Extent of adoption of farm machineries
Only 45 percent of farmers had adopted seed cum fertilizer drill, and the rest, 55 percent, did not adopt it. Among the adopter farmers, more than 65 percent of farmers used seed cum fertilizer drill in the range of 6.60 to10.69 acres of land, which fell in the group of medium and about 16 percent farmers in the category of low as well as and high category. In zero tillage, only 32.78 percent of farmers had adopted, and 67.22 percent still belonged to the non-adopter category. This dossier was matched with the outcome of Mottaleb et al, 2016.
Discussion explored the prerequisite condition for adopting farm machinery was large operational land. However, most of the farmers were small or marginal farmers. The land was also scattered over many areas were the reason of lower adoption. Financial limitations were another impeding factor that refrained them from adoption. Farmers were solely resourcing poor and were not able to purchase farm machineries. They were not able to recognise the use as well as the working mechanism of farm machinery. Another significant reason for non-adoption was the unavailability of machinery on a custom hiring basis as well as availability to auxiliary parts and maintenance thereof. Focus group discussion implied peoples’ mindset and the potentiality to make farmers aware of the farm machineries and benefits were the main reasons for non-adoption. However, once the crop germinated, other farmers wanted to know how they accomplished the results and were persuaded that it would work, although many waited until the harvesting was done for the final decision (seeing and doing). Since farmers rely upon farming for a livelihood, they will change production practices after being convinced.
Table 1: Extent of adoption of farm machineries (N=360)
Farm Machinery
|
Seed cum fertilizer Drill (N=360)
|
Adopter
|
Non-Adopter
|
F
|
%
|
F
|
%
|
161
|
44.72
|
199
|
55.27%
|
Frequency distribution of adopter categorisation based on OLH
|
|
Category
|
Range
|
Frequency
|
Percentage
|
Low
|
4.5-6.6
|
26
|
16.14
|
Medium
|
6.60-10.38
|
109
|
67.70
|
High
|
10.38-13
|
26
|
16.14
|
Zero Tillage (N=360)
|
Adopter
|
Non-Adopter
|
f
|
%
|
F
|
%
|
118
|
32.78
|
242
|
67.22
|
Frequency distribution of adopter categorisation based on OLH
|
|
Category
|
Range
|
Frequency
|
Percentage
|
Low
|
6-7.42
|
15
|
12.71
|
Medium
|
7.42-10.69
|
83
|
70.33
|
High
|
10.69-13
|
20
|
16.94
|
Determinants of adoption of Seed cum fertilizer drill and Zero tillage
The observed adoption choice of farm machineries was speculated to be the consequence of a tangled set of inter-technology favoured evaluations created by farmers. It was obligatory to scrutinize the elements persuading the adoption of farm machineries by evaluating a binary logic model with independent variables. This model represented the cumulative outcome of the explanatory variables impeding or encouraging the adoption of farm machinery. For Seed cum fertilizer drill, the Pseudo R2 value showed that the model is a 91 percent best fit (Table 2). Log-likelihood function -22.30 and LR Chi2 value 450.44 (significance 1%) suggested that the equation was fit and explanatory variables used in the model were cumulatively capable to elucidate determinants and magnitude of adoption. This model also constituted odds ratios and 95% confidence intervals for them for testing the hypothesized relationships. Similarly, for Zero tillage, Pseudo R2 value showed that the model is a 40 percent best fit than any other model. The log probability function -140.99 and the LR Chi2 value 171.48 (significance 1 percent) indicated that the framework was optimized and that the explanatory variables shown in the equation might altogether elaborate the determinants and the scale of adoption.
Availability of information (0.00*), mass media exposure (0.02**), risk orientation (0.06***), innovativeness (0.09***) and farm asset (0.00*) were the significant factors of adoption. The odds ratio indicated that with a one-unit increase in available information, the adoption level could increase by 5.15 percent. Similarly, with a one-unit increase in mass media exposure, the adoption level had risen by 2.02 percent. With one unit increase of risk orientation, innovativeness, and farm asset, the adoption level increased by 0.76, 1.13, and 13.73 percent, respectively. A similar study was carried out by Ainembabazi et al, 2017, which reported that accessibility of information enhanced awareness level, which eventually increased the adoption of technologies.
Table 2: Binary Logistic Regression analysis for determinants of adoption
Binary Logistic Regression analysis for determinants of adoption
|
Independent variable
|
Seed Cum Fertilizer Drill
|
Zero tillage
|
LR chi2(19): 450.44, Prob> chi2: 0, Pseudo R2: 0.9099 , Log likelihood = -22.303804
|
LR chi2(18): 171.48, Prob> chi2: 0, Pseudo R2: 0.37
Log likelihood =141.99
|
Explanatory Variables
|
Odds Ratio
|
Z
|
P>z
|
Odds Ratio
|
Z
|
P>z
|
Operational land holding
|
1.37
|
1.20
|
0.22
|
1.27
|
2.55
|
0.01*
|
Age
|
0.93
|
-1.25
|
0.21
|
0.98
|
-1.05
|
0.29
|
Educational status
|
1.26
|
0.45
|
0.65
|
1.07
|
0.47
|
0.636
|
Family type
|
0.21
|
-0.95
|
0.34
|
0.94
|
-0.13
|
0.893
|
Extension contact
|
0.87
|
-0.79
|
0.42
|
1.08
|
1.26
|
0.209
|
Extension activity
|
0.62
|
-1.55
|
0.12
|
1.11
|
0.99
|
0.323
|
Availability of information
|
5.15
|
3.82
|
0.00*
|
1.06
|
0.37
|
0.713
|
Marital status
|
0.52
|
-0.55
|
0.58
|
0.59
|
-1.19
|
0.235
|
MME
|
2.02
|
2.26
|
0.02**
|
1.15
|
1.62
|
0.106
|
Freq of use
|
1.28
|
1.26
|
0.20
|
1.13
|
2.35
|
0.01*
|
Risk orientation
|
0.76
|
-1.88
|
0.06***
|
1.04
|
1.02
|
0.30
|
Innovativeness
|
1.13
|
1.66
|
0.09***
|
1.08
|
2.97
|
0.00*
|
Availability of credit
|
2760.86
|
0.28
|
0.77
|
1.29
|
0.8
|
0.423
|
Distance from input market
|
2.14
|
0.96
|
0.33
|
1.28
|
1.2
|
0.231
|
Distance from output market
|
0.38
|
-1.14
|
0.25
|
0.85
|
-0.65
|
0.513
|
Experience in farming
|
1.01
|
0.29
|
0.77
|
0.99
|
-0.11
|
0.911
|
Farm asset
|
13.73
|
4.13
|
0.00*
|
|
|
|
Livestock
|
0.26
|
-0.98
|
0.32
|
1.081
|
0.23
|
0.818
|
Possession of vehicle
|
2.56
|
1.17
|
0.24
|
2.14
|
-5.46
|
0
|
_cons
|
3.02
|
-1.44
|
0.15
|
2.14
|
-5.46
|
0
|
*significance 1%, **significance 5%, *** significance 10%
More mass media exposure enhanced ability to procure, analyse and elucidate information of using seed cum fertilizer drill. This discovery is following the studies of Feder et al. (1985). Farmers had more data sources that would increase the capabilities of decision-making to embrace seed cum fertilizer drill. Farmers with more risk-bearing capacity, sometimes, lead to higher adoption as compared to other fellow farmers. They could resist with success as well as failure, simultaneously leading to more adoption. Focus group discussion with the non-adopted farmers disclosed that before adoption, they thought more about the outcome. They thought of using seed cum fertilizer drill could not have any significant increase in yield. Similarly, the numbers of farm assets had significant involvement in this regard. It referred that the farmers who had higher financial resources purchase more farm assets for use in their field. So, it can be expressed that establishing a custom hiring center is mandatory, which would increase more adoption of machinery. As anticipated, operational landholding (OLH) has an upbeat and noteworthy collision on zero tillage technology adoption. With the increase of every added unit of an acre of land, the chance of adoption was increased by 1.27 percent. On average, the result of zero tillage machine manifested that farming with each additional acre of land had enhanced the number of an acre of land under by wheat cultivation. Level of utilisation of agricultural operation quantified the number of trips per month by the extension representative to a farmer who had a favourable and meaningful impact on adoption with the likelihood of adoption by 1.13%.
To measure the ordinary linkage among two quantities, Kendall tau coefficient was employed. In this Fig 2, crucial independent variable had been picked up to measure the determinants of the level of adoption for seed cum fertilizer drill. Fig 2, showed that accessibility of information (0.45), mass media exposure (0.26), innovativeness (0.25) and farm asset (0.78) had significant positive with adoption level for seed cum fertilizer drill. Farmers with more access to information and mass media exposure had more awareness regarding the uses of resources efficiently which, in turn, lead to increase of income. Similar finding was also reported by Beyene and Menale, 2015. In case of Zero tillage adoption, Fig 2 indicated that frequency of use (0.26) and innovativeness (0.43) had significant positive relation with adoption level. It could also be concluded that with increase of mass media, awareness might be increased as well as farmers could understand the benefit of zero tillage. Hence, adoption levels automatically increase with innovativeness.
Socio-Agro-Economic impact of farm machineries:
Social impact mentioned the adoption of farm machineries that lead to any change in an individual, family, and community well-being like time-saving, employment generation, depletion in labour requirement, etc. Agronomic impact referred to change in parameters like coverage (field capacity), seed rate, germination percentage, crop establishment, plant population per ha, etc. Economic aspects dealt with BCR (benefit-cost ratio), Net Return, and RR (rate of return) from adopted technologies.
An attempt had been made to note down the influence of seed cum fertilizer drill adoption on socio- agronomic issues. DID method was applied to evaluate the technical as well as social features with ex-ante (before application) and ex-post (after application) analytical approach. Table 3 showed the attributes of the findings therein. Results depicted that labour cost was lessened by nearly 80 per cent from 9.82 MD to 2.80MD which was followed by saving of seed by more 20 per cent from 151.04 kg/ha to 108.65 kg/ha. Similar finding was reported by Dongarwar et al, 2015. Apart from it all others aspects represented positive impacts in contrast to non-adoption of seed drill. Hence, farmer could have got financial advantages for adoption of seed cum fertilizer drill in farming operations. Besides, performance in various aspects showed same results as found without adoption for all non-adopters of seed drill in their farming. Similar finding was reported by Kumar and Umesh, 2018; Rawat et al, 2007, Singh et al, 2017
Table 3: Impact of Seed Cum Fertilizer Drill adoption on Socio-agronomic aspects (DID method) (N=360)
Particulars
|
Seed Cum Fertilizer Drill
|
Zero Tillage
|
Adopter (n=161)
|
Adopter (n=161)
|
Non Adopter (n=199)
|
Adopter (n=114)
|
Adopter (n=114)
|
Non Adopter (n=246)
|
After
|
Before
|
After
|
Before
|
Field capacity (Hour/ha)
|
2.5
|
5.12
|
2.5
|
5.16
|
2.5
|
5.98
|
2.5
|
5.98
|
Seed rate (kg/ha)
|
108.65
|
151.04
|
108.65
|
150.77
|
100.61
|
138.64
|
100.61
|
138.64
|
Labour requirement for land preparation (MD/ha)
|
2.80
|
9.81
|
2.80
|
9.93
|
2.69
|
8.29
|
2.69
|
8.29
|
Germination percentage (%)
|
94.81
|
82.80
|
94.81
|
80.98
|
90
|
80
|
90
|
80
|
Crop establishment (%)
|
90.29
|
79.84
|
90.29
|
74.77
|
90
|
85
|
90
|
85
|
Plant population per ha of area (%)
|
90
|
81.43
|
90
|
85.26
|
90
|
75
|
90
|
75
|
NPK application (kg/ha)
|
120.18
|
158.38
|
120.18
|
158.59
|
359.39
|
382.85
|
359.39
|
382.85
|
Positive impact of ZT technology application was found to be very convincing on all aspects of agronomic as well as economics issues (field capacity, seed rate, labour, germination, establishment, plant population and nutrients) taken into account in the study. Differences in performances were very prominent when compared with or without (before) adoption of ZT by the same farmers. Among them, labour requirement was reduced by 30 per cent from 8.3MD to 2.69 MD. Related conclusion was reported by Yogi et al, 2015, Iqbal et al, 2002, Performances of the non-adopters were also studied which also established the same response as obtained in case of before adoption in all respects. Hence, adoption of ZT helped farmers to increase productivity as well as net return in farming. Similar study carried out by Verma and Tamrakar, 2017 reported similar finding.
Economic impact of adoption of seed cum fertilizer drill
Estimated fixed cost of seed cum fertilizer drill owner was Rs. 362.75 per year and variable cost was Rs. 11619.32. Total costs incurred for seed cum fertilizer drill owner per year was Rs.11982.07 and total return from the machine was Rs. 51479.26. The net benefit over investment was Rs. 39504.09 per year. Estimated return from use of it from own farm was Rs.1479.26. Farmers inferred on an average, 50 days in a year on custom hiring basis with Rs.1000 per day. As a result, return from custom hiring was Rs.50000 in a year. Value of benefit -cost ratio and rate of return for using seed cum fertilizer drill machine was 1:3.29 and 329% respectively. Further, expenditure had been worked out for conventional method (broadcasting) before adoption of seed cum fertilizer drill. It was observed that total cost incurred in sowing of wheat through broadcasting method was Rs. 15746.17. Compare the cost of sowing through seed cum fertilizer drill and broadcasting, established seed cum fertilizer drill saved an extra (Rs.15746.17-11982.07) Rs. 3764.10 from 1 ha of land.
Table 4. Comparative economic analysis of Seed cum fertilizer drill and broadcasting
Economic Analysis Seed cum fertilizer drill and broadcasting (n=161)
|
Economic analysis of seed cum fertilizer drill adopter and Non-adopter (n=360)
|
Adopter category
|
Adopter
|
Non-adopter
|
Farmer own Seed cum fertilizer Drill
|
Broadcasting
|
Seed cum fertilizer Drill
|
Broadcasting
|
- Fixed costs
|
Rs (per/year)
|
- Variable costs
|
Rs. (per ha)
|
- Fixed costs
|
Rs (per/year)
|
|
Rs. (per ha)
|
Interest costs
|
361.25
|
Seed cost (Rs/ha)
|
6037.78
|
Interest costs
|
361.25
|
Seed cost (Rs/ha)
|
6030.75
|
Insurance and taxes
|
0.00
|
Manpower cost (Rs/ha)
|
3928.40
|
Insurance and taxes
|
0.00
|
Manpower cost (Rs/ha)
|
3973.87
|
Depreciation costs
|
1.50
|
Expenditure on NPK (Rs/ha))
|
3800
|
Depreciation costs
|
1.50
|
Expenditure on NPK (Rs/ha))
|
3806.23
|
Total fixed costs
|
362.75
|
Land preparation cost
|
1980.00
|
Total fixed costs
|
362.75
|
Land preparation cost
|
1980.00
|
2. Variable costs
|
|
Total
|
15746.17
|
2. Variable costs
|
|
Total
|
15790.85
|
Fuel cost
|
285.00
|
|
|
Fuel cost
|
285.00
|
|
|
Land preparation cost
|
1980.00
|
|
|
Land preparation cost
|
1980.00
|
|
|
Seed cost (Rs/ha)
|
4343.89
|
|
|
Seed cost (Rs/ha)
|
4343.89
|
|
|
Lubricant cost
|
4.25
|
|
|
Lubricant cost
|
4.25
|
|
|
Manpower cost (Rs/ha)
|
1121.74
|
|
|
Manpower cost (Rs/ha)
|
1121.74
|
|
|
Expenditure on NPK (Rs/ha))
|
2884.44
|
|
|
Expenditure on NPK (Rs/ha))
|
2884.44
|
|
|
Maintenance cost
|
1000.00
|
|
|
Maintenance cost
|
1000.00
|
|
|
Total variable costs
|
11619.32
|
|
|
Total variable costs
|
11619.32
|
|
|
3.Total costs
|
11982.07
|
|
|
3.Total costs
|
11982.07
|
|
|
4.Return from own farm
|
1479.26
|
|
|
|
|
|
|
5.Income from custom hiring
|
50000
|
|
|
|
|
|
|
6.Total income
|
51479.26
|
|
|
|
|
|
|
7.Net income
|
39504.09
|
|
|
|
|
|
|
BCR for Seed drill: 1:3.29
|
Paired t test
|
126.36***
|
|
|
Independent t test
|
62.55***
|
**Price of Seed cum fertilizer drill: Rs. 50000/ unit (Unit costs for investment activities in agriculture and allied sectors Uttarakhand 2019- 20)
*** Significance 1%
Table 4 presented comparative economic analysis of adopter and non-adopter. It was witnessed in case of non-adopter, cost incurred for sowing of 1 ha was Rs. 15790.85 whereas Rs.11982.07 for adopted farmers. If compared with expenditure on sowing between adopter and non-adopter, it was found that adopter had saved Rs. (15790.85-11982.07) i.e., 3808.78 by adoption of seed cum fertilizer drill. An identical study was carried out by Satish and Umesh, 2018 reported that mechanized sowing of rabi crops were more profit making in financial terms rather than the conventional one. Both paired‘t’-test (with in adopter) and independent t test (between adopter and non-adopter) value specified that there was positive significant difference in sowing cost.
Economic impact of adoption of Zero tillage
Estimated fixed cost of Zero tillage owner was Rs. 378.6 per year and variable cost was Rs. 9805.43. Total costs incurred for zero tillage owner per year was Rs.10184.06. and return from the zero tillage machine was Rs. 51568.64. The net benefit over investment was Rs. 41384.59 per year (Table 5). Fixed cost was the 0.91 percent of the overall gain whereas variable cost was 23.69 percent of the total compensation. Value of benefit-cost ratio and rate of return for using zero tillage machines was 1:4.06 and 406% respectively. In the contrary, expenditure had been worked out for conventional method (broadcasting) before adoption of zero tillage. It was found that total cost incurred sowing of wheat through broadcasting method was Rs. 14063.92. Comparing the cost of sowing through zero tillage and broadcasting, it was found that zero tillage saved an extra (Rs.14063.92-Rs.10184.06) Rs. 3879.86 from 1 ha of land.
Table5: Comparative economic analysis of ZT and broadcasting
Economic Analysis zero tillage and broadcasting (n=114)
|
Economic analysis of Zero till adopter and Non-adopter (n=360)
|
Adopter category
|
Adopter
|
Non-Adopter
|
Farmer own zero till
|
Broadcasting
|
Zero till
|
Broadcasting
|
1.Fixed costs
|
Rs (per/year)
|
|
Rs. (per ha)
|
1.Fixed costs
|
Rs (per/year)
|
|
Rs. (per ha)
|
Interest costs
|
375.7
|
Seed cost (Rs/ha)
|
5374.576
|
Interest costs
|
375.7
|
Seed cost (Rs/ha)
|
5545.528
|
Insurance and taxes
|
0
|
Manpower cost (Rs/ha)
|
3320.339
|
Insurance and taxes
|
0
|
Manpower cost (Rs/ha)
|
3317.073
|
Depreciation costs
|
2.93
|
Expenditure on NPK (Rs/ha))
|
3389
|
Depreciation costs
|
2.93
|
Expenditure on NPK (Rs/ha))
|
3389
|
Total fixed costs
|
378.63
|
fuel cost
|
1980
|
Total fixed costs
|
|
Fuel cost
|
1980
|
2. Variable costs
|
|
Total
|
14063.92
|
2. Variable costs
|
|
Total
|
14231.6
|
Fuel cost
|
285
|
|
|
Fuel cost
|
285
|
|
|
Land preparation cost
|
0.00
|
|
|
Land preparation cost
|
0.00
|
|
|
Seed cost (Rs/ha)
|
4024.57
|
|
|
Seed cost (Rs/ha)
|
4024.576
|
|
|
Lubricant cost
|
4.28
|
|
|
Lubricant cost
|
4.28
|
|
|
Manpower cost (Rs/ha)
|
1074.58
|
|
|
Manpower cost (Rs/ha)
|
1074.58
|
|
|
Expenditure on NPK (Rs/ha))
|
3417
|
|
|
Expenditure on NPK (Rs/ha))
|
3417
|
|
|
Maintenance cost
|
1000
|
|
|
Maintenance cost
|
1000
|
|
|
Total variable costs
|
9805.43 (23.69)
|
|
|
Total variable costs
|
|
|
|
3.Total costs
|
10184.06
|
|
|
3.Total costs
|
10184.06
|
Independent t test: 114.50***
|
|
4.Return from own farm
|
1568.64
|
|
|
|
|
|
|
5.Income from custom hiring
|
50000
|
|
|
|
|
|
|
6.Total income
|
51568.64
|
|
|
|
|
|
|
7.Net income
|
41384.59
|
|
|
|
|
|
|
BCR for ZT: 4.06
|
Paired t test= 30.098***
|
|
|
|
|
|
Value in parenthesis indicate percent of cost with respect to net income, *** Significance 1 %
Table 5 presented comparative economic analysis of adopter and non-adopter. It was found that in case of non-adopter, cost incurred for sowing of 1 ha was Rs.14231.60 and in case of adopter, it was Rs. 10184.06. In case of adopter, net return over cost was Rs. 41384.59. The B:C value of adopter of zero tillage machine was 1:4.06. If compared to the expenditure on sowing between adopter and non-adopter, it was found that adopter had saved Rs. (14231.60- 10184.06) 4047.54 by adoption of zero tillage machine. The comparable outcome was stated by Yogi et al, 2015.Results of paired t test (with in adopter) as well as independent ‘t’-test (between adopter and No adopter) implied positive significance difference between zero till sowing adopter and non-adopter (Broadcasting). Similar finding was reported by Verma et al,2018, Krishna and Prakashan, 2014.
Farm level efficiency of improved machineries for upscaling:
This part is focused on devising an approach for out scaling of selected technologies for Lower Shivalik range of Uttarakhand as well as other regions on lower Shivalik hills. It alludes to the most suitable variety of wheat crop, for that particular region in economic parameters and economic efficacy of each machinery, related to each selected improved varieties in lower Shivalik hills. It aids farmers to step up agricultural production and income which results in economic growth, redistribution of resources at individual, local and regional level.
To estimate economic efficiency (in terms of yield and seed rate quantity) of seed cum fertilizer drill on selected wheat varieties, Seeming Unrelated Regression (SUR) model has been adopted. In the Table 6, RMSE score 11.48 shows model presents 11.48% absolute fit of predicted results of yield estimation of HD-2967 by using seed drill than OLS (Ordinal least Square) model. It presents 27.63% and 26.17% absolute fit predicted results for yield estimation for HD-3086 and HD-3059 than OLS model. In the similar way, model present 24.42%, 63.31% and 64.56% more efficient absolute predicted results for seed rate estimation in HD 2967, HD 3086 and HD 3059, respectively. Lesser RMSE values instructed for better fit. Here RMSE value 11.48 indicated in Table 6 that Yield estimation of HD 2967 is the best fit model. The data in Table 6 explains the efficacy of seed drill on yield and seed rate of different wheat varieties. It was observed that the use of seed drill enhances 13.39 q more yield per ha for the variety HD 2967. The varieties HD 3086 and HD 3059 have 12.39q and 10.34q more yield per ha respectively when sown through seed drill. Similarly, it was found that use of seed drill lessened seed rate by 27.71 kg/ha for HD-2967 which was followed by 3.96 kg/ha and 2.90 kg/ha for HD 3086 and HD 3059, respectively.
Table 6: Efficacy Measurements of Seed Drill on Wheat Crop
Equation
|
Variety
|
RMSE
|
R-sq
|
F
|
P
|
Yield
|
HD 2967
|
11.48
|
0.169
|
72.85488
|
0.000
|
HD 3086
|
27.63
|
0.047
|
17.86826
|
0.000
|
HD 3059
|
26.17
|
0.037
|
13.87793
|
0.000
|
Seed rate
|
HD 2967
|
24.42
|
0.2422
|
114.434
|
0.000
|
HD 3086
|
63.31
|
0.001
|
0.348682
|
0.05
|
HD 3059
|
64.56
|
0.0005
|
0.179927
|
0.02
|
SUR Model for efficiency measurements on yield variation
|
Yield Variation(q)
|
Coef.
|
Std. Err.
|
T
|
P>|t|
|
[95% Conf.
|
Interval]
|
HD 2967
|
13.39
|
1.217
|
8.54
|
0
|
7.99
|
12.78
|
HD 3086
|
12.39
|
2.93
|
4.23
|
0
|
6.62
|
18.15
|
HD 3059
|
10.34
|
2.77
|
3.73
|
0
|
4.88
|
15.79
|
SUR Model for efficiency measurements on Seed Rate Variation (kg)
|
|
HD 2967
|
-27.7175
|
2.59
|
-10.70
|
0
|
-32.81
|
-22.62
|
HD 3086
|
-3.96
|
6.715
|
-0.59
|
0.555
|
-17.17
|
9.24
|
HD 3059
|
-2.90
|
6.84
|
-0.42
|
0.672
|
-16.37
|
10.56
|
Table 7 explain the efficacy of zero tillage on yield and seed rate for different wheat varieties. It was observed that use of zero tillage enhance yield by 6.00q more per ha for the variety HD2967. The HD 3086 and HD 3059 have 5.08q and 10.08q more yield per hectare, respectively when sown by zero tillage. As such, it was found that of use of zero tillage adoption helps to, seed rate reduction 24.20 kg per ha in case 2967 which was followed by 4.49 kg and 1.79 kg per ha for HD 3086 and HD 3059. Thus, it saved the cost of seed which, in turn, increased farmers’ net income.
Table 7: Efficacy Measurements of Zero Tillage on Wheat Crop
Equation
|
Variety
|
RMSE
|
R-sq
|
F
|
P
|
Yield
|
HD 2967
|
12.79
|
0.046
|
17.46
|
0
|
HD 3086
|
30.66
|
0.007
|
2.84
|
0.09
|
HD 3059
|
28.89
|
0.026
|
9.662
|
0.00
|
Seed rate
|
HD 2967
|
27.42
|
0.1471
|
61.75
|
0.00
|
HD 3086
|
60.74
|
0.0012
|
0.43
|
0.51
|
HD 3059
|
64.70
|
0.0002
|
0.06
|
0.80
|
SUR Model for efficiency measurements on yield variation
|
Yield Variation(q)
|
Coef.
|
Std. Err.
|
T
|
P>|t|
|
[95% Conf.
|
Interval]
|
HD 2967
|
6.0029
|
1.436
|
4.18
|
0
|
3.178
|
8.827
|
HD 3086
|
5.8032
|
3.4422
|
1.69
|
0.09
|
-0.967
|
12.57
|
HD 3059
|
10.08
|
3.24
|
3.11
|
0.002
|
3.70
|
16.46
|
SUR Model for efficiency measurements on Seed Rate Variation (kg)
|
|
HD 2967
|
-24.20
|
3.07
|
-7.86
|
0
|
-30.25
|
-18.14
|
HD 3086
|
-4.48
|
6.82
|
-0.66
|
0.51
|
-17.89
|
8.92
|
HD 3059
|
-1.79
|
7.26
|
0.25
|
0.80
|
-12.48
|
16.08
|
Scenario Building and Synchronous Bootstrapping Towards Farm Mechanization:
In view of the efficacy of above discussed machineries on wheat varieties, an attempt has been made to study the improved varieties of wheat grown all over India in the context of future prospects of scaling up of these varieties in those particular regions where the usage of machinery in cultivation of these crops can lead to enhanced production and productivity.
Based on the above results, an effort has been made to present the state-wise scenario in respect of economic benefits where these varieties are grown and the selected mechanizations have been done. Fig 4 presents the scenarios of farm income for application of seed drill and zero till with reference to wheat varieties. Estimates based on the state-wise secondary data on cost of cultivation (Rs/q), price of seed (Rs/kg), farm harvest price (Rs/q) showed that the cost of cultivation (Rs/q) was the lowest in Punjab. It implied that farmers earned an additional benefit of at least more than Rs. 13000/- per ha and varied according to the cost of production. Similarly, for HD 3086 and for HD 3059, it was at least Rs. 10,000/ per ha each.If the farmers sold their product at MSP, they could earn additional income of Rs 24107.85/-, Rs22797.6/ and Rs.19026.74/- per ha from grain yield with respect to variety HD-2967, HD-3086 and HD-3059. But farmers reported that they were forced to sell their product at farm harvest price which was below the rate of MSP.
Considering this, estimation was made which afforded an additional income ranging Rs 20000-25000/- per ha for variety HD-2967, Rs. 16094.61-25622.52/- per ha for variety HD-3086 and Rs.13432.47-21384.4/ per ha for HD-3059 respectively. In case of Zero till, it implied farmers earned an additional benefit of at least Rs. 6000/- per ha which is varied according to the variety and cost of production. The farmers selling their product at MSP, could earn additional income of Rs 11040/-, Rs9347.20/ and Rs.18547.20/per ha from grain yield with respect to variety HD-2967, HD-3086 and HD-3059. In case for Farm Harvest Price, estimation was made which afforded an additional income ranging Rs 10000/- -Rs. 21000/ per ha which varies variety wise.
Constraints analysis for strategy formulation for out scaling of farm machineries
Expert opinion has been taken to identify the constraints related to out scaling of farm machineries.
According to experts view point lack of custom hiring services (farm machinery) was the major Organisational constraint (average score of 65.8) followed by unavailability of proper government support (avg score 51.55), poor cooperation at grass root level organizations (avg score 52.85).Related discovery was conveyed by Loon et al, 2020 and Gowda, 2012, Kelsey 2011, Balachandran, 2004. The custom hiring service could also be fruitful solution for using machinery as the initial investment was comparatively high. Another fascinating apprehension spot lighted that custom hiring farmer that possession of the tractor could be a sign of aristocracy in this sort of circumstances, indeed little ranches purchased tractor which turns into obstruction in customized hiring services. Hence it is imperative to create additional awareness regarding custom hiring of machinery which can facilitate to reduce the fixed costs of farm operations and lessen the burden of heavy capital investments.
Experts advised that farmers had to confront multidimensional problem for adoption of farm machinery and, as consequence, it had reflected to the level of up-scaling and out-scaling of farm machinery. As small and marginal farmers were predominant in the study area and they were no longer in a position to compete with the rich farmers. Thus, the Fig 5b showed competition with large and resource rich farmers was the main situational problem and accordingly got first rank which was followed by land fragmentation of holdings, soil and land degradation with avg. score of 59.5 and 31 and stationed at the 2nd and 3rd position, respectively. Similar problems had been reported by Loon et al, 2020, Balachandran, 2004 Arun et al, 2019. Rich farmers were benefitted from the different government subsidy schemes. Most farmers own sets of separate small and scattered plots. In some cases it used to be beyond the location of the village too, which made it impossible for them to use uniform farming techniques on their entire holding. Therefore, farmers were unable to use the equipment effectively. Henceforth, farm mechanization of scattered, parcels of land became too troublesome and inefficient, which, in turn, reduced agricultural production and owner’s incentive for land improvement and accomplishment of high production.
Non-availability of machines was the predominant technological problems (rank first) for out-scaling of farm machinery. Moreover, lack of skills, expert guidance, skilled personnel for handling machine and poor technicalities were also correlated with the technical problems of out- scaling of machinery. This finding lies with Kelsey, 2011, Balachandran, 2004. Availability of spare components was being imported. Similar finding was reported by Gowda, 2012. It is assumed that lot of attempts have to be taken up in this sector for mechanization of agriculture in this area for which govt. initiatives are assumed to be more important.
Dearth of manufacturers in the state led to buying of farm implements from other states. It required excessive capital investment for a small farm which made it uneconomical for small farmers. The excessive cost of farm machinery had continually been a serious limiting element in mechanization of agriculture. Farmers in this study area pay appreciably greater for machines than their counterparts in industrialized states like Punjab, Haryana, and U.P. Lack of genuine spare parts of machinery parts at a reasonable price and at a convenient distance had badly affected the operating of machines. Thus, lack of spare components for agricultural machinery and equipment used to be a primary trouble which notably affected all mechanization.
Fig 5d reiterated that the main problem is low level of fund/capital (ranking first) which stands in the way of agricultural development on the part of the small and marginal farmers who dominated the farming. This finding was also reported by Baliwala et al, 2017. Acquiring of fund is possible through credit but is has lot of lacunae and, as such, delay in loan disbursement by bank was given the 2nd rank. It was followed by more cost of machine and higher interest rate of loan. This verdict was matched with the judgement of Loon et al, 2020 and Gowda, 2012. Besides, lack of payment of subsidy and non-implementation of govt. scheme and inefficient service of bank (rural) are also recorded towards out-scaling of the technology. This finding lies with Kelsey, 2011, Balachandran, 2004. Hence, awareness about the govt. credit facilities and accountability of implementing agencies may be taken into consideration while framing any govt. policy in this regard. More than 70 per cent farmers have been economically poor. Due to this financial condition, in spite of their willingness to own farm implements, they were unable to purchase as these did not come in their order of priority of day to day requirements. Majority of the farmers having small farm holdings couldn't bear to save capital out of their own investment funds to purchase farm implements. If farmers had to take care of each tractor drawn and animal drawn implements, it might be uneconomical. On discussion with the experts it was disclosed that the farmers of that region facing unbelievable trouble in availing credit facilities for the purchase of implements.
They reported that huge formalities and a numerous visits were essential for availing credit facility. They additionally feared as to how many times they would be needed to reimburse the deposit obtained on account of suspected foul play in societies. They enquire interest free credit for buying implements and machinery. Moreover it was found that majority of farmers were having poor educational status and were unaware of such facilities. Some of them who were eager to take benefit of modern technology were excessively poor and could not mortgage their property to secure a bank loan. In addition, banking services had not been proper in rural areas. Indeed, even if it was accessible, farmers had not been well trained to take advantage of the banking facilities. They weren't habituated to deposit their cash in a bank or required a loan from a bank and refund it. Henceforth, it is needed to educate them, to expose them to banking and make them habitual of using bank. Thus lack of well organised workshops in the areas has made it hard to carryout mechanization programmes programs effectively.
Strategy for out-scaling of improved machineries (Expert Opinion):
Following Alfares and Duffuaa method, the aggregate weight (W) has been calculated for each dimension (criteria) assuming 100% for rank-1 which is evident from the Table 8. Economic problems (aggregate wt. 1823.11) were the most serious problems having first ranked for up-scaling and out-scaling of machinery followed by technical issues (1646.23), organisational problem (1241.92) and situational factors (1014.51) with rank of 2nd, 3rd and 4th, respectively.
Average size of operational holdings is shrinking resulting in higher proportion of marginal and small holdings hence making possession of agriculture equipment economic and unviable. Establishment of Farmer’s Cooperatives and Farm Machinery Utilization Centres or customized hiring service for multi-farm use are crucial. Necessary steps for improvement of supply chain and value chain mechanism must be developed and communicated to the farmers. Financial incentives, sponsored loans, low interest rate, provision of training will motivate the farmers for using farm machinery. Besides, dissemination of information, technical guidance and alternatives farm income may be promoted collectively. But poor information dissemination refrained them from use and management of those expensive resources. Experts felt that this drawback may be resolved by introducing joint farming or farming on co-operative basis resulting in increase in farm holding size. Other organizations like KVK, NGO, SHGs, etc. may play an important role for promotion of machinery in agricultural sector.
Table 8: Strategy for out-scaling of farm machineries by Alfares and Duffuaa method (Expert Opinion):
Areas
|
Respondents’ rank
|
1st
|
2nd
|
3rd
|
4th
|
Wr,n
|
Organisational problems
|
0
|
0
|
13
|
7
|
100
|
Situational factors
|
2
|
0
|
7
|
11
|
74.73
|
Economic problems
|
16
|
2
|
0
|
2
|
62.09
|
Technical factors
|
2
|
18
|
0
|
0
|
49.46
|
Frequency
|
20
|
20
|
20
|
20
|
|
1/f
|
0.05
|
0.05
|
0.05
|
0.05
|
0.05
|
Wrn,f (aggregate weight)
|
1241.92
|
1014.50*
|
1823.11***
|
1646.23
|
|
Rank
|
3
|
4
|
1
|
2
|
|
*** Most serious * Least serious
Therefore, the extension programmes of agricultural engineering ought to be strong. This was reflected in the perception of farmers and use of farm implement and machinery. Similarly manufacturers have not been accessible. This was because of poor extension intervention towards this path. Henceforth mechanisms and incentives should be put in place to encourage the procurement of machinery by smallholders and a favourable tax, liability, and/or excise regime should be enforced. Relevant economic instruments need to be established in this setting to placed small farmers into the financial aid beneficiary circle. It is important to consider whether farmers in real need of access to loans are excluded because of their ineligibility under established criteria. They were determined to prepare for land consolidation by government agencies. Such consolidation might improve cooperative farming or mechanized farming. The education of most of the farmers was poor. The majority of farmers have very little or no exposure to improve machines for farm operation and were hesitant to even attempt handling motor/ electric operated equipment. A programme of farmers training in handling and managing farm equipment used to be felt an urgent need to make mechanization a success. Custom hiring is a successful strategy to promote mechanization and tackle numerous constraints for the overall improvement of the agricultural sector, such as labour shortage, climatic disasters, as well as timely bound activities. It was orated that farmers had been looking for hands-on training experience starting from tractor driving, hitching of implements, ploughing, harrowing, seed sowing, cultivating, spraying, dusting, harvesting, and their repair and maintenance. They also want a knowledge/training programme for restoring and renovating diesel engines and electric motors. Due to the lack of perfect technical knowledge they had been unable to utilize the machine properly even after hiring it. So it has been conveyed that training centers might be established in the study area either by government organizations, agriculture universities, NGO, Agriculture line departments, or KVK to take up this task. It may be referred to that a higher percentage of labour association in agriculture with a lower contribution to GDP creates agriculture even less satisfying is the cause of ranchers’ destitution. With the estimation of reverse fashion in the populace and agricultural growth, it will undesirably effect on production. In such scenario agricultural mechanization is an answer to address the trouble of growing call for of food grains. However, farmers' incapacity to buy farm implements and commercial banks' reluctance to finance farm machinery is one of the major obstacles to the rise in the pace of mechanization which leads to an upward push in entering costs attributable to labour charges. The production of economical technology alongside state support for tiny farmers needs to be checked out and encouraged at the village level, often near the place where small and marginal farmers live. Custom Hiring Centres (CHCs) must be formed through Public-Private Partnership (PPP), non-profit organizations, co-operative foundation, farmer groups, and charitable trusts. A few governments like Andhra Pradesh, Gujarat, Karnataka, Madhya Pradesh, Punjab, etc have been endorsing farm mechanization through CHCs and such endeavours should be ventured up. Governments must catalyze to inspire them and enable farmers to recognize new economic models, such as custom hiring. In this particular circumstance, cross-sectoral collaborations with the non-government segment, financial institutions, and farmers' associations must be pursued to focus the numerous monetary blessings of customized hiring and the possible contribution in the direction of modernizing agricultural operations and improving rural livelihoods. Steps need to be taken to provide meaningful and accurate information to key stakeholders to promote and disseminate this activity for customized hiring. Besides, it is important to encourage proper communication along with the value chain including the dissemination of information on the overall performance of agricultural machinery, appropriate training modules developed, and regular training sessions organized to train people involved in machine operations and those involved in repair and maintenance services. Training may also add an instrument to make bigger consciousness among the various stakeholders.