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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
Xu Zhang Jian Hua Ravi Singh (2022, [Artículo])
LysM PRR Haynaldia villosa CERK1-V CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FUNGAL DISEASES DASYPYRUM VILLOSUM WHEAT DISEASE RESISTANCE
sridhar bhavani (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS DISEASE RESISTANCE WHEAT GENETIC DIVERSITY (AS RESOURCE) ADULT PLANT RESISTANCE
Gender, rainfall endowment, and farmers’ heterogeneity in wheat trait preferences in Ethiopia
Hom Nath Gartaula Moti Jaleta (2024, [Artículo])
Wheat is a vital cereal crop for smallholders in Ethiopia. Despite over fifty years of research on wheat varietal development, consideration of gendered trait preferences in developing target product profiles for wheat breeding is limited. To address this gap, our study used sex-disaggregated survey data and historical rainfall trends from the major wheat-growing regions in Ethiopia. The findings indicated heterogeneity in trait preferences based on gender and rainfall endowment. Men respondents tended to prefer wheat traits with high straw yield and disease-resistance potential, while women showed a greater appreciation for wheat traits related to good taste and cooking quality. Farmers in high rainfall areas seemed to prioritize high straw yield and disease resistance traits, while those in low rainfall areas valued good adaptation traits more highly. Most of the correlation coefficients among the preferred traits were positive, indicating that farmers seek wheat varieties with traits that serve multiple purposes. Understanding men's and women's preferences and incorporating them in breeding and seed systems could contribute to the development of more targeted and effective wheat varieties that meet the diverse needs of men and women farmers in Ethiopia.
Trait Preferences Multivariate Probit Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT AGRONOMIC CHARACTERS GENDER RAINFALL PROBIT ANALYSIS
sridhar bhavani (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS DISEASE RESISTANCE WHEAT
Gopalareddy Krishnappa Govindan Velu (2023, [Artículo])
DArT-Seq Gene Mapping Yield Component Traits CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT QUANTITATIVE TRAIT LOCI CANDIDATE GENES QUANTITATIVE TRAIT LOCI MAPPING YIELD COMPONENTS BIOFORTIFICATION
C.M. Parihar Dipaka Ranjan Sena Prakash Chand Ghasal Shankar Lal Jat Yashpal Singh Saharawat Mahesh Gathala Upendra Singh Hari Sankar Nayak (2024, [Artículo])
Context: Agricultural field experiments are costly and time-consuming, and their site-specific nature limits their ability to capture spatial and temporal variability. This hinders the transfer of crop management information across different locations, impeding effective agricultural decision-making. Further, accurate estimates of the benefits and risks of alternative crop and nutrient management options are crucial for effective decision-making in agriculture. Objective: The objective of this study was to utilize the Crop Environment Resource Synthesis CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based wheat system. The study aimed to calibrate the model using data from a field experiment conducted during the 2019-20-2020-21 growing seasons and evaluation it with independent data from the year 2021–22. Method: Crop simulation models, such as the Crop Environment Resource Synthesis CERES-Wheat (DSSAT v 4.8), may provide valuable insights into crop growth and nitrogen dynamics, enabling decision makers to understand and manage production risk more effectively. Therefore, the present study employed the CERES-Wheat (DSSAT v 4.8) model and calibrated it using field data, including plant phenological phases, leaf area index, aboveground biomass, and grain yield from the 2019-20-2020-21 growing seasons. An independent dataset from the year 2021–22 was used for model evaluation. The model was used to investigate the relationship between growing degree days (GDD), temperature, nitrate and ammonical concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on wheat yields. The experimental site is situated at ICAR-Indian Agricultural Research Institute (IARI), New Delhi, representing Indian Trans-Gangetic Plains Zone (28o 40’N latitude, 77o 11’E longitude and an altitude of 228 m above sea level). The treatments consist of four nitrogen management options, viz., N0 (zero nitrogen), N150 (150 kg N ha−1 through urea), GS (Green seeker based urea application) and USG (urea super granules @150 kg N ha−1) in two contrasting tillage systems, i.e., CA-based zero tillage (ZT) and conventional tillage (CT). Result: The outcomes exhibited favorable agreement between the model’s simulations and the observed data for crop phenology (With less than 2 days variation in 50% onset of flowering), grain and biomass yield (Root mean square error; RMSE 336 kg ha−1 and 649 kg ha−1, respectively), and leaf area index (LAI) (RMSE 0.28 & normalized RMSE; nRMSE 6.69%). The model effectively captured the nitrate-N (NO3−-N) dynamics in the soil profile, exhibiting a remarkable concordance with observed data, as evident from its low RMSE = 12.39 kg ha−1 and nRMSE = 13.69%. Moreover, as it successfully simulated the N balance in the production system, the nitrate leaching and ammonia volatilization pattern as described by the model are highly useful to understand these critical phenomena under both conventional tillage (CT) and CA-based Zero Tillage (ZT) treatments. Conclusion: The study concludes that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western Indo-Gangetic Plains (IGP) region. By providing reliable forecasts within the growing season, this modeling approach can facilitate better planning and more efficient resource management. Future implications: The successful implementation of the DSSAT-CERES-Wheat model in this study highlights its applicability in assessing crop performance and soil dynamics. Future research should focus on expanding the model’s capabilities by reducing its sensitivity to initial soil nitrogen levels to refine its predictions further. Moreover, the model’s integration with decision support systems and real-time data can enhance its usefulness in aiding agricultural decision-making and supporting sustainable crop management practices.
Nitrogen Dynamics Mechanistic Crop Growth Models Crop Simulation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NITROGEN CONSERVATION AGRICULTURE WHEAT MAIZE CROP GROWTH RATE SIMULATION MODELS
Mandeep Randhawa (2021, [Artículo])
Grain Yield Yield Stability Genotype x Season Interaction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT HERITABILITY YIELDS RUSTS GENOTYPES
Ricardo Hernández Mejía FRANCISCO JAVIER IBARRA VILLEGAS CAIN PEREZ WENCES (2023, [Artículo])
This work was originated from the increasing interest in several industries to implement voice based virtual assistant solutions powered by the Natural Language Processing field of study. This work is focused on the automotive industry Human Machine Interface related products, specifically the Instrument Panel. Nowadays people are constantly using virtual assistants like Google Assistant, Alexa, Cortana or Siri on their electronic devices. Furthermore, 31% of cars have a built-in virtual assistant, for example Ford uses Alexa, Mercedes-Benz and Hyundai use Google Assistant, BMW and Nissan use Cortana, GM uses IBM Watson, Honda uses Hana and Toyota uses YUI. Apart from the proprietary solutions described earlier, there are also contemporary open-source generic solutions available on the market, such as Mycroft AI which stands out from other technologies due to ready to deploy, well documented, simple installation on a Linux PC or RPI SoC, and simple execution. This paper presents a way to use Mycroft AI as an alternative to add artificial intelligence-based voice assistance to applications in the automotive domain. The voice communication module presented here drives notifications related to three different entities: seat belt, fuel level and battery level, all of them are telltales present in any automotive Instrument Panel. Since the Mycroft AI design approach is based on Human Centered Design (HCD), the voice communication module presented here provides real user experience (UX) based design. As a conclusion, Mycroft AI demonstrates great potential as an alternative to add voice assistance to automotive industry Human Machine Interface related products. About future work, due to the fact that Mycroft AI is based on Python, there are many possibilities for connecting and expanding the voice communication module by using countless Python libraries in order to import and process any type of information, in any format or source, for example the information from communication technologies like CAN, LIN, Ethernet, MOST, GPS or any other device or technology in order to create comprehensive automotive solutions.
Este trabajo se originó del creciente interés por parte de diferentes industrias para implementar soluciones de asistente virtual basado en voz impulsadas por el campo de estudio del Procesamiento del Lenguaje Natural. Este trabajo está enfocado en los productos relacionados a la Interfaz Humano Máquina de la industria automotriz, específicamente el Panel de Instrumentos. Hoy en día las personas usan constantemente asistentes virtuales como Google Assistant, Alexa, Cortana o Siri en sus dispositivos electrónicos. Más aún, 31% de los autos tienen un asistente virtual integrado, por ejemplo, Ford usa Alexa, Mercedes-Benz y Hyundai usan Google Assistant, BMW y Nissan usan Cortana, GM usa IBM Watson, Honda usa Hana y Toyota usa YUI. Aparte de las soluciones de marca registrada descritas anteriormente, también hay soluciones genéricas de código abierto contemporáneas disponibles en el mercado, tales como Mycroft AI que se hace notar por sobre otras tecnologías por características como listo para usar, bien documentada, instalación simple en una PC Linux o RPI SoC, y una ejecución simple. Este artículo presenta una manera de usar Mycroft AI como una alternativa para agregar inteligencia artificial basada en asistencia de voz a aplicaciones en el dominio automotriz. El módulo de comunicación de voz presentado aquí maneja notificaciones relacionadas a tres diferentes entidades: cinturón de seguridad, nivel de gasolina y nivel de batería, todos ellos son indicadores virtuales presentes en cualquier Panel de Instrumentos Automotriz. Dado que el enfoque de diseño de Mycroft AI se basa en Diseño Centrado en el Human (HCD), el módulo de comunicación por voz presentado aquí provee un diseño basado en experiencia de usuario (UX) real. Como conclusión, Mycroft AI demuestra gran potencial como una alternativa para agregar asistencia de voz a los productos relacionados a Interfaz Humano Máquina de la industria automotriz. Acerca del trabajo a futuro, debido al hecho que Mycroft AI está basado en Python, existen muchas posibilidades para conectar y expandir el módulo de comunicación por voz a través del uso de innumerables bibliotecas de Python para importar y procesar cualquier tipo de información, en cualquier formato o fuente, por ejemplo la información proveniente de tecnologías de comunicación tales como CAN, LIN, Ethernet, MOST, GPS o cualquier otro dispositivo o tecnología para crear soluciones automotrices integrales.
Authorship acknowledgment. Ricardo Hernández Mejía: Conceptualization, Methodology, Software, Validation, Formal analysis, Research, Resources, Original draft, Visualization, Project administration. Francisco Javier Ibarra Villegas: Review and Editing, Supervision, Project Administration. Cain Pérez Wences: Review and Editing.
Acknowledgment. To Posgrado CIATEQ A.C. due to the institutional support and guidance received to conclude this work in a professional and successful way. To Continental Automotive Occidente due to the sponsorship provided to perform the master’s degree along with Posgrado CIATEQ A.C. which made possible this work. To Dr. Francisco Javier Ibarra Villegas due to their guidance and support on the process to shape and concrete this work.
Instrument panel Virtual assistant Voice communication module Mycroft AI Human centered design User experience Panel de instrumentos Asistente virtual Módulo de comunicación por voz Diseño centrado en el humano Experiencia de usuario INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS