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, 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,blueberry in pot 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. Following the approach developed in previous studies , we assumed a generic factor for the fraction of pesticides in aquatic systems through leaching and runoff. However, this factor is likely to vary by pesticide – due to differences in their intrinsic physio-chemical properties – and by location – due to differences in local topographic, climatic, and soil conditions. To better estimate pesticide emissions after application, future studies may conduct field experiments – at least for the key pesticides identified – or rely on more sophisticated models than used in this dissertation, such as the PestLCI, that take into consideration pesticides’ properties, environmental factors, and application methods . 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. The potential for adaptive plant-microbe feed backs is especially relevant for acquisition of nitrogen , an essential nutrient whose availability in agroecosystems is controlled by interactions between fertility management practices and microbial metabolic processes. Microbial communities supply plant-available N through biological N fixation and mineralization of organic forms, and limit N losses by immobilizing it in soil organic matter. Conventional and organic agroecosystems establish unique contexts in which these transformations occur, shaping microbial communities through system-specific differences in soil N availability and dominant N forms as well as quantity and quality of soil organic matter. Organic fertility inputs such as compost and cover crop residues alter the abundance, diversity,plastic planters wholesale and activity of various nitrogen-cycling microorganisms, while synthetic fertilizers mainly increase the abundance of Acidobacteria and can decrease the abundance of ammonia-oxidizing archaea. Synthetic fertilizers may affect microbial community structure via changes in pH, increasing the abundance of acid-tolerant taxa indirectly through soil acidification, or may alter the relative abundance of specific taxa even when pH is relatively constant. Changes in microbial community structure and activity in bulk soil affect not just the rates but also the outcomes of agriculturally and environmentally relevant Ncycling processes such as denitrification. Roots are also key regulators of N transformations, leading to higher rates of N cycling that are more closely coupled to plant demand in the rhizosphere than in bulk soil compartments. The maize rhizosphere harbors a distinct denitrifier community and is enriched in functional genes related to nitrogen fixation , ammonification , nitrification , and denitrification relative to soil beyond the influence of roots. Understanding regulation of tight coupling of rhizosphere N cycling processes to plant demand could provide new avenues for more efficient and sustainable N management, particularly in an era of global change. However, it is necessary to go beyond exploration of individual effects of plant selection and agricultural management on rhizosphere microbial communities and consider how these factors interact. This knowledge can contribute to managing rhizosphere interactions that promote both plant productivity and agroecosystem sustainability. While management-induced shifts in bulk soil microbiomes affect environmental outcomes, plant-regulated rhizosphere communities are more directly relevant to yield outcomes. Improved understanding of how plant selection changes across management systems is thus an essential component of sustainable intensification strategies that decouple agroecosystem productivity from environmental footprints, particularly in organic systems where yields are formed through transformation of natural resources rather than transformation of external synthetic inputs.
When management and plant rhizosphere effects shape rhizosphere microbial communities, a number of scenarios are possible: one could be greater than the other , their effects could be additive , or they could interact . Typically, these effects are considered additive , where management shapes bulk soil communities and plant effects act consistently, such that rhizosphere communities are distinct from bulk soil and differ from one another to the same degree as their respective bulk soil communities. However, variation in rhizosphere microbiomes and co-occurrence networks between management systems and the unique responses of bulk soil and rhizosphere bacteria to cropping systems point toward M × R interactions shaping microbial community composition. Nonetheless, the functional significance of these interactive effects on critical functions such as N cycling is complex and remains difficult to predict. For example, biological N fixation is driven in large part by plant demand, but high inputs of synthetic fertilizer reduce rates of biological N fixation, diminishing the role of soil microbial communities in supplying plant nutrients and increasing the potential for reactive N losses. Understanding how the M × R interaction affects ecological functions is thus a knowledge gap of critical agricultural and environmental relevance. Adaptive plant-microbe feed backs in the rhizosphere have been described for natural ecosystems, but whether this can occur in intensively managed agricultural systems where resources are more abundant is less clear. We asked whether adaptation to contrasting management systems shifts the magnitude or direction of the rhizosphere effect on rhizosphere community composition and/or N-cycling functions across systems. For instance, can the same genotype selectively enrich adaptive functions that increase N mineralization from cover crops and compost when planted in an organic system and also reduce denitrification loss pathways from inorganic fertilizer when planted in a conventional system? We hypothesized that an M × R interaction would result in differences in the magnitude or direction of the rhizosphere effect on microbial community structure and functions and that differences between rhizosphere communities, cooccurrence network structure, or N-cycling processes would reflect adaptive management-system-specific shifts. To test these hypotheses, we investigated microbial community composition and co-occurrence patterns in bulk and rhizosphere samples from a single maize genotype grown in a long-term conventional-organic field trial. We further quantified the abundance of six microbial N-cycling genes as case study for M × R impacts on rhizosphere processes of agricultural relevance. Our approach integrated ordination, differential abundance and indicator species analyses, construction of co-occurrence networks, and quantitative PCR of N-cycling genes to gain a deeper understanding of the factors that shape rhizosphere community and ecological interactions.A greater number of ASVs showed a significant response to plant selection in conventional than organic soil . Five bacterial and five fungal ASVs were differentially abundant between the conventional bulk and rhizosphere soils , as compared to one bacterial and two fungal ASVs in the organic bulk and rhizosphere soils . The number of differentially abundant taxa between the rhizosphere communities of the two systems was at least as great as the number responding to within-system rhizosphere effects . More fungal than bacterial ASVs were differentially abundant between these rhizosphere communities: 24 fungal ASVs but only six bacterial ASVs were significantly different in abundance between CR and OR, indicating strong M × R interactions.