Abstract
Precision agriculture, combined with Geographic Information Systems (GIS), is revolutionizing the way we manage soil nutrients for optimal crop production. In this study, we analyzed soil nitrogen (N) availability and its relationship with paddy leaf N content, using total nitrogen, available nitrogen in nitrate (NO₃⁻) and ammonium (NH₄⁺) forms. By collecting point-based soil and leaf samples, we generated spatial maps to visualize nutrient distribution and identify patterns of N availability across the field.
Our correlation analysis revealed a moderate relationship (0.375) between soil NO₃⁻ and leaf N content, while a stronger correlation (0.590) was observed between soil NH₄⁺ and leaf N. These findings indicate that ammonium plays a more significant role in paddy nitrogen uptake under the given field conditions. The spatial mapping approach enabled us to identify variability in soil nutrient levels, paving the way for more precise nutrient management strategies.
To further enhance efficiency, future advancements can incorporate drone-based spatial photography equipped with multispectral and thermal imaging. This technology will allow real-time assessment of plant nitrogen status, detecting N deficiencies with high accuracy. By integrating drone-based monitoring with GIS-based nutrient maps, farmers can apply fertilizers at precise rates, reducing overuse and minimizing environmental impact. This precision-driven approach not only optimizes crop yields but also promotes sustainable and cost-effective farming practices.
Introduction and Problem Statement
Efficient nitrogen (N) management is crucial for maximizing paddy yield while minimizing environmental impact. However, soil nitrogen availability varies across fields, making it challenging to apply fertilizers effectively. Traditional fertilization methods often lead to overuse or deficiency, reducing crop productivity and increasing costs. By using GIS-based spatial mapping and drone-based remote sensing, we can analyze soil N distribution, correlate it with plant uptake, and develop precision fertilization strategies. This approach ensures optimal nitrogen use, improving yield, reducing waste, and promoting sustainable agriculture.
Objectives
Main Objective
To assess the spatial distribution of soil nitrogen (N) availability in different forms (NO₃⁻ and NH₄⁺) and its correlation with paddy leaf N content using GIS-based precision agriculture techniques, in order to optimize nitrogen management for improved crop productivity and sustainability.
Specific objective
- Measure total nitrogen, available NO₃⁻ , and NH₄⁺ in soil, and analyze paddy leaf nitrogen content.
- Develop GIS-based spatial maps to visualize soil nitrogen distribution and its correlation with leaf N.
- Assess the relationship between soil NO₃⁻, NH₄⁺, and leaf N content to determine nutrient uptake efficiency.
- Integrate drone-based remote sensing to detect nitrogen deficiencies and enhance real-time monitoring.
- Optimize nitrogen fertilization by implementing precision application strategies to reduce waste and improve sustainability.
Methodology
This study followed a structured approach to assess leaf nitrogen availability, its correlation with soil available nitrogen content in paddy fields, and the integration of drone-based spatial photography for optimized fertilizer application. The methodology consisted of multiple phases, including field data collection, laboratory analysis, spatial mapping, correlation analysis, and drone-based nitrogen assessment.
1. Field Data Collection
Soil and leaf samples were collected from multiple georeferenced locations across the study area. A systematic grid sampling method was employed to ensure uniform coverage of the field.
- Soil Sampling: At each georeferenced point, soil samples were taken from the top 0–20 cm depth using an auger. Samples were stored in labeled airtight bags and transported for analysis.
- Leaf Sampling: Paddy leaf samples were collected from the same locations where soil samples were taken. The youngest fully expanded leaves were chosen for nitrogen analysis, ensuring consistency in plant nutrient uptake measurements.
- Geospatial Data Recording: Each sampling point was recorded using a GPS device to integrate spatial data into GIS for further analysis.
2. Laboratory Analysis of Soil and Leaf Nitrogen
Soil and leaf samples were analyzed in the laboratory to determine nitrogen content using standard analytical methods:
- Soil Analysis:
- Total nitrogen (TN): Measured using the Kjeldahl method.
- Available nitrate (NO₃⁻) and ammonium (NH₄⁺): Extracted using a KCl solution and quantified using a spectrophotometer.
- Leaf Nitrogen Content:
- Dried leaf samples were ground and analyzed using the dry combustion method in a CHNS analyzer to determine total nitrogen content.
3. GIS-Based Spatial Analysis
The collected soil and leaf nitrogen data were spatially processed using GIS software.
- Interpolation Techniques: Inverse distance weighting (IDW) was applied to generate spatial distribution maps for soil NO₃⁻, NH₄⁺, and leaf nitrogen content.
- Overlay analysis: Soil and leaf nitrogen maps were overlaid to visualize spatial correlations between soil nitrogen availability and plant uptake.
4. Correlation Analysis Between Soil and Leaf Nitrogen
A statistical correlation analysis was performed to examine the relationship between soil nitrogen availability and leaf nitrogen content:
- Pearson correlation coefficient (r): Used to quantify the strength of the relationship between soil NO₃⁻ and leaf nitrogen, as well as soil NH₄⁺ and leaf nitrogen.
5. Drone-Based Spatial Photography for Nitrogen Deficiency Detection
To enhance nitrogen management, a drone equipped with multispectral and thermal cameras was deployed to assess plant nitrogen availability across the field.
- Drone Specifications: A high-resolution multispectral sensor capable of capturing red, green, blue, near-infrared (NIR), and red-edge bands was used.
- Vegetation Indices Calculation:
Normalized Difference Vegetation Index (NDVI): Used to assess plant health based on chlorophyll content.
Red Edge Position (REP): Analyzed to detect early nitrogen deficiencies in plants.
- Image Processing: Drone images were processed using specialized software, such as Pix4D, to create nitrogen availability maps. These maps were overlaid with GIS spatial data for improved precision.
6. Variable-Rate Fertilizer Application Strategy
Based on the nitrogen deficiency maps generated from both GIS and drone imagery, a variable-rate fertilizer application (VRA) strategy was developed:
- Deficiency Identification: Zones with nitrogen deficiencies were identified from the spatial maps.
- Targeted Fertilization: A prescription map was created for site-specific fertilizer application, ensuring that nitrogen is applied only where needed in precise amounts.
- Implementation: A GPS-enabled variable-rate fertilizer spreader was used to apply nitrogen fertilizer according to the prescription map, optimizing nutrient use efficiency while reducing excess application and environmental impact.
7. Data Validation and Evaluation
To ensure accuracy and effectiveness, the following validation steps were performed:
- Ground-Truthing: Random field measurements of leaf nitrogen content were taken to validate drone-based nitrogen assessment.
- Yield Assessment: Crop yield data were recorded at harvest to evaluate the impact of precision nitrogen management on productivity.
- Statistical Analysis: A comparative analysis was conducted between traditional uniform fertilization and precision nitrogen management to quantify improvements in nitrogen use efficiency and crop yield.
Results
The spatial analysis of nitrogen content across the experimental paddy field provided critical insights into variability in both soil and plant nitrogen distribution. The following key findings were derived from the analysis of spatial maps:
Leaf Nitrogen Content: The spatial map (Figure 1) illustrates the variation in leaf nitrogen across the experimental plot. Higher nitrogen content was concentrated in specific areas, corresponding to zones of optimal nutrient uptake. Regions with lower nitrogen content were identified as potential areas of deficiency.
Soil Available Nitrogen (NO3): As depicted in Figure 2, the distribution of soil-available nitrogen in nitrate form shows distinct zones of high and low availability. A moderate correlation (R = 0.37506) was observed between NO3 levels and leaf nitrogen content, indicating a partial influence of nitrate availability on plant nitrogen uptake.
Soil Available Nitrogen (NH4): Figure 3 highlights the spatial variability of ammonium-based nitrogen. A stronger correlation (R = 0.59070) between NH4 levels and leaf nitrogen suggests that ammonium availability plays a more significant role in nitrogen assimilation in paddy plants.
Discussion
The observed correlations between soil nitrogen forms and leaf nitrogen content reveal critical insights into nitrogen dynamics in paddy fields:
Role of NO3 in Plant Nutrition: The moderate correlation between soil NO3 and leaf nitrogen content (R = 0.37506) suggests that while nitrate contributes to plant nitrogen uptake, its influence is limited by factors such as leaching, soil texture, and microbial activity. Nitrate is highly mobile in soil, which can lead to uneven distribution and reduced availability in certain zones, as indicated in the spatial map.
Importance of NH4: The stronger correlation between NH4 and leaf nitrogen (R = 0.59070) highlights the significance of ammonium as a readily available nitrogen source for paddy plants. Unlike nitrate, ammonium is less prone to leaching, resulting in more consistent availability across the field. This makes it a critical parameter to monitor for precision nitrogen management.
Spatial Variability: The spatial maps underscore the heterogeneity in soil nitrogen distribution, influenced by factors such as water flow, organic matter content, and localized microbial activity. This variability necessitates site-specific interventions to address nitrogen deficits effectively.
Implications for Fertilizer Application: The findings emphasize the need for balanced fertilizer strategies that account for both nitrate and ammonium forms. Over-reliance on one form may lead to suboptimal nitrogen availability and uptake. By identifying zones with deficiencies, spatial maps can guide precise fertilizer application, improving nutrient use efficiency and crop yields.
Future Perspectives
Integrating drone-based spatial photography with GIS analysis can further enhance nitrogen management. Drones equipped with multispectral and hyperspectral sensors can capture high-resolution data on plant health, identifying nitrogen deficits with greater accuracy. This real-time monitoring capability enables dynamic adjustments to fertilizer application rates, reducing wastage and environmental impacts.
Additionally, coupling drone imagery with machine learning algorithms can provide predictive insights into nitrogen dynamics, optimizing resource allocation and maximizing productivity. The adoption of such technologies aligns with sustainable farming practices, promoting environmental stewardship while meeting the growing demand for food security.
Conclusion
This study demonstrates the potential of GIS-based spatial analysis in understanding nitrogen dynamics in paddy fields. The moderate and strong correlations observed between soil nitrogen forms and leaf nitrogen content highlight the critical role of targeted nutrient management. By leveraging advanced technologies such as drones and machine learning, precision agriculture can achieve significant improvements in efficiency and sustainability, paving the way for a more resilient agricultural future.