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Journal articles on the topic 'Field sampling'

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Published: 4 June 2021
Last updated: 17 June 2025

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1

Siciliano, Steven. "Field Sampling." Journal of Environmental Quality 34, no. 2 (2005): 732. http://dx.doi.org/10.2134/jeq2005.0732.

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2

Zhang, Cha, and Tsuhan Chen. "Light Field Sampling." Synthesis Lectures on Image, Video, and Multimedia Processing 2, no. 1 (2006): 1–102. http://dx.doi.org/10.2200/s00035ed1v01y200606ivm006.

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3

Santagata, Marika, Joseph V. Sinfield, and John T. Germaine. "Laboratory Simulation of Field Sampling: Comparison With Ideal Sampling and Field Data." Journal of Geotechnical and Geoenvironmental Engineering 132, no. 3 (2006): 351–62. http://dx.doi.org/10.1061/(asce)1090-0241(2006)132:3(351).

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4

Haig, C. W., W. G. Mackay, J. T. Walker, and C. Williams. "Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies." Journal of Hospital Infection 93, no. 3 (2016): 242–55. http://dx.doi.org/10.1016/j.jhin.2016.03.017.

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5

Capozzoli, A., C. Curcio, A. Liseno, and P. Vinetti. "Field sampling and field reconstruction: A new perspective." Radio Science 45, no. 6 (2010): n/a. http://dx.doi.org/10.1029/2009rs004298.

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6

Webb, Cameron E. "Mosquito Ecology: Field Sampling Methods." Australian Journal of Entomology 47, no. 4 (2008): 382–83. http://dx.doi.org/10.1111/j.1440-6055.2008.00673.x.

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7

Renault, Mikael, Yassine Hadjar, Sylvain Blaize, et al. "Bidimensional near-field sampling spectrometry." Optics Letters 35, no. 19 (2010): 3303. http://dx.doi.org/10.1364/ol.35.003303.

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8

Folium, OA, and KA Moe. "The GEEP Workshop: field sampling." Marine Ecology Progress Series 46 (1988): 7–12. http://dx.doi.org/10.3354/meps046007.

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9

Cox, Jennie, Hamza Mbareche, William G. Lindsley, and Caroline Duchaine. "Field sampling of indoor bioaerosols." Aerosol Science and Technology 54, no. 5 (2019): 572–84. http://dx.doi.org/10.1080/02786826.2019.1688759.

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10

Leake, C. J. "Mosquito ecology field sampling methods." Transactions of the Royal Society of Tropical Medicine and Hygiene 88, no. 5 (1994): 606. http://dx.doi.org/10.1016/0035-9203(94)90186-4.

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11

Meier, Janosch, Arijit Misra, Stefan Preusler, and Thomas Schneider. "Orthogonal Full-Field Optical Sampling." IEEE Photonics Journal 11, no. 2 (2019): 1–9. http://dx.doi.org/10.1109/jphot.2019.2902726.

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12

Liddell, Craig M. "Field Sampling and Chemical Analysis." Journal of Forensic Sciences 42, no. 3 (1997): 14137J. http://dx.doi.org/10.1520/jfs14137j.

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13

Onural, Levent. "Sampling of the diffraction field." Applied Optics 39, no. 32 (2000): 5929. http://dx.doi.org/10.1364/ao.39.005929.

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14

Burgess, P. J. "Field exploration and sampling (geological)." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 22, no. 6 (1985): 184. http://dx.doi.org/10.1016/0148-9062(85)90144-5.

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15

Ping-Hsien Lin and Tong-Yee Lee. "Camera-sampling field and its applications." IEEE Transactions on Visualization and Computer Graphics 10, no. 3 (2004): 241–51. http://dx.doi.org/10.1109/tvcg.2004.1272724.

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16

Dawson, Gaynor. "Field sampling methods for remedial investigations." Journal of Hazardous Materials 45, no. 1 (1996): 88. http://dx.doi.org/10.1016/s0304-3894(96)90042-4.

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17

Mroz, Glenn D., and David D. Reed. "Forest Soil Sampling Efficiency: Matching Laboratory Analyses and Field Sampling Procedures." Soil Science Society of America Journal 55, no. 5 (1991): 1413–16. http://dx.doi.org/10.2136/sssaj1991.03615995005500050035x.

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18

Rose, Lisa. "BEHAVIORAL SAMPLING IN THE FIELD: CONTINUOUS FOCAL VERSUS FOCAL INTERVAL SAMPLING." Behaviour 137, no. 2 (2000): 153–80. http://dx.doi.org/10.1163/156853900502006.

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AbstractI compared data collection rates for continuous and interval focal samples during a two-year, single-observer field study of white-faced capuchins (Cebus capucinus) in Costa Rica. I also compared the basic activity budgets generated by the two sampling methods, estimates of numbers in proximity, and rates at which additional ad libitum observations could be recorded. I collected 1238 hours of focal data (620 hr continuous, 618 hr interval). I found focal interval sampling to be 25% more time efficient, despite higher rate of sample loss, partly because interval samples are easier to obtain in difficult conditions. I found no evidence that interval sampling provided better opportunities for ad libitum observation than continuous sampling. Overall, the two methods yielded similar estimates of activity budgets. However, continuous sampling resulted in somewhat higher estimates of time spent eating, while interval data gave somewhat lower estimates of time spent foraging (looking for or handling food items) and moving, resulting in lower estimates of foraging success. Interval sampling also yielded slightly lower estimates of time spent vigilant. I attribute these patterns to two major effects: (1) errors of omission (missing rare behaviors of short duration) during interval samples and (2) a greater tendency toward conditional sampling bias (under-representing behaviors due to difficult sampling conditions such as rapid travel) under a continuous sampling regime.
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19

Pitard, Francis. "Theory of Sampling and Sampling Practice, Third Edition." TOS Forum 2020, no. 10 (2020): 17. http://dx.doi.org/10.1255/tosf.120.

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20

Ilic, Milos, Ruzica Igic, Mirjana Cuk, and Dragana Vukov. "Field sampling methods for investigating forest-floor bryophytes: Microcoenose vs. random sampling." Archives of Biological Sciences 70, no. 3 (2018): 589–98. http://dx.doi.org/10.2298/abs180422020i.

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Because of the high importance of bryophytes in forest ecosystems, it is necessary to develop standardized field sampling methodologies. The quadrat method is commonly used for bryophyte diversity and distribution pattern surveys. Quadrat size and the position of quadrats within the studied area have a significant influence on different analyses. The aim of the present study was to define the minimum quadrat size appropriate for sampling ground bryophytes in temperate beech forests, to compare two different field sampling methods for research on ground bryophytes, the random and microcoenose methods; and to test the adequacy of the microcoenose sampling method in temperate beech forests. Research was carried out on Fruska Gora mountain (Serbia) at four different sites. All sites contained temperate broadleaf forest vegetation, predominantly Fagus sylvatica, but also included various other tree species. Systematic sampling based on nested quadrats was used to determine the minimum sampling area. Random sampling was performed using 10 or 20 microplots (minimum area quadrat), randomly located within 10x10 m plots. Microcoenose sampling is a systematic sampling method based on the fact that every bryophyte fragment on the forest floor is a separate microcoenose. These methods were compared using the following criteria: species richness; Shannon?s diversity index and evenness measure; coverage of dominant species, and the time needed for sampling. The microcoenose sampling method has proven to be highly applicable in temperate beech forests in terms of species richness and diversity, in contrast to random sampling, which was not suitable for bryophyte flora with a patchy distribution.
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21

Fragaszy, D. M., S. Boinski, and J. Whipple. "Behavioral sampling in the field: Comparison of individual and group sampling methods." American Journal of Primatology 26, no. 4 (1992): 259–75. http://dx.doi.org/10.1002/ajp.1350260404.

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22

Penttinen, A. "A random field approach to Bitterlich sampling." Annales Academiae Scientiarum Fennicae. Series A. I. Mathematica 13 (1988): 259–68. http://dx.doi.org/10.5186/aasfm.1988.1319.

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23

Liu, Xiaodong, Shixu Meng, and Bo Zhang. "Modified Sampling Method with Near Field Measurements." SIAM Journal on Applied Mathematics 82, no. 1 (2022): 244–66. http://dx.doi.org/10.1137/21m1432235.

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24

Desbat, L. "Efficient parallel sampling in vector field tomography." Inverse Problems 11, no. 5 (1995): 995–1003. http://dx.doi.org/10.1088/0266-5611/11/5/004.

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25

Shin, Dong-Bin, and Gerald R. North. "Errors Incurred in Sampling a Cyclostationary Field." Journal of Atmospheric and Oceanic Technology 17, no. 5 (2000): 656–64. http://dx.doi.org/10.1175/1520-0426(2000)017<0656:eiisac>2.0.co;2.

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26

Blank, R. H. "Sampling black field cricket (Teleogryllus commodus) eggs." New Zealand Journal of Agricultural Research 30, no. 3 (1987): 333–39. http://dx.doi.org/10.1080/00288233.1987.10421892.

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27

Riek, C., D. V. Seletskiy, A. S. Moskalenko, et al. "Direct sampling of electric-field vacuum fluctuations." Science 350, no. 6259 (2015): 420–23. http://dx.doi.org/10.1126/science.aac9788.

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28

Bragg, L., Z. Qin, M. Alaee, and J. Pawliszyn. "Field Sampling with a Polydimethylsiloxane Thin-Film." Journal of Chromatographic Science 44, no. 6 (2006): 317–23. http://dx.doi.org/10.1093/chromsci/44.6.317.

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29

Yoo, Chulsang. "On optimal sampling design for rainfall field." KSCE Journal of Civil Engineering 10, no. 1 (2006): 47–52. http://dx.doi.org/10.1007/bf02829303.

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30

Plumejeaud-Perreau, Eric Quinton, Cécile Pignol, et al. "Towards better traceability of field sampling data." Computers & Geosciences 129 (August 2019): 82–91. http://dx.doi.org/10.1016/j.cageo.2019.04.009.

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31

Hagen, D. E., M. B. Trueblood, and P. D. Whitefield. "A FIELD SAMPLING OF JET EXHAUST AEROSOLS." Particulate Science and Technology 10, no. 1 (1992): 53–63. http://dx.doi.org/10.1080/02726359208906598.

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32

Tumbar, Remy, and David J. Brady. "Sampling field sensor with anisotropic fan-out." Applied Optics 41, no. 31 (2002): 6621. http://dx.doi.org/10.1364/ao.41.006621.

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33

Norval, R. A. I., C. E. Yunker, J. D. Gibson, and S. L. D. Deem. "Field sampling of unfed nymphs ofAmblyomma hebraeum." Experimental and Applied Acarology 4, no. 2 (1988): 173–77. http://dx.doi.org/10.1007/bf01193875.

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34

Curtis, C. F. "Mosquito ecology: field sampling methods (2nd edn)." Parasitology Today 9, no. 9 (1993): 346. http://dx.doi.org/10.1016/0169-4758(93)90242-8.

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35

Randle, W. M., D. A. Kopsell, D. E. Kopsell, R. L. Snyder, and R. Torrance. "Field Sampling Short-day Onions for Bulb Pungency." HortTechnology 8, no. 3 (1998): 329–32. http://dx.doi.org/10.21273/horttech.8.3.329.

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The marketing of onions (Allium cepa L.) based on bulb pungency as a measure of overall flavor intensity is being considered by the onion industry. Pungency is highly variable within and among fields due to genetic and environmental factors. Therefore, a study was undertaken to develop a sampling procedure to estimate onion pungency means and variances from field-grown onions with predetermined degrees of accuracy and confidence. Two shortday onion cultivars, commonly grown in the Vidalia, Ga., area, were each randomly sampled from four different fields. The sampled bulbs were analyzed for enzymatically formed pyruvic acid (EPY) and soluble solids content (SSC) to assess pungency and sugars, respectively. EPY concentration and SSC varied between the two cultivars, among the four fields within cultivars, and among the fifty samples within each field. In a combined analysis of all eight fields, at least 1.3 ten-bulb samples would be needed per acre to come within ±0.5 μmol EPY of a field's true EPY mean with 95% confidence. If the accuracy of the estimation was lowered to ±1.0 μmol EPY of a field's true mean, then at least 0.4 ten-bulb samples would be needed per acre. Because SSC was less variable than EPY, the number of ten-bulb samples needed per acre to estimate a field's true mean was lower than the number required to estimate EPY. Establishing a sampling method to estimate an onion field's EPY and SSC will provide the mechanism to standardize onion flavor in the market place and instill greater consumer confidence in purchasing onions.
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36

Schmidt, Carsten H., and Thomas F. Eibert. "Assessment of Irregular Sampling Near-Field Far-Field Transformation Employing Plane-Wave Field Representation." IEEE Antennas and Propagation Magazine 53, no. 3 (2011): 213–19. http://dx.doi.org/10.1109/map.2011.6028465.

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37

Oger, Baptiste, Cécile Laurent, Philippe Vismara, and Bruno Tisseyre. "Is the optimal strategy to decide on sampling route always the same from field to field using the same sampling method to estimate yield?" OENO One 55, no. 1 (2021): 133–44. http://dx.doi.org/10.20870/oeno-one.2021.55.1.3334.

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Aim: This short communication aims at providing insights to verify whether common yield sampling protocols (i.e., one round trip within the fields over two representative rows) are optimal whatever the considered fields. In addition, it aims to show how factors like the spatial organisation of the within-field yield variability, the length of the rows, the erratic variance, etc. may affect the optimal sampling route and the error of the yield estimation.Methods and Material: A new algorithm based on constraint programming and stochastic approaches was used to provide optimal sampling routes for vineyards. This algorithm guarantees the representativeness of the measurement sites and a minimization of the walking distance. Practical constraints (trellised structure, starting point, etc.) are considered by the algorithm to optimise the walking distance and the resulting sampling route. The algorithm has been applied to 60 simulated vineyards with known yield variability. Characteristics like yield spatial structure, row length and proportion of erratic variance were controlled during the simulation process and were used to study how they affect the optimal sampling route derived from the algorithm.Results: The row length as well as the spatial organization of the within-field yield variability are the main factors that determine the optimality of a sampling route. Spatial organisation of the yield happens to have a strong incidence; fields with small yield patterns (Range of the semi-variogram = 25 m) showed a yield estimation error of less than 2 % with an optimal sampling route of three minutes with 7 sampling sites, whereas it takes more than 5 minutes (with 9 sampling sites) to achieve the same estimation error for fields with larger spatial patterns (range &gt; 50 m). Results also highlight the relevance of original sampling routes which intend to sample only the beginnings of rows or mixed approaches based on a round trip in two inter-rows and complementary samples on the beginnings of one or more rows.Conclusions: This study shows that an optimal sampling route strongly depends on the field characteristics. The optimal sampling route should therefore be tailored to each field. This approach is a first step which shows how this methodology could be used to identify other factors of influence. It could also apply to real fields to optimise other logistic operations in viticulture.Significance and Impact of the Study: This short communication demonstrates the necessity to tailor sampling strategy to characteristics of each field to provide both an optimised sampling route (minimum walking distance with minimum samples) and the best possible estimate. It also proposes an original approach based on field simulations and an optimal sampling route generation algorithm. This approach makes it possible to produce new insights (and also to validate empirical practices) that can help the wine industry to better manage the logistics at harvest. This paper also gives considerations when it comes to the choice of a sampling route for a given field.
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38

Grawe, Matthew A., Jonathan J. Makela, Mark D. Butala, and Farzad Kamalabadi. "The Impact of Magnetic Field Temporal Sampling on Modeled Surface Electric Fields." Space Weather 16, no. 11 (2018): 1721–39. http://dx.doi.org/10.1029/2018sw001896.

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39

Goyet, Catherine. "Design of Sampling Strategy Measurements of Co2/Carbonate Properties." International Journal of Oceanography & Aquaculture 6, no. 3 (2022): 1–11. http://dx.doi.org/10.23880/ijoac-16000227.

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In order to study a (terrestrial or oceanic) field area, scientists need first to design a sampling strategy. At first, when nothing is known about this field, there is no other choice than to sample as much as possible wherever it is possible. Then, as something become known about some properties of the field, it becomes possible to use mathematical equations to design a scientifically sound sampling strategy based upon the various constraints (aimed accuracy, number of samples/measurements, etc.), of the study. Based upon available sea-surface salinity and sea-surface temperature data, this work shows a practical and simple way to design a sampling strategy with known accuracy for total CO2 and total alkalinity measurements in sea-surface waters. The results indicate the need to continue to sample the sea-surface waters but with specific designs of sampling strategy to reach the scientific objectives with known maximum error.
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40

Clay, Sharon A., G. Jason Lems, David E. Clay, Frank Forcella, Michael M. Ellsbury, and C. Gregg Carlson. "Sampling weed spatial variability on a fieldwide scale." Weed Science 47, no. 6 (1999): 674–81. http://dx.doi.org/10.1017/s0043174500091323.

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Site-specific weed management recommendations require knowledge of weed species, density, and location in the field. This study compared several sampling techniques to estimate weed density and distribution in two 65-ha no-tillZea mays–Glycine maxrotation fields in eastern South Dakota. The most common weeds (Setaria viridis, Setaria glauca, Cirsium arvense, Ambrosia artemisiifolia, andPolygonum pensylvanicum) were counted by species in 0.1-m2areas on a 15- by 30-m (1,352 points in each field) or 30- by 30-m (676 points in each field) grid pattern, and points were georeferenced and data spatially analyzed. Using different sampling approaches, weed populations were estimated by resampling the original data set. The average density for each technique was calculated and compared with the average field density calculated from the all-point data. All weeds had skewed population distributions with more than 60% of sampling points lacking the specific weed, but very high densities (i.e., &gt; 100 plants m−2) were also observed. More than 300 random samples were required to estimate densities within 20% of the all-point means about 60% of the time. Sampling requirement increased as average density decreased. The W pattern produced average species densities that often were similar to the field averages, but information on patch location was absent. Weed counts taken on the 15- by 30-m grid were dependent spatially and weed contour maps were developed. Kriged maps presented both density and location of weed patches and could be used to establish management zones. However, grid-sampling production fields on a small enough scale to obtain spatially dependent data may have limited usefulness because of time, cost, and labor constraints.
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41

Stellingwerf, D. A., and S. Lwin. "Stratified sampling compared with two-phase stratified cluster sampling for timber volume estimation." Netherlands Journal of Agricultural Science 33, no. 2 (1985): 151–60. http://dx.doi.org/10.18174/njas.v33i2.16861.

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Two sampling methods were compared on orthophotos (scale 1:10 000) of a rectangular 12 855-ha area of intensively managed Norway spruce forest in Upper Austria. Trees other than spruce &gt;40 yr old were ignored. 'Method 1' used stratified cluster sampling to determine the area proportion of spruce in the total area: 2 clusters of 5 circular 0.05-ha photo plots were randomly selected within each of 8 rectangular sub-blocks in each of 72 blocks into which the area was 'stratified' (systematically divided). Vol. was determined by 2-phase sampling: (i) 2 clusters were randomly selected in each block, and the % crown cover of spruce was determined in each photo plot; (ii) one photo plot was selected in each block (all crown cover classes being represented) for subsequent vol. determination on 72 plots in the field. 'Method 2' used areas of mature spruce on forest management maps, which were copied on to the orthophotos and then divided into 2 nearly equal strata of higher and lower density whose areas were measured by planimeter. Ten field plots were located in each stratum and enumerated in order to determine the s.d. of each stratum and hence the number of additional random field plots required; the total number of field plots was 55. The 2 methods required the same number of man-days (36), method 1 requiring less office work and more field work and computation. If the sampling intensity was reduced, method 1 became less time-consuming than method 2 (except in the field) for the same vol. error. However, method 2 with sampling errors only in vol., is preferred over method 1 which has sampling errors in both area and volume. (Abstract retrieved from CAB Abstracts by CABI’s permission)
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42

郑, 武杰. "Comparison of the Differences between Field Engineering Geological Drilling Field Sampling Methods." Advances in Geosciences 04, no. 04 (2014): 241–48. http://dx.doi.org/10.12677/ag.2014.44029.

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43

Franzen, David W., and Ted R. Peck. "Field Soil Sampling Density for Variable Rate Fertilization." Journal of Production Agriculture 8, no. 4 (1995): 568–74. http://dx.doi.org/10.2134/jpa1995.0568.

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44

Duan, Fuzhou, Ying Zuo, Hongliang Guan, and Tian Guo. "Light Field Acquisition Method Based on Depth Sampling." Sensors and Materials 33, no. x (2021): 1. http://dx.doi.org/10.18494/sam.2021.3509.

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45

Tartaron, Thomas F. "The Archaeological Survey: Sampling Strategies and Field Methods." Hesperia Supplements 32 (2003): 23. http://dx.doi.org/10.2307/1354045.

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46

Casey, Francis X. M. "Field Sampling: Principles and Practices in Environmental Analysis." Vadose Zone Journal 4, no. 4 (2005): 1219. http://dx.doi.org/10.2136/vzj2005.0003br.

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47

Lu, Heqi, Romain Pacanowski, and Xavier Granier. "Position-Dependent Importance Sampling of Light Field Luminaires." IEEE Transactions on Visualization and Computer Graphics 21, no. 2 (2015): 241–51. http://dx.doi.org/10.1109/tvcg.2014.2359466.

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48

Tollefson, Jeff. "Air sampling reveals high emissions from gas field." Nature 482, no. 7384 (2012): 139–40. http://dx.doi.org/10.1038/482139a.

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49

Tumbar, Remy, Ronald A. Stack, and David J. Brady. "Wave-front sensing with a sampling field sensor." Applied Optics 39, no. 1 (2000): 72. http://dx.doi.org/10.1364/ao.39.000072.

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50

Duranleau, Deena L. "Random Sampling of Regional Populations: A Field Test." Field Methods 11, no. 1 (1999): 61–67. http://dx.doi.org/10.1177/1525822x9901100105.

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