Due to the dominant use of HR and Bt crops, pests and weeds have evolved to be increasingly resistant . As a result, farmers may need to resort to earlier pest control practices that rely more on conventional pesticides, hence increasing crops’ freshwater ecotoxicity impact. Early LCA estimates differed with respect to whether corn ethanol offers carbon benefits in displacing gasoline . Notably, the findings of the Cornell Professor David Pimentel were all negative , leading him to strongly oppose the use of corn ethanol . But subsequent LCA studies, with updated data and ethanol coproducts correctly accounted for, seemed to converge on that corn ethanol has a moderately smaller carbon footprint than gasoline, thus contributes to climate goals . However, a core factor was neglected in all these LCA studies, that is, land use change . The reason land use change did not come into play in these LCA studies is that they were basically a portrayal of exiting corn ethanol with corn grown on long-standing cornfield. But with increasing ethanol demand driven by federal policies like the renewable fuel standard aimed partly at mitigating climate change , what mattered was not the carbon footprint of existing corn ethanol but of additional corn ethanol. The key issue then became the supply of additional corn. Yield increase through intensification could produce more corn in the long run, but was hardly enough, and too uncertain, to meet annual ethanol expansion. The pressure was on land resources . Higher corn prices between 2005 and 2008 were driving farmers to bring new cornfield into production by converting natural habitats or to reallocate existing cropland to growing more corn . Either way, however,growing bags has dire carbon consequences that run counter to the initial climate goal of the federal policies.
Direct conversion of forest or grassland to grow corn for ethanol production would release a substantial amount of carbon stored in soil and plant biomass, creating a “carbon debt” that may take dozens of years to be repaid by carbon savings from substituting corn ethanol for gasoline . Similarly, reallocation of existing cropland to growing more corn could generate similar nets effects through market mediated mechanisms . For example, if the extra corn came at the expense of reduced soybean production, this could drive up global soybean prices and led farmers across the world to produce more soybeans by converting forest and grassland, resulting in loss of large amounts of carbon as well. In hindsight, that the majority of LCA studies failed to take account of land use change has a lot to do with the methodology they took, namely, attributional LCA . In these studies, corn ethanol’s carbon footprint was quantified in the simple accounting manner. They first estimated carbon emissions at different life-cycle stages based on existing, average corn farming practices and ethanol conversion technologies, and then summed them up and compared the total against the carbon footprint of gasoline. If they found that corn ethanol has a lower carbon footprint, they would conclude that corn ethanol offers carbon benefits in displacing gasoline. Underneath the conclusion was the implicit assumption that the finding based on existing, average technologies would hold true for any amounts of additional corn ethanol. As argued above, however, the assumption is invalid. Because of land constraints, carbon emissions associated with additional corn ethanol would be much different from that associated with existing corn ethanol based on corn from long-standing cornfield . And it is the additional corn ethanol and associated carbon emissions that ultimately matter from both a policy perspective and in terms of reducing greenhouse gas emissions.
In a word, consequential LCA looking into changes and effects is more relevant and better suited for addressing policy questions with potentially large economic and environmental consequences . But it should be noted that which specific methods to use for consequential modelling needs further research .The core to consequential modelling is the consideration of marginal changes, or processes actually to be affected by decisions at hand . In the case of dLUC, marginal changes include land conversion, additional corn production on the converted land, and additional ethanol produced and used. Particularly, the additional corn grown on the converted land sequesters additional carbon from the atmosphere. Without the additional carbon uptake, corn ethanol’s carbon benefits would not be possible as rightly pointed out by Searchinger . In short, it is everything that takes place on the converted land, together with additional ethanol production and use, that should serve as the basis for calculating corn ethanol’s total life-cycle carbon emissions in the case of dLUC . Although Fargione et al. rightly considered land conversion and associated carbon loss, they relied on prior LCA studies , which were based on corn from long-standing cornfield, to estimate everything else. In so doing, they failed to recognize that newly converted land is generally not as fertile as cornfield persisting in cultivation and that corn ethanol originating from low-fertility land would provide smaller carbon benefits than corn ethanol originating from long-standing cornfield. Accounting for the actual yield of the converted land , as demonstrated by Yang and Suh , could substantially increase the time it takes for the use of corn ethanol to repay the carbon debt created by the initial land conversion. Exiting iLUC studies calculate corn ethanol’s total carbon emissions in the same way as do previous dLUC studies by adding carbon loss from land conversion to the carbon footprint of corn ethanol. When exposed with the same consequential reasoning, however, the iLUC literature commits the same error as committed in previous dLUC studies. But for iLUC effect it is beyond the actual yield or fertility of the converted land; what and how new crops are produced following land conversion matters. To drive home, let us consider a simple, hypothetical example of iLUC.
Suppose, in response to increasing ethanol demand, part of U.S. corn was diverted to ethanol production at the expense of reduced exports to China. Total U.S. corn production and areas thus remained unchanged. This drove up Chinese corn prices and subsequently led Chinese people in rural areas to eat more rice, which drove up rice prices there and led Chinese subsistence farmers to convert reforested land to rice cultivation. In the chapter on carbon payback time , we assumed a perfect 1:1 displacement ratio between corn ethanol and gasoline on an energy basis, an assumption also used in previous carbon payback time studies . That is to say, 1 additional MJ of corn ethanol is assumed to take the place of 1 MJ of gasoline. For example, suppose gasoline production is 500 MJ this year and is predicted to reach 600 MJ next year to keep up with rising demand under business-as-usual , and then comes 100 MJ of corn ethanol in the second year. If gasoline production remains 500 MJ in the second year, with the other 100 MJ of demand met by corn ethanol, this is considered a perfect 1:1 displacement ratio. Due to the complexity of economic systems and human behaviour, however, it is more likely less than one unit of gasoline will be displaced by corn ethanol . The introduction of corn ethanol into the market will put downward pressure on gasoline prices, leading to a higher demand for the fuel. To continue with our example,nursery grow bag because of the higher demand, suppose 550 MJ of gasoline and 100 MJ of corn ethanol are produced and consumed in the second year, all else being equal. Thus the net result is that 50 MJ of gasoline is displaced by 100 MJ of corn ethanol .A 10% decrease from the perfect displacement ratio would increase the CPT by 63% for unproductive land yield) to 27% for highly productive land . If only 0.6 MJ of gasoline is displaced, most of the marginal land would fail to provide any carbon benefits within the 100-year time horizon studied. If only 0.5 MJ of gasoline is displaced, even the most productive land would fail to yield any carbon benefits within the time horizon studied. These results suggest that whether corn ethanol provides carbon benefits depends importantly on the extent to which gasoline can be displaced by additional corn ethanol production. In future research, effort may be directed to estimate a more realistic displacement ratio that takes into account such market mechanisms as supply-demand price changes than the perfect ratio assumed in this and previous CPT studies. Models such as the partial equilibrium analyses can be used to derive such market-mediated displacement ratios . Concern has been raised over the eco-toxicity impact of emerging pesticides and the lack of characterization models to evaluate them. This is a general question of data gap. In fact,in addition to emerging pesticides, there are also pesticides whose usage data are withheld by the USDA . However, the ecotoxicity impact of these ‘undocumented’ pesticides is likely small as a large majority of the pesticides applied to the crops studied are covered by both usage and characterization data. Specifically, such data are available for 50 to 90 different types of pesticides; they generally account for 90% to 95% of the total amount of all pesticides applied; and they include the key pesticides that contribute the largest toxicity impacts identified by recent research.
It is worth noting that in terms of the number of pesticides covered, our analyses in chapters 2 and 4 are by far the most comprehensive in comparison to similar studies, which evaluated at most a dozen of pesticides . Nevertheless, our analyses may benefit from evaluating the possible ecotoxicity impact of the “uncovered” pesticides. For emerging pesticides, their characterization factors may be derived from models such as the USEtox based on their physicochemical properties and ecotoxicity effect data if available. For pesticides without usage data, their total usage is in fact aggregated in the total amount of pesticides applied and can be derived by subtracting the pesticides with usage data. Next, sensitivity analysis can be carried out to compute the possible range of their total ecotoxicity impact by assuming different amounts for individual pesticides subject to the total usage derived. Soil microbial communities are shaped by diverse, interacting forces. In agroecosystems, management practices such as crop rotation, fertilization, and tillage alter soil physicochemical parameters, influencing the diversity and composition of bulk soil bacterial and fungal communities. Plant roots create additional complexity, establishing resource-rich hotspots with distinct properties from the bulk soil and selectively recruiting microbial communities in the rhizosphere. Root uptake of ions and water coupled with exudation of carbon-rich compounds results in a rhizosphere soil compartment where microbial cycling of nitrogen, phosphorous, and other nutrients is rapid, dynamic, and competitive in comparison to the bulk soil. Although impacts of agricultural management and the rhizosphere environment on microbiomes and their ecological outcomes have frequently been analyzed separately, understanding interactions has important implications for assembly, ecology, and functioning of rhizosphere microbial communities which are critical to plant health and productivity. Agricultural management establishes soil physicochemical properties that influence microbial community composition, structure, and nutrient-cycling functions. Organic fertilizer increases bulk soil microbial diversity and heterogeneity, and organically managed systems differ from conventional systems in bacterial and fungal community composition. Co-occurrence network analysis has shown that these taxonomic shifts can shape patterns of ecological interactions regulating structure, function, and potential resilience of soil microbial communities. In fact, nutrient management strategies are strong drivers of co-occurrence network structural properties, although outcomes across regions and agroecosystems are inconsistent and also a function of other environmental and management factors. Plant roots are similarly powerful drivers of microbial community assembly, creating rhizosphere communities that are taxonomically and functionally distinct from bulk soil. The strength of plant selection, or rhizosphere effect, is evident in observations of core microbiomes across different field environments. As for management, plant effects on microbial communities also extend beyond taxonomy to network structure. Rhizosphere networks have frequently been found to be smaller, less densely connected, and less complex than bulk soil networks, although counterexamples exist. Whether plasticity in rhizosphere recruitment can occur across management gradients and how such plasticity could impact plant adaptation to varying resource availabilities in agroecosystems remains unclear.