65 resultados para Agriculture Forecasting
Resumo:
During the early medieval period, Ireland was politically organised into a large number of very small kingdoms. Unlike much of Western Europe, it had not been incorporated into the Roman Empire, and as a consequence, settlement remained exclusively rural in character until the Viking period. Extensive documentary, archaeological, zooarchaeological and macro-plant evidence provides a detailed reconstruction of the livestock and arable economy of the period. Cattle ownership formed the basis of wealth as well as being an indicator of status in society, and this is reflected in its clear dominance of the livestock economy during this period. From the eighth century onwards, however, cereal production appears to grow in importance as subsistence farming gave way to the production of agricultural surplus. This is reflected in cereal diversification and in the construction of watermills and more efficient grain-drying kilns. At the same time, settlement underwent significant changes and the relative importance of cattle in some areas began to decline.
Resumo:
Climate and other environmental change presents a number of challenges for effective food safety. Food production, distribution and consumption takes place within functioning ecosystems but this backdrop is often ignored or treated as static and unchanging. The risks presented by environmental change include novel pests and diseases, often caused by problem species expanding their spatial distributions as they track changing conditions, toxin generation in crops, direct effects on crop and animal production, consequences for trade networks driven by shifting economic viability of production methods in changing environments and finally, wholesale transformation of ecosystems as they respond to novel climatic regimes.
Resumo:
In this paper, an automatic Smart Irrigation Decision Support System, SIDSS, is proposed to manage irrigation in agriculture. Our system estimates the weekly irrigations needs of a plantation, on the basis of both soil measurements and climatic variables gathered by several autonomous nodes deployed in field. This enables a closed loop control scheme to adapt the decision support system to local perturbations and estimation errors. Two machine learning techniques, PLSR and ANFIS, are proposed as reasoning engine of our SIDSS. Our approach is validated on three commercial plantations of citrus trees located in the South-East of Spain. Performance is tested against decisions taken by a human expert.
Resumo:
The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.