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United States Department of Agriculture

Agricultural Research Service

Research Project: Increasing Inland Pacific Northwest Wheat Production Profitability

Location: Soil and Water Conservation Research

2013 Annual Report


1a.Objectives (from AD-416):
The goal of this research project is to identify cultural practices and technologies that improve economic viability and environmental sustainability of inland PNW dryland wheat production systems. The specific objectives are fourfold and include:

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).


1b.Approach (from AD-416):
Field experiments will assess the production potential of perennial bioenergy and forage crops in lower, middle, and upper slope positions. Biomass will be harvested at each slope position. Soil/air temperature and rainfall will be measured daily. Soil samples will be analyzed for water content and soil organic matter. A water gradient experiment will study the effect of the water-by-N interaction on grain protein and yield of spring wheat. A second experiment will compare economic returns from precision vs. conventional uniform N placement. Precision N placement uses yield and protein values from the water gradient study to compute the total N required for a following crop. Previous year’s plots of the water gradient study will be replanted. N fertilizer will be applied to half of plots based on N factors computed from previous protein/yield measurements (precision N placement), and to remaining plots based on single uniform N rate (uniform N placement). Uniform and precision N placement will be compared in terms of uniformity in grain protein. Dollar returns will be determined using a partial budget analysis of market quotes and production costs. Grain yield and N supply will be computed for each plot and averaged to arrive at N use efficiency for each N placement strategy. The bulk tank of a combine will be divided into two bins. An optical sensor will sense grain protein and control a mechanism that diverts grain into a bin for ordinary grain or one for high quality grain. Site-specific measurements of grain protein and yield will be used to determine the dollar value of grain. Partial budget analysis will be conducted to compare the profitability of wheat production with and without grain segregation. The water gradient study will provide a wide range in values for yield, grain protein, plant height, and straw yield. Linear regression will be used to develop straw yield prediction equations that include terms for yield, protein, and plant height. Models will be evaluated by comparing predictions against actual measurements obtained from fields. On-combine measurements of grain yield, protein, and straw yield will be obtained using a yield monitor, optical grain quality sensor, and crop height sensor. Maps of straw yield will be used with current price quotes to estimate the net value of straw. Dollar returns will be contrasted with contract payments that would be received under conservation programs if straw is retained. Amount of carbon to maintain soil organic C at current levels will be estimated using the C sequestration model CQESTR. From this, the amount of straw that may be removed while maintaining soil quality will be assessed. Long-term studies will be examined to assess their value for calibration and corroboration of various simulation models (CQESTR, C-Farm, CropSyst, and RZWQM2). For RZWQM2, relevant soil, weather, and plant growth parameters will be calibrated from available data that have measured values of phenology, biomass, and leaf area at different stages, and yield. Modeling will extend study results for a more diverse set of weather conditions and soil types across the region.


3.Progress Report:
This is the final report for this project. Substantial results were realized over the 5 years of the project. High yielding perennial biofuel and forage crops, incorporated as riparian buffers, were evaluated in degraded portions of agricultural land. Economic feasibility of variable-rate nitrogen (N) placement was determined in dryland wheat production systems, with less dollar returns than conventional uniform placement unless conservation program payments are considered. The role of free-living diazotrophs was studied and they were found to contribute significantly to plant available N by fixing atmospheric N to plant available ammonium. Ability to quantify leaf N status using ground-based optical sensing was investigated in water-gradient studies with spring wheat. A new spectral index was developed that is sensitive to crop N and resistant to soil variation. A green scanning laser was found to be useful for measuring the N status of wheat at early growth stages. A remote sensing method was developed that uses off-the-shelf, light detection and ranging technology to measure crop height from a combine and predict where excess straw is available in farm fields beyond soil conservation needs. An optical-mechanical system was developed for segregating grain by protein content on a combine. By scanning the grain as it is conveyed by the combine’s grain bin filling auger, the sensor triggers a logic circuit to effectively control a mechanical diverter valve for routing the grain into one of two bins. A web-based software was developed that allows growers to calculate the cutoff value to use for segregating wheat into two lots such that the prices received for average protein levels in the two lots maximize profit. Soil, crop, and weather data were compiled for calibration of the process-oriented model RZWQM2. Collaborations with ARS scientists in Ft. Collins, Colorado, have been re-established to continue our 2013 experiments with ongoing fieldwork as needed to simulate wheat yield and biomass from conventional tillage and zero tillage in a modified Mediterranean climate. This project has more relevance now since we now know the frequency and timing of soil water measurements needed to calibrate the model and conclude whether the grain yield differences are due to plant water availability, tillage regime, or both. The impact of the research was that high yielding perennial species were investigated to give the NRCS and FSA conservation programs extra practice options for degraded portions of agricultural landscapes. Additional impact was that methods were developed to give producers uses for advanced sensing technologies to manage N fertilizer, and variation in grain quality and straw levels within farm fields. Factors were identified that can be used to improve estimates of N use efficiency and reduce fertilizer use. An advantage of the on-combine grain segregation system is that prior knowledge of harvesting zones is not required. The overall impact of the accomplishments is that producers have new information and practice options on which to make decisions concerning production techniques to maximize profits while sustaining yield.


4.Accomplishments
1. Web-based calculator estimates the profit potential of grain segregation by protein content. Some crops are sold under a quality payment system that rewards growers for maximizing the protein concentration of their grain. On-combine optical sensing creates an opportunity to segregate grain by protein concentration during harvesting. Scientists in Pendleton, Oregon, developed the software, the “Grain Segregation Profit Calculator”, to calculate the cutoff value to use for segregating wheat into two lots on the combine such that the prices received for average protein levels in the two lots maximize profit. Results indicated that dollar returns from grain segregation are sensitive to the average level of a field’s protein, the protein variability within a field, and premium schedules being paid in the marketplace. The software is now freely available to the public as an interactive web-based application.

2. Optical-mechanical system segregates grain by protein concentration on the combine. Conventional harvesting, which mixes the grain in one bin, lessens the ability of growers to capture price premiums for high protein grain found in wheat fields. Scientists in Pendleton, Oregon, developed an on-combine system for automatically segregating wheat by grain protein concentration during harvest. Light from a multispectral optical probe is read by a spectrometer, which determines the spectral characteristics of grain as it is conveyed by the combine’s grain bin-filling auger. The instrument control software processes this information to calculate the grain protein content and trigger a logic circuit for operating a mechanical valve that diverts the grain into one of two bins. Results from field tests showed that it is possible to use grain protein measurements to effectively control a mechanical diverter valve for routing the grain into different bins on a combine. An advantage of this approach is that prior knowledge of harvesting zones is not required.


Review Publications
Long, D.S., Mccallum, J.D. 2013. Mapping straw yield using on-combine light detection and ranging (LiDAR). International Journal of Remote Sensing. 34: 6127-6134.

Martin, C.T., Mccallum, J.D., Long, D.S. 2013. A web-based calculator for estimating the profit potential of grain segregation by protein concentration. Agronomy Journal. 105: 721-726.

Mitchell, A.C., Peterson, L., Reardon, C.L., Reed, S.B., Culley, D.E., Romine, M.F., Geesey, G.G. 2012. Role of outer membrane c-type cytochromes MtrC and OmcA in Shewanella oneidensis MR-1 cell production, accumulation and detachment during respiration on hematite. Geobiology. 10, 355-370.

Clarke, T., Edwards, M., Gates, A., Hall, A., White, G.F., Bradley, J., Reardon, C.L., Shi, L., Beliaev, A.S., Marshall, M.J., Wang, Z., Watmough, N.J., Fredrickson, J.K., Zachara, J.M., Butt, J.N., Richardson, D.J. 2011. Structure of a bacterial cell surface decaheme electron conduit. Proceedings of the National Academy of Sciences. 108:9384-8389.

Long, D.S., Engel, R. 2011. Computing wheat nitrogen requirements from grain yield and protein maps. In: Clay, D.E., Shanahan, J.F., editors. GIS applications in agriculture. 2:321-335.

Long, D.S., Mccallum, J.D., Scharf, P.A. 2013. Optical-mechanical system for on-combine segregation of wheat by grain protein concentration. Agronomy Journal. 105:1529-1535.

Chatterjee, A., Long, D.S. 2013. Switchgrass influences soil biogeochemical processes in dryland region of the Pacific Northwest. Communications in Soil Science and Plant Analysis. 44:2314-2326.

Kumar, D., Juneja, A., Hohenschuh, W.E., Williams, J.D., Murthy, G.S. 2012. Chemical composition and bioethanol potential of different plant species found in Pacific Northwest conservation buffers. Journal of Renewable and Sustainable Energy. DOI: 10.1063/1.4766889.

Last Modified: 9/10/2014
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