Data partnership synergy: The Cropland Data Layer

Mueller, Rick; Boryan, Claire G.; Seffrin, Robert

doi:10.1109/geoinformatics.2009.5293489

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…One explanation for regional OFC variation is environmental exposure to factors that increase the risk of OFC development. Historically, OFC occurrence was more evenly distributed throughout the state and not associated with areas of high agricultural activity, such as the northern region and the Delta 17,35,36 . Now, there is a much higher burden of OFC diagnosis on children presenting from the central and southern regions of the state.…”

Section: Discussionmentioning

confidence: 99%

Epidemiologic Trends of Cleft Lip and Palate in a Southern State: A 30-Year Follow-Up

Brown,

McCandless,

Hopper

et al. 2024

Southern Medical Journal

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Section: Discussionmentioning

confidence: 99%

Epidemiologic Trends of Cleft Lip and Palate in a Southern State: A 30-Year Follow-Up

Brown,

McCandless,

Hopper

et al. 2024

Southern Medical Journal

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“…The CDL is a crop-specific, rasterized, geo-referenced land-cover map produced by NASS [32,46,47]. NASS is the agency responsible for administering the USDA's U.S. program, collecting and publishing agricultural statistics at the national, state, and county levels.…”

Section: The Usda Nass Cropland Data Layer (Cdl)mentioning

confidence: 99%

Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

et al. 2018

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Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

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“…Annual areas planted to corn and grain sorghum were obtained from the National Agricultural Statistics Survey (NASS) Cropland Data Layer (CDL; USDA-NASS, 2010). The 56-m resolution CDL is created annually by NASS using multispectral imagery from Resourcesat-1 AWiFS, ground truth training data from the USDA Farm Services Agency and various ancillary data sets (Mueller et al, 2009). Using ArcGIS W (ESRI, Redlands, CA), CDL data Table I sets for each year were overlain with watershed boundaries, and the area within each watershed in corn, grain sorghum and other land uses was determined.…”

Section: Cropland Areamentioning

confidence: 99%

“…Daily planting progress was used as a surrogate for herbicide application timing, and these data were obtained from weekly planting progress data for the northeastern Missouri crop reporting district (USDA-NASS, 1992-2009). They are shown in Figure 4 with the 1992-2005 data included to demonstrate the interannual variability in the field operations.…”

Section: Timing Of Applicationmentioning

confidence: 99%

A simple index explains annual atrazine transport from surface runoff-prone watersheds in the north-central USA

et al. 2012

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Abstract:Year-to-year dynamics in weather affect both the timing of application and the potential hydrologic transport of pesticides. Further, the most commonly used pesticides dissipate in the environment during the growing season. Interactions among these factors -hydrology, timing of application and dissipation kinetics -hinder the detection of temporal trends in transport. It is increasingly important to be able to discern such trends, to judge effectiveness of management practices or to determine whether observed changes were caused by management or weather. In previous work, a cumulative vulnerability index was developed to account for these three factors. It explained 63% of annual variation in atrazine load in the Goodwater Creek Experimental Watershed (GCEW). The objectives of the current work were (i) to generalize the cumulative vulnerability index to explicitly account for variation in watershed size, area treated with atrazine and average application rate; (ii) to test the overall performance on watersheds showing such variation; and (3) to test whether the generalized index properly accounted for the additional input parameters. The generalized index was tested using data from GCEW (73.7 km 2 ) and seven additional watersheds in the northeast Missouri claypan region that varied in size from 212 to 1180 km 2 and from 4% to 23% of watershed area planted to corn or sorghum. Across 32 site-years, the generalized index explained 84% of variation in annual atrazine load. Further, tests of residuals showed no dependence on either watershed area or fraction of area planted to corn and sorghum, indicating that these parameters were properly integrated into the index. The performance of the index supports the conclusion that data obtained from GCEW

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Data partnership synergy: The Cropland Data Layer

Cited by 6 publications

References 8 publications

Epidemiologic Trends of Cleft Lip and Palate in a Southern State: A 30-Year Follow-Up

Epidemiologic Trends of Cleft Lip and Palate in a Southern State: A 30-Year Follow-Up

Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

A simple index explains annual atrazine transport from surface runoff-prone watersheds in the north-central USA

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