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Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p

Abstract Introduction Crop production contribution to food security faces unprecedented challenge of increasing human population. This is due to the decline in major cereal crop yields including maize resulting from climate change and declining soil infertility. Changes in soil nutrient status and climate have continued to occur and in response, new fertilizer recommendations in terms of formulations and application rates are continuously developed and applied globally. In this sense, this review was conducted to: (i) identify the key areas of concentration of research on fertilizer and climate change effect on maize grain yield, (ii) assess the extent of the effect of climate change on maize grain yield, (iii) evaluate the extent of the effect of fertilization practices on maize grain yield, and (iv) examine the effect of interaction between climate change factors and fertilization practices on maize grain yield at global perspective. Methodology Comprehensive search of global literature was conducted in Web of Science (WoS) database. For objective 1, metadata on co-authorship (country, organisation), and co-occurrence of keywords were exported and analysed using VOSviewer software. For objective 2–4, yield data for each treatment presented in the articles were extracted and yield increment calculated. Results The most significant keywords: soil fertility, nutrient use efficiency, nitrogen use efficiency, integrated nutrient management, sustainability, and climate change adaptation revealed efforts to improve maize production, achieve food security, and protect the environment. A temperature rise of 1–4 °C decreased yield by 5–14% in warm areas and increased by

Abstract This study examined the physical properties of agricultural drought (i.e., intensity, duration, and severity) in Hungary from 1961 to 2010 based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The study analyzed the interaction between drought and crop yield for maize and wheat using standardized yield residual series (SYRS), and the crop-drought resilient factor (CDRF). The results of both SPI and SPEI (-3, -6) showed that the western part of Hungary has significantly more prone to agricultural drought than the eastern part of the country. Drought frequency analysis reveals that the eastern, northern, and central parts of Hungary were the most affected regions. Drought analysis also showed that drought was particularly severe in Hungary during 1970–1973, 1990–1995, 2000–2003, and 2007. The yield of maize was more adversely affected than wheat especially in the western and southern regions of Hungary (1961–2010). In general, maize and wheat yields were severely non-resilient (CDRF

Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria.

ABSTRACT Extreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa.

The migration of chemical compounds from packaging polymers to food presents a multifaceted challenge with implications for food safety and public health. This review explores the interaction between packaging materials and food products, focusing on permeation, migration, and sorption processes. The different migration mechanisms of contact migration, gas phase migration, penetration migration, set-off migration, and condensation/distillation migration have been discussed comprehensively. The major migrating compounds are plasticizers, nanoparticles, antioxidants, light stabilizers, thermal stabilizers, monomers, oligomers, printing inks, and adhesives, posing potential health risks due to their association with endocrine disruption and carcinogenic effects. Advanced analytical methods help in the monitoring of migrated compounds, facilitating compliance with regulatory standards. Regulatory agencies enforce guidelines to limit migration, prompting the development of barrier coatings and safer packaging alternatives. Furthermore, there is a need to decipher the migration mechanism for mitigating it along with advancements in analytical techniques for monitoring the migration of compounds.

Sodium hazard poses a critical threat to agricultural production globally and regionally which has been previously predicted from ground or surface water. Monitoring rainwater quality in this context is ignored but essential for agricultural water management in central Europe. Our study focused to predict sodium adsorption ratio (SAR) from 1985 to 2021 from ten ionic species of rainwater (pH, EC, Cl-, SO4−2, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) employing four machine learning (random forest (RF), gaussian process regression (GU), random subspace (RSS), and artificial neural network-multilayer perceptron (ANN-MLP)) methods at three stations K-puszta (KP), Farkasfa (FAK), and Nyirjes (NYR) of Hungary, central Europe. Exploratory data analysis was performed using the Mann-Kendall test, Pearson correlation, and principal component analysis (PCA). Rainwater composition revealed the highest percentage of SO4−2 ions i.e., 21 to 31%, followed by 10 to 15% of Na+ ions. Mann-Kendall test revealed a significant (p

Globally, countries are legitimizing actions to curtail the malevolent impacts of environmental degradation. This study examined the interaction between CO2 emissions and selected economic variables within the framework of Saudi Arabia's Vision 2030. The Autoregressive distributed lag model (ARDL) was used to analyze the long-run relationships and short-run dynamics between studied variables (1970–2020). The Mann-Kendall (MK) test revealed a significant (p

Phalsa is a tropical and subtropical fruit that is high in nutritional value and is primarily cultivated for its fruit. As, Phalsa fruit contain high number of vitamins (A and C), minerals (calcium, phosphorus, and iron), and fibre while being low in calories and fat. The fruit and seed of Phalsa contain 18 amino acids, the majority of which are aspartic acid, glutamic acid, and leucine. Based on in vivo and in vitro studies phalsa plant possess high antioxidant, anti-inflammatory, anticancer, antimicrobial, antidiabetic properties. However, antioxidant properties are found in the form of vitamin C, total phenolic, anthocyanin, flavonoid, and tannin. The phalsa plant's fruits and leaves have substantial anticancer action against cancer cell lines. Because of the presence of a broad range of physiologically active chemicals, investigations on phalsa plants revealed that some plant parts have radioprotective qualities. The anti-glycosidase and anti-amylase activity of aqueous fresh fruit extract was shown to be substantial. The phalsa plant contains an abundance of biologically active chemicals, allowing it to control microorganisms through a variety of processes. Phalsa methanolic leaf extract was revealed to have antimalarial and antiemetic effects. The hot and cold polysaccharide fractions extracted from the phalsa plant have potent hepatoprotective and therapeutic properties. Therefore, this review is based on the nutritional, bioactive, phytochemicals, and potential pharmacological uses of phalsa. The potential health benefits and economic potential of the phalsa berry's phytochemicals are promising areas for further study.

Aonla, commonly known as Indian gooseberry (Phyllanthus emblica), is a plant native to India with various therapeutic and dietary benefits. This review covers the taxonomical, morphological, and species-level classifications of aonla fruit, including its flower biology, maturation, harvesting, and yield metrics. It also discusses the nutritional, physico-chemical, and phytochemical characteristics and the total antioxidant and antimicrobial activities and mineral compositions of several aonla fruit cultivars. Additionally, the health benefits of aonla are reviewed, including its analgesic, antipyretic, antioxidative, anti-inflammatory, anti-aging, ulcerogenic, chemo-protective, neuroprotective, free radical scavenging, hypoglycaemic, and immunogenic properties, which make it beneficial in the treatment and prevention of various illnesses. Further various forms of fruit extract are also considered to be beneficial for the improvement of plant and animal health. Overall, aonla is a valuable fruit with significant potential for use in improving human health.

Agricultural soil pollution has attracted the attention of scientists and agricultural managers because of its importance to food security worldwide. In Syria, limited studies have been conducted to determine heavy metals (HMs) concentrations in arable lands. The objective of this study was to investigate the concentration of HMs in the southern part of Syria, and to identify the potential sources HMs. To achieve the study goals, 38 soil samples (0–25 cm) were collected and analysed for physical and chemical soil properties, while the DTPA (Diethylenetriamine pentaacetate) method was to extract HMs from soil.The results of this study showed that the average concentration of HMs in the study area are 2.00; 12.70; 8.56; 4.37; 0.12; and 0.12 ppm for Cu; Fe; Mn; Zn; Cd; and Pb, respectively. A multivariate statistical analysis showed that Cu, Fe, and Mn originated from parent material (natural resources), while Zn and Cd were related to agricultural activities (anthropogenic one) and Pb was relatively related to industrial activities. Interestingly, the spatial distribution indicates that Cu, Fe, and Mn distributed in the southern and eastern part of the study area, while Pb and Zn were dominant in the western and northern part. In addition, Cd was observed in the central part of the study area. We conclude that human activities are the main contributor of HMs accumulation in soil. The distribution of these HMs could affect the safety of agricultural product in the future.

Sappan wood (Caesalpinia sappan) is a tropical hardwood tree found in Southeast Asia. Sappan wood contains a water-soluble compound, which imparts a red color named brazilin. Sappan wood is utilized to produce dye for fabric and coloring agents for food and beverages, such as wine and meat. As a valuable medicinal plant, the tree is also known for its antioxidant, anti-inflammatory, and anticancer properties. It has been observed that sappan wood contains various bioactive compounds, including brazilin, brazilein, sappan chalcone, and protosappanin A. It has also been discovered that these substances have various health advantages; they lower inflammation, enhance blood circulation, and are anti-oxidative in nature. Sappan wood has been used as a medicine to address a range of illnesses, such as gastrointestinal problems, respiratory infections, and skin conditions. Studies have also suggested that sappan wood may have anticarcinogenic potential as it possesses cytotoxic activity against cancer cells. Based on this, the present review emphasized the different medicinal properties, the role of phytochemicals, their health benefits, and several food and nonfood applications of sappan wood. Overall, sappan wood has demonstrated promising medicinal properties and is an important resource in traditional medicine. The present review has explored the potential role of sappan wood as an essential source of bioactive compounds for drug development.

Alzheimer’s disease (AD) is an ascending, neurodegenerative disorder that attacks the brain’s nerve cells, i.e., neurons, resulting in loss of memory, language skills, and thinking and behavioural changes. It is one of the most common causes of dementia, a group of disorders that is marked by the decline of cognitive functioning. Probiotics are living microorganisms that are beneficial for human well-being. They help in balancing the extent of bacteria in the gut and support the defensive immune system of the body. Studies have found that probiotics can help with a variety of conditions, including mental health. Probiotics are beneficial bacteria that can help to maintain and strengthen a healthy gut microbiome. The gut microbiome is important for healthy brain function, as it is linked to the production of neurotransmitters and hormones that regulate mood and behaviour. This review article includes detailed review on the origination of probiotics and its significance in the treatment of AD.

This review describes the various innovative approaches implemented for naringin extraction as well as the recent developments in the field. Naringin was assessed in terms of its structure, chemical composition, and potential food sources. How naringin works pharmacologically was discussed, including its potential as an anti-diabetic, anti-inflammatory, and hepatoprotective substance. Citrus flavonoids are crucial herbal additives that have a huge spectrum of organic activities. Naringin is a nutritional flavanone glycoside that has been shown to be effective in the treatment of a few chronic disorders associated with ageing. Citrus fruits contain a common flavone glycoside that has specific pharmacological and biological properties. Naringin, a flavone glycoside with a range of intriguing characteristics, is abundant in citrus fruits. Naringin has been shown to have a variety of biological, medicinal, and pharmacological effects. Naringin is hydrolyzed into rhamnose and prunin by the naringinase, which also possesses l-rhamnosidase activity. D-glucosidase subsequently catalyzes the hydrolysis of prunin into glucose and naringenin. Naringin is known for having anti-inflammatory, antioxidant, and tumor-fighting effects. Numerous test animals and cell lines have been used to correlate naringin exposure to asthma, hyperlipidemia, diabetes, cancer, hyperthyroidism, and osteoporosis. This study focused on the many documented actions of naringin in in-vitro and in-vivo experimental and preclinical investigations, as well as its prospective therapeutic advantages, utilizing the information that is presently accessible in the literature. In addition to its pharmacokinetic characteristics, naringin’s structure, distribution, different extraction methods, and potential use in the cosmetic, food, pharmaceutical, and animal feed sectors were discussed.

Abstract Background Soil erosion is a major threat to the natural ecosystem and agricultural sector in the western part of Lattakia Governorate, Syrian Arab Republic. The main goals of this research are to investigate erosion risk by using the Coordination of Information on the Environment (CORINE) Model and to prioritize areas for conservation practices. To achieve these goals, soil samples were collected from the field, the climatic data (i.e., rainfall) and Digital Elevation Model (DEM) were obtained and utilized to perform CORINE model in Geographic Information System (GIS) environment. Results The results showed that only 13.2% of the study area was classified as high erodible. In addition, 45.24%, 49.15% and 5.29% of the study area were under low, moderate and high actual erosion risk, respectively. This research identified slope and land use/land cover as key factors responsible for soil erosion in the study area. Conclusions The CORINE model acknowledged as a good tool for predicting soil erosion and highlighting the areas affected by soil erosion in the study area with high precision.

Poniol (Flacourtia jangomas) has beneficial health effects due to its high polyphenolic and good antioxidant activity content. This study aimed to encapsulate the Poniol fruit ethanolic extract to the sucrose matrix using the co-crystallization process and analyze the physicochemical properties of the co-crystalized product. The physicochemical property characterization of the sucrose co-crystallized with the Poniol extract (CC-PE) and the recrystallized sucrose (RC) samples was carried out through analyzing the total phenolic content (TPC), antioxidant activity, loading capacity, entrapment yield, bulk and traped densities, hygroscopicity, solubilization time, flowability, DSC, XRD, FTIR, and SEM. The result revealed that the CC-PE product had a good entrapment yield (76.38%) and could retain the TPC (29.25 mg GAE/100 g) and antioxidant properties (65.10%) even after the co-crystallization process. Compared to the RC sample, the results also showed that the CC-PE had relatively higher flowability and bulk density, lower hygroscopicity, and solubilization time, which are desirable properties for a powder product. The SEM analysis showed that the CC-PE sample has cavities or pores in the sucrose cubic crystals, which proposed that the entrapment was better. The XRD, DSC, and FTIR analyses also showed no changes in the sucrose crystal structure, thermal properties, and functional group bonding structure, respectively. From the results, we can conclude that co-crystallization increased sucrose’s functional properties, and the co-crystallized product can be used as a carrier for phytochemical compounds. The CC-PE product with improved properties can also be utilized to develop nutraceuticals, functional foods, and pharmaceuticals.

Corn smut fungus (Ustilago maydis [DC.] Corda) is a globally widespread pathogen affecting both forage and sweet maize hybrids, with higher significance in sweet maize. Remote sensing technologies demonstrated favorable results for disease monitoring on the field scale. The study focused on the changes in vegetation index (VI) values influenced by the pathogen. In this study, four hybrids, two forage maize and two sweet maize hybrids were examined. Artificial infection was carried out at three different doses: a low (2500 sporidium number/mL), medium (5000 sporidium number/mL) and high dose (10,000 sporidium number/mL) with a non-infected control plot for each hybrid. The experimental plots were monitored using a multispectral UAV sensor of five monochrome channels on three different dates, i.e., 7, 14 and 21 days after infection. Five different indices (NDVI, GNDVI, ENDVI, LCI, and NDRE) were determined in Quantum GIS 3.20. The obtained results demonstrated that the infection had a significant effect on the VI values in sweet maize hybrids. A high-dose infection in the Dessert R 73 hybrid resulted in significantly lower values compared to the non-infected hybrids in three indices (NDVI, LCI and GNDVI). In the case of the NOA hybrids, GNDVI and ENDVI were able to show significant differences between the values of the infection levels.

Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature °C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-SC1 and ANN-MLP-SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-SC1) and 110.7 (ANN-MLP-SC4). In the testing phase, the ANN-MLP-SC4 had the highest r value (0.96), followed by ANN-MLP-SC1 (0.94) and RF-SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-SC4 and ANN-MLP-SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.

Enhancing green energy consumption is the most important strategy to achieve environmental goals and control global temperature rise. Unquestionably, political intuitions make decisions for developing environmental technologies and imposing environmental taxes for phasing out fossil fuels and achieving energy transition. Therefore, this study explores the role of environmental technologies, political risk, and environmental taxes in green energy consumption considering the potential impacts of population density and economic growth in G7 countries. Second-generation tests are applied for analyzing the long-run equilibrium connection and stationarity features. Finally, the CuP-FM and CuP-BC estimators are applied for assessing long-run linkage and Dumitrescu-Hurlin causal test is applied to reveal causal flow among variables. The estimates uncovered that enhancing environmental technologies and environmental taxes upsurges the consumption of green energy. Reducing political risk in G7 countries also boosts green energy consumption. Economic growth is evidenced to stimulate the consumption of green energy, while population density limits the consumption of green energy. Moreover, environmental technologies and political risk Granger cause green energy utilization, while a feedback relationship exists between environmental taxes and green energy usage. Based on the results, this study suggests that G7 countries should allocate more funds to accelerate innovation in environmental technologies and, at the same time, reduce the political risk to boost green energy consumption.

The agricultural sector is the second contributor to the worldwide emissions of greenhouse gases (GHGs), as it is responsible for 13.5% of GHG emissions. The main aim of this research is to track GHG emission from the agricultural sector in the EU-27 between 1990 and 2016 in order to determine trends and changes of emission on a country scale. To achieve the study goal, data were collected from the Organization for Economic Co-operation and Development (OECD) website, followed by the application of the Simple Linear Regression Model (SLRM). The obtained results showed that most of the EU-27 countries witnessed a significant reduction of GHG emissions from the agricultural sector, except for Iceland and Spain. Interestingly, the highest reduction conducted by the United Kingdom was followed by Germany and France, where the reduction reached 385.27; 226.72 and 294.92 tons of CO2-equivalent per year, respectively. Thus, we can conclude that most EU countries significantly reduced GHG emissions to the atmosphere.

Abstract Background Recently, many parts of Hungary, as well as central Europe, have been hit by successive extreme climatic events. The main aim of this research was to analyze hydrological and agricultural drought episodes in the Debrecen Region in eastern Hungary from 1950 to 2010. Thus, data of monthly precipitation were collected and tested. After that, three indices were applied, the Precipitation Concentration Index (PCI), dry Precipitation Concentration Index (dPCI), and the Standardized Precipitation Index (SPI). Meanwhile, the correlation between SPI and the normalized difference vegetation index (NDVI) was calculated. Results The results showed that the PCI values ranged between 9.44 and 15.8 with an average of 11.8, while the dPCI values ranged between 8.6 and 20.7 with an average of 10.9, which indicates heterogeneity in rainfall distribution from year to year. More than 103 events of severe agricultural drought were detected for the whole studied period. In connection to this, severe hydrological drought was recorded in 16% of the total monthly rainfall events from 1950 to 2010. Interestingly, a significant correlation was obtained between SPI-3, -6 (i.e., drought), and NDVI in the summer season. Conclusions The SPI index could provide an easy tool for drought monitoring meanwhile drought evaluation and monitoring should be taken seriously in the central Europe.

Predominantly, field experiments aim to determine the optimal plant density for a unit area. High spatial variation results in lower attained yield than the genetic potential. To find the highest value of the product of mean individual production and crop density, their mathematical relationship is needed to be known. In this study, the relationship between the population and the weight of the individual fresh, dehusked ear of grain maize (Zea mays L.) was investigated, and a relevant mathematical simulation model was developed. Linear regression was disproved to describe this relationship properly based on the analysis of different mosaic sizes within a plot. The distribution of plant-to-plant distance was found to be binomial. The distribution of the ear weight was found to be lognormal. For the description of the population homogeneity, neither the standard deviation nor the variation coefficient was found to be applicable apart from the scale-independent relative variance. The simulation model, based on the above statistical functions, was validated with a real dataset, and it is recommended for the optimization of the plant population at farm scale.

As non-renewable natural resources, restoring Syrian soil quality is a vital issue for sustainable future planning after conflict ends. The data provided in this research exhibit features and physiochemical properties for soils from the southern part of Syria until the Jordanian border, which can provide decision-makers with sufficient information for rehabilitation stage after conflict in a regional scale. The data were collected from 107 representative soil profiles covering diverse agroecosystems throughout the area (i.e. Dara and Alswieda governorates). The most important data findings of this research included the first detection of Palygorskite {(Mg,Al)2Si4O10(OH)•4(H2O)} in Syrian soils, which is considered a strong evidence for the direct effects of the climate change on agroecosystem. Vertisols, Inceptisols, Entisols, Mollisols, and Aridisols were the most widespread soil types in the area. Overall, the database involves the field morphological characteristics, physicochemical, and mineralogical analyses.

Reference crop evapotranspiration (ETo) 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 ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo 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 ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo 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.

In recent decades, the agricultural sector has witnessed rapid technological interventions from field to the production stage. Thus, the importance of these technological interventions must be strictly evaluated. The traditional statistical method often deems low statistical differences as a significant one, which cannot be considered effective from different perspectives. In this sense, the aim of this research was to develop a new statistical method for evaluating agricultural experiments based on different criteria; hence, the significant importance of the technological interventions can be clearly determined. Data were collected from of a long-term (13-year) crop production experiment (Central Europe, Hungary), which involved five different fertilization levels, along with non-fertilized treatment (control), two irrigation treatments (irrigated and non-irrigated), and 15–20 genotypes of maize. The output of this research showed that the classic statistical approach for testing the significant differences among treatments should be accompanied with our new suggested approach (i.e., professional test), which reflect whether treatments were professionally effective or not. Also, results showed that good statistical background is not enough for interoperating the analysis of agricultural experiments. This research suggested that erroneous conclusions can be avoided by merging classical and professional statistical tests, and correct recommendations could be provided to decision makers and farmers based on their financial resources.

In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CARBATCLIM database (1961–2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as −3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.

Climate mitigation and adaptation planning (CMAP) has recently been implemented across the EU-28 to reduce GHG emissions (CO2, CH4, N2O). Thus, the aim of this study was to provide an overview of GHG emissions from the agricultural sector in the EU-28 from 1990 to 2019, and cluster the EU-28 countries regarding their total GHG emissions. The results emphasize the positive impact of CMAP through a negative trend of the total GHG emissions (−2653.01 thousand tons/year, p < 0.05). Despite the positive and not significant trend of the total CO2 emissions, both CH4 and N2O exhibited a negative and significant trend. At the country scale, Italy, the United Kingdom, and the Netherlands showed the highest reduction in total GHG emissions, by −282.61thousand tons/year (p < 0.05), −266.40 thousand tons/year (p < 0.05), and −262.91 thousand tons/year (p < 0.05), respectively. The output of the multivariate analysis approach indicates changes in the pattern of GHG emissions between 1990 and 2019, where CO2 emissions decreased in the case of Poland and Czechia. The output of this study highlights the positive impact of CMAP, adopted by EU countries, in minimizing GHG emissions. Despite some fluctuations in CO2 emissions, strategies for attaining carbon neutrality in the agricultural sector, across the European Union, should be pursued.

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.