Agricultural Research Service - Plains Area

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Geneticists and breeders are positioned to breed plants with root traits that improve productivity under drought. However, a better understanding of root functional traits and how traits are related to whole plant strategies to increase crop productivity under different drought conditions is needed. Root traits associated with maintaining plant productivity under drought include small fine root diameters, long specific root length, and considerable root length density, especially at depths in soil with available water. In environments with late season water deficits, small xylem diameters in targeted seminal roots save soil water deep in the soil profile for use during crop maturation and result in improved yields. Capacity for deep root growth and large xylem diameters in deep roots may also improve root acquisition of water when ample water at depth is available. Xylem pit anatomy that makes xylem less "leaky" and prone to cavitation warrants further exploration holding promise that such traits may improve plant productivity in water-limited environments without negatively impacting yield under adequate water conditions. Rapid resumption of root growth following soil rewetting may improve plant productivity under episodic drought. Genetic control of many of these traits through breeding appears feasible. Several recent reviews have covered methods for screening root traits but an appreciation for the complexity of root systems (e.g., functional differences between fine and coarse roots) needs to be paired with these methods to successfully identify relevant traits for crop improvement. Screening of root traits at early stages in plant development can proxy traits at mature stages but verification is needed on a case by case basis that traits are linked to increased crop productivity under drought. Examples in lesquerella (Physaria) and rice (Oryza) show approaches to phenotyping of root traits and current understanding of root trait genetics for breeding.

Labile, 'high-quality', plant litters are hypothesized to promote soil organic matter (SOM) stabilization in mineral soil fractions that are physicochemically protected from rapid mineralization. However, the effect of litter quality on SOM stabilization is inconsistent. High-quality litters, characterized by high N concentrations, low C/N ratios, and low phenol/lignin concentrations, are not consistently stabilized in SOM with greater efficiency than 'low-quality' litters characterized by low N concentrations, high C/N ratios, and high phenol/lignin concentrations. Here, we attempt to resolve these inconsistent results by developing a new conceptual model that links litter quality to the soil C saturation concept. Our model builds on the Microbial Efficiency-Matrix Stabilization framework (Cotrufo et al., 2013) by suggesting the effect of litter quality on SOM stabilization is modulated by the extent of soil C saturation such that high-quality litters are not always stabilized in SOM with greater efficiency than low-quality litters.

The herbicide glyphosate became widely used in the United States and other parts of the world after the commercialization of glyphosate-resistant crops. These crops have constitutive overexpression of a glyphosate-insensitive form of the herbicide target site gene, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS). Increased use of glyphosate over multiple years imposes selective genetic pressure on weed populations. We investigated recently discovered glyphosate-resistant Amaranthus palmeri populations from Georgia, in comparison with normally sensitive populations. EPSPS enzyme activity from resistant and susceptible plants was equally inhibited by glyphosate, which led us to use quantitative PCR to measure relative copy numbers of the EPSPS gene. Genomes of resistant plants contained from 5-fold to more than 160-fold more copies of the EPSPS gene than did genomes of susceptible plants. Quantitative RT-PCR on cDNA revealed that EPSPS expression was positively correlated with genomic EPSPS relative copy number. Immunoblot analyses showed that increased EPSPS protein level also correlated with EPSPS genomic copy number. EPSPS gene amplification was heritable, correlated with resistance in pseudo-F(2) populations, and is proposed to be the molecular basis of glyphosate resistance. FISH revealed that EPSPS genes were present on every chromosome and, therefore, gene amplification was likely not caused by unequal chromosome crossing over. This occurrence of gene amplification as an herbicide resistance mechanism in a naturally occurring weed population is particularly significant because it could threaten the sustainable use of glyphosate-resistant crop technology.

We must mine the biodiversity in seed banks to help to overcome food shortages, urge Susan McCouch and colleagues.

Extreme climatic events (ECEs) – such as unusual heat waves, hurricanes, floods, and droughts – can dramatically affect ecological and evolutionary processes, and these events are projected to become more frequent and more intense with ongoing climate change. However, the implications of ECEs for biological invasions remain poorly understood. Using concepts and empirical evidence from invasion ecology, we identify mechanisms by which ECEs may influence the invasion process, from initial introduction through establishment and spread. We summarize how ECEs can enhance invasions by promoting the transport of propagules into new regions, by decreasing the resistance of native communities to establishment, and also sometimes by putting existing non‐native species at a competitive disadvantage. Finally, we outline priority research areas and management approaches for anticipating future risks of unwanted invasions following ECEs. Given predicted increases in both ECE occurrence and rates of species introductions around the globe during the coming decades, there is an urgent need to understand how these two processes interact to affect ecosystem composition and functioning.

We present new estimates of global nitrous oxide (N 2 O) emissions for the period 1500–1994 based on revised Intergovernmental Panel on Climate Change guidelines [ Intergovernmental Panel on Climate Change (IPCC) , 1997; Mosier et al. , 1998]. Use of these estimates as input to a simple atmospheric box model resulted in a closed N 2 O budget over time, showing that increases in atmospheric N 2 O can be primarily attributed to changes in food production systems. We hypothesize that before the ninetheenth century conversion of natural land to agriculture had no net effect on N 2 O. During the twentieth century a fast expansion of agricultural land coupled with intensification of land use may have caused a net increase in N 2 O. In our base scenario the total N 2 O emissions increased from 11 Tg N yr −1 in 1850 to 15 Tg N yr −1 in 1970 and to 18 Tg N yr −1 in 1994.

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.

Climate change and biological invasions are primary threats to global biodiversity that may interact in the future. To date, the hypothesis that climate change will favour non-native species has been examined exclusively through local comparisons of single or few species. Here, we take a meta-analytical approach to broadly evaluate whether non-native species are poised to respond more positively than native species to future climatic conditions. We compiled a database of studies in aquatic and terrestrial ecosystems that reported performance measures of non-native (157 species) and co-occurring native species (204 species) under different temperature, CO(2) and precipitation conditions. Our analyses revealed that in terrestrial (primarily plant) systems, native and non-native species responded similarly to environmental changes. By contrast, in aquatic (primarily animal) systems, increases in temperature and CO(2) largely inhibited native species. There was a general trend towards stronger responses among non-native species, including enhanced positive responses to more favourable conditions and stronger negative responses to less favourable conditions. As climate change proceeds, aquatic systems may be particularly vulnerable to invasion. Across systems, there could be a higher risk of invasion at sites becoming more climatically hospitable, whereas sites shifting towards harsher conditions may become more resistant to invasions.

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitation changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. We used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. This highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes.

Abstract There is a need for methodology to warm open‐field plots in order to study the likely effects of global warming on ecosystems in the future. Herein, we describe the development of arrays of more powerful and efficient infrared heaters with ceramic heating elements. By tilting the heaters at 45° from horizontal and combining six of them in a hexagonal array, good uniformity of warming was achieved across 3‐m‐diameter plots. Moreover, there do not appear to be obstacles (other than financial) to scaling to larger plots. The efficiency [ η h (%); thermal radiation out per electrical energy in] of these heaters was higher than that of the heaters used in most previous infrared heater experiments and can be described by: η h = 10 + 25exp(− 0.17 u ), where u is wind speed at 2 m height (m s − 1 ). Graphs are presented to estimate operating costs from degrees of warming, two types of plant canopy, and site windiness. Four such arrays were deployed over plots of grass at Haibei, Qinghai, China and another at Cheyenne, Wyoming, USA, along with corresponding reference plots with dummy heaters. Proportional integral derivative systems with infrared thermometers to sense canopy temperatures of the heated and reference plots were used to control the heater outputs. Over month‐long periods at both sites, about 75% of canopy temperature observations were within 0.5 °C of the set‐point temperature differences between heated and reference plots. Electrical power consumption per 3‐m‐diameter plot averaged 58 and 80 kW h day − 1 for Haibei and Cheyenne, respectively. However, the desired temperature differences were set lower at Haibei (1.2 °C daytime, 1.7 °C night) than Cheyenne (1.5 °C daytime, 3.0 °C night), and Cheyenne is a windier site. Thus, we conclude that these hexagonal arrays of ceramic infrared heaters can be a successful temperature free‐air‐controlled enhancement (T‐FACE) system for warming ecosystem field plots.

Existing strategies for long-term bovine tuberculosis (bTB) control/eradication campaigns are being reconsidered in many countries because of the development of new testing technologies, increased global trade, continued struggle with wildlife reservoirs of bTB, redistribution of international trading partners/agreements, and emerging financial and animal welfare constraints on herd depopulation. Changes under consideration or newly implemented include additional control measures to limit risks with imported animals, enhanced programs to mitigate wildlife reservoir risks, re-evaluation of options to manage bTB-affected herds/regions, modernization of regulatory framework(s) to re-focus control efforts, and consideration of emerging testing technologies (i.e. improved or new tests) for use in bTB control/eradication programs. Traditional slaughter surveillance and test/removal strategies will likely be augmented by incorporation of new technologies and more targeted control efforts. The present review provides an overview of current and emerging bTB testing strategies/tools and a vision for incorporation of emerging technologies into the current control/eradication programs.

Hydrology deals with the occurrence, movement, and storage of water in the earth system. Hydrologic science comprises understanding the underlying physical and stochastic processes involved and estimating the quantity and quality of water in the various phases and stores. The study of hydrology also includes quantifying the effects of such human interventions on the natural system at watershed, river basin, regional, country, continental, and global scales. The process of water circulating from precipitation in the atmosphere falling to the ground, traveling through a river basin (or through the entire earth system), and then evaporating back to the atmosphere is known as the hydrologic cycle. This introductory chapter includes seven subjects, namely, hydroclimatology, surface water hydrology, soil hydrology, glacier hydrology, watershed and river basin modeling, risk and uncertainty analysis, and data acquisition and information systems. The emphasis is on recent developments particularly on the role that atmospheric and climatic processes play in hydrology, the advances in hydrologic modeling of watersheds, the experiences in applying statistical concepts and laws for dealing with risk and uncertainty and the challenges encountered in dealing with nonstationarity, and the use of newer technology (particularly spaceborne sensors) for detecting and estimating the various components of the hydrologic cycle such as precipitation, soil moisture, and evapotranspiration.

Root exudates influence the surrounding soil microbial community, and recent evidence demonstrates the involvement of ATP-binding cassette (ABC) transporters in root secretion of phytochemicals. In this study, we examined effects of seven Arabidopsis (Arabidopsis thaliana) ABC transporter mutants on the microbial community in native soils. After two generations, only the Arabidopsis abcg30 (Atpdr2) mutant had significantly altered both the fungal and bacterial communities compared with the wild type using automated ribosomal intergenic spacer analysis. Similarly, root exudate profiles differed between the mutants; however, the largest variance from the wild type (Columbia-0) was observed in abcg30, which showed increased phenolics and decreased sugars. In support of this biochemical observation, whole-genome expression analyses of abcg30 roots revealed that some genes involved in biosynthesis and transport of secondary metabolites were up-regulated, while some sugar transporters were down-regulated compared with genome expression in wild-type roots. Microbial taxa associated with Columbia-0 and abcg30 cultured soils determined by pyrosequencing revealed that exudates from abcg30 cultivated a microbial community with a relatively greater abundance of potentially beneficial bacteria (i.e. plant-growth-promoting rhizobacteria and nitrogen fixers) and were specifically enriched in bacteria involved in heavy metal remediation. In summary, we report how a single gene mutation from a functional plant mutant influences the surrounding community of soil organisms, showing that genes are not only important for intrinsic plant physiology but also for the interactions with the surrounding community of organisms as well.

The sodium-bicarbonate (NaHCO3) method of Olsen et al. was primarily designed to extract inorganic P (P1) and to correlate this P-pool with plant response. This procedure was used to follow the transformations of organic P (P0) substrates in soil. Various commercial P0 substrates were added to a sandy loam soil, and were extracted immediately and after 1 to 18 days of incubation at field capacity. Glycerophosphate and all 3‘ nucleotide components of RNA were completely mineralized and accounted for in the NaHCO3 solution after 3 days. While RNA degraded in 18 days, sodium inositol hexaphosphate (Na-phytate) was relatively unaffected during this time and unrecoverable in the NaHCO3 solution upon immediate extraction. Thus, the labile compounds, like RNA, its four 3’ nucleotides, and glycerophosphates were recoverable in the 0.5-M NaHCO3-extracting solution (pH 8.5) of Olsen et al., while the Na-phytate, a relatively resistant compound, was not. Native P0 was only slightly affected, however. A relatively constant amount of native P0 was extracted, irrespective of extraction periods lasting 0.5 to 6 h.

A biogeochemical model, Denitrification‐Decomposition (DNDC), was modified to enhance its capacity of predicting greenhouse gas (GHG) emissions from paddy rice ecosystems. The major modifications focused on simulations of anaerobic biogeochemistry and rice growth as well as parameterization of paddy rice management. The new model was tested for its sensitivities to management alternatives and variations in natural conditions including weather and soil properties. The test results indicated that (1) varying management practices could substantially affect carbon dioxide (CO 2 ), methane (CH 4 ), or nitrous oxide (N 2 O) emissions from rice paddies; (2) soil properties affected the impacts of management alternatives on GHG emissions; and (3) the most sensitive management practices or soil factors varied for different GHGs. For estimating GHG emissions under certain management conditions at regional scale, the spatial heterogeneity of soil properties (e.g., texture, SOC content, pH) are the major source of uncertainty. An approach, the most sensitive factor (MSF) method, was developed for DNDC to bring the uncertainty under control. According to the approach, DNDC was run twice for each grid cell with the maximum and minimum values of the most sensitive soil factors commonly observed in the grid cell. The simulated two fluxes formed a range, which was wide enough to include the “real” flux from the grid cell with a high probability. This approach was verified against a traditional statistical approach, the Monte Carlo analysis, for three selected counties or provinces in China, Thailand, and United States. Comparison between the results from the two methods indicated that 61‐99% of the Monte Carlo‐produced GHG fluxes were located within the MSA‐produced flux ranges. The result implies that the MSF method is feasible and reliable to quantify the uncertainties produced in the upscaling processes. Equipped with the MSF method, DNDC modeled emissions of CO 2 , CH 4 , and N 2 O from all of the rice paddies in China with two different water management practices, i.e., continuous flooding and midseason drainage, which were the dominant practices before 1980 and in 2000, respectively. The modeled results indicated that total CH 4 flux from the simulated 30 million ha of Chinese rice fields ranged from 6.4 to 12.0 Tg CH 4 ‐C per year under the continuous flooding conditions. With the midseason drainage scenario, the national CH 4 flux from rice agriculture reduced to 1.7–7.9 Tg CH 4 ‐C. It implied that the water management change in China reduced CH 4 fluxes by 4.2–4.7 Tg CH 4 ‐C per year. Shifting the water management from continuous flooding to midseason drainage increased N 2 O fluxes by 0.13–0.20 Tg N 2 O‐N/yr, although CO 2 fluxes were only slightly altered. Since N 2 O possesses a radiative forcing more than 10 times higher than CH 4 , the increase in N 2 O offset about 65% of the benefit gained by the decrease in CH 4 emissions.

The protocols presently established for optimum seed storage do not account for the chemical composition of different seed species, the physiological status of the seed, and the physical status of water within the seed. The physiological status of seeds from five species with varying chemical compositions was determined by measurements of rates of oxygen uptake and seed deterioration. The physical status of water was determined by water sorption characteristics. For each species studied, there was a specific moisture content for the onset of respiration, chemical reactions, and accelerated aging rates. The moisture contents at which these physiological levels were observed varied among the species and correlated with the lipid content of the seed. However, the changes in physiological activities and the physical status of water occurred at specific relative humidities: 91% for the onset of respiration, 27% for the increased rates of thermal-chemical reactions, and 19% for optimum longevity. Based on these observations, we propose that equilibrating seeds between 19 and 27% relative humidity provides the optimum moisture level for maintaining seed longevity during longterm storage.

Climate change, in combination with the expanding human population, presents a formidable food security challenge: how will we feed a world population that is expected to grow by an additional 2.4 billion people by 2050? Population growth and the dynamics of climate change will also exacerbate other issues, such as desertification, deforestation, erosion, degradation of water quality, and depletion of water resources, further complicating the challenge of food security. These factors, together with the fact that energy prices may increase in the future, which will increase the cost of agricultural inputs, such as fertilizer and fuel, make the future of food security a major concern.

Three mixed prairie sites at Mandan, N.D. were grazed heavily (0.9 ha steer-1), moderately (2.6 ha steer-1), or left ungrazed (exclosure) since 1916. These sites provided treatments to study the effects of long-term grazing on soil organic carbon and nitrogen content and to relate changes in soil carbon and nitrogen to grazing induced changes in species composition. Blue grama [Bouteloua gracilis (H.B.K) Lag. ex Griffiths] accounted for the greatest change in species composition for both grazing treatment. Relative foliar cover of blue grama was 25% in 1916 and 86% in 1994 in the heavily grazed pasture and 15% in 1916 to 16% in 1994 in the moderately grazed pasture. Total soil nitrogen content was higher in the exclosure (1.44 kg N ha-1) than in either grazing treatment (0.92 and 1.07 kg N ha-1 for moderately and heavily grazed, respectively) to 107-cm depth. Soil organic carbon content avg 72, 6.4, and 7A kg m-2 to 30.4 cm soil depth and 14.1,11.7, and 14.0 kg m-2 to 106.7 cm soil depth for the exclosure, moderately grazed, and heavily grazed treatments, respectively. Compared to the exclosure the moderately grazed pasture contained 17% less soil carbon to the 106.7 cm depth. Heavy grazing did not reduce soil carbon when compared to the exclosure. Based on 13C analysis and soil organic carbon data to 15.2 cm depth, blue grama or other C4 species contributed 24% or 12 kg m-2 of the total carbon in the heavily grazed and 20% or 0.8 kg m-2 of the total carbon in the moderately grazed pastures during the 1916 to l99l time period. The increase in blue grama, a species with dense shallow root systems, in the heavily grazed pasture probably accounted for maintenance of soil carbon at levels equal to the exclosure. These results suggest that changes in species composition from a mixed prairie to predominantly blue grama compensated for soil carbon losses that may result from grazing native grasslands.

Quantifying where, when, and how much denitrification occurs on the basis of measurements alone remains particularly vexing at virtually all spatial scales. As a result, models have become essential tools for integrating current understanding of the processes that control denitrification with measurements of rate-controlling properties so that the permanent losses of N within landscapes can be quantified at watershed and regional scales. In this paper, we describe commonly used approaches for modeling denitrification and N cycling processes in terrestrial and aquatic ecosystems based on selected examples from the literature. We highlight future needs for developing complementary measurements and models of denitrification. Most of the approaches described here do not explicitly simulate microbial dynamics, but make predictions by representing the environmental conditions where denitrification is expected to occur, based on conceptualizations of the N cycle and empirical data from field and laboratory investigations of the dominant process controls. Models of denitrification in terrestrial ecosystems include generally similar rate-controlling variables, but vary in their complexity of the descriptions of natural and human-related properties of the landscape, reflecting a range of scientific and management perspectives. Models of denitrification in aquatic ecosystems range in complexity from highly detailed mechanistic simulations of the N cycle to simpler source-transport models of aggregate N removal processes estimated with empirical functions, though all estimate aquatic N removal using first-order reaction rate or mass-transfer rate expressions. Both the terrestrial and aquatic modeling approaches considered here generally indicate that denitrification is an important and highly substantial component of the N cycle over large spatial scales. However, the uncertainties of model predictions are large. Future progress will be linked to advances in field measurements, spatial databases, and model structures.