INTRODUCTION:

The efficiency of a farm unit can be denoted in terms of allocative efficiency which shows the potential of a farm to use inputs in most favourable proportions, given their respective prices and technical efficiency. In this study, we discuss about Technical Efficiency. Technical Efficiency is the proportion between actual and potential output of a production unit. A few empirical studies provide the estimates of Technical Efficiency of raising apple crop within a state/region. For instance, Gul (2006), measured the Technical Efficiency of apple farmers in Isparta, Karaman and Nigde provinces of Turkey. Lijia, Huo and Kabir (2013), measured the Technical Efficiency of apple farms in Shaanxi region of China. Minhas (2014), measured the Technical Efficiency of apple growers in Kullu District of Himachal Pradesh. Murtaza (2017), measured the technical Efficiency of apple growers in Balochistan Plateau of Pakistan. The results of these studies are useful for policymakers to rationalize the development policies for apple crop. However, no efforts has been made to analyse the technical efficiency of apple growers in Jammu and Kashmir. In this paper, we measure the technical efficiencies of apple growers in Jammu and Kashmir. We also tried to identify the various factors determining the Technical Efficiency levels in Jammu and Kashmir.

OBJECTIVES OF THE STUDY:

1. To estimate and compare technical efficiency of apple growers in the study area.

2. To estimate and compare determinants of inefficiency in the study area.

REVIEW OF LITERATURE:

Bhatt and Bhat (2014), in their study estimates the technical efficiency of 461 farmers from district Pulwama of Jammu and Kashmir for the year 2013-14 by employing Non-parametric Data Envelopment Analysis. Their study indicated that most of the farms were operating at low level of technical efficiency. Only 48 per cent farmers were found to be technically efficient which means that 52 per cent farmers are technically inefficient. Farm experience, occupation, Farm size, Household size, Membership and Seed type were assumed to be main determinants of their inefficiency.

Gul (2006), estimated technical efficiencies of apple production of 129 agricultural enterprises in Turkey during 2001 by employing Data Envelopment Analysis experienced that mean efficiency of sample apple farms were estimated to be 0.60 for constant return to scale assumption and 0.90 for variable returns to scale assumption. Total farm size likely to be the most significant factor affecting efficiency. Shanmugam and Venkataramani (2006), in their study talked about district level cross sectional secondary data of agricultural output for the year 1990-91 by employing Stochastic frontier production function model. They observed that the average technical efficiency is found to be 79.32 per cent in India which means 20.68 per cent farmers need to increase their agricultural output through existing inputs and technology. Health, Education, and Infrastructure were supposed to be main sources of efficiency.

MATERIALS AND METHODS:

Materials:

The present study based on primary data gathered through a well-structured interview schedule. The main purpose of this section is formulating a methodology comprising of the following.

Selection of the Area:

The universe for conducting the present study is Baramulla, Kupwara and Shopian districts of Jammu and Kashmir are selected purposively for field study. The reason behind this is that these districts cover highest area and production of apple cultivation.

Selection of sample Apple Growers:

Baramulla: In Baramulla district there are eight tehsils viz. Baramulla, Pattan, Sopore, Tanghmarg, Rohama, Boniyar, Keerri and Uri. Among them majority of apple growers are concentrated in Baramulla and Sopore. Therefore, these two tehsils are selected and a total number of 40 apple growers, 20 from each tehsil are selected purposively as sample for collecting the primary data for study purpose.

Kupwara: In the Kupwara district, there is three tehsils viz. Handwara, Karnah and Kupwara. Therefore, Hindwara and Kupwara tehsils are selected purposively because most apple growers are concentrated in these tehsils.

Shopian: In shopian district there is only one tehsil shopian. Therefore, 40 apple growers are selected from the same tehsil for the study purpose.

Methodology:

For estimating technical efficiency score and its various sources, Data Envelopment Approach (DEA)is used. For computing DEA, DEAP (Ver 2.1), a freely available software was used.

Estimation of Tobit Regression:

Tobit regression model is used to calculate the determinants of inefficiencies of apple growers in Jammu and Kashmir and Himachal Pradesh. It is estimated as follows:

Y = β₀ + β₁X₁+ β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇+ β₈X₈+ Ui

Where,

Y is the dependant variable (Technical Efficiency Score ranges between 0 to 1). The proposed determinants of technical efficiency include: X₁= Education (years of schooling); X₂=Farm size (Kanals); X₃= Farm Experience (years); X₄= Occupation (1= if agriculture and 0 = Non-agriculture); X₅= Age of the farmer (years); X₆= Household size (number of family members); X₇= Owned Assets (in rupees); X₈= Distance to farmland (kms); U_i is the error term.

RESULT:

Descriptive Statistics of sample apple growers in Jammu and Kashmir

The descriptive statistics of sample apple growers in Jammu and Kashmir are shown in the following table. The output (Farm Income) is the monetary value evaluated at current market price. The following table depicts that inputs used in the apple production vary across different farm sizes (Small, Medium, Large). The input Labour expressed as hired labour per year. The area under Apple Cultivation expressed as total area under apple cultivation in Kanals. The fertilizers used is expressed as Kg per Kanal and pesticides expressed in litre per Kanal.

The average Farm income of small, medium and large farmers is Rs 92735.87, Rs 109481.13 and Rs 92047.62 per Kanal. The average farm income increases with the increase in the farm size. The average area under apple cultivation of small, medium and large farmers is 10.20 Kanals, 15.60 Kanals and 27.24 Kanals. The average labour used by the large farmers was higher than the medium and small farmers. The average quantity of fertilizer used by small medium and large farmers was 1019.57 kg ,1560.38 kg and 2723.81 kg per Kanal. Similarly, the average quantity of Pesticides used by small, medium and large farmers was 20.39 litres, 31.21 litres and 54.48 litres per Kanal.

Table 1: Descriptive Statistics of sample apple growers in Jammu and Kashmir

Variable	Unit	Small (N=46)			Medium (N=53)
Variable	Unit	Minimum	Maximum	Mean	Minimum	Maximum	Mean
Farm Income	Rs/Kanal	34000	160000	92735.87	46000	190000	109481.13
Area under Cultivation	Kanal	3	16	10.25	4	30	14.61
No. of Labour days	Per Kanal	60	180	119.70	50	300	143.85
Fertilizer	Kg/Kanal	300	1500	1019.57	400	3000	1560.38
Pesticides	Litre/Kanal	6	32	20.39	8	60	31.21

Continue Table 1:

Variable	Unit	Large (N=21)
Variable	Unit	Minimum	Maximum	Mean
Farm Income	Rs/Kanal	65000	150000	92047.62
Area under Cultivation	Kanal	12	55	34.07
No. of Labour days	Per Kanal	50	260	161.43
Fertilizer	Kg/Kanal	1200	5500	2723.81
Pesticides	Litre/Kanal	24	110	54.48

Source: Field Survey (2018).

Table 1 shows the frequency distribution of all the variables used in Tobit Regression Model. Small farmers are more educated than medium and large farmers as shown in the Table 1. Only education is not enough to increase technical efficiency, but farm experience is also important factor to enhance the technical efficiency of growers. There were 6.52 per cent small growers, 9.43 per cent medium growers and only 19.05 per cent large apple growers having farm experience up to 10 years. There were 23.91 per cent small growers, 37.74 per cent medium farmers and 52.38 per cent large apple growers having farm experience between 11 to 20 years and 69.57 per cent small growers, 52.83 per cent medium growers and 28.57 per cent large apple growers having experience of more than 21 years in Jammu and Kashmir.

Table 2: Descriptive Statistics of the Variables used in Tobit Regression Model for Jammu and Kashmir

Variables	Unit	Small N=46		Medium N=53		Large N=21
Variables	Unit	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
Education	Illiterate	4	8.70	7	13.21	2	9.52
	Up to 8th	12	26.09	13	24.53	12	57.14
	9th to 12th	19	41.30	20	37.74	6	28.57
	Above 12th	11	23.91	13	24.53	1	4.76
Total		46	100	53	100	21	100
Farm Experience	Up to 10 years	3	6.52	5	9.43	4	19.05
	11-20 years	11	23.91	20	37.74	11	52.38
	21 Y and above	32	69.57	28	52.83	6	28.57
	Total	46	100	53	100	21	100
Occupation	Agriculture	29	63.04	34	64.15	11	52.38
Occupation	Non-Agriculture	17	36.96	19	35.85	10	47.62
Total		46	100	53	100	21	100
Age	Up to 50 Y	30	65.22	17	32.08	11	52.38
Age	Above 50 Y	16	34.78	36	67.92	10	47.62
	Total	46	100	53	100	21	100
Family Size	Up to 10	44	95.65	46	86.79	19	90.48
Family Size	Above 10	2	4.35	7	13.21	2	9.52
Total		46	100	53	100	21	100
Family Assets	Up to 5 Lac	46	100.00	33	62.26	5	23.81
Family Assets	Above 5 Lac	0	0.00	20	37.74	16	76.19
Total		46	100	53	100	21	100
Distance in Km	Up to 1 km	30	65.22	27	50.94	14	66.67
Distance in Km	Above 1 km	16	34.78	26	49.06	7	33.33
Total		46	100	53	100	21	100

Source: Field Survey (2018).

There were 63.04 per cent small growers, 64.15 per cent medium growers and 52.38 per cent large growers have agriculture as main occupation and 36.96 per cent small growers, 35.85 per cent medium growers and 47.62 per cent large apple growers having agriculture as subsidiary occupation. There were 65.22 per cent small growers, 32.08 per cent medium growers and 52.38 per cent large growers having age up to 50 years and 34.78 per cent small growers, 67.92 per cent medium growers and 47.62 per cent large growers having age more than 50 years. There were 95.65 per cent small growers, 86.79 per cent medium growers and 90.48 per cent large growers having family members up to 10 and only 4.35 per cent small growers, 13.31 per cent medium growers and 9.52 per cent large growers having more than 10 family members. Similarly, there were 100 per cent small farmers, 62.26 per cent medium farmers and 23.81 per cent large farmers have up to Rs 500000 family assets and only 37.74 per cent medium grower and 76.19 per cent large growers have more than Rs 500000 family assets.

Efficiency Estimates through Data Envelopment Analysis (DEA) in Jammu and Kashmir:

In order to determine the causes of inefficiency, technical efficiency (CRS), Pure Technical efficiency (VRS) and Scale efficiency were estimated. A farmer having technical efficiency score between 0.90 and 1 is treated as efficient farmer. The estimated results suggest that scale efficiency rather than technical efficiency is the major source of overall inefficiency.

Technical Efficiency (CRS) of sample apple growers in Jammu and Kashmir:

In Jammu and Kashmir Average scale efficiency came out to be 0.780 which is greater than pure technical efficiency i.e. 0.615. The estimated inefficiencies are mainly due to excessive use of inferior or low quality of inputs and lack of management. The average technical efficiency is 0.483 which implies that, on an average, the respondents are able to obtain around 48.3 per cent of potential outputs from a given mix of inputs. This also implies that around 51.7 per cent on an average is forgone due to technical inefficiency. In other words, around 51.7 per cent output shortfalls depicts inefficient use of factors that were within the control of growers. The technical efficiency level of the farms ranged from 0.165 to 1. The efficiency level varies across different farm sizes for small, medium, and large farmers ranges between 0.482 to 1, 0.165 to 1and 0.165 to 1 respectively. The average technical efficiency of small growers (0.719) is higher than that of medium (0.507) and large growers (0.677). There is only 8.70 per cent small farmers were technically efficient. The percentage of technical efficient farmers decreases to 7.55 percent for medium farmers and increases to 14.29 per cent for large farmers. Overall, 3.33 per cent farmers were technically efficient in Jammu and Kashmir as shown in the table 5.3.

Table 3: Percentage Distribution of the sample Apple Growers by Technical Efficiency Estimates (CRS) in Jammu and Kashmir

	Small		Medium		Large		Total
	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
<0.50	2	4.35	30	56.60	4	19.05	67	55.83
0.50<0.90	40	86.96	19	35.85	14	66.67	49	40.83
0.90<1	4	8.70	4	7.55	3	14.29	4	3.33
Total	46	100	53	100	21	100	120	100
Minimum	0.482		0.165		0.481		0.165
Maximum	1		1		1		1
Mean	0.719		0.507		0.677		0.483

Source: Field Survey (2018).

Pure Technical Efficiency (VRS) of sample apple growers in Jammu and Kashmir:

The table 3 shows the pure technical efficiency which account for variable returns to scale (VRS). The mean technical efficiency score for small, medium and large growers turned out to be 0.831, 0.661 and 0.773 respectively. The technical efficiency under variable returns to scale for small, medium and large apple growers ranged between 0.667 to 1, 0.345 to 1 and 0.575 to 1 respectively. The overall technical efficiency under variable returns to scale varied between 0.265 to 1. The estimated results states that overall mean technical efficiency under variable returns to scale is 0.615 (61.5 per cent) which means apple growers in Jammu and Kashmir were not operating at optimal scale. There is large scope for reducing the cost of inputs or maximizing the output at the same level of inputs. the results also show that only 9.17 per cent sample apple growers in Jammu and Kashmir were technically efficient under variable returns to scale.

Table 4: Percentage Distribution of the sample Apple Growers by Pure Technical Efficiency Estimates (VRS) in Jammu and Kashmir

	Small		Medium		Large		Total
	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
<0.50	0	0	14	26.42	0	0	40	33.33
0.50<0.90	35	76.09	28	52.83	17	80.95	69	57.50
0.90<1	11	23.91	11	20.75	4	19.05	11	9.17
Total	46	100	53	100	21	100	120	100
Minimum	0.667		0.345		0.575		0.265
Maximum	1		1		1		1
Mean	0.831		0.661		0.773		0.615

Source: Field Survey (2018).

Scale Efficiency:

Scale efficiency allows us to gain insights into the main source of inefficiencies. The value of scale efficiency (SE) equals to 1 implies that apple grower operating at the most productive scale size (MPSS) which corresponds to constant returns to scale. At most productive scale size, the apple grower operates at minimum point of its long-run average cost curve. If scale efficiency is less than 1, then the apple grower is experiencing overall technical inefficiency (TIE) because it is not operating at its optimal scale size. Thus, scale efficiencies are usually a consequence of the better and more efficient use of production factors (Bhatt and Bhat, 2014).

Table 5.5 depicts that average scale efficiency for small, medium, and large growers were 86.6 per cent (0.868), 75.2 per cent (0.752) and 87.1 per cent (0.871) respectively. The scale efficiency was higher relatively as compared to technical efficiency under variable returns to scale. At the aggregate level, the mean scale efficiency worked out to be 78 per cent (0.78) which was also relatively higher compared to technical efficiency under variable returns to scale. The table shows that overall scale efficiency scores ranged between 0.225 to1. For small apple growers scale efficiency varied between 0.482 to 1. For medium apple growers scale efficiency varied between 0.342 to 1 and for large apple growers it ranged between 0.565 to 1 respectively. This implies that average level of scale inefficiencies (SIE) of apple growers in study area were to the tune of about 22 per cent. The percentage of scale efficient farmers varies across different farm sizes it first decreases from 50 per cent for small growers to 30.19 per cent for medium growers and then increases to 47.62 per cent for large apple growers.

Table 5: Percentage Distribution of the sample Apple Growers by Scale Efficiency Estimates in Jammu and Kashmir

	Small		Medium		Large		Total
	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
<0.50	1	2.17	5	9.43	0	0	11	9.17
0.50<0.90	22	47.83	32	60.38	11	52.38	65	54.17
0.90<1	23	50	16	30.19	10	47.62	44	36.67
Total	46	100	53	100	21	100	120	100
Minimum	0.482		0.342		0.565		0.225
Maximum	1		1		1		1
Mean	0.868		0.752		0.871		0.78

Source: Field Survey (2018).

Comparison of sample Apple Growers with various return to scale in Jammu and Kashmir:

Table 6 shows that 3.33 per cent apple growers attain scale efficiency score equal to 1 (Constant returns to scale) which means 3.33 per cent apple growers operating at Most Productive Scale Size (MPSS). About 86.67 per cent of apple growers are operating with increasing returns to scale and 10 per cent growers operated under decreasing returns to scale during study period. The table shows that most of the apple growers were in the early expansionary stage and hence lot of scope there to improve the efficiency through proper reallocation of the resource use. Out of total number of growers only 4 (3.33 per cent) are operating efficiently under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) working under Most Productive Scale Size (MPSS). There are 12 (10 per cent) growers operating under Decreasing Returns to Scale (DRS) and 104 (86.67 per cent) apple growers are operating under Increasing Returns to Scale (IRS). Table 5.6 also shows that high percentage share of scale efficient growers was in the group of large apple growers (14.29 per cent). More than 14 per cent apple growers operating at Most Productive Scale Size (MPSS) while only 6.52 per cent small farmers operating at Most Productive Scale Size (MPSS). Majority of the apple growers operated below the optimal scale that is 65.22 per cent small apple growers,75.47 per cent medium apple growers and 61.90 per cent large apple growers are operating under Increasing Returns to Scale (IRS). It means that their productivity could increase further. It also shows that about 28.26 per cent small apple growers, 18.87 per cent medium apple grower and 23.81 per cent large apple growers are operating under Decreasing Returns to Scale (DRS) implying that their productivity could increase by smaller proportion. Thus, Decreasing Returns to Scale seems to be an appropriate strategic option for these apple growers.

Table 6: Comparison of the number and percentage of sample Apple Growers with various return to scale in Jammu and Kashmir

Category	Small		Medium		Large		Total
Scale Efficient Farmers	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
Constant	3	6.52	3	5.66	3	14.29	4	3.33
Decreasing	13	28.26	10	18.87	5	23.81	12	10.00
Increasing	30	65.22	40	75.47	13	61.90	104	86.67
Total	46	100	53	100	21	100	120	100

Source: Field Survey (2018).

Tobit Regression Model Results of sample apple growers of Jammu and Kashmir:

Previously, we estimated technical efficiency of sample apple growers by DEA approach. Now, in this section the determinants of inefficiency are computed by using Tobit Regression Model. Table 7 shows the estimated results of the Tobit Regression. The model is fit since the p-value is 0.0387134 and is significant at 1 per cent level. In addition to this the Mean of Dependent Variable (TE) is 0.614983, Standard Deviation of Dependent Variable is 0.189346 and log likelihood is 51.909.

Education of the farmers found to be positively related to farm efficiency, but the relationship was not significant. The result shows that one year of increase in schooling will increase the farm efficiency by 0.8 per cent. More educated respondents were likely to be more efficient as compare to less educated respondents. Possible reason for positive correlation could be their better skills and access to information. Similar results were reported by Haq and Boz (2019).

Farm size is found to have a negative effect on technical efficiency, but it is significant at 5 per cent. Which means that one kanal increase in the farm size results 0.6 per cent falls in the efficiency of the growers. Similar results are also reported by Tipi.et.al. (2009) and Mohsen (2017).

Farm experience has positive impact on technical efficiency of the sample apple growers but not significant. The results indicate that apple growers with more years of experience are technically efficient. Similar results are also confirmed by Mohsen (2017).

Table 7: Tobit Regression Results of sample apple growers in Jammu and Kashmir

VARIABLE	COEFFICIENT	STD.ERROR	T- STAT	P-VALUE
Constant	0.538222	0.106372	5.06	<0.00001
Education	0.00886	0.006899	1.284	0.19921
Farm Size	-0.00625	0.002421	-2.583	0.00980 **
Farm exp.	0.00773	0.006384	1.211	0.22600
Occupation	-0.08666	0.052481	-1.651	0.09867 *
Age	0.002936	0.001545	1.9	0.05741 *
Household Size	0.011113	0.011818	0.94	0.34704
Owned Assets	0.185542	0.04499	4.124	0.00004 **
Distance	-0.014828	0.020752	-0.715	0.47492
Mean of Dependent Variable = 0.614983 Standard Deviation of Dependent Variable. = 0.189346		Sigma = 0.156999 Log-likelihood = 51.909 Test statistic: Chi-square (2) = 6.50314 with p-value = 0.0387134

Source: Field Survey (2018).

Note: *Significant at 5% Level, **Significant at 10% Level.

In Jammu and Kashmir apple growers’ primary occupation showed a negative effect on technical efficiency. The estimated results show that as the occupational pattern shifts from agriculture as main occupation to agriculture as secondary occupation, the probability of technical efficiency decreases by 8.6 per cent.

The age of the household head shows a positive effect on technical efficiency and the relationship is significant at 5 per cent level. The result shows that an increase in farmer’s age by one year, increase the level of probability of technical efficiency by 0.2 per cent. This implied that aged apple growers are more technical efficient than their younger counterpart. Similar results are also reported by Tipi.et.al. (2009) and Mohsen (2017).

Household size is an important variable especially in Indian agriculture which is labour intensive. In this study our results show that household size positively related but insignificant. This means that increase in the number of family members, increases the probability of technical efficiency of farmers. Due to insignificant relationship, apple growers did not benefit by increasing number of family members. Similar results were reported by Haq and Boz (2019).

Household assets shows positive relation and significant at 10 per cent level. The results show that household assets led to increase in the probability of technical efficiency of apple growers by 18.5 per cent. According to Sibiko.et.al (2012), owing household assets are important to access credit by which farmers can purchase different kinds of agricultural implements. Similar results are reported by Yaw-Shun Yu (2012).

Distance between apple growers’ home and farmland shows a negative relation with technical efficiency of farm productivity but the relationship is not significant. The results show that an increase in the distance to the farmland by one kilometre led to decrease in the technical efficiency by 1.4 per cent. This means that farther the farm from the respondent’s home greater is the cost of transport, management, and opportunity cost (Bhatt and Bhat,2014).

CONCLUSION:

Data Envelopment Analysis is used to estimate technical efficiencies in Jammu and Kashmir. It has been concluded that in Jammu and Kashmir 48.3 per cent apple growers are found technical efficient. Small and large farmers are more efficient as their value of technical efficiency is higher. In case of pure technical efficiency (VRS) mean technical efficiency of Jammu and Kashmir is lower. It was 61.5 per cent only. Apple growers of Jammu and Kashmir are only 78 per cent scale efficient. In Jammu and Kashmir 86.67 per cent apple growers are operating under increasing returns to scale, 10 per cent of the total are operating under decreasing returns to scale and only 3.33 per cent apple growers are under constant returns to scale.

Tobit regression results shows that in Jammu and Kashmir farm size, occupation, and distance from home to farm showed an inverse relation with the efficiency of the farmers which means that as the farm size, distance and occupation from agriculture to non-agriculture increases, efficiency of the growers decreases simultaneously. But education farm experience, age of the farmer, their family size, and assets they owned are related to their efficiency.

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Received on 24.05.2024 Revised on 19.08.2024

Accepted on 23.10.2024 Published on 18.12.2024

Available online on December 27, 2024

Int. J. Ad. Social Sciences. 2024; 12(4):195-202.

DOI: 10.52711/2454-2679.2024.00031

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