Agronomy4future

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Agronomy4future

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

Coding environment

March 4, 2024 JK

Data Science

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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()

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

https://twitter.com/agronomy4future/status/1898040162229403743

https://twitter.com/agronomy4future/status/1896429984878825652

I became curious about mapping U.S. agriculture universities, so I used the #Folium package in #Python to create an easy-to-use #GIS map. Here’s the code to visualize their locations across the U.S. (https://t.co/mupLakgyms) pic.twitter.com/LB5u4lb2gy
— J.K Kim (@agronomy4future) February 12, 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 share a simple R code to create a GIS map like the one below.

□ Code explained: https://t.co/IdIlabCRns pic.twitter.com/5WOXubwxrm
— J.K Kim (@agronomy4future) September 22, 2024

Soybeans are nearing final maturity in the Champaign area of IL, with an estimated harvesting date in the 2nd week of October. pic.twitter.com/15czIZuuw3
— J.K Kim (@agronomy4future) September 19, 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

Using a simple Python code, a GIS map can be created if we have coordinates (latitude and longitude) along with yield data. I will share the Python code. "Python GIS: Interpolating and Plotting Corn Grain Yield Data." https://t.co/RMS9MoWEZl pic.twitter.com/RUCP7O1SCX
— J.K Kim (@agronomy4future) August 18, 2024

The booting stage of grain #sorghum occurs ca. 50 to 60 days after emergence and is characterized by the sorghum panicle being enclosed in the flag leaf sheath. During this time, the panicle is pushed up through the flag leaf collar by the upper stalk, known as the peduncle. pic.twitter.com/j40XsR4WLy
— J.K Kim (@agronomy4future) July 24, 2024

The female flower of Cannabis sativa characterized by its resinous buds, which are the primary source of cannabinoids such as THC and CBD. pic.twitter.com/otqoPvyFQq
— J.K Kim (@agronomy4future) June 14, 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

Data normalization improves scale uniformity and enhances visualization interpretability. I introduce several methods for data normalization.

Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning (https://t.co/5yUE2fjHfn) pic.twitter.com/7x6mU2ccgf
— J.K Kim (@agronomy4future) April 29, 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
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