Using factor analysis to distinguish between effective and ineffective aggregate stability indices

Several existing aggregate stability indices are commonly used to represent aggregate stability of soil. Consequently, there is a need to determine how well these common indices characterize or represent aggregate stability. The main objective of this study was to use a multivariate statistical method called factor analysis to determine the effectiveness of eight common indices in measuring aggregate stability. Eighty soil samples (Oxisols and Ultisols) were taken from soil depth of 0-150 mm and from different land uses, such as oil palm, coffee, tea, rubber, pine, fallow, vegetables, and grassland. Aggregate stability of these soils were determined by wet-sieving and water dispersion of the primary particles. Eight aggregate stability indices were used: AIA (average fraction of intact aggregates), WSA >0.3 and >0.5 (water-stable aggregates larger than size 0.3 and 0.5 mm, respectively), MWD (mean weight diameter), CR (clay ratio), WDC (water-dispersible clay), WDCS (water-dispersible clay plus silt), and TP (turbidity percentage). The factor analysis showed that all the aggregate stability indices were related to two common factors, namely, aggregate breakdown resistance and dispersion. By determining how well an aggregate stability index is correlated to either one or both these common factors, the factor analysis ranked the effectiveness of the indices as follows: WSA >0.3 = WDCS > AIA > MWD > WDC > CR. Due to the fact that WSA >0.5 is correlated very strongly with WSA >0.3, both the indices ought to be as effective as the other. The TP index, however, had a questionable efficacy as an aggregate stability index. Based on the findings of this study, it was therefore concluded that only two indices, WSA >0.3 (or WSA >0.5) and WDCS, were sufficient to represent the whole soil aggregate stability

Overcoming Microsoft Excel's weaknesses for crop model building and simulations

Using spreadsheets such as Microsoft Excel for building crop models and running simulations can be beneficial. Excel is easy to use, powerful, and versatile, and it requires the least proficiency in computer programming compared to other programming platforms. Excel, however, has several weaknesses: it does not directly support loops for iterative calculations, and it does not allow one cell to alter the contents of another cell. Thus, the objective of this study was to develop an Excel add-in, called BuildIt, that overcomes some of Excel's weaknesses by: (1) providing a loop for repetitive calculations and (2) providing several operations (called actions) typically needed in building crop models. These actions are such as for numerical integration, initialization of variables, and solving differential equations using the Runge-Kutta method, as well as for copying and manipulation of cell ranges. BuildIt was written in Excel's script language, Visual Basic for Applications (VBA), but it does not require users to program in VBA to build their models. Several examples of models were used in this article to illustrate how BuildIt implements the infrastructure in Excel, and how it can be used to build models and run model simulations. With BuildIt, users are able to use Excel to build and run their mathematical models, without requiring any knowledge in VBA

Development and validation of an unsaturated soil water flow model for oil palm

The development and use of a soil water model to predict the soil water flow and content under oil palm would be useful as a tool for more effective oil palm water management. Although many soil water models exist, none of them has been specifically developed, applied, and validated for oil palm. Consequently, the purpose of this study is to develop and validate such a model. Water flow was modelled following a one-dimensional "tipping bucket" system, and the soil profile was divided into several soil layers where the soil water and hydraulic characteristics for each layer were estimated based on the soil carbon content and soil texture. Darcy's law was applied to estimate the various soil water fluxes. The soil water model included algorithms to estimate the root water uptake and water stress response by oil palm. Raw data of measured soil water content for several soil depths (up to 90 cm) from two studies (Moraidi et al., 2015; Nur Farahin, 2013) were obtained, so that the accuracy of the soil water model could be validated by comparing simulations of soil water content with measured values. The model was satisfactorily accurate, showing similar daily trend as that observed for the measured soil water content. Goodness-of-fit indexes further indicated that the model simulations showed little to no overall model bias and with an average absolute prediction error of only 10%. Future work is to increase model accuracy by estimating the daily actual evapotranspiration instead as assumed constant in this study

Soil Aggregate Stability: Its Evaluation and Relation to Organic Matter Constituents and Other Soil Properties

The purpose of this study were: 1) to compare the aggregate stability of individual aggregate size fractions, 2) to determine the interrelationship and efficiency of several aggregate stability indices, and 3) to determine the relationship and importance of organic matter and other soil constituents on aggregate stability. To compare the aggregate stability of individual aggregate size fractions, a mathematical model was developed to estimate the breakdown of individual aggregate size fractions in the wet-sieving (using nested sieves) method. This model was validated and calibrated by comparing the estimation values to the actual aggregate breakdown values by paired sample t-test, linear regression and prediction error sum of squares. The average percentage of stable aggregates for all aggregate size fractions were represented in an index called average intact aggregates (AlA). Factor analysis was used to determine the interrelationship and efficiency of several aggregate stability indices. Aggregate stability of eight soil samples were measured with eight indices: percentage of water-stable aggregates >0.3 mm (WSA >0.3) and >0.5 mm (WSA >0.5), AlA, water-dispersible clay (WDC), waterdispersible clay and silt (WDCS), mean weight diameter after wet-sieving (MWDw), turbidity percentage (TP), and clay ratio (CR)

Aggregate stability of tropical soils in relation to their organic matter constituents and other soil properties

This study was carried out to determine the distribution of organic matter and its constituents, as well asother soil properties, in various aggregate size fractions for four soil types in Malaysia, and to investigate therelationship and relative importance of these soil properties on aggregate stability. The four soil series withcontrasting particle size distributions used were Munchong (Typic Hapludox), Melaka (Xanthic Hapludox),Rengam (Typic Paleudult), and Bungor (Typic Paleudult). The top soil, i.e. 0-15 cm of the soils of the four soilseries was sampled and analyzed for their particle size distribution, aggregation, aggregate stability, organicmatter, humic acids (HA), fulvic acids (FA), polysaccharides, functional groups of HA and FA (carboxylic,COOH, and phenolic-OH), and free Fe and Al oxides. Multiple linear regression revealed that silt, followedby free Fe oxides, fine sand, FA-OH, and HA-COOH, were the most important soil constituents to explainthe observed differences in the aggregate stability between the four soil types. Generally, as the aggregatesize decreased, the amount of clay, silt, OM, and free Fe oxides would also increase, while the aggregationand the amount of sand would decrease. As for the Rengam and Bungor series, the aggregate stability wouldgenerally increase with the decreasing aggregate size. Meanwhile, the observed differences in the amountsof HA, FA, and polysaccharides were mainly due to the differences in the soil types

Accuracy of the saxton-rawls method for estimating the soil water characteristics for mineral soils of Malaysia

The purpose of this study was to determine the accuracy of the Saxton-Rawls method in estimating the soil water characteristics of a wide range of mineral soils of Malaysia. This study found that it was necessary to calibrate the Saxton-Rawls method for the soils of Malaysia. The developed equation for calibration was Pti = a $ Pi ]1 - Pig, where Pi and Pi t are the uncalibrated and calibrated estimated values, respectively, for the soil sample no. i, and the parameter values of a were 2.225, 1.605, and 1.528 (for saturation, field capacity, and permanent wilting point) respectively. The calibrated method was validated against three independent soil data sets. The validation tests showed that the calibrated method remained stable and was more accurate than that without any calibration, by an average between 8 to 49%

Building mathematical models in excel: a guide for agriculturists

This book is for agriculturists, many of whom are either novices or non-computer programmers, about how they can build their mathematical models in Microsoft Excel. Of all modeling platforms, spreadsheets like Excel require the least proficiency in computer programming. This book introduces an Excel add-in called BuildIt (available for free as download) that shields users from having to use Excel’s VBA (Visual Basic for Applications) programming language and yet allows agriculturists to build simple to large complex models without having to learn complicated computer programming techniques or to use sophisticated Excel techniques. This book first discusses how BuildIt works and how it is used to build models. Examples range from the simple to progressively more complex mathematical models. Ultimately, readers are taught how to build a generic crop growth model from its five core components: meteorology, canopy photosynthesis, energy balance, soil water, and crop growth development. Ultimately, agriculturists will be able to build their own mathematical models in Excel and concentrate more on the science and mathematics of their modeling work rather than being distracted by the intricacies of computer programming

Modeling soil water flow in Python and Excel

Build a complete and working soil water model in either Python or Microsoft Excel. With this book, you will: understand how and why water flows in the soil and what factors affect its flow; how the soil water flow can be described as a set of mathematical equations; and how these equations can be implemented into a computer program in Python language and in Excel to simulate the vertical soil water flow. Fair knowledge in either Python or Excel is required to build the model (but knowledge in Visual Basic for Applications is not needed). Several scenarios are presented to describe how the model can be applied, and the simulation results for each scenario are discussed

Design of an object-oriented framework for modelling the partitioning of captured solar radiation and evapotranspiration in intercropping systems

x-library is a C++ object-oriented framework for modelling the partitioning of captured solar radiation and evapotranspiration in intercropping systems. The design and analysis of the x-library are done to ensure that the soil-plant-atmosphere system is categorised into classes, such as weather, microclimate, intercrop, crop, canopy, leaf, roots, soil, heat, and radiation. Meanwhile, x-library implements two kinds of solar radiation models; namely, one-dimensional (1-D), and two-dimensional (2-D) model, where irradiance varies in one dimension (vertical) and in two dimensions (vertical and horizontal), respectively. Radiation partitioning is based on weighting criteria so that a crop having the larger leaf area index and extinction coefficient would have greater share of captured radiation. Evapotranspiration partitioning is calculated using the Shuttleworth- Wallace equation. Model comparisons with a field experiment showed an overall good agreement between the simulated and measured solar radiation and transpiration values. A graphical user interface front-end for the x-library known as the x-model was also developed, primarily for non-modellers and non-programmers

A computer program to determine the soil textural class in 1-2-3 for Windows and EXCEL

Spreadsheets are now widely used for data entry and analysis. Therefore, Texture AutoLookup (TAL) is a computer program designed to work within 1–2–3 for Windows and EXCEL to determine the USDA soil textural class. TAL determines the textural class without having to repeat data entry because data is taken directly from the spreadsheet itself. Moreover, TAL works even with two particle size data or with imperfect data (that is, the sum of the three particle sizes being unequal to 100%). TAL is independent of the particle‐size analysis method, and TAL allows textural class names to be modified or be translated into another language