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Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts

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Title Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts
 
Creator Nidumolu, Uday Bhaskar
 
Contributor Lubbers, Ingrid
Kanellopoulos, Argyris
van Ittersum, Martin
Kadiyala, M.D.M.
Sreenivas, G.
 
Subject probabilistic seasonal forecasts
crop choice
small holder farmers
profit maximisation
climate risk
 
Description 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 amathematical programmingmodel 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 farmplan and corresponding
agricultural management decisions at a village scale. The second step uses a ‘revised forecast’ that is
given sixweeks later during the growing season. In scenarioswhere 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 scenarioswhere a ‘normal’ season is expected, themodel chooses predominantly
cotton andmaize in addition to paddy rice and redgram. As part of the stakeholder engagement process,
we operated the model in an iterative waywith 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 redgrammainly 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.
 
Date 2016-09-29
2017-02-08T23:17:56Z
2017-02-08T23:17:56Z
 
Type Journal Article
 
Identifier https://mel.cgiar.org/dspace/limited
http://oar.icrisat.org/id/eprint/9739
Uday Bhaskar Nidumolu, Ingrid Lubbers, Argyris Kanellopoulos, Martin van Ittersum, M. D. M. Kadiyala, G. Sreenivas. (29/9/2016). Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts. Agricultural Systems, 149, pp. 175-184.
https://hdl.handle.net/20.500.11766/5605
Limited access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher Elsevier Masson
 
Source Agricultural Systems;149,(2016) Pagination 175,184