Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs by Lu Chen, Luca Sartore, Habtamu Benecha, Valbona Bejleri, Balgobin Nandram

“…An application to the county-level corn yield data from the County Agricultural Production Survey of the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) is used to illustrate the proposed approaches. …”
Get full text

Using Small Area Estimation to Produce Official Statistics by Linda J. Young, Lu Chen

“…The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. …”
Get full text

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses? by David M. Johnson, Arthur Rosales, Richard Mueller, Curt Reynolds, Ronald Frantz, Assaf Anyamba, Ed Pak, Compton Tucker

“…Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. …”
Get full text

Model-Based Estimates for Farm Labor Quantities by Lu Chen, Nathan B. Cruze, Linda J. Young

“…The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce estimates of the number of workers, duration of the workweek, and wage rates for all agricultural workers. …”
Get full text

Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset by Feng Gao, Martha C. Anderson, David M. Johnson, Robert Seffrin, Brian Wardlow, Andy Suyker, Chunyuan Diao, Dawn M. Browning

“…Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. …”
Get full text

Indian River Millennium Block Shows Rootstock Effects by Rhuanito Soranz Ferrarezi, J. Martin Zapien-Macias, Mac Hossain, Herbert T James, Ronald D Cave, Peter Spyke, Willian Castle, Jude Grosser, Fred Gmitter, Ed Stover

“…This decrease is significant for the Indian River District, which produces approximately 80% of the total grapefruit crop grown in Florida (National Agricultural Statistics Service, 2021). Several strategies have been considered to keep citrus groves productive in HLB times. …”
Get full text

Near-real-time MODIS-derived vegetation index data products and online services for CONUS based on NASA LANCE by Chen Zhang, Zhengwei Yang, Liping Di, Eugene G. Yu, Bei Zhang, Weiguo Han, Li Lin, Liying Guo

Get full text

Floriculture Crops Economic Outlook for 2014 by Hayk Khachatryan, Alan W. Hodges, Shawn Steed

“…Data sources include the United States Department of Agriculture National Agricultural Statistics Service (USDA/NASS), US Census Bureau, the IBIS World Industry Reports, National Association of Home Builders, S&P/Case-Shiller Home Price Indices, US Bureau of Labor Statistics, University of Florida Bureau of Economic and Business Research, AIA Economics and Market Research Group, and Florida Realtors®. …”
Get full text

Watching the grass grow: does recreational cannabis legalization affect retail and agricultural wages? by Sichao Jiang, Keaton Miller

“…We employ employment data from the Quarterly Census of Employment and Wages, state-level demographic data from the US Census Bureau, and agricultural data from the National Agricultural Statistics Service. We use the least absolute shrinkage and selection operator (LASSO) for variable selection and classification and regression trees (CART) for chained imputation of missing values. …”
Get full text

Mississippi state and county level yield gap in corn production by Jagman Dhillon, Xiaofei Li, Raju Bheemanahalli, Vaughn Reed

“…Production levels considered were Mississippi yield contest (Yc), Mississippi State University hybrid testing trials under irrigation (Yp) and dryland (Yw), and actual yield (Ya) from USDA National Agricultural Statistics Service. Since 2012, Yc, Yp, and Ya are stagnant, and Yw has a nonsignificant positive trend. …”
Get full text

Spatiotemporal Economic Analysis of Corn and Wheat Production in the Texas High Plains by Aminun Naher, Lal K. Almas, Bridget Guerrero, Sania Shaheen

“…Historical county-level agricultural census data were collected from the United States Department of Agriculture and the National Agricultural Statistics Service from 1978 to 2017. Exploratory data analysis techniques were employed to examine the geographical concentration and spatial dependence of crop production among nearby locations. …”
Get full text

Albedo Impacts of Changing Agricultural Practices in the United States through Space-Borne Analysis by Jon Starr, Jianglong Zhang, Jeffrey S. Reid, David C. Roberts

“…Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. …”
Get full text

A Comparison of Raster-Based Forestland Data in Cropland Data Layer and the National Land Cover Database by Chinazor S. Azubike, Lyubov A. Kurkalova, Timothy J. Mulrooney

“…The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. …”
Get full text

Leveraging Important Covariate Groups for Corn Yield Prediction by Britta L. Schumacher, Emily K. Burchfield, Brennan Bean, Matt A. Yost

“…Unfortunately, the most representative yield data in the US, provided by the US Department of Agriculture, National Agricultural Statistics Service (USDA-NASS) Surveys, are spatiotemporally patchy and inconsistent. …”
Get full text

A dataset cataloging product-specific human appropriation of net primary production (HANPP) in US counties by Suman Paudel, Kaeli Mueller, Gustavo Ovando-Montejo, Lauren Tango, Richard Rushforth, Christopher Lant

“…To calculate HANPP, raw agricultural data were downloaded from public databases such as USDA-National Agricultural Statistics Service Quick Stats and Cropland Data Layer, US Forest Service Timber Product Output, and NPP data from MODIS. …”
Get full text

Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017 by Dinesh Shrestha, Jesslyn F. Brown, Trenton D. Benedict, Daniel M. Howard

“…Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. …”
Get full text

Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018 by Xiaocui Wu, Xiangming Xiao, Jean Steiner, Zhengwei Yang, Yuanwei Qin, Jie Wang

“…First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008–2018. …”
Get full text

Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach by Yuchi Ma, Zhengwei Yang, Qunying Huang, Zhou Zhang

Get full text

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States by Pang-Wei Liu, Rajat Bindlish, Peggy O'Neill, Bin Fang, Venkat Lakshmi, Zhengwei Yang, Michael H. Cosh, Tara Bongiovanni, Chandra Holifield Collins, Patrick J. Starks, John Prueger, David D. Bosch, Mark Seyfried, Mark R. Williams

Get full text

Orchard Establishment Budget for Peaches and Nectarines in Florida by Mercy Olmstead, Kim Morgan

“…Although the National Agriculture Statistics Service does not keep annual production and acreage statistics for peaches because of the industry’s small size, a recent poll revealed just over 670 acres in Florida, with about 700 unreported acres (Table 1) (Morgan and Olmstead 2013). …”
Get full text