M. Reyniers

Analysis of Soil and Crop Properties for Precision Agriculture for Winter Wheat

In a precision farming research project financed by the Belgian Ministry of Small Trade and Agriculture, the methods of precision agriculture are tested on grain fields with a view of implementation of precision agriculture methods in Belgian field agriculture. The project encompasses methods for automatic information gathering on soil and crop and analysis of this data for management of within-field variability. Automatic information capturing is combined with traditional data sources of soil sample analysis and crop observations. The measurements and part of the results on one particular field in Sauveniere are presented here. Five nitrogen management strategies were compared, but the resulting differences in nitrogen dose were small and did not lead to significantly different yield results. The yield results were correlated to topography-related variations in soil texture and chemical components and to crop reflectance measurements in May.

COMPARISON OF TWO FILTERING METHODS TO IMPROVE YIELD DATA ACCURACY

Yield maps are useful management tools, so it is important that they represent as closely as possible the true yield in the field and not an artifact generated by the combine harvester or other systematic errors. Two filtering techniques were compared in their usefulness when processing grain yield data from a combine harvester. The first technique used a normal low-pass filter. The second technique used a filter model based on the characteristics of the machine dynamics, after a low-pass filter. The accuracy of both filters on combine yield was tested under extreme field conditions and abrupt field changes. Compared to yield reference measurements, it was clear that when one of the filters was used in the yield processing, yield values were more accurate than when no filter was used. The low-pass filtering turned out to be robust and useful, but not where abrupt speed or cutting width variations were imposed. An advantage of using the low-pass filter was that when abrupt yield variations existed in the field, they stayed so throughout the yield processing. Where the low-pass filtering did not compensate for abrupt grain yield variations because of changing field conditions, machine dynamics filtering partly did. Machine dynamics filtering appeared very useful when measurement conditions changed suddenly. With machine dynamics filtering, there was a decrease in points containing extreme and impossible yield values. A great advantage of using the machine dynamics filtering method was that it solved a great part of the errors introduced by the delay time, the lead time, and the lag time. In fact, both filtering techniques had their own advantages and can be seen as complementary with respect to grain yield variation from different origins.

Fine-scaled optical detection of nitrogen stress in grain crops

What is lacking in precision farming at present are more comprehensive and fast non-destructive methods for obtaining the data needed to prescribe varia*ble treatments. In precision farming there is a demand for sensors that can easily monitor crop nitrogen requirements throughout the growing season on a high resolution. Currently used optical measurement platforms like satellites, airplanes and hand-held sensors, do not meet the needs of precision agriculture for good nitrogen management possibilities. An automated sensor system mounted on a tractor was developed and used to detect crop nitrogen status optically. A line spectrograph was used to detect amount of nitrogen (kgN/ha) and chlorophyll (kg/ha) in a wheat crop (Triticum aestivum L.). By calculating the red edge inflection point of the plant spectra in the images, wheat crop nitrogen stress within small areas in the field could be detected. Spectrograph red edge was highly correlated with applied nitrogen to the wheat crop (0.90), with crop nitrogen uptake (0.89) and with chlorophyll amount in the crop (0.86). The average errors when estimating those variables with the red edge inflection point were -0.4% (24.15kgN/ha), -1% (17.25kgN/ha) and -10% (14.74kg/ha) respectively. This means that spectrograph red edge measurements of the wheat crop during the growing season can be a predictor of topdress nitrogen needs.

Analysis of Spatial Soil, Crop and Yield Data in a Winter Wheat Field

In 2001 and 2002, soil and crop parameters were measured on a winter wheat field in Sauveniere, as part of a precision farming research project. One of the objectives was to study the processing of precision farming data for correct use in precision management. Different methods to study the relation between soil and crop were tested: correlation analysis, principal component analysis of soil parameters and clustering of soil and yield parameters. Crop and soil data were interpolated to a 6m grid and a 10 m grid to check the effect of grid size on the correlations between field data. Correlations were very similar for the 2 datasets, with slightly higher values for the 10 grid data (difference of 0 to 0.02 in correlation values). Grain and straw yield in 2001 were correlated to soluble phosphate, texture parameters, soil electrical conductivity, and potassium (coefficient of determination R² values up to 0.30 for grain yield). Crop optical measurements in May 2001 had lower correlation to soil parameters than yield (coefficient of determination R²= 0 to 0.18). Correlations were higher in March 2002, with coefficient of determination values up to 0.66 for optical mesurements. Correlation of grain yield to soil was very low in 2002, in part because of the incidence of eye spot disease. Principal component analysis of soil parameters resulted in three principal components describing the overall soil texture variation over the field, soil organic matter and soil nitrogen in early spring and acidity and phosphate variability. Correlations between yield and crop measurements and soil principal components were as expected from the correlation analysis. Clustering soil parameters resulted in soil zones that did not coincide with crop variability. Clustering yield and soil electrical conductivity did lead to zones that could be used to set up management zones. The average soil properties of these zones could be used to find parameters linked to yield variability and as a start to determine causes of variability in crop growth and yield, using a broader knowledgde on the soil-plant interaction. The grid size influenced the results of the analysis and should be further investigated, to determine the best methods for processing precision farming data. The soil data collected from the top 30 cm layer and the soil electrical conductivity only explain a limited part of variability in crop growth and yield. This means that either the top 30 cm was not representative of growth conditions or that other, non-measured soil parameters were important for crop growth. Root depth, water availability and soil compaction were probably important for the crop growth in 2001.

Using a Virtual Combine Harvester as an Evaluation Tool for Yield Mapping Systems

Different types of yield mapping systems for combine harvesters have been proposed in the literature. Each system is characterized by its composition of positioning system, grain flow measurement device, ground speed sensor and eventually cutting width sensors. The individual accuracy of each device has been optimised, without looking at their own impact on the accuracy of the global system.By developing a virtual harvesting process, it is possible to “harvest” virtual fields several times with different machine settings, ground speed strategies and measurement devices. Subsequently, the accuracy of different set-ups can be compared and the required accuracy for each individual measurement device can be quantified. In this way, it is possible to design a balanced composition of measurement devices for a predefined budget.

Precision farming through variable fertilizer application by automated detailed tracking of in-season crop properties

What is lacking in precision farming at present, are more comprehensive and non-destructive methods for obtaining the data needed to prescribe variable treatments. A farmer needs to be informed in order to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. Current remote sensing (satellite images) sources are too coarse in multispectral spatial resolution and too infrequent in time to allow detailed tracking of phenological stages during the growing season. In this research very detailed and automated on-the-go optical monitoring of the crop is used for detecting and managing zones with different crop yield potential on a seasonal scale. In particular, reflectance properties are used to identify and evaluate optical indicators of the nutritional status of the crop. These indicators should allow site-specific in-seasonal correction of N-application to come to optimal crop yield all over the field. Based on these indicators, site-specific fertilization is done with a variable fertilizer equipped with DGPS. At the end of the season, the crop was harvested with a combine harvester, equipped with precision farming sensors to map final crop yield. In this way final results could be evaluated and analyzed.

Grain Yield Mapping on Combine Harvesters: A Model-Based Approach

Abstract One of the latest trends on commercial combine harvesters is online mapping of grain yield. Together with sensors registering the surface harvested each instance and a position system, a grain flow measurement is installed at the end of the threshing process. Due to this position, the filtering impact of the machine smoothes the mass flow variations, weakening the economic value of the constructed maps. Here, a method is proposed for reducing this drawback based upon an earlier constructed grain flow model. The results are presented from a wheat field fertilized with different doses of nitrogen and sowed with different crop densities.