From 06aaaff8b7dc2e2c89d2ddae140c1b6b8df9218e Mon Sep 17 00:00:00 2001 From: Mengqi Zhao Date: Fri, 2 Aug 2024 17:56:13 -0700 Subject: [PATCH] Update paper, meta data. Update test_gaea. Improve vignettes --- README.md | 6 +- _pkgdown.yml | 2 +- paper/paper.md | 14 +- paper/workflow.jpg | Bin 859631 -> 1713778 bytes tests/testthat/helper.R | 126 ++++++++++++++++++ tests/testthat/test-gaea.R | 78 +++++++++-- vignettes/vignette.R | 48 +++++++ vignettes/vignette.Rmd | 118 ++++++++++++++-- vignettes/vignetteFigs/figures.pptx | Bin 1782797 -> 0 bytes .../vignetteFigs/rtemplate_activate_docs.png | Bin 506274 -> 0 bytes .../rtemplate_build_your_own_package.png | Bin 140024 -> 0 bytes vignettes/vignetteFigs/rtemplate_codecov.png | Bin 643018 -> 0 bytes .../rtemplate_package_structure.png | Bin 253317 -> 0 bytes vignettes/vignetteFigs/workflow.jpg | Bin 0 -> 1713778 bytes 14 files changed, 361 insertions(+), 31 deletions(-) create mode 100644 tests/testthat/helper.R create mode 100644 vignettes/vignette.R delete mode 100644 vignettes/vignetteFigs/figures.pptx delete mode 100644 vignettes/vignetteFigs/rtemplate_activate_docs.png delete mode 100644 vignettes/vignetteFigs/rtemplate_build_your_own_package.png delete mode 100644 vignettes/vignetteFigs/rtemplate_codecov.png delete mode 100644 vignettes/vignetteFigs/rtemplate_package_structure.png create mode 100644 vignettes/vignetteFigs/workflow.jpg diff --git a/README.md b/README.md index 389063d..bb23358 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ `gaea` is an open-source R package designed to estimate crop yield shocks in response to annual climate variations and CO2 concentrations at the country scale for 12 major crops. `gaea` streamlines the workflow from raw climate data processing to the production of different forms of yield shock, such as agricultural productivity changes at the region-basin level, which can be directly integrated into the latest Global Change Analysis Model (GCAM). -
+

https://jgcri.github.io/gaea/ > Zhao, M., Waldhoff, S., Tebaldi, C., Snyder, A. 2024. gaea: An R package to estimate crop yield responses to temperature and precipitation. (In progress) Journal of Open Source Software, DOI: XXXX +
+ # Installation Guide @@ -64,7 +66,7 @@ remotes::install_github("JGCRI/gaea") > Waldhoff, S.T., Wing, I.S., Edmonds, J., Leng, G. and Zhang, X., 2020. Future climate impacts on global agricultural yields over the 21st century. Environmental Research Letters, 15(11), p.114010. https://doi.org/10.1088/1748-9326/abadcb - +
diff --git a/_pkgdown.yml b/_pkgdown.yml index 6f6f795..827cb29 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -19,7 +19,7 @@ navbar: text: "News" href: news/index.html - text: "Contribute" - href: CONTRIBUTE.html + href: CONTRIBUTING.html - icon: fas fa-quote-left text: Citation href: CITATION.html diff --git a/paper/paper.md b/paper/paper.md index ddd166d..7e678e5 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -8,13 +8,13 @@ authors: orcid: 0000-0001-5385-2758 affiliation: 1 - name: Stephanie Waldhoff - orcid: + orcid: 0000-0002-8073-0868 affiliation: 2 - name: Claudia Tebaldi - orcid: + orcid: 0000-0001-9233-8903 affiliation: 2 - name: Abigail Snyder - orcid: + orcid: 0000-0002-9034-9948 affiliation: 2 affiliations: @@ -28,7 +28,7 @@ bibliography: paper.bib # Summary -`gaea` is an open-source R package designed to estimate crop yield shocks in response to annual climate variations and CO2 concentrations at the country scale for 12 major crops. This innovative tool streamlines the workflow from raw data processing to the production of different forms of yield shock, such as agricultural productivity changes at the basin level, which can be directly integrated into the latest Global Change Analysis Model (GCAM) [@Bond-Lamberty_2024; @Calvin_2019]. At its core, `gaea` employs an empirical econometric model that leverages historical crop yield data (e.g., from [FAOSTAT](https://www.fao.org/faostat/en/#data/QCL)) and climate data for robust empirical fitting across diverse country-crop-climate combinations. `gaea` is fully compatible with the widely-used climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), bias-adjusted by the Inter-Sectoral Impact Model Intercomparison Project ([ISIMIP](https://www.isimip.org/)). For future projections, the package aggregates global gridded precipitation and temperature data to the national level, weighted by cropland area derived from MIRCA [@Portmann_2010]. This approach enables the projection of annual yield shocks under various future climate scenarios, differentiated by crop type, country, and time. More broadly, `gaea` serves as a lightweight yet powerful model that equips researchers with the tools to explore the possibility space of global crop yields responses to future climate uncertainties, enhancing human-Earth system analysis capabilities. +`gaea` is an open-source R package designed to estimate crop yield shocks in response to annual climate variations and CO2 concentrations at the country scale for 12 major crops. This innovative tool streamlines the workflow from raw climate data processing to the production of different forms of yield shock, such as agricultural productivity changes at the region-basin level, which can be directly integrated into the latest Global Change Analysis Model (GCAM) [@Bond-Lamberty_2024; @Calvin_2019]. At its core, `gaea` employs an empirical econometric model that leverages historical crop yield data (e.g., from [FAOSTAT](https://www.fao.org/faostat/en/#data/QCL)) and climate data for robust empirical fitting across diverse country-crop-climate combinations. `gaea` is fully compatible with the widely-used climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), bias-adjusted by the Inter-Sectoral Impact Model Intercomparison Project ([ISIMIP](https://www.isimip.org/)). For future projections, the package aggregates global gridded precipitation and temperature data to the national level, weighted by cropland area derived from MIRCA [@Portmann_2010]. This approach enables the projection of annual yield shocks under various future climate scenarios, differentiated by crop type, country, and time. More broadly, `gaea` serves as a lightweight yet powerful model that equips researchers with the tools to explore the possibility space of global crop yields responses to future climate uncertainties, enhancing human-Earth system analysis capabilities. # Statement of need @@ -46,15 +46,15 @@ The exploration of future climate change impacts on agricultural production is c The primary functionality of `gaea` is encapsulated in the `yield_impact` wrapper function, which executes the entire workflow from climate data processing to yield shock estimation. Users can also execute individual functions to work through the main steps of the process (\autoref{fig:workflow}). Detailed instructions on `gaea` can be accessed at https://jgcri.github.io/gaea. -1. `weighted_climate`: Processes CMIP-ISIMIP climate NetCDF data and calculates cropland-weighted precipitation and temperature at the country level, differentiated by crop type, irrigation type, and country. The function accepts both daily or monthly climate data. +1. `weighted_climate`: Processes CMIP-ISIMIP climate NetCDF data and calculates cropland-weighted precipitation and temperature at the country level, differentiated by crop type and irrigation type. The function accepts both daily or monthly climate data. 2. `crop_calenders`: Generates crop planting months for each country based on crop calendar data [@Sacks_2010]. 3. `data_aggregation`: Calculates crop growing seasons using climate variables processed by `weighted_climate` and crop calendars for both historical and projected periods. This function prepares climate and yield data for subsequent model fitting. -4. `yield_regression`: Performs regression analysis based on historical crop yields and climate variations. The default econometric model employed in `gaea` is derived from @Waldhoff_2020. +4. `yield_regression`: Performs regression analysis based on historical crop yields and climate variations. The default econometric model applied in `gaea` is from @Waldhoff_2020. User can provide other formulas as long as it is using the same data from `data_aggregation`. 5. `yield_projection`: Projects yield shocks for future climate scenarios using the fitted model. 6. `gcam_agprodchange`: Remaps country-level yield shocks to GCAM-required spatial scales (i.e., region and basin intersections) based on harvest area and converts crops to GCAM commodities. This function calculates agricultural productivity growth (a key parameter for GCAM to estimate future yield) and creates ready-to-use XML outputs for GCAM. -![The gaea workflow showing the functions and the steps of modeling crop yield shocks to climate variations using empritical econometric model. \label{fig:workflow}](workflow.jpg) +![The gaea workflow showing the functions and the corresponding outputs of modeling crop yield shocks to climate variations using empritical econometric model. \label{fig:workflow}](workflow.jpg) # Acknowledgements diff --git a/paper/workflow.jpg b/paper/workflow.jpg index 501290fefd2850a9e08f7b5fb495d9c58ceae9ff..5b241c088af02db28a4679bd8c784a39350e8a80 100644 GIT binary patch literal 1713778 zcmeEucT`hZ+ix5TGj;|6rRl&73MdSQULB=4BZ0vXLPJXYX_NbAIRS z^0aLCZhrzD{>A2J8_;CcTmWe`uBl4 zW&!@VFKvCHFB0#+j)6ge->-qM5(o>r46+5C|8oDpK0tv5Adt}_2(pAa9U1z?}vC2+{=^Z1X@DKs&$sLSME!0ot`^ z*BAPF&z{|TzWI9Z-fzDC=9|6y5AEN(@8G^~zBwR$;NYRdhouki-T$r3w})i_{_vNJ z?D%r#&Rt&vCm!DS%|77h|0Qj|21$Q?r2P9IcJ5FGeI>nPr}U2PdXNeL_ZPW#{O8RA zn18cJYWLo+cJBI8jynvzpn&8%cO8^DuzUBe9kQSuz|QVHQqo_4``tG(x<4I}J$g*l z(8v*f6_@&eCV%!a)X6zGBou$^VfvF7m7O0sa%x~beS`BbFYKd=uHVv%NS}Rf6uf!+ z{RJ2A+w{603h&&lAD*%L{prxPS#{J&>+3c^vqyLQN&gfIc-CM2DbOitK;Rv_b^_%f zK z8-c$O_#1)05%?Q{zY+Kwf&V`UDC{f}UP77{VE7;OH^gbFOdn)B^ByGIxNCShvw+{D z-v+gg+Hh=n7`7fARwHZ;=(uv`(#D0rO{3E{owq^xx(xH1be-7)wF|<(@Sk4naNmk* z^`04$|QIR*uh|F>GmivSXWw_2W(`{j-@LKZj7kZRvW#){if77 z#KdCJ3rQ4plo^sot;90FqK^x2(YQGi`Rm_{2uncJ*&BWkIE=1Pa zW8s&=n|SaLLEZ@4m{jf;Hq(K-I!Qz}v(gbXFa+;6+CdHxILh)$A8wR6e%pE^m@gTz zpuLTGzzejammmu-47eT{cO&VzMGE@ZeJR+|Pm`SyH%8%iH7M0HFs-KlL$Lm?_U}Vr z$DAm(H69*LDI}5`2@MgXA1kk z$|E#d{l@&5f$+Ip(bjj2tsJ>?TCp9Oyu8uA-ZIGZfL@bn1`tv5G;YCdjNwiLA z*bSbVn%8wqv@MiV46T*J?G5&pYvoe}ucE{~-9(b0y}jMhE-bv#;s2`tci_D-b9sob zy23Xr+<_6`xyo@#v*B1hFMGKAK$sIo`J{|fu>w04>Fl*h#NgCR;u1(EeCAwo)UA%2 ze|ZeU%LYOo8+@)^U`lhV`xu&J&^6f7*20z(yB3%lbDfrK{p)SZ!P3ev?(*hYoX!z{ zA^&xMgxshg6+&?Fim(xmk*uJz6jz@pWzCoMP4Ix%!^i;I5S19({;g6&K9J1anJJsT|DxD_y5 zQPqQoS_qDGz6?`LiD70=A~^cQGAmn)z65Re(tIE7_DaM2Ly;8oqsw>j!Gs?+2{*1I zrYN}6+UqHH+n`7kIzJ|JSEJIN_CcOnoEuVMpg};iuept|YS^^YPF!fRl(Kl%;T%mK z)sFg~$o$Rxe?&lbP>{0la%h_EYI&H_KwkFV)G>;bbUV~*KL1myivvPJsn*?X&^6&U zXwmWqr3Z#gfv9;Z% zrC}f1ahy1IO-4eV()zJ{xs^MlnZmp7*>P(%p^XNEoIunZCEOavahMK`b}J?NreKPZ zZ7tRM5MK2ngj+T^y_QVNjrMKb{1vwD`txa82f#b5gOOik@LUBA-tEy_(FnX!d zocDN9XxD{tBZ?6W&j6R@0(1GxC{?c#po9&Aue1hWQxPu~6 z%xA0Cm4csMhg$culIJH3>mc%%7%&2v9~REVg|8{P^r)$RUUxj=MnT?6-S>F;T$^R# z_k)9xJx&eX%L7l6j4C|zV4sm^8=x{sHq^5+Xe^H|!8U=C(Sv59y z5$b#6Sd8%$Djn7wGg)y^-Pr>FLk{1)l3$UE$*!6tg*Q0Cl!m=U^{h@E!edKWi%ZJ# zRdWP@xZ4ggqYY`PQ3aj6|_y^PhVxhT_$+8Pf>Ptx-44sNv{TSLuqK1ac z%bB+6L&b1C7*@p3-+5663~u+S1c8*}SY~TK&G5vRE^f4QsFM})w)qz=6r;($$FutP zy!i{VJ0ODY0C`J?iJy`vpIs=rbO`+pRz;{G;&xHRvbL5U>Uq73loaGXcoRpyz!GUc!gjdUMp>(zA2pkxB%qU{NJI)FhG1sm=5M#& z%ME(9Rg;Kek_IsGX@1(=@stUu6-?fD8-z}{Ce3^vVJ=46Mh16W6-R3daG!;k{ijG8 zLYX5RJaMu#Crnj?M9!ME!lHQYj~SQD2O|Wz;NMKP^bmQ-X!|ptpmx|%FgS){YT41y z=AA2LnqOsNEacX|`d;J`@oI(bReJQ%^D#-+(nN{+hG&J=d=DG z{soZ#U=QtM1h~+VlRX*?i~tP7am~o2OmS9L{w^~Lg{VQKP*gLMv0(Gmz3GzQ7M9zF zZJw0OT(})kgEzSQLvsZ^*Ij{`Ku>(wp;_imF+19++l;cjlewCVydpqqMv}9Aya!tP z%vGKZVUTDp%v+b_mvXL z+8mE}Qq^9tVrfoxk(ZRm83NZPZb&fR=VyQaC@qHinO?zBgNR3{a`^{SoVok~_vG^1 zxzb7^*vi3{h?pn0O0RgMS8n9kQxYG-lqM*|!WUiuT~Psif`6efBpcm8nI2?9*!T(JB$Yf0(QEQ= zZC&|pEE)MhMb}}^nl2{#f|TZI(fR`-ek^NpxiymlJnTx2SN5XS+5%_tcILZ@mQF2& z&DKbMD6b-f3$=EHz|qOUX2n&fMlFMf#-_%TrY0G+>jcrMU!|^XmNS>O7I-U)rD#!= z{+W|d+OeZqcy&}_xA2V=5$kk$SF`t?|+*GnWuyX4j;$g4G2d-v;UXdgi+0RsR=B|K9z#Ah4V8mnB~XI=Xio zbg5lsot`$k_t}p2DRZ@bL4mpK$0Da5BjTg7_;z3GaIwXYKEmf<--ljm# zkR~tap$0TkG)>&Dbt$wrO@bK7$JY2bzXHjnK%;+}gV+KgoGS{zDe!|Cd!}z~1 zd+|zk;j8hXH#M6@!CKdDDAeQzT2sU>8E5s~8D$*9GjKGSs5zB?A%yBNsBsbKe$uy# z11}xmbvmE}qWx|J68VQteL{$-FY_wuQ-iUSCqFfUi4V33`dE}}E$yM#oDv9(XOXbPx&Z6!lV?gV7{b96$3_+fC5nv|>a1cGXbVVf%Y8mhN`&h*j$30w|f! 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zw2$#Mti9|FD!PHS=1a(~0z=5zzed+Ax*Vu|30j1ZzsZD$G;K==IF^+#S}X7{t$h}>W#a<;hIZ#{zb7)eot6<8dmqQ_0l5jMLY-fhQ; zkKQQxNNy#+v=2yi-x%OVW=d*g*Icg7K2cQgafdM7l)5RuaR+?^dv)OSl+ed<;Pc0@ zz3kRBpSc^|Qv+&wGVuA|k-RM$vdVJv>{lxXElcAz$BtCS1k|jHfLeA~V%CB@hHkh^ zvakZvdN)4{ovB#=9jbMuIC_4imCwNpNk~4gs{fpv_#I^*g8ohRNf3V$o$2>T`3{EE g4dA`)(;V9OuA#sFH}Bg2|M~wb@PA - - ```{=html} @@ -41,25 +39,129 @@ resourse_files: color: gold; } +p.comment { +background-color: #DBDBDB; +padding: 10px; +border: 1px solid black; +margin-left: 25px; +border-radius: 5px; +font-style: italic; +} + ``` + +# Workflow + + -# Introduction +

    + + +`gaea` is designed with a climate-driven empirical model at its core, integrated into an efficient modular structure. This architecture streamlines the entire workflow, from initial climate and crop data processing through empirical model fitting, yield shock projections under future climate scenarios, to the calculation of agricultural productivity changes for GCAM. The modular design also facilitates comprehensive diagnostic outputs, enhancing the tool's utility for researchers and policymakers. + +The primary functionality of `gaea` is encapsulated in the `yield_impact` wrapper function, which executes the entire workflow from climate data processing to yield shock estimation. Users can also execute individual functions to work through the main steps of the process (\autoref{fig:workflow}). Detailed instructions on `gaea` can be accessed at https://jgcri.github.io/gaea. + +1. `weighted_climate`: Processes CMIP-ISIMIP climate NetCDF data and calculates cropland-weighted precipitation and temperature at the country level, differentiated by crop type and irrigation type. The function accepts both daily or monthly climate data. + +2. `crop_calenders`: Generates crop planting months for each country based on crop calendar data from [Sacks et al., (2020)](https:/doi/org/10.1111/j.1466-8238.2010.00551.x). + +3. `data_aggregation`: Calculates crop growing seasons using climate variables processed by `weighted_climate` and crop calendars for both historical and projected periods. This function prepares climate and yield data for subsequent model fitting. + +4. `yield_regression`: Performs regression analysis based on historical crop yields and climate variations. The default econometric model applied in `gaea` is from [Waldhoff et al., (2020)](https://doi.org/10.1088/1748-9326/abadcb). User can provide other formulas as long as it is using the same data from `data_aggregation`. + +5. `yield_projection`: Projects yield shocks for future climate scenarios using the fitted model. + +6. `gcam_agprodchange`: Remaps country-level yield shocks to GCAM-required spatial scales (i.e., region and basin intersections) based on harvest area and converts crops to GCAM commodities. This function calculates agricultural productivity growth (a key parameter for GCAM to estimate future yield) and creates ready-to-use XML outputs for GCAM. + +
    + +

    +
    +

    **Figure 1:** The gaea workflow showing the functions and the corresponding outputs of modeling crop yield shocks to climate variations using empritical econometric model.

    +
    + + + +# Example + + -

    +

    + +## Example Climate Data + +`gaea` requires global climate data from the Inter-Sectoral Impact Model Intercomparison Project ([ISIMIP](https://www.isimip.org/)) or data with the same format of ISIMIP data. In addition, `gaea` also allows climate data that has been aggregated to monthly time step. Given the large size of the climate data at daily time step, we have provided an example climate data at monthly time step from 2015 to 2100 ((download link)[]). Please download the data and make sure to unzip the data to your desired location. + +
    +NOTE: In the case where there is no available historical climate data, `gaea` will use the default fitted model that has already been fitted with WATCH historical climate forcing data ([Weedon et al., (2011)](https://doi.org/10.1175/2011JHM1369.1)). +
    + - +## Run gaea! -

    +Once the example data is in place, we can run `gaea` with a single function `yield_impact` that streamlines the entire workflow. For the explanation of each argument of `yield_impact`, refer to this [reference page](https://jgcri.github.io/gaea/reference/yield_impact.html). -This is an example autogenerated documentation page with a basic empty strucure. Please see step by step instructions on how to modify the package and documentation at https://github.com/JGCRI/gaea. -

    +```{r eval=FALSE} + +# load gaea +library(gaea) + +# NOTE: please change `data_dir` to your desired location for downloaded data +data_dir <- gaea::get_example_data( + download_url = 'https://zenodo.org/records/13179630/files/gaea_example_climate.zip?download=1', + data_dir = 'path/to/desired/location' +) + +# Path to the climate NetCDF files +# NOTE: Each variable can have more than one file +# historical climate data +pr_historical_file <- file.path(data_dir, 'pr_monthly_canesm5_w5e5_rcp7_1950_2014.nc') +tas_historical_file <- file.path(data_dir, 'tas_monthly_canesm5_w5e5_rcp7_1950_2014.nc') + +# projected climate data +pr_projection_file <- file.path(data_dir, 'pr_monthly_canesm5_w5e5_rcp7_2015_2100.nc') +tas_projection_file <- file.path(data_dir, 'tas_monthly_canesm5_w5e5_rcp7_2015_2100.nc') + +# Run gaea +# The full run with raw climate data can take up to an hour +gaea::yield_impact( + pr_hist_ncdf = pr_historical_file, + tas_hist_ncdf = tas_historical_file, + pr_proj_ncdf = pr_projection_file, + tas_proj_ncdf = tas_projection_file, + timestep = 'monthly', # specify the time step of the NetCDF data (monthly or daily) + historical_periods = c(1950:2014), # vector of historical years selected for fitting + climate_model = 'canesm5', # label of climate model name + climate_scenario = 'gcam-ref', # label of climate scenario name + member = 'r1i1p1f1', # label of ensemble member name + bias_adj = 'w5e5', # label of climate data for bias adjustment + cfe = 'no-cfe', # label of CO2 fertilization effect in the formula (default is no CFE) + gcam_version = 'gcam7', # output is different depending on the GCAM version (gcam6 or gcam7) + use_default_coeff = FALSE, # set to TRUE when there is no historical climate data available + base_year = 2015 # GCAM base year + start_year = 2015, # start year of the projected climate data + end_year = 2100, # end year of the projected climate data + smooth_window = 20, # number of years as smoothing window + co2_hist = NULL, # historical annual CO2 concentration. If NULL, will use default value + co2_proj = NULL, # projected annual CO2 concentration. If NULL, will use default value + diagnostics = TRUE, # set to TRUE to output diagnostic plots + output_dir = 'path/to/output/folder' # path to the output folder +) + +``` + +
    + +

    +NOTE: The arguments `climate_model`, `climate_scenario`, `member`, `bias_adj`, and `cfe` require corresponding strings that demonstrate climate model information in the output files. These arguments will not affect `gaea` model simulation, other than the meta information of climate data in the output files. +
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