Soil texture is one of the soil properties influencing most physical, chemical, and biological soil processes. Information on soil texture is important to support the agronomic decisions for farm management. The problem is how to provide reliable, fast and inexpensive information of soil texture in numerous soil samples and repeated measurement. The objective of this research was to generate the soil texture map based on laboratory Vis-NIR (Visible - Near Infra-Red) spectroscopy and inverse distance weighted (IDW) interpolation method. An ASD Fieldspec 3 with a spectral range from 350 nm to 2500 nm was used to measure the soil reflectance. Pipette method was used to measure the silt, clay and sand fractions. The partial least square regression (PLSR) was performed to establish the prediction model of soil texture. The predicted values were mapped and showing the information of spatial and temporal variability of soil texture.
Keywords: Vis-NIR, spectroscopy, soil texture, PLSR, IDW
The process of fertilization is one of the phase that is very important in improving the quality and quantity crops. The needs of plants nutrient on one stretch of the fact that not entirely uniform. In addition the use of chemical fertilizer continously with the doses increasing each year can upset the balance lands nutrient therefore it is necessary variable-rate liquid fertilizer applicator to set a dose of fertilizer required by the plant. This study attempts to design and test the performance of variable-rate liquid fertilizer applicator and soil nutrrient test. The applicator can be used to 4 line the soybean plant with distance cropping 40x20 cm. This applicator can control travel speed and dosage of fertilizers that were out of 4 Nozzle. Evaluation and its performance tested in the laboratorium. Discharge that was issued on PWM motor sprayer variations 40 to 100% produce discharge markedly dissimilar variations on each of the PWM sprayer. In application uniformity evaluations use the coefficient of variation (CV). The coefficient of variation of the discharge range 2.71-12.37% and there is a diversity of N, P and K soil contents in each land tested although the changes are not too different.
The visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to explore how significant the relationship between the soil spectral reflectance and soil organic matter (SOM) content. Some soil samples in Yogyakarta were taken for SOM content and spectroscopy measurement. The SOM was analyzed using Walkley and Black method, while the spectral reflectance was determined using ASD Field-spectrophotometer by scanned the sample with Vis-NIR spectrum. Pearson’s coefficient showed that there was a strong negative correlation between SOM and soil spectral of certain wavelengths. Soil with less organic matter content performed high reflectance. Keywords: Soil organic matter; Vis-NIR spectroscopy; soil reflectance; Pearson’s correlation coefficient.
Precision Farming (PF) has been developed in many countries. It is need an appropriate and integrated technology to achieve the goals, such as global positioning system (GPS) to mark the geographical coordinate, ArcGIS to map the spatial data, Variable-rate Application (VRA) to show the variability of soil properties on the field and Decision Support System (DSS) to establish the best management practices for the field operation. The objective of this research was to develop a decision support system for mapping VRA of precision fertilizing on soybean cultivation. The system generates a VRA map and shows the amount of required fertilizer for soybean according to the field and soil properties data. The DSS was developed using web application to facilitate the flexibility, and scalable access via internet. To execute the application, user requires to select the coordinates of the field and soil sample points and fertilizer dose data. The friendly user of DSS program was successfully produced with the VRA map showed the data of recommendation fertilizer as the decision support system. The outcome from this study was the application mapping VRA. Further steps are needed in order to apply this application to the farmers.
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