Browsing by Autor "Joy Singarayer"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item type: Item ,
    An exploration of using large language models to integrate farmer behaviour into an agricultural systems model of the Peruvian Andes
    (2024) Joy Singarayer; Richard Bailey; Patrick McGuire; Francisco Araujo- Ferreira; Nicholas Branch; Fernando González; Diana Santos Shupingahua; Douglas B. Walsh; Alexander Herrera; Andrew J. Wade
    The implications of climate change on agro-pastoral farming systems in the Peruvian Andes are not fully understood. There is already a significant impact on agricultural productivity from current climate variability and extreme weather in the region. This is exacerbated by chronic poverty in many rural areas and the need for improved government-led strategic planning. Tools to assist with policy planning for climate change adaptations that achieve environmental and social resilience are vital, and these require collaboration with rural communities to incorporate the complexities of behavioural responses to climate change, market dynamics, and policy shifts in agricultural and water management. In this study we further develop a recent agricultural systems model (the TELLUS model; Pilditch et al., in review). The model is an agent-based simulation focussed on the behaviour of interacting populations of individual farming agents. TELLUS offers the opportunity to analyse the impact of interventions/policies in light of key scenarios and conditions of interest, with potential to uncover unforeseen emergent behaviours within farming systems (e.g., tipping points, amplifiers, system adaptations) and potential unintended consequences of scenarios and policies (e.g., increasing in equalities; increased system fragility). A difficulty in applying such models to specific case studies is in choosing valid parameter values, especially for model behaviour associated with human behaviour and decision-making.Our work over recent years includes extensive fieldwork in the Cordillera Negra and Cordillera Blanca, involving interviews and workshops with farming communities, and collaboration with regional NGOs. These interactions have been instrumental in understanding local challenges and priorities. The challenge in terms of modelling this system is turning information gained from qualitative methods (e.g. interviews) into parameter values for the model. Our novel approach is to assess the extent to which modern AI systems, specifically, Large Language Models (LLMs) can help perform this task.  We leverage the reasoning abilities of LLMs to directly estimate relevant model parameters from automated interview transcription/translations. We will discuss the extent to which this integration has aided the creation of a TELLUS model tuned specifically to the Peruvian Andes context. Our approach will hopefully serve as a novel tool, combining empirical research, community involvement, and advanced computational modelling, to explore future climate scenarios and the potential effects of policy interventions.
  • Thumbnail Image
    Item type: Item ,
    Crop modelling with AquaCrop during climate change in the Ancash region of the Peruvian Andes
    (2024) Patrick McGuire; Joy Singarayer; Andrew J. Wade; Harvey J. E. Rodda; Nicholas Branch; Dionisa Joseph Mattam; Francisco Araujo-Ferreira; Eric Capoen; Alden A. Everhart; Christian Florencio
    Peruvian Andean rural farmers often have precarious livelihoods and already experience less predictable weather conditions than in recent decades. With the goal of investigating hydrological and agricultural resilience in a region with an uncertain climate future (with regard to both temperature and precipitation), we present here the results obtained from using the AquaCrop software to model both crop growth and the consequent harvest yields in the valleys of the Peruvian Andes, including the Rio Santa Valley in the Ancash region. The crop models are presented for 1970-2099 (the historical and the future during climate change), using RCP2.6 & RCP8.5 Regional Climate Models (RCMs) from CORDEX at a spatial resolution of 0.22 degrees. We chose the CORDEX RCM data that was dynamically downscaled from the CMIP5 GCMs instead of the CHELSA statistically-downscaled data, since the downscaling of the CORDEX RCM data produces more locally-heterogeneous climate averages, which are more consistent with the variable topography. The CORDEX RCM model data has subsequently been bias-corrected to monthly CHIRPS precipitation and monthly ECMWF ERA-Interim temperature extremes from 1981-2005 for locations in the Ancash region, including Yungay and Aija. For the various crops that we modelled (maize/corn, potatoes, dry beans, quinoa, wheat), we find significant interannual variability of the dry yields from crop harvest (without irrigation or fertilizers), particularly when the climate is transitioning to a warmer one for those crops that prefer warmer climates. Without the consideration of irrigation or fertilizers, the possibility of high yield interannual variability could make it difficult for the Peruvian Andean farmers to plan ahead, and maintaining a diversity of crops within the Rio Santa Valley and the wider Ancash region could be advantageous for these farmers.