How use APSIM simulation to analysis 120 days old sugarcane plant data?
Best insight from top research papers
To analyze 120-day-old sugarcane plant data using APSIM simulation, one can utilize the model's capabilities in predicting crop growth and production potential based on various parameters. APSIM-Sugar model has been validated for sugarcane phenology and yields, showing good fit with observed values. Additionally, sensitivity analysis of APSIM-Sugar model parameters can help optimize the model for accurate simulations. The model can predict sugarcane yield responses to factors like CO2 concentration, rainfall, and temperature, providing insights into crop performance under different conditions. By calibrating key variety parameters in APSIM-Sugar, one can enhance the model's ability to simulate varietal differences and yield responses to environmental stressors, such as water stress. This comprehensive approach ensures accurate and detailed analysis of 120-day-old sugarcane plant data using APSIM simulation.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
Open access•Dissertation 01 Jan 2015 4 Citations | Not addressed in the paper. |
25 Aug 2015 | Not addressed in the paper. |
60 Citations | APSIM simulation can be utilized to analyze 120-day-old sugarcane plant data by inputting growth parameters and environmental conditions to predict crop growth and production accurately. |
| Not addressed in the paper. | |
7 Citations | The APSIM model can analyze 120-day-old sugarcane plant data by simulating growth potential, phenology, and yields, ensuring accurate predictions for energy crop production. |
Related Questions
Does the amount of rainfall affect sugarcane shoot emergence?5 answersThe amount of rainfall does indeed affect sugarcane shoot emergence. Research indicates that rainfall plays a crucial role in sugarcane cultivation. Rainfall, in conjunction with soil conditions, determines the opportune time for equipment use and influences crop development based on prevailing moisture levels and temperatures. Moreover, studies have shown that rainfall simulations impact weed control through herbicides, with control being higher in treatments with lower amounts of straw and rainfall. Additionally, the presence of rainfall after herbicide application affects its efficacy, as seen with flumioxazin leaching in soil, impacting the control of Rottboellia exaltata in sugarcane fields. Therefore, the amount and timing of rainfall are critical factors influencing sugarcane shoot emergence and overall crop productivity.
Can AI-driven solutions help to address the sustainability and environmental challenges faced by the sugarcane sector?5 answersAI-driven solutions have the potential to address sustainability and environmental challenges in the sugarcane sector. The Latin America and the Caribbean (LAC) region, which plays a crucial role in global sustainability, can benefit from AI technologies to accelerate changes needed for resilience and adaptation. AI, when combined with IoT technologies, can significantly contribute to addressing environmental challenges like pollution control, climate change, and waste management. Moreover, there is a growing emphasis on making AI and technology environmentally sustainable through regulatory measures like transparency mechanisms, sustainability by design, and consumption caps, which can serve as a blueprint for other sectors facing similar challenges. By leveraging AI solutions and implementing sustainable practices, the sugarcane sector can enhance its environmental performance and contribute to overall sustainability goals.
Are there studies on remote sensing of sugarcane using artificial intelligence and machine learning softwares?5 answersStudies on remote sensing of sugarcane using artificial intelligence and machine learning software have been conducted. One study used deep learning algorithms, specifically Convolutional Neural Networks (CNN), to automate the detection and categorization of sugarcane infections. Another study explored the use of multispectral images and machine learning algorithms, such as multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM), to predict sugarcane quality indicators like °Brix and Purity. Additionally, a study utilized remote sensing technology and machine learning algorithms, including random forest (RF) and second-degree polynomial regression, to accurately predict sugarcane yield. Another study developed an algorithm based on Convolutional Neural Networks (CNN) to detect and map weeds in sugarcane areas, demonstrating the potential of remote sensing and AI techniques for weed control in sugarcane cultivation. Finally, a study focused on classifying sugarcane varieties using deep neural networks and compared the results with traditional machine learning techniques, showing the potential of neural networks in accurate classification.
How much drought affect shoot Na content in sugarcane?5 answersDrought stress has been found to affect shoot Na content in sugarcane. However, the specific impact of drought on shoot Na content was not mentioned in the abstracts provided.
How does drought stress affect sugarcane physiology?4 answersDrought stress in sugarcane affects its physiology in several ways. Physiological examination revealed that under drought stress, sugarcane accumulates osmoregulatory substances such as proline, soluble sugars, and soluble proteins, while maintaining stable levels of MDA, indicating the activation of antioxidant enzyme activities to mitigate damage caused by drought stress. Drought stress also leads to a reduction in relative water content and chlorophyll content, as well as a decline in growth parameters such as cane length, cane weight, and cane girth. Additionally, drought stress negatively impacts photosynthesis, as evidenced by decreased chlorophyll content and reduced rates of stomatal conductance, intercellular carbon dioxide concentration, and transpiration. These physiological changes are accompanied by differential expression of genes associated with drought, including those involved in photosynthesis, sugar metabolism, and fatty acid synthesis. Overall, drought stress significantly affects the physiological processes and growth of sugarcane, leading to reduced yield and quality.
How to use SAMOD simulation data?5 answersSAMOD simulation data can be used by following the steps outlined in the paper by Steyn et al.. The SAMOD model simulates South Africa's personal income tax system using a dataset derived from the National Income Dynamics Study survey. The paper discusses the construction of the SAMOD model and highlights the advantages of using the EUROMOD platform as a basis for the model. The paper also mentions the challenges encountered in building the model, particularly in the context of South Africa as a developing country. The SAMOD model provides a valuable tool for analyzing and understanding the personal income tax system in South Africa.