Poverty eradication is one of the global challenges, and land transfer provides an effective path to address farmers’ poverty; however, the effect of poverty reduction can show heterogeneity depending on the location, household, and head of household. This study employs the propensity value matching technique to compare the effects of the land transfer on the future alleviation of poverty among farm households, based on the vulnerability as expected poverty, using data from 4608 household tracking surveys. The findings point to the following: In general, rural land transfers can significantly lessen farm households’ VEP. In terms of regional variations, the positive effects of land transfers on farm households’ VEP are mainly in the west. In terms of the differences among households, it was found that land transfers contribute to lower VEP for non-poor, non-financing-constrained, and government-subsidized farm households. With regard to differences in household headship, land transfers have abating effects on the VEP of self-employed heads of farm households. The results of the study can provide a useful reference for policy-making on land management and poverty reduction among farmers

The Age-Period-Cohort Model is used in this paper to examine how farmers’ confidence has changed in response to various measures for reducing poverty, based on data from 13,559 household tracking surveys, with a view to inform rural poverty reduction policies within Targeted Poverty Reduction Strategy (TPRS). The findings indicate that: (1) Farmers who get monetary grants have significantly lower levels of confidence than farmers who do not. The difference between the ages of 18 and 70, where this issue is more noticeable, grew between 2013 and 2018. (2) Between 2010 and 2018, transfer employment was more likely than monetary handouts to increase farmers’ confidence, and this difference was particularly obvious among young people (18–45 years old) and elderly individuals (65+). (3) The confidence gap between farmers with and without medical insurance has widened over time. Farmers with medical insurance have significantly higher confidence than farmers without it. Lessons for TPRS suggest that to reduce poverty among poor groups in a way that is both stable and sustainable, poverty alleviation strategies should take psychological factors into account when evaluating their efficacy. They should also concentrate on how employment boosts self-confidence.

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use.

China’s rapid income growth and urbanization have led to significant changes in food consumption patterns, and these dietary changes may increase the demand for agricultural land for food supply. This study explores the changes in urban and rural residents’ cropland demand based on dietary data of Chinese residents from 1990 to 2020, using dietary nutritional analysis and a cropland footprint method. The results show that during the study period, the total cropland demand of rural residents decreased by 31.02%, from 61.31 million hm2 to 42.29 million hm2, while the total cropland demand of urban residents increased by 246.83%, from 22.91 million hm2 to 79.46 million hm2. The results reveal that the primary drivers of increased cropland demand are urbanization and rising animal-based dietary patterns. However, the contribution of increased agricultural productivity to decreased demand for cropland is relatively small and inadequate to compensate for the increased demand. In short, the pressure to protect arable land in China will continue to rise, and protecting existing arable land, improving cropland productivity, and making full use of international resources are the main ways to alleviate the pressure on arable land protection.