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Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

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Title: Using canopy hyperspectral reflectance to predict root biomass carbon and nitrogen content

item Peterson-Munks, Brekke
item Starks, Patrick
item SADOWSKY, COOPER - Redlands Community College
item SCOTT, TREY - Redlands Community College

Submitted to: Environment and Natural Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2018
Publication Date: 3/1/2018
Citation: Peterson-Munks, B.L., Starks, P.J., Sadowsky, C., Scott, T. 2018. Using canopy hyperspectral reflectance to predict root biomass carbon and nitrogen content. Environment and Natural Resources Research. 8(1):84-93.

Interpretive Summary: A novel approach to monitoring root carbon and nitrogen of living forage. Premise: utilize remote sensing technology, normal root carbon and nitrogen sampling and analysis, to determine the amount of root carbon and nitrogen for crop health, crop longevity and inputs to soil carbon and nitrogen pools upon harvest of decomposition. This will provide a more precise value of soil carbon and nitrogen and available carbon and nitrogen for regrowth, allowing for better management practices thru reduction of chemical inputs to perennial grasses. Through two years of study a predictive model was established and tested with certainty.

Technical Abstract: Monitoring of root carbon (C) and nitrogen (N) is tedious in practice and commonly not done. Assessment of root C and N can provide vital information on plant health and soil nutrient cycling leading to better management practices by producers. Utilizing modern remote sensing technology, to monitor root C and N would remove the tedium and provide root C and N data. A novel approach utilizing remote sensing of living forage canopy to assess root C and N in grasslands of the Southern Plains was produced. This method proved to be predictive in nature for root C and N. Further study is required to improve predictive nature of model.