Brian J. Wienhold

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).

Cropping system effects on soil quality in the Great Plains: Synthesis from a regional project

Soils perform a number of essential functions affecting management goals. Soil functions were assessed by measuring physical, chemical, and biological properties in a regional assessment of conventional (CON) and alternative (ALT) management practices at eight sites within the Great Plains. The results, reported in accompanying papers, provide excellent data for assessing how management practices collectively affect agronomic and environmental soil functions that benefit both farmers and society. Our objective was to use the regional data as an input for two new assessment tools to evaluate their potential and sensitivity for detecting differences (aggradation or degradation) in management systems. The soil management assessment framework (SMAF) and the agro-ecosystem performance assessment tool (AEPAT) were used to score individual soil properties at each location relative to expected conditions based on inherent soil-forming factors and to compute index values that provide an overall assessment of the agronomic and environmental impact of the CON and ALT practices. SMAF index values were positively correlated with grain yield (an agronomic function) and total organic matter (an agronomic and environmental function). They were negatively correlated with soil nitrate concentration at harvest (an indicator of environmental function). There was general agreement between the two assessment tools when used to compare management practices. Users can measure a small number of soil properties and use one of these tools to easily assess the effectiveness of soil management practices. A higher score in either tool identifies more environmentally and agronomically sustainable management. Temporal variability in measured indicators makes dynamic assessments of management practices essential. Water-filled pore space, aggregate stability, particulate organic matter, and microbial biomass were sensitive to management and should be included in studies aimed at improving soil management. Reductions in both tillage and fallow combined with crop rotation has resulted in improved soil function (e.g., nutrient cycling, organic C content, and productivity) throughout the Great Plains.

Distribution and Quantification of Antibiotic Resistant Genes and Bacteria across Agricultural and Non-Agricultural Metagenomes

There is concern that antibiotic resistance can potentially be transferred from animals to humans through the food chain. The relationship between specific antibiotic resistant bacteria and the genes they carry remains to be described. Few details are known about the ecology of antibiotic resistant genes and bacteria in food production systems, or how antibiotic resistance genes in food animals compare to antibiotic resistance genes in other ecosystems. Here we report the distribution of antibiotic resistant genes in publicly available agricultural and non-agricultural metagenomic samples and identify which bacteria are likely to be carrying those genes. Antibiotic resistance, as coded for in the genes used in this study, is a process that was associated with all natural, agricultural, and human-impacted ecosystems examined, with between 0.7 to 4.4% of all classified genes in each habitat coding for resistance to antibiotic and toxic compounds (RATC). Agricultural, human, and coastal-marine metagenomes have characteristic distributions of antibiotic resistance genes, and different bacteria that carry the genes. There is a larger percentage of the total genome associated with antibiotic resistance in gastrointestinal-associated and agricultural metagenomes compared to marine and Antarctic samples. Since antibiotic resistance genes are a natural part of both human-impacted and pristine habitats, presence of these resistance genes in any specific habitat is therefore not sufficient to indicate or determine impact of anthropogenic antibiotic use. We recommend that baseline studies and control samples be taken in order to determine natural background levels of antibiotic resistant bacteria and/or antibiotic resistance genes when investigating the impacts of veterinary use of antibiotics on human health. We raise questions regarding whether the underlying biology of each type of bacteria contributes to the likelihood of transfer via the food chain.

Protocol for indicator scoring in the soil management assessment framework (SMAF).

Assessment tools are needed to evaluate agronomic management effects on critical soil functions such as carbon sequestration, nutrient cycling and water partitioning. These tools need to be flexible in terms of selection of soil functions to be assessed and indicators to be measured to ensure that assessments are appropriate for the management goals. The soil management assessment framework (SMAF) is being developed to meet this need. The SMAF uses soil physical, chemical and biological indicator data to assess management effects on soil function using a three-step process for (1) indicator selection, (2) indicator interpretation and (3) integration into an index. While SMAF is functional in its present format, it is intended to be malleable so that user needs can be met. Development of additional indicator interpretation scoring curves is one way that this framework can be expanded. Scoring curve development is a multi-step process of identifying an indicator, determining the nature of the relationship of the indicator to a soil function, programming an algorithm and/or logic statements describing that relationship and validating the resulting scoring curve. This paper describes the steps involved in developing an SMAF scoring curve. Scoring curves for interpreting water-filled pore space (WFPS) and Mehlich extractable potassium (K) were developed using the described protocol. This protocol will assist users of the SMAF in understanding how the existing scoring curves were developed and others interested in developing scoring curves for indicators that are not in the current version.

Comparison of two soil quality indexes to evaluate cropping systems in northern Colorado

Various soil management or quality assessment tools have been proposed to evaluate the effects of land management practices on soil, air, and water resources. Two of them are the Soil Management Assessment Framework and the Soil Conditioning Index (SCI). This study was conducted to test the hypothesis that the Soil Quality Index (SQI) estimated by the Soil Management Assessment Framework can detect more minute changes in soil management than SCI and to test SCI response to other soil quality (SQ) indicators. These SQ indexes were tested on irrigated cropping systems near Fort Collins, Colorado, that included no-till and conventionally-tilled corn (Zea mays L.), and no-till corn with rotations including barley (Hordeum distichon L.), soybean (Glycine max (L.) Merr.), and dry bean (Phaeseolus vulgaris L.) at three levels of nitrogen varying from 0 to 224 kg N ha-1 (0 to 200 lb ac-1). Both SQ indexes clearly separated the plots with very high levels of N from plots with no N. However, for SQI the mid-level of N was statistically the same as both extreme levels. Statistical differences were observed among all N levels for the SCI. The SQI seemed to make more detailed differentiation among crop management systems than the SCI. The SCI separated the cropping systems into three groups with no overlap among groups. All no-till systems had the statistically same higher SCI than the conventionally-tilled continual corn system. The SQI separated the cropping systems into three groups with decreasing SQI as tillage intensity increased and as lower residue crops were introduced into the cropping system. The systems that included tillage and a low residue crop (soybean) had the lowest SQI. The SQI allowed overlap among cropping groups not recognized by SCI. Selection of the most appropriate SQ index seems to be a tradeoff between data requirements, resolution required, and the desired use of the evaluation tool.

Comparison of Laboratory Methods and an In Situ Method for Estimating Nitrogen Mineralization in an Irrigated Silt-Loam Soil

Abstract Nitrogen (N) mineralization makes a considerable contribution to crop‐available N and is difficult to estimate. Reliable methods for measuring N mineralization are needed to produce data sets for developing N‐mineralization models, as a component in fertilizer recommendation algorithms, and to assess the effect of management practices on N mineralization. Numerous methods are available for estimating N mineralization. Laboratory methods are relatively easy but may not reflect conditions in the field, and field methods are usually labor‐intensive. A study was conducted to compare N‐mineralization estimates using anaerobic and aerobic laboratory methods and an in situ field method for the 0‐ to 15‐cm depth of a silt loam soil under irrigated corn (Zea mays L.). Mineralization estimates were also compared to N mineralization based on crop N content. Estimates of N mineralization were 101 kg ha−1 for the anaerobic laboratory method, 284 kg ha−1 for the aerobic laboratory method, and 134 kg ha−1 for the in situ field method. The in situ field method provided a reasonable estimate of N mineralization (0 to 15 cm) when compared to the estimate of mineralized N (root zone) based on crop N content (215 kg ha−1). The in situ field method can be used to measure N mineralization during the growing season and for comparing N mineralization among management practices.

The effect of soil moisture on mineral nitrogen, soil electrical conductivity, and pH

Inorganic nitrogen in the soil is the source of N for non-legume plants. Rapid methods for monitoring changes in inorganic N concentrations would be helpful for N nutrient management. The effect of varying soil moisture content on soil mineral nitrogen, electrical conductivity (EC), and pH were studied in a laboratory experiment. Soil NO3-N increased as soil water-filled pore space (WFPS) increased from 0 to 80 cm3 cm−3. At soil moisture levels greater than 80 cm3 cm−3, NO3-N concentration declined rapidly and NH4-N concentration increased, likely due to anaerobic conditions existing at higher WFPS levels. Soil pH did not change as soil moisture increased from 100 g kg−1 to 400 g kg−1 and increased from 6.2 to 6.6 at higher levels of soil moisture. Soil EC was correlated with soil mineral N concentration when measured in situ with a portable EC meter (R2=0.85) or in the laboratory as 1:1 soil water slurries (R2=0.92). Results suggest that EC can be used to rapidly detect changes in soil inorganic N status in soils where salts and free carbonates are not present in large amounts.

Arbuscular mycorrhizal fungi differ in their ability to regulate the expression of phosphate transporters in maize (Zea mays L.)

Previous studies have found that some phosphate (Pi) starvation inducible transporter genes are downregulated and arbuscular mycorrhizal (AM) inducible Pi transporter genes are upregulated in maize roots associated with the fungus Glomus intraradices. However, little is known about the functional diversity of different AM fungal species in influencing the expression of Pi transporters in maize roots. Here, we studied the expression of two Pi transporter genes ZEAma:Pht1;3 (Pi starvation inducible) and ZEAma:Pht1;6 (AM inducible) in maize root colonized by different AM fungal inoculants. Non-mycorrhizal maize, maize colonized by Glomus deserticola (CA113), Glomus intraradices (IA506), Glomus mosseae (CA201), Gigaspora gigantea (MN922A) and the co-inoculation of all four species were established. The expression patterns of the two genes were quantified using real-time, reverse transcription polymerase chain reaction. The expression level of ZEAma:Pht1;6 was 26–135 times higher in AM plants than in non-mycorrhizal maize roots, whereas the expression level of ZEAma:Pht1;3 was five to 44 times lower in AM plants than in non-mycorrhizal plants. Expression of the two genes differed with inoculation treatment, and increasing the diversity of AM fungi in maize roots led to greater expression of ZEAma:Pht1;6 as well as Pi uptake in shoots. The expression of ZEAma:Pht1;6 was significantly positively correlated with AM colonization rate, concentration of AM biomarkers in maize roots, Pi uptake and dry weight of shoot, but negatively correlated with the expression of ZEAma:Pht1;3. Addition of Pi fertilizer at a low concentration significantly increased the expression of ZEAma:Pht1;6 but had no effect on the expression of ZEAma:Pht1;3.

Links among Nitrification, Nitrifier Communities, and Edaphic Properties in Contrasting Soils Receiving Dairy Slurry

Soil biotic and abiotic factors strongly influence nitrogen (N) availability and increases in nitrification rates associated with the application of manure. In this study, we examine the effects of edaphic properties and a dairy (Bos taurus) slurry amendment on N availability, nitrification rates and nitrifier communities. Soils of variable texture and clay mineralogy were collected from six USDA-ARS research sites and incubated for 28 d with and without dairy slurry applied at a rate of ~300 kg N ha(-1). Periodically, subsamples were removed for analyses of 2 M KCl extractable N and nitrification potential, as well as gene copy numbers of ammonia-oxidizing bacteria (AOB) and archaea (AOA). Spearman coefficients for nitrification potentials and AOB copy number were positively correlated with total soil C, total soil N, cation exchange capacity, and clay mineralogy in treatments with and without slurry application. Our data show that the quantity and type of clay minerals present in a soil affect nitrifier populations, nitrification rates, and the release of inorganic N. Nitrogen mineralization, nitrification potentials, and edaphic properties were positively correlated with AOB gene copy numbers. On average, AOA gene copy numbers were an order of magnitude lower than those of AOB across the six soils and did not increase with slurry application. Our research suggests that the two nitrifier communities overlap but have different optimum environmental conditions for growth and activity that are partly determined by the interaction of manure-derived ammonium with soil properties.

Protocols for Nationally Coordinated Laboratory and Field Research on Manure Nitrogen Mineralization

Abstract The National Program structure of USDA‐ARS provides an opportunity to coordinate research on problems of national and global significance. A team of USDA‐ARS scientists is conducting nationally coordinated research to develop predictions of manure N availability to protect water quality and improve farm solvency. Experimental design and research protocols were developed and used in common across all participating locations. Laboratory incubations are conducted at each location with a minimum of three soils, three temperatures, two wetting/drying regimes, and two manure treatments. A soil from the central United States (Catlin silt loam, fine‐silty, mixed, superactive, mesic Oxyaquic Argiudoll) is used as an internal reference across all locations. Incubation data are compiled across locations to develop generalized predictions of manure nitrogen mineralization (Nmin). Field validation data are then obtained by monitoring nitrogen (N) transformations in manure‐amended soil cores equipped with anion exchange resin to capture leached nitrate. This field data will be used to compare laboratory‐based predictions with field observations of Nmin in each soil, climatic zone, and manure type represented. A Decision Support System will then be developed for predicting manure N mineralization across ranges in soil, climate, and manure composition. Protocols used by this research team are provided to 1) document the procedures used and 2) offer others detailed information for conducting research on nutrient transformation processes involving collaboration across locations or complementary research between laboratory and field environments.