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 more than 14 hours) to initiate flowering and subsequent seed production. The longer days signal to the plant that the favorable growing season has arrived, prompting it to transition from vegetative growth to reproductive development.
Photoperiodicity in long-day plant is crucial because:
- It ensures flowering and grain production occur during the optimal time of the year when conditions are favorable for pollination and seed development.
- It allows growers to select wheat varieties adapted to specific latitudes and sowing times, maximizing yields in different regions.
- Understanding the photoperiod requirements helps determine the ideal sowing dates and predict flowering times for efficient crop management.
■ Short-day Plant
For example, hemp is a short-day plant, meaning it requires shorter days (typically less than 12-14 hours of daylight) to initiate flowering. The decreasing day length signals to the plant that the growing season is ending, and it needs to transition from vegetative growth to reproductive development.
Photoperiodicity in short-day plant is important because:
- It allows growers to select cultivars adapted to specific latitudes, with northern cultivars flowering earlier under longer days compared to southern cultivars.
- Understanding the critical photoperiod (day length threshold for flowering) helps predict flowering times and optimize cultivation for different purposes (e.g., fiber, grain, or essential oil production).
- Manipulating the photoperiod through artificial lighting or shading can control the vegetative and flowering phases, enabling indoor cultivation or extending the growing season in greenhouses.
Understanding and managing the photoperiod requirements of these crops is essential for optimizing yields, quality, and adapting cultivation practices to different regions and production systems
■ Data for Day and Night Length
Let’s check the actual day and night length in Illinois.
# required package
if (require("readr") == F) install.packages("readr")
library(rio)
if (require("tidyr") == F) install.packages("tidyr")
library(tidyr)
if (require("dplyr") == F) install.packages("dplyr")
library(dplyr)
if (require("ggplot2") == F) install.packages("ggplot2")
library(ggplot2)# upload data
github="https://raw.githubusercontent.com/agronomy4future/raw_data_practice/main/day_length_Illinois.csv"
df= data.frame(read_csv(url(github), show_col_types=FALSE))
head (df, 5)
month1 month day1 day day_length night_length
1 Jan 1 1 1 09:22:00 14:38:00
2 Jan 1 2 2 09:23:00 14:37:00
3 Jan 1 3 3 09:23:00 14:37:00
4 Jan 1 4 4 09:24:00 14:36:00
5 Jan 1 5 5 09:25:00 14:35:00
.
.
.This data was created from US Naval Observatory Astronomical Applications Department (https://aa.usno.navy.mil/data/Dur_OneYear)
I’ll reorganize data structure as below.
df1= data.frame(df %>%
pivot_longer(
cols= c(day_length, night_length),
names_to= "category",
values_to= "length"))
head (df1, 5)
month1 month day1 day category length
1 Jan 1 1 1 day_length 09:22:00
2 Jan 1 1 1 night_length 14:38:00
3 Jan 1 2 2 day_length 09:23:00
4 Jan 1 2 2 night_length 14:37:00
5 Jan 1 3 3 day_length 09:23:00To show the day or night length as numeric, I’ll use the following code.
# Ensure time data is treated as character
df1$length= as.character(df1$length)
# Function to convert hh:mm to decimal hours
convert_to_decimal = function(time_str) {
parts= strsplit(time_str, ":")[[1]]
hours= as.numeric(parts[1])
minutes= as.numeric(parts[2])
decimal_time= hours + minutes / 60
return(decimal_time)
}
# Apply the conversion function to the length column
df2= df1 %>%
mutate(length_decimal= sapply(length, convert_to_decimal))
head (df2, 5)
month1 month day1 day category length length_decimal
1 Jan 1 1 1 day_length 09:22:00 9.366667
2 Jan 1 1 1 night_length 14:38:00 14.633333
3 Jan 1 2 2 day_length 09:23:00 9.383333
4 Jan 1 2 2 night_length 14:37:00 14.616667
5 Jan 1 3 3 day_length 09:23:00 9.383333
.
.
.Fig= ggplot(data=df2, aes(x=day, y=length_decimal)) +
geom_line(aes(color=category)) +
scale_color_manual(values= c("red","black"))+
geom_vline(xintercept= 60, linetype="dashed", color="black") +
geom_vline(xintercept= 152, linetype="dashed", color="black") +
geom_vline(xintercept= 244, linetype="dashed", color="black") +
geom_vline(xintercept= 335, linetype="dashed", color="black") +
annotate("text", label=paste("March"), x=60, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("June"), x=152, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("Sep"), x=244, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("Dec"), x=335, y=16, angle=90, hjust=0,
vjust=1.5)+
scale_x_continuous (breaks=seq(0,365,50), limits = c(0,365)) +
scale_y_continuous (breaks=seq(6,18,2), limits = c(6,18)) +
labs(x="Days of 2023", y="Day or Night length") +
theme_grey(base_size=18, base_family="serif")+
theme(legend.position="bottom",
legend.title=element_blank(),
legend.key.size=unit(0.5,'cm'),
legend.key=element_rect(color=alpha("white",.05),
fill=alpha("white",.05)),
legend.text=element_text(size=15),
legend.background= element_rect(fill=alpha("white",.05)),
strip.background=element_rect(color="white", size=0.5,
linetype="solid"),
axis.line=element_line(linewidth=0.5, colour="black"))
Fig + windows(width=5, height=5.5)
ggsave("C:/Users/kimjk/Desktop/Coding_Output/Fig.jpg",
Fig, width=5*2.54, height=5.5*2.54, units="cm", dpi=1000)
# required package
if (require("readr") == F) install.packages("readr")
library(rio)
if (require("tidyr") == F) install.packages("tidyr")
library(tidyr)
if (require("dplyr") == F) install.packages("dplyr")
library(dplyr)
if (require("ggplot2") == F) install.packages("ggplot2")
library(ggplot2)
# upload data
github="https://raw.githubusercontent.com/agronomy4future/raw_data_practice/main/day_length_Illinois.csv"
df= data.frame(read_csv(url(github), show_col_types=FALSE))
df1= data.frame(df %>%
pivot_longer(
cols= c(day_length, night_length),
names_to= "category",
values_to= "length"))
# Ensure time data is treated as character
df1$length= as.character(df1$length)
# Function to convert hh:mm to decimal hours
convert_to_decimal = function(time_str) {
parts= strsplit(time_str, ":")[[1]]
hours= as.numeric(parts[1])
minutes= as.numeric(parts[2])
decimal_time= hours + minutes / 60
return(decimal_time)
}
# Apply the conversion function to the length column
df2= df1 %>%
mutate(length_decimal= sapply(length, convert_to_decimal))
# Create a graph
Fig= ggplot(data=df2, aes(x=day, y=length_decimal)) +
geom_line(aes(color=category)) +
scale_color_manual(values= c("red","black"))+
geom_vline(xintercept= 60, linetype="dashed", color="black") +
geom_vline(xintercept= 152, linetype="dashed", color="black") +
geom_vline(xintercept= 244, linetype="dashed", color="black") +
geom_vline(xintercept= 335, linetype="dashed", color="black") +
annotate("text", label=paste("March"), x=60, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("June"), x=152, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("Sep"), x=244, y=16, angle=90, hjust=0,
vjust=1.5)+
annotate("text", label=paste("Dec"), x=335, y=16, angle=90, hjust=0,
vjust=1.5)+
scale_x_continuous (breaks=seq(0,365,50), limits = c(0,365)) +
scale_y_continuous (breaks=seq(6,18,2), limits = c(6,18)) +
labs(x="Days of 2023", y="Day or Night length") +
theme_grey(base_size=18, base_family="serif")+
theme(legend.position="bottom",
legend.title=element_blank(),
legend.key.size=unit(0.5,'cm'),
legend.key=element_rect(color=alpha("white",.05),
fill=alpha("white",.05)),
legend.text=element_text(size=15),
legend.background= element_rect(fill=alpha("white",.05)),
strip.background=element_rect(color="white", size=0.5,
linetype="solid"),
axis.line=element_line(linewidth=0.5, colour="black"))
Fig + windows(width=5, height=5.5)
ggsave("C:/Users/kimjk/Desktop/Coding_Output/Fig.jpg",
Fig, width=5*2.54, height=5.5*2.54, units="cm", dpi=1000)■ If wheat is a long-day plant, does late planting have a negative effect?
Late planting of wheat, which is a long-day plant, can have negative effects on its growth and yield due to the shorter day lengths during that time. Here are some potential negative impacts of late planting for wheat:
1) Delayed flowering:
Wheat requires long days (typically more than 14 hours of daylight) to initiate flowering. With late planting, the plant may not receive the required day length to trigger flowering at the optimal time, leading to delayed flowering and maturity.
2) Reduced vegetative growth:
The shorter days associated with late planting can limit the vegetative growth phase of wheat, resulting in shorter plants with fewer tillers (stems) and reduced biomass accumulation.
3) Lower grain yield:
The combination of delayed flowering and reduced vegetative growth due to shorter days can ultimately lead to lower grain yields in wheat when planted late.
4) Increased risk of heat stress:
Late-planted wheat may experience higher temperatures during the grain-filling stage, which can accelerate maturity and reduce grain size and weight, further impacting yield potential.
To mitigate these negative effects, growers need to carefully consider the optimal planting window for their specific wheat varieties and local climatic conditions. Early planting is generally recommended for long-day wheat cultivars to ensure they receive the required day length for proper growth and development, leading to higher yields and better quality.
■ If hemp is a short-day plant, does late planting have a positive effect?
Hemp requires short day lengths (less than 12-14 hours of daylight) to initiate flowering and seed/fiber production. When hemp is planted late in the growing season, the decreasing day lengths in late summer/early fall provide the critical short-day signal to trigger flowering. Some key benefits of late planting for hemp as a short-day plant:
1) Faster flowering:
The shorter days associated with late planting induce the flowering process more rapidly compared to early planted hemp that experiences longer days during early growth stages.
2) Avoids premature flowering:
Early planting when days are long can cause hemp to remain in the vegetative stage for an extended period before flowering, which can reduce yields. Late planting aligns better with the plant’s short-day requirement for flowering.
3) Extends growing season:
Day-neutral hemp cultivars allow growers to take advantage of mild spring weather by planting earlier. For photoperiod-sensitive cultivars, late planting extends the potential growing window into late summer/early fall.
- Day-neutral hemp cultivars:
- These cultivars are not sensitive to the length of daylight hours.
- Growers can plant them earlier in the season, taking advantage of mild spring weather.
- This allows for potentially earlier harvests and may help avoid some of the risks associated with late-season weather events.
- Photoperiod-sensitive hemp cultivars:
- These cultivars flower based on the length of daylight, typically requiring longer nights to initiate flowering.
- Planting these cultivars later in the season can extend the growing window into late summer and early fall.
- This can be advantageous in regions where a longer growing season is desirable or necessary for optimal plant development and yield.
By understanding the different growth requirements of these two types of hemp cultivars, growers can optimize their planting schedules to maximize yield and quality.
4) Adaptation to latitude:
Late planting of short-day hemp cultivars adapted to northern latitudes can induce faster flowering and seed maturation before cold temperatures arrive.
However, excessively late planting can also have drawbacks, such as reduced vegetative growth before flowering and potential exposure to early frosts. Growers need to balance the planting date with the specific cultivar’s photoperiod sensitivity and the local climate to optimize yields and quality.
