Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”
Abstract
:1. Introduction
1.1. Background Global Climate Change
1.2. Background to the “Stable 4.0” Research Initiative
1.3. Objectives and Problem Description in the “CowEnergySystem” Research Project
2. Materials and Methods
- (a)
- the application practice that influences its relative advantage,
- (b)
- the user population and its environment, which influence the perception of the relative advantage in practice,
- (c)
- the ease and speed of learning, which influence the willingness to use it in practice,
- (d)
- the potential user’s ability to put the application into practice.
3. Results
3.1. Main Results
3.2. Analysis of the Influence on the Acceptance Level
3.3. Analysis on the Influence on Adoption Duration
4. Discussion
4.1. Discussion of the Main Results
4.2. Discussion of the Learning Properties Category
4.3. Discussion Category Advantageousness
5. Conclusions
- (1)
- For the EMS in question, a market acceptance rate of 98% was calculated within the pre-defined user group and the diffusion period until maximum market penetration was estimated at 8 years. This is the first time that valid figures on the diffusion of this innovation have been presented.
- (2)
- However, when using ADOPT, it must be critically noted that in some cases the calculation algorithms and the variable weighting in the tool are not described in a sufficiently comprehensible manner. This puts the (comparatively very positive) overall result in a somewhat critical light.
- (3)
- In the in-depth impact analysis of the (2 × 2) variable categories used in the tool, individual main influencing factors such as “company size”, and “current and future benefit expectation”, were found to be significant for the intensity of adoption, indicating a need for further investigation. This finding is in line with the value drivers mentioned in the survey, which can directly and extensively influence a purchase decision. It will therefore be necessary for the project team to develop economic principles and the necessary framework conditions, especially for the economic added value, to provide farmers with reliable economic information about the EMS used, which can have a positive influence on the purchase decision.
- (4)
- The other references to the detailed results of individual factors, which provide an interesting statement on the spread of new technology, are also very useful. The analysis of the influencing factors and the measures derived from them is particularly important for developing a future marketing concept. The focus here is on both target-group-oriented and region-adapted advertising of the on-farm energy management system and the appropriate variable assignment in the tool.
- (5)
- Although ADOPT was originally designed purely for the agricultural sector, it is already being used for analyses outside the agricultural sector [28,29]. In comparisons, good correlations have already been established between the forecast results of ADOPT and practical figures, for example for automatic steering systems and direct sowing [26]. For example, factors influencing the introduction of photovoltaic systems for water pumping in Australian sugar cane irrigation systems have also been evaluated by the ADOPT tool [30].
Outlook
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Question | Response | Scaling |
---|---|---|
Relative Advantage for the Population | ||
1. Profit orientation | Almost all have maximising profit as a strong motivation | 5-(1-5) |
2. Enviromental orientation | About half have protection of the environment as a strong motivation | 3-(1-5) |
3. Risk orientation | A majority have risk minimisation as a strong motivation | 4-(1-5) |
4. Enterprise scale | About half of the target farms have a major enterprise that could benefit | 3-(1-5) |
5. Management horizon | A majority have a longterm management horizon | 4-(1-5) |
6. Short term constraints | About half currently have a severe short-term financial constraint | 3-(1-5) |
Learnability Characteristics of the Innovation | ||
7. Trialable | Easily trialable | 4-(1-5) |
8. Innovation complexity | Not at all difficult to evaluate effects of use due to complexity | 5-(1-5) |
9. Observability | Not observable at all | 1-(1-5) |
Learnability of Population | ||
10. Advisory support | About half use a relevant advisor | 3-(1-5) |
11. Group involvement | A majority are involved with a group that discusses farming | 4-(1-5) |
12. Relevant existing skills & knowledge | About half will need new skills and knowledge | 3-(1-5) |
13. Innovation awareness | A minority are aware that it has been used or trialed in their district | 2-(1-5) |
Relative Advantage of the Innovation | ||
14. Relative upfront cost of the project | Moderate initial investment | 3-(1-5) |
15. Reversibility of the innovation | Difficult to reverse | 2-(1-5) |
16. Profit benefit in yearsthat it is used | Moderate profit advantage in years that it is used | 6-(1-8) |
17. Future profit benefit | Moderate profit advantage in the future | 6-(1-8) |
18. Time until any future profit benefits are likely to be realised | Immediately | 5-(1-6) |
19. Environmental costs & benefits | Very Large environmental advantage | 8-(1-8) |
20. Time to environmental benefit | Immediately | 5-(1-6) |
21. Risk exposure | Large reduction in risk | 7-(1-8) |
22. Ease and convenience | Large increase in ease and convenience | 7-(1-8) |
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Bader, C.; Stumpenhausen, J.; Bernhardt, H. Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”. Energies 2024, 17, 435. https://doi.org/10.3390/en17020435
Bader C, Stumpenhausen J, Bernhardt H. Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”. Energies. 2024; 17(2):435. https://doi.org/10.3390/en17020435
Chicago/Turabian StyleBader, Christoph, Jörn Stumpenhausen, and Heinz Bernhardt. 2024. "Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”" Energies 17, no. 2: 435. https://doi.org/10.3390/en17020435
APA StyleBader, C., Stumpenhausen, J., & Bernhardt, H. (2024). Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”. Energies, 17(2), 435. https://doi.org/10.3390/en17020435