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This paper used the micro panel data from 2016 to 2019 of 2031 registered poor households in B Town, W County, Lu'an City of Anhui Province in China to analyze the diversified patterns and poverty alleviation effect of paired assistance based on the PSM-DID model. The empirical results show that paired assistance provided by social forces can significantly contribute to the poverty alleviation of poor households, promoting the poverty alleviation rate by 7.8%, which can be concluded through sample matching and control of relevant variables. Furthermore, based on the subsample of poor households with social assistance, we found that external social assistance subject to paired assistance can significantly improve the poverty alleviation rate of poor households by 14.26%, mainly hung on their economic base and strength of poverty alleviation. [ABSTRACT FROM AUTHOR]

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas. [ABSTRACT FROM AUTHOR]

Urbanization in Central and Western China has attracted increasing attention in the advent of new-type urbanization in China and the age of 'Global Urbanism'. Although land urbanization is at the epicenter of Chinese urbanization, its process and driving forces in counties beyond the Eastern coastal areas are less known. This paper therefore seeks to investigate the spatial expansion and driving forces of land urbanization in Feixi county, a relatively advanced county neighboring Hefei city proper in Anhui province in Central China. Based on the land-use change survey data, remote sensing interpretation data, and statistical yearbook data of Feixi county from 2002 to 2016, it is revealed that the overall scale of urban land in Feixi county increased remarkably, though with obvious temporal and geographical variants. The year 2009 appears to be the cutting line between quantity-based land urbanization and quality-based land urbanization. Land urbanization rate is sensitive to both geographical distance and traffic accessibility to Hefei city proper. Driving forces of land urbanization in Feixi county are summarized as: (1) central city expansion, (2) government-led industrial park construction, and (3) large industrial projects along traffic corridors. A better understanding of urbanization in Feixi county cannot be fulfilled without taking into account the wider spatial process in Hefei city and the Yangtze River Delta city-region. [ABSTRACT FROM AUTHOR]

This study delves into the factors influencing the willingness of rural land transfers in different terrain areas, aiming to promote the improvement of land transfer institutions and accelerate the process of scale farming. Based on rural survey data from Anhui and Qinghai provinces in China, this research uses geographical detector and Binary Logistic Model to explore the differential factors affecting the willingness of farmers to participate in land contract transfer in the first and third terrain areas of China. The study examines four dimensions, including individual characteristics, family endowments, social support strategies, and geographical environment. The findings reveal the following: (1) By comparing the mean values, standard deviations, and coefficients of variation of the data from both provinces, it is evident that the indicators of individual characteristics, family endowments, social support strategies, and geographical environment differ significantly between the two provinces. This indicates substantial disparities in the basic attributes of farmers and their living environments. (2) The single-factor explanatory power significantly influencing farmers' willingness to engage in land transfer varies considerably and is statistically significant at the 1% level. The types of interaction between two factors mainly include dual-factor enhancement, nonlinear enhancement, single-factor nonlinear attenuation, and nonlinear attenuation. (3) There are commonalities and differences in the factors that significantly influence farmers' willingness to participate in land transfer in the two provinces. Common factors influencing farmers' land transfer willingness in both provinces include: the educational level of household heads, the health status of household heads, the number of family laborers, the arable land area, the differentiation of agricultural management objectives, the proportion of agricultural operating income, labor service economy, and relocation policies. Factors showing different influences include: the age of household heads, school-age children, the number of family members engaged in different occupations, the proportion of income from off-farm employment, minimum guarantee policies credit support, location distance, and terrain undulation. Therefore, in formulating land transfer policies, the government should prioritize significant driving factors influencing farmers' decision-making behavior in different regions. It is essential to develop and implement land transfer policies tailored to local conditions with the primary goal of safeguarding the rights and interests of the principal stakeholders, thus achieving sustainable land utilization. [ABSTRACT FROM AUTHOR]

Given the importance and complexity of crop evapotranspiration estimation under drought stress, an experiment tailored for maize under drought stress was completed using six sets of large-scale weighing lysimeters at the Xinmaqiao Comprehensive Experimental Irrigation and Drainage Station, Anhui Province, China. Our aim was to analyze maize evapotranspiration under different drought conditions. Based on estimates of maize evapotranspiration under no drought stress using the dual crop coefficient approach, we optimized and calibrated basic crop coefficients Kcbini, Kcbmid, Kcbend, and the maximum crop coefficient Kcmax using a genetic algorithm. Measurements of solar radiation at the experimental station were used to derive the empirical parameters a and b from the Angstrom formula through the genetic algorithm, and then evapotranspiration was calculated for the reference crop (ET0). We then estimated the maize evapotranspiration under drought using the dual crop coefficient approach. The results indicated that a slight water deficit during the earlier stage of vegetative growth may stimulate the maize homeostatic mechanism and increase tolerance to drought stress in later growth periods. Maize evapotranspiration significantly decreased if drought stress continued into the elongation stage, and the same degree of drought stress had a greater influence on the middle and later stages of vegetative and reproductive growth. The calibrated results for Kcbini, Kcbmid, Kcbend, and Kcmax were 0.155, 1.218, 0.420 and 1.497 respectively. We calculated the root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) of maize evapotranspiration under no drought stress over the full growing season using a dual crop coefficient approach, and the results were 1.33 mm/day, 0.99 mm/day, and 1.30%, respectively, or 18.40%, 17.50%, and 91.11% lower than results using the recommended coefficients. The RMSE, MAE, and MRE results for maize under drought stress during two full growth periods were 1.18 mm/day, 0.98 mm/day, and 13.92%, respectively. These results were higher than maize without drought stress, but better than the estimated results based on FAO-56 recommended values. Therefore, maize evapotranspiration estimation under drought stress using the dual crop coefficient approach and genetic algorithm was reasonable and reliable. This study provides a theoretical basis for developing suitable regional irrigation programs and decreasing losses due to agricultural drought. [ABSTRACT FROM AUTHOR]