Agronomy4future
  • CURRICULUM VITAE
  • DATABASE
    • Linux
    • Structured query language (SQL)
    • EXCEL/VBA
    • Power Query
    • Data Science
  • CODING
    • R coding
    • SAS
    • PYTHON
    • Machine Learning
  • STATISTICAL MODEL
    • ANOVA
    • REGRESSION
    • SPLIT-PLOT
    • GLM
    • Mixed Model
    • REML
    • NONLINEAR
  • BASIC STAT

Agronomy4future

Stories about cereals and statistics (plus coding). We aim to develop open-source code for agronomy.
Linux

Linux

■ Sync Cloud Drive to Local

  • How to Sync OneDrive on Linux using Rclone?

JK Kim 
([email protected])
"We aim to develop open-source code for agronomy"

• GitHub: github.com/agronomy4future
• YouTube: @agronomy4future

annotate() aov () arrange() c(rep() case_when() colnames() data.frame() ddply () dplyr facet_grid() facet_wrap() filter() fwrmodel() gdds() geom_abline () geom_bar () geom_vline() github HSD.test() if() ifelse() intercept0() kimindex() mutate() nls() nlsLM() normtools() pivot_longer() poly() predict() probdistz() rbind() reshape2::melt() residuals() sapply() scale_fill_manual () spread() str_replace_all () subset() table() tdist() theme_grey() windows () write_xlsx() xtabs()

While high-throughput image scanning is standard for grain size analysis, we recently developed phenokio(), an R function specifically for estimating cereal grain area.
□ Code explained: https://t.co/QUe8bFpFMS pic.twitter.com/zcnTklcXl6

— J.K Kim (@agronomy4future) March 3, 2026

This is one of my recently published studies, conducted in Spain, on how post-anthesis #heatwaves affect grain weight in winter #wheat, and whether these effects are driven by source–sink limitations or by direct impacts on grain growth capacity.https://t.co/Mzd9XhkQ3J pic.twitter.com/ty3Tl9OWdS

— J.K Kim (@agronomy4future) January 9, 2026

New R function: agronomymap()
agronomymap() provides functions for generating heatmaps and spatial field visualizations from agronomic trial data, designed for grid-based field layouts (row/column).

■ Code explained: https://t.co/7I36HLAvwm pic.twitter.com/doq7KMYYag

— J.K Kim (@agronomy4future) November 18, 2025

The Gamma distribution is ideal for modeling strictly positive, asymmetric data unlike the Normal distribution. In this post, I demonstrate how to calculate the Gamma PDF manually and in Excel, and introduce gammacurve(), an R package.

■ Full article: https://t.co/ApzxrAvZ4t pic.twitter.com/RPbS6bfLGz

— J.K Kim (@agronomy4future) November 5, 2025

[R package] colorcapture(): Segment and measure colored objects in images.

By simply switching the HSV segmentation, different colors of fruits (or grains) can be detected by R, and its surface area automatically calculated.

□ Code explained: https://t.co/HR8ga1xa1Z pic.twitter.com/QNdhDu5MTq

— J.K Kim (@agronomy4future) October 26, 2025

[R package] datacume()
• Compute Cumulative Summaries of Grouped Data

Just released a new R function. This function helps you easily calculate cumulative values over time (or trials), grouped by category (also supports averaging).

□ code explained: https://t.co/7Az1kmXsEl pic.twitter.com/Vdjt41xwAD

— J.K Kim (@agronomy4future) September 7, 2025

Just released a new R function: #datacooks(). It adds diagnostic stats (residuals, leverage, studentized residuals, Cook’s distance, etc.) directly to your model dataset — then automatically spots and flags potential outliers.

□ code explained: https://t.co/nbwk2dwZtY pic.twitter.com/ZInoHebVAi

— J.K Kim (@agronomy4future) August 17, 2025

[R package] #rnmodel()
The concept behind this code is that plasticity can be quantified by estimating slopes for individual genotypes across environments. It offers an easy way to obtain these slopes for each specific environment.

□ Code explained: https://t.co/2KsHyADjWO pic.twitter.com/RDrPnsJXEF

— J.K Kim (@agronomy4future) June 23, 2025

Previously, I developed two R packages, descriptivestat() and deltactrl(). By combining these packages, it becomes easy to create a responsiveness graph.

[Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View (https://t.co/Yz3I6bbMsF) pic.twitter.com/eBesZBJUY9

— J.K Kim (@agronomy4future) June 9, 2025

□ New R package: #deltactrl(); delta control

This package is designed to easily calculate the responsiveness of each treatment relative to a control.

□ Github: https://t.co/BYCSf7KU9v
□ Code explained: https://t.co/GlXvnhh4mV pic.twitter.com/iIhWHLDAAH

— J.K Kim (@agronomy4future) June 9, 2025

We’re working on measuring crop canopy with image analysis and comparing it to actual leaf area. If the model fits well, this could be a fast way to estimate canopy size. Currently setting up the frame and code. pic.twitter.com/GE33yujdKi

— J.K Kim (@agronomy4future) May 21, 2025

New R package: #descriptivestat()
This package automatically embeds descriptive statistics into the dataset, allowing for clear visualization alongside the raw data.

□ Github: https://t.co/tYJPmzvCzJ
□ Code explained: https://t.co/sPOqQwIDgu pic.twitter.com/EL330dIH21

— J.K Kim (@agronomy4future) May 18, 2025

When the intercept is forced to 0 in a simple linear regression, most software programs report an incorrect R². Therefore, I developed a new R package, #intercept0(), which provides the correct R².

□ Github: https://t.co/In3GS6szFI
□ Code explained: https://t.co/EAL3Rbeo6U pic.twitter.com/LJ2eoUlLeT

— J.K Kim (@agronomy4future) May 11, 2025

This is a simple crop growth simulation code based on the Sigmoid Growth Model. If you assume a certain crop growth curve (e.g., biomass or canopy size) over time, you can set up this simulation curve and track it over time. I am sharing the Python code (https://t.co/RiBpXzpXkP) pic.twitter.com/YatW9RcikP

— J.K Kim (@agronomy4future) March 21, 2025

I developed an R package, #interpolate() to facilitate data interpolation, particularly by grouping. With this R package, you can easily predict intermediate data points based on actual data points.
□ Github: https://t.co/ClJuZPAMfH
□ Code explained: https://t.co/RRoaphRSi8 pic.twitter.com/u6SVdY1tBO

— J.K Kim (@agronomy4future) March 3, 2025

I will continue my #Agrivoltaics study at Cornell University. Over the past 1.5 years, I have conducted research on the source-sink strength of crops in response to shading at the University of Illinois Urbana-Champaign, and I look forward to gaining further insights at Cornell. pic.twitter.com/o1kxJLR32e

— J.K Kim (@agronomy4future) January 13, 2025

At the 2024 ASA meeting in San Antonio, I presented my current #agrivoltaics study and proposed distinct farming strategies for sorghum and soybean at pre- and post-anthesis, respectively, focusing on yield components (grain number and weight) in terms of source-sink strength. pic.twitter.com/QdV2kEP9zQ

— J.K Kim (@agronomy4future) November 19, 2024

The Log-Likelihood is a crucial component in statistical modeling, as it helps evaluate how well a model fits the data. In this article, I will explain how to calculate the Log-Likelihood by hand. There is an exercise you can try with an actual dataset (https://t.co/Q1b7693UUB) pic.twitter.com/YrV2SxnzzE

— J.K Kim (@agronomy4future) November 8, 2024

[STAT Article] Step-by-Step Guide to Calculating and Analyzing Principal Component Analysis (PCA) by Hand (https://t.co/IB1kXGYGiJ)

In this article, I introduce how to calculate and analyze Principal Component Analysis (PCA) by hand step by step. pic.twitter.com/HmjHOvOtaB

— J.K Kim (@agronomy4future) November 1, 2024

□ R package: normtools() for Normalization Methods for Data Scaling.

Recently, I developed an R package to normalize data using various methods (Z-test, Robust Scaling, Min-Max Scaling, and Log Transformation) for data scaling.

□ Code explained: https://t.co/EE8fatSqks pic.twitter.com/0u99SrvPLn

— J.K Kim (@agronomy4future) October 8, 2024

I've developed an R package, #gdds(), to easily calculate Growing Degree Days (#GDDs) with a base temperature (BT).

□ Cumulative temp when BT is 0
GDDs = gdds(df, "date", "temp", "group", date= c("0000-00-00", "0000-00-00"), BT= 0)

□ Github: https://t.co/p4oUI3XEnM pic.twitter.com/psKtHWZNiR

— J.K Kim (@agronomy4future) September 15, 2024

#kimindex() is a simple R package I developed to predict grain weight from area based on ŷ=x^1.32

# basis code
□ predicted_gw=kimindex(df, "grain_area", remove_na= TRUE)
□ predicted_area=kimindex1(df, "grain_weight", remove_na= TRUE)

□ Code explained: https://t.co/ISaQIGuQTr pic.twitter.com/ERguU3fee7

— J.K Kim (@agronomy4future) September 10, 2024

probdistz() R package
When creating a probability curve, the process can be a bit tricky. Recently, I developed an R package called #probdistz() to simplify this.

# basic code
probdistz(data, env_cols, yield_cols, smooth= TRUE)
#

□ Github: https://t.co/rKw7FkyHPq pic.twitter.com/bv2VjZ6vdx

— J.K Kim (@agronomy4future) September 2, 2024

Stepwise regression is a method that iteratively constructs a regression model by selecting independent variables to include in the final model, and I summarized the basic concept.
#
Stepwise Regression: A Practical Approach for Model Selection using R (https://t.co/YeLnex72ng) pic.twitter.com/HFOIukVzhk

— J.K Kim (@agronomy4future) May 8, 2024

#Agrivoltaic conference in June 2024. In shaded environments, various studies on crop physiology are available without the need for additional shading installations. Agrivoltaics also makes it an energy-efficient approach in crop science. I love this concept. pic.twitter.com/OiDtyKUxAC

— J.K Kim (@agronomy4future) April 17, 2024

Solar Farm 2.0 #SCAPES Crops Research @ University of Illinois at Urbana-Champaign.
Last season, I altered the sources-sink strength of sorghum and soybean and collected valuable data. This upcoming season, I will analyze the genotypic variance of soybeans in response to shading. pic.twitter.com/76EqBF8kuB

— J.K Kim (@agronomy4future) April 9, 2024

Solar Farm 2.0 Crops Research at UIUC. This is one of my researches I've been involved, and my main interest lies in understanding how shading affects the main yield components and determining the best strategy for managing crop growth under solar panels (https://t.co/ztp25lHCky)

— J.K Kim (@agronomy4future) October 28, 2023

Solar Farm 2.0 SCAPES Crops Research @ University of Illinois Urbana-Champaign. https://t.co/fIqsmoMJYu

— J.K Kim (@agronomy4future) September 12, 2023

Source-sink manipulation was performed on sorghum by removing alternate leaves throughout the entire canopy and half of the head 10 days after flowering, as referenced in Gambín and Borrás (2007). How does sorghum grain weight respond to the change in assimilates? pic.twitter.com/omR7oDQTO8

— J.K Kim (@agronomy4future) August 9, 2023
  • Agricultural Institute
  • AI
  • Article
  • Canada
  • Cereals
  • Conference
  • Crop breeding
  • Crop physiology
  • Data Science
  • España
  • Excel
  • Fertilizer
  • GHGs
  • Google Colab
  • hemp (Cannabis sativa)
  • JK column
  • JMP
  • Linux
  • Machine learning
  • Maize
  • Nederland
  • Principle-Centered Leadership
  • Python programming
  • R programming
  • SAS
  • Soil
  • Sorghum
  • source-sink dynamics
  • Soybean
  • SQL
  • Statistics
  • Tool
  • Uncategorized
  • United States
  • VBA
  • Wheat
  • A Step-by-Step Guide to Installing MySQL Workbench on Linux
  • How to Sync OneDrive on Linux using Rclone?
  • phenokio() R Package: Grain Size Analysis – Length, Width, and Area Metrics
  • [Agronomy article] Nitrogen Cycle in Soil
  • Literature Mining for Meta-Analysis Using the scopusmining Package
  • Converting Rows to Columns in R: A Guide to Transposing Data (feat. pivot_wider and pivot_longer)
  • [R package] Log Response Ratio and Effect Size Calculation (feat. lrr)
  • Confidence interval (CI) formula for a two-sample t-test
  • Understanding Bayes’ Theorem Step by Step
  • Connecting R to Linux-Based Virtual Private Server (VPS) by DigitalOcean for Secure Data Access
  • [R package] Cook’s Distance Diagnostics and Outlier Detection (Feat. datacooks)
  • [R package] Spatial Heatmap Visualization for Agronomic Grid-based Field Layouts Trials (Feat. agronomymap)
  • Visualizing Yield Data with a Heat Map in R
  • Simplify Your Data Cleaning: Replace Text in R
  • How to analyze quadratic plateau model in R Studio?
  • What is the Gamma Distribution? Shape and Scale Parameters, and the Probability Density Function (PDF)
  • guides(fill=”none”)
  • How to Set Up RStudio Server on a Linux-Based Virtual Private Server (VPS)
  • [Data article] How to Import Data from MySQL Server to R?
  • [R package] Segment and Measure Colored Objects in Images (Feat. colorcapture)
  • [Data article] How to Import Data to a Cloud MySQL Server (DigitalOcean) Using Python from Command Prompt
  • [R package] Segment and Measure Green Objects in Images (Feat. greencapture)
  • [R package] Compute Cumulative Summaries of Grouped Data (Feat. datacume)
  • [R package] Streamlined Mixed-Effects Analysis for Agrivoltaics Experiments (Feat. agrivoltaics)
  • geom_mark_ellipse
  • [STAT Article] Statistical Models in Agrivoltaics: Linear Mixed Models Across Different Field Layouts
  • [R package] Quantifying Reaction Norm Plasticity from Slopes to Individual Responses (Feat. nrmodel)
  • [STAT Article] How to calculate reaction norm in crop physiology?
  • [Data article] Visualizing Responsiveness: Integrating Raw Data for a Holistic Dataset View
  • [R package] Calculate the responsiveness of each treatment relative to a control (Feat. deltactrl)
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