Concept: National Agricultural Statistics Service
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 7 years ago
In the US Corn Belt, a recent doubling in commodity prices has created incentives for landowners to convert grassland to corn and soybean cropping. Here, we use land cover data from the National Agricultural Statistics Service Cropland Data Layer to assess grassland conversion from 2006 to 2011 in the Western Corn Belt (WCB): five states including North Dakota, South Dakota, Nebraska, Minnesota, and Iowa. Our analysis identifies areas with elevated rates of grass-to-corn/soy conversion (1.0-5.4% annually). Across the WCB, we found a net decline in grass-dominated land cover totaling nearly 530,000 ha. With respect to agronomic attributes of lands undergoing grassland conversion, corn/soy production is expanding onto marginal lands characterized by high erosion risk and vulnerability to drought. Grassland conversion is also concentrated in close proximity to wetlands, posing a threat to waterfowl breeding in the Prairie Pothole Region. Longer-term land cover trends from North Dakota and Iowa indicate that recent grassland conversion represents a persistent shift in land use rather than short-term variability in crop rotation patterns. Our results show that the WCB is rapidly moving down a pathway of increased corn and soybean cultivation. As a result, the window of opportunity for realizing the benefits of a biofuel industry based on perennial bioenergy crops, rather than corn ethanol and soy biodiesel, may be closing in the WCB.
Honey bee (Apis mellifera L., Hymenoptera: Apidae) colonies have experienced profound fluctuations, especially declines, in the past few decades. Long-term datasets on honey bees are needed to identify the most important environmental and cultural factors associated with these changes. While a few such datasets exist, scientists have been hesitant to use some of these due to perceived shortcomings in the data. We compared data and trends for three datasets. Two come from the US Department of Agriculture’s National Agricultural Statistics Service (NASS), Agricultural Statistics Board: one is the annual survey of honey-producing colonies from the Annual Bee and Honey program (ABH), and the other is colony counts from the Census of Agriculture conducted every five years. The third dataset we developed from the number of colonies registered annually by some states. We compared the long-term patterns of change in colony numbers among the datasets on a state-by-state basis. The three datasets often showed similar hive numbers and trends varied by state, with differences between datasets being greatest for those states receiving a large number of migratory colonies. Dataset comparisons provide a method to estimate the number of colonies in a state used for pollination versus honey production. Some states also had separate data for local and migratory colonies, allowing one to determine whether the migratory colonies were typically used for pollination or honey production. The Census of Agriculture should provide the most accurate long-term data on colony numbers, but only every five years.
This study was conducted to examine the incidence trend of campylobacteriosis in Michigan over a 10-year period and to investigate risk factors and clinical outcomes associated with infection. Campylobacter case data from 2004 to 2013 was obtained from the Michigan Disease Surveillance System. We conducted statistical and spatial analyses to examine trends and identify factors linked to campylobacteriosis as well as ecological associations using animal density data from the National Agricultural Statistics Service. An increasing trend of Campylobacter incidence and hospitalization was observed, which was linked to specific age groups and rural residence. Cases reporting ruminant contact and well water as the primary drinking source had a higher risk of campylobacteriosis, while higher cattle density was associated with an increased risk at the county level. Additional studies are needed to identify age-specific risk factors and examine prevalence and transmission dynamics in ruminants and the environment to aid in the development of more effective preventive strategies.
A probabilistic approach for estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments
- Integrated environmental assessment and management
- Published over 4 years ago
A crop footprint refers to the estimated spatial extent of growing areas for a specific crop, and is commonly used to represent the potential “use site” footprint for a pesticide labeled for use on that crop. A methodology for developing probabilistic crop footprints to estimate the likelihood of pesticide use and the potential co-occurrence of pesticide use and listed species locations was tested at the national scale and compared to alternative methods. The probabilistic aspect of the approach accounts for annual crop rotations and the uncertainty in remotely sensed crop and land cover datasets. The crop footprints used historically are derived exclusively from the National Land Cover Database (NLCD) Cultivated Crops and/or Pasture/Hay classes. This approach broadly aggregates agriculture into two classes, which grossly overestimates the spatial extent of individual crops that are labeled for pesticide use. The approach also does not use all the available crop data, represents a single point in time, and does not account for the uncertainty in land cover dataset classifications. The probabilistic crop footprint approach described herein incorporates best available information at the time of analysis from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) for 5 years (2008-2012 at the time of analysis), the 2006 NLCD, the 2007 NASS Census of Agriculture, and 5 years of NASS Quick Stats (2008-2012). The approach accounts for misclassification of crop classes in the CDL by incorporating accuracy assessment information by state, year, and crop. The NLCD provides additional information to improve the CDL crop probability through an adjustment based on the NLCD accuracy assessment data using the principles of Bayes' Theorem. Finally, crop probabilities are scaled at the state level by comparing against NASS surveys (Census of Agriculture and Quick Stats) of reported planted acres by crop. In an example application of the new method, the probabilistic crop footprint for soybean resulted in national and statewide soybean acreages that are within the error bounds of the average reported NASS yearly soybean acreage over the same time period, whereas the method using only NLCD resulted in an acreage that is over four times the survey acreage. When the probabilistic crop footprint for soybean was used in a co-occurrence analysis with listed species locations, the number of potentially proximal species identified was half the number based on the standard NLCD crop footprint method (276 species with the probabilistic crop footprint versus 511for the conventional method). The probabilistic crop footprint methodology allows for a more comprehensive and representative understanding of the potential pesticide use footprint co-occurrence with endangered species locations for use in effects determinations. This article is protected by copyright. All rights reserved.