A novel method for area frame stratification based on geospatial crop planting frequency data layers
This paper proposes a novel method for land cover area frame stratification based on corn planting frequency and percent cultivation. South Dakota U.S. geospatial crop frequency (2008-2013) and cultivation (2013) data layers created from NASS Cropland Data Layers are utilized to develop a novel area sampling frame (ASF) stratification design. Eight corn planting frequency strata are derived using a k-means clustering method based on mean corn planting frequency calculated at the NASS ASF primary sampling unit level. The corn planting frequency strata are then sub stratified based on percent cultivation, which, together, provide more crop specific information than the current NASS ASF based on percent cultivation alone. Using 2014 Farm Service Agency Common Land Unit Data as in situ validation, it is found that this novel ASF design predicts crop specific planting patterns well. These results indicate that the new stratification method has potential to improve ASF accuracy, efficiency and crop estimates.
Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers
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.
Development of a Web‐Based Decision Support System For Crop Managers:Structural Considerations and Implementation Case
Rapid developments since the mid 1990s of the Internet and the World Wide Web have created great opportunities to develop new approaches for the transfer of technology to farmers in the form of online computer-assisted decision support systems. This paper discusses structural considerations regarding the development of Web-based decision support systems in general and describes the development and deployment of The Crop Weather Program for South Texas, a decision support system for crop managers using modern Web-based technology. The primary goal of this online system was to provide cotton growers easy access to (a) weather data collected by a network of weather stations and (b) a suite of powerful numerical simulation tools for field-specific calculations of a variety of state variables describing the crop and its environment, such as heat unit calculation, occurrence of phenological events, soil moisture content, canopy growth, and crop water use. The online tools currently available to registered users are briefly described. After more than 6 yr, the Crop Weather Program for South Texas is still growing and continues to be a robust, reliable, and easy-to-use Web-based decision support system for crop managers.