The paper investigates the linkages between temperature anomalies, radiative forcing and ENSO. By means of a new flexible trend modeling approach, we uncover a nonlinear linkage between radiative forcing and global temperature anomalies. The nonlinear trend closely tracks the low frequency evolution in temperature anomalies, also accounting for the mid-end 1990s level switch, the 1998-2013 "warming hiatus" and the current steepening in trend temperatures. Radiative forcing is also found to account for trend dynamics in the Southern Oscillation Index (SOI), therefore providing support for the view that global warming might affect natural variability oscillations such as ENSO, and therefore enhance their disruptive effects. We also document the feature of time-varying volatility of temperature anomalies and SOI, which is well described by an IGARCH process. By means of a new dynamic conditional correlation model (SP-DCC), we finally document the presence of time-varying conditional correlations relating temperature anomalies across various zones and SOI. The correlation pattern is found to be consistent with the effects of ENSO events in the Tropics and their teleconnections.
This paper considers the role of decision support systems to apply seasonal climate information in agriculture by documenting the development and application of the Australian Rainman computer package as a case study. Rainman aims to develop knowledge and skills for managing climate variability in agriculture by analysing effects of the El Nino/Southern Oscillation (ENSO) on rainfall to derive probability-based seasonal climate forecasts. The two main seasonal forecast tools used in Rainman are the Southern Oscillation Index (SOI) and an index of Sea Surface Temperature (SST). The Rainman version 4 prototype is due for release and has improved seasonal forecast analyses and capacity for world-wide mapping of seasonal rainfall information at district and regional scales. There has also been interest in applying seasonal forecast technology to water supplies and irrigation systems and has led to developing the StreamFlow supplement for analysis of streamflow and run-off data. A central principle used in developing Rainman has been to include only seasonal forecast methods that have been well established and accepted by the scientific community and national organizations with responsibility in seasonal climate forecasting. Thus, the participative process to define and review Rainman has been an important element in the development of Rainman as a decision support product. Peer review is a necessary part of the quality assurance process in developing decision support systems. In communicating knowledge of risk, we have found that cumulative probability distributions work well for scientists. However, in communicating with the farming community, other ways of expressing risk have been more effective such as frequency plots, pie charts, box plots and time series. Rainman analyses follow accepted scientific conventions by applying several statistical tests to seasonal forecasts so that: (a) users have some guidance regarding the statistical reliability of the forecast information, and (b) duty of care is discharged in providing forecast information to users. The Rainman case study shows that software is an effective way to provide people with climatic information because it can be detailed but easy to use, comprehensive and locally relevant. Learning to use ENSO information is maximised by combining “hands-on” learning with the software with participation in a workshop where people share ideas and experiences. Benefits of using Rainman include improved knowledge and skills about the variable climate and seasonal climate forecasts, enhanced agriculture and resource management decisions, and reduced climate risk exposure.
Contributors:Keil, Alwin, Zeller, Manfred, Wida, Anastasia, Sanim, Bunasor, Birner, Regina et al
Crop production in the tropics is subject to considerable climate variability caused by the El Nino-Southern Oscillation (ENSO) phenomenon. In Southeast Asia, El Niño causes comparatively dry conditions leading to substantial declines of crop yields. In concert with global warming, the frequency and severity of the phenomenon are likely to increase during the 21st century. Little is known about the impact of ENSO-related drought on the welfare of farm households in developing countries. This paper seeks to contribute to closing this knowledge gap with a case study from Central Sulawesi, Indonesia. Its objective is to measure household resilience towards drought periods and identify its influencing factors to deduce policy implications. Using consumption-related indicators, we develop an index measuring household drought resilience; we then identify its determinants applying an asset-based livelihood framework encompassing the household-specific level of technical efficiency in agricultural production. Most of the drought-affected farm households are forced to substantially reduce expenditures for basic necessities, whereby the drastic cuts in food expenditures are particularly alarming. Households' drought resilience is strengthened by the possession of liquid assets, access to credit, and a high level of technical efficiency in crop production. The results suggest a number of policy recommendations, namely the improvement of the farmers' access to ENSO forecasts, the provision of formal credit at moderate interest rates to facilitate consumption smoothing, and the intensification of agricultural extension efforts in view of low levels of technical efficiency found in agricultural production.
Contributors:Zilli, Julcemar Bruno, Silva, Adriana Ferreira, Campos, Silva Kanadani, Costa, Jaqueline Severino, Zilli, Julcemar Bruno et al
A gestão dos resultados das atividades agropecuárias tem se tornado um constante desafio para os empresários rurais e a sua mensuração é imprescindível para o planejamento e análises de desempenho. No caso do mercado do boi gordo não tem sido diferente, principalmente, no que se refere às oscilações apresentadas nos preços. Nesse sentido, o mercado futuro tem se traduzido em um importante instrumento para reduzir os riscos de oscilação de preços. Porém, o pecuarista precisa identificar qual a proporção da produção que deve ser protegida. Assim, o objetivo principal do presente estudo consiste em estimar a razão ótima de hedge (ROH) para os pecuaristas da região de Cuiabá/MT e Campo Grande/MS utilizando o Mecanismo de Correção de Erros (MCE) para os dados diários, semanais e mensais. Os resultados mostraram que a razão ótima de hedge é muito sensível a freqüência dos dados. A Região de Campo Grande/MS apresentou ROH superiores as de Cuiabá/MT, pois regiões com maiores volatilidades devem proteger uma parcela maior da produção. Além disso, concluiu-se que a razão ótima de hedge apresenta melhores índices quanto se insere o Mecanismo de Correção de Erros no processo de estimação confirmando que séries não estacionárias podem fornecem estimativas errôneas da razão ótima de hedge, quando não considerado as relações de co-integração entre as variáveis.----------------------------------------------The management of the results of the farming activities has if become a constant challenge for the agricultural entrepreneurs and its measure is essential for the planning and analyses of performance. In this case of the market of the fat cattle it has not been different, mainly, as for the oscillations presented in the prices. In this direction, the futures market if has translated an important instrument to reduce the risks of price fluctuation. However, the producer needs to identify to which the ratio of the production that must be protected. Thus, the main goal of the present study consists of esteem the Optimal Hedge Ratio (OHR) for the producer of the region of Cuiabá/MT and Campo Grande /MS using the Error-Correction Mechanism (ECM) for the daily, weekly and monthly data. The results had shown that the Optimal Hedge Ratio is many sensible the frequency of the data. The Region of Campo Grande/MS presented high OHR of Cuiabá/MT, therefore regions with higher volatile must protect a bigger share of the production. Moreover, concluded that the OHR presents better index how much the Error Correction Mechanism in the process of esteem is inserted confirming that not stationary series can supply mistaken estimates of the OHR, when not considered the relations of co-integration between the variables.