High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images

Francisco Pinto Matthew Paul Reynolds Robert Furbank (2024, [Artículo])

Deep Learning Object-Based Image Analysis Optical Imagery CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE IMAGE ANALYSIS PLANT BREEDING REMOTE SENSING MACHINE LEARNING

Genomic regions associated with resistance to three rusts in CIMMYT wheat line “Mokue#1”

Naeela Qureshi Ravi Singh sridhar bhavani (2023, [Artículo])

Genetic Analysis Leaf Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS RUSTS QUANTITATIVE TRAIT LOCI MAPPING DISEASE RESISTANCE STRIPE RUST STEM RUST

Statistical machine-learning methods for genomic prediction using the SKM library

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023, [Artículo])

Sparse Kernel Methods R package Statistical Machine Learning Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MACHINE LEARNING GENOMICS METHODS

Chapter 22. Heat and climate change mitigation

Fatima Camarillo-Castillo (2022, [Capítulo de libro])

Leaf Epicuticular Wax Source and Sink Relationships CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE MITIGATION RESPIRATION HEAT SHOCK ETHYLENE

DESIGN AND FABRICATION OF SPECIAL PHOTONIC CRYSTAL FIBERS WITH HIGH COUPLING OF THE FUNDAMENTAL MODE TO LEAKY MODES FOR REFRACTIVE INDEX

Johan Sebastian Buriticá Bolaños (2023, [Tesis de maestría])

"This work explores the possibility of implementing a detection mechanism in optical fibers called “Lossy Mode Resonance” (LMR), which has begun to grow in popularity because, compared to other similar detection mechanisms in the area of optical fibers, has high flexibility with respect to its implementation."

Crystal Fiber sensor Leaky modes LMR CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA FÍSICA ÓPTICA FIBRAS ÓPTICAS FIBRAS ÓPTICAS

Teacher training in the state of Chihuahua: Between the health challenge and teacher resilience

Evangelina Cervantes Holguín Pavel Roel Gutiérrez Sandoval Cely Celene Ronquillo Chávez (2023, [Artículo, Artículo])

The article proposes to recover the response of the Teacher Training and Updating Institutions in the state of Chihuahua regarding the various challenges imposed by the Coronavirus Disease (COVID-19). The qualitative exercise analyzes the experience of 10 institutions based on the voice of their students, teachers, and principals regarding changes in academic, administrative, and organizational processes. It is concluded that the pandemic has affected each institution in different ways and with diverse intensity. Despite the achievements, the experience analyzed reveals the relative success of the using virtual platforms in the face of three basic conditions: connectivity, technological competencies, and socio-emotional skills of the teaching staff. It highlights the importance of implementing tutoring, resilience, or awareness actions of teachers and students' needs, feelings, and sufferings. It is opportune to recover the experiences of other institutions and to question especially students, thesis students and graduates.

Acceso a la educación Aprendizaje en línea Educación a distancia Formación de docentes Tecnología educacional HUMANIDADES Y CIENCIAS DE LA CONDUCTA HUMANIDADES Y CIENCIAS DE LA CONDUCTA Access to education online learning distance education teacher education educational technology

Una articulación fundamental: la emancipación

Nicolás Amoroso Boelcke (2023, [Capítulo de libro])

El concepto, la circunstancia de la enseñanza que aquí abordaremos, tiene el propósito de restituir ciertas situaciones que corresponden al propio ejercicio de la instrucción y a aquellas otras que hablan del acontecer, por ello el trabajo. Tiene las variables tanto de la experiencia real como de las otras sobre las que se han escrito, una de ellas, al final, plantea una situación paradójica que se refiere a la del maestro ignorante que, básicamente, tiene como propósito mostrar que el conocimiento es algo que está en el alumno y que el docente debe limitarse a tratar de que aflore y se exprese de la mejor manera posible. Nos habla de la independencia y de las características que puede tener una educación rigurosa y rígida, en contraposición con una más flexible. Lo mismo sucede con el pensamiento desde nuestra América, de la educación dependiente a una educación libertaria.

The concept, the circumstance of the teaching that we will address here has the purpose of restoring certain situations that correspond to the exercise itself as those others that speak of the happening for it the work. It has the variables of real experience as of the others about which they have been written, one of them, in the end, poses a paradoxical situation that refers to that of the ignorant teacher that, basically, has the purpose of showing that knowledge is something that is in the student and that the teacher must limit himself to trying to bring it out and express itself in the best possible way. It tells us about independence and the characteristics that a rigorous and rigid education can have with a more flexible one and the same happens with the thought from our America, from dependent education to a libertarian education.

Emancipación, docencia, alumnos, investigación, juego. Emancipation, teaching, students, research, play. Interaction analysis in education. Microteaching. Teacher-student relationships. Transformative learning. Educational innovations. Análisis de interacción en la educación. Relaciones maestro-estudiante. Aprendizaje transformador. Cambio educativo. LB1034 HUMANIDADES Y CIENCIAS DE LA CONDUCTA PEDAGOGÍA TEORÍA Y MÉTODOS EDUCATIVOS

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Multimodal deep learning methods enhance genomic prediction of wheat breeding

Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])

Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

Rodomiro Ortiz Paulino Pérez-Rodríguez Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])

Potato Traits Cross-Validation Breeding Data CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING