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@article{deRaymond2021,

title = {Systemic risk and food security. Emerging trends and future avenues for research},

author = {Antoine Bernard de Raymond and Arlène Alpha and Tamara Ben-Ari and Benoît Daviron and Thomas Nesme and Gilles Tétart},

url = {http://agri.ckcest.cn/file1/M00/02/DB/Csgk0WC5rL2ALdKRABEk1jUjpcE370.pdf},

doi = {https://doi.org/10.1016/j.gfs.2021.100547},

year  = {2021},

date = {2021-05-14},

urldate = {2021-05-14},

journal = {Global Food Security},

abstract = {The unanticipated international food price spike of 2008 has raised concerns about global food security. Might food  systems  lastingly  fail  to  supply, trade,  and  distribute  food?  Might  widespread unsustainable  agricultural practices irreversibly alter ecosystems? Or might large scale food shortages trigger political unrest? To answer these questions, we reflect upon the concept of systemic risk and conduct a review of the literature on systemic risks and food security. First, we present the concept of systemic risk and current trends in systemic risk research. 

We then analyze contributions on systemic risk and food security. We first show that the literature has so far focused  on  a)  agricultural  production  and  correlated  yield-losses,  and  on  ways  of  pooling  risk  at  regional  or global-level, and b) the role of international trade in increasing or decreasing systemic risk. We then identify avenues for further research, highlighting the impact of intensive farming on ecosystems. Finally, we discuss the concept of systemic risk: we show that scholars need to be careful when assuming that there exists just one global food system; we show that systemic risk can be understood in various ways, beyond the domino effect paradigm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cardon2020,

title = {Bounded Futures: Growing a Boundary Foreknowledge Infrastructure in Food Security Research},

author = {Vincent Cardon},

doi = {10.1177/0971721819889918},

year  = {2020},

date = {2020-03-03},

urldate = {2020-03-03},

journal = {Science, Technology and Society},

volume = {25},

number = {1},

pages = {38-66},

abstract = {One preferred way of coping with the openness or indeterminacy of future is to elaborate ‘fictional expectations’ that enable action by defining possible outcomes. In this article, I propose to focus on the career of the impact foresight model to analyse how agro-economists combine imaginaries, narratives, data and calculative technologies addressing the long-term future of agriculture. Impact is a partial equilibrium model, which has become increasingly comprehensive. Its modular structure now enables it to interweave scenarios produced by other legitimate institutions, and to run simulations for a number of configurations of climate change and socio-economic evolutions.In this article, foresight models are taken to be material discursive devices. My argument is that their evolutions as technologies and the framing of the future they operate should not be analysed separately. Transforming radical uncertainty into controlled variability – magnitude of change, they explicitly endeavour to ‘bound’ uncertainty. But it is ‘bounded’ in a way that is highly dependent on the knowledge infrastructure upon which the models rely. Quantified modelling also makes it possible for economists to compare rival models and create alignments or negotiate zones of consensus, that is, a certain form of knowledge on the future. In the case under scrutiny, technological choices and data processing work contribute to reinforce a certain point of view – market, production and technology-oriented – on food security. Studying infrastructure and model design therefore allows a better understanding of path dependency and cognitive lock-in effects regarding the way the future is envisaged and narrated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ruiz2015indicators,

title = {Indicators of agricultural intensity and intensification: a review of the literature},

author = {Irune Ruiz-Martinez and Elisa Marraccini and Marta Debolini and Enrico Bonari},

url = {https://hal.archives-ouvertes.fr/hal-01277628},

doi = {10.4081/ija.2015.656},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Italian Journal of Agronomy},

volume = {10},

number = {2},

pages = {74--84},

abstract = {Since the 1960s, research has dealt with agricultural intensification (AI) as a solution to ensure global food security. Recently, sustainable intensification (SI) has increasingly been used to describe those agricultural and farming systems that ensure adequate ecosystem service provision. Studies differ in terms of the application scales and methodologies, thus we aim to summarize the main findings from the literature on how AI and SI are assessed, from the farm to global levels. Our literature review is based on 7865 papers selected from the Web of Science database and analysed using CorText software. A further selection of 105 relevant papers was used for an in-depth full-text analysis on: i) farming systems studied; ii) related ecosystem services; iii) indicators of intensity; and iv) temporal and spatial scales of analysis. Through this two-step analysis we were able to highlight three main research gaps in the AI research indicators. Firstly, the farming systems analysed for assessing AI are often quite simplified or monoculture-oriented, and they do not take the diversity and complex organisation of farming systems into account. Secondly, these studies mainly focus on northern countries or developing countries, whereas there is a gap of knowledge in Mediterranean areas, which are the areas with a high complexity of farming systems and diversity in ecosystem services. Finally, AI is mostly assessed through nitrogen inputs and economic yield, which are used the most both at very local and global levels. Intermediate regional or local levels, which are relevant for policy implementation and local planning, are often neglected. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}