EnvRtype is an R package for enviromics — the study of the envirome, the set of environmental conditions linked to the biological performance of living beings (Costa-Neto and Fritsche-Neto, 2021; Crossa et al., 2021). It collects worldwide daily weather and soil data, derives agro-meteorological parameters, builds environmental covariable matrices and relatedness kernels, mines environmental typologies, delineates soil zones with Gaussian mixture models, and runs a FAO-56 soil water balance for use in enviromics and genotype-by-environment (GxE) analyses.
This is a fully re-engineered successor to the original EnvRtype (allogamous/EnvRtype, 2021), extending it from a weather-typing toolbox into an end-to-end envirotyping, simulation and prediction framework.
📄 Original publication: Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics, 11(4), jkab040. Read the paper →
📖 Documentation: https://gcostaneto.github.io/EnvRtype/
Installation
CRAN Version (0.1.3)
install.packages("EnvRtype")
# https://cran.r-project.org/web/packages/EnvRtype/index.htmlDeveloper Version
if (!require("devtools")) install.packages("devtools")
devtools::install_github("gcostaneto/EnvRtype")What’s new compared to the original EnvRtype (2021)
The original EnvRtype (Costa-Neto et al., 2021, G3) focused on collecting NASA POWER weather data, computing environmental typologies and building environmental kernels for reaction-norm models. This version keeps that workflow and adds several new layers:
Package modules & workflow
The package is organised in four data layers — acquisition, processing, characterisation and prediction — plus a simulation engine that generates ground-truth data to validate each layer. Each subsection lists the functions involved and the primary references used to build them.
Overview — the four layers + simulation
Design follows the enviromics reaction-norm framework of Costa-Neto et al. (2021, G3) and its envirome-wide extension (Costa-Neto et al., 2023, G3).
flowchart TB
L1["<b>1 · ACQUIRE</b><br/>get_weather · get_soil · get_elevation<br/>get_bioclim · get_spatial · get_AEZ<br/>get_climate_scenario · + resumable/hourly variants"]
L2["<b>2 · PROCESS</b><br/>processWTH · summaryWTH · param_*<br/>water_balance · env_phenology"]
L3["<b>3 · CHARACTERISE</b><br/>W_matrix · T_matrix · env_indices · env_pca<br/>soil_classification · env_typing · env_risk_profile"]
L4["<b>4 · MODEL & SCAN</b><br/>env_kernel · get_kernel · kernel_model<br/>kernel_cv · scan_untested_envs · map_scan"]
SIM["<b>SIMULATE</b><br/>sim_markers · sim_envirome<br/>sim_met() → C_env → sim_W()<br/><i>ground truth for every layer</i>"]
L1 --> L2 --> L3 --> L4
SIM -.->|"validates"| L3
SIM -.->|"validates"| L4
classDef l1 fill:#1565c0,stroke:#0d47a1,color:#fff
classDef l2 fill:#1976d2,stroke:#0d47a1,color:#fff
classDef l3 fill:#2e7d32,stroke:#1b5e20,color:#fff
classDef l4 fill:#c62828,stroke:#b71c1c,color:#fff
classDef sim fill:#6a1b9a,stroke:#4a148c,color:#fff
class L1 l1
class L2 l2
class L3 l3
class L4 l4
class SIM sim1–2 · Data acquisition and processing
Weather and geodata come from NASA POWER via nasapower (Sparks, 2018), SoilGrids 2.0 (Poggio et al., 2021), WorldClim 2 (Fick & Hijmans, 2017), bioclimatic predictors (O’Donnell & Ignizio, 2012), CMIP6 / ScenarioMIP scenarios (Hawkins et al., 2013; O’Neill et al., 2016) and GAEZ v4 (FAO & IIASA, 2021). Agro-meteorological processing, the FAO-56 soil water balance and phenology follow Allen et al. (1998), Pereira et al. (2021), Doorenbos & Kassam (1979), Steduto et al. (2012), Borg & Grimes (1986), Forsythe et al. (1995), Zadoks et al. (1974) and Counce et al. (2000).
flowchart LR
GW["get_weather()"]
GWH["get_weather_hourly()"]
GWR["get_weather_resumable()"]
RPL["read_progress_log()"]
RFL["restart_from_log()"]
GS["get_soil()"]
GSR["get_soil_resumable()"]
GEL["get_elevation()"]
GBC["get_bioclim()"]
GSP["get_spatial()"]
GAEZ["get_AEZ()"]
GCS["get_climate_scenario()"]
PRAD["param_radiation()"]
PATM["param_atmospheric()"]
PTMP["param_temperature()"]
PWT["processWTH()"]
SWT["summaryWTH()"]
EPH["env_phenology()"]
PHT["phenology_templates()"]
PWD["planting_window_table()"]
BPD["best_planting_date()"]
SPH["show_phenology()"]
PPW["plot_planting_window()"]
WBAL["water_balance()"]
SWB["summary_water_balance()"]
GWR --> RPL --> RFL --> GW
GW --> PWT
GWH --> PWT
PRAD --> PWT
PTMP --> PWT
GEL --> PATM
PATM --> PWT
PWT --> SWT
PWT --> EPH
PHT --> EPH
EPH --> PWD
EPH --> SPH
PWD --> BPD
PWD --> PPW
PWT --> WBAL
GSR --> GS
GS --> WBAL
GEL --> WBAL
WBAL --> SWB
classDef collect fill:#1565c0,stroke:#0d47a1,color:#fff
classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
classDef wb fill:#0097a7,stroke:#006064,color:#fff
class GW,GWH,GWR,RPL,RFL,GS,GSR,GEL,GBC,GSP,GAEZ,GCS,PRAD,PATM,PTMP,PWT,SWT collect
class EPH,PHT,PWD,BPD,SPH,PPW char
class WBAL,SWB wb3 · Characterisation and dissection
Environmental covariables, typologies and kernels follow Costa-Neto et al. (2021, G3); interval-resolved indices follow Della Coletta et al. (2023); breeding-zone typologies and target-population importance follow Costa-Neto et al. (2023, Agronomy Journal); copula reparameterisation follows Sklar (1959), Genest & Rivest (1993) and Salvadori et al. (2007); Gaussian-mixture soil zoning follows Fraley & Raftery (2002), Scrucca et al. (2016) and Egozcue et al. (2003).
flowchart LR
SWT["summaryWTH()"]
GS["get_soil()"]
WM["W_matrix()"]
ETYP["env_typing()"]
TM["T_matrix()"]
EEX["env_expand()"]
ECOP["env_copula()"]
ERP["env_risk_profile()"]
TPW["tpe_weights()"]
PRK["project_risk()"]
ETI["env_target_importance()"]
CVS["coverage_summary()"]
GCS["get_climate_scenario()"]
SCL["soil_classification()"]
EIX["env_indices()"]
EPC["env_pca()"]
EPS["env_pca_scree()"]
EPB["env_pca_biplot()"]
ELC["env_loading_curve()"]
ECOR["env_cor()"]
ECH["env_cor_heatmap()"]
EPA["env_pc_associate()"]
SWT --> WM
WM --> ETYP
ETYP --> TM
WM --> EEX
WM --> ECOP
WM --> ETI
ERP --> TPW
ERP --> PRK
GCS --> PRK
TPW --> CVS
GS --> SCL
EIX --> WM
EIX --> EPC
EIX --> ECOR
ECOR --> ECH
EPC --> EPS
EPC --> EPB
EPC --> ELC
EPC --> EPA
classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
classDef dis fill:#6a1b9a,stroke:#4a148c,color:#fff
classDef collect fill:#1565c0,stroke:#0d47a1,color:#fff
class WM,ETYP,TM,EEX,ECOP,ERP,TPW,PRK,ETI,CVS,SCL char
class EIX,EPC,EPS,EPB,ELC,ECOR,ECH,EPA dis
class SWT,GS,GCS collect4 · Prediction and scanning
Reaction-norm and kernel models follow Costa-Neto et al. (2021, G3), the nonlinear / Gaussian kernels of Costa-Neto, Fritsche-Neto & Crossa (2021, Heredity) and the deep kernels of Cuevas et al. (2019); the GxE modelling engine follows the BGGE approach of Granato et al. (2018); scanning of untested environments follows the envirome-wide prediction of Costa-Neto et al. (2023, G3).
flowchart TB
EK["env_kernel()"]
GK["get_kernel()<br/>K_G · K_E · K_S"]
DK["decompose_kernels()"]
UDK["undecompose_kernels()"]
TGK["truncate_gxe_kernel()"]
KM["kernel_model()"]
KCV["kernel_cv()"]
KMC["kernel_model_clustered()"]
KMM["kernel_model_mc()"]
ECL["env_cluster()"]
CLE["cluster_environments()"]
VCS["varcomp_summary()"]
SUE["scan_untested_envs()"]
GSC["grid_scan()"]
MSC["map_scan()"]
SST["scan_spatial_table()"]
CVS["coverage_summary()"]
EK --> GK
GK --> DK
DK --> UDK
DK --> TGK
TGK --> KM
DK --> KM
KM --> KCV
KM --> VCS
KM --> SUE
ECL --> CLE
CLE --> KMC
KM --> KMM
SUE --> GSC
SUE --> MSC
SUE --> SST
SUE --> CVS
classDef pred fill:#c62828,stroke:#b71c1c,color:#fff
classDef rel fill:#ef6c00,stroke:#e65100,color:#fff
classDef char fill:#2e7d32,stroke:#1b5e20,color:#fff
class GK,DK,UDK,TGK,KM,KCV,KMC,KMM,VCS,SUE,GSC,MSC,SST,CVS pred
class EK rel
class ECL,CLE charSimulation — closing the validation loop
The simulator encodes a known environmental covariance and reaction-norm structure to benchmark every layer, following the envirome-wide prediction framework of Costa-Neto et al. (2023, G3) and the kernel models of Costa-Neto et al. (2021, G3). sim_markers() and sim_envirome() additionally generate marker and envirome data with controllable genetic and environmental diversity (MAF spectrum, population structure, linkage; mega-environments, gradients and covariable redundancy), feeding sim_met() and get_kernel() directly.
flowchart LR
SMK["sim_markers()<br/><i>genetic diversity</i>"]
K["genomic kinship K<br/><i>n × n lines</i>"]
SEV["sim_envirome()<br/><i>environmental diversity</i>"]
SMC["sim_met_C()<br/><i>q × q envs</i>"]
SM["sim_met()"]
CE["$C_env<br/><i>q × q correlation<br/>among environments</i>"]
PH["$data<br/>env · gid · rep · value"]
SW["sim_W()"]
SWG["sim_W_grid()"]
W["simulated W<br/><i>known % explained</i>"]
GK["get_kernel()"]
KM["kernel_model()"]
VCS["varcomp_summary()"]
CHK{{"compare to<br/>known truth"}}
SMK --> K
K --> SM
K --> GK
SEV --> SM
SEV --> GK
SMC --> SM
SM --> CE
SM --> PH
CE --> SW
SW --> W
SWG --> W
W --> GK
PH --> KM
GK --> KM
KM --> VCS
VCS --> CHK
SM -.->|"$truth"| CHK
classDef sim fill:#6a1b9a,stroke:#4a148c,color:#fff
classDef pred fill:#c62828,stroke:#b71c1c,color:#fff
classDef data fill:#455a64,stroke:#263238,color:#fff
classDef chk fill:#f9a825,stroke:#f57f17,color:#000
class SM,SW,SWG,SMC,SMK,SEV sim
class GK,KM,VCS pred
class K,CE,PH,W data
class CHK chkPrediction
EnvRtype fits enviromics-enabled reaction-norm models in which phenotypes are regressed on genomic, environmental and GxE relatedness kernels. The current prediction workflow provides:
-
Kernel construction —
get_kernel()assembles genomic (K_G), environmental (K_E) and soil (K_S) kernels;env_kernel()builds Gaussian / linear environmental kernels;decompose_kernels(),undecompose_kernels()andtruncate_gxe_kernel()manage the GxE block structure. -
Model fitting —
kernel_model()fits the multi-kernel Bayesian reaction-norm model;kernel_model_mc()runs multiple chains;kernel_model_clustered()fits environment-clustered models built withenv_cluster()/cluster_environments(). -
Validation & variance —
kernel_cv()runs cross-validation (CV1 / CV2 / CV0 schemes) andvarcomp_summary()reports variance components and the share of variance explained by each kernel. -
Untested environments —
scan_untested_envs()predicts genotype performance in environments with no phenotypes, whilegrid_scan(),map_scan(),scan_spatial_table()andcoverage_summary()summarise the target population of environments (TPE).
These tools implement the reaction-norm and kernel methods of Costa-Neto et al. (2021, G3), the nonlinear / Gaussian kernels of Costa-Neto, Fritsche-Neto & Crossa (2021, Heredity), the deep kernels of Cuevas et al. (2019, G3), the BGGE GxE engine of Granato et al. (2018, G3), and the envirome-wide prediction of untested environments of Costa-Neto et al. (2023, G3). Full citations are listed under References for the new functions below.
Acknowledgements
Original EnvRtype team (2020–2021)
EnvRtype began in 2020 and was first published as allogamous/EnvRtype in Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes|Genomes|Genetics, 11(4), jkab040. We gratefully acknowledge the original authors and the community of users whose feedback shaped this new version.
Current maintainers and developers
- Germano Costa-Neto — maintainer & lead developer <germano.cneto@gmail.com>
- Fernanda Pontes — developer <ferpoontes@gmail.com>
References for the new functions
The methods and data sources underlying the new and extended functions are documented below, grouped by module.
Enviromics framework, kernels and genomic prediction
- Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes|Genomes|Genetics, 11(4), jkab040. https://doi.org/10.1093/g3journal/jkab040
- Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, 126, 92–106. https://doi.org/10.1038/s41437-020-00353-1
- Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2023). Envirome-wide associations enhance multi-environment prediction. G3: Genes|Genomes|Genetics, 13(2), jkac313. https://doi.org/10.1093/g3journal/jkac313
- Cuevas, J., Montesinos-López, O., Juliana, P., Guzmán, C., Pérez-Rodríguez, P., González-Bucio, J., Burgueño, J., Montesinos-López, A., & Crossa, J. (2019). Deep kernel for genomic and near infrared predictions in multi-environment breeding trials. G3: Genes|Genomes|Genetics, 9(9), 2913–2924. https://doi.org/10.1534/g3.119.400493
- Granato, I., Cuevas, J., Luna-Vázquez, F., Crossa, J., Montesinos-López, O., Burgueño, J., & Fritsche-Neto, R. (2018). BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models. G3: Genes|Genomes|Genetics, 8(9), 3039–3047. https://doi.org/10.1534/g3.118.200435
Environmental characterisation, typologies and target-population importance
- Costa-Neto, G., da Matta, D., Fernandes, I. K., & Heinemann, A. B. (2023). Environmental clusters defining breeding zones for tropical irrigated rice in Brazil. Agronomy Journal, 115(5). https://doi.org/10.1002/agj2.21481
- Della Coletta, R., Liese, S. E., Fernandes, S. B., Mikel, M. A., Bohn, M. O., Lipka, A. E., & Hirsch, C. N. (2023). Linking genetic and environmental factors through marker effect networks to understand trait plasticity. GENETICS, 224(4), iyad103. https://doi.org/10.1093/genetics/iyad103
Copula-based environmental indices (env_copula)
- Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229–231.
- Genest, C., & Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88(423), 1034–1043. https://doi.org/10.1080/01621459.1993.10476372
- Salvadori, G., De Michele, C., Kottegoda, N. T., & Rosso, R. (2007). Extremes in Nature: An Approach Using Copulas. Springer.
Environmental data acquisition (weather, soil, elevation, bioclimate, scenarios, agro-ecological zones)
- Sparks, A. H. (2018). nasapower: NASA-POWER Data from R. Journal of Open Source Software, 3(30), 1035. https://doi.org/10.21105/joss.01035
- Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H., & Schoolfield, R. M. (1995). A model comparison for daylength as a function of latitude and day of the year. Ecological Modelling, 80(1), 87–95. https://doi.org/10.1016/0304-3800(94)00034-F
- Peng, S., Huang, J., Sheehy, J. E., Laza, R. C., Visperas, R. M., Zhong, X., Centeno, G. S., Khush, G. S., & Cassman, K. G. (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences, 101(27), 9971–9975. https://doi.org/10.1073/pnas.0403720101
- Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021
- Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
- O’Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series, 691. https://doi.org/10.3133/ds691
- Hawkins, E., Osborne, T. M., Ho, C. K., & Challinor, A. J. (2013). Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agricultural and Forest Meteorology, 170, 19–31. https://doi.org/10.1016/j.agrformet.2012.04.007
- O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
- FAO & IIASA (2021). Global Agro-Ecological Zones (GAEZ v4). FAO, Rome. https://gaez.fao.org
Water balance, evapotranspiration and phenology
- Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome.
- Pereira, L. S., Paredes, P., López-Urrea, R., Hunsaker, D. J., Mota, M., & Mohammadi Shad, Z. (2021). Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management, 243, 106466. https://doi.org/10.1016/j.agwat.2020.106466
- Doorenbos, J., & Kassam, A. H. (1979). Yield response to water. FAO Irrigation and Drainage Paper 33. FAO, Rome.
- Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water. FAO Irrigation and Drainage Paper 66. FAO, Rome.
- Borg, H., & Grimes, D. W. (1986). Depth development of roots with time: an empirical description. Transactions of the ASAE, 29(1), 194–197. https://doi.org/10.13031/2013.30125
- Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x
- Counce, P. A., Keisling, T. C., & Mitchell, A. J. (2000). A uniform, objective, and adaptive system for expressing rice development. Crop Science, 40(2), 436–443. https://doi.org/10.2135/cropsci2000.402436x
Soil classification and Gaussian-mixture risk zoning
- Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. https://doi.org/10.1198/016214502760047131
- Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 205–233. https://doi.org/10.32614/RJ-2016-021
- Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300. https://doi.org/10.1023/A:1023818214614
- Zhang, F., Jia, Z., Wu, S., Chen, C., Chen, X., Zheng, C., & Xu, M. (2025). Machine learning and Gaussian mixture model for delineating soil cadmium risk zones. Ecosystem Health and Sustainability. https://doi.org/10.34133/ehs.0402
Citation
If you use EnvRtype in your research, please cite the original publication:
Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics, 11(4), jkab040. https://doi.org/10.1093/g3journal/jkab040
BibTeX
@article{costaneto2021envrtype,
title = {EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture},
author = {Costa-Neto, Germano and Galli, Giovanni and Carvalho, Humberto Fanelli
and Crossa, Jos{\'e} and Fritsche-Neto, Roberto},
journal = {G3 Genes|Genomes|Genetics},
volume = {11},
number = {4},
pages = {jkab040},
year = {2021},
doi = {10.1093/g3journal/jkab040},
url = {https://academic.oup.com/g3journal/article/11/4/jkab040/6129777}
}From within R:
citation("EnvRtype")Please also kindly let the maintainer know when you are using EnvRtype (contact germano.cneto@gmail.com).