Yield Monitoring and Mapping in Precision Agriculture
Crop Yield Mapping
Crop yield mapping transforms the data collected through yield monitoring into visual representations of field productivity. The yield calculated at each field location can be displayed on a map using a geographic information system (GIS) software package that allows data analysis and provides tools for making decisions. These maps provide invaluable insights into spatial variability across agricultural lands, allowing farmers to make targeted decisions about resource allocation and management practices. Yield maps created with on-the-go yield monitoring systems and positioning equipment record yield flow data with spatially referenced field locations as the crop is being harvested. Field maps with yield data can show variability in yield patterns across fields, pointing to high as well as low production areas. Areas with lower yields can be correlated with other field data, such as soil fertility maps, drainage flow maps, or field scouting data. Yield maps provide a wealth of information that is useful for directing site-specific scouting and management techniques or identifying issues with field equipment mechanics or operations. Yield maps are often utilized to delineate management zones for variable-rate fertilizer or seeding applications. Areas that need drainage tile installation can also be located using information from yield maps. Additionally, yield maps can be useful for on-farm research studies to compare hybrids, chemical products, or other management practices.
Data Cleaning
Yield mapping has become an important function in site-specific management systems. Decisions of economic significance are made based upon the patterns and summary statistics of the yield data recorded during harvest. Unfortunately, yield maps frequently contain data points that are not accurate estimates of the yield at that point. The yield estimate at any given point is affected by a number of factors, including the number of combines used in a field, the shape of the field, yield and moisture calibration, harvest pattern, operator practices, sensor malfunctions, or overlapping data.
Yield Data Cleaning Workflow
A typical yield data cleaning workflow involves several steps to ensure the accuracy and reliability of collected data for analysis and decision-making. These steps include identifying and removing erroneous data points based on physical measurements, statistical analysis, and field boundaries, as well as standardizing data and verifying accuracy.
Georeferencing
Georeferencing in yield mapping is the process of assigning accurate geographic coordinates (latitude, longitude, and often elevation) to each yield data point collected by harvesting equipment. This allows the yield data to be spatially aligned with other field data layers (like soil maps, satellite imagery, or management zones) and displayed on maps. Each yield data point is tagged with location (from GPS/GNSS), timestamp, yield value (e.g., kg/ha or bu/ac), moisture content, harvester speed, and swath width/header position.
Interpolation in Yield Mapping
Interpolation is a mathematical method used to estimate unknown values at unsampled locations based on known values (e.g., measured yield points). It creates a continuous surface of data. This process is crucial for visualizing the spatial variability of crop yield, identifying areas with low or high yields, and ultimately informing management decisions in precision agriculture. The two most commonly used forms of interpolation are Kriging (KRG) and Inverse Distance Weighted (IDW); both options are offered in most FMIS software.
Interpolation in Yield Mapping Software
Interpolation in yield mapping software is used to estimate crop yields at unsampled locations across a field by using measured yield data from GPS-equipped combine harvesters, UAVs, or sensors. It creates continuous surfaces like contour or raster maps, which help visualize yield variability and inform precision agriculture decisions.
Yield Map Generation
Yield map generation is the final step of yield monitoring, where spatial data collected by sensors on harvesters is processed and visualized as maps showing yield variability across a field. These maps are crucial for identifying high- and low-performing areas, and guiding precision agriculture decisions such as variable rate applications.
Grid-Based Verus Contour-Based Yield Maps
Grid-based and contour-based yield maps are techniques used in spatial representation of yield data to visualize variability across a field. They differ mainly in how the spatial data is organized, analyzed, and displayed.
Analysis and Interpretation
After collecting and processing yield data, the analysis and interpretation phase is where insights are extracted to inform better decision-making. The goal is to understand spatial variability in crop productivity and relate it to underlying factors like soil properties, weather, topography, and management practices.
Yield Data Layer Visualization
Yield data layer visualization is the process of converting raw yield data into clear, informative maps that display the spatial distribution of crop productivity across a field. These maps help farmers and agronomists identify patterns, assess variability, and guide precision agriculture decisions. A yield data layer is a georeferenced dataset collected during harvest. It typically includes:
Interpretation of Yield Variability
Interpreting yield variability involves analyzing spatial differences in crop productivity across a field to understand underlying causes and support data-driven decisions in precision agriculture. Yield maps reveal patterns influenced by soil, weather, topography, and management practices. Yield maps are usually divided into zones (Table 11.3).
Analysis of Geospatial Layers
Yield maps can be overlaid with other geospatial layers in a GIS or farm management software to interpret causes of variability. Analyzing geospatial layers alongside yield data enhances understanding of the spatial factors influencing crop performance. This approach enables precision agriculture decisions based on not just what happened (yield), but why it happened. Analysis techniques for layer comparison include the following:
Statistical and Spatial Analysis Techniques
Yield mapping combines statistical and spatial analysis techniques to visualize and understand crop production across a field, aiding in precision agriculture practices. It involves collecting georeferenced yield data, typically using harvest equipment with GPS, and then employing various methods to map and interpret the spatial patterns. Analyzing yield data using statistical and spatial techniques helps understand the variability, identify yield-limiting factors, and support precision agriculture decisions. Below is a structured overview of the main techniques used.
Temporal Analysis
Temporal analysis in yield mapping refers to evaluating yield patterns across multiple seasons or years. This helps uncover trends, stability, and variability over time, making it a critical tool for long-term decision-making in precision agriculture.
Management Recommendations
Yield maps, which depict crop yield variations across a field, can guide management decisions to optimize inputs and improve crop productivity. Yield data can be used for many applications, including diagnosing crop production problems, assessing the effectiveness of a wide range of inputs, selecting varieties or hybrids, conducting on-farm studies, conducting profitability assessments, and identifying management zones.
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