Burning of crop residue and its potential for electricity generation.
Ahmed, Tanvir ; Ahmad, Bashir
This paper identified the factors influencing the rice crop residue
burning decision of the farmers and the potential of the burnt residue
to generate electricity. For this study, data were collected from 400
farmers in the rice-wheat cropping system. Effects of different
variables on the burning decision of rice residue are investigated
through logit model. A number of factors had significant effects on the
burning decision of crop residue. These included farming experience of
the farmer, Rajput caste, farm size, owner operated farm,
owner-cum-tenants operated farm, silty loam soil type, livestock
strength, total cost associated with the handling of residue and
preparation of wheat field after rice, availability of farm machinery
for incorporation, use of residue as feed for animals, use of residue as
fuel, intention of the respondent to reduce turnaround time between
harvesting of rice and sowing of wheat, convenience in use of farm
machinery after burning of residue and the geographic location of farm.
The overall quantity of rice straw burnt is estimated to be 1704.91
thousand tonnes in the rice-wheat cropping areas with a potential to
generate electric power of 162.51 MW. This power generation from crop
residues would be a source of income for the farmers along with
generation of additional employment opportunities and economic
activities on sustainable basis. In order to minimise the cost of
haulage of rice straw, installation of decentralised power plants at
village level would be a good option. Further, use of rice crop residue
as an energy source can help in reducing foreign exchange requirements
for import of furnace oil.
JEL Classification: 044, Q12, Q16, Q42, Q48
Keywords: Bioenergy, Crop Residue, Electricity, Energy, Growth,
Rice
1. INTRODUCTION
Most of the villages in Punjab have inadequate electricity supply.
These villages have to face electricity shut downs because of severe
electricity shortage in Pakistan. In Pakistan, household sector was the
largest consumer of electricity with a share of 46.5 percent while major
sources of electricity generation were fossil fuel (64.1 percent) and
hydro (31.9 percent) during 2011-12 [Pakistan (2013)]. Due to political
reasons, Government of Pakistan is not developing new hydro resources
for electricity generation but generates electricity through burning of
fossil fuel, which produces greenhouse gases. Moreover, high oil prices
have adverse impacts on the economy of Pakistan. Thus, it is important
to explore new means of electricity generation.
Bioenergy accounts for about 10 percent of total energy consumption
in the world and it is expected that this source will play greater role
in near future [Jiang, et al. (2012)]. Research work indicates that open
field burning of crop residue is a common practice in many countries
[Gadde, et al. (2009)]. It has been estimated that annually on average
730 teragram (tg) of biomass are burnt in Asia and out of which 250 tg
are from agricultural burning. Open burning of biomass is emitting 0.37
tg of S02, 2.8 tg of NOx, 1100 tg of C[O.sub.2], 67 tg of CO and 3.1 tg
of methane. However, emissions of crop residues burning is contributing
about 0.10 tg of S[O.sub.2], 0.96 tg of NOx, 379 tg of C[O.sub.2], 2j tg
of CO and 0.68 tg of C[H.sub.4] [Streets, et al. (2003)]. A growing
concern regarding residue burning emerges from its effects on air
pollution and climate change. Incomplete combustion of biomass such as
agricultural residues generates black carbon [Kante (2009); Bond, et al.
(2013)] which is the second largest contributor to global warming after
carbon dioxide [UNEP (2009); Chung, et al. (2005); Ramanathan and
Carmichael (2008)]. Black carbon absorbs radiation and warms the
atmosphere at regional and global scales. Increased concentration of
black carbon and other pollutants, observed in the high Himalayas, is
expected to enhance glacier melting. Black carbon emissions and other
types of aerosols have also given rise to atmospheric brown clouds
(ABCs) in Asia [Nakajima (2009)]. The aerosols in ABCs decrease the
amount of sunlight reaching the earth's surface by 10 to 15 percent
and enhance atmospheric solar heating by as much as 50 percent. In
general, ABCs and their interactions with greenhouse gases significantly
affect climate, hydrological cycle, glacier melting, agricultural and
human health [UNEP.RRC.AP (2012)]. Thus, all it indicates that open
field burning of crop residue is the most undesirable treatment of crop
residue from the perspective of environmentalists. This treatment of
crop residue also worsens the problem of global warming.
Rice-wheat cropping system is dominant in the Indo-Gangetic Plain
(IGP) which comprises of parts of Pakistan, India, Bangladesh, and
Nepal. IGP is producing enormous quantity of rice straw and it is
usually not used as feed for animals [Badarinath, et al. (2006)].
Consequently, rice residues are generally burnt and it is often
questioned, why farmers burn it? Research work done shows that burning
of rice residues increases the short-term availability of some nutrients
i.e. P and K [Erenstein (2002)] it also results in the loss of plant
nutrients [Biederbeck, et al. (1980); Gupta, et al. (2004); Heard, et
al. (2006); 1RR1-CIMMYT Alliance Cereal Knowledge Bank (2007)] in
addition, it also creates health and environmental problems [The Lung
Association (2009); Nori (2005), Graham, et al. (1986); Prasad and Power
(1991)]. Burning of crop residues also reduces microbial population
[Raison (1979)] and organic carbon [Rasmussen, et al. (1982), Heard, et
al. (2006)]. However, incorporation of crop residue increases organic
carbon and nutrient contents of soils and crop yield [Sharma, et al.
(1985); Sidhu and Beri (1989); Ganwar, et al. (2006); Hartley and Kessel
(2005); Kessel and Horwath; Prasad, et al. (1999); Hooker, et al.
(1982); Bhatnagar, et al. (1983); Garg (2008); Surekha, et al. (2006);
Prasad, et al. (1999); Tripathi, et al. (2007)].
There is an increasing interest in converting crop residues to
energy products due to new emerging technologies and rising energy
prices [Idania, et al. (2010), Scarlat, et. al. (2010)]. There are
number of studies that indicate the existence of potential of
electricity generation through the usage of crop residue as a fuel in
power generation plants [Freedman (1983); Ergudenler and Isigigur
(1994); Shyam (2002); Jingura and Matengaifa (2008); Karaj, et al.
(2010); Hiloidhari and Baruah (2011); Nguyen, et al. (2013)]. Liquid or
gaseous biofuel can be produced from crop residues like cereals and
corn, by using thermo-chemical or biological techniques [Elmore, et al.
(2008)]. Hiloidhari and Baruah (2011) found 16 different types of crop
residue in Sonitpur district of Assam, India. They found rice crop as a
dominant source of residue and about 0.17 million tonnes of residue
biomass has a potential to produce about 17MW power. According to them,
decentralised crop residue based power generation can solve the problem
of acute shortage of grid connected power supply. Similarly, Nguyen, et
al. (2013) estimated the electricity generation from wheat straw instead
of coal and natural gas. Their study also indicates that usage of straw
will reduce global warming and use of non-renewable energy. Hence, there
is an increasing recognition that interrelations between agriculture,
biomass production, bio-energy and climate should be better understood
in order to estimate the realistic bioenergy potential [Haberal, et al.
(2011)]. According to Freedman (1983), a huge potential of biomass
energy is available in rural areas in the form of rice crop residue.
Potential amount of energy that can be obtained from this residue is
3.70x[10.sup.10] J/ha/year under traditional methods, 7.93 X 1010 J
under labour intensive and 8.36 X [10.sup.10] J under capital intensive
methods. Accurate estimates of the amounts of produced crop residues,
their disposal pattern (quantity used as feed for animals, quantity used
as fuel for cooking, quantity incorporated into soil, quantity burnt to
clear the field in order to improve the performance of farm machinery
for bed preparation for the next crop, etc.) and the potential amount of
crop residue that can be saved from burning and used for bioenergy
generation on sustainable basis is very important. According to Jingura
and Matengaifa (2008), biomass can provide 47 percent of the energy
consumption in Zimbabwe and crop residue is its major component.
According to them, estimated annual amount of crop residue in Zimbabwe
is 7.805 Mt and it has an energy potential of 81.5 PJ per year. Thus
crop residue can be used for energy generation besides feeding of
animals and improvement of soil fertility. Moreover, environmental
advantage connected with burning of residue for electricity generation
can be revealed from the fact that this usage does not compete with food
or cash crops and no land use change is required [Barz and Delivand
(2011)]. Shyam (2002) identified crop residue as a sustainable source of
energy supply and suggested establishment of decentralised electricity
supply system based on crop residue in rural areas. Likewise, Karaj, et
al. (2010) analysed the existence of potential of electricity generation
in Albania through biomass (bioenergy crops, agricultural and forestry
residues and wastes). They considered generation of steam and biogas
from the biomass to run steam generators and turbines for the generation
of electricity. Energy content in biomass was estimated theoretically by
estimating biomass using statistical reports, literature review and
personal investigations. For Albania, it is found that 4.8 million tons
of dry biomass was produced in year 2005 with energy content 11.6
million MWh/a. This energy content has technical potential of 3 million
MWh/a of electrical energy production. This amount of electrical energy
is equal to 45.8 percent of total electrical consumption of Albania.
Study of Ergudenler and Isigigur (1994) identified agricultural residue
as a potential fuel for sustainable electricity generation in Turkey.
According to them, usage of agricultural residue in power plants has
less environmental impacts and results in the reduction of net emissions
of C02, S02 and NOx as compared to thermal power plants in which lignite
is major source of fuel. Open field burning of residue has adverse
impact on the soil fertility [Malhi and Kutcher (2007)] and on the
environment because of greenhouse gas emissions. So by using this
residue for electricity generation, one can avoid the problem of
greenhouse gas emissions and intensity of electricity shortage problem.
As in Pakistan, no comprehensive study has been carried out to identify
the factors influencing the rice crop residue burning decision by the
farmers and the potential of burnt rice residue for electricity
generation, so this study is conducted to answer this question. The
specific objectives of the study are:
(1) To determine the factors, which influence farmers to make
decision of burning of rice crop residue, and
(2) To find out the quantity of electricity that can be produced by
using the rice straw that is currently being burnt.
The rest of paper is organised as follows. Section 2 describes the
methodology along with model specification and description of data set.
Section three discusses the results of models and key determinants of
the rice crop residue burning decision by the farmers along with
potential of the burnt residue for electricity generation. Last section
deals with summary and suggestions for the generation of electricity.
2. METHODOLOGY
The first part of the methodology presents a model to answer the
question why the farmers burn the rice residue. The second part is
concerned with the methodology used in estimating the potential
electricity that can be generated from the residue, which is being burnt
by farmers. Finally, procedure used for data collection is presented.
2.1. Logit Model of Residue Burning Decision
Adoption of burning or non-burning (i.e. complete
removal/incorporation) residue management practice essentially involves
a choice by the farmer. Binary choice models are more appropriate when a
choice is made between the two alternatives [Judge, et al. (1980);
Pindyck and Rubinfeld (2000)]. The linear probability model suffers from
a number of deficiencies i.e. variance of the disturbance is
heteroscedastic--the distribution of this term is not normal and it does
not constrain the predicted values to lie between 0 and I- [Amemiya
(1981); Capps and Kramer (1985)]. Problems of the linear probability
model can be overcome through the monotonic transformation (Probit or
logit specification), which guarantees that predictions lie in the unit
interval [Capps and Kramer (1985)]. The choice of model i.e. probit or
logit is mainly a question of convenience [Hanushek and Jackson (1977)].
In this paper, logit model is used. A fanner will make his choice based
on the rule of utility maximisation. According to this rule, farmer i
selects the alternative from the choice set that maximises his utility
[U.sub.i]. Since the researcher does not have complete information about
all the factors that are considered important in the decision making
process by farmers while making a choice, so the utility function
[U.sub.ij] is broken down into two components [Guadagni and Little
(1983)], i.e.
[U.sub.ij] = [V.sub.ij] + [[epsilon].sub.ij] Where, [U.sub.ij] is
the overall utility of /'th farmer fory'th choice,
[V.sub.ij] is a systematic utility component of ith farmer for jth
choice,
[V.sub.ij] is a stochastic component of ith farmer for jth choice.
The decision maker chooses the alternative from which he gets the
maximum utility. In the binomial or two alternatives case, farmer
chooses alternative 1 if and only if.
[U.sub.i1] [greater than or equal to] [U.sub.i2] or [U.sub.i1] +
[[epsilon].sub.i1] [greater than or equal to] [U.sub.i2] +
[[epsilon].sub.i2]
In probabilistic terms, the probability that alternative 1 is
selected is given by
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
It states that the probability of choosing alternative 1 is equal
to the probability of the difference in stochastic utility of choice 1
and 2 being less than or equal to the difference in systematic utility
of choice 1 and 2. Assuming that [[epsilon].sub.i2] - [[epsilon].sub.i1]
has a logistic distribution, the probability ([P.sub.i]) that farmer i
burns residue is a function of an index variable ([Z.sub.i]) summarising
a set of farmer attributes, which can be written as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where [beta] is a vector of coefficients; [X.sub.i] is a vector of
the ith farmer attributes and e is the base of natural logarithm. Z, is
a dichotomous variable, it takes the value of one if a farmer has
adopted the practice of residual burning and takes the value zero
otherwise. The change in [P.sub.i] with respect to change in X] is given
by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where [[beta].sub.k] is the kth element of the parameter vector p.
As [P.sub.i] is equal to one if a choice is made and zero otherwise
so the correct estimation procedure is maximum likelihood. The
probability that the farmer burns the rice residue depends upon various
attributes like farm size, number of farm fragments, livestock strength,
age, education, farming experience and caste of farmer, ownership of
farm, soil type, use of rice residue as feed, fuel, cost of collection
and transportation of rice residue etc. Therefore, the following model
is used to analyse the decision of rice residue burning:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where, the variables are defined in Table 1.
2.2. Methodology for Determining the Potential of Electricity
Generation from Rice Residue
Following steps are involved for calculating the generation of
electricity from rice residue.
2.2.1. Determining the Total Yield of Rice Crop and Residue
Availability of accurate data about the crop residue is very
essential for determining the potential of bioenergy in any country.
Previous studies estimated the straw produced from the main product like
grain and used a specific ratio of main product to straw to estimate the
straw produced. Such a ratio of main product to straw varies from
variety to variety and sometime even for a specific product because of
differences in climatic and agronomic conditions under which the main
product is produced. Consequently, the estimate of amount of crop straw
produced either overestimated or underestimated the actual amount of
straw produced. This study uses primary data collected from the farmers
for the assessment of the quantity of straw produced and its disposal
pattern. In this study in to order obtain the yield of rice crop and its
residue, farmers were asked about the variety grown, area under each
variety, yield of paddy and straw. This information was used to
calculate the paddy yield and straw yield which came to 1624 kg and 1602
kg, respectively. Thus the ratio of paddy to straw was 1:0.99. This
ratio was quite comparable with the ratio of 1:1 reported by Jiang, et
al. (2012).
2.2.2. Rice Area under Various Residue Management Practices
In the study area, farmers were following different practices to
manage the rice residue. Therefore, farmers were asked about the rice
area managed under various residue management practices i.e. area from
which residue was removed 100 percent (REMV), area from which pural was
removed and lower parts of rice plant were burnt (RPBL), area from which
pural was removed and lower parts of stem were burnt (BPLP), area from
which pural was removed and lower parts of stem were incorporated
(RPINC) and the area where the entire residue was incorporated (INC).
The area where traditional manual method was used for harvesting, the
residue was removed 100 percent and was used mainly as feed for animals.
2.2.3. Estimation of Quantity of Rice Residue Burnt
In two practices (i.e. RPBL and BPLP), burning of residue is
involved. Moreover, there is not complete burning of residue in these
practices as the lower parts of rice plant are not dry enough to catch
fire. Consequently, we asked farmers about the proportion of rice
residue burnt in these practices. This proportion was used to determine
the quantity of rice residue burnt from the straw yield produced for
each variety grown under these two practices. A weighted average
quantity of residue burnt was obtained by weighting the quantity of
straw burnt with the acreage of each variety for the practice RPBL and
BPLP. Finally, quantity of residue burnt per acre under various residue
management practices was weighted according to the acreage under each
practice to determine the quantity of residue burnt per acre of rice
harvested. This quantity of residue per acre was multiplied with the
rice acreage in the rice wheat cropping system of Punjab, to estimate
the total quantity of residue burnt. Assuming the same quantity of
residue burnt per acre for the rice-wheat cropping system area, we
estimated the total quantity of burnt residue in Punjab, Pakistan.
2.2.4. Estimation of Biomass Power Potential
Conversion of biomass to energy can be done by using various
technologies i.e. thermo-chemical and bio-chemical [Jiang, et al.
(2012)]. Thermo-chemical conversion technology is specifically suitable
for loose biomass [Nussbaumer (2003)]. The most common process involves
the direct combustion of fuels to produce thermal energy, which is used
to produce steam and further to generate electricity by using steam
turbines, steam engines or other energy converters [Barz (2008)].
Biomass power plants with different sizes of combustion can generate
electricity from a few kilowatts to 100 MW with net conversion
efficiency from 20 to 40 percent [Mckendry (2002); Nussbaumer (2003)].
In order to estimate the power potential, following expression is
used.
[RRPP.sub.J] = K x [ACR.sub.J] x WAQRB x LHVR/T
Where [RRPP.sub.J] is the rice residue biomass power potential of
the J-th area; K is the overall energy conversion efficiency assuming a
value of 20 percent [Hiloidhari and Baruah (2011)]; [ACR.sub.J] is the
rice acreage in acres in the J-th area; WAQRB is the weighted average
quantity of rice residue burnt per acre; LHVR is the lower heating value
of the rice straw. It is taken to be 15.03 (G) [t.sup.-1] [Singh, et al.
(2008)]; T is the annual operating duration in seconds.
2.3. Data
The data for this study were collected during the year 2010 from
the two most important districts (i.e. Gujranwala and Sialkot) having
share of maximum acreage in the rice-wheat system of the Punjab [Punjab
(2009)]. Ten villages were selected randomly from the 36 villages
already selected by the Federal Bureau of Statistics from each of the
districts for the estimation of acreage and yield of various crops.
These villages were considered as primary sampling units (PSU). Farmers
within the PSUs were taken as secondary sampling units. A list of
fanners was prepared in each village and then 20 fanners were randomly
selected from different sizes in proportion to their number. Total
sample comprised of 400 respondents. For the collection of data, a
comprehensive questionnaire was constructed, which was modified after
pre-testing. The data were collected by using personal interview method.
3. RESULTS
3.1. Influence of Different Factors on the Decision of Burning of
Residue
Descriptive statistics of the variables used in the model are
exhibited in Table 2.
The means of the qualitative variables refer to the proportion of
respondents taking on particular qualitative attributes. For example,
approximately 77 percent of the respondents are owner operators, roughly
20 percent of the respondents are owner-cumtenants. Similarly,
approximately 57 percent of the respondents are Jat, 13 percent Rajput
and 6 percent Arian. The continuous variables indicate that each farm
has, on average about 11.93 acres of land and the collection and
transportation cost per acre of rice residue is Rs 485.84 (Rs 104 = 1
US$).
The maximum likelihood estimates of the logit model are presented
in Table 3. Likelihood ratio indicates that the amount of variation
explained is significantly different from zero. Pseudo [R.sup.2] value
is 0.433. The probability of burning rice residue was significantly
associated (at 20 percent level) with fourteen variables out of twenty
six variables included in the model. These factors were: (a) Farming
experience of the farmer (EXP), (b) Rajput caste (RAJPUT), (c) Farm size
(SIZE), (d) Farmer is owner operator (OWNER), (e) Farmer is
owner-cum-tenant (OWNCT), (f) Soil type is silty loam (SILTL), (g)
livestock strength on the farm (ANIMAL), (h) Total cost associated with
the handling of the residue and preparation of wheat field after rice
(TCBURN), (i) Farm k2 machinery availability for incorporation (MACH),
(j) Use of residue as feed for animals (FEED), (k) Use ot residue as
fuel (FUEL), (I) Intention of the respondent to reduce turnaround time
between harvesting of rice and sowing of wheat (REDTURN), (m) Burning of
residue results in convenient use of farm machinery (CONMACH) and (n)
The geographic location of farm in Gujranwala (GUJRAN) district.
The farming experience (EXP) had positive influence on the
probability of burning rice residue. The probability of burning
increased by one percent for each one percent increase in farming
experience. A possible explanation for this behaviour is that 53.75
percent and 15.15 percent farmers perceive that residue burning improves
the physical properties and increase soil nutrients of soil,
respectively. Moreover, the results of the study show that 70.50 percent
and 64.75 percent of the farmers perceive that burning of rice residue
increases the yield of wheat and rice, respectively. The increase in the
yield of both wheat and rice crops is due to substantial and ready
availability of nutrients through ash to plants due to incomplete
burning of rice residue as the temperature desired for complete burning
is not achieved during the burning of residue [Kumar and Goh (2000)].
Further there is rapid conversion of nutrients from organic form to
inorganic form N, P, K, Ca and Mg [Surekha, et al. (2006)].
The probability of burning of rice residue was increased by 1.91
percent for each percent increase in farm size (SIZE). This results from
the fact that livestock strength per unit area decreases with increase
in farm size and consequently the use of rice residue as feed falls.
Total cost associated with the preparation of field for wheat crop
after rice was significantly related with the increase in probability of
rice residue burning. The survey results show that the total cost
associated with the preparation of wheat field after rice was Rs 3536.79
where the rice straw was burnt in the field compared with Rs 4097.83 for
the incorporation of rice residue practice. This shows that farmers are
adopting the burning practice as the cost associated with burning
practice was substantially less than non-burning practice. Under the
prevailing cost conditions, farmers will not stop rice residue burning
practice unless they are compensated appropriately by other measures.
Tenure type i.e. owner operator (OWNER) and owner-cum-tenant
(OWNCT) were significantly associated with the decrease in probability
of rice residue burning by 55.87 percent and 53.49 percent,
respectively. This shows that owner operators and owner-cum-tenant have
long-term planning horizon and are concerned more with the
sustainability of land resource.
The probability of burning of rice residue was decreased by 0.65
percent for each 1 percent increase in animal strength (ANIMAL). Because
the effect of animal strength on the use of rice residue is positive,
therefore, farmers have adopted less burning practice.
Availability of farm machinery for incorporation (MACH) of rice
residue in the soil was significantly associated with the decrease in
probability of rice residue burning by 20.89 percent. This suggests that
ensuring the availability of farm machinery for incorporation can help
in reducing the practice of burning.
Use of rice residue as feed (FEED) and fuel (FUEL) were both
significantly associated with decrease in probability of rice residue
burning by 55.30 percent and 23.35 percent, respectively. Thus the
farmers can reduce the adoption of burning practice by utilising the
residue for domestic purposes.
The probability of burning of rice residue was increased by 29.45
percent with the intention of the producers to reduce turnaround time
between harvesting of rice and sowing of wheat (REDTUURN). Delay in
sowing of wheat reduces its yield by 30 kg/day [Akhtar, et al. (1992)]
and in order to sow on time fanners are burning residue to clear the
field. Intention of the farmers to burn rice residue for the convenient
use of fann machinery had positive and significant impact on the
probability of residue burning by 41.49 percent. Thus fanners used
burning practice for the convenient use of farm machinery for the
preparation of fields for the wheat crop. Thus the reduction of
turnaround time between harvesting of rice and sowing of wheat and
convenient use of farm machinery demand the proper disposal of rice
residue for obtaining better wheat yield.
Not surprisingly, producers in the Gujranwala district exhibited
higher probability of rice residue burning than Sialkot district, the
calculated change in probability was 16.53 percent. Larger farm size in
Gujranwala district compared to Sialkot district probably contributed to
this difference.
3.2. Potential for Electricity Generation
If one looks at the overall area of rice allocated to different
residue management practices, then the full burn method ranks as first
and removal ranks as second (Table 4). 58 percent of area under rice is
fully burned, while 25 percent of rice area has full removal of residue.
The remaining area is either partially burnt or a small portion is
incorporated into the field. We observed a similar pattern of adoption
of different residue management practices for different varieties of
rice (see Table 4).
The results of logit model indicate that total cost associated with
the handling of residue and preparation of field for wheat crop after
rice was significantly related with the increase in probability of rice
residue burning. The survey results show that the total cost associated
with the handling of rice residue and preparation of the wheat field
after various rice residue management practices was the highest at Rs
4585.72 for the REMV practice and the lowest at Rs 3423.94 for the BPLP
practice. The total cost was higher for RPBL, RPfNC and INC by 25.56
percent, 26.51 percent and 19.68 percent, respectively, in comparison
with BPLP. Thus, the burning of residue is the most economical method
for handling rice residue and preparing the wheat field. Under the
prevailing cost conditions, farmers will not stop rice residue burning
unless they are compensated appropriately by other measures.
The proportion of the straw burnt for various varieties; ranged
from 53.75 to 58.12 percent for the practice of removal of pural and the
burning of the lower parts of rice plant; from 63.48 to 69.26 percent
for the practice of burning the pural and the lower parts of the rice
plant. In terms of quantity 931 kg and 1034 kg of rice straw per acre
was burnt under these practices, respectively. On overall basis, 712 kg
of rice straw per acre was burnt in the study area. Of the total
surveyed respondents, 61 percent were of the view that the trend in rice
residue burning was increasing although 31 percent thought it was
decreasing. About eight percent thought there is no change. As reported
by 46 percent and 65 percent of the respondents, respectively, the short
turn-around time between the harvesting of the rice crop and the sowing
of the wheat crop and inconvenience in the use of farm machinery were
the major reasons for the burning of rice residue. Major reasons for not
burning the residue included its use as feed for animals and for home
cooking as reported by 95 percent and 24 percent of respondents,
respectively.
On the basis of results of survey conducted in rice-wheat cropping
system, total quantity of rice residue burnt is estimated at 1704.91
thousand tonnes. Using same basis as used for rice-wheat cropping
system, total quantity of rice residue burnt is estimated at 3106.68
thousand tonnes for Punjab and 4159.05 thousand tonnes for Pakistan,
which could be used for electric power generation.
On the basis of the quantity of rice residue burnt, the potential
for electric power generation is estimated as 162.51 MW, 296.13 MW and
396.44 MW for the rice-wheat cropping system, areas of Punjab and
Pakistan, respectably. The power scenario in the rice-wheat cropping
area and in other areas in Punjab and Pakistan is characterised by
fluctuating voltage, load shedding and unreliable supply. However,
demand for electricity is increasing over time and is expected to
increase many folds in coming years in Pakistan. Electricity is required
for improving health facilities, education system, living standard and
for other economic activities including running of tubewells for meeting
the water requirements of rice and other crops. Major part of the demand
is met through fossil fuels. Diminishing fuel reserves, mounting oil
prices and Green House Gases emission from burning of fossil fuels
resulting in global environment problems demand to look for renewable
energy for meeting future energy requirement. Thus significant part of
future energy must be met from renewable energy sources to meet the
rising demand and to reduce Green House Gases emission. According to
World Bioenergy Association (2010), reasonable and sustainable use of
world biomass energy can meet energy demand globally. The European
Commission has set an overall target of 20 percent share of renewable
energy and a 10 percent share of renewable energy in transport for the
year 2020 [Dam and Junginger (2011)]. U.S. Department of energy has set
a target that biomass will supply energy equivalent to 30 percent of
current petroleum consumption [Fengxiang, et al. (2011)]. Similarly,
targets have been fixed by Romania [Scarlat, et al. (2011)] and
Australia [Herr and Dunlop (2011)]. Demirbas (2011) has reported that
biomass energy can meet half of the present global energy consumption by
the year 2050. In view of the haulage cost associated with rice crop
residue, installation of crop residue biomass power plants at the
village level would be an attractive option for improving electricity
supply. Such decentralised units can benefit the rural population in
many ways. First, these can generate income for farmers from rice
residue, which is presently being burnt by them. Second, these can
generate employment through involvement of rural population in
collection, transport, loading and other activities. Third,
decentralised power units at the village level can stimulate economic
activities through assured power supply [Hiloidhari and Baruah (2011)].
4. CONCLUSIONS
This paper addresses two very important issues i.e. why farmers
burn rice residue and what is the potential of electricity generation
from the residue being burnt? Burning of rice crop residue has
significant effect on the yield of crops, physical properties of soil
and environment. The results obtained by using logit model provide
policy-makers with additional insight into the relations between the
adoption of rice residue burning practice and the various factors which
influence its adoption. There will not be significant decline in rice
residue burning under prevailing government policies as the other
practices are costly in terms of handling of rice residue and
preparation of wheat field after rice. Application of choice logit model
has identified farming experience, farm size, farmer's caste, soil
type, tenure type, animal strength, use of residue as feed and fuel,
cost of preparation of wheat field after rice, reduction in turnaround
time between harvesting of rice and sowing of wheat, convenience in use
of farm machinery, availability of machinery for incorporation and
geographic location of farm as key explanatory variables of rice crop
residue bunting decision.
The present study also attempted to estimate the quantity of burnt
rice residue, which could be used for the generation of electricity. The
results indicate that 58 percent of area under rice is fully burnt,
while in 12 percent area, pural is removed and lower parts of rice plant
are burnt. The proportion of the straw burnt ranged from 53.75 to 58.12
percent of the total straw produced for various varieties of rice when
the farmer removed the pural and burnt the lower parts of rice plant,
while this proportion varied from 63.48 to 69.26 percent when the
farmers burnt both pural and lower parts of rice plant. On overall
basis, 712 kg per acre of rice straw was burnt in the study area. The
overall quantity of rice straw burnt is estimated to be 1704.91 thousand
tonnes for the rice-wheat cropping system area, 3106.68 thousand tonnes
for Punjab and 4159.05 thousand tonnes for Pakistan. The rice straw
burnt has the potential to generate 162.51 MW, 296.13 MW and 396.44 MW
electric power in the rice-wheat cropping system area, Punjab and
Pakistan, respectively. In order to minimise the cost of haulage of rice
straw, installation of decentralised power plants at village level would
be a good option. Further, use of rice crop residue as an energy source
can help in reducing foreign exchange requirements as four kg of crop
residue can substitute one litter of furnace oil or one [m.sup.3] of
natural gas [Dubey, et al.]. Moreover, power generation from crop
residues would be a source of income for the farmers from the rice
residue along with generation of additional employment opportunities and
economic activities on sustainable basis.
REFERENCES
Amemiya, T. (1981) Qualitative Response Models: A Survey. Journal
of Economic Literature20, 1483-1536.
Ahmed, T. and B. Ahmad (2013) Why do Farmers Burn Rice Residue?
Examining Farmers' Choices in Punjab, Pakistan. South Asian Network
for Development and Environmental Economics, Kathmandu, Nepal. (Working
Paper No. 76-13).
Akhtar, M., M. A. Zahid, D. Byerlee, and K. Tetlay (1992)
Developing Appropriate Cropping Patterns for the Cotton-Wheat System of
the Northern Punjab: Plant Breeding and Policy Perspectives. In D.
Byerlee and T. Husain (eds). Farming Systems of Pakistan. Vanguard Books
Pvt Ltd. Printed at Intekhab-e-Jadeed Press, Lahore.
Badarinath, K. V. S, T. R. K. Chand, and V. K. Prasad (2006)
Agriculture Crop Residue Burning in the Indo-Gangetic Plains--A Study
Using IRS-P6 AWiFS Satellite Data. Current Science 91, 1085-1089.
Barz, M. (2008) Biomass Technology for Electricity Generation in
Community, International Journal of Renewable Energy 3:1.
Barz, M. and M. K. Delivand (2011) Agricultural Residues as
Promising Biofuels for Biomass Power Generation in Thailand. Journal of
Sustainable Energy and Environment Special Issue, 21-27.
Bhatnagar, V. K, T. N. Choudhary, and B. R. Sharma (1983) Effect of
Tillage Ad Residue Management on Properties of Two Coarse Textured Soils
and Yield of Irrigated Wheat and Groundnut. Soil Tillage Research 3,
27-37.
Biederbeck, V. O., C. A. Campbell, K. E. Bowren, M. Schnitzer, and
R. V. Mclver (1980) Effect of Burning Cereal Straw on Soil Properties
and Grain Yields in Saskatchewan. Soil Science Society of American
Journal 44, 103-11.
Bond, T. C., S. J., Doherty, D. W. Fahey, P. M. Forster, T.
Berntsen, B. J. DeAngelo, M. G. Flanner, S. Ghan, B. Karcher, D. Koch,
S. Kinne, Y. Kondo, P. K. Quinn, M. C. Sarofini, M. G. Schultz, M.
Schulz, C. Venkataraman, H. Zhang, S. Zhang, N. Bellouin, S. K.
Guttikunda, P. K. Hopke, M. Z. Jacobson, J. W. Kaiser, Z. Klimont, U.
Lohmann, J. P. Schwarz, D. Shindell, T. Storelvmo, S.G. Warren, and C.
S. Zender (2013) Bounding the Role of Black Carbon in the Climate
System: A Scientific Assessment. Journal of Geophysical Research:
Atmosphere 118:11, 5380-552.
Capps jr., O. and R. A. Kramer (1985) Analysis of Food Stamp
Participation Using Qualitative Choice Models. American Journal of
Agricultural Economics 67:1, 49-59.
Chung, C. E., V. Ramanathan, D. Kim, and 1. A. Podgorny (2005)
Global Anthropogenic Aerosol Direct Forcing Derived from Satellite and
Ground-based Observations. Journal of Geophysical Research: Atmospheres
110.
Dam, V. J. and M. Junginger (2011) Striving to Further
Plarmonization of Sustainability Criteria for Bioenergy in Europe:
Recommendations from a Stakeholder Questionnaire. Energy Policy 39,
4051-66.
Demirbas, A. (2001) Biomass Resource Facilities and Biomass
Conversion Processing for Fuels and Chemicals. Energy Conversation and
Management 42, 1357-78.
Dubey, A. K, P. Chandra, D. Padhee, and S. Gangil (2013) Energy
from Cotton Stalks and other Crop Residues, CIAE, Bhopal, India,
Available at https://www.icac.
org/projects/CommonFund/20_ucbvp/papers/15_chandra.pdf. (accessed on
September 05, 2013)
Elmore, A. J., X. Shi, J. Nathaniel, X. Li, H. Jin, F. Wang, and X.
Zhang (2008) Spatial Distribution of Agricultural Residue from Rice for
Potential Biofuel Production in China. Biomass and Bioenergy 32:1, 22-7.
Erenstein, O. (2002) Crop Residue Mulching in Tropical and
Semi-Tropical Countries: An Evaluation of Residue Availability and Other
Technological Implications. Soil and Tillage Research 67, 115-133.
Ergudenler, A. and A. Isigigur (1994) Agricultural Residues as a
Potential Resource for Environmentally Sustainable Electric Power
Generation in Turkey. Renewable Energy 5: Part-11, 786-90.
Freedman, S. M. (1983) The Use of Rice Crop Residues as a
Non-commercial Energy Source in Developing World: The Energy and
Environmental Implications, Agriculture. Ecosystems and Environment 10,
63-74.
Gadde, B., S. Bonnet, C. Menke, and S. Garivait (2009) Air
Pollutant Emissions from Rice Straw Open Field Burning in India,
Thailand and the Philippines. Environmental Pollution 157, 1554-1558.
Ganwar, K. S., K. K. Singh, S. K. Sharma, and O. K. Tomar (2006)
Alternative Tillage and Crop Residue Management in Wheat After Rice in
Sandy Loam Soils of Indo-Gangetic Plains. Soil and Tillage Research 88,
242-257.
Garg, S. C. (2008) Trace Gases Emission from Field Burning of Crop
Residues. Indian Journal of Air Pollution Viii, 76-86.
Pakistan, Government of (2013) Economic Survey 2012-13. Ministry of
Finance, Government of Pakistan.
Punjab, Government of the (2009) Punjab Development Statistics
2009. Bureau of Statistics, Lahore.
Graham, J. P., F. B. Ellis, D. G. Christian, and R. O. Canned
(1986) Effects of Straw Residues on the Establishment and Yield of
Autumn Sown Cereals. Journal of Agricultural Engineering Research 33,
39-49.
Guadagni, P. M. and J. D. C. Little (1983) A Logit Model of Brand
Choice Calibrated on Scanner Data. Marketing Science 2:3, 203-238.
Gupta. P. K., S. Sahai, N. Singh, C. K. Dixit, D. P. Singh, C.
Sharma, M. K. Tiwari, R. K. Gupta and, S. C. Garg (2004) Residue Burning
in Rice-Wheat Cropping System: Causes and Implications. Current Science
87:2, 1713-1717.
Han, F. X., R. L., King, J. S. Lindner, T-Y. Yu, S. S. Durbha, N.
H. Younan, D. L. Monts, Y. Su, J. C. Luthe, and M. J. Plodinec (2011)
Nutrient Fertilizer Requirements for Sustainable Biomass Supply to Meet
U.S. Bioenergy Goal. Biomass and Bioenergy 35, 253-62.
Hanushek, E. A. and J. E. Jackson (1977) Statistical Methods for
Social Scientists. New York: Academic Press.
Hartley, C. and C. V. Kessel (2005) Residue Management, Soil
Organic Matter and Fertility in California Rice Systems. Conference
Proceedings California Plant and Soil Conference. California Chapter of
the American Society of Agronomy. Modesto, California.
Heard. J., C. Cavers, and G. Adrian (2006) Up in Smoke Nutrient
Loss with Straw Burning. Better Crops 90, 10-11.
Herr, A. and M. Dunlop (2011) Bioenergy in Australia: An Improved
Approach for Estimating Spatial Availability of Biomass Resources in the
Agricultural Production Zones. Biomass and Bioenergy 35, 2298-305.
Hiloidhari, M. and D.C. Baruah (2011) Crop Residue Biomass for
Decentralised Electrical Powr Generation in Rural Areas (Part 1):
Investigation of Spatial Availability. Renewable and Sustainable Energy
Reviews 15, 1885-1892.
Hooker, M. L, G. M. Herron, and P. Penas (1982) Effect of Residue
Burning, Removal and Incorporation on Irrigated Cereal Yields and Soil
Chemicals Properties. Soil Science Society of America Journal 46,
122-126.
Idania, V-V, J. A. Acevedo-Benitezand, and C. Hernandez-Santiago
(2010) Distribution and Potential of Bioenergy Resources from
Agricultural Activities in Mexico. Renewable and Sustainable Energy
Reviews 14, 2147-53.
IRR1-CIMMYT Alliance Cereal Knowledge Bank (2007) Crop Residue
Management, Fact Sheet. Developed with Input from M. Chandiramani, J.
Jones, and P. Kosina (knowledgebank.cimmyt.org).
Jiang, D, D. Zhuang, F. J. Yaohuan, Y. Huang, and K. Wen (2012)
Bioenergy Potential from Crop Residues in China: Availability and
Distribution. Renewable and Sustainable Energy Reviews 16, 1377-1382.
Jingura, R. M. and R. Matengaifa, (2008) The Potential for Energy
Production from Crop Residues in Zimbabwe. Biomass and Bioenergy 32,
1287-92.
Judge, G. G., W. E. Griffiths, R. C. Hill, and T. C. Lee (1980) The
Theory and Practice of Econometrics. New York: John Wiley & Sons.
Karaj, S., T. Rehl, H. Leis, and J. Muller (2010) Analysis of
Biomass Residues Potential for Electrical Generation in Albania.
Renewable and Sustainable Energy Reviews 14, 493-99.
Kante, B. (2009) Benefits of Black Carbon Mitigation for Developing
Countries. Black Carbon-e Bulletin 1, 2.
Kumar, K. and K. M. Goh (2000) Crop Residues and Management
Practices: Effects on Soil Quality, Soil Nitrogen Dynamics, Crop Yield
and Nitrogen Recovery. Advances in Agronomy 68, 197-319.
Kessel, C. V. and W. R. Horwath (1999) Long Term Rice Straw
Incorporation: Does it Impact Maximum Yield. University of California,
Davis.
Malhi, S. S. and H. R. Kutcher (2007) Small Grains Stubble Burning
on Tillage Effects on Soil Organic C and N and Aggregation in
North-Eastern Saskatchewan. Soil and Tillage Research 94, 353-361.
Mckendry, P. (2002) Energy Production from Biomass (Part 2):
Conversion Technologies. Bioresource Technology 83:1, 47-54.
Nakajima, T. (2009) New Demands on Black Carbon Sciences. Black
Carbon-e Bulletin 1: 2.
Nori, S. J. (2005) Study of Particulate Matter (PM) in Air in
Taiyun. M. Sc. Thesis Department of Chemistry. Faculty of Mathematics
and Natural Sciences. University of Oslo, Shanxi, China.
Nussbaumer, T. (2003) Combustion and Co-combustion of Biomass:
Fundamentals Technologies, and Primary Measures for Emission Reduction.
Energy Fuels 17, 1510-21.
Nguyen, T. L. T., J. E. Hermansen, and L. Mogensen (2013)
Environmental Performance of Crop Residues as an Energy Source for
Electricity Production: The Case of Wheat Straw in Denmark. Applied
Energy 104, 633-41.
Pindyck, R. S. and D. L. Rubinfeld (2000) Econometric Models and
Economic Forecasts. New York: McGraw-Hill Book Co.
Prasad, R., B. Gangaiah, and K. C. Aipe (1999) Effect of Crop
Residue Management in a Rice Wheat Cropping System on Growth and Yield
of Crops and on Soil Fertility. Experimental Agriculture 35, 427-435.
Prasad, R. and J. F. Power (1991) Crop Residue Management. Advances
in Soil Science 15, 205-251.
Raison, R. J. (1979) Modification of Soil Environment by Vegetation
Fires, with Particular Reference to Nitrogen Transformation a Review.
Plant and Soil 51, 73-108.
Ramanathan, N., I. H. Rehman, A. Kar, N. Bhatt, and V. Ramanathan
(2009) Project Surya: Mitigation of Global and Regional Climate Change,
Buying the Planet Time for Reducing Black Carbon, Methane and Ozone.
Black Carbon-e Bulletin 1:2.
Ramanathan, V. and G. Carmichael (2008) Global and Regional Climate
Changes Due to Black Carbon. Nature Geoscience 1: 221-227.
Rasmussen, P. E., R. R. Allmaras, C. R. Rohde, and N. C. Roager
(1980) Crop Residue Influence on Soil Carbon and Nitrogen in a Wheat
Fallow System. Soil Science Society of America Journal 44, 596-600.
Scarlat, N., V. Blujdea, and J-F. Dallemand (2011) Assessment of
the Availability of Agricultural and Forest Residues for Bioenergy
Production in Romania. Biomass and Bioenerg)' 35, 1995-2005.
Scarlat, N., M. Martinov, and J-F. Dallemand (2010) Assessment of
the Availability of Agricultural Crop Residues in the European Union:
Potential and Limitation for Bioenergy Use. Waste Management 30,
1889-97.
Sharma, H. L., S. C. Modgal, and M. P. Singh (1985) Effect of
Applied Organic Manure, Crop Residues and Nitrogen in Rice Wheat
Cropping System in North Western Himalayas. Himachal Journal of
Agricultural Research 11, 63-68.
Shyam, M. (2002) Agro-residue-based Renewable Energy Technologies
for Rural Development. Energy for Sustainable Development 6:2,37-42.
Sidhu, B. S. and V. Beri (1989) Effect of Crop Residue Management
on the Yields of Different Crops and on Soil Properties. Biological
Wastes 27, 15-27.
Singh, J., B. S. Panesar, and S. K. Sharma (2008) Energy Potential
Through Agricultural Biomass Using Geographical Information System--A
Case Study of Punjab. Biomass and Bioenergy 32, 301-307.
Streets, D. G., K. F. Yarber, J. H. Woo, and G. R. Carmichael
(2003) Biomass Burning in Asia: Annual and Seasonal Estimates and
Atmospheric Emissions. Global Biogeochemical Cycles 17, 1099-1118.
Surekha, K., P. C. Reddy, A. P. P. Kumari, and P. C. S. Cruz (2006)
Effect of Straw on Yield Components of Rice (Oryza Sativa L,) under
Rice-Rice Cropping System. Journal of Agronomy and Crop Science 192,
92-101.
The Lung Association (2009) Heating Methods and Open Burning.
Available at http://www.lung.ca/protect-protegez/pollution-pollution/outdoor-exterior/heatingchauffage_e.php. (accessed on July 25, 2009)
Tripathi, R., P. Sharma, and S. Singh (2007) Influence of Tillage
and Crop Residue on Soil Physical Properties and Yield of Rice and Wheat
Under Shallow Water Table Condones. Soil and Tillage Research
92,221-226.
UNEP (2009) A Major Study on Black Carbon Intercomparison. Black
Carbon-e Bulletin 1:2.
UNEP.RRC.AP (2012) Atmospheric Brown Clouds. Available at
http://www. rrcap.unep.org/abc/ (accessed on October 2, 2012)
World Bioenergy Association (2010) Certification Criteria for
Sustainable Biomass for Energy. WBA Position Paper, Available at
http://www.worldbioenergy.org/
sites/default/files/Certification%20Criteria%20for%20Sustainable%20Biomass%20fo r%20Energy Position%20Paper.pdf. (accessed on September 5,
2013).
Tanvir Ahmed <
[email protected]> is Associate Professor,
Department of Economics, Forman Christian College (A Chartered
University), Lahore. Bashir Ahmad <
[email protected]> is
Professor Emeritus, Institute of Agricultural and Resource Economics,
University of Agriculture, Faisalabad.
Authors' Note'. This work has been undertaken with the
financial support of the South Asian Network for Development and
Environmental Economics (SANDEE). We are thankful for the technical
support and guidance provided by several SANDEE advisors and peers for
this study.
Table 1
Variable Definitions
Variable
Name Description
BURN 1 if farmer adopted the practice of rice crop residue
burning; 0 otherwise
AGE Age of farmer in years
K2EXP Farming experience of farmer in years
PRIM 1 if farming is the primary occupation; 0 otherwise
UPMAT 1 if educational level of farmer is up to matric;
0 otherwise
AMATR 1 if education level of farmer is above matric; 0
otherwise
JAT 1 if caste of farmer is Jat; 0 otherwise
ARIAN 1 if caste of farmer is Arian; 0 otherwise
RAJPUT 1 if caste of farmer is Rajput; 0 otherwise
SIZE Operational size of farm in acres
OWNER 1 if farmer is owner operator; 0 otherwise
OWNCT 1 if farmer is owner-cum-tenant; 0 otherwise
FRAGM Number of places where the farm land is situated
SILTL 1 if the dominant soil type is silt loam; 0 otherwise
CLAY 1 if the dominant soil type is clayey; 0 otherwise
ANIMAL Number of animal units on the farm
TCBURN Total cost associated with the handling the residue and
preparation of wheat field after rice
WHTSOWN 1 if wheat is sown before the end of November; 0
otherwise
MACH 1 if farm machinery is available for incorporation; 0
otherwise
FEED 1 if rice residue is used as feed for animals; 0
otherwise
FUEL 1 if rice residue is used as fuel; 0 otherwise
PBASM Proportion of rice acreage allocated to super basmati
and 385 basmati to total rice acreage
INSECT 1 if the intention of respondent is to control insects,
weeds and diseases; 0 otherwise
REDTURN 1 if the intention of respondent is to reduce
turnaround time between harvesting of rice and sowing
of wheat; 0 otherwise
CON MACH 1 if burning of residue results in convenience in use
of farm machinery; 0 otherwise
COLTRAN Total cost associated with collection and
transportation of rice residue
GUJRAN 1 if farm is located in Gujranwala district; 0
otherwise
Table 2
Descriptive Statistics for the Variables
Used in Logit Analysis
Variable Mean Std. Dev. Minimum Maximum
AGE 47.49 15.637 17 80
EXP 27.63 15.978 1 70
PRIM 0.923 0.268 0 1
UPMAT 0.403 0.491 0 1
AMATR 0.088 0.283 0 1
JAT 0.570 0.496 0 1
ARIAN 0.063 0.242 0 1
RAJPUT 0.128 0.334 0 1
SIZE 11.929 14.934 0.62 100
OWNER 0.765 0.425 0 1
OWNCT 0.198 0.399 0 1
FRAGM 1.508 0.779 1 4
SILTL 0.623 0.485 0 1
CLAY 0.348 0.477 0 1
ANIMAL 8.921 11.406 0 130
TCBURN 3061.639 1246.474 0 7850
WHTSOWN 0.835 0.371 0 1
MACH 0.103 0.304 0 1
FEED 0.740 0.439 0 1
FUEL 0.120 0.325 0 1
PBASM 73.551 38.001 0 100
INSECT 0.330 0.417 0 1
REDTURN 0.095 0.294 0 1
CONMACH 0.580 0.494 0 1
COLTRAN 485.835 478.800 0 4556.794
GUJRAN 0.50 0.501 0 1
Table 3
Maximum Likelihood Estimates for Logit Model
Change in
Variable Estimate Probability Z statistic
AGE -0.0191 -0.0048 -1.100
EXP 0.0398 * 0.0099 2.290
PRIM -0.5552 -0.1357 -0.910
UPMAT 0.2375 0.0593 0.710
AMAIR -0.4940 -0.1219 -0.720
JAT 0.0191 0.0048 0.050
ARIAN -0.5119 -0.1260 -0.780
RAJPUT 0.98573 0.2332 1.680
SIZE 0.0766 ** 0.0191 4.400
OWNER -2.8688 * -0.5587 -2.240
OWNCT -2.7415 * -0.5349 -2.070
FRAGM -0.0493 -0.0123 -0.220
SILTL 1.1686 (b) 0.2832 1.310
CLAY 0.9606 0.2341 1.080
ANIMAL -0.0261 (b) -0.0065 -1.540
TCBURN 0.00023 0.0001 1.820
WITHSOWN 0.4141 0.1028 0.940
MACH -0.8 701 (b) -0.2089 -1.550
FEED -2.7507 ** -0.5530 -6.300
FUEL -0.9806 * -0.2335 -2.020
PBASM 0.0026 0.0007 0.640
INSECT 0.1035 0.0259 0.220
REDTURN 1.3046 * 0.2945 2.280
CONMACH 1.7715 ** 0.4149 4.090
COLTRAN -0.0001 -0.0001 -0.160
GUJRAN 0.6672 * 0.3186 2.090
CONSTANT 0.7673 1.9147 0.400
Table 4
Proportion of Rice Area with Various Varieties under Different
Residue Management Practices
Pattern of Residue Management (Percent of Total Rice Area)
Removal of
pural and
Burning of
Complete Lower
Area Removal of Parts of
Variety (Acres) Residue Rice Plant
Super Basmati 2677 25 12
Basmati 386 810 26 12
Other Varieties 303 23 12
All Varieties 3790 25 12
Removal of
Burning of pural and
Pural and Incorporation
Lower of Lower
Parts of Parts of Complete
Variety Rice Plant Rice Plant Incorporation
Super Basmati 59 3 1
Basmati 386 53 9 0
Other Varieties 62 3 0
All Varieties 58 4 I
