Key factors affecting watermelon yield loss in different growing seasons

Pereira, Anielli Souza; Santos, Gil Rodrigues dos; Sarmento, Renato Almeida; Galdino, Tarcísio Visintin da Silva; Lima, Carlos Henrique de Oliveira; Picanço, Marcelo Coutinho

doi:10.1016/j.scienta.2017.02.030

Cited by 15 publications

(12 citation statements)

References 37 publications

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“…To determine the critical yield component and key loss factor, using a k-factor analysis, the yield-loss data were transformed to log10 in each experimental replication, obtaining the total loss (K) and the partial loss from a factor (ki) (Varley and Gradwell 1960, Bacci et al 2006, Pereira et al 2017. The total K loss was obtained by the sum of the k losses, (i.e., K ki), = Σ…”

Section: Discussionmentioning

confidence: 99%

“…The partial loss component that showed the steepest slope (different from the others, t-test, P ≤ 0.05) was recognized as the critical component of yield loss. A similar procedure was used when determining the key loss factor within the critical yield component (Picanço et al 2007, Pereira et al 2017). Using a one-way ANOVA (α = 0.05), we compared the yield losses caused by the key factors in Cry1Ab and non-Bt corn hybrids at each location.…”

Section: Discussionmentioning

confidence: 99%

“…Although some researchers recognize the importance of understanding the components of yield losses for crops (Head et al 2005, Bacci et al 2006, Picanço et al 2007, Hutchison et al 2010, Pereira et al 2017, as well as the impact of Bt corn on the infestation levels by target insects (Hutchison et al 2010) and the abundance of natural enemies (Fernandes et al 2007), integrative studies considering all these aspects are scarce to date. Here we determined the critical components, key loss factors, and the potential role of natural biological control agents in fields of non-Bt and Bt Cry1Ab corn, which was the first transgenic Bt corn technology introduced in Brazil in 2007 aiming to provide some level of control against Lepidoptera (Mendes et al 2011, Okumura et al 2013.…”

Section: Discussionmentioning

confidence: 99%

“…A number of approaches have been proposed to document yieldloss factors in crop production (Harcourt 1970, Picanço 1992, Oerke 2006, Savary et al 2006). The crop life table tool, which is based on methods and concepts pioneered by Varley and Gradwell (1960) and latter adapted by Picanço (1992), can be used to quantify yield-loss factors in different crop stages (Harcourt 1970;Picanço et al 1998Picanço et al , 2007Bacci et al 2006;Pereira et al 2017). By using this analytic tool, one can determine the critical yield component and the key loss factor, which can be used to inform decisions on crop management.…”

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confidence: 99%

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Yield Losses in Transgenic Cry1Ab and Non-Bt Corn as Assessed Using a Crop-Life-Table Approach

Silva

Picanço

Ferreira

et al. 2018

Journal of Economic Entomology

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In this study, we constructed crop life tables for Bacillus thuringiensis Berliner (Bt) Cry1Ab and non-Bt corn hybrids, in which yield-loss factors and abundance of predaceous arthropods were recorded during 2 yr at two locations. Corn kernel/grain was the yield component that had the heaviest losses and that determined the overall yield loss in the corn hybrids across years and locations. Yield losses in both corn hybrids were primarily caused by kernel-destroying insects. Helicoverpa zea (Boddie) and Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) were the key loss factors at one location, while at the other, the key loss factor was the silk fly larvae, Euxesta spp. (Diptera: Ulidiidae). Although the realized yield of corn grains was not different (P > 0.05) between Cry1Ab and non-Bt corn hybrids, the Bt corn hybrid reduced (P < 0.05) the damage by H. zea and S. frugiperda in three of the four field trials, particularly at the location where Lepidoptera were the key loss factors. As expected, no reduction in the abundance of predaceous arthropods was observed in Cry1Ab corn fields. Various species of natural enemies were recorded, particularly the earwig Doru luteipes (Scudder) (Dermaptera: Forficulidae), which was the most abundant and frequent predaceous insect. These results indicate that integration of pest management practices should be pursued to effectively minimize losses by kernel-destroying insects during corn reproductive stages when growing non-Bt or certain low-dose Bt corn cultivars for fall armyworm and corn earworm, such as those producing Cry1Ab or other Cry toxins.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Yield Losses in Transgenic Cry1Ab and Non-Bt Corn as Assessed Using a Crop-Life-Table Approach

Silva

Picanço

Ferreira

et al. 2018

Journal of Economic Entomology

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“…Biotic constraints such as the vascular wilt disease caused by Fusarium oxysporum f.sp. niveum [ 4 ] significantly limits yield potential [ 5 , 6 ]. This ubiquitous soil-borne fungal pathogen has been reported in watermelon fields throughout the world, yet within the watermelon production areas of the United States, race 2 appears to be the dominant concern due to its widespread distribution and the lack of resistant watermelon cultivars [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Phylogenetic and phenotypic characterization of Fusarium oxysporum f. sp. niveum isolates from Florida-grown watermelon

et al. 2021

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Fusarium wilt of watermelon (Citrullus lanatus) caused by Fusarium oxysporum f. sp. niveum (Fon), has become an increasing concern of farmers in the southeastern USA, especially in Florida. Management of this disease, most often through the use of resistant cultivars and crop rotation, requires an accurate understanding of an area’s pathogen population structure and phenotypic characteristics. This study improved the understanding of the state’s pathogen population by completing multilocus sequence analysis (MLSA) of two housekeeping genes (BT and TEF) and two loci (ITS and IGS), aggressiveness and race-determining bioassays on 72 isolates collected between 2011 and 2015 from major watermelon production areas in North, Central, and South Florida. Multilocus sequence analysis (MLSA) failed to group race 3 isolates into a single large clade; moreover, clade membership was not apparently correlated with aggressiveness (which varied both within and between clades), and only slightly with sampling location. The failure of multilocus sequence analysis using four highly conserved housekeeping genes and loci to clearly group and delineate known Fon races provides justification for future whole genome sequencing efforts whose more robust genomic comparisons will provide higher resolution of intra-species genetic distinctions. Consequently, these results suggest that identification of Fon isolates by race determination alone may fail to detect economically important phenotypic characteristics such as aggressiveness leading to inaccurate risk assessment.

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