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

65

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

87

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


Standarderror


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


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

109

Table 4.18 Details regarding the coefficients regression equation for sieve losses


Standarderror


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.

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


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

a study was undertaken in raichur district of...

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