Critical period of weed interference on total polyphenol content in quinoa

Merino, Jorge; Pedreros, A.; Fischer, Susana; López, María Dolores

doi:10.4067/s0718-58392019000300405

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…This variable was related directly to plant yield, which was higher in this treatment (data not shown). This can be due to the fact that when there is no competition with weeds or applied herbicides, photoassimilates are used in the growth and storage processes and form the optimal number of grains they can fill (Merino et al, 2019). There is also a direct influence of factors such as irradiance levels, water potential, nutrients, and duration of the formation of potential harvest organs on the number of these organs.…”

Section: Agronomic Variables and Yield Componentsmentioning

confidence: 99%

Effect of post-emergence herbicides on stress indicators in quinoa

Merino

Pedreros

Fischer

et al. 2020

Chil. j. agric. res.

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The impact of herbicide application on the accumulation of secondary metabolites and photosynthesis as stress indicators in the quinoa (Chenopodium quinoa Willd.) crop is unknown. The objective of the present study was to evaluate the effect of two post-emergence herbicides on production, total polyphenol content, and chlorophyll fluorescence in quinoa. The experiments were conducted for two consecutive seasons using a completely randomized block design with seven treatments, including two herbicides and three commercial product rates, as well as a control without herbicide application. Production parameters, yield components, and stress indicators were evaluated. Yield was affected (P < 0.05) by herbicide application in some treatments; control and treatment with only one bentazon application varied from 1851.23 to 1235.03 kg ha-1 , respectively. Yield affected the number of grains per plant (P < 0.05); control and treatment with only one bentazon application decreased yield from 3984.60 to 2040.94 kg ha-1 , respectively. Total polyphenol content and percentage of germination in quinoa grains were not affected by stress caused by herbicide application (P > 0.05). The herbicide fomesafen did not affect the maximum quantum yield, while the herbicide bentazon had a significant effect (P < 0.05) during the application. Three sequential applications of bentazon and fomesafen in the absence of weeds were sufficient to achieve a yield that was equal (P > 0.05) to the control.

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Section: Agronomic Variables and Yield Componentsmentioning

confidence: 99%

Effect of post-emergence herbicides on stress indicators in quinoa

Merino

Pedreros

Fischer

et al. 2020

Chil. j. agric. res.

Self Cite

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“…Studies have already reported that the lack of weed control may reduce the crop yield close to 100% 8–10 . Some crops have shown greater capacity to endure weed interference, but high losses were observed for soybean (70%), 11 corn (70%), 12 sesame (20%), 13 and cotton (18%) 14 .…”

Section: Introductionmentioning

confidence: 99%

“…7 Studies have already reported that the lack of weed control may reduce the crop yield close to 100%. [8][9][10] Some crops have shown greater capacity to endure weed interference, but high losses were observed for soybean (70%), 11 corn (70%), 12 sesame (20%), 13 and cotton (18%). 14 Even generating costs for producers, weed control can be economically viable if implemented at the necessary time to minimize interference.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models as an alternative to determine productivity losses caused by weeds

Souza

Monteiro

Silva

et al. 2021

Pest Management Science

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BACKGROUND Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models have a low generalization capacity to understand different scenarios. However, computational development facilitated the implementation of supervised machine learning models, as artificial neural networks (ANNs), capable of understanding complex relationships. The objectives of our work were to evaluate the ability of ANNs to estimate yield losses in onion (model crop) due to weed interference and compare with multiple linear regression (MLR) and empirical models. RESULTS MLR constructed from non‐destructive and destructive methods show R2 and root mean square error (RMSE) values varying between 0.75% and 0.82%, 13.0% and 19.0%, respectively, during testing step. The ANNs has higher R2 (higher than 0.95) and lower RMSE (less than 6.95%) compared to MLR and empirical models for training and testing steps. ANNs considering only the coexistence period and system have similar performance to MLR models. However, the insertion of variables related to weed density (non‐destructive ANN) or fresh matter (destructive ANN) increases the predictive capacity of the networks to values close to 99% correct. CONCLUSION The best performing ANNs can indicate the beginning of weed control since they can accurately estimate losses due to competition. These results encourage future studies implementing ANNs based on computer vision to extract information about the weed community.

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“…It is also known as the critical period of weed control [23] and defined as the duration of the crop life cycle in which it must be kept weed-free to prevent yield loss from weed interference. This period has been determined for many crop species, including wheat and found different between crops [24][25][26][27][28][29]. In competition of wheat (Triticum spp.)…”

Section: Introductionmentioning

confidence: 99%

Competition of Weeds Dominated by Wild Oat (Avena sterilis L.) in Wheat (Triticum durum Desf.) in Jordan

Qasem

2021

Agronomy

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Wheat is the main field crop grown in Jordan. Productivity is low due to different factors, including erratic rainfall, poor soil fertility and weed competition. A field experiment was carried out at the University of Jordan Research Station for two growing seasons to determine the effect of weed competition on growth and yield of wheat (Triticum durum Cv. “Hourani”). Treatments consisted of either allowing weeds to infest the crop or maintaining the crop weed-free for increasing durations after emergence. Results showed that, the longer the periods of weed competition, the greater the loss in crop growth and yield. Average reductions in grain and straw yields were 41% and 37%, respectively. The highest grain yield obtained was from weed-free, and the lowest was in weed-infested plots for the entire growing season. Average grain yield was not significantly different at 14 to 49 days of weed-infested periods, while none of the weed-free periods produced a yield similar to that of the weed-free control. However, maintaining a weed-free crop for three weeks after emergence significantly increased grain yield compared with the weed-infested control. High rainfall in the first season almost doubled weed growth and greatly reduced wheat growth and yield compared with the second season. To determine the critical period of weed competition and the influence of weed infestation on wheat grain yield, Gompertz and logistic equations were fitted to data representing increasing duration of weed-free and weed-infested periods, respectively. Based upon an arbitrary 5% level of average grain yield loss in the two years, the critical period of weed competition occurred at 0–49 days after wheat emergence, which corresponded with the rapid increase in weed biomass.

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Critical period of weed interference on total polyphenol content in quinoa

Cited by 8 publications

References 23 publications

Effect of post-emergence herbicides on stress indicators in quinoa

Effect of post-emergence herbicides on stress indicators in quinoa

Machine learning models as an alternative to determine productivity losses caused by weeds

Competition of Weeds Dominated by Wild Oat (Avena sterilis L.) in Wheat (Triticum durum Desf.) in Jordan

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