using yield monitor data to determine spatial crop ... - CiteSeerX

USING YIELD MONITOR DATA TO DETERMINE SPATIAL CROP PRODUCTION POTENTIAL R. K. Taylor, G. J. Kluitenberg, M. D. Schrock, N. Zhang, J. P. Schmidt, J. L. Havlin

ABSTRACT. Consistent spatial–temporal yield patterns should help determine spatial production potential. Our objective was to evaluate methods for using yield monitor data to develop spatial yield goal maps. Three to seven years of yield monitor data were analyzed for five sprinkler–irrigated cornfields in central and western Kansas. Yield data were block–averaged to 55 m square cells, normalized based on the mean yield, and then used to develop spatial yield goals for subsequent years using six different methods. One method used a uniform yield goal, two methods combined normalized yield monitor data with a uniform yield goal (transitional), and three methods used only normalized yield monitor data from previous years. Methods were evaluated based on their ability to predict the spatial yield pattern of the subsequent year better than the uniform method. Yield monitor data were also segregated based on the temporal CV of each field during the time of the study, and the six methods were evaluated only on the data that were deemed temporally stable. The result of incorporating yield monitor data into yield goals was inconsistent across sites and years. For one site, the two transitional and three yield monitor methods were significantly better predictors of normalized yield. On another field, the uniform method was a better predictor of normalized yield than the yield monitor methods in three of six years, while the yield monitor methods were better than the uniform method in another year. On a third field, the yield monitor method predicted normalized yield better than the uniform method in one of four years with no difference in the other three years. In general, when the correlation coefficient between two years of yield monitor data exceeded 0.70, the methods that incorporated yield monitor data into the yield goal were better predictors of normalized yield than the uniform method. Evaluating these methods using only data from cells where the temporal CV was less than the average temporal CV for the field did not improve the results sufficiently to warrant widespread use of this practice. Keywords. Precision agriculture, Site–specific crop management, Yield maps, Corn, Irrigation.

T

otal nutrient use is greater on corn than any other major field crop grown in the United States, and most Kansas corn acreage requires the addition of at least some nitrogen (N) fertilizer to achieve full production potential (Lamond, 1994). As agricultural producers strive to become more efficient with inputs and as environmental concerns related to soil nitrate leaching increase, N fertilizer applications are being examined more closely for better management opportunities. With the availability of precision agriculture technologies, variable– rate management of N is currently a possible means of improving N use efficiency (Snyder et al., 1997).

Article was submitted for review in December 2000; approved for publication by the Power & Machinery Division of ASAE in July 2001. Presented at the 1998 ASAE Annual Meeting as Paper No. 98–1048. Contribution No. 01–217–J Kansas Agricultural Experiment Station, Kansas State University. The authors are Randal K. Taylor, ASAE Member Engineer,Associate Professor, Biological and Agricultural Engineering, Gerard J. Kluitenberg, Professor, Agronomy, Mark D. Schrock, ASAE Member Engineer, Professor, Biological and Agricultural Engineering, Naiqian Zhang, ASAE Member Engineer, Professor, Biological and Agricultural Engineering, John P. Schmidt, Assistant Professor, Agronomy, Kansas State University, Manhattan, Kansas; and John L. Havlin, Professor and Head, Soil Science, North Carolina State University, Raleigh, North Carolina. Corresponding author: Randal K. Taylor, 237 Seaton Hall, Kansas State University, Manhattan, KS 66506; phone: 785–532–2931; fax: 785–532–6944; e–mail: [email protected].

In Kansas, the N recommendation model for corn incorporates use of a yield goal (Lamond, 1994). Nitrogen recommendation models for corn in several other states also make use of a yield goal or are based on yield potential. Schrock (1994) documented spatial corn yield variations in several Kansas fields. This spatial yield variation certainly could imply spatially variable yield potential and, thus, an opportunity for the development of spatial yield goals. Traditionally, yield goals have been calculated on a whole–field basis in which a single value for yield goal is established for a field. With the availability of grain yield monitors, the development of spatially variable yield goals, or yield goal maps, can be considered. Inasmuch as yield monitors have been available for a relatively short time and their use in production agriculture will increase, farmers and researchers are faced with the problem of making the transition from the use of whole–field yield goals to yield goal maps. Traditionally, multiple years of production history are used to establish the yield goal for a particular field. Observing the actual yield for a field for multiple years and tempering this information with knowledge of seasonal growing conditions and yields from similar fields is a typical approach to determining whole–field yield goals. Determining yield goals for areas within a field is simply a change in scale. Yield monitors have given us the ability to measure the output of areas within the field. We can now use this yield information together with the knowledge of growing conditions and yield from similar areas to determine an appropriate yield goal for localized

Transactions of the ASAE Vol. 44(6): 1409–1414

E 2001 American Society of Agricultural Engineers ISSN 0001–2351

1409

areas within a field. However, if yield maps are to be used in the development of a yield goal map, an appropriate method is needed for combining yield maps from multiple years. A yield map for any given year may contain “noise” that is relevant to that particular year and not related directly to yield potential. Furthermore, with a limited number of yield maps in some cases, there needs to be a method to incorporate spatial and field–level yield data into a continuous yield goal map. Other researchers have concluded that long–term yield monitoring may be necessary to characterize production potential (Jaynes and Colvin, 1997; Colvin et al., 1997). Jaynes and Colvin (1997) examined the spatiotemporal stability of yield patterns across a field that was rotated between corn and soybean. They determined that long–term monitoring of yield was necessary to fully characterize spatiotemporal yield patterns. Colvin et al. (1997) observed spatial coefficients of variation (CV) in yield ranging from 12% to greater than 30% during a 6–year period for a single field. They ranked relative yields for 224 locations in this field and did not observe yield stability even after the sixth year. Lamb et al. (1997) collected data over a period of 5 years from 60 hand–harvested corn plots that were distributed spatially within a 1.8 ha field. Coefficients of variation ranged from 8% to 15%, and correlation coefficients between yield for various years ranged from 0.19 to 0.65. They concluded that 4 years of yield data was not satisfactory for determining yield goals. The objectives of this study were to characterize spatiotemporal variability of corn yield and to evaluate various methods for using spatially variable yield monitor data (yield maps) to develop spatially variable yield goals (yield goal maps).

METHODS FIELDS AND DATA COLLECTION Yield monitor data were obtained for five fields in central and western Kansas over the course of 3 to 7 years. Field sizes and harvest years are shown in table 1. All fields were cropped continuously to sprinkler–irrigated corn. Hybrids, seeding rates, planting dates, and cultural practices varied across years and sites, but were representative of production practices in their respective areas. Corn at all sites was planted in 0.76 m rows. Yield was measured with a variety of yield monitors and obtained in several formats. Field A was located near Pretty Prairie, Kansas, and had soils mapped as Shellabarger fine sandy loam and Farnum loam. The Shellabarger (fine–loamy, mixed, superactive, mesic Udic Argiustolls) and Farnum (fine–loamy, mixed, superactive, mesic Pachic Argiustolls) series consist of deep, sandy and loamy soils on uplands. The Farnum series has Table 1. Field sizes, number of cells, and harvest years. Area Total Harvest years Field (ha) cells A B C D E

1410

47 53 65 53 53

158 162 164 164 158

93 93

94 94

95 95

96 96

97 97

98 98

99 99

96 96

97 97

98 98

99

00

moderately slow permeability. Both series are well drained and have moderate natural fertility (USDA–SCS, 1960). Yield data from field A were measured at 1 s intervals with a Greenstar yield–mapping system on a John Deere combine equipped with a 6–row corn head. Field B was located near Great Bend, Kansas. The soil at this site was mapped as Pratt loamy fine sand (USDA–SCS, 1981). The Pratt series (sandy, mixed, slightly acid, thermic Psammentic Haplustalfs) consists of deep, well drained, rapidly permeable soils formed in sandy eolian deposits on uplands (USDA–SCS, 1981). The 1993–1995 yield data for field B were obtained with a Case–IH 1680 (KSU’s combine) equipped with a triangular elevator flow sensor (Schrock et al., 1995), and yield data in the subsequent 4 years were measured with a John Deere Greenstar yield–mapping system owned by the cooperator. The KSU combine was operated with a 6–row corn head, while the cooperator’s combine had a 12–row corn head. Data from both combines were recorded at 1 s intervals. Field C was located near Stafford, Kansas. The soil at this site was mapped as Pratt–Tivoli loamy fine sand (USDA–SCS, 1978a). The Pratt series (sandy, mixed, slightly acid, thermic Psammentic Haplustalfs) and the Tivoli series (sandy, mixed, slightly acid, thermic Typic Ustipsamments) consist of deep, well drained, rapidly permeable soils formed in sandy eolian deposits on uplands (USDA–SCS, 1978a). Characteristics for both series include slow runoff and low natural fertility and available water capacity. Three years of yield data for field C were obtained with the KSU combine by recording data at 1 s intervals. A 6–row corn head was used to harvest this field in all years. Field D was located near Inman, Kansas, and the soils were mapped as Crete silt loam and Ladysmith silty clay loam. The Crete (fine, montmorillonitic, mesic Pachic Argiustolls) and Ladysmith (fine, montmorillonitic, mesic Pachic Argiustolls) series consist of deep, moderately well drained, nearly level soils on uplands. Characteristics for both series include slow to very slow permeability and medium to slow runoff. Fertility is medium to high and available water capacity is high (USDA–SCS, 1978b). Field D was harvested with a John Deere combine equipped with a 12–row corn head and a Greenstar yield–mapping system. Yield data were recorded at 1 s intervals Field E was located in southwest Kansas. The soils were mapped as a Ulysses silt loam and Satanta loam. The Ulysses (fine–silty, mixed, mesic Typic Haplustolls) and Satanta (fine–loamy, mixed, mesic Typic Argiustolls) series consist of nearly level to moderate sloping soils on loess–mantled uplands. Characteristics for both series include good drainage, moderately slow permeability, slow to medium runoff, and medium infiltration. Fertility is moderately high, and available water capacity is high (USDA–SCS, 1969). The field was harvested with KSU’s combine in 1996 recording data at 1 s intervals and in the following 2 years with a Case–IH 2166 combine equipped with an AFS yield monitor recording data at 2 s intervals. In all years, the combine was equipped with an 8–row corn head. DATA PREPROCESSING Data obtained from all yield monitors were converted to point yield data, which then were filtered to omit unrealistic values. Unrealistic data included negative and zero yield

TRANSACTIONS OF THE ASAE

points and exceptionally high yields. The upper threshold was set at 22 t/ha, and all points above this value were removed from the files. The filtered point yield data from each field were block averaged to 55 m square cells using a MathCad 6.0 (Mathsoft, Inc.) program. The number of point yield observations in each cell varied with the frequency of data acquisition and header width of each combine. All fields were harvested in a back and forth pattern. Cells from the periphery of the field were excluded from the data sets to minimize edge effects, resulting in cell numbers ranging from 158 to 164 for each field (table 1). Figure 1 shows a typical field boundary and cell arrangement. Yield data for each year were normalized by dividing the cell yield values by the mean yield for all cells in a particular field. This removed temporal variability by scaling all yields based on the mean yield for the year, while preserving spatial variability relative to the mean. Six methods for calculating spatial yield goals (yield goal maps) using the normalized data were evaluated (table 2). In describing these methods, “previous year(s)” is used to indicate yield monitor data in the year(s) immediately prior to the year in which yield was predicted. The uniform method assigned the same value to each cell. Because yield goals are also normalized, a yield goal of 1 (table 2) is equivalent to mean yield goal. This method is identical to the traditional approach of computing yield goal on a whole–field basis. The transitional methods (TR1 and TR2) combined normalized yield monitor data with a uniform yield goal. These methods were evaluated because they may be useful in making the transition from whole–field yield goals to yield goal maps. The other three methods (YM1, YM2, and YM3) were based entirely on normalized yield monitor data from previous years. These methods were evaluated by examining the difference between yield goals and actual yields. The absolute difference between the normalized yield goal and the normalized yield for each cell was calculated using the expression: ADi = abs(NYGi – NYi ) where ADi NYGi NYi i

= = = =

(1)

absolute difference for cell i normalized yield goal for cell i normalized yield for cell i cell number.

Figure 1. Typical cell arrangement and field boundary. Cell size is 55 m y 55 m.

Vol. 44(6): 1409–1414

Table 2. Methods for determining normalized yield goals for a cell (i) in a year (j) from normalized yield (NY). Method Calculation Uniform TR1i,j TR2i,j YM1i,j YM2i,j YM3i,j

1 (NYi,j–1 + 2)/3 (NYi,j–1 + NYi,j–2 + 1)/3 NYi,j–1 (NYi,j–1 + NYi,j–2 )/2 (NYi,j–1 + NYi,j–2 + NYi,j–3 )/3

The average absolute difference (AD) was calculated for each method, field, and year. Lower values of average AD indicate better agreement between yield goal and actual yield. Each cell, as illustrated in the typical layout in figure 1, had an AD for the six methods of determining yield goal. This resulted in a 2–factor design, with cell and yield goal method as the 2 factors. The data for each field and year were analyzed as a randomized complete block design using the general linear model (GLM) procedure in SYSTAT 8.0 (SPSS, Inc). The absolute difference between normalized yield and normalized yield goal was modeled as a function of cell and yield goal method. Fisher’s least significant difference (LSD) test was used to compare the methods that used yield monitor data to the uniform method. All statistics were computed at the p < 0.01 level.

RESULTS AND DISCUSSION YIELD VARIATION Average yield and coefficient of variation (CV) for each field and year are shown in table 3. Spatial variability in yield was greatest for field A (high CV) and smallest for field E (low CV). Field B had CVs ranging from about 6% to nearly 18%, and they were negatively correlated with yield (r = –0.87). Higher average yield was associated with greater uniformity in yield. The limited yield monitor data for fields A, C, and E showed a similar trend. In contrast, five years of yield monitor data for field D indicated a positive relationship between spatial CV and mean yield (r = 0.54). Correlation analysis was conducted on the yield data from each site to assess temporal stability in spatial patterns of yield (table 4). A higher correlation coefficient indicates greater similarity in the spatial patterns of the yield maps between two years. In other words, consistently higher correlation coefficients indicate greater temporal stability in spatial yield patterns. Correlation coefficients ranged from a low of 0.03 to a high of 0.87, and substantial variation was evident among sites and years. Thus, the predictive value of a single year’s yield map varies substantially. This clearly suggests that caution must be employed in using the spatial information from yield maps to establish spatial estimates of production potential or yield goal maps, supporting the conclusions by Colvin et al. (1997) and Lamb et al. (1997). Yields for the 4 years of yield monitor data at field A were correlated significantly among years, and all coefficients exceeded 0.75 (table 4). This indicated a fairly high predictive value of the yield monitor data for this field. The correlation coefficients for field B were extremely variable, ranging from 0.03 to 0.77, and less than half of them were significant. Yield from 1995 was not correlated significantly to yield from any other year. Yield was relatively low in 1995

1411

Table 3. Mean yield and coefficient of variation of block– averaged yield data for each field and year. Yield data (t/ha) Std. CV Mean Median Max. Min. Field Year dev. (%) A 1996 10.1 10.6 13.3 2.1 2.3 23.2 1997 10.4 11.2 12.9 1.2 2.4 23.0 1998 9.5 10.1 11.1 2.3 1.8 18.7 1999 8.2 8.7 11.3 1.6 2.0 24.5 B

1993 1994 1995 1996 1997 1998 1999

10.4 11.7 8.0 12.4 13.4 9.8 12.2

10.7 11.8 8.3 12.7 13.6 10.1 12.4

12.6 15.5 10.0 13.9 14.7 11.4 15.4

5.8 8.3 0.2 8.9 8.5 6.4 5.8

1.4 1.3 1.4 0.9 0.7 1.0 1.4

13.2 11.0 17.6 7.6 5.9 10.2 11.8

C

1993 1994 1995

7.7 10.9 8.2

7.8 10.9 8.3

11.3 14.6 12.3

3.9 5.7 0.2

1.7 1.6 2.0

21.7 14.5 23.9

D

1996 1997 1998 1999 2000

12.5 11.6 9.0 8.9 10.7

13.1 11.9 9.2 9.1 10.9

15.4 13.8 11.1 10.6 12.5

6.3 6.0 6.0 3.5 5.4

1.8 1.5 0.9 1.0 0.9

14.7 12.5 10.4 11.6 8.2

E

1996 1997 1998

13.0 14.2 14.2

13.1 14.9 14.4

15.1 14.9 15.5

8.8 12.4 11.6

0.8 0.4 0.9

6.3 2.9 6.1

Table 4. Correlation coefficients for yield among years. 1993 1994 1995 1996 1997 1998 1999 2000 Field A 1996 1997 1998 1999 Field B 1993 1.00[a] 1994 0.28[a] 1.00[a] 1995 0.14 0.15 1.00[a] 1996 0.42[a] 0.21 0.24 1997 0.42[a] 0.36[a] 0.21 1998 0.31[a] 0.19 0.24 1999 0.03 0.11 0.15

1.00[a] 0.83[a] 1.00[a] 0.82[a] 0.87[a] 1.00[a] 0.75[a] 0.85[a] 0.81[a] 1.00[a]

due to late planting caused by early season rainfall and ponded water that damaged the crop in topographically low areas. The correlation coefficients for comparing yield among years for fields C, D, and E were all significant, but generally not as high as the coefficients obtained at field A (table 4). Although the correlation coefficients for the 3 years of yield monitor data for field C were fairly consistent, they were not high (0.44 to 0.50). The correlation coefficients at field D ranged from 0.44 to 0.73, which indicates moderate predictive ability of yield monitor data for this field. Though yields for the 3 years of yield monitor data at field E were correlated significantly, the values were fairly low, ranging from 0.26 to 0.46. EVALUATION OF METHODS FOR DETERMINING SPATIAL YIELD GOALS Six methods for determining spatial yield goals were compared by averaging the absolute difference between normalized yield goal and normalized yield for each field and year (table 5). Comparing the results in tables 3 and 5 illustrates that fields with high spatial CVs for a particular year also had high absolute differences for the uniform yield goal. For field A, all methods (TR1, TR2, YM1, YM2, and YM3) were significantly better than the method using a uniform yield goal (table 5). The consistently high correlation coefficients between yield for each year allowed the yield monitor data for this field to be highly predictive. For field B, the methods using transitional yield goals (TR1 and TR2) were not significantly different than the uniform method. The three methods using only yield monitor data to determine yield goal (YM1, YM2, and YM3) were significantly worse than the uniform method in 3 of the years (1994, 1996, and 1997). However, the uniform method was Table 5. Average absolute differences between normalized yield goal and normalized yield for each method. Field Year Uniform TR1 TR2 YM1 YM2 YM3 A

1.00[a] 0.51[a] 1.00[a] 0.58[a] 0.37[a] 1.00[a] 0.32[a] 0.04 0.77[a] 1.00[a]

B

Field C 1993 1.00[a] 1994 0.47[a] 1.00[a] 1995 0.50[a] 0.44[a] 1.00[a] Field D 1996 1997 1998 1999 2000 Field E 1996 1997 1998 [a]

C 1.00[a] 0.64[a] 0.73[a] 0.47[a] 0.53[a]

1.00[a] 0.70[a] 1.00[a] 0.52[a] 0.44[a] 1.00[a] 0.64[a] 0.51[a] 0.65[a] 1.00[a]

1.00[a] 0.26[a] 1.00[a] 0.41[a] 0.46[a] 1.00[a]

Significant at the 0.01 probability level.

1412

D

E

[a]

1996 1997 1998 1999

0.180 0.169 0.131 0.191

1993 1994 1995 1996 1997 1998 1999

0.107 0.082 0.129 0.064 0.035 0.078 0.086

0.079 0.123 0.065 0.032 0.073 0.067

1993 1994 1995

0.185 0.119 0.185

0.108 0.167

1996 1997 1998 1999 2000

0.110 0.091 0.072 0.075 0.053

1996 1997 1998

0.043 0.019 0.048

0.126[a] 0.103 [a] 0.092[a] 0.067[a] 0.089 [a] 0.080[a] 0.160[a] 0.120[a] 0.110[a] 0.097[a] 0.102 [a]

0.126 0.068 0.051 0.065 0.068

0.111[a] 0.138 0.132 0.130[a] 0.085 0.073 0.056[a] 0.074[a] 0.059 [a] 0.072 0.062 0.068 0.056[a] 0.064[a] 0.067

0.151

0.153 [a] 0.161

0.154

0.090 0.070 0.085 0.065

0.059 [a] 0.082 0.051

0.040[a] 0.042

0.040

0.077 0.057[a] 0.048[a] 0.072 0.072 0.043 0.042 0.021 0.045

0.041

0.082 0.051



not significantly different than the other methods in 1995 and 1998. Two of the methods using only yield monitor data (YM1 and YM2) to determine a yield goal for 1999 were significantly better than the uniform method. The surprisingly high correlation (0.77) between yield for 1998 and 1999 likely caused the improvement in these methods. The next greatest correlation between yield for any 2 years on this field was 0.58. Methods using transitional yield goals (TR1 and TR2) were not significantly different than the uniform method for fields C and E. The YM1 method was a significantly worse predictor of yield than the uniform method in one year for each of these fields. Correlations between yield among the three years of yield monitor data ranged from 0.44 to 0.50 on field C and from 0.26 to 0.46 on field E. Although significant (table 4), these low correlation coefficients probably contributed to the reduced predictive value of the yield monitor data. The results for comparing methods for determining an adequate yield goal map were mixed for field D. Yields for the first 3 years were moderately correlated, ranging from 0.64 to 0.73. However, the correlations between yields for 1999 and the first 3 years were lower, while the correlations between yields for 2000 and the previous 4 years of yield monitor data were moderate. The higher correlations between yields for 1998 and the first 2 years (0.73 and 0.70) probably contributed to the significant improvements for TR1, TR2, and YM2 methods over the uniform method, but it is unclear why there was no difference between the YM1 and uniform methods. No differences occurred among yield goal prediction methods for the other 3 years at this field. SEGREGATING DATA BASED ON TEMPORAL COEFFICIENT OF VARIATION Visual examination of the yield maps revealed that some portions of these fields exhibited much stronger temporal stability than other portions. As a first attempt to quantify these apparent differences in temporal stability for a given field, year–to–year variation in yield for each cell was characterized by computing a CV from the block–averaged yield data for each cell before the normalizing calculation. Seven yield observations were used to compute the temporal CVs for each cell in field B, five observations for field D, four observations for field A, and three observations each for fields C and E. Blackmore and Larscheid (1997) used temporal CVs to classify management zones within a field. Temporally stable cells were defined as having a CV that was less than the field–level temporal CV. This approach was used to classify temporally stable cells with the fields studied. Field–level temporal CVs were calculated from the mean yields shown in table 3. The field–level temporal CVs (table 6) were then used to segregate the cell–based temporal CV data. Though the field–level temporal CVs varied among sites, some expected value likely exists for consistent production practices within a geographic region. The number of temporally stable cells for each field is shown in table 6. Approximately 30% to 40% of each field was classified as temporally stable. Comparing the baseline temporal CVs for each field (table 6) to the spatial CVs shown in table 3 indicates there was minimal relationship between these two values. Field A had consistently high spatial CVs for the 4 years of yield monitor data, but it had the second lowest

Vol. 44(6): 1409–1414

Table 6. Total cells, temporally stable cells, and baseline temporal CVs. Total Temporally Baseline Field cells stable cells temporal CV A B C D E

158 162 164 164 158

46 53 68 49 58

9.7 16.8 19.1 15.0 5.1

baseline temporal CV. Field C had spatial CVs similar to field A, but it had the highest baseline temporal CV. Field E, on the other hand, exhibited small variability in both space and time. The correlation analysis with yield data from the temporally stable cells resulted in higher correlation coefficients between yields from different years in 30 of 43 instances (table 7). Yields were correlated significantly for the temporally stable cells in all years at fields A, C, D, and E, similar to results when cells representing the entire field were used (table 4). However, yields were correlated significantly between only 2 years at field B. In general, the average absolute difference between normalized yield and normalized yield goal was smaller for the temporally stable cells regardless of method (table 8) than for all cells in each field (table 5). The smaller average absolute differences give the appearance of improved predictive value, but this is not the case. As in the analysis for all cells (table 5), predictive value was evaluated by examining how the various methods perform relative to the uniform method. The results in table 8 show that segregating Table 7. Correlation coefficients for yield among years for the temporally stable cells. 1993 1994 1995 1996 1997 1998 1999 2000 Field A 1996 1997 1998 1999

1.00[a] 0.80[a] 1.00[a] 0.83[a] 0.89[a] 1.00[a] 0.82[a] 0.90[a] 0.86[a] 1.00[a]

Field B 1993 1.00[a] 1994 0.45 1.00[a] 1995 0.34 0.32 1.00[a] 1996 0.33 0.30 0.38 1.00[a] 1997 0.39 0.22 0.57[a] 0.31 1.00[a] 1998 0.31 0.28 0.07 0.42 0.29 1.00[a] 1999 0.22 0.09 0.05 0.23 0.10 0.47[a] 1.00[a] Field C 1993 1.00[a] 1994 0.58[a] 1.00[a] 1995 0.51[a] 0.63[a] 1.00[a] Field D 1996 1997 1998 1999 2000

1.00[a] 0.67[a] 0.83[a] 0.79[a] 0.70[a]

Field E 1996 1997 1998

1.00[a] 0.41[a] 1.00[a] 0.54[a] 0.47[a] 1.00[a]

[a]

1.00[a] 0.79[a] 1.00[a] 0.66[a] 0.72[a] 1.00[a] 0.71[a] 0.70[a] 0.67[a] 1.00[a]


1413

Table 8. Average absolute differences between normalized yield goal and normalized yield for each method for only the temporally stable cells. Field Year Uniform TR1 TR2 YM1 YM2 YM3 A

B

C

D

E

[a]

1996 1997 1998 1999

0.087 0.082 0.076 0.084

1993 1994 1995 1996 1997 1998 1999

0.086 0.125 0.253 0.089 0.042 0.135 0.129

0.116 0.242 0.062 0.037 0.129 0.100

1993 1994 1995

0.120 0.107 0.124

0.096 0.107

0.102

0.113 0.106

0.102

1996 1997 1998 1999 2000

0.139 0.088 0.070 0.070 0.062

0.071 0.050 0.059 0.047

0.034 [a] 0.055 0.041

0.105 0.058 0.060 0.057

0.052 0.061 0.047

1996 1997 1998

0.026 0.018 0.033

0.017 0.031

0.028

0.026 0.029

0.028

0.064 0.061 0.051[a] 0.033 [a] 0.038[a] 0.034 [a] 0.065 0.044 [a] 0.046[a] 0.037 [a] 0.037 [a] 0.158 0.197 0.238 0.176 [a] 0.069 0.187[a] 0.098 0.091 0.083 [a] 0.080[a] 0.134 [a] 0.099 [a] 0.109 0.119 0.098 [a] 0.062 [a] 0.097 0.083 0.088 0.088

0.055 0.048


the data and evaluating only temporally stable cells typically did not improve the predictive value of yield monitor data. In fact, for fields A and B, using yield monitor data from only the temporally stable cells reduced the predictive value in some years, as compared to using data for the entire field.

CONCLUSIONS Using yield monitor data to define the spatiotemporal variability has produced mixed results. For the fields evaluated in this study, yield monitor data was valuable for determining spatial yield goals for fields if correlation coefficients were consistently high (greater than 0.70) among years. However, this characteristic was observed at only one of the fields. In addition, to determine if this characteristic exists for a specific field would require multiple years of yield mapping history. The transitional yield goals presented here minimize the risk of using yield monitor data to determine yield goals, and could be used while determining the spatial correlation of yield monitor data. Though the

1414

transitional yield goal methods were not significantly better than a uniform yield goal, they typically had lower average absolute differences between normalized yield goal and normalized yield. Temporal stability has been observed within areas of some fields. However, segregating yield monitor data based on temporal stability did not improve the predictive ability of the data.

REFERENCES Blackmore, B. S., and G. Larscheid. 1997. Strategies for managing variability. In Proc. 1st European Conference on Precision Agriculture, 851–859. Warwick, U.K. 8–10 September 1997. Oxford, U.K.: BIOS Scientific Publishers. Colvin, T. S., D. B. Jaynes, D. L. Karlen, D. A. Laird, and J. R. Ambuel. 1997. Yield variability within a central Iowa field. Trans. ASAE 40(4): 883–889 Jaynes, D. B., and T. S. Colvin. 1997. Spatiotemporal variability of corn and soybean yield. Agronomy J. 89(1): 30–37. Lamb, J. A., R. H. Dowdy, J. L. Anderson, and G. W. Rehm. 1997. Spatial and temporal stability of corn grain yield. J. Prod. Agric. 10(3): 410–414. Lamond, R. E. 1994. Nutrient management. In Corn Production Handbook, 12–15. Publ. No. C–560 Rev. Manhattan, Kansas: Kansas State University, Agric. Experiment Station and Cooperative Extension Service. Schrock, M. D. 1994. The role of combine yield monitors in nutrient management. In Proc. 24th North Central Extension–Industry Soil Fertility Workshop, 161–166. St. Louis, Mo. 27 October 1994. Norwich, Georgia: Potash and Phosphate Institute. Schrock, M. D., D. K. Kuhlman, R. T. Hinnen, D. L. Oard, and J. L. Pringle. 1995. Sensing grain yield with a triangular elevator. In Proc. 2nd International Conference on Site–Specific Management for Agricultural Systems, 637–650. P. C. Robert, et al., eds. Bloomington/Minneapolis, Minn. 27–30 March 1994. Madison, Wisc.: ASA–CSSA–SSSA. Snyder, C., T. Schroeder, J. L. Havlin, and G. J. Kluitenberg. 1997. An economic analysis of variable rate nitrogen management. In Proc. 3rd International Conference on Precision Agriculture, 989–998. Minneapolis, Minn. 23–26 June 1996. Madison, Wisc.: ASA–CSSA–SSSA. USDA–SCS. 1960. Soil survey of Reno County, Kansas. Washington, D.C.: U.S. Govt. Printing Office. _____. 1969. Soil survey of Grant County, Kansas. Washington, D.C.: U.S. Govt. Printing Office. _____. 1978a. Soil survey of Stafford County, Kansas. Washington, D.C.: U.S. Govt. Printing Office. _____. 1978b. Soil survey of Harvey County, Kansas. Washington, D.C.: U.S. Govt. Printing Office. _____. 1981. Soil survey of Barton County, Kansas. Washington, D.C.: U.S. Govt. Printing Office.
