Please wait a minute...
Journal of Integrative Agriculture  2017, Vol. 16 Issue (02): 312-323    DOI: 10.1016/S2095-3119(16)61396-5
Section 3: Cropland cover mapping and change Advanced Online Publication | Current Issue | Archive | Adv Search |
Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers
Claire G. Boryan1, Zhengwei Yang1, Patrick Willis1, Liping Di2

1 Research and Development Division, USDA National Agricultural Statistics Service, VA 22030, USA

2 Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA

Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
Abstract  Area Sampling Frames (ASFs) are the basis of many statistical programs around the world.  To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed.  This paper investigates using 2008–2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota (SD) U.S. ASF stratifications.  Corn, soybeans and wheat are three major crops in South Dakota.  The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit (PSU) level based on the Crop Frequency Data Layers.  The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer.  The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service (NASS) ASF based on percent cultivation alone.  Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers.  It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency (FSA) Common Land Unit (CLU) and 578 administrative data.  This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops.  Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.
Keywords:   cropland data layer      crop planting frequency data layers      automated stratification      crop specific stratification      multi-crop stratification  
Received: 22 October 2015   Accepted:
Corresponding Authors:  Claire G. Boryan, Tel: +1-202-690-0449, E-mail:    

Cite this article: 

Claire G. Boryan, Zhengwei Yang, Patrick Willis, Liping Di. 2017. Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers. Journal of Integrative Agriculture, 16(02): 312-323.

Arroway P, Abreu D A, Lamas A C, Lopiano K K, Young L Y. 2010. An alternate approach to assessing misclassification in JAS. In: Proceedings of the Section on Survey Research Methods JSM 2010. American Statistical Association, Alexandria, VA.
Benedetti R, Postiglione P, Piersimoni F. 2015. Sampling Spatial Units for Agricultural Surveys. Advances in Spatial Science. Springer, Berlin.
Bethel J. 1989. Sample allocation in multivariate surveys. Survey Methodology, 15, 47–57.
Boryan C, Yang Z. 2014. Operational implementation of a new automatic stratification method using geospatial cropland data layers in the NASS area frame section. In: Proceedings of IGARSS 2014, IGARSS 2014 & 35th Canadian Symposium on Remote Sensing. Quebec City, Canada.
Boryan C, Yang Z, Di L. 2012. Deriving 2011 cultivated land cover data sets using USDA national agricultural statistics service historic Cropland Data Layers, In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium. July 22–27, 2012. Munich, Germany.
Boryan C, Yang Z, Di L, Hunt K. 2014a. A New automatic stratification method for U.S. agricultural area sampling frame construction based on the Cropland Data Layer. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4317–4327.
Boryan C, Yang Z, Mueller R, Craig M. 2011. Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service Cropland Data Layer Program. Geocarto International, 5, 341–358.
Boryan C G, Yang Z, Willis P. 2014b. US geospatial crop frequency data layers. In: Proceedings of the Third International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2014), August 11–14, 2014. IEEE, Beijing, China.
Boryan C G, Yang Z, Willis P. 2015. A novel method for area frame stratification based on geospatial crop planting frequency data layers.  In: Proceedings of IEEE, International Geoscience and Remote Sensing Symposium.July 26–31, 2015. IEEE, Milan, Italy.
Carfagna E, Gallego F J. 2005. Using remote sensing for agricultural statistics. International Statistical Review, 73, 389–404.
Census of Agriculture. 2012. [2016-1-23].
Cotter J, Davies C, Nealon J, Roberts, R. 2010. Area frame design for agricultural surveys. In: Benedetti R, Bee M, Espa G, Piersimoni F, eds., Agricultural Survey Methods. John Wiley & Sons, Chichester, UK.
Cotter J, Tomczac C M. 1994. An image analysis system to develop area sampling frames for agricultural surveys, Photogrammetric Engineering and Remote Sensing, 60, 299–306.
Faulkenberry G, Garoui A. 1991. Estimating a population total using an area frame. Journal of the American Statistical Association, 86, 445–449.
Ford B I, Nealon J, Tortora R D. 1986. Area frame estimators in agricultural surveys - Sampling versus nonsampling errors. Agricultural Economics Research, 38, 1–10.
Forgy E. 1965. Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Biometrics, 21, 768–769.
FSA (Farm Service Agency). 2014. Farm service agency (FSA) common land unit (CLU) information worksheet. [2015-9-10].
Gallego F J, Feunette I, Cargagna E. 1994. Two stage area frame sampling on square segments for farm surveys. Survey Methodology, 20, 107–115.
Gallego J, Delincè J. 2010. The European land use and cover area-frame statistical survey. In: Benedetti R, Bee M, Espa G, Piersimoni F, eds., Agricultural Survey Methods, John Wiley & Sons, Chichester, UK. pp. 151–168.
Han H, Yang Z, Di L, Mueller R. 2012. CropScape: A web service based  application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computer and Electronics in Agriculture, 84, 111–123.
Hanuschak G A, Morrissey K M. 1978. Pilot Study of the Potential Contributions for Landsat Data in the Construction of Area Sampling Frames, Statistical Reporting Service Staff Report 1977. Washington, D.C.
Hartigan J A, Manchek A W. 1979. Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society (Series C: Applied Statistics), 28, 100–108.
Heard J. 2002. USDA establishes a common land unit. [2015-9-24].
MacQueen J B. 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press, USA. pp. 281–297.
Nusser S M, House C C. 2009. Sampling, data collection, and estimation in agricultural surveys. In: Pfeffermann D, Rao C R, eds., Handbook of Statistics 29A, Sample Surveys: Design, Methods and Applications. Elsevier, The Netherlands. pp. 471–486.
Pradhan S. 2001. Crop area estimation using GIS, remote sensing and area frame sampling. International Journal of Applied Earth Observation and GeoInformation, 3, 86–92.
Tsiligirides T. 1998. Remote sensing as a tool for agricultural statistics: A case study of area frame sampling methodology in Hellas. Computers and Electronics in Agriculture, 20, 45–77.
USDA, NASS. 2016. Cropland data layer, cultivation layer and crop frequency data layer “Metadata”. [2016-1-13].
Villalobos A G, Wallace M A. 1998. Multiple Frame Agricultural Surveys. Volume 2: Agricultural Survey Programmes Based on Area Frame or Dual Frame (Area and List) Sample Designs. FAO Statistical Development Series no. 10, Rome. p. 274.
Vogel F A. 1995. The evolution and development of agricultural statistics at the United States Department of Agriculture. Journal of Official Statistics, 11, 161–180.
Workneh F,  Tylka G, Yang X, Faghihi J, Ferris J. 1999. Regional assessment of soybean brown stem rot, phytophthora sojae, and heterodera glycines using area-frame sampling: Prevalence and effects of tillage. Phytopathological, 89, 204–211.
No related articles found!
No Suggested Reading articles found!