How to identify soybean vegetative growth stages?

How to identify soybean vegetative growth stages?

Identifying the vegetative growth stages of soybean is crucial for effective crop management. These stages are marked by the development of trifoliolate leaves, which are a key indicator starting from the V1 stage. Here’s a brief guide on how to recognize these stages: ■ Emergence (VE) The first stage is emergence (VE), where the soybean seedling breaks through the soil surface. At this stage, the cotyledons, or seed leaves, are visible. They supply nutrients to the young plant before the…

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Machine Learning: Modeling with Random Forest Using Python

Machine Learning: Modeling with Random Forest Using Python

In my previous post, I introduced stepwise regression to select the best model. I suggested that grain yield = -4616.47 + 10.53 * stem biomass + 41.03 * height, indicating that stem biomass and height are the most important variables affecting grain yield. ■ Stepwise Regression: A Practical Approach for Model Selection using R Now, I’ll find the best model using machine learning. This is a small dataset, which might not be suitable for machine learning, but it serves as…

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In R Studio, how to exclude missing value (NA)?

In R Studio, how to exclude missing value (NA)?

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I’ll create one data. In genotype D, yield data was missed, so it was indicated as NA. Now I’ll calculate the mean of total yield across all genotypes. As you see above, we can’t calculate the mean dud to NA. To obtain the mean of total yield, we should exclude NA. Using subset(), we can simply exclude Genotype D, But, a much simpler way is to use the code na.rm=TRUE, which enables you to avoid using subset(). When the data…

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[Meta-Analysis] Mining Academic Papers from SCOPUS with Pybliometrics in Python

[Meta-Analysis] Mining Academic Papers from SCOPUS with Pybliometrics in Python

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SCOPUS is one of the largest abstract and citation databases, providing access to a wide range of peer-reviewed literature across various disciplines. It ensures researchers have access to high-quality, up-to-date academic papers, conference proceedings, and other scholarly materials. Pybliometrics is a Python library that streamlines the retrieval of bibliometric data from SCOPUS. It simplifies accessing and manipulating large datasets, saving researchers time and effort compared to manual data collection. Using Pybliometrics to mine academic papers from SCOPUS enables efficient data…

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[Data article] How many times do we need to compare each other according to group numbers ?

[Data article] How many times do we need to compare each other according to group numbers ?

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All of a sudden, I became curious about this question, How many time do we need to compare each other according to group numbers?” and searched for the answer on a website, but I couldn’t find a clear answer. Therefore, I calculated it myself. For example, when there are two groups, we will compare them only once. When there are three groups, we need to compare each group with every other group, resulting in three comparisons. With four groups, we…

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How to Sample a Portion of Data using R?

How to Sample a Portion of Data using R?

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I have one big dataset. Let’s upload to R. This data has 96,319 data rows. I want to use some part of this data. How can I randomly extract some data from the whole dataset. First, I’ll add number from 1 to the end of the data row to provide ID of each data row. Caret package The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. You can find…

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Stepwise Regression: A Practical Approach for Model Selection using R

Stepwise Regression: A Practical Approach for Model Selection using R

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Stepwise selection, forward selection, and backward elimination are all methods used in the context of building statistical models, specifically regression models, where the goal is to select the most relevant predictors. In this section, I’ll introduce one by one. Let’s generate one dataset. This dataset includes grain yield data, along with measurements of stem biomass, grain weight (agw), and grain number (gn). I would now like to determine which variables are the most critical factors in influencing the final grain…

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In R, how to check the data structure?

In R, how to check the data structure?

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When uploading data to R, we first need to check the data structure before analyzing it. Here are some tips for checking the data structure in R. First, I’ll upload a dataset from my GitHub. In this dataset, let’s check the structure of the data. ■ Code to display the first or last certain rows When we examine the data, we can simply run the variable df or use print(df) to display it. However, if we want to quickly understand…

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A Practical Guide to Data Normalization using Z-Tests in Python

A Practical Guide to Data Normalization using Z-Tests in Python

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Today, I’ll introduce one method for data normalization, utilizing the biomass with N and P uptake data available on my GitHub. I also aim to create regression graphs illustrating the relationship between biomass and either nitrogen or phosphorus. First, I’ll generate a regression graph for biomass with either nitrogen or phosphorus to observe the data patterns. I notice a clear pattern between biomass and nitrogen. However, when combining nitrogen and phosphorus in the same panel due to their different data…

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Coding Light Spectrum Curves for Plant Growth in R

Coding Light Spectrum Curves for Plant Growth in R

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Let’s say we collected relative light intensity data across a wide range of the light spectrum in an LED experiment. and I’d like to create light spectrum curves regarding relative light intensity. First, I’ll define wavelength colors. The color at different ranges of wavelengths is always the same, so if we run this code, we can obtain the same color range at wavelength (which would be the x-axis of the graph). and let’s create curve graph. I’ll highlight the ranges…

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[Data article] Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning

[Data article] Data Normalization Techniques: Excel and R as the Initial Steps in Machine Learning

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In my previous post, I introduced the necessity of data normalization in visualizing data. By following that post, you may gain an understanding of how we can organize data according to our preferences. □ Why is data normalization necessary when visualizing data? Today, I’ll introduce various methods for data normalization, utilizing the biomass with N and P uptake data available on my GitHub. R coding Python coding I also aim to create regression graphs illustrating the relationship between biomass and…

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[Data article] Why is data normalization necessary when visualizing data?

[Data article] Why is data normalization necessary when visualizing data?

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Data normalization is necessary when visualizing data for several key reasons, and I believe the most important reason is for scale uniformity. Different data variables can have vastly different scales and units. For example, grain yield might be in Mg/ha, while nutrient contents might typically range from %. Normalizing these data to a common scale (like 0 to 1) allows them to be compared and visualized on the same axis without one overshadowing the other due to its scale. Additionally,…

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How to draw a y-axis border when using facet_wrap() in R? (feat. scales=”free”)

How to draw a y-axis border when using facet_wrap() in R? (feat. scales=”free”)

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Here is one dataset, and I’ll use facet_wrap() to create bar graphs. First, let’s summarize the data. Then, I’ll create a bar graph using facet_wrap() to divide panels by irrigation. Now, I want to draw a y-axis border for the ‘Irrigation_Yes’ panel. We can achieve this simply by adding scales=”free”. © 2022 – 2023 https://agronomy4future.com

How to randomize treatments using R?

How to randomize treatments using R?

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Setting up experimental design according to your experiment goal is the first step to achieve your experiment’s success. In Agronomy studies, experimental design involves the combination of treatments deployed in the field, and these treatments should be randomized. Randomization is important in experimental design as it helps our experiments avoid biases due to physical or biological factors. Of course, there are no specific, unconditional rules for randomization. In a very old-fashioned way, you can write treatment numbers on paper, and…

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Achieving Smooth Curve Graphs with R

Achieving Smooth Curve Graphs with R

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□ How to convert character to POSIXct format in R? In my previous post, I created a curve graph like the one shown below. The curve on the graph appears to be not very smooth, and I want to make it smoother. Therefore, I will add geom_smooth(), but the method will be method=”gam” code summary: https://github.com/agronomy4future/r_code/blob/main/Achieving_Smooth_Curve_Graphs_with_R.ipynb © 2022 – 2023 https://agronomy4future.com

How to convert character to POSIXct format in R?

How to convert character to POSIXct format in R?

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Here is one dataset Let’s check the data type of each variable. The time column is in character format. When opening the data in Excel, it is considered text. I wish to create a time series graph, but this cannot be accomplished when the variables are in text format. Therefore, we need to convert the text to a time format. Now we can adjust time using scale_x_datetime() full summary: https://github.com/agronomy4future/r_code/blob/main/How_to_convert_character_to_POSIXct_format_in_R.ipynb © 2022 – 2023 https://agronomy4future.com

How to Convert Time to Numeric for Line Graphs in R?

How to Convert Time to Numeric for Line Graphs in R?

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Here is one dataset. With this data, I’ll create a line graph to show the change in day length over time. First, let’s transpose the columns to rows using pivot_longer(). I’ll sort the data by Day and Month, but since the month column is in text format, sorting it from January to December directly isn’t feasible. Therefore, I’ll add a number corresponding to each month for sorting purposes. Now, I can sort by ‘month1’ and ‘Day’ from January 1 to…

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Summarizing Data by Group: Mean and Standard Error with MS Azure

Summarizing Data by Group: Mean and Standard Error with MS Azure

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□ Creating an Azure SQL Database: A step-by-step guide In my previous post, I introduced how to set up Azure SQL Database. Today, let’s practice some SQL coding! 1) to create data table I just created two data tables YieldData, and BiomassData. 2) to summarize data I will summarize the data tables by calculating the mean and standard error for each. How to merge two datasets? Here is one more tip. I want to merge two datasets. Here is the…

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Converting Character Values to Numeric in R: A How-To Guide

Converting Character Values to Numeric in R: A How-To Guide

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First, let’s create a dataset. and observe the different data formats of each value. I have two sets of yield data: one in character format (yield column) and the other in numeric format (yield1 column). How to convert missing value to 0 when data is numeric? When data is numeric (yield1 column), and if there are missing values, how can we replace it to 0? or you can also use the following code. How to convert missing values to 0…

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How to add separate text to panels divided by facet_wrap() in R?

How to add separate text to panels divided by facet_wrap() in R?

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□ Graph Partitioning Using facet_wrap() in R Studio□ How to customize the title format in facet_wrap()? In my previous posts, I introduced how to divide panels in one figure using facet_wrap(). Today, I’ll introduce how to add separate text to panels. First, let’s make sure we have the required packages installed. I’ll create a dataset as shown below: Next, I’ll reshape the dataset into columns to facilitate data analysis. And then, I’ll summarize this data using descriptive statistics. Finally, I’ll…

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The Agrivoltaics Image created from DALL∙E3

The Agrivoltaics Image created from DALL∙E3

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DALL·E3, developed by OpenAI, is an advanced AI model capable of generating images from textual descriptions. It can create images based on a wide variety of prompts, ranging from straightforward descriptions to more imaginative or abstract concepts. ChatGPT – DALL·E (openai.com) I requested images from DALL·E depicting Agrivoltaics farming, and these are the results.

Quantifying pre- and post-anthesis heat waves on grain number and grain weight of contrasting wheat cultivars

Quantifying pre- and post-anthesis heat waves on grain number and grain weight of contrasting wheat cultivars

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Quantifying pre- and post-anthesis heat waves on grain number and grain weight of contrasting wheat cultivars The study titled “Quantifying pre- and post-anthesis heat waves on grain number and grain weight of contrasting wheat cultivars” investigates the impact of heat stress on wheat productivity. As temperatures rise, wheat faces challenges in maintaining grain yield. Heat stress adversely affects two critical components: grain number per m2 (GN) and average grain weight (AGW). However, it remains unclear whether the sensitivity of these components differs and…

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How to summarize data using Python?

How to summarize data using Python?

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In my previous post, I demonstrated how to create a data table using Python. If you’re interested, please refer to the post below. ■ How to create a data table in Python? I’ll summarize this data by mean and standard error. full code: https://github.com/agronomy4future/python_code/blob/main/How_to_summarize_data_using_Python.ipynb