Topics
in Precision Agriculture |
BIOEN/SOIL
4213 |
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M-W-F 1:30 to 2:20 | Room 202 | ||||

Date |
Subject | Description | Classroom Hours (45) | Assignment | Instructor |

17-Jan |
Round-Table | History of the Green Revolution…. Need for Precision Agriculture… Cost of N Fertilizer…. Hypoxia…...natural and man-made variability. | 1 | ALL | |

Spatial
Variability |
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19-Jan |
Inherent and acquired field variability | Become familiar with and be able to identify the sources and differences between inherent and acquired field variability (macro scale) and their influences on yield response to production inputs. Review influence of soil forming factors on morphology | 1 | Use Soil Survey maps and other field maps to separate inherent variability from acquired variability. Delineate and identify the percentage and acreage of variable units, estimate the impact of each on response to production inputs. | Johnson/Raun/Solie |

22-Jan |
Descriptive Statistics | Review mean, median, maximum, standard deviation, coefficient of variation, and variance. Apply these statistics to the 1x1 data sets. Discuss calculating these statistics in Excel. | 1 | Each student will be provided a 1x1 data set for a specific soil variable. The students will calculate the descriptive statistics for their particular data set. | Solie |

GRID
SAMPLING |
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24-Jan |
Random and Fixed (Grid) Sampling | Sampling an area on random basis will be discussed. Descriptive statistics for the underlying soil variable will be calculated. Error from the population mean will be calculated. Actual samples needed based on the population variance will be calculated | 1 | Students will "randomly" sample their 1x1 data and calculate the descriptive statistics. They will vary the number of samples and determine the error from the true value. | Solie |

26-Jan |
Grid Sampling | The use of grids to control variability will be discussed. Descriptive statistics for the underlying soil variable will be calculated for different size grids. Error from the mean population will be calculated for various grid sizes. | 1 | Students will sample their 1x1 data based on various grid sizes. They will calculate the error from the true value based on grid sample size | Solie |

29-Jan |
Grid Sampling, Future of soil sampling, grids | Examples; Georgia, data (grid sampling 100' apart) and resultant contour maps. What are MANAGEMENT ZONES? Alternatives to Grid Sampling.. | 1 | Raun | |

31-Jan |
Grid Sampling | Grid sampling (field variability, Class Experiments Conducted at EFAW) | 1 | Johnson | |

GPS |
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2-Feb |
Introduction to GPS | Describe the Navstar system and how it operates. Summarize errors in the system. | 1 | Read GPS handout | Stone |

Afternoon
Lab, 2-Feb |
GPS | GPS… Map a field bounday | Solie | ||

5-Feb |
Differential GPS | Describe DGPS technique, Standard data exchange, Different correction strategies with disadvantages, advantages and current costs. | 1 | Collect single point data with differential and DGPS using Omnistar. Plot the x,y vs. Time diagrams. | Stone |

Geostatistics/Field
Element Size |
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7-Feb |
Geostatistics | Geostatistics. Introduction, uses, applications | 1 | Solie | |

9-Feb |
Semivariance | Concept of population variation, population mean and relatedness. Calculation of semivariograms using an Excel macro. Construction of semivariograms. Interpretation of semivariograms (nugget, sill, range, departure distance, drift, pseudocycling). | 1 | Students will plot 1x1 data for seven transects | Solie |

12-Feb |
Application based on fundamental field element | Fundamental field elements will be calculated for a 1x1 data set. Interpretation of the transformed data set and its implications for variable measurement and applications will be discussed. Error will be calculated and a map of error will be built. | 1 | Students will build a new data set based on the fundamental field element size. Students will calculate the error from the true value for a fundamental field element and for a larger size element. Biological differences needed to declare significance. | Solie/Raun (note, Solie at KSU) |

Fundamentals
of Radiant Energy |
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14-Feb |
History of Using Indirect Measures for detecting Nutrient Status | Chlorophyll meters, NIR, Soil mapping… FIELD COLLECTION OF SOIL SAMPLES, 1x1m | 1 | Raun | |

16-Feb |
Absorption Spectra for Plants and Soil | Relate absorption of radiant energy in plants to chemical processes in plants. Explain the absorption of radiant energy in soils. Explain the resulting radiance spectra in soils and plants. | 1 | Measure the radiance of plants and soil using a spectrophotometer. Develop an understanding of the difference between radiance and reflectance | Stone |

19-Feb |
Instrumentation for detection of absorbed radiation for satellites and for ground based remote sensing. | Describe the mechanics of measurement of radiance from plants. Photodiode mechanics, amplification, A/D conversion, signal handling. Describe the techniques for compensation for sunlight when artificial lighting is used. | 1 | Use serial based sensor to measure and discriminate between plant material and soils. | Stone |

21-Feb |
Patchen Sensor and EM-VERIS | How the Patchen Sensor Works. What actual conductance (Electromagnetic induction) is used for/agronomic significance | 1 | Stone/Raun | |

Agronomic
Issues |
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23-Feb |
Review of Bray and Mitscherlich response models | Understand the relationship of realistic yield goal and crop N requirement. Understand the influence of P, K, and pH levels on yield potential and N crop requirement. | 1 | Optional (instructor may do this in class): Calculate a "realistic" yield goal from a long-term N-trial data base. Calculate influence of P or K deficiency on response to N at different yield goals | Raun |

26-Feb |
Soil/Plant Analyses Review | Discuss how sensor based technologies will ultimately replace soil testing. What should be learned from soil testing? Address the importance of Bray's mobility concept relative to using sensors. Importance of subsoil nutrient availability. | 1 | Raun | |

28-Feb |
Use of Organic C and Soil Texture | Applications of using indirect measures of organic C, and soil texture for the ultimate refinement of 'yield potential.' | 1 | Raun | |

2-Mar |
Correlation/Calibration/Recommendation | Describe the steps involved in taking an indirect measure of nutrient status (e.g., spectral radiance) all the way to a nutrient recommendation. Limited discussion will focus on the use of statistical models for interpretation of response. | 1 | Students will be given example data (soil and sensor) and from this data they will establish a sufficiency table, establish yield goals, relate yield goals to fertilizer need and ultimately make a recommendation. | Raun |

5-Mar |
Interfering factors affecting the use of sensors for indirect measures of nutrient status | Delineate interfering agronomic factors that are and/or will be encountered when using indirect measures for determining nutrient status of crops (weeds, clouds, variety, stage of growth, freeze damage, soil coverage, etc.) | 1 | Example data will be provided where interfering factors are known. Can they detect the interference? How can it be removed? Influence of soil coverage? | Raun |

7-Mar |
Late-season Prediction of Protein & Yield | History of OSU's research in this area, and why it is important to match predicted yield potential with a specific resolution… Field Collection of NDVI Data from same spots soil sampled on February 2 | 1 | Raun | |

9-Mar |
RI harvest, RI NDVI, NFOA | Predicting yield, predicting the response index (RI), using RI to adjust N based on yield potential. | 1 | Raun/Johnson | |

12-Mar |
Analyzing and displaying spatial data | Introduction to GIS. Create maps and tables of properties associated with layer. Calculate new layers based on mathematical and/or logical combinations of properties of other layers. Summarize results as tables, maps, and graphs. | 1 | Display point data with associated values. Given set of data, have students draw contours by hand. Draw maps of yield data and sensor data for same field. Tabulate results and areas. Calculate total yield for the field and compare it to the amount hauled | Solie |

Afternoon
Lab, 12-Mar |
Analyzing and displaying spatial data | Mapping with the Patchen Sensor (field project) | Solie | ||

14-Mar |
Analyzing and displaying spatial data (continued) | Create maps and tables of properties associated with layer. Calculate new layers based on mathematical and/or logical combinations of properties of other layers. Summarize results as tables, maps, and graphs. | 1 | Display point data with associated values. Given set of data, have students draw contours by hand. Draw maps of yield data and sensor data for same field. Tabulate results and areas. Calculate total yield for the field and compare it to the amount hauled | Solie |

TOOL
BOX |
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16-Mar |
ToolBox | Tool Box | 1 | Solie | |

16-Mar |
ToolBox LAB | Tool Box LAB | Solie | ||

19-23
Mar |
SPRING BREAK | ||||

26-Mar |
Remote Sensing | Satellite & Aerial images | 1 | Solie | |

28-Mar |
Yield Mapping | Yield mapping, variable rate application mechanisms, yield and grain moisture sensors. How they work, their strengths, and their limitations | 1 | Solie | |

30-Mar |
1x1, at what resolution do we make agronomic decisions? | True variability (X study and other data). Combine induced error. How can we use yield maps. | 1 | Solie/Raun | |

2-Apr |
Variable Rate Mechanisms | What can be controlled? Liquid - Droplet flight time, flow rate control by pressure, droplet size, quad-binary. Granules flight time, metering gate control, swath width for various distribution mechanisms. | 1 | Solie for Whitney | |

Applications of VRT | |||||

4-Apr |
Estimating RI, and making a fertilizer Recommendation, Understanding Yield Goals, Yield Potential and INSEY | Field measurements, field application, field implementation of NFOA (Efaw or Stillwater). Use of hand-held sensor, and algorithm developed at OSU | 1 | Raun | |

6-Apr |
Landscape/Topography | Importance of topography and position relative to predicting yield potential (erosion losses/gains) | 1 | Raun | |

9-Apr |
Evaluate the economics of variable rate N and P applications | Compare and contrast the economic return from variable and constant rate application of N and P to a field of known variability, when the field element size for treatment is changed from whole field to one yard2. | 1 | Using Excel and terms from NPK$PLUS model, calculate return from variable rate fertilizer application under different nutrient deficiency levels, crops, fertilizer prices, commodity prices, and growing conditions (yield potential) | Johnson |

11-Apr |
Evaluate the economics of variable rate N and P applications | cont. | 1 | Johnson | |

13-Apr |
MAP Warnings | Errors and uncertainty in data and results. How to lie with maps (unintentionally, of course)! | 1 | Gregory | |

16-Apr |
Student Presentations | 1 | |||

18-Apr |
Student Presentations | 1 | |||

20-Apr |
Student Presentations | 1 | |||

23-Apr |
Student Presentations | 1 | |||

25-Apr |
Student Presentations | 1 | |||

27-Apr |
Student Presentations | 1 | |||

30-Apr |
Student Presentations | 1 | |||

2-May |
Student Presentations | 1 | |||

4-May |
OPEN DISCUSSION | Do precision agriculture and/or variable rate technologies equate with environmental safety | 1 | ALL | |

TOTAL Hours | 44 | ||||

7-11
May |
FINALS WEEK | ||||

8-May |
2:30 Final Exam | ||||

GRADING PROCEDURES | |||||

Homework (inst. specific) | 50% | ||||

Final Exam | 50% | ||||

A 90-100; B 80-89; C 70-79; D 60-69; F<60 | |||||

Instructors | Department | Office | Telephone | ||

Dr. Bill Raun | Plant and Soil Sciences | 044 N. Ag. Hall | 744-6418 | ||

Dr. John Solie | Biosystems and Agricultural Engineering | 112 S. Ag. Hall | 744-7893 | ||

Dr. Marvin Stone | Biosystems and Agricultural Engineering | 213 S. Ag. Hall | 744-4337 | ||

Dr. Gordon Johnson | Plant and Soil Sciences | 269 N. Ag. Hall | 744-9590 | ||

Dr. Mark Gregory | Plant and Soil Sciences | 271 N. Ag. Hall | 744-9603 |