Data paper¶

Paper¶

Young apple tree development under a gradient of agroforestry radiative conditions: a dataset of morphological and architectural responses at various topological scales

Authors: F. Reyes, B. Pitchers, C. Pradal, P.E. Lauri¶

This Notebook showcase how to load the data and extract the analysis of apple tree under a gradient of agroforestry radiative conditions

In [1]:

from gafam import analysis

Load the data set of 45 apple tree under different treatment¶

Extract the information at 3 different scales : Plant, Branch and Growth Unit¶

In [2]:

tree_df, branch_df, GU_df = analysis.full_analysis(verbose=False)

NODE   < < False

Analysis at plant scale¶

In [3]:

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="ticks", palette="crest")

In [4]:

fig, ax =plt.subplots(1,2, sharey=True)
sns.boxplot(x="trt", y="LA_2018",
            data=tree_df, hue='trt', ax=ax[0])
sns.boxplot(x="trt", y="LA_2019",
            data=tree_df,hue='trt', ax=ax[1])

Out[4]:

<Axes: xlabel='trt', ylabel='LA_2019'>

Analysis at Growth Unit scale¶

In [5]:

GU_df

Out[5]:

	tree	trt	years	GU	LeafArea	nb_GU	nb_leaves	nb_flowers	nb_fruits
0	1	AC	2018	A	627.20200	1	0	0	0
1	1	AC	2018	B	6512.77992	43	58	54	0
2	1	AC	2018	C	7938.62165	94	153	120	0
3	1	AC	2018	D	0.00000	0	0	0	0
4	1	AC	2018	E	0.00000	0	0	0	0
...	...	...	...	...	...	...	...	...	...
445	148	AF_IR	2019	A	97.33408	1	0	0	0
446	148	AF_IR	2019	B	5478.28288	44	34	24	4
447	148	AF_IR	2019	C	14091.22799	112	72	55	14
448	148	AF_IR	2019	D	5928.16141	65	0	0	0
449	148	AF_IR	2019	E	0.00000	0	0	0	0

450 rows × 9 columns

In [6]:

fig, ax =plt.subplots(2,4, sharey='row')
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2018" and GU=="A"'), 
            hue='trt', ax=ax[0][0])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2018" and GU=="B"'), hue='trt', ax=ax[0][1])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2018" and GU=="C"'), hue='trt', ax=ax[0][2])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2018" and GU=="D"'), hue='trt', ax=ax[0][3])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2019" and GU=="A"'), hue='trt', ax=ax[1][0])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2019" and GU=="B"'), hue='trt', ax=ax[1][1])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2019" and GU=="C"'), hue='trt', ax=ax[1][2])
sns.boxplot(x="trt", y="LeafArea",
            data=GU_df.query('years == "2019" and GU=="D"'), hue='trt', ax=ax[1][3])

Out[6]:

<Axes: xlabel='trt', ylabel='LeafArea'>