nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R

Sparks, Adam H.

doi:10.21105/joss.01035

Cited by 204 publications

(115 citation statements)

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“…First, daily environmental data were obtained from NASA orbital sensors (Sparks 2018 ). Next, additional variables describing ecophysiological processes (e.g., evapotranspiration, the impact of air temperature on radiation-use efficiency) were computed as extensively described by Allen et al ( 1998 ) and Soltani and Sinclair ( 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

2020

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Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model–kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.

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Section: Methodsmentioning

confidence: 99%

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

2020

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“… a Collected from NASA orbital sensors ( Sparks 2018 ). b Shuttle Radar Topography Mission integrated with the raster R package.…”

Section: Methodsmentioning

confidence: 99%

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

Costa‐Neto

Galli

Carvalho

et al. 2021

G3 Genes|Genomes|Genetics

105

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Envirotyping is an essential technique used to unfold the non-genetic drivers associated with the phenotypic adaptation of living organisms. Here we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically-smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

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“…The trial was rainfed, and standard agronomic practices for grain sorghum were used; these included preplant fertilization at 0.1 Mg ha −1 and a seed‐applied fungicide to prevent early seedling death due to fungal infections. Additional information on weather and climate conditions (Supplemental Figure S1) were obtained using nasapower (Sparks, 2018) under the EnvRtype R library (github.com/allogamous/EnvRtype). Traits evaluated in this experiment hereafter are referred to as primary (ground truth or target) and secondary (surrogate or remotely acquired).…”

Section: Methodsmentioning

confidence: 99%

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

Galli

Horne

Collins

et al. 2020

The Plant Phenome Journal

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High-throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high-throughput traits could lead to significant optimization of HTP-assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS-based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field-based breeding program and suggest additional optimization options. Abbreviations: AUDPC, area under the disease progress curve; CIg, chlorophyll index; CIre, red-edge chlorophyll index; DAS, days after sowing; GY, grain yield; HTP, high-throughput phenotyping; ML, machine learning; MSR, modified simple ratio; MSRre, red-edge modified simple ratio; NDVI, normalized difference vegetation index; NDVIre, red-edge normalized difference vegetation index; PLSR, partial least squares regression; UAS, unmanned aerial system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R

Cited by 204 publications

References 10 publications

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

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