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Supplementary.Rmd
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Supplementary.Rmd
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---
title: "Supplementary materials for 'Climate change impacts on winter chill in Mediterranean temperate fruit orchards'"
author:
- Eduardo Fernandez^1,2^[^*], Hajar Mojahid^1^, Erica Fadón^3,4^, Javier Rodrigo^3,4^, David Ruiz^5^,
- Jose A. Egea^5^, Mehdi Ben Mimoun^6^, Ossama Kodad^7^, Adnane El Yaacoubi^8^, Mohamed Ghrab^9^,
- José Egea^5^, Haifa BenMoussa^6,10^, Nadia Borgini^6^, Olfa Elloumi^9^ and Eike Luedeling^1^
date: ^1^ Department of Horticultural Science, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, 53121 Bonn, Germany \newline
^2^ Escuela de Agronomía, Pontificia Universidad Católica de Valparaíso, Casilla 4-D, Quillota, Chile \newline
^3^ Department of Plant Science, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain \newline
^4^ Instituto Agroalimentario de Aragón-IA2 (CITA-Universidad de Zaragoza), Calle Miguel Servet 177, 50013 Zaragoza, Spain \newline
^5^ Department of Plant Breeding, Fruit Breeding Group, CEBAS-CSIC, PO Box 164, Espinardo, 30100 Murcia, Spain \newline
^6^ University of Carthage, Institut National Agronomique de Tunisie (INAT), Laboratory GREEN-TEAM (LR17AGR01), 43 avenue Charles Nicolle, 1082 Tunis, Tunisia \newline
^7^ National School of Agriculture of Meknès (ENAM), Department of Arboriculture and Viticulture, PB. S/40, 50000 Meknès, Morocco \newline
^8^ University Sultan Moulay Slimane, Higher School of Technology Khenifra, P.B. 170, Khenifra, Morocco \newline
^9^ University of Sfax, Olive Institute, LR16IO01 & LR16IO02, BP 1087 Sfax 3000, Tunisia \newline
^10^ University of Carthage, Higher School of Agriculture of Mograne, Department of Agricultural Production, Zaghouan, Tunisia
geometry: margin=2cm
output:
pdf_document: default
bibliography: packages.bib
header-includes:
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhead[R]{Future winter chill for temperate fruit orchards in the Mediterranean}
\fancyhead[L]{Fernandez et al. (2022)}
\usepackage{pdfpages}
\usepackage{pdflscape}
\renewcommand{\figurename}{Figure S}
\makeatletter
\def\fnum@figure{\figurename\thefigure}
\makeatother
\renewcommand{\tablename}{Table S}
\makeatletter
\def\fnum@table{\tablename\thetable}
\makeatother
---
<style>
body {
text-align: justify}
</style>
```{r internal, echo = FALSE, message = FALSE}
library(kableExtra)
library(tidyverse)
```
[^*]: Corresponding author: Eduardo Fernández (eduardo.fernandez.c@pucv.cl)
## Introduction
In this document, we provide supplementary materials for the work 'Climate change impacts on winter chill in Mediterranean temperate fruit orchards' by Eduardo Fernandez and co-authors. The study is published in the journal Regional Environmental Change under the doi: [10.1007/s10113-022-02006-x](https://doi.org/10.1007/s10113-022-02006-x). We conducted this work in collaboration with researchers from northern and southern Spain, Tunisia, Morocco and Germany under the umbrella of an international project [(AdaMedOr)](https://mel.cgiar.org/projects/adamedor) funded by the Partnership for Research and Innovation in the Mediterranean Area (PRIMA).
Compared to previous similar studies, we provide now an analysis that combines the spatial interpolation of winter chill accumulation in the Mediterranean region under future scenarios with expert knowledge regarding the impacts of climate change on temperate orchards as well as future concerns of farmers cultivating temperate species. Our approach allowed us to frame and contextualize the results of our chill estimations, potentially contributing to the development of management strategies to adapt Mediterranean orchards to future climate conditions.
We offer figures that were not included in the main manuscript, as well as additional information about the weather stations used for the analysis.
\newpage
## Weather stations used in the analysis
For this study, we used 387 weather stations as primary sources of minimum and maximum temperature records between 1974 and 2020. In the following table (Table S1), we provide the name, location (coordinates) and percentage of data complete for each weather station.
```{r table_s1, echo = FALSE, message = FALSE, warning = FALSE}
table_s1 <- read.csv("data/table_s1.csv")
table_s1 <- select(table_s1, -chillR_code, -BEGIN, -END, -distance, -starts_with("Total"))
names(table_s1) <- c("Name", "Country", "Latitude (dec. degrees)", "Longitude (dec. degrees)",
"Data complete Tmin (%)", "Data complete Tmax (%)")
table_s1$Name <- stringr::str_to_title(table_s1$Name)
table_s1$Country <- countrycode::countrycode(table_s1$Country, "fips", "country.name")
#column_width <- c("3.5cm", "2.5cm", "2.25cm", "2.25cm", "2.25cm", "2.25cm")
digits <- c(1, 1, 2, 2, 1, 1)
kableExtra::kable(table_s1[1 : 42, ], format = "latex", booktabs = T, escape = T, digits = digits,
caption = "Weather stations used as primary sources of minimum and maximum temperature records between 1974 and 2020 in the Mediterranean basin", align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_1, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_b <- table_s1[43 : 92, ]
rownames(table_s1_b) <- NULL
kableExtra::kable(table_s1_b, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_2, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_c <- table_s1[93 : 142, ]
rownames(table_s1_c) <- NULL
kableExtra::kable(table_s1_c, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_3, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_d <- table_s1[143 : 192, ]
rownames(table_s1_d) <- NULL
kableExtra::kable(table_s1_d, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_4, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_e <- table_s1[193 : 241, ]
rownames(table_s1_e) <- NULL
kableExtra::kable(table_s1_e, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_5, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_f <- table_s1[242 : 291, ]
rownames(table_s1_f) <- NULL
kableExtra::kable(table_s1_f, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_6, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_g <- table_s1[292 : 341, ]
rownames(table_s1_g) <- NULL
kableExtra::kable(table_s1_g, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
```{r table_s1_cont_7, echo = FALSE, message = FALSE, warning = FALSE}
table_s1_h <- table_s1[342 : 387, ]
rownames(table_s1_h) <- NULL
kableExtra::kable(table_s1_h, format = "latex", booktabs = T, escape = T, digits = digits,
align = c("l", "l", "r", "r", "r", "r")) %>%
kableExtra::kable_styling(full_width = T, latex_options = "HOLD_position") %>%
kableExtra::column_spec(column = 1, width = "3.5cm")
```
\newpage
## Climate models used in the projections
In Table S2, we show the 15 climate models used in the analysis to obtain future temperature data from the ClimateWizard data base. As described in the main manuscript, we later grouped these models into "pessimistic", "intermediate" and "optimistic" classes according to Safe Winter Chill distributions.
```{r table_s2, echo = FALSE, message = FALSE, warning = FALSE}
table_s2 <- read.csv("data/table_s2.csv")
kableExtra::kable(table_s2[, 1 : 2], format = "latex", booktabs = T, escape = T,
align = "l",
caption = "Climate models used in the analysis to obtain temperature data under future climate scenarios") %>%
kableExtra::kable_styling(latex_options = "HOLD_position")
```
\newpage
## Correction model
As described in the main manuscript, we implemented a spatial interpolation and used a 3D model to correct for large errors that originated from the initial Kriging procedure. This 3D correction model (Fig. S1) consisted of the relationship between the monthly minimum and maximum temperatures in January (x- and y-axis, respectively) and the observed chill in each weather station (color surface). This allowed us to identify the combination of temperatures that was associated with a given amount of chill. We later used this combination to estimate chill values from the co-variables (mean daily minimum and maximum temperatures) from both data sources (weather stations and WorldClim) and obtain a chill correction map.
```{r fig_S1, echo = FALSE, fig.cap = "Three-dimensional model used to correct for the inital error generated by interpolating Safe Winter Chill only based on data from the weather stations. In the figure, we show the 3D correction model for the 1975 scenario. Different relationships were established for each of the many scenarios evaluated in this study", out.width = '100%', fig.align='center'}
knitr::include_graphics("figures/interpolation/correction_plane/Past_sim_1975supplementary.png")
```
## Additional figures
In the following figures, we show the expected change in Safe Winter Chill compared to the baseline period (median SWC across the historic simulated scenarios) for the "pessimistic" and "optimistic" climate model classes for the RCP4.5 and RCP8.5 scenarios by 2050 and 2085. As expected, major chill losses will occur under the "pessimistic" version of the RCP8.5 scenario by 2085, whereas minor changes may be expected by the near future under the RCP4.5 scenario.
```{r fig_S2, echo = FALSE, fig.cap = "Estimation of Safe Winter Chill change for the Mediterranean region under the RCP4.5 scenario by two time horizons relative to historic simulated scenarios. In both panels, we show the difference in SWC between the respective combination of future scenario and year and the median across historic simulated scenarios between 1975 and 2019. For future scenarios, we used the results of the “pessimistic” climate model class (15th percentile among 15 General Circulation Models). The grey hatched areas in the maps represent regions excluded from the 3D correction model. The red crosses represent the locations of the weather stations used in the analysis", out.width = '92%', fig.align = 'center'}
knitr::include_graphics("figures/final_figures/figure_s2.png")
```
```{r fig_S3, echo = FALSE, fig.cap = "Estimation of Safe Winter Chill change for the Mediterranean region under the RCP8.5 scenario by two time horizons relative to historic simulated scenarios. In both panels, we show the difference in SWC between the respective combination of future scenario and year and the median across historic simulated scenarios between 1975 and 2019. For future scenarios, we used the results of the “pessimistic” climate model class (15th percentile among 15 General Circulation Models). The grey hatched areas in the maps represent regions excluded from the 3D correction model. The red crosses represent the locations of the weather stations used in the analysis", out.width = '92%', fig.align = 'center'}
knitr::include_graphics("figures/final_figures/figure_s3.png")
```
```{r fig_S4, echo = FALSE, fig.cap = "Estimation of Safe Winter Chill change for the Mediterranean region under the RCP4.5 scenario by two time horizons relative to historic simulated scenarios. In both panels, we show the difference in SWC between the respective combination of future scenario and year and the median across historic simulated scenarios between 1975 and 2019. For future scenarios, we used the results of the “optimistic” climate model class (85th percentile among 15 General Circulation Models). The grey hatched areas in the maps represent regions excluded from the 3D correction model. The red crosses represent the locations of the weather stations used in the analysis", out.width = '92%', fig.align = 'center'}
knitr::include_graphics("figures/final_figures/figure_s4.png")
```
```{r fig_S5, echo = FALSE, fig.cap = "Estimation of Safe Winter Chill change for the Mediterranean region under the RCP8.5 scenario by two time horizons relative to historic simulated scenarios. In both panels, we show the difference in SWC between the respective combination of future scenario and year and the median across historic simulated scenarios between 1975 and 2019. For future scenarios, we used the results of the “optimistic” climate model class (85th percentile among 15 General Circulation Models). The grey hatched areas in the maps represent regions excluded from the 3D correction model. The red crosses represent the locations of the weather stations used in the analysis", out.width = '92%', fig.align = 'center'}
knitr::include_graphics("figures/final_figures/figure_s5.png")
```