Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts. (November 2016)

• Record Type: Journal Article
• Authors: Nidumolu, U.B.; Lubbers, M.; Kanellopoulos, A.; van Ittersum, M.K.; Kadiyala, D.M.; Sreenivas, G.
• Is Part Of: Agricultural systems. Volume 149(2016)
• Page Start: 175
• Page End: 184
• Publication Date: 2016-11
• Subjects: Mathematical programming -- Probabilistic seasonal forecasts -- Crop choice -- Climate risk -- Small holder farmers -- Profit maximisation; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2016.09.011
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: Seasonal climate forecasts (SCFs) can be used to identify appropriate risk management strategies and to reduce the sensitivity of rural industries and communities to climate risk. However, these forecasts have low utility among farmers in agricultural decision making, unless translated into a more understood portfolio of farm management options. Towards achieving this translation, we developed a mathematical programming model that integrates seasonal climate forecasts to assess 'what-if?' crop choice scenarios for famers. We used the Rayapalli village in southern India as a case study. The model maximises expected profitability at village level subject to available resource constraints. The main outputs of the model are the optimal cropping patterns and corresponding agricultural management decisions such as fertiliser, biocide, labour and machinery use. The model is set up to run in two steps. In the first step the initial climate forecast is used to calculate the optimal farm plan and corresponding agricultural management decisions at a village scale. The second step uses a 'revised forecast' that is given six weeks later during the growing season. In scenarios where the forecast provides no clear expectation for a dry or wet season the model utilises the total agricultural land available. A significant area is allocated to redgram (pigeon pea) and the rest to maize and paddy rice. In a forecast where a dry season is more probable, cotton is the predominant crop selected. In scenarios where a 'normal' season is expected, the model chooses predominantly cotton and maize in addition to paddy rice and redgram. As part of the stakeholder engagement process, we operated the model in an iterative way with participating farmers. For 'deficient' rainfall season, farmers were in agreement with the model choice of leaving a large portion of the agriculture land as fallow with only 40 ha (total area 136 ha) of cotton and subsistence paddy rice area. While the model crop choice was redgram in 'above normal and wet seasons, only a few farmers in the village favoured redgram mainly because of high labour requirements, and the farmers perceptions about risks related to pests and diseases. This highlighted the discrepancy between the optimal cropping pattern, calculated with the model and the farmer's actual decisions which provided useful insights into factors affecting farmer decision making that are not always captured by models. We found that planning for a 'normal' season alone is likely to result in losses and opportunity costs and an adaptive climate risk management approach is prudent. In an interactive feedback workshop, majority of participating farmers agreed that their knowledge on the utility and challenges of SCF have highly improved through the participation in this research and most agreed that exposure to the model improved their understanding of the role of SCF in crop choice decisions and that the modelling tool was useful to discuss climate risk in agriculture. Highlights: A bioeconomic crop choice model that integrates seasonal climate forecasts (SCF) is developed. Model uses SCFs as weights and model parameterised in consultation with farmers. Model is used as interactive tool to discuss SCF utility in climate risk management. Farmer vs model crop choice discrepancy provides insights on farmer decision making. Planning for 'average' season alone likely to result in losses and opportunity costs.