No abstract

Assessing changes in the extent and intensity of cropland use is essential to understanding the processes underlying agricultural development. However, our knowledge of the spatiotemporal dynamics of cropland intensification, and how they connect to the livelihoods of rural households, is currently limited. This paper aims to quantify key components of cropland intensification in China through trend analysis of cropping intensity and land productivity over time. This information is then used to model the effects of cropland intensification on farmers’ livelihoods. We found that most croplands were under intensive use characterized by steady cropping frequency or multi‐cropping from 2001 to 2018, while the variation in cropping frequency exhibited a significant north–south spatial disparity. High cropping intensity increased land productivity. However, over 25% of the total cropland area experienced productivity improvements that were characterized as inconsistent. Our work suggests that the economic output of farming is greatly driven by land management intensity and that fertilizer use is the predominant driver of this. We also found that cropping intensity at the landscape scale showed no correlation with agricultural income, but land productivity correlated significantly with both land management intensity and rural livelihood metrics. The findings presented here highlight the importance of integrating the long‐term consistency of land productivity and rural livelihoods into the research framework of land use intensification. Doing so advances the current understanding of diverse cropland use change in China.

In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

No abstract

In recent years, China has put forward comprehensive land consolidation projects to solve problems in rural areas, such as cultivated land fragmentation, scattered spatial pattern of construction land and ecological environment pollution, and boost the rural revitalization strategy. Constructing ecological networks is important for maintaining ecological security. This study built an ecological network using morphological spatial pattern analysis (MSPA), spatial principal component analysis (SPCA) method and minimum cumulative resistance model (MCR) models to analyze the spatial and temporal characteristics and ecological security pattern. Finally, it was optimized by analyzing ecological network indices and using two methods of adding additional ecological sources and stepping stones. The results show that ecological sources and ecological corridors for three phases are located in the central and northern parts with an uneven distribution. In fact, adding new ecological sources is more efficient in balancing the ecological pattern of a study area. The ecological network indices α, β, γ and C values increased by 15.3%, 8.4%, 8.5% and 3.3%, respectively. Constructing and optimizing an ecological network is expected to provide scientific basis for small-scale landscape design, provide theoretical reference for spatial pattern optimization of comprehensive land consolidation projects and coordination of regional development and ecological protection.