At present, the potato's mechanized harvesting rate in hilly and mountainous areas is very low. The reasons for this are that in heavy soil, the separation of potato rhizomes from soil or vines is not sufficient, harvesting machinery is seriously damaged by the potato epidermis, and the harvested potato is easily buried in soil, resulting in a missed harvest. In this paper, a two-stage cleaning potato harvester with wave-type and roller-group-type separating mechanisms was designed, and its overall structure and working principle are introduced in detail. The new cleaning mechanism can increase the effective separating length and effective contact area of the potato-soil mixture so as to achieve the purpose of removing clay and heavy soil. The main separator uses a structure that combines offset waves with opposite waves and a staggered arrangement of large-small diameter straight bars. The secondary separator adopts a device combining left-hand and right-hand separating rollers. The discrete element model of the whole machine was established, and the results of the theoretical analysis were verified by simulation. The key factors affecting the harvest quality were analyzed by variance analysis and response surface analysis, and the field experiment was carried out with the rate of clean potatoes, damaged potatoes, and peeled potatoes harvested as the indexes. The field experiments showed that the machine achieved a rate of photos on or out of the earth of 98.87%, a damaged potato rate of 0.91%, and a peeled potato rate of 1.13%. The research results provide theoretical support and a technical reference for the design and optimization of potato harvesters, as well as the improvement of the potato-soil separating efficiency and harvest quality.

PurposeThe ecological damage caused by cut slopes in mountainous areas is serious, and ecological restoration is urgently needed. In this context, outside soil spray seeding (OSSS) combined with a frame beam is often used in mountainous areas of southwestern China. The aims of this study were (1) to determine the differences in soil organic carbon (SOC) and its fractions of cut slopes under different restoration methods and (2) to explore the factors influencing SOC and its fractions of cut slopes in this study area.Materials and methodsTwo cut slopes restored by different restoration methods (framed slope, using OSSS combined with a frame beam, FS; rimless slope, unassisted restoration, RS) were selected, and a nearby naturally developed slope that had not been cut was used as a reference (NS). The SOC, SOC fractions, and related soil parameters were investigated.Results and discussionCompared with RS, the available phosphorus, urease activity, amylase activity, microbial biomass carbon (MBC), and light-fraction organic carbon (LFOC) levels of FS were significantly higher. However, there were no significant differences in pH, bulk density, available nitrogen, saccharase activity, SOC, particulate organic carbon (POC), and readily oxidizable organic carbon (ROC) between FS and RS. Notably, the MBC contents of FS and RS were higher compared to that of NS, which may be due to the fact that the deep soil was exposed to the air after stripping the surface soil of the cut slopes, which facilitated the growth of aerobic microorganisms. The dissolved organic carbon (DOC) content of FS was lower than that of RS, most likely because of the higher MBC content of FS compared with RS. The main soil parameters influencing soil SOC and its fractions were available nitrogen, available phosphorus, and bulk density.ConclusionsDespite the implementation of ecological restoration measures, the SOC and its fractions of the cut slope did not fully recover, and there was a gap between the soil quality of FS and NS. Further research is needed to determine whether OSSS combined with frame beams is an effective ecological restoration method for cut slopes in this area.

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R-CV(2) and RMSECV of 0.59 and 82 kg m(-3), respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe.