4.1. Technical Efficiency of Agricultural Holdings
We estimate the technical efficiency of agricultural holdings by DEA input–orientation model with the aim to minimize inputs given the output. We use the EMS software (Efficiency Measurement System) to calculate the relevant technical efficiency. The technical efficiency scores for 1109 agricultural holdings were distributed into the regions (NUTS III), districts (LAU 1), and production areas. We analyzed firstly the administrative classification by the regions and districts, and the results are presented in
Table 3. The administration units are characterized by the geometric mean, maximum, and minimum of technical efficiency scores. We used the geometric mean, because it is less influenced by the outliers than the arithmetic mean.
The results suggest that the technical efficiency scores of the agricultural holdings in the particular regions range from 23% to 54% in 2016 and an input waste ranges from 46% to 77%. The smallest technical efficiency is in the Žilina region on the north of the country where the poorest soils are concentrated and the mountain occupied the most area of the region. There are 11 districts and the district Turčianske Teplice has received the biggest technical efficiency score (45.52%) from all districts in the Žilina region. However, there are also agricultural holdings in Čadca district, Tvrdošín district, or Ružomberok district where some of the agricultural holdings are effective (maximum efficiency score is 100%). It means that it is possible to be effective also in the regions where the conditions for agricultural business are not ideal. The most efficient region is the Trnava region with the technical efficiency score 54.2%. However, there is also a very big gap among its seven districts, mainly between the Senica districts and the rest of them. In spite of this fact, the Senica district is one of those where the agricultural holdings are able to be effective for 100%. We can stay that the natural conditions for agriculture are not the barriers to be effective in agricultural business. The most of agricultural holdings do not know or do not want to transform inputs to effective output.
To compare the results among the regions, we use the non-parametric Kruskal–Wallis test and the multiple range tests available in Statgraphics Plus. The technical efficiency is statistically significant different among the regions (Test statistic 198.11 with the
p-value = 0.0). The multiple range tests showed that the statistically significant differences are among three groups of regions. The first one is created by the Bratislava region, the Nitra region, and the Trnava region with the best technical efficiency scores (from 49.01% to 54.2%). The second group is created by the Košice region (36.04%) and the Trenčín region (38.63%). The third group with the smallest technical efficiency scores is created by the Žilina region (23.98%) and the Prešov region (24.41%). Only the Banská Bystrica region (30.59%) cannot be without doubt to include in the second or the third group. On the basis of multiple range tests (the Tukey highly significant difference (HSD) test, the Scheffe test, and the Bonferroni test), the Banská Bystrica regions should be included in the third group. However, the least significant difference (LSD) test, Duncan test, and Newmann–Keuls test are confirmed as included in the second group. We prefer to include the Banská Bystrica region in the second group on the basis of the interval classification of districts presented below. Regardless of the groups of regions, the results confirm that it is necessary to increase the technical efficiency scores of agricultural holdings in each region from 46% to 77%. The results confirm that the most of the effective agricultural holdings are concentrated on the west of Slovakia. Similar results were confirmed by Bartová et al. [
46] and Čechura et al. [
47] that analyzed the technical efficiency of dairy farms in the Czech Republic and Slovakia on the level NUTS II. In both studies, the best technical efficiency scores of dairy farms were found in west Slovakia in comparison to Middle and East Slovakia. Bartová et al. [
46] argued that the problem of inefficiency of dairy farms is related to all Slovak regions, and was mainly a result of managerial underperformance and then a non-optimal farm scale. However,
Table 3 confirms that not only the districts of West Slovakia are able to be effective. Regarding the minimum and maximum values of the technical efficiency scores in
Table 3, it is not possible to conclude that the technical efficiency of the agricultural holdings is more homogenous on in West Slovakia than in the other regions of the country. Therefore, we try to analyze the statistical significance differences among the districts. The technical efficiency is statistically significantly different among the districts (Test statistic 302.611 with the
p-value = 0.0). The multiple range tests showed that the statistically significant differences are between districts of Gelnica, Žilina, and Bytča on the one hand, and Senec, Malacky, Trenčín, and Partizánske on the other hand. For better visualizing the technical efficiency of agricultural holdings in the districts, we used
Figure 1. We joined the districts Bratislava I to V into one district Bratislava and districts Košice I to IV and Košice-okolie into one district Košice, due to the small number of agricultural holdings in the particular districts. We have 71 districts displayed in
Figure 1.
The smallest technical efficiency scores were calculated in the Žarnovica district, and the highest one was calculated in the Senec district. The districts were divided into six classes; the first class includes the districts with the most effective agricultural holdings. The classes were calculated as the share of the range (the difference between the largest and smallest values) and the number of classes. The number of classes was calculated as follows:
, where n is the number of the districts, and k is the number of classes. We chose six classes from the calculated interval <3.026; 6.88> because of a detailed view. According to
Figure 1, we can conclude that five districts have an average technical efficiency that is much higher than the average technical efficiency of the regions where the districts belong, and four districts have an average technical efficiency that is much lower than the average technical efficiency of the regions where the districts belong. The outliers are documented in
Table 4.
Table 4 confirms that the homogenous group of the agricultural holdings in relation to the technical efficiency scores are concentrated only in the Bratislava region and the Nitra region. Other regions received the heterogeneity of the agricultural holdings in relation to the technical efficiency scores in the particular districts that the regions include. These facts should be regarded when creating the regional policy for agriculture. Most of the regions regarding the heterogeneity of agricultural holdings in the particular districts need to take into account the special acceptation of these districts in the process of preparing the new political measures for agricultural sustainability in those districts. The results of DEA analysis enable identifying the perspective agricultural districts for agriculture as well as the districts with special needs. The comparison of technical efficiency differences could be a basic material for the adoption of new political measures to support sustainable agriculture in the particular districts in Slovakia.
Secondly, we expected that the heterogeneity of the technical efficiency of the agricultural holdings in the particular districts of a region is dependent on the agricultural classification of the country. We have five production areas in Slovakia:
maize production area (up to 200 m above sea level; south-west and south-east of Slovakia where most of the lowlands are situated);
sugar beet production area (200–300 m a.s.l.; north parts of the lowlands mentioned above);
potato production area (300–500 m a.s.l.; lower parts of the highlands in Middle and North Slovakia);
potato–oat production area (500–600 m a.s.l.; lower parts of the mountains in Middle and North Slovakia);
mountain production area (over 600 m a.s.l.; mountains region in North Slovakia).
Therefore, we expected the statistically significant differences of the technical efficiency scores among the production areas. We found out by the Kruskal–Wallis test (test statistic 154.99 with the
p-value = 0.0) and multiple range tests that there are statistically significant differences among the maize production area, sugar beet production area, and the rest of them; however, there are no statistically significant differences among the potato production area, potato–oat production area, and mountain production area. The results of technical efficiency scores are in
Table 5.
Then, we compared the results of
Table 4 and
Table 5, and concluded that the agricultural holdings are able to be effective, regardless of the production area. Therefore, we supposed that production areas are not one of the most important factors that are able to predict the efficiency of an agricultural holding. Our hypothesis was partially confirmed by the econometric models in the further analysis.
4.2. Factors Influencing the Technical Efficiency of the Agricultural Holdings
Some of the scientific studies try to identify the factors of technical efficiency of agricultural holdings. Madau et al. [
48] argued that differences in the technical efficiency may be caused by the structural differences among the agricultural holdings, such as the size of an agricultural holding, age of managers, climatic changes, and managerial skills. Nowak et al. [
21] confirmed that the technical efficiency is positively influenced by factors such as the soil quality, the age of the head of the household, and the surcharges for investments; on the other hand, the size of the farm appeared to be irrelevant from the viewpoint of the technical efficiency of the agricultural sector. Pechrová [
49] confirmed the positive and statistically significant impact of agricultural subsidies from the Programme of Rural Development on the technical efficiency of the agricultural holdings in the Czech Republic, but she added that the projects financed from the Programme of Rural Development are different, and further research of these projects on the technical efficiency of the agricultural holdings is necessary. In Slovakia, Adamišin et al. [
50] analyzed the impact of legal forms of the agricultural holdings on the productivity of the agricultural holdings by the paired
t-test, and they concluded that the business companies are able to manage an agricultural business better than the agricultural cooperatives. Moreover, the effective management of an agricultural holding in a worse production area is able to be a good inspiration for other agricultural holdings not only in the surroundings, but also in the better production areas. Sojková and Covaci [
51] evaluated the technical efficiency by the parametric Stochastic Frontier Analysis (SFA analysis), and concluded that the agricultural holdings with the higher acreage of agricultural land are more effective. On the contrary, Fandel [
38], Bielik, and Rajčániová [
37] evaluated the technical efficiency by the non-parametric DEA analysis, and concluded that the most effective agricultural holdings cultivate up to 500 ha of agricultural land [
37] or cultivate up to 100 ha and more than 1000 ha of agricultural land [
38]. Sojková and Covaci [
51] considered that the problem of different results consists in outliers and in data likelihood. Kollár and Sojková [
39] confirmed the statistically significant impact of agricultural investment subsidies on the technical efficiency evaluated by SFA. However, the legal form and specialization of agricultural holdings were not confirmed as statistically significant factors. Sojková et al. [
40] evaluated the technical efficiency of agricultural holdings in the production areas and ANC areas by the SFA. They found out the statistically significant differences in the technical efficiency of the agricultural holdings between the production areas and LFA areas; however, a higher variability of the technical efficiency was found among the agricultural holdings in the better production areas. On the base of a brief summary of the results from the studies of the technical efficiency of agricultural holdings, mainly in Slovakia, we tried to evaluate the impact of selected natural, geographical, social, and economic factors on the technical efficiency of the agricultural holdings by the econometric models.
Firstly, we considered as independent variables all of these variables:
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natural, production, and geographical conditions such as region, production area, distance from the city, soil quality, and agricultural production diversity expressed by the Shannon’s equitability index;
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social conditions such as the education of managers, membership in the farmers’ associations, number of members in an agricultural holding, number of new jobs, share of employees with the university education to all employees in an agricultural holding;
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legal and economic conditions such as legal form, acreage of agricultural land, average number of employees, CAP subsidies per 1 ha of land (SAPS, greening, ANC, agri-environmental scheme payments, payments for ecological agriculture, WELFARE, state subsidies, payments for the common organization of the markets, investment subsidies), share of revenues from the agricultural production in total revenues, share of revenues from the agricultural production in total costs, share of revenues from the animal production in revenues from the agricultural production, share of total revenues in total costs, share of costs for material, services, and energy in total costs, share of costs for material, services, and energy in total revenues, rent payments for 1 ha of land, share of labor costs in total costs, share of property taxes in total costs, land tax for one hectare of agricultural land, share of ANC land in the total agricultural land, and share of irrigated land in the total agricultural.
However, the models consist of the independent variables described in
Table 2. Although we tried to include all above-mentioned variables, they should be excluded due to the multicollinearity of variables, heteroscedasticity of the model, or absence of the normality of residuals. We choose two models estimated by the OLS and TOBIT methods. The results are documented in
Table 6. Most of the results are similar in both models; therefore, we interpreted the variables from the OLS model, and we mentioned the variable in the TOBIT model only if there is a different result. In spite of the censured variable of technical efficiency, there are no serious differences between the models.
Agricultural production diversity is the statistically significant factor with a positive effect on the technical efficiency of the agricultural holdings. The positive regressive coefficient indicates that the technical efficiency increases with the higher agricultural production diversity. It means if the variable agricultural production diversity increases by 1 percentage point, the technical efficiency is expected to increase on average by 0.069 percentage points. This was confirmed by both models. Agricultural production diversity is one of the important factors of sustainable agriculture. Karley [
4] summarized research to assess the benefits to agricultural sustainability of introducing diversity at crop, field, farm, and regional scales. In the recent years, discussions on the biodiversity conservation on agricultural land also include energy crop production [
52], especially short-rotation coppice plants such as willows or poplars [
53,
54]. Practices that increase the diversity of crop and non-crop components of the field can contribute ecosystem services that benefit agriculture. For example, crop variety mixtures could be designed to promote the survival of rare arable plants [
55]. It follows that the political measures should consider also the support of agricultural production diversity within the agricultural policy.
The second group of independent variables included in the model consists of particular subsidies of the CAP. These are statistically significant only regarding the payments for areas with natural constraints; however, the coefficient is negative one, and is close to zero. It results that the ANC payments are not an important factor of technical efficiency, although they are statistically significant coefficient. The payment for animal welfare is not statistically significant in the OLS model. In the TOBIT model, this payment was statistically significant, but only at the
p-value of 10%. The welfare payment has a positive impact on the technical efficiency; however, the coefficient is close to zero. If the welfare payment increases by 1 euro per hectare, the technical efficiency is expected to increase on average by 0.0017 percentage points. We can conclude that in general, the CAP payments do not form a statistically a significant factor regarding the technical efficiency of the agricultural holdings. Pechrová [
49] arrived at a similar conclusion; she argued that on one hand, the Rural Development Programme subsidies contributed to the decrease of variance of the inefficiency term, but on the other, the effect is statistically significant only at a 90% level of significance [
49]. Kollár and Sojková [
39] confirmed the statistically significant impact of agricultural subsidies on the technical efficiency evaluated by SFA, but the coefficients were negative ones. Galluzzo [
56] recommended that Italian small farms smaller than 5 hectares need adequate financial supports allocated by the second pillar of the Common Agricultural Policy, and these subsides are pivotal in particular toward family farms located in the upland and hilly areas. The regression coefficients of the subsidies from the second pillar of CAP for greater farms were not statistically significant. We can conclude that the present CAP subsidies do not motivate the farmers to improve their technical efficiency. Of course, there is also the question of the distribution of subsidies. We had to exclude more than 160 agricultural holdings from the econometric model because of zero revenues from the agricultural production; however, they received the particular subsidies. On the other hand, there were smaller agricultural holdings with the revenues from the agricultural production, but they refuse the subsidies because of huge administration burden.
Other statistically significant indicators include the share of total revenues in the total costs (with the positive effect), labor costs, and property taxes in total costs (with the negative effect). The lawmaker should regard these results as very sensitive when considering the legislative amendment related to the labor market and property taxes. The share of revenues from the animal production in relation to the agricultural production revenues is one of the statistically significant factors, but with a negative effect. It means that if this variable increases by 1 percentage point, the technical efficiency is expected to decrease on average by 0.08 percentage points. The reason is that firstly, animal production needs higher costs than crop production, and secondly, most of the agricultural holdings are not able to sell their animal products with the profit: they are usually able only to cover their costs. This problem could be solved at least partially by membership in the farmers´ associations. There is a positive significant effect on the technical efficiency of the agricultural holdings. It means that membership in the farmers´ associations increases the technical efficiency on average by 0.03 percentage points.
The factors such as distance from the city, number of members in an agricultural holding, number of new jobs, and rent for land are not statistically significant factors for technical efficiency. The statistically significant factor is land tax, which has a positive effect on the technical efficiency. The land tax depends significantly on the soil quality and is derived from Act no. 582/2004 Coll. on local taxes. The higher land tax means usually higher soil quality, which enables receiving an agricultural holding with a higher technical efficiency. However, the positive significant effect expressed by the coefficient is minimal (only 0.0025).
The production areas are not significant factors regarding technical efficiency in the most of the cases. The regression coefficient of production areas is statistically significant only for the potato production areas. All the regression coefficients are negative ones, regardless of statistical significance. It means that the agricultural holdings in the maize production areas have the highest technical efficiency. The agricultural holdings received the technical efficiency that was 0.068 percentage points smaller in the potato production areas than in the maize production areas. In other production areas, the regression coefficient is not statistically significant, but in spite of this fact, it is interesting that the technical efficiency decreases in the sugar beet production areas on average by 0.03 p.p., and in the potato areas on average by 0.06 p.p., but in the potato–oat production areas, the technical efficiency decreases only on average by 0.01 p.p., and in the mountain production areas, it decreases only on average by 0.03 p.p. as in the sugar beet production areas. However, we would expect that the decrease of the technical efficiency would be still higher with the higher production areas. It is possible to consider that drought and missing rainfall moves the technical efficiency from the production areas to the mountain areas. Of course, there is necessary further research to confirm this opinion, because our models do not provide a statistically significant regression coefficient.
The last factor—region (NUTS III)—was considered in the econometric models. The statistical significance was received only in the Bratislava region and the Trnava region in the TOBIT model. The Banská Bystrica region is a benchmark; then, the regression coefficients confirm that the higher technical efficiency was received in the regions of the first group (the Bratislava region, the Trnava region, and the Nitra region). The regression coefficient of the Trenčín region is positive; therefore, the technical efficiency in this region is higher than in the Banská Bystrica region, but the regression coefficient of the Košice region is negative, and the technical efficiency in this region is smaller than in the Banská Bystrica region. The Trenčín region and the Košice region were included in the second group of the regions together by the Kruskal–Wallis test and multiple-range tests, and the including of the Banská Bystrica region was not without doubt in the second or third group. Therefore, the Banská Bystrica region should be included in this group with the highest probability rather than in the third group. It follows that the location of an agricultural holding in the particular region regardless of administrative or agricultural classification is not statistically significant in favor of its technical efficiency.