2009 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).
For Obj. 3A, a study was established to compare wheat yields, grain protein, and dollar returns from conventional uniform nitrogen (N) placement vs. spatially variable N placement. In 2009, spring wheat was grown under three water regimes and a wide range of N levels. To control non-treatment variation, the experiment design was changed from the established project plan. Accordingly, within a water regime, each N level was split to create a pair of neighboring plots: one receiving N at the average rate at which it had been removed by the previous crop in plots comprising all N rates (uniform), and the other at the rate at which it had been removed in that specific plot (variable).
For Obj. 3B, a mechanical system was constructed to segregate the grain based on protein concentration. The bulk tank of the combine was divided into a bin for ordinary grain and one for high quality grain. The system consists of an in-line optical sensor, mechanical segregator valve, and an elevating auger for the high quality bin. The optical sensor measures the protein concentration by reflectance of near infrared light. Grain is diverted by the mechanical segregator valve to the ordinary bin if protein is below a certain cutoff value, and to the high quality bin if above this value. Grain flows into the high quality bin by gravity down an inclined tube. The mechanical valve is powered by an electric linear actuator that is controlled by a relay that opens or closes with the binary input-output from the computer interface to the optical sensor. When tested on the combine, grain could be segregated that differed in protein concentration by as little as 1%.
For Obj. 3C, measurements of grain yield, protein, and crop height were obtained from an N fertility-water gradient experiment with spring wheat. Simple regression was used to produce a linear model for predicting straw yield from yield, protein, and height [straw yield (kg/ha) = -3348 + (50×yield) + (158×protein) + (55×height)]. A combine was then equipped with a mass flow yield monitor, optical protein sensor, ultrasonic height sensor, and GPS receiver. The software for the protein sensor was configured to accept yield monitor and height sensor data so that the three could be simultaneously recorded and arithmetically combined on a single logging device. Maps of estimated straw yield were produced during harvest of farm fields using the prediction model. Straw yield was measured in micro-plots so that the straw yield predictions could be validated.
Eitel, J.U.H., D.S. Long, P.E. Gessler, E.R. Hunt. 2008. Combined Spectral Index to Improve Ground-Based Estimates of Nitrogen Status in Dryland Wheat. Agronomy Journal. 100:1694-1702.
Pimstein, A., Eitel, J.U., Long, D.S., Mufradi, I., Karnieli, A., Bonfil, D.J. 2009. A spectral index to monitor the head-emergence of wheat in semi-arid conditions. Field Crops Research. 111:218-225.
Eitel, J.U., Long, D.S., Gessler, P.E., Hunt, E.R., Brown, D.J. 2009. Sensitivity of Ground-Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance. Soil Science Society of America Journal. 73:1715-1723