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United States Department of Agriculture

Agricultural Research Service

2010 Annual Report

1a.Objectives (from AD-416)
Acquire new knowledge important for managing agricultural practices on spatially-variable soil landscapes and for assessing their impacts and sustainability. Develop and evaluate in-field sensing technologies and data interpretation methods for spatially- and temporally-variable soil properties important in assessing and managing soils and crops. Develop, implement at field scale, and assess innovative site-specific management systems for improved profitability and for water and soil quality.

1b.Approach (from AD-416)
In this project, our interdisciplinary team will address key limitations to the overall goal of developing sustainable, site-specific soil and crop management systems. We will investigate spatially-variable soil-plant water relationships on hydrologically complex soils and the use of soil and rhizosphere biological measurements for soil quality assessment. We will explore the use of commercial sensor technology to provide estimates of soil hydraulic properties and of soil quality indicator variables. Building on our previous research, we will combine multiple soil sensor technologies into integrated, on-the-go tools for efficiently mapping within-field soil variability. We will investigate and compare analysis techniques available for understanding relationships between soil and landscape properties and crop yield, and will develop site-specific estimators of productivity that can help assess production risk. Building on our decade of experience in measuring and understanding within-field spatial variability, we will assess the profitability and water and soil quality of site-specific management systems. Management system evaluations will include on-farm research with active participation by crop producers and crop advisors. Products of this research project will include soil quality indicators, sensors for measurement of multiple soil properties, and agricultural and conservation practices specifically designed to deal with landscape variability.

3.Progress Report
Soil quality indicators (SQI): (1) Additional soil enzyme activities were assessed to expand the suite of SQI’s available as candidates for determining a soil quality index. Phenol oxidase activity shows promise for differentiating relative proportions of soil organic matter fractions because it is involved in degradation of complex C (i.e., lignin) and may indicate levels of stable C or C sequestration in soils under different management. (2) SQI’s were assessed for soils under grazed pastures, which will give valuable information on impact of livestock activity on soil quality in diversified agricultural production systems. Sensor development and application: Analyzed near-infrared (NIR) reflectance data from soil samples obtained under a variety of management systems and soil types to investigate potential of NIR for estimating important variables related to soil quality without the need for standard analysis. Ground-sample NIR analysis shows promise for discriminating soil carbon levels between cropped (tilled and no-till) and perennial grass management systems. Analysis of site-specific data: Added data to a grain yield map database, increasing it to over 120,000 acre-years. Developed relationships of mapped yield to soil and landscape properties, and used these relationships to estimate crop production at unmapped locations. Management system evaluation: (1) Monitoring crop production and soil and water quality changes on a field-scale site-specific management system (PAS) has continued. Exceptionally high-precipitation growing seasons (2008-2010) are allowing for evaluation of the system under high erosion potential conditions. (2) An Agricultural Policy/Environmental eXtender (APEX) model of the precision agriculture system (PAS) field was developed and calibrated using available flow, atrazine, and sediment data. Simulation results agreed with yield map data, with lower yields at backslope positions than at summit or footslope positions. Simulation indicated that these lower productivity areas also contributed higher runoff and pollutant loads than other landscape positions. (3) Findings from a first cycle of canopy sensor-guided variable-rate N application studies have led to follow-up studies focused on the highly-productive loess soils along the Missouri River. On-farm research in loess soil fields is being used to guide algorithm improvement and to evaluate the suitability of newly available commercial sensors.

1. Detecting herbicide effects in the soil environment. Biological processes are sensitive indicators that contribute to an overall assessment of impacts of management practices on soil quality. The most popular management practice in current conventional crop production is the use of varieties genetically modified to resist the herbicide glyphosate (‘Roundup’) on >90% and >60% of the soybean and corn acreage, respectively, in the U.S. ARS researchers at Columbia, Missouri summarized results of research conducted during 1997-2008 on this widely adopted crop management system to better understand potential impacts on biological indicators of soil quality. Most of the indicators were sensitive in detecting glyphosate-induced changes in soils and plant root zones including increased root colonization of corn and soybean by potential fungal disease agents, reduced nodular growth on soybean roots that contain nitrogen-fixing bacteria, decreased micronutrient availability thereby reducing plant use, and decreased numbers of the beneficial bacterial that control disease-causing fungi and provide plant growth enhancing substances. This information is important for improving productivity of genetically-engineered crops by developing innovative and combined strategies including soil management for balanced biological activity, proper plant nutrition, and suppression of potential microbial disease agents.

2. Improved understanding of landscape position effects on soil compaction. Soil compaction caused by farming practices can reduce crop production and promote water quality problems with increased runoff. However, little is known about the vulnerability of soils to management-induced compaction at different landscape positions. ARS researchers at Columbia, Missouri collected compaction data at summit, backslope, and footslope positions of a claypan-soil landscape cropped with various grain and perennial grass management systems. As expected, less compaction was found with grass management systems, because of much less farm machinery traffic. Most significantly, grain crops managed without tillage but including winter cover crops were especially vulnerable to extreme compaction in the lower portions of the landscape. Soils there were consistently wet when spring operations needed to be performed. Although cover crops are an important part of conservation management systems, they need to be removed in the early spring, especially in the lower portion of claypan-soil landscapes, to facilitate soil drying before planting. This increased understanding of the interactions of management practices over varying landscape positions is crucial in helping farmers employ best management practices that prevent compaction, thus improving yields and reducing environmental problems.

3. Methods to map topsoil depth variation within fields. Maps of soil variability within fields can provide farmers with useful information for management decisions. Such maps can be readily created using commercially available soil apparent electrical conductivity (EC) sensors, which provide a reading of EC averaged over a unique measurement depth. The purpose of this research was to determine which sensor data would give the best picture of topsoil depth variability for the claypan soils found in several states of the U.S. Midwest, because topsoil depth is an important factor in the productivity of these soils. ARS researchers in Columbia, Missouri collected data with three commercial EC sensors on two fields in Missouri and related the data to topsoil depth with two different methods. Topsoil depth estimates by two methods were very similar, EC sensors with medium measurement depths gave the best results, and estimates were improved by combining data from multiple sensors. These results will benefit users of EC instruments by providing guidance on instrument selection and on methods to calculate soil variables from EC data.

5.Significant Activities that Support Special Target Populations
Participated in activities targeting small farmers, including on-farm organic research on soils under orchard and restored prairie at a woman-owned operation near Liberty, Missouri; also participated in field tour for organic producers held at the farm. Continued collaboration with Lincoln University (1890 Land-Grant Institution in Jefferson City, Missouri) on greenhouse gas emission and microbial relationships, and impact of herbicide surfactants on soil microbial activity. Project scientist serves as co-advisor for one PhD and one MS student conducting research within these projects.

Review Publications
Jang, G.S., Sudduth, K.A., Sadler, E.J., Lerch, R.N. 2009. Watershed-Scale Crop Type Classification using Seasonal Trends in Remote Sensing-Derived Vegetation Indices. Transactions of the ASABE. 52(2):1535-1544.

Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Scharf, P.C., Palm, H.L., Roberts, D.F., Vories, E.D. 2010. Ground-Based Canopy Reflectance Sensing for Variable-Rate Nitrogen Corn Fertilization. Agronomy Journal. 102:71-84.

Roberts, D.F., Kitchen, N.R., Scharf, P.C., Sudduth, K.A. 2010. Will Variable-Rate Nitrogen Fertilization Using Corn Canopy Reflectance Sensing Deliver Environmental Benefits? Agronomy Journal. 102:85-95.

Kim, H.J., Sudduth, K.A., Hummel, J.W. 2009. Soil Macronutrient Sensing for Precision Agriculture. Journal of Environmental Monitoring. 11:1810-1824.

Jung, K., Kitchen, N.R., Sudduth, K.A., Lee, K., Chung, S. 2009. Soil Compaction Varies by Crop Management System over a Claypan Soil Landscape. Soil and Tillage Research. 107:1-10.

Lee, K., Sudduth, K.A., Drummond, S.T., Lee, D., Kitchen, N.R., Chung, S. 2010. Calibration Methods for Soil Property Estimation Using Reflectance Spectroscopy. Transactions of the ASABE. 53(3):675-684.

Souza, E.G., Scharf, P.C., Sudduth, K.A. 2010. The Influence of Sun Position and Clouds on Reflectance and Vegetation Indices of Greenhouse-Grown Corn. Agronomy Journal. 102:734-744.

Williams, J.D., Kitchen, N.R., Scharf, P.C., Stevens, W.E. 2009. Within-field Corn Nitrogen Response Related to Aerial Photograph Color. Precision Agriculture. 11:291-305.

Kremer, R.J., Moncef, B. 2009. Allelopathic Plants. Hordeum vulgare L. Allelopathy Journal. 24(2):225-242.

Kremer, R.J., Means, N.E. 2009. Glyphosate and Glyphosate-Resistant Crop Interactions with Rhizosphere Microorganisms. European Journal of Agronomy. 31(3):153-161.

Zobiole, L.H., Oliveira, R.S., Kremer, R.J., Constantin, J., Yamada, T., Castro, C., Oliveira, F.A., Oliveria, A. 2010. Effect of Glyphosate on Symbiotic N2 Fixation and Nickel Concentration in Glyphosate-Resistant Soybean. Applied Soil Ecology. 44:176-180.

Zobiole, L.H., Oliveira, R.S., Viesentainer, J.V., Kremer, R.J., Bellaloui, N., Yamada, T. 2010. Glyphosate affects seed composition in glyphosate-resistant soybean. Journal of Agricultural and Food Chemistry. 58:4517-4522.

Last Modified: 11/26/2015
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