Location: Great Plains Agroclimate and Natural Resources Research
2019 Annual Report
Accomplishments
1. Parameterizing and validating APEX model for nutrient trading. The Agricultural Policy Environmental eXtender (APEX) model is the scientific basis for the Nutrient Tracking Tool (NTT), which is a user-friendly web-based computer program developed to estimate reductions in nutrient losses to the environment, associated with alternative practices. Open-source, user-friendly software was developed to automate parameterization and model evaluation of the APEX model to enable deployment of NTT nationwide by USDA Office of Environmental Markets (OEM). The initial release of NTT by USDA OEM that is now online, includes validated parameters for the Ohio and the Western Lake Erie Basin that were developed in collaboration with OEM as part of these efforts. The software was used in other project-related studies and can be used for other hydrologic and water quality modeling studies using APEX around the globe. Recommendations of data needs, appropriate methods to use within APEX, and processes in APEX model that need improvements were published and communicated to the developers. This research will help producers determine alternative agricultural production systems with least impacts on soil and water resources.
2. Performance of soil moisture sensors for irrigation assessed. Agricultural producers are increasingly using commercial soil moisture sensors to manage irrigation scheduling. There are different types of commercially available soil moisture sensors but their performance in soils with different levels of salinity and clay content has not been evaluated. The performance of Time Domain Reflectometry (TDR315), Campbell Scientific (CS655), METER Group Sensor (GS1), Spectrum (SM100), and CropX commercial soil moisture sensors at factory settings was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma with variable levels of soil salinity and clay content. It was determined that only the CS655, TDR315, and GS1 sensors measured soil moisture accurately at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. In addition, a wide range of accuracies was noted among soil moisture sensors and methods for determining soil moisture thresholds, thus making it difficult to utilize soil moisture sensors for irrigation scheduling applications without being tested and customized. Therefore, we recommend that studies like this need to be conducted under variable field conditions to evaluate the performance of new sensors being developed in order to provide guidelines on how they can be used for irrigation scheduling purposes. Determination of appropriate soil moisture sensors will help producers efficiently use limited irrigation water resources for crop production.
3. Soil disturbance increased greenhouse gas emissions. A research study found that chisel plow tillage negatively impacted greenhouse gas (GHG) emissions. Following tillage, carbon dioxide (CO2) flux from the soil was doubled and remained elevated for one week. Nitrous oxide (N2O) flux from soil also increased after chisel plow tillage while methane assimilation decreased after chisel plow tillage. This study confirmed that water-filled pore space (WFPS) was an important driver of CO2 emissions. Mechanical disturbance through tillage appeared to be the biggest driver of N2O efflux and produced a flush of soil nitrogen (N). However, soil N availability, as a result of tillage, caused a transition of assimilation to efflux for methane that was not affected by WFPS. No-till practices reduced GHG efflux, resulting in soil carbon and nitrogen conservation. Findings from this study can be used to identify or improve land management practices to reduce greenhouse gas emissions from agricultural lands, which should result in improved environmental conditions.
4. Southern Plains LTAR Grazing Experiment. The prairie ecosystems of the Southern Great Plains are important for livestock grazing and provide benefits that include habitat for avian, terrestrial and aquatic species, carbon regulation, and hydrologic function. The impact of grazing management systems (continuous (C), vs rotational (R) stocking) on many of these functions is unknown and the results reported in the literature are often contradictory. Results from a multi-year grazing study revealed that microbial biomass in the soil surface layer decreased in the C treatments but increased in R treatments; that there were no treatment differences in total, particulate, microbial mass, and mineralizable carbon and nitrogen fractions between treatments; individual calf weaning weights were higher in C than in R; available plant biomass did not differ between treatments, but concentrations of nitrogen and in vitro true digestibility were higher and concentrations of acid detergent fiber and neutral detergent fiber were lower in R than in C; and that phenology and gross primary productivity of tallgrass pastures were similar between treatments and that both treatments were resilient to drought. Findings from this study contribute directly to the goals of LTAR (i.e., development of tools and information to establish resilient and sustainable agricultural systems) to meet growing demands for feed and fiber, and improve rural prosperity.
Review Publications
Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Steiner, J.L., Gowda, P.H., Starks, P.J., Tadesse, H.K. 2018. Use of soft data for multicriteria calibration and validation of agricultural policy environmental eXtender: impact on model simulations. Journal of Soil and Water Conservation. 73(6):623-636. https://doi:10.2489/jswc.73.6.623.
Sarker, N.C., Borhan, M., Fortuna, A., Rahman, S. 2019. Understanding gaseous reduction mechanisms in swine manure resulting from nanoparticle treatments under anaerobic storage conditions. Journal of Environmental Science. https://doi.org/10.1016/j.jes.2019.03.005.
Datta, S., Taghvaeian, S., Ochsner, T.E., Moriasi, D.N., Gowda, P.H., Steiner, J.L. 2018. Performance assessment of five different soil moisture sensors under irrigated field conditions in Oklahoma. Sensors. 18(11): 1-17. https://doi:10.3390/s18113786.
Zou, C.B., Twidwell, D., Bielski, C.H., Fogarty, D.T., Mittelstet, A.R., Starks, P.J., Will, R., Zhong, Y., Acharya, B. 2018. Impact of eastern redcedar proliferation on water resources in the Great Plains USA – current state of knowledge. Water. 10(12). https://doi:10.3390/w10121768.
Joshi, S., Garbrecht, J.D., Brown, D.P. 2019. Observed spatiotemporal trends in intense precipitation events across United States: applications for stochastic weather generation. Climate. 7(3): 36. https://doi.org/10.3390/cli7030036.
Garbrecht, J.D., Zhang, X.J., Brown, D.P., Busteed, P.R. 2019. Generation of synthetic daily weather for climate change scenarios and extreme storm intensification. Environment and Natural Resources Research. 9(2). https://doi.org/10.5539/enrr.v9n2p1.
Talebizadeh, M., Moriasi, D.N., Steiner, J.L., Gowda, P.H., Tadesse, H.K., Nelson, A.M., Starks, P.J. 2018. APEXSENSUN: An open-source package in R for sensitivity analysis and model performance evaluation of APEX. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12686.
Neel, J.P., Moriasi, D.N., Brown, M.A., Belesky, D.P. 2019. Model predicted DMI, nitrogen (N) excretion and N use efficiency utilizing plasma urea nitrogen (PUN) versus values estimated in conjunction with viable dry matter intake estimates in lambs grazing pasture. Journal of Animal Science and Research. 3(1). https://doi.org/10.16966/2576-6457.123.
Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Tadesse, H.K., Steiner, J.L., Gowda, P.H., Starks, P.J. 2019. Comparing the effects of inputs for NTT and ArcAPEX interfaces on model outputs and simulation performance. Water. 11:554-580. https://doi.org/10.4236/jwarp.2019.115032.
Maina, C.W., Sang, J.K., Raude, J.M., Mutua, B.M., Moriasi, D.N. 2019. Sediment distribution and accumulation in Lake Naivasha, Kenya over the past 50 years. Lakes and Reservoirs. 24:162-172. https://doi.org/10.1111/lre.12272.
Talebizadeh, M., Moriasi, D.N., Steiner, J.L., Gowda, P.H., Tadesse, H.K., Nelson, A.M., Starks, P.J. 2019. A parallel computation tool for dynamic sensitivity and model performance analysis of APEX: Evapotranspiration modeling. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12758.
Tadesse, H.K., Moriasi, D.N., Gowda, P.H., Steiner, J.L., Talebizadeh, M., Nelson, A.M., Starks, P.J., Marek, G.W. 2019. Comparison of evapotranspiration simulation performance by APEX model in dryland and irrigated cropping systems. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12759.
Franzluebbers, A.J., Starks, P.J., Steiner, J.L. 2019. Conservation of soil organic carbon and nitrogen fractions in a tallgrass prairie in Oklahoma. Agronomy. 9(4):204. https://doi.org/10.3390/agronomy9040204.
Ma, S., Zhou, Y., Gowda, P.H., Chen, L., Steiner, J.L., Starks, P.J., Neel, J.P. 2019. Evaluating the impacts of continuous and rotational grazing on tallgrass prairie landscape using high spatial resolution imagery. Agronomy. 9(5):238. https://doi.org/10.3390/agronomy9050238.
Northup, B.K., Starks, P.J., Turner, K.E. 2019. Stocking methods and soil macronutrient distributions in southern tallgrass paddocks: Are there linkages? Agronomy. 9(6):281. https://doi.org/10.3390/agronomy9060281.
Northup, B.K., Starks, P.J., Turner, K.E. 2019. Soil macronutrient responses in diverse landscapes of southern tallgrass to two stocking methods. Agronomy. 9(6):329. https://doi.org/10.3390/agronomy9060329.
Starks, P.J., Steiner, J.L., Neel, J.P., Turner, K.E., Northup, B.K., Gowda, P.H., Brown, M.A. 2019. Assessment of the standardized precipitation and evaporation index (SPEI) as a potential management tool for grasslands. Agronomy. 9(235). https://doi.org/10.3390/agronomy9050235.
Wang, J., Xiao, X., Bajgain, R., Starks, P.J., Steiner, J.L., Doughty, R.B., Chang, Q. 2019. Estimating leaf index and aboveground biomass of grazing pastures using sentinel-1, sentinel-2 and landsat images. Journal of Photogrammetry and Remote Sensing. 154:189-201. https://doi.org/10.1016/j.isprsjprs.2019.06.007.
Zwieback, S., Bosch, D.D., Cosh, M.H., Starks, P.J., Berg, A. 2019. Vegetation-soil moisture coupling metrics from dual-polarization microwave radiometry using regularization. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111257.
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. https://doi.org/10.5539/enrr.v8n1p84.
Starks, P.J., Brown, M.A. 2018. Estimation of dry-matter intake in lambs via field-based NIR proximal sensing. Grass and Forage Science. https://doi.org/10.1111/gfs.12381.
