Cotton Yield Monitor Research Articles

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

Modeling local and global spatial correlation in field‐scale experiments

AbstractPrecision agriculture has renewed the interest of farmers and researchers to conduct on‐farm planned comparisons and researchers with respect to field‐scale research. Cotton (Gossypium hirsutum L.) yield monitor data collected on‐the‐go from planned field‐scale on‐farm experiments can be used to make improved decisions if analyzed appropriately. When farmers and researchers compare treatments implemented at larger block designs, treatment edge effects and spatial externalities need to be considered so that results are not biased. Spatial analysis methods are compared for field‐scale research using site‐specific data, paying due attention to local and global patterns of spatial correlation. Local spatial spillovers are explicitly modeled by spatial statistical techniques that led to improved farm management decisions in combination with the limited replication strip trial data farmers currently collect.

A Note on the Application of an AgLeader® Cotton Yield Monitor for Measuring Peanut Yield: An Investigation in Two US states.

ABSTRACT Previous researchers demonstrated the ability to adapt an AgLeader® Cotton Monitor to a peanut combine. It was demonstrated that the field weight could be accurately predicted with average errors of less than 10% across all trials when at least five calibration loads are applied. This project focused on expanding previous work performed at the University of Georgia and other peanut optical yield monitor work by incorporating a protective deflector plate for the sensors, obtaining multiple field weights, and using the peanut sale sheets to correlate yield monitor yield to sale weight. This study was a two-university, two-state effort, including Oklahoma State University (Oklahoma), and Mississippi State University (Mississippi). Data collected during this study included multiple loads which included yield monitor weight, field weight, field moisture content, and all the information presented on the standard USDA peanut grade sheet, when available. The multi-state effort allowed for the incorporation of the two major peanut types and for the incorporation of different soil types. The goal of this study was to develop guidelines for using, calibrating, and adapting the AgLeader® Cotton Monitor for peanut harvest. Five calibration loads referenced to buy-point net weight were typically needed to bring error within acceptable limits. Results indicated that multiple local calibrations were needed to ensure high data validity and yield estimation across multiple harvest environments. The data showed that peanut type (virginia, runner and spanish) and variable soil conditions impacted yield estimation.

Variety Effects on Cotton Yield Monitor Calibration

Abstract.Cotton ( L.) producers were some of the earliest adopters of practices employing site-specific management. Yield monitors are an essential component of those precision agriculture methods. The agreement between measured cotton yields and those estimated with yield monitors must be improved to adequately advance precision agriculture, particularly the site-specific planting of multiple varieties. Six site-years of observations with two yield monitor systems were studied with the objective to compare yield monitor-estimated weights to observed weights in replicated cotton variety trials and investigate factors that could relate to any observed varietal effect. The results supported the findings of earlier research indicating that cotton yield monitor calibration is affected by variety. For the six site-years and three combinations of two site-years, correlations were sometimes observed between calibration errors and turnout or some of the High Volume Instrument (HVI) fiber quality measurements, but no factor was consistently correlated across multiple studies. In one study, none of the included factors was significantly correlated with calibration error, while the HVI properties reflectance (Rd) and leaf grade were both significantly correlated in three comparisons, which was more than any other factor. Data from additional site-years must be collected and analyzed and other factors investigated to find what variety-related properties affect yield monitor errors and to allow the determination of calibration adjustments based on those factors. Keywords: Cotton, Cotton picker, Harvest, Precision agriculture, Yield monitor.

An Optical Yield Monitor for Peanuts – Proof of Concept and Evaluation

Peanut (Arachis Hypogea) is one of the few major agronomic crops for which a yield monitor is not commercially available. This paper describes an ongoing project whose long-term goal is to adapt an optical sensor originally developed for cotton yield monitoring for use as a peanut yield monitor (PYM). The immediate objective of the work reported here was to evaluate the PYM under harvest conditions typical in southern Georgia, USA. The PYM consists of two mass-flow sensors, a data acquisition system, and a DGPS receiver. The PYM was evaluated on three fields totaling 29 ha during the 2016 harvest season. Percent error between the scale load and calculated load was 2% or better for the first field tested, but increased greatly for subsequent fields that were tested, most likely caused by damage to the sensor lens from the impact of pebbles.

Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

Abstract. Crop growth simulation models can address a variety of agricultural problems, but their use to directly assist in-season irrigation management decisions is less common. Confidence in model reliability can be increased if models are shown to provide improved in-season management recommendations, which are explicitly tested in the field. The objective of this study was to compare the CSM-CROPGRO-Cotton model (with recently updated ET routines) to a well-tested FAO-56 irrigation scheduling spreadsheet by (1) using both tools to schedule cotton irrigation during 2014 and 2015 in central Arizona and (2) conducting a post-hoc simulation study to further compare outputs from these tools. Two replications of each irrigation scheduling treatment and a water-stressed treatment were established on a 2.6 ha field. Irrigation schedules were developed on a weekly basis and administered via an overhead lateral-move sprinkler irrigation system. Neutron moisture meters were used weekly to estimate soil moisture status and crop water use, and destructive plant samples were routinely collected to estimate cotton leaf area index (LAI) and canopy weight. Cotton yield was estimated using two mechanical cotton pickers with differing capabilities: (1) a two-row picker that facilitated manual collection of yield samples from 32 m2 areas and (2) a four-row picker equipped with a sensor-based cotton yield monitoring system. In addition to statistical testing of field data via mixed models, the data were used for post-hoc reparameterization and fine-tuning of the irrigation scheduling tools. Post-hoc simulations were conducted to compare measured and simulated evapotranspiration, crop coefficients, root zone soil moisture depletion, cotton growth metrics, and yield for each irrigation treatment. While total seasonal irrigation amounts were similar among the two scheduling tools, the crop model recommended more water during anthesis and less during the early season, which led to higher cotton fiber yield in both seasons (p < 0.05). The tools calculated cumulative evapotranspiration similarly, with root mean squared errors (RMSEs) less than 13%; however, FAO-56 crop coefficient (Kc) plots demonstrated subtle differences in daily evapotranspiration calculations. Root zone soil moisture depletion was better calculated by CSM-CROPGRO-Cotton, perhaps due to its more complex soil profile simulation; however, RMSEs for depletion always exceeded 20% for both tools and reached 149% for the FAO-56 spreadsheet in 2014. CSM-CROPGRO-Cotton simulated cotton LAI, canopy weight, canopy height, and yield with RMSEs less than 21%, while the FAO-56 spreadsheet had no capability for such outputs. Through field verification and thorough post-hoc data analysis, the results demonstrated that the CSM-CROPGRO-Cotton model with updated FAO-56 ET routines could match or exceed the accuracy and capability of an FAO-56 spreadsheet tool for cotton water use calculations and irrigation scheduling. Keywords: Cottonseed, Crop coefficient, Decision support, Depletion, Evapotranspiration, Fiber, Management, Simulation, Soil moisture, Yield.

Changes in Producers' Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors

This article investigates how information from cotton yield monitors influences the perceptions of within-field yield variability of cotton producers. Using yield distribution modeling techniques and survey data from cotton producers in 11 southeastern states, we find that cotton farmers who responded to the survey tend to underestimate within-field yield variability (by approximately 5-18%) when not using site-specific yield monitor information. Results further indicate that surveyed cotton farmers who responded to a specific question about yield monitors place a value of approximately $20/acre/year (on average) on the additional information about within-field yield variability that the yield monitor technology provides.

Development of sensor systems for precision agriculture in cotton

Precision agriculture (PA) is an information-based technology, using detailed information within an agricultural field to optimize production inputs on a spatially variable basis, maximize farm profit, and minimize environmental impact. Information collection and processing plays a very important role in PA. In recent years PA technologies have been gradually adopted in cotton production. Several sensor systems for PA were developed and field-evaluated in cotton, including a plant height measurement system (PHMS), the Mississippi cotton yield monitor (MCYM), and cotton fiber quality mapping. The PHMS used an ultrasonic sensor to scan the plant canopy and determine plant height in real time in situ. A plant height map was generated with the data collected with the PHMS. Cotton plant height showed a close relationship with yield (R2=0.63) and leaf-nitrogen content (R2=0.48). The MCYM was developed for cotton yield mapping. A patented mass-flow sensor technology was employed in the MCYM. The sensor measured optical reflectance of cotton particles passing through the sensor and used the measured reflectance to determine cotton-mass flow rates. Field tests indicated that the MCYM could measure cotton yield with an average error less than 5%, and it was easy to install and maintain. The cotton fiber-quality mapping research involved a wireless cotton module-tracking system (WCMTS) and a cotton fiber quality mapping system (CFQMS). The WCMTS was based on the concept that a cotton fiber-quality map could be generated with spatial information collected by the system during harvesting coupled with fiber quality information available in cotton classing offices. The WCMTS was constructed and tested, and it operated according to design, with module-level fiber-quality maps easily made from the collected data. The CFQMS was designed and fabricated to perform real-time measurement of cotton fiber quality as the cotton is harvested in the field. Test results indicated that the sensor was capable of accurately estimating fiber micronaire in lint cotton (R2=0.99), but estimating fiber quality in seed cotton was more difficult. Cotton fiber quality maps can be used with cotton yield maps for developing field profit maps and optimizing production inputs.

Cotton-harvester-flow simulator for testing cotton yield monitors

An experimental system was developed to simulate the pneumatic flow arrangement found in picker-type cotton harvesters. The simulation system was designed and constructed for testing a prototype cotton yield monitor developed at Mississippi State University. The simulation system was constructed to approximate the pneumatic cotton flow system of a cotton picker, and was capable of operating with varying cotton flow rates. The simulator was tested with different cotton flow rates, and the relationship between feeder rate and amount of conveyed seed cotton was found to be consistent. Further, the simulator was used to conduct tests with the novel optical cotton yield monitor, which proved accurate at measuring the amount of seed cotton flowing through the simulator. Finally, some differences between laboratory testing and field-testing were noted: seed cotton becomes fluffed and twisted when recycled through the simulator, and seed cotton stored in the laboratory tends to be of lower moisture content than cotton during harvest. These differences should be considered when using a laboratory simulator to test cotton yield monitors.

A module-specific post-processing calibration method to improve cotton yield mapping

Calibrating cotton yield monitor data is difficult, because there are many confounding factors, such as landscape, soil, ambient light and temperature, defoliation level, and variety that can cause sensor response to shift from field to field and even within a field. The conventional calibration method involves in situ weighing of basket loads, which is costly, logistically unfavorable, and not adequate to account for inconsistent performance of yield monitor sensors. In this work a module-specific post-processing method was used to calibrate cotton yield data. The method was enabled by a wireless communication and GPS based electronic system that can delineate the harvest location of each cotton module in the field during machine harvest. Module-specific correction coefficients were calculated as the ratio of module weight determined at the gin to the yield monitor based module weight integrated over the module harvest area. Module-specific lint turnout, also determined at the gin, was used to convert seed cotton yield to lint yield. The method was field tested on 16 cotton modules harvested from two study areas near College Station, TX. Results showed that yield monitor based weight deviated substantially from the actual weight for some modules, with errors ranging from −41.0% to +13.9%. Lint yield maps produced with module-specific post-processing calibration showed more reliable spatial patterns than maps produced with field-based post-processing. Combining the wireless communication and GPS system with the cotton yield monitor could greatly enhance the quality of cotton yield maps, making them more suitable for various precision agriculture applications.

Generalized analysis of spatial variation in yield monitor data

Australian lupin and cotton yield monitor data were analysed using spatial models from the Matérn class of spatial covariance functions. Despite difficulties with the spatial disposition of the data, the analysis supports the statistical model in which the variation is a linear combination of white noise and the de Wijs process. The de Wijs process, also called the logarithmic covariance function, is a generalized covariance function that is conformally invariant and suggests that there is variation at all spatial scales. The present work also indicates that anisotropy and convolution are properties of yield monitor data and that it is hard to distinguish the two. The degree and causes of anisotropy require further investigation. Fitting this model is relatively easy for small, precision-agriculture datasets and open source software is available to this end. Comparing the de Wijs model with more general models in the Matérn class is computationally intensive for precision-agriculture datasets.

Evaluating High-Resolution QuickBird Satellite Imagery for Estimating Cotton Yield

High spatial resolution imagery from recently launched satellite sensors offers new opportunities for crop management. The objective of this study was to evaluate QuickBird satellite imagery for mapping yield variability in cotton fields. A QuickBird image scene with 2.8 m resolution and four spectral bands (blue, green, red, and near-infrared) was acquired from an intensively cropped area in south Texas. Airborne color-infrared imagery was also collected from two cotton fields within the satellite scene. Yield data were collected from the two fields using a cotton yield monitor. Images for the two fields were extracted from the QuickBird scene, and both the satellite and airborne images were aggregated to a pixel resolution of 8.4 m, about twice the harvester swath. Vegetation indices including band ratios and normalized differences were calculated from the spectral bands, and cotton yield was related to the bands and vegetation indices for both types of imagery. The extracted QuickBird images were classified into two to ten zones using unsupervised classification, and mean yields among the zones were compared. Results showed that cotton yield was correlated to both types of imagery and that the QuickBird imagery had similar correlations with yield to the airborne imagery. Moreover, the unsupervised classification maps effectively differentiated cotton production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for estimating crop yield.

A computer decision aid for the cotton yield monitor investment decision

This article introduces the Cotton Yield Monitor Investment Decision Aid (CYMIDA), a stand alone, interactive, and user-friendly computerized decision aid for analyzing the cotton yield monitor information system investment choice. Because electronic cotton yield monitors are a relatively new technology, the decision aid was developed to meet the need for better educational information about annual ownership costs and required returns for the cotton yield monitoring information investment decision. The partial budgeting and breakeven analysis methods and data used in CYMIDA are documented. In addition, the use of the decision aid is explained and three example investment analyses using the decision aid are described.

Development of practical site-specific management methods for reclaiming salt-affected soil

Sustaining irrigated agricultural production systems in semi-arid and arid regions requires consideration of with saline and sodic soil conditions. The spatial variability of these conditions makes soil reclamation an ideal practice in which to apply site-specific management (SSM). We discuss an application of SSM in which preliminary measurements of apparent soil electrical conductivity (EC a), supplemented by EC a-directed soil sampling, are used to construct a GIS map of salinity zones on which site-specific amendment application can be based. Our primary objective was to develop a field-implementable methodology for site-specific soil amendment application. The focus of the program was on cotton production, although the method should be applicable to other crops. A second objective was to establish experiments to test the effectiveness of this program. Evaluating the effectiveness of a sprinkler application for the first irrigation was incorporated as a part of this program in hopes of reducing variability due to poor germination. This study consisted of two commercial fields located in the San Joaquin Valley of California. Based on the consideration that EC a is directly related to electrical conductivity from soil saturation paste extract (EC e) for known saline-sodic conditions, we developed a four-step method for site-specific salinity management in commercial fields. The steps included the following: (1) generation of an EC a map; (2) directed soil sampling for EC e; (3) determination of the estimated amendment requirement as a function of location in the field; and (4) integration of the individual amendment requirements into a practical spatial pattern for amendment application. Cotton yield monitors were utilized to indicate spatial yield variation throughout the replicated plots instead of aggregated yield values. Because of the high levels of variability in these commercial fields and the time required for amendments to have a substantial effect, we can provide only short-term results of these experiments. In both of the experiments no significant differences existed between the irrigation and amendment treatments ( p > 0.05), but increased yield trends supported the benefit of sprinkler application of the first irrigation. A Scheffe test of the cotton yield monitor data indicated significant yield differences between in-plot treatment zones.

ADAPTATION AND TESTING OF THE AGLEADER COTTON YIELD SENSOR ON A PEANUT COMBINE

An Agleader cotton yield monitor was modified for use as a peanut yield monitor for harvest during the 2000, 2001, and 2002 seasons. A single pair of optical sensors was mounted on the vertical portion of the delivery chute between the air fan and basket. The first season’s harvest resulted in severe pitting and scarring of a glass plate used to protect the sensors from peanut and foreign material abrasion. Even so, mean load absolute error was only 11.6% for 25 loads of peanuts harvested. For the second and third seasons, a deflection baffle and a small air circulation opening at the bottom of each sensor were made to prevent abrasion from peanuts and foreign material and to reduce dust build-up, respectively. Inspection of the sensors showed that the modifications did prevent pitting and scarring on the sensor covering and that the dust build-up was reduced significantly. Absolute mean load error for three different fields harvested was 9.4%, 3.2%, and 4.9%. It was determined that adjustments to the combine at the beginning of the harvest season created the larger error in the first field. Once the machine was adjusted and the yield monitor calibrated to that setting, the Agleader yield monitor results were comparable to the results when used to monitor cotton yield.

The influence of cotton variety in the calibration factor of a cotton yield monitor

A three years study on precision fanning in cotton in central Greece showed serious infield variability of yield and soil physical and chemical properties. Yield mapping constitutes the starting and ending point of the whole process chain in a precision farming system. Yield mapping was performed for three consecutive years in the same cotton fields of central Greece in a wide range of cotton varieties and field conditions with a yield monitor installed on a cotton picker. The yield sensors estimate the flow volume of cotton conveyed from the picking units in the basket through air ducts. The main control unit performs the transformation of the estimated cotton volume in cotton weight using a factor named Calibration Coefficient or Factor. Every time the picking conditions change, like going to a different field or change cotton variety, there is the need to pick a whole load basket and make an actual weighing to inform the main unit to adapt the calibration factor to the correct value. After three cotton yield mapping seasons (overall 200ha) it was observed that there is a straight relation of the value of calibration factor with the cotton variety. The present study shows these results that group the values of calibration factor with corresponding cotton varieties. The results would help in the calibration of the monitors.

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.

Mississippi cotton yield monitor: beta test for commercialization

Based on a novel cotton-flow sensor, the Mississippi Cotton Yield Monitor has been under development at Mississippi State University since 1999, when one prototype of the yield monitor was field tested in Mississippi. Three prototypes were constructed and field tested in Texas, Georgia, and Mississippi in 2000. Five prototypes of an improved version were fabricated and field tested in 2001. All three years’ testing results were promising. In 2001, Mississippi State University and Agricultural Information Management, LLC, signed a licensing option agreement to prepare for the eventual manufacture and marketing of the Mississippi cotton yield monitor. Research towards commercialization of the Mississippi cotton yield monitor was conducted in 2002. In this beta test, 10 prototypes of the Mississippi cotton yield monitor were built and extensively tested on commercial and research farms during the 2002 cotton harvesting season. Harvesting was conducted with both cotton pickers and strippers. The yield monitor system’s accuracy was evaluated on a load-by-load weight basis. Reliability was tested under commercial harvesting conditions. All systems performed well during the tests. Each one was easy to install, maintain, and operate. No hardware problems occurred. The system’s average absolute error was 3.8%. Cotton yield maps created with the data collected by the monitors realistically exhibited yield variations within the fields. Evaluator’s suggestions, mainly related to software performance, are to be addressed in the commercial version of the Mississippi Cotton Yield Monitor.

SIMULTANEOUS ASSESSMENT OF COTTON YIELD MONITOR

The most essential component of precision farming is the yield monitor a sensor or group of sensors installedon harvesting equipment that dynamically measures spatial yield variability. Yield maps, which are produced with data fromyield monitors, are extremely useful in providing a visual image to clearly show the variability of yield across a field. Inresponse to the demand for a reliable and accurate cotton yield monitor, several have recently become commerciallyavailable. We assessed the AgLeader, AgriPlan, FarmScan, and MicroTrak cotton yield monitors in southern Georgia forfive harvest seasons from 1997 to 2001. During 2001 we also assessed a prototype yield monitor. Each year, two or more yieldmonitors were mounted on a cotton harvester and were used during the harvest of several farmerowned and managed fields.The accuracy of each yield monitor was tested by comparing the weight of each harvested load to data produced by the yieldmonitor. Yield maps from each yield monitor were also produced with the respective software packages and compared.Features of the monitors were also compared. Each of the cotton yield monitoring systems we assessed has something to offera user interested in creating yield maps. All are capable of producing an adequate yield map provided the system is properlycalibrated, operated, and maintained.

MISSISSIPPI COTTON YIELD MONITOR: THREE YEARS OF FIELD-TEST RESULTS

The Mississippi cotton yield monitor, which is based on an optical cotton-flow sensor comprised of emitters anddetectors in one unit that can be affixed to only one side of a pneumatic duct, has been under development at Mississippi StateUniversity since 1999. One prototype of the yield monitor was field tested that year in Mississippi. In 2000, three prototypeswere constructed and field tested in Texas, Georgia, and Mississippi on about 530 ha (1300 acre) of cotton. Different varietieswith large yield variations were harvested. In the Texas test, average absolute errors for two fields were 5.9% and 5.4%.Results from the Georgia test indicated an average absolute error of 5.7%. System reliability was tested in Mississippi on about445 of the total 530 ha (1100 out of 1300 acre) of cotton. Based on the 2000 results, a new version of the Mississippi cottonyield monitor was designed to include anti-stray-light and temperature-stabilization features. Five prototypes of the newversion were fabricated and field tested in 2001 on three cotton pickers and two cotton strippers at five locations in Georgia,Texas, and Mississippi. A total of 1230 ha (3000 acre) of cotton was harvested with the yield monitors from September toDecember of 2001. Results indicated that the average absolute errors of the system were 3.7% and 4.9% in two fields wheresystem accuracy was evaluated load by load. Considering all three years data, the average measurement error of theMississippi cotton yield monitor was less than 5%, and the latest prototype is improved over previous versions. Cotton yieldmaps have been created with data from all the field tests, and the maps realistically exhibit yield variations within fields, basedon the expectations of experienced producers and consultants. The tests also indicated that the system was reliable and easyto install, operate, and maintain.