Thomas S. Colvin

Twelve-year tillage and crop rotation effects on yields and soil chemical properties in northeast Iowa

Abstract Long‐term tillage and crop management studies may be useful for determining crop production practices that are conducive to securing a sustainable agriculture. Objectives of this field study were to evaluate the combined effects of crop rotation and tillage practices on yield and changes in soil chemical properties after 12 years of research on the Clyde‐Kenyon‐Floyd soil association in northeastern Iowa. Continuous corn (Zea mays L.) and a corn‐soybean [Glycine max L. (Herr.)] rotation were grown using moldboard plowing, chisel plowing, ridge‐tillage, or no‐tillage methods. Tillage and crop rotation effects on soil pH, Bray P1, 1M NH4OAc exchangeable K, Ca, and Mg, total C, and total N in the top 200 mm were evaluated. Profile NO3‐N concentrations were also measured in spring and autumn of 1988. Crop yields and N use efficiencies were used to assess sustainability. Bray P1 levels increased, but exchangeable K decreased for all cropping and tillage methods. Nutrient stratification was evident for...

Relationship Between Six Years of Corn Yields and Terrain Attributes

Crop yield, soil properties, and erosion are strongly related to terrain attributes. The objectives of our study were to examine the relationship between six years of corn (Zea mays L.) yield data and relative elevation, slope, and curvature, and to develop a linear regression model to describe the spatial patterns of corn yield for a 16 ha field in central Iowa, USA. Corn grain yield was measured in six crop years, and relative elevation was measured using a kinematic global positioning system. Slope and curvature were then determined using digital terrain analysis. Our data showed that in the four years with less than normal growing season precipitation, corn yield was negatively correlated with relative elevation, slope, and curvature. In the two years with greater than normal precipitation, yield was positively correlated with relative elevation and slope. A multiple linear regression model based on relative elevation, slope, and curvature was developed that predicted 78% of the spatial variability of the average yield of the transect plots for the four dry years. This model also adequately identified the spatial patterns within the entire field for yield monitor data from 1997, which was one of the dry years. The relationship between terrain attributes and corn yield spatial patterns may provide opportunities for implementing site-specific management.

ANALYSIS OFWATER STRESS EFFECTS CAUSING SPATIAL YIELD VARIABILITY IN SOYBEANS

Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability has had limited success. In this article, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16-ha field in Iowa using three years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over three years. The root mean square error was 286 kg ha–1 representing approximately 12% of the three-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water run on into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.

Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors

Research findings are reviewed focusing on yield sensing methods, yield reconstruction, mapping, and errors. Yield sensing methods were explained and yield mapping process was briefly introduced. Grain flow through different combines was explained and the effects of combine dynamics on yield measurement accuracy were discussed. Other errors caused by various sensors that are utilized by a yield monitor were included. It was concluded that with proper installation, calibration, and operation of yield monitors, sufficient accuracy can be achieved in yield measurements to make site-specific decisions. Nevertheless, attention must be paid when interpreting yield maps since yield measurement accuracy can vary depending upon the measurement principle, combine grain flow model, size of management area chosen, and the operators capabilities and carefulness in following instructions to obtain the best accuracy possible under varying field operating conditions. Some of the errors can be filtered out by careful analysis of the raw yield (or flow rate) data provided by yield monitors. Researchers have focused on crop flow models to improve yield reconstruction process. A yield reconstruction algorithm that effectively handles non-linear combine dynamics has not been developed by researchers yet. More efforts towards yield reconstruction should be encouraged.

SPATIO-TEMPORAL ANALYSIS OF YIELD VARIABILITY FOR A CORN-SOYBEAN FIELD IN IOWA

Spatio-temporal analyses of yield variability are required to delineate areas of stable yield patterns for application of precision farming techniques. Spatial structure and temporal stability patterns were studied using 1995- 1997 yield data for a 25-ha field located near Story City, Iowa. Corn was grown during 1995-1996, and soybean in 1997. The yield data were collected on nine east-west transects, consisting of 25 yield blocks per transect. The two components of yield variability, i.e., large-scale variation (trend) and small-scale variation, were studied using median polishing technique and variography, respectively. The trend surface, obtained from median polishing, accounted for the large-scale deterministic structure induced by treatments and landscape effects. After removal of trend from yield data, the resulting yield residuals were used to analyze the small-scale stochastic variability using variography. The variogram analysis showed strong spatial structure for the yield residuals. The spatial correlation lengths were found to vary from about 40 m for corn to about 90 m for soybean. The range parameter of the variograms showed a significant correlation coefficient of –0.95 with the cumulative growing season rainfall. The total variance of 1995 corn yield was partitioned as 56% trend, 37% small-scale stochastic structure, and 7% as an interaction of both. Yield variance of 1996 corn was about 80% trend and 20% small-scale stochastic structure. Contrary to corn years, the total yield variance for soybean in 1997 was partitioned as about 25% trend and about 75% small-scale stochastic structure. The significant negative correlation of range with rainfall shows that small-scale variability may be controlled by factors induced directly or indirectly by rainfall. More years of data are required to substantiate these relationships. The lack of temporal stability in large-scale and small-scale variation suggest that longer duration yield data analyses are required to understand and quantify the impact of various climatic, and management factors and their interaction with soil properties on delineation of areas under consistent yield patterns before applying variable rate technology.

CROPPING SYSTEM EFFECTS ON NO3–N LOSS WITH SUBSURFACE DRAINAGE WATER

An appropriate combination of tillage and nitrogen management practices will be necessary to develop sustainable farming practices. A six–year (1993–1998) field study was conducted on subsurface–drained Clyde–Kenyon–Floyd soils to quantify the impact of two tillage systems (chisel plow vs. no tillage) and two N fertilizer management practices (preplant single application vs. late–spring soil test based application) on nitrate–nitrogen (NO3–N) leaching loss with subsurface drain discharge from corn (Zea mays L.) soybean (Glycine max L.) rotation plots. Preplant injected urea ammonium nitrate solution (UAN) fertilizer was applied at the rate of 110 kg ha–1 to chisel plow and no–till corn plots, while the late–spring N application rate averaged 179 and 156 kg ha–1 for the no–till and chisel plow corn plots, respectively. Data on subsurface drainage flow volume, NO3–N concentrations in subsurface drainage water, NO3–N loss with subsurface drainage flow, and crop yield were collected and analyzed using a randomized complete block design. Differences in subsurface drainage flow volume due to annual variations in rainfall significantly (P = 0.05) affected the NO3–N loss with subsurface drainage flows. High correlation (R2 = 0.89) between annual subsurface drainage flow volume and the annual NO3–N leaching loss with subsurface drainage water was observed. The flow–weighted average annual NO3–N concentrations varied from a low of 6.8 mg L–1 in 1994 to a high of 13.9 mg L–1 in 1996. Results of this study indicated that NO3–N losses from the chisel plow plots were 16% (16 vs. 19 kg–N ha–1) lower in comparison with no–till plots, while corn grain yield was 11% higher in the chisel plow plots (8.3 vs. 7.5 Mg ha–1). Late–spring N application applied as a sidedress resulted in 25% lower NO3–N leaching losses with subsurface drainage water in comparison with preplant single N application and also significantly (P = 0.5) higher corn grain yield by 13% (8.4 vs. 7.4 Mg ha–1). These results clearly demonstrate that chisel plow tillage with late–spring soil test based N application for corn after soybean can be a sustainable farming practice for the northeast part of Iowa.

YIELD VARIABILITYWITHINACENTRAL IOWA FIELD

Technologies to support precision farming (PF) began to emerge in 1989 when the Global Positioning System (GPS) became available to a limited extent and was tested as a means for locating farm equipment within fields. Substantial PF technology is available with rapidly decreasing costs and increasing capabilities. However, one major class of information that is missing is a method for determining how much material to apply or what action to take as a result of a specific condition at any position within a field. Developing this information will require knowing the spatial and temporal variability of plant response and will most likely be obtained by measuring yield variability. This field study was designed to quantify yield variability within a 16 ha field which has had consistent practices for several years. Crop yields showed a coefficient of variation ranging from near 12% in 1989 and 1992 to over 30% in 1990 and 1993. Rankings of the long-term relative yield for 224 locations were not stable even after 6 years when recalculated each year. Many PF scenarios are based on the assumption of a stable yield pattern within a field, but only a few points in this field have exhibited such a pattern. Perhaps stable patterns will eventually emerge, but the time frame for this to occur may be quite long. Overall, this study suggests that implementation of PF practices within the Clarion-Nicollet-Webster soil association area will reveal both difficulties and opportunities.

Using Soil Attributes and GIS for Interpretation of Spatial Variability in Yield

Precision farming application requires better understanding of variability in yield patterns in order to determine the cause-effect relationships. This field study was conducted to investigate the relationship between soil attributes and corn (Zea mays L.)-soybean (Glycine max L.) yield variability using four years (1995-98) yield data from a 22-ha field located in central Iowa. Corn was grown in this field during 1995, 1996, and 1998, and soybean was grown in 1997. Yield data were collected on nine east-west transects, consisting of 25-yield blocks per transect. To compare yield variability among crops and years, yield data were normalized based on N-fertilizer treatments. The soil attributes of bulk density, cone index, organic matter, aggregate uniformity coefficient, and plasticity index were determined from data collected at 42 soil sampling sites in the field. Correlation and stepwise regression analyses over all soil types in the field revealed that Tilth Index, based upon soil attributes, did not show a significant relationship with the yield data for any year and may need modifications. The regression analysis showed a significant relationship of soil attributes to yield data for areas of the field with Harps and Ottosen soils. From a geographic information system (GIS) analysis performed with ARC/INFO, it was concluded that yield may be influenced partly by management practices and partly by topography for Okoboji and Ottosen soils. Map overlay analysis showed that areas of lower yield for corn, at higher elevation, in the vicinity of Ottosen and Okoboji soils were consistent from year to year; whereas, areas of higher yield were variable. From GIS and statistical analyses, it was concluded that interaction of soil type and topography influenced yield variability of this field. These results suggest that map overlay analysis of yield data and soil attributes over longer duration can be a useful approach to delineate subareas within a field for site specific agricultural inputs by defining the appropriate yield classes.

An Evaluation of the Response of Yield Monitors and Combines to Varying Yields

Comparisons were made between yield measurements with an impact based yield sensor and an electronic scale in adjacent harvest strips and on the same grain stream within a combine. Yield measurements in adjacent strip comparisons were more prone to errors as the segment lengths decreased. Grain yield difference between the yield sensor and electronic scale ranged from 5 to 14%, 4 to 13%, 3 to 12%, and 2 to 11% for 15, 30, 60, and 300 m long segments. The yield differences between neighboring segments might have been caused by yield variability to a degree; however, a consistent decrease in yield differences with increasing segment lengths implied that better accuracies could be obtained in longer management areas. The combine responses to grain yield changes and the effect of varying ground speed on accuracy were also evaluated by creating artificial yield patterns in harvest strips. Grain diffusion within the combine was more obvious when abrupt yield changes were introduced at known locations. Grain mixing and redistribution inside the combine may dictate the selection of segment sizes in the site-specific decision making process. Constant ground speed provided more stable grain flow values than varying ground speed. The average error in yield estimate was 3.4% and 5.2% at constant ground speed and varying speed, respectively. Careful calibration and constant combine speed were important to achieve better accuracy with the grain yield monitor.

Prediction of NO3-N losses with subsurface drainage water from manured and UAN-fertilized plots using GLEAMS

Excessive application of swine manure to a field over long durations can increase nitrate-nitrogen (NO 3 -N) leaching as a result of accumulation of more nutrients in the root zone than the subsequent crops may need. The objective of this study was to use the GLEAMS (V.2.1) model to compare measured versus simulated effects of swine manure application with urea-ammonium-nitrate (UAN) on subsurface drain water quality from beneath long-term corn (Zea mays L.) and soybean (Glycine max L.) plots. Four years (1993-1996) of field data from an Iowa site were used for model calibration and validation. The SCS curve number and effective rooting depth were adjusted to minimize the difference between simulated percolation below the root zone and measured subsurface drain flows. Model predictions of percolation water below the root zone followed the pattern of measured drain flow data, giving an average difference of 10%, and –5% between predicted and measured values for manured and UAN-fertilized plots, respectively, for four years from 1993 to 1996. Model simulations for overall NO 3 -N losses with percolation water were comparable to measured NO 3 -N losses with subsurface drain water giving an average difference of 20% for manured plots. The model overpredicted NO 3 -N losses, particularly for soybean on plots, which received manure in the previous year. Predicted NO 3 -N losses with subsurface drainage from fertilized plots were much lower than measured values with an average difference of –32%. The best fit line with zero intercept showed correlation coefficients of 0.73 and 0.66 between monthly predicted and measured NO 3 -N losses with subsurface drain flows for manured and UAN-fertilized plots for four years from 1993 to 1996, respectively. The results of the study show that the N-transformation processes and the associated rate factors based on soil temperature and soil water levels may need to be refined for consistent simulation of NO 3 -N losses with subsurface drainage water when fertilized with either swine manure or UAN for corn production.