Accelerating agriculture: Data-intensive plant breeding and the use of genetic gain as an indicator for agricultural research and development

Williamson, Hugh F.; Leonelli, Sabina

doi:10.1016/j.shpsa.2022.08.006

Cited by 8 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include reducing the length of time that breeding takes (i.e. fewer breeding generations), improving selection accuracy, and/or increasing selection intensity (Williamson & Leonelli, 2022). The methods available to achieve these goals frequently involve complex forms of data linkage that have emerged since the turn of the millennium.…”

mentioning

confidence: 99%

“…The case study presented here draws on a more detailed analysis of genetic gain as an indicator, how it is being implemented in international plant breeding networks, and some key implications (seeWilliamson & Leonelli, 2022).…”

mentioning

confidence: 99%

“…On 'visions' or 'imaginaries' of socio-technical systems (including data systems) and their implications for science, society and the future, seeJasanoff and Kim (2015) andLeonelli (2021).3 Other solutions involve the use of policy levers to encourage or oblige seed companies and farmers to distribute and adopt new varieties, respectively(Spielman & Smale, 2017). These solutions have quite significant political, legal and economic implications regarding the control of seed systems, but we leave that topic aside for the purposes of this chapter (seeWilliamson & Leonelli, 2022).…”

mentioning

confidence: 99%

“…See footnote 2, and cf Williamson & Leonelli, 2022. for a more detailed discussion of seed system issues.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Cultivating Responsible Plant Breeding Strategies: Conceptual and Normative Commitments in Data-Intensive Agriculture

Williamson

Leonelli

2022

Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development

View full text Add to dashboard Cite

This chapter argues for the importance of considering conceptual and normative commitments when addressing questions of responsible practice in data-intensive agricultural research and development. We consider genetic gain-focused plant breeding strategies that envision a data-intensive mode of breeding in which genomic, environmental and socio-economic data are mobilised for rapid crop variety development. Focusing on socio-economic data linkage, we examine methods of product profiling and how they accommodate gendered dimensions of breeding in the field. Through a comparison with participatory breeding methods, we argue that the conceptual commitments underpinning current methods of integrating socioeconomic data into calculations of genetic gain can preclude the achievement of key social development goals, and that better engagement with participatory approaches can help address this problem. We conclude by identifying three key avenues towards a data-intensive approach to plant breeding that utilises the diverse sources of relevant evidence available, including socio-economic data, and maximises the chance of developing sustainable and responsible strategies and research practices in this domain: (1) reliable, long-term management of data infrastructures; (2) ongoing critical analysis of the conceptual foundations of specific strategies; and (3) regular transdisciplinary consultations including expertise in the social studies of agricultural science as well as participatory breeding techniques.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…See footnote 2, and cf Williamson & Leonelli, 2022. for a more detailed discussion of seed system issues.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cultivating Responsible Plant Breeding Strategies: Conceptual and Normative Commitments in Data-Intensive Agriculture

Williamson

Leonelli

2022

Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development

View full text Add to dashboard Cite

show abstract

“…I thank the referees that rightly encouraged me to highlight this crucial issue; see alsoWilliamson and Leonelli (2022b). OPEN ACCESS -PTPBIO.ORG…”

mentioning

confidence: 99%

Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science

Leonelli

2022

Philosophy, Theory, and Practice in Biology

View full text Add to dashboard Cite

This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic diversity affecting the identification and description of plant traits, the methods used to generate and process data, and the goals and skills of those with relevant expertise—including farmers and breeders. Through a study of the Crop Ontology (which explicitly recognizes and negotiates diversity) and its application to cassava breeding, I argue for a process-sensitive approach to the naming of plant traits that focuses on documenting environmental processes instead of biological products. I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions. I conclude that the study of data semantics and related descriptors constitutes a productive and underexplored way to think about the epistemic import of naming traits within plant science. The effort to articulate semantic differences among plant varieties and methods of data processing can generate newly inclusive ways to develop and communicate biological knowledge. In turn, such practices have the potential to defy existing understandings of systematisation and hierarchies of expertise in biology, thus bolstering the extent to which plant science can support biodiversity and sustainable agriculture.

show abstract

Biodiversity skepticism and measurement practices

Bocchi

2024

Biol Philos

View full text Add to dashboard Cite

This paper challenges “biodiversity skepticism:” an inferential move that acknowledges the proliferation, heterogeneity, and lack of covariance of biodiversity measurements, and concludes that we should doubt the scientific validity of the biodiversity concept. As a way out of skepticism, philosophers have advocated for eliminating “biodiversity” from scientific inquiry, revising it, or deflating its meaning into a single measurable dimension. I present a counterargument to the inferential move of the skeptic by revealing how it stands on two unstated premises, namely a reflective view of measurements and the unidirectional dynamics between definitional and measurement practices, and corollary assumptions. These premises and assumptions are misaligned with a richer theoretical understanding of measurement and are sometimes inconsistent with how science operates. A more nuanced view of measurement could better explain measurement proliferation while being consistent with new ways in which the general biodiversity concept could be useful. To conclude, I urge philosophers of measurement and conceptual engineers to collaborate in tackling the interplay between conceptual change and measurement practices.

show abstract

Accelerating agriculture: Data-intensive plant breeding and the use of genetic gain as an indicator for agricultural research and development

Cited by 8 publications

References 45 publications

Cultivating Responsible Plant Breeding Strategies: Conceptual and Normative Commitments in Data-Intensive Agriculture

Cultivating Responsible Plant Breeding Strategies: Conceptual and Normative Commitments in Data-Intensive Agriculture

Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science

Biodiversity skepticism and measurement practices

Contact Info

Product

Resources

About