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J.R. Davenport, C.A. Redulla, M.J. Hattendorf, R.G. Evans and R.A. Boydston

An accurate yield map is imperative for successful precision farming. For 3 years (1998 to 2000) two to four potato (Solanum tuberosum) fields on a commercial farm in southeastern Washington were yield-monitored using commercial yield monitoring equipment without operator interaction. Multiple potato diggers were used to harvest the fields and diggers used were not necessarily the same at each harvest. In all years, yield monitoring data were missing due to equipment failure or lack of yield monitoring equipment on all diggers. Banding, due to dissimilar calibrations, different equipment used, or differential digger performance was observed in 1998 and 2000. Based on experience described here, some yield monitor data need minimal postprocessing or correction, other data need substantial postprocessing to make them usable, and other data may not be reliable due to equipment failure, improper calibration, or other causes. Even with preharvest calibration, it is still likely that the potato yield monitor data will need differential postprocessing, indicating that yield maps lack accuracy. In addition, comparison to yield data collected at multiple points within the field, this study found that the yield monitor over estimated potato yield. Thus, with some postprocessing, a useful yield map showing within field differences is possible. However, without significant postprocessing, the practice of using multiple diggers and yield monitors for potato harvest, both within and between fields, severely limits the ability to make consistent yield maps in commercial potato operations.

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Yiannis G. Ampatzidis and Matthew D. Whiting

along the row (e.g., UFO). Literature Cited Ampatzidis, Y.G. 2010 Modeling and electronic monitoring of activities during manual harvested of specialty crops with application to precision farming and traceability [in Greek]. PhD thesis, Aristotle

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Amy Barr, Mark Bennett and John Cardina

The objective of this study was to ascertain if stand establishment of sh2 sweet corn (Zea mays L.) would benefit from variable planting depths determined by the use of geographic information systems (GIS). Spring and fall research plots were established in a field [80 × 20 m (262 × 66 ft)] containing Crosby silt loam and Kokomo silty clay loam soil series in Columbus, Ohio. Three sh2 sweet corn cultivars (Starship, Skyline, and Confection) were planted at three depths on the two soil types in the fall study, with an additional transition soil added in the spring. Emergence counts as well as soil moisture and temperature were monitored. In the spring, sites were also sampled for nutrient levels and soil compaction. Significant variability was found within the field with respect to soil moisture, temperature, nutrient levels, and compaction. Seedling emergence fluctuated with average soil moisture increasing in blocks with up to 24% moisture and then leveling off. Daily minimum soil temperatures impacted stand establishment. Although heat units accumulated faster on the Crosby soil, emergence was slower and less complete on these soil series than on Kokomo soil series. Further investigation determined that although temperatures of the Crosby soil were 3 to 4.5 °C (5.4 to 8.1 °F) warmer during the day than the Kokomo soil, temperatures on the Crosby soil averaged 2 °C (3.6 °F) cooler at night. Analysis of emergence patterns and field variability was performed on ArcView mapping software. Although `Skyline' planted at 2 cm (0.8 inches) had the best emergence overall, final stand would have been increased with `Skyline' planted variably at 2 and 4 cm (1.6 inches). Mapping the field under these different scenarios showed that although area with less than 70% stand would exist with a 2-cm uniform planting depth, the entire field would have a stand of 70% or greater with variable planting depth using a high vigor seedlot.

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Ronnie W. Heiniger

New technologies such as differential global positioning systems (DGPS) and geographical information systems (GIS) are making it possible to manage variability in soil properties and the microenvironment within a field. By providing information about where variability occurs and the patterns that exist in crop and soil properties, DGPS and GIS technologies have the potential of improving crop management practices. Yield monitoring systems linked to DGPS receivers are available for several types of horticultural crops and can be used in variety selection and/or improving crop management. Precision soil sampling and remote sensing technologies can be used to scout for infestations of insects, diseases, or weeds, to determine the distribution of soil nutrients, and to monitor produce quality by measuring crop vigor. Combined with variable rate application systems, precision soil sampling and remote sensing can help direct fertilizer, herbicide, pesticide, and/or fungicide applications to only those regions of the field that require soil amendments or are above threshold levels. This could result in less chemical use and improved crop performance. As with any information driven system, the data must be accurate, inexpensive to collect, and, most importantly, must become part of a decision process that results in improvements in crop yield, productivity, and/or environmental stewardship.

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Pierre C. Robert

A better awareness of soil and crop condition variability within fields brought the notion, in the early 1980s that variable management within fields by zones rather than whole fields would increase profitability by doing the right thing at the right place in the right way. At the same time, the microcomputer became available and made possible the acquisition, processing, and use of spatial field data as well as the development of a new kind of farm machinery with computerized controllers and sensors. Precision agriculture (PA) has been considered for most common cropping systems and some specialty crops, worldwide. It is particularly well adapted to high value crops such as many horticultural crops. PA is still in infancy and its adoption varies greatly but precision agriculture is the agricultural system of the future. It offers a variety of potential benefits in profitability, productivity, sustainability, crop quality, food safety, environmental protection, on-farm quality of life, and rural economic development.

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N. Tremblay and C. Bélec

The necessity of achieving appropriate nitrogen fertilization of vegetable crops relates to both economical and environmental sustainability. Split nitrogen applications have been shown to improve N-use efficiency, in line with the aforementioned objective and should therefore be encouraged. Given the variation in the amount of N naturally provided to, or uptaken by, the crop, strategies are required to tailor supplementary fertilization to actual crop needs, keeping in mind the absolute requirement for optimal yield in quality and quantity. It is suggested that the fertilization rates applied at sowing or later in the season can be figured in two manners. The first relies on modelling; the second on measurements. The modelling (N budget) approach, mostly linked to initiatives on the European continent, would be most applicable to the determination of the first fertilizer dressing. When a plant stand is established, however, canopy-based measurements made either directly or remotely could be developed to make use of the capability of the plants to integrate the properties of the soil environment and to decide upon further top-dressed applications. For this purpose, a fully fertilized “reference plot” has to be introduced in the field in order to overcome the variability induced by season, site and cultivar. With the emergence of “precision farming” and “remote sensing technologies” it is now possible to adjust fertilizer inputs not only at the field level but also within fields based on actual, localized requirements.

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Pierre C. Robert

The new agricultural system called soil/site specific crop management (SSCM), now more generally named precision agriculture (precision farming) is the start of a revolution in natural resource management based on INFORMATION TECHNOLOGY AND CONTROL: it is bringing agriculture in the digital and information age. New technologies in the early 80s, particularly the microprocessor, made possible the development in the United States of farm machinery computers and controllers, the electronic acquisition and process of spatial field data to build farm geographic record keeping systems, the production of soil/site specific condition and management maps using GIS, the positioning of machines using GPS, and the development of real-time soil and crop sensors, particularly yield sensors. The concept of precision agriculture originated from a better awareness of soil and crop conditions variability within fields. The variability of soil conditions within parcels in the U.S. has been demonstrated in many ways (soil survey, soil sampling, and remote sensing) for both soil nutrients and soil physical properties (e.g., available water and compaction). It is progressively found that the concept of precision agriculture can be applied to a variety of crops and practices; management technological levels; and farm types and sizes. For example, in addition to grain crops (corn, soybeans, and wheat), applications are now developed for sugar beet and sugar cane, potato, cotton, peanut, vegetables, turf, or- chard, livestock, tree plantation, etc. Precision agriculture is still in infancy but it is the agricultural system of the future because it offers a unique variety of potential benefits in profitability, productivity, sustainability, crop quality, food safety, environmental protection, on-farm quality of life, and rural economic development.

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Gabriele Gusmini and Todd C. Wehner

the world. High-yielding cultivars, precision-farming systems, increased use of chemicals for fertilization and weed and disease control, and proper training of the local farmers allowed a significant change in agriculture. As a result, crop yields

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Yun-wen Wang, Bruce L. Dunn and Daryl B. Arnall

. 2003 Remote sensing and SGIS as a tool for precision farming in horticulture sector in India, p. 37–38. In: Singh, H.P., G. Singh, J.C. Samuel, and R.K. Pathak (eds.). Precision farming in horticulture. NCPAH, DAC/PFDC, CISH, Lucknow, India. Peng, S

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Arnold W. Schumann

spatial and temporal variations in potato yield and in soil properties Amer. J. Potato Res. 83 381 395 Doerge, T. 1999 Defining management zones for precision farming Crop Insights 8 1 5 Elings, A. de Visser, P.H.B. Marcelis, L.F.M. Heinen, M. Boogaard, H