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.

Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

Floristic studies were conducted in 2011 and 2012 on the soil reclaimed using composts made of sewage sludge with the addition of various amounts of ash from power plant and sawdust. The experiment was carried out in 2002 on devastated soilless formation in the area of "Jeziórko" sulfur mine. Strongly acidic soilless formation (weak loamy sand) was reclaimed using post-flotation lime for deacidification at the dose of 300 t/ha and compost in various variants: municipal sewage sludge, sewage sludge (80%) + ash (20%), sewage sludge (70%) + ash (30%), and sewage sludge (70%) + sawdust (30%). The compost was added at following doses of dry weight: 90, 180, and 270 t/ha. In the prepared plots, each with the area of 15 m 2 , a mixture of reclamation grasses was sown: Festuca pratensis-41.2%, Festuca rubra-19.2%, Lolium perenne-14.7%, Lolium multiflorum-12.4%, Dactylis glomerata-6.5%, Trifolium pratense-6%. The phyto-indication method was used to evaluate the impact of different ways of the soilless formation remediation on the habitat development. The assessment took into account following indicators: soil moisture, trophism, pH, organic matter content, resistance to salinity, and increased content of heavy metals. The largest number of species was found on plots where compost made of sewage sludge was used, while the smallest-on those reclaimed with sewage sludge compost with sawdust addition. In terms of habitat conditions, species preferring wet habitats typical of fresh soils, trophism of the subsoil corresponding to the abundant soils (eutrophic), neutral soil reaction, and subsoil with organic matter like in humus and mineral soils, dominated. The most favorable habitat conditions were found in plots reclaimed using sewage sludge compost.

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