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Inexpensive and accurate methods for spatially measuring soil properties are needed that enhance interpretation of yield maps and improve planning for site‐specific management. This study was conducted to investigate the relationship of apparent profile soil electrical conductivity (ECa) and grain yield on claypan soils (Udollic Ochraqualfs). Grain yield data were obtained by combine yield monitoring and ECa by a mobile, on‐the‐go electromagnetic (EM) induction meter. Investigations were made on four claypan fields between 1993 and 1997 for a total of 13 site‐years. Crops included five site‐years of corn (Zea mays L.), seven site‐years of soybean [Glycine max (L.) Merr.], and one site‐year of grain sorghum [Sorghum bicolor (L) Moench]. Transformed ECa (l/ECa was regressed to topsoil thickness giving r2 values > 0.75 for three of the four fields. The relationship between grain yield and ECa was examined for each site‐year in scatter plots. A boundary line using a log‐normal function was fit to the upper edge of data in the scatter plots. A significant relationship between grain yield and ECa (boundary lines with r2 > 0.25 in nine out of 13 site‐years) was apparent, but climate, crop type, and specific field information was needed to explain the shape of the potential yield by ECa interaction. Boundary line data of each site‐year fell into one of four condition categories: Condition 1–site‐years where yield increased with decreasing ECa; Condition 2–site‐years where yield decreased with decreasing ECa; Condition 3–where yield was less at low and high ECa, values and highest at some mid‐range values of ECa; and Condition 4–site‐years where yield variation was mostly unrelated to ECa. Soil ECa provided a measure of the within‐field soil differences associated with topsoil thickness, which for these claypan soils is a measure of root‐zone suitability for crop growth and yield. Research Question Grain yield mapping has demonstrated to farmers that much of the yield variability within fields seems to be associated with soil and landscape properties. A basic premise for a successful site‐specific management program is that the causes of grain variability can be identified and quantified, hopefully by using automated sensors or devices. An inexpensive and accurate method for spatially measuring soil properties that explain variability in grain crop production would greatly enhance the interpretation of yield maps and improve planning for site‐specific management. Apparent soil electrical conductivity (ECa) obtained using mobile on‐the‐go sensors has been suggested as one such measure. The objective of this research was to evaluate claypan ECa as a measure of the relative within‐field variability of grain crop production. Literature Summary Many procedures have been examined for measuring the effects of soil and landscape properties on crop production. Traditional soil surveys give a general understanding of the impact that soil mapping units have on crop productivity. Slope position and landform are topographic features t...

Understanding the relationships between yield and soil properties and topographic characteristics is of critical importance in precision agriculture. A necessary first step is to identify techniques to reliably quantify the relationships between soil and topographic characteristics and crop yield. Stepwise multiple linear regression (SMLR), projection pursuit regression (PPR), and several types of supervised feed-forward neural networks were investigated in an attempt to identify methods able to relate soil properties and grain yields on a point-by-point basis within ten individual site-years. To avoid overfitting, evaluations were based on predictive ability using a 5-fold cross-validation technique. The neural techniques consistently outperformed both SMLR and PPR and provided minimal prediction errors in every site-year. However, in site-years with relatively fewer observations and in site-years where a single, overriding factor was not apparent, the improvements achieved by neural networks over both SMLR and PPR were small. A second phase of the experiment involved estimation of crop yield across multiple site-years by including climatological data. The ten site-years of data were appended with climatological variables, and prediction errors were computed. The results showed that significant overfitting had occurred and indicated that a much larger number of climatologically unique site-years would be required in this type of analysis.

Nitrogen available to support corn (Zea mays L.) production can be highly variable within fields. Canopy reflectance sensing for assessing crop N health has been proposed as a technology to base side‐dress variable‐rate N application. Objectives of this research were to evaluate the use of active‐light crop‐canopy reflectance sensors for assessing corn N need, and derive the N fertilizer rate that would return the maximum profit relative to a single producer‐selected N application rate. A total of 16 field‐scale experiments were conducted over four seasons (2004–2007) in three major soil areas. Multiple blocks of randomized N rate response plots traversed the length of the field. Each block consisted of eight treatments from 0 to 235 kg N ha−1 on 34 kg N ha−1 increments, side‐dressed between the V7–V11 vegetative growth stages. Canopy sensor measurements were obtained from these blocks and adjacent N‐rich reference strips at the time of side‐dressing. Within fields, the range of optimal N rate varied by >100 kg N ha−1 in 13 of 16 fields. A sufficiency index (SI) calculated from the sensor readings correlated with optimal N rate, but only in 50% of the fields. As fertilizer cost increased relative to grain price, so did the value of using canopy sensors. While soil type, fertilizer cost, and corn price all affected our analysis, a modest ($25 to $50 ha−1) profit using canopy sensing was found. These results affirm that, for many fields, crop‐canopy reflectance sensing has potential for improving N management over conventional single‐rate applications.

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Along with yield mapping, producers have expressed increased interest in characterizing soil and topographic Many producers who map yield want to know how soil and landvariability (Wiebold et al., 1998). Numerous properties scape information can be used to help account for yield variability influence the suitability of soil as a medium for crop and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical root growth and yield. These include soil water-holding conductivity (EC a) and topographic measures to grain yield for three capacity, water infiltration rate, texture, structure, bulk contrasting soil-crop systems. Yield data were collected with combine density, organic matter, pH, fertility, soil depth, topograyield-monitoring systems on three fields [Colorado (Ustic Haplarphy features (i.e., slope, aspect, etc.), the presence of gids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiarestrictive soil layers, and the quantity and distribution qualfs)] during 1997-1999. Crops included four site-years of corn (Zea of crop residues. These properties are complex and vary mays L.), three site-years of soybean (Glycine max L.), and one sitespatially (and with some, temporally) within fields. No year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter single measurement adequately describes the influence wheat (Triticum aestivum L.). Apparent soil electrical conductivity of the soil environment on rooting and crop growth and was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time yield. Georeferenced soil sampling for fertility status, kinematic global positioning system, was used to determine slope, typically from the surface layer from 0 to 20 cm, is often curvature, and aspect. Four analysis procedures were employed to used by producers in developing recommendation maps investigate the relationship of these variables to yield: correlation, for variable-rate fertilizer application. Information obforward stepwise regression, nonlinear neural networks (NNs), and tained from these samples [including fertility, organic boundary-line analysis. Correlation results, while often statistically matter, cation exchange capacity (CEC), and texture] significant, were generally not very useful in explaining yield. Using has also been used in some research to evaluate yield either regression or NN analysis, EC a alone explained yield variability variation (Kravchenko and Bullock, 2000; Nolin et al., (averaged over sites and years R 2 ϭ 0.21) better than topographic 2001; Ward and Cox, 2001), but usually little or no variables (averaged over sites and years R 2 ϭ 0.17). In six of the nine site-years, the model R 2 was better with EC a than with topography. significance has been found. Combining EC a and topography measures together usually improved Inexpensive and accurate methods for measuring model R 2 values (averaged over sites and years R 2 ϭ 0.32). ...

Producers using site‐specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c‐means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.

Along with yield mapping, producers have expressed increased interest in characterizing soil and topographic Many producers who map yield want to know how soil and landvariability (Wiebold et al., 1998). Numerous properties scape information can be used to help account for yield variability influence the suitability of soil as a medium for crop and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical root growth and yield. These include soil water-holding conductivity (EC a) and topographic measures to grain yield for three capacity, water infiltration rate, texture, structure, bulk contrasting soil-crop systems. Yield data were collected with combine density, organic matter, pH, fertility, soil depth, topograyield-monitoring systems on three fields [Colorado (Ustic Haplarphy features (i.e., slope, aspect, etc.), the presence of gids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiarestrictive soil layers, and the quantity and distribution qualfs)] during 1997-1999. Crops included four site-years of corn (Zea of crop residues. These properties are complex and vary mays L.), three site-years of soybean (Glycine max L.), and one sitespatially (and with some, temporally) within fields. No year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter single measurement adequately describes the influence wheat (Triticum aestivum L.). Apparent soil electrical conductivity of the soil environment on rooting and crop growth and was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time yield. Georeferenced soil sampling for fertility status, kinematic global positioning system, was used to determine slope, typically from the surface layer from 0 to 20 cm, is often curvature, and aspect. Four analysis procedures were employed to used by producers in developing recommendation maps investigate the relationship of these variables to yield: correlation, for variable-rate fertilizer application. Information obforward stepwise regression, nonlinear neural networks (NNs), and tained from these samples [including fertility, organic boundary-line analysis. Correlation results, while often statistically matter, cation exchange capacity (CEC), and texture] significant, were generally not very useful in explaining yield. Using has also been used in some research to evaluate yield either regression or NN analysis, EC a alone explained yield variability variation (Kravchenko and Bullock, 2000; Nolin et al., (averaged over sites and years R 2 ϭ 0.21) better than topographic 2001; Ward and Cox, 2001), but usually little or no variables (averaged over sites and years R 2 ϭ 0.17). In six of the nine site-years, the model R 2 was better with EC a than with topography. significance has been found. Combining EC a and topography measures together usually improved Inexpensive and accurate methods for measuring model R 2 values (averaged over sites and years R 2 ϭ 0.32). ...