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

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

Correlation of banana productivity levels and soil morphological properties using regularized optimal scaling regression

Soil morphological properties described in the field, such as texture, consistence or structure, provide a valuable tool for the evaluation of soil productivity potential. In this study, we developed a regression model between the soil morphological variables of banana plantations and a crop Productivity Index (PI) previously developed for the same areas in Venezuela. For this, we implemented categorical regression, an optimal scaling procedure in which the morphological variables are transformed into a numerical scale, and can thus be entered in a multiple regression analysis. The model was developed from data from six plantations growing “Gran Nain” bananas, each with two productivity levels (high and low), in two 4-ha experimental plots, one for each productivity level. Sixty-three A horizons in thirty-six soils were described using 15 field morphological variables on a nominal scale for structure type, texture and hue, and an ordinal scale for the rest (structure grade, structure size, wet and dry consistence, stickiness, plasticity, moist value, chroma, root abundance, root size, biological activity and reaction to HCl). The optimum model selected included biological activity, texture, dry consistence, reaction to HCl and structure type variables. These variables explained the PI with an R2 of 0.599, an expected prediction error (EPE) of 0.645 and a standard error (SE) of 0.135 using bootstrapping, and EPE of 0.662 with a SE of 0.236 using 10-fold cross validation. Our study showed how soil quality is clearly related to productivity on commercial banana plantations, and developed a way to correlate soil quality indicators to yield by using indicators based on easily measured soil morphological parameters. The methodology used in this study might be further expanded to other banana-producing areas to help identify the soils most suitable for its cultivation, thereby enhancing its environmental sustainability and profitability

The advance of Fusarium wilt tropical race 4 in Musaceae of Latin America and the Caribbean: Current situation

The fungus Fusarium oxysporum f. sp. cubense tropical race 4 (syn. Fusarium odoratissimum) (Foc TR4) causes vascular wilt in Musaceae plants and is considered the most lethal for these crops. In Latin America and the Caribbean (LAC), it was reported for the first time in Colombia (2019), later in Peru (2021), and recently declared in Venezuela (2023). This work aimed to analyze the evolution of Foc TR4 in Musaceae in LAC between 2018 and 2022. This perspective contains a selection of topics related to Foc TR4 in LAC that address and describe (i) the threat of Foc TR4 in LAC, (ii) a bibliometric analysis of the scientific production of Foc TR4 in LAC, (iii) the current situation of Foc TR4 in Colombia, Peru, and Venezuela, (iv) medium-term prospects in LAC member countries, and (v) export trade and local food security. In this study, the presence of Foc TR4 in Venezuela and the possible consequences of the production of Musaceae in the long term were reported for the first time. In conclusion, TR4 is a major threat to banana production in Latin America and the world, and it is important to take measures to control the spread of the fungus and minimize its impact on the banana industry. It is important to keep working on the control of Foc TR4, which requires the participation of the local and international industry, researchers, and consumers, among others, to prevent the disappearance of bananas

Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela

The present work aims to analyze the relationship of microbial activity with the physicochemical properties of the soil in banana plantations in Venezuela. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were differentiated with two levels of productivity (high and low) of the “Gran Nain” banana. Ten variables were selected: total free-living nematodes (FLN), bacteriophages, predators, omnivores, Phytonematodes, saturated hydraulic conductivity, total organic carbon, nitrate (NO3), microbial respiration and the variable other fungi. Subsequently, machine learning algorithms were used. First, the Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to find the soil properties that could distinguish the banana productivity levels. Second, the Debiased Sparse Partial Correlation (DSPC) algorithm was applied to obtain the correlation network of the most important variables. The variable free-living nematode predators had a degree of 3 and a betweenness of 4 in the correlation network, followed by NO3. The network shows positive correlations between FLN predators and microbial respiration (r = 1.00; p = 0.014), and NO3 (r = 1.00; p = 0.032). The selected variables are proposed to characterize the soil productivity in bananas and could be used for the management of soil diseases affecting bananas

Mapping Projected Variations of Temperature and Precipitation Due to Climate Change in Venezuela

The impacts of climate change will not be homogeneous in all countries or between regions within each country. Mapping projected changes in temperature and precipitation is crucial for formulating region-specific agricultural adaptation measures. The spatial variation of projected changes in temperature and annual precipitation for 1970–2000 and 2041–2060 in Venezuela was analyzed using the WorldClim 2.1 data. Both variables have been analyzed in fourteen physiographic regions that differ in climate, geology, geomorphology, soils, and land use. The results reveal that western regions experience higher temperature increases, while the regions located in the east and center of the country are projected to experience greater decreases in rainfall. Likewise, temperature and precipitation will increase from north to south. Thus, there are differences in how different regions will be affected by variations in temperature and annual precipitation associated with climate change. It is concluded that physiographic regions can be used as large spatial units to plan future land use and design agricultural adaptation measures to climate change at the national scale

Impacto del cambio climático en zonas bananeras de la Región Central de Venezuela: El futuro de los bananos en un escenario hídrico incierto

VII Congreso Científico de Investigadores en Formación de la Universidad de Córdoba.[EN] The expected increase in average temperatures and the consequent decrease in rainfall in different parts of the country might have serious consequences for man, significantly decreasing the production of basic foods, and encouraging the emergence of food crises. The aim of this study was to describe the possible impact of climate change on banana-growing areas of the Central Region of Venezuela. Two models of General Circulation of the Atmosphere (UKTR and CCC-EQ) for the year 2060. The impact was evaluated by studying changes in the system of annual precipitation were used, that is, both the volume of precipitation and reduction the number of wet months using the methodology Growth Period. According to the UKTR model, there is a decrease in annual rainfall and an increase in the agricultural area affected by water deficit. It is evident, a drier future, being clearly distinguishable banana-growing areas of the region in which the greatest impact occurs. This research will support to define strategies within the framework of sustainable land management, achieving a contribution to reducing vulnerability of agriculture to climate change.[ES] El incremento esperado de las temperaturas medias y la disminución consecuente de la precipitación en diferentes zonas del país puede tener consecuencias graves para el hombre, perjudicando considerablemente la producción de alimentos básicos, y propiciando la aparición de crisis alimentarias. El objetivo de este trabajo fue describir el posible impacto del cambio climático en zonas bananeras de la Región Central de Venezuela. Se utilizaron dos modelos de Circulación General de la Atmósfera (UKTR y CCC-EQ) para el año 2060. El impacto se evaluó mediante el estudio de cambios en el régimen de precipitación anual, esto es, tanto el volumen de precipitación como la reducción del número de meses húmedos por medio del uso de la metodología de Periodo de Crecimiento. De acuerdo con el modelo UKTR, existe una disminución de la precipitación anual y una ampliación de la superficie agrícola afectada por déficit hídrico. Se evidencia, un futuro más seco, siendo claramente distinguibles las zonas bananeras de la región en las cuales se produce el mayor impacto. Esta investigación servirá de apoyo para definir estrategias en el marco del manejo sostenible de tierras, logrando así una contribución a la reducción de la vulnerabilidad del sector agrícola al cambio climático.N

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis

Fusarium Wilt of Bananas: A Review of Agro-Environmental Factors in the Venezuelan Production System Affecting Its Development

Bananas and plantains (Musa spp.) are among the main staple of millions of people in the world. Among the main Musaceae diseases that may limit its productivity, Fusarium wilt (FW), caused by Fusarium oxysporum f. sp. cubense (Foc), has been threatening the banana industry for many years, with devastating effects on the economy of many tropical countries, becoming the leading cause of changes in the land use on severely affected areas. In this article, an updated, reflective and practical review of the current state of knowledge concerning the main agro-environmental factors that may affect disease progression and dissemination of this dangerous pathogen has been carried out, focusing on the Venezuelan Musaceae production systems. Environmental variables together with soil management and sustainable cultural practices are important factors affecting FW incidence and severity, excluding that the widespread dissemination of Foc, especially of its highly virulent tropical race 4 (TR4), is mainly caused by human activities. Additionally, risk analysis and climatic suitability maps for Foc TR4 in Venezuela have been developed. Although currently there are no effective management solutions available for FW control, this perspective provides an overview on the influence that environmental and agricultural variables would have on FW incidence and severity, giving some insight into management factors that can contribute to reducing its detrimental effects on banana production and how climate change may affect its development.This work was partially funded by projects “Technological innovations for the management and improvement of the quality and health of banana soils in Latin America and the Caribbean” financed by FONTAGRO and coordinated by Bioversity International (before INIBAP). BOO was recipient of a PhD fellowship from The Iberoamerican Association of Postgraduate Universities (Spanish: Asociación Universitaria Iberoamericana de Postgrado), the international mobility fellowship from the Ibero-American General Secretary of the Carolina Foundation in Costa Rica (2019) and Action KA107 of Erasmus+ Program from Agrifood Campus of International Excellence (ceiA3) (2020)