Of all human activities, agriculture has one of the highest environmental impacts, particularly related to Greenhouse Gas (GHG) emissions, energy use and land use change. Soybean and maize are two of the most commercialized agricultural commodities worldwide. Argentina contributes significantly to this trade, being the third major producer of soybeans, the first exporter of soymeal and soybean oil, and the third exporter of maize. Despite the economic importance of these crops and the products derived, there are very few studies regarding GHG emissions, energy use and efficiencies associated to Argentinean soybean and maize production. Therefore, the aim of this work is to determine the carbon and energy footprint, as well as the carbon and energy efficiencies, of soybeans and maize produced in Argentina, by analyzing 18 agronomic zones covering an agricultural area of 1.53millionkm. Our results show that, for both crops, the GHG and energy efficiencies at the Pampean region were significantly higher than those at the extra-Pampean region. The national average for production of soybeans in Argentina results in 6.06ton/ton CO-eq emitted to the atmosphere, while 0.887ton of soybean were produced per GJ of energy used; and for maize 5.01ton/ton CO-eq emitted to the atmosphere and 0.740ton of maize were produced per each GJ of energy used. We found that the large differences on yields, GHGs and energy efficiencies between agronomic regions for soybean and maize crop production are mainly driven by climate, particularly mean annual precipitation. This study contributes for the first time to understand the carbon and energy footprint of soybean and maize production throughout several agronomic zones in Argentina. The significant differences found in the productive efficiencies questions on the environmental viability of expanding the agricultural frontier to less suitable lands for crop production.
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http://dx.doi.org/10.1016/j.scitotenv.2017.12.286 | DOI Listing |
J Am Chem Soc
January 2025
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China.
Palladium (Pd) catalysts are promising for electrochemical reduction of CO to CO but often can be deactivated by poisoning owing to the strong affinity of *CO on Pd sites. Theoretical investigations reveal that different configurations of *CO endow specific adsorption energies, thereby dictating the final performances. Here, a regulatory strategy toward *CO absorption configurations is proposed to alleviate CO poisoning by simultaneously incorporating Cu and Zn atoms into ultrathin Pd nanosheets (NSs).
View Article and Find Full Text PDFPLoS One
January 2025
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia.
The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pathology and Parasitology, Chittagong Veterinary and Animal Sciences University (CVASU), Chittagong, Bangladesh.
The three rickettsial parasites- Babesia bovis, Theileria annulata and Anaplasma Marginale are responsible for causing Babesiosis, Theileriosis and Anaplasmosis among cattle. These diseases exist due to spreading of infected ticks. A large number of cattle were found to suffer from mixed infections caused by the three parasites at the same time.
View Article and Find Full Text PDFPLoS One
January 2025
Engineering Research Center of Hydrogen Energy Equipment& Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, China.
The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected.
View Article and Find Full Text PDFJ Org Chem
January 2025
Department of Chemistry and Chemical Engineering, Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, Liaocheng University, Liaocheng 252059, China.
Multipalladium clusters possess peculiar structures and synergistic effects for reactivity and selectivity. Herein, -symmetric tripalladium clusters (, 0.5 mol %) afford C-regioselective SMCC of 2,4-dibromopyridine with phenylboronic acids or pinacol esters (C:C up to 98:1), in contrast to Pd(OAc) in ligand-free conditions.
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