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Martin, R
Yost, M
McArthor , B
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Authors
Cointault, F
Marin, A
Journaux, L
Miteran, J
Martin, R
Conway, L
Yost, M
Kitchen, N
Sudduth, K
Myers, B
Bobryk, C.W
Yost, M
Kitchen, N
Skouby, D
Schumacher, L
Yost, M
Kitchen, N.R
Stewart, S
Kitcken, N
Yost, M
Conway, L
Flint, E.A
Yost, M
Hopkins, B.G
McArthor , B
Prestholt, A
Kyveryga, P
Topics
Modeling and Geo-statistics
Spatial Variability in Crop, Soil and Natural Resources
Agricultural Education
Site-Specific Nutrient, Lime and Seed Management
In-Season Nitrogen Management
Decision Support Systems
Type
Oral
Year
2010
2016
2018
2022
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Authors

Filter results7 paper(s) found.

1. Wheat Growth Stages Discrimination Using Generalized Fourier Descriptors In Pattern Recognition Context

... F. Cointault, A. Marin, L. Journaux, J. Miteran, R. Martin

2. Claypan Depth Effect on Soil Phosphorus and Potassium Dynamics

Understanding the effects of fertilizer addition and crop removal on long-term change in spatially-variable soil test P (STP) and soil test K (STK) is crucial for maximizing the use of grower inputs on claypan soils. Using apparent electrical conductivity (ECa) to estimate topsoil depth (or depth to claypan, DTC) within fields could help capture the variability and guide site-specific applications of P and K. The objective of this study was to determine if DTC derived from ECa... L. Conway, M. Yost, N. Kitchen, K. Sudduth, B. Myers

3. Field Potential Soil Variability Index to Identify Precision Agriculture Opportunity

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These concerns... C.W. Bobryk, M. Yost, N. Kitchen

4. A Content Review of Precision Agriculture Courses Across the US

Knowledge of what precision agriculture (PA) content is currently taught across the United States will help build a better understanding for what PA instructors should incorporate into their classes in the future. The University of Missouri partnered with several universities throughout the nation on a USDA challenge grant. Precision Agriculture faculty from 24 colleges/universities from across the U.S. shared their PA content by sharing their syllabi from 43 different courses. The syllabi were... D. Skouby, L. Schumacher, M. Yost, N.R. Kitchen

5. Optimizing Corn Seeding Depth by Soil Texture to Achieve Uniform Stand

Corn (Zea mays L.) yield potential can be affected by uneven emergence. Corn emergence is influenced by both management and environmental conditions. Varying planting depth and rate as determined by soil characteristics could help improve emergence uniformity and grain yield. This study was conducted to assess varying corn seeding depths on plant emergence uniformity and yield on fine- and coarse-textured soils. Research was conducted on alluvial soil adjacent to the Missouri river with contrasting... S. Stewart, N. Kitcken, M. Yost, L. Conway

6. Variable Rate Nitrogen Approach in a Potato-wheat-wheat Cropping System

Nitrogen application in agriculture is a vital process for optimal plant growth and yield outcomes. Different factors such as topography, soil properties, historical yield, and crop stress affect nitrogen (N) needs within a field. Applying variable N within a field could improve precision agriculture. Optimal N management is a system that involves applying a conservative variable base rate at or shortly after planting followed by in-season assessment and, if needed, variable rate application—with... E.A. Flint, M. Yost, B.G. Hopkins

7. Soybean Variable Rate Planting Simulator Using Economic Scenarios

Soybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implementation.... B. Mcarthor , A. Prestholt, P. Kyveryga