Improvements in sustainability at the farm level are the basic driver of agricultural sustainability at the macro level. This is a challenge that can only be met by farms which efficiently process inputs into products. The increase in the efficiency of European farms is largely conditioned by measures taken under the Common Agricultural Policy (CAP), especially its second pillar. The purpose of this study was to determine the net effect of pro-investment instruments available under the second pillar of the CAP in selected Central and Eastern European countries. Unpublished Farm Accountancy Data Network (FADN) microdata provided by the European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI) were used as the source material. The study presented in this paper is unique in that the research tasks are based on unpublished microdata of selected Central and Eastern European farms. The study relied on the Propensity Score Matching approach; the net effect of pro-investment mechanisms was analyzed using productivity and profitability indicators calculated for farms which have been keeping FADN records for a continuous period of no less than 6 years. As shown by the study, structural funds available under the CAP clearly provided an investment incentive for farms. The conclusion from the assessment of changes in the availability of productive inputs is that the beneficiaries reported a greater increase in fixed asset value and in farm area in all countries except for the Czech Republic and Slovakia. The comparative analysis of countries covered by this study failed to clearly confirm that labor is substituted with capital to a significant extent. Every country covered by this study experienced a noticeable negative net effect on both the productivity and profitability of capital. When considering all the countries, the beneficiary group has no clear advantage over the control group in terms of changes in land and labor productivity and profitability (a statistically significant positive effect was recorded for land productivity and profitability in Slovenia). As regards labor, a statistically significant positive net effect (a difference in growth rate between the beneficiary group and the control group) was recorded in Slovenia, but also in Poland, where beneficiary farms reported a greater increment in labor profitability and reduced the negative difference in labor productivity.

The functioning and development of each economic entity requires the implementation of investments. In the case of farms in Poland, which are characterised by structural problems and a lack of capital, it is essential to undertake investment activities of a modern nature. The main objective of this research was to assess agricultural investments made in farms, considering the example of the Wielkopolska Voivodship in Poland, from the point of view of their impacts on changes in the economic efficiency of production factors. The data source was unpublished individual data from the agricultural accounting “Polish FADN” results for all farms in the Wielkopolska Voivodship who had continuously participated in the data collection system from 2009 to 2021. The project’s complexity was determined by measuring the scale of investments, relating the sum of investment expenditures to the average annual value of fixed assets (reduced by land value). Farmers endowed with a greater asset base had the ability to invest more comprehensively (i.e., in their most desirable scale regarding the value of possessed fixed assets) in a manner primarily concerning the value of fixed assets, especially the agricultural area, as a primary agricultural production factor. In terms of the effects of differentiating the scale of investment outlays (which was the aim of the study), a better initial state allowed farms to increase their area significantly, with an average annual rate of change of 3%. On the other hand, a lack of investment led to the decapitalisation of assets (−6.7% per year), which also concerned land sales (−1.5% per year). These research results indicate that not only does the undertaking of investments, in general, impact the development possibilities of farms—and, therefore, improvements in the resource situation of farms—but also the scale of investment (with respect to the value of possessed fixed assets).

Farms need to invest in order to earn incomes and maintain their competitive edge. However, the scale and extent of investments must be aligned with resources of other productive inputs, primarily including land, because otherwise there is risk of overinvestment. Since 2004, Central and Eastern European countries have been provided with access to investment support programs; these are non-repayable aid funds which can potentially lead to overinvestment issues. Therefore, this paper attempts to answer the question on the scale of overinvestment in the countries covered. This is all the more important since that problem has rarely been addressed in economic and agricultural research. The study presented in this paper is unique in that the research tasks are based on unpublished Farm Accountancy Data Network (FADN) microdata for 5839 selected Central and Eastern European farms provided by the European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI). Based on variables relating to the amount of productive inputs and production volumes, the authors developed their own typology of farms which includes the following categories: optimum investment levels (the growth rate of labor productivity is greater than growth in the assets-to-land ratio); relative overinvestment (while labor productivity grows, it does so at a slower rate than the assets-to-land ratio); absolute overinvestment (labor productivity declines while the assets-to-land ratio grows); underinvestment (decline in both labor productivity and the assets-to-land ratio). The authors demonstrated that members of the ‘absolute overinvestment’ group made flagrant mistakes in investment planning and implementation, whereas members of the ‘relative overinvestment’ group did record an improvement in labor productivity which ultimately can be considered a positive outcome. Underinvested farms were a very small minority in each country. In addition to filling a gap in the methodology for determining agricultural overinvestment, this paper also indicates the scale of that issue in Central and Eastern European countries. This study may be of importance both to farms (as guidelines for investment planning) and to agricultural policymakers who develop investment support programs.

In many circles, brown coal continues to be viewed as a cheap source of energy, resulting in numerous investments in new opencast brown coal mines. Such a perception of brown coal energy is only possible if the external costs associated with mining and burning coal are not considered. In past studies, external cost analysis has focused on the external costs of coal burning and associated emissions. This paper focuses on the extraction phase and assesses the external costs to agriculture associated with the resulting depression cone. This paper discusses the difficulties researchers face in estimating agricultural losses resulting from the development of a depression cone due to opencast mineral extraction. In the case of brown coal, the impacts are of a geological, natural-climatic, agricultural-productive, temporal, and spatial nature and result from a multiplicity of interacting factors. Then, a methodology for counting external costs in crop production was proposed. The next section estimates the external costs of crop production arising from the operation of opencast mines in the Konin-Turek brown coal field, which is located in central Poland. The analyses conducted showed a large decrease in grain and potato yields and no effect of the depression cone on sugar beet levels. Including the estimated external costs in the cost of producing electricity from mined brown coal would significantly worsen the profitability of that production.

Ensuring adequate profitability of production, which can be ensured by optimal investments, can encourage farmers to be more caring about sustainable development. Several existing studies indicate that technical efficiency in agriculture varies regionally. Investments comprise a basic way to increase efficiency and thus reduce polarisation between regions. However, contrary to established assumptions, not every investment leads to increased efficiency, which entails a phenomenon of overinvestment. Investments should, by definition, be positively correlated with efficiency. However, existing studies indicate the existence of a significant problem of overinvestment, where increased efficiency may not occur. While for about 40% of farms in Poland the scale of investments can be assessed as optimal, more than quarter of farms exhibited absolute overinvestment and nearly one in five farms is underinvested. In response to this problem, this study aimed to identify regional differences in Poland with regard to overinvestment in farms, as well as to determine changes in farm efficiency depending on the region and level of overinvestment. The source material used in the following article consisted of unpublished Farm Accountancy Data Network (FADN) microdata derived from the DG AGRI of the European Commission. The study covered the period 2004–2015. For an original classification of farms according to their level of overinvestment the technical efficiency, using the stochastic frontier analysis approach, was used for determining regional differences that occurred as a result of overinvestment. Stochastic frontier analysis shown noticeable differences in the average technical efficiency for different overinvestment groups. As expected, underinvested farms are the least efficient (general in Poland and in all analyzed regions) and average technical efficiency did not increase. Interestingly, optimally investing farms do not have the highest technical efficiency. Higher efficiency was achieved by both relatively and absolutely overinvested farms. This is due to the fact that in order to produce efficiently in agriculture, it is necessary to at least maintain the level of tangible assets provision, and preferably to increase it as well. In terms of overinvestment levels, farm structure does not differ significantly between individual regions in Poland. However, there are differences between regions in terms of farm efficiency within each group. In all regions, only the underinvested farms did not increase their efficiency over the period under review and the highest efficiency growth rate was in regions where farms were least efficient at baseline.

The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.