Conference Paper

Aplikasi Pemetaan Tanah Digital untuk Pemetaan Sifat Tanah Menunjang Rekomendasi Pemupukan

Conference: PROSIDING SEMINAR NASIONAL TEKNOLOGI PEMUPUKAN DAN PEMULIHAN LAHAN TERDEGRADASI

ABSTRACT The better recommendation of fertilizer application can be formulated is
supported by voluminous, well distributed soil data. Yet, this effort requires much money
and consume much time. In the other hand, there is available legacy soil data that can be
used to derive soil-landcape model. This model can be used to predict and map soil
properties. This paper discusses digital soil mapping approach to provide quantitative
soil properties as base for formulating fertilizer recommendation. For a given region,
digital soil mapping is applied following 3 main steps i.e. (i) dataset preparation by
collecting previous soil data and auxiliary information, (ii) soil-landscape model development, and (iii) model application to derive digital soil properties map. The
framework was applied in Java where 301 soil profiles were re-documented and used to
develop soil-landscape model for predicting sand fraction, clay fraction, soil organic
matter, soil organic carbon, nitrogen total, pH, base saturation, and cation exchange
capacity. This model is used to create soil property map in Subang Regency. The
application of digital soil mapping technique can complement current technique in
providing soil property map to support fertilizer recommendation. Based on data and
these baseline map, soil sampling can be done efficiently, and better fertilizer
recommendation can be formulated.

Download full-text

Full-text

Available from: Yiyi Sulaeman, Jul 04, 2015
8 Followers
 · 
693 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Predictive soil mapping (PSM) can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic data base to create a predictive map. PSM is made possible by geocomputational technologies developed over the past few decades. For example, advances in geographic information science, digital terrain modeling, remote sensing, fuzzy logic has created a tremendous potential for improvement in the way that soil maps are produced. The State Factor soil-forming model, which was introduced to the western world by one of the early Presidents of the American Association of Geographers (C.F. Marbut), forms the theoretical basis of PSM. PSM research is being driven by a need to understand the role soil plays in the biophysical and biogeochemical functioning of the planet. Much research has been published on the subject in the last 20 years ( mostly outside of geographic journals) and methods have varied widely from statistical approaches (including geostatistics) to more complex methods, such as decision tree analysis, and expert systems. A geographic perspective is needed because of the inherently geographic nature of PSM.
    Progress in Physical Geography 06/2003; 27(2):171-197. DOI:10.1191/0309133303pp366ra · 3.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Conventional survey methods have efficiencies in medium to low intensity survey because they use relationships between soil properties and more readily observable environmental features as a basis for mapping. However, the implicit predictive models are qualitative, complex and rarely communicated in a clear manner. The possibility of developing an explicit analogue of conventional survey practice suited to medium to low intensity surveys is considered. A key feature is the use of quantitative environmental variables from digital terrain analysis and airborne gamma radiometric remote sensing to predict the spatial distribution of soil properties. The use of these technologies for quantitative soil survey is illustrated using an example from the Bago and Maragle State Forests in southeastern Australia. A design-based, stratified, two-stage sampling scheme was adopted for the 50,000 ha area using digital geology, landform and climate as stratifying variables. The landform and climate variables were generated using a high resolution digital elevation model with a grid size of 25 m. Site and soil data were obtained from 165 sites. Regression trees and generalised linear models were then used to generate spatial predictions of soil properties using digital terrain and gamma radiometric survey data as explanatory variables. The resulting environmental correlation models generate spatial predictions with a fine grain unmatched by comparable conventional survey methods. Example models and spatial predictions are presented for soil profile depth, total phosphorus and total carbon. The models account for 42%, 78% and 54% of the variance present in the sample respectively. The role of spatial dependence, issues of scale and landscape complexity are discussed along with the capture of expert knowledge. It is suggested that environmental correlation models may form a useful trend model for various forms of kriging if spatial dependence is evident in the residuals of the model.
    Geoderma 04/1999; DOI:10.1016/S0016-7061(98)00137-2 · 2.51 Impact Factor