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The genetic yield potential of soybeans in the Midwestern United States is estimated to be approximately 100 bushels per acre, based on results from small-plot studies. However, field and statewide average yields are much lower. Soybean yield is the result of complex interactions between genetics, management, environment, fertility, and stresses. Water stress is often viewed as the biggest underlying factor resulting in yield loss. However, other factors such as soybean cyst nematodes, Rhizoctonia root rot, and hail damage can also cause significant injury to soybean yields

Soybean composition variance in fields

Much attention has been given to determining the causes of soybean yield variability across fields. However, little attention has been given to whether seed composition may contribute to the variability. In 1998, researchers at Iowa State University measured soybean protein and oil variability across a 50-acre field in central Iowa. The field contained a single variety, and approximately 10 soybean plants were collected from 50 points uniformly distributed across the field. The seeds were sampled for oil and protein content. Protein ranged from 34.4 to 37.9 percent, whereas oil ranged from 18.1 to 19.8 percent

Using Crop Growth Models for Soybean and Corn Management

Corn and soybeans are the two primary row-crops grown in Iowa. In 1993, 11 million acres of corn was planted, with an average yield of 149 bu/ ac. In the same year, 8.1 million acres of soybean was planted, with an average yield of 44 bu/ ac (Iowa Crop Report, 1994). Growers are under increasing pressure to produce crops with minimum effects to the environment. This must be done without compromising the economic sustainability of the farm. It is becoming more difficult to determine the optimum crop production strategy because the system constraints continue to increase. In the future, farmers will rely more heavily upon the use of computers to aid in decision making to determine the optimum crop production strategy including variety selection, planting date, irrigation, pesticide applications, fertilizer strategies, and manure applications

Crop growth models and yield variability

Crop growth models are computer programs that integrate information on daily weather, genetics, management, soil characteristics, and pest stress to determine daily plant growth and subsequent yield. Researchers at Iowa State University (ISU) have used a soybean growth model as a tool to evaluate causes of yield variability in several fields in Iowa. The idea is to calibrate the model to mimic historic yields within small grids in a field. Once calibrated, the model can be used to evaluate performance of prescriptions over many different weather conditions

The Role of Water Stress in Creating Spatial Yield Variability in Soybeans

Recent advancements in yield monitors and global positioning systems that can create spatial yield maps has generated much excitement and controversy among farmers and researchers. Site-specific field management promises to maximize field level net return and minimize environmental impact by managing fields using spatially variable management practices. The success of site-specific field management depends upon discovery of relationships between environment, management, and resulting yield variability, and ultimately, how these relationships can be exploited to compute optimum prescriptions. Farmers are faced with trying to determine how to manage variability to improve profits. Researchers are trying to develop methods to analyze causes of yield variability, and determine how to develop prescriptions for fertility, and cultural practices to capitalize on variability across field. While environmental, management, soil, and pest factors have been studied for many years, researchers are just beginning to determine how these factors vary across fields, contributing to spatial yield variability

Evaluation of interactions within a shelterbelt agroecosystem

Yield data for corn (eight years) and soybeans (six years) were collected and analyzed to determine the impacts of a hybrid poplar shelterbelt on crop production on a central Iowa farm

Gestalt Principles in Ligeti’s Piano Etude “Desordre”

This document is a study of how Gestalt principles of organization are at work in “Désordre” (1985), the first etude in the first book of piano etudes by György Ligeti (1923–2006). After explaining how Gestalt principles can be applied to the analysis of music, the study presents an analysis of the etude in four main parts. The first part identifies elements of the composition that help the listener define boundaries between phrases, phrase groups, and sections. The second part discusses how foreground and background layers are articulated. The third part discusses the polytempo illusion. The fourth and final part identifies elements that contribute to large-scale unity in the composition. Finally, some pedagogical applications for teaching composition are briefly addressed

Probabilistic Neural Networks for Segmentation of Features in Corn Kernel Images

A method is presented for clustering of pixel color information to segment features within corn kernel images. Features for blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch were identified by red, green, and blue (RGB) pixel value inputs to a probabilistic neural network. A data grouping method to obtain an exemplar set for adjustment of the Probabilistic Neural Network (PNN) weights and optimization of a universal smoothing factor is described. Of the 14,427 available exemplars (RGB pixel values sampled from previously collected images), 778 were used for adjustment of the network weights, 737 were used for optimization of the PNN smoothing parameter, and 12,912 were reserved for network validation. Based on a universal PNN smoothing factor of 0.05, the network was able to provide an overall pixel classification accuracy of 86% on calibration data and 75% on unseen data. Much of the misclassification was due to overlap of pixel values among classes. When an additional network layer was added to combine similar classes (blue–eye mold and germ damage, sound germ and shadow in sound germ, and hard and soft starch), network results were significantly enhanced so that accuracy on validation data was 94.7%. Image quality was shown to be important to the success of this algorithm as lighting and camera depth of field effects caused artifacts in the segmented images