[R package] Calculation for Growing Degree Days (GDDs, ºCd)

[R package] Calculation for Growing Degree Days (GDDs, ºCd)

Growing Degree Days (GDDs) are a measure of heat accumulation used to predict crop development rates such as the growth of crops. The GDDs are calculated to provide a simple model to estimate the growth and development of plants, especially crops, based on the daily temperature. To calculate GDDs, the base temperature for each crop should first be identified. The base temperature is the temperature below which crop growth is minimal or stops. This temperature varies by crop. For example,…

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[R package] Prediction of Grain Weight and Area in Bread Wheat (feat. kimindex)

[R package] Prediction of Grain Weight and Area in Bread Wheat (feat. kimindex)

These days, image analysis equipment can easily provide grain area measurements (mm²), and the large datasets acquired instantly from this equipment offer more insights into wheat grains. While grain weight can be a good indicator of wheat yield, obtaining data on grain weight is challenging with the available equipment. Currently, average grain weight is calculated using thousand kernel weight (TWK), a process that is time-consuming and labor-intensive. Therefore, predicting wheat grain weight from the grain area would allow us to…

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[R package] Probability Distribution and Z-Score Calculation Function (feat. probdistz)

[R package] Probability Distribution and Z-Score Calculation Function (feat. probdistz)

■ Introduction ■ What is Probability Density Function (PDF) and Cumulative Distribution Function (CDF): How to calculate using Excel and R? In my previous post, I explained what the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) are. I also explained the formula for the PDF and demonstrated how to manually calculate it in Excel. Additionally, I mentioned the Excel function that performs the same calculation for the PDF, as follows: I then introduced how to create a probability distribution…

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[R package] Finlay-Wilkinson Regression model (feat. fwrmodel)

[R package] Finlay-Wilkinson Regression model (feat. fwrmodel)

■ What is Finlay-Wilkinson Regression Model? In my previous post, I introduced what Finlay-Wilkinson Regression Model is and how to calculate adaptability (or stability). Actually, adaptability and stability are opposite concept with the same data. Have you ever heard heritability (h2)? Heritability is a key concept in genetics and breeding that measures how much of the variation in a trait within a population is due to genetic differences among individuals. In other words, it quantifies the proportion of phenotypic variation…

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Python GIS: Interpolating and Plotting Corn Grain Yield Data

Python GIS: Interpolating and Plotting Corn Grain Yield Data

I have corn yield data (Mg/ha) that I want to visualize. First, let’s upload the data. First, I’ll create yield distribution data. Now, we can see that the general grain yield varies from 10 to 30 Mg/ha, with some outliers. I’ll create a yield map to visualize this variation. https://github.com/agronomy4future/python_code/blob/main/Python_GIS_Interpolating_and_Plotting_Corn_Grain_Yield_Data.ipynb If you have the ArcGIS program, you can create a more detailed GIS map.

Machine Learning: Predicting Values with Multiple Models- Part II

Machine Learning: Predicting Values with Multiple Models- Part II

In my previous post, I predicted grain weight from length and width of grains using Random Forest. ■ Machine Learning: Predicting Values with Multiple Models- Part I Now, my next question is how the model accuracy changes when grain area and genotype are added. If you followed my previous post closely, you should be able to understand the code below. ■ Data upload ■ Data Splitting Unlike the previous data, I have now added genotype and grain area to the model. ■ Machine…

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Machine Learning: Predicting Values with Multiple Models- Part I

Machine Learning: Predicting Values with Multiple Models- Part I

Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data. Rather than being explicitly programmed to perform a specific task, ML algorithms use data to identify patterns and make inferences or predictions. Machine Learning can be divided into supervised and unsupervised learning. In supervised learning, the model is trained on labeled data, which means the input data is paired with the correct output, and it can…

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Machine Learning: How to Perform Classification with Different Models?

Machine Learning: How to Perform Classification with Different Models?

Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data. Rather than being explicitly programmed to perform a specific task, ML algorithms use data to identify patterns and make inferences or predictions. What is Classification in Machine Learning? Classification is a type of supervised learning where the goal is to categorize data into predefined classes. For example, classifying emails as “spam” or “not spam.” Different models…

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Sorghum grain weight in response to assimilate availability

Sorghum grain weight in response to assimilate availability

Sorghum panicle de-graining is an experimental technique used to study grain size and assimilate availability. De-graining is used to artificially increase assimilate availability to the remaining grains by removing a portion of the panicle. Typically, researchers remove the top half of selected panicles at anthesis (flowering stage). This is done before significant grain development has occurred. Removing part of the panicle generally results in an increase in grain weight for the remaining grains. This technique helps isolate genetic effects associated with…

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Is no-tillage farming beneficial to farmers?

Is no-tillage farming beneficial to farmers?

This year, in the no-tillage field, the farmer has sprayed herbicide three times since planting. These days, the hot trend is carbon farming, and all approaches aim to mitigate GHG emissions, suggesting that no-tillage is beneficial for carbon sequestration. Although the academic trend consistently indicates that carbon farming is important, in reality, farmers have been using more herbicides to suppress weeds. Is carbon farming good for farmers and real farming systems? No-tillage might mitigate GHG emissions, but more herbicides are…

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Soybean Growing Stage at R1 @ Illinois, Champaign (26 June 2024)

Soybean Growing Stage at R1 @ Illinois, Champaign (26 June 2024)

Soybean growth is divided into two main phases: Vegetative (V) stages and Reproductive (R) stages. The vegetative stages are characterized by leaf and node development, while the reproductive stages begin with flowering and include pod development, seed development, and plant maturation. The reproductive stage R1, also known as the beginning bloom stage, is defined as having one open flower at any node on the main stem. This marks the start of the reproductive phase, even if the plant continues to produce new…

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Urea Application in Sorghum Field @ Champaign, Illinois (25 June 2024)

Urea Application in Sorghum Field @ Champaign, Illinois (25 June 2024)

■ Unit conversion □ Area Acre ha m2 ft2 Acre 1 0.404686 4,046.86 43,560 ha 2.47105 1 10,000 107,639 m2 0.000247105 0.0001 1 10.7639 ft2 0.000022956 0.00000929 0.092903 1 □ Weight lbs kg lbs 1 0.453592 kg 2.20462 1 bushel [corn] 56 56 * 0.453592 = 25.4 bushel [wheat] 60 60 * 0.453592 = 27.2 bushel [soybean] 60 60 * 0.453592 = 27.2 My target nitrogen application rate is 100 N lbs / acre in sorghum, and my plot size is…

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[Paper review] Weight of individual wheat grains estimated from high-throughput digital images of grain area

[Paper review] Weight of individual wheat grains estimated from high-throughput digital images of grain area

Kim, J., Savin, R. and Slafer, G.A., 2021. Weight of individual wheat grains estimated from high-throughput digital images of grain area. European Journal of Agronomy, 124, p.126237. https://www.sciencedirect.com/science/article/pii/S1161030121000095 ■ Context and Objective This study focuses on estimating the weight of individual wheat grains using high-throughput digital images of grain area. Given the importance of average grain weight (AGW) as a key component of wheat yield, the researchers aimed to develop a reliable model to convert grain dimensions from 2D images…

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Long and Short-Day Plants: The Significance of Photoperiodicity

Long and Short-Day Plants: The Significance of Photoperiodicity

Photoperiodicity refers to the response of plants to the relative lengths of day and night, which regulates their growth and flowering. It is an important factor in agriculture as it allows growers to optimize crop yields and quality by understanding and manipulating the photoperiod requirements of different plant species. In terms of photoperiodicity, plants can be divided into long-day and short-day plants. ■ Long-day Plant For example, wheat is a long-day plant, meaning it requires extended periods of daylight (typically…

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Understanding Autotrophic and Heterotrophic Respiration in Crop Science: Importance and Impact

Understanding Autotrophic and Heterotrophic Respiration in Crop Science: Importance and Impact

Understanding the difference between autotrophic and heterotrophic respiration is crucial in crop science. These processes play a vital role in the carbon cycle and have significant implications for carbon emissions, climate change, and sustainable agriculture. 1. Autotrophic Respiration Autotrophic respiration is the process by which plants convert the carbohydrates produced during photosynthesis into energy. This energy is used for growth, maintenance, and reproduction. There are three main types of autotrophic respiration: During autotrophic respiration, plants release carbon dioxide back into…

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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)?

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

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 ?

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?

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

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?

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

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

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

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?

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”)

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