Estimates of the ratio of marginal to average yield for US corn based on different methods range from 47 to 82 % . This range is consistent with estimates on the global scale . In our analysis, we test how different MtA yield ratios affect ethanol’s CPT. We also identify the best scenario for CRP land with comparable fertility because the CRP program can sometimes retire highly productive land . For the carbon debt caused by CRP land conversion, we use the field measurement by Gelfand et al. , estimated at 68 Mg CO2e ha−1, with the assumption that no-till practices are used for corn farming after land conversion. This estimate is similar to that by Fargione et al. for CRP land conversion, i.e., 69 Mg CO2e ha−1. Following Fargione et al., 83 % of the total carbon debt is allocated to ethanol and 17 % to coproducts, primarily distiller grains with solubles , based on their economic values . As discussed in the Introduction section, a number of approaches to accounting for emission timing effects in LCA and carbon accounting are reported in the literature with various rationales. These approaches are referred to as dynamic characterization in this paper. Dynamic characterization uses temporally specific emissions and characterization factors instead of using time-integrated life cycle inventory and characterization factors . In general, emissions and characterization factors are calculated for each annual time-step and summed up to produce a cumulative impact over a certain period of time. Just like any other characterization methods, dynamic characterization approaches are not free from subjective choices. In this study,grow bag we select 100 years as the time horizon, and following the approach by Levasseur et al. , we then calculate the cumulative radiative forcing , over the 100-year time horizon, for 1 kg CO2 emitted in different years.
Similar to Kendall , we further normalize the CRF results of different years by that of the year when the carbon debt occurs . This step yields a set of weights of decreasing value from 1 for year 0, 0.5 for year 60, to nearly 0 towards year 100. Finally, we assume that carbon debt occurs all at once in the year of land conversion . The assumption of instantaneous carbon loss is somewhat unrealistic but can be considered as a worst-case scenario to indicate the impact of considering emissions timing. Following the argument of Hellweg et al. , we do not further discount future emissions, a practice that is common in economics to account for time value of money. The general approach to CPT calculation outlined in this paper could, however, employ other dynamic characterization approaches discussed earlier; we select the approach by Levasseur et al. with 100-year time horizon only for the purpose of illustration. Taking into account potential corn yield differences, productivity improvements within the corn ethanol system, and emissions timing, our analysis shows that the CPT ranges from 43 to 24 years for CRP land with MtA yield ratios from 60 to 80 % , taking 2001 as the base or land conversion year . In other words, these lands would start to generate carbon benefits from year 24 to 43 after land conversion. For CRP land of low soil fertility with 50 % of average yield, however, the CPT is estimated to be 88 years, which is more than double the CPT estimate for CRP land with 60%of average yield. If the converted land, on the other hand, is highly productive with MtA yield ratio of 100 %, the payback time would be as short as 17 years. Previous estimates of 40 and 48 years of payback time are close to our estimate for 60 % MtA yield ratio, which is 43 years , but with very different underlying reasons. We have re-examined corn ethanol’s carbon payback time in the case of converting CRP land for corn ethanol production taking into account three factors that were neglected in previous studies: yield differences on newly converted land, productivity improvements within the corn ethanol system, and emissions timing . Our results show that CPT estimates for converting low-fertility CRP land with 50 % marginal-to-average yield ratio ranges from 65 to 88 years . For highly productive CRP land, the payback time could be reduced to less than 20 years.
For CRP lands with 60–80 % of marginal-to-average yield ratio, which is considered to be a more typical case, the payback time range from 19 to 43 years . Previous estimates of 40and 48 years of payback time are near the upper bound of our estimates. Technological advances within the corn ethanol system are the key for the CRP-corn ethanol system to be able to generate positive climate impacts. Without technological advances, CRP land with ≤80 % of marginal-to-average yield ratio would fail to provide any carbon benefits over the 100 years after the land conversion. Note that our study does not consider the reversion of land use, which, if included, would further shorten our estimates of CPT for the CRP-corn ethanol system . Overall, our study confirms the importance of understanding marginal technologies and efficiency changes in LCA ; LCAs based on a static productivity assumption may fail to recognize the long-term benefits of the technology as it matures. Also, our study demonstrates the relevance of considering the actual yield of the converted land rather than the average yield, as direct corn expansion will most likely bring marginal, less-fertile land into production. One of the key questions in bio-fuel policies is whether additional corn ethanol production would reduce GHG emissions. Therefore, LCA studies based on average data from existing corn ethanol systems fall short of offering adequate insights for the policy questions at hand. Ideally, such policy questions can be answered using a prospective model that embraces the complex dynamics between and within marginal technologies, marginal impacts, displacement mechanisms and behavioral changes. Our analysis highlights the importance of taking the underlying dynamics into account in understanding the implications of a technology, which can be referred to as “consequential thinking” as an analytical paradigm . However, we acknowledge and admit that our analysis neglects many other factors that would influence the system. That is one of the main reasons why we believe that the term, “consequential LCA”, which implies the existence of a well-defined, operational modelthat is capable of showing the future trajectories of human-nature complexity, can be misleading . Instead, our study employs a scenario approach to answer what-if questions focusing on marginal yield and ethanol system productivity.
Needless to say, our results shall be interpreted only under the assumptions employed as well as the limitations associated with them. Agriculture is essential for feeding a majority of the global population, but it has also been identified as one of the major drivers behind various global environmental degradations . For example, due to a quintupling of global fertilizer use in the past decades, agriculture has greatly disturbed the global nitrogen and phosphorus cycles . This results in a wide range of environmental issues from release of N2O, formation of photochemical smog over large regions of earth, to accumulation of excessive nutrients in estuaries and costal oceans . Agriculture dominates pesticide use , which contaminates surface and ground water and threatens human and ecological health . So also does agriculture dominate freshwater withdrawal worldwide ,grow bag gardening adding stresses where there are competing needs for water . Despite the severity of existing environmental impacts of agriculture, the challenge of addressing them is compounded by increasing global food demand . Continuous global population growth and spread of economic prosperity , mainly in developing countries, will likely drive the global food demand to double by 2050 . Over the past decade, life cycle assessment has been increasingly applied to agricultural and food products , with a number of agricultural LCA databases developed worldwide recently . LCA is a tool that quantifies products’ environmental emissions and resource use throughout the life cycle and evaluates the potential impacts they generate on human and ecological health . Impact categories evaluated in LCA span a wide range, from global warming, ozone depletion, acidification, eutrophication, to ecotoxicity, human health cancer, and non-cancer . Applications of LCA in agriculture include comparing the environmental performance of alternative products or technologies , such as organic versus conventional farming , and identifying hotspots and improvement opportunities . In particular, LCA has played an active and important role in assessing the environmental benefits of bio-energy and contributed to the making of public climate policies . As with LCA studies in general, agricultural LCAs often rely on static and single-year inventory data with commonly 5 to 10 years of data age. In Ecoinvent database, for example, the data year for U.S. Corn Farming is around 2005 and for Swiss Corn Farming is around 2000 . Literature suggests, however, that agricultural systems may be highly dynamic due in part to the increasingly changing climate and technological advances such as improved yield and energy efficiency . These factors may bring about substantial changes in the use of input materials and the yield of crops, hence substantial changes in the environmental impacts. For example, direct energy inputs per ha corn produced in the U.S. declined by about 40% between 1996 and 2005 and in the meantime corn yield increased by about 30% .
In this study, we seek to evaluate if on-going changes in input use and structure of four major crops in the U.S. might have resulted in substantial changes in their environmental impacts over the past decade, focusing on regional issues such as eutrophication, acidification, and ecological toxicity. The crops studied are corn, soybean, wheat, and cotton, which together account for around 70% of total harvested area domestically . The main objectives of the study are to understand the extent to which different environmental impacts might have changed and to identify major drivers behind such changes. Following previous LCA studies we analyzed the cradle-to-gate life cycle environmental impacts of 1 ton and 1 hectare -year of crop production. Direct emissions, such as nutrient leaching and runoff, result from the use of agricultural inputs, and indirect emissions that occur along the supply chain, including emissions from production and transportation of agricultural inputs like synthetic fertilizers. We focused on the estimation of direct emissions for their substantial contribution to the overall life-cycle environmental impacts of crops , and used the Ecoinvent database to calculate indirect emissions . We began with collecting data on the use of agricultural inputs in different years, and then estimated associated emissions based on emission statistics and models. The emission data compiled were next aggregated using characterization models from Life Cycle Impact Assessment to quantify their relative magnitudes of environmental impact. Major agricultural inputs include fertilizers, pesticides, irrigation water, and energy . Data on fertilizer and pesticide use are from the U.S. Department of Agriculture , which surveys farmers in top-producing states annually on a rotating basis . We selected the years with the largest number of states covered for each crop to best represent U.S. national situations. We found that top producing states were consistently surveyed in the years selected for each crop, which ensures comparability across years. For example, the same 19 states were covered for corn and they accounted for around 95% of total corn area harvested in each of the years selected. Similarly, the same 9, 19, and 15 states were covered for cotton, soybean, and wheat, and these stated accounted for around 92%, 96%, and 88% of total area harvested, respectively.Irrigation water use data are from the Farm and Ranch Irrigation surveys conducted also by USDA , and the most recent three surveys for 2002, 2007, and 2012 were used for our analysis. State-level energy use data were also compiled from the USDA , but the data are somewhat outdated as they reflect crops planted in late 1990s or early 2000s. USDA has unfortunately ceased to update such data for most crops except for corn, which was updated to the year 2005 . On the other hand, farms have become more efficient in response to rise in fuel and fertilizer prices in the last decade . For example, on-farm energy use in corn production reduced by >20% between 2001 and 2005.