Challenges and Opportunities for Mitigating Nitrous Oxide Emissions from Fertilized Cropping Systems

Nitrous oxides are a potent greenhouse gas emitted from a multitude of sources.  Agricultural processes constitute a significant portion of these emissions, and attempts have been made at reducing nitrous oxide.  This paper intends to be a thorough review of the potential strategies and future research needs specific to nitrous oxide emissions by focusing on the management of individual fertilized cropping systems.   Venterea et al. (2012) investigate methods in reducing nitrous oxide emissions, and goes into detail about what has and hasn’t worked, as well as why.  What follows is a summary of this investigation as well as recommendations that the authors have in reducing nitrous oxide emissions. — Anthony Li
Venterea, R. T., Halvorson, A. D., Kitchen, N., Liebig, M. A., Cavigelli, M. A., Del Grosso, S. J., Motavalli, P. P., Nelson, K. A., Spokas, K. A., Singh, B. P., Stewart, C. E., Ranaivoson, A., Strock, J., Collins, H. 2012. Challenges and opportunities for mitigating nitrous oxide emissions from fertilized cropping systems.  Frontiers in Ecology and the Environment 10. 10: 562-570

Nitrous oxide emissions are the product of several processes occurring in the soil, which include nitrification, nitrifier-denitrification, chemo-denitrification, and denitrification.  All these processes are exacerbated in crop systems, as the addition of nitrogen fertilizers provide the necessary ingredients.  Because these processes can occur under a range of soil conditions, optimizing soil conditions to reduce nitrous oxides production can be difficult and may not be feasible.  Nitrous oxide is of particular concern, as it is 300 times more potent as a greenhouse gas than carbon dioxide, and is expected to increase by 2% per year through 2015.  Experts agree that the main challenge in reducing agroecosystem nitrogen losses and nitrous oxide emissions is to maximize the amount of nitrogen fertilizer that is actually used by the crops, or the optimization of nitrogen use efficiency (NUE).  The authors investigate current methods of optimizing NUE, and how they can be improved upon.
The synchronization of the application of nitrogen fertilizers with the demand for nitrogen in plants holds a lot of potential in improving NUE.  Currently, large amounts of nitrogen fertilizers are applied before the growing season, resulting in lower than 40-50% recovery in crops while contributing to unnecessary nitrous oxide production.  The nitrogen that is not recovered by plants is often moved to downwind/stream ecosystems where it is still converted into nitrous oxides.  However, the timing of fertilizer use not only has to be in sync with crop demands, but also during appropriate climatic conditions.  In past cases, applying nitrogen fertilizers during the growing seasons in warmer or wetter conditions has led to an increased amount of nitrous oxides emitted.  With this in mind, synchronizing fertilizer application will require the need for accurate systems that can predict nutrient demand in crops while accounting for climatic conditions.  It’s also common for large amounts of nitrous oxides to be emitted in response to management practices and climatic events.  We can address these emissions by investigating these practices and events individually and seeing how they affect nitrous oxides production.  On a broader scale, we can reduce nitrous oxide emissions from soils overall by using smaller and more frequent nitrogen applications, which would lessen fertilizer-induced pulses of nitrous oxides emissions, and by using carbon rich residues for short-term nitrogen immobilization, which can reduce pulses of nitrous oxides emissions during the decomposition of nitrogen rich residues.
Another solution for the nitrous oxides is reducing the nitrogen rate, or the amount of nitrogen applied per area of field during a growing season.  This cuts straight to the source of the emissions, while also solving problems such as nutrient runoff and a dwindling nitrogen supply.  However, “because crop yields, and therefore farmers’ profits, are also highly sensitive to nitrogen rate, the feasibility of nitrogen rate reduction as a strategy for mitigating nitrous oxide must consider economic impacts and other policy ramifications.”  Solutions that result in economic damage are unattractive and will unlikely be implemented.  It should also be noted that reducing nitrogen rate in one area might increase the nitrogen rate, and its associated nitrous oxide emissions, in another area via leakage effect.
In dealing with this nitrous oxides issue, the authors have a number of suggestions they believe to be most effective.  They recommend frequent additions of nitrogen fertilizer that are applied to coincide with crop demand and avoid wet conditions.  This would maximize amount of nitrogen absorbed by crops, while avoiding conditions that can result in elevated nitrous oxides production.  The authors also recommend using nitrogen rates that are adjusted spatially to match in-field variations in crop nitrogen demand and soil nitrogen supply, as the demand and supply of nitrogen in a field varies spatially and would benefit from maximum nitrogen use efficiency.  The authors finally recommend developing a system that can deliver nitrogen close to the root system in a chemical form that is stabilized to minimize losses of all other reactive nitrogen species.  This solution would reduce nitrogen runoff, which would in turn reduce eutrophication and the likelihood of nitrous oxides being produced downstream.  It should be noted that all these solutions are in common in that they focus on improving the efficiency of nitrogen use.  Increasing nitrogen use to compensate for low nitrogen recovery rate in crops would only produce more nitrous oxides, while decreasing nitrogen will result in a leakage effect that would offset any amount of nitrous oxide saved.  The lesson we should take away from this is that improving efficiency should be our goal in addressing any environmental issue.  The consequences of increasing/decreasing the inputs for a greenhouse gas are reviewed here and should be considered when addressing other greenhouse gases.

Effects of Nitrogen Deposition on Greenhouse-Gas Fluxes for Forests and Grasslands of North America

Nitrogen deposition across North America has been influencing fluxes in greenhouse gas pollutants such as carbon dioxide, nitrous oxide, and ozone.  In the face of climate change, it is crucial for us to understand the spatial patterns and effects of nitrogen deposition on greenhouse gasses. Templer et al.(2012) discussed spatial patterns of nitrogen deposition and tropospheric ozone and then summarized the known effects of nitrogen deposition on greenhouse gases in North American terrestrial ecosystems.  The authors investigated the causes and implications of emissions such as nitrogen and ozone, and then created an exposure index that analyzed regions affected by both emissions.  The index showed that forests in Eastern U.S. and California’s Sierra Nevada mountains had the highest levels of nitrogen deposition and ozone exposure.—Anthony Li
Templer, P. H., Pinder, R. W., Goodale, C. L. 2012. Effects of nitrogen deposition on greenhouse-gas fluxes for forests and grasslands of North America.  Frontiers in Ecology and the Environment  10.10 547–553

In this paper, the authors investigate the spatial patterns of nitrogen deposition and tropospheric ozone and its implications on other greenhouse-gas pollutants.  Due to the industrial sector, emissions of nitrogen oxides and ammonia have been on the rise.   Both of these pollutants contribute to the formation of aerosols in the atmosphere, which are responsible for decreasing the amount of light available.  Nitrogen oxides also drive the formation of tropospheric ozone and the removal of atmospheric methane.  Nitrogen deposition also largely impacts carbon released from plants and soils, because it enhances carbon dioxide uptake in plants and reduces carbon dioxide release from soils by decomposition. Tropospheric ozone is a byproduct of the photo-oxidation process of nitrous oxides, and often occurs in areas with nitrogen deposition.  Ozone is itself a greenhouse gas and can decrease carbon dioxide uptake in plants by damaging their root production and stomates.  Tropospheric ozone currently accounts for 22% of global warming, and is predicted to reduce rates of primary productivity by up to 16%.  Methane is another greenhouse gas pollutant that was also investigated in this study.  Methane concentrations are affected by nitrogen oxides multiple ways.  In soils, nitrogen enrichment slows the consumption of atmospheric methane by bacteria whereas in wetlands, nitrogen enrichment can enhance methane production.  However, the effect that increased nitrous oxides have on removing methane from the atmosphere is more prominent than the previous two effects.  Data for these pollutants were obtained from previous studies and were then compared with each other regionally using an exposure index that the authors developed.
Collected data show that nitrogen deposition on Eastern U.S. averages 8.5 kg nitrogen per hectare year while on the West coast it averages less than 4 kg nitrogen per hectare year.  The Eastern U.S. has hotspots of nitrogen deposition near urban areas and sites of intensive livestock operations, but the differences between rural and urban areas are not as pronounced as those in the West.  Along the East Coast, an analysis revealed that the nitrogen deposition stimulated forest growth in the hardwood forests.  The forests on the West Coast are noticeably drier and limited in nitrogen, so increased fire frequency and subsequent carbon release may result from the nitrogen deposition.  Southern Canada also experienced elevated rates of atmospheric nitrogen deposition, more so in areas downwind of the agricultural and industrial regions of southern Ontario.  Nitrogen deposition on the East Coast also increased rates of nitric oxide and nitrous oxide production, with the former being produced significantly faster.  Authors note how this is for the better, as nitrous oxide is roughly 300 times more potent in warming potential than carbon dioxide is per molecule.  Nitrogen deposition causes nitrous oxide emissions to increase by 30 to 90 gigagrams while causing atmospheric methane to increase by 40 to 110 gigagrams. Nitrous oxide and methane emissions contribute to warming and can partially offset the cooling that results from increased uptake of carbon dioxide.  The index that the authors used showed forests in Eastern U.S. and California’s Sierra Nevada mountains amongst the regions with highest levels of nitrogen deposition and ozone exposure.  The index also showed that the highest exposure for grasslands occurred in California, Texas, and Kansas.
While the results of this study show how detrimental nitrogen deposition can be, the authors also note that regulation of nitrous oxide emissions has resulted in significant reductions over the past decade.  With more standards being implemented, nitrogen oxides are predicted to decrease by up to 47% by 2030 and 67% by 2050, relative to 2006 nitrogen oxide levels.   Lower levels of nitrogen oxides will likely lead to lower carbon dioxide uptake by plants and lowered nitrous oxide emitted from soils.  As opposed to nitrous oxide however, ammonia levels are unlikely to decline.  Even though standards are projected to decrease nitrogen deposition, increasing levels of atmospheric carbon dioxide are likely to increase the demand for nitrogen in vegetation.  This shows how implemented standards may mean nothing if we do not seek to lower carbon dioxide emissions.  In summary, rates of nitrous oxide, ammonia emissions, and atmospheric nitrogen deposition are elevated over much of North America.  Heightened nitrogen deposition results in greater carbon dioxide uptake by vegetation and increased nitrous oxide emissions from soils.  The net effect of nitrogen deposition in the U.S. is equivalent to an annual uptake of 170 Tg of carbon dioxide equivalent gases.  Despite the heightened nitrous oxide, ammonia, and nitrogen deposition, standards are expected to decrease nitrogen deposition in the coming years.  The impacts of this will be two-sided, as lowered rates of nitrogen deposition result in slower forest growth and carbon storage, but also result in lower emissions of nitrous oxide.

Measuring Agricultural Land–Use Intensity — A Global Analysis Using a Model–Assisted Approach

As future demand for agricultural produce rises, we must develop methods to increase production and satisfy our food needs.  The conversion of land into farms is a solution, but comes with the price of habitat destruction, altered CO2 output and input, and land erosion.  A more sensible solution would be to improve production on farmlands we currently have.  Dietrich et al. (2012) investigates and compares the agricultural land–use intensities for 10 world regions and 12 crops.  The authors use the Lund–Potsdam–Jena dynamic global vegetation model with managed Land (LPJmL) to project reference yields that are compared to current observed yields.  The results show parts of Russia, Asia, and Africa having low agricultural land–use intensities, whereas Eastern U.S., Western Europe, and parts of China have high agricultural land–use intensities.  Measuring for agricultural land–use intensity differs from the more commonly used method of calculating gap yields.  In light of the author’s results and analysis, this paper shows the value in using the t-factor for measuring land-use intensity which can be more accurate than other measuring methods.—Anthony Li
Dietrich, J. P., Schmitz, C., Mueller, C., Fader, M., Lotze-Campen, H., Popp, A., Measuring agricultural land-use intensity — A global analysis using a model-assisted approach.  Ecological Modeling May 10th, 2012

In calculating land–use intensities, the authors introduce a new measure called the t–factor.  The t–factor is the ratio between actual, observable yield to a reference, calculated yield under well–defined management and technology conditions.  The t–factor is independent of the physical environment and is proportional to the agricultural land–use intensity.  The reference yield used to calculate the t–factor can be either deduced from models or statistical analysis.  In this study, the authors use the LPJmL model to project the reference yield.  The LPJmL model simulates the growth, production, and phenology of plant and functional crop types, and reports it as maximum leaf area index, scaling factor from simulated leaf-level photosynthesis to field scale, and harvesting index.  Actual yields were compiled from the Food and Agricultural Organization of the United Nations Statistics (FAOSTAT), but were first applied to the same LPJmL model in order to account for discrepancies due to bias or systematic errors.  The authors calculated t–factors for 10 global regions and 12 different crop types for 2000, and omitted any data for crops that produced less than 0.1% of the global crop production.
The authors compared variations in t–factor, homogeneity in t–factor and the t–factor itself between the regions of the world.  Europe had the highest total t–factor, as well as the highest crop–specific t–factors for wheat, millet, field peas, and rapeseed.  The Middle East and North Africa ranked lowest in total t–factors, but was closely followed by Africa despite the fact that Africa had more crops with the lowest t–factor.  Africa and Europe had the least variation in their t–factors, which contributed to their lowest and highest t–factors, respectively.  In contrast, the Pacific Organization for Economic Cooperation and Development and the Middle East and North Africa showed the strongest variations in t–factor between crops.  In terms of homogeneity in t–factors across each country, Ireland, the United Kingdom, France, Germany, and Sweden show homogeneous values at a high t–factor while Madagascar and Mozambique had homogeneous values at a low t–factor. 
The t–factor measure used in this study differs from other measures of land–use intensity by eliminating the environmental component from observed actual yields via a reference yield.  Other typical, input–oriented measures determine land–use intensity by measuring individual drivers of land–use intensity such as fertilizer use, labor use, and machinery.  Since the t–factor is independent of natural conditions, it can be used as a measure of yield differences due to human activity.  If sample region A has twice the t–factor than sample region B, then it will have twice the yield due to human activities.  The same could be said for the reverse; assuming they have the same t–factors, if the physical conditions in region A are half as good as in region B, then the yields in both regions will be equal.  While the t–factor estimates total land–use intensity which cannot be attributed to individual factors, input–oriented measures record the relevance of certain individual inputs to the attributing factors of overall land–use intensity.  The authors make the point that low agricultural land–use intensities do not necessarily mean higher yield increases in the future.  Factors such as political instability or weak governing bodies may inhibit a nation’s opportunity to improve yields.  While these results may not accurately represent yield increases in specific countries, it is still representative of global agricultural conditions as the results are similar with FAO/OECD yield growth projections.  Without taking into account political stability or effectiveness of governing body, regions such as Africa, South and Eastern Europe, Russia, South Asia, and Latin America show long-term chances for yield increases.  This paper introduces a new method, the t–factor, for measuring agricultural land–use intensity that does not take into account environmental conditions.  Because of this, the t–factor is proportional to agriculture land–use intensity and is a good measurement if we want to calculate land–use intensity solely as a result of human activity.

Measuring Agricultural Land–Use Intensity — A Global Analysis Using a Model–Assisted Approach

As future demand for agricultural produce rises, we must develop methods to increase production and satisfy our food needs.  The conversion of land into farms is a solution, but comes with the price of habitat destruction, altered CO2 output and input, and land erosion.  A more sensible solution would be to improve production on farmlands we currently have.  Dietrich et al. (2012) investigates and compares the agricultural land–use intensities for 10 world regions and 12 crops.  The authors use the Lund–Potsdam–Jena dynamic global vegetation model with managed Land (LPJmL) to project reference yields that are compared to current observed yields.  The results show parts of Russia, Asia, and Africa having low agricultural land–use intensities, whereas Eastern U.S., Western Europe, and parts of China have high agricultural land–use intensities.  Measuring for agricultural land–use intensity differs from the more commonly used method of calculating gap yields.  In light of the author’s results and analysis, this paper shows the value in using the t-factor for measuring land-use intensity which can be more accurate than other measuring methods.—Anthony Li
Dietrich, J. P., Schmitz, C., Mueller, C., Fader, M., Lotze-Campen, H., Popp, A., Measuring agricultural land-use intensity — A global analysis using a model-assisted approach.  Ecological Modeling May 10th, 2012

In calculating land–use intensities, the authors introduce a new measure called the t–factor.  The t–factor is the ratio between actual, observable yield to a reference, calculated yield under well–defined management and technology conditions.  The t–factor is independent of the physical environment and is proportional to the agricultural land–use intensity.  The reference yield used to calculate the t–factor can be either deduced from models or statistical analysis.  In this study, the authors use the LPJmL model to project the reference yield.  The LPJmL model simulates the growth, production, and phenology of plant and functional crop types, and reports it as maximum leaf area index, scaling factor from simulated leaf-level photosynthesis to field scale, and harvesting index.  Actual yields were compiled from the Food and Agricultural Organization of the United Nations Statistics (FAOSTAT), but were first applied to the same LPJmL model in order to account for discrepancies due to bias or systematic errors.  The authors calculated t–factors for 10 global regions and 12 different crop types for 2000, and omitted any data for crops that produced less than 0.1% of the global crop production.
The authors compared variations in t–factor, homogeneity in t–factor and the t–factor itself between the regions of the world.  Europe had the highest total t–factor, as well as the highest crop–specific t–factors for wheat, millet, field peas, and rapeseed.  The Middle East and North Africa ranked lowest in total t–factors, but was closely followed by Africa despite the fact that Africa had more crops with the lowest t–factor.  Africa and Europe had the least variation in their t–factors, which contributed to their lowest and highest t–factors, respectively.  In contrast, the Pacific Organization for Economic Cooperation and Development and the Middle East and North Africa showed the strongest variations in t–factor between crops.  In terms of homogeneity in t–factors across each country, Ireland, the United Kingdom, France, Germany, and Sweden show homogeneous values at a high t–factor while Madagascar and Mozambique had homogeneous values at a low t–factor. 
The t–factor measure used in this study differs from other measures of land–use intensity by eliminating the environmental component from observed actual yields via a reference yield.  Other typical, input–oriented measures determine land–use intensity by measuring individual drivers of land–use intensity such as fertilizer use, labor use, and machinery.  Since the t–factor is independent of natural conditions, it can be used as a measure of yield differences due to human activity.  If sample region A has twice the t–factor than sample region B, then it will have twice the yield due to human activities.  The same could be said for the reverse; assuming they have the same t–factors, if the physical conditions in region A are half as good as in region B, then the yields in both regions will be equal.  While the t–factor estimates total land–use intensity which cannot be attributed to individual factors, input–oriented measures record the relevance of certain individual inputs to the attributing factors of overall land–use intensity.  The authors make the point that low agricultural land–use intensities do not necessarily mean higher yield increases in the future.  Factors such as political instability or weak governing bodies may inhibit a nation’s opportunity to improve yields.  While these results may not accurately represent yield increases in specific countries, it is still representative of global agricultural conditions as the results are similar with FAO/OECD yield growth projections.  Without taking into account political stability or effectiveness of governing body, regions such as Africa, South and Eastern Europe, Russia, South Asia, and Latin America show long-term chances for yield increases.  This paper introduces a new method, the t–factor, for measuring agricultural land–use intensity that does not take into account environmental conditions.  Because of this, the t–factor is proportional to agriculture land–use intensity and is a good measurement if we want to calculate land–use intensity solely as a result of human activity.

The Effects of Potential Changes in United States Beef Production on Global Grazing Systems and Greenhouse Gas Emissions

As much as agriculture and forestry are affected by climate change, they are also promoters of climate change, being responsible for 13.5% and 17.4% of global anthropogenic greenhouse gas emissions, respectively.  Dumortier et al. (2012) decided to assess the leakage effect that will occur as U.S. cattle production declines.  The authors used a combination of a global agricultural production and trade model and a greenhouse gas model to project global leakage of carbon dioxide-equivalent pollutants as a result of declining U.S. cattle production.  The leakage effect occurs when countries increase their agricultural production in order to maintain the demand after another nation decreases their production.  They found a net increase of 37–85 kg CO2-equivalent per kg of beef globally.  The authors attributed this change to inelastic domestic demand and land-intensive cattle production systems that are used internationally. —Anthony Li
Dumortier J., Hayes D. J., Carriquiry M., Dong F., Du X., Elobeid A., Fabiosa J. F., Martin P. A., Mulik K. 2012. The effects of potential changes in United States beef production on global grazing systems and greenhouse gas emissions.  Environmental Research Letters 7, 024023

The authors used an agricultural production and trade model along with a greenhouse gas model to project CO2–equivalent pollutants released.   The production model projects crop and livestock production, commodity prices, utilization, and crop area based on U.S. beef production decline.  Once these projections were made, the authors were able to apply the greenhouse gas model to it.  The greenhouse gas model is designed to calculate emissions associated with land-use change and agricultural production.  The land-use change component contains emissions as a result of biomass and soil carbon.  The agricultural production component accounts for emissions from enteric fermentation, manure management, and agricultural soil management, which all release methane and nitrous oxide.  The researchers also accounted for a country’s stocking rate, which is the number of animals allotted to an area for a given length of time.  They provided “a sensitivity analysis for major livestock producing countries to identify the carbon savings that could potentially be achieved between the baseline and the scenario by allowing for intensification” of stocking rate.  For all countries, they held stocking rates constant except for Brazil, where they allowed it to increase based on historical stocking rate increases.
The findings of this paper were split based on source of emission.  For agricultural production, the results based on a U.S. beef production drop of 17.06% showed an increase of emissions by 10.94 Mt CO2-e from enteric fermentation and 13.83 Mt CO2-e from manure management in Brazil and the rest of the world.  Overall, emissions from livestock increased slightly by 3.22 Mt CO2-e.  For land-use change, results show pasture area in Brazil increasing by 6.85 million ha after a decrease in U.S. pasture of 33.90 million ha.  On a global scale, almost 10 million fewer ha of pasture are used.  Despite the amount of land saved, the models predict a significant release of carbon in Brazil, projecting that 1961 and 3641 Mt CO2–e is emitted from land-use change in croplands and pastures, respectively.  The results show that a decrease in production in one country results in an increase in production in other countries, and subsequent land-use change varies between countries based on stocking rates.
The results show a mixed future for agricultural systems if regulations decrease cattle production.  For agricultural production, there is a difference in meat production per head in the US compared to other countries, as the U.S. generally has lower emissions of methane per unit of meat due to the high energy diet the livestock receive and the cattle’s shorter lifetime.  Brazil’s cattle’s higher methane per unit of meat is the reason why emissions from enteric fermentation and manure management will increase as U.S. cattle production lowers and foreign production increases.  Generally, emissions in the US.. will decline as a result of reduced livestock population, but emissions in other countries will increase because of expanded herd size.  The expansion of herd size largely depends on the country’s stocking rate.  Countries with lower stocking rates will have to expand their farm area more to compensate for demand for cattle.  Subsequently, this causes land-use change and its associated emissions to vary widely from country to country.  Emissions also depend on the type of production system that is being decreased and increased.  Feedlot production systems are generally more energy and CO2 intensive than pastoral production systems.  Although this study did not take into account energy and CO2 associated with production systems, it cited previous studies on this topic.
Adoption of greenhouse gas policies in the agricultural sector may not have its intended consequences.  The results of this study generally show that an increasing number of greenhouse gas policy adopting countries will be matched by an increasing amount of emissions by leakage in other countries.  This leakage is widely dependent on which countries assume which role in this situation.  From this paper, we should note how climate change is a global issue, and policies that address this must take into account its repercussions on a global scale.

Recent Land Use Change in the Western Corn Belt Threatens Grasslands and Wetlands

The recent boom in the biofuel industry, in part due to incentives that promote the conversion of grassland to corn and soybean cropping, is reshaping the landscape of the US Corn Belt. Wright et al. (2013) sought to study the extent to which this land use conversion is occurring, and what its implications may mean for the environment.  The researchers used the National Agricultural Services (NASS) Cropland Data Layer (CDL) to examine the rate at which grasslands have been converted into corn/soy cultivation over five states of the Western Corn Belt: North and South Dakota, Nebraska, Minnesota, and Iowa.  The authors considered the agronomic and environmental attributes of lands on which grassland conversion was occurring, as well as the effects on nearby waterfowl nesting sites, and included these in the results as well.  The results of this study show that the rate at which land was being converted has not been seen in the US since the advent of the mechanization of US agriculture in the 1920s.  The implications of this rate are bleak as it threatens waterfowl populations, soil quality, and water resources.  The authors recommend we shift to biofuels produced from perennial feedstocks, as these fuels have desirable traits with respect to net energy and greenhouse gas balances and wildlife conservation. —Anthony Li
Wright, C. K., Wimberly, M. C., 2013. Recent land use change in the Western Corn Belt threatens grasslands and wetlands.  Proceedings of the National Academy of Sciences of the United States of America published ahead of print February 19, 2013

The authors acquired land cover data from 2006 to 2011 of the Western Corn Belt from the NASS CDL.  They selected this year range because the extent of the data recording goes back to 2006. The NASS CDL uses land cover data acquired from satellite imagery and maps agricultural land cover at a very high crop-type specificity.  Using the 2006 NASS CDL data and comparing it with the 2011 NASS CDL on a per-pixel basis allowed the researchers to observe a general grass-dominated land cover be converted into a general corn/soy cultivation land.  In order to see if the land use data derived from the NASS CDL was representative of long-term land cover change region-wide, they performed a trend analysis of grassland conversion in North Dakota and Iowa.  The analysis showed that the data were representative.  The researchers also took note of the agronomic and environmental attributes of the lands in which NASS CDL recorded data on.  Lastly, the authors examined the relationship between grassland conversion and lands protected under the Conservation Reserve Program (CRP).  The CRP “pays farmers to establish and maintain grassland cover on retired cropland in exchanged for a fixed rental payment over a fixed period,” but in recent years with the rise of corn and soybean prices as well as a projected consistently high commodity prices, more farmers have not been renewing their CRP contracts.  By examining this relationship, the authors were able to see which recently converted areas were formerly protected by the CRP, showing some insight in the farmer’s reasons for changing crop.
The results showed that across the Western Corn Belt, there was a net decline in grass-dominated land cover totaling near 530,000 ha, more than 1.3 million acres, from 2006 to 2011.  This change in land cover was concentrated in South Dakota and Iowa.  The rates at which grassland is being converted to corn/soy is comparable to the deforestation rates in Brazil, Malaysia, and Indonesia.  The authors make the comparison that the current rates of grassland conversion have not been seen in the Corn Belt since the advent of agriculture’s mechanization in the 1920’s.  Grassland conversion is also occurring dangerously close to the Prairie Pothole Region, a wetland region that acts as a climate-change refugia for North American waterfowl.  The current rate of grassland conversion threatens one of the few breeding grounds of waterfowl.  The authors found that grassland conversion was concentrated on relatively high quality lands in Minnesota and the Dakotas, suggesting that the local landowners are seeking higher rates of return by swapping to corn and soybean cultivation.  This trend has become increasingly consistent due to the emerging market of corn/soy production and its rate of return.  In Iowa, they found grassland conversion was occurring on less suitable land, reflecting the lack of high quality land for soybean/corn cultivation.  Similar to Iowa, Nebraska was also shown to have used unsuitable land for crop production, suggesting that both these states will have to acquire more resource-intensive irrigation practices to sustain the soy/corn crops.  The authors also predicted that fewer landowners will be renewing their CRP contracts as the higher rates of return for soybean/corn cultivation is more economically viable.
While this paper shows the rate at which the biofuel industry has grown, it also shows the daunting implications for such a growth. Grassland conversion into corn/soy production is characterized by high erosion risk and vulnerability to drought.  This grassland conversion also threatens waterfowl populations, as the soy/corn fields encroach upon diminishing waterfowl breeding sites.  The grassland conversion also effects the soil’s carbon sequestration ability.  The authors predict that with the reductions in soil sequestration caused by grassland conversion, “more than three decades of biofuel substitution” will be required to counteract this.  In the face of all this the researchers suggest an alternative, saying that biofuels derived from perennial feedstocks are more efficient with respect to net energy and greenhouse gas balances as well as wildlife conservation.

Closing the Gap: Global Potential for Increasing Biofuel Production Through Agricultural Intensification

In the past couple of decades, the global agricultural industry has seen a massive boom, in part due to a combination of fertilizers, pesticides, herbicides, smart management techniques, mechanization, irrigation, and optimized seed varieties and genetic engineering.  This jump in agriculture not only provides the opportunity to feed our growing population, but to also create ethanol and biodiesel to meet our energy demands.  Johnston et al. (2011) looked at the magnitude and spatial variation of new agricultural production potential from closing of ‘yield gaps’ for 20 major ethanol and biodiesel crops.  By using data sets of annual crop yields to determine the amount of additional biofuel produced from obtaining yield gaps up to the global median yield, the researchers deduced that approximately 112.5 billion liters of ethanol and 8.5 billion liters of biodiesel could be made.  While this shows an optimistic future for energy security, it also has a profound effect on policymakers and how individuals will determine goals of reaching a level of biofuel use.  —Anthony Li
Johnston M., Licker R., Foley J., Holloway T., Mueller N. D., Barford C., Kucharik C. 2011. Closing the gap: global potential for increasing biofuel production through agricultural intensification. Environmental Research Letters 6, 034028

The authors of this paper investigated 20 common biofuel and biodiesel crops, some notable ones include maize, rice, sugarcane and wheat for biofuel, or ethanol, and soybean, rapeseed, and oil palm for biodiesel crops.  The researchers obtained the M3 data set of global farming yields for these 20 crops and organized the data based on region.  With information on the average global yields of crops, the authors were able to calculate the yield gaps, which they defined for this study as the “difference between current agricultural yields and future potential based on climatic and biophysical characteristics of the growing region.”  They calculated the potential yields of biodiesel and ethanol if yield gaps of these crops were closed to multiple degrees, such as the global median or the 90thpercentile gap of what is completely attainable.  In order to observe the effects of unequal distributions of irrigation infrastructure and sustainable water resources on crop yields, the researchers re-ran their analysis with irrigated areas excluded.  In order to get a rough idea of what was needed to increase crop yield, the authors calculated the growing degree days for each crop, which is a measure of heat to predict plant development rates.
The researchers found that increasing yield gaps to the median global yield would result in 112.5 billion additional liters of ethanol and 8.5 billion liters of ethanol, while obtaining the 90th percentile gap would result in 450 billion liters of additional ethanol and 33 billion liters of biodiesel.  While the new tonnage varied considerably between biodiesel and ethanol, the overall percentage increase between the two were roughly equal, ranging from 10%–17%.  The majority of ethanol potential identified was attributable to maize, wheat, and rice crops, while the majority of biodiesel potential was attributable to soybean, rapeseed, and oil palm.  Biodiesel fuel production was generally more evenly distributed amongst its constituent crops, whereas ethanol fuel production was incredibly uneven between the crops.
The implications of this study in energy security are obvious, but they also provide a benefit to policymakers or anyone setting goals for biofuel use.  The research performed here shows policymakers how much additional biofuel we can expect from closing various yield gaps to different degrees, allowing them to make more accurate goals.  For example, The Renewable Fuel Standard Program Final Rule of the 2007 Energy Independence and Security Act made a goal for the US to blend 36 billion gallons of biomass-based fuels by 2022.  As ambitious as this goal was, this study showed that even if all the countries were to increase their biofuel crop yields to only the median level, there would still not be enough fuel to meet this goal.  This study is also useful in that it shows the biofuel and biodiesel distribution based on specific crop.  For ethanol, a very notable crop for fuel production was sugarcane.  While this may not mean that sugarcane produces the most ethanol of any other crop per mass, if we can identify whichever crop produces more fuel than others, we can focus our biofuel industry to take advantage of these specific crops.
In the face of our energy and food crisis, our nations should begin looking towards agriculture for potential solutions.  Johnston et al.’s study shows how much additional biofuel can be produced by closing various yield gap levels per crop.  This information will prove useful to governments seeking to implement goals of reaching certain levels of biofuel use and individuals such as farmers who want to capitalize on the most biofuel yielding crop.