2010 Annual Report
Objective 1: Develop cropping practices for improving crop water use in dryland production systems and landscapes across PNW agroecological zones. (Pullman all of Obj 1) Sub-objective 1A: Optimize crop establishment practices and crop water use for improving the performance of winter canola. Sub-objective 1B: Improve stand establishment methods for spring canola to minimize weed competition and increase crop water use. Sub-objective 1C: Contrast fall-planted facultative wheat and spring-planted wheat for abilities to suppress weeds and increase yield, profitability, and crop water use. Sub-objective 1D: Determine effects of Russian thistle on crop water use, and production costs and quality of forage spring triticale.
Objective 2: Evaluate cropping system diversification strategies (forage and biofuels) for increasing agronomic performance of agricultural landscapes across PNW agroecological zones. Sub-objective 2A: Determine productivity and profitability of integrating alternative forage and biofuel crops into wheat-based production systems. (Pullman) Sub-objective 2B: Determine production potential of perennial biofuel and forage crops incorporated as riparian buffers in agricultural landscapes. (Pendleton)
Objective 3: Assess how new optical light reflectance spectrometers (advanced technology) can be used to increase cropping system performance in agricultural landscapes. (Pendleton – all of Obj 3) Sub-objective 3A: Apply information from on-combine yield monitors and optical sensors into site-specific nitrogen (N) application thereby improving grain quality and yield, and N use efficiency of cereal crops. Sub-objective 3B: Assess the quantity and quality of wheat residue at site-specific field locations across farm fields. Sub-objective 3C: Measure and map determinants of grain quality value (i.e. test weight, protein concentration, and foreign weed material), and apply this information into grain segregation on a combine harvester.
Objective 4: Synthesize available crop and cropping systems research across PNW agro-ecological zones to assess biophysical production factors influencing cropping system performance and ecosystem services. Sub-objective 4A: Compile and summarize existing databases of dryland crops and cropping systems to calibrate and corroborate process-oriented models. (Pendleton) Sub-objective 4B: Utilize existing datasets and process-oriented models to spatially evaluate the suitability of past, present, and future cropping system strategies. (Pullman). Replaces 5356-13210-002-00D (10/08). NP216 Cross-location project associated with Pullman, WA, 5348-22610-002-00D (Young).
The third and final year of a water gradient study was completed in which spring wheat was grown under varying water and nitrogen (N) regimes. Data analysis was initiated to identify the critical protein level below which N was likely deficient for yield, and determine the effect of the water-by-N interaction on grain yield and protein of spring wheat. Plus, the 2nd and final year of a field study was completed that determines agro-economic returns from conventional uniform application of N and precision application based on removal of N in grain. In 2010, work will continue to examine these data as needed to determine profitability of uniform N placement vs. spatially variable N placement.
Grain protein and yield were mapped within OR and MT wheat fields in the 2009 growing season. These data were added to a multi-year database comprising site-specific yield and protein measurements for wheat in dryland fields. A general computer model was developed to demonstrate when grain segregation is advantageous. For hard red wheat, profit opportunities exist provided the shape of the grain price curve is convex such that wheat of higher value is present to offset wheat of lower value.
A technique was developed that applies information from on-combine crop sensors into estimation of straw yield across fields. Straw yield could be predicted within 10% (R^2 > 0.90) using grain yield, grain protein, and crop height as regression estimators. When validated in the field, the straw yield model provided a modest fit to the data (R^2 = 0.58). Preliminary results show that straw yield can be estimated from maps of grain yield and grain protein on the combine during harvest. Straw yield maps may be useful for determining in farm fields where excess crop residue could be removed as a feedstock for bioenergy production. The feasibility of measuring wheat biomass by means of a Light Detection and Ranging (LIDAR) sensor was also investigated. The method estimates the crop’s biomass from the average crop height computed over a 6-m wide profile that is scanned in front of the moving combine.
Reece, C.L., Long, D.S., Clay, S.A., Clay, D.E., Beck, D. 2010. Nitrogen and Water Stress Impacts Hard Red Spring Wheat (Triticum aestivum) Canopy Reflectance. Journal of Terrestrial Observation. 2(1):Article 7. Available:http://docs.lib.purdue.edu/jto/vol2/iss1/art7.
Long, D.S., Eitel, J.U., Huggins, D.R. 2009. Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index. Crop Management [Online]. Available: http://www.plantmanagementnetwork.org/cm/element/sum2.aspx?id=8617
Eitel, J.U., Keefe, R., Long, D.S., Davis, A., Vierling, L. 2010. Active Ground Optical Remote Sensing for Improved Monitoring of Seedling Stress in Nurseries. Sensors. 10(4):2843-2850. Available: http://www.mdpi.com/journal/sensors.