Effects of erosion and deposition on particle size distribution of deposited farmland soils on the chinese loess plateau

Particle size distribution (PSD) in the soil profile is strongly related to erosion, deposition, and physical and chemical processes. Water cycling and plant growth are also affected by PSD. Material sedimented upstream of the dam constructions formed large areas of deposited farmland (DF) soils on the Chinese Loess Plateau (CLP), which has been the site of the most severe soil erosion in the world. Two DFs without tillage on the CLP were chosen to study the combined effect of erosion and check dams on PSD. Eighty-eight layers (each 10 cm thick) of filled deposited farmland (FDF) soils and 22 layers of silting deposited farmland (SDF) soils of each studied soil profile were collected and 932 soil samples were investigated using laser granulometry. The particle sizes were stratified in both DFs based on soil properties and erosion resistance. The obtained results of clay and silt fractions showed similar horizontal distribution, indicating parallel characteristics of erosion and deposition processes. Fine sand represented the largest fraction, suggesting the preferential detachment of this fraction. The most erodible range of particle sizes was 0.25-0.5 mm, followed by 0.2-0.25 mm in the studied soil profiles. The correlation between particle size and soil water contents tended to increase with increasing water contents in FDF. Due to the abundant shallow groundwater, the relationship between particle size and soil water content in SDF was lost. Further studies on PSD in the DF area are needed to enhance the conservation management of soil and water resources in this region.

Sandfly Lutzomyia longipalpis in a cutaneous leishmaniasis focus in central Colombia

Lutzomyia longipalpis, 15 other species of the genus Lutzomyia, and one species of Brumptomyia were collected in an endemic focus of cutaneous leishmaniasis in a river canyon 450 m above sea-level, in Rio Claro, Antioquia, Colombia. The presence of Lu. longipalpis is associated with the destruction of the primary forest and the development of new farmland and rural settlement in this region. The composition of species identified a different habitat for Lu. longipalpis in Colombia. Lu. yuilli and Lu. longipalpis were predominant (68.26%) followed by Lu. trapidoi, Lu. hartmani, Lu. triramula, Lu. panamensis, Lu. gomezi.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Radarsat backscattering from an agricultural scene

Orbital remote sensing in the microwave electromagnetic region has been presented as an important tool for agriculture monitoring. The satellite systems in operation have almost all-weather capability and high spatial resolution, which are features appropriated for agriculture. However, for full exploration of these data, an understanding of the relationships between the characteristics of each system and agricultural targets is necessary. This paper describes the behavior of backscattering coefficient (sigma°) derived from calibrated data of Radarsat images from an agricultural area. It is shown that in a dispersion diagram of sigma° there are three main regions in which most of the fields can be classified. The first one is characterized by low backscattering values, with pastures and bare soils; the second one has intermediate backscattering coefficients and comprises well grown crops mainly; and a third one, with high backscattering coefficients, in which there are fields with strong structures causing a kind of double bounce effect. The results of this research indicate that the use of Radarsat images is optimized when a multitemporal analysis is done making the best use of the agricultural calendar and of the dynamics of different cultures.