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Key researcher: Dr. Aimrun Wayayok

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Introduction

Soil sensor such as the VerisEC sensor is a useful tool in mapping apparent soil electrical conductivity (ECa) in order to identify areas of contrasting soil properties. In non-saline soils, EC values are measurements of soil texture—relative amounts of sand, silt and clay. Soil texture is directly related to both water holding capacity and cation exchange capacity which are key ingredients of productivity (Veris Technologies 2001). The crop management system known as precision farming relies on geospatial information to facilitate the treatment of small portions of fields as individual management units. Although agriculturalists have long known that fields are heterogeneous, only recently the technologies become available that allow production practices to efficiently take this variability into account. Key technologies include GPS, GIS, electronic sensors, and ruggedized computers are being used for within-field data acquisition and operation control. Although it is now relatively easy to collect geospatial data for precision farming, it is difficult to apply effectively those data in making crop management decisions. An important step in these management decisions is to understand the relationship, on a spatial basis, of crop yields to the myriad of agronomic factors which may potentially be causing yield variations.

Soil scientists collect soil samples based on soil map created by semi-detailed sampling which means only one sample from several hectares. Then, agricultural inputs were added following this prescription or action maps, while a good management needs the details of every foot step. Grid sampling involves few samples per hectare. For 50-m grid sampling, four samples will be collected for a hectare field. Using ECa sensor to show the contrast of soil properties in the field, the soil ECa across the field can be determined rapidly with detailed features of the soil, and operated by a few workers. Data can be collected for every second. Therefore, numerous data points can be presented on an ECa map.

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 Sample areas covered:

  • Assimilation of Remotely Sensed and In-Situ Measurements to Improve Hydroecological Models
  • Assimilation of Remote Sensing and In-Situ Data into Crop Models for Estimation of Yield and Soil Carbon
  • Determination of 'Best Management Practice' Effectiveness
  • Evaluation of Alternative Sources of Water Supply
  • Evaluation of Soil-Water-Air-Plant Relationships
  • Evaluation of Water Use and Soil Carbon Sequestration Under Ridge Tillage in Mali
  • Integration of Hydrologic/Water Quality Models with GIS
  • Integration of Remote Sensing Models with Crop and Hydrologic Models
  • Integration of Satellite Data, GIS, and GPS in Water Resources Management
  • Linking Crop Growth and Water Quality Models
  • Microwave Remote Sensing for Soil Moisture Estimation
  • Modeling the Dynamics of Crop Water and Nutrient Use, Stress Development and Yield
  • Optimization of Agricultural Watershed Management Systems
  • Optimization of Irrigation Management Strategies Under Climate Change
  • Remote Sensing for Weed and Invasive Species Detection in Wetlands
  • Simulation of Water Quality using Hydrologic Models
  • Stochastic Modeling of Hydrologic Processes
  • Systems Design for Microirrigation
  • Waste Treatment by Crop Irrigation and Overland Flow
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