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The present study aimed to contribute to the diagnosis and advance the knowledge of the impacts of land use change and climate change on the tropical longleaf forest biome at the continental scale in South America (Biome 1 according to the WWF classification) for realizing scientific progress in the search for convincing strategies and actions by different actors for the preservation of forests in the continent. The status and climate of the area, which harbors the tropical longleaf forests of South America, were assessed. Moreover, volumetric soil moisture (VSM) was evaluated through maps and simulation using the autoregressive integrated moving average model (ARIMA). Furthermore, future climate scenarios were predicted based on El Niño–Southern Oscillation phenomena, meteorological systems, and scientific evidence, such as the shared socioeconomic pathways (SSPs) and sociopolitical dynamics evident in the region from the case analysis of the Brazilian states of Acre and Rio de Janeiro. An increase was noted in the temperature and range of precipitation variation in the biome. ARIMA analysis indicated changes of up to 0.24 m3 m−3 and an increased range of future VSM values. The December–January–February (DJF) quarter recorded the highest VSM median with the measurement scale of 0.05 to 0.44 m3 m−3, while the June–July–August (JJA) quarter recorded the lowest value. The regions of the biome with the lowest VSM values included southern Amazon (Ecuador, Peru, and the Brazilian states of Acre, Mato Grosso, Pará, and Maranhão), Brazilian Atlantic Forest, Southeast Region, and the Brazilian state of Bahia. [ABSTRACT FROM AUTHOR]

The present study aimed to contribute to the diagnosis and advance the knowledge of the impacts of land use change and climate change on the tropical longleaf forest biome at the continental scale in South America (Biome 1 according to the WWF classification) for realizing scientific progress in the search for convincing strategies and actions by different actors for the preservation of forests in the continent. The status and climate of the area, which harbors the tropical longleaf forests of South America, were assessed. Moreover, volumetric soil moisture (VSM) was evaluated through maps and simulation using the autoregressive integrated moving average model (ARIMA). Furthermore, future climate scenarios were predicted based on El Niño–Southern Oscillation phenomena, meteorological systems, and scientific evidence, such as the shared socioeconomic pathways (SSPs) and sociopolitical dynamics evident in the region from the case analysis of the Brazilian states of Acre and Rio de Janeiro. An increase was noted in the temperature and range of precipitation variation in the biome. ARIMA analysis indicated changes of up to 0.24 m3 m−3 and an increased range of future VSM values. The December–January–February (DJF) quarter recorded the highest VSM median with the measurement scale of 0.05 to 0.44 m3 m−3, while the June–July–August (JJA) quarter recorded the lowest value. The regions of the biome with the lowest VSM values included southern Amazon (Ecuador, Peru, and the Brazilian states of Acre, Mato Grosso, Pará, and Maranhão), Brazilian Atlantic Forest, Southeast Region, and the Brazilian state of Bahia.

The aim of this research are to know the result of rice product forecasting at Lamongan regency in 2016 and identify the factor influence rice production. This research was done at Lamongan by using cluster method. The total number of respondents are 60 farmers. Data were analyzed with ARIMA and multiple regression method. The results show that the rice production forecasting at Lamongan in 2016 will increas approximately 1.022.488 ton, and the factors influence rice production are land area, urea, labor and capital. If each those variables are increased, it will be able to increase the result of rice production.

We examine and evaluate the concept of risk for the financial market of telecommunications in Europe using the Value-at-Risk (VaR) method. In particular, we compare the estimates of risk between stock market indices and stock prices of telecommunications institutions in Europe. The estimates of risk are obtained as a one-step-ahead forecast using AutoRegressive Integrated Moving Average (ARIMA) analysis with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. [ABSTRACT FROM AUTHOR]

This article focuses on the ecological theory, which asserts that the climate of a region exerts top-down control on regional ecosystem patterns and processes, across space and time. Ecological processes may respond differently to climatic variation at each of these scales, depending on the region and ecosystem. Climate affects ecological processes both directly and indirectly, and complex-lagging patterns may exist. For example, interannual to decadal-scale changes in climatic regimes and ecosystem responses affect the demographic patterns of forests and meadows. Fire regimes in semiarid forests and woodlands in the southwestern U.S., in Colorado, and in Patagonia, Argentina, appear to be sensitive to climatic regimes characterized by interannual switching from wet to dry conditions on time scales of about two to four years. This wet/dry switching and low/high fire activity is often in phase with the ENSO and may be related to the importance of fine fuel for example growth, accumulation, and moisture content. In contrast, interannual switching from wet to dry is apparently less important in more productive pine forests of eastern Oregon than in the Southwest, but multiyear drought conditions may be more important to fire frequency patterns.

Nonstationary time series are frequently detrended in empirical investigations by regressing the series on time or a function of time. The effects of the detrending on the tests for causal relationships in the sense of Granger are investigated using quarterly U.S. data, The causal relationships between nominal or real GNP and M1, inferred from the Granger-Sims tests, are shown to depend very much on, among other factors, whether or not series are detrended. Detrending tends to remove or weaken causal relationships, and conversely, failure to detrend tends to introduce or enhance causal relationships. The study suggests that we need a more robust test or a better definition of causality. ABSTRACT FROM AUTHOR [ABSTRACT FROM AUTHOR]