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Wide research attention has been paid in the last two decades to the thermal comfort conditions of different outdoor and semi-outdoor urban spaces. Field studies were conducted in a wide range of geographical regions in order to investigate the relationship between the thermal sensation of people and thermal comfort indices. Researchers found that the original threshold values of these indices did not describe precisely the actual thermal sensation patterns of subjects, and they reported neutral temperatures that vary among nations and with time of the year. For that reason, thresholds of some objective indices were rescaled and new thermal comfort categories were defined. This research investigates the outdoor thermal perception patterns of Hungarians regarding the Physiologically Equivalent Temperature (PET) index, based on more than 5800 questionnaires. The surveys were conducted in the city of Szeged on 78 days in spring, summer, and autumn. Various, frequently applied analysis approaches (simple descriptive technique, regression analysis, and probit models) were adopted to reveal seasonal differences in the thermal assessment of people. Thermal sensitivity and neutral temperatures were found to be significantly different, especially between summer and the two transient seasons. Challenges of international comparison are also emphasized, since the results prove that neutral temperatures obtained through different analysis techniques may be considerably different. The outcomes of this study underline the importance of the development of standard measurement and analysis methodologies in order to make future studies comprehensible, hereby facilitating the broadening of the common scientific knowledge about outdoor thermal comfort.

This paper reviews the works related to the effect of soil compaction on cereal yield and focuses on research of field experiments. The reasons for compaction formation are usually a combination of several types of interactions. Therefore one of the most researched topics all over the world is the changes in the soil's physical and chemical properties to achieve sustainable cereal production conditions. Whether we are talking about soil bulk density, physical soil properties, water conductivity or electrical conductivity, or based on the results of measurements of on-line or point of soil sampling resistance testing, the fact is more and more information is at our disposal to find answers to the challenges.Thanks to precision plant production technologies (PA) these challenges can be overcome in a much more efficient way than earlier as instruments are available (geospatial technologies such as GIS, remote sensing, GPS with integrated sensors and steering systems; plant physiological models, such Decision Support System for Agrotechnology Transfer (DSSAT), which includes models for cereals etc.). The tests were carried out first of all on alteration clay and sand content in loam, sandy loam and silt loam soils. In the study we examined especially the change in natural soil compaction conditions and its effect on cereal yields.Both the literature and our own investigations have shown that the soil moisture content changes have the opposite effect in natural compaction in clay and sand content related to cereal yield. These skills would contribute to the spreading of environmental, sustainable fertilizing devoid of nitrate leaching planning and cereal yield prediction within the framework of the PA to eliminate seasonal effects.

Summary Weed species loss due to intensive agricultural land use has raised the need to understand how traditional cropland management has sustained a diverse weed flora. We evaluated to what extent cultivation practices and environmental conditions affect the weed species composition of a small‐scale farmland mosaic in Central Transylvania (Romania). We recorded the abundance of weed species and 28 environmental, management and site context variables in 299 fields of maize, cereal and stubble. Using redundancy analysis, we revealed 22 variables with significant net effects, which explained 19.2% of the total variation in species composition. Cropland type had the most pronounced effect on weed composition with a clear distinction between cereal crops, cereal stubble and maize crops. Beyond these differences, the environmental context of croplands was a major driver of weed composition, with significant effects of geographic position, altitude, soil parameters (soil pH, texture, salt and humus content, CaCO3, P2O5, K2O, Na and Mg), as well as plot location (edge vs. core position) and surrounding habitat types (arable field, road margin, meadow, fallow, ditch). Performing a variation partitioning for the cropland types one by one, the environmental variables explained most of the variance compared with crop management. In contrast, when all sites were combined across different cropland types, the crop‐specific factors were more important in explaining variance in weed community composition.

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Abstract:This study provides evidences on the bene cial small-scale human-biometeorological e ects of a large shade tree during the daytime in summer. We carried out detailed measurement from 10 am to 6 pm with two human-biometeorological stations on a popular square in Szeged, Hungary. One of the stations stood under a great Sophora japonica, while the other in the sun. Compared to the sunny location, we found 0.5°C lower air temperature, 2% higher relative humidity and 0.4 hPa higher vapor pressure under the tree. From human-biometeorological point of view, we observed more signi cant di erences. The tree reduced the mean radiant temperature by 22.1°C and the physiological equivalent temperature by 9.3°C -indicating about two categories lower physiological stress on the human body. In order to demonstrate the background mechanisms of these di erences, we analyzed separately the components of the radiation budget. The e ect of tree crown on radiation components was found to be greater in the short-wave domain than in the long-wave domain. The extended foliage reduced the solar radiation from the upper hemisphere and thus lowered the radiation from the ground (the re ected short-wave and the emitted longwave ux densities) along with the radiation from the lateral directions.

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.