Tuesday, September 11, 2007 - 10:45 AM
ECONOMICS OF NATURALLY COLOURED COTTON PRODUCTION UNDER CONTRACT FARMING IN INDIA
Cotton (Gossypium spp.), ‘the white Gold’ or the “king of fibres” is closely linked to human civilization itself. Cotton is one of the most important commercial crop playing a key role· in economic, political and social affairs of the world. Cotton is one of the most important fibre crop constituting more than 37 per cent share in total fibre usage. About 78 countries are growing cotton in the world on an area of 32.80 million hectares with annual production of 94.63 million bales of 170 kgs each. At present, China, USA, India, Pakistan and Uzbekistan are the top five cotton producing countries of the world. However, of them only US is having considerable share in the world exports. At present, the average per capita annual consumption of textile fibres in the world is about 8 kgs of which 3 kgs is cotton.
India is the third largest producer of cotton in the world with production of around 3.95 million MT (Approximately 15.71% of world Production). Area under cotton is around 9.50 million hectares contributing about 29 per cent to the world cotton area and keeps fluctuating owing to monsoon and other factors. Despite having the largest area under cotton in the world, India ranks third in world output of cotton due to its abysmally low average yield of 415 kgs against a world average of 723 kgs per hectare. Cotton is cultivated in almost all the states in the country, the 9 states of Punjab, Haryana, Rajasthan, Gujarat, Maharashtra, Madhya Pradesh, Andhra Pradesh, Tamil Nadu and Karnataka account for more than 95 per cent of the area under cotton.
Karnataka is one of the nine major cotton-growing states in the country. Area under cotton in the state is around 5.12 lakh hectares, with the production and productivity of 8.00 lakh bales and 266 kg/ha, respectively (AICCIP, Annual Report 2005-06). The main cotton growing districts in Karnataka are Dharwad, Haveri, Mysore, Gadag, Bellary, Belgaum, Raichur, and Gulbarga. There is a fluctuation in cotton production over the years. The same trend is also seen with area and productivity. Dharwad district had the largest area accounting for 23.26 per cent of state’s total area and it ranked second in production with 39,526 bales during 2003-04.
Cotton provides nearly 50 per cent of global fibre requirements, using 10 per cent of pesticides and 22.5 per cent of all insecticides applied in agriculture (on only 2.5% of agricultural land). These pesticides cause a range of health and environmental problems. Organic cotton is grown in India, Turkey, China, the USA, Peru and Paraguay. The organic cotton market offers major opportunities to help improve livelihoods for farmers through business and trade. Some of the more experienced farmers in the study area are obtaining higher yields than conventional farmers and organic farmers are clear that they will not go back to using chemicals unless forced to. Their incomes are usually higher as their purchases of inputs are reduced, and their health improves, reducing spending on health care, which is usually relatively expensive in developing countries.
Farmers who cultivated chemically had a greater incidence of pest attack in their fields. In the chemically cultivated cotton crop, flower drop was higher. Though these plants appeared tall and green, it was attacked by aphids and fruit borers. The cotton crop cultivated organically was not green and was short in stature. But the weight and colour of the cotton lint was superior in the organically cultivated plants. Using chemical fertilizers and pesticides for cotton crop, only increases the cost of cultivation. On the other hand, it does not increase the yield.
Cotton with naturally coloured lint, other than white is referred as coloured cotton. In nature, coloured cotton and white linted cotton are found from time immemorial. Colour cotton was used to create nets, clothing, tapestries, shrouds, saddlebags and blankets. Fossils obtained indicated the cultivation of colour cottons like blue, purple, green, tan and red colours in Peru. But Peru was the main producer of colour cotton in the World. Around 10 to 15 thousand of tribals were used to grow this cotton. Use of artificial dyes is avoided when the fabrics are manufactured from naturally coloured cotton. Even those having sensitive skin can safely use such fabric. Thus, fabric manufactured from naturally coloured cotton has been found to be best for human health (Waghmare et al, 2005).
The present study was undertaken
with the following specific objectives.
To estimate the costs and returns in naturally coloured cotton production. To analyze the resource use efficiency in naturally coloured cotton production.
Multistage purposive sampling
technique was adopted in the selection of the district, taluk,
village and cultivators. Dharwad district was purposively selected
for the study, as this district is the major cotton-growing district
in the state. Dharwad district ranked first and third in cotton area
(16.39 %) and production (11.64%) of cotton respectively in the state
and coloured cotton is also grown in this
district. Dharwad taluk was purposively selected for the study, as
coloured cotton is grown in Uppinbetageri village of this taluk under
contract farming. Talukwise area under cotton in Dharwad. From
Uppinbetageri village of Dharwad taluk, all the 80 farmers
cultivating naturally coloured cotton under the system of contract
farming were chosen purposively for the study. Contract was made
between the University of Agricultural Sciences, Dharwad and Khadi
Nekar Sahakari Utpadak Sangha Niyamit, Uppinbetageri. The study was
based on the data collected from 80 farmers using purposive sampling
technique.
The analytical techniques used to
evaluate the objectives of the present study are summarized below.
The data collected were presented
in tabular form to facilitate easy comparisons. This technique of
tabular analysis was employed for estimating the cost and return
structure, processors as well as garments manufacturer. The problems
in production and processing units faced by the contract farmers and
processors elicited were also analyzed by this technique. Terms of
contract and modus operandi between UAS, Dharwad and Khadi Nekar
Sahakari Utpadak Sangh Niyamit, Uppin Betageri were documented using
averages, means and percentages.
The data were summarized with the
aid of statistical tools like averages, percentages etc. to
obtain the meaningful results.
The Cobb-Douglas type of
production function was used to study the effect of various inputs on
coloured cotton output. On account of its well known properties like
its computational simplicity that justify its wide application in
analyzing production relations (Handerson
and Quandt, 1971). It being a homogenous
function provided a scale factor enabling one to measure the returns
to scale. The estimated regression coefficients represented the
production elasticities.
The form of Cobb-Douglas
production function used in the present study is as follows.
Y = aX1b1X2b2X3b3
X4b4 X5b5X6b6
X7b7eu _____________ (1)
Where,
Y = Gross output in rupees
a = Intercept (efficiency) term
X1 = Farm size (ha)
X2 = quantity of seeds
in kgs
X3 = quantity of FYM
in tonnes
X4 = Human labour in
mandays
X5 = Bullock labour in
pair days
X6 = quantity of
Biopesticides in liters
X7= quantity
Trichocard in numbers
eu = Random error term
bi’s = Output elasticities
of respective factor inputs, i = 1, 2….7 and
The Cobb-Douglas production
function was converted into log linear form and parameters
(coefficients) were estimated by employing Ordinary Least Square
Technique (OLS) as given below.
log Y = log a + b1 log
X1 + b2 log X2 + b3 log
X3 + b4 log X4 + b5 log
X5 + b6 log X6 + b7 log
X7 + u log e _____________ (2)
The regression coefficients
(bi’s) were tested using ‘t’ test at
choosen level significance.
I
Where,
R
R
R2 =
n = Number of observations in the
sample
P = Number of parameters in the
function
The analysis of efficiency should
help to identify the possibilities for increasing income while
conserving resources. The role of efficiency may be viewed as an
important component in policy making to stimulate income and/or
promote resource conservation.
The concept of efficiency was
first defined by Farrel (1957) in terms of its two dimensions,
technical efficiency and allocative efficiency. Technical efficiency
arises when the maximum output is obtained from a given bundle of
inputs and allocative efficiency arises when inputs are used in
proportion, which yield maximum output. Allocative efficiency exists
when resources are allocated within the farm according to market
prices. It is therefore, suggested that within a static framework
measures of technical efficiency retain validity as a measure of goal
achievement in a materialistic world (Russel and Young, 1983). The
idea of frontier production function is built around the concept of
efficiency adduced by Farrel (1957).
Timmer (1971) imposed the
Cobb-Douglas production function on the frontier and computed an
output-based measure of efficiency. The approach adopted here is to
specify a fixed parameter frontier amenable to statistical analysis.
This takes the following general form.
Y = f(x) eu, u≤0
____________________ (5)
a
1n Y = a +
bj
log xj + u, u≤0 ______________ (6)
In estimating the above equation,
the Corrected Ordinary Least Squares (COLS) regression is chosen as
the most convenient means. This method is briefly outlined as under.
As a first step, the foregoing
equation is estimated by the method of OLS yielding the best linear
unbiased estimates of bi’s coefficients. The
intercept ‘a’ is then corrected by shifting the function
until no residual is positive and one case is zero. This is done by
adding the largest error term of the fitted model to the intercept.
Greene (1980) has shown that a consistent, though biased, estimate of
‘a’ which imposes the sign uniformity on the residuals
will be generated by this procedure.
Thus, Timmer measure of technical
efficiency (TEi) of a farm ‘i' is the ratio of actual output to
potential (Frontier) output, given the level of input use on farm
‘i’. It thus indicates how much extra output could be
obtained if farm ‘i' were on the frontier with the given
technology and level of input.
T _____________ (7)
TEi = Y 1 Y*
Where,
Y = Actual output
Y* = The potential output
obtainable for given level of inputs
Kopp (1981) suggested a different
approach within the Farrell framework, which involves the econometric
estimation of a parametric frontier function followed by the
algebraic identification of the efficiency standard for each data
point. The Kopp measure of technical efficiency compares the actual
level of input use to the frontier level of input use given the input
use ratios. For this, the following procedure was used.
Ri = X2X1,
R2 = X3X1,……….
Rn = XnX1
__________ (8)
n n ln X1* = (ln Y – A* ∑ bi ln Ri) ∑ bi i=1 i=1
_____ (9)
‘bis’ are
the respective production function estimates; ‘n’
indicate the number of inputs and other terms are the same as defined
earlier.
The degree of efficiency of X1
resource among the sample farms is assessed by dividing the frontier
usage (X1*) with the actual quantity used. Subsequently
technical efficiency will be:
KTE = (X1*
X1)
___________________ (10)
Similar procedure was used for
calculating the frontier usage of other inputs.
Given the technology, allocative
efficiency exists when resources are allocated within the farm
according to market prices and it implies the proper level of input
use in production. To decide whether a particular input is used
rationally or irrationally, its marginal value products will be
computed. If the marginal value product of an input just covers its
acquisition cost it is said to be used most efficiently.
T
MVP ith resource = bi
__________________ (11)
Where,
Y
Xi = Geometric mean of
ith independent variable
bi = The regression
coefficient of the ith independent variable
In order to determine the
efficiency of allocation of the resources or price efficiency, the
value of the marginal product obtained by multiplying the marginal
product (bi) by the price of the product was compared with
its marginal cost. A ratio of the value of marginal product to the
factor price more than unity implied that the resources were
advantageously employed. If the ratio was less than one, it suggested
that resource was over utilized.
Finally, the Economic Efficiency
(EE) was estimated as the product of technical efficiency and
allocative efficiency.
EE = TE x AE
________________________ (12)
III. RESULTS
Labour utilization pattern in
naturally coloured cotton cultivation
The results presented in Table 1
indicated average human labour utilization in coloured cotton
cultivation. Among the various operations, harvesting/picking
utilized higher proportion of human labour as this operation was
carried out for 2-3 times. The entire crop cannot be harvested at one
stretch and the picking of the opened bolls has to be carried out at
suitable intervals. Spreading of picking operation over several weeks
requires huge amount of human labour. Weeding was the next major
operation, which consumed substantial amount of human labour. This
was mainly because weeding was carried out two-three times. This was
followed by loading, transportation and spreading of FYM (14.30%),
baling / packing (5.28%) and spraying (3.83%).
The results on bullock labour and
tractor labour utilization presented in the Table 1 which indicated
that, among the various operations harrowing operation utilized more
bullock labour (4.63 bullock pair days), as many of the farmers
carried out harrowing operation through bullock labour for 2-3 times.
The bullock labour utilization for ploughing (2.28 pair days) and
transportation of FYM (1.37 pair days) was less, as some of the
sample farmers employed tractor for these operations.
Input use pattern in naturally
coloured cotton cultivation
Inputs used per hectare in
naturally coloured cotton cultivation in the study area (Table 2)
revealed that the farmers used 67.92 man days of human lobour because
most of the operations such as harvesting/picking, weeding were human
labour intensive. Most of the farmers used bullock labour (14.70 pair
days) as against use of tractor labour (1.43 hours) because use of
bullock labour worked out to be cheaper than tractor labour use, but
some large farmers used tractor for ploughing and other operations.
Farmers in the study area used large quantity of farmyard manures
(7.02 tonnes), as there was no application of chemical fertilizers in
anticipation of good yield. Bio-pesticides were also used to minimize
/ control the pests/ diseases as there was no spraying of chemicals.
But it involved minimum cost as the variety (DDCC-1) used was
diseases/pest resistant variety. This variety was exclusively
cultivated organically under contract farming in the study area.
Cost of production of
naturally coloured cotton
Results on per hectare and per
quintal cost of production of coloured cotton in the study area
(Table 3) revealed that the total cost incurred by the farmers in the
cultivation of coloured cotton was Rs.15,934.30 per hectare and
Rs.1,868.02 per quintal. About 82.08 per cent of this cost was formed
by the variable cost and remaining 17.92 per cent by fixed cost. The
cost of human labour, farmyard manures, nimbucidine and bullock
labour were the items of cost with major share in the variable costs,
because most of the operations like harvesting/picking, spraying and
weeding are human labour intensive operations and the other
operations like harrowing and inter-cultivation were bullock labour
intensive.
As farmyard manure is an
important input in the cultivation of coloured cotton, all the
coloured cotton growers applied farmyard manure to the crop. The
supply of farmyard manure being limited and the demand for it from
the coloured cotton growers in the region being high the naturally
coloured cotton farmers had to purchase farmyard manure at higher
prices. Because, farmers had not applied any chemical fertilizers in
the study area. The farmyard manures accounted for 14.32 per cent of
the total cost of cultivation because the farmers in the study area
wanted to maintain the quality of coloured cotton to get the higher
returns. The cost on bio-pesticides accounted for 19.25 per cent of
the total cost of cultivation because the farmers in the study area
had to control pest and diseases. The interest on fixed and working
capital together account for about 7.99 per cent because of
prevailing nominal rates of interest.
All the farmers cultivated
naturally coloured cotton on their own land and hence imputed rental
value of owned land was the major item of the fixed cost.
The cost of marketing of coloured
cotton was Rs.440.64 per hectare and Rs.51.66 per quintal of coloured
cotton of which the cost on packing material and packing charges
accounted for 73.36 per cent were the major items of cost of
marketing because the packing material (bales) for coloured cotton
costs more. The transportation cost was low (17.98 %) because it was
locally transacted. The study conducted by Ramasundaram et al.
(2005) on the economics of rainfed hybrid cotton production in
Central India revealed similar results with respect to total cost of
cultivation per hectare (Rs.15,640) and per quintal (Rs.2148).
Cost and Returns Profile of
coloured cotton production
Table 4 reveals that the total
cost of cultivation (Cost C) was Rs.16,374.94 per hectare while the
gross returns realized was Rs.19,772.50 per hectare indicating a net
income (profit at Cost-C) of Rs.3,838.20 per hectare and per hectare
net income (profit at Cost-D) of Rs.3,397.56. The marketing cost of
coloured cotton was Rs.440.64. The Per hectare yield of naturally
coloured cotton was 8.53 quintals. The cost of production per quintal
was Rs.1919.68 and the benefit-cost ratio obtained was 1.21.
The study carried out by
Mahantesh (2002) in the economics of cotton production in the Belgaum
District revealed similar results with respect to net income
(Rs.3088.98/ha) and the benefit-cost ratio (1.10).
Resource use Efficiency in
Naturally Coloured Cotton Production
The Cobb-Douglas production
function was employed to analyze the relationship between the
resources used and productivity of coloured cotton using the field
level data of sample farmers The total gross returns realized
as the dependent variable and the amount of seeds, FYM, human labour,
bullock and tractor charges, bio-pesticides and trichocards as
independent variables for naturally coloured cotton production. The
results presented in the Table 5 revealed that the variables included
in the function satisfactorily explained the variation in the
dependent variable (92.00 %). The regression equation was estimated
in order to capture the nature and magnitude of the effects of the
independent variables on the productivity of naturally coloured
cotton production.
The output elasticity of human
labour was positive and significant, which implies the increased
usage of labour and thus the gross income. Since the coloured cotton
crop was labour intensive and the operations such as manures
application, hand weeding, spraying of bio-pesticides, which
significantly contributes towards increased yield and thus the
income.
The other inputs such as land and
farmyard manure were significant and had positive impact on gross
income. This reveals that as more land brought under coloured cotton
cultivation in the study area only, which seeks application of more
manures, seeds and bio-pesticides as a result increasing the income.
The sum of production
elasticities (bi
= 1.30) revealed increasing returns to scale in
naturally coloured cotton production. A one per cent increase in all
the inputs used in the production simultaneously would result in an
average increase of gross returns by 1.30 per cent.
Allocative
efficiency in naturally coloured cotton production.
The ratio of Marginal Value
Product (MVP) to Marginal Factor Cost (MFC), presented in the Table 6
reveals that allocative efficiency was positive and greater than
unity in the case of land, seeds, manures, human labour,
bio-pesticides and trichocard indicating that still there is scope to
use these inputs and increase the gross returns of coloured cotton
production.
The Marginal Value Product (MVP)
to Marginal Factor Cost (MFC) ratio for land was 1.52, which
reflected the scope for increasing area under coloured cotton to
increase the gross income in the study area.
The MVP to MFC ratio for seeds
(3.09), manures (1.16), human labour (3.16), bio-pesticides (1.97)
and trichocard (10.25) were more than one indicating that still there
is scope for higher utilization of these inputs and which in turn
would increase the gross income. This would help to maximize their
profit in naturally coloured cotton production.
The MVP to MFC ratio for bullock
labour was less than unity (0.09) indicated excessive use of this
input for the sole reason of increasing the yield. The results
obtained in respect of land and labours are in conformity with the
results of Mahantesh (2002).
Technical Efficiency in
Naturally Coloured Cotton Production
a. Timmers measure of
technical efficiency in naturally coloured cotton production
The technical efficiency in
naturally coloured cotton production was measured as a ratio of
actual output to maximum attainable physical output by each farmer
based on timmer measure of technical efficiency (Table 7). The
average technical efficiency was 0.75654. The proportion of farmers
in different technical efficiency ratings, about six per cent of
farmers were found to operate in technical efficiency rating of below
0.75. Less than three per cent of farmers were found to be operating
under high level of technical efficiency rating of above 0.90.
About 15 per cent of farmers were
found to operate in technical efficiency ratings of 81-90 per cent.
Thus, the study revealed that a large majority (more than 50%) of the
farmers in the study area were found to have achieved only 75.65 per
cent of average technical efficiency and hence there is vast scope
for improving naturally coloured cotton productivity by reducing
technical inefficiency without using additional resources.
b. Kopp measure of technical
efficiency
The results presented in Table 8
indicated the extent of excess use of resources in view of the
existence of technical inefficiency in naturally coloured cotton
production. The quantities of different inputs required for the
farmers to produce the existing level of output at the highest level
of technical efficiency were called as frontier level of inputs. The
coloured cotton-growing farmers by enhancing technical efficiency
could save 23.75 per cent of land, 14.40 per cent of seeds, 5.31 per
cent of manures, 12.74 per cent of human labour, 16.67 per cent of
bullock labour, 12.67 per cent of bio-pesticides and 4.60 per cent of
trichocard.
The farmers could produce 11.27
quintals of coloured cotton against 8.53 quintals by using existing
quantities of different inputs if they operate at the highest
technical efficiency level. Thus the analysis of technical efficiency
in naturally coloured cotton production in the study area revealed
that by improving the technical efficiency of the farmers, about
15-21 per cent of cost on different inputs could be saved. Thereby,
there will be a substantial reduction in the cost of cultivation and
increase in the returns from naturally coloured cotton to the farmers
Technical, allocative and
economic efficiency for naturally coloured cotton farmers are
presented in Table 9. The average technical efficiency was 0.756.
This implied that there existed 24 per cent potential for increasing
income of farmers by using existing quantities of resources.
Allocative efficiency was 0.585. The allocative inefficiency was also
more pronounced. Thus, the study indicated that allocative
inefficiency in naturally coloured cotton production was more than
the technical inefficiency. This implied that returns from naturally
coloured cotton production in the study area could be maximized by
reorganization of resources and by enhancing the technical
efficiency. In view of the high allocative inefficiency, the economic
inefficiency was also more in naturally coloured cotton production in
the study area. By improving the efficiency (both technical and
allocative) in naturally coloured cotton production, the profits
could be maximized on farmers in the study area. Hence, more
concerted efforts are needed to improve efficiency (both allocative
and technical) in naturally coloured cotton production.
IV. REFERENCES
ACHARYA, S. S. AND AGARWAL, N. L., 1989, Economics of marketing and processing of rape seed in Haryana. Agricultural Situation in India, 44 (7): 42-47. ASWATHAREDDY, K. P., CHANDRASHEKAR, K. S. AND SRINIVASAGOWDA, M. V., 1997, Resource efficiency in groundnut production under rainfed conditions – A study in Challakere taluk of Karnataka. Agricultural Situation in India, 53(12) : 829-831. BALLAPPA SHIVARAYA, 1997, Production, marketing and processing of redgram in Gulbarga district – An economic analysis. Unpublished M.Sc. Thesis, University of Agricultural Sciences, Dharwad. BASAVARAJ KULKARNI AND KUNNAL, L.B., 2002, Constraints in production, marketing and processing of soybean. Rural India, 65 (4): 68-71. BAWA, A. S. AND KAINTH, G. S., 1989, Cost and return analysis of rice milling industry, Journal of Agricultural. Economics, 44(3) : 326-327. DAMTE, S., SINGH, B.AND SINGH, J., 2003, Changes in Costs and Returns of Major crops in Punjab. Agricultural Situation in India.60 (1-6):11-17. FARRELL, M. J., 1957, The measurement of production efficiency. Journal of Royal Statistical Society, AIZO (2): 253-281 GADDI, G.M., MUNDINAMANI, S.M. AND PATIL, S.A., 2002, Yield Gaps and Constraints in Cotton Production in Karnataka, Indian Journal of Agricultural Economics, 57(3):722-734. GADRE, N.A. AND MAHALE, Y.P., 1988, Comparative economics of production of cotton vis-à-vis its substitute crops in Vidarbha region. Indian Journal of Agricultural Economics, 34: 57-60. HAZARIKA. C. AND SUBRAMANIAN. S. R., 1999, Estimation of technical efficiency in the stochastic frontier production function model- an economic application to the industry in Assam. Indian Journal of Agricultural Economics. 54: 201-211. KIRESUR, V. R., PATIL, S. A. AND VIJAYAKUMAR, H. S., 2001, Contract farming – An opportunity for small and marginal farmers in the context of trade liberalization. Paper presented at Annual Conference on Impact of WTO on Indian Agriculture, 21-22 November 2001, organized by IARI and NCAEPR, New Delhi. KOPP, R, J., 1981, The measurement of production efficiency. Australian Journal of Economics, 97(3): 457-503. PORTER GINA AND KEVIN PHILLIPS-HOWARD, 1997, Comparing contracts: An evaluation of contract farming schemes in Africa, World Development, 25 (2): 227-238. VERMA, A. R., 2002, Economics of production, resource use efficiency and constraints: A case study of onion in Shajapur district of Madhya Pradesh. The Bihar Journal of Agricultural Marketing, 10(4): 429-439. VERMA, A.R., 2005, Economic Analysis of Production, Resource-use Efficiency, Marketing and Constraints of Garlic in Indore District of Madhya Pradesh. Agricultural Marketing, XLVII (2):37-48. VIKAS SINGHAL, 2003, Cotton. Indian Agriculture. Vikas Publishing House, New Delhi. pp. 310-330.
Table-1: Labour utilization
pattern in naturally coloured cotton cultivation
(Hectare)
SN Operations Unit Numbers Percentage I. 1. Ploughing a. Bullock labour Pair days 2.28 15.51 b. Tractor labour Hours 1.29 90.21 2. Harrowing a. Bullock labour Pair days 4.63 31.50 3. Transportation of FYM a. Bullock labour Pair days 1.37 9.32 b. Tractor labour Hours 0.14 9.80 4. Sowing a. Bullock labour Pair days 2.68 18.23 5. Inter cultivation a. Bullock labour Pair days 3.74 25.44 Total Bullock labour Pair days 14.70 100.00 Total Tractor labour Hour 1.43 100.00 II. 1. Loading, transportation and spreading of FYM Mandays 9.71 14.30 2. Sowing Mandays 7.42 10.92 3. Weeding Mandays 12.10 17.81 4. Spraying Mandays 2.60 3.83 5. Harvesting/picking Mandays 32.50 47.85 6. Baling/packing Mandays 3.59 5.28 Total Human labour Mandays 67.92 100.00
Table-2: Input use
pattern and output obtained in naturally coloured cotton cultivation (Hectare)
Units Quantity 1. Seeds Kgs 6.25 2. Human labour Mandays 67.92 3. Bullock labour Pair days 14.70 4. Tractor labour Hours 1.43 5. Farm yard manure Tonnes 7.02 6. Biopesticides i. Numbucidine Ltrs. 13.91 ii. NPV LE 400.00 7. Trichocard Nos. 10.00 8. Average Yield i. Main Product (Kapas) Qtls. 8.53 ii. By-product (stalk)) Qtls. 29.00
Table-3: Costs in Production and
Marketing of Naturally Coloured Cotton
SN Particulars Rs./Hectare Rs./Quintal Percentage I. Variable cost 1 Human labour 3396.00 398.12 21.31 2 Bullock labour 2205.00 258.50 13.84 3 Tractor power 429.00 50.29 2.69 4 Seeds 625.00 73.27 3.92 5 Farm yard manure 2281.50 267.47 14.32 6 Biopesticides a a. Nimbucidine 2267.33 265.81 14.23 b b. NPV 800.00 93.79 5.02 7 Trichocard 50.00 5.86 0.31 8 Interest on working capital 1024.58 120.11 6.43 Sub total (I) 13078.41 1533.22 82.08 II. Fixed cost 1 Rental value of land 2500.00 293.08 15.69 2 Land revenue 25.00 2.93 0.16 3 Depreciation 83.12 9.74 0.52 4 Interest on fixed capital 247.77 29.05 1.56 Sub total (II) 2855.89 334.80 17.92 Total cost of cultivation (I + II) 15934.30 1868.02 100.00 III. Marketing cost 1 Packing material and packing charges 323.22 37.90 73.36 2 Loading and unloading charges 38.17 4.47 8.66 3 Transport cost 79.25 9.29 17.98 Sub total (III) 440.64 51.66 100.00
Table-4: Cost and Returns Profile
of Naturally Coloured Cotton Production
Particulars Rs./ Hectare 1 Cost-A 10006.53 2 Cost-B 12754.30 3 Cost-C 15934.30 4 Cost-D 16374.94 5 Cost of production (Rs./qtl) 1868.02 6 Gross returns including by-products (Rs./ha) 19772.50 7 Yield (Qtls/ha) 8.53 8 Farm business income (Profit at cost-A) 9765.97 9 Farm labour income (Profit at cost-B) 7018.20 10 Net income (Profit at cost-C) 3838.20 11 Net income (Profit at cost-D) 3397.56 12 B:C ratio 1.21
Table-5: Estimated
Cobb-Douglas Production Function Coefficients
SN Explanatory variables Unit Parameters 1. Intercept a -0.1519 2. Land Hectare b1 0.4942** (0.1265) 3. Seeds Kgs b2 0.1182 (0.1027) 4. Farmyard manure Tonnes b3 0.1118* (0.0490) 5. Human labour Mandays b4 0.4612** (0.0811) 6. Bullock labour Pair days b5 0.0078 (0.0476) 7. Biopesticides Rs b6 0.0722 (0.0690) 8. Trichocards Nos. b7 0.0318 (0.0651) 9. Coefficient of multiple determination (R2) 0.9210 10. Returns to scale (Sbi) 1.2971 Note:
Figures in the parentheses indicate their respective standard errors
** - Significant at one per
cent probability level
* - Significant at five per
cent probability level
Table-6: MVP to MFC ratios of resources in Naturally
Coloured Cotton Production Explanatory variable Parameters MVP: MFC Ratios 1 Land in hectare b1 1.517 2 Seeds in kgs b2 3.091 3 Farmyard manure in tones b3 1.164 4 Human labour in mandays b4 3.159 5 Bullock labour in pair days b5 0.089 6 Biopesticides in Rupees b6 1.970 7 Trichocards in numbers b7 10.248 Note:
MVP – Marginal value product, MFC – Marginal factor cost
Table-7: Distribution of
Coloured Cotton Producers according to Technical Efficiency ratings Relative efficiency (%) Number Percentage 1. < 70 5 6.25 2. 70-80 61 76.25 3. 81-90 12 15.00 4. > 90 2 2.50 80 100.00 Average Technical Efficiency 0.75654
Table-8: Actual and frontier
input use level in naturally coloured cotton production
SN Items Units Actual Savings Savings (%) 1. Land Ha. 0.80 0.61 0.19 23.75 2. Seeds Kg. 6.25 5.35 0.90 14.40 3. Farmyard manure Tonnes 7.02 6.647 0.373 5.31 4. Human labour Mandays 67.92 59.27 8.65 12.74 5. Bullock labour Pairdays 14.70 12.25 2.50 16.67 6. Biopesticides Rs. 3067.33 2678.67 388.66 12.67 7. Trichocards Numbers 10.00 9.54 0.46 4.65 8. Yield Quintals 8.53 11.27
Table-9: Efficiency of sample
farmers
SN Particulars Rating 1. Technical efficiency 0.756 2. Allocative efficiency 0.585 3. Economic efficiency 0.443
t = ______________________ (3)
n
order to know the goodness of fit, the adjusted co-efficient of
multiple determination R2 was calculated by using the
formula. 
R2 = 1 – (1 -
R2) ______________________(4)2
= The adjusted coefficient of multiple determination (adjusted for
the size of the sample)
2
= The coefficient of multiple determination which is given by 
nd
the Cobb-Douglas production function in natural logarithmic form
would be:
Where, A* is sum of the intercept
and the maximum positive error term (Y-Ŷ); 
he
Marginal Value Products (MVP) were calculated at the geometric mean
levels of variables by using the formula. 
= Geometric mean of the output
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