Jeffrey L. Willers

Analysis of a precision agriculture approach to cotton production

The hope of precision agriculture is that through more precise timing and usage of seed, agricultural chemicals and irrigation water that higher economic yields can occur while enhancing the economic production of field crops and protecting the environment. The analyses performed in this manuscript demonstrate proof of concept of how precision agriculture coupled with crop simulation models and geographic information systems technology can be used in the cotton production system in the Mid South to optimize yields while minimizing water and nitrogen inputs. The Hood Farm Levingston Field, located in Bolivar County, Mississippi, next to the Mississippi River, was chosen as the test sight to obtain a one hectare soil physical property grid over the entire 201 ha field. The 1997 yield was used as a comparison for the analysis. Actual cultural practices for 1997 were used as input to the model. After the 201 simulations were made using the expert system to optimize for water and nitrogen on a one hectare basis, the model predicted that an increase of 322 kg/ha could be obtained by using only an average increase of 2.6 cm of water/ha and an average decrease of 35 kg N/ha.

Designing experiments to evaluate the effectiveness of precision agricultural practices on research fields: part 1 concepts for their formulation

A better method is needed to evaluate the effectiveness of precision agricultural practices on research farm fields. We present a novel methodology for formulating the design of an experiment to evaluate the effectiveness of precision agricultural practices. The method combines a georeferenced treatment structure and a georeferenced design structure to build a mixed model that describes and analyzes the site-specific experiment. One or more layers of georeferenced information (obtained by various remote-sensing systems) describing the topography of the research field and its crop attributes may be included as covariates in the mixed model. The concepts of this approach are illustrated through the use of a hypothetical field. Current limitations are also discussed.

Geographical Approaches for Integrated Pest Management of Arthropods in Forestry and Row Crops

With the proper technology and access to geographical information, it is more important to spend time developing an excellent classification scheme of a remotely sensed attribute of crop and forest vigor than to spend that time collecting multiple samples of insect counts . The ability to define zones from remote sensing images of crop or forest systems provides a vastly improved capacity to assess the sample variability of insect counts. Perspectives on defining zones from remote sensing information, including an examination of some relationships between these zones and insect sample counts, are discussed.

Wireless Local Area Networking for Farm Operations and Farm Management

High speed wireless data communication on farms has a large potential application which has already shown economical benefits in the two test farms where we have applied the technology. Because information is entered only once in hand-held devices connected wirelessly to the WLAN, transcription errors are minimized. Application maps which have been hand-carried by high-level technicians or professionals to the application equipment now can be transmitted directly to the equipment, saving significant amount of time and labor of high-level (high-cost) personnel. Similarly, data from the farm equipment can be transmitted directly to the farm base of operations at high speed saving time and money.

Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields

There is a lot of interest in the concept of precision farming, also called precision agriculture or site-specific management. Although the total acreage managed by these concepts is increasing worldwide each year, there are several limitations and constraints that must be resolved to sustain this increase. These include (1) collecting and managing the large amounts of information necessary to accomplish this micromanagement, (2) building and delivering geo-referenced fine-scale (i.e., change every few meters or less) prescriptions in a timely manner, (3) finding or developing agricultural machines capable of quickly and simultaneously altering the rates of one or more agri-chemicals applied to the crop according to a geo-referenced prescription, (4) the need to have personnel stay “current” with advancements in developing technologies and adapting them to agriculture, (5) refining existing and/or creating new analytical theories useful in agriculture within a multidisciplinary, multi-institutional, and multibusiness environment of cooperation, and (6) modification of agricultural practices that enhances environmental conservation and/or stewardship while complying with governmental regulations and facing difficult economic constraints to remain profitable. There are many myths that overshadow the realities and obscure the true possibilities of precision agriculture. Considerations to establish productive linkages between the diverse sources of information and equipment necessary to apply site-specific practices and geographically monitor yield are daunting. It is anticipated that simulation models and other decision support systems will play key roles in integrating tasks involved with precision agriculture. Discovering how to connect models or other software systems to the hardware technologies of precision agriculture, while demonstrating their reliability and managing the flows of information among components, is a major challenge. The close cooperation of the extension, industrial, production, and research sectors of agriculture will be required to resolve this constraint.

Remote Sensing in Dryland Cotton: Relation to Yield Potential and Soil Properties

The use of soil and topography information to explain crop yield variation across fields is often applied for crop management purposes. Remote sensed data is a potential source of information for site-specific crop management, providing both spatial and temporal information about soil and crop condition. Studies were conducted in a 104-acre (42-hectare) dryland cotton field in 2001 and 2002 in order to (1) qualitatively assess the spatial variability of soil physical properties from kriged estimates, (2) compare actual yields with normalized difference vegetation reflectance indices (NDVI) obtained from multispectral imagery and from in situ radiometer data, and (3) predict site-specific cotton yields using a crop simulation model, GOSSYM. An NDVI map of soybean in 2000 obtained from a multispectral image was used to establish four sites in each low, medium and high NDVI class. These 12 sites were studied in 2001 and 12 more sites selected at random were studied in 2002 (n = 24). Site-specific measurements included leaf area index (LAI), canopy hyperspectral reflectance, and three-band multispectral image data for green, red, and near-infrared reflectance wavebands at spatial resolutions of 2 m in 2001 and 0.5 m in 2002. Imagery was imported into the image analysis software Imagine (ERDAS, v. 8.5) for georegistration and image analysis. A 6x6 pixels (144 m2) area of interest was established on top of each field plot site and digital numbers (DN) from reflectance imagery were extracted from each band for derivation of NDVI maps for each of four sampling dates. Lint yield from each plot site was collected by hand and also by a cotton picker equipped with AgLeader yield monitor and OmniStar differential global positioning system. We found plant height, leaf area index, and lint yield were closely associated with NDVI maps and with NIR band values acquired from either an aircraft or handheld (GER-1500) sensor during peak bloom in mid July. Results indicate NDVI and NIR bands could be used to produce estimated field maps of plant height, leaf area index and yield, which offer a potentially attractive mid-season management tool for site specific farming in dryland cotton.

Variable-Rate Spray System Dynamic Evaluation

A MidTech TASC 6300 variable-rate spray control system was interfaced with a John Deere 4700 spray tractor. The dynamic response of the system was examined by measuring the response time between when a control signal was initiated and when the solution at the nozzle stabilized at the resulting concentration. These measurements were evaluated with three injection dyes at two tractor speeds and three application rates. Three FD&C dyes were chosen to simulate three chemicals in the injection tanks. A total of 1176 samples of solution from the nozzles were collected and analyzed with a spectrophotometer. Their absorbance characteristics were used to determine the response time of the spray system under various testing conditions. The results showed that the overall average delay time of the system was 38.3 seconds, and the rise time was 65.9 seconds. The response time varied with speed, but the product of speed and response time remained almost constant. The system took longer to achieve steady-state when the application rate varied from high to low than when it varied from low to high.

Development of a Crop Yield Stability Methodology for a Field

Here we present a methodology used to develop a yield stability map for a field. We proposed that there exist yield stability patters for commercial field crop production which growers can use to optimize crop production while minimizing inputs. The methodology uses multiple years of multi-crop yield monitor data and a high resolution, high density LIDAR (light detection and ranging using a laser light source similar to radar but much higher accuracy) digital elevation map of the field. These data are analyzed using ESRI GIS (geographic information system), ERDAS Imagine image processing, and SAS® (Company and brand names are used for information only and do not represent a recommendation or endorsement by the USDA-ARS) (SAS®2008). statistical analysis to produce detailed, multiple component GIS map which shows transitions for low yielding through medium yielding to the most productive high yielding areas of the field. The statistical procedures make use of krigging, cluster analysis, and simple quadratic regression to produce a statistically sound three-component map showing low-, medium- and high-yield zones in the field. The three-component map is based on a 3 m by 3 m pixel density and provides an immediate visualization tool for the grower to plan crop management practices.