Figure 1.
Figure 2.
Figure 3.
Variables used to determine the synthetic measure of sustainability of surveyed farms in Poland, Romania and Lithuania
| Sustainability component | Variable name | Variable type* | Weight of variable for the individual sustainability component | Weight for the synthetic measure of sustainability |
|---|---|---|---|---|
| Economic | Income gap indicator (difference between average income in the national economy and total income of the agricultural holding) | D | 0.1280 | 0,3304 |
| Subjective assessment of the household's financial situation | S | 0.3398 | ||
| Level of agricultural investment | S | 0.3356 | ||
| Estimated market value of the holding | S | 0.1967 | ||
| Social | Dwelling/house furnishing index | S | 0.1819 | 0,3089 |
| Usable floor area of dwelling/house per family member | S | 0.0959 | ||
| Participation in lifelong learning system | S | 0.1511 | ||
| Participation in social or cultural events | S | 0.2823 | ||
| Membership in an organisation, club, association, etc. | S | 0.2887 | ||
| Environmental | Livestock Units (LSU) per ha of UAA** | D | 0.1383 | 0,3608 |
| Monoculture index | D | 0.2730 | ||
| Eco-efficiency (according to DEA) | S | 0.1133 | ||
| Share of forest in the farm area | S | 0.0315 | ||
| Share of permanent grassland in the farm area | S | 0.0784 | ||
| Share of arable land covered with vegetation during winter | S | 0.1992 | ||
| Balance of soil organic matter*** | S | 0.1664 |
The most important barriers to the use of artificial intelligence among small farms in Poland, Romania, and Lithuania
| Poland | Romania | Lithuania |
|---|---|---|
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Basic statistics for the ‘Top 20’ farms, 2020 (values in brackets for the entire population involved in the questionnaire survey)
| Farm characteristics | Average value | ||
|---|---|---|---|
| Poland | Romania | Lithuania | |
| Farm area (ha of UAA) | 13.4 (14.1) | 13.2 (12.1) | 10.3 (10.5) |
| Standard output (EUR/year) | 17.905 (12.830) | 12.650 (10.320) | 7.501 (5.614) |
| Household income (EUR/month)-only from agriculture | 1.917 (1.843) | 1.219 (1.106) | 1.230 (1.022) |
| Share of support in agricultural income | 39% (35%) | 57% (50%) | 58% (55%) |
| Estimated farm value (thous. EUR) | 209.6 (n/a) | 25.7 (24.5) | 51.5 (49.7) |
| Estimated farm liabilities (thous. EUR) | 6.6 (n/a) | 3.0 (2.6) | 0.4 (0.5) |
| Age of farm manager | 49 (49) | 46 (47) | 48 (48) |
| Level of education of farm manager* | 4.9 (4.6) | 4.8 (4.5) | 5.1 (4.9) |
The average value of indications regarding the statements on attitude towards AI technologies among farm owners from Poland, Romania, and Lithuania
| Component | The statement | Poland | Romania | Lithuania |
|---|---|---|---|---|
| Cognitive (Behavioural beliefs) | Most AI technologies have features assigned to them. | 5.45 | 5.20 | 5.10 |
| The use of AI technologies improves efficiency of farm's production. | 5.15 | 4.65 | 4.50 | |
| Emotional (Normative beliefs and subjective norms) | I am full of appreciation seeing what applications AI technologies can have. | 5.25 | 4.35 | 4.10 |
| I would have confidence in using AI technology. | 4,45 | 3.65 | 3.25 | |
| Behavioural (Behaviour) | I would not have a problem with implementing AI technology in my work. | 3.55 | 3.20 | 3.05 |