Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Nature And Properties Of Soils

The Analysis of Variance.

Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.

Soil quality – A critical review

Preparation, modification and environmental application of biochar: A review

The soil moisture retention curve (MRC) is time consuming and expensive to measure directly. Several attempts have been made to establish a relation between readily available soil properties, like particle-size distribution, organic matter content, and bulk density, and the soil moisture retention curve. Those relationships are referred to as pedotransfer functions (PTFs). The objective of this study was to evaluate some PTFs with respect to their accuracy in predicting the soil moisture retention curve. Five widely used and four more recently developed PTFs were selected for evaluation. Seven of the selected PTFs predict moisture retention function parameters, whereas the other two predict the moisture content at certain matric potentials. In order to quantify the prediction accuracy, the mean of the absolute value of mean differences (MAMD), the mean and the standard deviation of the root of mean squared differences (MRMSD and SDRMSD, respectively), and the mean of the Pearson correlation coefficient (Mr) were used. The evaluated PTFs were finally ranked based on these validation indices. The PTFs showed good to poor prediction accuracy with MAMD values ranging from 0.0312 to 0.0603 m 3 m -3 and with MRMSDs between 0.0412 and 0.0774 m 3 m -3 , The SDRMSDs and Mrs ranged from 0.0212 to 0.0349 m 3 m -3 , and from 0.9468 to 0.9980, respectively. The validation indices computed by the PTF of Vereecken and coworkers gave the best results. Moreover, it predicts moisture retention function parameters, and therefore, this PTF is recommended most to predict the moisture retention curve from readily available soil properties.

Evaluation of Pedotransfer Functions for Predicting the Soil Moisture Retention Curve

Optimal windbreak design for wind-erosion control

Impact of mechanized logging on compaction status of sandy forest soils

The positive relationship between soil quality and crop production: A case study on the effect of farm compost application

Erosion and loss of organic carbon (OC) result in degradation of the soil surface. Rill and interrill erosion processes on a silt loam soil were examined in laboratory rainfall and flume experiments. These experiments showed that rill and interrill erosion processes have contrasting impacts on enrichment of OC in transported sediment. Rill erosion was found to be nonselective, while for interrill erosion the enrichment ratio of OC, ER OC , varied between 0.9 and 2.6 and was inversely related to the unit sediment discharge. At unit sediment discharge values >0.0017 kg s -1 m -1 , the ER OC remained equal to 1. The enrichment process was not influenced by raindrop impact. Enrichment of OC by "aggregate stripping" was found to be unimportant in our study. This was attributed to the low aggregate stability of the soil and the equal distribution of OC within the different soil aggregate classes.

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Wim Cornelis

Papers

Evaluation of Pedotransfer Functions for Predicting the Soil Moisture Retention Curve

Optimal windbreak design for wind-erosion control

Impact of mechanized logging on compaction status of sandy forest soils

The positive relationship between soil quality and crop production: A case study on the effect of farm compost application

Enrichment of Organic Carbon in Sediment Transport by Interrill and Rill Erosion Processes