This article discusses the outcomes of a quantitative analysis using econometric panel data models of the competitiveness of grains in the countries of the Eurasian Economic Union (EAEU). The analysis was based on the public authorities’ statistics of EAEU countries as well as the United Nations Comtrade Database, which is a repository of official international trade statistics. The results of the analysis allowed us to assess the level of competitiveness of the agricultural products produced in EAEU countries and to determine the extent to which various factors affect the competitiveness. The research conclusions can be used to develop and adjust the agricultural policy in the EAEU.

For over two decades, the construction of wind turbines in Germany has been supported by guaranteed feed-in tariffs determined by the Renewable Energy Sources Act (EEG), the primary goal of which is climate protection, in addition to reducing the country’s dependence on the import of (finitely available) fossil fuels. After China and the United States, Germany ranks third worldwide in the production of wind energy. The number of onshore wind turbines in Germany has risen to approximately 30,000 plants, of which approximately 10,000 wind turbines will fall out of the guaranteed EEG funding window in the next one to two years. There are basically two alternatives for these wind turbines: either continuing operations, with the sale of electricity at relatively low and fluctuating electricity stock prices, or repowering, which opens access to the fixed feed-in tariffs for another 20 years. However, repowering has the disadvantages that an approval process must be carried out and the investor must participate in a tender. There is no guarantee for the granting of a building permit; economically feasible operations also depend on the fact that one can win a contract without the submitted price being set too low. This area of tension is illustrated by a wind farm in Mecklenburg-Western Pomerania and analysed economically. The investment in new, more efficient, and larger wind turbines currently promises a high return. The profitability of the investment in wind turbines is determined using the net present value (NPV) method. In addition, a risk analysis is carried out using stochastic simulation. As a result, the feed-in tariff contributes to over 95% of the variance in the net present value (NPV).

Only a small number of German farmers invest in wind turbines on the land they own; rather, they tend to lease land for that purpose to external investors. In this study, an explorative qualitative analysis of the reasons for this investment pattern was conducted. Calculations of the economic efficiency (e.g., net present value, payoff) of wind turbines built the foundation of the study. In addition, farmers in the northeastern federal state of Mecklenburg-Western Pomerania were interviewed to assess their motives for capital expenditure. A considerable amount of equity capital is required to set up a wind turbine. The capital invested frequently competes with the purchase of agricultural land. Erecting wind turbines involves risks. Substantial financial means for planning permission and other examinations precede the construction of a wind turbine. Only at the very end of these costly investigations will a construction permit be issued; as such, farmers face a high level of uncertainty during the whole process. Fluctuations in wind yield resulting in volatile revenues confront farmers with additional financial uncertainties. The risk-taking behavior of farmers was assessed normatively by the Hurwicz criterion. The results showed that farmers with the necessary funds at their disposal and a high level of optimism were more likely to take the risk to set up a wind turbine on their own land. They make this decision despite the fact that leasing one’s own land to external investors is predictably profitable and clearly less risky.

A climate-friendly and CO₂-neutral energy supply for agricultural farms is the subject of investigation of this study. The supply concerns the internal economy (buildings and animal husbandry) as well as the production of synthetic fuels for outdoor work (cultivation of the fields). This energy is in demand with many customers, e.g., the dairy cooperative Arla Foods, whose goal is the production of cow’s milk with net-zero CO₂ emissions by the year 2050. The operational energy system considered here included renewable electricity generation, covering electricity consumption in the cowshed, battery storage for times without electricity generation, the production of synthetic fuels and feeding into the public power grid. Fluctuations depending on the day and the season were taken into account for electricity at 15-min intervals and for fuel per calendar week for one year. The aim was to determine the necessary capacities of renewable energy (RE) generation systems and production systems for synthetic fuel, as well as an economic evaluation with the calculation of the energy costs per kWh and the break-evens for the capital expenses (CapEx). Two different scenarios were developed using the example of a survey dairy farm with an annual electricity consumption of approximately 80,000 kWh in the cowshed and an annual diesel consumption of 35,000 L, corresponding to 350,000 kWh for field work. To ensure the energy supply, Scenario 1 required a photovoltaic system (PV) on the roof with an output of 125 kWp, a 250 kW small wind turbine, a battery with a storage capacity of 2 kWh and synthetic fuel production with an output of 210 kW. Scenario 2 required a 200 kWp PV system on the roof and a 520 kWp PV system in the open fields, a battery with a 105 kWh storage capacity and a synthetic fuel production facility with an output of 385 kW to cover the farm’s energy needs. The results showed that a farm’s own electricity production is currently profitable; however, a farm’s production of synthetic fuel still has comparatively high costs and therefore is not yet profitable. Further technical advances, rising prices of fossil fuels and economies of scale, e.g., larger cooperatively-operated plants, could help new technology to make a breakthrough.

The production of renewable energy fluctuates in terms of sun and wind and must be supplemented by storage in the system. On an individual basis, i.e., for centralized electricity production and predominantly self-consumption, the use of batteries is considered here. Possible future development scenarios were simulated based on current price relationships (status quo). In the status quo, a selling price for PV electricity of 13 Euro cents (ct)ct/kWh was assumed with a production cost of 11 ct/kWh. The selling price of wind power is 5 ct/kWh with a production cost of 3 ct/kWh. The cost of storing electricity in a battery increases the price by 33 ct/kWh. A price of 20 ct/kWh is assumed for electricity purchases by companies. In the status quo, the use of batteries is not economical given the assumed price relationships. Changing the framework conditions, such as those of the legislature in Germany with the nuclear power phase-out and in the EU with the coal exit and decarbonization, will lead to increased availability of (fluctuating) renewable electricity, especially during the day. The purchase of electricity at other times, when the supply is scarce, can lead to increased electricity prices, especially at night. Together with falling costs for storage, the use of batteries for centralized power generators could be very interesting in the future. The method used in this study is nonlinear optimization of the target function costs of electricity supply in the developed simulation model. The results can also be transferred to other countries, as the assumed trends apply worldwide.

Combine harvesters used today can achieve throughput rates of over 70 tons of grain per hour; however, their technical performance potential is currently not being exhausted by any means. The global market for combine harvesters, its stocks and recent production is described. According to farm size distribution and regional field sizes, Germany was divided into three combine-harvesting regions. The simulation results show that in Germany about 45,000 units of combine harvesters in three performance categories are necessary. However, the calculations also show that future domestic sales of combine harvesters will depend greatly on the service life of the units currently in use.