Distribution of Puccinia striiformis f. sp. tritici races and virulence in wheat growing regions of Kenya from 1970 to 2014

Lesley Boyd sridhar bhavani Cristobal Uauy Annemarie Fejer Justesen Mogens Hovmoller (2022, [Artículo])

Cereals and Grains Pathogen Diversity Puccinia f. sp. tritici Stripe Rust Yellow Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CEREALS FIELD CROPS FUNGI PATHOGENICITY RUSTS TRITICUM AESTIVUM

Trichoderma: recurso microbiológico y sus aplicaciones en la agricultura en Yucatán, México

Elizabeth de los Ángeles Herrera Parra MARIA MANUELA DE JESUS REYES ESTEBANEZ Jairo Cristóbal Alejo CAROLINA ISABEL BASTO POOL MANUEL JESUS ZAVALA LEON (2023, [Artículo])

Los productos sintéticos como nematicidas, fungicidas y fertilizantes empleados en la producción agrícola, contaminan suelo, agua y ocasionan daño a la salud de los productores y consumidores. Una alternativa al uso de estos agroquímicos son los microorganismos que habitan en el suelo, como los hongos del género Trichoderma, que promueven el crecimiento de cultivos y controlan enfermedades de plantas que se originan en la raíz, lo que favorece el cultivo agrícola libre de agroquímicos sintéticos. En esta nota se dan a conocer los modos de acción de Trichoderma y los avances de estudios realizados con cepas nativas del estado de Yucatán, para el control de patógenos en solanáceas.

ASCOMYCOTA BIOCONTROLADOR CULTIVOS TROPICALES FUNGI MICROORGANISMO PROMOTOR DE CRECIMIENTO BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Genética de la resistencia al complejo mancha de asfalto en 18 genotipos tropicales de maíz

George Mahuku Ignacio Benítez-Riquelme Serafin Cruz-Izquierdo (2015, [Artículo])

Horizontal Resistance Tar Spot Complex CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FUNGI MONOGRAPHELLA PHYLLACHORALES ZEA MAYS DIALLEL ANALYSIS DISEASE RESISTANCE

La pandemia de los anfibios: el caso del hongo Batrachochytrium dendrobatidis

EMILIANO HERNANDEZ VALDES (2022, [Artículo])

Así como ocurren brotes de nuevos patógenos en humanos, también sucede con otras especies de animales. Actualmente, los anfibios se encuentran sufriendo la consecuencia de un patógeno nuevo que está acabando drásticamente con las poblaciones de diferentes especies de ranas. El hongo, denominado Batrachochytrium dendrobatidis, es una amenaza para la biodiversidad y los ecosistemas en general, debido a que los anfibios son organismos fundamentales para la sustentabilidad de todos los procesos ecológicos que se llevan a cabo en los diferentes ecosistemas que habitan. La quitridiomicosis es la enfermedad causada por este hongo y es responsable de la mayor pérdida de biodiversidad. Por esto, es vital comprender la distribución espacial de esta pandemia y su propagación.

ANURA BATRACHOCHYTRIUM FUNGI PLAGA QUITRIDIOMICOSIS RANA BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Transpiration of a tropical dry deciduous forest in Yucatan, Mexico

EVELYN RAQUEL SALAS ACOSTA José Luis Andrade Torres Jorge Perera ROBERTH ARMANDO US SANTAMARIA bernardo figueroa-espinoza Jorge M. Uuh-Sonda EDUARDO CEJUDO ESPINOSA (2022, [Artículo])

The study of forest hydrology and its relationships with climate requires accurate estimates of water inputs, outputs, and changes in reservoirs. Evapotranspiration is frequently the least studied component when addressing the water cycle; thus, it is important to obtain direct measurements of evaporation and transpiration. This study measured transpiration in a tropical dry deciduous forest in Yucatán (Mexico) using the thermal dissipation method (Granier-type sensors) in representative species of this vegetation type. We estimated stand transpiration and its relationship with allometry, diameter-at-breast-height categories, and previously published equations. We found that transpiration changes over time, being higher in the rainy season. Estimated daily transpiration ranged from 0.562 to 0.690 kg m–2 d–1 in the late dry season (April–May) and from 0.686 to 1.29 kg m–2 d–1 in the late rainy season (September–October), accounting for up to 51% of total evapotranspiration in the rainy season. These daily estimates are consistent with previous reports for tropical dry forests and other vegetation types. We found that transpiration was not species-specific; diameter at breast height (DBH) was a reliable way of estimating transpiration because water use was directly related to allometry. Direct measurement of transpiration would increase our ability to accurately estimate water availability and assess the responses of vegetation to climate change. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

SAP FLUX SEASONALITY STAND TRANSPIRATION EVAPOTRANSPIRATION DRY DECIDUOUS FOREST BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Does access to improved grain storage technology increase farmers' welfare? Experimental evidence from maize farming in Ethiopia

Hugo De Groote Bart Minten (2024, [Artículo])

Seasonal price variability for cereals is two to three times higher in Africa than on the international reference market. Seasonality is even more pronounced when access to appropriate storage and opportunities for price arbitrage are limited. As smallholder farmers typically sell their production after harvest, when prices are low, this leads to lower incomes as well as higher food insecurity during the lean season, when prices are high. One solution to reduce seasonal stress is the use of improved storage technologies. Using data from a randomised controlled trial, in a major maize-growing region of Western Ethiopia, we study the impact of hermetic bags, a technology that protects stored grain against insect pests, so that the grain can be stored longer. Despite considerable price seasonality—maize prices in the lean season are 36% higher than after harvesting—we find no evidence that hermetic bags improve welfare, except that access to these bags allowed for a marginally longer storage period of maize intended for sale by 2 weeks. But this did not translate into measurable welfare gains as we found no changes in any of our welfare outcome indicators. This ‘near-null’ effect is due to the fact that maize storage losses in our study region are relatively lower than previous studies suggested—around 10% of the quantity stored—likely because of the widespread use of an alternative to protect maize during storage, for example a cheap but highly toxic fumigant. These findings are important for policies that seek to promote improved storage technologies in these settings.

Hermetic Storage Randomised Controlled Trial CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA STORAGE PILOT FARMS SEASONALITY WELFARE MAIZE

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

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

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images

Francisco Pinto Matthew Paul Reynolds Robert Furbank (2024, [Artículo])

Deep Learning Object-Based Image Analysis Optical Imagery CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE IMAGE ANALYSIS PLANT BREEDING REMOTE SENSING MACHINE LEARNING