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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

Project Number: 5356-21610-001-00
Project Type: Appropriated

Start Date: Nov 26, 2008
End Date: Oct 20, 2013

Objective:
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).

Approach:
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.

Last Modified: 9/1/2014
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