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: github.com/agronomy4futur…

□ 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: agronomy4future.org/archives/23225

[STAT Article] Step-by-Step Guide to Calculating and Analyzing Principal Component Analysis (PCA) by Hand (agronomy4future.org/archives/16454) In this article, I introduce how to calculate and analyze Principal Component Analysis (PCA) by hand step by step.

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 (agronomy4future.org/archives/23447)

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.

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.

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: github.com/agronomy4futur… □ Code explained: agronomy4future.com/archives/23834

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 (agronomy4future.com/archives/24018)

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: github.com/agronomy4futur… □ Code explained: agronomy4future.com/archives/24170

New R package: #descriptivestat() This package automatically embeds descriptive statistics into the dataset, allowing for clear visualization alongside the raw data. □ Github: github.com/agronomy4futur… □ Code explained: agronomy4future.com/archives/24197

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.

□ New R package: #deltactrl(); delta control This package is designed to easily calculate the responsiveness of each treatment relative to a control. □ Github: github.com/agronomy4futur… □ Code explained: agronomy4future.com/archives/24266

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 (agronomy4future.com/archives/24294)

[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: agronomy4future.com/archives/24358

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: agronomy4future.com/archives/24565

[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: agronomy4future.com/archives/24603

[R package] greencapture(): Segment and Measure Green Objects in Images. This function provides tools for capturing, and quantifying green plant tissue area from digital images. Designed for high-throughput in vitro or greenhouse image analysis (agronomy4future.com/archives/24628)

Local SQL servers limit data access. Cloud-based SQL offers better efficiency. I’ve simplified importing data to Cloud-based SQL with Python via Command Prompt. For big data, Cloud-based SQL servers are key. * Full article: agronomy4future.com/archives/24674

[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: agronomy4future.com/archives/24714

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: agronomy4future.com/archives/24792