The generation challenge programme platform: Semantic standards and workbench for crop science

Richard Bruskiewich Guy Davenport Mathieu Rouard Reinhard Simon Samart Wanchana Trushar Shah Victor Jun Ulat Andrew Farmer Pankaj Jaiswal Mark Wilkinson David Marshall Alyssa Collins (2008, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP IMPROVEMENT GENETIC RESOURCES PLANT BREEDING BIODIVERSITY COMPUTER APPLICATIONS DIGITAL TECHNOLOGY DATA PROCESSING

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Development of a thermoelectric test for electrical contactors

PABLO OROZCO CORRAL FRANCISCO JAVIER IBARRA VILLEGAS NOE VILLA VILLASEÑOR (2023, [Artículo])

In the economic model in which we develop, there is a dependence on the means of transport that every day is increasing and due to the importance of electrification in the issue of efficient and clean transportation, has caused the need for companies supplying components for automotive manufacturers to evolve and develop new technologies to stay ahead of the needs of their customers being in full evolution towards electrified transport. This evolution extends not only to manufacturing but to the test methods that currently exist, since automotive standards require suppliers to test in laboratory, the components they sell to their customers to ensure their correct operation.

Electrical contactors Powered thermal cycle endurance Busbars Electrification industry INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Seed systems in Sudan: an annotated bibliography

Hugo De Groote Paswel Marenya (2023, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SYSTEMS SEED INDUSTRY BIBLIOGRAPHIES

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

Nepal Seed And Fertilizer Project

Dyutiman Choudhary (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SEED INDUSTRY PRIVATE SECTOR MAIZE RICE INTEGRATED SOIL FERTILITY MANAGEMENT COVID-19

Movements of scalloped hammerhead sharks (Sphyrna lewini) at Cocos Island, Costa Rica and between oceanic islands in the Eastern Tropical Pacific

Elena Nalesso (2019, [Artículo])

Many species of sharks form aggregations around oceanic islands, yet their levels of residency and their site specificity around these islands may vary. In some cases, the waters around oceanic islands have been designated as marine protected areas, yet the conservation value for threatened shark species will depend greatly on how much time they spend within these protected waters. Eighty-four scalloped hammerhead sharks (Sphyrna lewini Griffith & Smith), were tagged with acoustic transmitters at Cocos Island between 2005–2013. The average residence index, expressed as a proportion of days present in our receiver array at the island over the entire monitoring period, was 0.52±0.31, implying that overall the sharks are strongly associated with the island. Residency was significantly greater at Alcyone, a shallow seamount located 3.6 km offshore from the main island, than at the other sites. Timing of presence at the receiver locations was mostly during daytime hours. Although only a single individual from Cocos was detected on a region-wide array, nine hammerheads tagged at Galapagos and Malpelo travelled to Cocos. The hammerheads tagged at Cocos were more resident than those visiting from elsewhere, suggesting that the Galapagos and Malpelo populations may use Cocos as a navigational waypoint or stopover during seasonal migrations to the coastal Central and South America. Our study demonstrates the importance of oceanic islands for this species, and shows that they may form a network of hotspots in the Eastern Tropical Pacific. © 2019 Nalesso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

article, Cocos Island, human, monitoring, nonhuman, resident, shark, South America, animal, Costa Rica, environmental protection, island (geological), movement (physiology), physiology, season, shark, Animals, Conservation of Natural Resources, Costa CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA

Monitoreo e Instalación visual de señales a un motor eléctrico de inducción de instalación trifásica de forma jaula de ardilla, mediante la implementación de tecnología industria 4.0

Luis Ricardo Uribe Dávila (2023, [Tesis de maestría])

Vivimos la industria 4.0, misma que no es nueva, ya que sus orígenes se remontan a finales de la década de los 2000, en Alemania. Un pilar de la industria 4.0 es el análisis de datos, conocido como Big Data. El conocer los datos de un proceso, de un estudio, ayuda en gran medida a predecir el comportamiento que tendrá el proceso o la máquina a estudiar en un periodo a corto o mediano plazo. En el presente proyecto se analizan los datos arrojados por un motor eléctrico de corriente alterna, del tipo inducción, jaula de ardilla. El motor está diseñado para trabajar de manera continua, sin embargo, el uso que se le da, es meramente educativo; es decir, no sobre pasa las 15 horas por semana de uso. Mediante la toma de datos de las tres fases de corriente RMS o corriente de valor eficaz que posee el motor eléctrico que se realizará con el microcontrolador Arduino UNO, se analizarán los mismos mediante el software de cómputo numérico MATLAB, ordenando los datos, descartando valores que no aporten información relevante para lograr la predicción de datos. Por último, se llevará a conocer este proyecto a la carrera mecatrónica, área sistemas de manufactura flexible y área automatización, con el fin de que puedan observar de una mejor manera la aplicación y funcionamiento de uno de los pilares de la actual industria 4.0.

We live in industry 4.0, which is not new, since its origins date back to the late 2000s, in Germany. One pillar of industry 4.0 is data analysis, known as Big Data. Knowing the data of a process, of a study, helps greatly to predict the behavior that the process or machine will have to study in a short- or medium-term period. This project analyzes the data released by an electric motor of alternating current, of the type induction, squirrel cage. The engine is designed to work continuously, however, the use given to it is merely educational, that is; only not over spends 15 hours per week of use. By taking data from the three phases of RMS current or effective value current of the electric motor that will be made with the Arduino UNO micro controller, they will be analyzed using MATLAB numerical computing software, ordering the data, discarding values that do not provide relevant information to achieve data prediction. Finally, this project will be presented to the mechatronics career, flexible manufacturing systems area and automation area, so that they can observe in a better way the application and operation of one of the pillars of the current industry 4.0.

Mantenimiento predictivo Regresión lineal Industria 4.0 Big data Corriente RMS Predictive maintenance Linear regression Industry 4.0 Big data RMS Current INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Nepal Seed And Fertilizer Project

Dyutiman Choudhary (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED FERTILIZERS SEED INDUSTRY PRIVATE SECTOR MAIZE RICE INTEGRATED SOIL FERTILITY MANAGEMENT COVID-19

Last mile seed delivery approaches in Sudan and beyond: an annotated bibliography

Hugo De Groote Paswel Marenya (2023, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SYSTEMS SEED INDUSTRY BIBLIOGRAPHIES