Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.

In the next few years, the demand for organic crops, including barley, will grow. Barley is one of the world’s most important crops cultivated for food and feed. With the forecasted increase in cropped area, there is a need for stable, well-adapted and high-yielding varieties. The aim of this study was to assess the yield stability of ten varieties tested in the Polish organic post-registration trials in the years 2020–2022. For this purpose, we fitted a linear mixed model on plot data. Additionally, for each variety, we calculated the probability of the yield falling to a certain threshold. It is shown that the Bente variety was the highest-yielding among the tested varieties. The Pilote variety was the most stable in terms of Shukla’s stability variance. Furthermore, for the three highest-yielding varieties, the lowest values of the simultaneous selection index and the probability of falling below a certain threshold were obtained. We can, therefore, conclude that the highest-yielding varieties should be promoted for cultivation. Moreover, new varieties suitable for organic farming can be bred from the highest-yielding and most stable varieties.

Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most environmentally friendly method to control diseases is to cultivate resistant varieties. The aim of the current study was to identify barley varieties with an improved resistance to leaf rust and net blotch in Polish organic post-registration trials conducted in the years 2020–2022. For this purpose, the cumulative link mixed model with several variance components was applied to model resistance to leaf rust and net blotch. It was found that the reference variety Radek was the most resistant to leaf rust, whereas variety Avatar outperformed the reference variety in terms of resistance to net blotch, although the difference between the two varieties was non-significant. In the present study, the use of the cumulative link mixed model framework made it possible to calculate cumulative probabilities or the probability of a given score for each variety and disease, which might be useful for plant breeders and crop experts. Both, the method of analysis and resistant varieties may be used in the breeding process to derive new resistant varieties suitable for the organic farming system.

Climate-driven changes have raised concerns about their long-term impacts on the yield resilience of cereal crops. This issue is critical in Poland as it affects major cereal crops like winter triticale, spring wheat, winter wheat, spring barley, and winter barley. This study investigates how soil nutrient profiles, fertilization practices, and crop management conditions influence the yield resilience of key cereal crops over a thirteen-year period (2009–2022) in the context of changing climate expressed as varying Climatic Water Balance. Data from 47 locations provided by the Research Centre for Cultivar Testing were analyzed to assess the combined effects of agronomic practices and climate-related water availability on crop performance. Yield outcomes under moderate and enhanced management practices were contrasted using Classification and Regression Trees to evaluate the relationships between yield variations and agronomic factors, including soil pH, nitrogen, phosphorus, potassium fertilization, and levels of phosphorus, potassium, and magnesium in the soil. The study found a downward trend in Climatic Water Balance, highlighting the increasing influence of climate change on regional water resources. Crop yields responded positively to increased agricultural inputs, especially nitrogen. Optimal soil pH and medium phosphorus levels were identified as crucial for maximizing yield. The findings underscore the importance of tailored nutrient management and adaptive strategies to mitigate the adverse effects of climate variability on cereal production. The results provide insights for field crop research and practical approaches to sustain cereal production in changing climatic conditions.

The aim of the study was to evaluate the dependence of potato crops on the level of irrigation in three mesoregions of Poland. The field experiments were carried out in 2009–2011 according to an obligatory methodology for evaluation of crop cultivars. Three factors were tested: two cultivation practices (with irrigation and without irrigation as control), five potato cultivars, and three locations (Masłowice, Szczecin-Dąbie, and Węgrzce). The study was conducted in randomized blocks in triplicate. The study included the same nutrition across locations and protection against potato blight. Irrigation was applied according to the criterion of optimal soil moisture at a humidity decrease below 70% of the field water capacity. At the time of harvest, total and commercial yields of tubers were determined. Detailed analysis of the dependent variables, total and marketable yield, and the independent variables for the second harvest date, confirmed confidence in the achieved results. The coefficients of variation for total and marketable yield, on the second harvest date, were 23% and 25%, respectively, which means high stability for the results. Irrigation of potato plantations contributed to an increase in the total yield of tubers in the first harvest term by 3.22 t·ha⁻¹ and by 7.23 t·ha⁻¹ -in the second term; and the commercial yield of tubers by 3.45 t·ha⁻¹ in the first term and by 7.42 t·ha⁻¹ -in the second term of tuber harvest. The highest watering efficiency in the first harvest time, 60 days after planting, was distinguished by the “Miłek” variety, and in the second harvest date by the “Denar” variety.