**3.1 Spatial variation in soil water use**

Results from the studies where crop water use was quantified across the field revealed major differences among soil types. An example is shown in Fig. 2 for crop water use patterns for a Clarion and Webster soil with two different N rates. There was only a single meteorological unit in each soil type; however, error analysis for daily measurements are less than 10% of the daily total water use so the differences shown among soil types and N rates in Fig. 2 are significantly different at the end of the growing season. Crop water use patterns began to deviate shortly after crop establishment and continued throughout the growing season with the most noticeable difference occurring after Day of Year (DOY) 200 at the beginning of the grain-filling period (Fig. 2). The differences between the Clarion and Webster soil are due to their organic matter content with the Clarion soil having organic matter content between 1-2% and the Webster soil between 4-5%. This leads to a difference

**Center Wavelength (µm)** 

1 0.426 0.003 13 0.681 0.006 25 0.797 0.006 2 0.445 0.006 14 0.689 0.006 26 0.805 0.006 3 0.506 0.006 15 0.698 0.006 27 0.819 0.005 4 0.520 0.006 16 0.707 0.006 28 0.826 0.005 5 0.530 0.006 17 0.715 0.006 29 0.833 0.005 6 0.540 0.006 18 0.724 0.006 30 0.851 0.005 7 0.549 0.006 19 0.734 0.006 31 0.860 0.006 8 0.561 0.006 20 0.743 0.006 32 0.869 0.006 9 0.580 0.006 21 0.755 0.006 33 0.880 0.006 10 0.599 0.006 22 0.766 0.006 34 0.890 0.006 11 0.620 0.006 23 0.774 0.006 35 0.899 0.006

Table 2. Wavebands and bandwidth for the AISA hyperspectral data collected over the field

Data were analyzed on the individual N strips within the field and across the field. Vegetative indices were computed from the reflectance data obtained from the aircraft data and correlated to strip yield and field level yield observations. Correlation and regression analysis were conducted between N rate and corn yield for each individual field and across all fields for each year of the study. T-tests among means were conducted on differences between soils within a field strip while analysis of variance was conducted on N rates across a field with an interaction term based on the soil type by N rate comparison. These analyses were made using ANOVA and GLM models in SAS (SAS 2009). Spatial analysis was conducted for each field using GS+ version 5.1 to determine the spatial relationships among yield and the vegetative indices across different fields. Autocorrelation values were computed across the field using the field location points as coordinates to compute the

Results from the studies where crop water use was quantified across the field revealed major differences among soil types. An example is shown in Fig. 2 for crop water use patterns for a Clarion and Webster soil with two different N rates. There was only a single meteorological unit in each soil type; however, error analysis for daily measurements are less than 10% of the daily total water use so the differences shown among soil types and N rates in Fig. 2 are significantly different at the end of the growing season. Crop water use patterns began to deviate shortly after crop establishment and continued throughout the growing season with the most noticeable difference occurring after Day of Year (DOY) 200 at the beginning of the grain-filling period (Fig. 2). The differences between the Clarion and Webster soil are due to their organic matter content with the Clarion soil having organic matter content between 1-2% and the Webster soil between 4-5%. This leads to a difference

**Band Width (µm)** 

**Band Numbe r** 

**Center Wavelength (µm)** 

**Band Width (µm)** 

**Band Number** 

**Center Wavelength (µm)** 

sites in 2000 to 2002.

**Band Width (µm)** 

12 0.639 0.006 24 0.785 0.006

range and sill for yield and the red/green index.

**3. Results and discussion** 

**3.1 Spatial variation in soil water use** 

**Band Number** in water holding capacity of nearly 100 mm in the upper 1 m for these two soil profiles. By extension, this creates a difference in the amount of soil water available to the growing crop during the season. In this comparison, Clarion soil with the lower amount of N applied showed the larger amount of crop water use during the season and ultimately showed the larger grain yield at the end of the growing season compared to high rates of N application. In typical growing seasons in central Iowa, there is adequate soil water available for plant growth early in the growing season and it is not until the onset of the reproductive period in which crop water use rates increase and precipitation amounts begin to diminish and to meet atmospheric demand there is a reliance on the amount of water stored within the soil profile.

A recent study by Hatfield et al. (2007) revealed large differences in the daily and seasonal amounts of crop water use across corn and soybean fields in central Iowa. They found the primary reasons for these differences were due spatial variation in precipitation and spatial variation in soils across various fields. These results confirm the observations collected from multiple soils within the same field. The observations collected from several different studies across multiple years reconfirm the observations shown in Fig. 2.

Fig. 2. Crop water use patterns during the 2000 growing season for corn on two soil types with two nitrogen application rates.

An extension beyond the crop water use patterns is the development of an assessment of water use efficiency. Water use efficiency is expressed as the amount of grain yield relative to the seasonal total of transpiration. In this case, transpiration was determined by removing the soil water evaporation component from the total crop water use amounts. In this analysis, water use efficiency was calculated for the 150 kg ha-1 N application rate. What is

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 83

Nitrogen Rate (kg ha-1)

Nitrogen Rate (kg ha-1)

Nitrogen Rate (kg ha-1)

Fig. 4. Corn yield response to applied nitrogen in different fields in central Iowa from 2000

0 50 100 150 200 250

0 50 100 150 200 250

0 50 100 150 200 250

Corn Strip Trials 2000

Calhoun Field 1 Dallas Field 1 Sac Field 1 Shelby Field 1 Story Field 1 Story Field 2

Corn Strip Trials 2001

Story Field 1 Story Field 2 Story Field 3

Corn Strip Trials 2002

Calhoun Field 1 Dallas Field 1 Coon Rapids Field 1

Yield (kg ha-1)

Yield (kg ha-1)

Yield (kg ha-1)

to 2002.

5000

5000

6000

8000

10000

12000

14000

6000

7000

8000

9000

10000

11000

12000

6000

7000

8000

9000

10000

11000

12000

striking in this figure is the observation that we have a large number of points which are below the line which can be interpreted as using water but not producing a large yield. Some of these are related to low N rates; however, others have large amounts of N (Fig. 3). The primary reason for this type of relationship is due to the seasonal pattern of crop water use (Fig. 2) which causes water deficit conditions in the grain-filling period. Water is the primary variable causing variation in corn yields more than N application rates. These observations prompted the extension of the N studies to be conducted across a production fields in central Iowa.

Fig. 3. Water use efficiency for corn for multiple years with different nitrogen application rates based on observation of seasonal totals of transpiration and grain yield.

#### **3.2 Field scale response to nitrogen**

Corn yield response to N applications varied across fields and years (Fig. 4). These data were separated by field and year to remove the potential confounding effects of weather variation among years. Analysis of the relationship between N rate and yield revealed that most fields showed no response to N (Table 3). The Sac Field 1 showed a negative response to N rate while the other fields showed a positive response to N (Table 3). The different response to N poses a problem in developing a general set of guidelines for N management and raises questions about why these fields differ in their yield response to N rate. However, this observation provides an opportunity for application of precision agricultural tools if the underlying mechanism for the difference can be identified and is consistent among growing seasons to allow for application of this information for decision-making.

striking in this figure is the observation that we have a large number of points which are below the line which can be interpreted as using water but not producing a large yield. Some of these are related to low N rates; however, others have large amounts of N (Fig. 3). The primary reason for this type of relationship is due to the seasonal pattern of crop water use (Fig. 2) which causes water deficit conditions in the grain-filling period. Water is the primary variable causing variation in corn yields more than N application rates. These observations prompted the extension of the N studies to be conducted across a production

> Water Use (mm) 200 250 300 350 400 450 500 550

Fig. 3. Water use efficiency for corn for multiple years with different nitrogen application

Corn yield response to N applications varied across fields and years (Fig. 4). These data were separated by field and year to remove the potential confounding effects of weather variation among years. Analysis of the relationship between N rate and yield revealed that most fields showed no response to N (Table 3). The Sac Field 1 showed a negative response to N rate while the other fields showed a positive response to N (Table 3). The different response to N poses a problem in developing a general set of guidelines for N management and raises questions about why these fields differ in their yield response to N rate. However, this observation provides an opportunity for application of precision agricultural tools if the underlying mechanism for the difference can be identified and is consistent among growing seasons to allow for application of this information for

rates based on observation of seasonal totals of transpiration and grain yield.

= 0.63

fields in central Iowa.

4000

decision-making.

**3.2 Field scale response to nitrogen** 

6000

8000

10000

12000

14000

16000

18000

20000

168 kg ha-1

116 kg ha-1 232 kg ha-1 58 kg ha-1

Yield = 3022 + 27.9 Water Use r<sup>2</sup>

22000

Yield (kg ha-1

)

Fig. 4. Corn yield response to applied nitrogen in different fields in central Iowa from 2000 to 2002.

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 85

All soils showed an increase in yield above the 56 kg ha-1 rate but only the Webster soil showed a positive relationship to increasing N above this rate (Fig. 5). These results show that soils with a higher water holding capacity (Webster) had a different response to N compared to soils with lower water holding capacity (Clarion, Canisteo). Even though the studies were conducted at a different location, the soils were similar to those in these N studies so these results help explain the results shown in Fig. 4. The lack of response to N across the majority of the fields was related to the distribution of soils within the field. The Sac Field, in 2000, had a decrease in yield with increasing N was caused by the large amount of highly eroded Clarion soils within the field resulting in a limitation on the water holding capacity in the soil profile causing yields to be severely limited by water. These results are similar to those reported by Massey et al. (2008) in which the eroded soils had the lower yields and in this study lower yields were associated with soils having lower water holding capacity. Similar findings were reported by Sadler et al. (2000b) in which they suggested that understanding of site-specific yield maps would be enhanced by observations of water stress within the field. Their observations and those from this study suggest that spatial yield patterns in response to N management are dictated by soil types within the field and

In order to understand the spatial patterns of yield within a strip, a stepwise approach was taken to evaluate these patterns. The first step was to evaluate the frequency distribution of yields for the different N rates as shown for the Coon Rapids field (Fig. 6). These frequency distributions are similar to other fields in this study. In all fields, the lower N rate had a lower mean yield but a similar range of minimum and maximum yields compared to the other N rates; however, the distribution showed a wider dispersion and more variation. As the N rate increased the variation pattern showed reduced dispersion with a higher frequency of values near the mean value (134 kg ha-1 compared to 200 kg ha-1). In all fields we observed that the higher the N rate the less variation in frequency distribution with a similar shape of the yield distribution and the range of maximum and minimum values. The distribution of the yields based on the percentiles showed the 134 and 200 kg ha-1 rates were the same. All three rates showed a

To further evaluate the spatial patterns within fields, yields were summarized by each soil type within each field for N rates. Spatial variation of yields within fields was significant in their relationship to soil variation within the field. Across all of the N rates there was a similar pattern with the higher yields in the Webster soils and the lower yields in the Clarion soils (Fig. 7). Yields in the Webster soils were larger than the yields in the Clarion soils at all N rates and showed less variation than those in the Clarion soils (Fig. 7). This type of analysis was completed for all of the different fields evaluated in this study and the results shown in Fig. 7 were consistent among all of the fields with the soils having a higher water holding capacity producing the larger yields compared to those soils with lower

When the yields are aggregated to create a N response curve across fields, yield differences were significant using a simple T-test between these two soils. These two soils were chosen because they were the most dominant in all of the different fields measured in this study. Yield differences between the Clarion and Webster soil at the 134 and 190 kg ha-1 rates were over 1000 kg ha-1 and there was a decrease in the corn yield with the N rate of 190 kg ha-1 in

the interaction with soil water availability.

skewed distribution toward the lower yields.

water holding capacity.


ns- Not significant, \*-p<0.1, \*\*-p<0.05, \*\*\*-p<0.001

Table 3. Analysis of the effect of nitrogen rates on corn yields from the fields in central Iowa.

There was a range of soils within these fields and across the study sites. Variation among strips within a field for a given N rate was not significant for all fields when evaluated with a simple analysis of variance (ANOVA) using like strips as replicates. Understanding that N response is not consistent among the different fields creates questions about reasons for N responses observed among fields. Development of processes for the application of N within a field that takes advantage of information about corn response to N would greatly enhance the efficiency of N use. To explore this question and the lack of a consistent response, further evaluations were conducted on the data set. An additional data set on N response across a single field obtained by Hatfield and Prueger (2001) showed that N response was related to soil water holding capacity (Fig. 5).

Fig. 5. Corn yield response to applied nitrogen across five different soils for a field in central Iowa for 1998 to 2001.

**Year/Field Significance Slope** 

2000/Sac \* -

2000/Story 2 \*\* +

2001/Story 2 \*\*\* + 2000/Story 3 \*\* + 2002/Calhoun East \* +

2002/Coon Rapids \*\* +

Corn Nitrogen Study 1998-2001

Table 3. Analysis of the effect of nitrogen rates on corn yields from the fields in central Iowa.

There was a range of soils within these fields and across the study sites. Variation among strips within a field for a given N rate was not significant for all fields when evaluated with a simple analysis of variance (ANOVA) using like strips as replicates. Understanding that N response is not consistent among the different fields creates questions about reasons for N responses observed among fields. Development of processes for the application of N within a field that takes advantage of information about corn response to N would greatly enhance the efficiency of N use. To explore this question and the lack of a consistent response, further evaluations were conducted on the data set. An additional data set on N response across a single field obtained by Hatfield and Prueger (2001) showed that N response was

> Nitrogen Rate (kg ha-1) 40 60 80 100 120 140 160 180 200 220

Fig. 5. Corn yield response to applied nitrogen across five different soils for a field in central

2000/Carroll 1 ns 2000/Carroll 2 ns

2000/Shelby ns 2000/Story 1 ns

2001/Story 1 ns

2002/Dallas South ns

ns- Not significant, \*-p<0.1, \*\*-p<0.05, \*\*\*-p<0.001

related to soil water holding capacity (Fig. 5).

Clarion Webster Canisteo Nicollett Okoboji

Yield (kg ha-1

Iowa for 1998 to 2001.

)

All soils showed an increase in yield above the 56 kg ha-1 rate but only the Webster soil showed a positive relationship to increasing N above this rate (Fig. 5). These results show that soils with a higher water holding capacity (Webster) had a different response to N compared to soils with lower water holding capacity (Clarion, Canisteo). Even though the studies were conducted at a different location, the soils were similar to those in these N studies so these results help explain the results shown in Fig. 4. The lack of response to N across the majority of the fields was related to the distribution of soils within the field. The Sac Field, in 2000, had a decrease in yield with increasing N was caused by the large amount of highly eroded Clarion soils within the field resulting in a limitation on the water holding capacity in the soil profile causing yields to be severely limited by water. These results are similar to those reported by Massey et al. (2008) in which the eroded soils had the lower yields and in this study lower yields were associated with soils having lower water holding capacity. Similar findings were reported by Sadler et al. (2000b) in which they suggested that understanding of site-specific yield maps would be enhanced by observations of water stress within the field. Their observations and those from this study suggest that spatial yield patterns in response to N management are dictated by soil types within the field and the interaction with soil water availability.

In order to understand the spatial patterns of yield within a strip, a stepwise approach was taken to evaluate these patterns. The first step was to evaluate the frequency distribution of yields for the different N rates as shown for the Coon Rapids field (Fig. 6). These frequency distributions are similar to other fields in this study. In all fields, the lower N rate had a lower mean yield but a similar range of minimum and maximum yields compared to the other N rates; however, the distribution showed a wider dispersion and more variation. As the N rate increased the variation pattern showed reduced dispersion with a higher frequency of values near the mean value (134 kg ha-1 compared to 200 kg ha-1). In all fields we observed that the higher the N rate the less variation in frequency distribution with a similar shape of the yield distribution and the range of maximum and minimum values. The distribution of the yields based on the percentiles showed the 134 and 200 kg ha-1 rates were the same. All three rates showed a skewed distribution toward the lower yields.

To further evaluate the spatial patterns within fields, yields were summarized by each soil type within each field for N rates. Spatial variation of yields within fields was significant in their relationship to soil variation within the field. Across all of the N rates there was a similar pattern with the higher yields in the Webster soils and the lower yields in the Clarion soils (Fig. 7). Yields in the Webster soils were larger than the yields in the Clarion soils at all N rates and showed less variation than those in the Clarion soils (Fig. 7). This type of analysis was completed for all of the different fields evaluated in this study and the results shown in Fig. 7 were consistent among all of the fields with the soils having a higher water holding capacity producing the larger yields compared to those soils with lower water holding capacity.

When the yields are aggregated to create a N response curve across fields, yield differences were significant using a simple T-test between these two soils. These two soils were chosen because they were the most dominant in all of the different fields measured in this study. Yield differences between the Clarion and Webster soil at the 134 and 190 kg ha-1 rates were over 1000 kg ha-1 and there was a decrease in the corn yield with the N rate of 190 kg ha-1 in

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 87

Yield (kg ha-1) 6000 7000 8000 9000 10000 11000 12000

Yield (kg ha-1) 5000 6000 7000 8000 9000 10000 11000 12000

Yield (kg ha-1) 2000 4000 6000 8000 10000 12000

Fig. 7. Frequency distribution of corn yields for the 78, 134, and 190 kg N ha-1 rate for the

N Rate 78 kg ha-1

N Rate 134 kg ha-1

N Rate 190 kg ha-1

four soil types within the field in the Calhoun East field in 2002.

Okoboji Nicollett Clarion Webster

Okoboji Nicollett Clarion Webster

Okoboji Nicollett Clarion Webster

Frequency

0

7

Frequency

Frequency

0

1

2

3

4

5

6

2

4

6

8

10

12

14

Fig. 6. Frequency distribution of corn yields at the 67, 134, and 200 kg N ha-1 rate for the Coon Rapids field in 2002.

Yield (kg ha-1) 4000 6000 8000 10000 12000 14000 16000 18000

Yield (kg ha-1) 4000 6000 8000 10000 12000 14000 16000

Yield (kg ha-1) 4000 6000 8000 10000 12000 14000 16000

Fig. 6. Frequency distribution of corn yields at the 67, 134, and 200 kg N ha-1 rate for the

Coon Rapids 2002 (134 kg ha-1)

Coon Rapids 2002 (200 kg ha-1)

Coon Rapids 2002 (67 kg ha-1)

Frequency

Frequency

Frequency

0

50

0

0

Coon Rapids field in 2002.

10

20

30

40

50

60

10

20

30

40

10

20

30

40

Fig. 7. Frequency distribution of corn yields for the 78, 134, and 190 kg N ha-1 rate for the four soil types within the field in the Calhoun East field in 2002.

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 89

78 129.9 14.8 -0.09 -1.46 134 140.6 21.6 -0.59 -0.65 190 140.8 20.7 -2.29 7.63

78 124.5 22.5 0.65 -1.72 134 143.2 15.9 -0.37 -0.49 190 114.7 10.6 0.41 -1.21

78 147.0 12.3 -0.50 1.62 134 158.8 11.5 -0.21 -0.48 190 149.2 16.9 -2.55 8.80

78 162.2 7.4 -7.7 -0.42 134 165.4 6.9 0.08 0.35 190 160.5 5.9 0.42 -0.03

**Soil Type N Rate Mean Std. Dev. Skewness Kurtosis** 

Table 4. Mean, standard deviation, skewness, and kurtosis for corn yields within each soil

Harvested yield represents one point in the season which is the result of all of the interacting factors during the season. One question is whether the factors that affect yield patterns at harvest persist throughout the growing season or are there changes which occur and are detectable only in grain yield. Application of techniques related to improved management decisions require that observations within a field be able to detect a plant response that is ultimately related to crop yield as part of the decision making process. Sadler et al. (2000b) suggested that yield patterns could be explained by following the patterns of crop stress during the season. These data sets contain a sequence of measurements during the growing season that may be related to crop yield. The hyperspectral remote sensing data allowed for several indices to be calculated; however, one of the relationships we examined was the red (0.681µm) /green (0.561µm) ratio. This ratio was selected because of the strong relationship to crop biomass and crop yield. There was an inverse relationship between yield and the August red/green index for the fields (Fig. 9). Although there was a large variation about the regression line, this index showed a significant relationship with yield compared to other vegetative indices. Seasonal patterns of different vegetative indices provide insights into the spatial patterns of vegetative response during the course of the growing season. In this study we evaluated the NIR (0.819 µm) /red (0.681 µm) ratio and found that this vegetative index was not consistently related to yield across all of the fields while the red/green relationship showed a more consistent relationship across all of the fields. There are large varieties of vegetative indices that can be computed from the wavebands shown in Table 2; however, the consistency of this index across the fields in this study was one of the primary reasons for its use in these analyses. Hatfield et al. (2008) reviewed the different indices derived from remote sensing signals and their relationship to various agronomic variables and there are a variety of different indices which can be applied to these fields and in this study the red/green index provided a useful method of assessing response across

type for different N rates within the Calhoun East field in 2002.

Clarion

Nicollett

Webster

Okoboji

fields.

**3.3 Seasonal patterns in fields** 

the Clarion soil (Fig. 8). This was a similar response as to that observed in the Sac field in 2000 (Fig. 4). The N response curve shown in Fig. 5 suggests that the improved water holding capacity of the Webster soil allows for enhanced yield compared to the other soils because of the increased available soil water during grain-filling. Seasonal water use patterns between a Clarion and Webster soil were significant during the reproductive stage of growth because at this time of the year, crop water use was dependent upon stored soil water in the soil profile (Fig. 2). These water use patterns lead to differences in crop water stress which affects yield patterns as suggested by Sadler et al. (2000b). This was a consistent finding across all of the fields examined in this study in which the higher water holding capacity soils had a higher yield regardless of N rate. Observations from the 78 kg

Fig. 8. Corn yield response to applied nitrogen for Clarion and Webster soils using three N rates in 2002.

N ha-1 rate showed that yields in the Clarion soil were distributed across the range of yields observed in the field. There were changes in the statistical moments for the yields in the different soils within N rates as shown in Table 3. Yields in the 134 and 190 kg N ha-1 rate were not significantly different for any of soils (Table 4). The yield distribution within the soil types reveals the effects of the soil water availability as a major factor in determining yield response to N rates (Fig. 7). Evaluation of the N response across fields will have to account for the water holding capacity of the soil and the precipitation during the growing season in order to interpret the results. Analysis of the yield distributions within fields segregated by soil type demonstrates the impact that available soil water has on determining the spatial pattern of corn yield.


Table 4. Mean, standard deviation, skewness, and kurtosis for corn yields within each soil type for different N rates within the Calhoun East field in 2002.

## **3.3 Seasonal patterns in fields**

88 Agricultural Science

the Clarion soil (Fig. 8). This was a similar response as to that observed in the Sac field in 2000 (Fig. 4). The N response curve shown in Fig. 5 suggests that the improved water holding capacity of the Webster soil allows for enhanced yield compared to the other soils because of the increased available soil water during grain-filling. Seasonal water use patterns between a Clarion and Webster soil were significant during the reproductive stage of growth because at this time of the year, crop water use was dependent upon stored soil water in the soil profile (Fig. 2). These water use patterns lead to differences in crop water stress which affects yield patterns as suggested by Sadler et al. (2000b). This was a consistent finding across all of the fields examined in this study in which the higher water holding capacity soils had a higher yield regardless of N rate. Observations from

> N rate (kg ha-1) 78 134 190

Fig. 8. Corn yield response to applied nitrogen for Clarion and Webster soils using three N

N ha-1 rate showed that yields in the Clarion soil were distributed across the range of yields observed in the field. There were changes in the statistical moments for the yields in the different soils within N rates as shown in Table 3. Yields in the 134 and 190 kg N ha-1 rate were not significantly different for any of soils (Table 4). The yield distribution within the soil types reveals the effects of the soil water availability as a major factor in determining yield response to N rates (Fig. 7). Evaluation of the N response across fields will have to account for the water holding capacity of the soil and the precipitation during the growing season in order to interpret the results. Analysis of the yield distributions within fields segregated by soil type demonstrates the impact that available soil water has on

Clarion Webster

Calhoun East 2002

the 78 kg

Yield (kg ha-1)

rates in 2002.

6000

determining the spatial pattern of corn yield.

7000

8000

9000

10000

11000

Harvested yield represents one point in the season which is the result of all of the interacting factors during the season. One question is whether the factors that affect yield patterns at harvest persist throughout the growing season or are there changes which occur and are detectable only in grain yield. Application of techniques related to improved management decisions require that observations within a field be able to detect a plant response that is ultimately related to crop yield as part of the decision making process. Sadler et al. (2000b) suggested that yield patterns could be explained by following the patterns of crop stress during the season. These data sets contain a sequence of measurements during the growing season that may be related to crop yield. The hyperspectral remote sensing data allowed for several indices to be calculated; however, one of the relationships we examined was the red (0.681µm) /green (0.561µm) ratio. This ratio was selected because of the strong relationship to crop biomass and crop yield. There was an inverse relationship between yield and the August red/green index for the fields (Fig. 9). Although there was a large variation about the regression line, this index showed a significant relationship with yield compared to other vegetative indices. Seasonal patterns of different vegetative indices provide insights into the spatial patterns of vegetative response during the course of the growing season. In this study we evaluated the NIR (0.819 µm) /red (0.681 µm) ratio and found that this vegetative index was not consistently related to yield across all of the fields while the red/green relationship showed a more consistent relationship across all of the fields. There are large varieties of vegetative indices that can be computed from the wavebands shown in Table 2; however, the consistency of this index across the fields in this study was one of the primary reasons for its use in these analyses. Hatfield et al. (2008) reviewed the different indices derived from remote sensing signals and their relationship to various agronomic variables and there are a variety of different indices which can be applied to these fields and in this study the red/green index provided a useful method of assessing response across fields.

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 91

Frequency

Frequency

Frequency

0

0

0

100

200

300

400

500

600

700

100

200

300

400

500

600

100

200

300

400

500

Red/Green 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

Red/Green 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

Red/Green 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

Fig. 10. Frequency distribution of red/green ratio for the four observation times during the

2002 growing season for the Coon Rapids field with 67, 134, and 200 kg N ha-1 rate.

Coon Rapids 2002 (134 kg ha-1)

Coon Rapids 2002 (200 kg/ha)

Coon Rapids 2002 67 kg/ha

May July August September

> May July August September

May July August September

Fig. 9. Relationship between corn yield and red/green ratio observed in August with an aircraft scanner for the Dallas South field in 2002.

A more detailed examination of the red/green ratio was conducted with the observations collected four times during the growing season. Observations throughout the season represent unique characteristics of the growing season, May observations represent the soil background, July represents maximum vegetative cover, August the point of mid-grain fill, and September the time near physiological maturity (Fig. 10). At each of these times the frequency distribution of the red/green ratio was computed for each N rate within the field. There is a seasonal trend in the frequency distributions with a decrease in the variation found in the distribution from the May to July or August observations and then an increase in variation for the September observations (Fig. 10). Variation in the red/green ratios early in the season was related to the soil variation within each N rate. The variation in the July and August observations was small for all three N rates. Observations of the water use patterns among soils within a field showed little difference at this time of the growing season because there was adequate soil water in all soils to meet crop demands. Later in the growing season the crop water demand exceeds the precipitation and crop water use is dependent upon stored soil water and variation among soils becomes evident and the variation in the red/green ratio is similar to the bare soil distribution (Fig. 10). There was no significant difference among the 67, 134, or 200 kg N ha-1 rates for the frequency distributions of the red/green ratio (Fig. 10). The frequency patterns of the red/green ratios within N rates follow the yield patterns. Spatial patterns of reflectance reveal the seasonal dynamics of the interactions of soil types with N rates. These same patterns of red/green reflectance throughout the season were the same across all of the fields within this study. There is a consistent pattern in terms of a decreasing variation as the crop develops until mid-grain fill and then variation increases during the later grain-fill stages. The only difference among fields was whether the early grain-fill observations began to reveal spatial variation because of the lack of soil water in the profile and limited precipitation to meet the crop water demands. In fields with adequate soil water during grain-fill the variation is less pronounced.

Red/Green Ratio 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00

Fig. 9. Relationship between corn yield and red/green ratio observed in August with an

A more detailed examination of the red/green ratio was conducted with the observations collected four times during the growing season. Observations throughout the season represent unique characteristics of the growing season, May observations represent the soil background, July represents maximum vegetative cover, August the point of mid-grain fill, and September the time near physiological maturity (Fig. 10). At each of these times the frequency distribution of the red/green ratio was computed for each N rate within the field. There is a seasonal trend in the frequency distributions with a decrease in the variation found in the distribution from the May to July or August observations and then an increase in variation for the September observations (Fig. 10). Variation in the red/green ratios early in the season was related to the soil variation within each N rate. The variation in the July and August observations was small for all three N rates. Observations of the water use patterns among soils within a field showed little difference at this time of the growing season because there was adequate soil water in all soils to meet crop demands. Later in the growing season the crop water demand exceeds the precipitation and crop water use is dependent upon stored soil water and variation among soils becomes evident and the variation in the red/green ratio is similar to the bare soil distribution (Fig. 10). There was no significant difference among the 67, 134, or 200 kg N ha-1 rates for the frequency distributions of the red/green ratio (Fig. 10). The frequency patterns of the red/green ratios within N rates follow the yield patterns. Spatial patterns of reflectance reveal the seasonal dynamics of the interactions of soil types with N rates. These same patterns of red/green reflectance throughout the season were the same across all of the fields within this study. There is a consistent pattern in terms of a decreasing variation as the crop develops until mid-grain fill and then variation increases during the later grain-fill stages. The only difference among fields was whether the early grain-fill observations began to reveal spatial variation because of the lack of soil water in the profile and limited precipitation to meet the crop water demands. In fields with adequate soil water during grain-fill the variation is less

Yield = -3.105 + 7.343 R/G r2 = 0.45

Dallas South 2002

Data

aircraft scanner for the Dallas South field in 2002.

Yield (Mg ha-1

pronounced.

5

6

7

8

9

10

11

12

)

Fig. 10. Frequency distribution of red/green ratio for the four observation times during the 2002 growing season for the Coon Rapids field with 67, 134, and 200 kg N ha-1 rate.

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 93

Fig. 12. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the August

Fig. 13. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the

Spatial variation maps for the Coon Rapids field in 2002 showed the yield variation patterns that indicated evidence of a relationship to N rates at the higher N rate of 200 kg ha-1. The May spatial analysis image had a significant correlation with the yield of 0.65. These strips had higher yields in parts of the field but were not consistent across the entire strip and there was a difference between the west and east end of the field (Fig. 14). The September red/green patterns were significantly correlated with the May red/green values with a

image.

September image.

aug r/g 0.711 0.704 0.697 0.690 0.684 0.677 0.670 0.663 0.656 0.649 0.642 0.635 0.629 0.622 0.615

sept r/g 0.862 0.850 0.839 0.827 0.815 0.804 0.792 0.780 0.769 0.757 0.745 0.734 0.722 0.710 0.699

Spatial analysis of the red/green index for the May and August periods during the 2002 growing season showed the effect of the soil differences for the May image with the differences from west to east that were related to the distribution of soil types within the field (Fig. 11). The presence of waterway was very evident in this kriged map of the field. The range of the samples was 20 m indicating there were detectable differences over relatively short distances within the field. In other fields the range was considerably longer and on the order of 80 to 100m. Spatial analysis was able to reveal the patterns of the soil types within the fields. This is in contrast with August image in which there is little variation across the field in the red/green ratio (Fig. 12). There is one spot with poor plant growth that was detectable in the field. In this analysis there was no stable range in the data because there were no significant spatial patterns detected within the field. These interpolated maps confirm the analysis conducted within each strip that showed the July and August periods have the least variation in vegetative indices across the field because the crop growth is uniform (Fig. 12). The growth of the crop reduces the variation within the field and there is no detectable variation caused by the N rates within this field. Spatial analysis of the September red/green ratios showed the variation had reoccurred within the field (Fig. 13). This temporal pattern was common across all of the fields in which the variation in the red/green ratio decreased in the July and August observations and there was no correlation of these ratios with soil types within the field. The reason for this pattern is that during this phase of crop growth the water use rate is small and with the soil profile completely recharged at the beginning of the growing season there is more than sufficient soil water along with the precipitation to produce a uniform growth across the field. During the grain-fill period when crop water use rates are larger and precipitation is more infrequent then soil water availability from the soil profile becomes a critical factor and influences the red/green ratio because of the effect on leaf senescence.

may r/g 1.09 1.07 1.06 1.05 1.04 1.02 1.01 1.00 0.98 0.97 0.96 0.95 0.93 0.92 0.91

Fig. 11. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the May image.

Spatial analysis of the red/green index for the May and August periods during the 2002 growing season showed the effect of the soil differences for the May image with the differences from west to east that were related to the distribution of soil types within the field (Fig. 11). The presence of waterway was very evident in this kriged map of the field. The range of the samples was 20 m indicating there were detectable differences over relatively short distances within the field. In other fields the range was considerably longer and on the order of 80 to 100m. Spatial analysis was able to reveal the patterns of the soil types within the fields. This is in contrast with August image in which there is little variation across the field in the red/green ratio (Fig. 12). There is one spot with poor plant growth that was detectable in the field. In this analysis there was no stable range in the data because there were no significant spatial patterns detected within the field. These interpolated maps confirm the analysis conducted within each strip that showed the July and August periods have the least variation in vegetative indices across the field because the crop growth is uniform (Fig. 12). The growth of the crop reduces the variation within the field and there is no detectable variation caused by the N rates within this field. Spatial analysis of the September red/green ratios showed the variation had reoccurred within the field (Fig. 13). This temporal pattern was common across all of the fields in which the variation in the red/green ratio decreased in the July and August observations and there was no correlation of these ratios with soil types within the field. The reason for this pattern is that during this phase of crop growth the water use rate is small and with the soil profile completely recharged at the beginning of the growing season there is more than sufficient soil water along with the precipitation to produce a uniform growth across the field. During the grain-fill period when crop water use rates are larger and precipitation is more infrequent then soil water availability from the soil profile becomes a critical factor and influences the red/green ratio because of the effect

Fig. 11. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the May

may r/g 1.09 1.07 1.06 1.05 1.04 1.02 1.01 1.00 0.98 0.97 0.96 0.95 0.93 0.92 0.91

on leaf senescence.

image.

Fig. 12. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the August image.

Fig. 13. Spatial map of the red/green ratio for the Coon Rapids field in 2002 for the September image.

Spatial variation maps for the Coon Rapids field in 2002 showed the yield variation patterns that indicated evidence of a relationship to N rates at the higher N rate of 200 kg ha-1. The May spatial analysis image had a significant correlation with the yield of 0.65. These strips had higher yields in parts of the field but were not consistent across the entire strip and there was a difference between the west and east end of the field (Fig. 14). The September red/green patterns were significantly correlated with the May red/green values with a

Spatial Patterns of Water and Nitrogen Response Within Corn Production Fields 95

spatial patterns, these spatial patterns were related to the water use patterns and soil water holding capacity. The observations from this study revealed that N impacts on crop yield were directly related to soil water holding capacity and to improve N response an

Observations of the changes in the spatial patterns during the growing season have shown that there is complex interaction between the patterns of soil within the field and the final pattern of corn yield as a function of the patterns of soil water use and N management inputs. Agriculture will benefit from an enhanced understanding of the interactions of soil water use and N management and how these interact across a production field. The combination of remote sensing along with yield maps offers an enhanced method to evaluate field scale responses to both weather and management which will benefit production efficiency. These efforts will lead toward improved production efficiency and enhance the capability of

The support of the Risk Management Agency and especially Virginia Guzman and Dave Fulk are greatly acknowledged and this research is under the agreement 07-IA-0831-0210. This effort would not be possible without the capable support of Brooks Engelhardt, Wolf Oesterreich, and Bert Swalla in their efforts to collect and process the data from the field experiments and the interactions with Galen Hart for his encouragement and insights. Likewise, the support of the producers Don Ferguson, Mike Hermanson, Nels Leo, Kriss Lightener, Dale Pennington, and David Schroeder who willingly let us use their fields for these studies and the efforts of the Farnhamville Cooperative of Farnhamville, IA (Jeff True, Gabe Tar) for helping identify the producers. The support from CALMIT at the University of Nebraska (Rich Perk and Don

Blackmer, T.M., Schepers, J.S. (1996). Aerial photography to detect nitrogen stress in corn. *J.* 

Blackmer, T.M., Schepers, J.S., Varvel, G.E. (1994). Light reflectance compared with other

Blackmer, T.M., Schepers, J.S., Varvel, G.E., Meyer, G.E. (1996a). Analysis of aerial photography for nitrogen stress within corn fields. *Agron. J.* 88:729-733. Blackmer, T.M., Schepers, J.S., Varvel, G.E., Walter-Shea, E.A. (1996b). Nitrogen deficiency

Blackmer, T.M., Schepers, J.S., Vigil, M.F. (1993). Chlorophyll meter readings in corn as affected by plant spacing. *Commun. Soil Sci. Plant Anal.* 24:2507-2516. Brock, A., Brouder, S.M., Blumhoff, G., Hoffman, B.S. (2005). Defining yield-based management zones for corn-soybean rotations. *Agron. J.* 97:1115-1128. Brouder, S.M., Mengel, D.B., Hoffman, B.S. (2000). Diagnostic efficiency of the black layer stalk nitrate and grain nitrogen tests for maize. *Agron. J.* 92:1236-1247. Burkart, M.R., James. D.E. (1999). Agricultural nitrogen contributions to hypoxia in the Gulf

detection using reflected shortwave radiation from irrigated corn canopies. *Agron.* 

nitrogen stress measurements in corn leaves *Agron. J.* 86:934-938.

improvement in soil water availability during grain-filling would be necessary.

agricultural systems to become more efficient in terms of water and N use.

Rundquist) to obtain the hyperspectral data is greatly appreciated.

*Plant Physiol.* 148:440-444.

of Mexico. *J. Environ. Qual.* 28:850-859.

**5. Acknowledgements** 

**6. References** 

*J.* 88:1-5.

value of 0.70 and are indicative of the role of soil variation and the effect on soil water dominating the effect of N. The higher yields were found in the Webster soils as shown earlier with the frequency distribution of yields. The spatial map of yields show these high yields but the high yields are not consistent with N rates. There was the reemergence of the presence of the waterway within the field in the yield map that was present in the May spatial map of the field. These patterns within fields were found in the other fields we examined in this study and there was a significant correlation between the yield and soil type across all fields of 0.58. The growing season conditions during these study years had normal or slightly below normal precipitation amounts during the growing season and above normal precipitation during grain fill would offset these relationships and allow for more potential benefit of applied N. This finding confirms the observations from Jaynes and Colvin (1997) in which the spatial variation of yield was related to seasonal precipitation and extends their results to include the interacting effects of N management.

Fig. 14. Interpolated yield map from the Coon Rapids field in 2002 using spatial analysis software.

## **4. Conclusions**

Nitrogen response across agricultural fields is more complex than observing a consistent response across a change in management practices. Observations among fields has shown that when multiple soils are encountered within a production field there are spatial patterns in both water use and N impacts on crop yield. There have been few studies which have coupled water and N dynamics across corn production fields. It has been assumed that water patterns operate separately from N management practices; however, the spatial patterns within a field show there is a temporal and spatial pattern determined by the combination of the precipitation patterns during the season, the soil water holding capacity, and the crop growth (crop water use) patterns. Observations of N impacts on corn yield across production scale fields revealed that yield responses were dependent upon the soil type and within a rate strip there were a range of yields and when further dissected into the spatial patterns, these spatial patterns were related to the water use patterns and soil water holding capacity. The observations from this study revealed that N impacts on crop yield were directly related to soil water holding capacity and to improve N response an improvement in soil water availability during grain-filling would be necessary.

Observations of the changes in the spatial patterns during the growing season have shown that there is complex interaction between the patterns of soil within the field and the final pattern of corn yield as a function of the patterns of soil water use and N management inputs. Agriculture will benefit from an enhanced understanding of the interactions of soil water use and N management and how these interact across a production field. The combination of remote sensing along with yield maps offers an enhanced method to evaluate field scale responses to both weather and management which will benefit production efficiency. These efforts will lead toward improved production efficiency and enhance the capability of agricultural systems to become more efficient in terms of water and N use.
