Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES

A novel method for genomic-enabled prediction of cultivars in new environments

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

Genomic Best Linear Unbiased Prediction Gains in Accuracy Genomic Prediction Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOTYPE ENVIRONMENT INTERACTION METHODS ENVIRONMENT

Comparación entre un modelo hidrodinámico completo y un modelo hidrológico en riego por melgas

Comparison between a hydrodynamic full model and a hydrologic model in border irrigation

LEONID VLADIMIR CASTANEDO GUERRA HEBER ELEAZAR SAUCEDO ROJAS CARLOS FUENTES RUIZ (2013, [Artículo])

Con el fin de conseguir un manejo más eficiente del agua en la producción agrícola, se llevó a cabo una comparación entre el modelo hidrodinámico completo y el modelo hidrológico en riego por melgas. Para reducir las variaciones, originadas por diferencias en la lámina infiltrada, obtenidas con las ecuaciones de Green-Ampt y de Richards utilizadas en los modelos hidrológico e hidrodinámico completo, se realizó el ajuste del parámetro de succión en el frente de humedecimiento de la ecuación de Green-Ampt; con ello se reproduce el cambio de la lámina infiltrada obtenida con la ecuación de Richards. La comparación se efectuó a partir del análisis de los perfiles de flujo superficial y subsuperficial, que se presentan en el riego, y de la distribución final de la lámina infiltrada.

Hidrodinámica Modelos hidrológicos Modelación hidrológica Riego de superficie INGENIERÍA Y TECNOLOGÍA

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

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

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

Detección de eventos violentos en publicaciones de redes sociales

Detection of violent events in social media publications

Esteban Ponce León (2023, [Tesis de maestría])

En los últimos años, ha habido un interés creciente en el monitoreo de redes sociales para recopilar información y, en algunos casos, para examinar la ocurrencia de delitos. Sin embargo, gran parte de las investigaciones hasta ahora solo se han centrado en ciudades de EE. UU. o extranjeras, y por ende, en publicaciones y conjuntos de datos en inglés El objetivo principal de esta tesis es diseñar un método que permita la identificación de publicaciones de eventos violentos en español y en Twitter, utilizando información multimodal y técnicas de aumento de datos que mejoren el rendimiento de los modelos. Para esto, el trabajo de investigación se dividió en dos fases experimentales. La primera orientada a identificar publicaciones a partir de solo texto, explorando diferentes técnicas de aumento de datos para texto y modelos de aprendizaje máquina y profundo. En la segunda fase, se extendió el método propuesto para abordar la identificación en un contexto multimodal, es decir, considerando tanto los textos de los tweets como las imágenes compartidas que los acompañan. En este caso el método propuesto consideró utilizar descripciones textuales de las imágenes y abordar la problemática desde el dominio textual, además se hicieron 2 tipos de aumento de datos para cada tipo de información. La evaluación de los métodos se hizo utilizando las colecciones de la tarea de evaluación DA-VINCIS 2022 y 2023. Los resultados demostraron una mejora en el rendimiento de los modelos al considerar el uso de información multimodal y el uso de aumento de datos.

In recent years, there has been a growing interest in monitoring social networks to gather information and, in some cases, to examine the occurrence of crime. However, much of the research so far has only focused on US or foreign cities, and thus on English-language publications and data sets. The main objective of this thesis is to design a method that allows the identification of publications of violent events in Spanish and on Twitter, using multimodal information and data augmentation techniques that improve the performance of the models. For this, the research work was divided into two experimental phases. The first aimed at identifying publications from only text, exploring different data augmentation techniques for text and machine and deep learning models. In the second phase, the proposed method was extended to address identification in a multimodal context, that is, considering both the texts of the tweets and the shared images that accompany them. In this case, the proposed method considered using textual descriptions of the images and addressing the problem from the textual domain, in addition, 2 types of data augmentation were made for each type of information. The evaluation of the methods was done using the collections of the DA-VINCIS 2022 and 2023 evaluation task. The results demonstrated an improvement in the performance of the models when considering the use of multimodal information and the use of data augmentation.

Detección de Violencia, Redes Sociales, Aumento de Datos, Procesamiento del Lenguaje Natural, BERT, BETO, Descripción de Imágenes Violence Detection, Social Networks, Data Augmentation, Natural Language Processing, BERT, BETO, Image Captioning INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES MODELOS CAUSALES MODELOS CAUSALES

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

Calibración de un modelo hidrológico aplicado en el riego tecnificado por gravedad

Calibration of an hydrology model applied in the technology irrigation by gravity

LUIS RENDON PIMENTEL JORGE DIONISIO ETCHEVERS BARRA JESUS CHAVEZ MORALES HUMBERTO VAQUERA HUERTA LUIS RENDON PIMENTEL (2001, [Artículo])

Este estudio tuvo como propósitos comparar la tecnología de riego tradicional con una metodología tecnificada, así como medir la respuesta de la producción de maíz (Zea mays) a la aplicación de nitrógeno en forma tradicional y en fertirriego. El experimento se desarrolló en el módulo 2 del distrito de riego (DR076), en el Valle del Carrizo, Sinaloa, México. La tecnología denominada riego tradicional es la utilizada por los agricultores, y la tecnificada es una propuesta del Instituto Mexicano de Tecnología del Agua.

Cultivos alimenticios Maíz Fertirriego Modelos matemáticos Riego tecnificado INGENIERÍA Y TECNOLOGÍA