 8 | Issue 12 | e81039
1 (Table 3, Figure 3). For both soybean and wheat no yield
differences were detected between CON and CONBtC farming
systems, except for significantly higher wheat yields in CON in
cycle 1 (Table 3).
Regarding the total productivity per crop rotation in terms of
summed-up dry matter yields of cotton, soybean and wheat
(including wheat 2 in CONBtC in cycle 2), a significant effect
(P,0.001) of the System6Cycle interaction was found (Table 3).
Both of the conventional farming systems were significantly more
productive (+30%, Table 3) than the organic farming systems in
cycle 1. However, in cycle 2 only CONBtC showed significantly
higher productivity (+28%, Table 3) when compared to the other
three farming systems, due to the additional wheat crop (wheat 2).
Differences in yield between BIODYN and BIOORG were minor
and not statistically significant for all crops and total productivity
of the whole crop rotation (Table 3). Unexpectedly, there was a
significant Strip effect for total productivity across the whole crop
rotation. This may be explained by the fact that different crops
were cultivated on the two strips in a given year (Figure 2). The
compilation of whole crop rotations (compiling years) thus led to
the combination of the observed variability for each crop across
the four years, and subsequently to the Strip effect becoming
significant (Table 3).
In general, the first four years of the farming systems
comparison trial in India revealed that there was a significant
yield gap in cycle 1 (2007–2008) for cotton (229%) and wheat
(227%), in organic compared to conventional farming systems,
whereas in cycle 2 yields of the three crops were similar in all
farming systems due to low yields in the conventional systems
(Table 3). Because there was no clear trend of yield development
for cotton and wheat during the four year period in any of the
systems, observed results rather reflect growth conditions in
respective years than long-term yield trends of cotton and wheat.
However, the marginal yield gap between the BIOORG system
and the conventional systems, and the par soybean yields of
BIODYN and the conventional systems show that leguminous
crops are a promising option for conversion to organic systems
under the given conditions. The yield development across the
whole crop rotation needs to be verified during future crop cycles.
2 Economic analysis
The production costs (i.e. labor and input costs) in our trial
(Tables S2, S3 and S4) were in a similar range as reported by the
Ministry of Agriculture, Government of India [67]. The variable
production costs of conventional (CON, CONBtC) compared to
organic (BIOORG, BIODYN) farming systems were on average
38%, 66%, and 49% higher in cotton, soybean and wheat (Table
S2, S3 and S4). This is in agreement with findings for cotton in
Gujarat, but contradicts findings for wheat in Punjab and Uttar
Pradesh [53]. The main reason for the differences observed in our
study were the higher input costs (fertilizer, pesticides) in the
conventional farming systems, which is in accordance with the
findings of a study conducted in Kyrgyzstan [27]. Labor costs were
similar among all farming systems, as organic and conventional
farming systems did not differ greatly with regard to time
requirements of activities (Table S2, S3 and S4). For instance,
weeding was done manually in all systems and no herbicides were
applied in the conventional farming systems except for soybean
and wheat in cycle 2, reflecting the common practice of most
smallholder cotton farmers in India [66]. This practice, however,
might change in the near future, as labor costs in Indian
Figure 3. Yield (mean ±standard error) 2007-2010 in cotton,
soybean and wheat. Farming systems: (N) biodynamic (BIODYN), (&)
organic (BIOORG), (¤) conventional (CON), (m) conventional with Bt
cotton (CONBtC), (.) wheat after Bt cotton (wheat 2); In 2009 and 2010
Bt cotton was uprooted 2 months earlier to grow a second wheat crop
(wheat 2) to reflect common practice of local Bt cotton farmers. Non-
GM soybean and wheat varieties were cultivated in the CON and CON-
BtC plots throughout the trial. Note the different scales on y-axes in the
different panels of the graph.
doi:10.1371/journal.pone.0081039.g003
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 10 December 2013 | Volume 8 | Issue 12 | e81039
agriculture are on the rise [77]. The variable production costs
of the two organic farming systems were similar for all crops. This
was also true for the two conventional farming systems, except
for cotton, where the variable production costs of CONBtC
were 17% higher compared to CON due to both the higher
seed price of Bt cotton (Table S2) [34,36,47] and production cost
of the additional wheat crop in cycle 2. The prices we present here
for Bt seed material are in the same range as reported by Singh
et al. [78].
Cotton was the most important cash crop and accounted for
48% of the total gross return in the crop rotation, irrespective of
the system. The System6Cycle interaction had a significant effect
(P,0.001) on cotton gross margins (Table 4). Due to much higher
yields, conventional cotton led to 32% higher gross margins
compared to organic cotton in cycle 1 (2007–2008), which is in
accordance with several international meta-studies [34,65,71].
However, the opposite was true in cycle 2 (2009–2010), where we
observed 32% lower gross margins in conventional cotton,
supporting the findings of Bachmann [27]. The significant Strip
effect for cotton gross margin can be explained by the highly
variable cotton prices across the four years (Table 2).
For soybean, the System6Cycle interaction was not significant
(Table 4) which allowed for an analysis of gross margin data across
both cycles (see 2.4). Considerably higher gross margins were
obtained in organic systems (+10%) compared to conventional
systems between 2007 and 2010. The difference was statically
significant for BIOORG (+11%, P,0.05) and almost significant
for BIODYN (+8%, P,0.1). These results indicate that the slightly
lower productivity of organic soybean was balanced out by lower
production costs rendering soybean production considerably more
profitable in organic systems when compared to conventional
farming systems.
For wheat gross margins, the System6Cycle interaction was found
to be significant (P,0.001, Table 4). Under organic farming,
wheat obtained significantly lower gross margins in cycle 1
(226%), but significantly higher gross margins (+18%) in cycle 2
(Table 4). The earlier removal of Bt cotton from the field in order
to grow another wheat crop in CONBtC, before the start of the
Zaid (summer) season in cycle 2, only provided minor economic
benefits compared to CON (Table 4). This was mainly due to low
yields of wheat 2 (,50% compared to regular wheat crop,
Figure 3) and lower market prices for wheat compared to cotton
(Table 2). Thus, the additional wheat crop could not compensate
for the missed cotton yield of the last picking period with respect to
economic profitability, a result contradicting total yield perfor-
mance across all crops.
A highly significant (P,0.001) System6Cycle interaction was
found for the total gross margin per crop rotation. In cycle 1,
favorable weather conditions allowed for the realization of the
anticipated yield potential in conventional farming systems, and
thus led to both higher cotton and wheat yields (Figure 3), and
concomitantly significantly higher gross margins (+29%, Table 4).
However, In cycle 2 the gross margins of the organic farming
systems were significantly higher (+25%) (Table 4, Figure 4) due to
par yields as measured in the conventional systems (Figure 3), but
lower variable production costs (Tables S2 and S4). If the
premium price in 2010 would not have been considered, the total
gross margin per crop rotation in cycle 2 would not be
substantially lower and still be significantly higher in organic
farming systems (statistical analysis not shown). This could imply
that, in favorable years (e.g. good yield, high price for commod-
ities, etc.), premium prices are not required for achieving
comparable economic returns in organic and conventional
farming systems. However, the premium is needed in unfavorable
years in order to compensate for yield gaps, and to avoid that
organic farmers sell their produce to the local conventional
market. The significant Strip effect for total gross margin can be
explained by compiling the individual gross margins of the
different crops, thereby transferring the significant Strip effect of
cotton to the total gross margin (Table 4).
The results of cycle 2 suggest that under certain conditions,
organic farming can be an economically viable and less capital-
intensive production system compared to conventional farming
systems, which is in accordance with the findings by Ramesh et al.
[76] and Panneerselvam et al. [79]. However, long-term studies are
needed in order to substantiate these findings. Moreover, the
viability of organic farming systems strongly depends on farmers
having access to knowledge, purchased inputs such as organic
fertilizers, pesticides and non-GM seeds, and assuming that there
is a market demand and well developed certification system.
These are vital components for the economic profitability of
organic farming systems [27] especially against the backdrop of
increasing labor costs in Indian agriculture [77]. The costs for
organic certification are substantial in case individual farmers
have to undergo this process, and premium prices may also have
to cover these costs. Up to now, certification costs are usually
covered by the cotton organization that is purchasing seed
cotton from smallholders (here: bioRe India Ltd.). This includes
extensive testing of seeds and seed cotton for GM contamina-
tion, as well as the implementation of Tracenet, an internet
based electronic service offered by the Agricultural and
Processed Food Products Export Development Authority
(APEDA) for facilitating certification of organic export products
from India which comply with the standards of the (National)
Programme for Organic Production (NPOP/NOP). This is a big
challenge of certified organic cotton compared to fair trade
cotton [15], and further organic cotton initiatives rely on cost-
efficient and trustful certification and education programs as
well.
Figure 4. Gross margins (mean ±standard error) of four crop
rotations. Farming systems: (N) biodynamic (BIODYN), (&) organic
(BIOORG), (¤) conventional (CON), (m) conventional with Bt cotton
(CONBtC) (includes wheat cultivated after Bt cotton on the same plots
in 2009 and 2010); C = cotton, S-W = soybean-wheat; Exchange rate
Indian rupee (INR): US Dollar (USD) = 50:1 (stand October 2012),
premium price on organic cotton only in 2010.
doi:10.1371/journal.pone.0081039.g004
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 11 December 2013 | Volume 8 | Issue 12 | e81039
3 Transferability of field trial results
So far little is known about the comparative performance of
cotton-based farming systems under organic and conventional
management. To our knowledge, this is the first publication
comparing the agronomic and economic performance of biody-
namic, organic, conventional and conventional with Bt cotton-
based farming systems. The few studies published to date
compared either organic vs. conventional [9,28,80] or conven-
tional vs. conventional with Bt cotton production systems [36].
By including two organic (BIOORG and BIODYN) and two
conventional (CON and CONBtC) farming systems in our trial,
we were able to cover a wide range of current cotton-based
farming systems in central India (see 2.2). Forming close links to
local partners and having practitioners in the steering committee
of our systems comparison trial guaranteed that the various
agronomic activities that were involved represented local farmers’
practice. Due to the cooperative initiative of bioRe, cotton farmers
are trained in compost preparations, and organic inputs are
purchased collectively and distributed among the farmers. Farmers
associated with bioRe may not face the various problems
commonly observed during conversion to organic farming [75]
to a similar extent as do farmers without affiliation to similar
institutions. This is likely due to the experienced and well-
functioning extension service of bioRe. Drip irrigation is strongly
promoted and subsidized by the Indian government and is not
specific to our experiment. However, a direct extrapolation of our
results to the reality of smallholder farmers is not possible due to
the fact that farmers are confronted with several obstacles not
considered in our study; These are for example market access,
access to inputs and know-how and in particular costs associated
with the organic certification process (see 3.2). One also has to
consider that yield estimates from optimally managed trial plots
are usually higher than the average yield of smallholder farmers.
This is due to the fact, that such optimal crop management, as it
was applied in this farming systems comparison trial, might not
always be possible under the real-world smallholder conditions.
This is especially true as the trial was conducted on a fertile
Vertisol soil. Based on a survey of more than 1’000 smallholders in
Madhya Pradesh, the average yield levels in the respective time
period (2007–2009) were 1’416 kg ha
21
, 1’285 kg ha
21
,and
2’426 kg ha
21
for seed cotton, soybean, and wheat [67], as
compared to 2’585 kg ha
21
, 1’761 kg ha
21
, and 3’658 kg ha
21
found in our trial. In a survey performed between 2006 and 2008
among 700 smallholder cotton farmers in India, average yields of
1’743 and 2’048 kg ha
21
seed cotton were reported for conven-
tional (without Bt) and Bt cotton, respectively [36], as compared to
2’700 kg ha
21
and 3’133 kg ha
21
in our trial in the same time
period (2007–2008). Thus, our yields might be generally overesti-
mated, but within the range of other field trials in India [78].
The following examples show that comparative findings on yield
and economics between organic and conventional cotton are
highly contextual. Eyhorn et al. [9] surveyed more than 50
conventional (without Bt cotton) and 30 organic cotton farmers in
the Nimar Valley, Madhya Pradesh, India, during the period of
2003–2004. Their findings support our results of cycle 2 (years
three and four, 2009–2010): yields of cotton and other cash crops
were on par with conventional farmers, but with economic benefits
for the organic farmers due to lower production costs. These
findings also underline the practical relevance of our results for
cotton production in the in the smallholder context in Madhya
Pradesh. Likewise, Venugopalan et al. [81] reported similar or
slightly higher cotton yields in an organic compared to a non-
organic system under low input and semi-arid conditions in
the Yavatmal district, Maharashtra, India (observation phase
2001–2005). In a long-term trial under rainfed conditions in
Nagpur (Maharashtra, India), Menon [82] reported a yield gap of
25% of organic cotton compared to the modern method of
cultivation ( = conventional without Bt cotton) within the first six
years after conversion (1994–2000). Thereafter (2002–2004), the
organic farming systems outyielded the conventional systems by up
to 227 kg seed cotton ha
21
[28].
However, our findings from India are in contrast to the results
from a survey on cotton farms in Northern San Joaquin Valley,
California [80]. There, averaged over a six-year observation period,
yields of organic cotton were 19 and 34% lower (P,0.05) than those
of cotton under conventional and integrated pest management
(reduced insecticide input). It has to be taken into account that for
two out of the six years assessed in their study, different varieties
were compared under conventional and organic management.
Production costs of organic cotton were 25 and 60% higher than
those of cotton under conventional and integrated pest manage-
ment, respectively. This was mainly due to the higher labor costs for
manual weeding. In our trial, there was less difference in weed
control, as manual weeding is still the common practice of most
smallholder cotton farmers in India [66]. This example underlines
the contextual nature of the findings concerning the agronomic and
economic performance between organic and conventional cotton,
which was also pointed out by Seufert et al. [17] and de Ponti et al.
[16] for other crops than cotton.
Building on unique panel data on Indian cotton farming of
smallholder farmers between 2002 and 2008, Kathage and Qaim
[36] showed that the use of conventional Bt cotton led to a 24%
yield increase and a 50% gain in cotton profit compared to
smallholders growing conventional non-Bt cotton. In contrast to
the systematic farm survey by Kathage and Qaim [36], our study
represents a pairwise comparison of cotton-based farming systems
under identical pedo-climatic conditions. While the study by
Kathage and Qaim [36] can better depict the actual situation on
real farms for a given region, our results can better represent the
potential outcomes that are achievable under optimal conditions
with respect to inputs and knowledge access. In our study, there
were comparatively little differences between the CONBtC and
the CON farming systems regarding cotton yield (+16 and +14%
in cycle 1 and 2, respectively) and cotton gross margin (+17 and
+2% in cycle 1 and 2, respectively). Furthermore, it needs to be
taken into account that with the introduction of Bt cotton to India
in 2002, the provincial governments began to subsidize Bt cotton
considerably, especially between the years of 2002 and 2008. This
led to the rapid spread of Bt cotton and the breakdown of the non-
Bt cotton seed chain. The relatively weak performance of the non-
Bt cotton in the farm survey by Kathage and Qaim [36] could
partly be explained by the poor quality of non-Bt cotton seeds, as
propagation of non-Bt cotton was abandoned and led to limited
availability of non-Bt cotton seeds from old stocks of probably poor
quality and mainly older cultivars [59,83].
In contrast to Kathage and Qaim [36], our study also includes
other cash crops such as soybean and wheat as part of cotton-
based farming systems. These are essential components for
enabling long-term cotton cropping and for securing livelihoods
of smallholders, as they enable the distribution of risks. According
to our findings, the investigated organic farming systems also
showed a significant yield gap compared to the conventional
farming systems in wheat in cycle 1, as well as for total productivity
per crop rotation (including additional wheat crop after Bt cotton
(wheat 2)) in cycle 2. Furthermore, a smaller yet significant yield
gap was observed for soybean in cycle 2 for the BIOORG system
(but not for the BIODYN system) (Table 3). Nevertheless, as in our
study organic farming systems were less capital-intensive than
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 12 December 2013 | Volume 8 | Issue 12 | e81039
conventional ones for all crops, they may be of particular interest
to smallholder farmers who often do not have the financial means
to purchase inputs and would thus need to seek loans. If this can be
verified in on-farm trials, organic farmers might be less exposed to
financial risks associated with fluctuating market prices of synthetic
fertilizers and crop protection products [27,79]. Additional on-
farm investigations have been started in order to classify regional
farms into several farm typologies with corresponding levels of
available production factors. This should allow for the assessment
of the perspectives of each farm type regarding conversion to
organic farming systems. If organic farming is to be adopted more
widely, more inter- and transdisciplinary research giving focus to
the problems and benefits of organic management practices needs
to be undertaken [84]. Furthermore, large efforts have to be made
to gather and disseminate knowledge on production techniques.
Intensifying research on organic farming systems to a similar
extent as was the case for research on GM crops [85] may help to
provide additional relevant information to policy makers, advisors
and farmers about comparative advantages and limitations of
different cotton production systems.
Conclusions
With this publication we respond to the urgent need for farming
systems comparison trials in the tropics and subtropics [17,26].
Here we show results from the conversion period (first four years
after inception of the trial) of cotton-based farming systems
representative for Vertisol soils in Madhya Pradesh, central India.
Due to the short-term nature of our results and the observed
System6Cycle interactions (no clear trend of system performance
over time) for yield and gross margin data of cotton and wheat,
definitive conclusions about the comparative agronomic and
economic performance of the investigated farming systems cannot
be drawn. However, our results show that organic soybean
productivity can be similarly high as in conventional systems at
lower input levels, which can make organic soybean production -
as part of cotton-based crop rotations - more profitable. Future
research will bring further insights on agronomic and economic
performance of the different farming systems after the conversion
period, thus providing indications on the long-term sustainability
across the whole crop rotation. Furthermore, the effects of the
farming systems on biodiversity, soil fertility, other ecological co-
benefits such as climate change mitigation by means of C
sequestration, and product quality need to be elucidated.
Supporting Information
Figure S1 Experimental design of the farming systems
comparison trial in Madhya Pradesh, India. Farming
systems: biodynamic (BIODYN), organic (BIOORG), convention-
al (CON), conventional with Bt cotton (CONBtC), CONBtC
includes wheat cultivated after Bt cotton on the same plots in 2009
and 2010, open squares belong to Strip 1, closed squares belong to
Strip 2, distance between two plots within a strip = 6 m, distance
between the two strips = 2 m.
(TIF)
Table S1 Fertilizer and plant protection practices in the farming
systems compared in central India (2007–2010). BIODYN:
biodynamic, BIOORG: organic, CON: conventional, CONBtC:
conventional with Bt cotton, Ntotal: total nitrogen, OF: organic
fertilizers (compost, FYM and castor cake), Ntotal includes only
fertilizer derived N, nutrient inputs by green manures were not
considered, DAP: Diammonium phosphate, MOP: muriate of
potash, SSP: single super phosphate, 1BeavicideH: organic
pesticide containing Beauveria bassiana,2GOC: slurry made from
rotten garlic, onion and chili with water, 3NeemAzalH: insecticide
made from neem kernels, 4Top Ten: slurry made from leaves of
ten wild plants and water, 5Verelac: organic pesticide containing
Verticillium lecanii.
(DOCX)
Table S2 Detailed list of variable production costs in cotton of
the farming systems compared in central India (2007–2010).
1
in
the text, BIODYN and BIOORG are referred to consistently as
organic farming systems.
2
in the text, CON and CONBtC are
referred to consistently as conventional farming systems.
3
figures
include time for preparation of organic fertilizers to account for
their market value.
4
figures represent subsidized prices for mineral
fertilizers set by the Government of India.
5
longer time required
for soil cultivation in CON and CONBtC due to soil compaction.
6
figure includes application of biodynamic preparations.
7
figures
include uprooting cotton and removing the straw from the field.
8
figures include time required to purchase inputs (organic/
synthetic) from the market and to produce organic (natural)
pesticides and biodynamic preparations.
(DOCX)
Table S3 Detailed list of variable production costs in soybean of
the farming systems compared in central India (2007–2010).
1
in
the text, BIODYN and BIOORG are referred to consistently as
organic farming systems.
2
in the text, CON and CONBtC are
referred to consistently as conventional farming systems.
3
figures
include time for preparation of organic fertilizers to account for
their market value.
4
figures represent subsidized prices for mineral
fertilizers set by the Government of India.
5
longer time required
for soil cultivation in CON and CONBtC due to soil compaction.
6
figure includes application of biodynamic preparations.
7
figures
include removing soybean bundles from the field and threshing.
8
figures include time required to purchase inputs (organic/
synthetic) from the market and to produce organic (natural)
pesticides and biodynamic preparations.
(DOCX)
Table S4 Detailed list of variable production costs in wheat of
the farming systems compared in central India (2007–2010).
1
in
the text, BIODYN and BIOORG are referred to consistently as
organic farming systems.
2
in the text, CON and CONBtC are
referred to consistently as conventional farming systems.
3
figures
include time for preparation of organic fertilizers to account for
their market value.
4
figures represent subsidized prices for mineral
fertilizers set by the Government of India.
5
longer time required
for soil cultivation in CON and CONBtC due to soil compaction.
6
figure includes application of biodynamic preparations.
7
figures
include removing wheat bundles from the field and threshing.
8
figures include time required to purchase inputs (organic/
synthetic) from the market and to produce organic (natural)
pesticides and biodynamic preparations.
(DOCX)
Acknowledgments
Special thanks go to Andreas Gattinger (Research Institute of Organic
Agriculture, FiBL) for helping to prepare the manuscript and for his many
valuable inputs. We thank Kulasekaran Ramesh (Indian Institute of Soil
Science, IISS), Padruot Fried (Swiss Federal Agricultural Research Station
Agroscope, ART), Monika Schneider, Franco Weibel, Andreas Fliessbach
(Research Institute of Organic Agriculture, FiBL), Georg Cadisch
(University of Hohenheim), and Philipp Weckenbrock (die Agronauten)
for fruitful discussions. The field and desktop work of the whole bioRe
Association team is also gratefully acknowledged. We thank Christopher
Hay, Ursula Bausenwein and Tal Hertig for the language editing of the
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 13 December 2013 | Volume 8 | Issue 12 | e81039
manuscript. We acknowledge the inputs by Fra¨nzi Korner and Bettina
Almasi regarding statistical analysis and data interpretation. Finally, we
sincerely thank the anonymous reviewers for their very constructive and
helpful comments and suggestions.
Author Contributions
Conceived and designed the experiments: DF CZ PM. Performed the
experiments: DF RV CZ. Analyzed the data: CA MM. Wrote the paper:
CA DF MM PM CZ RV.
References
1. Trewavas A (2002) Malthus foiled again and again. Nature 418: 668–670.
2. Badgley C, Moghtader J, Quintero E, Zakem E, Chappell MJ, et al. (2007)
Organic agriculture and the global food supply. Renewable Agriculture and
Food Systems 22: 86–108.
3. Singh RB (2000) Environmental consequences of agricultural development: a
case study from the Green Revolution state of Haryana, India. Agriculture
Ecosystems & Environment 82: 97–103.
4. Pimentel D (1996) Green revolution agriculture and chemical hazards. Science
of the Total Environment 188: 86–98.
5. IAASTD (2009) International assessment of agricultural knowledge, science and
technology for development (IAASTD): Executive summary of the synthesis
report. Washington, DC: Island Press. 606 p.
6. Kilcher L (2007) How organi c agriculture contributes to sustainable develop-
ment. In: Willer H, Yussefi M, editors. The world of organic agriculture -
Statistics and emerging trends 2007. Rheinbreitbach: Medienhaus Plump. pp.
82–91.
7. Altieri MA (1999) The ecological role of biodiversity in agroecosystems.
Agriculture Ecosystems & Environment 74: 19–31.
8. Valkila J (2009) Fair Trade organi c coffee production in Nicaragua - Sustainable
development or a poverty trap? Ecological Economics 68: 3018–3025.
9. Eyhorn F, Ramakrishnan M, Ma¨ der P (2007) The viability of cotton-based
organic farming systems in India. Internationa l Journal of Agricultural
Sustainability 5: 25–38.
10. Lyngbaek AE, Muschler RG, Sinclair FL (2001) Productivity and profitability of
multistrata organic versus conventional coffee farms in Costa Rica. Agroforestry
Systems 53: 205–213.
11. Mendez VE, Bacon CM, Olson M, Petchers S, Herrador D, et al. (2010) Effects
of Fair Trade and organic certifications on small-scale coffee farmer households
in Central America and Mexico. Renewable Agriculture and Food Systems 25:
236–251.
12. Borlaug NE (2000) Ending world hunger. The promise of biotechnology and the
threat of antiscience zealotry. Plant Physiology 124: 487–490.
13. Trewavas AJ (2001) The population/biodiversity paradox. Agricultural
efficiency to save wilderness. Plant Physiology 125: 174–179.
14. Nelson L, Giles J, MacIlwain C, Gewin V (2004) Organic FAQs. Nature 428:
796–798.
15. Makita R (2012) Fair Trade and organic initiatives confronted with Bt cotton in
Andhra Pradesh, India: A paradox. Geoforum 43: 1232–1241.
16. de Ponti T, Rijk B, van Ittersum MK (2012) The crop yield gap between organic
and conventional agriculture. Agricultural Systems 108: 1–9.
17. Seufert V, Ramankutty N, Foley JA (2012) Comparing the yields of organic and
conventional agriculture. Nature 485: 229–U113.
18. Reganold JP (2012) Agriculture Comparing apples with oranges. Nature 485:
176–176.
19. Watson CA, Walker RL, Stockdale EA (2008) Research in organic production
systems - past, present and future. Journal of Agricultural Science 146: 1–19.
20. Reganold JP, Glover JD, Andrews PK, Hinman HR (2001) Sustainability of
three apple production systems. Nature 410: 926–930.
21. Ma¨ der P, Fliessbach A, Dubois D, Gunst L, Fried P, et al. (2002) Soil fertility and
biodiversity in organic farming. Science 296: 1694–1697.
22. Hepperly P, Douds Jr. D, Seidel R (2006) The Rodale faming systems trial 1981
to 2005: longterm analysis of organic and conventional maize and soybean
cropping systems. In: Raupp J, Pekrun C, Oltmanns M, Ko¨ pke U, editors. Long-
term field experiments in organic farming. Bonn: International Society of
Organic Agriculture Resarch (ISOFAR). pp. 15–32.
23. Fliessbach A, Oberholzer H-R, Gunst L, Maeder P (2007) Soil organic matter
and biological soil quality indicators after 21 years of organic and conventional
farming. Agriculture Ecosystems & Environment 118: 273–284.
24. Teasdale JR, Coffman CB, Mangum RW (2007) Potential long-term benefits of
no-tillage and organic cropping systems for grain production and soil
improvement. Agronomy Journal 99: 1297–1305.
25. Birkhofer K, Bezemer TM, Bloem J, Bonkowski M, Christensen S, et al. (2008)
Long-term organic farming fosters below and aboveground biota: Implications
for soil quality, biological control and productivity. Soil Biology & Biochemistry
40: 2297–2308.
26. Gattinger A, Muller A, Haeni M, Skinner C, Fliessbach A, et al. (2012)
Enhanced top soil carbon stocks under organic farming. Proceedings of the
National Academy of Sciences of the United States of America 109: 18226–
18231.
27. Bachmann F (2012) Potential and limitations of organic and fair trade cotton for
improving livelihoods of smallholders: evidence from Central Asia. Renewable
Agriculture and Food Systems 27: 138–147.
28. Blaise D (2006) Yield, boll distribution and fibre quality of hybrid cotton
(Gossypium hirsutum L.) as influenced by organic and modern methods of
cultivation. Journal of Agronomy and Crop Science 192: 248–256.
29. Rasul G, Thapa GB (2004) Sustainabilit y of ecological and conventional
agricultural systems in Bangladesh: an assessment based on environmental,
economic and social perspectives. Agricultural Systems 79: 327–351.
30. FAO (2013) FAOSTAT database on agriculture. Available: http://
faostatfaoorg. Accessed 11 October 2013.
31. Herring RJ (2008) Whose numbers count? Probing discrepant evidence on
transgenic cotton in the Warangal district of India. International Journal of
Multiple Research Approaches 2: 145–159.
32. James C (2012) Global status of commercialized biotech/GM crops: 2012.
ISAAA Brief 44. ISAAA: Ithaca, NY. ISBN: 978-1-892456-53-2. Available:
http://www.isaaa.org/resources/publications/briefs/44/executivesummary/
pdf/Brief%2044%20-%20Executive%20Summary%20-%20English.pdf. Ac-
cessed 11 October 2013.
33. Qaim M KS (2013) Genetically Modified Crops and Food Security. PLoS ONE
8(6): e64879 doi:101371/journalpone0064879.
34. Finger R, El Benni N, Kaphengst T, Evans C, Herbert S, et al. (2011) A Meta
Analysis on Farm-level Costs and Benefits of GM Crops. Sustainability 3: 743–
762.
35. Krishna VV, Qaim M (2012) Bt cotton and sustainability of pesticide reductions
in India. Agricultural Systems 107: 47–55.
36. Kathage J, Qaim M (2012) Economic impacts and impact dynamics of Bt
(Bacillus thuringiensis) cotton in India. Proceedings of the National Academy of
Sciences of the United States of America 109: 11652–11656.
37. Stone GD (2011) Field versus Farm in Warangal: Bt Cotton, Higher Yields, and
Larger Questions. World Development 39: 387–398.
38. Crost B, Shankar B, Bennett R, Morse S (2007) Bias from farmer self-selection in
genetically modified crop productivity estimates: Evidence from Indian data.
Journal of Agricultural Economics 58: 24–36.
39. Bennett R, Kambhampati U, Morse S, Ismael Y (2006) Farm-level economic
performance of genetically modified cotton in Maharashtra, India. Review of
Agricultural Economics 28: 59–71.
40. Qaim M, Subramanian A, Sadashivappa P (2010) Socioeconomic impacts of Bt
(Bacillus thuringiensis) cotton. In: Zehr UB, editor. Biotechnology in Agriculture
and Forestry 65: Cotton. pp. 221–240.
41. ICAR (2002) All India coordinate d cotton improvement project: Annual report
2001-02. Coimbatore: Indian Council for Agricultural Research. 97 p.
42. Singh NB, Barik A, Gautam HC (2009) Revolution in Indian Cotton.
Directorate of Cotton Development, Ministry of Agriculture, Department of
Agriculture & Cooperation, Government of India, Mumbai & National Center
of Integrated Pest Management, ICAR, Pusa Campus, New Delhi. Available:
www.ncipm.org.in/NCIPMPDFs/Revolution_in_Indian_Cotton.pdf. Accessed
11 October 2013.
43. Grue` re GP, Sun Y (2012) Measuring the contribution of Bt cotton adoption to
India’s cotton yields leap. IFPRI Discussion Paper 01170. Available: http://
www.ifpri.org/sites/default/files/p ublications/ifpri dp01170.pdf. Accesse d 11
October 2013.
44. Kranthi K (2011) 10 years of Bt in India - Three-part feature story on the history
of Bt cotton in India.Cordova: Cotton Media Group. Available: http://www.
cotton247.com/article/27520/part-ii-10-years-of-bt-in-india. Accessed 11 Oc-
tober 2013.
45. Aris A, Leblanc S (2011) Maternal and fetal exposure to pesticides associated to
genetically modified foods in Eastern Townships of Quebec, Canada.
Reproductive Toxicology 31: 528–533.
46. Marvier M, McCreedy C, Regetz J, Kareiva P (2007) A Meta-Analysis of Effects
of Bt Cotton and Maize on Nontarget Invertebrates. Science 316: 1475–1477.
47. Azadi H, Ho P (2010) Genetically modified and organic crops in developing
countries: A review of options for food security. Biotechnology Advances 28:
160–168.
48. Qaim M (2009) The economics of genetically modified crops. Annual Review of
Resource Economics: 665–693.
49. Jacobsen S-E, Sørensen M, Pedersen S, Weiner J (2013) Feeding the world:
genetically modified crops vers us agricultural biodiversity. Agronomy for
Sustainable Development: 1–12.
50. Radhakrishnan S (2012) 10 years of Bt cotton: False hype and failed promises.
Coalition for a GM-Free India. Available: http://indiagminfo.org/wp-content/
uploads/2012/03/Bt-Cotton-False-Hype-and-Failed-Promises-Final.pdf. Ac-
cessed 11 October 2013.
51. Herring RJ, Rao NC (2012) On the ‘failure of Bt cotton’ - Analysing a de cade of
experience. Economic & Political Weekly XLVII: 45–54.
52. Gruere G, Sengupta D (2011) Bt Cotton and Farmer Suicides in India: An
Evidence-based Assessment. Journal of Development Studies 47: 316–337.
53. Charyulu K, Biswas S (2010) Econo mics and efficiency of organic farming vis-a`-
vis conventional farming in India. Ahmedabad: Indian Institute of Management.
Available: http://www.iimahd.ernet.in/publications/data/2010-04-03Charyulu.
pdf. Accessed 11 October 2013.
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 14 December 2013 | Volume 8 | Issue 12 | e81039
54. IFOAM (2012) The IFOAM norms for organic production and processing:
Version 2012. Bonn: Die Deutsche Bibliothek. 134 p.
55. Koepf HH, Petersson BD, Schaumann W (1976) Biodynamic Agriculture: An
Introduction. Anthroposophic Press, Hudson, New York. 430 p.
56. Kanwar JS (1988) Farming systems in swell-shrink soils under rainfed conditions
in soils of semi-arid tropics. In: Hirekerur LR, Pal DK, Sehgal JL, Deshpande
CSB, editors. Transactions of International Workshop on Swell-Shrink Soils.
National Bureau of Soil Survey and Land Use Planning, Nagpur. pp.179–193.
57. Ministry of Agriculture GOI (2011a) State -wise estimates of area and production
of cotton released. Bt cotton constitutes about 90% of total area under cotton
cultivation. Press release of Department of Agriculture & Cooperation,
Government of India, Mumbai. Release ID: 73448. Available: http://pib.nic.
in/newsite/erelease.aspx?relid = 73448. Accesse d 11 October 2013.
58. Choudhary B, Gaur K (2010) Bt Cotton in India: A Country Profile . ISAAA
Series of Biotech Crop Profiles. ISAAA: Ithaca, NY. ISBN: 978-1-892456-46-X.
Available: http://www.isaaa.org/resources/publications/biotech_crop_
profiles/bt_cotton_in_india-a_country_profile/downlo ad/Bt_Cotton_in_India-
A_Country_Profile.pdf. Accessed 11 October 2013.
59. Nagarajan P (2012) Fiber production report for India 2010-11. Texti le
exchange. Available: http://farmhub.textileexchange.org/upload/library/
Farm%20and%20fiber%20report/Regional%20Reports%20-%20India%20-
%20English%202010-11-FINAL.pdf. Accessed 11 October 2013.
60. IFOAM (2006) The IFOAM norms for organic production and processing:
Version 2005. Bonn: Die Deutsche Bibliothek. 136 p.
61. Demeter International e.V. (2012) Production standards for the use of Demeter,
biodynamicHand related trademarks. Demeter International production
standards: The standards committee. 45 p. Available: http://www.demeter.
net/certification/standards/production. Accessed 11 October 2013.
62. ICAR (2009) Handbook of Agriculture. New Delhi: Indian Council of
Agricultural Research. 1617 p.
63. Beej SA, Hamara BA (2012) A decade of Bt cotton in Madhya Pradesh: A
report. India Environment Portal, Centre for Science and Environment (CSE),
National Knowledge Commission (NKC), Government of India. Available:
http://www.indiaenvironmentportal.org.in/files/file/MP-DECADE-OF-BT-
COTTON-2012.pdf. Accessed 11 October 2013.
64. Carpenter-Boggs L, Kennedy AC, Reganold JP (2000) Organic and biodynamic
management: Effects on soil biology. Soil Science Society of America Journal 64:
1651–1659.
65. Brookes G, Barfoot P (2011) The income and production effects of biotech crops
globally 1996-2009. International Journal of Biotechnology 12: 1–49.
66. Majumdar EG (2012) CICR technical bulletin - Mechanisation of cotton
production in India. Nagpur: Central Institute for Cotton Research (CICR).
Available: http://www.cicr.org.in/pdf/mechnaisation_cotton.pdf. Accessed 11
October 2013.
67. Ministry of Agriculture GOI (2011b) Cost of cultivation/production & related
data. Directorate of Economics and Statistics, Department of Agriculture &
Cooperation, Government of India, Mumbai. Available: http://eands.dacnet.
nic.in/Cost_of_Cultivation.htm. Accessed 11 October 2013.
68. Pinheiro J, Bates D, DebRoy S, Sarkar D and the R Develop ment Core Team
(2013) nlme: Linear and Nonlinear Mixed Effects Models.R package version 3.1-
110. Available: http://cran.r-project.org/web/packages/nlme/nlme.pdf. Ac-
cessed 11 October 2013.
69. R Core Team (2012) A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
Available: http://www.R-project.org/. Accessed 11 October 2013.
70. Loughin TM (2006) Improved experimental design and analysis for long-term
experiments. Crop Science 46: 2492–2502.
71. Carpenter JE (2010) Peer-reviewed surveys indicate positive impact of
commercialized GM crops. Nature Biotechnology 28: 319–321.
72. Glover D (2010) Is Bt Cotton a Pro-Poor Technology? A Review and Critiqu e of
the Empirical Record. Journal of Agrarian Change 10: 482–509.
73. Konduru S, Yamazaki F, Paggi M (2012) A Study of Indian Government Policy
on Production and Processing of Cotton and Its Implications. Journal of
Agricultural Science and Technology B 2: 1016–1028.
74. Sadashivappa P, Qaim M (2009) Bt cotton in India: Development of benefits
and the role of government seed price interventions. AgBioForum 12: pp. 172–
183. Available: http://agbioforum.org/v12n2/v12n2a03-sadashivappa.pdf. Ac-
cessed 11 October 2013.
75. Panneerselvam P, Halberg N, Vaarst M, Hermansen JE (2012) Indian farmers’
experience with and perceptions of organic farming. Renewable Agriculture and
Food Systems 27: 157–169.
76. Ramesh P, Panwar NR, Singh AB, Ramana S, Yadav SK, et al. (2010) Status of
organic farming in India. Current Science 98: 1190–1194.
77. Ministry of Agriculture GOI (2011b) Agricultural wages in India. Directorate of
Economics and Statistics, Department of Agriculture & Cooperation, Govern-
ment of India, Mumbai. Available: http://eands.dacnet.nic.in/Cost_of_
Cultivation.htm. Accessed 11 October 2013.
78. Singh RJ, Ahlawat IPS, Kumar K (2013) Productivity and profitability of the
transgenic cotton-wheat production system through peanut intercropping and
FYM addition. Experimental Agriculture 49: 321–335.
79. Panneerselvam P, Hermansen JE, Halberg N (2011) Food Security of Small
Holding Farmers: Comparing Organic and Conventional Systems in India.
Journal of Sustainable Agriculture 35: 48–68.
80. Swezey SL, Goldman P, Bryer J, Nieto D (2007) Six-year comparison between
organic, IPM and conventional cotton production systems in the Northern San
Joaquin Valley, California. Renewable Agriculture and Food Systems 22: 30–40.
81. Venugopalan MV, Rajendran TP, Chandran P, Goswami SN, Challa O, et al.
(2010) Comparative evaluation of organic and non-organic cotton (Gossypium
hirsutum) production systems. Indian Journal of Agricultural Sciences 80: 287–
292.
82. Menon M (2003) Organ ic cotton re-inventing the wheel. Hyderabad: Booksline,
SRAS Publications. Available: http://www.ddsindia.com/www/PDF/
Organiccotton_Cover_Text.pdf. Accessed 11 October 2013.
83. Nemes N (2010) Seed security among organic cotton farmers in South India.
University of Hohenheim: Department of Rural Communication and Extension.
Available: http://www.organiccotton.org/oc/Library/library_detail.
php?ID = 305. Accessed 11 October 2013.
84. Forster D, Adamtey N, Messmer MM, Pfiffner L, Baker B, et al. (2012) Organic
Agriculture—Driving Innovations in Crop Research. In: Bhullar G, Bhullar N,
editors. Agricultural Sustainability - Progress and Prospects in Crop Research:
Elsevier. pp 21–46.
85. Vanloqueren G, Baret PV (2009) How agricultural research systems shape a
technological regime that develops genetic engineering but locks out agroeco-
logical innovations. Res Policy 38: 971–983.
Organic vs. Conv. Cotton Farming Systems in India
PLOS ONE | www.plosone.org 15 December 2013 | Volume 8 | Issue 12 | e81039
