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Uncertainties in Soil Model Projections

Maas, Ellen DvL
http://orcid.org/0000-0002-1184-9056
http://rave.ohiolink.edu/etdc/view?acc_num=osu1587396700081549

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Environment and Natural Resources.
Soil organic matter is a critical component of soil quality and directly contributes to many processes important to agriculture and society such as water infiltration and retention, nutrient cycling, and contaminant remediation. It is also a key component of the global carbon cycle and can influence climate change. It is extremely important to understand how land management decisions contribute to its accumulation or depletion. Agroecosystem models are tools which can assist in developing the current understanding of terrestrial processes as well as predict the response of soil and crops to management decisions under future scenarios. However, all models have limitations, and there are numerous sources of uncertainty influencing their output: model inputs, model parameters, model structure, study scale, and future scenarios. This research project explored these five issues through three case studies. The first study addressed model input uncertainty with the RothC soil carbon model. Three potential evapotranspiration (PET) models were used to estimate open pan evaporation and model runs constructed with the results of each to test the sensitivity of RothC to this input element. Annual carbon input is also required, and this study explored the feasibility of calculating this input through remote sensing, using three different vegetation indices. A quadratic relationship was found between the percent bias of each PET model with the soil organic carbon (SOC) output of RothC, with SOC content increasing as bias increased. Despite considerable differences in the predictive power of vegetation indices to estimate crop yield, there were no significant differences in the C input calculated from them, nor in the results of the RothC model runs constructed with them. The second study addressed model parameter, model structure, and study scale uncertainty with RothC and the DAYCENT agroecosystem model at two sites currently growing sorghum [Sorghum bicolor (L.) Moench] in a model ensemble approach. DAYCENT was statistically calibrated and RothC manually calibrated. Then future climate data was supplied to project the SOC at each site. Despite projected decreases in yield by DAYCENT, both DAYCENT and RothC project either steady SOC content, or even increases. The third study addressed future scenario uncertainty with an extension of the second study, using only DAYCENT. The same sites and data were used over the historical periods at each site, but while the second study averaged the future climate-related data, this study constructed RothC model runs with each future climate model’s data individually. Despite substantial evidence of bias among the future climate data sets resulting in significant differences, the spread of results was small. While there were numerous limitations identified in the techniques used in this research project, there was notable resilience demonstrated in the models used to the inputs supplied, as well as some substantial sensitivity of parameters to calibration, and scenarios presented to them. This project should help highlight the need to quantify uncertainties and the variable results possible from agroecosystem models.
Rattan Lal (Advisor)
Steve Culman (Committee Member)
Desheng Liu (Committee Member)
Umakant Mishra (Committee Member)
Alvaro Montenegro (Committee Member)
212 p.
Agriculture; Environmental Science; Soil Sciences
soil carbon; modeling; modelling; uncertainties; sorghum; corn; soybeans; remote sensing

Recommended Citations

Citations

  • Maas, E. D. (2020). Uncertainties in Soil Model Projections [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587396700081549

    APA Style (7th edition)

  • Maas, Ellen. Uncertainties in Soil Model Projections. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1587396700081549.

    MLA Style (8th edition)

  • Maas, Ellen. "Uncertainties in Soil Model Projections." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587396700081549

    Chicago Manual of Style (17th edition)

osu1587396700081549
574
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This open access ETD is published by The Ohio State University and OhioLINK.