A study was undertaken in Raichur District of...
Transcript of A study was undertaken in Raichur District of...
IV. RESULTS AND DISCUSSION
A study was undertaken in Raichur District of Karnataka to assess
the grain losses by using of rice combine harvester and compared with
conventional system. Farmers’ opinions were obtained over the usage of
combined harvester. Field performance of the combine harvester was
evaluated in farmers’ fields and grain losses for Rabi and Kharif seasons
in the consecutive years 2001-02 and 2002-2003 were assessed. Based on
the field data, a mathematical model for rice combine harvester was
developed to predict post harvest grain losses and verified the same in the
farmers’ fields in the Kharif season, 2003. In this chapter, the results
obtained from the study are presented and discussed.
4.1 Farmers’ opinion on Rice Combine Harvester
The rice combine harvesters are popular, among irrigated farmers
in the study area. Farmers having irrigation facilities generally grow rice
twice in a year with ruling variety pertain to the seasons and the same
method of planting. These fields are found comparatively soft and
levelled, and are preferred by the operators. Most of the farmers using
combine harvester plant high yielding varieties of rice, which suits the
machine requirements.
To assess the opinion of farmers, about the rice combine harvester,
a survey of 60 farmers in two selected taluks viz., Sindhanur and Manvi in
Raichur district was conducted through a questionnaire and the findings
are summarized in table 4.1.
From the table 4.1 it is seen that, out of 60 farmers 52 farmers
(87 %) were using rice combine harvester, whereas the remaining resort to
the conventional method of harvesting. Reasons provided for using rice
combine harvester and conventional harvesting were studied.
The major reasons for preferring to use rice combine harvester in
their fields by the farmers are due to timely harvest, more area coverage
with minimum grain loss, minimum operational cost and possibility of
early preparation of land for next season. Scarcities of labour, high wages
and early returns are other supporting factors which force the fanners to
adopt rice combine harvester in their fields.
The reasons for not using combine harvester are due to lack of
confidence in the machine coupled with other factors like: small land
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Table 4.1 Farmers’ opinion on rice combine harvester
SI.No Parameters
Farmers using rice combine
harvester (%)
Farmers using
conventions! method (%)
Reasons for using machine1 Shortage of labour 872 High wage of labour 783 High machine capacity 894 Lower grain losses 735 Easy and convenient 856 Cost of operation less 907 Saves time 938 Timely harvesting 949 Land preparation next season 9510 Immediate returns 8511 Searching of labour avoided 91
Reasons for conventional harvesting
1 Small land holding 572 Water logging condition 353 Lack of confidence 234 Non-availability of the machine
in time45
5 Difficult in collection of straw 38General inference
1 Mechanization improve nation’s productivity
48
2 Machine affects daily wage labourers
18
3 Need of multi crop combine harvester
63
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holdings, water logged conditions, difficult in collection of straw and
non-availability of machine in time.
Further few general opinions observed from the statements of the
farmers are: to improve nation’s productivity by using mechanization
(48 %), to develop all crop combine harvester (63 %) and machine affects
labourers who depend on daily wages (18 %).
Hence it has been observed that, majority of the farmers in both
taluks are willing to adopt rice combine harvester in their fields.
4.2 Estimation of grain losses in conventional harvest system
Studies were conducted to assess the amount of grain losses, time
taken and field capacity in the first and second crop seasons in ten
farmers’ fields each in Sindhanur and Manvi taluks by the conventional
harvesting and post harvesting of the rice crop. The values are given in
tables 4.2,4.3, 4.4 and 4.5.
Table 4.2 gives the details regarding the experiments conducted in
ten different Helds of Sindhanur taluk in the first season. The total grain
loss ranged from 10.24 to 12.36 percent with an average of 11.02 percent.
The field capacity varied from 0.0096 to 0.0134 hectare per hour with an
average of 0.0081 hectare per hour. The. moisture content of the grain was
found in the range of 17.48 to 19.36 percent. The average value of the
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estimated yield and purity of grain were found 2.75 tonnes per hectare and
92.41 percent, respectively, in Manvi taluk the data collected in the fields
were recorded and presented in the table 4.3. The total grain loss ranged
from 9.21 to 13.35 percent with an average of 11.26 percent. The field
capacity varied from 0.0099 to 0.0102 hectare per hour with an average of
0.0104 hectare per hour. The moisture content of the grain was found in
the range of 17.48 to 19.25 percent. The average value of the estimated
yield and purity of grain were found to be 2.95 tonnes per hectare and
92.11 percent, respectively.
From table 4.4 it could be seen that in the second season of
Sindhanur taluk, the total grain losses ranged from 9.48 to 12.01 percent
with an average of 10.82 percent. The field capacity varied from 0.0098 to
0.0100 hectare per hour with an average of 0.0098 hectare per hour. The
moisture content of the grain was found in the range of 17.98 to
19.36 percent. The average value of the estimated yield and purity of
grain were found 2.88 tonnes per hectare and 92.14 percent, respectively.
It could be observed from table 4.5 that Manvi taluk, the total grain losses
ranged from 9.28 to 12.42 percent with an average of 11.02 percent. The
field capacity varied from 0.0098 to 0.0100 hectare per hour with an
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average of 0.0094 hectare per hour. The moisture content of the grain was
found in the range of 17.30 to 18.47 percent. The average value of the
estimated yield and purity of grain were found 2.90 tonnes per hectare and
91.99 percent, respectively.
While comparing all the four tables, which represented two taluks
for two crop seasons in two successive years, it reveals that variation in
field capacity between the taluks could be due to the favourable climatic
conditions at: the time of harvest coupled with engaging of experienced
labourers for attending different field operations especially in Manvi
taluks.
In all the four tables, it has been observed that among the grain
losses, percentage of losses are found high in threshing operations
followed by losses due to cutting, bundling, conveying, winnowing and
drying irrespective of seasons, variety and locations. It could be due to the
adoption of tractor treading for threshing to separate grains from earheads
which caused more losses in the form of unthreshed, broken grain and
rubbish. Using country sickles with unserrated teeth, taking the harvested
produce by head load, winnowing in the elevated position against natural
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wind flow are some of the lengthy and labour intensive conventional
operations attributed grain losses.
Further from the results it has been noticed that threshing
operations required more time when compare to other operations,
irrespective of season, variety and location. It could be due to the adoption
of conventional methods, which again depend upon the weather,
availability of skill labour and space availability for carrying out the
operations in time.
4.2 J Cost of operation in conventional harvest system
The cost of operations involved in conventional harvesting and
other post harvesting operations were calculated by taking the number of
labourers involved in each operations and their wages. The actual values
which were observed in the study area and the average of it is presented in
table 4.6. The total cost for conventional operations was found
Rs. 880.00 per hectare. The cost of operation mainly depends on the crop
condition and the availability of labour. The cost for bundling and
transporting harvested material to threshing floor was
Rs. 640.00 per hectare. The cost of tractor treading for threshing and
labour involved for winnowing was Rs. 400.00 and
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Table 4.6 Cost of operation in conventional harvesting system
SLNo
Parameters Workinghours
RateRs/hr
AmountRs/ha
Percent of utilization
1 Manualharvesting
110 man- h/ha
8.0 880.00 40.44
2 Bundling and conveying
80 man-h/ha
8.0 640.00 29.41
3 Threshing Tractortreading
Approximately400.00
18.38
4 Winnowing 32 man- h/ha
8.0 256.00 11.77
5 Cost of operation (1 -1-2+3+4) 2176.00 1006 Considering
Grain loss - 11.03 percent Estimated yield - 2.87 tonnes per hectareCost of grain - Rs. 3.00 per kilogram
950.00
Total cost 3126.00
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Rs. 256.00 per hectare, respectively. From the field data it was estimated
that, an average estimated yield of 2.87 tonnes per hectare with an average
losses of 11.03 percent. The cost of grain losses was calculated by
considering the regional market price of grain Rs. 3.00 per kilogram as
Rs.950.00 per hectare. Thus the total cost of operations, which includes
harvesting, bundling and transporting, threshing and winnowing and grain
losses come to Rs. 3126.00 per hectare.
4.3 Estimation of grain losses in Rice Combine Harvester
The rice harvesting operations which carried out by combine
harvester were monitored in the two taluks and data were collected for
two seasons separately and the performance of rice combine harvester is
presented in tables 4.7, 4.8,4.9 and 4.10.
The machine was tested in 25 fields for first season in Sindhanur
taluk and the field parameters observed are given in table 4.7. The
effective field capacity of the machine varied from 0.48 to
0.58 hectare per hour with an average of 0.53 hectare per hour. In terms of
crop handled, the crop capacity varied from 1.29 to 2.02 tonnes of grain
per hour with an average of 1,66 tonnes of grain per hour. The average
field efficiency was found 94.62 percent. The total grain losses varied
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from 2.40 to 3.41 percent with an average of 2.91 percent. The pre harvest
losses varied from 0.13 to 0.20 percent with an average of 0.16 percent.
The average cutter bar and threshing loss was found 0.68 and
1.58 percent, respectively. The separation losses (straw walker and sieve
losses) varied from 0.36 to 0.64 percent with an average of 0.49 percent.
The moisture content of grain was found in the range of 16.28 to
19.32 percent at the time of harvest. The average value of purity of the
grain was recorded at 92.35 percent.
The machine was used in Manvi taluk for the first season in 25
different fields and the observed data are given in the table 4.8. The
effective field capacity of the machine varied from 0.49 to
0.68 hectares per hour with an average of 0.60 hectare per hour. In terms
of crop handled, the crop field capacity varied from 1.45 to
2.12 tonnes of grain per hour with an average of 1.74 tonnes of grain per
hour. The average field efficiency was found to be 95.79 percent in the
tested fields. The total grain losses varied from 2.49 to 3.43 percent with
an average of 2.94 percent. The average grain loss was found less
(0.03 %) as compared to the results found in tested fields in Sindhanur
taluk, which might be due to efficiency of operators skill in controlling
the travel speed of the machine corresponding to the crop conditions. The
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pre harvest losses varied from 0.13 to 0.21 percent with an average of
0.16 percent. The threshing loss was found maximum when compared to
cutter bar, separation loss and pre harvest losses. The separation losses
(straw walker and sieve loss) varied from 0.36 to 0.67 percent with an
average ol 0.43 percent. The occurrence of separation losses depends
upon the louvers adjustment at the upper concave in the threshing unit and
on the fan speed. From the field observations it has been found that at
higher inclination angle of louvers and higher fan speed could cause more
separation losses. However at lower inclination angle of louvers, earheads
could be easily blocked in the threshing drum, will minimize the
discharge of grain to the main outlet. This might take most of the time of
the operators to adjust the angle of inclination for preventing blocks of
earheads in threshing drum, which in turn depend upon the crop
condition. The average moisture content and purity of grain was found to
be 17.80 percent and 91.76 percent, respectively.
The effective field capacity of the machine was found high with
minimum total grain losses in the tested fields in Manvi taluk when
compared to Sindhanur taluk. This could happen due to the favourable
climatic conditions at the time of harvest and the availability of
efficiency of the operator in Manvi taluk. Similarly the crop handled was
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found high in the fields of Manvi taluk due to good crop stand. The
purity of grain was almost found similar in both taluks because of the
well setting of the machine.
In both the taluks, among the total grain loss, losses due to
threshing was found high followed by cutter bar loss and separation loss
irrespective of season, variety, age of the machine and operators’ skill.
However, in the second season for Sindhanur taluk for 25 fields
(Table 4.9) it was observed that the effective field capacity varied from
0.48 to 0.74 hectare per hour with an average of 0.59 hectare per hour
and crop capacity varied from 0.95 to 2.35 tonnes of grain per hour with
an average of 1.55 tonnes of grain per hour and the average field
efficiency was 96.05 percent. The average field efficiency was higher
because of optimum grain moisture content found during harvesting. The
total grain losses varied from 2.31 to 3.23 percent with an average of
2.79 percent. The pre harvest losses varied from 0.12 to 0.19 percent with
an average of 0.15 percent. The average moisture content and purity of
grain was found 17.01 percent and 91.76 percent, respectively. The
threshing loss was found maximum followed by cutter bar, separation and
pre harvest losses.
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The results of the second season harvesting in tested fields in
Manvi taluk are presented in table 4.10. The effective field capacity of the
machine varied from 0.44 to 0.74 hectare per hour with an average of
0.56 hectare per hour. In terms of crop handled, the machine field
capacity varied from 0.74 to 2.35 tonnes of grain per hour with an average
of 1.59 tonnes of grain per hour. The average field efficiency was
96.16 percent. The total grain losses varied from 2.36 to 3.21 percent with
an average of 2.84 percent. The pre harvest losses varied from 0.12 to
0.18 percent with an average of 0.14 percent. The average moisture
content and purity of grain was found 16.64 percent and 93.10 percent,
respectively.
The variations in the field efficiency might be due to the differences
in operator’s skill. It was observed that the operator in the second season
was more experienced than the operator engaged during the first season.
Besides operators’ skill, the variations in the field capacities might have
occurred due to the machine age, field size, ground and crop conditions,
which affected its forward speed of the machine. The field capacity and
efficiency were also affected by the time spent for turning, repair and
adjustment. The average field capacity and efficiency of second season
was higher than the first season. It might be due to the climatic conditions
favourable at the time of harvesting i.e., moisture content in straw and
grain.
The total crop harvest losses depend on the time of harvest, crop
variety, height of the crop, crop inclination, grain moisture content, travel
speed of machine, reel speed, age of machine and machine adjustments
including blower speed and louvers. The harvest loss seemed to increase
with travel speed of the machine, reel speed and crop condition at the time
of harvesting.
Therefore, mechanization of this operation would not only reduce
drudgery of the human labour but would also relieve the farmer from the
labour shortage problems.
The quality of grain in terms of percent purity, as observed from the
collected samples from the output of the two taluks of rice combine
harvesters, varied from 88.48 to 94.28 percent with an average of
92.24 percent. In the case of peak harvesting time and hasty harvesting it
was observed that the operator did not take care to adjust the air flow rate,
thus resulting in low purity percentage. It appears that the purity can be
improved by adjusting the quantity of air with the help of flap of the
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blower unit. However, while adjusting air quantity it should be ensured
that the flow does not carry excessive grain along with the straw.
4.3.1 Cost of operation in rice combine harvester
The cost of operations involved in a rice combine harvester was
assessed in two taluks for two seasons and the average values are
presented in table 4.11. The average field capacity of the machine was
taken 0.57 hectare per hour. The total cost of harvesting was calculated as
Rs. 877.07 per hectare by taking into account of fixed cost and variable
cost of the machine. However, for arriving at the actual cost of harvesting
two more components to be added. One is the clearing cost of headlands
for accommodating the machine in the field and another is the cost of
grain loss. From the field observations, the actual labours involved in
clearing the headlands were taken for calculation. The estimated grain
yield and grain losses were taken as 2.87 tonnes per hectare and
2.87 percent, respectively. These values arrived by taking the averages of
respective field data observed in two taluks for two seasons. Regional
market price of grain as Rs. 3.00 per kilogram was taken for calculation of
cost of grain loss, which was worked out Rs. 247.11 per hectare. The total
cost of operation by using combine harvester was Rs. 1274.00 per hectare.
Table 4.11 Cost of operation in rice combine harvester
Particulars Unit Amo ii nit1. Machine cost Rs 15,00,000.002. Yearly use h 1,000.003. Useful life Years 10.004. Salvage value (10 % Machine cost) Rs 1,50,000.005. Fixed costs per houra. Depreciation Rs 135.00b. Interest (@11.5 / year) Rs 94.87Total fixed cost (Rs/h) Rs 229.876. Variable cost per houra. Fuel cost ((a), 20.20,6 lit/h) Rs./h 121.20b. Lubricant cost (30 % of fuel cost) Rs./h 36.36c. Labour costi) Two operator (@ Rs. 150 per operator) Rs./h 37.50d. Repair and maintenance (5 % of Machine cost per year)
Rs./ h 75.00
Total variable cost Rs./h 270.067. Total machine cost (5+6) Rs./h 499.938. Average field capacity of the machine ha/h 0.579. Variable cost of operation (6/8) Rs./ ha 473.7810. Operation cost of the machine (7/8) Rs./ ha 877.0711. Average cost for clearing the headlands
Labour required -16 man-hour Labour rate - Rs. 75.0 per day of 8 hour
Rs./ ha 150.00
12. Considering Grain loss - 2.87 percent Estimated yield - 2.87 tonnes per hectare Cost of grain - Rs. 3.00 per kilogram
Rs./ ha 247.11
12. Total cost of harvesting with machine operation was (10+11+12)
Rs./ha 1274.18
Rounded off 1274.00Transporting charges Rs./ha 100.00Total cost 1374.00
In addition to this, it was observed that normally the contractor claim
Rs. 100.00 per hectare for the transportation charges. Thus for the farmer,
the operational cost worked out as Rs. 1374.00 per hectare.
4.3.2 Comparison of rice combine harvester with conventional
harvesting system
The figure 9 shows the comparison between rice combine harvester
and conventional harvesting system. The parameters considered were time
for completing the operation (min), total grain losses (%), purity of
grain (%) and cost of operation (Rs/ha). The average value of each
parameter was taken from the field data which observed in two taluks for
two crop seasons, as there is no significant variations when compared the
said parameters in location and season wise.
The total time required to complete the operation in conventional
harvesting was 1545.45 min, whereas in the combine harvester required
26.32 min. The total grain losses were found more in conventional
method of harvesting (11.03 %) as compared to combine harvester
(2.87 %). The purity of the grain was found almost equal i.e.,
92.17 percent in conventional method and 92.24 percent in combine
harvester. The cost of operation in conventional method was 2.28 times
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more than that of combine harvester. Hence, rice combine harvester is
found efficient and economic when compared to the conventional harvest
system in the study area irrespective of season and location. There are
possibilities to reduce post harvest losses further in engaging rice combine
harvester, by reducing the farmers to keep the crop and field conditions
suit to the machine requirement.
4.3.3 Break even, pay back period analysis and comparison with
conventional harvesting
The break even analysis was done considering the actual cost of
operation of the machine and the prevailing cost of operation in
conventional harvesting system. Accordingly, referring to table 4.12, the
break even area was calculated and it was found 92 hectares.
The relationship between the total operation cost per hectare and
annual harvested area by using rice combine harvester were compared
with conventional harvesting system. Results of the cost analysis shows
that the economics of using rice combine harvester is highly dependent on
the prevailing rate of conventional harvesting in the area. Rice combine
harvester becomes more economical as the cost of conventional
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harvesting goes up. Table 4.12 shows the break even area of the
harvesting machine for different contract rates of conventional harvesting
from Rs. 3,200 to Rs. 4,500 per hectare. At the highest assumed
conventional harvesting rate of Rs. 4,500 per hectare, the break even area
of using the machine goes down to 59 hectares.
The pay back period of the machine is given in table 4.13. At
present, the custom hiring charge for combines in the study area is
found Rs. 1200.00 per hour (excluding the cost of labour for clearing
headlands). The rate was considered for calculating the pay back period
for different annual usage. Normally the machine could cover one acre in
one hour, so the hiring charges are taken as Rs. 3000.00 per hectare by
considering the time spent for turning, adjustment and crossing the bunds.
Therefore, payback period is found in less than three years, if the machine
can harvest 400 hectares annually. The pay back period would go down to
two years, if the machine can harvest 500 hectares per year. Generally,
more than 800 hectares per year are easily available for harvesting to
contractors, due to variations in dates of harvesting seasons. If the net
availability of the area would be about 1000 hectare then the respective
pay back period would be about 0.93 year. Therefore, it can be concluded
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Table 412 Break Even area of rice combine harvester
SL No. Contract / Harvesting Rate (Rs/ha)
Break even area (ha)
1 3126.00 922 3200.00 893 3500.00 804 3800.00 725 4100.00 666 4500.00 59
Table 413 Pay Back Period of rice combine harvester
Area Annualuse (h)
Revenue year (Rs) *
Expenditure per year
(Rs)
Net- return
per year(Rs)
Paybackperiod(year)
400 1000 1200000 553600 646400 2.32450 1125 1350000 622800 727200 2.06500 1250 1500000 692000 808000 1.86550 1375 1650000 761200 888000 1.69600 1500 1800000 830400 969600 1.54700 1750 2100000 968800 1131200 1.32800 2000 2400000 1107200 1292800 1.16900 2250 2700000 1245600 1454400 1.031000 2500 3000000 1384000 1616000 0.93
* Custom hiring rate Rs. 1200 per hour
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that the machine can pay for itself in less than a year period, if the
contractors manage the harvesting schedules and keeping their machines
well.
4.4 Development of a Mathematical model for estimating the grain
tosses in rice combine harvester
4.4.1 Estimation of Cutter Bar loss
The primary objective of the study is to evolve a suitable
Mathematical Model, which could provide optimum levels of moisture
content, width of cut and speed to reduce the losses. In order to achieve
this, the scatter diagram of each one of the independent variables with the
loss was observed. The scatter diagram expressed a ‘U’ shaped variation,
which implies that there exists an optimum level for each one of the
parameters, which can minimize the loss. Apart from this the ‘ANOVA’
carried out between the taluks and also between the seasons did not show
any significant difference in any of the mean value of any of these
parameters. This helped to collate all the data available for combine
harvester in both the taluks for two seasons. Hence all the 100
observations were taken together. Even in the combined data, the scatter
diagram exhibited same type of variations with respect to each one of the
variable. Hence a ‘Multivariate Quadratic’ type of function was prepared.
The variables selected are cutter bar loss (Y), moisture content of straw
(xi), width of cut (x2) and time taken for 20 m travel i.e., speed (X3).
The Mathematical form of the proposed function was,
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The mixed terms like (xix2), (xix3) and (X2X3) were used to know
whether there are any interactions present among the independent
variables considered. The step-down procedure was preferred in the
estimation of the parameters in the proposed Mathematical Model since it
eliminates those variables in the equation, which do not contribute
anything towards the explanatory power of the dependent variable. The
Statistical Package for Social Science (SPSS) 10.0 package was used in
the estimation. The relationship between independent variables and losses
occurred in cutter bar are represented in figures 10, 11 and 12. The
estimated regression equation in its Mathematical form is:
The details of the coefficients are presented below
Table 4.14 Details regarding the coefficients of the regression equation for cutter bar loss
Sl.N Variables Regressioncoefficients
Standarderror
Level of significance
R2
1 Constant (bo) 67.7534 - -■ 0.95492 Moisture content
of straw (xi)-0.5066 0.0184 0.0000
3 Width of cut (x2) Time taken for 20 m distance (x3)
-46.0350 0.0219 0.00004 -0.4287 0.0153 0.0000
5 Square of xi (xi2) 0.0035 0.0187 0.00006 Square of X2 (x2 ) 11.9991 0.0219 0.00007 Square of x3 (x3“) 0.0095 0.0187 0.0000
The R2, the coefficient of multiple determination is 0.9549 which is
significant at one percent level of probability indicating the fact that
95.49 percent of the variations in the dependent variable is being
explained by the three independent variables included in the regression.
Another noteworthy result is that, in the final form of the function all the
three interaction coefficients are deleted in the equation indicating that the
interactions do not contribute anything towards the grain loss i.e., all the
three variables have only direct effects towards the grain loss.
Since the objective the estimation of parameters for minimizing the
grain loss, the estimated equation was partially differentiated with respect
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Thus when the moisture content of straw (%) is 72.37, width of cut
(m) is 1.91 and speed of the machine (sec) is 22.56, the total grain loss
will be minimum (0.43 %).
In order to know the validity of the solution .obtained the Chi-
square was worked out. The observed loss was taken as the ‘O’ value and
the value obtained by substituting the used set of triplets on moisture
content of straw (x^, width of cut (x2) and speed of the machine (x3) in
the estimated equation was taken as ‘E\ The Chi-square equation is
Chi-square was worked out, when it was found to be 0.0247, which
is not at all significant indicating the justification for using the estimated
parameter values for predictions.
Since wanted to suggest the interval for each one, such that it
allows for variations, simulated values from the model presented below.
In the first stage the variations are allowed on only one variable
below and above the optimal solution. The increase and decrease on both
sides were stopped whenever the loss goes beyond a critical level (it has
fixed 0.65 % loss as the critical level). This process was repeated for each
one of the variables and the resulting interval in each are 68.25 to 76.50
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for moisture content of straw, 1.85 to 2.03 for cutter bar width (m) and
21.50 to 23.50 for time taken (sec). Now in order to test verify the
combined effect simulated 25 such types of values for the trials and their
losses are presented below:
Table 4.15 Random simulation of model with different combinations for cutter bar loss
SLNo.
Moisture content of straw
Width of cut
Speed Cutter bar loss
1 68.25 1.85 21.5 0.562 68.25 1.85 21.0 0.573 68.50 1.92 21.5 0.494 69.00 1.92 21.5 0.485 69.25 1.95 23.0 0.486 70.00 2.03 23.5 0.617 70.25 2.02 22.0 0.578 70.75 1.99 23.5 0.519 71.00 1.89 23.5 0.4610 71.25 1.99 21.5 0.5111 72.00 2.02 23.5 0.5612 72.50 2.03 22.0 0.5813 73.00 1.95 23.5 0.4514 73.25 1.98 23.0 0.4815 73.50 2.02 22.0 0.5616 73.75 1.98 22.5 0.4817 74.00 2.03 23.0 0.5918 74.50 2.03 23.5 0.6119 74.75 1.95 23.0 0.4720 75.00 1.95 22.0 0.4721 75.25 2.03 21.5 0.6222 75.50 1.95 21.5 0.4923 76.00 2.03 22.0 0.6324 76.25 2.02 23.0 0.6125 76.50 2.03 23.5 0.65
The table reveals that for every set of trial within this interval, the
cutter bar loss was found minimum. Thus the result reveals that one can
choose any moisture content level of straw, width of cut and speed within
the above three intervals of the machine for minimizing the loss. These
intervals can be nomenclatured as a ‘Confidence Band’ in the three
dimensional space for minimizing the cutter bar losses
The intervals are:
For Moisture content of straw (%) - 68.25 to 77.49
For Width of cut (m) - 1.89 to 2.04
For speed (sec) - 19.45 to 25.12
4.4.2 Estimation of Threshing loss
Since the machine is a combined harvester, after harvesting, the
harvested material goes for threshing and hence the threshing loss is a
second most important factor to be seen. In order to have a good
combined effect one more important point is that a Confidence Band was
already finalized such that any triplet chosen inside this band will always
minimize the loss in harvesting. Hence threshing loss, also must fall
inside the Confidence Band. With this in view the analysis was carried
out on the same lines as before by taking the loss due to threshing as the
dependent variable (Y), and moisture content of grain (xi), width of cut
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(x2) and speed i.e., time taken to harvest for 20 m travel (x3) as the three
independent variables.
The estimated equation by the same step-down procedure here
deleted the three interacting variables leaving only the linear and
quadratic terms in each case of the variables. The relationship between
independent variables and losses occurred in threshing are represented in
figure 13, 14 and 15. The estimated equation is:
Y = - 197.5331 + 3.2818 X] + 173.87 x2+ 0.4480 x3
- 0.0933 x,2 - 45.6620 x22 - 0.0099 x3
2
The details of the coefficients are presented below
Table 4.16 Details regarding the coefficients of regression equation for threshing loss
Sl.N Variables Regressioncoefficients
Standarderror
Level of significance
R2
1 Constant (bo) -197.5331 - - 0.96092 Moisture content
of grain (x)3.2818 0.0907 0.0000
3 Width of cut (x2) 173.87 0.1380 0.00004 Time taken for 20
m distance (x3)0.4480 0.0679 0.0000
5 Square of X (xt2) - 0.0933 0.0898 0.0000
6 Square of x2 (x22) - 45.6620 0.1387 0.0000
7 Square of x3 (x3“) - 0.0099 0.0675 0.0000
101
The R2, the coefficient of multiple determination is 0.9609 which is
significant at one percent level of probability indicating the fact that
96.09 percent of the variations in the threshing loss is being accounted by
the changes in the three variables. Another important result is that in the
final form of the function all the three interaction coefficients are deleted
in the equation indicating that the interactions do not contribute anything
towards the threshing loss i.e., all the three variables have only direct
effect towards the grain loss viz., moisture content of grain, width of the
cut and speed.
For the estimation of parameters which minimizes the loss, the
estimated equation was partially differentiated with respect to each one of
the variables and equated to the zero. The differentiated equations are as
follows:
Y = - I97.5331 + 3.2818 xi + 173.87x2+0.4480 x3
- 0.0933 xj2 - 45.6620 x22 - 0.0099 x3
2
Differentiating the above equation with respect to x \partially
dy/dxi =3.2818-0.1866
= 0 gives
.’. xi = 17.58
The values obtained for x2 (1.90) and X3 (22.62) lie completely
inside the Confidence Band already obtained. The extra information
gathered from this is that the moisture content of the grain should be
17.58 percent. It gives the minimum threshing loss. The coefficient of
second degree term is ‘Negative’ for all the variables. This shows that
second derivative is always negative, as seen in the graph, the increase in
the parameters brings only decrease in the threshing loss i.e., the right
portion of the parabola. Since the ‘Confidence Band’ is already fixed the
two values obtained through this equation will be at a minimum level. If
proceeded further, there may be increase in the grain loss, however it will
indirectly add for the loss due to harvesting. Hence a balance is needed in
105
between the two and the interval that lies within the Confident level is
chosen as sufficient enough for the threshing loss also.
When the moisture content of grain is 17.58 percent, width of cut is
1.90 m and speed of the machine is 22.62 sec, the total grain loss will be
minimum (1.91 %).
In order to know the validity of the solution obtained, the
Chi-square test was worked out. The observed loss was taken as the ‘O’
value and the value obtained by substituting the used set of triplets on
moisture content of grain (xt), width of cut (X2) and speed of the machine
(x3) in the estimated equation was taken as ‘E’ value and the Chi-square
equation is as follows,
Chi-square = X (0 - E)2 / E
Chi-square was worked out and it was found to be 0.0761, which is
not at all significant indicating the justification for using the parameter,
obtained through the equation values for predictions.
In order to suggest the ‘Confidence Band’ for each one of the
variable, to allow variations, simulated values from the model is presented
below.
106
In the first stage, variations are allowed in one variable, below and
above the optimal range as in the previous case, the increase and decrease
on both limits were recorded stopped whenever the loss goes beyond a
critical level (1.58 % fixed a loss as the critical level). This process was
repeated for each one of the variables and the resulting interval in each are
19.50 to 21.00 for moisture content of grain (%), 1.85 to 1.95 for cutter
bar width (m) and 19.00 to 25.00 for time taken (sec). Now in order to test
verify the combined effect 25 such type of values for the trails and their
losses are simulated and presented in table 4.17.
The table reveals that for every set of trials within this interval, the
cutter bar loss was found minimum. Thus the results reveal that one can
choose any moisture content level of straw, width of cut and speed of the
machine within these intervals for minimizing the loss. As in the previous
case, these intervals can be nomenclatured as a ‘Confidence Band’ in the
three dimensional space for minimizing the threshing loss.
The intervals are:
For Moisture content of grain (%) - 19.50 to 21.00
For Width of cut (m) - 1.85 to 1.95
For speed (sec) - 19.00 to 25.00
107
Table 4.17 Random simulation of model with different combinations for threshing loss
SLNo.
Moisture content of grain
Width of cut
Speed Threshingloss
1 19.50 1.85 19.00 1.302 19.50 1.95 19.00 1.333 19.50 1.85 19.00 1.304 19.50 1.86 19.00 1.345 19.50 1.89 19.00 1.426 19.50 1.86 19.50 1.387 20.00 1.85 20.00 1.168 20.00 1.86 20.00 1.209 20.00 1.88 21.00 1.3110 20.00 1.95 23.00 1.2611 20.00 1.92 24.00 1.3312 20.05 1.94 25.00 1.2213 20.50 1.92 24.50 1.0714 20.50 1.93 23.50 1.0715 20.50 1.94 22.50 1.0516 20.50 1.88 21.50 1.0717 20.75 1.89 22.50 0.9618 20.75 1.93 21.50 0.9319 20.75 1.92 22.50 0.9620 20.75 1.92 21.50 0.9521 21.00 1.93 23.50 0.7822 21.00 1.93 24.50 0.7523 21.00 1.95 25.00 0.6624 21.00 1.95 25.00 0.6625 21.00 1.95 25.00 0.66
108
Two more important parameters in the design of combined
harvester where losses occur are in sieve and straw walker. The data when
executed showed very high correlation of 0.932 between the two variables
moisture content of grain and moisture content of straw. Hence in order to
avoid multicollinarity, it was decided to take one of the two since the
result, which is true for one, will be exactly true for the other. In view of
this sieve loss was used as the dependent variable (Y) and moisture
content of straw (xi), width of cut (x2), speed (X3) and moisture content of
grain (x4) as independent variables.
The estimated equation by the same step-down procedure here
again deleted all the interacting variables leaving only the linear and
quadratic terms in each one of the variables. The relationship between
independent variables and losses occurred in sieve are represented in
figures 16, 17, 18 and 19. The estimated equation is:
Y = 29.8395 - 0.2063 xi - 19.6767 x2- 0.0637 x3 - 0.3038 x4
+ 0.0014 x,2 + 5.1247 x22 + 0.0014 x3
2 + 0.0088 x42
The details of the coefficients are presented below:
4.43 Estimation of Sieve and straw walker losses
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Table 4.18 Details regarding the coefficients regression equation for sieve losses
Sl.N Variables Regressioncoefficients
Standarderror
Level of significance
R
1 Constant (bo) 29.8395 - - 0.91742 Moisture content
of straw (xi)- 0.2063 0.0142 0.0000
3 Width of cut (x2) - 19.6767 0.0206 0.00004 Time taken for 20
m distance (x3)-0.0637 0.0147 0.0000
5 Moisture content ofgrain (X4)
-0.3038 0.0193 0.0000
6 Square of xi (xi ) 0.0014 0.0144 0.00007 Square of x2 (x2
2) 5.1247 0.0206 0.00008 Square of x3 (x3
2) 0.0014 0.0147 0.00009
- — - 2Square of X4(X4 ) 0.0088 0.0193 0.0000
• The best fitted equation as in the earlier cases is with R2 , the
coefficient of multiple determination is 0.9174 which is significant at one
percent level of probability indicating the fact that 91.74 percent of the
variations in the sieve loss is being explained by the changes in the four
independent variables included in the model. Here also in the final form,
all the four interaction coefficients are deleted indicating that the
interactions do not contribute anything towards the sieve loss i.e., all the
four variables have only direct effects towards the grain loss viz.,
moisture content of straw, width of cut, speed and moisture content of
grain.
113
114
Chi-square was found to be 0.2101, which is not significant
indicating the justification for using parameter values for predictions.
To suggest the interval for each one of the variable such that it
allows for variations, the simulated values from the model is presented
below.
As in the earlier cases, variations are allowed in one variable below
and above the optimal range. The increase and decrease on both limits
were stopped whenever the loss goes beyond the critical level (it has fixed
a 0.23 % loss as the critical level). This process was repeated for each one
of the variables and the resulting interval in each case here are 68.25 to
77.49 for moisture content of straw, 16.25 to 19.50 for cutter bar width,
1.89 to 2.04 for time taken and 19.45 to 25.12 for moisture content of
grain. Now in order to test verify the combined effect, 25 such type of
values for the trials and their losses are simulated and presented in
table 4.19.
The table reveals that for every set of trials within this interval, the
cutter bar loss is found minimum. Thus the result reveals that one can
choose any moisture content level of straw, width of cut and speed of the
machine for minimizing the loss. The intervals can be nomenclatured as a
115
Table 4.19 Random simulation of model with differentcombinations for sieve loss
sl.No.
Moisture content of
straw
Width of cut
Speed Moisture content of
grain
Sieve loss
1 68.25 1.89 19.45 16.25 0.1722 69.00 1.89 19.45 17.00 0.0753 69.50 1.90 20.00 17.50 0.0564 69.25 1.91 21.00 17.50 0.0425 69.26 1.92 23.00 18.50 0.0386 70.00 1.93 25.12 19.50 0.0467 71.00 1.94 24.20 17.50 0.0768 71.50 1.95 24.23 18.00 0.0209 71.50 2.02 21.52 18.50 0.02410 72.00 2.04 23.25 19.00 0.07911 72.50 1.95 23.54 19.25 0.11012 73.00 1.89 21.65 18.00 0.04713 73.50 2.02 24.50 18.00 0.01714 74.00 2.03 25.00 19.25 0.06515 74.00 2.01 23.00 16.50 0.10916 74.00 1.99 19.50 17.00 0.05217 74.50 1.96 19.85 18.00 0.04618 75.00 1.89 21.00 18.50 0.03119 75.50 1.89 20.00 19.00 0.03020 76.00 2.02 21.00 19.25 0.05121 76.50 2.03 23.00 18.50 0.10322 76.50 2.02 25.00 18.00 0.09223 77.00 2.03 25.12 19.25 0.07924 77.49 2.04 25.12 19.35 0.12525 77.49 2.04 25.12 19.50 0.146
116
‘Confidence Bands’ in the three dimensional space for minimizing the
sieve and straw walker losses.
The intervals are:
For Moisture content of straw (%) - 68.25 to 77.49
For Moisture content of grain (%) - 16.25 to 19.50
4.4.4 Estimation of moisture content in grain and straw
One point of interest in a study of this type is whether it would be
able to predict the losses as soon as one enters in the field. Once the
farmer is familiar with the crop, then by experience he will be able to
predict the moisture content without actually using any instrument. The
cutter bar width is constant and the adjustment is only on the speed. One
more parameter to be known is the moisture content of the grain. From the
observed data, it has been estimated the relationship between moisture
content of straw and that of the grain. Straw is the source of moisture to
the grain. Hence from the moisture content of the grain could predict the
straw moisture content. The relationship between independent variable
and dependent variable are represented in figure 20. The estimated
equation is:
For Width of cut (m) 1.89 to 2.04
For speed (sec) 19.45 to 25.12
Y = 104.922-2.4473 x, + 0.0166 xj2
where, xi is the moisture content of straw
Y is the moisture content of grain
The R2, the coefficient of determination is 0.8820, which is
significant at one percent level of probability indicating the fact that
88.20 percent of the variations in the moisture content in the grain is being
accounted by the changes in the independent variable.
Hence the grain moisture can also be estimated through this
equation. Parameters needed for forecasting losses in the field condition
are available.
4.5 Test and verifying the model in the field condition
To verify the practical working of the model developed, test run
was conducted in 20 samples. Field observations viz., speed of the
machine, width of cut, moisture content of straw and grain were collected
in ten randomly selected fields in each taluk in the month of April 2003
and the values are presented in table 4.20.
The total losses involved in mathematical model for combine
harvester has four coefficients. The structure of these coefficients helps to
design parameters of the machine and know crop parameters affecting the
value of the coefficients. Therefore, the model coefficients were
119
determined for different losses viz., harvesting, threshing and separation
losses.
These values were substituted in the different models and the
results obtained from the model for each one of the losses. By taking the
observed results from the fields and the predicted values from the model
are taken as expected values. To calculate the expected values in the
functions respective equations are used from its individual equation,
which was discussed in earlier estimation. The Chi-Square test was
worked out for each loss. The Chi-Square values are 0.0175 for cutter bar
loss, 0.0003 for threshing loss and 0.0011 for separation loss. In each case
the Chi-Square value was very low justifying the factor that the working
condition of the machine in the field is justifiable i.e. the working of the
model is valid in the field condition.
120
Table 4.20 Test and verifying the mode! in the field condition
SLNo.
Harvesting loss (%)
Threshing loss (%)
Separation loss. . . (%X_.
Observedvalues
Modeledvalues
Observedvalues
Modeledvalues
Observedvalues
Modeledvalues
1 0.48 0.48 1.65 1.64 0.24 0.242 0.45 0.45 1.84 1.85 0.26 0.233 0.38 0.46 1.85 1.83 0.25 0.254 0.45 0.43 1.89 1.82 0.21 0.245 0.35 0.36 1.84 1.84 0.19 0.206 0.48 0.48 1.88 1.85 0.18 0.167 0.35 0.25 1.96 1.96 0.16 0.168 0.48 0.45 1.84 1.86 0.15 0.149 0.45 0.48 1.54 1.57 0.18 0.1510 0.35 0.26 1.65 1.68 0.23 0.2311 0.48 0.29 1.68 1.65 0.23 0.2212 0.35 0.25 1.87 1.85 0.19 0.2013 0.35 0.48 1.85 1.86 0.22 0.2114 0.48 0.42 1.77 1.75 0.19 0.2015 0.48 0.45 1.79 1.77 0.21 0.1916 0.39 0.46 1.85 1.84 0.23 0.2217 0.45 0.45 1.49 1.49 0.22 0.2318 0.35 0.34 1.48 1.45 0.22 0.2119 0.48 0.36 1.65 1.64 0.19 0.2020 0.46 0.46 1.89 1.88 0.19 0.19
ill