Are small farms sustainable and technologically smart? Evidence from Poland, Romania, and Lithuania
Sebastian Stępień
Poznań University od Economics and Business, Department of Macroeconomics and Agricultural EconomicsPoznań, Poland
Profilo OrcidCerca questo autore su
, Katarzyna Smędzik-Ambroży
Poznań University od Economics and Business, Department of Macroeconomics and Agricultural EconomicsPoznań, Poland
Profilo OrcidCerca questo autore su
, Jan Polcyn
Stanislaw Staszic State University of Applied Sciences, Department of EconomicsPiła, Poland
Profilo OrcidCerca questo autore su
, Aleksy Kwiliński
London Academy of Science and BusinessLondon, England
Profilo OrcidCerca questo autore su
e 30 mag 2023
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 30 mag 2023
Pagine: 116 - 132
DOI: https://doi.org/10.2478/ceej-2023-0007
Parole chiave
© 2023 Sebastian Stępień et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Variables used to determine the synthetic measure of sustainability of surveyed farms in Poland, Romania and Lithuania
| 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
|
too small area of the farm (45%) too small scale of production (50%) too high price/cost of new technologies (30%) lack of knowledge in this field (35%) attachment to traditional production methods (15%) use of artificial intelligence is risky (10%) |
too small area of the farm (35%) too small scale of production (40%) too high price/cost of new technologies (45%) lack of knowledge in this field (50%) attachment to traditional production methods (45%) artificial intelligence will not replace humans (10%) |
too small area of the farm (35%) too small scale of production (30%) too high price/cost of new technologies (40%) lack of knowledge in this respect (40%) attachment to traditional production methods (25%) using AI is ineffective (15%) |
Basic statistics for the ‘Top 20’ farms, 2020 (values in brackets for the entire population involved in the questionnaire survey)
| 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
| 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 |