Viacheslav I. Adamchuk

Precision Agriculture and Food Security

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Adapting production inputs site-specifically within a field and individually for each animal allows better use of resources to maintain the quality of the environment while improving the sustainability of the food supply. Precision agriculture provides a means to monitor the food production chain and manage both the quantity and quality of agricultural produce.

Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time

Abstract This chapter reviews proximal soil sensing (PSS). Our intent is for it to be a source of up-to-date information on PSS, the technologies that are currently available and their use for measuring soil properties. We first define PSS and discuss the sampling dilemma. Using the range of frequencies in the electromagnetic spectrum as a framework, we describe a large range of technologies that can be used for PSS, including electrochemical and mechanical sensors, telemetry, geographic positioning and elevation, multisensor platforms, and core measuring and down-borehole sensors. Because soil properties can be measured with different proximal soil sensors, we provide examples of the alternative techniques that are available for measuring soil properties. We also indicate the developmental stage of technologies for PSS and the current approximate cost of commercial sensors. Our discussion focuses on the development of PSS over the past 30 years and on its current state. Finally, we provide a short list of general considerations for future work and suggest that we need research and development to: (i) improve soil sampling designs for PSS, (ii) define the most suitable technique or combination of techniques for measuring key soil properties, (iii) better understand the interactions between soil and sensor signals, (iv) derive theoretical sensor calibrations, (v) understand the basis for local versus global sensor calibrations, (vi) improve signal processing, analysis, and reconstruction techniques, (vii) derive and improve methods for sensor data fusion, and (viii) explore the many and varied soil, agricultural, and environmental applications where proximal soil sensors could be used. PSS provides soil scientists with an effective approach to learn more about soils. Proximal soil sensors allow rapid and inexpensive collection of precise, quantitative, high-resolution data, which can be used to better understand soil spatial and temporal variability. We hope that this review raises awareness about PSS to further its research and development and to encourage the use of proximal soil sensors in different applications. PSS can help provide sustainable solutions to the global issues that we face: food, water, and energy security and climate change.

Impact of Robotics and Geospatial Technology Interventions on Youth STEM Learning and Attitudes

Abstract This study examined the impact of robotics and geospatial technologies interventions on middle school youth’s learning of and attitudes toward science, technology, engineering, and mathematics (STEM). Two interventions were tested. The first was a 40-hour intensive robotics/GPS/GIS summer camp; the second was a 3-hour event modeled on the camp experiences and intended to provide an introduction to these technologies. Results showed that the longer intervention led to significantly greater learning than a control group not receiving the instruction, whereas the short-term intervention primarily impacted youth attitude and motivation. Although the short-term intervention did not have the learning advantages of a more intensive robotics camp, it can serve a key role in getting youth excited about technology and encouraging them to seek out additional opportunities to explore topics in greater detail, which can result in improved learning.

APPLICATION OF A STRAIN GAUGE ARRAY TO ESTIMATE SOIL MECHANICAL IMPEDANCE ON–THE–GO

A vertical smooth blade with an array of strain gauges was designed to dynamically measure mechanical impedance of soil at multiple depths. As the blade (tapered cantilevered beam) cuts through the soil, strain gauges detect deformation caused by varying soil resistance. Recorded strain data was used to estimate resistance pressure at different depths that caused the same deformation of the blade with known geometry and material properties. Verification of the analytical solution was performed in the laboratory by applying varying combinations of static loads. Field experiments involved on–the–go measurement of soil resistance coordinated using the global positioning system (GPS). Integrated resistance pressures were compared to cone penetrometer readings obtained from the same area within the field.

The use of digital manipulatives in k-12: robotics, GPS/GIS and programming

Faculty from 4-H Youth Development, Biosystems Engineering, and Education have collaborated to develop and implement an innovative robotics and geospatial technologies program, delivered in an informal learning setting of 4-H clubs and afterschool programs. Aimed at middle school youth, the program uses robotics and global positioning system (GPS) receivers and geographic information system (GIS) software to provide hands-on, self-directed learning experiences that promote personalized comprehension of science, technology, engineering, and math (STEM) concepts through experimentation. The goals of the program are to prepare youth for the 21st Century workplace by providing them opportunities to learn STEM concepts and foster positive attitudes about STEM. Funded by the National Science Foundation, the project has undergone extensive research and evaluation over the three years of the project. Results have focused on the projects impact on: a) youth learning of computer programming, mathematics, geospatial concepts, and engineering/robotics concepts and b) youth attitudes and motivation towards science, technology, engineering, and mathematics. In contrast to the preponderance of research on educational robotics relying on anecdotal and descriptive strategies, this research uses empirical, quantitative methods involving the use of comparison groups and pre-post analyses.

A Model for Agro-Economic Analysis of Soil pH Mapping

Core soil sampling followed by laboratory analysis is the traditional method used to map soil pH prior to variable rate application (VRA) of lime on cropland. A recently developed automated soil sampling system capable of measuring soil pH on-the-go has significantly increased sampling resolution. However, adoption of such systems must be justified economically. This paper presents a method for assessing the economic benefit from automated mapping of soil pH prior to variable rate lime application. In this work, geostatistical, agronomic, and economic methods were used to generate a comprehensive numerical model for quantitative assessment of the net return over cost of liming for different lime management strategies. The strategies included: automated pH mapping, manual grid soil sampling, and whole field sampling used in combination with either variable or fixed rate liming. The model was demonstrated using a simulated field with known average pH and semivariogram model. The analysis showed the largest benefit (

Sensor Fusion for Precision Agriculture

6.13ha−1year−1) from using VRA with automated soil pH mapping versus VRA based on 1ha (2.5acres) manual grid point sampling for the selected simulated field conditions. A sensitivity analysis demonstrated that for a wide range of field conditions and crop prices, VRA plus automated mapping promises higher relative benefits than VRA based on either manual grid point or grid cell sampling.

Development of On-the-Go Proximal Soil Sensor Systems

With the rapid rise in demand for both agricultural crop quantity and quality and with the growing concern of non-point pollution caused by modern farming practices, the efficiency and environmental safety of agricultural production systems have been questioned (Gebbers and Adamchuk, 2010). While implementing best management practices around the world, it was observed that the most efficient quantities of agricultural inputs vary across the landscape due to various naturally occurring, as well as man-induced, differences in key productivity factors such as water and nutrient supply. Identifying and understanding these differences allow for varying crop management practices according to locally defined needs (Pierce and Nowak, 1999). Such spatially-variable management practices have become the central part of precision agriculture (PA) management strategies being adapted by many practitioners around the world (Sonka et al., 1997). PA is an excellent example of a system approach where the use of the sensor fusion concept is essential. Among the different parameters that describe landscape variability, topography and soils are key factors that control variability in crop growing environments (Robert, 1993). Variations in crop vegetation growth typically respond to differences in these microenvironments together with the effects of management practice. Our ability to accurately recognize and account for any such differences can make production systems more efficient. Traditionally differences in physical, chemical and biological soil attributes have been detected through soil sampling and laboratory analysis (Wollenhaupt et al., 1997; de Gruijter et al., 2006). The cost of sampling and analysis are such that it is difficult to obtain enough samples to accurately characterize the landscape variability. This economic consideration resulting in low sampling density has been recognized as a major limiting factor. Both proximal and remote sensing technologies have been implemented to provide highresolution data relevant to the soil attributes of interest. Remote sensing involves the deployment of sensor systems using airborne or satellite platforms. Proximal sensing requires the operation of the sensor at close range, or even in contact, with the soil being

FIELD EFFICIENCY DETERMINATION USING TRAFFIC PATTERN INDICES

To implement sustainable agricultural and environmental management, a better understanding of the soil at increasingly finer scales is needed. Conventional soil sampling and laboratory analyses cannot provide this information because they are slow and expensive. Proximal soil sensing (PSS) can overcome these shortcomings. PSS refers to field-based techniques that can measure soil properties from 2 m or less above the soil surface. The sensors may be invasive, or not, and may or may not be mounted on vehicles for on-the-go operation. Much research is being conducted worldwide to develop sensors and techniques that may be used for proximal soil sensing. These are based on electrical and electromagnetic, optical and radiometric, mechanical, acoustic, pneumatic, and electrochemical measurement concepts. This chapter reviews the latest of these technologies and discuss their applications.

MAPPING SOIL MECHANICAL RESISTANCE WITH A MULTIPLE BLADE SYSTEM

Field efficiency is an important criterion for determining field capacity during field operations and, indirectly, for making important machinery management decisions. Geographic location data gathered with a yield monitor during harvest and a data logger during planting were used to provide time-motion studies of equipment and operator productivity. This study used these spatial and temporal data to quantify field performance of a combine and a planter. Seven Nebraska fields were used to compare results from soybean and corn production systems. Fields that were relatively flat with straight rows were contrasted with contoured fields with slopes of 3% to 5%. Two unique traffic patterns in fields with a center pivot were compared. Four traffic pattern indices were developed and averaged across each field to indicate the steering behavior (or adjustments) made during field operations. Geo-referenced data were used to predict field efficiency for various traffic patterns. Of the four indices compared, the average steering angle (.) and its standard deviation had the strongest association with field efficiency with Pearson correlation coefficients of -0.654 and -0.664, respectively. The average steering angle for contoured traffic patterns were two to four times in magnitude that of straight- and gently curved-row traffic patterns. The steering angle index gave valuable information about field operating conditions but differences in data recording methods and operational characteristics imposed limitations on statistically appropriate comparison analyses.