University of Kentucky From

University of Kentucky from

Fluid Fertilizer FoundationProposal

Title:Evaluation of Optical Sensor Based Nitrogen Algorithms for Corn Using Sidedress Liquid Application

Investigators:Greg Schwab, Assistant Professor

University of Kentucky

Department of Agronomy

N-106 Agricultural Science Bldg.-North

Lexington, KY 40546-0091

Phone: 859-257-9780Email:

Robert Mullen, Assistant Professor

Ohio State University/OARDC

School of Natural Resources

1680 Madison Ave.

Wooster, OH44691

Phone: 330-263-3785Email:

Wade Thomason, Assistant Professor

Virginia PolytechnicInstitute

Department of Crop and Soil Environmental Sciences

422 Smyth Hall

Blacksburg, VA24061

Phone: 540-231-2988Email:

Justification:

Improving the nitrogen use efficiency (NUE) of agricultural production systems has been an intense area or researchin recent years. Current NUE of cereal crop production is estimated to be near 33% worldwide (Raun and Johnson, 1999). This implies that 67% of the N applied for cereal crop production is not in the harvested grain and may be susceptible to loss which can negatively affect the environment. With the development of advanced optical sensing technologies and improvements in fluid delivery systems (specifically variable rate applications), sensor based N algorithms have been developed. To utilize in-season optical sensing tools, sidedress application of N is necessary which can potentially expand application of liquid N products.

Split applications of N have been documented to result in yield increases and improved NUE, specifically in agricultural systems which are susceptible to early season losses. Delaying N application until plant need has been recognized as a method to improve nitrogen efficiency and avoid potential N loss mechanisms (Russelle et al., 1983). Randall et al. (2003) reported that split application of N between planting (40%) and sidedress (60%)at V8 (Ritchie et al., 1997)) resulted in higher yields than when N was applied in the spring as a single event. Nitrogen recovery was also increased by split application of N compared to spring application. Scharf et al. (2002) reported that maximum yield could be achieved with N applications as late a V11, but applications delayed until silking resulted in a 15% yield loss. Split applications also allow for adjustment of N management based on environmental conditions encountered since planting. For example, if plant population has been affected by excess water, N rates can be adjusted accordingly to account for lower yield potential.

To improve N management, crop evaluation tools have been developed that utilize canopy reflectance or leaf absorbance as an indication of plant health and N status. Chlorophyll meters (SPAD Minolta 502) have been used extensively to identify N status and response to improve N recommendations (Schepers et al., 1992; Varvel et al., 1997). Canopy reflectance sensors which measure the reflectance of light at specified wavelengths has also been used to measure plant health and N status (Wade et al., 1994; Stone et al., 1996). While SPAD meters collect point data from specific leaves of a plant and require multiple plant samples (Peterson et al., 1993), canopy reflectance sensors measure information from a specified area (Raun et al., 1997; Raun et al., 2001). The size of the area depends upon the sensor and the platform used (satellite, airplane, or ground-based). While raw reflectance values of individual wavelengths can be used, equations that utilize multiple wavelengths are common. The Normalized Difference Vegetative Index (NDVI) is typically used as an indicator of plant health or biomass (Tucker et al., 1979; Raun et al., 1997; Shanahan et al., 2001). NDVI is computed using the following equation:

Equation 1

where NIRref is the reflected near-infrared light and VISref is the reflected visible light (can be multiple wavelengths - most commonly red or green) (Tucker et al., 1979; Gitelson et al., 1996). Passive sensors which use incident light from the sun to measure canopy reflectance have been used historically, but new active sensors which generate their own light source are now readily available. Active sensors do not rely on solar radiation to collect reflectance measurements, so they are not subject to the limitations of passive sensors such as sun angle and variations in intensity caused by cloud cover. Sensors provide a way to objectively determine crop status and may lead to improved N management.

A reference strip has been proposed as an appropriate way to identify crop response to N and provide a calibration point to determine N response (Peterson et al., 1993; Johnson and Raun, 2003; Schepers and Meisinger, 1994). Many studies have documented that N response is spatially and temporally variable and that yield response to added N changes dramatically (Bundy and Andraski, 1997; Johnson and Raun, 2003). Mineralization of the N fraction of the soil organic matter is identified as the primary cause of variable N response (Johnson and Raun, 2003). Unfortunately, predicting mineralization rates of organic matter has proven to be difficult because mineralization is controlled by unpredictable environmental conditions. The N reference strip allows the opportunity to identify if response to additional N is likely. Previous work in winter wheat has shown that in-season estimates of the response index (RINDVI) using optical sensors is highly correlated with the response index measured at harvest (RIHarvest) (Mullen et al., 2003). Similarly, work in corn has shown that N response measured at various stages of growth (V6-R3) with a SPAD meter is indicative of N response observed at harvest (Varvel et al., 1997). The use of a N reference strip and active sensors to quantify N response allowsN rate adjustments based on plant available nitrogen.

Varvel et al. (1997) utilized SPAD meters and a N reference strip to manage an irrigated corn trial. SPAD readings were taken periodically throughout the growing season to monitor N response by comparing the reference strip values to areas of lower N rates (Varvel et al., 1997). When SPAD readings of the non-reference area were less than 95% of the reference area (sufficiency index – SI),N was recommended at a rate of 30 kg ha-1 (Blackmer and Schepers, 1995; Varvel et al., 1997). This method of N application has been found to improve NUE significantly while maintaining yield levels provided SI was not significantly less than 95% at the V8 growth stage (Varvel et al., 1997). This will be one of the algorithms evaluated in this experiment.

Active optical sensors have also been used to develop N algorithms in corn (Raun et al., 2003). The use of the reference strip remains, but instead of using SI to compute responsiveness, RI is calculated (RI is simply the reciprocal of SI). The RI determined for a specific environment,from sensor readings (NDVI), is used as a multiplier to adjust N recommendations based on variations in yield potential (Raun et al., 2002). As sensor readings are collected, at a defined resolution which is correlated to yield potential, N recommendationsare changed based on yield potential and site responsiveness. In order to vary rates quickly and accurately, liquid forms of N must be used.

The use of liquid forms of N allows variable rate application based upon algorithms that use reference strips and sensor measures to compute N recommendations. Fluid application systems are much more dynamic than solid forms and therefore have greater potential for use in precision ag applications. Fluid delivery can also be varied at a higher resolution than solid delivery systems.

Objective

The objective of this work is to evaluate different N recommendation algorithms which are based on remotely sensed information. This work will also compare current state N application algorithms as well as determining the optimal N rate based on incremental N treatments. This trial will be conducted across several states including: Kentucky, Oklahoma, Missouri, Illinois, Iowa, Kansas, Nebraska, Minnesota,Virginia, and Ohio. Results will be collected and disseminated to coordinating researchers in each state and presented at an annual N conference. Results may also be reported at other national and regional meetings.

Materials and Methods

All experiments will be conducted using a randomized complete block design with a minimum of three replications. Plot sizes will be 4 rows wide by 20-40 feet long, and plant spacing will depend upon region and equipment (30 inches or 36 inches). One treatment will receive 160-200 lbs N/acre preplant, or shortly after planting, and will be used as the reference/N-rich strip. A zero N check will also be included. Sidedress N will be applied as urea-ammonium nitrate (UAN) at rates of 40, 80, 120, 160 lbs N/acre between growth stages V8 and V12, and all plots that will receive flat sidedress application rates of UAN will receive 40 lbs N/acre as a starter (either as urea or UAN). Two treatments will evaluate the sufficiency concept proposed by Varvel et al. (1997) with and without preplant N. Two other treatments will evaluate the response-index coefficient of variation (RI-CV) approach proposed by OklahomaStateUniversity (Raun et al., 2003). The final two treatments will evaluate the algorithm proposed by USDA-ARS at Columbia, MO with and without preplant N. Additional treatments may be included, but these are the common treatments that will be implemented by all cooperating researchers.

Preplant soil samples will be collected to a depth of 24 inches and will be sectioned into 12 inch depths. Soil samples will be analyzed for phosphorus, potassium, pH, ammonium-N, nitrate-N, total N, and organic carbon. Preplant P and K will be applied to 100% sufficiency based on upon soil test. Lime will be applied if necessary based on state recommendations.

Chlorophyll and sensor readings will be taken weekly to monitor crop development and determine N rates based on the various algorithms evaluated. GreenSeeker (NTech Industries, Inc.) active sensors which measure reflected red and NIR light to compute NDVI will be used on each plot. If available Crop Circle (Holland Scientific) sensors will also be used to collect NDVI. Minolta 502 SPAD meter readings will also be collected from each plot. The upper-most fully developed leaf will be sampled from a minimum of 15 plants per plot.

At harvest, the two center rows from each plot will be harvested. The grain will be shelled and moisture will be determined. Subsamples of grain from each plot will be taken for total N analysis. Nitrogen use efficiency (NUE) will be determined using the method proposed by Moll et al., (1982). Statistical analysis will be performed using the PROC GLM statement in SAS (SAS, 1997).

Project Duration

The funds requested are for the first year of the study (March 1, 2005-February 28, 2006). We anticipate that the study will be conducted for at least three years.

Regional Collaboration

The research conducted will be replicated across several different research institutions including:

USDA-ARS Columbia, MO

University of Illinois

University of Minnesota

OklahomaStateUniversity

USDA-ARS Lincoln, NE

USDA-ARS Ames, IA

Virginia Polytechnic Institute

KansasStateUniversity

University of Kentucky

OhioStateUniversity

References

Blackmer, T.M., and J.S. Schepers. 1995. Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation of corn. J. Prod. Agric. 8:56-60.

Bundy, L.G., D.T. Walters, and A.E. Olness. 1999. Evaluation of soil nitrate tests for predicting corn nitrogen response in the North Central Region. North Central Regional Research Publication No. 342. Wisconsin Agricultural Experiment Station, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, Wisconsin.

Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298.

Johnson, G.V., and W.R. Raun. 2003. Nitrogen response index as a guide to fertilizer management. J. Plant Nutr. 26:249-262.

Moll, R.H., E.J. Kamprath, and W.A. Jackson. 1982. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron. J. 74:562-564.

Mullen, R.W., K.W. Freeman, W.R. Raun, G.V. Johnson, M.L. Stone, and J.B. Solie. 2003. Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agron. J. 95:347-351.

Peterson, T.A., T.M. Blackmer, D.D. Francis, and J.S. Schepers. 1993. Using a chlorophyll meter to improve N management. NebGuide G93-1171-A. Cooperative Extension, Institute of Agriculture and Natural Resources, Univ. of Nebraska-Lincoln.

Randall, G.W., J.A. Vetsch, and J.R. Huffman. 2003. Corn production on a subsurface-drained mollisol as affected by time of nitrogen application and nitrapyrin. Agron. J. 95:1213-1219.

Raun, W.R., and G.V. Johnson. 1999. Improving nitrogen use efficiency for cereal production. Agron. J. 91:357-363.

Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, E.V. Lukina, W.E. Thomason, and J.S. Schepers. 1997. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93:131-138.

Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, E.V. Lukina, W.E. Thomason, and J.S. Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93:131-138.

Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, R.W. Mullen, K.W. Freeman, W.E. Thomason, and E.V. Lukina. 2002. Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron. J. 94:815-820.

Raun, W.R., G.V. Johnson, and J.B. Solie. 2003. Variable nitrogen management for cereal production in the United States and Mexico. In Agronomy Abstracts. ASA, Madison, WI.

Ritchie, S.W., J.J. Hanway, and G.O. Benson. 1997. How a corn plant develops. Spec. Publ. 48. Iowa State Univ. Coop. Ext. Serv., Ames, IA.

Russelle, M.P., R.D. Hauck, and R.A. Olson. 1983. Nitrogen accumulation rates of irrigated corn. Agron. J. 75:593-598.

Scharf, P.C., W.J. Wiebold, and J.A. Lohry. 2002. Corn yield response to nitrogen fertilizer timing and deficiency level. Agron. J. 94:435-441.

Schepers, J.S., D.D. Francis, M.F. Vigil, and F.E. Below. 1992. Comparisons of corn leaf nitrogen and chlorophyll meter readings. Commun. Soil Sci. Plant Anal. 23:2173-2187.

Schepers, J.S. and J.J. Meisinger. 1994. Field indicators of nitrogen mineralization. p.31-47. In J.L. Havlin and J.S. Jacobsen (ed.) Soil testing: prospects for Improving Nutrient Recommendations. SSSA Spec. Publ. 40. ASA, CSSA, and SSSA, Madison, WI.

Shanahan, J.F., J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, M.R. Schlemmer, and D.J. Major. 2001. Use of remote sensing imagery to estimate corn grain yield. Agron. J. 93:583-589.

Tucker, C.J., J.H. Elgin, Jr., and J.E. McMurtrey III. 1980. Relationship of spectral data to grain yield variation. Photogramm. Eng. Rem. Sens. 46:657-666.

Varvel, G.E., J.S. Schepers, and D.D. Francis. 1997. Ability for in-season correction of nitrogen deficiency in corn using chlorophyll meters. Soil Sci. Soc. Am. J. 61:1233-1239.

Wade, G., R. Mueller, P. Cook, and P. Doraiswamy. 1994. AVHRR map products for crop condition assessment: A geographic information systems approach. Photogramm. Eng. Rem. Sens. 60:1145-1150.

Budget

Table 1. Estimated budget for 12 months of activity (March 1, 2005-February 28, 2006).

Allocation / Dollars Requested
University of Kentucky
Materials and Supplies / $2,800
Undergraduate Student Labor / $1,000
Travel / $500
OhioStateUniversity
Materials and Supplies / $1,300
Travel / $1,000
Virginia Polytechnic Institute / $1,400
Total / $8,000

Budget Justification

The University of Kentucky will subcontract with OhioStateUniversity and Virginia Polytechnic Institute to establish at least one field research experiment within their state. All plant and soil samples collected from the study sites will be sent to the University of Kentucky for analysis. Materials, supplies, and labor are needed for plot establishment, sample preparation, and sample analysis. Travel funds will be used to travel to and from research plots for weekly data collection. Funds appropriated to OSU and VT will be used in a similar fashion. Additional funds are allocated to OSU to allow Robert Mullen to travel to the Foundation’s Fluid Forum in Scottsdale, AZ to present the research results.

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