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.
US geospatial crop frequency data layers
Information on future crop specific planting is valuable for improving agricultural survey estimates. This information is critical for agricultural production planning, agricultural product commodity inventory control, natural resource allocation and conservation, etc. However, future crop planting details are generally unavailable. This paper proposes to use crop specific planting frequency data as indicators to indirectly provide information regarding future crop planting. A methodology to derive crop planting frequency data layers based on 2008-2013 Cropland Data Layers has been presented in this paper. Crop frequency layers for corn, soybeans, wheat and cotton were successfully built at the national level and for two states including Indiana and Mississippi, USA. Multi-year (2008-2013) Farm Service Agency (FSA) Common Land Unit (CLU) data were utilized to assess the accuracy of the derived crop frequency data layers. The accuracies of the national scale crop frequency data layers are 91.00%, 90.13%, 87.67% and 85.96% for corn, cotton, soybean and wheat respectively.
Athenian Democracy at War
Classical Athens perfected direct democracy. The plays of this ancient Greek state are still staged today. These achievements are rightly revered. Less well known is the other side of this success story. Democratic Athens completely transformed warfare and became a superpower. The Athenian armed forces were unmatched in size and professionalism. This book explores the major reasons behind this military success. It shows how democracy helped the Athenians to be better soldiers. For the first time David M. Pritchard studies, together, all four branches of the armed forces. He focuses on the background of those who fought Athens' wars and on what they thought about doing so. His book reveals the common practices that Athens used right across the armed forces and shows how Athens' pro-war culture had a big impact on civilian life. The book puts the study of Athenian democracy at war on an entirely new footing.