Effects of payments for ecosystem services programs in China on rural household labor allocation and land use: Identifying complex pathways

Identifying and monitoring of abandoned farmland in key agricultural production areas on the Qinghai‒Tibet Plateau: A case study of the Huangshui Basin

Curbing the continuous abandonment of large areas of farmland is important for meeting the global food demand and promoting agricultural and rural development. Accurate identification is the key to the effective management and utilization of abandoned farmland. The identification of abandoned land based on a long time series of remote sensing data has become rapid and effective. Therefore, a set of training and test datasets generated from invariant samples and reference sample sets is established in this paper. On this basis, the Google Earth Engine (GEE) is used to classify Landsat and Sentinel high-precision long-term remote sensing images from 2000 to 2022. In addition, a change detector based on the sliding window algorithm is proposed to extract abandoned farmland in the Huangshui Basin from 2002 to 2020, and the intensity, trend, frequency, reclamation rate and utilization efficiency are analyzed. The results revealed that the OA of land use classification in the Huangshui Basin from 2000 to 2022 was between 0.852 and 0.91, and the kappa coefficient was between 0.822 and 0.89, indicating a good classification effect. From 2002 to 2020, the accumulated abandoned farmland area in the Huangshui Basin continued to increase, showing a fluctuating upward trend, and the phenomenon of farmland abandonment and reclamation occurs repeatedly in some areas. From the overall distribution, the abandoned area gradually increased from the central region to the southeast. With the passage of time, the amount of abandoned farmland in the valley increased gradually, and the abandoned area was transferred from the high mountains to the valley area. The average annual abandonment rate of supplementary farmland was 50.45%, which was much greater than that of basic farmland. Most of the supplementary farmland could not be effectively and judiciously used, and the utilization efficiency was low. The research results provide data support for the reuse of abandoned farmland in ecologically fragile plateau areas, the formulation of targeted strategies, the implementation of timely adjustments, and the establishment of new ideas and methods for the accurate identification of abandoned farmland.

The pattern of abandoned cropland and its productivity potential in China: A four-years continuous study

The increased requirement of food production with the rising population challenges limited cultivation land in China. The abandoned cropland has high potential in grain production to ensure China's food security. However, the spatial distributions of abandoned cropland in China are understudied and therefore it is difficult to estimate its potential grain production. Our study proposed a new definition of abandoned cropland considering unique multiple cropping systems in China, and estimate the abandoned cropland distribution and grain productivity potential by using Landsat-8 and GF-1 images under deep learning technology. The area of abandoned cropland in three main grain-producing regions was approximately 1.53 million hectares during 2014-2017. The estimated images agreed with the field survey and the national agricultural statistical data with the accuracy larger than 87 %. The spatial distribution of abandoned cropland in China was scattered and a high abandonment rate observed in the Middle-lower Yangtze River Plain. Moreover, the uncultivated cropland accounted for approximately 50 % of the total area of abandoned cropland. The maximum production potential of abandoned cropland could reach 8.5 million tons, including 2.7, 2.5 and 3.3 million tons of maize, wheat and rice, respectively. The exploitation of abandoned cropland is also beneficial for additional soybean production in China. National-scale estimation of abandoned cropland in China is crucial for land use policy making and cropland protection, as well the implementation of national food security strategy.

Dynamic Evolution of Land Use/Land Cover and Its Socioeconomic Driving Forces in Wuhan, China

Human activities are considered as the main driving forces of land use/land cover (LULC) variation at city scales. Monitoring the dynamic variation of LULC and its socioeconomic driving forces helps to reveal the response of LULC change to human activities and land use policies. However, this issue remains poorly understood. In this study, the spatiotemporal transitions among different LULC types during nearly three decades in Wuhan, China, were modeled in detail using the transfer matrix method. Ten socioeconomic factors indicating the population level, economic condition and social development were selected to quantitatively explain LULC variation. Some typical policies were discussed for the LULC transitions. The results showed that construction land was detected to continuously increase, with the fastest change rate of 560.48% during the 29-year period. Farmland area significantly declined by 1855 km2, decreasing by 31.21%, contributing to 86.14% of the area increase in construction lands. To some extent, the net area increase in construction land was at the expense of farmland area. All 10 indicators considered in this study were positively correlated with the construction land area (R2 of 0.783~0.970) and negatively correlated with farmland area (R2 of 0.861~0.979). In general, social and economic development contributed considerably to urban expansion and cultivated land loss. The largest contributors were non-agricultural population and economic conditions (secondary industry output, primary industry output and local revenues). Governmental guidance and behavior were considered the original impetus for LULC transition, while the impact of land use policies and human activities on LULC transitions varied across the subperiods. These findings provide decision-making support for appropriate urban planning and efficient land use management.

Impacts of COVID-19 pandemic on the livelihoods of rural households in the community forestry landscape in the Middle Hills of Nepal

The novel coronavirus disease (COVID-19) has severely affected all sectors of the economy, and the impacts are expected to last-long. One major impact is that migrants return to their original households in rural communities due to loss of jobs. Since rural communities are highly dependent on forest and agriculture for livelihoods, an influx of return migrants likely increases the consumption of forest products and intensifies the agriculture practices, increasing the pressure on forest resources. Based on in-person interview of 215 in 2018 before the pandemic and a phone interview of the same 215 rural households in 2021 at the peak of the pandemic in Kavrepalanchowk district in Nepal, this study addresses the following research questions: (1) Does COVID-19 exert differential impacts among the socio-economic groups? (2) How do return migrants affect the rural land use? (3) Do return migrants put additional pressure on forests resources? The rare before-and-after dataset provide a precious opportunity to assess the COVID-19 impacts on the livelihoods of rural households in the community forestry landscape in the Middle Hills of Nepal. We found that the impacts of COVID-19 were severe on the households with larger family size, those belonging to the marginalized caste groups, having lower number of livestock, low wellbeing index, those who rely on daily wage-based occupation, with low level of education, and the households with return migrants. A significant number of migrants were found to return to their village of origin. As a result, there was a decrease in abandoned land and an increase in the livestock number and forest product use. These findings provide timely insights for the post-pandemic recovery efforts in better targeting needy household with limited resource in the community forestry landscape in the Middle Hills of Nepal.

Does Classification-Based Forest Management Promote Forest Restoration? Evidence from China’s Ecological Welfare Forestland Certification Program

Classification-based forest management (CFM) is generally regarded as an important political means of achieving sustainable forest development. However, in the upsurge of publicly managed forest devolution, the impact of CFM policies on forestland restoration remains uncertain and needs to be explored. This study contributes to the scant literature on this topic in China, where CFM has long been implemented based on the ecological welfare forestland (EWF) certification program. We use provincial data from China to examine the relationship between EWF-certified areas and forest restoration. Based on inter-provincial panel data from the third to the ninth consecutive forest resource inventories in China (1984–2018), we use a dynamic spatial autoregressive model to analyze the impact of forest classification management on forest restoration. The results show that, contrary to appearances, increasing EWF-certified areas promotes forest restoration. However, after controlling for other possible influencing factors, increasing EWF-certified areas plays a minimal role in promoting forest restoration and regrowth by inhibiting investment in forest management and even has a negative impact on forest restoration in the southern collective forest area.

Afforestation Subsidy Determination for Haloxylon ammodendron (C.A.Mey.) Bunge in China Based on Cost–Benefit Analysis

Subsidies are an effective instrument to encourage farmers to engage in afforestation which provides many ecosystem services. A scientific and reasonable subsidy level should reflect the demands of various stakeholders so that the subsidy scheme can be accepted by both farmers and the government. This study aimed to establish a framework for designing an afforestation subsidy to attract voluntary participation in a way that seeks to raise both farmers’ and government’s returns while reducing financial expenditure. The framework determines the optimal subsidy level by integrating direct costs, opportunity costs, and ecological benefits to conduct cost–benefit analysis over the period of afforestation. As a case study, we focus on the planting of Haloxylon ammodendron (C.A.Mey.) Bunge in Alxa, Inner Mongolia. The results show that the subsidy level required to motivate farmers is lower than the subsidy level according to the government’s willingness to pay. Under the optimal subsidy level, the government and farmers can reach a balance point that provides effective incentives for afforestation without requiring unreasonable spending. Additionally, the current subsidy is substantially lower than the recommended subsidy; there is still room for improvement in the subsidy policy. The research framework developed herein can help policy and decision makers to design afforestation subsidy schemes and contribute to ecological restoration in the future.

Understanding the Effects of China's Agro-Environmental Policies on Rural Households' Labor and Land Allocation with a Spatially Explicit Agent-Based Model

Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households' land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns show that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.