2020
DOI: 10.1016/j.indcrop.2020.112749
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Data-based agroecological zoning of Acrocomia aculeata: GIS modeling and ecophysiological aspects into a Brazilian representative occurrence area

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Cited by 24 publications
(17 citation statements)
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“…Results from an agroclimatic zoning model indicate that north and northeast Argentina are also suitable (Falasca et al 2017). Similar predictions are available for Brazil suggesting the importance of the entire natural occurrence area of A. aculeata for exploring its productive potential (Resende et al 2020). Plath et al (2016) found that 2.8% of the potential area (3.7 M km 2 under present climate conditions) is considered highly suitable by the modeling study and 37.1% moderately suitable.…”
Section: Cultivation Systemssupporting
confidence: 53%
“…Results from an agroclimatic zoning model indicate that north and northeast Argentina are also suitable (Falasca et al 2017). Similar predictions are available for Brazil suggesting the importance of the entire natural occurrence area of A. aculeata for exploring its productive potential (Resende et al 2020). Plath et al (2016) found that 2.8% of the potential area (3.7 M km 2 under present climate conditions) is considered highly suitable by the modeling study and 37.1% moderately suitable.…”
Section: Cultivation Systemssupporting
confidence: 53%
“…We estimated variance components and related genetic parameters for the following quantitative traits: plant height (HEI), trunk circumference 10 cm above ground level (CIR), fruit volume (FRV), and estimated fruit yield (FRY). To calculate the fruit volume (FRV), we used the equation adopted by Resende et al (2020) and Yunes et al (2015), which considers the radial and longitudinal circumferences of fruits. The estimated fruit yield (FRY) was obtained from the product of the fruit mass and number of fruits per plant (developed or immature fruits present on the tree in September 2021, a period marked by the appearance of "falling fruits").…”
Section: Variance Components and Genetic Parametersmentioning
confidence: 99%
“…Environmental covariates can also be modeled using crop growth models (CG GBLUP) and deep kernel approaches [67,[80][81][82]. Recently, environmental covariates using geographic information system information have been used to better deal with GE [83]. Further, Costa-Neto et al [63] developed an R package called "EnvRtype" to integrate large-scale envirotyping (enviromics) into quantitative genomics for implementation in GS.…”
Section: Modelmentioning
confidence: 99%