Agronomic-problems project includes analysis of one-year data collected in the field from the grain hemp nitrogen trial conducted in summer 2021. This experiment was conducted in the sandy soils of central Florida. Two hemp varieties, i.e., X-59 and Bialobrzeskie, were sown on 4 May 2021 in randomized complete block design with factorial treatment design. Six nitrogen rates i.e., 0, 56, 112, 168, 224 & 280 kg/ha were applied using urea as a nitrogen source. Further details of the data collected are provided in the wiki.

For project review, there are three CSV format files named; Grain_N_rate_trial_harvest_data_2021.csv (all data collected at harvesting), Grain_N_trial_inseason_data_2021.csv (data collected during crop growth season), and Grain_N_trial_NDVI_NDRE_data_2021.csv (data collected by the handheld sensor). Along with these, there are three R files (Grain_N_rate_trial_harvest_data_2021.R, Grain_N_trial_inseason_data_2021_2.R, Grain_N_trial_NDVI_NDRE_data_2021.R) which include codes for the data analysis.

Learning experience: This course offered a great learning experience. I acknowledge that Dr. Zachary Brym was instrumental in completing this course. I was completely new to R and git hub when I started this course. The first few weeks were a bit of a struggle because of the constant errors I experienced in my code files. But eventually, I learned how to fix errors. I learned that consistent efforts are required to learn new skills. Along with that design of the project and dividing it into weeks helped me understand and keep easy track of the project. Readings helped solve most of my questions. While doing analysis, I learned how to proceed, like starting with essential data management and visualization and then moving on to statistical models. Issues tab on git hub was beneficial as whenever I experienced errors and posted that as an issue and got them solved from Dr. Zachary Brym's guidance. Since this was a self-paced course, it was helpful to complete it based on my schedule.
For future students, this course could be a great learning experience too. Consistent efforts are essential to learning new skills like R. Try exploring other sources and watching youtube videos related to the course besides the readings provided; this can help a lot. This course provides the direction to move, and then we can explore further.

Course assessment questions:
Were the readings effective in providing background information to complete the assignment?
Answer - Yes, the readings were constructive and provided background information for completing the assignment.

Were the assignments helpful to practice programming topics presented in the reading?
Answer – Yes, I appreciate that assignments provided us with a way to see analysis and readings. There was a lot of information in the readings; assignments helped me extract the vital information that otherwise I could lose. All the codes and commands provided in the assignments were beneficial not only for the project but also for future analysis.

Was the course effectively structured around your analysis plan?
Answer – Yes, the course was effectively structured around the analysis plan.

What was your favorite thing about the course?
Answer – My favorite thing was the way assignments were framed and the issues tab.

How can the course be improved for future students?
Answer – I was entirely new to git hub and had some idea about the preface of R when I started this course. Understanding the git hub preface and then using terminal and repositories was difficult. I believe providing a link to some videos and the written readings explaining using GitHub and R will be helpful. I am also providing here link to some of the websites which I referred and were helpful.
https://thomasadventure.blog/posts/ggplot-regression-line/
https://rpkgs.datanovia.com/ggpubr/reference/stat_regline_equation.html
https://www.datacamp.com/community/tutorials/facets-ggplot-r