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El fondo Bonos y Acciones, contiene información desde 1889 hasta 1928. Los títulos de acciones reúnen información acerca de una determinada empresa, el nombre de un accionista, el valor comercial de cada acción, y el trabajo litográfico del momento, para entonces en desarrollo, en el caso de estas acciones fueron elaboradas en la litografía de J. L. Arango, trabajo rico en detalles, que hacen de estos títulos un buen ejemplo litográfico. Títulos acciones de la Fábrica de Baldosas Roca, empresa dedicada a la industria de la cerámica, constituida el 31 de marzo de 1920, por Víctor E. Arbeláez y Cía., Enrique, Correa y Rafael Posada. Fueron nombrados como cajeros Enrique Correa y Julio Arbeláez, en representación de Víctor E. Arbeláez y Cía., y como gerente Rafael Posada. Los títulos aparecen a nombre de Enrique Correa y Víctor E. Arbeláez y Cía. Títulos de acciones de la Compañía Manufacturera de Camisas y Cuellos, fundada por Rubén M. Arcila; Carlos, Rodríguez; Manuel José, Tobón; Clodomiro, Ramírez, el 14 de agosto de 1917. Con un capital de $1.200 oro inglés, representadas en 600 acciones de $2 oro inglés cada una. Como gerente fue nombrado Rubén M. Arcila, quien aparece como titular de las acciones. Títulos de acciones de la compañía de Impermeables Colombia, constituida el 3 de septiembre de 1918 por Ángel López y Cía., Carlos Montoya G., Alejandro Restrepo L., Ramón A. Vélez, Jesús M. López. Con un capital de $60 dividido en 60 acciones de $1 cada uno. Como gerente fue nombrado Jesús M. López y como suplente, Ramón A. Vélez y Alejandro Restrepo L., los títulos aparecen a nombre de Raúl Uribe A., y Ramón A. Vélez. Los Bonos por su parte, ofrece información acerca de algunas ferias y exposiciones que tuvieron lugar en París, Francia a finales del siglo XIX y principios del XX., El fondo está conformado por 8 carpetas y 1,945 folios agrupados en las siguientes series documentales: Titulos de Acciones: 8 carpetas para un total de 1,945 folios.

Simulation models represent a low-cost approach to evaluating agricultural systems. In the current study, the precision and accuracy of the RUMINANT model to predict dry matter intake (DMI) and methane emissions from beef cattle fed tropical diets (characteristic of Colombia) was assessed. Feed intake (DMI) and methane emissions were measured in Brahman steers housed in polytunnels and fed six forage diets. In addition, DMI and methane emissions were predicted by the RUMINANT model. The model’s predictive capability was measured on the basis of precision: coefficients of variation (CV%) and determination (R2, percentage of variance accounted for by the model), and model efficiency (ME) and accuracy: the simulated/observed ratio (S/O ratio) and slope and mean bias (MB%). In addition, combined measurements of accuracy and precision were carried out by means of mean square prediction error (MSPE) and correlation correspondence coefficient (CCC) and their components. The predictive capability of the RUMINANT model to simulate DMI resulted as valuable for mean S/O ratio (1.07), MB% (2.23%), CV% (17%), R2 (0.886), ME (0.809), CCC (0.869). However, for methane emission simulations, the model substantially underestimated methane emissions (mean S/O ratio = 0.697, MB% = −30.5%), and ME and CCC were −0.431 and 0.485, respectively. In addition, a subset of data corresponding to diets with Leucaena was not observed to have a linear relationship between the observed and simulated values. It is suggested that this may be related to anti-methanogenic factors characteristic of Leucaena, which were not accounted for by the model. This study contributes to improving national inventories of greenhouse gases from the livestock of tropical countries.

Chapter 3 assesses the emissions pathways literature in order to identify their key characteristics (both in commonalities and differences) and to understand how societal choices may steer the system into a particular direction (high confidence). More than 2000 quantitative emissions pathways were submitted to the IPCC’s Sixth Assessment Report AR6 scenarios database, out of which 1202 scenarios included sufficient information for assessing the associated warming consistent with WGI.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Rang