Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

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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.

Liberalism has been fundamental to the making of the modern world, at times shaping basic assumptions as to the nature of the political, and in other cases existing as a delimited political project in contention with others. Across its long history, liberal projects have taken a diverse range of forms, which resist easy reduction to a single logic or history. This diversity, however, has often escaped anthropological attention. In this introduction to our special section on Grammars of Liberalism, we briefly trace this historical diversity, interrogate anthropological approaches to conceptualising liberalism and offer a broad framework for studying liberalism that remains attentive to both continuity and difference. First, we argue for attention to how the political claims made by liberal projects unfold at the levels of values, their interrelations (morphology) and the underlying rules governing the expression and combination of values, and their intelligibility as liberal (grammar). Second, we argue for empirical attention to how values are expressed and liberal projects assembled across different social forms. We argue that this approach enables anthropology to grasp the diversity of liberal political projects and subject positions while still allowing scholars to approach liberalism critically and to interrogate its underlying logics.

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.