Highlights of the 2023 Southern Africa regional trials coordinated by CIMMYT

Xavier Mhike (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HYBRIDS SELECTION MAIZE FOLIAR DISEASES DROUGHT STRESS

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

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

Genetic structure analysis and identifying key founder inbred lines in diverse elite sub-tropical maize inbred lines

Melaku Gedil Ana Luisa Garcia-Oliveira Nnanna Unachukwu Cesar Petroli Sarah Hearne Abebe Menkir (2023, [Artículo])

Genetic Relationship Desirable Target Traits Parental Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC STRUCTURES INBRED LINES MAIZE BREEDING PROGRAMMES

Editorial: Model organisms in plant science: Maize

Manje Gowda (2023, [Artículo])

Model Organism Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PLANT SCIENCES RESEARCH CROP IMPROVEMENT PLANT PHYSIOLOGY PLANT BREEDING

Silage maize as a potent candidate for sustainable animal husbandry development—perspectives and strategies for genetic enhancement

Yogesh Vikal Manje Gowda (2023, [Artículo])

Brown Mid-Rib Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOMASS SILAGE DIGESTIBILITY GENOME-WIDE ASSOCIATION STUDIES MARKER-ASSISTED SELECTION MAIZE

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

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

Reciprocal recurrent selection based on genetic complementation: An efficient way to build heterosis in diploids due to directional dominance

GIOVANNY COVARRUBIAS-PAZARAN Christian Werner Dorcus Gemenet (2023, [Artículo])

Selection Strategies Reciprocal Recurrent Selection Dominance-Based Heterosis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS HETEROSIS MAIZE HYBRIDS FOOD SECURITY COMBINING ABILITY

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

XUECAI ZHANG Zefeng Yang Yang Xu (2022, [Artículo])

Genotyping by Sequencing Genomic Selection SNP Array Marker Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MAIZE SINGLE NUCLEOTIDE POLYMORPHISM HYBRIDS