ag5Tools: An R package for downloading and extracting agrometeorological data from the AgERA5 database

Brown, David; Sousa, Kauê de; Etten, Jacob van

doi:10.1016/j.softx.2022.101267

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…We used the following R (R Core Team, 2022) packages for data management and preparation: readr , readxl, janitor , dplyr, gosset , and caret (de Sousa, Brown et al., 2023; Firke, 2021; Kuhn, 2022; Wickham & Bryan, 2022; Wickham, François et al., 2022, 2022). Climatic variables were obtained using the packages ag5Tools and climatrends (Brown et al., 2023; de Sousa, van Etten et al., 2023). The statistical modeling was performed with the packages PlackettLuce , stablelearner , gosset , and qvcalc (de Sousa, Brown et al., 2023; Firth, 2020; Philipp et al., 2016; Turner et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

“…To convert the data from numerical measurements to ranks, we used the function rank_numeric() from the R package gosset (de Sousa, Brown et al, 2023). The rankings were constructed separately for each evaluation and then aggregated into a sparse matrix, where the columns are the evaluated genotypes, and the rows correspond to the evaluations.…”

Section: Core Ideasmentioning

confidence: 99%

“…We used the following R (R Core Team, 2022) packages for data management and preparation: readr, readxl, janitor, dplyr, gosset, and caret (de Sousa, Brown et al, 2023;Firke, 2021;Kuhn, 2022;Wickham, François et al, 2022, 2022. Climatic variables were obtained using the packages ag5Tools and climatrends (Brown et al, 2023;.…”

Section: Supporting Softwarementioning

confidence: 99%

“…Climatic variables were obtained using the packages ag5Tools and climatrends (Brown et al, 2023;. The statistical modeling was performed with the packages PlackettLuce, stablelearner, gosset, and qvcalc (de Sousa, Brown et al, 2023;Firth, 2020;Philipp et al, 2016;Turner et al, 2020). The figures were made with packages ggplot2, ggparty, patchwork, GGally, and tmap (Borkovec & Madin, 2019;Pedersen, 2020;Schloerke et al, 2021;Tennekes, 2018;Wickham, 2016).…”

Section: Supporting Softwarementioning

confidence: 99%

See 3 more Smart Citations

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

et al. 2023

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Synthesis of crop trial data can generate insights that are not available from the analysis of individual studies, but such synthesis is often constrained by the heterogeneity of data among studies. Rank‐based data synthesis provides the flexibility to combine data of heterogeneous types and from different sources. We demonstrate the application of rank‐based data synthesis of heterogeneous trial data to assess the effect of climatic factors on the reaction of several Musa genotypes to black leaf streak disease (BLSD; caused by Pseudocercospora fijiensis [sexual morph: Mycosphaerella fijiensis]). We aggregated data from the main public repositories of Musa trial data. We applied model‐based recursive partitioning with the Plackett‐Luce model, using climatic data as covariates. The model identified the maximum length of the dry spell as the main variable influencing differences in genotypic response to BLSD, dividing the aggregated trial dataset into humid and dry environments. We found differences in the reaction of genotypes to BLSD between these environments. In humid environments, NARITA 8 was found to be the most resistant genotype, while in dry environments FHIA‐01 was the best performing improved genotype. We also assessed reliability, which is the probability of outperforming the reference genotype (Calcutta 4). In humid environments NARITA 2, NARITA 8 and FHIA‐01 had the highest reliability, while in dry environments only the landrace Saba surpassed 50% reliability. The information generated by our data synthesis approach supports selecting Musa genotypes for further evaluations at new locations.This article is protected by copyright. All rights reserved

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Section: Methodsmentioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

Section: Supporting Softwarementioning

confidence: 99%

Section: Supporting Softwarementioning

confidence: 99%

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Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

et al. 2023

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“…However, there is a pressing need for a comprehensive and user‐friendly package that seamlessly manages the selection, download, extraction and preparation of climate data for diverse terrestrial areas or specific sampling points. Although existing packages are used by scientists in various research fields such as agriculture (Brown et al., 2023) and forestry (Reyer et al., 2020), they often provide specialised solutions focused on specific regions or limited spatial resolutions, lacking a unified and user‐friendly workflow. This gap in the current landscape of R packages motivated the development of our climenv package, short for climatic envelope.…”

Section: Introductionmentioning

confidence: 99%

climenv: Download, extract and visualise climatic and elevation data

Tsakalos,

Smith,

Luebert

et al. 2023

J Vegetation Science

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Understanding the relationship between climate and vegetation requires climate data to be linked with ecological data, including habitat types and vegetation mapping. Our new R package climenv allows researchers to efficiently acquire, extract and visualise data sets that are commonly used by researchers to quantify the climatic envelope of vegetation. climenv offers integrated downloading and processing capabilities for three globally recognised data sets: WorldClim 2, CHELSA and the National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM) elevation data. The package allows users to easily download and extract these data sets for single and multi‐geospatial polygon and point data sets, facilitating studies that explore the relationship between vegetation and climate. Furthermore, climenv allows users to plot traditional Holdridge life zone classification, Walter–Lieth climate diagrams and new customised plots, which combine aspects of both these systems with other biologically relevant climate variables. By enhancing the usability and flexibility of these data sets, climenv helps to explore the intricacies of the relationships between climate and vegetation. Our package is accessible from CRAN (https://CRAN.R‐project.org/package=climenv) or GitHub (https://github.com/jamestsakalos/climenv).

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On‐farm evaluation of cassava clones using the triadic comparison of technology options approach

Nanyonjo,

Angudubo,

Iragaba

et al. 2024

Crop Science

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The triadic comparison of technology options (tricot) approach to participatory varietal selection has been demonstrated to scale out the on‐farm testing of elite candidate crop varieties. In this study, we evaluated elite clones of cassava (Manihot esculenta Crantz) using the tricot approach. We sought to (1) synthesize on‐farm trial data from two cycles evaluating different sets of elite cassava clones; (2) assess the agronomic performance of elite cassava clones considering sociodemographic and climatic factors; and (3) assess the performance of elite cassava clones for both agronomic and food quality traits. The study involved 10 districts in Uganda, two cycles/seasons of evaluation, 20 elite cassava clones, one check variety, and 294 men and 320 women farmers. Our results indicate that the agronomic performance of elite cassava clones was influenced more by geographic than sociodemographic factors. Our analysis identified the number of days with rainfall higher than 20 mm as the most influencing climatic factor over agronomic performance. Further, the study identified superior elite cassava clones UG110164, UGC14170, and UG120193 as promising candidate varieties for release, targeting food products. Overall, our study emphasizes the important contribution of end‐users to crop improvement and provides insights into use of tricot on‐farm testing methodology to evaluate elite cassava clones during cassava variety development in Uganda, which can be used to support decision making for variety release.

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ag5Tools: An R package for downloading and extracting agrometeorological data from the AgERA5 database

Cited by 17 publications

References 8 publications

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

Rank‐based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of Musa genotypes to black leaf streak disease

climenv: Download, extract and visualise climatic and elevation data

On‐farm evaluation of cassava clones using the triadic comparison of technology options approach

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