Análisis de las conexiones matemáticas en la enseñanza y aprendizaje de la derivada basado en un networking of theories entre la Teoría de las conexiones y el Enfoque ontosemiótico.

CAMILO ANDRES RODRIGUEZ NIETO (2021, [Tesis de doctorado])

Consejo Nacional de Ciencia y Tecnología No. 602990

In research in Mathematics Education, models have been reported to analyze mathematical connections in which specific connection categories are considered. In the literature, it was identified that the most used model is the Businskas with contributions from other researchers. However, the problem refers to the fact that some categories of connections limit the analysis of mathematical activity and, therefore, the research suggests that the established categories are validated and, if possible, new categories of connections are reported. Other investigations focused on exploring mathematical connections and understanding the derivative reveal that high school students, pre-service teachers, and some in-service mathematics teachers have difficulty connecting multiple representations of the derivative (e.g., algebraic, or symbolic, verbal, graphic, tabular) and establish connections between partial meanings about this concept.

Networking of theories Mathematical connections Onto-semiotic approach semiotic function derivative teacher students HUMANIDADES Y CIENCIAS DE LA CONDUCTA PEDAGOGÍA TEORÍA Y MÉTODOS EDUCATIVOS TEORÍAS EDUCATIVAS

Exploring the agronomic performance and molecular characterization of diverse spring durum wheat germplasm in Kazakhstan

Kadyrzhan Mukin Abdelfattah DABABAT Timur Savin Vladimir Shamanin Vadim Yusov Alexey Morgounov (2023, [Artículo])

Germplasm Characterization Molecular Markers CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TRITICUM TURGIDUM GERMPLASM GENETIC MARKERS BREEDING

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

Point placement of late vegetative stage nitrogen splits increase the productivity, N-use efficiency and profitability of tropical maize under decade long conservation agriculture

Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022, [Artículo])

Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT

Response of African sorghum genotypes for drought tolerance under variable environments

Hussein Shimelis Baloua Nébié (2023, [Artículo])

Additive Main Effect and Multiplicative Interaction Best Linear Unbiased Estimates Drought Tolerance Indices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ABIOTIC STRESS DROUGHT TOLERANCE SORGHUM GENOTYPES

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

GIOVANNY COVARRUBIAS-PAZARAN Hans-Peter Piepho (2023, [Artículo])

Average Semivariance Linear Mixed Model Variance Component Estimation Polygenic Inheritance Oligogenic Inheritance Mendelian Inheritance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MENDELISM GENETIC VARIANCE GENOME-WIDE ASSOCIATION STUDIES PHENOTYPES CHROMOSOME MAPPING

Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones

Germano Costa Neto Jose Crossa (2024, [Artículo])

Forest Tree Breeding Genomic Relationship Matrix Genomic Selection Best Linear Unbiased Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOREST TREES BREEDING MARKER-ASSISTED SELECTION MYRTACEAE EUCALYPTUS GLOBULUS

Análisis diferencial de expresión génica de los fotosistemas I y II en cloroplastos y proplástidos aislados de plántulas in vitro verdes y albinas de Agave angustifolia Haw.

LUIS FERNANDO NUÑEZ BECERRIL (2024, [Tesis de maestría])

BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOQUÍMICA BIOLOGÍA MOLECULAR BIOLOGÍA MOLECULAR

Cloning of the broadly effective wheat leaf rust resistance gene Lr42 transferred from Aegilops tauschii

Guifang Lin Hui Chen Bin Tian Sunish Sehgal Jingzhong Xie Philomin Juliana Narinder Singh Sandesh Kumar Shrestha Ravi Singh Harold Trick Jesse Poland Robert Bowden guihua bai bikram gill (2022, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ALLELES CLONES GENE EXPRESSION GRASSES MUTATION RUSTS WHEAT BASIDIOMYCOTA DISEASE RESISTANCE GENETICS MOLECULAR CLONING PLANT BREEDING PLANT DISEASES

Enhancement of plant variety protection and regulation using molecular marker technology

Yunbi Xu Jian Zhang Jiansheng LI (2022, [Artículo])

Plant Variety Protection Distinctness-Uniformity-Stability Essentially Derived Variety Molecular Markers Molecular Diagnostics Genetic Similarity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS GENETIC MARKERS PLANT BREEDING VARIETIES