In the article “Professor Tinbergen’s Method” (Keynes J. M. Professor Tinbergen’s Method. − The Economic Journal. 1939. Vol. 49. No. 195) John Maynard Keynes expressed critical notes concerning insufficient scientific level of the Jan Tinbergen’s results obtained with the help of econometric methods. The conclusion of present research is that the critical appraisal of econometric applying given by John Maynard Keynes in 1939 can be completely assigned to many published in Latvia research in economics and business done by inductive statistics because scientists carelessly apply the linear regression model in absence of the knowledge a priori required by proper theorems and algorithms. Moreover, many times there is no sense even to speak about stochastic experiment because the most important condition − ceteris paribus does not fulfil, namely, economic environment is not homogeneous enough. As the result, the causal inferences derived from Regression Report are not scientifically justified. The second conclusion is that the objective criticism in econometrics applications area in order to keep the satisfactory level of scientific correctness and scientific ethics in Latvia has to be established. We also assert that the applications of inductive econometrics in agriculture are much more justified due to possibility to repeat the stochastic experiments many times in constant circumstances like in physics and mechanics. The meta-target of present article is to remind the protests of John Maynard Keynes to the careless utilization of econometric theorems and to raise a wide discussion about the problems of correctness in applying inductive statistics in economics and agriculture in Latvia.

The increasing reliance on renewable energy sources such as solar and wind power necessitates the development of advanced forecasting techniques to address the inherent variability and unpredictability of these energy systems. Accurate forecasting is vital for optimising energy production, maintaining grid stability, and effectively integrating renewable energy into power systems. Traditional forecasting methods often struggle to adapt to rapidly changing environmental conditions and new data inputs, limiting their effectiveness in dynamic contexts. This study introduces the Self-Improvement Cycle (SIC) module, which is designed to enhance forecasting accuracy through continuous learning, adaptation, and feedback integration. The SIC module leverages advanced machine learning algorithms, reinforcement learning techniques, and reflective practice principles to create a self-improving framework that dynamically updates models based on real-time data and external feedback. The module’s design incorporates multiple feedback loops, enabling the system to iteratively refine its performance and remain robust in the face of changing conditions. Reflective practice, a concept drawn from psychology, plays a critical role in the SIC module by facilitating ongoing evaluation and adaptation. By learning from previous predictions and continuously adjusting algorithms, the SIC module demonstrates its potential to improve forecasting accuracy across various domains, with a particular emphasis on renewable energy forecasting. The theoretical and mathematical foundations of the SIC module are explored, showcasing its capability to enhance predictive accuracy and resilience in an evolving energy landscape.

The aim of the present work is to propose methods and tools for classifying sweet pepper into groups according to their degree of maturity based on colour and spectral characteristics extracted from colour images on the surface of the vegetables. The investigated pepper is two varieties of sweet — red Banji and yellow Liri. Five groups were formed, depending on the degree of maturity, and 16 colour and 11 spectral indices were calculated for each of the groups. By successively using the ReliefF and PLSR methods, a selection of informative features and subsequent reduction of the vector formed by them was carried out, thereby aiming to increase the predictive results and minimize the time for data processing. The obtained classification errors between the individual stages of ripening vary according to the type of pepper and depending on which of the two types of maturity the fruits are in — technical or biological. For red sweet pepper, the separation inaccuracy obtained using a discriminant classifier with a quadratic separation function is in the range of 8–19%, while for yellow it is from 5 to 23%. The results obtained in the present work for the classification of pepper into groups according to their degree of maturity would support decision-making in selective harvesting and overall more accurate and efficient management of the harvesting process from the point of view of precision agriculture. The work will continue with studies related to the prediction of various compounds indicating changes in the colour of peppers, including chlorophylls, carotenes and xanthophylls. In this way, it is possible to increase the accuracy in determining the degree of ripeness, since in pepper the colour does not always follow the same pattern of change from green to yellow to orange to red.