Trace Elements Loss Characteristics in Runoff Discharge from Tobacco-Growing Red Soil in Sichuan Province of China

Trace elements are found in small concentrations in soil, yet plants require them for physiological functions. The runoff process leads to soil fertility loss by shifting soil particles and elements, and deposits them to a different position. However, there is a lack of information about the amount of trace elements that flow in tobacco-growing red soil during the natural rainy seasons due to runoff. In this study, runoff discharge was collected from two different soil mulching conditions (straw and no straw) at 15?, in Miyi county of Sichuan province, to evaluate the characteristics of trace elements in runoff discharge. The runoff discharge was filtered to separate water (runoff) from sediment. The concentrations of the elements were analyzed in samples obtained from 9 erosive rainfall events, with 3 replications for every sample. The considered trace elements were Zinc (Zn), Copper (Cu), and Molybdenum (Mo). In addition, the total amount of each element loss per unit area (total loss) was also calculated statistically. The results revealed different concentrations and total losses for the selected trace elements. The total loss in runoff ranged from 10.82 to 194.05 mg/ha, 0.62 to 18.91 mg/ha, and 0.32 to 2.37 mg/ha for Zn, Cu, and Mo, respectively. The total loss in sediment ranged from 54.65 to 12036.34 mg/ha, 44.74 to 5285.30 mg/ha, and 1.78 to 399.82 mg/ha for Zn, Cu, and Mo, respectively. Rainfall intensity, runoff depth, and sediment yield showed distinct positive correlations with the trace elements losses. The loss reduced with the addition of straw in the experimental area. Since each trace element showed distinct characteristics in the runoff and sediment, it is crucial to assess the loss of trace elements in runoff discharge from different agronomic practices. In turn, various sustainable practices of preventing soil fertility loss will be identified.

Effects of planting distance and depth on PTO load spectrum of a small riding-type transplanter

The aim of this study was to analyze the effects of the planting distance and depth on the power take-off(PTO)load spectrum of a small riding-type transplanter for the optimal design of the transplanter.To measure load data during actual planting operation,a load measurement system was developed using a torque sensor,a data acquisition system,and an inverter.Field experiments were conducted at four planting distances(26 cm,35 cm,43 cm,and 80 cm)and three planting depths(85 mm,105 mm,and 136 mm)in a field with similar soil conditions.The measured load data were inverted into a load spectrum using rain-flow counting and Smith-Watson-Topper(SWT)methods.The safety factor of a transplanter according to the planting conditions was analyzed using the converted load spectrum and commercial software.The load spectrum for all planting conditions showed torque ratios similar within a high cycle region of 108 to 109.The torque ratio increased when the planting depth increased and planting distance decreased in the low cycle region under less than 108 cycles.The safety factors of the PTO driving gear and the driven gear increased as the planting distance increased at all planting depths.When the planting depth decreased at the same planting distance,the safety factor of the PTO gears increased.The results of this study might provide useful information for a transplanter PTO design considering the working load according to the various planting conditions.

Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research.In the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was proposed.First,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data.Second,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps.Then,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory point.To validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories.On the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.