Simulação da produtividade de cana-de-açúcar nas regiões nordeste e sudeste do Brasil, utilizando o modelo DSSAT

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelos agrometeorológicos para previsão de atributos de qualidade em laranjas 'Valência' para o estado de São Paulo

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

A hybrid commodity price-forecasting model applied to the sugar-alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision-making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar-alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software

Seasonal climate forecasting offers potential for improving management of crop production risks in the cropping systems of NE Australia. But how is this capability best connected to management practice? Over the past decade, we have pursued participative systems approaches involving simulation-aided discussion with advisers and decision-makers. This has led to the development of discussion support software as a key vehicle for facilitating infusion of forecasting capability into practice. In this paper, we set out the basis of our approach, its implementation and preliminary evaluation. We outline the development of the discussion support software Whopper Cropper, which was designed for, and in close consultation with, public and private advisers. Whopper Cropper consists of a database of simulation output and a graphical user interface to generate analyses of risks associated with crop management options. The charts produced provide conversation pieces for advisers to use with their farmer clients in relation to the significant decisions they face. An example application, detail of the software development process and an initial survey of user needs are presented. We suggest that discussion support software is about moving beyond traditional notions of supply-driven decision support systems. Discussion support software is largely demand-driven and can compliment participatory action research programs by providing cost-effective general delivery of simulation-aided discussions about relevant management actions. The critical role of farm management advisers and dialogue among key players is highlighted. We argue that the discussion support concept, as exemplified by the software tool Whopper Cropper and the group processes surrounding it, provides an effective means to infuse innovations, like seasonal climate forecasting, into farming practice. Crown Copyright (C) 2002 Published by Elsevier Science Ltd. All rights reserved.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

An assessment of the economic impact of climate change on the Agriculture, Health and Tourism sectors in Saint Lucia

This report analyses the agriculture, health and tourism sectors in Saint Lucia to assess the potential economic impacts of climate change on the sectors. The fundamental aim of this report is to assist with the development of strategies to deal with the potential impact of climate change in Saint Lucia. It also has the potential to provide essential input for identifying and preparing policies and strategies to help advance the Caribbean subregion closer to solving problems associated with climate change and attaining individual and regional sustainable development goals. Some of the key anticipated impacts of climate change for the Caribbean include elevated air and sea-surface temperatures, sea-level rise, possible changes in extreme events and a reduction in freshwater resources. The economic impact of climate change on the three sectors was estimated for the A2 and B2 IPCC scenarios until 2050. An evaluation of various adaptation strategies for each sector was also undertaken using standard evaluation techniques. The key subsectors in agriculture are expected to have mixed impacts under the A2 and B2 scenarios. Banana, fisheries and root crop outputs are expected to fall with climate change, but tree crop and vegetable production are expected to rise. In aggregate, in every decade up to 2050, these sub-sectors combined are expected to experience a gain under climate change with the highest gains under A2. By 2050, the cumulative gain under A2 is calculated as approximately US$389.35 million and approximately US$310.58 million under B2, which represents 17.93% and 14.30% of the 2008 GDP respectively. This result was unexpected and may well be attributed to the unavailability of annual data that would have informed a more robust assessment. Additionally, costs to the agriculture sector due to tropical cyclones were estimated to be $6.9 million and $6.2 million under the A2 and B2 scenarios, respectively. There are a number of possible adaptation strategies that can be employed by the agriculture sector. The most attractive adaptation options, based on the benefit-cost ratio are: (1) Designing and implementation of holistic water management plans (2) Establishment of systems of food storage and (3) Establishment of early warning systems. Government policy should focus on the development of these adaption options where they are not currently being pursued and strengthen those that have already been initiated, such as the mainstreaming of climate change issues in agricultural policy. The analysis of the health sector placed focus on gastroenteritis, schistosomiasis, ciguatera poisoning, meningococal meningitis, cardiovascular diseases, respiratory diseases and malnutrition. The results obtained for the A2 and B2 scenarios demonstrate the potential for climate change to add a substantial burden to the health system in the future, a factor that will further compound the country’s vulnerability to other anticipated impacts of climate change. Specifically, it was determined that the overall Value of Statistical Lives impacts were higher under the A2 scenario than the B2 scenario. A number of adaptation cost assumptions were employed to determine the damage cost estimates using benefit-cost analysis. The benefit-cost analysis suggests that expenditure on monitoring and information provision would be a highly efficient step in managing climate change and subsequent increases in disease incidence. Various locations in the world have developed forecasting systems for dengue fever and other vector-borne diseases that could be mirrored and implemented. Combining such macro-level policies with inexpensive micro-level behavioural changes may have the potential for pre-empting the re-establishment of dengue fever and other vector-borne epidemic cycles in Saint Lucia. Although temperature has the probability of generating significant excess mortality for cardiovascular and respiratory diseases, the power of temperature to increase mortality largely depends on the education of the population about the harmful effects of increasing temperatures and on the existing incidence of these two diseases. For these diseases it is also suggested that a mix of macro-level efforts and micro-level behavioural changes can be employed to relieve at least part of the threat that climate change poses to human health. The same principle applies for water and food-borne diseases, with the improvement of sanitation infrastructure complementing the strengthening of individual hygiene habits. The results regarding the tourism sector imply that the tourism climatic index was likely to experience a significant downward shift in Saint Lucia under the A2 as well as the B2 scenario, indicative of deterioration in the suitability of the island for tourism. It is estimated that this shift in tourism features could cost Saint Lucia about 5 times the 2009 GDP over a 40-year horizon. In addition to changes in climatic suitability for tourism, climate change is also likely to have important supply-side effects on species, ecosystems and landscapes. Two broad areas are: (1) coral reefs, due to their intimate link to tourism, and, (2) land loss, as most hotels tend to lie along the coastline. The damage related to coral reefs was estimated at US$3.4 billion (3.6 times GDP in 2009) under the A2 scenario and US$1.7 billion (1.6 times GDP in 2009) under the B2 scenario. The damage due to land loss arising from sea level rise was estimated at US$3.5 billion (3.7 times GDP) under the A2 scenario and US$3.2 billion (3.4 times GDP) under the B2 scenario. Given the potential for significant damage to the industry a large number of potential adaptation measures were considered. Out of these a short-list of 9 potential options were selected by applying 10 evaluation criteria. Using benefit-cost analyses 3 options with positive ratios were put forward: (1) increased recommended design speeds for new tourism-related structures; (2) enhanced reef monitoring systems to provide early warning alerts of bleaching events, and, (3) deployment of artificial reefs or other fish-aggregating devices. While these options had positive benefit-cost ratios, other options were also recommended based on their non-tangible benefits. These include the employment of an irrigation network that allows for the recycling of waste water, development of national evacuation and rescue plans, providing retraining for displaced tourism workers and the revision of policies related to financing national tourism offices to accommodate the new climate realities.

Severe storms [microform] : hearing before the Subcommittee on Natural Resources, Agriculture Research, and Environment of the Committee on Science and Technology, U.S. House of Representatives, Ninety-seventh Congress, first session, October 6, 1981.

"No. 177."

Forecasting from synoptic weather charts /

Contribution from Weather Bureau.

Forecasting from synoptic weather charts /

Originally issued April 1936.

Forecasting retail values and spreads for the market basket of U.S. farm foods /

Chiefly tables.

Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km(2) were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km(2). We observe an increase of 2000 km(2) of agricultural intensification, where areas of single crops were converted to double crops during the study period. (C) 2007 Elsevier Inc. All rights reserved.

Thermodynamic and topological instability approaches for forecasting glass-forming ability in the ternary Al-Ni-Y system

A thermodynamic approach to predict bulk glass-forming compositions in binary metallic systems was recently proposed. In this approach. the parameter gamma* = Delta H-amor/(Delta H-inter - Delta H-amor) indicates the glass-forming ability (GFA) from the standpoint of the driving force to form different competing phases, and Delta H-amor and Delta H-inter are the enthalpies for-lass and intermetallic formation, respectively. Good glass-forming compositions should have a large negative enthalpy for glass formation and a very small difference for intermetallic formation, thus making the glassy phase easily reachable even under low cooling rates. The gamma* parameter showed a good correlation with GFA experimental data in the Ni-Nb binary system. In this work, a simple extension of the gamma* parameter is applied in the ternary Al-Ni-Y system. The calculated gamma* isocontours in the ternary diagram are compared with experimental results of glass formation in that system. Despite sonic misfitting, the best glass formers are found quite close to the highest gamma* values, leading to the conclusion that this thermodynamic approach can lie extended to ternary systems, serving as a useful tool for the development of new glass-forming compositions. Finally the thermodynamic approach is compared with the topological instability criteria used to predict the thermal behavior of glassy Al alloys. (C) 2007 Elsevier B. V. All rights reserved.