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Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

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Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
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Video Experimental Relacionado

Updated: Jan 13, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Predicción de variedades adaptadas al entorno con big data

Abhishek Gogna1, Bahareh Kamali2, Valentin Wimmer3

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstraße, Gatersleben, 306466, Germany.

Genome biology
|January 6, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los modelos de predicción genómica ahora pueden seleccionar variedades de trigo de invierno de alto rendimiento para entornos específicos. El aprendizaje automático y el aprendizaje profundo mejoran las predicciones, acelerando el progreso de la mejora para los agricultores.

Palabras clave:
Inteligencia artificialBig dataProgramas de mejoraAprendizaje profundoVariedades adaptadas al entornoRendimiento del genotipoInteracciones genotipo por entornoAprendizaje automáticoTrigo de invierno

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Área de la Ciencia:

  • Ciencia Agrícola
  • Genética
  • Mejora de Cultivos

Sus antecedentes:

  • Los programas de mejora tradicionales se centran en el rendimiento promedio del genotipo, lo que puede pasar por alto adaptaciones específicas del entorno.
  • La selección de genotipos para entornos específicos es crucial para optimizar los rendimientos de los cultivos.

Objetivo del estudio:

  • Desarrollar un marco de predicción genómica para seleccionar genotipos de trigo de invierno de alto rendimiento adaptados a entornos individuales.
  • Mejorar la predicción del rendimiento específico del genotipo teniendo en cuenta las interacciones genotipo-entorno.

Principales métodos:

  • Se compilaron datos extensos de rendimiento de grano de trigo de invierno de 13 285 genotipos en 31 sitios de Europa Central (2010-2022).
  • Se utilizaron redes neuronales convolucionales (CNN) y la predicción lineal insesgada genómica tradicional (GBLUP) para predecir el rendimiento del genotipo.
  • Se incorporaron datos ambientales para modelar las interacciones genotipo-entorno (G×E) utilizando aprendizaje automático.

Principales resultados:

  • Las CNN demostraron un rendimiento competitivo o superior en comparación con GBLUP para predecir el rendimiento promedio del genotipo a medida que aumentaba el tamaño de los datos de entrenamiento.
  • Se observó una mejora del 23% en la predicción del rendimiento de híbridos específicos del entorno utilizando modelos GBLUP con interacciones G×E.
  • Se identificaron variables ambientales clave que impulsan las interacciones G×E y la agrupación de genotipos en los sitios de estudio de Europa Central.

Conclusiones:

  • El big data, el aprendizaje automático y el aprendizaje profundo ofrecen enfoques novedosos para superar los cuellos de botella genéticos en la mejora de cultivos.
  • Estos métodos avanzados facilitan el desarrollo y la entrega más rápidos de variedades de cultivos mejoradas a los agricultores.