Arable land ecosystems are among the most important terrestrial systems. The issues of carbon sequestration and emission reductions in arable land ecosystems have received extensive attention. Countries around the world have actively issued policies to manage arable land ecosystems. At present, more than 100 countries have made carbon neutralization target commitments. Various arable land management measures and arable land planting strategies have important impacts on the carbon storage of arable land ecosystems. Research on arable land carbon is of great significance to global climate change. This study attempts to investigate the problems and deficiencies in the current research by summarizing a number of studies, including the main methods for the quantitative research of carbon sources and sinks as well as the influencing factors in these ecosystems. In this study, it is found that due to the differences of climate patterns, soil properties and management practices in arable land ecosystems, the factors affecting carbon sources and sinks are of great heterogeneity and complexity. Generally, variations in natural factors affect the carbon balance in different regions, while human management measures, such as irrigation, fertilization and the degree of agricultural mechanization, are the leading factors causing changes to carbon sources and sinks in these ecosystems. In addition, there are still great uncertainties in the evaluation of carbon sources and sinks in these ecosystems caused by different estimation models and methods. Therefore, emphasis should be placed on model parameter acquisition and method optimization in the future. This review provides a scientific basis for understanding carbon sources and sinks in arable land ecosystems, enhancing their carbon sink capacity and guiding low-carbon agriculture on arable land.

With continuous population growth and farmland decrease, the food security is seriously threatened. Farmland reclamation has been used as a means of raising the agricultural productivity and improving the ecological environment. However, the lack of reclaimed soil represents a serious problem. To verify the feasibility and effect of using large amounts of iron tailings to construct farmland, ten treatments (T1–T10) were designed to represent different soil profiles of regional normal farmland and constructed profiles using iron tailings. All treatments involving an iron tailings layer below topsoil exhibited higher soil water contents. The field capacity under T3 (20-cm iron tailings layer below cinnamon soil (b)) was 19.20% higher than that under T7 (20-cm red clay layer below cinnamon soil (b)), and the field capacity under T5 (20-cm iron tailings layer below cinnamon soil (a)) was 2.26% higher than that under T9 (20-cm red clay layer below cinnamon soil (a)). The soil water contents under T3 and T5 were almost the same as those under T7 and T9, respectively. The water-holding capacity of the 30-cm iron tailings layer (T6) was better than that of the 20-cm iron tailings layer (T2). Additionally, none of the treatments caused salt injury to maize. The maize height and stem thickness under the treatments employing iron tailings layers below topsoil were significantly greater than those in normal farmland; the maize height and stem thickness under T3 were 136.82% and 32.02% greater, respectively, than those under T7, and the values under T5 were 9.13% and 9.56% greater, respectively, than those under T9. The maize yields matched or even surpassed those in normal farmland, namely, the maize yield under T5 was equal to that under T9, and the maize yield under T3 was 12.69% higher than that under T7. In general, the application of an iron tailings layer below topsoil to construct farmland is a feasible and environmentally friendly way to realize sustainable farmland utilization and is beneficial to soil quality and crop yield improvement. Collectively, these results provide insight into the efficient utilization of iron tailings and environmental protection.

Accurate digital mapping of farmland soil organic carbon (SOC) contributes to sustainable agricultural development and climate change mitigation. Farmland landscape pattern has changed greatly under anthropogenic influence, which should be considered an environmental variable to characterize the impact of human activities on SOC. In this study, we verified the feasibility of integrating landscape patterns in SOC prediction on Lower Liaohe Plain. Specifically, ten variables (climate, topographic, and landscape pattern variables) were selected for prediction with Random Forest (RF) and Support Vector Machines (SVMs). The effectiveness of landscape metrics was verified by establishing different variable combinations: (1) natural variables, and (2) natural and landscape pattern variables. The results confirmed that landscape variables improved mapping accuracy compared with natural variables. R² of RF and SVM increased by 20.63% and 20.75%, respectively. RF performed better than SVM with smaller prediction error. Ranking of importance of variables showed that temperature and precipitation were the most important variables. The Aggregation Index (AI) contributed more than elevation, becoming the most important landscape variable. The Mean Contiguity Index (CONTIG-MN) and Landscape Contagion Index (CONTAG) also contributed more than other topographic variables. We conclude that landscape patterns can improve mapping accuracy and support SOC sequestration by optimizing farmland landscape management policies.