Irrigation Decision Tool Development for Water Conservation for Citrus and their Application to Bioreactor Landfill Technology

Texas A&M University-Kingsville graduate students and faculty from the Texas A&M University Citrus Center in Weslaco, Texas, are working on the development of decision tools for irrigation operators and citrus growers to maximize yield but minimize water loss, which can also be applied to complex solid waste technologies such as bioreactor

Bioreactor landfills are designed to degrade organic wastes to conserve landfill space and also produce energy in the form of methane gas.Optimal soil moisture management is crucial for biological degradation of solid waste and energy production in a bioreactor landfill.  Accurate soil moisture monitoring of the movement of water through the waste containment area provides useful information for irrigation decision making. In-situ moisture content monitoring remains a challenge because of the heterogeneous nature of natural materials and especially solid wastes.  Moisture sensors proposed for bioreactor landfill use are an adaptation from devices used for agriculture irrigation applications.The in-situ moisture sensors have advantages and limitations. 

This research aims to consider these uncertainties by utilizing fuzzy logic analysis.  Its output can be used to control leachate recirculation or water addition for landfill applications.In this project, Watermark® granular matrix sensors were used in silty sand soil for citrus production in South Texas.  The sensor readings combined with evapotranspiration (ET) data were used as input parameters to a fuzzy logic irrigation decision matrix to create an output that could tell when and how much water is needed for an irrigation event. This approach can be modified for use as a strategy to irrigate bioreactor landfill areas based on in-situ moisture content measurements.

Project PI:  Dr. Kim Jones, STEI 
Students:  Irama Wesselman
Project partners:  TAMU Citrus Center, Dr. Shad Nelson, TAMUK Agronomy
Project Sponsor:  NSF sponsored CREST-RESSACA
Funding Level:  $13,200