The objective of this investigation is to determine the possibility of rapidly sensing soil mineral-N content using near infrared (NIR) reflectance. Simulation studies were conducted to determine the ability of Partial Least Squares (PLS) and Principal Components Regression (PCR) techniques to relate NIR spectral data to soil nitrate content in the presence of interfering effects and experimental noise. The simulation studies revealed that both PLS and PCR techniques were quite robust in predicting soil nitrate content provided the calibration set included the same interfering effects. These techniques failed completely if the prediction set contained interfering effects which were not included in the calibration set. This implies that a site-specific calibration is necessary for this technique to work successfully. Laboratory tests using Yolo loam and Capay clay soil samples as well as verification tests using field soils (Yolo loam and Capay clay) mixed with nitrogen fertilizer indicated that soil mineral-N content can be determined using the NIR technique provided site-specific calibration is used.

A NIR Technique for Rapid Determination of Soil Mineral Nitrogen

https://works.bepress.com/stuart_birrell/41/download/

Comparison of sensors and techniques for crop yield mapping

Research findings are reviewed focusing on yield sensing methods, yield reconstruction, mapping, and errors. Yield sensing methods were explained and yield mapping process was briefly introduced. Grain flow through different combines was explained and the effects of combine dynamics on yield measurement accuracy were discussed. Other errors caused by various sensors that are utilized by a yield monitor were included. It was concluded that with proper installation, calibration, and operation of yield monitors, sufficient accuracy can be achieved in yield measurements to make site-specific decisions. Nevertheless, attention must be paid when interpreting yield maps since yield measurement accuracy can vary depending upon the measurement principle, combine grain flow model, size of management area chosen, and the operator's capabilities and carefulness in following instructions to obtain the best accuracy possible under varying field operating conditions. Some of the errors can be filtered out by careful analysis of the raw yield (or flow rate) data provided by yield monitors. Researchers have focused on crop flow models to improve yield reconstruction process. A yield reconstruction algorithm that effectively handles non-linear combine dynamics has not been developed by researchers yet. More efforts towards yield reconstruction should be encouraged.

Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors

A field mapping system for an agricultural vehicle such as a combine, planter or cultivator is disclosed herein. The system includes a circuit for determining the position of the vehicle relative to a field, and a sensor for sensing a characteristic (e.g., grain moisture content, grain harvest yield, soil compaction, altitude, etc.) at locations of the vehicle within the field. The system also includes an electronic display controlled by a control circuit coupled to the position determining circuit and the sensor. The control circuit produces display signals applied to the electronic display to generate a map of an area of the field at a desired scale which includes indicia of the current vehicle location and of the characteristic data being gathered. The map is dynamically redrawn at the desired scale to keep the visual indicia displayed when the vehicle has moved such that the vehicle location no longer corresponds to the displayed area.

Panning display of GPS field maps

To maximize economic return from agricultural production units, costs have to be minimized and benefits maximized. For grain, kernel yield and quality have to be maximized while the use of seeds, fertilizer, herbicides and fungicides have to be optimized. The best location to evaluate productivity levels, by measuring yield and quality of grain and straw, is the combine harvester. Moreover, other grain quality characteristics like density or test weight can be determined for use as an evaluation tool. In this paper, an overview is given of the past and current research toward the evaluation of currently available commercial sensors (e.g., for measuring grain yield and grain moisture content) as well as toward the development of new sensors (e.g., grain protein content and straw yield).

A Review of Combine Sensors for Precision Farming

GPS for yield mapping on combines

In the harvest seasons 1991 and 1992 two yield measurement systems for combines were investigated on over 300 ha of small grain. Both systems, one basing on volume measurement, the other on measuring flow with a radiometric system, were investigated on reliability, measuring accuracy, range of measurement errors and reasons for measurement errors.

Yield Measurements on Combine Harvesters

Fertilizing in future will be based on local yield detection, local soil and weather conditions and on use of expert knowledge systems. Position detection will play the key role in a closed loop system for data collection and process control. Engineers have to solve the challenge of the nineties to develop and to implement reliable sensors, actuators and control equipments for practical use. High sophisticated technique will then be able to save costs and to protect environment.

T. Muhr

Papers

GPS for yield mapping on combines

Yield Measurements on Combine Harvesters

Future Developments for Fertilizing in Germany