2008 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.
Soil quality indicators: (1) Biologically-active soil organic matter was analyzed using dilute permanganate to assess its potential as a readily-measured soil quality indicator in a field test kit. Analyses of grid-sampled soils from a long-term research field in two different years and from soils of different organic C contents showed that the active C represented a consistent proportion of total organic C, suggesting that the analyses could detect a biologically active C portion from the total soil C pool. (2) Analyses of selected soil enzymes as soil quality indicators of pesticide degradation accommodated by vegetative buffer strips revealed that certain warmseason grasses enhance soil enzyme activity and concomitant atrazine degradation. Results contribute to understanding of complex interactions of plants and rhizosphere microorganisms involved in efficient atrazine biodegradation. Sensor development and evaluation: (1) Completed visible-near-infrared (VNIR) reflectance analysis of a 5-state soil dataset, investigating effects of various calibration methods on accuracy. (2) Used a sensor fusion approach to combine VNIR and electrochemical sensor data for soil P and K estimation, finding that results were better than either method alone. (3) Investigated a combination of sensors and empirical functions of soil profile properties to develop high resolution 3-D models of soil landscapes. The approach included high-resolution estimation of soil profile properties using VNIR reflectance and several soil electrical conductivity (EC) sensors, modeling of sensor-predicted soil profile properties with nonlinear functions, and mapping the parameters of the functions across the landscape to produce a continuous numerical soil-landscape model. Analysis of site-specific data: (1) Compiled a database of grain yield maps from over 10 growing seasons, representing about 40,000 acre-years of data. Merged a subset of the yield data with digital soil survey information and produced frequency distribution diagrams of yield by soil series as an initial assessment of soil productivity. (2) Developed a mixed model methodology for analyzing treatment effects on spatially variable landscapes. Application to several datasets is in process. Management system evaluation: (1) Implemented precision conservation components of a field-scale study. Switchgrass was established in the primary waterway and perennial rye grass was established as a cover crop to protect the soil over the winter season. Crop, water and soil monitoring is progressing as scheduled. (2) Results from 16 field-scale experiments in 2004-2007 were analyzed to determine the ability of active crop-canopy reflectance sensing to assess corn N need. Algorithms were developed for optimizing economic returns with variable-rate N fertilizer application. (3) Identified an alternate location for the site-specific deep tillage study in 2009. If cropping sequence at the initial site allows, we will return and complete that study. (4) Completing original three-year cover crop study in cotton; however, a high degree of soil variability combined with relatively small plot size forced changes in procedures.
Software for Improved Crop Yield Maps. Yield maps are a key component of precision agriculture, used in both developing and evaluating precision management strategies. Unfortunately, yield monitors produce complex datasets that often contain a large number of errors, which should be removed to prevent erroneous conclusions from any subsequent analysis or interpretation. To help yield map users remove errors from their maps, we developed the Yield Editor, interactive software that incorporates several automated filters and also allows the user to manually remove bad data. Tests of Yield Editor on several datasets showed its usefulness and provided information about the relative importance of the various filter types, which is a step toward a standardized procedure to clean yield data. The software, available at: http://www.ars.usda.gov/Services/Services.htm?modecode=36-22-15-00, has been downloaded over 1000 times and is used by farmers, consultants, educators, and scientists around the US and in at least 10 other countries. (NP202, Problem Area 4: Nutrient Management for Crop Production and Environmental Protection)
Estimating Plant-Available Water. Understanding how well the soil at a specified location in a field allows water to move into the soil (infiltration) and retains water for plant growth (plant available water capacity or PAW) is basic information for explaining crop growth and yield. Both of these soil properties are used in a general way to describe soil differences at large scales, such as contrasting soils among or between regional or political boundaries, but are seldom used at small scales such as within a single farm field, due to the expense of measurements. In this study, we investigated two sensor-driven methods of estimating PAW on the poorly drained claypan soils in northeast Missouri – (1) using a crop modeling program and yield maps from a combine yield monitor; and (2) using soil electrical conductivity (EC), a property which has been found to be a good estimator of topsoil depth on claypan soils. We found that the first method was not as accurate as previously found on well-drained soils, perhaps due to the hydrologic complexity of the claypan soils. The second method, using EC data that was quick and inexpensive to obtain, produced more accurate estimates of profile PAW, with an average error of about +/- 2/3 of an inch of water. These results will benefit producers who use site-specific information when making soil and water management decisions to improve crop water use efficiency and the general public through improved water utilization that reduces surface runoff into lakes and streams. (NP202, Problem Area 2: Soil Management to Improve Soil Structure and Hydraulic Properties)
Separating Spatial and Temporal Variation. Almost every application of computer models requires some proof of performance, which usually involves some statistical comparison of simulated model result to observations. However, where multiple factors contribute variation to a dynamic observation, a model may perform well in explaining one source of variation but not the other. In precision agriculture, multiple-year yield datasets are often compared with simulated yield for the same combinations of weather, crops, and soils. In this situation, year-to-year variation in weather often dominates over variation in space. Testing model performance in explaining variation in spatial dimensions requires explicit acknowledgement of the two sources of variation in yield during the testing procedure, and the two methods presented in this research illustrate both how to perform the test and how important it is to separate the model’s performance in the spatial and temporal domains. The precision agriculture and computer modeling communities will benefit from this research in both establishing proper confidence in the model performance, and isolating areas of model development that should be examined. (NP202: Problem Area 5: Adoption and Implementation of Soil and Water Conservation Practices and Systems)
Assessing Conservation Practices for Soil Quality. Current soil conservation practices used to reduce soil erosion and improve soil productivity include grass buffer strips, composed of forage or native grasses and legumes, and agroforestry buffers characterized by multiple-cropping systems involving simultaneous production of trees and agricultural crops. Despite the known benefits of these conservation practices, little is known about the effects on biological properties that influence plant productivity and soil quality -- information that would be valuable in assessing effectiveness of land management practices associated with agroforestry/grass buffer systems. The objective was to examine changes in soil carbon and nitrogen, soil microbiological activity, and soil structure in an agroforestry system established on a silt loam within a northeast Missouri watershed. Soil samples were collected during 2006-07 from cool-season grass strips, rows of 10-year old pin oak trees established along the landscape contour, and the corn crop areas between either the grass strips or tree rows. Soil quality parameters including soil enzyme activities, and soil C and N contents were greater in grass and agroforestry soils compared with soil planted to corn and were directly related to improved soil structure indicated by higher stable soil aggregates, which provided better aeration, water infiltration, and organic matter stabilization. Results are important because they illustrate the value of simple conservation practices in improving soil quality and that the measurements reported can be easily applied to other areas for assessing soil conservation effects. (NP202, Problem Area 1: Understanding and Managing Soil Biology and Rhizosphere Ecology)
5.Significant Activities that Support Special Target Populations
Continued collaboration with small farmers (one of which is a woman owner-operator of an organic farming enterprise) in monitoring on-farm soil quality attributes of various management practices. A portion of the research results was co-presented with the farmer at the 2008 Organic Farming Conference in Lacrosse, WI.
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Bullock, D.S., Kitchen, N.R., Bullock, D.G. 2007. Multi-disciplinary teams - a necessity for research in precision agriculture systems. Crop Science. 47:1765-1769.
Chung, S.O., Sudduth, K.A., Plouffe, C., Kitchen, N.R. 2008. Soil bin and field tests of an on-the-go soil strength profile sensor. Transactions of the ASABE. 51(1):5-18.
Jiang, P., Anderson, S.H., Kitchen, N.R., Sudduth, K.A., Sadler, E.J. 2007. Estimating plant-available water capacity for claypan landscapes using apparent electrical conductivity. Soil Science Society of America Journal. 71:1902-1908
Jiang, P., Kitchen, N.R., Anderson, S.H., Sudduth, K.A., Sadler, E.J. 2008. Estimating plant-available water using the simple inverse yield model for claypan landscapes. Agronomy Journal. 100:830-836.
Kim, H.J., Hummel, J., Sudduth, K.A., Motavalli, P.P. 2007. Simultaneous analysis of soil macronutrients using ion-selective electrodes. Soil Science Society of America Journal. 71:1867-1877.
Myers, D.B., Kitchen, N.R., Sudduth, K.A., Miles, R.J., Sharp, R.E. 2007. Soybean root distribution related to claypan soil properties and apparent soil electrical conductivity. Crop Science. 47:1498-1509.
Kitchen, N.R. 2008. Emerging technologies for real-time and integrated agriculture decisions. Computers and Electronics in Agriculture. 61(1):1-3.
Sadler, E.J., Sudduth, K.A., Jones, J.W. 2008. Separating spatial and temporal sources of variation for model testing in precision agriculture. Precision Agriculture. 8:297-310.
Shanahan, J.F., Kitchen, N.R., Raun, W., Schepers, J.S. 2008. Responsive in-season nitrogen management for cereals. Computers and Electronics in Agriculture. 61:51-62
Sudduth, K.A., Drummond, S.T. 2007. Yield editor: Software for removing errors from crop yield maps. Agronomy Journal. 99:1471-1482.
Sudduth, K.A., Chung, S.O., Andrade-Sanchez, P., Upadhyaya, S.K. 2008. Field comparison of two prototype soil strength profile sensors. Computers and Electronics in Agriculture. 61:20-31.
Fang, M., Motavalli, P.P., Kremer, R.J., Nelson, K.A. 2007. Assessing changes in soil microbial communities and carbon mineralization in Bt and non-Bt corn residue-amended soils. Applied Soil Ecology. 37:150-160.
Nasraoui, B., Hajlaoui, M.R., Aissa, A.D., Kremer, R.J. 2007. Biological control of wheat take-all disease: I - characterization of antagonistic bacteria from diverse soils toward Gaeumannomyces graminis var. tritici. Tunisia Journal of Plant Protection. 2:23-34.
Nasraoui, B., Hajlaoui, M.R., Gargouri, S., Kremer, R.J. 2007. Biological control of wheat take-all disease: II – rapid selection of bacteria suppressive to Gaeumannomyces graminis var. tritici in laboratory with greenhouse and field confirmation trials. Tunisia Journal of Plant Protection. 2:35-46.
Park, K., Kremer, R.J. 2007. Effects of a biological amendment on chemical and biological properties and microbial diversity in soils receiving different organic amendments. Korean Journal of Soil Science and Fertilizer. 40:234-241.
Udawatta, R.P., Kremer, R.J., Adamson, B.W., Anderson, S.H. 2008. Variations in soil aggregate stability and enzyme activities in a temperate agroforestry practice. Applied Soil Ecology. 39:153-160.
Clay, D.E., Kitchen, N.R., Carlson, C.G., Kleinjan, J.L. 2007. Using historical management areas to reduce soil sampling errors. In: Pierce, F.J., Clay, D. E., editors. GIS Applications in Agriculture. Boca Raton, FL: CRC Press. p. 49-64.