ALTERNATIVE DATA MANAGEMENTS AND INTERPRETATIONS FOR STRIP TRIALS HARVESTED WITH YIELD MONITORS

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1 ALTERNATIVE DATA MANAGEMENTS AND INTERPRETATIONS FOR STRIP TRIALS HARVESTED WITH YIELD MONITORS A.P. Mallarino, M. Bermudez, D.J. Wittry, and P. N. Hinz Iowa State University Ames, Iowa ABSTRACT This research demonstrates possible field methods and alternative data analyses for on-farm comparisons of fertilization practices for grain crops using precision agriculture technologies. Soil samples and grain yields were collected using intensive soil sampling techniques and yield monitors from field-scale, replicated strip trials. The data were managed and analyzed using various geographical information systems (GIS) and statistical procedures. Use of yield monitors allowed for yield measurements for different areas of the field, which is a major advantage compared with the traditional method of weighing grain harvested from strips as long as the field length. This capability lead to various statistically valid estimates of treatment effects on crop yield and different interpretations of the results. These estimates arise from dividing the field into different areas in ways that still conform to sound field experimental design. Consideration of the spatial correlation of yield in analyses of variance by means of nearest neighbor analysis or modeled semivariograms improved the assessment of treatment effects compared with classic analyses. However, accounting for spatial correlation usually resulted in similar treatment means. Subjective scientific and agronomic judgment continues to play a major role in selecting the most appropriate analysis to achieve the objectives of the experiments and in interpreting the results. KEYWORDS: on-farm experiments, yield monitor, precision agriculture. INTRODUCTION Agricultural experimentation often involves comparisons of current and new products, technologies, systems, or recommendations. New products, technologies, systems, or management practices are recommended after statistical analyses confirm the advantages over existing practices. Field tests usually are replicated in several locations over a period of years using a variety of experimental designs. Environmental replication is necessary to be able to extend recommendations to larger geographical areas. On-farm research using strip plots is an accepted methodology for complementing traditional small-plot research, for generating local recommendations, and for demonstrating management practices. It is used by

2 farmers, dealers, industry, and universities (Rzewnicki et al., 1988; Shapiro et al., 1989). Crop yield is measured with farm-size combines equipped with weighing devices or by weighing large loads. In recent years new technologies available to farmers allow for georeferencing of measurements such as soil tests, scouting counts, various agronomic observations, and crop yields. These measurements can be collected using conventional methods or with sensing devices. The most commonly used sensor is the grain yield monitor. After several cropping seasons all these layers of information will generate extensive farm databases. Precision agriculture technologies can be successfully adapted to these types of field trials (Oyarzabal et al., 1996; Mallarino and Wittry, 1997). Significant advantages of using these new technologies include better data collection and increased number of observations often at a low cost. Typical examples are the numerous soil-test sampling points and yield-monitor data points that can be collected for each experimental unit (each unit area to which a specific treatment is applied) and georeferenced. These advantages exist even when conventional methods of analysis such as analysis of variance (ANOVA), regression, and others that do not involve spatial analysis techniques are used. The benefit of using precision agriculture technologies likely is fully achieved when spatial analysis techniques are used. This possibility can greatly improve both the analysis and interpretation of the results of on-farm field trials. However, use of these techniques also creates new questions and uncertainties concerning data management, analysis, and interpretation. The objective of this study was to demonstrate and discuss a variety of data management and statistical procedures that can be applied to data collected with yield monitors and intensive soil sampling methods from fertilization, onfarm strip-trials. The treatments applied to two fields were variable-rate and fixed-rate P fertilization for soybean [Glycine max (L.) Merr.] and fixed-rate starter fertilization for corn (Zea mays L.), and measurements were soil-test values and grain yields collected with yield monitors. The two experiments are part of two ongoing multi-site research projects. The agronomic justification for the experiments is obvious and will not be discussed in this article. MATERIALS AND METHODS Fields, Treatments, and Measurements Data from two responsive trials that were part of multi-site research projects were used for this article. One trial involved the comparison of liquid starter vs. no starter fertilization for corn. The other trial compared variable-rate and fixed-rate P fertilization for soybean. The treatments were applied to strips whose width and length varied depending on the planting or fertilization equipment used and on the length of the fields. In the Starter trial the strip width was 12 m (two passes of a 6-m planter) and the strip length was 480 m. In the P fertilizer trial the strip length was 18 m (the width of the bulk fertilizer spreader) and strip width was 360 m. The treatments were replicated four times for the Starter trial and three times for the P fertilization trials following a completely randomized block design. The strips were the experimental units that received the different treatments. Treatments in the Starter trial were application of a

3 N-P 2 O 5 -K 2 O liquid fertilizer in the row and no starter fertilization. The amount of starter applied was 6.5 kg N/ha and 9.6 kg P/ha. Treatments in the P fertilization trial were a nonfertilized control, a fixed P rate, and a variable P rate in which the rate applied varied along the strip depending on soil-test P measurements made before planting. Several soil samples were collected from the experimental areas before planting following a systematic grid-point sampling scheme. The grid lines across crop rows (and across the future treatment strips) were spaced to coincide with the width of each replication (block). The spacing between grid lines along crop rows (and along the future treatment strips) was 24 m in the Starter trial and 45 m in the P fertilization trial. Composite soil samples were collected from an 80-m 2 central area of each cell (12 cores from a 15-cm depth). The soil was analyzed at the Iowa State University Soil and Plant Analysis Laboratory for P by the Bray-1 method, K, Ca, and Mg by the ammonium acetate method, organic matter by the Walkley Black method, and ph. Iowa State University soil test interpretation classes for P (Voss et al., 1996) will be used at times to classify soil test ranges in this report. Classes and values are Very Low (0 to 8 mg kg -1 ), Low (9 to 15 mg kg -1 ), Optimum (16 to 20 mg kg -1 ), High (21 to 30 mg kg -1 ), and Very High (>30 mg kg -1 ). Crop yields were measured and recorded using yield monitors equipped with real-time differential global positioning systems (DGPS) receivers. The grain was harvested with combines equipped with Green Star (John Deere Inc.) monitors and moisture sensors. The yield monitor points were recorded at 1-s intervals. The spatial accuracy was checked by georeferencing several positions in the field with a hand-held DGPS receiver. Yield data were unaffected by field borders or end-of-field yield monitor errors because yield monitor data points for a distance of approximately 40 m from each field end were deleted from the data set. While harvesting, each combine trip (a 6-m swath for the Starter trial and a 4.5-m swath for the P fertilization trial) was identified with a unique number. The yield data recorded by the yield monitors were carefully analyzed for common errors, such as incorrect geographic coordinates due to partial loss of good differential correction, the effects of waterways or grass strips, and incorrect settings in the time lag for the grain path through the combine (from the combine head to the yield monitor). The data were imported into ArcView (ESRI, 1998) and the SAS statistical package (SAS Institute, 1996) for data management and analysis. Data Management and Analysis The yield response data and the relationships between yield response and soil-test values were analyzed by various procedures. Yield responses were analyzed by five procedures. The first three procedures analyzed treatment effects on yield assuming a randomized complete block design (RCBD) without accounting for the spatial correlation of yield, whereas the last two included ANOVA and spatial statistics. For Procedure 1, the data input were yield means for the entire length of the strips (i. e., the experimental units), a procedure that is equivalent to the one used for traditional strip trials in which yields are harvested and weighed using conventional methods. For Procedure 2, a similar ANOVA

4 was performed on a similar type of yield means that were calculated after deleting very low or variable yields for usually small areas that were obviously affected by adverse soil characteristics or management. These are yield levels for which an analysis of treatment effects makes no agronomic sense. A similar concept was used for Procedure 3, but in this instance ANOVA was used to study treatments effects for areas along the strips that had either high or low yields. The threshold yield level was chosen arbitrarily after analysis of yields and other layers of information. Procedures 4 and 5 accounted for the spatial correlation of yields in conjunction with ANOVA in two different ways. The yield input data for these two procedures were means of all yield monitor points within small areas delineated by the width of the combine head and a distance of 24 m (Starter trial) or 20 m (P fertilization trial) along crop rows. The individual data recorded by the yield monitors were not directly considered because of the known lack of accuracy of yield monitors over short distances (Lark et al., 1997). In Procedure 4, nearest neighbor analysis (NNA) was used to calculate values of a covariate that was included into the ANOVA for a RCBD following a procedure used before (Hinz and Lagus, 1991, Mallarino et al., 1998). A covariate value was calculated to correspond to each number input for the ANOVA. The first step in the calculation was to calculate yield residuals by removing treatment and block effects with a conventional ANOVA. Afterwards, covariate values were calculated by subtracting each yield residual from the mean value of its residual neighbors. Four neighbors (one from each N, S, E, and W direction) were used because preliminary work in our group (D. Dousa, P. Hinz, and A. P. Mallarino, personal communication) found that using four neighbors was more efficient in reducing experimental error than using six to 14 neighbors. In Procedure 5, the spatial correlation was accounted for by using a mixed model with SAS proc mixed (Littell et al., 1996). Initial estimates of the sill, nugget, and range parameters of the semivariance model were calculated on a data set of residuals from a conventional ANOVA. In a second step, the initial estimates were included in appropriate statements of the proc mixed procedure to estimate treatment effects on yields. The relationship between treatment effects and soil-test P values were analyzed by three procedures (the same procedure can be used for other measurements). One procedure assessed treatment effects separately for parts of the experimental areas with different soil-test P values following a procedure described by Oyarzabal et al. (1996). The yield input data were means for areas defined by the width of each strip (12 m for the Starter trial and 18 m for the P fertilization trial) and the separation distance of the soil sampling grid lines along the strips (24 m for the Starter trial and 45 m for the P fertilization trial). The soil-test P input data were the values for areas defined by the width of each replication and the separation distance of the sampling grid lines along the strips. Each yield value was classified according to the soil-test P interpretation class. Values were not considered for this analysis when there were less than three cells for a similar class within a field. In this case, the data for those cells were merged with a neighboring class. The ANOVA included estimates of soil-test P class and interaction treatments by soil-test class effects. The soil-test classes were considered as repeated measures within the experimental units. A significant

5 interaction soil-test class by treatment suggests that treatment effects differed for areas of the field with different soil-test levels. When the interaction was significant, an additional ANOVA estimated the significance of treatment effects for each soil-test class. In the second and third procedures, regression analysis was used to study the relationship between yield response (absolute and relative yield increases) and soil test P across the field in two different ways. For one procedure, the data pairs were the same used for the previous analysis (yield means and soil-test P for areas defined by each strip and soil sampling cell). The data pairs for the other procedure were calculated by interpolating and surfacing yield-monitor data points and soil test values. Before interpolating the yield data, the data set (with yield points corresponding to all observations) was split into two data sets. In the Starter trial, one data set contained data for the nonfertilized control and the other one contained data for the starter treatment. In the P fertilization trial, one data set contained data for the nonfertilized treatment and the other contained data for the two fertilized treatments. The fixed-rate and variable-rate treatments were not separated because they produced statistically similar yields. Data in all data sets were interpolated using a weighted inverse-distance method and were surfaced to a 5-m 2 grid size. In a second step, the yield data set containing the nonfertilized treatment was subtracted from the data set containing the fertilized treatments using GIS spatial analysis methods, which resulted in a map with treatment differences. In a third step, the yield differences data were regressed on the soiltest P data. Figure 1 shows, as an example, selected maps for the Starter trial. RESULTS AND DISCUSSION The soil test data from samples collected before fertilization showed large nutrient variability in all fields. Table 1 shows descriptive statistics for selected soil-test values. The soil-test P data encompassed several classes used by Iowa State University in both fields. According to current interpretations for corn and soybean, large to moderate yield responses to P should be expected in the very low and low classes, small or no responses should be expected in the optimum class, and no responses should be expected within the high or very high classes. The initial soil-test K data for the trials ranged from Low to Very High, and a high K fertilization rate was uniformly applied over the field. Table 2 shows yield means and relevant statistics for the procedures that analyzed yield responses using a conventional ANOVA. The main objective of these comparisons is to address one major advantage of harvesting strip trials using yield monitors compared with the traditional practice of weighing the grain harvested from the entire length of each strip. The yield monitor allows for estimating treatments differences for the entire field, for parts of the field, or for different yield levels. This is a significant aspect that may lead to different interpretation of treatment effects when there are contrasting soil differences over a field.

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7 Table 1. Descriptive statistics for selected soil tests for four strip trials. Trial Soil test Mean Minimum Maximum SD Starter P (mg kg -1 ) K (mg kg -1 ) ph Org. matter (g kg -1 ) P P (mg kg -1 ) K (mg kg -1 ) ph Org. matter (g kg -1 ) Table 2. Effect of the yield data used on estimates of yield response to fertilization in two strip trials. Yield data used Treatment Complete Problem areas High Low Trial and statistics data set excluded yield area yield area kg/ha of grain and statistics Starter No starter Starter P>F P Control Fixed Variable P>F of P P>F of F-V See the Methods section for a description of the data sets used. F-V = comparison of the fixed and variable fertilization treatments. A capability for either considering or eliminating areas also is useful when known or unknown growth factors other than those under study seriously limit yield levels in some parts of the field. The latter situation occurs very frequently in production agriculture, and usually is associated to areas that are highly eroded or have severe pest infestation, very poor drainage, or soil with low moisture holding capacity. The results show that yield monitor data provides much more flexibility concerning yield response interpretation compared with the classic weigh wagon harvesting method. However, decisions about the yield data management before a conventional analysis of variance may influence the conclusions of a study. In

8 both trials, use of the yield monitor data after a normal correction of errors (wrong yield monitor delay setting, waterways, combine stops, etc.) or a more strict cleaning of the data resulted in similar estimates of both yield and treatment differences. However, we believe that the data management corresponding to the problem areas excluded method is more appropriate from an agronomic perspective. The analysis for high or low yielding areas produced markedly different results and, moreover, the trends were different for the two trials. Starter fertilization had a smaller effect on yields in high yielding areas than in low yielding areas. In the P fertilization trials, on the other hand, P fertilization had a higher effect in the high yielding areas. Detailed explanations of these differences are beyond the scope of this article. Our objectives were to show that these types of analyses are possible and that different interpretations also are possible. Aspects related to nutrient dynamics in soils and crop response to both types of fertilization undoubtedly are involved. Explanations could also be found after study of differences in other layers of information for these high or low yielding areas. In the Starter experiment, for example, the lower corn response to starter in high yielding areas coincided with higher elevation, different soil types, higher soil-test P, faster corn early growth, and smaller differences between treatments in early growth. Table 3 shows the mean yields and relevant statistics for the procedure that analyzed yield response using a conventional ANOVA, the procedure involving NNA, and the procedure involving a semivariance model. The means and statistics for the conventional ANOVA are the observed means for the complete yield data set presented in Table 2, and are shown again to facilitate the comparison of results. The treatment means for the two procedures that accounted for spatial correlation are estimated means (adjusted for spatial correlation). In agronomic terms, there was a high response of corn to starter fertilization, a moderate response of soybean to P fertilization, and no difference between fixed or variable methods of P fertilization. Adjusting for spatial correlation with NNA almost did not change the treatment means compared with observed values. Adjusting for spatial correlation with the semivariance model produced slight changes in the size of the means but changed little the relative difference between treatments. Both spatial analysis methods tended to reduce standard errors and to increase the level of significance of treatment differences. This adjustment resulted in similar interpretation of the results in both trials, however. The statistics for all procedures confirmed that starter and P fertilization increased grain yield at commonly used probability levels. The statistical significance of treatment differences was much higher for the two methods that accounted for spatial correlation. Use of these or related spatial analysis methods for other field studies (Bhatti et al., 1991; Stroup et al., 1994; Mallarino and Wittry, 1998) has shown that sometimes these differences lead to statistical confirmation of treatment differences only when the spatial analysis is used. This marked difference may occur and likely is important only when researchers use the probability classes commonly used in agricultural research (for example, P > F of 0.05). The differences in adjusted means and standard errors between the NNA and SEM cannot be explained with certainty. Observation of sample semivariograms (not shown) showed, however, evident spatial structures, good fits of the spherical model, and suggested no obvious

9 explanation for the differences. Differences likely were related to different structures of the spatial correlation of yields and to the way in which the spatial correlation is accounted for by the two procedures. The fact that using more than four neighbors did not improve the NNA analysis (not shown) suggests that a covariate calculated from few residuals may have accounted for localized variability better than procedures that considered greater number of observations. Table 3. Effect of fertilization on grain yield as evaluated by three methods of analysis for two strip trials. Treatment Method of analysis Trial and statistics RCBD NNA SEM --- kg/ha and level of significance --- Starter No starter Starter P>F P Control Fixed Variable P>F of P P>F of F-V RCBD = observed means and statistics for the randomized complete block design, NNA = analysis combined with nearest neighbor analysis, and SEM = SAS proc mixed analysis including a spherical semivariance model. F-V = comparison of the fixed and variable fertilization treatments. Table 4 shows results of the procedures that assessed treatment effects for areas of the field with different soil-test P. The results suggest that within-field variation in soil-test P influenced the effect of P fertilization in both trials. At the Starter trial, there was a significant interaction (P < 0.10) between the treatments and the soil-test P classes, and responses were greater when P was low. The significance of the interaction probably was not higher because of a small responsive trend observed for the Optimum soil-test interpretation class. In the P fertilization trial, the interaction between the treatments and the soil-test P classes also was significant, and the response was significant only for the Low class. Table 5 shows the results of regression analyses of yield response on soiltest P by two procedures. The procedure without interpolation was based on yield means for areas measuring 288 m 2 (Starter trial) or 810 m 2 (P trial) and on observed soil-test P data representing sampling areas measuring 576 m 2 (Starter trial) or 810 m 2 (P trial). The other procedure was based on interpolation and surfacing of yield monitor points and soil-test P values, and the pair of values corresponded to an output grid size of 25 m 2. The relationships were linear in all cases (fit of curvilinear models did not improve the model sums of squares

10 significantly), probably because there were few high-testing values in both fields. As expected, yield increases expressed in either absolute or relative terms were negatively correlated with soil-test P. Table 4. Mean grain yield as affected by fertilization for areas of two fields having different soil-test P values. Soil-test P class Trial Treatment Very low Low Optimum High kg ha Starter No starter na Starter na P>F na P Control na na Fixed na na Variable na na P>F of P na na P>F of F-V na na F-V = comparison of the fixed and variable fertilization treatments. na = No data or too few numbers of cells to obtain a reasonable estimate. Table 5. Relationship between yield response and soil-test P assessed by two procedures. No interpolation With interpolation Trial Response Equation r P >F Equation r P >F Starter Absolute P P Relative (%) P P P Absolute P P Relative (%) P P The units are kg ha -1 for absolute yield increases and % for relative increases. Use of relative responses slightly improved the correlations only in the starter trial. This method often reduces the variation due to different yield levels in different parts of the field. The correlations were statistically significant for both methods, but were slightly stronger for the interpolation method in the

11 Starter trial and stronger for the method without interpolation in the P trial. The higher statistical significance for the interpolation method in both fields may be explained by the higher number of observations. Observations of the equations for both trials and both procedures show that slopes of the relationships differed greatly between methods for both trials. The interpolation method produced a more rapid reduction in yield response as soil P increased (i.e., the negative linear coefficient was greater). The results of the three procedures used to study relationships between yield response and soil-test P show how different approaches may result in markedly different interpretations. Any of the procedures used is valid, and decisions concerning what method is better or what interpretation is more appropriate requires scientific judgement and are strongly dependent on assumptions concerning what set of data best represents what happened in the field. Other procedures are possible, of course, and some methods used for the three procedures demonstrated could have been different. For example, use of a different interpolation method and larger output grid sizes resulted in slightly different correlations and statistical significance. In our experience, we are finding that the interpolation method is useful to visualize treatment differences over a field. However, the other two methods usually provide more reasonable and useful agronomic interpretations concerning the relationships under study. CONCLUSIONS The method of data management and analysis markedly influenced interpretation of results of on-farm trials harvested with yield monitors. The strip trial methodology and the yield monitor allow for assessments of treatment effects over an entire experimental area or for different areas of the field. The different areas of the field can be defined based on yield levels, soil characteristics, or other agronomic aspects. This has great potential for improving the conclusion and extrapolation value of field trials. Consideration of the spatial correlation of yield in analyses of variance by means of nearest neighbor analysis or a modeled semivariogram improved the assessment of treatment effects compared with classic analyses. However, accounting for spatial correlation usually resulted in similar treatment means. The study of treatment effects for different areas of the field was a major advantage compared with the traditional method based on grain weights for strips as long as the length of the fields. This capability lead to different estimates of treatment effects and interpretations of the results. Subjective agronomic judgment continues to play a major role in selecting the most appropriate analysis to achieve the objectives of the experiments and in interpreting the results. REFERENCES Bhatti, A.U., D.J. Mulla, F.E. Koehler, and A.H. Gurmani Identifying and removing spatial correlation from yield experiments. SSSAJ 55: Hinz, P.N. and J.P. Lagus Evaluation of four covariate types used for

12 adjustment of spatial variability. p In Applied Statistics in Agriculture Conf. Kansas State Univ., Manhattan. Lark, R.M., J.V. Stafford, and H.C. Bolam Limitations on the spatial resolution of yield mapping for combinable crops. J. Agric. Eng. Res. 66: Littell, R.C., G.A. Milliken, W.W. Stroup, and R.D. Wolfinger SAS System for Mixed Models. SAS Institute, Cary, NC. Mallarino, A.P., and D.J. Wittry Use of DGPS, yield monitors, soil testing, and variable rate technology to improve phosphorus and potassium management. In The Integrated Crop Management Conference. Proceedings. Nov , Iowa State Univ. Extension. Ames. Mallarino, A.P., D.J. Wittry, D. Dousa, and P.N. Hinz Variable-rate phosphorus fertilization: On-farm research methods and evaluation for corn and soybean. p In P.C. Robert et al. (ed.). Proceedings, 4d. Int. Conf. on Site-Specific Management for Agricultural Systems. July 19-22, 1998 Minneapolis, MN. ASA, SSSA, ASA. Madison, WI. Marx, D.B., and W.W. Stroup Analysis of spatial variability using proc mixed. p In Applied Statistics in Agriculture Conf. Kansas State Univ., Manhattan. Oyarzabal, E.S., A.P. Mallarino, and P.N. Hinz Using Precision Farming Technologies for Improving Applied On-Farm Research. In P.C. Robert et al. (ed.). Proceedings, 3d. Int. Conf. on Site-Specific Management for Agricultural Systems. June 23-27, 1996 Minneapolis, MN. ASA, SSSA, ASA. Madison, WI. Rzewnicki, P.E., R. Thompson, G.W. Lesoing, R.W. Elmore, C.A. Francis, A.M. Parkhurst, and R.S. Moomaw On-farm experiment designs and implications for locating research sites. Am J. Alt. Agric. 3: SAS Institute SAS/STAT User's Guide, Release 6.11 Edition. SAS Institute, Cary, NC. Shapiro, C.A., W.L. Kranz, and A.M. Parkhurst Comparison of harvest techniques for corn field demonstrations. Am. J. of Alt. Agric. 4: Stroup, W.W., P.S. Baenziger, and K.D. Mulitze Removing spatial variation from wheat yield trials: A comparison of methods. Crop Sci. 34:62-66.