Determinación de la influencia potencial del suelo en la diferenciación de la productividad y en la clasificación de áreas susceptibles a la marchitez del banano en Venezuela

Banana, the edible fruit of Musaceae, is a staple food for more than 400 million people worldwide due to their nutritional and energy attributes. This makes Musaceae a crop of worldwide relevance, particularly in tropical regions, highlighting the impact of improved Musaceae cropping systems in the current efforts worldwide oriented towards a new agricultural revolution based on sustainable intensification. To achieve this, better practices for food production based on scientific and technical research capable to consider the complexity and variability within the agri-food sector are necessary. The research presented in this PhD Thesis is oriented towards providing answers to the causes of two aspects considered of high relevance for banana production, both affecting productivity and sustainability, always addressed for the Venezuelan conditions, one of the world’s largest producing countries: 1- The impact of phytosanitary risks related to Fusarium wilt and the influence of the soil on the incidence of Banana Wilt (BW) caused by a fungal-bacterial complex. 2- An observed trend towards loss of productivity and decline of soil quality in some commercial farms of Aragua and Trujillo states in Venezuela. The first issue, related to banana plant health, has been covered in two consecutive studies. Firstly, in Chapter I a systematic review on the effect of agro-environmental factors on the impact of Fusarium Wilt of Bananas, caused by Fusarium oxysporum f. sp. cubense (Foc) tropical race 4 (TR4), and the implications for the Venezuelan production system of this disease is presented. This Chapter synthetically characterizes reliable information on the biotic and abiotic factors related to Foc TR4 occurrence, in conjunction with a risk analysis and climate suitability maps for Foc TR4 in Venezuela. This chapter can serve as a basic summary of the available knowledge for use by plant health technicians and professionals, as well as for other stakeholders concerning disease management. The research oriented towards the plant health issues in banana is completed with the study presented in Chapter II. This chapter analyzes the relationship between soil properties and the incidence of Banana Wilt (BW), a disease of unknow etiology, that is attributed to be caused by a fungal-bacterial complex, in a case study of a commercial banana farm in the state of Aragua in Venezuela, whose incidence has reduced the planted area by more than 35.0% in recent years. The application of the Random Forest algorithm allowed to classify with good precision the incidence of BW in lacustrine soils of Venezuela based on the physical and chemical soil properties, being an effective tool for decision-making in the field. In addition, the use of soil information in banana areas of Venezuela allowed the identification of banana lots with high and low incidence of BW using also the Random Forest algorithm. The model showed that the incidence level (low or high) of Banana Wilt could be distinguished through its relationship with Zn, Fe, K, Ca, Mn and Clay content in the soil. These results can contribute to improve our understanding of the basic mechanisms and progression of BW incidence and identify soil variables that can play a determinant role in predicting risk and evolution of BW in banana farms in tropical lacustrine soils. The second issue, related to the relationship between banana productivity and soil properties, has been covered also in two studies. Chapter III contains the research oriented toward the development of an empirical correlation model to predict productivity based on soil characteristics. Five soil properties were found to have a clear agronomic and environmental importance: Mg, resistance to penetration, total microbial respiration, soil bulk density, and free-living omnivorous nematodes. This model could be used at the field level for the reliable identification of areas of high and low banana productivity in the studied areas of Venezuela. Finally, Chapter IV presents a study which can broaden the usefulness of soil information derived from soil profile descriptions. It validated the hypothesis that it is possible to delimit areas of different productivity within banana farms, in the two main banana producing areas of Venezuela (Aragua and Trujillo states) using soil morphological properties (e.g., soil structure). For this, we developed a model of categorical regression prediction calibrated with soil morphological properties such as biological activity, texture, dry consistency, reaction to HCl and structure type. In the future, if further studies are conducted validating this approach in other environmental conditions, banana productivity could be improved using information which might be already available or can be acquired at a moderate cost using standard soil profile descriptions. This PhD Thesis, has combined a systematic bibliographic review, crop and soil information from a systematic survey of different farm types in Venezuela with soil profile descriptions. Using that information, it has validated the hypothesis that by identifying the abiotic properties of the soil, the predisposition of the banana plant to the BW disease, and the potential productivity of the crop can be predicted. This approach can allow the differentiation of zones with different levels of productivity and BW risk, and as an immediate consequence, avoid areas of high risk or low productivity, or adapt agronomical practices to enhance productivity and sustainability of banana cropping systems in Venezuela.La banana, fruta comestible de las Musáceas, es un alimento básico para más de 400 millones de personas en todo el mundo debido a sus atributos nutricionales y energéticos. Esto hace de las Musáceas cultivos de importancia global, particularmente en regiones tropicales, remarcando la importancia de la mejora de los sistemas de cultivo en Musáceas dentro de los esfuerzos actuales a nivel mundial orientados a una nueva revolución agrícola basada en la sostenibilidad productiva. Para lograrlo, son necesarias buenas prácticas para la producción de alimentos basadas en la investigación científica y técnica capaces de considerar la complejidad y variabilidad dentro del sector agroalimentario. La investigación presentada en esta Tesis Doctoral está orientada a dar respuesta a las causas de dos aspectos considerados de alta relevancia para la producción bananera, que afectan tanto la productividad como la sostenibilidad, siempre dirigidas hacia las condiciones de Venezuela, uno de los principales países productores a nivel mundial: 1- El impacto del riesgo fitosanitario relacionado con la Fusariosis Vascular y la influencia del suelo en la incidencia de la Marchitez del Banano (MB) causada por un complejo fúngico-bacteriano. 2- Una tendencia observada hacia la pérdida de productividad y la disminución de la calidad del suelo en algunas fincas comerciales de los estados de Aragua y Trujillo en Venezuela. El primer tema, relacionado con la sanidad vegetal del banano, se ha abordado en dos estudios consecutivos. En primer lugar, en el Capítulo I se presenta una revisión sistemática sobre el efecto de los factores agroambientales en el impacto de la Fusariosis Vascular del banano, causada por Fusarium oxysporum f. sp. cubense (Foc) raza tropical 4 (TR4), y las implicaciones de esta enfermedad para el sistema de producción venezolano. Este Capítulo caracteriza sintéticamente información fiable sobre los factores bióticos y abióticos relacionados con la ocurrencia de Foc TR4, de forma conjunta al desarrollo de un análisis de riesgos y mapas de idoneidad climática para Foc TR4 en Venezuela. Este capítulo puede servir como un resumen básico del conocimiento disponible para el manejo de la enfermedad para que lo utilicen los técnicos y profesionales de la sanidad vegetal, así como para otras partes interesadas. La investigación orientada a los aspectos fitosanitarios del banano se completa con el estudio presentado en el Capítulo II. Este capítulo analiza la relación entre las propiedades del suelo y la incidencia de la Marchitez del Banano (MB) una enfermedad de etiología desconocida, atribuida a un complejo fúngico-bacteriano, en un estudio de caso de una finca comercial bananera en el estado de Aragua en Venezuela, cuya incidencia ha reducido la superficie plantada en más de un 35,0% en los últimos años. La aplicación del algoritmo Random Forest permitió clasificar la incidencia de MB en suelos lacustres de Venezuela con base a las propiedades físicas y químicas del suelo con buena precisión, siendo una herramienta eficaz para la toma de decisiones en campo. Además, el uso de información de suelos en áreas bananeras de Venezuela permitió la identificación de lotes de banano con alta y baja incidencia de MB utilizando también el algoritmo Random Forest. El modelo mostró que el nivel de incidencia (alta o baja) de la MB se puede distinguir a través de su relación con el contenido de Zn, Fe, K, Ca, Mn y arcilla en el suelo. Estos resultados contribuyen a mejorar nuestra comprensión acerca de los mecanismos básicos y la progresión de la incidencia de MB, e identifican las variables del suelo que pueden jugar un papel determinante en la predicción del riesgo y la evolución de MB en fincas bananeras de suelos lacustres tropicales. El segundo tema, relacionado con la productividad del banano y las propiedades del suelo, también se ha abordado en dos estudios. El Capítulo III contiene la investigación orientada al desarrollo de un modelo de correlación empírico para predecir la productividad del banano en base a las características del suelo. Se encontró que cinco propiedades del suelo tienen una clara importancia agronómica y ambiental: Mg, resistencia a la penetración, respiración microbiana total, densidad aparente del suelo y nematodos omnívoros de vida libre. Este modelo podría utilizarse a nivel de campo para la identificación confiable de áreas de alta y baja productividad bananera en las zonas estudiadas de Venezuela. Finalmente, el Capítulo IV presenta un estudio que puede ampliar la utilidad de la información derivada de las descripciones del perfil del suelo. Se validó la hipótesis de que es posible delimitar áreas de diferente productividad dentro de las fincas bananeras, en las dos principales áreas productoras de banano de Venezuela (estados Aragua y Trujillo) utilizando propiedades morfológicas del suelo (por ejemplo, estructura del suelo). Para ello, se desarrolló un modelo de predicción de regresión categórica calibrado con propiedades morfológicas del suelo tales como actividad biológica, textura, consistencia seca, reacción al HCl y tipo de estructura. En el futuro, si se llevan a cabo más estudios que validen este enfoque en otras condiciones ambientales, la productividad del banano podría mejorarse utilizando información que podría estar ya disponible o puede adquirirse a un costo moderado utilizando descripciones estándar del perfil de suelo. Esta Tesis Doctoral ha combinado una revisión sistemática de literatura, información de cultivos y suelos a partir de un muestreo sistemático de diferentes tipos de fincas en Venezuela con descripciones de perfiles de suelos. Con esa información, se ha validado la hipótesis de que, al identificar las propiedades abióticas del suelo, se puede predecir la predisposición de la planta de banano a la enfermedad de la MB y la productividad potencial del cultivo. Esta aproximación puede permitir la diferenciación de zonas con diferentes niveles de productividad y riesgo de la MB y, como consecuencia inmediata, evitar áreas de alto riesgo o baja productividad, incluso adaptar prácticas agronómicas para mejorar la productividad y sostenibilidad de los sistemas bananeros en Venezuela

Inteligencia de mercados: comportamientos estratégicos sobre precios de oferta en el mercado spot eléctrico Colombiano

El mercado de energía mayorista es uno de los sectores industriales más competitivos de Colombia, y representa uno de los ejes principales en la economía del país. Este mercado ha sido objeto de estudio de varias áreas de conocimiento como la ingeniería eléctrica, economía, finanzas y otros. Aquí se presenta un análisis de los posibles comportamientos estratégicos de los principales agentes de la industria, desde la perspectiva de la inteligencia artificial orientada a la inteligencia de mercados, es decir, un trabajo multidisciplinar centrado en la explicación y emulación de la conducta inteligente y posiblemente estratégica de los agentes involucrados en la actividad de generación de energía en Colombia.The energy market is one of the most competitive Colombian industry sectors, and represents one of the main strategic focus in economy and development of the country. This market has been under study of many knowledge areas such as electric engineering, economy, financial and others. In this work, is presented an analysis of all the possible strategic behaviors of major industry players, from the basis of artificial intelligence oriented to market intelligence, that is, a multidisciplinary work focused on the explanation and emulation of intelligent behavior and possibly strategic of the actors involved in each market activities and in particular the behavior of energy-generating agents in Colombia

Changes in metabolic profiling of sugarcane leaves induced by endophytic diazotrophic bacteria and humic acids.

Plant growth-promoting bacteria (PGPB) and humic acids (HA) have been used as biostimulants in field conditions. The complete genomic and proteomic transcription of Herbaspirillum seropedicae and Gluconacetobacter diazotrophicus is available but interpreting and utilizing this information in the field to increase crop performance is challenging. The identification and characterization of metabolites that are induced by genomic changes may be used to improve plant responses to inoculation. The objective of this study was to describe changes in sugarcane metabolic profile that occur when HA and PGPB are used as biostimulants. Inoculum was applied to soil containing 45-day old sugarcane stalks. One week after inoculation, the methanolic extracts from leaves were obtained and analyzed by gas chromatography coupled to time-of-flight mass spectrometry; a total of 1,880 compounds were observed and 280 were identified in all samples. The application of HA significantly decreased the concentration of 15 metabolites, which generally included amino acids. HA increased the levels of 40 compounds, and these included metabolites linked to the stress response (shikimic, caffeic, hydroxycinnamic acids, putrescine, behenic acid, quinoline xylulose, galactose, lactose proline, oxyproline and valeric acid) and cellular growth (adenine and adenosine derivatives, ribose, ribonic acid and citric acid). Similarly, PGPB enhanced the level of metabolites identified in HA-treated soils; e.g., 48 metabolites were elevated and included amino acids, nucleic acids, organic acids, and lipids. Co-inoculation (HACPGPB) boosted the level of 110 metabolites with respect to non-inoculated controls; these included amino acids, lipids and nitrogenous compounds. Changes in the metabolic profile induced by HA+PGPB influenced both glucose and pentose pathways and resulted in the accumulation of heptuloses and riboses, which are substrates in the nucleoside biosynthesis and shikimic acid pathways. The mevalonate pathway was also activated, thus increasing phytosterol synthesis. The improvement in cellular metabolism observed with PGPB+HA was compatible with high levels of vitamins. Glucuronate and amino sugars were stimulated in addition to the products and intermediary compounds of tricarboxylic acid metabolism. Lipids and amino acids were the main compounds induced by co-inoculation in addition to antioxidants, stress-related metabolites, and compounds involved in cellular redox. The primary compounds observed in each treatment were identified, and the effect of co-inoculation (HACPGPB) on metabolite levels was discussed

Effect of directional pulling on mechanical protein degradation by ATP-dependent proteolytic machines

AAA+ proteases and remodeling machines couple hydrolysis of ATP to mechanical unfolding and translocation of proteins following recognition of sequence tags called degrons. Here, we use single-molecule optical trapping to determine the mechanochemistry of two AAA+ proteases, Escherichia coli ClpXP and ClpAP, as they unfold and translocate substrates containing multiple copies of the titin[superscript I27] domain during degradation initiated from the N terminus. Previous studies characterized degradation of related substrates with C-terminal degrons. We find that ClpXP and ClpAP unfold the wild-type titin I27 domain and a destabilized variant far more rapidly when pulling from the N terminus, whereas translocation speed is reduced only modestly in the N-to-C direction. These measurements establish the role of directionality in mechanical protein degradation, show that degron placement can change whether unfolding or translocation is rate limiting, and establish that one or a few power strokes are sufficient to unfold some protein domains. Keywords:protein degradation; AAA+ proteases; directional unfolding; AAA+ motorsNational Institutes of Health (U.S.) (Grant GM-101988)National Institutes of Health (U.S.) (Grant AI-15706

Morteros de cemento mejorados con la adición de cenizas volantes carbonatadas provenientes de la incineración de residuos

This article presents the results of a research developing high performance cement mortars with the addition of municipal solid waste incineration fly ash (MSWIFA) stabilized as insoluble carbonates. The encapsulation of hazardous wastes in mortar matrixes has also been achieved. The ashes present high concentrations of chlorides, Zn and Pb. A stabilization process with NaHCO3 has been developed reducing 99% the content of chlorides. Developed mortars replace 10% per weight of the aggregates by treated MSWIFA. Physical/mechanical properties of these mortars have been studied. Presence of Zn, Pb, Cu and Cd has been also analyzed confirming that leaching of these heavy metal ions is mitigated. Conclusions prove better behavior of CAC and CSA mortars than those of CEM-I and CEM-II cement. Results are remarkable for the CAC mortars, improving reference strengths in more than 25%, which make them a fast-curing product suitable for the repair of structures or industrial pavements.Este artículo presenta los resultados del desarrollo de morteros mejorados con la incorporación de cenizas volantes de residuos sólidos urbanos inertizadas en forma de carbonatos. Además se consigue la encapsulación de un residuo peligroso. Las cenizas presentan una alta concentración de cloruros, Zn y Pb. Se ha desarrollado un proceso de estabilización con NaHCO3 reduciendo en un 99% el contenido de cloruros. Los morteros reemplazan un 10% en peso del árido por cenizas tratadas. Se han analizado sus propiedades físico/mecánicas y la presencia de Zn, Pb, Cu y Cd. Se demuestra un mejor comportamiento de los morteros de CAC y CSA que los de CEM-I y CEM-II y se mitiga el lixiviado de metales pesados. Los resultados son significativos en los morteros CAC al mejorar las resistencias de los de referencia en un 25%. Los morteros desarrollados son de curado rápido adecuados para la reparación de estructuras o soleras industriales

Assessing the spatiotemporal patterns and impacts of droughts in the Orinoco river basin using earth observations data and surface observations

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region

Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures

Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coherence in the literature. Maintaining hierarchical coherence while producing accurate forecasts can be a challenging problem, especially in the case of probabilistic forecasting. We present a novel method capable of accurate and coherent probabilistic forecasts for hierarchical time series. We call it Deep Poisson Mixture Network (DPMN). It relies on the combination of neural networks and a statistical model for the joint distribution of the hierarchical multivariate time series structure. By construction, the model guarantees hierarchical coherence and provides simple rules for aggregation and disaggregation of the predictive distributions. We perform an extensive empirical evaluation comparing the DPMN to other state-of-the-art methods which produce hierarchically coherent probabilistic forecasts on multiple public datasets. Compared to existing coherent probabilistic models, we obtained a relative improvement in the overall Continuous Ranked Probability Score (CRPS) of 11.8% on Australian domestic tourism data, and 8.1% on the Favorita grocery sales dataset.Comment: Probabilistic Hierarchical Forecasting, Neural Networks, Poisson Mixtures, Preprint submitted to IJ