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Our findings confirm the existence of educational quality agglomeration in Cali (Moran’s Index = 0.311). High educational quality is concentrated in schools in the south of the city (commune 22), while low educational quality is found in schools located in the east (communes 13, 14 and 15). Our study also confirms the importance of the determinants for school performance identified in the previous literature. The main contribution of this study is the finding that crime negatively affects educational quality. When the number of homicides in the neighbourhood in which a school is located increases by one percentage point, the quality of education decreases, on average, by 0.63 points.

This study seeks to measure the degree of agglomeration of educational quality in Colombia, based on the non-socialization of the population that exhibits low educational quality, with the population that exhibits high educational quality, and thus determine how such agglomeration affects the phenomenon of academic segregation. To this end, we perform a spatial analysis of the educational quality in Colombia and of variables that may influence to the phenomenon of educational agglomeration. The level of agglomeration in educational quality in Colombia is demonstrated by the calculation of the Moran’s Index, in which a result of 0.62 was obtained. High educational quality is concentrated in the Andean region, while low educational quality is agglomerated in the periphery of the country, in areas such as the Pacific region. A spatial regression model was carried out to measure the dependence of municipalities on their neighbors, and to determine the main socio-economic factors affecting the phenomenon of educational agglomeration in Colombia, finding that living conditions, unsatisfied basic needs and fiscal transparency have all an impact on the educational quality of the municipalities. It is also found that the number of homicides in the municipalities does not seem to have a significant relationship with education.

Objetivo Describir la distribución espacio-temporal del COVID-19 en la ciudad de Cali durante el primer mes de epidemia.Métodos Se realizó un análisis exploratorio de datos espaciales, compuesto por un análisis de densidad de Kernel y se verificó la presencia de patrones espaciales por medio de la función K de Ripley.Resultados La distribución espacial de los casos tiende a concentrarse inicialmente en el norte y sur de la ciudad, con una dinámica cambiante hacia el oriente y occidente.Conclusiones El patrón espacial identificado puede estar influenciado por las medidas de aislamiento tomadas a nivel local y nivel nacional, además no se puede descartar el efecto del poco acceso que tiene la población general a las pruebas diagnósticas,los retrasos y represamientos para conocer los resultados de las mismas y aun los posibles sesgos por dificultades en la técnica de toma de la muestra o su conservación.

Objetivo Predecir el número de casos de COVID-19 en la ciudad de Cali-Colombia mediante el desarrollo de un modelo SEIR.Métodos Se utilizó un modelo determinista compartimental SEIR considerando los estados: susceptibles (S), expuestos (E), infectados (I) y recuperados (R). Los parámetros del modelo fueron seleccionados de acuerdo a la revisión de literatura. En el casode la tasa de letalidad, se usaron los datos de la Secretaría de Salud Municipal de Cali. Se plantearon varios escenarios teniendo en cuenta variaciones en el número básico de reproducción (R0) y en la tasa de letalidad; además, se comparó la predicción hasta el 9 de abril con los datos observados.Resultados A través del modelo SEIR se encontró que, con el número básico de reproducción más alto (2,6) y utilizando la letalidad calculada para la ciudad de 2,0%, el número máximo de casos se alcanzaría el primero de junio con 195 666 (prevalencia); sin embargo, al comparar los casos observados con los esperados, al inicio la ocurrencia observada estaba por encima de la proyectada; pero luego cambia la tendencia con una disminución marcada de la pendiente.Conclusiones Los modelos epidemiológicos SEIR son métodos muy utilizados para la proyección de casos en enfermedades infecciosas; sin embargo, se debe tener en cuenta que son modelos deterministas que pueden utilizar parámetros supuestos y podrían generar resultados imprecisos.

Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.

Introduction Cameras for detecting traffic violations have been used as a measure to improve road safety in different countries around the world. In Cali, Colombia, fixed cameras were installed in March 2012 on a number of roads and intersections. All camera devices are capable of detecting simultaneously the following traffic violations: driving over the speed limit, running a red light or stop sign, violation of the traffic ban schedule, and blocking the pedestrian crosswalk. Objective To evaluate the impact of camera enforcement of traffic violations in Cali, Colombia. Methods A quasi-experimental difference-in-differences study with before and after measurements and a comparison group was conducted. We observed 38 intervention areas and 50 comparison areas (250 m radius), during 42 months before and 34 months after the installation of cameras. Effects were estimated with mixed negative binomial regression models. Results In intervention areas, after 12 months, there was a reduction of 19.2% of all crashes and a 24.7% reduction of injury and fatal crashes. In comparison areas, this reduction was 15.0% for all crashes and 20.1% for injury and fatal crashes. After adjusted comparisons, intervention sites outperformed comparison sites with an additional yearly reduction of 5.3% (p = 0.045) for all crashes. Conclusions The use of cameras for detecting traffic violations seems to have a positive effect on the reduction of crashes in intervention areas. A beneficial spillover effect was found as well in comparison areas; but more evaluations are needed.

Rice is one of the most important crops in terms of harvested area and food security both globally and for Colombia. Improvement of technical efficiency levels in rice production in order to close yield gaps in a context in which rice demand increases, natural resources are depleted, and where there are growing expectations about both climate changes and trade agreements is likely the most important challenge that farmers confront. This research assessed the main management factors that limit both rice crop productivity and the likely drivers of non-optimal technical efficiency levels (a proxy for yield gaps). This study focused on both upland and irrigated direct seeding systems across a variety of environments in Colombia. Stochastic frontier models were used to integrate microeconomic theory and empirical regression analysis in conjunction with a large commercial rice production database developed by the Colombian rice growers' federation (Fedearroz). A large variation was found in technical efficiency (from 40 to 95%) levels for both upland and irrigated systems, and major differences were obtained in the limiting factors of the two systems (e.g. seed availability, variety type, market accessibility, fertilizer type, and use rate). This suggests both substantial and varied opportunities for improvements in current technical efficiency levels. Across systems, the correct choice of variety was identified as a common key factor for maximizing yield for a particular environment. For upland systems, optimal choices were F174 and F2000, whereas for irrigated rice F473 was found to produce the highest yield. Additionally, numerical analysis suggests a yield impact of ca. 0.18% for each 1% increase in the nitrogen application rate for upland systems. For irrigated rice, phosphorous rather than nitrogen application rates were found to be more important. Since our analysis is based on farm-scale commercial production data, we argue that once our results are brought to consensus with local extension agents, technicians and agronomists, then management recommendations for closing yield gaps can be used to improve rice productivity. RESUMEN El cultivo de arroz es uno de los más importantes en términos de área cosechada y seguridad alimentaria tanto a nivel global como en Colombia. Mejorar los niveles de eficiencia técnica en la producción de arroz para cerrar las brechas de rendimiento en un contexto en el que la demanda de alimento aumenta, los recursos naturales escasean y las expectativas sobre los impactos potenciales del cambio climático y los tratados de libre comercio crecen es probablemente el desafío más importante que enfrentan los agricultores. Esta investigación evaluó los principales factores de gestión que limitan la productividad del cultivo de arroz, y los posibles impulsores de niveles de eficiencia técnica no óptimos (un proxy de las brechas de rendimiento). El estudio se enfocó en sistemas de siembra directa de tierras altas y de riego en diferentes ambientes en Colombia. Utilizamos modelos de frontera estocástica para integrar la teoría microeconómica y el análisis de regresión empírica junto con una gran base de datos de producción comercial de arroz, desarrollada por la Federación colombiana de productores de arroz (Fedearroz). Se encontró una gran variación en los niveles de eficiencia técnica (del 40 al 95%) para los sistemas de tierras altas y de riego, y se obtuvieron diferencias importantes en los factores limitantes entre los dos sistemas (por ejemplo, disponibilidad de semillas, tipo de variedad, accesibilidad al mercado, tipo de fertilizante, y tasa de uso). Esto sugiere oportunidades sustanciales y variadas para mejorar los niveles actuales de eficiencia técnica. En todos los sistemas, la elección correcta de la variedad se identificó como un factor clave común para maximizar el rendimiento por ambiente. Para los sistemas de tierras altas, las opciones óptimas fueron F174 y F2000, mientras que para el arroz de riego se encontró que F473 era el de mayor rendimiento. Además, el análisis numérico sugiere un impacto en el rendimiento de ca. 0.18% por cada 1% de aumento en la tasa de aplicación de nitrógeno para sistemas de tierras altas. Para el arroz de riego, se encontró que las tasas de aplicación de fósforo en lugar de nitrógeno eran más importantes. Como nuestro análisis se basa en datos de producción comercial a escala de finca, se argumentó que una vez que nuestros resultados llegan a un consenso con los agentes de extensión, técnicos y agrónomos locales, las recomendaciones de gestión para cerrar las brechas de rendimiento se pueden utilizar para mejorar la productividad del arroz.

Increasing weather risks threaten agricultural production systems and food security across the world. Maintaining agricultural growth while minimizing climate shocks is crucial to building a resilient food production system and meeting developmental goals in vulnerable countries. Experts have proposed several technological, institutional, and policy interventions to help farmers adapt to current and future weather variability and to mitigate greenhouse gas (GHG) emissions. This paper presents the climate-smart village (CSV) approach as a means of performing agricultural research for development that robustly tests technological and institutional options for dealing with climatic variability and climate change in agriculture using participatory methods. It aims to scale up and scale out the appropriate options and draw out lessons for policy makers from local to global levels. The approach incorporates evaluation of climate-smart technologies, practices, services, and processes relevant to local climatic risk management and identifies opportunities for maximizing adaptation gains from synergies across different interventions and recognizing potential maladaptation and trade-offs. It ensures that these are aligned with local knowledge and link into development plans. This paper describes early results in Asia, Africa, and Latin America to illustrate different examples of the CSV approach in diverse agroecological settings. Results from initial studies indicate that the CSV approach has a high potential for scaling out promising climate-smart agricultural technologies, practices, and services. Climate analog studies indicate that the lessons learned at the CSV sites would be relevant to adaptation planning in a large part of global agricultural land even under scenarios of climate change. Key barriers and opportunities for further work are also discussed.

The upland rice (UR) cropped area in Brazil has decreased in the last decade. Importantly, a portion of this decrease can be attributed to the current UR breeding programme strategy, according to which direct grain yield selection is targeted primarily to the most favourable areas. New strategies for more-efficient crop breeding under non-optimal conditions are needed for Brazil's UR regions. Such strategies should include a classification of spatio-temporal yield variations in environmental groups, as well as a determination of prevalent drought types and their characteristics (duration, intensity, phenological timing, and physiological effects) within those environmental groups. This study used a process-based crop model to support the Brazilian UR breeding programme in their efforts to adopt a new strategy that accounts for the varying range of environments where UR is currently cultivated. Crop simulations based on a commonly grown cultivar (BRS Primavera) and statistical analyses of simulated yield suggested that the target population of environments can be divided into three groups of environments: a highly favorable environment (HFE, 19% of area), a favorable environment (FE, 44%), and least favourable environment (LFE, 37%). Stress-free conditions dominated the HFE group (69% likelihood) and reproductive stress dominated the LFE group (68% likelihood), whereas reproductive and terminal drought stress were found to be almost equally likely to occur in the FE group. For the best and worst environments, we propose specific adaptation focused on the representative stress, while for the FE, wide adaptation to drought is suggested. 'Weighted selection' is also a possible strategy for the FE and LFE environment groups.