The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén, Viktor; Czvetkó, Tímea; Abonyi, János

doi:10.3389/fenvs.2021.619092

Cited by 57 publications

(27 citation statements)

References 202 publications

(250 reference statements)

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“…This subset is later on partitioned between (b) training and (c) testing populations. The training population is used to calibrate (d) ML models that aim using genomic information to predict genomic estimated adaptive values (GEAVs, an analogous rank to the polygenic risk score (PGS) and genomic estimated breeding value (GEBV) from the quantitative genomics literature, e.g., [102,136]). The computer screen depicts a hypothetical hidden neural network (HNN) algorithm, which is one among many potential ML tools; the repertoire includes several regressions, classification, and deep learning models, thoughtfully reviewed this year by Sebestyén et al [137] and Tong and Nikoloski [138].…”

Section: Figurementioning

confidence: 99%

Harnessing Crop Wild Diversity for Climate Change Adaptation

Cortés

López-Hernández

2021

Genes

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Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent ‘big data’ developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these ‘big data’ approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

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Section: Figurementioning

confidence: 99%

Harnessing Crop Wild Diversity for Climate Change Adaptation

Cortés

López-Hernández

2021

Genes

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“…According to Sebestyen et al, data and systems science tools can be crucial in recognizing climate challenges and mitigation opportunities by integrating heterogeneous data and models and exploring the relationship between environmental and social factors. This integrated thinking lays the foundation for promising future trends in climate informatics [19]. For the case of Agricultural Big Data, few papers applied to mitigation situations.…”

Section: Main Challenges and Trendsmentioning

confidence: 99%

“…Mitigating climate change impacts and successful adaptation require effective strategic climate change planning by countries worldwide, whose decision making requires complex models and information sources [19]. The Big Data toolkit enables the systematization, processing, and evaluation of heterogeneous data and information sources, which are infeasible with traditional analysis tools.…”

Section: Main Challenges and Trendsmentioning

confidence: 99%

“…The Big Data toolkit enables the systematization, processing, and evaluation of heterogeneous data and information sources, which are infeasible with traditional analysis tools. Harmonizing the constant scientific knowledge and data sources related to climate change may be one of the most researched areas in the future [19]. Thirty-three papers using Big Data to analyze and adapt agriculture processes due to problems caused by climate change were reviewed.…”

Section: Main Challenges and Trendsmentioning

confidence: 99%

“…On the other hand, the dynamism and complexity of climate issues that entail an interdisciplinary approach and the interweaving of various disciplines must not be neglected. According to Sebestyen et al, a System of Systems is urgently required, called "climate computation" [19]. According to the authors, to resolve any of the challenges related to climate change, "it is essential to obtain and integrate knowledge in an entire series of systems that serve as the basis for the design of solutions that take the complex and uncertain nature of individual systems and their interrelations into account" [19].…”

Section: Introductionmentioning

confidence: 99%

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Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

et al. 2022

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Climate change is currently one of agriculture’s main problems in achieving sustainability. It causes drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that improve the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data, to discuss the use of the architectures in crop, water, climate, and soil management, and especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. However, despite advances in this line of research, few papers were found that mention architecture components, in addition to a lack of standards and the use of reference architectures that allow the proper development of Agricultural Big Data in the context of climate change.

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