Growing climate change concerns have intensified the focus on agribusiness sustainability,driving an urgent energy transition to improve production efficiency and mitigate environmental harm.The complex interplay betw...Growing climate change concerns have intensified the focus on agribusiness sustainability,driving an urgent energy transition to improve production efficiency and mitigate environmental harm.The complex interplay between energy efficiency and energy consumption highlights the essential role of strategic energy policies in ensuring sustainable development.This study used the Double-Log regression model with bootstrap resampling to examine the rebound effect in the energy transition of agribusiness focusing on five Latin American countries including Brazil,Argentina,Uruguay,Colombia,and Mexico based on the agricultural sector data during 2010–2022.The findings revealed that the rebound effect significantly influences energy transition,with varying degrees of impact across agricultural sectors.This study identified partial rebound effect across all five countries,with elasticity coefficient varying from 9.63%(Colombia’s coffee sector)to 89.12%(Brazil’s livestock sector).In Brazil’s sugarcane sector,nonrenewable energy,agricultural employment,and irrigation efficiency were identified as key factors influencing energy consumption,while in livestock sector,energy consumption was affected by CH_(4)emissions,income and well-being of farmers,water consumption,and water conservation practices.In Mexico’s livestock sector,CH_(4)emissions,nonrenewable energy,and water conservation practices were the key factors affecting energy consumption.In Argentina’s sugarcane sector,pesticides,NO_(2)emissions,renewable energy,and agricultural employment were the key factors affecting energy consumption,while renewable energy,income and well-being of farmers,and water consumption were the key factors affecting energy consumption in livestock sector.In Uruguay’s livestock sector,non-renewable energy,income and well-being of farmers,and irrigation efficiency were the key factors affecting energy consumption.In Colombia’coffee sector,NO_(2)emissions and irrigation efficiency were identified as key factors influencing energy consumption.Finally,this study reinforces the importance of aligning energy transition with Sustainable Development Goals(SDGs),ensuring that energy efficiency gains do not inadvertently increase energy consumption or environmental degradation.展开更多
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered tex...We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.展开更多
We investigated the correlation of large fires（[300 ha） from 1992 to 2013 within the borders of the Antalya Regional Directorate of Forestry using the Keetch–Byram drought index（KBDI）. Daily KBDI values were calc...We investigated the correlation of large fires（[300 ha） from 1992 to 2013 within the borders of the Antalya Regional Directorate of Forestry using the Keetch–Byram drought index（KBDI）. Daily KBDI values were calculated for each year, and values for the period before the year 2000 differed significantly from those after2000. After 2000（large fires occurred in 2004, 2006, 2007,2008, 2010, and 2013）, when KBDI values increased, the KBDI, but not the number of fires, was inversely correlated with the natural log of the burned area（NLBA）. While there were both high and low KBDI values when the NLBA was small, only high KBDI values were associated with high NLBA values. Particularly for logarithmic values of 4 and higher, KBDI values increased in parallel with increases in NLBA values. On the basis of a Mann–Whitney U test done in addition to a Pearson correlation test, we found that when the burned areas were grouped according to small and large areas, the KBDI could be used to distinguish the two groups. Using a conditional probability analysis, we found that 4th, 5th and 6th class KBDI values may lead to large fires at the 60 % possibility.Similarly, the possibility of large fires greater than the median burned area in any given 6 years was found to be48 %. In addition, while the mean value of KBDI is 390.51 for the period from May to September for these 6 years, it is 359.93 for the other years. Consequently, the area burned also increased as the KBDI classes（Class 0： 0–99, Class 1：100–199, Class 2： 200–299, Class 3： 300–399, Class 4：400–499, Class 5： 500–599, Class 6： 600–699, and Class 7：700–800） increase.展开更多
Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area...Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area to the left for road widening on horizontal curves. In order to provide safe and continuous shipment and transportation,appropriate curve widening areas should be provided for long vehicles along horizontal curves. In this study, a statistical model was developed to provide curve-widening solutions for long trucks（e.g., those with 18 wheels） considering various curve radius and deflection angles. The dynamic curve widening feature of Plateia 2013 program was employed to calculate curve widening for the specified vehicle. During the solution process, nine different horizontal curve diameters from 10 to 50 m（by 5 m intervals）and 17 different deflection angles from 90° to 170°（by 5°intervals） were evaluated to run horizontal curve-widening analysis. Using a multiple regression model, we made suitable predictions about curve widening. The curvewidening areas decrease as the horizontal curve radius increases, while increasing the deflection angle on horizontal curves increases curve widening areas. Clearly, the computer-based dynamic curve widening model developed in this study can be effectively used in determining optimum widening for horizontal curves by evaluating the number of alternatives that fit geometrical specifications and vehicle types.展开更多
Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, touri...Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, tourism riches, as well as ecological benefits, such as nutrient regulation and carbon sequestration. Thus, this work aimed to generate potential distribution modeling for the Brazilian forest species, to provide information that will serve as a strategy for conservation, restoration and commercial plantation of them, that is, encouraging the use of legal native species in the forest sector. Eleven tree species and 19 bioclimatic variables were selected. The software Maxent 3.3.3 was applied in the generation of the distribution models and the area under the curve of receiver operating characteristic （AUC） was used to analyze the model. The Jackknife test contributed to identify which bioclimatic variables are most important or influential in the model. The models showed AUC values ranged from 0.857 to 0.983. The species with higher AUC values were Araucaria angustifolia, Mimosa scabrella and Euterpe edulis, respectively. The maximum temperature of warmest month showed the highest influence for the most species, followed by the mean diurnal range and annual precipitation. It was observed that for some species, there were restricted areas of environmental suitability, such as Araucaria angustifolia, Ilex paraguariensis and Mimosa scabrella. The models used could trace the potential distribution areas using the environmental variables, and these models contribute significantly to sustainable forest management.展开更多
Aiming to use lignocellulosic biomass as energy source, one of the process that may aggregate values is the densification process, which allows the production of bioenergy using solid fuels, mainly for reducing transp...Aiming to use lignocellulosic biomass as energy source, one of the process that may aggregate values is the densification process, which allows the production of bioenergy using solid fuels, mainly for reducing transportation costs. In this research, solid fuel from co-briquetting of wood residues from sawmill using commercial kraft lignin as binder was investigated. The effects of compression pressure (900, 1200 and 1500 PSI) and briquette formulation (varying wood and kraft lignin proportion) on the quality and characteristics of briquettes were evaluated. The main findings were that briquetting of wood residues with kraft lignin resulted in an improvement of bulk density, strength rupture modulus, low heating value (LHV) and high heating value (HHV). The briquettes using 4% and 6% of kraft lignin, and submitted to 1200 to 1500 PSI, presented higher bulk density and strength resistance, respectively. On the other hand, the heating values showed the highest results with the addition of 2% lignin at 900 PSI, being the legal range for additives in briquettes for many countries such as in European Union.展开更多
Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviro...Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.展开更多
文摘Growing climate change concerns have intensified the focus on agribusiness sustainability,driving an urgent energy transition to improve production efficiency and mitigate environmental harm.The complex interplay between energy efficiency and energy consumption highlights the essential role of strategic energy policies in ensuring sustainable development.This study used the Double-Log regression model with bootstrap resampling to examine the rebound effect in the energy transition of agribusiness focusing on five Latin American countries including Brazil,Argentina,Uruguay,Colombia,and Mexico based on the agricultural sector data during 2010–2022.The findings revealed that the rebound effect significantly influences energy transition,with varying degrees of impact across agricultural sectors.This study identified partial rebound effect across all five countries,with elasticity coefficient varying from 9.63%(Colombia’s coffee sector)to 89.12%(Brazil’s livestock sector).In Brazil’s sugarcane sector,nonrenewable energy,agricultural employment,and irrigation efficiency were identified as key factors influencing energy consumption,while in livestock sector,energy consumption was affected by CH_(4)emissions,income and well-being of farmers,water consumption,and water conservation practices.In Mexico’s livestock sector,CH_(4)emissions,nonrenewable energy,and water conservation practices were the key factors affecting energy consumption.In Argentina’s sugarcane sector,pesticides,NO_(2)emissions,renewable energy,and agricultural employment were the key factors affecting energy consumption,while renewable energy,income and well-being of farmers,and water consumption were the key factors affecting energy consumption in livestock sector.In Uruguay’s livestock sector,non-renewable energy,income and well-being of farmers,and irrigation efficiency were the key factors affecting energy consumption.In Colombia’coffee sector,NO_(2)emissions and irrigation efficiency were identified as key factors influencing energy consumption.Finally,this study reinforces the importance of aligning energy transition with Sustainable Development Goals(SDGs),ensuring that energy efficiency gains do not inadvertently increase energy consumption or environmental degradation.
基金work conducted under COST Action CA21125-a European forum for revitalisation of marginalised moun-tain areas(MARGISTAR)supported by COST(European Cooperation in Science and Technology)gratefully acknowledges the support received for the research from the University of Ljubljana’s research program Forest,forestry and renewable forest resources(P4-0059).
文摘We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.
文摘We investigated the correlation of large fires（[300 ha） from 1992 to 2013 within the borders of the Antalya Regional Directorate of Forestry using the Keetch–Byram drought index（KBDI）. Daily KBDI values were calculated for each year, and values for the period before the year 2000 differed significantly from those after2000. After 2000（large fires occurred in 2004, 2006, 2007,2008, 2010, and 2013）, when KBDI values increased, the KBDI, but not the number of fires, was inversely correlated with the natural log of the burned area（NLBA）. While there were both high and low KBDI values when the NLBA was small, only high KBDI values were associated with high NLBA values. Particularly for logarithmic values of 4 and higher, KBDI values increased in parallel with increases in NLBA values. On the basis of a Mann–Whitney U test done in addition to a Pearson correlation test, we found that when the burned areas were grouped according to small and large areas, the KBDI could be used to distinguish the two groups. Using a conditional probability analysis, we found that 4th, 5th and 6th class KBDI values may lead to large fires at the 60 % possibility.Similarly, the possibility of large fires greater than the median burned area in any given 6 years was found to be48 %. In addition, while the mean value of KBDI is 390.51 for the period from May to September for these 6 years, it is 359.93 for the other years. Consequently, the area burned also increased as the KBDI classes（Class 0： 0–99, Class 1：100–199, Class 2： 200–299, Class 3： 300–399, Class 4：400–499, Class 5： 500–599, Class 6： 600–699, and Class 7：700–800） increase.
文摘Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area to the left for road widening on horizontal curves. In order to provide safe and continuous shipment and transportation,appropriate curve widening areas should be provided for long vehicles along horizontal curves. In this study, a statistical model was developed to provide curve-widening solutions for long trucks（e.g., those with 18 wheels） considering various curve radius and deflection angles. The dynamic curve widening feature of Plateia 2013 program was employed to calculate curve widening for the specified vehicle. During the solution process, nine different horizontal curve diameters from 10 to 50 m（by 5 m intervals）and 17 different deflection angles from 90° to 170°（by 5°intervals） were evaluated to run horizontal curve-widening analysis. Using a multiple regression model, we made suitable predictions about curve widening. The curvewidening areas decrease as the horizontal curve radius increases, while increasing the deflection angle on horizontal curves increases curve widening areas. Clearly, the computer-based dynamic curve widening model developed in this study can be effectively used in determining optimum widening for horizontal curves by evaluating the number of alternatives that fit geometrical specifications and vehicle types.
文摘Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, tourism riches, as well as ecological benefits, such as nutrient regulation and carbon sequestration. Thus, this work aimed to generate potential distribution modeling for the Brazilian forest species, to provide information that will serve as a strategy for conservation, restoration and commercial plantation of them, that is, encouraging the use of legal native species in the forest sector. Eleven tree species and 19 bioclimatic variables were selected. The software Maxent 3.3.3 was applied in the generation of the distribution models and the area under the curve of receiver operating characteristic （AUC） was used to analyze the model. The Jackknife test contributed to identify which bioclimatic variables are most important or influential in the model. The models showed AUC values ranged from 0.857 to 0.983. The species with higher AUC values were Araucaria angustifolia, Mimosa scabrella and Euterpe edulis, respectively. The maximum temperature of warmest month showed the highest influence for the most species, followed by the mean diurnal range and annual precipitation. It was observed that for some species, there were restricted areas of environmental suitability, such as Araucaria angustifolia, Ilex paraguariensis and Mimosa scabrella. The models used could trace the potential distribution areas using the environmental variables, and these models contribute significantly to sustainable forest management.
文摘Aiming to use lignocellulosic biomass as energy source, one of the process that may aggregate values is the densification process, which allows the production of bioenergy using solid fuels, mainly for reducing transportation costs. In this research, solid fuel from co-briquetting of wood residues from sawmill using commercial kraft lignin as binder was investigated. The effects of compression pressure (900, 1200 and 1500 PSI) and briquette formulation (varying wood and kraft lignin proportion) on the quality and characteristics of briquettes were evaluated. The main findings were that briquetting of wood residues with kraft lignin resulted in an improvement of bulk density, strength rupture modulus, low heating value (LHV) and high heating value (HHV). The briquettes using 4% and 6% of kraft lignin, and submitted to 1200 to 1500 PSI, presented higher bulk density and strength resistance, respectively. On the other hand, the heating values showed the highest results with the addition of 2% lignin at 900 PSI, being the legal range for additives in briquettes for many countries such as in European Union.
基金R.T.R.,L.L.P.,and G.E.M.thank the Brazilian agencies Coordenac¸ao de Aperfeic¸oamento de Pessoal de Nıvel Superior(CAPES)and Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico(CNPq)for the financial support,which was instrumental in the successful execution of this project.L.H.was supported through an ARC Future Fellowship(FT220100350)from the Australian Research Council.C.H.A.was supported by The University of Colorado Boulder Grand ChallengeCIRES Earth Lab.Y.X.was supported by the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences,Shenzhen Science and Technology Program(KQTD202303010928390070)Hebei Science and Technology Program(215A7612D),and the Provincial Technology Innovation Program of Shandong,China.
文摘Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
