Genetic diversity and genome-wide association study for the phenology response of winter wheats of North America, Western Asia, and Europe

Susanne Dreisigacker Marta Lopes Miguel Sanchez-Garcia (2023, [Artículo])

Winter Wheat Panel Precision Phenology Effective Markers CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC DIVERSITY (AS RESOURCE) GENOME-WIDE ASSOCIATION STUDIES PHENOLOGY PHOTOPERIODICITY POPULATION STRUCTURE VERNALIZATION WINTER WHEAT

Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands

João Vasco Silva Frits K. Van Evert Pytrik Reidsma (2023, [Artículo])

Context: Wheat crop growth models from all over the world have been calibrated on the Groot and Verberne (1991) data set, collected between 1982 and 1984 in the Netherlands, in at least 28 published studies to date including various recent ones. However, the recent use of this data set for calibration of potential yield is questionable as actual Dutch winter wheat yields increased by 3.1 Mg ha-1 over the period 1984 – 2015. A new comprehensive set of winter wheat experiments, suitable for crop model calibration, was conducted in Wageningen during the growing seasons of 2013–2014 and of 2014–2015. Objective: The present study aimed to quantify the change of winter wheat variety traits between 1984 and 2015 and to examine which of the identified traits explained the increase in wheat yield most. Methods: PCSE-LINTUL3 was calibrated on the Groot and Verberne data (1991) set. Next, it was evaluated on the 2013–2015 data set. The model was further recalibrated on the 2013–2015 data set. Parameter values of both calibrations were compared. Sensitivity analysis was used to assess to what extent climate change, elevated CO2, changes in sowing dates, and changes in cultivar traits could explain yield increases. Results: The estimated reference light use efficiency and the temperature sum from anthesis to maturity were higher in 2013–2015 than in 1982–1984. PCSE-LINTUL3, calibrated on the 1982–1984 data set, underestimated the yield potential of 2013–2015. Sensitivity analyses showed that about half of the simulated winter wheat yield increase between 1984 and 2015 in the Netherlands was explained by elevated CO2 and climate change. The remaining part was explained by the increased temperature sum from anthesis to maturity and, to a smaller extent, by changes in the reference light use efficiency. Changes in sowing dates, biomass partitioning fractions, thermal requirements for anthesis, and biomass reallocation did not explain the yield increase. Conclusion: Recalibration of PCSE-LINTUL3 was necessary to reproduce the high wheat yields currently obtained in the Netherlands. About half of the reported winter wheat yield increase was attributed to climate change and elevated CO2. The remaining part of the increase was attributed to changes in the temperature sum from anthesis to maturity and, to a lesser extent, the reference light use efficiency. Significance: This study systematically addressed to what extent changes in various cultivar traits, climate change, and elevated CO2 can explain the winter wheat yield increase observed in the Netherlands between 1984 and 2015.

Light Use Efficiency Potential Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING LIGHT PHENOLOGY MAXIMUM SUSTAINABLE YIELD TRITICUM AESTIVUM WINTER WHEAT

Emmer wheat eco-geographic and genomic congruence shapes phenotypic performance under mediterranean climate

Dana Fuerst SHAILESH YADAV Rajib Roychowdhury Carolina Sansaloni Sariel Hübner (2022, [Artículo])

Emmer Wheat CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENETIC VARIATION CLIMATE PHENOLOGY YIELDS MEDITERRANEAN CLIMATE

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield

Ahmed Kayad Francelino Rodrigues Marco Sozzi Francesco Pirotti Francesco Marinello Urs Schulthess Bruno Gerard Marie Weiss (2022, [Artículo])

PROSAIL Vegetation Indices Field Variability Digital Farming CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PRECISION AGRICULTURE MAIZE GRAIN YIELD BIOMASS VEGETATION VEGETATION INDEX

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

Gestión del riego enfocada a la variabilidad climática en el cultivo de papa: aplicación al Distrito de Riego 075, Río Fuerte, Sinaloa, México

JAIME MACIAS CERVANTES WALDO OJEDA BUSTAMANTE (2016, [Artículo])

Se desarrolló una plataforma computacional basada en tecnología de información y comunicaciones (TIC) para programación integral y gestión de riego por Internet en el cultivo de papa, la cual ajusta las demandas hídricas del cultivo de papa a la variabilidad climática observada en los últimos años en el Valle del Fuerte, Sinaloa, México. El fundamento de la tecnología fue un modelo de programación integral del riego basado en grados-día crecimiento, integrándose a un software (Irrimodel) operado a través de Internet. En este trabajo se presenta un análisis de esta tecnología a gran escala como alternativa para la adaptación de la programación del riego ante los efectos del cambio climático.

Riego de precisión Cambio climático Distritos de riego CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Drones agrícolas, clave para la adopción y transición hacia la agricultura 4.0 en México

JUAN MANUEL GODOY NAVARRO (2023, [Otro, Trabajo de grado, maestría])

La agricultura 4.0 es un enfoque revolucionario que utiliza tecnologías de vanguardia para impulsar la innovación y mejorar la eficiencia en el sector agrícola. Una tecnología clave para iniciar la adopción y transición hacia esta nueva era son los drones agrícolas. En este trabajo buscaremos explorar en detalle sus aplicaciones y usos primordiales, junto con sus características, componentes y las regulaciones que guían su uso en México.

Agricultura 4.0 Agroindustria 4.0 Agricultura de precisión Agricultura inteligente Drones agrícolas Tecnología agrícola INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LAS TELECOMUNICACIONES TECNOLOGÍA DE LAS TELECOMUNICACIONES

Los programas de medicina de precisión y los desafíos para la gestión de la salud pública

Guillermo Foladori Guillermo Foladori (2023, [Artículo, Artículo])

Varios países iniciaron programas para el desarrollo de medicina de precisión. Con miles o millones de datos de salud, priorizando los mapas genéticos, mediante inteligencia artificial se elaboran algoritmos que identifican subpoblaciones con susceptibilidad a determinadas enfermedades para luego establecer la correspondencia farmacogenómica con las medicinas adecuadas. Este artículo realiza una acotada caracterización de esta propuesta y sistematiza aspectos críticos para su implementación desde una perspectiva de salud pública. Dados los argumentos expuestos, resulta probable que la medicina personalizada lleve a una mayor desigualdad entre países y regiones y al interior de los diferentes sectores sociales.

Medicina de precisión Inteligencia artificial Farmacogenómica Tendencias socioeconómicas CIENCIAS SOCIALES; INGENIERÍA Y TECNOLOGÍA; MEDICINA Y CIENCIAS DE LA SALUD CIENCIAS SOCIALES INGENIERÍA Y TECNOLOGÍA MEDICINA Y CIENCIAS DE LA SALUD

Aplicaciones de los vehículos aéreos no tripulados en la ingeniería hidroagrícola

WALDO OJEDA BUSTAMANTE AZUCELLI MAYTHE MAURICIO PEREZ JORGE FLORES VELAZQUEZ (2017, [Artículo])

El monitoreo remoto de variables agrícolas sigue siendo un reto ante los altos costos que representa la adquisición manual de datos con alta frecuencia. La puesta en órbita de satélites facilitó la obtención de información espacial y temporal de variables de interés hidroagrícola. Sin embargo, ante la demanda de información detallada, los satélites tienen la limitante de suministrar información temporal discontinua e información espectral de baja resolución. Una alternativa emergente la proporcionan los Vehículos Aéreos No Tripulados, comúnmente llamados drones. Ante el gran desarrollo que presentan estas herramientas, es de utilidad conocer las principales características y componentes de estos equipos utilizados con fines agrícolas, así como los trabajos relacionados y su uso potencial en las diferentes áreas de aplicación hidroagrícola. Los estudios también indican que estos vehículos son de gran utilidad para el monitoreo y supervisión de la superficie terrestre a través de imágenes georreferenciadas de alta resolución espacial, temporal y espectral de baja altura.

Infraestructura hidroagrícola Aviones no tripulados Agricultura de precisión CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

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