cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19929274
title: Growth and yields parameters of cassava crop (Manihot esculenta Crantz) as affected by plant density, genotype and planting system

description: ABSTRACT In Argentina, cassava (Manihot esculenta) production does not meet the national demand. The aim of this work was to analyze the effects of two planting systems of the stem cuttings (horizontal and vertical), two plant densities (10,000 and 20,000 plant ha-1) and two cassava genotypes (cv. Palomita and Blanca de Santa Catarina) on biometric variables and on root and starch yields by means of a field experiment carried out in Corrientes, Argentina. Eight treatments were distributed following a randomized complete block design. The following variables were evaluated: cutting sprouting percentage, plant height, percentage of intercepted photosynthetically active radiation (PARi); total, shoot and root fresh weight per hectare and/or per plant; starch production per hectare and weed dry biomass. Blanca de Santa Catarina (BSC) demonstrated the best yield potential. Vertical planting determined higher sprouting percentages, plant length and PARi, consequently greater conversion into biomass and starch. Higher plant density was associated with lower weed dry biomass and with lower root yield per plant. In conclusion, in cassava producing areas, such as Corrientes, which has sandy soils, the vertical planting system at a density of 10,000 plant ha-1 with the cv. BSC could be recommended to increase cassava yield without additional costs.
DOI: 10.6084/m9.figshare.19929275.v1
title: Blossom blight resistance in peach: heritability and segregation in progenies from reciprocal crosses

description: ABSTRACT Blossom blight and brown rot caused by Monilinia fructicola is the most important peach disease in Brazil. Genetic resistance is a control strategy that is gaining importance in breeding programs worldwide. This study aimed to identify genotypes with higher levels of blossom blight resistance to estimate the heritability of this character; study the frequency distribution in populations; and test the possibility of maternal effect. Blossom blight susceptibility was tested in reciprocal hybridizations seedlings, as well as their parents. The detached flower technique was used in a randomized complete block design, considering each genotype as a treatment. Flower inoculation was made by spraying a M. fructicola suspension and evaluations were carried out after 72 and 120 hours using a scale of five severity levels. The studied populations presented low phenotypic variability regarding the flower resistance/susceptibility to M. fructicola, being most of them susceptible or very susceptible. Among the tested genotypes, the cultivars Maciel and Cerrito showed less blossom blight susceptibility, transmitting this character to their progenies. Heritability estimates of the blossom blight resistance were medium to low. The low heritability and its distribution in the progenies suggest that the character has additive inheritance, without detecting deviations associated with maternal effects.
DOI: 10.6084/m9.figshare.19929275
title: Blossom blight resistance in peach: heritability and segregation in progenies from reciprocal crosses

description: ABSTRACT Blossom blight and brown rot caused by Monilinia fructicola is the most important peach disease in Brazil. Genetic resistance is a control strategy that is gaining importance in breeding programs worldwide. This study aimed to identify genotypes with higher levels of blossom blight resistance to estimate the heritability of this character; study the frequency distribution in populations; and test the possibility of maternal effect. Blossom blight susceptibility was tested in reciprocal hybridizations seedlings, as well as their parents. The detached flower technique was used in a randomized complete block design, considering each genotype as a treatment. Flower inoculation was made by spraying a M. fructicola suspension and evaluations were carried out after 72 and 120 hours using a scale of five severity levels. The studied populations presented low phenotypic variability regarding the flower resistance/susceptibility to M. fructicola, being most of them susceptible or very susceptible. Among the tested genotypes, the cultivars Maciel and Cerrito showed less blossom blight susceptibility, transmitting this character to their progenies. Heritability estimates of the blossom blight resistance were medium to low. The low heritability and its distribution in the progenies suggest that the character has additive inheritance, without detecting deviations associated with maternal effects.
DOI: 10.6084/m9.figshare.19929277
title: Vigor assessment of juvenile rubber tree clones in Northwestern São Paulo State, Brazil

description: ABSTRACT Besides rubber production, one of the main goals of the genetic improvement of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] is to develop vigorous clones. The objective of the present study was to evaluate the vigor of 34 elite juvenile rubber clones. To this end, three experiments were conducted in the municipality of Pontes Gestal, in the northwest region of the São Paulo State. Each experiment consisted of 10 treatments, and 2 controls in common to the 3 experiments. The experiments were arranged in the randomized block design, with three replications and ten plants per plot, in the 8 m x 2.5 m spacing. Individual and joint analyses of variance were performed. The means were compared using the Dunnett test at 5%. Significant effect of treatments was found in the three experiments and in the joint analysis. The most vigorous clones were not different from the controls in the comparison of means by the individual and joint analyses. The less vigorous clones were statistically different from the others and from the controls, reflecting this difference in the genetic variability observed. The breeding program must consider the lack of difference between the most vigorous clones and the controls when recombining superior clones, since genetic gain depends on variability.
DOI: 10.6084/m9.figshare.19929277.v1
title: Vigor assessment of juvenile rubber tree clones in Northwestern São Paulo State, Brazil

description: ABSTRACT Besides rubber production, one of the main goals of the genetic improvement of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] is to develop vigorous clones. The objective of the present study was to evaluate the vigor of 34 elite juvenile rubber clones. To this end, three experiments were conducted in the municipality of Pontes Gestal, in the northwest region of the São Paulo State. Each experiment consisted of 10 treatments, and 2 controls in common to the 3 experiments. The experiments were arranged in the randomized block design, with three replications and ten plants per plot, in the 8 m x 2.5 m spacing. Individual and joint analyses of variance were performed. The means were compared using the Dunnett test at 5%. Significant effect of treatments was found in the three experiments and in the joint analysis. The most vigorous clones were not different from the controls in the comparison of means by the individual and joint analyses. The less vigorous clones were statistically different from the others and from the controls, reflecting this difference in the genetic variability observed. The breeding program must consider the lack of difference between the most vigorous clones and the controls when recombining superior clones, since genetic gain depends on variability.
DOI: 10.5281/zenodo.6579267
title: SuperDARN data in netCDF format (2008-Oct)

description: 2008-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6578738
title: SuperDARN data in netCDF format (2009-May)

description: 2009-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579361
title: SuperDARN data in netCDF format (2008-Sep)

description: 2008-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6578660
title: SuperDARN data in netCDF format (2009-Jul)

description: 2009-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6578830
title: SuperDARN data in netCDF format (2009-Apr)

description: 2009-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579188
title: SuperDARN data in netCDF format (2008-Nov)

description: 2008-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579004
title: SuperDARN data in netCDF format (2009-Feb)

description: 2009-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.6084/m9.figshare.19929278
title: Chemical management of weeds in cassava crop, cultivar ‘Santa Helena’

description: ABSTRACT The objective of this study was to evaluate the selectivity of cassava cultivar ‘Santa Helena’ and the efficiency of weed control using herbicides applied alone or tank mixed with and without sequential applications. Two experiments were carried out, both in a randomized block design with four replications. The treatments for the 1st experiment were: Hand hoeing (weed-free control); No hand hoeing (non-weeded control); sulfentrazone (500 g ha-1); sulfentrazone/[clomazone + clethodim] (500/[1125 + 120] g ha-1); sulfentrazone/mesotrione (500/240 g ha-1); S-metolachlor (1920 g ha-1); S-metolachlor/[clomazone + clethodim] (1920/[1125 + 120] g ha-1); S-metolachlor/mesotrione](1920/240 g ha-1); [sulfentrazone + S-metolachlor]/[clomazone + clethodim] ([500 + 1920]/[1125 + 120] g ha-1); [sulfentrazone + S-metolachlor]/mesotrione ([500 + 1920]/240 g ha-1). The treatments of the 2nd experiment were: Hand hoeing (weed-free control); No hand hoeing (non-weeded control); glyphosate (360 g ha-1) + hand hoeing; glyphosate + sulfentrazone (360 + 500 g ha-1); glyphosate + flumioxazin (360 + 50 g ha-1); glyphosate + clomazone (360 + 1125 g ha-1); glyphosate + S-metolachlor (360 + 1920 g ha-1); glyphosate + sulfentrazone + clomazone (360 + 400 + 900 g ha-1); glyphosate + sulfentrazone + S-metolachlor (360 + 400 + 1440 g ha-1); glyphosate + flumioxazin + clomazone (360 + 40 + 900 g ha-1); glyphosate + flumioxazin + S-metolachlor (360 + 40 + 1440 g ha-1). In the first experiment, only the treatments S-metolachlor, S-metolachlor/[clomazone + clethodim] and S-metolachlor/mesotrione did not present efficient weed control, causing reduction in yields; the other treatments were crop selective and efficient. In the second experiment, all treatments showed excellent levels of weed control up to 65 DAA and were crop selective. It is concluded that applications of tank mixtures, sequential mixtures and the use of glyphosate mixed with pre-emergent herbicides constitute excellent alternatives of weed management strategies in ‘Santa Helena’ cassava.
DOI: 10.6084/m9.figshare.19929278.v1
title: Chemical management of weeds in cassava crop, cultivar ‘Santa Helena’

description: ABSTRACT The objective of this study was to evaluate the selectivity of cassava cultivar ‘Santa Helena’ and the efficiency of weed control using herbicides applied alone or tank mixed with and without sequential applications. Two experiments were carried out, both in a randomized block design with four replications. The treatments for the 1st experiment were: Hand hoeing (weed-free control); No hand hoeing (non-weeded control); sulfentrazone (500 g ha-1); sulfentrazone/[clomazone + clethodim] (500/[1125 + 120] g ha-1); sulfentrazone/mesotrione (500/240 g ha-1); S-metolachlor (1920 g ha-1); S-metolachlor/[clomazone + clethodim] (1920/[1125 + 120] g ha-1); S-metolachlor/mesotrione](1920/240 g ha-1); [sulfentrazone + S-metolachlor]/[clomazone + clethodim] ([500 + 1920]/[1125 + 120] g ha-1); [sulfentrazone + S-metolachlor]/mesotrione ([500 + 1920]/240 g ha-1). The treatments of the 2nd experiment were: Hand hoeing (weed-free control); No hand hoeing (non-weeded control); glyphosate (360 g ha-1) + hand hoeing; glyphosate + sulfentrazone (360 + 500 g ha-1); glyphosate + flumioxazin (360 + 50 g ha-1); glyphosate + clomazone (360 + 1125 g ha-1); glyphosate + S-metolachlor (360 + 1920 g ha-1); glyphosate + sulfentrazone + clomazone (360 + 400 + 900 g ha-1); glyphosate + sulfentrazone + S-metolachlor (360 + 400 + 1440 g ha-1); glyphosate + flumioxazin + clomazone (360 + 40 + 900 g ha-1); glyphosate + flumioxazin + S-metolachlor (360 + 40 + 1440 g ha-1). In the first experiment, only the treatments S-metolachlor, S-metolachlor/[clomazone + clethodim] and S-metolachlor/mesotrione did not present efficient weed control, causing reduction in yields; the other treatments were crop selective and efficient. In the second experiment, all treatments showed excellent levels of weed control up to 65 DAA and were crop selective. It is concluded that applications of tank mixtures, sequential mixtures and the use of glyphosate mixed with pre-emergent herbicides constitute excellent alternatives of weed management strategies in ‘Santa Helena’ cassava.
DOI: 10.5281/zenodo.6578711
title: SuperDARN data in netCDF format (2009-Jun)

description: 2009-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579075
title: SuperDARN data in netCDF format (2009-Jan)

description: 2009-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'