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SOIL FERTILITY & PLANT NUTRITION

Potential Impact of Precision Nitrogen Management on Corn Yield, Protein Content, and Test Weight

^a College of Resources and Environmental Sciences, China Agricultural Univ., Beijing 100094, China
^b Precision Agriculture Center, Univ. of Minnesota, St. Paul, MN 55108
^c Mosaic Crop Nutrition, 616 S. Jefferson Ave., Paris, IL 61944

^* Corresponding author (ymiao{at}cau.edu.cn).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Precision crop management for optimizing yield and quality is important for developing a consistent product for different end uses of grain. This study was conducted to evaluate the potential impact of variable-rate N (VRN) application, hybrid selection, and hybrid-specific N management on corn (Zea mays L.) yield, protein content, and test weight. On-farm experiments were conducted during three site-years in eastern Illinois using a split-plot design, with the main plots consisting of five N rates and the subplots two corn hybrids (Pioneer 33G26 and 33J24). Nitrogen response curves of corn yield and quality were fitted at 19 and 16 within-field locations in Fields 1 and 2, respectively, and the potential impacts of different N management strategies were evaluated. Results indicated that within-field economically optimum N rates (EONR) ranged from 82 to 336 kg N ha^–1, while N rates that would maximize grain quality ranged from 0 to 336 kg N ha^–1. Compared with a uniform-rate N (URN) application of 168 kg N ha^–1, the VRN application at EONR would increase corn yield for hybrid 33J24 while having an inconsistent impact on yield of 33G26, without significantly improving grain quality of either hybrid. Hybrid 33J24 would have higher yield, quality, and economic returns than 33G26 under either URN or VRN application. Hybrid-specific N applications could have either negative or positive impacts on corn yield and protein content, without significantly affecting test weight. These results suggest that selecting the right hybrid(s) was more important and practical than the evaluated precision N management practices for optimizing both corn yield and grain quality during the study site-years.

Abbreviations: EONR, economically optimum N rate • MAXN, N rates that would maximize corn grain quality parameters • PNM, precision N management • URN, uniform-rate N • VRN, variable-rate N • VRT, variable-rate technology

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Profitable crop production is not only affected by crop yield, but also by the quality of the product. For example, Montana producers can receive a premium for hard red wheat (Triticum aestivum L.) with 13 to 15% protein content (Long et al., 1999). In the Japanese market, an additional US$9.44 to $30.68 Mg^–1 (US$0.24 to $0.78 bu^–1) can be paid when corn grain quality meets certain standards (Paulsen et al., 1996). The existence of spatial and temporal variability in important corn quality parameters (Stroshine et al., 1986; Bullock et al., 1989; Hurburg, 1994; Roggenbuck, 2004; Miao et al., 2006a) has not only influenced end use efficiency and profit, but also made it difficult for farmers to get premium prices for their products. As a result, precision corn management for both optimum corn yield and quality is becoming increasingly important.

Nitrogen is one of the most important nutrients influencing both corn yield and grain quality. Crop demand for N varies spatially within a field due to variations in crop yield potential and plant-available soil N (Pan et al., 1997; Scharf et al., 2005). Precision N management by adjusting N application to crop demand has the potential to improve fertilizer use efficiency, reduce environmental pollution, maintain or improve crop yield, produce better and more consistent grain quality, and as a result, increase economic returns (Doerge, 2002; Delin, 2004). The impact of precision N management (PNM) on corn yield has been evaluated by researchers taking three basic approaches (Pierce and Nowak, 1999): (i) application of current N recommendations at specific sites within a field; (ii) site-specific N recommendations developed from condition-specific N response curves as related to landscape attributes; and (iii) site-specific N intervention management based on monitoring crop N status. Researchers taking the first approach have had limited success in increasing yield or profit or reducing soil residual NO₃ (Shapiro et al., 2000; Ferguson et al., 2002; Eghball et al., 2003). With the second approach, Mamo et al. (2003) found that VRN at site-specific EONRs would reduce N application rates by 69 and 75 kg ha^–1 without having any significant impact on corn yield. With the third approach, Hendrickson and Han (2000) applied 20 to 50% of the recommended N rates at emergence and then additional N was applied using variable-rate technology (VRT) before anthesis based on N stress maps derived from infrared aerial photos. They found that average corn yields were generally similar for the different N management approaches, but less N was used, except at one site due to wet spring conditions.

The impact of PNM on crop quality has been studied by several researchers with mixed results. Mulla et al. (1992) found that protein content of soft white winter wheat could be reduced using a VRN program, without reducing crop yields relative to wheat receiving URN application. Algerbo and Thylén (1999) found that VRN application (according to expected yield) did not significantly increase winter wheat yield, but increased protein content, N uptake, and the Zeleny number, while reducing variability in protein content and thousand-kernel weight. Gooding et al. (1999) found wheat yield and quality responses to late N fertilization varied across within-field locations, but wheat quality and economic returns were similar between variable and uniform N application strategies. Financial margin was not significantly affected by considering only winter wheat yield in comparison to considering both yield and quality. Link et al. (2002) found that both wheat grain yield and protein content were increased by VRN application with the Hydro N-Sensor, a tractor-mounted multispectral scanner, compared with uniform N management, based on multicountry study results. But Jørgensen and Jørgensen (2001) did not find any significant difference in winter wheat protein content between VRN application by the Hydro N-Sensor system and uniform N application. Ehlert et al. (2004) evaluated a VRN strategy based on plant mass estimated with a mechanical sensor (pendulum meter), with N fertilizer being increased in areas with high plant mass, but decreased in low plant mass locations. They achieved a 10 to 20% reduction in N fertilizer without reducing winter wheat yield or affecting grain quality (protein content and quality, falling number, and thousand-kernel mass).

Studies on the impact of PNM on corn grain quality parameters and their variability across the landscape have been very limited. Miao et al. (2006b) found significant within-field variation in corn yield response to N fertilizer rate in five out of six site-years, but significant variations were observed in no more than a third of site-years for corn oil, protein, and starch content or test weight. In addition to significant hybrid differences in yield and grain quality parameters in at least half of the site-years, they also found significant hybrid x N interactions for test weight, suggesting the potential for hybrid-specific N management for this quality parameter. Miao et al. (2006b) used ANOVA to determine if corn yield, quality, their responses to N, and hybrid differences varied significantly within a field. Lacking from this previous analysis was N response modeling at different within-field locations to calculate year-, hybrid-, and site-specific EONRs, and evaluate the potential impact of VRN application on yield and quality. The objectives of this analysis were to: (i) quantify within-field variability of corn yield and quality (protein content and test weight) responses to N rate; and (ii) evaluate the potential impact of VRN application, hybrid selection, and hybrid-specific N management on corn yield and grain quality (protein content and test weight) compared with URN management.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Study Site and Experimental Design
Two corn–soybean [Glycine max (L.) Merr.] rotation fields in Paris, IL, were selected for this study. Field 1 is a 12.5-ha no-till field with a history of manure application between 1978 and 1996. It is relatively flat, with a difference in elevation of 1.98 m. An Order 1 soil survey (1:8000) conducted by the USDA-NRCS in Illinois reveals that the dominant soils are Drummer silty clay loam (fine-silty, mixed, mesic Typic Endoaquolls), Brenton silt loam (fine-silty, mixed, mesic Aquic Argiudolls), and Raub silt loam (fine-silty, mixed, mesic, Aquic Argiudolls). Field 2 is 13.4 ha, with a long history of reduced tillage. This field is more rolling, with a difference in elevation of 5.80 m. To prepare the seedbed for soybean, a chisel plow was used in the fall and two passes of a cultivator were used in the spring. To prepare the corn seedbed, a cultivator was used in the spring. Two soil types dominate this field according to the Order 1 soil survey: Xenia silt loam (fine-silty, mixed, mesic Aquic Hapludalfs) and Elpaso silt loam (fine-silty, mixed, superactive, mesic Typic Endoaquolls). Both fields have subsurface tile drainage.

Four replications of five side-dressed N rate treatments (as anhydrous NH₃) were established in early June in a split-plot design in both fields. The main plot consisted of five N rates: 0, 112, 168, 224, and 336 kg N ha^–1. Each N rate was randomly assigned to 18.24-m (60-ft) wide strips running across the length of the whole field in Field 1. In Field 2, the strips ran half the length of the field. The split plot consisted of two Pioneer hybrids, 33G26 and 33J24 (relative maturity 112 d), planted side by side using the split-planter technique (systematic rather than random arrangement). Field 1 was used in the study in both 2001 and 2003, while Field 2 was used only in 2003. The planting rates were about 76,600 seed ha^–1. Scouting was conducted on a regular basis during the growing seasons and no major pests were found to be a serious problem. Phosphorus and K fertilizers were applied using VRT to compensate for any P and K deficiencies in the study fields based on 0.4-ha grid point sampling results. Kriging was used to interpolate the soil test data into surface maps based on semivariogram analysis results performed with the Geostatistical Analyst extension in ArcGIS (ESRI, Redlands, CA). The surface maps were used to create P and K recommendation maps following guidelines provided by Hoeft and Peck (1996) and VRT application was performed by local fertilizer dealers.

Weather
Growing season (April–September) average temperature was near normal (20°C) in 2001 and slightly below normal in 2003. In general, the growing season in 2001 was nearly ideal for corn growth and development, with 64% more precipitation than normal in the important month of July, when kernel size and weight can be significantly affected. Year 2003 growing season precipitation was close to normal, except for a very wet September.

Corn Sampling and Analysis
Before harvest, five (in Field 1) or four (in Field 2) transects across all N x hybrid treatments were superimposed over the experimental site to determine sampling locations. The average spacing between transects was about 50 m in Field 1 (Fig. 1 ). In Field 2, sampling locations were positioned by soil drainage class to evaluate the impact of soil drainage classes on corn N response variability (separate objective not reported here). Thus, sample spacing was adjusted so that samples for one within-field location (or station) would come from the same or similar drainage class. At each sampling point, five consecutive corn ears (8–10 ears for control strips) were hand collected. A total of 190 samples were collected in Field 1 (samples were collected only on four transects in the fourth replication or block due to field dimension limitation) each year and 160 samples in Field 2. Corn protein content was analyzed using the Perten DA 7000 NIR Grain Analyzer (Perten Instruments, Springfield, IL) and the results are reported on a dry-matter basis. Corn test weight was determined with the Dickey-John GAC 2000 grain analysis computer (Auburn, IL) and the results are reported on a 15.5% moisture basis. Corn yield was measured using a combine equipped with a calibrated AgLeader yield monitor (AgLeader Technology, Ames, IA). Yield data were cleaned by removing yield points that were near the start or end of harvest passes, and removing yield values or grain flow rates that exceeded three standard deviations; however, yield values exceeding three standard deviations in check strips (with no N application) were not removed. The two closest yield points were averaged to represent the yield at each corn quality sampling location.

View larger version (80K): [in this window] [in a new window]   Fig. 1. Field plot showing Order 1 soil survey (USDA-NRCS), N treatment strips, corn quality sampling points, and the concept of "within-field locations" or "stations" in Field 1. Nitrogen treatment levels were 0, 112, 168, 224, and 336 kg N ha–1, and the N treatment strips run north–south.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Averaged across site-years and hybrids, N application significantly increased corn yield, protein content, and test weight (Tables 1 and 2). Corn yield response to N was significantly affected by both environment and hybrid, while N responses of protein content and test weight were significantly affected by only environment and hybrid, respectively (Table 1). The two hybrids were significantly different in test weight, and their differences were consistent across environments (Table 1).

View larger version (17K): [in this window] [in a new window]   Fig. 2. Sample corn test weight responses to N rate: (A) linear response (negative), Hybrid 33J24, Field 1, 2001, Within-Field Location 2; (B) linear response (positive), Hybrid 33G26, Field 1, 2003, Within-Field Location 3; (C) linear with plateau response, Hybrid 33G26, Field 1, 2001, Within-Field Location 13; (D) quadratic response, Hybrid 33J24, Field 1, 2003, Within-Field Location 4; (E) quadratic with plateau response, Hybrid 33G26, Field 1, 2003, Within-Field Location 13; (F) no response, Hybrid 33J24, Field 2, 2003, Within-Field Location 14.

View larger version (39K): [in this window] [in a new window]   Fig. 3. Spatial distribution of economically optimum N rate (EONR), N treatment strips, and Order 1 soil survey (USDA-NRCS) in Field 1: (A) Hybrid 33G26, 2001; (B) Hybrid 33J24, 2001; (C) Hybrid 33G26, 2003; and (D) Hybrid 33J24, 2003.

View larger version (141K): [in this window] [in a new window]   Fig. 4. Spatial distribution of economically optimum N rate (EONR), N treatment strips, experimental area, and Order 1 soil survey in Field 2: (A) Hybrid 33G26; (B) Hybrid 33J24. The N treatment levels were 0, 112, 168, 224, and 336 kg N ha–1 and the N treatment strips run west–east.

For test weight, MAXN was consistently lower than the EONR for 33J24, but was not significantly different from the EONR for 33G26, so the N rates that optimize corn yield would simultaneously optimize corn test weight.

Corn Yield and Quality at Economically Optimum Nitrogen Rates
Averaged across site-years, the impact of N rate on corn yield and test weight differed by hybrid (Tables 1 and 2). For Hybrid 33G26, corn yield and test weight were not significantly increased with N rates above 112 kg ha^–1, while for 33J24, corn yield continued to increase with N rates, but test weight was not significantly affected by N application (Table 2). For protein content, both hybrids increased with N rates up to 224 kg ha^–1.

The impact of VRN application on corn yield varied across hybrids and years. Average yield at EONR for 33J24 was significantly higher than yield at a URN application of 168 kg N ha^–1, which was achieved using an average of 27 (N price = $0.46 kg^–1) or 15 kg ha^–1 (N price = $0.93 kg^–1) more N. For Hybrid 33G26, however, an average of at least 25 to 33 kg ha^–1 less N could have been used with VRN in Field 1 in 2001 and Field 2 in 2003 without causing any yield reduction. For 33G26 in Field 1 in 2003, VRN application would significantly increase corn yield with a field-average N rate similar to the rate of URN application (Tables 3 and 4). For these fields, VRN would have been better at supplying the N needed to optimize corn yield potential than URN. This work also suggests that hybrid differences in N response need to be considered in PNM.

The magnitude of variability in corn yield would be significantly reduced by N application compared with the variability with no applied N fertilizer. As for quality, the variability was smaller than yield, and was less affected by N application as well (Table 2). The VRN application at spatially varying EONR values would not significantly reduce the variability in either corn yield or quality, and in some cases, the variability could even be increased compared with variability at a URN application (Table 4). This suggests that yield and quality potential and N needs vary spatially, probably due to soil moisture supply and losses of N by leaching and denitrification. The VRN involves adjusting N application rates to match site-specific N needs so that yield and quality potentials can be fully expressed, assuming no other stresses. In contrast, a URN application may limit yield or quality potential at locations needing more N, and as a result, the variability may be smaller than with a VRN application. This may also suggest that factors other than N are limiting, such as deficiencies in micronutrients or soil moisture, or the presence of pests.

In a similar analysis of corn protein content and ethanol yield, Roggenbuck (2004) also found that VRN application at the EONR would not reduce variability in corn quality compared with variability at a uniform N application of 112 or 168 kg ha^–1. Given such findings, the concept of sorting grain into different quality bins (Stafford, 1999; Thylén and Algerbo, 1999; Thylén et al., 2002; Stewart et al., 2002) or differential harvesting (Stafford, 1999; Kravchenko and Bullock, 2002) may be a more practical way to reduce the impacts of spatial variability in grain quality for end users and increase the economic value of the grain for producers. Stewart et al. (2002) estimated that income could be increased by up to US$34 ha^–1 simply by using the additional information on protein concentration to separate durum wheat grain into two grades. In evaluating the economic potential of sorting barley (Hordeum vulgare L.) grain for malting or feeding, wheat for milling or feeding, and grain for different feed uses, Thylén and Rosenquist (2002) found that fewer than 100 ha were required for a return on investment with malting barley, while very large areas would be required for milling wheat. Similar studies need to be conducted to evaluate the feasibility and economic potential of sorting corn grain into different quality classes for different end uses.

Potential Economic Benefit of Variable-Rate Nitrogen Application at the Economically Optimum Nitrogen Rate
Applying N fertilizers variably at site-specific EONRs for corn yield has been found to be US$11 to $72 ha^–1 (Malzer et al., 1996) or $8 to $23 ha^–1 (Mamo et al., 2003) more profitable than URN application. In this study, the potential field-average economic benefit of VRN application according to year-, hybrid-, and site-specific EONR varied from US$25 to $61 ha^–1 across site-years and hybrids, with an average of $41 ha^–1 over URN at 168 kg ha^–1 (Table 5). Within each field, the potential profit varied widely, with standard deviations ranging from $13 to $48 ha^–1. Variable-rate N application would be more profitable with 33J24 than 33G26 at lower N prices ($0.46 kg^–1). At higher N prices ($0.93 kg^–1), however, VRN application would have been equally profitable with the two hybrids in Field 1, and more profitable with the less responsive hybrid (33G26) in Field 2 in 2003 (Table 5). This analysis does not include costs associated with variable-rate management, such as soil or crop sampling or sensing, map analysis, and variable-rate application, etc. (Whipker and Akridge, 2001). In addition, as our current technologies have seldom realized more than 50% of the potential (Malzer et al., 1996), the actual profitability of VRN application will be lower than the numbers presented in this study.

Hybrid Differences in Yield, Quality, and Potential Economic Returns
Hybrid 33J24 consistently had higher quality (protein content and test weight) than 33G26 with similar or higher yield (Table 4) and the hybrid differences were generally more distinctive under VRN than for a URN application. These results indicate that selecting the right hybrids is the most important step to optimize both corn yield and quality. After a suitable hybrid with high quality is selected, the main management objective can simply be to optimize yield. Depending on the uniform N rate, hybrid, and environment, variable N application for economically optimum yield may widen spatial and hybrid differences in yield and protein content, thus increasing grain quality variability at a farm or regional level.

The importance of hybrid selection was further demonstrated with economic analysis. The costs of seed for 33G26 and 33J24 were similar, but planting 33J24 would make an average of US$38 ha^–1 ($65, $37, and $6 ha^–1 for the three site-years, respectively) more than planting 33G26 when N was applied uniformly at 168 kg N ha^–1, without considering quality (Table 6). This improvement by planting 33J24 rather than 33G26 was comparable to the average potential economic profit of VRN application according to EONR ($41 ha^–1). When both hybrids received a VRN application at EONR, planting 33J24 would make an average of $54 ha^–1 (N price = $0.46 kg^–1, $95, $40, and $20 ha^–1 in the three site-year comparisons, respectively) or $35 ha^–1 (N price = $0.93 kg^–1, $66, $35 and –$3 ha^–1 in the three site-year comparisons, respectively) more than 33G26. These results indicate that selecting the right hybrid itself may result in a significant profit increase, whether N was applied uniformly or variably.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
In recent years, there has been an increasing interest in using precision agriculture technologies to improve both crop yield and grain quality. It has been hypothesized that PNM may maintain or improve crop yield and produce better and more consistent grain quality. The primary objective of this study was to evaluate the potential impact of PNM on corn yield, protein content, and test weight to see if they could be improved or their variability could be reduced for more consistent quality. Results from this three-site-year study indicated that within-field EONR and MAXN for grain quality varied from 82 to 336 and from 0 to 336 kg ha^–1, respectively. In general, N rates that would maximize protein content were 44.5 kg ha^–1 (N price = $0.46 kg^–1) or 49.5 kg ha^–1 (N price = $0.93 kg^–1) higher than the EONR for yield, while N rates to maximize test weight were similar to or less than the EONR. Variable-rate N application at year-, hybrid-, and site-specific EONR would significantly increase corn yield of 33J24 at all three site-years with an average of 27 kg ha^–1 (N price = $0.46 kg^–1) or 15 kg ha^–1 (N price = $0.93 kg^–1) more N, maintain yield of 33G26 with an average of 25 to 33 kg ha^–1 less N in two site-years, and improve yield with a similar amount of N in the third site-year, compared with URN application. In general, VRN application according to EONR would not significantly improve any quality parameter or reduce yield or quality variability compared with URN application, thus grain sorting or separate harvesting may be a more practical alternative to managing the quality of grain harvested.

Another objective of this study was to evaluate the potential impact of hybrid selection and hybrid-specific N management on corn yield, protein content, and test weight. Consistent hybrid differences were observed, with 33J24 having higher yield, quality (protein content and test weight), and economic returns than 33G26 under either uniform- (at 168 kg N ha^–1) or variable-rate N application scenarios. The two hybrids also differed in N responses, with yield and test weight of 33J24 being more and less responsive, respectively, to N rate than 33G26.

The results of this study suggested that selecting the right hybrid(s) to plant was more important and practical than the evaluated PNM practices for optimum corn yield and grain quality during the study site-years. Economic returns could be improved by adjusting N application rates according to the N response characteristics of the selected hybrid(s) or within-field variations in N responses. More studies are needed to evaluate the potential impact of PNM in more variable fields; to evaluate the stability of hybrid differences in yield, grain quality, and their N responses across variable environments; and to determine the potential impact of in-season site-specific N management on corn yield, grain quality, and economic returns.

	ACKNOWLEDGMENTS

 
ACKNOWLEDGMENTS

We would like to thank Cargill Crop Nutrition (now Mosaic, Inc.), Cargill Dry Corn Ingredients, and Pioneer Hi-Bred International, Inc., for funding the research project and assisting in experimental design, sampling, and corn quality analysis. Special appreciation is given to Ron Olson, Mosaic, Inc., for overall coordination of the research project; Harry Frost, Cargill Dry Corn Ingredients, for test weight measurement; Kirby Wuethrich, Joe Walker, and Shaun Schmidt, Pioneer Hi-Bred International, and Dr. Dan Frochlich, Cargill Crop Nutrition (now Mosaic, Inc.), for grain sampling assistance; and Pioneer Hi-bred International, Inc., for analyzing corn protein content. We also thank local farmers Gene Barkley and Steve Brinkerhoff for their strong support and cooperation in this project, and USDA-NRCS in Illinois for conducting the Order 1 soil survey of the research fields.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
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Received for publication December 8, 2005.

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