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

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.

Closing the Gap between Climate Information Producers and Users: Assessment of Needs and Uptake in Senegal

“…Similar to countries such as India and Malawi where weather information is used, Senegal is facing multiple challenges which, if unchecked, could undermine the long-term sustainability of delivery and uptake of CIS. The common issues encountered are: (i) the production of information is not locally relevant, fit-for purpose and available in a timely manner [17,38,39]; (ii) the non-existence of appropriate governance and institutional structures for the provision of climate information [19]; (iii) there is not enough emphasis on socio-economic value in the uptake of climate information provided and subsequent decision-making [40]; and (iv) the lack of appropriate boundary organizations to effectively communicate between information providers and users. There are additional major challenges that are specific to the case of Senegal and these have to do with the funding sources, the production of missing but awaited CIS and the timely implementation of the training on the ground.…”

The utility of weather and climate information for adaptation decision-making: current uses and future prospects in Africa and India

“…Examples of this are already being seen in India (Lobo et al, 2017;Manjula & Rengalakshmi, 2015;Nidumolu et al, 2016) and across Africa (Dorward et al, 2015;Ouma, Ogallo, & Onyango, 2013 (Singh, Urquhart, et al, 2016) and this slowly fostered recognition of the utility of climate information to manage risk. Today, the benefits of those investments are visible through farmer-led demands of better forecasts, the private sector seeing value in investing in climate information delivery, and integration of climate information in adaptation initiatives (Lobo et al, 2017 The framework aims to provide a way forward in demonstrating how actions at one temporal scale are interlinked with actions across different temporal scales.…”

“…As evidenced in a study by Cooper et al (2008), which focuses on rain-fed farming systems in sub-Saharan Africa, the ability of decision-makers to utilize short-term information and manage current climate risks is a precursor to better management of future climate risks. Yet, there are relatively few examples of long-term climate information informing decision-making Nidumolu et al, 2016), implying that there are issues around the relevance, provision and usability of climate information on longer time scales.…”

“…Perceptions of climate variability as held by farmers may differ from meteorological data and (Osbahr, Dorward, Stern, & Cooper, 2011) this may constrain uptake of CI because of different frames of constructing and planning for uncertainty and risks.…”

“…Since climate information is often given in isolation, 'more often than not [it] is disconnected from the real life agricultural decisions' (Manjula & Rengalakshmi, 2015, p. 13). Our review identifies characteristics key to defining success in the context of using climate information for decision-making in adaptation: (1) decision-makers (farmers, policy-makers) receive, trust and understand information (Mase & Prokopy, 2014;Singh, Urquhart, et al, 2016); (2) information is locally relevant, fit-forpurpose and available in a timely manner (Lackstrom, Kettle, Haywood, & Dow, 2014; Lobo, Chattopadhyay, & Rao, 2017Nidumolu et al, 2016;Vaughan & Dessai, 2014); (3) there are appropriate governance and institutional structures for the provision of climate information (Vaughan & Dessai, 2014); and (4) there is an emphasis on socio-economic value in the uptake of climate information provided and subsequent decision-making (Dorward et al, 2015;Nidumolu et al, 2016;Vaughan & Dessai, 2014).…”