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Published online 5 January 2006
Published in Agron J 98:129-140 (2006)
DOI: 10.2134/agronj2005.0120
© 2006 American Society of Agronomy
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Production Papers

Within-Field Variation in Corn Yield and Grain Quality Responses to Nitrogen Fertilization and Hybrid Selection

Yuxin Miao*, David J. Mulla, Pierre C. Robert and Jose A. Hernandez

Dep. of Soil, Water, and Climate, Univ. of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108. Contribution of the Precision Agriculture Center, University of Minnesota

* Corresponding author (ymiao{at}umn.edu)

Received for publication April 25, 2005.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
This study was conducted in Paris, IL, from 2001 to 2003 involving three corn (Zea mays L.) hybrids, five N rates (0, 112, 168, 224, and 336 kg ha–1), and six site-year comparisons to determine the significance of within-field variation in corn yield and quality responses to N fertilization, differences between hybrids in yield and quality, and the feasibility of within-field variable hybrid selection. On average, N fertilization significantly increased corn yield, protein content, and test weight, but decreased corn oil and starch content. The overall economically optimum nitrogen rate (EONR) was 125 kg ha–1, but EONR varied from 93 to 195 kg ha–1 in different environments. The N rates that would maximize protein content and test weight (MAXN) varied from 143 to 303 kg ha–1 and 0 to 235 kg ha–1 in different environments, respectively. Significant within-field variability in N response was detected in five of six environments for yield, but not in more than two environments for any quality parameter. Hybrid differences were significant in all six environments for test weight, followed by oil content (five), protein and starch content (four), and yield (three). Hybrid differences between 33G26 and 33J24 in test weight response to N were consistent across environments, showing the potential of hybrid-specific N management for this quality parameter. However, hybrid differences in yield and quality did not vary significantly over space in most environments, showing limited potential of within-field variable hybrid selection. Further studies involving more diverse within-field soil–landscape conditions and hybrids are needed.

Abbreviations: ANOVA, analysis of variance • CV, coefficient of variation • DGPS, differential global positioning system • EONR, economically optimum nitrogen rate • GLM, general linear model • MAXN, nitrogen rates to maximize a grain quality parameter • RM, relative maturity • SD, standard deviation • VRN, variable rate nitrogen


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
CORN is the leading U.S. cereal crop with an annual production of around 0.2 billion Mg (tonne) (9 billion bushels). Recent interests in using precision agricultural technologies to improve N management of corn for both optimum corn yield and grain quality have led to several questions: (i) How do corn quality parameters respond to N fertilization? (ii) Do their N responses vary spatially within-fields or with different hybrids? (iii) Do corn hybrid differences in yield and grain quality vary spatially as well, so that variable hybrid selection is necessary or practical? Such questions need to be answered before proper precision crop management strategies can be used to optimize both corn yield and end use quality profitably and practically.

Nitrogen management studies have mainly focused on corn yield, with less attention being paid to grain quality. Protein content, the most commonly studied quality parameter, generally increases with N fertilizer rate (Bauer and Carter, 1986; Sabata and Mason, 1992; Tsai et al., 1992; Zhang et al., 1993; and Oikeh et al., 1998), and the response can be linear (Sabata and Mason, 1992) or quadratic (Oikeh et al., 1998). However, the specific response may be an increase, decrease, or no response to N fertilizer, depending on the genotype or hybrid, soil N supply, environmental conditions, N fertilizer type, time, and method of application (Bates and Heyne, 1980). Zuber et al. (1954) observed a decrease in protein content as N increased from 0 to 56 kg ha–1, but protein content significantly increased at the other two rates (134 and 280 kg ha–1). Verma and Singh (1976) did not find any significant difference in corn protein content among three N application rates (80, 120, and 160 kg ha–1).

Compared with protein content, studies of N fertilization on other quality parameters have been limited. Welch (1969) found that N fertilization significantly increased corn oil content compared with a control (0 kg ha–1 N), but there were no significant differences in oil content across the higher N rates (67, 134, 201, and 268 kg ha–1). Most researchers have found that corn oil content was not significantly affected by environmental factors, including N fertilization (Genter et al., 1956; Jellum et al., 1973; Verma and Singh, 1976; Zhang et al., 1993; Singh et al., 2002). Both starch and extractable starch content were decreased by N fertilization in the study of Singh et al. (2002). Increasing N levels increased kernel density and test weight (Bauer and Carter, 1986), and decreased kernel breakage susceptibility (Bauer and Carter, 1986; Kniep and Mason, 1989; Sabata and Mason, 1992). A quadratic response of kernel weight to N rate was reported by Oikeh et al.(1998) when the data were averaged across cultivars and years. Bauer and Carter (1986) found that kernel weight increased with N levels in both study years. However, Zhang et al. (1993) found that 1000-kernel weight increased with N rate in only half of the site-years. Corn hardness, indicated by kernel translucence, vitreousness, or floater percentages, was found to be increased by N fertilization (Tsai et al., 1992; Oikeh et al., 1998).

What makes N management challenging is that the impact of N on corn yield and quality can be affected by many factors, including hybrid genetic differences, climatic conditions, management practices, soil landscape factors, and their dynamic interactions (Tsai et al., 1992; Sabata and Mason, 1992; Asghari and Hanson, 1984; Ahmadi et al., 1993; Sogbedji et al., 2001). Corn yield response to N has been found to vary spatially within a field (Malzer et al., 1996; Schmidt et al., 2002; Mamo et al., 2003; Katsvairo et al., 2003) due to variations in crop N requirements, soil N supply and losses, and water availability (Fiez et al., 1995; Hergert et al., 1995; Simmelsgaard and Djurhuus, 1997; Baxter et al., 2003). These factors may also cause within-field variation in corn quality responses to N fertilization. Corn genotypic differences in yield responses to N have been reported as early as the 1930s (Smith, 1934; Stringfield and Salter, 1934) and confirmed by later researchers (Tsai et al., 1984, 1992; Mackay and Barber, 1986). However, some researchers found that hybrid differences in N response were not consistent across different environments and were thus unpredictable. They concluded that adjusting N application rates according to different hybrids was not likely to improve yield or N utilization efficiency (Bundy and Carter, 1988; Gardner et al., 1990; Iragavarapu, 1998). Hybrid x N interactions have also been reported for corn quality parameters by a few researchers (Zuber et al., 1954; Sabata and Mason,1992), but the results have not been very conclusive, and more studies are needed. Variable hybrid selection has been found to have limited potential for corn yield (Katsvairo et al., 2003; Shanahan et al., 2004), but Heiniger and Dunphy (1999) found that field topography could be used as guide for site-specific hybrid planting and corn yield could be increased an average of 0.21 Mg ha–1 in comparison to uniform planting of a single hybrid.

To date, no studies have been reported to investigate within-field variability in corn quality responses to N fertilizer and the potential of variable hybrid selection for grain quality. The objectives of this study are to determine (i) corn yield and quality responses to N fertilization; (ii) the significance of spatial variability in corn yield and quality responses to N within field; (iii) the significance of hybrid differences in corn yield and quality response to N; and (iv) the feasibility of variable hybrid selection for corn yield and grain quality.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
Study Sites and Experimental Design
Field studies were performed during the 2001, 2002, and 2003 growing seasons on two farms near Paris, IL. All the fields have been in a corn–soybean [Glycine max (L.) Merr.] rotation for many years. Three fields (Field 1, 2, and 3) from Farm 1 have been under no-till since 1991. Two fields (Field 4 and 5) selected from Farm 2 have been managed using reduced tillage. When these two fields were harvested for corn, a chisel plow was used to incorporate corn stalks into the soil after harvest, and one or two passes of a field cultivator were used in the spring to prepare the soil for planting. When the fields were harvested for soybean, field cultivators were used for spring tillage.

All the fields have subsurface tile drainage except Field 2. Field 1 also had manure applications between 1978 and 1996. Order 1 soil surveys of the study fields were conducted by USDA-NRCS in Illinois, and the dominant soils in each field are listed in Table 1.


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  		Table 1. Farm, field size, dominate soils, and years of the five sites used in the study.

 
On-farm experiments were conducted using a split-plot design, with three or four blocks depending on the field size. The main plot consisted of five nitrogen rates: 0, 112, 168, 224, and 336 kg N ha–1 (0, 100, 150, 200, and 300 lb N acre–1) except Field 2, where the 112 kg N ha–1 rate was not included due to the narrow dimensions of the field. Each N rate was assigned to 18.24 m wide strips (25.84 m in Field 4) running the length of the whole field except in Field 5, in which case the strips run half the length of the field. In some fields (Field 3 and 4), only a quarter of each control strip received no fertilizer and the rest of the strip received 168 kg N ha–1 to reduce yield losses for the farmers. Anhydrous ammonia was side-dressed in early to middle June. The subplot treatments were two corn hybrids: Pioneer 33G26 (relative maturity [RM] 112 d) and 33J24 (RM 112 d) in 2001 and 2003, Pioneer 33J24 and 34B97 (RM 108 d) in 2002. The hybrids were planted side-by-side using the split-planter technique (Doerge and Gardner, 1999). 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 major problem in the study site-years. Phosphorus and K fertilizers were applied using variable rate technology to compensate for any P and K deficiencies in the study fields based on 0.4 ha grid point sampling results. The mean and standard deviation of major soil chemical properties at the study sites are summarized in Table 2.


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  		Table 2. The mean and standard deviation of major soil chemical properties in the study sites.

 
Corn Sampling and Analysis
Before harvest, four or five transects across all treatments were superimposed over each experimental site to determine the sampling locations. The longitude/latitude coordinates were determined in SSToolBox (SST, Stillwater, OK) GIS and differential global positioning system (DGPS) was used to identify the sampling locations in the field during sampling. In Field 1, the sampling locations were kept the same for 2001 and 2003, except for a few locations near the southeast corner where corn was replanted due to water logging during the early growing season in 2003. They were shifted north, but were kept as close to the original locations as possible. At each sampling location, five corn ears (8–10 ears for control strips) were hand-collected for both hybrids. The samples were then taken to Pioneer Research Station in Urbana, IL, for drying, shelling, and cleaning. Corn oil, protein, and starch content were analyzed using the Perten DA 7000 NIR Grain Analyzer (Perten Instruments, Springfield, IL), and the results were reported on a dry matter basis. Corn test weight was determined with the Dickey-John GAC 2000 grain analysis computer (Dickey-John Corp., Auburn, IL) and the results were 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). Each hybrid was harvested separately and identified in the yield data file. Yield data were cleaned, with yield points being removed if they were near the start or end of harvest passes, or the yield values or grain flow rates exceeded ±3 SD (Kleinjan et al., 2002; Simbahan et al., 2004). However, the yield values exceeding ±3 SD in check strips (with no N fertilization) were not removed. After cleaning, the two closest yield points were averaged to represent the yield value at each corn quality sampling location.

Statistical Analysis
The overall impact of environment, N rate, and their interactions were evaluated with all the site-year and hybrid data (except Field 2 due to the missing 112 kg ha–1 N rate) using Generalized Linear Models (GLM). The environment (site-year) variable was treated as random, and N rate was treated as fixed. The effect of N rate was partitioned into linear, quadratic, and cubic components. The yield and quality response curves to N rate were generated using the NLIN procedure in SAS (SAS Institute, 1998). Four different N response models were evaluated: linear, linear with plateau, quadratic, and quadratic with plateau. The criteria for model selection was mainly based on the smallest residual sum of squares (SS), but the fitted response curves and calculated EONR and MAXN were also examined to make sure the selected statistical model was reasonable. The EONR was calculated assuming the price of corn and N fertilizer to be $0.09833 kg–1 ($2.5 bu–1) and $0.46 kg–1 ($0.21 lb–1), respectively. A corn yield response model was fitted for each hybrid in each environment, and the corresponding EONRs were calculated.

To determine if spatial variability in corn yield and quality, and their responses to N were significant, the term "Station" was used to represent selected corn sampling locations within a field as used in Katsvairo et al. (2003). In this case, "Station" represents a sub-area within a field that includes a set of neighboring sampling locations across the five N rates and two hybrids (Fig. 1 ). Station was treated as a random variable, and N rate and hybrid were treated as fixed variables. If the station effect is significant, it indicates that spatial variability in yield or quality is significant. If station x N or station x hybrid interactions are significant, then N responses of yield or quality parameters, or hybrid differences in yield or quality vary significantly over space. Due to practical field operation considerations, the arrangement of hybrids in each subplot was not random. This should be remembered when interpreting analysis of variance (ANOVA) results concerning hybrid. In fields where the zero N strip lengths did not match the other treatment strip lengths, the yield and quality values at the closest zero N strip sampling points were used for stations without zero N samples. The temporal stability in station, N, hybrid, and their interactions across years were tested using the 2001 and 2003 data from Field 1. The N rate and hybrid variables were treated as fixed and year (weather) and station effects were treated as random. The above analyses were conducted using the statistical package STATISTICA 6.0 (StatSoft, 2002) at the 0.05 significance level (P less double equals 0.05).



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  		Fig. 1. Field plot showing order 1 soil survey (USDA-NRCS), N treatment strips, corn sampling locations, and the concept of "Station."

 

   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
SUMMARY AND CONCLUSIONS
 REFERENCES
 
Weather Conditions
Growing season (April–September) average temperature during the 3-yr study period was close to normal (20°C), with 2003 being slightly cooler (Table 3). Year 2003 growing season accumulated precipitation was 100.6 mm (16.7%) higher than normal. Monthly precipitation varied greatly, ranging from around 64.5% less than normal in July and September 2002 to 182% more than normal in September 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 2002 had an unusually wet spring, with April and May having 127 and 92% more than normal precipitation, respectively. Planting was delayed until late May. However, in the following few months of the growing season, precipitation was 48 to 65% less than normal; this dry condition can significantly affect both corn yield and quality. Year 2003 growing season precipitation was close to normal, except for a very wet September, which may not have any significant impact on corn yield and quality.


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  		Table 3. Annual and growing season air temperature and precipitation departures from normal for 2001– 2003 in Paris, IL.

 
Corn Yield and Quality Variability
Averaged across years, fields, N rates, and hybrids, corn yield varied more (from 715 to 14 944 kg ha–1, CV = 27.8%) than corn quality parameters. Corn oil and protein content had moderate variability, with CV's averaging around 10% (Table 4). Corn starch content and test weight were the least variable quality parameters, with CV values of <2%. This lack of variability occurred despite the fact that N rate varied from 0 to 336 kg ha–1 and growing season rainfall varied from 19.6 mm lower to 100.6 mm more than normal. The average test weight (805.3 kg m–3 or 62.6 lb bu–1) agreed with that reported by Nelson (2002) (810 kg m–3 or 62.9 bu ac–1), which was significantly higher than the commonly used value of 720.7 kg m–3 (56 lb bu–1) for yield calculation. Corn starch content and test weight were not affected as much by the environmental conditions or management (N fertilization) as yield or other quality parameters.


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  		Table 4. Descriptive statistics of corn yield and quality parameters across N rates, hybrids, and site-years (2001–2003).

 
Corn Yield and Quality Responses to Nitrogen Fertilization
Averaged across site-years and hybrids, N fertilization significantly increased corn yield, protein content, and test weight, but decreased corn oil and starch content (Fig. 2 ; Table 5). Corn oil content and test weight was less responsive to N fertilization, because no significant differences among the four nonzero N rates (112, 168, 224, and 336 kg ha–1) were detected. The variability of yield and quality was reduced by N fertilization, as compared with the check plot, but the differences in corn yield and quality variability across nonzero N rates were minimal. The impact of N fertilization on corn oil content was different from previous reports that N fertilization significantly increased corn oil content compared with the control (Lang et al., 1956; Welch, 1969) or did not have any significant impact on oil content (Jellum et al., 1973; Verma and Singh, 1976; Zhang et al., 1993; Singh et al., 2002).



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  		Fig. 2. Corn yield and quality responses to N rate averaged across hybrids and environments. (A) Corn yield; (B) oil content; (C) protein content; (D) starch content; and (E) test weight. Vertical bars represent ±SE of the mean.

 

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  		Table 5. Significance of environment, N rate, and their interactions on corn yield and grain quality. The data included all the site-years except Field 2, 2001 due to omission of the 112 kg ha–1 N rate.

 
A linear with plateau model was fitted to the overall yield response, and the EONR was 125 kg ha–1 (Table 6). Quadratic models were fitted to overall protein and test weight responses, with the MAXN rates being 283 and 240 kg ha–1, respectively (Table 6). Corn protein content was more sensitive to N fertilization than yield, and more N was needed to maximize protein content than yield, which is consistent with previous research (Verma and Singh, 1976; Pierre et al., 1977; Zhang et al., 1993).


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  		Table 6. Economically optimum N rates (EONR) and N rates that would maximize protein content and test weight (MAXN) in different environments.

 
The significant impact of environment on corn yield, quality, and their responses to N has been recognized by many researchers (Earle, 1977; Thompson, 1988; Lamb et al., 1997; Timlin et al., 1998; Sogbedji et al., 2001; Andresen et al., 2001). In this study, environment also had a significant impact on corn yield, quality, and their N responses (Table 5; Fig. 3 Go Go Go –7) . The calculated EONR values varied from 93 to 195 kg ha–1 for corn yield, while MAXN varied from 143 to 327 kg ha–1 or from 0 to 235 kg ha–1 for protein or test weight, respectively (Table 6). Year 2002 had an extremely wet spring, but a very dry growing season, which was significantly shorter as well, due to delayed planting. As a result, corn yield was significantly lower and was less responsive to N fertilization than in the other 2 yr (2001 and 2003) (Fig. 3), and a lower N rate was more profitable for both hybrids (33J24 and 34B97) (Table 6). However, corn protein content continued to respond to N up to the highest rate in the study (336 kg ha–1) in Field 4 (Fig. 5D). The impact of environment (especially weather) on corn yield and quality can be quite large, and N management may not be able to significantly reduce quality variability due to variations in environmental factors caused by yearly weather differences.



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  		Fig. 3. Corn yield response to N rate in different environments. (A) Field 1, 2001; (B) Field 2, 2001; (C) Field 3, 2002; (D) Field 4, 2002; (E) Field 1, 2003; and (F) Field 5, 2003. Vertical bars represent ±SE of the mean.

 


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  		Fig. 4. Corn oil content response to N rate in different environments. (A) Field 1, 2001; (B) Field 2, 2001; (C) Field 3, 2002; (D) Field 4, 2002; (E) Field 1, 2003; and (F) Field 5, 2003. Vertical bars represent ±SE of the mean.

 


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  		Fig. 5. Corn protein content response to N rate in different environments. (A) Field 1, 2001; (B) Field 2, 2001; (C) Field 3, 2002; (D) Field 4, 2002; (E) Field 1, 2003; and (F) Field 5, 2003. Vertical bars represent ±SE of the mean.

 


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  		Fig. 6. Corn starch content response to N rate in different environments. (A) Field 1, 2001; (B) Field 2, 2001; (C) Field 3, 2002; (D) Field 4, 2002; (E) Field 1, 2003; and (F) Field 5, 2003. Vertical bars represent ±SE of the mean.

 


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  		Fig. 7. Corn test weight response to N rate in different environments. (A) Field 1, 2001; (B) Field 2, 2001; (C) Field 3, 2002; (D) Field 4, 2002; (E) Field 1, 2003; and (F) Field 5, 2003. Vertical bars represent ±SE of the mean.

 
Within-Field Variability
Within-field analyses showed that N effects were significant (P less double equals 0.05) for corn yield and protein content in all site-years (Tables 7 and 8). For other quality parameters, the N effect was not significant in at least one site-year. This result indicated that N fertilization was more important for yield and protein content than for other crop parameters.


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  		Table 7. Results of analysis of variance for corn yield and grain quality in 2001 and 2002, Paris, IL.

 

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  		Table 8. Results of analysis of variance for corn yield and grain quality in 2003, Paris, IL.

 
The Station effect was significant (P less double equals 0.05) for corn yield in four out of six site-year comparisons, but only in one or two site-years for any specific grain quality parameter (Tables 7 and 8), indicating that spatial variability was significant for corn yield in most site-years, but generally not significant for quality parameters. This result correlates with earlier findings that corn yield variability (CV = 27.8%) was higher than for quality parameters (CV < 13%) (Table 4). Station x N interactions were significant (P less double equals 0.05) for corn yield in five out of six site-years, while in only one or two site-years for quality parameters, so variable rate N application is likely to be beneficial for corn yield, but may have limited potential for improvements in corn grain quality. More studies are needed to further evaluate the potential of variable rate N application to improve corn grain quality parameters in fields with more rolling topography and variable soils, because fields used in this study are relatively level and all have fairly similar soils (either silt loams or silty clay loams).

Hybrid had a significant effect on test weight in all six site-years, followed by oil content (five site-years), protein and starch content (four site-years), and yield (three site-years) (Table 7 and 8), indicating that hybrid selection may be more important for test weight and oil content than yield or quality parameters like protein and starch content. The effects of hybrid on test weight were more important than N fertilization, because test weight for 33J24 was higher than 33G26 at any N rate (Fig. 7A, B, E, and F). These results demonstrated the importance of hybrid selection in precision corn management, and indicated that N management may have limited potential in minimizing the variability of certain quality parameters caused by hybrid differences.

Grain quality characteristics differ widely in their heritability, and Kettlewell (1996) ranked the relative importance of cultivar choice on wheat quality parameters in descending order: hardness, protein quality, {alpha}-amylase activity, test weight, protein concentration, kernel size, and dockage. It has been reported that corn oil concentration is largely controlled by genetics, and less by environmental factors and management practices (Dudley et al., 1977; Jellum and Marion, 1966; Jellum et al., 1973). The results of this study indicate that test weight, which is affected by kernel shape (Pomeranz et al., 1985), may also be largely influenced by genetic differences, and thus is less affected by management.

The N responses between Pioneer 33G26 and 33J24 (hybrid x N interactions) were significantly different in all the four site-years for starch content and test weight, in three out of four site-years for corn yield, and in half of the site-years for oil and protein content (Table 7 and 8; Fig. 3–7). The hybrid x N interaction was also significant between these two hybrids for test weight when the data were averaged across site-years, while it was not significant for yield or other quality parameters, indicating that the hybrid differences in N responses for test weight were consistent across site-years. Hybrid x N interactions were significant for test weight between 33J24 and 34B97 at both sites in 2002. These results indicated that hybrid-specific N management may have a higher potential for test weight than for yield or other quality parameters tested. Oikeh et al. (1998), however, found significant cultivar (hybrid) x N x year interactions for corn test weight, and studies with more hybrids and site-years are needed to confirm the above findings.

Station x hybrid interactions were significant for corn yield and quality parameters in only one or two site-years (Table 7 and 8). In most fields, the hybrid differences in yield and quality were spatially stable, and variable hybrid selection for yield or quality parameters within a field does not show much promise based on results of this study. More hybrids with distinctive genetic backgrounds need to be tested for such purposes.

To determine the temporal stability of the effects of station, N, hybrid, and their interactions, the 2001 and 2003 data in Field 1 were analyzed together. The results (data not shown) indicated that year x station interactions were significant only for corn yield, indicating that spatial patterns of corn yield were significantly different between the 2 yr. This is in agreement with findings of other researchers on temporal variability of crop yield (Lamb et al., 1997; Timlin et al., 1998; Porter et al., 1998). The station x N interaction was significant for yield, whereas year x station x N interactions were not significant. Thus, the spatial variations in yield N responses within Field 1 were stable between the two study years, which is desirable for site-specific N management. Many researchers have found that within the same field, corn yield N response patterns could vary drastically across years (Doerge, 2002); therefore, additional studies at this site are needed to confirm our findings.

The work reported in this paper is only a first step in the effort to evaluate the potential of using precision agricultural technologies to optimize both corn yield and grain quality, because the ANOVA can only tell us whether within-field variation in corn yield, quality, their N responses, and hybrid differences are significant or not, without telling us how they vary spatially. Further analyses are needed to fit N response models for both yield and quality parameters at different within-field locations and calculate the year-, hybrid-, and site-specific-EONRs, so that grain yield and quality under both uniform and variable rate N management scenarios can be compared to determine the potential benefits of precision N management of corn.


   SUMMARY AND CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
REFERENCES
 
Nitrogen fertilization significantly increased corn yield, protein content, and test weight, but decreased corn oil and starch content, when averaged across site-years and hybrids. A linear with plateau model was fitted to overall yield response data, and the EONR was 125 kg ha–1. Quadratic models were fitted to the overall N response data of both protein content and test weight, and the N rates to maximize these quality parameters (MAXN) were 283 and 240 kg ha–1. However, corn yield and quality responses to N were significantly affected by environment, with EONR varying from 93 to 195 kg ha–1 in different site-years, and MAXN varying from 143 to 327 kg ha–1, and from 0 to 240 kg ha–1 for protein content and test weight, respectively. Within-field analyses indicated that spatial variability in corn yield (station effect) was significant for corn yield in four out of six site-years, but in only one or two site-years for a specific grain quality parameter. Station x N interactions were significant for corn yield in five of six site-years, but only for one or two site-years for other quality parameters, so variable rate N (VRN) application may be beneficial for corn yield management, but may have limited potential for corn grain quality, based on the results on this study. Hybrid selection may be more important for test weight and oil content than for yield, protein, or starch content, and hybrid-specific N management has a good potential only for test weight. Variable hybrid selection within a field does not show much promise for improved yield and quality based on the results of this study. A practical strategy to optimize both yield and quality may be to select hybrids with the best quality, and then use precision N management practices to optimize grain yield. More studies are needed to evaluate the potential of variable rate N application for corn quality parameters in fields with more variable soil and landscape conditions, and the potential of variable hybrid selection involving hybrids with more distinctive genetic backgrounds. Further analyses are also needed to determine how corn yield and quality parameters of different hybrids vary in their responses to N fertilization at different within-field locations.


   ACKNOWLEDGMENTS
 
The authors thank Cargill Crop Nutrition (now Mosaic), Cargill Dry Corn Ingredients, and Pioneer Hi-Bred International for funding the research project and assisting in experimental design, sampling, and corn quality analysis. Special appreciation is given to Ron Olson, Mosaic, for overall coordination of the research project, and to Matt Wiebers, Harry Frost, and Kirby Wuethrich for their assistance and contribution. We also thank local farmers, Mr. 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.


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




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Y. Miao, D. J. Mulla, J. A. Hernandez, M. Wiebers, and P. C. Robert
Potential Impact of Precision Nitrogen Management on Corn Yield, Protein Content, and Test Weight
Soil Sci. Soc. Am. J., August 9, 2007; 71(5): 1490 - 1499.
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P. M. Kyveryga, A. M. Blackmer, and T. F. Morris
Alternative Benchmarks for Economically Optimal Rates of Nitrogen Fertilization for Corn
Agron. J., June 5, 2007; 99(4): 1057 - 1065.
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