TY - JOUR AU - Bouzghiba, Houria AU - Mendyl, Abderrahmane AU - Kenza, Khomsi AU - Gabor, Geczi TI - Short-term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling JF - CLEAN-SOIL AIR WATER J2 - CLEAN-SOIL AIR WATER PY - 2024 PG - 15 SN - 1863-0650 DO - 10.1002/clen.202300395 UR - https://m2.mtmt.hu/api/publication/34824732 ID - 34824732 N1 - Funding Agency and Grant Number: Thematic Excellence Program of the National Research, Development, and Innovation Office [TKP2021-NVA-22] Funding text: 2021 Thematic Excellence Program of the National Research, Development, and Innovation Office,Grant/Award Number: TKP2021-NVA-22 AB - Air pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short-term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey-Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short-term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region. LA - English DB - MTMT ER - TY - CHAP AU - Pápai, Bánk AU - Kovács, Zsófia AU - Bedő, Janka AU - Khin, Nyein Chan AU - Tóth-Lencsés, Andrea Kitti AU - Csilléry, Gábor AU - Szamosi, Csaba AU - Timár, Zoltán AU - Szőke, Antal AU - Veres, Anikó TI - BIOMECHANICAL PROFILING OF THE CAPSICUM ANNUUM FRX MUTANT GENOTYPE T2 - FIBOK 2024 6th National Conference of Young Biotechnologists PB - MTA Agrártudományok Osztálya, Mezőgazdasági Biotechnológiai Tudományos Bizottsága CY - Budapest SN - 9786156448453 PY - 2024 SP - 42 PG - 1 UR - https://m2.mtmt.hu/api/publication/34819531 ID - 34819531 LA - English DB - MTMT ER - TY - JOUR AU - Penksza, Károly AU - Saláta, Dénes AU - Fűrész, Attila AU - Penksza, Péter AU - Fuchs, Márta AU - Pajor, Ferenc AU - Sipos, László AU - Falusi, Eszter AU - Wagenhoffer, Zsombor AU - Szentes, Szilárd TI - Are Hungarian Grey Cattle or Hungarian Racka Sheep the Best Choice for the Conservation of Wood-Pasture Habitats in the Pannonian Region? JF - AGRONOMY (BASEL) J2 - AGRONOMY-BASEL VL - 14 PY - 2024 IS - 4 SP - 846 SN - 2073-4395 DO - 10.3390/agronomy14040846 UR - https://m2.mtmt.hu/api/publication/34798937 ID - 34798937 AB - Wood pastures have been characteristic farming types in the Pannonian biogeographical region over the centuries. In the present work, we studied wood-pastures of typical geographical locations in the North Hungarian Mountain Range of Hungary characterized by similar environmental conditions but grazed by different livestock. The sample area of Cserépfalu was grazed by Hungarian Grey Cattle, while the Erdőbénye was grazed by Hungarian Racka Sheep. Coenological records of the sites were collected from 2012 to 2021 in the main vegetation period according to the Braun-Blanquet method with the application of 2 × 2 m sampling quadrats, where the coverage estimated by percentage for each present species was also recorded. To evaluate the state of vegetation, ’ecological ordering’ distribution, diversity, and grassland management values were used. Between the two areas, the grazing pressure of the two studied livestock produced different results. Based on the diversity values, woody–shrubby–grassland mosaic diversity values were high (Shannon diversity: 2.21–2.87). Cattle grazing resulted in a variable and mosaic-like shrubby area with high cover values. Based on our results, grazing by cattle provides an adequate solution for forming and conserving wood-pasture habitats in the studied areas of Hungary. However, if the purpose is to also form valuable grassland with high grassland management values, partly sheep grazing should be suggested. LA - English DB - MTMT ER - TY - CHAP AU - Jahan, Almash AU - Várallyay, Éva TI - APPLE LUTEOVIRUS P0 PROTEIN IS A SUPPRESSOR OF LOCAL AND SYSTEMIC RNA SILENCING T2 - FIBOK 2024 6th National Conference of Young Biotechnologists PB - MTA Agrártudományok Osztálya, Mezőgazdasági Biotechnológiai Tudományos Bizottsága CY - Budapest SN - 9786156448453 PY - 2024 SP - 27 EP - 27 PG - 1 UR - https://m2.mtmt.hu/api/publication/34798018 ID - 34798018 LA - English DB - MTMT ER - TY - CHAP AU - Fűrész, Attila AU - Falusi, Eszter AU - Bajor, Zoltán AU - Sipos, László AU - Fuchs, Márta AU - Penksza, Péter AU - Szentes, Szilárd AU - Wagenhoffer, Zsombor AU - Penksza, Károly ED - Buró, Botond ED - Molnár, Mihály TI - Nyílt homoki gyepek regenerációjának monitorozása az Újpesti Homoktövis Természetvédelmi Területen (2006-2021) T2 - XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet PB - MTA Atommagkutató Intézet CY - Debrecen SN - 9789638321602 T3 - Kárpát-Medencei Környezettudományi Konferencia, ISSN 1842-9815 PY - 2024 SP - 138 UR - https://m2.mtmt.hu/api/publication/34796057 ID - 34796057 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Fintha, Gabriella AU - Fűrész, Attila AU - Falusi, Eszter AU - Járdi, Ildikó AU - Penksza, Károly ED - Buró, Botond ED - Molnár, Mihály TI - Házi vízibivalyok által, eltérő intenzitással legeltetett gyepek és legeltetési üzemmódból kivett gyepek florisztikai felmérése T2 - XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet PB - MTA Atommagkutató Intézet CY - Debrecen SN - 9789638321602 T3 - Kárpát-Medencei Környezettudományi Konferencia, ISSN 1842-9815 PY - 2024 SP - 136 UR - https://m2.mtmt.hu/api/publication/34796027 ID - 34796027 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Balogh, Dániel AU - Fűrész, Attila AU - Penksza, Károly AU - Lantos, Csaba AU - Szőke, Antal ED - Buró, Botond ED - Molnár, Mihály TI - Festuca taxonok ploid vizsgálata Magyarországon T2 - XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet PB - MTA Atommagkutató Intézet CY - Debrecen SN - 9789638321602 T3 - Kárpát-Medencei Környezettudományi Konferencia, ISSN 1842-9815 PY - 2024 SP - 131 UR - https://m2.mtmt.hu/api/publication/34795993 ID - 34795993 LA - Hungarian DB - MTMT ER - TY - CONF AU - Kovács, Flórián AU - Papdi , Enikő AU - Veres, Andrea AU - Szegő, Anita AU - Juhos, Katalin ED - Buró, Botond ED - Molnár, Mihály TI - Gyapjúhulladék alkalmazása a kertészetben és a talajerő-gazdálkodásban T2 - XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet PB - MTA Atommagkutató Intézet CY - Debrecen SN - 9789638321602 T3 - Kárpát-Medencei Környezettudományi Konferencia, ISSN 1842-9815 PY - 2024 SP - 9 UR - https://m2.mtmt.hu/api/publication/34788378 ID - 34788378 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Kipkulei, Harison Kiplagat AU - Bellingrath‐Kimura, Sonoko Dorothea AU - Lana, Marcos AU - Ghazaryan, Gohar AU - Baatz, Roland AU - Matavel, Custodio AU - Boitt, Mark AU - Chisanga, Charles B. AU - Brian Rotich, Kanyongi AU - Moreira, Rodrigo Martins AU - Sieber, Stefan TI - Agronomic management response in maize ( Zea mays L.) production across three agroecological zones of Kenya JF - AGROSYSTEMS GEOSCIENCES & ENVIRONMENT J2 - AGROSYSTEMS G EOSCI ENVIRON VL - 7 PY - 2024 IS - 1 SN - 2639-6696 DO - 10.1002/agg2.20478 UR - https://m2.mtmt.hu/api/publication/34730230 ID - 34730230 AB - Maize ( Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha −1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions. LA - English DB - MTMT ER - TY - JOUR AU - M'hamdi, Oussama AU - Takács, Sándor AU - Palotás, Gábor AU - Ilahy, Riadh AU - Helyes, Lajos AU - Pék, Zoltán TI - A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data JF - PLANTS-BASEL J2 - PLANTS-BASEL VL - 13 PY - 2024 IS - 5 SN - 2223-7747 DO - 10.3390/plants13050746 UR - https://m2.mtmt.hu/api/publication/34727185 ID - 34727185 AB - The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of −0.35. Shapley additive explanation’s (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models’ efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost’s superiority in handling complex agronomic data for quality assessment. LA - English DB - MTMT ER -