Rural communities are exposed to a large number of risks which may threaten their livelihoods, including climate risks (e.g. floods, cyclones, droughts), market risks (e.g. food price volatility), environmental risks (e.g. water quality, water depletion), social risks (e.g. social exclusion, increasing inequity), and health risks (e.g. loss of dietary diversity). As part of SIAGI’s research a risk assessment framework will be applied to investigate the wide range of risks associated with agricultural intensification in the study locations shown in the figure below. The range of potential consequences will be considered for each risk, and the magnitude of these consequences will be assessed. The likelihood and consequence of all identified risks will be assessed in addition to qualitative methods for eliciting further information and knowledge (e.g. expert/key informant interviews, focus group discussion). The identified risks will be ranked according to their likelihood and the assessment criteria. A set of scenarios will be developed for the risks ranked most important, and narratives describing each scenario, its socioeconomic and biophysical context and pathway will be formulated.
A framework for environmental risk assessment and management. The dashed line between the ‘formulate problem’ and ‘assess risk’ stages on the figure indicates the strong interdependencies between these two stages (Gormley et al, 2011)
Climate change poses a huge risk for smallholder farmers. Climate risk assessments will be based on climate data analysis and farmer experience using a participatory engagement process successfully used in the ACCA project (Nidumolu et al., 2015). Crop simulation modelling using the APSIM model (Holzworth et al., 2014) will assess production risks and management options. Key crops in the region will be identified for analysis in consultation with local farmers and researchers. Yield constraints will be identified through a Comparative Performance Analysis (CPA) (de Bie, 2000; Nidumolu, 2004). This research will use yield constraints to provide important quantitative insights into socio-economic constraints and risks, both actual and perceived, which incorporate and extend beyond bio-physical agronomic management issues. The results of this activity constitute key inputs back to the ACIAR sister projects.
de Bie, CAJM, 2000. Comparative performance analysis of agro-ecosystems. PhD thesis, Wageningen University, the Netherlands. ISBN: 90-5808-253-9
Holzworth, DP, NI Huth, PG de Voil, EJ Zurcher, NI Herrmann, G McLean, K Chenu, E van Oosterom, VO Snow, C Murphy, AD Moore, HE Brown, JPM Whish, S Verrall, JLWB Fainges, AS Peake, PL Poulton, Z Hochman, PJ Thorburn, DS Gaydon, NP Dalgliesh, D Rodriguez, H Cox, S Chapman, A Doherty, E Teixeira, J Sharp, R Cichota, I Vogeler, FY Li, E Wang, GL Hammer, MJ Robertson, J Dimes, PS Carberry, JNG Hargreaves, N MacLeod, C McDonald, J Harsdorf, S Wedgwood, BA Keating, 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327-350
Gormley, A, S Pollard, S Rocks, 2011. Guidelines for Environmental Risk Assessment and Management. Department for Environment, Food and Rural Affairs (DEFRA), London, UK.
Nidumolu, UB, PT Hayman, Z Hochman, H Horan, DR Reddy, G Sreenivas, DM Kadiyala, 2015. Assessing climate risks in small-holder rainfed farming using farmer perceptions, crop calendars and climate analysis. Journal of Agricultural Science, 153 (8), 1380-1393
Nidumolu, UB, 2004. Integrating Geo-Information Models with Participatory Approaches: Applications in Land Use Analysis. PhD thesis, Wageningen University, the Netherlands. ISBN: 90-8054-138-4