Speaker
Description
Introduction
Crop-livestock systems at landscape level consist of interactions between farms (often specialized) promoting ecological interactions over space and time between crops, pastures and livestock sub-systems. They can increase resources circularity to satisfy short-term (e.g. farm autonomy) and long-term goals (e.g. resilience to climatic hazards and agricultural practices change) (Martin et al. 2016).
Little research is available on the impact of farms involvement in exchanges of products with other farms (constituting a farm network) on autonomy and resilience, both at farm and network levels.
Agent-based models (ABM) represent individual agents (farms) making decisions and acting independently from each other while considering social behaviors such as social interactions (Huber et al. 2018). We aimed to assess, via an ABM, the autonomy and resilience of a farm network in a given region in response to different scenarios.
Material & Methods
We built an ABM in which agents are farms (cereal, crop-livestock, and livestock) and the cooperative. At each time step (year), on-farm products (grains, forage, straw, manure) can be traded if needed or in surplus with other farms, the cooperative or the global market. Decisions to trade with another farm are based on a score that takes into account i) the distance to cover, ii) the quantity of biomass that can be exchanged, iii) trust between farmers, representing farm trade habits and evolving according to past trade experiences, iv) individual strategy (preference for exchanged quantity or social connection).
The ABM was implemented on the GAMA platform for the Ariège region (France), then explored with the openmole platform (Reuillon et al. 2013). Explorations showed that 300 farms could be representative of the functioning of the region and that stability is reached after 8 steps. This stable state is our baseline scenario from which we will include perturbation to simulate scenarios.
Main outputs indicators for each farm are: i) local farm autonomy: in nitrogen, ratio of quantity of consumed products coming from the farm or other farms over total quantity of consumed products, ii) betweenness centrality: degree of inclusion of a farm in the network.
Results
For the baseline scenario, network autonomy is low (0.24), with farm autonomy much higher for crop-livestock farms (0.773), than livestock (0.279) and crop farmers (0.007) (See Attachement). This variability is also observed between livestock types: the local autonomy is higher in ovine crop-livestock farms (0.98) than in bovine ones (0.61) and ovine livestock farms have lower local autonomy (0.22) than bovine ones (0.31).
On average, crop-livestock farms have more connections than livestock and crop farms (respectively 3.04, 1.95 and 1.21), and show higher levels of betweenness centrality (respectively 0.019, 0.009 and 0.004).
Mean distance between trading farms is the lowest for protein grain, and differs whether the buyer is a livestock or crop-livestock farm (61km and 27km respectively). Highest mean distances are reached for manure and cereal straw (54km both).
Discussion and conclusion
Our modeling approach combines both technical and social aspects of farm interactions. Based on the baseline scenario results, we showed that crop-livestock farmers are better included inside the farm network, mainly as they can be supplier and requestors. We highlighted that manure is scarce in the region. Crop farmers are bound to a low autonomy in simulations as their only local need is for fertilizers (thus for manure).
We are simulating scenarios of perturbations to compare with the baseline: i) changes in resource availability, to simulate yield variability, ii) farm number, farm structure (crop area, number of livestock heads), iii) new practices (cover crop grazing). New practices may help increase farms’ autonomy, while decrease in farm number and increase in farm areas may increase attraction to the local cooperative (versus other farms) and/or lead to a concentration of connections between farms. These results could be presented at the conference. They will provide insights on performances and resilience of crop-livestock systems at landscape level facing perturbations.
References
Huber R, et al. (2018) Representation of decision-making in European agricultural agent-based models. Agricultural Systems 167:143–160. https://doi.org/10.1016/j.agsy.2018.09.007
Martin G, et al. (2016) Crop–livestock integration beyond the farm level: a review. Agron Sustain Dev 36:53. https://doi.org/10.1007/s13593-016-0390-x
Reuillon R, et al. (2013) OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Generation Computer Systems 29:1981–1990. https://doi.org/10.1016/j.future.2013.05.003
| Keywords | agent-based model; crop-livestock interaction; farm network; resilience; farm autonomy |
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