Evaluation of Quantitative and Qualitative Characteristics of Strawberry in Response to Bio- and Chemical Fertilizers

Habibzadeh, Farhad; Hazrati, Saeid; Gholamhoseini, Majid; Khodaei, Diako; Habashi, Delara

doi:10.1007/s10343-019-00455-9

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…As new studies evaluate a growing amount of traits aiming at better explaining the phenomena (e.g., Martínez et al, 2017;Habibzadeh et al, 2019;Sadowska et al, 2020), the challenge of summarizing the complex results into a succinct and easy-to-interpret recommen-dation arisen. Dierently from an approach in which a post-hoc test, e.g., Tukey's honest signicance test is used, we have shown here how treatments in experiments with strawberry can be ranked from most desired to most undesired based on the values of several traits (Fig.…”

Section: T1mentioning

confidence: 99%

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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Section: T1mentioning

confidence: 99%

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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“…Our results are in accordance with findings of Kachwaya and Chandel [32] research on strawberries who assumed, that this might be due to the increased synthesis of primary metabolites as there was more nutrients uptake and to their translocation to the fruits. While Habibdzeh et al [35] demonstrated, that chemical fertilizers produce the highest amount of nitrate in plant tissue, because of the direct release of nitrogen, plants requirements could be exceed when applied in higher doses (100% RDN).…”

Section: Discussionmentioning

confidence: 99%

Influence of Nitrogen, Calcium and Nano-Fertilizer on Strawberry (Fragaria × ananassa Duch.) Fruit Inner and Outer Quality

et al. 2021

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The production system, especially fertilisation has an important effect on yield and quality of strawberries. In the present study, plants were fertilized with different doses of nitrogen (0–100% recommended doses), calcium chelate, as well as nano fertilizer Lithovit. Strawberry cultivar ‘Clery’ yield and quality parameters (fruit color and firmness) including nutritional indicators (total soluble solids, sugars, organic acids, phenolic and volatile compounds) were monitored. Volatiles were identified and monitored using headspace solid phase microextraction and analysed using gas chromatograph-mass spectrometry (SPME/GC-MS) and sugars, organic acids, and phenolic compounds with high performance liquid chromatography. Organic acids and phenolic compounds were detected with mass spectrometer (HPLC/MS). Both nitrogen and calcium fertilisation had altered sugars, organic acids, volatile and phenolic contents in strawberry fruits. Fertilisation with higher doses of nitrogen and calcium increased the content of unpleasant aromas aldehydes hexanal (up to 3.8-fold) and (E)-2-hexen-1- (up to 3.7-fold). The content of fruity esters was uppermost in fruits fertilised with nano-fertiliser Lithovit (up to 2.3-fold). Fertilisation with N and Ca decreased the strength of ketone and terpenoids fruity aroma. The highest content of total phenols, as well as all individual hydroxycinnamic and hydroxybenzoic acid derivatives were obtained in the nano-fertiliser Lithovit treatment. Fertilisation, especially with nitrogen, had mostly negative impact on strawberry flavour while nano-fertilization with Lithovit improved strawberry phenolic content and aroma.

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“…As new studies evaluate a growing amount of traits aiming at better explaining the phenomena [42][43][44], the challenge of summarizing the complex results into an easy-to-handle and practical recommendation arises. Differently from an approach in which a post-hoc teste.g., Tukey's honest significance test-is used, we have shown here how to rank treatments in agronomic experiments based on the values of several traits (Figs.…”

Section: Why a Multi-trait Framework Analysis?mentioning

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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Evaluation of Quantitative and Qualitative Characteristics of Strawberry in Response to Bio- and Chemical Fertilizers

Cited by 9 publications

References 34 publications

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

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

Influence of Nitrogen, Calcium and Nano-Fertilizer on Strawberry (Fragaria × ananassa Duch.) Fruit Inner and Outer Quality

MGIDI: a powerful tool to analyze plant multivariate data

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