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

Australasia

Observed changes and impacts Ongoing climate trends have exacerbated many extreme events (very high confidence). The Australian trends include further warming and sea level rise sea level rise (SLR), with more hot days and heatwaves, less snow, more rainfall in the north, less April–October rainfall in the southwest and southeast and more extreme fire weather days in the south and east. The New Zealand trends include further warming and sea level rise (SLR), more hot days and heatwaves, less snow, more rainfall in the south, less rainfall in the north and more extreme fire weather in the east. There have been fewer tropical cyclones and cold days in the region. Extreme events include Australia’s hottest and driest year in 2019 with a record-breaking number of days over 39°C, New Zealand’s hottest year in 2016, three widespread marine heatwaves during 2016–2020, Category 4 Cyclone Debbie in 2017, seven major hailstorms over eastern Australia and two over New Zealand from 2014–2020, three major floods in eastern Australia and three over New Zealand during 2019–2021 and major fires in southern and eastern Australia during 2019–2020

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140436/1/6.2014-2301.pd

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

Frugal Innovation for Supply Chain Sustainability in SMEs: Multi-method Research Design

In this study we attempt to establish the missing links between supply chain sustainability and frugal innovation. Our study motivations stem from two facets of the emerging markets: firstly, the institutional barriers and secondly, the resource constraints. We argue that there is a synergy in the concepts of frugal innovation and sustainability in supply chains and there is a need to further explore this synergy. Furthermore, we claim that even in the wake of many success stories in the frugal innovative supply chain management practices from emerging markets such as India, there are very few, if any, attempts made to understand the implications of a sustainability oriented frugal innovations in the particular context. To address this gap we develop a model to establish the linkage between sustainable supply chains and frugal innovations. Our proposed conceptual framework depicts the hierarchy and interlinks of the identified enablers in developing sustainability oriented frugal innovative capabilities in supply chains. Furthermore, we have empirically validated our theoretical framework using survey data. We observed that most of the interpretive links are supported. These findings extend the understanding of frugal innovation for supply chain sustainability using multi-method research design, while also providing theoretically guidance to managers in the development of frugal innovation capability to achieve sustainability in supply chain in resource constrained environment

Taxonomy of delays in the implementation of hospital computerized physician order entry and clinical decision support systems for prescribing:a longitudinal qualitative study

BACKGROUND: Implementation delays are common in health information technology (HIT) projects. In this paper, we sought to explore the reasons for delays in implementing major hospital-based HIT, through studying computerized physician order entry (CPOE) and clinical decision support (CDS) systems for prescribing and to develop a provisional taxonomy of causes of implementation delays. METHODS: We undertook a series of longitudinal, qualitative case studies to investigate the implementation and adoption of CPOE and CDS systems for prescribing in hospitals in the U.K. We used a combination of semi-structured interviews from six case study sites and two whole day expert roundtable discussions to collect data. Interviews were carried out with users, implementers and suppliers of CPOE/CDS systems. We used thematic analysis to examine the results, drawing on perspectives surrounding the biography of artefacts. RESULTS: We identified 15 major factors contributing to delays in implementation of CPOE and CDS systems. These were then categorized in a two-by-two delay classification matrix: one axis distinguishing tactical versus unintended causes of delay, and the second axis illustrating internal i.e., (the adopting hospital) versus external (i.e., suppliers, other hospitals, policymakers) related causes. CONCLUSIONS: Our taxonomy of delays in HIT implementation should enable system developers, implementers and policymakers to better plan and manage future implementations. More detailed planning at the outset, considering long-term strategies, sustained user engagement, and phased implementation approaches appeared to reduce the risks of delays. It should however be noted that whilst some delays are likely to be preventable, other delays cannot be easily avoided and taking steps to minimize these may negatively affect the longer-term use of the system