Crop production potential varies spatially across many dryland farm fields in the Palouse and Columbia Plateau. Much of the crop variability results from differences in soil depth, slope aspect and steepness, soil parent material, soil fertility, and other natural or man-made factors. When delineated for management purposes, these distinct soil conditions can be considered as a basis for precision farming decisions.
The agronomy research effort is focusing on developing precision management strategies for both improving nitrogen (N) input efficiency and reducing within-field variability in wheat protein levels thus improving the output of high quality grain from dryland production systems.
- Dan Long, Research Agronomist
- John McCallum, Physical Science Technician
- Daryl Haasch, Agricultural Science Technician
- Fran Pierce, Washington State University
- Lee Vierling, University of Idaho
- Susan Capalbo, Oregon State University
- Steve Petrie, Oregon State University
- Frank Young, ARS-Pullman
- Rick Engel, Montana State University
Precision Nitrogen Management
Plant available water varies greatly across field landscapes and from one season to the next. Consequently, yield potential and N requirements can fluctuate greatly. In addition, cultivar development efforts by plant breeders and improved cultural practices to conserve water have led to higher yield potentials and plant N requirements. Future improvements in N fertility management, particularly in the arena of precision agriculture, will require an understanding of how we can best manage fertilizer resources under these spatio-temporally varying water conditions.
Maps of grain protein and grain yield have been used to compute the amount of N removed in the crop, which in turn is useful for identifying N management zones. The research at Pendleton is determining whether such an approach is applicable to the soils and winter rainfall conditions of the semiarid Pacific Northwest. Our goal is to develop a precision N management strategy for hard red spring wheat that uses information from grain yield monitors and grain quality sensors. Knowledge of the relationships among applied N, growing conditions, and crop response will help growers optimize grain protein and yields of hard red spring wheat in this region.
In related research, we have made a great effort to develop a remote sensing method for predicting the N status of dryland wheat so that growers could apply this information into late-season N topdressing applications. Conventional sensing methods are negatively affected by the lack of crop cover and soil background reflectance, especially under dryland conditions. We have developed new indices that have greater sensitivity to chlorophyll and are less affected by soil reflectance. With this information, the use of in-season N management, revolving around the use of ground-based, active light sensors, is possible for dryland wheat growers.
Grain Segregation by Protein Concentration
Buyers of grain exported from the Pacific Northwest are increasingly interested in purchasing wheat with the quality characteristics they need for milling and baking. Our goal is to better position growers for capturing protein premiums that may become available for soft white winter wheat during a 12 month marketing period.
Within-field variability in grain quality arises from differences in soil fertility, plant available water, and other yield determining factors. Despite this variability, traditional harvesting systems tend to keep the grain from a field together in the same bin. By ignoring spatial variability in grain quality, this approach increases the chance that growers will not capture price premiums for high quality grain found in smaller areas within fields.
Our plan is to understand the relationship between within-field variability in protein and grain prices as needed to predict when it is economically feasible to segregate. We have evaluated whole grain, near infrared (NIR) analyzers, already widely used in elevator companies and grain labs, and have found this technique to be accurate in the field on a combine. A mechanical-optical system for a combine harvester has been constructed and successfully operated t o segregate the grain based on protein concentration . When field tested, grain could be segregated that differed in protein concentration by as little as 1%.
Measuring and Mapping Straw Levels Across Wheat Fields
Crop residue as a material for biofuel feedstock is increasing in importance with the perceived national importance for energy security and environment . With k nowledge of straw levels within fields, wheat growers can determin e where surplus residue beyond soil conservation requirements can be removed for economic purposes, making precision nitrogen fertilizer recommendations, and implementing conservation compliance plans. Our research is assessing the design and performance of relatively low cost, tractor/combine-mounted systems, based on spectral, ultra sonic, and Light Detection and Ranging (LiDaR) sensors, potentially capable of reading wheat crop structure and mapping wheat straw within farm fields.