Behavior of strawberry production with growth models: a multivariate approach

Diel, Maria Inês; Lúcio, Alessandro Dal’Col; Sari, Bruno Giacomini; Olivoto, Tiago; Pinheiro, Marcos Vinícius Marques; Krysczum, Dionatan Ketzer; Melo, Patrícia Jesus de; Schmidt, Denise

doi:10.4025/actasciagron.v43i1.47812

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The values of parameter c, related to precocity (DIEl et al, 2021), are not different between the trials for logistics and Gompertz, but they are different between the models, where logistic model estimates are higher (Table 1). The nondifference of c between trials can be explained by the use of the same cultivar.…”

Section: Resultsmentioning

confidence: 96%

Nonlinear models in the height description of the Rhino sunflower cultivar

et al. 2022

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Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

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Section: Resultsmentioning

confidence: 96%

Nonlinear models in the height description of the Rhino sunflower cultivar

et al. 2022

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“…Low contributions of a factor indicate that the traits within such a factor are close to the ideal treatment. Data manipulation and the index computation were performed in the R Software version 4.0.2 (R Core Team, 2020) using the package metan (Olivoto and Lúcio, 2020).…”

Section: Selecting the Best Treatment Combinationsmentioning

confidence: 99%

“…While multivariate approaches have been used with the main goal of investigating the interrelationships between traits and grouping of the variables in a PCA plot (e.g., González-Domínguez et al, 2020;amec et al, 2016), its use in the analysis of strawberry experiments seems to be more an exception than a rule. A new approach that applies principal component analysis on coecients of nonlinear growth models was introduced by Diel et al (2020). In this approach, the use of growth models substantially increases the inferences that can be made about crop growth (fruit production), and the PCA summarizes these pieces of information, simplifying its interpretation.…”

Section: Strengths and Weaknesses Of Treatmentsmentioning

confidence: 99%

“…The cultivated strawberry has a limited genetic basis (Hardigan et al, 2018;Gaston et al, 2020) and because of this, it makes it dicult to quickly identify important target traits for the greatest success in improv-ing the strawberry (Gaston et al, 2020). Generally, it is sought for cultivars that present good productivity, that is precocious, and with a high rate of production (Diel et al, 2020). Sensory characteristics such as total soluble solids content, total titratable acidity, and their relationship, are essential to winning over consumers, as well as physical coloring characteristics (Adak et al, 2017;Diel et al, 2018;Gaston et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Multivariate analysis of strawberry experiments: where are we now and where can we go?

Olivoto¹,

Diel

Schmidt

et al. 2021

Preprint

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The multi-trait genotype-ideotype distance index (MGIDI) was used to select superior treatments in experiments with strawberries. Twenty-three productive, qualitative, physiological, and phenological traits with negative and positive desired gains were accessed in 16 treatments, a combination of two cultivars (Albion-neutral days, and Camarosa-short days), two transplants origins (National and Imported), and four organic substrates mixes (Crushed sugarcane bagasse, burnt rice husk, organic substrate, and Carolina commercial substrate). Our results suggest that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI index indicated that the Albion cultivar originated from imported transplants grown in substrates where the main component (70%) is burnt rice husk provides desired values for 20 of a total of 22 traits, which represents a success rate of ~ 91% in selecting traits with desired values. The strengths and weakness view provided by the MGIDI index revealed that the looking for an ideal treatment should direct the efforts on improving the water efficiency use and reducing total acidy of fruits. On the other hand, the strengths of selected treatments are mainly related to productive precocity, total soluble solids, and flesh firmness. The MGIDI index provides a unique, robust, and easy-to-handle process, standing out as a powerful tool to develop better treatment recommendations for experiments with strawberries when multiple traits are assessed.

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“…Experienced researchers often keep in mind a set of plant traits that an "ideal" treatment should provide. For strawberries, for example, it is sought for treatments that present productive precocity and a high rate of fruit production [9]. Sensory characteristics such as total soluble solids content, total titratable acidity, and their relationship, are essential to winning over consumers, as well as physical coloring characteristics [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

MGIDI: a powerful tool to analyze plant multivariate data

et al. 2022

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Background Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. Results In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. Conclusions Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.

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Behavior of strawberry production with growth models: a multivariate approach

Cited by 7 publications

References 24 publications

Nonlinear models in the height description of the Rhino sunflower cultivar

Nonlinear models in the height description of the Rhino sunflower cultivar

Multivariate analysis of strawberry experiments: where are we now and where can we go?

MGIDI: a powerful tool to analyze plant multivariate data

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