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The effective control of cropland soil erosion is urgent for all countries because of its threat to global food security. Cropland soil erosion is caused by agricultural production and driven indirectly by consumption. Analyzing the causes and preventive strategies from the consumption side is essential for soil erosion control. However, there is not yet sufficient research or practice. In this study, we estimated global cropland soil erosion with the revised universal soil loss equation, allocated it to specific types of crops, and quantified the cropland soil erosion footprint of the economies with a multiregional input-output analysis model. Our results showed that developed economies, usually importing cropland soil erosion from developing or agriculturally developed economies, are the beneficiaries in the current crop trading system. The European Union is the largest net importer, while Brazil is the largest exporter. The indirect and induced sectors are the main contributors, consuming approximately 70.48% of the total cropland soil erosion. Our results revealed the region- and product-specific contributors that could inform the reduction of global cropland soil erosion for sustainable food production and consumption.

In the mountainous area of China, the rock fragments in top soils are often removed by farmers because of their side effects on tillage. To understand hydrological processes and to assess the risk of soil erosion in purple soil areas with sloping farmlands, this study investigated the effects of rock fragment cover on surface runoff, infiltration, subsurface runoff, and erosion under field conditions using a portable rainfall simulator. Experimental twin plots (two subplots, 1 m wide by 2 m long, 32% slope) with different rock fragment covers ranging from 0 to 42% were exposed to four rainfall intensities: (I1), (I2), (I3), and (I4). Surface runoff, subsurface runoff, soil moisture, and sediment were measured. The results show that the hydrological response was influenced by the rock fragment cover and rainfall intensity. The rate of surface runoff in bare soils was affected by the rainfall intensity and increased with increasing rainfall intensity. The values of the surface runoff rates in bare soils were lower than under the I1, I2, and I3 rainfall intensities. As the rock fragment cover increased, the surface runoff rate decreased while the infiltration rate, the subsurface runoff rate and the deep percolation coefficient increased. On the other hand, when the surface runoff rates in the bare soils were higher than (in rainfall of I4), there were no significant differences in these hydrological variables among soils with different rock fragment covers. The differences in hydrological variables among soils with varying rock fragment covers decreased with increasing rainfall intensity. The rock fragment cover determined the erosive response. As the rock fragment cover increased, the sediment concentration decreased. The presence of surface rock fragments significantly reduces soil erosion, and the relationship between the soil erosion rate and rock fragment cover can be expressed by an exponential function with a high degree of reliability for different rainfall intensities. However, the effectiveness of the rock fragment cover in reducing soil erosion decreased with increasing rainfall intensity. [ABSTRACT FROM AUTHOR]

Accurate information on crop type spatial distributions is crucial for yield estimation, agroecological modeling, and agrarian policy development. In comparison with moderate resolution (10–30 m) satellite images, very high-resolution (VHR, ≤ 1 m) images are superior at describing the spatial details and complex texture features of agricultural land. However, VHR images generally suffer from insufficient spatiotemporal coverage and high spectral variation in crop type, making fine-scale crop type mapping at regional or larger scales challenging. In this study, we developed a novel data-driven residual attention U-shape network (RAUNet) to identify crop types at the regional scale based on VHR images. RAUNet was adapted from UNet by introducing a residual module and a gated attention mechanism to strengthen the multilevel representation of crop features from VHR images. We selected rice-crayfish fields (RCFs) in Hubei Province, China, as the case crop type and area to test the performance of RAUNet. Seven models, including RAUNet pan , ResUNet, AttnUNet, UNet, DeepLabV3 + and random forest at the pixel and object levels, were adopted for comparison with RAUNet. Moreover, the model transferability was evaluated over regions with different agricultural landscapes. Our results showed that RAUNet-derived RCF maps obtained an average F1-score and MCC of 0.90 and 0.85 at different cultivation stages, which significantly outperformed other methods. Additionally, RAUNet had good spatiotemporal transferability, with an average F1-score and MCC of 0.93 and 0.88 in four transfer areas. The visualization results of the attention weights indicated RAUNet's prominent capability in combining useful and relevant information from different feature levels to produce refinement predictions for the target crop type. Furthermore, the high-level features derived from RAUNet can well separate RCFs and non-RCFs over different landscapes, which were also consistent across different cultivation periods. These encouraging results suggest that RAUNet works well for fine-scale crop type mapping in smallholder farming systems, and it is an efficient method for land cover mapping based on VHR images at the regional scale. [ABSTRACT FROM AUTHOR]

Soil erosion on cropland is a result of the interaction between nature and human activities. The socioeconomic influencing factors of soil erosion have been less studied than the biophysical processes and previous studies have mainly focused on the impacts of local socioeconomic factors on soil erosion in the same region. However, since agricultural activities are densely connected to other socio-economic activities, the need for agricultural products from distant regions could potentially drive local soil erosion accompanying agricultural production. To the best of our knowledge, these telecoupling effects have not been studied. Here, we combined the Revised Universal Soil Loss Equation (RUSLE) and multiregional input-output analysis (MRIO) models to quantify the contribution of China's cross-provincial economic demand to local soil erosion at the provincial, sectoral, and supply chain levels. Our results show that a large amount of soil erosion in the southwest, northeast, and central regions is linked to the economic needs across provinces. Agriculture and food processing are the most important distant driving sectors. The driving effect of household consumption on soil erosion mainly occurs on shorter supply chains, while exports and capital formation drive soil erosion through longer chains. Our results indicate that local soil erosion management must consider the impact of distant agricultural product needs and coordinate food production and supply on a national scale to protect the ecological function of the land. [Display omitted] • Cropland soil erosion was linked to a multi-regional input-output table. • 28.19% of cropland soil erosion was driven by distant final demand. • Ten provinces had more than 1/4 of their soil erosion driven by other provinces' consumption. • Agriculture, Food Processing, and Construction contributed 72.28% telecouped erosion. • The telecoupled share increases along supply chains from 19.60% (tier 0) to 33.70% (tier 6). [ABSTRACT FROM AUTHOR]