Studies have focused on identifying combinations of insects and organic waste to optimise bio-conversion. Here, the effects of different diets (10% chicken feed complemented with 90% vegetable waste, garden waste, cattle manure, or horse manure) on growth and survival rates, and nutritional value of Zophobas morio and Tenebrio molitor larvae, and Acheta domesticus were investigated. Compared with chicken feed, organic waste decreased the individual larval weight, although green waste showed fewer negative effects than the manure. The macronutrient concentrations in garden waste were moderate compared with chicken feed, and vegetable waste was the poorest diet in terms of nutrient concentration, as revealed by the principal component analysis (PCA). There was no difference in weight between larvae reared on garden waste and those reared on vegetable waste. Tenebrio molitor and A. domesticus showed the maximum growth rates at 71–101 and 36–66 days of age at 22.5 ± 2.5 °C, respectively. The PCA showed that the protein and fibre concentrations were inversely proportional to fat concentration in the larvae. Acheta domesticus was rich in proteins, whereas Z. morio and T. molitor were rich in fat. Feeding nutrient-poor diets resulted in a lower protein and a higher fat concentration in the larvae.

Reference crop evapotranspiration (ET_o) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ET_o based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (T_min), maximum temperature (T_max), mean temperature (T_mean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (grad_max), minimum global radiation (grad_min), day length, and ET_o data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ET_o included T_max, T_min, H, and SR at hidden layers (4, 3); grad_min, SR, and WS at (6, 4); SR, day length, Ssh, and T_mean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ET_o in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.

The European Commission (EC) has monitored Member States' digital progress through the Digital Economy and Society Index (DESI) since 2014. The DESI index currently ranks the EU Member States and monitors their progress based on four core and 33 individual indicators. We sought to determine whether convergence between the Member States could be detected using the DESI’s annual databases. By examining the variation in the indices, we propose the existence of a so-called “Matthew effect”, i.e., the “rich get richer” syndrome among the 27 EU Member States. We also hypothesised that the COVID-19 pandemic would influence the change in the DESI. Issues investigated were those using bibliometric, statistical-mathematical methods. The σ-convergence analysis was used to estimate the reduction over time of the differences between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. A PCA analysis was performed to verify the Mathew effect with additional λ-variances considering real GDP per capita change. The σ-convergence was confirmed over the period 2016–2021. The β-convergence was significantly confirmed, and the research also revealed that the half-life of catching up is approximately 20 years. The suggestion of a Matthew effect in the 2016–2021 period, although not significantly confirmed, tends to suggest its existence. The COVID-19 pandemic’s impact on the value of the DESI index is likely to be affected, but future studies are needed to find support for this hypothesis. The study concludes that convergence between the EU-27 Member States can be detected based on the DESI, but this does not imply convergence for all four core DESI indicators.

It is difficult to estimate the contribution of individual sources to the total CO₂ efflux from soil with vegetation. Long-term experiments with bare soil will provide useful conclusions. In this study, we aimed to mathematize the effect of soil temperature and soil moisture content on bare soil CO₂ efflux in a four-season semiarid region to assess the adequacy of different models and to enable future predictions by seasons. We proved that the exponential model adequately described the relationship between the CO₂ efflux and the soil temperature. The model calculations showed no significant relationship in the case of an additional quadratic exponential function, while, in the case of the linear model, the homoscedasticity criteria were not met, and the accuracy of the estimation was found to be dependent on the level of CO₂ efflux. When the soil moisture content with either an exponential function or power was added to the exponential formula, the models did not provide more accurate results. Our findings confirm that the best-fitting models are dependent on the local environmental conditions, and there are areas in which the moisture content does not significantly affect the CO₂ efflux of bare soil. Using trends in historical hourly temperature data in the exponential model, the CO₂ emission was estimated to be in the range 772–898 g m⁻² y⁻¹ in 2050 in the location we used. Trends in climate change are expected to have considerable effects on the processes that govern the CO₂ emissions of soil.

The goals of our study were to evaluate the historical aspects of maize (Zea mays L.) production in Hungary, and to provide a prognosis for the yield for 2050 based on the trends of temperature, precipitation, and climatic water balance changes. Different climate zones for the period of 1970–2019 were investigated by means of correlation analyses, normality tests, time series analysis, and multiple linear regression analysis. Two well-distinguishable linear trends in the yields were found, the first representing large-scale farming, and the second starting with the change of the socio-economic system in 1989. The annual amount of precipitation showed high variations both spatially and temporally, although no significant change was identified for the last five decades. In the period 1990–2019, not only were higher temperatures characteristic, but the frequency of extreme high temperature values (T_max > 30 °C) also increased. We quantified the heat stress, expressing it in heat stress units (HSU, °C) derived from the heat-sum of the daily maximum air temperature values above 30 °C. By 2050, the average increase in HSUs may reach 35 °C. Increasing HSU causes yield depression; according to our estimations, a 1 °C increase in HSU results in a 23 kg ha⁻¹ yield depression of maize. Taking the unfavorable effect of heat stress and technological development into consideration, the average domestic yield of maize will be 8.2 t ha⁻¹. Our study revealed that without taking technological development into consideration, prediction models may overestimate the adverse effect of climate change on crop production.

Wheat is grown in the largest area in the world as well as in Hungary. Globally, the yield is predicted to decrease due to climate change; however, technological development can potentially compensate for it. In this study, the contribution of climatic and technological trends to the change in winter wheat yield in four sub-regions of Hungary with considerable spatial and temporal variations in weather conditions was evaluated. Long-term trends in both the weather conditions and the technology development, with the consideration of the socio-economic circumstances, were identified. For future yield prediction, non-climatic influences and critical climatic factors, as well as sensitivity in the phenological stages, were considered. In the past 50 years, the average yield variation was lower at regional than country scale. Winter wheat yield was not found to be sensitive to temperature, global degree days, precipitation, and climatic water balance, only to heat stress. Considering the technological development and the heat stress during the critical weeks in the last 30 years, an increase of yields can be expected by 2050 in Hungary’s western regions (0.72–1.55 t ha⁻¹), while yield depression is predicted (0.27–0.75 t ha⁻¹) in the eastern regions compared to the values estimated for 2019, ±1.5 t ha⁻¹ within a 95% confidence interval. We proved that yield estimations can show contradictory changes by sub-regions of an agricultural region if the contribution of site-specific technology development, the dominant weather stressor, and the most sensitive phenological phase is involved in the statistical analyses. Identification of the dominant climatic stressor(s) for the different crops is necessary to keep high yield or even increase it under the changing environmental circumstances. Our findings suggest that heat stress is the main concern to maximize winter wheat production in temperate climate zones.