Program R: applications in plant breeding

Peternelli, Luiz Alexandre

doi:10.1590/s1984-70332011000500014

Cited by 2 publications

(2 citation statements)

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“…The vast increase in affordable computational capacity has allowed each plant breeder to conduct complex analysis with extreme simplicity. The establishment of open‐source statistical packages has dramatically increased the number of users relying on them to perform selection (Alvarado et al., 2020; Peternelli, 2011). In parallel, the simplification of statistical analysis into user‐friendly packages has ensured that ever more complex analysis be performed routinely.…”

Section: What Worked and What Did Notmentioning

confidence: 99%

What plant breeding may (and may not) look like in 2050?

Bassi¹,

Sánchez-García²,

Ortíz

2023

The Plant Genome

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At the turn of 2000 many authors envisioned future plant breeding. Twenty years after, which of those authors’ visions became reality or not, and which ones may become so in the years to come. After two decades of debates, climate change is a “certainty,” food systems shifted from maximizing farm production to reducing environmental impact, and hopes placed into GMOs are mitigated by their low appreciation by consumers. We revise herein how plant breeding may raise or reduce genetic gains based on the breeder's equation. “Accuracy of Selection” has significantly improved by many experimental‐scale field and laboratory implements, but also by vulgarizing statistical models, and integrating DNA markers into selection. Pre‐breeding has really promoted the increase of useful “Genetic Variance.” Shortening “Recycling Time” has seen great progression, to the point that achieving a denominator equal to “1” is becoming a possibility. Maintaining high “Selection Intensity” remains the biggest challenge, since adding any technology results in a higher cost per progeny, despite the steady reduction in cost per datapoint. Furthermore, the concepts of variety and seed enterprise might change with the advent of cheaper genomic tools to monitor their use and the promotion of participatory or citizen science. The technological and societal changes influence the new generation of plant breeders, moving them further away from field work, emphasizing instead the use of genomic‐based selection methods relying on big data. We envisage what skills plant breeders of tomorrow might need to address challenges, and whether their time in the field may dwindle.

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Section: What Worked and What Did Notmentioning

confidence: 99%

What plant breeding may (and may not) look like in 2050?

Bassi¹,

Sánchez-García²,

Ortíz

2023

The Plant Genome

View full text Add to dashboard Cite

show abstract

“…In general, the software developed and used in the field of genetics and plant breeding has very efficiently prioritized aspects of biometrics applied to breeding (Cruz and Schuster 2004, Peternelli et al 2009, Peternelli 2011, Cruz 2013, Akbarpour et al 2016, Resende 2016, Bhering 2017, Matias et al 2018, agronomic experimentation (Ferreira 2011, Ferreira 2013, computational simulation (Abreu et al 2013), or even in teaching on genetics and plant breeding (Cruz et al 2011, Fritsche-Neto andMatias 2016). Different languages or programming environments such as Visual Basic, Delphi, Java, SAS, and R…”

Section: Introductionmentioning

confidence: 99%

IPRDataCol - software for field labelling and data storage

Garbuglio

2018

Crop Breed. Appl. Biotechnol.

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Program R: applications in plant breeding

Cited by 2 publications

References 1 publication

What plant breeding may (and may not) look like in 2050?

What plant breeding may (and may not) look like in 2050?

IPRDataCol - software for field labelling and data storage

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