Plant Density Differentially Influences Seed Weight
in Different Portions of the Raceme of Castor

Mai, Wenxuan; Xue, Xudong; Abdolzadeh, Ahmad

doi:10.15244/pjoes/159426

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The highest rank 1 was obtained for leaf area as shown in Table 3. This indicated that the leaf area had the highest influence on the dry weight production of W. trilobata under flooding and nitrogen enrichment [44]. It was also interpreted that the leaf area played an important role in the successful invasion of invasive plant species in flooding and nitrogen enrichment because the leaf area controls photosynthesis, respiration and evapotranspiration [7,45].…”

Section: Resultsmentioning

confidence: 94%

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Azeem,

Wenxuan,

Changyan

et al. 2023

Pol. J. Environ. Stud.

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The objective of this study is to produce multi-criteria model for the dry weight prediction of Wedelia trilobata under flooding and nitrogen conditions. Plants of W. trilobata were grown in a greenhouse, and treatments were given for two months. Growth parameters of 60 plants were used to build a numerical model. The neural network model was built using Quasi-Newton approaches that containing Broydenfletcher-goldfarb-shanno gradient (BFGS) learning algorithm, multilayer perceptron (MLP) training algorithm and sigmoid axon transfer function along with 10 neurons at the input network, 9 neurons in the hidden layer, and 1 neuron in the output layer (10-9-1). The selection and validation of the best predictor model were based on lower values of errors and higher value of R 2 . The selected model had a higher values of R 2 = 0.90 and lower values of errors i.e (relative approximate error, RAE = 0.004, root mean square error, RMS = 0.027, mean absolute error, MAE = 0.004, mean absolute percentage error, MAPE = 0.013). Moreover, the highest rank 1 was obtained for leaf area during sensitivity analysis followed by water potential and photosynthesis ranked 2 rd and 3 th , respectively. The constructed model of W. trilobata under flooding and nitrogen conditions is the new feature in the management of invasive plant species and gives direction to control its spread.

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Section: Resultsmentioning

confidence: 94%

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Azeem,

Wenxuan,

Changyan

et al. 2023

Pol. J. Environ. Stud.

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“…They are a few other mathematical models, such as the Gaussian model, Cubic polynomial model, Gaussian modified model and Log-normal model, that were used to predict the growth period of plants more accurately in every stage of plant growth [25,35]. However, these mentioned models have been developed for crops such as cotton [26], wheat [45], and maize [46,48] to predict growth under different environmental conditions. Simultaneously, there is a scarcity of studies examining these models for the growth of fodder crops in desert conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling Plant Height and Biomass Production of Cluster Bean and Sesbania across Diverse Irrigation Qualities in Pakistan’s Thar Desert

Azeem,

Mai,

Ali

2023

Water

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Plant height (PH) plays a crucial role in determining per-plant growth and biomass production. Various characteristics of PH, along with the formulation of mathematical growth models, can provide a theoretical yield or biomass production based on water quality, fruit quality, and yields. The aim of this study was to investigate the relationship between PH and biomass per-plant production of two fodder crops (Cluster bean and Sesbania) under different water quality irrigation parameters in the Thar desert of Pakistan. Universal models of PH were established in which growing degree days (GDDs) and different water quality irrigation techniques have been applied as independent variables to calculate the maximum plant height of both of these crops. For this purpose, the logistic growth model, Gaussian growth model, modified Gaussian growth model, and Cubic polynomial growth model were used. Furthermore, universal biomass per plant production models have been developed for both crops, between biomass per plant, GDDs, and PH. However, among all these developed models, the modified Gaussian and Cubic polynomial growth models produced the best results. The Cubic polynomial model has meaningless parameters that make the model not very accurate, so the modified Gaussian growth model is the best among all models. Furthermore, the relationship between biomass per plant and different water qualities was established using Michaelis–Menten equations for both crops. It was observed that an increase in salt concentration within the water quality led to a decline in biomass per plant, indicating a negative linear relationship between these factors. The growth of Cluster bean and Sesbania ceased when the electrical conductivity (EC) reached or exceeded 12.34 ds/m and 11.51 ds/m, respectively. Furthermore, the results show that Cluster bean and Sesbania have the maximum plant height under brackish water irrigation when the GDD is at 1500 °C, while in freshwater irrigation, the maximum plant height of Sesbania and Cluster bean was observed when the GDD is at 1444 °C and 1600 °C. It was concluded that these developed mathematical models can provide crucial insights for enhancing production in desert conditions by improving water use efficiency across diverse irrigation water qualities.

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Growth prediction models and dynamics of three fodder crops under fresh and brackish water irrigation in dry-land regions

Azeem,

Wenxuan,

Ali

et al. 2024

Sustainable Environment

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Plant Density Differentially Influences Seed Weight in Different Portions of the Raceme of Castor

Cited by 5 publications

References 24 publications

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Modeling Plant Height and Biomass Production of Cluster Bean and Sesbania across Diverse Irrigation Qualities in Pakistan’s Thar Desert

Growth prediction models and dynamics of three fodder crops under fresh and brackish water irrigation in dry-land regions

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