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

Predicting zinc-enhanced maize hybrid performance under stress conditions

Nakai Matongera THOKOZILE NDHLELA Angeline van Biljon Maryke Labuschagne (2023, [Artículo])

Combined Stress Zinc Biofortification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEAT STRESS DROUGHT STRESS HYBRIDS INBRED LINES ZINC BIOFORTIFICATION

Worldwide selection footprints for drought and heat in bread wheat (Triticum aestivum L.)

Ana Luisa Gómez Espejo Carolina Sansaloni Juan Burgueño Fernando Henrique Toledo Adalberto Benavides-Mendoza M. Humberto Reyes-Valdés (2022, [Artículo])

Genome–Environment Associations Climatic Variables Hormonal Elicitors CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ADAPTATION DROUGHT STRESS HEAT STRESS LANDRACES TRITICUM AESTIVUM

New genomic regions identified for resistance to spot blotch and terminal heat stress in an interspecific population of triticum aestivum and T. spelta

Sudhir Navathe Ramesh Chand Mir Asif Iquebal Govindan Velu arun joshi (2022, [Artículo])

Resistance Terminal Heat CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIPOLARIS SOROKINIANA HEAT STRESS WHEAT RESISTANCE VARIETIES

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

Can I speak to the manager? The gender dynamics of decision-making in Kenyan maize plots

Rachel Voss Zachary Gitonga Jason Donovan Mariana Garcia-Medina Pauline Muindi (2023, [Artículo])

Gender and social inclusion efforts in agricultural development are focused on making uptake of agricultural technologies more equitable. Yet research looking at how gender relations influence technology uptake often assumes that men and women within a household make farm management decisions as individuals. Relatively little is understood about the dynamics of agricultural decision-making within dual-adult households where individuals’ management choices are likely influenced by others in the household. This study used vignettes to examine decision-making related to maize plot management in 698 dual-adult households in rural Kenya. The results indicated a high degree of joint management of maize plots (55%), although some management decisions—notably those related to purchased inputs—were slightly more likely to be controlled by men, while other decisions—including those related to hiring of labor and maize end uses—were more likely to be made by women. The prevalence of joint decision-making underscores the importance of ensuring that both men’s and women’s priorities and needs are reflected in design and marketing of interventions to support maize production, including those related to seed systems, farmer capacity building, and input delivery.

Intrahousehold Jointness CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENDER HOUSEHOLDS MAIZE SEED SYSTEMS DECISION MAKING

Statistical machine-learning methods for genomic prediction using the SKM library

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023, [Artículo])

Sparse Kernel Methods R package Statistical Machine Learning Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MACHINE LEARNING GENOMICS METHODS

Genetic potential of tropically adapted exotic maize (Zea mays L.) heat-tolerant donor lines in sub-tropical breeding programs

Casper Nyaradzai Kamutando Cosmos Magorokosho Pervez Zaidi (2023, [Artículo])

Gene Action Grain Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COMBINING ABILITY EXOTIC GERMPLASM GRAIN YIELDS HEAT STRESS

GENERATION OF LG VECTOR MODES WITH ULTRAHIGH STABILITY BASED ON COMPLEX AMPLITUDE MODULATION IN AN ON-AXIS CONFIGURATION

Gloria Elizabth Rodríguez García (2023, [Tesis de maestría])

"This thesis presents a novel technique for generating vector beams using complex amplitude modulation (CAM) in an on-axis configuration. The holograms used to generate the beams were created using the Mathlab software and displayed on a reflective spatial light modulator (SLM). The main goal of this research was to address both the purity and stability of the beams during generation and propagation, introducing a quantitative approach to assess their stability. As a proof-of-concept, Laguerre-Gaussian vector beams have been generated and characterized using Stokes polarimetry with the proposed experimental set up."

Structured light Laguerre-Gauss vector beams Stokes polarimetry Beam generation Spatial light modulators CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA FÍSICA ÓPTICA OPTICA FÍSICA OPTICA FÍSICA

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

Rodomiro Ortiz Paulino Pérez-Rodríguez Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])

Potato Traits Cross-Validation Breeding Data CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING