John F. Shanahan

Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years

zones in agricultural fields (Franzen et al., 2002). This approach has been applied in Illinois and Indiana where Recent precision-agriculture research has focused on use of man40% of grain yield variability was explained by topoagement zones (MZ) as a method for variable application of inputs like N. The objectives of this study were to determine (i) if landscape graphical characteristics and selected soil properties attributes could be aggregated into MZ that characterize spatial varia(Kravchenko and Bullock, 2000). Aerial photographs, tion in soil chemical properties and corn yields and (ii) if temporal crop canopy images, and yield maps have also been variability affects expression of yield spatial variability. This work was suggested as approaches to delineate MZ (Schepers et conducted on an irrigated cornfield near Gibbon, NE. Five landscape al., 2000). Remote sensing technology is especially apattributes, including a soil brightness image (red, green, and blue pealing to identify MZ because it is noninvasive and bands), elevation, and apparent electrical conductivity, were acquired low in cost (Mulla and Schepers, 1997). Additionally, for the field. A georeferenced soil-sampling scheme was used to deterscientific evidence for suggesting practical use of remote mine soil chemical properties (soil pH, electrical conductivity, P, and sensing technology to delineate MZ is increasing (Varvel organic matter). Georeferenced yield monitor data were collected for et al., 1999). five (1997–2001) seasons. The five landscape attributes were aggregated into four MZ using principal-component analysis of landscape Another promising noninvasive approach to define attributes and unsupervised classification of principal-component the boundaries of MZ involves the use of electromagscores. All of the soil chemical properties differed among the four netic induction to measure apparent electrical conducMZ. While yields were observed to differ by up to 25% between the tivity (ECa). This approach has been used to effectively highestand lowest-yielding MZ in three of five seasons, receiving map variations in surface soil properties such as salinity, average precipitation, less-pronounced ( 5%) differences were noted water content, and percentage clay (Corwin and Lesch, among the same MZ in the driest and wettest seasons. This illustrates 2003; Kitchen et al., 2003). In a semiarid cropping systhe significant role temporal variability plays in altering yield spatial tem, Johnson et al. (2003) showed that ECa–determined variability, even under irrigation. Use of MZ for variable application MZ could be used to characterize spatial variation in of inputs like N would only have been appropriate for this field in wheat (Triticum aestivum L.) and corn (Zea mays L.) three out of the five seasons, seriously restricting the use of this approach under variable environmental conditions. yields. Magnetic induction has also been used to track soluble nutrient levels in soil (Eigenberg et al., 2002). Caution is necessary when using this approach because of the extreme sensitivity to soil type and management R research in precision agriculture has focused conditions, but its ease of use makes it an attractive tool on use of MZ as a method to more efficiently for precision farming applications (Lund et al., 1998). apply crop inputs such as N across variable agricultural Yield mapping is yet another approach to delineate landscapes (Franzen et al., 2002; Ferguson et al., 2003). MZ. This approach is considered to be the primary form Management zones, in the context of precision agriculof precision-agriculture technology in the USA (Pierce ture, are field areas possessing homogenous attributes and Nowak, 1999). However, practical application of in landscape and soil condition. When homogenous in yield mapping to identify zones has been plagued by a specific area, these attributes should lead to the same spatial and temporal variation in measured yield (Hugresults in crop yield potential, input use efficiency, and gins and Alderfer, 1995; Sadler et al., 1995). Conseenvironmental impact. quently, most efforts in yield map interpretation have Approaches to delineate MZ vary. Topography has focused on identifying generalized zones of low, mebeen suggested as a logical basis to define homogenous dium, and high yield (Stafford et al., 1998). While using MZ to characterize spatial variability in A.R. Schepers, J.F. Shanahan, J.S. Schepers, and S. Johnson, USDAARS, and Dep. of Agron. and Hortic., Univ. of Nebraska, Lincoln, soil and crop properties is important in site-specific studNE 68583; M.A. Liebig, USDA-ARS, Northern Great Plains Res. ies, it is equally important to consider the temporal Lab., Mandan, ND 58554; and A. Luchiari, Jr., Embrapa Meio Ameffects of climate variability on expression of spatial variabiente, Jaguariuna, SP, Brazil. Joint contribution of USDA-ARS and tion in crop yields. For example, Eghball and Varvel Agric. Res. Div. of the Univ. of Nebraska. Published as Journal Ser. (1997) and Lamb et al. (1997) found under rainfed conno. 14176. Mention of commercial products and organizations in this article is solely to provide specific information. It does not constitute ditions that temporal variability of corn yields was more endorsement by USDA-ARS over other products and organizations dominant than spatial variability, indicating that spatial not mentioned. The USDA-ARS is an equal opportunity/affirmative patterns in grain yields were greatly affected by yearly action employer, and all agency services are available without discrimination. Received 18 Aug. 2003. *Corresponding author (jshanahan1@ Abbreviations: CV, coefficient of variation; DGPS, differential global unl.edu). positioning system; DN, digital number; EC, electrical conductivity; ECa, apparent electrical conductivity; GIS, geographical information Published in Agron. J. 96:195–203 (2004).  American Society of Agronomy systems; MZ, management zones; OM, organic matter; PC, principal component; PCA, principal-component analysis. 677 S. Segoe Rd., Madison, WI 53711 USA

Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels

a b s t r a c t Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L.) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R2 of 0.74 and 0.94, respectively. Results showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780-800 nm) and either green (540-560 nm) or red-edge (730-750 nm) spectral bands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively.

Site-specific management zones based on soil electrical conductivity in a semiarid cropping system

geographic information systems (GIS) for spatial analysis and mapping, variable-rate applicators, and input preSite-specific management (SSM) can potentially improve both ecoscription maps to define management zones and direct nomic and ecological outcomes in agriculture. Effective SSM requires metering devices controlling input rates (Eliason et al., strong and temporally consistent relationships among identified man1995). While the first three components are currently agement zones; underlying soil physical, chemical, and biological parameters; and crop yields. In the central Great Plains, a 250-ha dryland available, the last, an effective and economical basis for experiment was mapped for apparent electrical conductivity (ECa). defining site-specific inputs, is lacking. In response to Eight fields were individually partitioned into four management zones this need, significant research effort has been directed based on equal ranges of deep (ECDP) and shallow (ECSH) ECa (aptoward evaluating a variety of individual and combined proximately 0–30 and 0–90 cm depths, respectively). Previous experiGIS databases as frameworks for identifying stratified ments documented negative correlations between ECSH and soil propwithin-field management zones (regions of similar proerties indicative of productivity. The objectives of this study were to duction potential). These include kriged soil test point examine ECSH and ECDP relationships with 2 yr of winter wheat (Tritidata (Mulla, 1991); soil survey maps (Robert, 1989); cum aestivum L.) and corn (Zea mays L.) yields and to consider the topography (Kravchenko et al., 2000); remote sensing potential applications of ECa–based management zones for SSM in (McCann et al., 1996); topography and remote sensing a semiarid cropping system. Within-zone wheat yield means were (Tomer et al., 1995); topography, remote sensing, and negatively correlated with ECSH (r 0.97 to 0.99) and positively farmer experience (Fleming et al., 1999); electrical concorrelated with ECDP (r 0.79–0.97). Within-zone corn yield means ductivity sensors (Sudduth et al., 1997; Lund et al., 1999); showed no consistent relationship with ECSH but positive correlation and yield maps (Eliason et al., 1995; Stafford et al., with ECDP (r 0.81–0.97). Equal-range and unsupervised classification methods were compared for ECSH; within-zone yield variances de1999). These approaches to SSM have met with varying clined slightly (0–5%) with the unsupervised approach. Yield response degrees of success that are often highly soil or region curves relating maximum wheat yields and ECSH revealed a boundary specific. line of maximum yield that decreased with increasing ECSH. In this Because some factors affecting crop yields occur unsemiarid system, ECSH–based management zones can be used in SSM predictably, including weather, human error, and equipof wheat for: (i) soil sampling to assess residual nutrients and soil ment malfunction (operator error, plugged spray nozattributes affecting herbicide efficacy, (ii) yield goal determination, zles or planters, herbicide drift, weed pressure, poor and (iii) prescription maps for metering inputs. seed viability, etc.), the potential impact of SSM may be limited in some years. At best, it will optimize the interactions between soil and inputs of nutrients, seed, D uniform management across a field, withinor pesticides by targeting soil indices related to producfield variability in crop yields is a well-recognized tion potential that are measurable, relatively stable, and phenomenon. For this reason, whole-field management manageable. The productivity of a given soil is deteris increasingly viewed as inefficient because it results in mined by the cumulative effect of natural factors inthe overapplication of inputs in low-producing areas and volved in its formation, including climate, topography, suboptimal application in areas with high-production parent material, biological activity, and time (Jenny, potential. Site-specific management—the spatially di1941), and management history. Management history rected management of soils, crops, and pests based on can significantly affect the range and spatial heterogenevarying conditions within a field (Larson and Robert, ity of soil chemical properties beyond that attributable 1991)—provides an alternative to the use of the field to natural processes. This is particularly true in organic as a primary management unit. Increasing fertilizer and systems where input applications are typically less unipesticide costs, coupled with environmental concerns form than in conventional systems (Cambardella and stemming from their use, conceptually advance SSM as Karlen, 1999). a means to improve economic (Griffith, 1995; Reetz While variations in individual soil factors have limited and Fixen, 1995) and ecological outcomes in agriculture utility for SSM, their combined impact on water and (Wallace, 1994; Castelnuovo, 1995; Larson et al., 1997). nutrient use efficiency is highly relevant to both producThe implementation of SSM requires real-time and tion potential and environmental concerns, such as NO3 accurate global positioning system (GPS) equipment, leaching (Bouma and Finke, 1993) and soil acidification (Malhi et al., 1991). Fields can be mapped for multiple C.K. Johnson, B.J. Wienhold, J.F. Shanahan, and J.W. Doran, USDAARS, 120 Keim Hall, Lincoln, NE 68583-0934; D.A. Mortensen, Dep. of Crop and Soil Sci., Pennsylvania State Univ., 116 ASI Building, Abbreviations: ECa, apparent electrical conductivity; ECDP, deep apUniversity Park, PA 16802; Received 14 Nov. 2001. *Corresponding parent electrical conductivity; ECSH, shallow apparent electrical conauthor (cjohnso2@bigred.unl.edu). ductivity; GIS, geographic information system; SSM, site-specific management. Published in Agron. J. 95:303–315 (2003).

Relay cropping for improved air and water quality.

Abstract Using plants to extract excess nitrate from soil is important in protecting against eutrophication of standing water, hypoxic conditions in lakes and oceans, or elevated nitrate concentrations in domestic water supplies. Global climate change issues have raised new concerns about nitrogen (N) management as it relates to crop production even though there may not be an immediate threat to water quality. Carbon dioxide (CO2) emissions are frequently considered the primary cause of global climate change, but under anaerobic conditions, animals can contribute by expelling methane (CH4) as do soil microbes. In terms of the potential for global climate change, CH4 is ~ 25 times more harmful than CO2. This differential effect is minuscule compared to when nitrous oxide (N2O) is released into the atmosphere because it is ~ 300 times more harmful than CO2. N2O losses from soil have been positively correlated with residual N (nitrate, NO3 -) concentrations in soil. It stands to reason that phytoremediation via nitrate scavenger crops is one approach to help protect air quality, as well as soil and water quality. Winter wheat was inserted into a seed corn/soybean rotation to utilize soil nitrate and thereby reduce the potential for nitrate leaching and N2O emissions. The net effect of the 2001- 2003 relay cropping sequence was to produce three crops in two years, scavenge 130 kg N/ha from the root zone, produce an extra 2 Mg residue/ha, and increase producer profitability by ~

Use of shoot reduction treatments as a means of simulating hail injury to proso millet

250/ha.

Economic and Environmental Benefits from Canopy Sensing for Variable-Rate Nitrogen Corn Fertilization

Abstract Proso millet, Panicum miliaceum (L.), is a warm‐season annual grass well adapted for grain production in the western Great Plains of the United States, where risk of hail injury is greater than any other region of the United States. Because adjustment procedures and loss equations are not available, proso millet producers in this region have had limited access to crop hail insurance as a risk management tool. Our research was conducted to assess impact of shoot reduction treatments imposed at different crop growth stages on grain yield loss of proso millet grown under several environments. Our goal was to provide information for development of crop insurance adjustment procedures. We also wanted to determine the impact of shoot reduction on various grain yield components. Treatments consisted of a control and three levels of shoot reduction (33,66, and 100% of full stand) applied at four growth stages (emergence, 4‐leaf, boot, and heading stages). The experiments were conducted at two locations (Akron, CO and Carrington, ND) during 1996 and 1997 to assess treatment impact on relative grain yield (RGY), expressed as percent of control. A significant shoot reduction χ growth stage interaction was observed for RGY, indicating yield loss from increasing shoot reduction varied with growth stage. A linear reduction in RGY to increasing levels of shoot reduction was observed for the 4‐leaf, boot and heading growth stages, while RGY displayed a segmented linear response to increasing shoot reduction at emergence. Variation in grain yield, induced by shoot reduction treatments, was more consistently correlated with variation in seed number than seed weight.

Use of remote-sensing imagery to estimate corn grain yield

Nitrogen (N) available to support corn production can be highly variable within fields. Canopy reflectance sensing for assessing crop N health has been proposed as a technology on which to base top-dress variable-rate N application. The objective of this research in Missouri and Nebraska was to evaluate the economic and environmental benefit of active-light crop-canopy reflectance sensors for corn N rate decisions. In Missouri, 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 8 treatments from 0 to 235 kg N ha-1 on 34 kg N ha-1 increments, top-dressed between V7-V11 vegetative growth stages. Canopy sensor measurements were obtained from these blocks and adjacent N-rich reference strips. A sufficiency index calculated from the sensor readings correlated with optimal N rate, but only in 50% of the fields. While soil type, fertilizer cost, and corn price all affected our analysis, a modest (

Field-scale electrical conductivity mapping for delineating soil condition

25 to

Responsive in-season nitrogen management for cereals

50 ha-1) profit using canopy sensing was found. Fertilizer savings of 10 to 50 kg N ha-1 could be expected in most situations, but savings also varied by reflectance readings, soil type, and fertilizer and grain prices. In the Nebraska studies, canopy sensing for one site allowed 39% savings in N applied compared to the traditional N management strategy, while producing similar grain yields. These results affirm using crop-canopy reflectance sensors for detecting corn N fertilizer needs that vary spatially within fields.