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

Agronomy4future

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

Mixed Model

■ Linear Mixed-Effects models (LMMs)

  • The Best Linear Unbiased Estimator (BLUE): Step-by-Step Guide using R (with AllInOne Package)
  • Calculating Predicted Values for Each Group in Basic Modeling
  • A Practical Approach to Linear Mixed-Effects Modeling in R

JK Kim (agronomy4future@gmail.com)
"We aim to develop open-source code for agronomy"

• GitHub: github.com/agronomy4future
• YouTube: https://www.youtube.com/@agronomy4future

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

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 (@el_trigo_JK) 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 (@el_trigo_JK) November 8, 2024
https://twitter.com/el_trigo_JK/status/1852494088215355775

I've participated in a machine learning (ML) project at UIUC, and I had my own ML project focused on agronomy. This is my first achievement in ML: predicting the total P in soil. By utilizing various soil property variables, total P was predicted, and a GIS P map was created. pic.twitter.com/o5m4vVMjNS

— J.K Kim (@el_trigo_JK) October 21, 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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 (@el_trigo_JK) 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

— Jin.W.Kim (JK) (@el_trigo_JK) June 14, 2024

Farming is the process of harvesting light energy. Plants convert sunlight into chemical energy stored in glucose, which is produced from CO2 and water. Thus, farming captures and utilizes solar energy to produce agricultural products.

— Jin.W.Kim (JK) (@el_trigo_JK) June 13, 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

— Jin.W.Kim (JK) (@el_trigo_JK) 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

— Jin.W.Kim (JK) (@el_trigo_JK) 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

— Jin.W.Kim (JK) (@el_trigo_JK) 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

— Jin.W.Kim (JK) (@el_trigo_JK) 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)

— Jin.W.Kim (JK) (@el_trigo_JK) October 28, 2023

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

— Jin.W.Kim (JK) (@el_trigo_JK) 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

— Jin.W.Kim (JK) (@el_trigo_JK) August 9, 2023
  • Agricultural Institute
  • AI
  • Article
  • Canada
  • Cereals
  • Conference
  • Crop breeding
  • Crop physiology
  • Data Science
  • El trigo column
  • España
  • Excel
  • Fertilizer
  • GHGs
  • Google Colab
  • hemp (Cannabis sativa)
  • JMP
  • 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
  • [STAT Article] Steps to Calculate Log-Likelihood Prior to AIC and BIC: [Part 2] ANOVA model
  • [STAT Article] Steps to Calculate Log-Likelihood Prior to AIC and BIC: [Part 1] regression model
  • [STAT Article] Step-by-Step Guide to Calculating and Analyzing Principal Component Analysis (PCA) by Hand
  • Understanding Mean Absolute Error (MAE) in ANOVA: A Step-by-Step Guide to Calculation in Excel
  • Practices in Data Normalization using normtools() in R
  • Sorghum panicle damage
  • How to install Llama 3 in your PC?
  • [R package] Normalization Methods for Data Scaling (Feat. normtools)
  • [코딩 교육 플랫폼 추천] 코드트리 (Code Tree)
  • 베이즈 (Bayes) 정리를 가장 쉽게 설명해 보자
  • R GIS: Interpolating and Plotting Corn Grain Yield Data
  • Graphing Normal Distributions with Varied Variances
  • [R package] Calculation for Growing Degree Days (GDDs, ºCd)
  • [R package] Prediction of Grain Weight and Area in Bread Wheat (feat. kimindex)
  • [R package] Probability Distribution and Z-Score Calculation Function (feat. probdistz)
  • [R package] Finlay-Wilkinson Regression model (feat. fwrmodel)
  • Python GIS: Interpolating and Plotting Corn Grain Yield Data
  • Machine Learning: Predicting Values with Multiple Models- Part II
  • Machine Learning: Predicting Values with Multiple Models- Part I
  • Machine Learning: How to Perform Classification with Different Models?
  • Sorghum grain weight in response to assimilate availability
  • Is no-tillage farming beneficial to farmers?
  • Soybean Growing Stage at R1 @ Illinois, Champaign (26 June 2024)
  • Urea Application in Sorghum Field @ Champaign, Illinois (25 June 2024)
  • [Paper review] Weight of individual wheat grains estimated from high-throughput digital images of grain area
  • Predicting Intermediate Data Points with Linear Interpolation in Excel and R
  • Urbana-Champaign Farming Record on May 2024
  • Long and Short-Day Plants: The Significance of Photoperiodicity
  • Understanding Autotrophic and Heterotrophic Respiration in Crop Science: Importance and Impact
  • How to identify soybean vegetative growth stages?



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