Editorial: Conservation agriculture: knowledge frontiers around the world

Stéphane Cordeau ML JAT Cameron Pittelkow Christian Thierfelder (2023, [Artículo])

No-Tillage Direct Seeding Cover Crops Crop Diversification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COVER PLANTS DIVERSIFICATION CROPPING SYSTEMS DIRECT SOWING ZERO TILLAGE

Genome-wide association study and genomic prediction on plant architecture traits in sweet corn and waxy corn

XUECAI ZHANG Ao Zhang (2023, [Artículo])

Genome-Wide Association Study Genomic Prediction Ear Height Tassel Branch Number Waxy Corn CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS PLANT HEIGHT SWEET CORN WAXY MAIZE

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

Tackling Maize Lethal Necrosis (MLN) in eastern Africa through effective phytosanitary approaches

Suresh L.M. Yoseph Beyene Dan Makumbi Manje Gowda Prasanna Boddupalli (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE NECROSIS DISEASE MANAGEMENT PLANT HEALTH GENE EDITING GERMPLASM

Biodiversidad en las ciudades: el caso de las epífitas vasculares

Demetria Martha Mondragón Chaparro MARTHA PATRICIA MORA FLORES (2022, [Artículo])

Cada vez más hay un reconocimiento del valor de las ciudades como reservorios de biodiversidad. ¿Qué tanto se resguardan las especies?, dependerá del grupo de organismos que se trate; por ello, nos dimos a la tarea de averiguar cuántas especies de epífitas vasculares se encuentran presentes en la ciudad de Oaxaca de Juárez, México, encontrando solo seis especies, todas pertenecientes al género Tillandsia (Bromeliaceae), siendo T. recurvata la más abundante y mejor distribuida dentro de la ciudad. Ahora queda por investigar, que factores pudieran explicar esta baja diversidad.

BROMELIACEAE CONSERVACION OAXACA PLANTAS EPIFITAS BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

La flora electrónica de México “eFloraMEX”: un sueño para los botánicos

MARIA VICTORIA SOSA ORTEGA Diego Angulo (2023, [Artículo])

La flora electrónica de México “eFloraMEX” documenta las especies de plantas vasculares nativas. Para iniciarla, se publicó en su portal la lista florística con aproximadamente 29,000 especies, representando el punto de partida del proyecto. La flora electrónica de México contendrá información e imágenes sobre las especies, así como claves de identificación y tratamientos taxonómicos. Taxónomos especialistas en grupos de plantas colaborarán en su desarrollo, coordinados por los comités editorial, ejecutivo y bioinformático. La eFloraMEX es un esfuerzo conjunto, por lo que cualquier taxónomo o institución interesada podrá participar.

BIODIVERSIDAD PLANTAS NATIVAS PLANTAS VASCULARES TAXONOMIA BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) TAXONOMÍA VEGETAL TAXONOMÍA VEGETAL

Efficacy of plant breeding using genomic information

Osval Antonio Montesinos-Lopez Alison Bentley Carolina Saint Pierre Leonardo Abdiel Crespo Herrera Morten Lillemo Jose Crossa (2023, [Artículo])

Genomic Selection Genomic Prediction Genomic Best Linear Unbiased Predictor CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT BREEDING GENOMICS MARKER-ASSISTED SELECTION ENVIRONMENT

Visualising the pattern of long-term genotype performance by leveraging a genomic prediction model

Vivi Arief Ian Delacy Thomas Payne Kaye Basford (2022, [Artículo])

Factor Analytic Genotype-By-Year Historical Data Relationship Matrix CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOTYPES PLANT BREEDING SPRING WHEAT RESEARCH

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

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