scholarly journals Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 933 ◽  
Author(s):  
Wenqing Li ◽  
John Aloysius Zinda ◽  
Zhiming Zhang
Keyword(s):  
Regression Models ◽  
Time Series Data ◽  
Ecological Factors ◽  
Landscape Fragmentation ◽  
Community Level ◽  
Landscape Patterns ◽  
Series Data ◽  
Farmer Livelihoods ◽  
Socioeconomic Patterns ◽  
Significant Factors

In China, the Returning Farmland to Forest Program (RFFP) has afforested large areas, transforming land and livelihoods. By impacting vegetation cover, it may also drive spatial pattern changes across landscapes. Most studies have focused on time series data as a means to determine the effectiveness of the program, but there is a paucity of community-level comparative studies. Twelve communities in Northwest Yunnan Province were selected to test whether the RFFP changed landscape patterns by testing the following hypotheses: with (or without) the RFFP, forest and shrubland fragmentations would decrease (or increase) and farmland fragmentation would increase (or decrease). Remote sensing images from 2000, 2010, and 2014 were used to compare the differences in landscape patterns. Survey data from 421 households were used to examine the socioeconomic and ecological factors that affect the differences in landscape fragmentation across communities. The results showed that landscape patterns and fragmentation metrics were not significantly different between communities with or without the RFFP, regardless of the class or landscape level. These communities showed consistent patterns of change in their fragmentation parameters between 2000 and 2014, with forest fragmentation decreasing and the fragmentation of farmland and the overall landscape increasing. The regression models suggest these changes were affected by the local natural conditions, socioeconomic patterns, policy implementation, and farmer livelihoods, with the proximity to market towns and elevation being significant factors. The RFFP alone did not directly drive the changes in landscape patterns for the considered region. For the new RFFP to effectively contribute to reducing fragmentation, managers of afforestation efforts should carefully consider livelihoods and biophysical factors that influence changes in landscape patterns.

scholarly journals The Analysis of the Impact of GDP, FDI, Minimum Wage on Employment in Indonesia

2019 ◽  
Vol 2 (2) ◽  
pp. 53-62
Author(s):  
Thomson Sitompul ◽  
Yansen Simangunsong
Keyword(s):  
Minimum Wage ◽  
Direct Investment ◽  
Regression Models ◽  
Time Series Data ◽  
Minimum Wages ◽  
Series Data ◽  
Economic Sector ◽  
Linear Regression Models ◽  
The Impact ◽  
Multiple Linear Regression Models

Unlike the previous study in determinant of labor absorption, which focused on economic sector and took up regional scope, this paper examines the impact of Gross Domestic Product, Foreign Direct Investment and Minimum Wages on labor absorption in Indonesia which take the national scope and aggregate labor by using secondary series of time series data (1990-2015). This study contributes to the limited literature on aggregate employment and national scope as the impact of the minimum wage, GDP, FDI in developing countries, especially in Indonesia. By using multiple linear regression models, surprisingly, we find that GDP and Minimum Wages have a positive and significant impact to increase employment while FDI  does not affect employment in Indonesia.

Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 179-190 ◽  
Author(s):  
Carl J. Markon ◽  
Michael D. Fleming ◽  
Emily F. Binnian
Keyword(s):  
Time Series ◽  
Photosynthetic Activity ◽  
Time Series Data ◽  
Landscape Patterns ◽  
Series Data ◽  
Vegetation Phenology ◽  
Regional Patterns ◽  
Landscape Processes ◽  
Composite Maps ◽  
Landscape Scales

AbstractAdvanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

scholarly journals Quantifying community responses to environmental variation from replicate time series

2021 ◽  
Author(s):  
Joseph S. Phillips ◽  
Lucas A. Nell ◽  
Jamieson C. Botsch
Keyword(s):  
Time Series ◽  
Community Composition ◽  
Mixed Models ◽  
Time Series Data ◽  
Environmental Variation ◽  
Community Change ◽  
Community Level ◽  
Series Data ◽  
Observation Error ◽  
Temporal Autocorrelation

AbstractTime-series data for ecological communities are increasingly available from long-term studies designed to track species responses to environmental change. However, classical multivariate methods for analyzing community composition have limited applicability for time series, as they do not account for temporal autocorrelation in community-member abundances. Furthermore, traditional approaches often obscure the connections between responses at the community level and those for individual taxa, limiting their capacity to infer mechanisms of community change. We show how linear mixed models that account for group-specific temporal autocorrelation and observation error can be used to infer both taxon- and community-level responses to environmental predictors from replicated time-series data. Variation in taxon-specific responses to predictors is modeled using random effects, which can be used to characterize variation in community composition. Moreover, the degree of autocorrelation is estimated separately for each taxon, since this is likely to vary due to differences in their underlying population dynamics. We illustrate the utility of the approach by analyzing the response of a predatory arthropod community to spatiotemporal variation in allochthonous resources in a subarctic landscape. Our results show how mixed models with temporal autocorrelation provide a unified approach to characterizing taxon- and community-level responses to environmental variation through time.

scholarly journals Impact of dispatcher-assisted cardiopulmonary resuscitation and myResponder mobile app on bystander resuscitation

2021 ◽  
Vol 50 (3) ◽  
pp. 212-221 ◽  
Author(s):  
Xiang Yi Wong ◽  
Qiao Fan ◽  
Nur Shahidah ◽  
Carl Ross De Souza ◽  
Shalini Arulanandam ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Cardiopulmonary Resuscitation ◽  
Time Series Data ◽  
Mobile App ◽  
Community Level ◽  
Series Data ◽  
Bystander Cardiopulmonary Resuscitation ◽  
Time Periods ◽  
The Impact ◽  
Hospital Cardiac Arrest

Introduction: Bystander cardiopulmonary resuscitation (B-CPR) is associated with improved out-ofhospital cardiac arrest survival. Community-level interventions including dispatcher-assisted CPR (DA-CPR) and myResponder were implemented to increase B-CPR. We sought to assess whether these interventions increased B-CPR. Methods: The Singapore out-of-hospital cardiac arrest registry captured cases that occurred between 2010 and 2017. Outcomes occurring in 3 time periods (Baseline, DA-CPR, and DA-CPR plus myResponder) were compared. Segmented regression of time-series data was conducted to investigate our intervention impact on the temporal changes in B-CPR. Results: A total of 13,829 out-of-hospital cardiac arrest cases were included from April 2010 to December 2017. Higher B-CPR rates (24.8% versus 50.8% vs 64.4%) were observed across the 3 time periods. B-CPR rates showed an increasing but plateauing trend. DA-CPR implementation was significantly associated with an increased B-CPR (level odds ratio [OR] 2.26, 95% confidence interval [CI] 1.79–2.88; trend OR 1.03, 95% CI 1.01–1.04), while no positive change was detected with myResponder (level OR 0.95, 95% CI 0.82–1.11; trend OR 0.99, 95% CI 0.98–1.00). Conclusion: B-CPR rates in Singapore have been increasing alongside the implementation of community-level interventions such as DA-CPR and myResponder. DA-CPR was associated with improved odds of receiving B-CPR over time while the impact of myResponder was less clear.

scholarly journals Factors Influencing Indonesia’s Cashew Export Volume

Agriekonomika ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 205-214
Author(s):  
Oni Ringgu Lero ◽  
Agnes Quartina Pudjiastuti ◽  
Sumarno Sumarno
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Influencing Factors ◽  
Regression Models ◽  
Time Series Data ◽  
Series Data ◽  
Factors Influencing ◽  
Multiple Regression Models ◽  
Export Volume ◽  
Per Capita

Cashews contribute significantly to the Indonesian economy because it is one of the exporting countries. However, volume of exports tends to fluctuate, so it is necessary to identify the influencing factors. This study aims to analyze volume of Indonesian cashew exports and its determinants. Time series data for 8 variables during 1985–2016 were analyzed descriptively by multiple regression models. The results again show fluctuations in export volume and value over 1985–2016 period. Lowest export volume occurred in 1989, but its value was in 1985. Highest export volume and value occurred in 2015. National cashew export volume depends on the domestic cashew price, exchange rate and income per capita. Peanuts and coffee have a complementary relationship with cashews, while sugar has a substitution relationship with this commodity. Cashews are an inferior goods.

scholarly journals Pengaruh penanaman modal dalam negeri, upah minimum dan belanja modal terhadap daya serap tenaga kerja di Provinsi Jambi

2019 ◽  
Vol 14 (1) ◽  
pp. 37-44
Author(s):  
Rizki Kenraraswati ◽  
M. Syurya Hidayat ◽  
Yohanes Vyn Amzar
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Minimum Wage ◽  
Regression Models ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Series Data ◽  
Domestic Investment ◽  
Average Growth ◽  
Multiple Regression Models

The study aims to analyze the effect of domestic investment (PMDN), minimum wage (UMP) and capital expenditure (BM) on employment absorption in Jambi Province. The data used is time series data of Jambi Province during the period 2000-2016. Data were analyzed descriptively as well as multiple regression models. The results of the study found that: 1) the average growth of employment is 3.11percent per year, domestic investment is 11.67 percent per year, UMP is 16.44 percent per year and capital expenditure is 20.00 percent per year; 2) Simultaneously PMDN, UMP and BM have a significant effect on employment in Jambi Province. Partially the BM variable does not have a significant effect while the PMDN and UMP variables have a significant effect on employment in Jambi Province.

Prediction in Two­Equation Linear Regression Models

2005 ◽  
Vol 57 (3-4) ◽  
pp. 195-208
Author(s):  
Amitava Dey ◽  
V. K. Sharma ◽  
Himadri Ghosh
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Series Data ◽  
Linear Regression Models ◽  
Error Terms ◽  
Best Linear Unbiased

In regression models using time series data, the errors are generally correlated. The sample residuals contain useful information for predicting post­sample observations. This information, which is generally ignored, has been exploited here in deriving the best linear unbiased predictors in a 2­equation linear regression model. The gain in efficiency of the proposed predictors over the usual generalized least ­ squares predictors has been obtained and the particular case when error terms in the two equations follow AR(l) process has also been disscussed.

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