Farming without a recipe: Wisconsin graziers and new directions for agricultural science

Lyon, Alexandra; Bell, Michael; Gratton, Claudio; Jackson, Randall D.

doi:10.1016/j.jrurstud.2011.04.002

Cited by 45 publications

(48 citation statements)

References 23 publications

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“…In biodiversity-based farming systems, agricultural practices (including species and cultivar choices) to provide high levels of input ecosystem services are sitedependent (Caron et al 2014;Duru et al 2015a;Giller et al 2015;Lyon et al 2011;Tittonell et al 2012). Thus, agricultural practices are considered as "situated action" (Crane et al 2011).…”

Section: Biodiversity-based Farming Systemsmentioning

confidence: 99%

A new analytical framework of farming system and agriculture model diversities. A review

Thérond

Duru

Roger-Estrade

et al. 2017

Agron. Sustain. Dev.

217

155

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In most current farming system classifications (e.g. "conventional" versus "organic"), each type of farming system encompasses a wide variety of farming practices and performances. Classifying farming systems using concepts such as "ecological", "sustainable intensification" or "agro-ecology" is not satisfactory because the concepts "overlap in…def-initions, principles and practices, thus creating…confusion in their meanings, interpretations and implications". Existing classifications most often focus either on biotechnical functioning or on socio-economic contexts of farming systems. We reviewed the literature to develop an original analytical framework of the diversity of farming systems and agriculture models that deal with these limits. To describe this framework, we first present the main differences between three biotechnical types of farming systems differing in the role of ecosystem services and external inputs: chemical input-, biological inputand biodiversity-based farming systems. Second, we describe four key socio-economic contexts which determine development and functioning of these farming systems: globalised commodity-based food systems, circular economies, alternative food systems and integrated landscape approaches. Third, we present our original analytical framework of agriculture models, defined as biotechnical types of farming systems associated with one or a combination of socio-economic contexts differing in the role of relationships based on global market prices and "territorial embeddedness". We demonstrate the potential of this framework by describing six key agriculture models and reviewing key scientific issues in agronomy associated with each one. We then analyse the added value of our analytical framework and its generic character. Lastly, we discuss transversal research issues of the agriculture models, concerning the technologies required, their function in the bioeconomy, their multi-criteria and multilevel assessments, their co-existence and the transitions between them.

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Section: Biodiversity-based Farming Systemsmentioning

confidence: 99%

A new analytical framework of farming system and agriculture model diversities. A review

Thérond

Duru

Roger-Estrade

et al. 2017

Agron. Sustain. Dev.

217

155

View full text Add to dashboard Cite

show abstract

“…Results of academic research such as academic papers are generally complex and theoretical, and therefore inappropriate information sources for practical farming problems (Sewell et al, 2014). Or putting it more drastically: many farmers consider 'expert knowledge' as being of limited practical value (Lyon, Bell, Gratton, & Jackson, 2011;Stuiver et al, 2004), and prefer insider information coming from other farmers (Hoffmann et al, 2007;Schneider, Fry, Ledermann, & Rist, 2009). The situation can be different in other contexts, e.g.…”

Section: Topics Motives and Information Sources For Farmers' Experimmentioning

confidence: 99%

Farmers’ Own Research: Organic Farmers’ Experiments in Austria and Implications for Agricultural Innovation Systems

Kummer

Leitgeb

Vogl

2017

SAR

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Farmers' experiments can be defined as the autonomous activities of farmers to try or introduce something new at the farm, and include evaluation of success or failure with farmers' own methods. Experiments enable farmers to adapt their farms to changing circumstances, build up local knowledge, and have resulted in countless agricultural innovations. Most research on the topic has been conducted in countries of the south. In this paper, however, we present experiments of randomly sampled organic farmers in Austria, and we discuss implications for agricultural innovation systems. In 76 structured questionnaire interviews we investigated topics, motives, methods and outcomes of farmers' experiments, and factors related to the frequency of experimentation. From the interviewed farmers, 90% reported experiments, and the majority of experiments (94%) involved monitoring and evaluation strategies. Farmers who reported a high frequency of experimentation showed a significantly higher propensity to plan, document and repeat their experiments, and had a more positive attitude towards experimenting than farmers that rarely experimented. We conclude that experimenting is a common activity among organic farmers in Austria, and that farmers have their own methods to conduct and assess their experiments. The most significant outcome is the creation of new knowledge, stressing the importance of experimentation for learning and adaptive farm management. Farmers' experiments are significant on two levels, i.e. at individual farm level and at the level of agricultural innovation systems. Taking full advantage of this innovative potential requires a better involvement of farmers as co-researchers into the development of agricultural innovations.

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“…Doing so, we could go beyond the assessment of the trade-off between production and resistance done in Sabatier et al (2015) and quantify the flexibility of the agroecosystem as well as the number of adaptations needed to overcome uncertainty. The aim of this study was not to provide recipes to be implemented in the field (which would be counterproductive; Lyon et al, 2011) but rather to take a wider point of view on the system so as to provide more general results on the behavior of the system in face of uncertainty. Our results showed that grazing sequences ranged from resistant and flexible with corresponding low production, to more productive sequences associated with low flexibility and resistance that required constant adaptations to cope with, and possibly benefit from, environmental variability.…”

Section: A Gradient Of Situationsmentioning

confidence: 99%

“…Knowledge of the principles required to conduct productive grazing has been available for many decades (Voisin, 1957), and complex knowledge of local pasture plant communities, soils, climate and weather, types of livestock, and marketing opportunities have improved (Franzluebbers et al, 2012), but graziers still face the issue of high unpredictability in resource availability and weather uncertainty (Lyon et al, 2011). Farmers should therefore not only design grazing sequences aimed at high levels of production, but also adopt management strategies that make it possible to cope with environmental uncertainty (sensu Westoby et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Management flexibility of a grassland agroecosystem: A modeling approach based on viability theory

Sabatier

Oates

Jackson

2015

Agricultural Systems

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Farming without a recipe: Wisconsin graziers and new directions for agricultural science

Cited by 45 publications

References 23 publications

A new analytical framework of farming system and agriculture model diversities. A review

A new analytical framework of farming system and agriculture model diversities. A review

Farmers’ Own Research: Organic Farmers’ Experiments in Austria and Implications for Agricultural Innovation Systems

Management flexibility of a grassland agroecosystem: A modeling approach based on viability theory

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