Spring 2027

Introduction to Precision Agriculture

Introduction to Precision Agriculture covers managing agricultural production systems, crop variability, geographic positioning, remote/proximal sensing, and variable-rate prescription technologies.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 2

Recent recorded grades — Fall 2025: 3.55 GPA, 78.6% A/AB (n=28 letter grades).

What you’ll be able to do

  • Define key terminology related to agricultural production systems and precision agriculture technologies.

  • Recognize factors contributing to variability in yield and quality of agricultural products.

  • Explain the technologies used to predict spatial and temporal variability in plant growth and development.

  • Assess management tools designed to optimize input usage and reduce environmental impact.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Sophomore standing

  • Sophomore standing
BSE 305

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Sophomore standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Fall 2025: 3.55 GPA, 78.6% A/AB (n=28 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Fundamentals of agricultural production systems and sources of crop variability.
  • Geographic positioning and information systems for monitoring spatial variability.
  • Temporal observation techniques using remote and proximal sensing systems.
  • Technologies for implementing and managing variable-rate prescriptions.

Skills

  • Recognize factors contributing to variability in yield and quality of agricultural products.
  • Explain the technologies used to predict spatial and temporal variability in plant growth and development.
  • Assess management tools designed to optimize input usage and reduce environmental impact.

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F
More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records

No offering records for the selected term.

Raw records 
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "c2508e0d0478b15d00b6161833c3da0b84cb757df0910823043599a0aab8b33b",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}