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Instructor(s)
⏱ Duration: 10 Weeks | 20 Contact Hours (2 Hours per Week) | Equivalent to 1 Credit Hour Mode of Delivery: Hybrid (online + in-person sessions) Class Size: 20-25 participants
Course Description
Basics of Organic Agriculture (MC55002) addresses the global organic food market's growth to $437B by 2026 and Bangladesh's rising demand for organic products. With increasing consumer preference for sustainable produce and government support for organic farming, this course equips learners with practical skills in soil management, crop production, and certification processes. Through hands-on training and field visits, participants will contribute to sustainable food systems and tap into local/export markets.
Evidence of Demand
Course Objectives
This course aims to:
Course Content & Class Plan (Modules)
Practical & Field Work
Learning Outcomes
After completing the course, participants will be able to:
Target Audience & Requirements
Career Pathways
After completing this micro-credential, learners may:
Tools & Resources
Assessment Criteria
Financial Information
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Uswatun Hasana Hashi : Assistant Professor, Department of Agricultural Science, Faculty of Agriculture Science, Daffodil International University
Duration: 2 weeks
Mode of Delivery: Blended learning
This course is designed to equip undergraduate and graduate students from agricultural and non-agriculture backgrounds with practical skills in designing, implementing, and managing field-based agronomic experiments. The course covers experimental layout, sampling protocols, use of field instruments, data collection, quality assurance, and preliminary data analysis to support evidence-based agricultural decision-making.
There is an increasing need for skilled field technicians in agricultural R&D. There is strong demand from public research institutes, private seed/agrochemical companies, and NGOs for professionals capable of data-driven crop management.
This course aims to:
Teach practical skills in designing, implementing, and managing field-based agronomic experiments.
Train participants in experimental layout and sampling protocols.
Familiarize learners with the use of field instruments and proper data collection methods.
Provide foundational knowledge in data quality assurance and preliminary data analysis to support evidence-based agricultural decision-making.
Module 1: Principles of Experimental Design (RCBD, Split-Plot, Factorial Designs)
Module 2: Field Plot Techniques (Land preparation, plot demarcation, treatment application)
Module 3: Sampling Methods (Plant sample, Soil, plant tissue, pest/disease, and yield sampling)
Module 4: Field Instrumentation (Use of different instruments used in agronomic research, soil probes, moisture meters, different sensors, etc.)
Module 5: Data Collection Protocols (Paper-based vs. electronic data capture, field notebooks)
Module 6: Data Quality Assurance
Module 7: Explanation of Statistical Analysis (ANOVA, mean separation, interpretation)
Module 8: Report Writing and Presentation of Field Results.
The course utilizes a blended delivery mode, with 60% of the training consisting of in-person field practical sessions at designated research farms.
Practical elements include field assignments for layout and sampling, as well as compiling a Field Data Collection Portfolio with quality checks.
After completing the course, participants will be able to:
Design and lay out a statistically sound field experiment.
Apply proper sampling techniques for soil, crops, and pests.
Operate standard field data collection instruments and sensors.
Collect, record, and manage field data with accuracy and integrity.
Perform basic statistical analysis and interpret experimental results.
Prepare a professional technical report on field research findings.
Target Audience:
Undergraduate and graduate students from agricultural and non-agriculture backgrounds.
Early-career agronomists, agricultural extension officers, and research assistants.
Graduate students in crop science and field technicians working in agricultural research stations or commercial farming operations.
Entry Requirements:
A diploma or currently studying Undergraduate in Agriculture, Agronomy, Crop Science, Soil Science, or a related field.
Minimum age of 18 years.
Facilities & Locations: Access to experimental field plots.
Instruments: Field instruments including lux meters, LAI, soil augers, moisture meters, GPS units, weighing scales, and canopy sensors.
Technology: Laptops/tablets with spreadsheet software (MS Excel, R, or SPSS) and internet access for online modules.
Materials: Printed field notebooks and data sheets.
After completing this micro-credential, learners may:
Proceed to advanced courses in Experimental Design and Data Analysis in Agronomy, Applied Field Research (Design, Data Management, Analysis and Reporting), or Advanced Agricultural Research Methods.
Use this credential as a foundation for pursuing a Master's degree in Agronomy or Plant Sciences.
Practical Field Assignment (Layout & sampling) – 30%
Field Data Collection Portfolio (with quality checks) – 30%
Final Data Analysis Report (using real field data) – 30%
Online Quiz (Theory & design principles) – 10% (Note: A pass mark of 60% is required out of the total 100%).
Achieving the pass mark of 60% across the practical and theoretical assessments qualifies the participant for course completion.
Tentative Course Fee: 1500 BDT
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Join thousands of students already enrolled
Md. Istiak Hossain Joy, Lecturer, Department of Agricultural Science (AGS), Daffodil International University (DIU).
Dr. Shihab Uddin, Assistant Professor, Department of Soil Science, Bangladesh Agricultural University (BAU).
Atiqur Rahman, Scientific Officer, Plant Breeding Division, Bangladesh Rice Research Institute (BRRI).
⏱ Duration: 06 Weeks | 16–18 Hours (10–12 Sessions) | Mode: Online (Google Meet)
This micro-credential course equips agriculture and environmental science students with practical, workbench-level competencies in statistical data analysis, visualization using R and RStudio, academic thesis formulation, scientific report writing, and journal manuscript submission. Additionally, it provides structured coaching for agricultural research station job preparation, covering recruitment procedures, written technical assessments, and interview strategies.
Agriculture students often lack practical skills in advanced data analysis using RStudio, academic thesis writing, scientific report preparation, and journal article submission. They also have limited exposure to agricultural research station recruitment procedures, technical examinations, and interview preparation. These gaps reduce their readiness for higher education, research publication, and professional employment. Training in RStudio, data visualization, thesis composition, and job screening addresses this critical skill gap.
The course aims to equip agriculture students with practical skills in advanced data analysis using RStudio, academic thesis writing, scientific report preparation, research article submission, and agricultural research station job preparation, enhancing their research competencies and career readiness.
Specific objectives include:
Develop practical skills in statistical data analysis, interpretation, and visualization using R and RStudio.
Equip learners with standard academic thesis writing, research organization, citation, and referencing conventions.
Build competencies in scientific report writing, manuscript preparation, journal selection, and submission procedures.
Prepare candidates for agricultural research station recruitment through guidance on written exams, technical assessments, and interview dynamics.
Module 1: Introduction to Research Methodology and RStudio (2 hrs)
Module 2: Statistical Data Analysis and Visualization Using R-Studio (4–5 hrs)
Module 3: Academic Thesis Writing Guidelines (3–4 hrs)
Module 4: Scientific Report Writing & Article Submission Guidelines (2 hrs)
Module 5: Guidelines for Agricultural Research Station Job Preparation (2 hrs)
Hands-on statistical analysis routines and scripting using RStudio via interactive Google Meet lab sessions.
Data transformation, hypothesis testing, model interpretation, and graphic generation/data visualization exercises.
Practical assignments structuring academic thesis chapters and reference lists.
Manuscript drafting workshop: scientific report composition and mock journal submission workflows based on selected research topics.
After completing the course, participants will be able to:
Conduct statistical data analysis and data visualization using R and RStudio.
Prepare academic theses following standard writing, structuring, and referencing guidelines.
Write scientific reports and research articles and navigate formal journal submission procedures.
Apply strategic guidelines for agricultural research station recruitment examinations and interviews.
Target Audience:
Undergraduate students in Agricultural Science or Environmental Science.
MS students and early-career scientists working in agricultural research sectors.
Entry Requirements:
Background in Agricultural Science, Environmental Science, Soil Science, Genetics, or related biological disciplines.
Basic computer literacy and fundamental knowledge of agricultural concepts.
Access to a computer/laptop with R and RStudio installed and a stable internet connection.
Minimum Age: 18 Years.
After completing this micro-credential, learners may:
Work as Agricultural Research Assistant, Young/Undergraduate Researcher, or Quality Control Analyst (Agri-Tech).
Successfully compete in recruitment examinations for national agricultural research stations (e.g., BRRI, BARI, BINA).
Progress to Advanced Micro-Credentials, postgraduate higher studies (MS/PhD), or international research fellowships.
Software & Digital Tools: R and RStudio software environment, Google Meet for live practical sessions.
Facilities & Hardware: Personal computer or laptop with active internet connectivity.
Learning Materials: Lecture slides, code scripts, demo agricultural datasets, thesis formatting guidelines, and job preparation briefs.
Aligned with Outcome-Based Education (OBE) frameworks:
Practical Assignments (R-Studio Data Analysis & Visualization – LO1): 60%
Report Writing Project based on Research Topic (Thesis & Article Writing – LO2 & LO3): 40% (Recommended Benchmarks: Minimum 80% attendance, minimum 60% pass score)
Tentative Course Fee: BDT 1500/-
Class Size: 30 to 60 participants (Maximum 60 per operational batch)
Certification: Micro-Credential Certificate issued by DIU Micro-Credentials Academy upon successful assessment completion
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