It is worth pointing out that that while using nutrients much more intensively, corn growth in TX does not generate a substantially larger eutrophication impact than cotton. This is because nutrient runoff and leaching rates of corn in TX are generally smaller than that of cotton . For all states, as with the average situation in Fig. 2.1, land shift from cotton to corn would relieve freshwater water ecotoxicity impact. In summary, our study calls for an attention to policy-induced land cover change from cotton to corn and associated environmental issues. In doing so, we demonstrate that average data reflecting national situations are inadequate to capture the likely environmental impacts of corn expansion into cotton on marginal land at regional level. Our results for three states North Carolina, Georgia, and Texas show that corn expansion into cotton in the South relieves freshwater ecotoxicity but may aggregate many other regional environmental impacts. Overall, our study confirms the earlier studies that demonstrated the importance of understanding “marginal” impacts in LCA : environmental consequences of the policies that encourage converting cotton to corn cultivation in the regions where corn is generally less suitable to grow cannot be understood by comparing average environmental profiles of cotton and corn. Our results also favor “consequential thinking,” as an analytical paradigm, in bio-fuel LCA,30 plant pot while our study is not intended to demonstrate how to perform a “consequential LCA,” as an operational model .
Corn ethanol, supported by several federal policies as a means of reducing GHG emissions by displacing gasoline , has been a point of heavy dispute in the last decade . However, it has become increasingly clear that although corn ethanol may have the potential to combat climate change , its large-scale expansion is reported to generate adverse environmental consequences including, notably, direct, and indirect land use changes .These adverse consequences, first, undermine the climate objectives of the public policies. Second, for intensive use of agrochemicals and irrigation water, corn expansion adds to the pressure on local water quality and scarcity issues . Our study focused on yet another consequence related to ethanol expansion, namely, land cover change from cotton to corn, and analyzed the potential implications of such change for local environments. Contrary to the previous view that land shift between cotton and corn, both high-input crops, may cause negligible net environmental impacts , our study revealed a more complex picture. Although land switch from cotton to corn relieves ecotoxicity, it likely aggravates other various environmental problems depending on where the crops are grown. Note that our study only covers part of the effects bio-fuels policies have generated on crop conversions. To understand the overall environmental impacts of bio-fuel policies through crop conversions, further research is needed to estimate the environmental aspects of other crops affected, particularly soybean , and the magnitude of land shifts between the crops. Our results highlight the importance of potential, unintended consequences that cannot be adequately captured when average data are employed. Understanding the actual mechanisms under which certain policy induces marginal changes at a regional and local level is crucial for evaluating its net impact. Our results also show the importance of recognizing potential trade-offs between environmental objectives in policy making.
Climate policies focusing narrowly on carbon, for instance, could shift burden to regional issues like water scarcity and eutrophication . Therefore, environmental policy making should attend to not only unintended effects within its targeted problems like the indirect LUC effect , but also those across impact categories to avoid or minimize burden shifting across impact categories. Also, our study reinforces previous research with respect to spatial variability in agricultural systems . Unlike industrial systems, agricultural systems are subject to the influence of weather patterns, soil type, geography, and management practices. Even the same agricultural product may have drastically different input structures, hence environmental impacts, in different regions. Therefore, average data with generic descriptions of material and energy fluxes are hardly adequate to capture the high degree of system variability of agricultural products. With the rising interests in bio-fuels as a means to combat climate change across the world, we strongly recommend future studies in this area to take into consideration the spatial variability of biomass growth. Just as technological and environmental variability exists across states, there is probably certain variability within a state, too, that may not be precisely captured by state average data. This does not mean, however, that state-level data should be dismissed for the research question at hand because they are still likely more reflective of local or farm-level practices than national averages. In addition, state average data are especially valuable and representative, more so than farm-level data, in situations in which massive land shift between crops takes place within a state. Nevertheless, we encourage finer-scale, more detailed studies into land shift between cotton and corn and associated environmental impacts, which could not have been conducted in our analysis due to the data limitation and resources constraints.
Additional research is needed to paint a more complete picture on the impact of cropland conversion to corn: In 2005, 41 states grew corn and 17 states grew cotton, among which only 19 of the corn-growing states and 7 of the cotton-growing states had data on major inputs that can be used to generate LCIs . Among these states, only three overlap, namely, North Carolina, Georgia, and Texas. Therefore, this study does not quantify the environmental impact and their trade-offs in other cotton-growing states where conversion to corn might have happened. Nevertheless, environmental implications of cotton-to-corn land shift in these other states are probably worse than that indicated by Fig. 2.1 and closer to that indicated in Fig. 2.2 because cropland in southern states are generally less suitable for corn growth than the Corn Belt. Future studies pursuing this line of research may make the effort to quantify the magnitude of land shift in each cotton-growing state when relevant data on agricultural inputs, environmental outputs, and acreage of conversion become available. Furthermore, it is worth noting that spatially detailed data are often unavailable or incomplete, although such data can improve the environmental relevance of an LCA study. In this case, one may rely on assumptions or spatially generic data to fill the gaps,grow raspberries in a pot and this may increase the uncertainty of the LCA results . In our study, data on agricultural inputs such as fertilizers and pesticides were available at the state level, but we often relied on spatially generic emission factors to estimate their emissions . Further, the LCA results for corn and cotton were found to be moderately sensitive to the emission factors which are likely to vary across regions . Future spatially explicit LCAs on agricultural systems may take this into account and direct efforts to estimate spatially differentiated emission factors.For the potential to mitigate climate change, reduce dependence on oil imports, and invigorate rural economic development, bio-fuel development in the USA has been supported by an array of policy measures . Among them is the federal Renewable Fuel Standard , a mandate that requires 140 billion liters bio-fuels to be produced annually from different sources by 2022. Corn ethanol is currently the primary bio-fuel and is likely to continue dominating US bio-fuels market as cellulosic and other advanced bio-fuels are far from mass production . Driven by the favorable policies and high oil prices, corn ethanol production has increased eight-fold since 2000, to the current level of about 50 billion liter per year.
Early Life Cycle Assessment research on corn ethanol was largely in support of the policies aiming partly at reducing greenhouse gas emissions. As is typically done in LCA, these studies quantified GHG emissions generated at each stage of corn ethanol’s life cycle, summed them up, and then compared the results against that of gasoline. Corn ethanol was found to have 10–20 % lower life cycle GHG emissions than gasoline and, therefore, concluded to provide a modest carbon benefit in replacing gasoline . However, the conclusion was later called into question, when the land use change effects of corn ethanol expansion emerged in the literature . Converting natural vegetation or forestland to corn field for ethanol production releases a substantial amount of carbon from soil and plant biomass, creating a “carbon debt” that could not be repaid in dozens of years or even a century . Similarly, diversion of existing cropland for ethanol could generate indirect LUC effect through market-mediated mechanisms . In this scenario, corn ethanol expansion reduces food supply, which could lead to conversion of natural vegetation or forestland elsewhere in the world to compensate for the diverted grains. While the concept of iLUC has become widely accepted in academic and policy arenas , quantification of iLUC emissions is known to be difficult and highly uncertain . Plevin et al. , for example, estimated the range from 10 to 340 CO2e MJ−1 y−1. This wide range is due in large part to a lack of quality data and detailed understanding as to how the global agricultural market would respond to bio-fuels expansion . In contrast, the direct land use change emissions can be relatively accurately quantified . Previous studies used the concept of carbon payback time to measure the magnitude of dLUC effect of corn ethanol. While the initial carbon debt due to land conversion may be large, it can be repaid over time by the annual carbon savings corn ethanol yields in displacing gasoline because corn ethanol has lower life cycle GHG emissions. The first dLUC study estimated that 48 years would be required for corn ethanol to pay back its carbon debt if the Conservation Reserve Program land is converted and that 93 years would be required if central grassland is converted .Gelfand et al. conducted a field experiment on CRP land conversion to measure its carbon loss. They found that approximately 40 years would be required for the use of corn ethanol to pay back this carbon loss with the converted land under no-till management. In another study, Piñeiro et al. arrived at a similar estimate of approximately 40 years for the payback time for CRP land conversion to corn ethanol. However, these studies were based on several oversimplifications that may substantially affect their results. First, these studies assumed that newly converted land has the same yield as existing cornfields, neglecting the potential yield differences of newly converted land. In particular, CRP land is generally less fertile than cornfields that have been in continuous use . Thus, corn ethanol from CRP land generates lower annual carbon savings, hence a longer payback time. Land with extremely low yield may even fail to provide any carbon savings, in which case the carbon loss due to land conversion is permanently lost. Second, the dLUC studies relied primarily on life cycle assessments based on early bio-fuel conversion processes that did not reflect the productivity improvements that have occurred in the past decade due to yield and energy efficiency increases at both the corn growing and ethanol conversion stages . Recent studies have shown that corn ethanol’s carbon benefit has increased to up to 50 % , compared with earlier estimates of 10–20 % . The productivity of the gasoline production system over the same period of time has been fairly steady . The productivity improvements in the corn ethanol system result in greater amounts of annual carbon savings that, if considered, would yield a shorter payback time than previously estimated. Finally, the dLUC studies used the global warming potential 100 to assess the global warming impact of corn ethanol, gasoline, and dLUC emissions. This approach assumes equal weights to GHGs emitted at different times. More recent literature explores the application of different weights to GHG emissions emitted in different times. First, from a scientific point of view, increasing background GHG concentrations in the atmosphere result in a diminishing marginal radiative forcing for a unit GHG emission . The rate at which the relative radiative forcing effect of a unit GHG emission diminishes depends on future atmospheric GHG concentrations.