These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
24. Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Gu J; Yin X; Zhang C; Wang H; Struik PC Ann Bot; 2014 Sep; 114(3):499-511. PubMed ID: 24984712 [TBL] [Abstract][Full Text] [Related]
25. ADAM-Plant: A Software for Stochastic Simulations of Plant Breeding From Molecular to Phenotypic Level and From Simple Selection to Complex Speed Breeding Programs. Liu H; Tessema BB; Jensen J; Cericola F; Andersen JR; Sørensen AC Front Plant Sci; 2018; 9():1926. PubMed ID: 30687343 [TBL] [Abstract][Full Text] [Related]
26. Understanding crop genetic diversity under modern plant breeding. Fu YB Theor Appl Genet; 2015 Nov; 128(11):2131-42. PubMed ID: 26246331 [TBL] [Abstract][Full Text] [Related]
27. Rapid transgenerational adaptation in response to intercropping reduces competition. Stefan L; Engbersen N; Schöb C Elife; 2022 Sep; 11():. PubMed ID: 36097813 [TBL] [Abstract][Full Text] [Related]
28. Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Yoosefzadeh Najafabadi M; Hesami M; Eskandari M Genes (Basel); 2023 Mar; 14(4):. PubMed ID: 37107535 [TBL] [Abstract][Full Text] [Related]
29. Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data. Vallejos CE; Jones JW; Bhakta MS; Gezan SA; Correll MJ BMC Plant Biol; 2022 Jun; 22(1):275. PubMed ID: 35658831 [TBL] [Abstract][Full Text] [Related]
30. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Reynolds D; Ball J; Bauer A; Davey R; Griffiths S; Zhou J Gigascience; 2019 Mar; 8(3):. PubMed ID: 30715329 [TBL] [Abstract][Full Text] [Related]
31. Plant science in the age of simulation intelligence. Stock M; Pieters O; De Swaef T; Wyffels F Front Plant Sci; 2023; 14():1299208. PubMed ID: 38293629 [TBL] [Abstract][Full Text] [Related]
32. Integrated physiological and agronomic modelling of N capture and use within the plant. Jeuffroy MH; Ney B; Ourry A J Exp Bot; 2002 Apr; 53(370):809-23. PubMed ID: 11912224 [TBL] [Abstract][Full Text] [Related]
33. Speed breeding in growth chambers and glasshouses for crop breeding and model plant research. Ghosh S; Watson A; Gonzalez-Navarro OE; Ramirez-Gonzalez RH; Yanes L; Mendoza-Suárez M; Simmonds J; Wells R; Rayner T; Green P; Hafeez A; Hayta S; Melton RE; Steed A; Sarkar A; Carter J; Perkins L; Lord J; Tester M; Osbourn A; Moscou MJ; Nicholson P; Harwood W; Martin C; Domoney C; Uauy C; Hazard B; Wulff BBH; Hickey LT Nat Protoc; 2018 Dec; 13(12):2944-2963. PubMed ID: 30446746 [TBL] [Abstract][Full Text] [Related]
34. Understanding of the various aspects of gene regulatory networks related to crop improvement. Bulbul Ahmed M; Humayan Kabir A Gene; 2022 Jul; 833():146556. PubMed ID: 35609798 [TBL] [Abstract][Full Text] [Related]
35. Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. van Eeuwijk FA; Bustos-Korts D; Millet EJ; Boer MP; Kruijer W; Thompson A; Malosetti M; Iwata H; Quiroz R; Kuppe C; Muller O; Blazakis KN; Yu K; Tardieu F; Chapman SC Plant Sci; 2019 May; 282():23-39. PubMed ID: 31003609 [TBL] [Abstract][Full Text] [Related]
36. Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Heslot N; Akdemir D; Sorrells ME; Jannink JL Theor Appl Genet; 2014 Feb; 127(2):463-80. PubMed ID: 24264761 [TBL] [Abstract][Full Text] [Related]
37. Simulating the impact of genetic diversity of Medicago truncatula on germination and emergence using a crop emergence model for ideotype breeding. Brunel-Muguet S; Aubertot JN; Dürr C Ann Bot; 2011 Jun; 107(8):1367-76. PubMed ID: 21504913 [TBL] [Abstract][Full Text] [Related]
38. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. Anilkumar C; Sunitha NC; Harikrishna ; Devate NB; Ramesh S Planta; 2022 Sep; 256(5):87. PubMed ID: 36149531 [TBL] [Abstract][Full Text] [Related]
39. Data-driven approaches to improve water-use efficiency and drought resistance in crop plants. Sharma N; Raman H; Wheeler D; Kalenahalli Y; Sharma R Plant Sci; 2023 Nov; 336():111852. PubMed ID: 37659733 [TBL] [Abstract][Full Text] [Related]
40. Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions. Kromdijk J; Bertin N; Heuvelink E; Molenaar J; de Visser PH; Marcelis LF; Struik PC J Exp Bot; 2014 Jan; 65(1):11-22. PubMed ID: 24227339 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]