News Article | November 19, 2015
A new study of the citrus greening bacterium's effects on its insect vector, the Asian citrus psyllid, reveals multiple changes within the insect. During the infection, the pathogen affects the 'good' bacteria living inside the insect and alters the psyllid's metabolism in ways that could help spread the pathogen further. Ultimately, the study may reveal weak points in the transmission cycle that could yield novel and highly specific targets for control strategies. These approaches could be more effective and environmentally friendly than large-scale pesticide use. The study appears in the journal PLOS ONE. "Our work in this area could not be more timely," said Michelle Cilia, an assistant professor at BTI and research molecular biologist in the U.S. Department of Agriculture's Agricultural Research Service. "Last week, the USDA's National Agricultural Statistics Service lowered its already dire projection for orange production in Florida. The catastrophe faced by Florida growers is a direct result of citrus greening disease. This shortage will affect every single American who consumes citrus products as part of their daily diet, so we are really racing against the clock." Citrus greening disease is a deadly bacterial infection of citrus plants that causes the tree to produce bitter green fruits. It is associated with infection by a bacterium called Candidatus Liberibacter asiaticus (CLas). There is no cure for the infection and currently, growers slow its spread by controlling the insect that carries it, the Asian citrus psyllid, with pesticides. "There's certainly a lot of concern about beneficial insects and honeybees when you talk about combatting citrus greening disease by simply using more insecticides to kill the insect," said John Ramsey, a USDA postdoctoral associate in Cilia's lab. "One of the most appealing aspects of a target is its uniqueness to this system, because then you don't have as much to worry about with off-target effects." To better understand what changes occur in the insect during infection, researchers in Cilia's lab worked with researchers in Michael MacCoss' lab at the University of Washington in Seattle to compare all of the proteins inside the body—called the proteome—of infected and uninfected psyllids. The analysis revealed that the psyllid responds to the citrus greening pathogen not as a harmless hitchhiker, but as an infection, which triggers the insect's immune system. The infection also causes the insect to ramp up production of certain metabolic enzymes, which break down glucose and fatty acids. The researchers suspect that CLas induces metabolic syndrome in the insect and may be manipulating the insect to change its feeding habits, thus increasing its chances of spreading, but behavioral studies will be needed to confirm this idea. The proteome experiments also show that CLas infection affects the helpful, symbiotic bacteria that live in the insect. Psyllids and other sap-sucking insects have long-standing relationships with bacterial partners, which help transform their sugar-rich diet of sap into usable amino acids. The Asian citrus psyllid houses a novel bacterium not found in other insects, called 'Candidatus Profftella armatura.' Though the bacterium's role in the psyllid is not yet well understood, it is known to produce conspicuously high levels of a metabolite called a polyketide. "We saw that a large number of Profftella proteins, including proteins involved in polyketide biosynthesis are upregulated in insects that have acquired CLas," said Ramsey. "The data indicate that Profftella could be playing a role in CLas transmission by the insect." While other researchers have shown that this polyketide, which is unique to the Asian citrus psyllid, is toxic to animal cells, no one knows if it has antibacterial effects. Co-author Jason Hoki, a Cornell University graduate student working in the lab of BTI Associate Professor Frank Schroeder, quantified the levels of this polyketide in infected and uninfected psyllids. In the process, the team identified a second type of polyketide and found that the quantities of the two related compounds change after an insect becomes infected. In future work, researchers in the Cilia lab plan to further explore the function of these polyketides in the insect. The protein work conducted in the Cilia lab also feeds into their collaboration with bioinformaticists in Associate Professor Lukas Mueller's lab, who are annotating the genes and other genetic regions in the recently sequenced genome of the Asian citrus psyllid. This work is part of a large USDA Specialty Crops Grant focused on the development of molecules that physically interfere with CLas transmission by the psyllid. By investigating the multi-faceted interactions between the symbiotic bacteria, the Asian citrus psyllid, the citrus plant and CLas, the researchers hope to develop treatments that can block key proteins in the pathogen transmission cycle, which will be more effective than pesticides. "We're probing all of these interactions to find the best tool to support citrus growers and help them fight this disease," said Ramsey. The study also brings up an interesting evolutionary question: Which host came first, the psyllid or the citrus plant? "Was this an insect pathogen that became adapted to a plant, or is it a plant pathogen that has found a way to infect the insect? We don't know, but it's one of the fascinating elements of CLas, the citrus greening pathogen." Explore further: Psyllid identification key to area-wide control of citrus greening spread More information: John S. Ramsey et al. Metabolic Interplay between the Asian Citrus Psyllid and Its Profftella Symbiont: An Achilles' Heel of the Citrus Greening Insect Vector, PLOS ONE (2015). DOI: 10.1371/journal.pone.0140826
News Article | November 2, 2016
WASHINGTON, Nov. 2, 2016 - Agriculture Secretary Tom Vilsack today announced that USDA will grant $20.2 million to help 34 small businesses move forward with innovative research and development projects to benefit food security, natural resources conservation and other agricultural issues. These competitive grants are made through the Small Business Innovation Research (SBIR) program, which is coordinated by the Small Business Administration and administered by 11 federal agencies including the U.S. Department of Agriculture's (USDA) National Institute of Food and Agriculture (NIFA). "I offer my sincere congratulations to these recipients who have demonstrated that their ideas have strong potential for commercialization and can provide real solutions to tough issues that the food, agriculture and forestry sectors are facing," said Vilsack. "Studies have shown that every dollar invested in agricultural research now returns over $20 to our economy, and that's why USDA has increased our investment in delivering problem-driven and solutions-based science from the farm to the lab to the boardroom. Since 2009, through the Small Business Innovation Research program alone, USDA has awarded nearly 850 research and development grants to American-owned, independently operated, for-profit businesses, allowing hundreds of small businesses to explore their technological potential, and providing an incentive to profit from the commercialization of innovative ideas." Since 2009, USDA has invested $19 billion in research and development touching the lives of all Americans from farms to the kitchen table and from the air we breathe to the energy that powers our country. Learn more about the many ways USDA scientists are on the cutting edge, helping to protect, secure and improve our food, agricultural and natural resources systems in USDA's Medium Chapter 11: Food and Ag Science Will Shape Our Future. The Small Business Innovation Research (SBIR) program offers two phases of investment. Phase I invests in feasibility studies of up to $100,000 and Phase II grants of up to $600,000 support project implementation by grantees who successfully completed Phase I. Recipients of today's announcement are all receiving Phase II grants. SBIR funding comes from multiple USDA agencies including NIFA, the Animal and Plant Health Inspection Service, Agricultural Research Service, Economic Research Service, National Agricultural Statistics Service and U.S. Forest Service. Examples of projects that will receive funding include: Details on all of the SBIR projects announced today are available on NIFA's SBIR webpage. Recent examples of successful NIFA-funded SBIR projects include work by the Nitrate Elimination Company, Inc., which developed kits that allow farm managers to determine nitrate accumulation levels on their farms, helping them manage nitrate concentration, reduce costly nitrogen fertilizer use, and reduce pollutants. Whole Trees, LLC, developed a new market in construction for small-diameter round timber, a natural waste product of well-managed forests. Stony Creek Colors used a SBIR grant to develop a more efficient way to produce natural indigo dyes using the indigo plant, replacing more commonly used synthetic indigo that pollutes waterways and is slow to decompose. See more examples of SBIR-funded research and development projects in the SBIR brochure available on the NIFA website. Since 1983, the SBIR program has awarded more than 2,000 research and development grants to American-owned, independently operated for-profit businesses with up to 500 employees. Funded research areas include air, soil and water; animal production and protection; aquaculture; biofuels and biobased products; food science and nutrition; forests and related resources; plant production and protection--biology and engineering; rural and community development; and small and midsized farms. NIFA invests in and advances innovative and transformative research, education and extension to solve societal challenges and ensure the long-term viability of agriculture. NIFA supports the best and brightest scientists and extension personnel whose work results in user-inspired, groundbreaking discoveries that combat childhood obesity, improve and sustain rural economic growth, address water availability issues, increase food production, find new sources of energy, mitigate climate variability and ensure food safety. To learn more about NIFA's impact on agricultural science, visit http://www. , sign up for email updates or follow us on @usda_NIFA, #NIFAimpacts. USDA is an equal opportunity provider, employer and lender.
Roy D.P.,South Dakota State University |
Wulder M.A.,Natural Resources Canada |
Loveland T.R.,U.S. Geological Survey |
C.E. W.,Boston University |
And 30 more authors.
Remote Sensing of Environment | Year: 2014
Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40. year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012-2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived 'higher-level' Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements. © 2014.
News Article | December 28, 2016
In preparation for the 2017 Census of Agriculture, close to one million potential farmers and ranchers will receive the National Agricultural Classification Survey (NACS) this month to help the U.S. Department of Agriculture identify all active farms and ranches in the United States. The result of the NACS will determine who receives a census of agriculture questionnaire next December. The census of agriculture, conducted every five years by the USDA’s National Agricultural Statistics Service (NASS), is the only source of uniform, comprehensive and impartial agricultural data for every county in the nation. Through the census of agriculture, producers are able to demonstrate the value and importance of agriculture, and influence decisions that will shape the future of the industry in this country. “NACS plays an integral role in getting a complete count of U.S. agriculture,” said NASS Census and Survey Division Director Barbara Rater. “We ask everyone who receives the survey to please respond by January 30, so that we can maintain an accurate and comprehensive census of agriculture mailing list. This is an important opportunity. The census of agriculture is the leading source of facts about American agriculture. Farm organizations, businesses, government decision-makers, commodity market analysts, news media, researchers and so many others use census of agriculture data. We are ensuring that every farm and ranch has a voice.” The census of agriculture defines a farm as any place, big or small, traditional or unique, urban garden to vast countryside that produces and sells, or could sell, $1,000 or more of agriculture products within a calendar year. NACS is required by law as part of the census of agriculture. By this same law, all information reported by individuals is protected. It is NASS’ goal to reach and collect data from every beginner and seasoned farmer. NASS will begin data collection for the 2017 Census of Agriculture in late 2017. The census of agriculture is Your Voice, Your Future, Your Opportunity. For more information about NACS and the 2017 Census of Agriculture, visit http://www.agcensus.usda.gov.
Wang C.,University of Missouri |
Fritschi F.B.,University of Missouri |
Stacey G.,University of Missouri |
Yang Z.,National Agricultural Statistics Service
Annals of the Association of American Geographers | Year: 2011
Biomass is the largest source of renewable energy in the United States, and corn ethanol currently constitutes the vast majority of the country's biofuel. Extended plantation of annual crops for biofuel production, however, has raised concerns about long-term environmental, ecological, and socioeconomic consequences. Switchgrass (Panicum virgatum L.), along with other warm-season grasses, is native to the precolonial tallgrass prairie in North America and is identified as an alternative energy crop for cellulosic feedstocks. This article describes a phenology-based geospatial approach to mapping the geographic distribution of this perennial energy crop in the tallgrass prairie. Time series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery (500-m resolution, eight-day interval) in 2007 were processed to extract five phenology metrics: end of season, season length, peak season, summer dry-down, and cumulative growth. A multitier decision tree was developed to map major crops, especially native prairie grasses in the region. The geographic context of the 20 million ha of perennial native grasses extracted in this study could be combined with economic and environmental considerations in a geographic information system to assist decision making for energy crop development in the prairie region. © 2011 by Association of American Geographers.
Berg E.J.,National Agricultural Statistics Service |
Fuller W.A.,Iowa State University
Computational Statistics and Data Analysis | Year: 2012
Prediction for the mixed model requires estimates of covariance matrices. There is often a direct estimate of the "within area" covariance matrix, and for survey samples this is an estimate of the sampling covariance matrix. The estimated covariance matrix may have large sampling variance, suggesting parametric modeling for the matrix. The model can play a role at various points in the construction of predictions for proportions for small areas. Simulations demonstrate that efficiency for predictions is improved by using a model for the covariance matrix in the estimator of mean parameters and in constructing the coefficients in the predictor. © 2011 Elsevier B.V. All rights reserved.
Nandram B.,Worcester Polytechnic Institute |
Berg E.,Iowa State University |
Barboza W.,National Agricultural Statistics Service
Environmental and Ecological Statistics | Year: 2014
Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the "speculative region," ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region. © 2013 Springer Science+Business Media New York.
Traver B.E.,Virginia Polytechnic Institute and State University |
Williams M.R.,National Agricultural Statistics Service |
Fell R.D.,Virginia Polytechnic Institute and State University
Journal of Invertebrate Pathology | Year: 2012
Nosema ceranae is a microsporidian parasite of the European honey bee, Apis mellifera, that is found worldwide and in multiple Apis spp.; however, little is known about the effects of N. ceranae on A. mellifera. Previous studies using spore counts suggest that there is no longer a seasonal cycle for N. ceranae and that it is found year round with little variation in infection intensity among months. Our goal was to determine whether infection levels differ in bees collected from different areas of the hive and if there may be seasonal differences in N. ceranae infections. A multiplex species-specific real-time PCR assay was used for the detection and quantification of N. ceranae. Colonies were sampled monthly from September 2009-2010 by collecting workers from honey supers, the fringe of the brood nest, and the brood nest. We found that all bees sampled were infected with N. ceranae and that there was no significant difference in infection levels among the different groups of bees sampled (P=0.74). However, significant differences in colony infection levels were found at different times of the year (P<0.01) with the highest levels in April-June and lower levels in the fall and winter. While our study was only performed for one year, it sheds light on the fact that there may be a seasonality to N. ceranae infections. Being able to predict future N. ceranae infections can be used to better advise beekeepers on N. ceranae management. © 2011 Elsevier Inc.
Boryan C.,National Agricultural Statistics Service |
Yang Z.,National Agricultural Statistics Service |
Mueller R.,National Agricultural Statistics Service |
Craig M.,National Agricultural Statistics Service
Geocarto International | Year: 2011
The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available statelevel crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.
Johnson D.M.,National Agricultural Statistics Service
Remote Sensing of Environment | Year: 2014
Four timely and broadly available remotely sensed datasets were assessed for inclusion into county-level corn and soybean yield forecasting efforts focused on the Corn Belt region of the central United States (US). Those datasets were the (1) Normalized Difference Vegetation Index (NDVI) as derived from the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS), (2) daytime and (3) nighttime land surface temperature (LST) as derived from Aqua satellite's MODIS, and (4) precipitation from the National Weather Service (NWS) Nexrad-based gridded data product. The originating MODIS data utilized were the globally produced 8-day, clear sky composited science products (MOD09Q1 and MYD11A2), while the US-wide NWS data were manipulated to mesh with the MODIS imagery both spatially and temporally by regridding and summing the otherwise daily measurements. The crop growing seasons of 2006-2011 were analyzed with each year bounded by 32 8-day periods from mid-February through late October. Land cover classifications known as the Cropland Data Layer as produced annually by the National Agricultural Statistics Service (NASS) were used to isolate the input dataset pixels as to corn and soybeans for each of the corresponding years. The relevant pixels were then averaged by crop and time period to produce a county-level estimate of NDVI, the LSTs, and precipitation. They in turn were related to official annual NASS county level yield statistics. For the Corn Belt region as a whole, both corn and soybean yields were found to be positively correlated with NDVI in the middle of the summer and negatively correlated to daytime LST at that same time. Nighttime LST and precipitation showed no correlations to yield, regardless of the time prior or during the growing season. There was also slight suggestion of low NDVI and high daytime LST in the spring being positively related to final yields, again for both crops. Taking only NDVI and daytime LST as inputs from the 2006-2011 dataset, regression tree-based models were built and county-level, within-sample coefficients of determination (R2) of 0.93 were found for both crops. Limiting the models by systematically removing late season data showed the model performance to remain strong even at mid-season and still viable even earlier. Finally, the derived models were used to predict out-of-sample for the 2012 season, which ended up having an anomalous drought. Yet, the county-level results compared reasonably well against official statistics with R2=0.77 for corn and 0.71 for soybeans. The root-mean-square errors were 1.26 and 0.42metrictonsper hectare, respectively. © 2013 Elsevier Inc.