Title page for ETD etd-10312011-162534

Type of Document Master's Thesis
Author Rojas Jimenez, Jose Pablo
Author's Email Address jrojas1@lsu.edu
URN etd-10312011-162534
Title Integrated Weather Sensor Platform and Decision Support System for Improved Sweet Potato Production
Degree Master of Science in Biological & Agricultural Engineering (M.S.B.A.E.)
Department Biological & Agricultural Engineering
Advisory Committee
Advisor Name Title
Sheffield, Ronald Committee Chair
Thomas, Daniel Committee Member
Villordon, Arthur Committee Member
  • weather data
  • irrigation scheduling
  • sweetpotato
  • soil water balance
  • Reference evapotranspiration
Date of Defense 2011-10-07
Availability unrestricted
Water management represents an essential component in all agricultural activities, where significant improvements can be achieved through the implementation of field measuring devices and irrigation scheduling models. The methods that integrate these tools may be based on information regarding the soil, crop, and weather. Evapotranspiration (ET) is one of the most important components of the soil water-balance used in modeling. A number of estimation methods have been developed to determine Reference Evapotranspiration (ETo) under various types of weather conditions. In this research, an analysis was conducted between different ETo estimation methods and ETo calculated from soil water content measurements and a soil-water budget, in Northeast Louisiana during the 2010 sweetpotato growing season. Similarly, the standardize ASCE Penman-Monteith equation was then compared to ETo equations using limited weather inputs. Additionally, a Sweetpotato Irrigation Scheduler (SPIS) based on a simple soil-water balance approach was developed to improve irrigation scheduling using weather, crop, and soil data. The modelís predictions were validated, for the critical first 30 Days after Transplanting (DAT) and for the entire growing season, against field data obtained from soil water content probes. A previously developed phenology-driven Bayesian belief network model was used to establish the timing and depth of irrigation.

Some difficulties where found during the assessment of ETo and the simulation of the soil-water content under unsaturated soil and dry weather conditions. These circumstances reduced the capacity of the soil to move water appropriately, slowing down some of the processes involved in the soil-water budget, causing a misrepresentation by the ETo equations and the irrigation scheduling model.

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