Cover crop mixture diversity, biomass productivity, weed suppression, and stability

The diversity-productivity, diversity-invasibility, and diversity-stability hypotheses propose that increasing species diversity should lead, respectively, to increased average biomass productivity, invasion resistance, and stability. We tested these three hypotheses in the context of cover crop mixtures, evaluating the effects of increasing cover crop mixture diversity on above ground biomass, weed suppression, and biomass stability. Twenty to forty cover crop treatments were replicated three or four times at eleven sites using eighteen species representing three cover crop species each from six pre-defined functional groups: cool-season grasses, cool-season legumes, cool-season brassicas, warm-season grasses, warm-season legumes, and warm-season broadleaves. Each species was seeded as a pure stand, and the most diverse treatment contained all eighteen species. Remaining treatments included treatments representing intermediate levels of cover crop species and functional richness and a no cover crop control. Cover crop seeding dates ranged from late July to late September with both cover crop and weed aboveground biomass being sampled prior to winterkill. Stability was assessed by evaluating the variability in cover crop biomass for each treatment across plots within each site. While increasing cover crop mixture diversity was associated with increased average aboveground biomass, we assert that this was the result of the average biomass of the pure stands being drawn down by low biomass species rather than due to niche complementarity or increased resource use efficiency. At no site did the highest biomass mixture produce more than the highest biomass pure stand. Furthermore, while increases in cover crop mixture diversity were correlated with increases in weed suppression and biomass stability, we argue that this was largely the result of diversity co-varying with aboveground biomass, and that differences in aboveground biomass rather than differences in diversity drove the differences observed in weed suppression and stability

Changes in ecosystem carbon following afforestation of native sand prairie

Includes bibliographical references (pages 1622-1624).Determining the dynamics of carbon (C) as a function of vegetation and residue inputs is important for predicting changes in ecosystem functions and the global C cycle. Litter and soil samples were analyzed from plantations of eastern red cedar (Juniperous virginiana) and ponderosa pine (Pinus ponderosa) and native prairie at the Nebraska National Forest to evaluate the impact of different types of land management on soil C contents and turnover rates. Total soil C to a depth of 1 m was greatest in the cedar stands. Pine ecosystems stored more C in the tree biomass and litter but lost more native prairie C from the soil. The soil 13C content showed 82% of the original, and prairie C remained under cedars compared with ∼45% under pine. Soil cation contents were greatest overall in cedar soils and lowest in pine. The C content in cedar soils was strongly related to Ca content. Differences in microbial community fatty acid profiles were related to vegetation type, and nutrients explained ∼60% of the variation in profiles. Our research indicates that changes in soil C and nutrient content following conversion from prairie to forest are dependent on tree species planted, characteristics of the plant litter, and cation cycling in the plant–soil system

Changes In Nitrogen Use Efficiency And Soil Quality After Five Years Of Managing For High Yield Corn And Soybean

Average corn grain yields in the USA have increased linearly at a rate of 1.7 bu/acre over the past 35 years with a national yield average of 140 bu/acre. Corn yield contest winners and simulation models, however, indicate there is ~100 bu/a in exploitable corn yield gap. Four years (1999-2002) of plant development, grain yield and nutrient uptake were compared in intensive irrigated maize systems representing (a) recommended best management practices for a yield goal of 200 bu/acre (M1) and (b) intensive management aiming at a yield goal of 300 bu/acre (M2). For each management level, three levels of plant density (30000-P1, 37000-P2 and 44000-P3 seed/acre) were compared in a continuous corn and corn- soybean rotation. Over five years, the grain yields increased 11% as a function of management and this effect was manifest under higher plant densities. A high yield of 285 bu/acre was achieved at the M2, P2 treatment in 2003. Higher population resulted in greater demand for N and K per unit grain yield. Over the past five years, nitrogen use efficiency has steadily improved in the M2 treatment due to improvements in soil quality. Intensive management and population levels significantly increased residue carbon inputs with disproportionately lower soil respiration. Closing the yield gap requires higher plant population and improved nutrient management to maintain efficient and profitable improvement in maize production. Soil quality improvements and higher residue inputs under intensive management should make this task easier with time

Developments in Agricultural Soil Quality and Health: Reflections by the Research Committee on Soil Organic Matter Management

The North Central Education and Research Activity Committee (NCERA-59) was formed in 1952 to address how soil organic matter formation and management practices affect soil structure and productivity. It is in this capacity that we comment on the science supporting soil quality and associated soil health assessment for agricultural lands with the goal of hastening progress in this important field. Even though the suite of soil quality indicators being applied by U.S. soil health efforts closely mirrors the “minimum data set” we developed and recommended in the mid-1990s, we question whether the methods or means for their selection and development are sufficient to meet current and emerging soil health challenges. The rush to enshrine a standard suite of dated measures may be incompatible with longer-term goals. Legitimate study of soil health considers soil change accrued over years to decades that influence on- and off-site function. Tailoring of methods to local conditions is needed to effectively apply and interpret indicators for different soil resource regions and land uses. Adherence to a set suite of methods selected by subjective criteria should be avoided, particularly when we do not yet have adequate data or agreed upon interpretive frameworks for many so-called “Tier 1” biological indicators used in soil health assessment. While pooling data collected by producer-groups is one of the most exciting new trends in soil health, standardizing methods to meet broad inventory goals could compromise indicator use for site or application-specific problem solving. Changes in our nation’s research landscape are shifting responsibility for soil stewardship from national and state government backed entities to public-private partnerships. As a result, it is critical to ensure that the data needed to assess soil health are generated by reproducible methods selected through a transparent process, and that data are readily available for public and private sector use. Appropriate methods for engagement need to be applied by public-private research partnerships as they establish and expand coordinated research enterprises that can deliver fact-based interpretation of soil quality indicators within the type of normative soil health framework conceived by USDA over 20 years ago. We look to existing examples as we consider how to put soil health information into the hands of practitioners in a manner that protects soils’ services

Developments in Agricultural Soil Quality and Health: Reflections by the Research Committee on Soil Organic Matter Management

The North Central Education and Research Activity Committee (NCERA-59) was formed in 1952 to address how soil organic matter formation and management practices affect soil structure and productivity. It is in this capacity that we comment on the science supporting soil quality and associated soil health assessment for agricultural lands with the goal of hastening progress in this important field. Even though the suite of soil quality indicators being applied by U.S. soil health efforts closely mirrors the “minimum data set” we developed and recommended in the mid-1990s, we question whether the methods or means for their selection and development are sufficient to meet current and emerging soil health challenges. The rush to enshrine a standard suite of dated measures may be incompatible with longer-term goals. Legitimate study of soil health considers soil change accrued over years to decades that influence on- and off-site function. Tailoring of methods to local conditions is needed to effectively apply and interpret indicators for different soil resource regions and land uses. Adherence to a set suite of methods selected by subjective criteria should be avoided, particularly when we do not yet have adequate data or agreed upon interpretive frameworks for many so-called “Tier 1” biological indicators used in soil health assessment. While pooling data collected by producer-groups is one of the most exciting new trends in soil health, standardizing methods to meet broad inventory goals could compromise indicator use for site or application-specific problem solving. Changes in our nation’s research landscape are shifting responsibility for soil stewardship from national and state government backed entities to public-private partnerships. As a result, it is critical to ensure that the data needed to assess soil health are generated by reproducible methods selected through a transparent process, and that data are readily available for public and private sector use. Appropriate methods for engagement need to be applied by public-private research partnerships as they establish and expand coordinated research enterprises that can deliver fact-based interpretation of soil quality indicators within the type of normative soil health framework conceived by USDA over 20 years ago. We look to existing examples as we consider how to put soil health information into the hands of practitioners in a manner that protects soils’ services

Corn Yield Potential and Optimal Soil Productivity in Irrigated Corn/Soybean Systems

In 1999, an interdisciplinary research team at the University of Nebraska established a field experiment to (1) quantify and understand the yield potential of corn and soybean under irrigated conditions, (2) identify efficient crop management practices to achieve yields that approach potential levels, and (3) determine the energy use efficiency, global warming and soil C-sequestration potential of intensively managed corn systems. The experiment compares systems that represent different levels of management intensity expressed as combinations of crop rotation (continuous corn, corn-soybean), plant density (low, medium, high) and nutrient management (recommended best management vs. intensive management). Detailed measurements include soil nutrient dynamics and C balance, crop growth and development, nutrient uptake and components of yield of corn and soybean, radiation use efficiency, soil surface fluxes of greenhouse gases, root biomass, C inputs through crop residues, translocation of non-structural carbohydrates, and amount, composition and activity of the microbial biomass. Selected results for corn are presented

Corn Yield Potential and Optimal Soil Productivity in Irrigated Corn/Soybean Systems

In 1999, an interdisciplinary research team at the University of Nebraska established a field experiment to (1) quantify and understand the yield potential of corn and soybean under irrigated conditions, (2) identify efficient crop management practices to achieve yields that approach potential levels, and (3) determine the energy use efficiency, global warming and soil C-sequestration potential of intensively managed corn systems. The experiment compares systems that represent different levels of management intensity expressed as combinations of crop rotation (continuous corn, corn-soybean), plant density (low, medium, high) and nutrient management (recommended best management vs. intensive management). Detailed measurements include soil nutrient dynamics and C balance, crop growth and development, nutrient uptake and components of yield of corn and soybean, radiation use efficiency, soil surface fluxes of greenhouse gases, root biomass, C inputs through crop residues, translocation of non-structural carbohydrates, and amount, composition and activity of the microbial biomass. Selected results for corn are presented

Cover crop mixture diversity, biomass productivity, weed suppression, and stability

The diversity-productivity, diversity-invasibility, and diversity-stability hypotheses propose that increasing species diversity should lead, respectively, to increased average biomass productivity, invasion resistance, and stability. We tested these three hypotheses in the context of cover crop mixtures, evaluating the effects of increasing cover crop mixture diversity on above ground biomass, weed suppression, and biomass stability. Twenty to forty cover crop treatments were replicated three or four times at eleven sites using eighteen species representing three cover crop species each from six pre-defined functional groups: cool-season grasses, cool-season legumes, cool-season brassicas, warm-season grasses, warm-season legumes, and warm-season broadleaves. Each species was seeded as a pure stand, and the most diverse treatment contained all eighteen species. Remaining treatments included treatments representing intermediate levels of cover crop species and functional richness and a no cover crop control. Cover crop seeding dates ranged from late July to late September with both cover crop and weed aboveground biomass being sampled prior to winterkill. Stability was assessed by evaluating the variability in cover crop biomass for each treatment across plots within each site. While increasing cover crop mixture diversity was associated with increased average aboveground biomass, we assert that this was the result of the average biomass of the pure stands being drawn down by low biomass species rather than due to niche complementarity or increased resource use efficiency. At no site did the highest biomass mixture produce more than the highest biomass pure stand. Furthermore, while increases in cover crop mixture diversity were correlated with increases in weed suppression and biomass stability, we argue that this was largely the result of diversity co-varying with aboveground biomass, and that differences in aboveground biomass rather than differences in diversity drove the differences observed in weed suppression and stability

Survey of Extreme Solvent Tolerance in Gram-Positive Cocci: Membrane Fatty Acid Changes in \u3ci\u3eStaphylococcus haemolyticus\u3c/i\u3e Grown in Toluene

We exploited the unique ecological niche of oil fly larval guts to isolate a strain of Staphylococcus haemolyticus which may be the most solvent-tolerant gram-positive bacterium yet described. This organism is able to tolerate 100% toluene, benzene, and p-xylene on plate overlays and saturating levels of these solvents in monophasic liquid cultures. A comparison of membrane fatty acids by gas chromatography after growth in liquid media with and without toluene showed that in cells continuously exposed to solvent the proportion of anteiso fatty acids increased from 25.8 to 33.7% while the proportion of 20:0 straight-chain fatty acids decreased from 19.3 to 10.1%. No changes in the membrane phospholipid composition were noted. Thus, S. haemolyticus alters its membrane fluidity via fatty acid composition to become more fluid when it is exposed to solvent. This response is opposite that commonly found in gram-negative bacteria, which change their fatty acids so that the cytoplasmic membrane is less fluid. Extreme solvent tolerance in S. haemolyticus is not accompanied by abnormal resistance to anionic or cationic detergents. Finally, six strains of Staphylococcus aureus and five strains of Staphylococcus epidermidis, which were not obtained by solvent selection, also exhibited exceptional solvent tolerance

Collapse, reorganization, and regime identity: breaking down past management paradigms in a forest-grassland ecotone

The identity of an ecological regime is central to modern resilience theory and our understanding of how systems collapse and reorganize following disturbance. However, resilience-based models used in ecosystem management have been criticized for their failure to integrate disturbance outcomes into regime identity. Assessments are needed to understand how well these classifications represent ecosystem responses that occur over management relevant time scales. We tracked post-wildfire forest and grassland dynamics 27 years after wildfire in eastern ponderosa pine savanna. We tested for differences between the assigned identity of a site (forest or grassland) versus classifications based on the site's disturbance history (burned/unburned and fire severity). Under current ecosystem models used to manage these forest-grassland ecotones, forests that experience high severity fire are expected to resemble an unburned grassland following fire, while forests and grasslands that experience low severity fire are expected to resemble unburned forests and grasslands, respectively. Twenty-seven years after wildfire, burned forests and grasslands displayed a high degree of departure from their expected regime identity. Plant and bird communities deviated significantly on sites that experienced low severity fire from undisturbed sites classified under the same ecological regime (grassland or forest). Forest sites that experienced high severity fire were the most unique of all disturbance history classes. Our results demonstrate that structures and communities predicted under resilience-based models used for eastern ponderosa pine management do not emerge over management relevant time scales following disturbance. Over 20% of variation in ecological structures and communities was explained by a single, 27-year-old disturbance. Integrating disturbance legacies will help improve applied models of ecosystem dynamics