2010 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). Replaces 5356-13210-002-00D (10/08).
NP216 Cross-location project associated with Pullman, WA, 5348-22610-002-00D (Young).
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.
Perennial biofuel and forage crops planted in 2009 were monitored for growth and development. Crop establishment was fair to good in four of five experiments in the low and intermediate rainfall zones of Umatilla Co., OR. Vole damage was problematic in some plots. Plots were mowed to control weeds. A sixth site in the high rainfall zone in Whitman Co., WA was planted in spring 2010. Planting of annual crops is scheduled at this site for fall 2010. Soil water data were collected continuously by means of digital acquisition, and seasonally through gravimetric sampling. By July 2010, species composition data had been collected in four of the six sites located in Umatilla County, OR.
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.
The CCCI is best for optical sensing in dryland wheat. Active sensing Ground based, optical crop sensing is now available to farmers for assessing potential crop nitrogen (N) response and applying N fertilizer. This technique developed by ARS researchers in Pendleton, OR, relies upon the Normalized Difference Vegetation Index (NDVI); however, the NDVI may not work well in semiarid environments where crop biomass and yields depend upon plant available water. The Canopy Chlorophyll Content Index (CCCI) was evaluated for predicting the leaf chlorophyll and nitrogen contents in dryland wheat. Chlorophyll and leaf N were well described by CCCI (r2 < 0.80) in two of three dryland wheat fields whereas NDVI performed well in only one field. The CCCI is easily computed using commercially available, active light sensors with sensitivity in the red, red edge, and near infrared wavebands. Use of the CCCI, which is resistant to soil background reflectance and LAI, may not only improve performance of optical sensing in dryland crops, but may also ease the requirement for multiple reference strips in farm fields.
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.