scholarly journals Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

2021 ◽  
Vol 13 (12) ◽  
pp. 5689-5710
Author(s):  
Yanhua Xie ◽  
Holly K. Gibbs ◽  
Tyler J. Lark
Keyword(s):  
Reference Data ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Irrigation Frequency ◽  
Department Of Agriculture ◽  
Time Step ◽  
Field Level ◽  
The Us ◽  
Period Derivative ◽  
Abstract Data

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents are vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the US. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30 m resolution annual irrigation maps covering the conterminous US (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5×106 ha of croplands and pasture–hay has been irrigated, among which the yearly active area ranged from ∼22.6 to 24.7×106 ha. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ∼10 000 visually interpreted ground reference locations for the eastern US where such data have been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern US, marking a significant advancement over other products, whose accuracies ranged from 50 % to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with US Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation, especially in challenging areas like the eastern US. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5548555 (Xie and Lark, 2021a).

scholarly journals Landsat-based Irrigation Dataset (LANID): 30-m resolution maps of irrigation distribution, frequency, and change for the U.S., 1997–2017

2021 ◽  
Author(s):  
Yanhua Xie ◽  
Holly K. Gibbs ◽  
Tyler J. Lark
Keyword(s):  
Reference Data ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Irrigation Frequency ◽  
Department Of Agriculture ◽  
Time Step ◽  
Field Level ◽  
Period Derivative ◽  
Abstract Data ◽  
The U.S

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents is vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the U.S. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30-m resolution annual irrigation maps covering the conterminous U.S. (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5 million hectares of croplands and pasture/hay have been irrigated, among which the yearly active area ranged from ~22.6 to 24.7 million hectares. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ~10,000 visually interpreted ground reference locations for the eastern U.S. where such data has been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern U.S., marking a significant advancement over other products, whose accuracies ranged from 50 to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with U.S. Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation especially in challenging areas like the eastern U.S. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5003976 (Xie et al., 2021).

Bringing it home: learning to index books by correspondence

1997 ◽  
Vol 20 (4) ◽  
pp. 185-187
Author(s):  
Shirley Manley ◽  
Norma Harwood
Keyword(s):  
Graduate School ◽  
Department Of Agriculture ◽  
Study Program ◽  
Home Learning ◽  
The Us ◽  
Correspondence Study

The US Department of Agriculture (USDA) Graduate School Correspondence Study Program offers two indexing courses. Over 2,600 students have enroled in these courses since their inception.

Welcome to Burke County

2018 ◽  
pp. 3-14
Author(s):  
Loka Ashwood
Keyword(s):  
Rural Poverty ◽  
Black Belt ◽  
Department Of Agriculture ◽  
Du Bois ◽  
The Past ◽  
The Us ◽  
Farm Structure ◽  
Persistent Poverty

This chapter describes the outcome of for-profit's rule in Burke County, Georgia. Burke County is what the US Department of Agriculture calls a persistent-poverty county, meaning that for the past thirty years, over 20 percent of the population has lived in poverty. The designation is not an easy one to get. Only 11.2 percent of counties nationally register as that poor, for that long. And most of such counties are rural. Poverty has been even worse lately in Burke County: 33.5 percent of the county lives in poverty. The region is part of what W. E. B. Du Bois called the Black Belt, for both its soil and people, where plantations once littered the landscape, providing the template for the later tenant-farm structure.

Letters to the Editor

PEDIATRICS ◽  
1981 ◽  
Vol 68 (3) ◽  
pp. 473-473
Author(s):  
David B. Nelson ◽  
Renate D. Kimbrough ◽  
Philip S. Landrigan ◽  
A. Wallace Hayes ◽  
George C. Yang ◽  
...  
Keyword(s):  
United States ◽  
Drug Administration ◽  
Food Supply ◽  
Food Industry ◽  
The United States ◽  
Letters To The Editor ◽  
Department Of Agriculture ◽  
The Us ◽  
The World ◽  
Food Commodities

Dr Wray's comments are, of course, very appropriate and encouraging. Aflatoxin was first detected in food commodities from other parts of the world. As concentrations in other parts of the world have usually been higher, little attention has been paid to the possibility of aflatoxin exposure in humans in the United States except by those who are directly involved in monitoring the human food supply (US Department of Agriculture, the food industry, and the US Food and Drug Administration).

Statistical Inference in Economics in the 1920s and 1930s

2021 ◽  
Vol 53 (6) ◽  
pp. 53-80
Author(s):  
Jeff Biddle
Keyword(s):  
United States ◽  
Probability Theory ◽  
Economic Analysis ◽  
Statistical Inference ◽  
Statistical Data ◽  
The United States ◽  
Interesting Case ◽  
Agricultural Products ◽  
Department Of Agriculture ◽  
The Us

Statistical inference is the process of drawing conclusions from samples of statistical data about things not fully described or recorded in those samples. During the 1920s, economists in the United States articulated a general approach to statistical inference that downplayed the value of the inferential measures derived from probability theory that later came to be central to the idea of statistical inference in economics. This approach is illustrated by the practices of economists of the Bureau of Economic Analysis of the US Department of Agriculture, who regularly analyzed statistical samples to forecast supplies of various agricultural products. Forecasting represents an interesting case for studying the development of inferential methods, as analysts receive regular feedback on the effectiveness of their inferences when forecasts are compared with actual events.

scholarly journals Development of a Miniaturized 24-well Strawberry Leaf Disk Bioassay for Evaluating Natural Fungicides

2008 ◽  
Vol 3 (7) ◽  
pp. 1934578X0800300 ◽  
Author(s):  
Xiaoning Wang ◽  
David E. Wedge ◽  
Nurhayat Tabanca ◽  
Robert D. Johnson ◽  
Stephen J. Cutler ◽  
...  
Keyword(s):  
Leaf Surface ◽  
Higher Plants ◽  
Polar Compounds ◽  
Leaf Disk ◽  
Biologically Active ◽  
Department Of Agriculture ◽  
Lead Compounds ◽  
The Us ◽  
Pure Compounds ◽  
Environmentally Safe

There is great incentive to discover biologically active natural products from higher plants that are more effective than synthetic agrochemicals and are environmentally safe. Research emphasis at the US Department of Agriculture has therefore been on the development of alternative approaches to utilizing natural plant products in pest management. Discovery and evaluation of natural product fungicides is largely dependent upon the availability of miniaturized antifungal bioassays. We report on the development of a miniaturized 24-well leaf disk assay for evaluating plant extracts and pure compounds. Compounds applied directly to the leaf surface can be evaluated in a dose-response for fungicidal activity and phytotoxicity. The assay is sensitive to microgram quantities, can determine chemical sensitivity between fungal isolates, and adaptable to complex mixtures, lipophilic extracts, and non-polar compounds. The use of digital imaging and analytical software provided quantitative data and the ability to fine tune the data analysis. Identification of new potential lead compounds can be repeated quickly in time and real on-the-leaf-surface activity can be evaluated in high throughput formats and published in a reasonable time.

scholarly journals SNAPScapes: Using Geodemographic Segmentation to Classify the Food Access Landscape

Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 71 ◽  
Author(s):  
Elizabeth Major ◽  
Elizabeth Delmelle ◽  
Eric Delmelle
Keyword(s):  
Food Access ◽  
Socioeconomic Indicators ◽  
Department Of Agriculture ◽  
Distance Measurements ◽  
Assistance Program ◽  
Neighborhood Walkability ◽  
Number Of Factors ◽  
The Us ◽  
Clustering Approach ◽  
Nutrition Assistance

Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of “food deserts”, including those identified as so by the US Department of Agriculture (USDA), only include a limited number of factors in their designation. In this article, we adopt a geodemographic approach to classifying the food access landscape that takes a multivariate approach to describing the food access landscape. Our method combines socioeconomic indicators, distance measurements to Supplemental Nutrition Assistance Program (SNAP) participating stores, and neighborhood walkability using a k-means clustering approach and North Carolina as a case study. We identified seven distinct food access types: three rural and four urban. These classes were subsequently prioritized based on their defining characteristics and specific policy recommendations were identified. Overall, compared to the USDA’s food desert calculation, our approach identified a broader swath of high-needs areas and highlights neighborhoods that may be overlooked for intervention when using simple distance-based methods.

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