Investigating lead and zinc uptake and accumulation by Stipa hohenackeriana trin and rupr. in field and pot experiments

Moameri, Mehdi; Jafari, M; Tabili, A.; Motasharezadeh, Babak; Chahouki, Zare; Díaz, Fernando Madrid

doi:10.14393/bj-v34n1a2018-37238

Cited by 8 publications

(4 citation statements)

References 19 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Total Chlorophyll (TCh) (The amount of chlorophyll in plant leaves was measured by a chlorophyll meter) 26 , leaf area index (LFA) (leaf area index was measured based on ground based method) 27 , relative water content (RWC) 28 and biological yield (BY) 29 were evaluated www.nature.com/scientificreports/ in this study. For this purpose, in the end of growth stage, 3 plants from each plot were randomly selected 30,31 . Summary of statistical indices for estimated characteristics are shown in Table 2.…”

Section: Study Area and Climatic Characteristicsmentioning

confidence: 99%

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Khalaki

Jahantab

Abdipour

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Festuca ovina L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R2 scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.

show abstract

Section: Study Area and Climatic Characteristicsmentioning

confidence: 99%

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Khalaki

Jahantab

Abdipour

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, SiO 2 application to M. sativa increased plant height, tiller count, yield, FW, and DW, chlorophyll content, and carotenoid levels ( Ma and Yamaji, 2006 ; Zmeeva et al, 2017 ). Govorov and Carmeli (2007) reported that SiO 2 NPs increase leaf FW and DW as well as chlorophyll content in O. basilicum , and also negatively affect shoot and root growth in S. bicolor , Stipa hohenackeriana , and Secale montanum ( Lee et al, 2012 ; Moameri et al, 2018b ; Moameri and Abbasi Khalaki, 2019 ). ZnO NPs increase biomass, root and shoot length, and chlorophyll content in many plant species ( Peyvandi et al, 2011b ; Raliya and Tarafdar, 2013 ; Najafi Disfani et al, 2017 ; García-López et al, 2018 ; Yuan et al, 2018 ).…”

Section: Npsmentioning

confidence: 99%

Role of Nanoparticles in Enhancing Crop Tolerance to Abiotic Stress: A Comprehensive Review

El‐Saadony

Saad²,

Soliman³

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Plants are subjected to a wide range of abiotic stresses, such as heat, cold, drought, salinity, flooding, and heavy metals. Generally, abiotic stresses have adverse impacts on plant growth and development which affects agricultural productivity, causing food security problems, and resulting in economic losses. To reduce the negative effects of environmental stress on crop plants, novel technologies, such as nanotechnology, have emerged. Implementing nanotechnology in modern agriculture can also help improve the efficiency of water usage, prevent plant diseases, ensure food security, reduce environmental pollution, and enhance sustainability. In this regard, nanoparticles (NPs) can help combat nutrient deficiencies, promote stress tolerance, and improve the yield and quality of crops. This can be achieved by stimulating the activity of certain enzymes, increasing the contents (e.g., chlorophyll) and efficiency of photosynthesis, and controlling plant pathogens. The use of nanoscale agrochemicals, including nanopesticides, nanoherbicides, and nanofertilizers, has recently acquired increasing interest as potential plant-enhancing technologies. This review acknowledges the positive impacts of NPs in sustainable agriculture, and highlights their adverse effects on the environment, health, and food chain. Here, the role and scope of NPs as a practical tool to enhance yield and mitigate the detrimental effects of abiotic stresses in crops are described. The future perspective of nanoparticles in agriculture has also been discussed.

show abstract

“…Siddiqui et al (2007) reported that SiO2 increased leaf fresh and dry weight and chlorophyll content in Ocimum basilicum. SiO2 NP application was found to affect shoot and root growth negatively in Sorghum bicolor, Stipa hohenackeriana and Secale montanum plants (Lee et al, 2012;Moameri et al, 2018b;Moameri and Abbasi Khalaki, 2019).…”

Section: Nanoparticlesmentioning

confidence: 99%

The Effect of Nanoparticle Applications on Plants under Some Stress Conditions

Yavaş

2021

Turkish Journal of Range and Forage Science

View full text Add to dashboard Cite

Plants are exposed to various abiotic stresses such as drought, salinity, high temperature, flooding and heavy metal stress. These stress factors have a significant negative effect on plant growth and yield and cause economic losses. Therefore, new approaches such as nanotechnology are used to reduce the harmful effects of these stresses on plants. Agricultural nanotechnology aims to improve sustainability in agriculture, to use water effectively and to protect against plant diseases, to eliminate environmental pollution and the effects of abiotic stress factors. Nanoparticles eliminate nutrient deficiencies in plants, increase the tolerance of plants to stress conditions by enabling the enzyme activities and the adhesion of bacteria that promote plant growth to the roots under abiotic stress conditions. In this review, the role of nanoparticles in ameliorating adverse effects on plants exposed to abiotic stress conditions will be emphasized.

show abstract

Investigating lead and zinc uptake and accumulation by Stipa hohenackeriana trin and rupr. in field and pot experiments

Cited by 8 publications

References 19 publications

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Role of Nanoparticles in Enhancing Crop Tolerance to Abiotic Stress: A Comprehensive Review

The Effect of Nanoparticle Applications on Plants under Some Stress Conditions

Contact Info

Product

Resources

About