Temporal Changes in the Spatial Variability of Soil Nutrients

This paper reports the temporal changes in the spatial variability of soil nutrient concentrations across a field during the growing season, over a four-year period. This study is part of the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. Uniform fertilization did not produce a uniform increase in fertility. During the growing season, several of the nutrients and micronutrients showed increases in concentration although no additional fertilization had occurred. Potato plant uptake did not explain all of these changes. Some soil micronutrient concentrations increased above levels considered detrimental to potatoes, but the plants did not show the effects in reduced yield. All the nutrients measured changed between the last sampling in the fall and the first sampling the next spring prior to fertilization. The soil microbial community may play a major role in the temporal changes in the spatial variability of soil nutrient concentrations. These temporal changes suggest potential impact when determining fertilizer recommendations, and when evaluating the results of spatially varying fertilizer application

Physical Separation of Straw Stem Components to Reduce Silica

In this paper, we describe ongoing efforts to solve challenges to using straw for bioenergy and bioproducts. Among these, silica in straw forms a low-melting eutectic with potassium, causing slag deposits, and chlorides cause corrosion beneath the deposits. Straw consists principally of stems, leaves, sheaths, nodes, awns, and chaff. Leaves and sheaths are higher in silica, while chaff, leaves and nodes are the primary source of fines. Our approach to reducing silica is to selectively harvest the straw stems using an in-field physical separation, leaving the remaining components in the field to build soil organic matter and contribute soil nutrients

From Prediction to Prescription: Intelligent Decision Support for Variable Rate Fertilization

We describe the use of machine learning methods in the analysis of spatial soil fertility, soil physical characteristics, and yield data, with a particular objective of determining local (field- to farm-scale) crop response patterns. For effective prescriptive use, the output of these tools is augmented with economic data and operational constraints, and recast as a rulebased decision support tool to maximize economic return in variable rate fertilization systems. We describe some of the practical issues addressed in development of one such system, including data preparation, adaptation of regression tree output for use in a rule-based expert system, and incorporation of real-world limits on system recommendations. Results from various field trials of this system are summarized