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DRAINAGE RESEARCH IN FARMERS' FIELDS: ANALYSIS OF
DATA This paper is about the analysis of agricultural drainage data. First it deals with frequncy analysis to judge how often certain magnitudes occur and how important they are. Secondly, it explains how to determine relations between important parameters and to what extent there is mutually a strong influence or not. To this end various types of regression analysis are used, including regression in segments. Finally drainage models and irrigation-rainfall-discharge interactions are reviewed. TABLE OF CONTENTS 1. Analysis of data 1.1 Types of analysis 1.2 Parameters 2. Standard statistical analysis 2.1 Frequency analysis Means and standard deviations Cumulative frequencies of crop yield Cumulative frequencies of water-table depth Cumulative frequencies of soil salinity Cumulative and interval frequencies of hydraulic conductivity Cumulative frequencies of rainfall Cumulative frequencies of river discharge Cumulative frequencies of drain discharge Missing data Outliers 2.2 Time-series analysis Time series (hydrograph) of drain discharge Time series (hydrograph) of water-table depth Frequency distribution in two sub-periods and mass method 2.3 Testing of differences 2.4 Spatial differences 2.5 Correlation analysis 3. Conceptual statistical analysis 3.1 Linear regression, ratio method 3.2 Linear regression, least squares method Linear relation between drain discharge and level of the water table Non-linear relation between drain discharge and water table Linear relation between crop production and depth of water table 3.3 Intermediate regression 3.4 Segmented two-variable linear regression Non-linear relation between water level and time Non-linear relation between crop production and soil salinity Non-linear relation between crop production and irrigation Non-linear relation between crop production water table 3.5 Segmented three-variable linear regression 4. Conceptual deterministic analysis 4.1 Introduction 4.2 Transient recharge-discharge relations References |
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Analysis of data in famers' fields encompasses the
regression of crop production on important growth
factors of the soil.
The figure shows a probability distribution fitted to rainfall data as the climatic factors are important for crop growth. While making assessments about the state of the agriculture it may be usefull to apply models to the relations between water table and hydraulic conductivity. Also factors like soil salinity, irrigation and drain discharge can be modelled. |