EC72-1226 Growing Vegetable Transplants

Extension circular 72-1226 shows how to grow vegetable transplants

EC73-1229 Growing and Harvesting the Cole Crops.....Broccoli, Brussels Sprouts, Cabbage, Cauliflower, Kohlrabi

Extension circular 73-1229 is about growing and harvesting the cole crops like broccoli, Brussels sprouts, cabbage, cauliflower, and kohlrabi

Growing Degree Days Predictions for Corn and Sorghum Development and Some Applications to Crop Production in Nebraska

The concept of growing degree days (GDD) originated with observations by Reamur (1735) that plant development is more closely related to the temperature accumulated to a given stage than with time alone. It was not until nearly 200 years later, however, that Merriam (1894), Livingston (1916) and Klages (1942) began to use temperature accumulations in plant distribution studies and in crop geography. In the early 1950\u27s, a system involving growing degree days became widely used in the canning industry to schedule plantings and thus control time of harvest of rapidly maturing vegetables. This system provided a more precise control of both quantity and maturity of produce delivered for processing. It had a profound effect on processing efficiency and the cost and quality of canned vegetables, particularly peas and sweet corn. This study concerns GDD requirement for the series of consecutive phenological stages of field corn (Zea mays L.) and grain sorghum (Sorghum bicolor (L.) Moench) from emergence to physiological maturity. Such data are important for crop management decisions throughout the growing season

Simulation Studies of Corn Hybrid-Climate Response in Nebraska

Crop development models can be used to determine the expected phenological responses of corn hybrids to different planting dates and locations. The number of days from planting to maturity for a particular hybrid varies considerably between different dates of planting at a single location and between locations at the same planting date

CROPSTATUS--A Computer Program to Assess the Effects of Seasonal Weather Changes on Nebraska\u27s Agriculture

CROPSTATUS is a series of programs residing in Nebraska\u27s AGNET system using daily weather data to assess seasonal changes in crops, livestock, and other agricultural conditions. Assessments are based on parameters developed from accumulations of current daily temperature and precipitation data collected from a network of synoptic, climate, and automated micrometeorological stations in Nebraska in comparison with daily normals. The daily normals were derived from monthly summaries using multiple regression models to compute daily values as a function of Julian day numbers. Crop phenology models based on growing degree days were used to monitor and forecast the progress of different crop strains and times of planting. Biological time scale statistical yield models are used for production estimates. Weather probability information is also available from CROPSTATUS. Long term climatic records have been used to determine spring and autumn freeze probabilities, preseason precipitation available for subsoil moisture recharge and the probabilities of weekly averages of daily maximum and minimum temperatures. These and other features are available in a menu of over 20 different agricultural weather information items developed from a network of 60 weather stations. CROPSTATUS is also used to prepare tabular data and computer maps showing changes in conditions throughout the state. These maps are used in meetings by an interdisciplinary committee of agricultural extension specialists to prepare weekly agweather situation/advisory reports

CROPSTATUS--A Computer Program to Assess the Effects of Seasonal Weather Changes on Nebraska\u27s Agriculture

CROPSTATUS is a series of programs residing in Nebraska\u27s AGNET system using daily weather data to assess seasonal changes in crops, livestock, and other agricultural conditions. Assessments are based on parameters developed from accumulations of current daily temperature and precipitation data collected from a network of synoptic, climate, and automated micrometeorological stations in Nebraska in comparison with daily normals. The daily normals were derived from monthly summaries using multiple regression models to compute daily values as a function of Julian day numbers. Crop phenology models based on growing degree days were used to monitor and forecast the progress of different crop strains and times of planting. Biological time scale statistical yield models are used for production estimates. Weather probability information is also available from CROPSTATUS. Long term climatic records have been used to determine spring and autumn freeze probabilities, preseason precipitation available for subsoil moisture recharge and the probabilities of weekly averages of daily maximum and minimum temperatures. These and other features are available in a menu of over 20 different agricultural weather information items developed from a network of 60 weather stations. CROPSTATUS is also used to prepare tabular data and computer maps showing changes in conditions throughout the state. These maps are used in meetings by an interdisciplinary committee of agricultural extension specialists to prepare weekly agweather situation/advisory reports

Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis

A large percentage of the students who drop out of K-12 schools in the United States do so at the end of high school, at some point after grade 10. Yet we know little about the differences between different types of students who drop out of the end of high school. The purpose of this study is to examine a typology of high school dropouts from a large nationally representative dataset (ELS:2002) using latent class analysis (LCA). We found three significantly different types of dropouts; Quiet, Jaded, and Involved. Based on this typology of three subgroups, we discuss implications for future dropout intervention research, policy, and practice

Numerical continuation in nonlinear experiments using local Gaussian process regression

Control-based continuation (CBC) is a general and systematic method to probe the dynamics of nonlinear experiments. In this paper, CBC is combined with a novel continuation algorithm that is robust to experimental noise and enables the tracking of geometric features of the response surface such as folds. The method uses Gaussian process regression to create a local model of the response surface on which standard numerical continuation algorithms can be applied. The local model evolves as continuation explores the experimental parameter space, exploiting previously captured data to actively select the next data points to collect such that they maximise the potential information gain about the feature of interest. The method is demonstrated experimentally on a nonlinear structure featuring harmonically coupled modes. Fold points present in the response surface of the system are followed and reveal the presence of an isola, i.e. a branch of periodic responses detached from the main resonance peak