The methods so far applied to determine the bound water diffusion coefficient in wood do not provide credible results on this coefficient as well as on the boundary condition. An alternative approach based on the concept of solving the inverse transfer problems was recently applied. Two European species were investigated in the present study. A series of sorption experiments was performed and followed by the numerical identification of the coefficients. Several case studies were carried out for the constant and bound water content dependent diffusion coefficients. The obtained results were validated by comparison to a set of experimental data.

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).

Ensuring reliability of data on thermal properties of wood-based panels is important for manufacturing processes, especially when it is recommended to shorten the cooling phase and stack the panels in hot conditions. Prediction of the heat transfer during cooling phase and normal or hot stacking based on accurate data is essentially important for attaining panels of required properties. The thermal properties are also required when designing houses, especially low-energy or passive ones. Therefore, a water calorimeter was adequately designed and constructed to ensure improvement in the accuracy of the specific heat measurements. The calorimeter was used to determine the specific heat. The attained accuracy estimated by the relative error was significantly increased, and the error values were less than 2 % for all types of the investigated particleboard and OSB. In case of low-density fiberboard (LDF), the maximum value of the relative error did not exceed 4 %. It was also shown that high accuracy required for the specific heat measurements was achieved for experiments in which high-mass samples were used, in contrast to measurements for such samples in traditional DSC systems. The results for the specific heat were within the range from 1420 to 1450 J/kg K for LDF and all types of particleboard. The effect of the investigated material density on the specific heat was not found. The only exception was in case of OSB for which the specific heat was ca. 1550 J/kg K, and it was approximately 100 J/kg K higher when compared to other panels.

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha⁻¹, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.