Daniel R. Ess

Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt

Abstract.Two mail surveys were carried out in Denmark and the Eastern Corn Belt, USA in 2002. Questionnaires were sent to 580 farmers who had used precision agriculture (PA) and 198 responses were received. The surveys focused on the current status of use of PA in both countries, including: PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service providers, data handling, interpretation, storage and ownership, value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in practicing PA between the two countries than differences. Time requirement and high cost of data handling were cited as the main problems. Survey respondents found soil maps to be more valuable than yield maps in management decisions. About 80% of the respondents would like to store the PA data themselves. The majority of the respondents indicated that they have changed their management practices due to PA, but not substantially. Some 90 of the respondents used the Internet and e-mail for agricultural purposes, but only a small number for PA websites.

Detection of Salmonella enteritidis Using a Miniature Optical Surface Plasmon Resonance Biosensor

The frequent outbreaks of foodborne illness demand rapid detection of foodborne pathogens. Unfortunately, conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Surface plasmon resonance (SPR) sensors have been widely adapted as an analysis tool for the study of various biological binding reactions. SPR biosensors could detect antibody-antigen bindings on the sensor surface by measuring either a resonance angle or refractive index value. In this study, the feasibility of a miniature SPR sensor (Spreeta, TI, USA) for detection of Salmonella enteritidis has been evaluated. Anti-Salmonella antibodies were immobilized on the gold sensor surface by using neutravidin. Salmonella could be detected by the Spreeta biosensor at concentrations down to 105 cfu/ml.

Use of CERES-Maize to study effect of spatial precipitation variability on yield

Abstract The objective of this study was to determine the usefulness of on-farm precipitation measurement, through determining spatial and temporal precipitation variability and its effect on corn yield. CERES-Maize (DSSAT version 3.5) was used with three precipitation data sources, for an Indiana farm—an on-farm National Weather Service (NWS) station, the nearest non-urban NWS station with electronic reporting (27 km from the farm), and a weighted mean of the three nearest such stations (27–35 km away)—to simulate 31 years of crop yield on 1-ha grid cells. Described as a percentage of the mean, spatial precipitation variability among the three data sources by corn phenological phase was 21–104%, while temporal (year-to-year) variability was 20–49%. The difference in simulated yield based on spatial precipitation variability was 15.8%, while year-to-year yield variability was 21.5%. The apparent yield difference based on spatial precipitation variability was of the same order as year-to-year variability, which suggests having on-farm precipitation data may be necessary for accurate yield modeling.

Binding Inhibition Assay Using Fiber-Optic Based Biosensor for the Detection of Foodborne Pathogens

Frequent outbreaks of foodborne illness have been increasing the need for simple, rapid and sensitive methods to detect foodborne pathogens. Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Some immunological rapid assays are developed, but these assays still require prolonged enrichment steps. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Among the biosensors, fiber-optic methods have much potential because they can be very sensitive and simple to operate. Fiber-optic biosensors typically use a light transmittable, tapered fiber to send excitation laser light to the detection surface and receive emitted fluorescent light. The fluorescent light excited by an evanescent wave generated by the laser is quantitatively related to fluorophor-labeled biomolecules immobilized on the fiber surface. A portable and automated fiber-optic biosensor, RAPTOR (Research International, Monroe, WA), was used to detect Salmonella enteritidis in food samples. A binding inhibition assay based on the biosensor was developed to detect the bacteria in hot dog samples. The biosensor and the binding inhibition assay could detect 104 cfu/ml of bacteria in less than 10 min of assay time.

Profitability of On-Farm Precipitation Data for Nitrogen Management Based on Crop Simulation

The purpose of this study was to determine the utility of on-farm precipitation measurement for nitrogen management decisions on an Indiana farm. Site-specific farming has led some producers to measure on-farm precipitation at multiple sites, but the profitability of such intense sampling for non-irrigated agriculture is not clear. The CERES-Maize model in Decision Support System for Agrotechnology Transfer (DSSAT) version 3.5 was used to simulate corn yield for a farm in east-central Indiana for 20years of weather data from three precipitation data sources—an on-farm station, the nearest non-urban National Weather Service (NWS) station, and the weighted mean of the three nearest such stations. Stochastic dominance and descriptive statistics were used to compare simulated yield and profitability for four nitrogen strategies: variable-rate versus whole-field fertilizer application and split application (starter urea-ammonium nitrate mixture at planting and sidedressed ammonia 37days later) versus sidedress application only. Off-farm data never led to a different choice of nitrogen strategy than on-farm data, but the ability to categorize a choice as risk averse or risk neutral depended on the precipitation data source used. This suggested that although on-farm precipitation measurement could be useful for risk management decisionmaking, it might not be profitable on average. The nearest NWS station would be the most profitable source of precipitation data, if it leads to the same management strategy as on-farm data.

Detection of Listeria Monocytogenes Using an Automated Fiber-Optic Biosensor: RAPTOR

Fiber-optic biosensor uses light transmittable tapered fiber to send excitation laser light and receive emitted fluorescent light. The fluorescent light excited by an evanescent wave generated by the laser is quantitatively related to biomolecules immobilized on the fiber surface [1]. An automated fiber-optic biosensor based detection method for Listeria monocytogenes was developed in this research. Detections of Listeria monocytogenes in hotdog sample were performed to evaluate the method. By using the detection method with automated fiber-optic biosensor, 5.4×107 cfu/ml of Listeria monocytogenes was able to detect.

Agricultural Systems Management Technologies for Precision Agriculture

This paper describes the content and experiences teaching an undergraduate elective course in Technologies for Precision Agriculture. The primary objective of this course is to prepare agricultural systems management students for successful careers in information-intensive agriculture. The subjects include the technologies of global positioning systems, yield monitors, geographic information systems, site-specific data acquisition, remote sensing, variable-rate application, and economics. Since the 2000 Fall semester, over 80 students have completed the 3-credit, junior-level course at Purdue. Student reviews of recent revisions to teaching methodologies are very positive. For each subject, students are required to complete a pre-test based on assigned reading materials; class and lab activities which provide hands-on (experiential learning) data collection, processing and analysis; and an in-lab unit evaluation.