390 results on '"Murray, Seth C"'
Search Results
2. Current challenges and future of agricultural genomes to phenomes in the USA
3. Genetic modification can improve crop yields — but stop overselling it
4. Photoperiod associated late flowering reaction norm: Dissecting loci and genomic-enviromic associated prediction in maize
5. 2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative
6. Genomes to Fields 2022 Maize genotype by Environment Prediction Competition
7. 2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project
8. Genetic mapping and prediction for novel lesion mimic in maize demonstrates quantitative effects from genetic background, environment and epistasis
9. Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
10. Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments
11. Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms
12. Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations
13. Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images
14. A Fond Farewell and Reflection on 2023
15. Crop Science Futurology: A Data-Driven Approach Through Phenomic, Genomic and Enviromic Insights
16. Facilitating community unoccupied aerial systems (UAS, drone) knowledge, communication, and data processing across agriculture
17. The Loneliness and Social Isolation Epidemic
18. Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments
19. Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data
20. Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition
21. Time to Modernize Our Metrics
22. 2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative
23. Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits
24. Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets
25. Environment‐specific selection alters flowering‐time plasticity and results in pervasive pleiotropic responses in maize
26. Change Is Upon Us, and We Need to Adapt
27. MUCH ROOM TO IMPROVE PLANTS
28. High-Resolution UAS Imagery in Agricultural Research Concepts, Issues, and Research Directions
29. Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights
30. The Importance of Awards for CSSA
31. Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction
32. An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize
33. Strategically Planning Our Future, Together
34. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions
35. Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding
36. Temporal field phenomics allows discovery of nature AND nurture, so can we saturate the phenome?
37. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize
38. Differentiation of Seed, Sugar, and Biomass-Producing Genotypes in Saccharinae Species
39. Prediction of regrowth and biomass of perennial sorghum using unoccupied aerial systems
40. Erratum to: Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds
41. Field Based High Throughput Phenotyping Enables the Discovery of Loci Linked to Senescence and Grain Filling Period
42. Genetic variation in hydrogen cyanide potential of perennial sorghum evaluated by colorimetry
43. Chapter Four - Remote and proximal sensing: How far has it come to help plant breeders?
44. Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding.
45. Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
46. Molecular characterization and phylogenetic analysis of ZmMCUs in maize
47. A response to Honnay et al.
48. Root system size and root hair length are key phenes for nitrate acquisition and biomass production across natural variation in Arabidopsis
49. Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize
50. Control of aflatoxin using atoxigenic strains and irrigation management is complicated by maize hybrid diversity
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