|Potential Class Projects|
Class Projects / Research Ideas
Why are RI and YP0 independent?
Global Warming, Review and Interpretation of Contrasting Estimates
Predicting Yield Goals using Long-Term Data (completed)
Updated NDVI-RI versus Yield-RI
warming gases from Animal Agriculture
Monitor Organic Carbon decline in a small plot within
Subsurface inorganic nitrogen
accumulation as a result of first-time tillage (implemented in a
historical site, graveyard, park area)
A. Biello, David. "The Carbon Capture Fallacy." Scientific American (2016): 58-65.
B. "Global Greenhouse Gas Emissions Data." 11 December 2012. United States Environmental Protection Agency . http://www3.epa.gov/climatechange/ghgemissions/global.html. 18 January 2016.
C. Kathleen Hogan, Michael Gibbs. "Methane." EPA (1990): 23-26.
D. R. W. Mullen, W. E. Thomason, W.R. Raun. "Estimated Increase in Atmospheric Carbon Dioxide Due to Worldwide Decrease in Soil Organic Matter ." Communication Soil Science Plant Analysis (1999): 1713-1719.
E. "Seven Environmental Threats ." Scholastic Update (1992): 6-7.
F. Singh, Hambir. "Climate Change: A Global Issue ." Special Focus on Carbon Dioxide (2010 ): 42-48.
G. "The Role of Livestock in Climate Change ." n.d. Food and Agriculture Organization of the United States . http://www.fao.org/agriculture/lead/themes0/climate/en/. 18 January 2016.
Determining the top five sources of global warming is dependent upon ones methods for grouping factors involved in global warming. Various methods can be employed to segregate sectors based upon either the type of pollution that contributes to increased greenhouse gases or, the industries themselves and the total emissions that they output. This analysis is based off of the approach of compiling all greenhouse gases produced by similar industries as opposed to entailing the top producers of specific greenhouse gases. As many agencies and parties of interest have conflicting measurements regarding greenhouse gas emission and categorization, this work seeks to find commonality amongst reported global greenhouse gas emission estimates. According to the Environmental Protection Agency of the United States of America, the following five industries represent the sectors who contribute the greatest to global warming in increasing order: buildings, transportation, industry, agriculture/forestry and electricity & heat production. The largest producing sector, electricity, emitted between 24 and 25% of global greenhouse emission in the mid-2000s. This narrowly exceeded the production of greenhouse gases from total global ag/land use, as estimates of 20-24% are generated from this sector. While this sector is extreme in its’ production of carbon dioxide, it also produces large amount of methane and nitrous oxide. Globally, the origin of 19 – 21% of the greenhouse gases emitted is a direct result of industrial manufacturing/production. While this estimate has declined recently, it still serves as an area for continued focus and improvement in lesser developed nations. Another leading producer of emissions, transportation, accounts for roughly 14% of global greenhouse gas emissions. This sector is largely targeted for its’ well known consumption of fossil fuels. Lastly, on-site generation of energy in buildings contributes around 10% of the global greenhouse gas emissions. The majority of emissions produced outside of these sectors can be attributed to indirect consequences of the energy sector. While these estimates are supported by various sources, it could be possible to view the production of greenhouse gases from agriculture or soil/environmental interactions as a more prevalent source for greenhouse gas emission. Based off of the historical loss of soil organic matter and deforestation practices, I would estimate that this industry contributes more greatly to the problem than estimated and could be closer to 30% of the total greenhouse gas production globally. With nearly 7 billion metric tons of carbon dioxide equivalent gases being produced annually, joint efforts will be needed between these major sectors and the countries abroad if outputs are to be reduced.
Enkvist, P., Naucler, T. and Rosander, J. 2007. A cost curve for greenhouse gas reduction. European Union Commission: The McKinsey Quarterly.
Eurostat. 2015. Greenhouse gas emission by industries and households. United Nations Climate Change Convention. http://ec.europa.eu/eurostat/statistics-explained/index.php/Greenhouse_gas_emissions_by_industries_and_households (accessed 19 Jan. 2016).
International Transport Forum. 2010. Reducing Transport Greenhouse Gas Emissions. http://www.internationaltransportforum.org/Pub/pdf/10GHGTrends.pdf (accessed 19 Jan. 2016).
IPCC. 2014. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment. Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. (accessed 19 Jan. 2016).
USEPA. 2014. Climate Change: Source of Greenhouse Gas Emissions. USEPA. http://www3.epa.gov/climatechange/ghgemissions/ (accessed 19 Jan. 2016).
Verge, X.P.C., Kimpe, C.De. and Desjardans, R.L. 2007. Agricultural production, greenhouse gas emissions and mitigation potential. Agricultural and Forest Meteorology. 142:255-269.
Title: 2 lb N/bu (60 lb N/bu wheat) Review and Analysis
50% of N in grain comes from soil/atmospheric deposition, 2.39% N in the grain
____ fertilizer use efficiency
60 * 0.0239 = 1.43 lbs N/bu
1.43*0.5 = 0.715 lbs N from soil
0.715/0.35 = 2.04 lbs N/bu
56 * 0.011 = 0.616 lb N/bu
0.616 * 0.5 = 0.308 lbs N from soil
0.308/0.3 = 1.0 lbs N/bu
Harvest index of 0.5 (grain N / grain + stover N)
10,000*0.012 = 120
10,000* 0.008 = 80
80 kg N/ha accumulated by V12, 80/200 = 40%
Fertilizer N Rate Survey
Corn and Wheat
Sub-meter Spatial Variability
Algorithms for in-season Nutrient Management in Cereals
The demand for improved decision making products for cereal production systems has placed added emphasis on using plant sensors in-season, and that incorporate real-time, site specific, growing environments.
The objectives of this work were to describe validated in-season sensor based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields.
A review of research programs in the Central Great Plains that have developed sensor-based nitrogen (N) recommendations for cereal crops was performed. Algorithms included multiple land-grant university, government, and industry programs.
A common thread in this review is the use of active sensors, particularly those using the Normalized Difference Vegetation Index (NDVI) for quantifying differences in fertilized and non-fertilized areas, within a specific cropping season. In-season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in-season environment-specific inputs have also been used. Early passive sensors have advanced to by-plant N fertilization using active NDVI and by-plant statistical properties. Most recently, sensor-based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. These algorithms rely on a priori understanding of crop nutrient use related to crop growth and yield.
While development and promotion of fee-based modeling approaches for nutrient management continues, algorithms using active sensors for in-season N management are available, affordable, field tested and that can be modified by producers.