Stakeholder perceptions on better management of Cross-timbers forest resources

The Cross-timbers ecoregion represents the broad ecotone between the Eastern Deciduous Forest and the Tallgrass Prairie. The region is threatened by both natural and anthropogenic factors including urban development, increasing climate variability, and the encroachment of eastern redcedar (Juniperus virginiana). In particular, the exclusion of fire has dramatically changed the composition and structure of the Cross-timbers forests, which historically experienced multiple fires per decade. Active management practices such as prescribed fire, timber thinning, and fuels reduction are largely absent in the region. These management practices are further limited by a lack of a forest resource market. This study utilized a mixed-mode data collection method, which involved focus group meetings as well as an online version of the survey to determine how stakeholders perceived both active management and market opportunities within the Cross-timbers. The requisite data were analyzed using the strengths, weaknesses, opportunities, and threats (SWOT)-Analytic Network Process (ANP) framework. The results suggested that the presence of healthy and resilient forests and the opportunities associated with increased revenue could be the driving forces in active Cross-timbers management. In addition, the availability of a variety of natural resources and the restoration of ecosystem services could be the key to developing a sustainable market within the Cross-timbers. However, stakeholders across-the-board revealed that the financial burden of management and the risk of uncontrolled fire were the major obstacles in these efforts. Further, uncertain markets, lack of enthusiasm from manufacturers, and low quality resources may be what currently hinder the market potential of the Cross-timbers

Discrete stochastic programming to address biomass yield variability and feedstock quality

While advances in improving biomass yields and conversion technologies will contribute toward the U.S. energy security goals, the lack of a large-scale stable supply of feedstock could limit this biobased venture. Therefore, optimizing logistics for collecting, storing, combining feedstock, and address potential supply risks are critical to facilitate a biobased industry and offset non-renewable sources consumption. This project determined the land to contract for a five-year biomass supply subject to the risk of year to year variation in feedstock availability of two dedicated energy crops that could be blended to meet carbohydrate and ash requirements. For this purpose, I built a discrete stochastic programming model that minimized costs subject to the inherent variability of biomass yield, quality specifications, and assumed plant capacity. This research introduced a risk management approach to address the risk of year to year biomass yield variability and contributes to the creation of a market for bioenergy sources in Oklahoma

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables