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125 resultados, página 3 de 10

DISEÑO Y SIMULACIÓN DE UNA PLANTA DE PROCESAMIENTO HIDROTERMAL UTILIZANDO ENERGÍA SOLAR: UN ANÁLISIS TECNO-ECONÓMICO

Eduardo Bautista (2023, [Tesis de maestría])

"En este trabajo de tesis se aborda el modelado tecno-económico de una planta de procesamiento hidrotermal, la cual acoplará en su operación tecnología de concentración solar con el objetivo de transformar biomasa de carácter lignocelulósico (residuos de madera triturada) para obtener productos objetivo de alta densidad energética como lo son los biocombustibles: bio-crudo y gas de síntesis.

El diseño de la planta se contempla para procesar 1 tonelada diaria de desechos de madera, la cual trabaja mediante el uso de energía solar de concentración y gas natural con el objetivo de tener una operación continua."

Energía solar de concentración Licuefacción hidrotermal Biomasa Desechos forestales INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA ENERGÉTICA FUENTES NO CONVENCIONALES DE ENERGÍA FUENTES NO CONVENCIONALES DE ENERGÍA

Using microsatellite data to estimate the persistence of field-level yield gaps and their drivers in smallholder systems

Balwinder-Singh Meha Jain (2023, [Artículo])

One way to meet growing food demand is to increase yields in regions that have large yield gaps, including smallholder systems. To do this, it is important to quantify yield gaps, their persistence, and their drivers at large spatio-temporal scales. Here we use microsatellite data to map field-level yields from 2014 to 2018 in Bihar, India and use these data to assess the magnitude, persistence, and drivers of yield gaps at the landscape scale. We find that overall yield gaps are large (33% of mean yields), but only 17% of yields are persistent across time. We find that sowing date, plot area, and weather are the factors that most explain variation in yield gaps across our study region, with earlier sowing associated with significantly higher yield values. Simulations suggest that if all farmers were able to adopt ideal management strategies, including earlier sowing and more irrigation use, yield gaps could be closed by up to 42%. These results highlight the ability of micro-satellite data to understand yield gaps and their drivers, and can be used to help identify ways to increase production in smallholder systems across the globe.

Yield Drivers Yield Mapping CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MICROSATELLITES YIELD GAP SMALLHOLDERS FOOD PRODUCTION YIELD INCREASES

Agricultural lime value chain efficiency for reducing soil acidity in Ethiopia

Moti Jaleta (2023, [Artículo])

Soil acidity is challenging agricultural production in Ethiopia. Above 43% of the farmland is under soil acidity problem and it leads to low crop yields and production losses. Ag-lime is widely considered as an effective remedy for amending soil acidity. This study assesses the current structure of ag-lime value chain and its functionality focusing on central parts of Ethiopia where lime is produced and channeled to acidity affected areas. The study uses Ethiopia as a case study and applies qualitative methods such as key informant interviews and focus group discussions to collect data from different actors in the ag-lime value chain. Key findings indicate that both public and private ag-lime producing factories are operating below their capacity. Due to limited enabling environments, the engagement of private sector in ag-lime value chain is minimal. In addition, farmers have a good awareness of soil acidity problem on their farms, and its causes and mitigation strategies in all regions. However, the adoption of ag-lime by smallholders was minimal. Overall, the current structure of the ag-lime value chain appears fragmented and needs improvement. Addressing soil acidity challenge through efficient ag-lime value chain could narrow lime supply-demand mismatches and increase widespread adoption by farmers to enhance crop productivity and food security in acidity-prone areas of the country.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LIMES PRODUCTION COSTS VALUE CHAINS SOIL PH

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE