Interview Questions for Harvest Mapping

Harvest mapping is the process of spatially representing yield and other harvest-related data across a field or farm. It’s a cornerstone of precision agriculture, allowing farmers to pinpoint areas of high and low productivity. Think of it like creating a detailed ‘map’ of your harvest, revealing variations in yield across the field. This allows for targeted interventions, improving efficiency and profitability. For example, identifying a low-yielding zone might prompt investigation into soil nutrient deficiencies or irrigation issues in that specific area.

In precision agriculture, harvest mapping helps optimize resource allocation. By identifying high-yielding zones, farmers can focus fertilizer and irrigation efforts there, maximizing returns. Conversely, understanding low-yielding areas helps determine the causes and implement corrective actions, preventing future losses. This targeted approach is far more efficient than blanket applications of inputs across the entire field.

Several methods are used to acquire harvest data for mapping. The most common is using yield monitors integrated into combine harvesters. These monitors measure yield in real-time as the machine harvests, recording data at frequent intervals (often every few seconds). This data, along with GPS coordinates, provides a highly accurate record of yield variations across the field.

Another method involves using post-harvest ground surveys. These involve manually collecting samples from different locations in the field, measuring yield and other characteristics. While less efficient than yield monitors for large fields, they provide valuable supplemental data and can be crucial when yield monitors are unavailable or malfunctioning.

Finally, remote sensing techniques, such as multispectral or hyperspectral imagery captured by drones or satellites, offer a broader perspective. Although they don’t directly measure yield, they can provide valuable proxy data like biomass or vegetation indices that correlate with yield. This information can be integrated with yield monitor data for a more comprehensive analysis.

The core data sources in harvest mapping are:

• Yield Monitors: These are the primary source, providing direct yield measurements with precise GPS location data. Data typically includes yield (e.g., bushels per acre), moisture content, and sometimes other factors like grain size.
• GPS Receivers: Essential for georeferencing the yield monitor data, ensuring accurate placement on the field map.
• Remote Sensing Data (Satellite or Drone Imagery): Provides valuable context and supplemental information on factors influencing yield, such as vegetation health, soil moisture, and topography. Indices like NDVI (Normalized Difference Vegetation Index) can be used as proxies for yield.
• Soil Data: Information on soil type, texture, and nutrient content can help explain yield variations observed in the map.
• Field Management Records: Details of farming practices like planting date, fertilizer application, irrigation, and pest control can provide valuable context for analysis.

Ensuring accuracy and reliability is crucial. We employ several strategies:

• Calibration of Yield Monitors: Regular calibration of yield monitors is vital to ensure accurate measurements. This involves verifying the monitor’s readings against known weights of harvested material.
• Data Validation and Cleaning: After data collection, a thorough review is necessary to identify and correct errors or outliers. This includes checking for implausible yield values or GPS inaccuracies. Automated data cleaning techniques can also be applied.
• Quality Control Checks: Comparing yield monitor data with ground truth measurements from a representative subset of the field can help assess the overall accuracy of the system. Discrepancies can highlight potential issues with the yield monitor or the data processing pipeline.
• Georeferencing Accuracy: Precise georeferencing is key. Using high-accuracy GPS with differential correction techniques (e.g., RTK-GPS) minimizes positional errors in the map.

GIS (Geographic Information System) software is indispensable for harvest mapping. It provides the framework for organizing, analyzing, and visualizing the spatial data. ArcGIS, QGIS, and other GIS platforms are commonly used.

GIS allows us to:

• Create thematic maps: Visualize yield variations across the field using color-coded maps or other cartographic representations.
• Overlay different datasets: Integrate yield data with other geospatial information (e.g., soil maps, topography, field boundaries) to understand the factors driving yield variability.
• Perform spatial analysis: Utilize GIS tools for advanced analysis, like calculating zonal statistics (average yield within specific zones) or spatial autocorrelation (identifying patterns in yield variations).
• Data Management: Organize and manage large volumes of geospatial data efficiently.
• Report Generation: Create professional reports and maps to communicate findings to stakeholders.

Several geospatial analysis techniques are valuable in harvest mapping:

• Zonal Statistics: Calculating summary statistics (e.g., mean, standard deviation, maximum) for yield within predefined zones (e.g., management zones, soil types).
• Spatial Interpolation: Estimating yield values at unsampled locations using techniques like kriging or inverse distance weighting, creating a continuous yield surface.
• Spatial Autocorrelation: Assessing the degree to which yield values are spatially correlated. This helps identify clusters of high or low yields.
• Regression Analysis: Investigating the relationships between yield and other factors (e.g., soil properties, management practices) to build predictive models.
• Geostatistics: Techniques like kriging are particularly useful for analyzing spatially autocorrelated data and creating accurate yield maps.

For example, we might use regression analysis to model the relationship between yield and soil nitrogen levels, helping farmers optimize nitrogen fertilizer application in subsequent seasons.

Missing or incomplete harvest data is a common challenge. Several approaches help mitigate this:

• Spatial Interpolation: Techniques like kriging or inverse distance weighting can be used to estimate missing yield values based on the values of neighboring points. This assumes spatial autocorrelation in the data.
• Data Imputation: Statistical methods can fill in missing values based on existing data patterns. Simple methods include replacing missing values with the mean or median yield.
• Data Augmentation: If the missing data is systematic (e.g., caused by a yield monitor malfunction in a specific area), we might supplement the data with remote sensing imagery or ground surveys.
• Sensitivity Analysis: Evaluating how the analysis results are affected by the choice of imputation or interpolation method. This helps assess the uncertainty associated with the missing data.
• Careful data collection planning: Planning ahead minimizes data gaps. This can include using redundant sensors, establishing a data backup system, and meticulous maintenance of equipment.

The best strategy depends on the extent and nature of the missing data, as well as the specific research question. It is crucial to document the methods used to handle missing data and acknowledge the uncertainties introduced by data gaps.

Integrating different data sources in harvest mapping presents several challenges. The primary hurdle is data heterogeneity – different datasets might have varying spatial resolutions, formats, projections, and accuracy levels. For instance, you might have high-resolution yield data from a combine harvester but coarser resolution data from satellite imagery representing soil properties. Harmonizing these differences is crucial for accurate analysis.

• Format inconsistencies: Yield data might be in a proprietary format, while soil data is in GeoTIFF. Conversion and standardization are needed.
• Spatial resolution differences: High-resolution yield data can reveal field-scale variations, while coarser resolution remote sensing data might only show larger-scale trends. This mismatch can lead to inaccuracies if not properly addressed through techniques like resampling or aggregation.
• Temporal mismatches: Yield data is a snapshot in time, while remote sensing data might represent average conditions over a longer period. Considering the timing of data collection is crucial for accurate interpretation.
• Data accuracy and uncertainty: Each data source has its inherent uncertainties. Yield monitors might have errors, and satellite imagery can be affected by cloud cover or atmospheric conditions. Accounting for these uncertainties is vital to avoid drawing misleading conclusions.
• Data registration and alignment: Ensuring that different datasets are correctly aligned geographically is essential to avoid misinterpretations in spatial relationships between variables.

Overcoming these challenges often involves employing geoprocessing techniques in GIS software, careful data pre-processing, and potentially the application of data fusion methodologies to combine information from multiple sources effectively.

Creating a harvest map from yield monitor data involves several key steps. Imagine it’s like piecing together a detailed picture of your field’s productivity.

1. Data Acquisition: Harvest yield data is collected by yield monitors on combines, recording yield in real-time, often as yield per hectare or bushel per acre. GPS coordinates are simultaneously recorded to geographically locate each measurement.
2. Data Cleaning and Preprocessing: Raw yield data often contains outliers or errors. This step involves identifying and correcting these anomalies, for example, removing data points collected during headland turns or instances of equipment malfunction. This ensures data quality and improves map accuracy.
3. Data Georeferencing: The yield data points are linked to their corresponding GPS coordinates, creating a point cloud representing yield variation across the field.
4. Interpolation: A spatial interpolation method, such as kriging or inverse distance weighting, is used to estimate yield values between the measured points, effectively creating a continuous yield surface across the entire field. The choice of interpolation method depends on the spatial structure of the data and desired level of smoothing.
5. Map Creation: The interpolated yield surface is visualized as a map, typically using a color scale to represent yield levels, ranging from low (e.g., blue) to high (e.g., red) yields. This visual representation facilitates easy identification of high- and low-yielding areas.
6. Data Export and Analysis: The resulting harvest map is usually exported in a GIS-compatible format (e.g., GeoTIFF) for further analysis and integration with other datasets, such as soil maps or remote sensing imagery.

For example, using software like ArcGIS or QGIS, you can generate a detailed map showing the yield variation across your farmland. This map serves as a foundation for making informed decisions about future planting, fertilization, and irrigation strategies.

Interpreting a harvest map involves analyzing the spatial patterns of yield variations. Imagine it’s like reading a topographical map, but instead of elevation, you’re looking at yield.

Identifying High-Yielding Areas: Areas depicted in darker shades (e.g., red or dark green depending on the color scheme) represent higher yield levels. These areas typically indicate optimal growing conditions, possibly due to factors such as favorable soil conditions, efficient irrigation, and timely fertilization.

Identifying Low-Yielding Areas: Lighter shades (e.g., blue or light green) represent lower yield levels. These zones might suggest areas with limitations such as soil compaction, nutrient deficiencies, water stress, disease outbreaks, or pest infestations.

Analyzing Spatial Patterns: Beyond simply identifying high and low areas, the map helps identify patterns of yield variation. Are there distinct zones of high and low yield? Is there a gradual change in yield across the field, suggesting a gradient in soil properties? This pattern analysis guides decision-making. For example, a consistently low-yielding area might indicate the need for soil testing and remediation, while a patchy distribution of low yield might suggest localized issues.

Software tools allow for quantitative analysis like calculating the average yield for different zones, determining the area affected by low yield, and assessing the overall yield variability across the field.

Spatial resolution is the crucial factor determining the level of detail in a harvest map. It represents the size of the area represented by a single data point or pixel. Higher resolution provides a more detailed picture, while lower resolution offers a more generalized view.

• High Spatial Resolution: A high-resolution map (e.g., data points every few meters) can reveal small-scale variations in yield, caused by factors such as individual plant variations, micro-topographic features, or localized soil variations. This level of detail is vital for precision management strategies.
• Low Spatial Resolution: A low-resolution map (e.g., data aggregated over larger areas) might only show broad trends in yield, masking the subtle variations. While simpler to interpret, it might lack the detail needed for precise interventions.

For example, a high-resolution map might show yield differences between rows in a field, while a low-resolution map might only show the average yield for the entire field. The choice of appropriate spatial resolution depends on the objectives of the mapping exercise. If precise management interventions are needed, high resolution is essential. If the focus is on overall field performance, lower resolution might suffice.

Environmental factors significantly influence crop yields and are crucial considerations in harvest mapping. These factors are often incorporated into the analysis through integration with ancillary data.

• Soil properties: Soil type, texture, organic matter content, nutrient levels, and drainage characteristics all affect crop growth and can be mapped using soil surveys or remote sensing techniques. Integrating soil maps with yield data can help identify areas where soil limitations are affecting yield.
• Topography: Elevation, slope, and aspect can influence drainage, sunlight exposure, and microclimate, impacting crop yields. Digital elevation models (DEMs) can provide this information and be integrated with yield maps to analyze topographic effects.
• Climate: Rainfall, temperature, and solar radiation patterns influence crop growth. Weather data can be incorporated to understand the impact of climatic variations on yield variability throughout the growing season.
• Remote sensing data: Satellite or drone-based imagery can capture information on vegetation health, canopy cover, and biomass, providing valuable indicators of crop performance. Combining yield maps with remote sensing data can reveal the relationship between these indicators and actual yields.

Incorporating environmental factors into harvest mapping is often achieved through geospatial analysis techniques, such as overlaying different datasets in a GIS environment, correlation analysis to establish relationships between yield and environmental factors, and the creation of composite indices that combine multiple environmental factors to represent overall growing conditions.

The effectiveness of harvest mapping is assessed using several key performance indicators (KPIs).

• Yield variability: Measured as the standard deviation or coefficient of variation of yield across the field. Higher variability suggests greater potential for improvement through precision agriculture techniques.
• Area of low-yielding zones: The percentage or total area of the field with yields below a specific threshold. This KPI helps quantify the extent of areas requiring targeted management interventions.
• Yield improvement after intervention: Comparing yield maps from different years to assess the effectiveness of implemented changes, such as variable rate fertilization or irrigation adjustments. This indicates the return on investment for precision agriculture strategies.
• Management zone identification accuracy: If management zones are defined based on the yield map, assessing the accuracy of this zonal delineation against field observations can be valuable.
• Correlation with environmental factors: Analyzing the correlation between yield and environmental factors (soil properties, topography, climate) helps understand the drivers of yield variation and improve future management decisions.

These KPIs, often visualized through charts and graphs alongside the yield map, provide a comprehensive assessment of the benefits of harvest mapping and its contribution to optimizing agricultural practices.

Soil maps and other ancillary data play a crucial role in enhancing the insights derived from harvest mapping. They provide crucial context for understanding the underlying reasons for yield variability.

• Soil maps: These maps provide information on soil type, texture, organic matter content, nutrient levels, and drainage characteristics. Overlaying soil maps with yield maps helps identify relationships between soil properties and yield variations. For instance, a consistently low-yielding area might correlate with poorly drained soil or low nutrient levels.
• Topography data (DEMs): Digital elevation models provide information on elevation, slope, and aspect. Integrating DEMs helps understand how topography affects drainage, sunlight exposure, and microclimate, influencing crop growth and yield.
• Remote sensing data: Satellite or drone imagery provides information on vegetation health, NDVI (Normalized Difference Vegetation Index), and other indicators of crop vigor. Combining remote sensing data with yield maps can reveal how vegetation health influences final yield.
• Weather data: Historical rainfall, temperature, and solar radiation data can be used to assess the influence of climate variations on yield across the field. This context is crucial for understanding yield variations that might not be immediately evident in the yield map alone.

By integrating these ancillary data sources, we move beyond simply visualizing yield variation and delve into understanding the causal factors that drive these variations, ultimately leading to more effective precision agriculture management strategies. For example, identifying a correlation between low yield and low soil organic matter content could direct efforts towards implementing organic matter-enhancing practices in those specific areas.

Harvest maps, which visually represent yield variations across a field, are invaluable tools for optimizing fertilizer application and irrigation. By identifying high-yielding and low-yielding zones, we can tailor resource allocation to maximize efficiency and profitability.

For fertilizer, a harvest map reveals areas that responded well to nutrients and those that didn’t. This allows for variable rate fertilizer application (VRA), where higher rates are applied to areas historically showing lower yields, while lower rates are used in high-yielding zones, minimizing waste and environmental impact. For example, if a map shows a consistently low yield in a specific corner of a field, we might investigate soil nutrient levels in that area and adjust the fertilizer blend accordingly in the following season.

Similarly, with irrigation, harvest maps pinpoint areas with consistently low yields that might be due to insufficient water. This allows for precise irrigation scheduling and targeted water application using technologies like drip irrigation or center pivots, reducing water waste and improving overall water-use efficiency. For instance, a field with patchy yields might benefit from installing a more precise irrigation system to precisely address the water needs of each identified zone.

Harvest mapping data provides crucial insights for informed planting decisions in subsequent seasons. Analyzing yield data from previous harvests helps to identify patterns and trends related to soil conditions, crop health, and environmental factors. This information allows farmers to make data-driven choices regarding seed selection, planting density, and overall field management strategies.

For instance, consistently low yields in a particular section of a field could indicate compaction, poor drainage, or nutrient deficiencies. A harvest map highlights these areas, enabling farmers to address the underlying issues before planting the next crop. Perhaps soil testing and amendment will be necessary, or perhaps a different crop variety better suited to the challenging conditions should be selected.

Furthermore, analyzing yield data in conjunction with other data sources such as soil maps, elevation data, and weather patterns can help predict yield potential for different parts of the field. This allows for strategic planning, like allocating higher-yielding varieties to the most productive areas of the field.

Effective communication of harvest mapping results is critical for adoption and impact. We employ various strategies tailored to the audience.

For farmers, we use clear and concise visuals, such as color-coded maps that are easy to interpret. We hold meetings or workshops to discuss the maps, explain the implications, and suggest management strategies. We may use simple terminology and relatable examples to improve understanding.

For other stakeholders (e.g., agronomists, researchers), we use more technical reports, statistical analyses, and GIS-based presentations with detailed data and insights. Interactive dashboards can be valuable to allow for dynamic exploration of data.

Regardless of the audience, we always emphasize the practical implications of the findings and the potential for improved efficiency and profitability. We actively encourage questions and feedback to ensure understanding and build trust.

Ethical considerations surrounding data privacy and security in harvest mapping are paramount. The data collected is sensitive and can reveal valuable information about a farmer’s operations. We must ensure data confidentiality and integrity.

We adhere to strict data governance protocols, including secure data storage, access control, and encryption. We obtain explicit consent from farmers before collecting and using their data and ensure data is only used for the intended purposes. Anonymization techniques, where possible, are applied to safeguard individual farmer identities when sharing aggregate data. We make transparent to farmers how data will be utilized and who will have access to it. It is important to maintain trust with the farmers.

Furthermore, we comply with all relevant data privacy regulations and standards (e.g., GDPR). Regular audits and security assessments are conducted to ensure ongoing compliance.

Integrating harvest mapping with other precision agriculture technologies significantly enhances its value and effectiveness. This synergy allows for a more holistic view of farm operations and facilitates data-driven decision-making.

For instance, integrating yield data with GPS-guided machinery allows for automatic variable rate application of inputs based on the harvest map. Combining it with soil sensors and remote sensing data improves the understanding of yield variation, helping to identify root causes of low yields. Integration with weather station data may further enhance predictive modelling capabilities for future yields.

Ultimately, this integrated approach leads to more accurate predictions, reduced input costs, and optimized resource utilization, thereby improving the overall profitability and sustainability of farming practices.

Machine learning (ML) significantly enhances the accuracy and efficiency of harvest mapping. ML algorithms can analyze large datasets of yield data, along with other relevant factors (soil properties, weather patterns, etc.), to identify patterns and make predictions that would be difficult or impossible for humans to discern.

For instance, ML can be used to:

• Improve yield prediction models by identifying key factors influencing yield and their interactions.
• Detect outliers and anomalies in the yield data, flagging areas requiring further investigation.
• Create more accurate yield maps by interpolating missing data or smoothing noisy data.
• Develop predictive models for diseases or pest infestations based on yield patterns.

By automating many aspects of data analysis and interpretation, ML frees up time for more strategic decision-making and allows for quicker identification of problem areas, ultimately increasing the efficacy and efficiency of precision agriculture strategies.

Throughout my career, I have worked extensively with various yield monitor systems and data formats, building proficiency in both their strengths and limitations.

My experience spans across different manufacturers, including John Deere, Claas, and Case IH yield monitors. I’m comfortable working with various data formats, including SHP, CSV, KML, and proprietary formats. I understand the importance of data cleaning and standardization before any analysis. I’m proficient in using data processing software like R and Python to handle and analyze this data.

In addition to traditional yield monitors, I am also familiar with newer technologies such as optical sensors and hyperspectral imaging, offering alternative methods for creating harvest maps with greater precision and added data dimensions beyond simple yield.

This broad experience has enabled me to effectively integrate data from diverse sources, ensuring accurate and comprehensive harvest maps for diverse agricultural settings.

Validating the accuracy of harvest maps is crucial for ensuring their reliability and usefulness in precision agriculture. We employ a multi-faceted approach, combining ground truthing with statistical analysis and comparing our maps against independent data sources.

• Ground Truthing: This involves physically collecting yield data from representative samples across the field using methods like yield monitors on combines or manual sampling. We compare these ground measurements to the yield values predicted by our maps at the corresponding locations. Discrepancies are analyzed to identify potential sources of error.
• Statistical Analysis: We calculate statistical measures like the Root Mean Square Error (RMSE) and R-squared to quantify the agreement between the mapped yields and the ground truth data. A lower RMSE indicates better accuracy, while a higher R-squared suggests a stronger correlation.
• Independent Data Comparison: Where possible, we compare our harvest maps to other independent yield data sources, such as data from neighboring farms or government agricultural statistics. This helps to identify biases and systematic errors in our mapping process.

For instance, in one project, we achieved an RMSE of less than 2% and an R-squared of over 95%, demonstrating high accuracy and reliability in our harvest maps.

My proficiency in harvest mapping extends across various software and tools. I’m adept at using GIS software like ArcGIS and QGIS for spatial data analysis and map visualization. I’m also experienced with precision agriculture software such as AgLeader, John Deere Operations Center, and Climate FieldView, which provide yield data directly from combines and other machinery. For data processing and statistical analysis, I rely on programming languages like R and Python, leveraging packages such as ggplot2 for visualization and spdep for spatial statistics.

Furthermore, I am comfortable using various remote sensing tools and software such as ERDAS Imagine and ENVI for analyzing satellite imagery data if needed to supplement yield data, especially in situations with limited ground-truthing data.

Data processing and cleaning are paramount to producing accurate harvest maps. My experience encompasses various techniques, including:

• Data Cleaning: This involves identifying and correcting errors or inconsistencies in yield data, such as outliers, missing values, and data entry mistakes. Techniques such as outlier detection using boxplots or Z-scores, followed by either removal or imputation of outliers using mean, median, or k-Nearest Neighbors methods are commonly employed.
• Data Transformation: Transformations like log transformation may be necessary to improve data normality and stabilize variance for certain statistical analyses.
• Data Aggregation: Yield data from different sources may need to be aggregated and standardized to a common spatial resolution and unit of measure (e.g., converting yields from bushels/acre to tons/hectare).
• Spatial Alignment: Ensuring that yield data is accurately georeferenced and aligns with the appropriate geographic coordinate system is critical for proper mapping and analysis.

For example, I once encountered a dataset with significant outliers due to equipment malfunction. By carefully investigating these outliers and using a robust imputation technique, I ensured the integrity of the data and prevented the outliers from skewing the final yield map.

Interpolation methods are essential for estimating yield values at unsampled locations within a field. Several methods are commonly used, each with its own strengths and weaknesses.

• Inverse Distance Weighting (IDW): This simple method assigns greater weight to closer data points when interpolating. It’s easy to understand and implement but can be sensitive to outliers.
• Kriging: A geostatistical method that considers spatial autocorrelation in the data. It provides more accurate estimations than IDW, particularly when spatial dependence is strong. Ordinary kriging and universal kriging are common variations.
• Spline Interpolation: This creates a smooth surface that passes through all data points. It is useful for generating visually appealing maps but can overfit to noisy data.
• Nearest Neighbor: This assigns the value of the nearest data point to unsampled locations. It is very simple but can generate a discontinuous, blocky surface.

The choice of interpolation method depends on the characteristics of the yield data and the desired level of accuracy. For instance, Kriging is often preferred when dealing with spatially correlated yield data, while IDW might suffice for initial exploratory analysis.

Outliers and spatial autocorrelation are common challenges in yield data. Addressing them is crucial for obtaining reliable harvest maps.

• Outliers: As mentioned before, outliers can be identified using statistical methods like boxplots or Z-scores. Handling them might involve removal (if justified and with caution) or imputation using appropriate methods like median imputation or K-Nearest Neighbors.
• Spatial Autocorrelation: This refers to the tendency of nearby locations to have similar yield values. Ignoring this can lead to inaccurate interpolation and statistical inferences. Kriging, which explicitly models spatial autocorrelation, is the most suitable method for interpolation. Moreover, Moran’s I or Geary’s C can be used to quantify the degree of spatial autocorrelation present.

In practice, I often use a combination of exploratory data analysis, statistical methods, and spatial analysis techniques to identify and address both outliers and spatial autocorrelation, ensuring robust and reliable harvest maps.

Imagine a farmer wanting to understand the yield variations across their field. Harvest mapping is like creating a detailed ‘yield map’ of the field. This map shows which areas produced high yields and which areas produced lower yields. It’s like a treasure map showing where the most ‘gold’ (high yield) is hidden!

This information is incredibly valuable. Farmers can use the map to identify areas needing improvement, such as adjusting irrigation, fertilizer application, or planting techniques in low-yield zones. They can also target high-yield areas for future optimization, potentially leading to higher overall crop production and profits. It’s all about making farming smarter and more efficient.

In a project with a large-scale corn farm, we used harvest mapping to significantly improve their efficiency. They had noticed variable yields across their fields, but lacked a clear understanding of the spatial patterns. Using yield data collected from their combines, we created high-resolution yield maps.

These maps revealed clear patterns related to soil drainage and nutrient levels. Specifically, areas with poor drainage showed significantly lower yields compared to well-drained areas. Using this information, the farmer implemented targeted drainage improvements and variable-rate fertilizer application. In the following growing season, they saw a 15% increase in overall yield and a more uniform yield distribution across their fields, demonstrating a significant return on investment in precision agriculture techniques like harvest mapping.