• Crop modeling to assess the impact of climate change on spring wheat growth in sub-Arctic Alaska

      Harvey, Stephen K.; Zhang, Mingchu; Karlsson, Meriam; Fochesatto, Gilberto (2019-05)
      In the sub-arctic region of Interior Alaska, warmer temperatures and a longer growing season caused by climate change could make spring wheat (Triticum aestivum L.) a more viable crop. In this study, a crop model was utilized to simulate the growth of spring wheat in future climate change scenarios RCP4.5 (medium-low emission) and RCP8.5 (high emission) of Fairbanks, Alaska. In order to fulfill such simulation, in 2018 high quality crop growth datasets were collected at the Fairbanks and Matanuska Valley Experiment Farms and along with historic variety trial data, the crop model was calibrated and validated for simulating days to maturity (emergence to physiological maturity) and yield of spring wheat in Fairbanks. In the Fairbanks 1989-2018 (baseline) climate, growing season (planting to physiological maturity) average temperature and total precipitation are 15.6° C and 122 mm, respectively. In RCP4.5 2020-2049 (2035s), 2050-2079 (2065s), and 2080-2099 (2090s) projected growing season average temperature and total precipitation are 16.7° C, 17.4° C, 17.8° C and 120 mm, 112 mm, 112 mm, respectively. In RCP8.5 2035s, 2065s, and 2090s projected growing season average temperature and total precipitation are 16.8° C, 18.5° C, 19.5° C and 120 mm, 113 mm, 117 mm, respectively. Using Ingal, an Alaskan spring wheat, the model simulated days to maturity and yield in baseline and projected climate scenarios of Fairbanks, Alaska. Baseline days to maturity were 69 and yield was 1991 kg ha-1. In RCP4.5 2035s, 2065s, and 2090s days to maturity decreased to 64, 62, 60 days, respectively, and yield decreased 2%, 6%, 8%, respectively. In RCP8.5 2035s, 2065s, and 2090s days to maturity decreased to 64, 58, 55 days, respectively, and yield decreased 1%, 3%, then increased 1%, respectively. Adaptation by cultivar modification to have a growing degree day requirement of 68 days to maturity in RCP4.5 2035s and RCP8.5 2035s resulted in increased yields of 4% and 5%, respectively. Climatic parameters of temperature and precipitation per growing season day are projected to become more favorable to the growth of spring wheat. However, precipitation deficit, an indicator of water stress was found to stay similar to the baseline climate. Without adaption, days to maturity and yield are projected to decrease. Selection and/or breeding of spring wheat varieties to maintain baseline days to maturity are a priority to materialize yield increases in the area of Fairbanks, Alaska.
    • Spatial and temporal trends in vegetation index in the Bonanza Creek Experimental Forest

      Baird, Rebecca A. (2011-08)
      Climate has warmed substantially in boreal Alaska since the mid-1970s. The direct effects of rising temperatures on sub-Arctic ecosystems are already being observed in the form of drought stress, increased fire frequency and severity, and increased frequency and severity of herbivorous insect outbreaks. These effects of climate change are having a direct impact on the vegetation of the boreal forest and leading to a decreased remotely sensed normalized difference vegetation index (NDVI), which is an effective proxy for landscape-scale plant productivity and photosynthesis. Therefore, NDVI is a useful tool to examine landscape-scale changes in vegetation over time, especially in the context of known climate change. The overarching goal of my research was to assess the change in vegetation index at multiple scales over a period of 23 years at Bonanza Creek Experimental Forest. I used a combination of remote sensing and field sampling to examine trends in NDVI across landscape units, topographic classes, and plant communities. My project consists of two main parts: 1) Create a floristically-based landcover classification through field sampling and incorporating the field data into a map using satellite imagery and 2) Examine trends in the vegetation index using 11 Landsat TM and ETM+ images from 1986-2009. By using Landsat imagery and doing a landcover classification of my study area I was able define trends in NDVI to specific landscape units, topographic classes, and plant communities in the study area.