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Jason P. Ackerson

Assistant Professor of Agronomy

Department: Agronomy
Phone: 765.494.5314
Fax: 765.496.2926
Office: Lilly 3-317
E-mail: jackers@purdue.edu
                                               Area of Expertise: Soil Science

Program Overview

Dr. Ackerson's research and extension program focusses on understanding the spatiotemporal dynamics of soil physical and chemical properties and developing systems to utilize this information to improve management of soil systems for improved agronomic output and ecosystem function.  His research focusses on two main pillars 1) developing and testing novel soil sensing systems for improved measurement of soil properties and 2) developing systems to integrate novel sensor information into spatiotemporal soil mapping and monitoring applications. His extension program focuses on information and technology transfer; translating data products and tools from the research sector to the commercial or public arenas.​ 

Sensor development

Low-cost and high-resolution soil data in needed for modern soil monitoring and mapping applications.  One avenue to collect this data is through the use of proximal (i.e. near or in contact with the soil) and remote (i.e. UAV, airborne, or satellite-based) sensors.   These sensors can generate large quantities of data and provide improved data for soil mapping and monitoring applications across a variety of scales.  This sensor data can be used to supplement or replace costly traditional soil sampling which in turn can improve the accuracy of soil monitoring and mapping applications.  Dr. Ackerson’s research in this area focuses on the development and testing of new proximal sensors and developing novel uses for existing remote sensing products (e.g. satellite imagery).

Soil mapping and monitoring

Soil physical, biological, and chemical properties can vary greatly in time and space.  This variability can pose a challenge to managers at a variety of scales.  For instance, at the field scale a farmer needs to manage field-level changes in soil properties to maximize agronomic profitability while minimizing environmental impact.  At the regional or watershed-scale, conservation and land use planners need to understand the spatial distribution of soil properties to develop effecting conservation interventions and monitor the success of these plans.   Regardless of scale, understating the spatial distribution of soil properties and functions (and the associated ecosystem services of these properties).  Dr. Ackerson work focuses on utilizing new and novel data sources to map and monitor the spatiotemporal distributions of soil properties across a variety of scales.  

 Specific Projects

In-situ measurement of soil properties with high depth-resolution multisensory penetrometer platform

This project focusses on sensor development and testing of a multisensory penetrometer platform.  The penetrometer can be inserted into the soil using a hydraulic soil sampling probe (Fig. 1a).  The probe is equipped with various sensors including a visible near infrared (VisNIR) spectrometer, electrical conductivity array, and insertion force sensors.   Theses sensors measure soil properties in situ and in real time at high (2-cm) depth resolution (Fig. 1b).  The penetrometer is capable of measuring soil organic carbon content, particle size (i.e. sand, silt, and clay content), cation exchange capacity, and soil water content. By collecting soil data in situ, the penetrometer system minimizes the need for soil sampling and traditional laboratory analysis.  This instrument can provide high-throughput data collection for digital soil mapping and soil assessment.  On-going research projects with the penetrometer platform include: 

  • ​Evaluation of the effectiveness of the probe to detect changes in soil physical properties (e.g. porosity, penetration resistance, soil water holding capacity, etc.)
  • Development of error-detection routines to determine real-time data acquisition errors. 
  • Sensor fusion to combine VisNIR and electrical conductivity data for improved sensor performance. 
  • Improved VisNIR library selection from a priori ancillary data.
Figure 1. The multi-sensor penetrometer platform deployed in the field (fig. 1a) and an example high-resolution soil profile mea 
 Figure 1. The multi-sensor penetrometer platform deployed in the field (fig. 1a) and an example 
high-resolution soil profile measurement generated using the penetrometer platform.

Informed soil sampling strategies for precision digital soil mapping

Soil sampling is often the most costly component of a soil mapping project.  To reduce the cost of sampling and take full advantage of limited resources, efficient soil sampling routines are needed.  While many strategies exist, few take into account the a prior information imbedded in existing soil maps.   This project focusses on the developing and validating a soil sampling scheme that uses machine learning to discover and formalize the embedded knowledge in existing soil maps.  This embedded knowledge is then used to determine the most informative landscape attributes to sample and guides sampling to the most informative locations. Specific goals of this project are to:

  •  Improve sampling efficiency through utilizing the embedded-knowledge in soil maps to direct sampling efforts.
  • Develop strategies to identify the economically optimum sampling rate from a priori information.

 

 
 Figure 2. Three-dimensional soil elevation model with optimized sampling locations.

Understanding trends in dynamic soil properties through improved spatiotemporal land use characterization

Many soil properties are dynamic and therefore are not stable in time.  These properties such as soil carbon stocks and soil health parameters often change due to changes in soil management systems (e.g. tillage regime, cover crop use, cropping system) and land use.   Understanding and monitoring the spatiotemporal trends in dynamic soil properties requires knowledge of the current and historical land use/management.  Improving monitoring of land use and management strategy requires a multifaceted approach that incorporates existing and emerging data products with smart soil monitoring and spatiotemporal statistical methods.  On-going research in this area includes:

  •  Development of automated "windshield survey" techniques for improved calibration and ground-truthing of remotely-sensed land use and management products.
  • Developing tools for remote-sensing of cover crop usage
  • Assessing various strategies to characterize and utilize spatiotemporal land use data for predicting soil carbon stocks and soil health parameters

 

 

 

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Department of Agronomy, 915 West State Street, West Lafayette, IN 47907-2053 USA, (765) 494-4773

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