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SET Crop variability in cotton fields

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Three experimental fields were cultivated with cotton (Gossypium hirsutum L) in Omorfochori Larissa in 2005. The correlation between electrical conductivity, plant density, plant height and yield were determined to draw conclusions and make farm decisions for the best management of the fields. The results showed significant variability in yield and Electrical Conductivity in the three fields. Electrical conductivity did not give adequate correlation to yield but it can be used to delineate management zones for targeted soil sampling. Plant height and plant density did not affect yield due to the uniformity of the plants.
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... From a visual comparison of the yield maps (Fig. 4a-c) with the EC a maps ( Fig. 3a and b), the spatial association does not appear so direct, because the high values of EC a extended up to the eastern border of the field, which was generally low producing. In literature, contrasting results are reported about the relationship between EC a and yield: positive correlation for Lund et al., 1999, whereas negative for Vardoulis et al., 2006. This is probably due to the fact that EC a is simultaneously affected by many mutually interacting soil factors, like texture, salinity, water content, temperature and organic matter content (Friedman, 2005). ...
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Management zone (MZ) delineation is important for the application of Precision Agriculture because farm management decisions are based on it. Several factors were used for the MZ delineation including crop and soil characteristics. In the present paper multivariate analysis was applied to delineate MZs. Soil and crop data, collected over 3 years from a Precision Agriculture project in an apple orchard in Greece, were used. The collected data were categorized in three groups, namely soil properties, yield and fruit quality. All data were analyzed for descriptive statistics and their distribution. Maps of the spatial variability for the 3 years were presented. Data were jointly analyzed for management zone delineation using a combination of multivariate geostatistics with a non-parametric clustering approach, and the orchard was divided in four zones which could be differently managed. However, further research and experimentation are needed before precision horticulture being confidently adopted in Greece.
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Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil-water-plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.
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The results of three years of yield mapping in cotton in Greece are reported. Yield mapping was taken with a FARMSCAN equipment installed on a two row picker. The equipment performed satisfactorily with the proper care. Data of soil properties using direct measurements (texture, chemical properties, compaction) and soil EC mapping by a VERIS machine were collected as well .Thematic maps were produced using GIS software. Correlations between yield and soil properties were investigated using SPSS software. The analysis showed that great yield variability was evident in all three years giving high spatial and temporal variability. Yield zones can be produced giving basis for field management. Yield was well correlated to VERIS EC mapping as well as to other soil properties like soil compaction.
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The application of precision agriculture to cotton (Gossypium hirsutum L.) production has been limited due to the lack of a suitable yield monitor. Cotton yield monitors under research have utilized load cells, sound sensors and electro-optical devices to measure seedcotton yield. A commercial electro-optical cotton yield sensor was introduced to the cotton industry in 1997 by the Zycom Corporation, Bedford, MA. The purpose of this study was to evaluate the precision of this new yield monitor on small research plots. Cotton research plots were harvested and monitor values recorded from a single harvesting head. Seedcotton was saved from each plot and weighed for comparison with yield monitor values. Monitor values were compared to cumulative plot weights. A total of 226.8 kg of seedcotton was harvested from 72 plots. A significant linear relationship (r2=0.99) was observed between monitor values and observed plot weights. This yield monitor looked very promising for estimating cotton yields and suggests that this technology may allow for the application of precision agriculture to cotton production.
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Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil-water-plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.
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The most common method for in situ assessment of soil salinity, namely the electrical conductivity (EC) of the soil solution (ECw), is to measure the apparent electrical conductivity (ECa) and volumetric water content (θ) of the soil and apply measured or predicted ECa(ECw, θ) calibration curves. The water content and electrical conductivity of a soil solution are indeed the major factors affecting its apparent electrical conductivity, which justifies the assessment of salinity from apparent EC measurements. However, the ECa(ECw, θ) relationship depends on some additional soil and environmental attributes affecting the soil ECa. Non-spherical particle shapes and a broad particle-size distribution tend to decrease ECa, and when non-spherical particles have some preferential alignment in space, the soil becomes anisotropic, i.e., its ECa depends on the direction in which it is measured. The electrical conductance of adsorbed counterions constitutes a major contribution to the ECa of medium- and fine-textured soils, especially under conditions of low solution conductivity. In such soils and with such salinity levels, the temperature response of the soil ECa should be stronger than that of its free solution, and care should be taken when extrapolating from field-measured ECa values to obtain the ECa at a given temperature. The above-mentioned and other secondary findings should, on one hand, indicate some limitations for the application of existing ECa–ECw models, and, on the other hand, can serve as guidelines for further development of such essential models.
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Electromagnetic induction (EM) is a commonly used tool for non-invasive mapping of apparent soil electrical conductivity (EC(a)). In this paper, we examine three applications of EM surveying used in arid southwestern US agriculture: repetitive salinity mapping, soil texture mapping, and locating buried tile lines. The basic statistical modeling techniques associated with each application are described and then demonstrated using data from three different field survey projects. In the first study, pre- and post-EM surveys are used to quantify the degree of salt removal from a field leaching event. These survey results demonstrate that the degree of salt removal was spatially variable and that the leaching process was not successful in sub-areas of the field that exhibited high pre-survey salinity concentration levels. The second study demonstrates the use of EM survey data for precision soil texture mapping under non-saline conditions, and illustrates how texture prediction maps can be generated from EM survey data. The final study represents an example of how EM survey data can be used to precisely locate the positions of buried tile lines. In this latter study, two different EM survey data sets that were collected 1 year apart produced estimates of tile line positions within 1 m of each other, validating the reliability and repeatability of the proposed tile line identification strategy. These projects demonstrate three applications of EC(a) surveying techniques used to derive spatial information that aids in the effective management of agricultural fields.
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Sustainable agriculture is considered the most viable means of meeting future food needs for the world's increasing population through its goal of delicately balancing crop productivity, profitability, natural resource utilization, sustainability of the soil-plant-water environment and environmental impacts. Precision agriculture is a proposed approach for achieving sustainable agriculture. Site-specific crop management (or site-specific management, SSM) refers to the application of precision agriculture to crop production. Site-specific crop management utilizes rapidly evolving information and electronic technologies to modify the management of soils, pests and crops in a site-specific manner as conditions within a field change spatially and temporarily. Geospatial measurements of apparent soil electrical conductivity (EC(a)) are the most reliable and frequently used measurements to characterize within-field variability of edaphic properties for application to SSM. The collection of papers that comprises this special issue of Computers and Electronics in Agriculture provides a review of the current technology and understanding of geospatial measurements of EC(a) and current approaches for their application in SSM. The objective of this preface is to run a thread through the papers to show their interrelationship and to identify significant points. The spectrum of topics covered by the papers include: (i) a review of the use of EC(a) measurements in agriculture, (ii) multi-dimensional EC(a) modeling and inversion, (iii) theory and principles elucidating the edaphic properties that influence the EC(a) measurement, (iv) EC(a) survey protocols for characterizing spatial variability, (v) EC(a)-directed response surface sampling design, (vi) designing and evaluating field-scale experiments using geospatial EC(a) measurements, (vii) mapping of soil properties with EC(a), (viii) spatially characterizing EC(a) and water content with time domain reflectometry (TDR), (ix) delineating productivity and SSM zones and (x) SSM methods for reclaiming salt-affected soils. The greatest potential for the application of geospatial measurements of EC(a) in SSM is to provide reliable spatial information for directing soil sampling to identify and characterize the spatial variability of edaphic properties influencing crop yield. This in turn can be used to delineate SSM units, which are key components of SSM. The future of SSM depends upon the continued development and integration of information and electronic technologies that can identify and characterize, both temporally and spatially, not only edaphic properties but also topographical, biological, meteorological and anthropogenic factors influencing within-field variations in crop productivity. The implementation of global positioning system (GPS)-controlled variable-rate equipment will need spatial information to effectively determine input application rates. Because of their reliability, ease of measurement and flexibility, geospatial EC(a) data will undoubtedly contribute a significant portion of the spatial soils-related information needed to direct variable-rate equipment.
Precision Farming in Cotton: Correlating yield maps and Electeical Conductivity maps. 3 rd Conference of the Hellenic Society of Agricultural Engineers
  • A Markinos
  • T A Gemtos
  • L Toulios
  • D Pateras
  • G Zerva
  • M Papaeconomou
Markinos, A.., Gemtos, T. A., Toulios, L., Pateras, D., Zerva, G. and Papaeconomou, M. (2003). Precision Farming in Cotton: Correlating yield maps and Electeical Conductivity maps. 3 rd Conference of the Hellenic Society of Agricultural Engineers, 29-31 May 2003, Thessaloniki, Greece (In Greek).
Comparison of five methods of tillage in a crop rotation with sugar beet, cotton and corn
  • C Cavalaris
Cavalaris C. (2004). Comparison of five methods of tillage in a crop rotation with sugar beet, cotton and corn. PhD Thesis. University of Thessaly.
Precision Farming in Europe Precision Farming; A global perspective
  • B S Blackmore
  • H W Greipentrog
  • . S M Pedersen
  • S Fountas
Blackmore, B. S., Greipentrog, H. W., Pedersen,.S. M. & Fountas, S.(2006). Precision Farming in Europe. Chapter on a book edited by Ancha Srinivasan: Precision Farming; A global perspective. In Press.