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

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

Assessing climate risks in rainfed farming using farmer experience, crop calendars and climate analysis

Climate risk assessment in cropping is generally undertaken in a top-down approach using climate records while critical farmer experience is often not accounted for. In the present study, set in south India, farmer experience of climate risk is integrated in a bottom-up participatory approach with climate data analysis. Crop calendars are used as a boundary object to identify and rank climate and weather risks faced by smallhold farmers. A semi-structured survey was conducted with experienced farmers whose income is predominantly from farming. Interviews were based on a crop calendar to indicate the timing of key weather and climate risks. The simple definition of risk as consequence × likelihood was used to establish the impact on yield as consequence and chance of occurrence in a 10-year period as likelihood. Farmers’ risk experience matches well with climate records and risk analysis. Farmers’ rankings of ‘good’ and ‘poor’ seasons also matched up well with their independently reported yield data. On average, a ‘good’ season yield was 1·5–1·65 times higher than a ‘poor’ season. The main risks for paddy rice were excess rains at harvesting and flowering and deficit rains at transplanting. For cotton, farmers identified excess rain at harvest, delayed rains at sowing and excess rain at flowering stages as events that impacted crop yield and quality. The risk assessment elicited from farmers complements climate analysis and provides some indication of thresholds for studies on climate change and seasonal forecasts. The methods and analysis presented in the present study provide an experiential bottom-up perspective and a methodology on farming in a risky rainfed climate. The methods developed in the present study provide a model for end-user engagement by meteorological agencies that strive to better target their climate information delivery

Climate Risk Management in Smallholder Farming Systems in the Semiarid Tropics

Climate risk management in the semi-arid tropics (SAT) is one of the major challenges to achieving food security and development in India and large parts of sub-Saharan Africa and also in the case of Australia. Climate-induced production risk associated with the current season-to-season variability of rainfall is a major barrier in making rainfed agriculture sustainable and viable farm business. Since season outcomes are uncertain, even with the best climate information, farmers have limited flexibility in applying management with confidence. In fact in risky environments, farmers most often respond by adapting a risk averse strategy and are reluctant to invest in even risk reducing measures (Leathers and Quiggin 1991). In the SAT agro-ecologies, there are a limited range of enterprise or crop options to consider which may be further restricted by cultural traditions, food preferences or market opportunities.While there are fundamental differences between large scale commercial farms in Australia compared to the predominantly smallholder resource poor farms found in India, when it comes to climate risk management in the SAT, there are many commonalities. The purpose of this paper is therefore to (i) establish a framework for managing climate variability and transforming farming systems to be more resilient and sustainable for future climates; and (ii) provide some case study examples from climate risk management in low rainfall cropping system in Australia and consider how they may be applied in smallholder systems of the SAT..

Sustainability, epistemology, ecocentric business and marketing strategy:ideology, reality and vision

This conceptual article examines the relationship between marketing and sustainability through the dual lenses of anthropocentric and ecocentric epistemology. Using the current anthropocentric epistemology and its associated dominant social paradigm, corporate ecological sustainability in commercial practice and business school research and teaching is difficult to achieve. However, adopting an ecocentric epistemology enables the development of an alternative business and marketing approach that places equal importance on nature, the planet, and ecological sustainability as the source of human and other species' well-being, as well as the source of all products and services. This article examines ecocentric, transformational business, and marketing strategies epistemologically, conceptually and practically and thereby proposes six ecocentric, transformational, strategic marketing universal premises as part of a vision of and solution to current global un-sustainability. Finally, this article outlines several opportunities for management practice and further research

Comparison of NDVI seasonal trajectories and modelled crop growth dynamics

Wheat growth in south-eastern Australia shows a strong seasonal pattern associated with a variable ‘break’ of season, a dry and usually warm season finish, and poor buffering from limited soil water storage. Our main aim was to interpret seasonal growth trajectories using Normalised Differential Vegetation Index (NDVI) time series for the period April 1998-August 2007 in selected locations in South Australia. Secondary aims were to (a) assess the sensitivity of APSIM simulations (b) upscaling from point to landscape to provide insights into a range of issues including spatial water stress. The method applied to this analysis combined three independent approaches. These were (a) an examination of spatial crop class overlays to examine the spatial and temporal variability of classes in the case study region; (b) a comparison of spatial class information and APSIM modelled data; and (c) curve fitting approach to explain the peak and the amplitude. The combination of these three approaches allowed simulated growth variables from APSIM, including green cover, biomass, soil water uptake, leaf area index and yield to be compared with NDVI. Strong correlations between NDVI and simulated crop growth parameters allowed to attempt benchmarking APSIM sowing rule, as an example.http://www.regional.org.au/au/asa/2008

A discussion support model for a regional dairy-pasture system with an example from Reunion island

International audienceRéunion Island, situated in the Indian Ocean, presents a unique case study for modelling regional bio-economic parameters of the dairy industry. It is a good example of a closed system for several parameters of the model such as movement of animals, labour, consumption and available land. The existence of several agro-ecological zones from tropical to temperate, and various different types of terrain and vegetation presents another unique opportunity to study the impact of these features on the dairy industry. The present study models the dairy sector at a regional (island) level to study the impact of new or adapted agricultural policies in relation to changes in subsidy levels, price fluctuations and environmental policies (mainly nitrogen management). The model can be used to generate a number of scenarios to explore the effects of various policy measures, such as fixing the stocking rate according to EU norms, increasing or decreasing the milk subsidy, intensification (such as an increase in milk production to the allotted quota of 40 million litres/yr) and varying labour/price constraints (such as a reduction in labour hours or an increase or decrease in the milk price). The model is being utilized by the local dairy cooperative as a discussion support tool to study the implications at the regional scale of expanding the sector and assessing its economic, environmental and social impact

The vulnerability of Australian rural communities to climate variability and change: Part II—Integrating impacts with adaptive capacity

In the first paper in this series [Nelson, R., Kokic, P., Crimp, S., Martin, P., Meinke, H., Howden, S.M. (2010, this issue)], we concluded that hazard/impact modelling needs to be integrated with holistic measures of adaptive capacity in order to provide policy-relevant insights into the multiple and emergent dimensions of vulnerability. In this paper, we combine hazard/impact modelling with an holistic measure of adaptive capacity to analyse the vulnerability of Australian rural communities to climate variability and change. Bioeconomic modelling was used to model the exposure and sensitivity of Australian rural communities to climate variability and change. Rural livelihoods analysis was used as a conceptual framework to construct a composite index of adaptive capacity using farm survey data. We then show how this integrated measure of vulnerability provides policy-relevant insights into the constraints and options for building adaptive capacity in rural communities. In the process, we show that relying on hazard/impact modelling alone can lead to entirely erroneous conclusions about the vulnerability of rural communities, with potential to significantly misdirect policy intervention. We provide a preliminary assessment of which Australian rural communities are vulnerable to climate variability and change, and reveal a complex set of interacting environmental, economic and social factors contributing to vulnerabilit

Developing a common language for transdisciplinary modelling teams using a generic conceptual framework

In developing countries, problems of poverty and the environment are inextricably intertwined, with any potential resolution requiring underlying political, social and economic causes to be addressed. An integrated research approach to examine such problems should not only involve a transdisciplinary team that covers the broad scope and perspective of relevant issues, but also ensure that interactions between different issues are deliberately explored. Differences in theories, methods, terminologies and research interests of team members, often hinders integration and leads to such complex projects being fragmented by disciplines. This paper describes a template for developing a conceptual framework for a project aimed at promoting socially inclusive and sustainable agricultural intensification in West Bengal (India) and southern Bangladesh. The project involves consideration of the various climate, market, environmental, social, political and health risks that threaten the livelihoods of these rural communities. The proposed template was designed to provide a common framework that the team can readily co develop and thus overcome some of the challenges of working with transdisciplinary teams. This framework underpins the integrated modelling activities of the team