Browsing by Author "Mohammed, Safwan"

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Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990e2019)
(Elsevier, 2023-09) Mohammed, Safwan; Gill, Abid Rashid; Ghosal, Kaushik; Al-Dalahmeh, Main; Alsafadi, Karam; Szabo, Szilard; Olah, Judit; Alkerdi, Ali; Ocwa, Akasairi; Harsanyi, Endre
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 þ1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO2 emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO2 emissions occurred in Germany (7.52 Mt CO2 annually), and the lowest in Latvia (0.087 Mt CO2 annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO2 emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP2 leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.
A bibliographic review of climate change and fertilization as the main drivers of maize yield: implications for food security
(Springer, 2023-06) Ocwa, Akasairi; Harsanyi, Endre; Széles, Adrienn; Holb, Imre János; Szabó, Szilárd; Rátonyi, Tamás; Mohammed, Safwan
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 < 5% in cold areas globally. Precipitation reduction decreased yield by 25–32%, while CO2 concentration increased and decreased yield by 2.4 to 7.3% and 9 to 14.6%, respectively. A promising fertilizer was a combination of urea + nitrapyrin with an average yield of 5.1 and 14.4 t ha− 1 under non-irrigation and irrigation, respectively. Fertilization under climate change was projected to reduce yield in the average range of 10.5–18.3% by 2099. Conclusion The results signified that sole fertilizer intensification is insufficient to attain sustainable maize yield. Therefore, there is need for integrated agronomic research that combines fertilizers and other technologies for enhancing maize yield, and consequently maize contribution to the attainment of global food security under climate change conditions. Keywords Climate change, Drought, Fertilizers, Heat stress, Maize, Nitrogen, Temperature, Yield
Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe
(MDPI, 2023-05) Harsányi, Endre; Bashir, Bashar; Arshad, Sana; Ocwa, Akasairi; Vad, Attila; Alsalman, Abdullah; Mohammed, Safwan; Széles, Adrienn; Hijazi, Omar; Rátonyi, Tamás; Bácskai, István
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 ofANNas an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management. Keywords: maize yield; climate; multilayer perceptron; random forest; optimum model
An environmental impact assessment of Saudi Arabia’s vision 2030 for sustainable urban development: A policy perspective on greenhouse gas emissions
(Elsevier, 2023-12) Altouma, Ahmed; Bashir, Bashar; Ata, Behnam; Ocwa, Akasairi; Alsalman, Abdullah; Harsanyi, Endre; Mohammed, Safwan
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 < 0.05) positive increase of GHGs emissions from all sectors across the KSA. The highest increased were captured at the electricity and heat by 7345454.47 tonnes of carbon dioxide-equivalents/year (p < 0.05). On the hand, the ARDL model indicates that GDP, agriculture, industry, services, and oil production have short-term effects on the environment through CO2 emissions. Therefore, GDP, agriculture, services and oil production contribute to increases in CO2 emissions. While industry contributes to decrease in CO2 emissions. The ARDL model also showed that an increase in GDP of 1 percent increases CO2 emissions by 3.46 percent, while an increase in oil production of 1 percent increases CO2 emissions by 4.04 percent. However, an increase in industry of 1 percent decreases CO2 emissions by 7.25 percent. The output of this research has a policy implication for addressing environmental concerns in the country.
Maize Grain Yield and Quality Improvement Through Biostimulant Application: a Systematic Review
(Springer, 2024-02) Ocwa, Akasairi; Mohammed, Safwan; Mousavi, Seyed Mohammad Nasir; Illés, Árpád; Bojtor, Csaba; Ragán, Péter; Rátonyi, Tamás; Harsányi, Endre
Increasing the productivity of cereals such as maize while protecting the environment remains a fundamental impetus of healthy food production systems. The use of biostimulants is one of the sustainable strategies to achieve this balance, although the ability of biostimulants to enhance maize productivity varies. Moreover, research on the efficacy of biostimulants is ubiquitous with limited comprehensive global analysis. In this context, this systematic review evaluated the sole and interactive effects of biostimulants on the yield and quality of maize grain from a global perspective. Changes in yield (t ha-1), protein content (%), starch content (%) and oil content (%) of maize grain were assessed. Results revealed that sole and combined application of biostimulants significantly improved grain yield. Irrespective of the region, the highest and the lowest grain yields ranged between 16-20 t ha-1 and 1-5 t ha-1, respectively. In sole application, the promising biostimulants were chicken feather (16.5 t ha-1), and endophyte Colletotrichum tofieldiae (14.5 t ha-1). Sewage sludge × NPK (15.4 t ha-1), humic acid × control release urea (12.4 t ha-1), Azospirillum brasilense or Bradyrhizobium japonicum × maize hybrids (11.6 t ha- 1), and Rhizophagus intraradices × earthworms (10.0 t ha- 1) had higher yield for the interactive effects. The effects of biostimulants on grain quality were minimal, and all attributes improved in the range from 0.1 to 3.7%. Overall, biostimulants had a distinct improvement effect on yield, rather than on the quality of grain. As one way of maximising maize productivity, soil health, and the overall functioning of crop agroecosystems, the integrated application of synergistic microbial and non-microbial biostimulants could provide a viable option. However, the ability to produce consistent yield and quality of grain improvement remains a major concern.