Lecture 14

Application of geo information in soil resource studies

Land Information System: GIS based land acquisition management system will provide complete information about the land. Land acquisition managements is being used for the past 3 or 4 years only. It would help in assessment, payments for private land with owner details, tracking of land allotments and possessions identification and timely resolution of land acquisition related issues.

Soil Mapping : Soil mapping provides resource information about an area. It helps in understanding soil suitability for various land use activities. It is essential for preventing environmental deterioration associated with misuse of land. GIS Helps to identify soil types in an area and to delineate soil boundaries. It is used for the identification and classification of soil. Soil map is widely used by the farmers in developed countries to retain soil nutrients and earn maximum yield.

Natural Resources Management: By the help of GIS technology the agricultural, water and forest resources can be well maintain and manage. Foresters can easily monitor forest condition. Agricultural land includes managing crop yield, monitoring crop rotation, and more. Water is one of the most essential constituents of the environment. GIS is used to analyze geographic distribution of water resources. They are interrelated, i.e. forest cover reduces the storm water runoff and tree canopy stores approximately 215,000 tons carbon. GIS is also used in afforestation.

Determine land use/land cover changes: Land cover means the feature that is covering the barren surface .Land use means the area in the surface utilized for particular use. The role of GIS technology in land use and land cover applications is that we can determine land use/land cover changes in the different areas. Also it can detect and estimate the changes in the land use/ land cover pattern within time. It enables to find out sudden changes in land use and land cover either by natural forces or by other activities like deforestation.

Agricultural Applications: GIS can be used to create more effective and efficient farming techniques. It can also analyze soil data and to determine: what are the best crop to plant?, where they should go? how to maintain nutrition levels to best benefit crop to plant?. It is fully integrated and widely accepted for helping government agencies to manage programs that support farmers and protect the environment. This could increase food production in different parts of the world so the world food crisis could be avoided.

Pedonwise soil database

Soil information of Tripura contains the soil database as detailed soil series information showing 30 parameters of site information, 17 morphological properties, 3 physical characteristics and 6 chemical properties12,14. It also shows details of mineralogical properties of various particle size fractions and soil groupings.

Concluding remarks

This article projects the need of relevant and pertinent datasets to develop a SIS for a state. In view of the global changing scenario the need of the hour is to produce a fresh group of earth scientists with specialization in soil and crop science, geology and geography with appreciable knowledge in GIS and other information technology software. They will be equipped to deal with data storage, and retrieval in a user-friendly mode for management recommendations, so that issues like land degradation, biodiversity, food security and climate change can be addressed adequately. In view of the global changing scenario with the developments of GIS and other web technologies, dissemination of spatial information is getting a paradigm shift. Natural resource information is an essential pre-requisite for monitoring and predicting global environmental change with special reference to climate. This article may not only serve as a ‘handbook’ for development purposes for the state, but may also encourage specialists in the subject to document natural resource information in a similar way.

Pedotransfer Functions for Estimation of K _s

The term pedotransfer function (PTF), coined by Bouma (1989), refers to statistical regression equations used to express relationships between soil properties. In K_s context, PTFs are used to develop relationships between Ks and more easily measured soil properties. Terminology is new, but concept is old. Many decades-old methods for Ks estimation can be considered PTFs.

Primary benefit of PTF concept?

Renewed interest in estimation of hydraulic properties, Focusing of effort in soil science community, Strong interest in PTFs mainly a result of new methods and tools for PTF development: Statistical regression techniques, Artificial neural networks, Group method of data handling, Regression tree modeling.

Considerable interest in neural network PTF of Schaap et al. (1998) for K_s estimation.Interest driven, in part, by availability of a graphical user interface (Rosetta) for implementing method.

Evaluation of PTFs for Estimating Ks

Methods

Pit excavated at each site and soil described by NRCS soil scientist. Samples from each horizon sent to NSSC Soil Survey Laboratory for physical property analysis. Field measurements of Ks obtained using constant-head well permeameter method (Amoozemeter) with five replicates per horizon. Where appropriate, horizons less than 15-cm thick were grouped to satisfy constraints of CHWP method. The 16 sites yielded 53 samples including 14 A horizons, 29 B horizons, and 10 C horizons. Relatively uniform distribution of textures with the exception of sandy clay. Estimation of Ks from physical property done using Rosetta (Schaap et al., 2001), and the methods of Ahuja et al. (1989) and Saxton et al. (1986).

        

Rosetta allows for five hierarchical levels of input data:

Textural class

Sand, silt and clay (SSC) percentages

SSC and bulk density (BD)

SSC, BD, and 33-kPa water content

SSC, BD, and 33- and 1500-kPa water contents

Method of Ahuja et al. (1989) uses effective porosity.

Method of Saxton et al. (1986) uses sand and clay percentages and total porosity

Results – Estimation using Rosetta

Results show only modest correlation between measured and Rosetta-predicted saturated hydraulic conductivity. Best estimation achieved with combination of sand, silt and clay percentages and bulk density. The use of 33- and 1500-kPa water contents did not enhance predictive ability over SSC and bulk density. Rosetta estimates were biased (rotational) towards overestimation at low Ks and underestimation at high Ks. Bias and modest correlation likely a result of the data set used for calibration of Rosetta.

Evaluation of PTFs for Estimating Ks

Results – Ks from Ahuja and Saxton Methods

Ahuja Method

Rotational bias in Ks estimates similar to that for Rosetta. Did not perform as well as Rosetta (larger RMSE) due to translational bias.

Saxton Method

Best of the three PTFs examined (lowest RMSE) due to minimal bias in Ks estimates.

Conclusions

A high-quality data set has been assembled for evaluating pedotransfer functions for Ks estimation. The results suggest that Rosetta is not well suited for estimating Ks due to modest correlation with measured values and substantial bias. Of the PTFs evaluated, the Saxton method proved to be the most effective for estimating Ks. Problems with bias in Ks estimation were most likely a result of the data sets used for PTF calibration.