diff --git a/data/exemple_data/exemple_4/itk_exemple_4.yaml b/data/exemple_data/exemple_4/itk_exemple_4.yaml
new file mode 100644
index 0000000..3bfd417
--- /dev/null
+++ b/data/exemple_data/exemple_4/itk_exemple_4.yaml
@@ -0,0 +1,33 @@
+DateSemis: 2020-5-1
+NI: .nan # intensification parameter ; if .nan computation will be done without taking intensification into consideration
+coefMc: 0.0 # impact of permanent covering effect on estimation of coefficient of evaporation from the soil (kce)
+
+densite: 53333.0 # sowing density (ha-1)
+
+nbjTestSemis: 0 # parameter for testing sowing date
+profRacIni: 0.0 # used in the initiatlization of root_tank_capacity
+seuilEauSemis: 8.0 # if surface_tank_stock is above this threshold, crop is initiated
+
+# irrigation related
+irrigAuto: false
+irrigAutoTarget: 0.0
+maxIrrig: 0.0
+
+# mulch related
+surfMc: 1.0 # overing capacity of the mulch (ha/t)
+biomIniMc: 0.0 # initial mulch biomass (kg/ha)
+humSatMc: 0.0 # saturation point of mulch, kg H2O/kg biomass (% m/m)
+mulch: 1.0 # permanent mulch effect
+
+KI: 0.0 # coefficient used in mulch calculations
+KNLit: 0.0 # coefficient used in mulch calculations
+KNUp: 0.0 # coefficient used in mulch calculations
+KT: 0.0 # coefficient used in mulch calculations
+
+# non utilisés dans le modèle python
+DisMc: 0
+TxRecolte: 0.0
+TxaTerre: 0.0
+NbUBT: 10.0
+dateFin: 300.0 # does not seem to be used ?
+precision: 0.0 
diff --git a/data/exemple_data/exemple_4/variety_exemple_4.yaml b/data/exemple_data/exemple_4/variety_exemple_4.yaml
new file mode 100644
index 0000000..a5fb450
--- /dev/null
+++ b/data/exemple_data/exemple_4/variety_exemple_4.yaml
@@ -0,0 +1,73 @@
+# params ok depuis MaisOPV_MLIV42 - fichier SyntheseCalibrationVarietes_04_2020 feuille maïsv42 
+AGauss: 1.0
+KRdtBiom: 0.0 # ok
+KRdtPotA: 0.4 # ok 
+KRdtPotB: 200.0 # ok 
+LGauss: 1.0
+NIYo: 1.0
+NIp: 0.0
+PFactor: 0.45 # ok
+PPCrit: 11.0 # ok
+PPExp: 0.0 # ok
+PPsens: 5.0 # ok
+
+# SARRA  |---Levée---|---BVP---|---PSP---|---RPR---|---MATU1---|---MATU2---|
+# WOFOST |---TSUMEM--|------------TSUM1------------|---------TSUM2---------|
+
+# WOFOST TSUMEM
+# temperature sum from sowing to emergence
+SDJLevee: 90.0 # ok
+SDJBVP: 500.0 # ok
+SDJRPR: 400.0 # ok
+SDJMatu1: 500.0 # ok
+SDJMatu2: 200.0 # ok
+
+SeuilPP: 13.6 # ok
+
+TBase: 8.0 # ok 
+
+TLim: 44.0 # ok
+TOpt1: 26.0 # ok 
+TOpt2: 34.0 # ok 
+VRacLevee: 30.0 # ok
+VRacBVP: 15.0 # ok
+VRacPSP: 15.0 # ok
+VRacRPR: 15.0 # ok
+VRacMatu1: 12.0 # ok 
+VRacMatu2: 12.0 # ok
+
+aeroTotBase: 0.6 # ok
+aeroTotPente: 3.5e-05 # ok 
+
+# optimal density
+# according to http://www.tropicultura.org/text/v29n3/183.pdf spacings go from 25 x 25cm to 40 x 40cm
+# -> 62500 to 160000 plants/ha
+densOpti: 65000.0 # ok
+densiteA: 0.7 # ok
+densiteP: 4.5 # ok
+
+feuilAeroBase: 0.6 # ok
+feuilAeroPente: -1.4e-04 # ok 
+kRespMaint: 0.01 # ok
+kcMax: 1.25 # ok 
+kdf: 0.4 # ok
+pcReallocFeuille: 0.7 # ok
+phaseDevVeg: 0
+poidsSecGrain: 0.38 # ok
+senCO2: 10.0 # ok
+seuilCstrMortality: 3.0 # ok
+
+slaMax: 0.006 # ok
+slaMin: 0.002 # ok 
+slaPente: 0.4 # ok 
+
+tempMaint: 25 # ok
+
+txAssimBVP: 1.0 # ok
+txAssimMatu1: 0.9 # ok
+txAssimMatu2: 0.1 # ok 
+
+# 
+txConversion: 5.8 # ok 
+txRealloc: 0.4 # ok
+txResGrain: 0.55 # ok 
\ No newline at end of file
diff --git a/notebooks/SARRA-Py - Exemple 4 - Cameroon maize determination of phenological phase length for best yield.ipynb b/notebooks/SARRA-Py - Exemple 4 - Cameroon maize determination of phenological phase length for best yield.ipynb
new file mode 100644
index 0000000..a62762f
--- /dev/null
+++ b/notebooks/SARRA-Py - Exemple 4 - Cameroon maize determination of phenological phase length for best yield.ipynb	
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exemple 4 - Determination of best reproductive phenological phase length for maize yield, based on 2020-2022"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The purpose of this notebook is to determine what would be the length of reproductive phenological phase that leads to the highest yield. This can be of interest for plant breeders, when working about phenological phasing and more globally cycle length of crops."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import datetime\n",
+    "from matplotlib import pyplot as plt\n",
+    "from tqdm import tqdm as tqdm\n",
+    "import xarray as xr\n",
+    "from sarra_py import *\n",
+    "import geopandas as gpd\n",
+    "from joblib import Parallel, delayed\n",
+    "from contextlib import redirect_stdout, redirect_stderr\n",
+    "import shutil"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Preparing climate and rainfall data, and crop parameters files"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example, we provide rainfall and climate data for north Cameroon in 2020-2022, sourced from CHIRPS v2.0 for rainfall, and AgERA5 for climate ; the following cell will download it and unzip it. \n",
+    "\n",
+    "- climate data includes daily min, max, mean temperatures (°C), solar irradiance (W/m2), and reference evapotranspiration (mm), sourced from AgERA5\n",
+    "- rainfall data includes... rainfall data (mm), sourced from CHIRPS v2.0\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File downloaded using urllib.\n",
+      "File unzipped.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import urllib.request\n",
+    "import zipfile\n",
+    "\n",
+    "# create a folder to store the data\n",
+    "os.makedirs('../data/exemple_data/exemple_4/', exist_ok=True)\n",
+    "\n",
+    "# download preformatted data from Zenodo repository\n",
+    "url = 'https://zenodo.org/records/11092880/files/SARRA-Py_north_cameroon_2020_2022_example_data.zip?download=1'\n",
+    "local_filename = '../data/exemple_data/exemple_4/SARRA-Py_north_cameroon_2020_2022_example_data.zip' # store the downloaded file in the ../data/exemple_data/ folder\n",
+    "urllib.request.urlretrieve(url, local_filename)\n",
+    "print(\"File downloaded using urllib.\")\n",
+    "\n",
+    "# unzip data\n",
+    "path_to_zip_file = \"../data/exemple_data/exemple_4/SARRA-Py_north_cameroon_2020_2022_example_data.zip\"\n",
+    "directory_to_extract_to = \"../data/exemple_data/exemple_4/\" # unzips the file in the same folder\n",
+    "\n",
+    "with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:\n",
+    "    zip_ref.extractall(directory_to_extract_to)\n",
+    "print(\"File unzipped.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We set the paths to the data :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rainfall_data_path = \"../data/exemple_data/exemple_4/CHIRPS_v2.0_Africa_north_cameroon/\"\n",
+    "climate_data_path = \"../data/exemple_data/exemple_4/AgERA5_north_cameroon/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we copy the crop parameters from the example folder to the appropriate locations :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../data/params/variety/variety_exemple_4.yaml'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we copy maize_cameroon_2020. yaml from the example folder to the ../data/params/itk/ folder \n",
+    "shutil.copy(\"../data/exemple_data/exemple_4/itk_exemple_4.yaml\", \"../data/params/itk/itk_exemple_4.yaml\")\n",
+    "\n",
+    "# we copy maize_north_cameroon.yaml from the example folder to the ../data/params/variete/ folder\n",
+    "shutil.copy(\"../data/exemple_data/exemple_4/variety_exemple_4.yaml\", \"../data/params/variety/variety_exemple_4.yaml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Defining yearly simulation function"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define a wrapper function that will perform for a given year all the operations for data loading and launching one simulation per SDJRPR to be tested. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_best_SDJRPR_length(year, SDJRPR_min, SDJRPR_max, interval):\n",
+    "\n",
+    "    # to be able to parallelize we first define a function that will run the simulation for a given SDJRPR value\n",
+    "    # it is defined inside the main function so that it has access to all the necessary variables\n",
+    "    def run_simulation(SDJRPR):\n",
+    "        \n",
+    "        print(\"-> estimating yield for SDJRPR = {} degree days\".format(SDJRPR))\n",
+    "\n",
+    "        data = base_data.copy() # creating simulation dataset by copying the base_data\n",
+    "        paramITK[\"DateSemis\"] = datetime.date(year, 5, 1) # setting the sowing date (this overrides the sowing date in the itk file)\n",
+    "        paramVariete[\"SDJRPR\"] = SDJRPR # setting the thermal time to flowering (this overrides the value in the variety file)\n",
+    "\n",
+    "        # initializing all the necessary variables\n",
+    "        data = initialize_simulation(data, grid_width, grid_height, duration, paramVariete, paramITK, date_start)\n",
+    "        data = initialize_default_irrigation(data)\n",
+    "        data = calculate_once_daily_thermal_time(data, paramVariete)\n",
+    "\n",
+    "        data = run_model(paramVariete, paramITK, paramTypeSol, data, duration) # running the model\n",
+    "        result = xr.where(data[\"numPhase\"][-1,:,:] != 0, data[\"rdt\"][-1,:,:], np.nan) # extracting the yield, excluding pixels where simulation never started (numPhase = 0)\n",
+    "\n",
+    "        del data # free memory\n",
+    "        return result\n",
+    "    \n",
+    "    print(\"== computing best SDJRPR for year {} ==\".format(year))\n",
+    "\n",
+    "    # hiding all the outputs of this code section\n",
+    "    with open(os.devnull, 'w') as devnull, redirect_stdout(devnull), redirect_stderr(devnull):\n",
+    "\n",
+    "        # defining the simulation period (interval in which data is loaded)\n",
+    "        # remember that we want to load data at least one month before the sowing date so that water balance can initialize properly\n",
+    "        date_start = datetime.date(year,4,1) # 01/04/year\n",
+    "        duration = 365 - datetime.date(year,4,1).timetuple().tm_yday # we define the duration of the simulation as the number of days remaining in the year\n",
+    "\n",
+    "        grid_width, grid_height = get_grid_size(rainfall_data_path, date_start, duration) # get grid size \n",
+    "        base_data = xr.Dataset() # initialize empty xarray dataset\n",
+    "        base_data = load_TAMSAT_data(base_data, rainfall_data_path, date_start, duration) # load rainfall data\n",
+    "        base_data = load_AgERA5_data(base_data, climate_data_path, date_start, duration) # load climate data\n",
+    "        base_data = load_iSDA_soil_data(base_data, grid_width, grid_height) # load soil parameters\n",
+    "        base_data = calc_day_length_raster_fast(base_data, date_start, duration) # compute day length raster\n",
+    "\n",
+    "        # load variety, cropping system and soil parameters\n",
+    "        file_paramVariete = \"variety_exemple_4.yaml\"\n",
+    "        file_paramITK = \"itk_exemple_4.yaml\"\n",
+    "        file_paramTypeSol = \"USA_iowa_V42.yaml\"\n",
+    "        paramVariete, paramITK, paramTypeSol = load_YAML_parameters(file_paramVariete, file_paramITK, file_paramTypeSol)\n",
+    "\n",
+    "    # parallel run the simulations for the different SDJRPR values to test\n",
+    "    # the function includes arguments defining the SDJRPR min and max, as well as the interval between each tested value\n",
+    "    parallel_jobs = 4 # if you have lots of RAM increase the number of parallel jobs\n",
+    "    results = Parallel(n_jobs=parallel_jobs)(delayed(run_simulation)(value) for value in np.arange(SDJRPR_min, SDJRPR_max, interval)) # parallel run the simulations\n",
+    "    rdt = xr.concat(results, dim=\"step\") # assemble the yield results from all the different SDJRPR values tested in a single xarray dataset\n",
+    "\n",
+    "    # find the best SDJRPR value (returning the highest yield) for each pixel with argmax\n",
+    "    # however as argmax is sensitive to NaN values we need to handle them\n",
+    "    all_nan_slices = rdt.isnull().all(dim=\"step\") # identify slices where all values are NaN\n",
+    "    argmax_result = rdt.fillna(np.inf).argmax(dim=\"step\") # compute argmax, ignoring slices with all NaN\n",
+    "    argmax_result = argmax_result.where(~all_nan_slices, np.nan) # Replace indices with NaN where all values were NaN\n",
+    "\n",
+    "    return argmax_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Run simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize best sowing date array\n",
+    "best_SDJRPR = xr.DataArray()\n",
+    "\n",
+    "# loop over the years 2020 to 2022 included\n",
+    "for year in range(2020, 2022 + 1):\n",
+    "\n",
+    "    # define best SDJRPR search parameters\n",
+    "    # here we will search for the best SDJRPR value between 360 and 630 degree days, with a 30 degree days interval\n",
+    "    SDJRPR_min, SDJRPR_max = 360, 630\n",
+    "    interval = 30\n",
+    "    argmax_result = find_best_SDJRPR_length(year, SDJRPR_min, SDJRPR_max, interval) # run simulations\n",
+    "    \n",
+    "    # store results, taking care of the first iteration\n",
+    "    if best_SDJRPR.size == 0:\n",
+    "        best_SDJRPR = argmax_result.expand_dims(\"year\")\n",
+    "    else:\n",
+    "        best_SDJRPR = xr.concat([best_SDJRPR, argmax_result], dim=\"year\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Plot results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have the determined the best phase length in degree days for SDJRPR for each year on the period 2020-2022, we can compute temporal agregates to determine the average best SDJRPR value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Skipping field centroid: unsupported OGR type: 3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# converting results to DOY\n",
+    "best_SDJRPR_DOY = best_SDJRPR.mean(dim=\"year\") * interval + SDJRPR_min\n",
+    "\n",
+    "# plot the results\n",
+    "best_SDJRPR_DOY.plot(cbar_kwargs={\"label\": \"degree days\"})\n",
+    "gdf = gpd.read_file(\"https://fdw.fews.net/api/feature/?layer=1704&format=geojson\")\n",
+    "gdf.plot(ax=plt.gca(), facecolor='none', edgecolor='red', linewidth=1.0)\n",
+    "plt.title(\"Average of best SDJRPR\\nfor maize in north Cameroon, 2020-2022\")\n",
+    "plt.xlabel(\"Longitude\")\n",
+    "plt.ylabel(\"Latitude\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On this map, we can see that most of the north Cameroon maize produces its highest yields when reproductive phase length is long ; however the further north we go, especially in the \"horn\" the shortest this period has to be in order for crops to reach their maximum attainable yield."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "my_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}