On the quality of USDA gridded crop condition layers
Abstract Precise, accurate, and reliable crop condition data continues to be in demand for farmers, agribusiness, government agencies, agroclimatologists, and research institutions. This study evaluated the data quality of four major United States field crops: corn (Zea mays L.), cotton (Gossypium h...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
|
| Series: | Agrosystems, Geosciences & Environment |
| Online Access: | https://doi.org/10.1002/agg2.70087 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849419077746950144 |
|---|---|
| author | Logan R. Bundy Vittorio A. Gensini Walker S. Ashley Alex M. Haberlie |
| author_facet | Logan R. Bundy Vittorio A. Gensini Walker S. Ashley Alex M. Haberlie |
| author_sort | Logan R. Bundy |
| collection | DOAJ |
| description | Abstract Precise, accurate, and reliable crop condition data continues to be in demand for farmers, agribusiness, government agencies, agroclimatologists, and research institutions. This study evaluated the data quality of four major United States field crops: corn (Zea mays L.), cotton (Gossypium hirsutum L.), soybeans (Glycine max L.), and winter wheat (Triticum aestivum L.) from the USDA National Agricultural Statistics Service's (NASS) Gridded Crop Progress and Condition dataset. Upon aggregating the weekly 9 km gridded data to the county level (and further to the state and national level) over the 2015–2023 period, no statistically significant differences emerged between the gridded condition data and the tabular condition data from the USDA NASS Crop Progress and Condition Report (CPCR). In line with state and national‐level analyses, a strong linear relationship between crop conditions and yield existed at the county scale. County‐level crop condition ratings were a statistically significant covariate of yield during the critical reproduction period through harvest for 90% of corn, 78% of cotton, 90% of soybean, and 96% of winter wheat‐producing counties. In addition, intramonthly county‐level crop conditions changed accordingly based on the magnitude of temperature and precipitation anomalies during certain phenological stages. In at least 80% of counties for each respective crop, temperatures and precipitation were statistically significant covariates for crop condition changes. The relationships between USDA NASS gridded crop condition data, CPCR data, yield, and climate substantiate the utility and fidelity of this dataset as a representation of confidential crop condition reports, supporting its practical application in research and operational decision‐making. |
| format | Article |
| id | doaj-art-dbc364e40d4e4e83856c0d96ffd1b14c |
| institution | Kabale University |
| issn | 2639-6696 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Agrosystems, Geosciences & Environment |
| spelling | doaj-art-dbc364e40d4e4e83856c0d96ffd1b14c2025-08-20T03:32:15ZengWileyAgrosystems, Geosciences & Environment2639-66962025-06-0182n/an/a10.1002/agg2.70087On the quality of USDA gridded crop condition layersLogan R. Bundy0Vittorio A. Gensini1Walker S. Ashley2Alex M. Haberlie3Department of Earth, Atmosphere, and Environment Northern Illinois University Dekalb Illinois USADepartment of Earth, Atmosphere, and Environment Northern Illinois University Dekalb Illinois USADepartment of Earth, Atmosphere, and Environment Northern Illinois University Dekalb Illinois USADepartment of Earth, Atmosphere, and Environment Northern Illinois University Dekalb Illinois USAAbstract Precise, accurate, and reliable crop condition data continues to be in demand for farmers, agribusiness, government agencies, agroclimatologists, and research institutions. This study evaluated the data quality of four major United States field crops: corn (Zea mays L.), cotton (Gossypium hirsutum L.), soybeans (Glycine max L.), and winter wheat (Triticum aestivum L.) from the USDA National Agricultural Statistics Service's (NASS) Gridded Crop Progress and Condition dataset. Upon aggregating the weekly 9 km gridded data to the county level (and further to the state and national level) over the 2015–2023 period, no statistically significant differences emerged between the gridded condition data and the tabular condition data from the USDA NASS Crop Progress and Condition Report (CPCR). In line with state and national‐level analyses, a strong linear relationship between crop conditions and yield existed at the county scale. County‐level crop condition ratings were a statistically significant covariate of yield during the critical reproduction period through harvest for 90% of corn, 78% of cotton, 90% of soybean, and 96% of winter wheat‐producing counties. In addition, intramonthly county‐level crop conditions changed accordingly based on the magnitude of temperature and precipitation anomalies during certain phenological stages. In at least 80% of counties for each respective crop, temperatures and precipitation were statistically significant covariates for crop condition changes. The relationships between USDA NASS gridded crop condition data, CPCR data, yield, and climate substantiate the utility and fidelity of this dataset as a representation of confidential crop condition reports, supporting its practical application in research and operational decision‐making.https://doi.org/10.1002/agg2.70087 |
| spellingShingle | Logan R. Bundy Vittorio A. Gensini Walker S. Ashley Alex M. Haberlie On the quality of USDA gridded crop condition layers Agrosystems, Geosciences & Environment |
| title | On the quality of USDA gridded crop condition layers |
| title_full | On the quality of USDA gridded crop condition layers |
| title_fullStr | On the quality of USDA gridded crop condition layers |
| title_full_unstemmed | On the quality of USDA gridded crop condition layers |
| title_short | On the quality of USDA gridded crop condition layers |
| title_sort | on the quality of usda gridded crop condition layers |
| url | https://doi.org/10.1002/agg2.70087 |
| work_keys_str_mv | AT loganrbundy onthequalityofusdagriddedcropconditionlayers AT vittorioagensini onthequalityofusdagriddedcropconditionlayers AT walkersashley onthequalityofusdagriddedcropconditionlayers AT alexmhaberlie onthequalityofusdagriddedcropconditionlayers |