Land suitability evaluation using GIS-based multi-criteria decision making for bio-fuel crops cultivation in KhonKaen, Thailand

The effective Multi-criteria Decision Making (MCDM) has been adopted by this study. Several studies agreed that one of the understandable principles of the Analytical Hierarchy Process (AHP) MCDM can be able to work on multiple criteria analysis. It can deal with the data uncertainties among several criteria which is the strength point to be chosen for land suitability evaluation for biofuel crops cultivation in KhonKaen, Thailand. Due to this study aims to allocate the scarcely land availability for the most suitable crops and turn into the higher beneficial incomes for farmers. Therefore, the sixteen criterion layers that related to the selected crop requirements were analysed using the GIS based approach. These include soil texture, soil reaction, soil drainage, soil depth, soil cat-ion exchange capacity (CEC), ground water, stream water, irrigation zone, slope, elevation, aspect, erosion, soil salinity, drought, rainfall and humidity. The results shown based on the objectives in different degrees. The suitable areas were extracted by matching the potential suitable areas with the existing land use dataset. It shown the total areas of land allocations by MCDM is as 71.86% and by individual crops in the three suitable classes that the rice areas should be preserved around 32.02% while the rest areas of around 24.34%, 10.87% and 4.63% were for sugarcane, oil palm and cassava respectively. While the results of total areas by FAO is 66.76% and provided the total areas by individual crops as around 28.94%, 25.92%, 8.35% and 3.52% for rice, sugarcane, oil palm and cassava respectively. The results can be simulated by multiplying the average cost and benefit values with the suitable areas to visualise the potential budgets and potential incomes for the decision makers

Identifying potential leakage zones in an irrigation supply channel by mapping soil properties using electromagnetic induction, inversion modelling and a support vector machine

The clay alluvial plains of Namoi Valley have been intensively developed for irrigation. A condition of a license is water needs to be stored on the farm. However, the clay plain was developed from prior stream channels characterised by sandy clay loam textures that are permeable. Cheap methods of soil physical and chemical characterisations are required to map the supply channels used to move water on farms. Herein, we collect apparent electrical conductivity (ECa) from a DUALEM-421 along a 4-km section of a supply channel. We invert ECa to generate electromagnetic conductivity images (EMCI) using EM4Soil software and evaluate two-dimensional models of estimates of true electrical conductivity (σ—mS m−1) against physical (i.e., clay and sand—%) and chemical properties (i.e., electrical conductivity of saturated soil paste extract (ECe—dS m−1) and the cation exchange capacity (CEC, cmol(+) kg−1). Using a support vector machine (SVM), we predict these properties from the σ and depth. Leave-one-site-out cross-validation shows strong 1:1 agreement (Lin’s) between the σ and clay (0.85), sand (0.81), ECe (0.86) and CEC (0.83). Our interpretation of predicted properties suggests the approach can identify leakage areas (i.e., prior stream channels). We suggest that, with this calibration, the approach can be used to predict soil physical and chemical properties beneath supply channels across the rest of the valley. Future research should also explore whether similar calibrations can be developed to enable characterisations in other cotton-growing areas of Australia

A Vis-NIR spectral library to predict clay in Australian cotton growing soil

To maintain profitability of cotton growing areas of Australia, information of nutrient management and water-use efficiency are needed. In this regard, information about clay is required. This is a time-consuming and expensive laboratory analysis to undertake. An alternative is to use visible-near infrared (vis-NIR) spectroscopy, which has shown potential at different scales (e.g., local and global). Here, we predicted clay using a machine learning algorithm (Cubist) from vis-NIR acquired from topsoil (0–0.3 m) and subsurface (0.3–0.6 m) in seven cotton growing areas. The first aim was to assess the ability of soil samples from each area to predict clay independently. The second aim was to determine the ability of the samples of six areas to predict clay in an area withheld from the calibration. The third aim was to explore the potential to improve prediction using “spiking”. The fourth was to determine how much data was necessary to establish a suitable library. We conclude that establishing a calibration from each area independently was more accurate than making a calibration from six areas and predicting clay from the area withheld from the calibration. We also found that improvements in model performance were possible using spiking. When using samples from topsoil or subsurface only, over 93 samples were required to obtain an accurate library. We also conclude that a combined dataset from topsoil and subsurface samples enabled a more consistent set of data with no loss of calibration and prediction accuracy, especially when considering the availability of calibration samples

Digital soil mapping using proximal and remote sensed geophysical data at field, farm and district levels in Thailand

The Northeast of Thailand is located in a tropical climate and dominated by sandy soil types. Owing to the high rainfall (1,162 mm/annum) and low clay content, the soil has low inherent fertility (e.g. pH < 5.5) and soil re-activity (i.e. CEC < 10 cmol(+)kg-1). In order to improve productivity of the main agricultural land uses (e.g. rice), addition of amendments and fertilisers are required to increase pH and exchangeable cations, respectively. Moreover, irrigation and associated infrastructure (e.g. cannals) have been introduced to supplement the variable rainfall. However, the canals are leaking through the low reactive soil, causing recharge water to interact with an underlying sequence of rock salts. The result has been extensive secondary salinization.To better understand the nature and extent of the sandier textured soil, as well as how to manage their infertility and improve water retention in the canals, detailed soil information is required. However, the traditional survey techniques and laboratory analysis are cost and time consuming. In order to develop information to enable soil improvement in terms of adding lime to increase pH and identify leakage areas, more technologically advanced method of digital soil mapping can be considered. Specifically, use easier to measure and acquire digital data to couple this to a limited number of measured soil chemical and physical properties via the use of predictive spatially mathematical models. This thesis therefore focuses on developing digital soil maps (DSM) to create base line information of soil properties including; clay content (%), cation exchange capacity (CEC), and soil salinity.In Chapter 1 the natural resources and land uses of northeast Thailand are described with the need to improve soil condition described. Chapter 2 introduced previous literature on the theory and application of mathematical models, to relate various soil physical and chemical properties to digital data including proximal and remote sensed. In Chapter 3, DSM of clay and CEC are developed at the field scale by inverting EM38 ECa and using a quasi-3D inversion algorithm. The approach is compared with a more commonly used approach of considering DSM of each layer independently. In Chapter 4, a similar approach is used to map soil salinity (ECe) in 3-D, but across multiple fields with a comparison of different EM instruments simulated. In Chapter 5, soil salinity is mapped adjacent to a canal to identity leakage by various ECa data (i.e. EM38, Dualem-421S and EM34) and using quasi-2D inversion modelling.The results confirmed DSM approach can be effectively used to map the spatial detailed soil properties even if in the field or farm scales. Practical guidelines for fulfilment mapping quality over those various morphological properties (e.g. sandy, salinity and acidity profiles) were informatively discussed in addressing and promising the future research. Specifically, for the combination use of available proximally and remotely sensed data with mathematical models including linear regression and machine learning algorithm. Overall, the accurately and economically in producing soil base line information are utmost required to this research