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Journal articles on the topic 'Yield Management'

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Published: 4 June 2021
Last updated: 9 March 2023

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1

Wood, William J. "Yield Management." Industrial Management & Data Systems 85, no. 7/8 (July 1985): 23–25. http://dx.doi.org/10.1108/eb057410.

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2

Dörnemann, Jörg. "Yield Management." Controlling 12, no. 12 (2000): 615–16. http://dx.doi.org/10.15358/0935-0381-2000-12-615.

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3

Jones, Peter, and Donna Hamilton. "Yield Management." Cornell Hotel and Restaurant Administration Quarterly 33, no. 1 (February 1992): 89–95. http://dx.doi.org/10.1177/001088049203300126.

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4

Pannell, David J. "Optimal Yield Management." Agriculture, Ecosystems & Environment 33, no. 1 (November 1990): 91–92. http://dx.doi.org/10.1016/0167-8809(90)90148-7.

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5

Elkins, Steven B. "Holistic yield management." International Journal of Services Technology and Management 2, no. 1/2 (2001): 46. http://dx.doi.org/10.1504/ijstm.2001.001590.

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6

Noone, Breffni. "Yield management conference." Progress in Tourism and Hospitality Research 4, no. 3 (September 1998): 293–94. http://dx.doi.org/10.1002/(sici)1099-1603(199809)4:3<293::aid-pth155>3.0.co;2-m.

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7

Yeoman, I., A. Ingold, and S. E. Kimes. "Yield Management: Editorial Introduction." Journal of the Operational Research Society 50, no. 11 (November 1999): 1083–84. http://dx.doi.org/10.1057/palgrave.jors.2600855.

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8

Wang, Xiubin, and Fenghuan Wang. "Dynamic network yield management." Transportation Research Part B: Methodological 41, no. 4 (May 2007): 410–25. http://dx.doi.org/10.1016/j.trb.2006.08.001.

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9

Donaghy, Kevin, Una McMahon, and David McDowell. "Yield management: an overview." International Journal of Hospitality Management 14, no. 2 (June 1995): 139–50. http://dx.doi.org/10.1016/0278-4319(95)00013-3.

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10

Чхиквадзе, Нелли, and Nelli Chkhikvadze. "HOTEL YIELD MANAGEMENT TECHNOLOGIES." Services in Russia and abroad 7, no. 9 (December 25, 2013): 104–10. http://dx.doi.org/10.12737/1853.

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The article deals with the issue of hotel development stabilization. The author describes Yield Management (Revenue Management) as especially relevant for the hotel industry, identifies the flaws in the current practices, provides details of strategic revenue management tools, dwells on the concept of yield management process and specifies the features requisite for Yield management implementation in the hotel industry.
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11

Irwin, Brian H., Mitchell H. Tsai, Simon C. Hillier, and Joel Goh. "Applying yield management to operating room management." Journal of Clinical Anesthesia 74 (November 2021): 110422. http://dx.doi.org/10.1016/j.jclinane.2021.110422.

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12

Lukic, Ljubisa, and Marko Trisic. "Risk management and yield management in banks." Trendovi u poslovanju 3, no. 1 (2015): 28–39. http://dx.doi.org/10.5937/trendpos1501028l.

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13

Johnston, A. M., G. W. Clayton, G. P. Lafond, K. N. Harker, T. J. Hogg, E. N. Johnson, W. E. May, and J. T. McConnell. "Field pea seeding management." Canadian Journal of Plant Science 82, no. 4 (October 1, 2002): 639–44. http://dx.doi.org/10.4141/p02-001.

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The impact of seed placement and seeding rate on crop yield is not clearly understood for field pea (Pisum sativum L.). A field experiment was conducted at Melfort, SK, and Lacombe, AB, in 1998 and 1999, to evaluate the effect of three seed placements (distinct row: 23 cm and 30 cm with a hoe opener; and spread band: a 20-cm spread using a 28-cm sweep on a 23-cm row spacing) and three seeding rates (50, 100, and, 150 seeds m-2) on pea seedling density, seed yield and seed weight of a leafy prostrate and semi-leafless upright cultivar. A follow-up experiment was conducted at seven sites across Saskatchewan in 2001 to further examine the influence of a wider range of seeding rates (20, 30, 40, 50, 60, 70, 80, 90, 100, and 120 target plants m-2). Pea productivity for both cultivars was not affected by the different seed placements, despite a 4 mg greater seed weight for distinct row seed placements compared with spread band placement across all 1998–1999 sites. Moreover, the absence of a seed placement by seeding rate interaction indicated that greater spacing between plants was not associated with improved pea yield when seeding rate was increased, regardless of the cultivar. Yield component compensation occurred where increased plant density from higher seeding rates reduced seed weight. In the 2001 study, seed yield benefits were small at seeding rates greater than 50 target plants m-2. There was a tendency for lower yields with seeding rates less than 50, especially at sites with higher yield potential. Yields of field peas grown under relatively weed-free conditions should be optimized with a seeding rate of 50 to 75 seeds m-2. Key words: Pea (Pisum sativum L.), plant arrangement, row spacing, opener type, seeding rate
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14

Sawhney, Sandeep, and Robert C. Lewis. "Hotel Yield Management in Practice:." Journal of Hospitality & Leisure Marketing 1, no. 2 (January 5, 1993): 7–25. http://dx.doi.org/10.1300/j150v01n02_02.

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15

Smith, Barry C., John F. Leimkuhler, and Ross M. Darrow. "Yield Management at American Airlines." Interfaces 22, no. 1 (February 1992): 8–31. http://dx.doi.org/10.1287/inte.22.1.8.

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16

Kimes, S. "Perceived fairness of yield management." Cornell Hotel and Restaurant Administration Quarterly 43, no. 1 (February 2002): 21–30. http://dx.doi.org/10.1016/s0010-8804(02)80005-2.

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17

Wang, Eric, Marius Holtan, Ram Akella, Iraj Emami, Mike McIntyre, Dave Jensen, and Dave Fletcher. "Valuation of yield management investments." Computer Standards & Interfaces 21, no. 2 (June 1999): 184. http://dx.doi.org/10.1016/s0920-5489(99)92247-x.

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18

Reynolds, Paul. "1st International yield management conference." Tourism Management 18, no. 4 (June 1997): 243. http://dx.doi.org/10.1016/s0261-5177(97)81013-8.

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19

Brotherton, Bob, and Sean Mooney. "Yield management—progress and prospects." International Journal of Hospitality Management 11, no. 1 (February 1992): 23–32. http://dx.doi.org/10.1016/0278-4319(92)90033-r.

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20

Kimes, Sheryl E. "The Basics of Yield Management." Cornell Hotel and Restaurant Administration Quarterly 30, no. 3 (November 1989): 14–19. http://dx.doi.org/10.1177/001088048903000309.

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21

Kimes, Sheryl E. "Perceived Fairness of Yield Management." Cornell Hotel and Restaurant Administration Quarterly 35, no. 1 (February 1994): 22–29. http://dx.doi.org/10.1177/001088049403500102.

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22

Lee‐Ross, Darren, and Nick Johns. "Yield management in hospitality SMEs." International Journal of Contemporary Hospitality Management 9, no. 2 (April 1997): 66–69. http://dx.doi.org/10.1108/09596119710164795.

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23

Kimes, S. "Perceived fairness of yield management." Cornell Hotel and Restaurant Administration Quarterly 35, no. 1 (February 1994): 22–29. http://dx.doi.org/10.1016/0010-8804(94)90060-4.

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24

Ferris-Prabhu, A. V. "Vlsi yield management and prediction." Quality and Reliability Engineering International 1, no. 4 (October 1985): 219–25. http://dx.doi.org/10.1002/qre.4680010403.

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25

Donaghy, Kevin, U. McMahon-Beattie, I. Yeoman, and A. Ingold. "The realism of yield management." Progress in Tourism and Hospitality Research 4, no. 3 (September 1998): 187–95. http://dx.doi.org/10.1002/(sici)1099-1603(199809)4:3<187::aid-pth156>3.0.co;2-b.

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26

DeVeau, Patricia M. "Teaching Yield Management in the Hospitality Management Curriculum." Hospitality Education and Research Journal 13, no. 3 (August 1989): 557. http://dx.doi.org/10.1177/109634808901300368.

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27

Hartemink, Alfred E. "Integrated Nutrient Management Research with Sweet Potato in Papua New Guinea." Outlook on Agriculture 32, no. 3 (September 2003): 173–82. http://dx.doi.org/10.5367/000000003101294442.

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This paper summarizes a series of field experiments that investigated the effects of organic and inorganic nutrients on sweet potato tuber yield in the humid lowlands of Papua New Guinea. In the first experiment, plots were planted with Piper aduncum, Gliricidia sepium and Imperata cylindrica, which were slashed after one year, whereafter sweet potato was planted. Sweet potato yield was lowest after Gliricidia fallow, but no yield differences were found after piper and imperata fallow. In the second season, there was no significant difference in sweet potato yields. The second experiment consisted of a factorial fertilizer trial with four levels of N (0, 50, 100, 150 kg ha–1) and two levels of K (0, 50 kg ha–1). Nitrogen fertilizers increased yield in the first season, but depressed tuber yields in the second and third seasons. Potassium fertilizer had no effect on marketable tuber yield. The third experiment consisted of a comparison between N from inorganic fertilizer and poultry litter at four rates (0, 50, 100, 150 kg ha–1). No difference was found between the inorganic fertilizer and poultry litter, and the highest yields were found at 100 kg N ha–1. In the second season no significant response was observed. Although yield variation was considerable, this series of experiments has shown that sweet potato yield can be significantly increased by inorganic or organic N applications. Sweet potato yields after fallows were less variable than after inorganic nutrient inputs. Inputs of inorganic fertilizer or poultry litter may strongly increase or decrease tuber yields.
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28

Stapper, C. H., and R. J. Rosner. "Integrated circuit yield management and yield analysis: development and implementation." IEEE Transactions on Semiconductor Manufacturing 8, no. 2 (May 1995): 95–102. http://dx.doi.org/10.1109/66.388016.

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29

Heino, Mikko. "Management of evolving fish stocks." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 8 (August 1, 1998): 1971–82. http://dx.doi.org/10.1139/f98-089.

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Mortality caused by harvesting can select for life history changes in the harvested stock. Should this possibility be taken into account in the management of renewable resources? I compare the performance of different harvest strategies when evolutionary change is accounted for with the help of an age-structured population dynamics model. Assuming that age of first reproduction is the only evolving trait, harvesting of only mature individuals selects for delayed maturation and results in increased sustainable yields. Unselective harvesting of both mature and immature fish selects for earlier maturation which causes the sustainable yield to decrease. Constant stock size and constant harvest rate strategies perform equally well in terms of maximum sustainable yield, both before and after evolutionary change. The maximum sustainable yield for fixed-quota strategies is lower. All those strategies have similar evolutionary consequences given a similar average harvest rate. Coevolutionary dynamics between fish stock and the stock manager indicate that the evolutionary benefits of selective harvesting are attainable without incurring yield losses in the near future.
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30

Raymond, Michael A., Jeffrey C. Stark, and Glen A. Murray. "Irrigation Management Effects on Spring Pea Seed Yield and Quality." HortScience 22, no. 6 (December 1987): 1262–63. http://dx.doi.org/10.21273/hortsci.22.6.1262.

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Abstract Seed yield and quality of four spring pea cultivars (Pisum sativum L.) were evaluated when subjected to deficient, optimal, and excessive irrigation levels. Irrigation at 100% of the estimated optimal irrigation amount (IR) resulted in the greatest seed yields, whereas irrigation at 60% or 140% IR reduced yields by 468 and 222 kg·ha−1, respectively. Yield reductions for individual cultivars were as large as 694 and 350 kg·ha−1 for deficient and excessive irrigation, respectively. Increased yields for the 100% IR treatment resulted from increases in pods per plant and seeds per pod. Irrigation at 140% IR reduced germination of pea seed by 5% to 13%. Deficient irrigation reduced seed yield more than did excessive irrigation, whereas excessive irrigation caused the greatest reduction of seed quality.
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31

Fateh, H., A. Toube, A. Q. Gholipuri, and Z. Djazouli. "The feasibility of reducing yield gap by improving crop management." Ukrainian Journal of Ecology 9, no. 3 (July 8, 2019): 21–30. http://dx.doi.org/10.15421/2019_704.

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An appropriate solution to increase crop production is to improve production per unit area. Reconsiderations are, therefore, necessary in various agricultural systems and their production potential. To evaluate agronomic factors affecting crop yield and feasibility of reducing wheat yield gap, a questionnaire was developed in terms of yield-affecting management factors. For this purpose, data of most important factors affecting wheat yield were recorded from farmers sampled from different parts of Piranshahr, a southern city in West Azerbaijan province, Iran. The results showed that the management applied throughout the season had a significant effect on wheat yield. The lands with more than three hectares, flat, and northward with loam-clay soil texture yielded relatively higher crop. The yields were also higher in the lands plowed in March and August with a three-plough moldboard connected to a four-cylinder Ferguson tractor, flattened using a trowel, and rotated with fallow and pea. The use of phosphate (100-150 kg ha−1), potash, and micro fertilizers, as well as 100-150 kg of urea (50 kg in spring and 50 kg in autumn) led to increased yield. The use of Granstar (60 g) and 1.0 L of 2, 4-D separately, and 0.5 L of a pesticide resulted in a better seed yield. Harvesting of wheat in July 15-25 using Hyundai and 130k combines resulted in higher yields. The percentages of studied managements, including land characteristics, planting, growing, and harvesting in wheat fields were 13, 35, 27, and 25 percent, respectively.
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32

Kulla, L., J. Tutka, and R. Marušák. "Forest yield index and its applicability to the assessment of future forest yields." Journal of Forest Science 55, No. 1 (February 3, 2009): 41–50. http://dx.doi.org/10.17221/45/2008-jfs.

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The paper suggests and examines a simplified relative indicator of forest production, with special regard to possibilities of its use in projecting future forests. Forest yield index (<I>I</I><sub>Y</sub>), based on an economic parameter “value of final cutting yield” was proposed, and examined in the model territory of Kysuce in north-western Slovakia. The current values of final cutting yield, dependent on tree species, site index and the length of rotation period served as a basis for the assessment of expected yields. The possibilities and limitations of index applicability in long-term strategic forest management decision-making are discussed, considering the uncertainty of ecological and economic conditions during the long forest production cycle, as well as the complexity of tree species growth and production in the mixed forests, uneven aged forests and forests under climate change.
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33

Miles, M. R., C. Levy, W. Morel, T. Mueller, T. Steinlage, N. van Rij, R. D. Frederick, and G. L. Hartman. "International Fungicide Efficacy Trials for the Management of Soybean Rust." Plant Disease 91, no. 11 (November 2007): 1450–58. http://dx.doi.org/10.1094/pdis-91-11-1450.

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The efficacy of fungicides in managing soybean rust was evaluated in 12 environments in South America and southern Africa over three growing seasons from 2002 to 2005. There were differences in final soybean rust severity, defoliation, and yield among the treatments at most locations. In locations where soybean rust was not severe, all the fungicides evaluated reduced severity. In locations where soybean rust was severe, applications of triazole and triazole + strobilurin fungicides resulted in lower severity and higher yields compared with other fungicides. The strobilurin fungicides provided the highest yields in many locations; however, severity tended to be higher than that of the triazole fungicides. There also were differences in yield and severity between the trials with two and three applications of several fungicides, with three applications resulting in less severe soybean rust and higher yields. However, the third application of tebuconazole, tetraconazole, and the mixtures containing azoxystrobin and pyraclostrobin was not needed to maintain yield. These fungicides were among the most effective for managing soybean rust and maintaining yield over most locations.
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34

Bavec, F., B. Čeh Brežnik, and M. Brežnik. "Hop yield evaluation depending on experimental plot area under different nitrogen management." Plant, Soil and Environment 49, No. 4 (December 10, 2011): 163–67. http://dx.doi.org/10.17221/4108-pse.

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Numerous agricultural and associated ecological effects such as mineral nitrogen fertilising influence the yield of hop (Humulus lupulus L.) cones and its quality. Using a&nbsp;wide spacing of plants (in our case 2.6 &times; 0.8 m) we want to answer a&nbsp;hypothetical question about an appropriate number of test plants per plot vs. experimental plot area. The aim of this study was to compare the effect of different rates of mineral nitrogen, fertiliser combinations and their nitrogen split application on hop yield evaluated from different plot areas (micro trial: 30 plants per plot; macro trial: 320 plants per plot). Hop yield varied significantly between treatments, plot areas, years and interactions (year &times; treatment, plot area &times; treatment) (all at P &pound; 0.01). Cone yield in a&nbsp;micro trial was higher in all treatments in comparison with yield in a&nbsp;macro trial. In spite of common intensive fertilisation the appropriate fertilising combination and mineral N rate can influence the yield. Target nitrogen rate of160 kg mineral N/ha (at the level from 40.0 to62.5 kg nitrate N/ha in soil depth to0.3 m) and cheaper combination of calcium-ammonium nitrate (50 kg N/ha) at the beginning of vegetation plus urea (110 kg N/ha) for top dressing can be recommended. On plot areas of each size and each year all treatments showed similar trends of fertilising effect on yield. In spite of higher yield in the micro trial and lower coefficient of variation in comparison with the macro trial, the results proved that a&nbsp;risk of incorrect yield analysing in macro trials is very low for field experiments.
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35

Kravchenko, Alexandra N., Sieglinde S. Snapp, and G. Philip Robertson. "Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems." Proceedings of the National Academy of Sciences 114, no. 5 (January 17, 2017): 926–31. http://dx.doi.org/10.1073/pnas.1612311114.

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Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based–organic, management practices for a corn–soybean–wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot- and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.
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36

Betancourt García, María Elena, Antonio Saavedra Espada, and Juan Llambias Estrada. "Aplicación del Yield Management a la gestión de las capacidades en el restaurante "La Campana de Toledo"." Revista de Investigaciones de la Universidad Le Cordon Bleu 2, no. 1 (July 5, 2015): 19–28. http://dx.doi.org/10.36955/riulcb.2015v2n1.002.

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37

Sisterson, Mark S., and Drake C. Stenger. "Roguing with Replacement in Perennial Crops: Conditions for Successful Disease Management." Phytopathology® 103, no. 2 (February 2013): 117–28. http://dx.doi.org/10.1094/phyto-05-12-0101-r.

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Replacement of diseased plants with healthy plants is commonly used to manage spread of plant pathogens in perennial cropping systems. This strategy has two potential benefits. First, removing infected plants may slow pathogen spread by eliminating inoculum sources. Second, replacing infected plants with uninfected plants may offset yield losses due to disease. The extent to which these benefits are realized depends on multiple factors. In this study, sensitivity analyses of two spatially explicit simulation models were used to evaluate how assumptions concerning implementation of a plant replacement program and pathogen spread interact to affect disease suppression. In conjunction, effects of assumptions concerning yield loss associated with disease and rates of plant maturity on yields were simultaneously evaluated. The first model was used to evaluate effects of plant replacement on pathogen spread and yield on a single farm, consisting of a perennial crop monoculture. The second model evaluated effects of plant replacement on pathogen spread and yield in a 100 farm crop growing region, with all farms maintaining a monoculture of the same perennial crop. Results indicated that efficient replacement of infected plants combined with a high degree of compliance among farms effectively slowed pathogen spread, resulting in replacement of few plants and high yields. In contrast, inefficient replacement of infected plants or limited compliance among farms failed to slow pathogen spread, resulting in replacement of large numbers of plants (on farms practicing replacement) with little yield benefit. Replacement of infected plants always increased yields relative to simulations without plant replacement provided that infected plants produced no useable yield. However, if infected plants produced useable yields, inefficient removal of infected plants resulted in lower yields relative to simulations without plant replacement for perennial crops with long maturation periods in some cases.
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38

Khadka, Ram B., and Norman Uphoff. "Effects ofTrichodermaseedling treatment with System of Rice Intensification management and with conventional management of transplanted rice." PeerJ 7 (January 11, 2019): e5877. http://dx.doi.org/10.7717/peerj.5877.

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Many benefits ofTrichodermainoculation for improving crop production have been documented, including growth and yield enhancement and the alleviation of biotic and abiotic stresses. However, because rice is usually cultivated under continuous flooding that creates anaerobic soil conditions, this limits the benefits of these beneficial fungi. Cultivating rice with the methods of the System of Rice Intensification (SRI) provides rice plants with a more favorable environment for their colonization by beneficial microbes in the soil because the soil is more aerobic under SRI management and contains more organic matter. This study evaluated the effects ofTrichodermainoculation of rice plants under SRI management compared with transplanted and flooded rice plants, considering also the effects of different means of fertilization and different varieties in rice. Experiments were conducted in 2015 and 2016 under the tropical climate of Nepal’s western terai (plains) during both the rainy season (July to November) and the dry season (March to July). The results indicated significantly better performance (P = 0.01) associated withTrichodermainoculation for both seasons and for both systems of crop management in terms of grain yield and other growth-contributing factors, compared to non-inoculated rice cropping. Relatively higher effects on grain yield were recorded also with organic compared to inorganic fertilization; for unimproved (heirloom) varieties compared with improved varieties; and from SRI vs. conventional flooded crop management. The yield increase withTrichodermatreatments across all trials was 31% higher than in untreated plots (4.9 vs 4.5 mt ha−1). WithTrichodermatreatment, yields compared with non-treated plots were 24% higher with organic SRI (6.38 vs 5.13 mt ha−1) and 52% higher with non-organic SRI (6.38 vs 3.53 mt ha−1). With regard to varietal differences, under SRI managementTrichodermainoculation of the improved variety Sukhadhan-3 led to 26% higher yield (6.35 vs 5.04 mt ha−1), and with the heirloom variety Tilkidhan, yield was 41% higher (6.29 vs 4.45 mt ha−1). Economic analysis indicated that expanding the organic cultivation of local landraces under SRI management should be profitable for farmers where such rice has a good market price due to its premium quality and high demand and when SRI enhances yield. These varieties’ present low yields can be significantly increased by integratingTrichodermabio-inoculation with SRI cultural methods. Other recent research has shown that such inoculation can be managed profitably by farmers themselves.
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39

Procházková, B., J. Málek, and J. Dovrtěl. "Effect of different straw management practices on yields of continuous spring barely." Plant, Soil and Environment 48, No. 1 (December 11, 2011): 27–32. http://dx.doi.org/10.17221/4204-pse.

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Field experiments were conducted in the maize-growing region on heavy gleic fluvisol from 1974 to 2000. Three variants of straw management (straw harvested, incorporated into soil and burned), two variants of soil tillage (conventional plough tillage to 0.22 m, shallow disc tillage to 0.12&ndash;0.15 m) and three variants of fertilization (30, 60 and 90 kg N.ha<sup>&ndash;1</sup>) were studied. After conventional tillage, the highest yield was obtained in the variant with burned straw (5.50 t.ha<sup>&ndash;1</sup>), followed by the variant with straw incorporated into soil (5.40 t.ha<sup>&ndash;1</sup>) and the lowest after harvested straw (5.01 t.ha<sup>&ndash;1</sup>). At shallow tillage, lower yields were assessed in all variants of straw management in comparison with conventional tillage (after straw burning 5.07 t.ha<sup>&ndash;1</sup>, incorporation into soil 4.66 t.ha<sup>&ndash;1</sup> and harvest 4.54 t.ha<sup>&ndash;1</sup>). The ranking of variants was identical to that in inversion tillage; however, the yield increased more after straw burning in comparison with its incorporation into soil. Yields increased regularly along with increasing rates of nitrogen. If long-term effects of straw incorporation on yields and yield trends were evaluated (in comparison with straw harvest), statistically significant decrease in yields was assessed after shallow in contrast with increase in yields after deeper straw incorporation.
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40

Dubois, Pierre-Louis, and Marie-Christine Frendo. "Yield management et marketing des services." Décisions Marketing 4 (January 1, 1995): 47–54. http://dx.doi.org/10.7193/dm.004.47.54.

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41

Hopkins, Bryan G., and Neil C. Hansen. "Phosphorus Management in High‐Yield Systems." Journal of Environmental Quality 48, no. 5 (September 2019): 1265–80. http://dx.doi.org/10.2134/jeq2019.03.0130.

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42

Malvestuto, Stephen P., and Michael D. Hudgins. "Optimum Yield for Recreational Fisheries Management." Fisheries 21, no. 6 (June 1996): 6–17. http://dx.doi.org/10.1577/1548-8446(1996)021<0006:oyfrfm>2.0.co;2.

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43

Martinez, Maria-Encarnacion Andres, Miguel-Angel Gomez Borja, and Juan-Antonio Mondejar Jimenez. "Yield Management As A Pricing Mechanism." Review of Business Information Systems (RBIS) 15, no. 5 (September 28, 2011): 51–60. http://dx.doi.org/10.19030/rbis.v15i5.6018.

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Abstract:
Consumers nowadays frequently make their purchases online. One common pricing strategy on the Internet is demand-based pricing, which allows firms to adapt prices to the demand for their products. This mechanism can undoubtedly be employed more efficiently on the Internet, due to how quickly demand information can be obtained. Within demand-based pricing, one very well-known practice is yield management. This practice was pioneered by airlines, but has spread in recent years to industries such as hotels, rental cars and cruise lines to name but a few industries. The application of yield management was an authentic revolution for the traditional concept of pricing, but has shown that it can be a good strategy when used correctly. Applying yield management requires firms to understand consumer purchasing behavior in order to compare present demand with the demand that is anticipated in the future. Yield management techniques imply the allocation of a fixed capacity to different prices and segments of consumers in order to maximize income. Yield management is sometimes confused with a well-known segmentation strategy called time-based pricing. For this reason, in this paper we highlight the main demand-based pricing strategies that are now being used on the Internet, placing special emphasis on yield management.
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44

Desiraju, Ramarao, and Steven M. Shugan. "Strategic Service Pricing and Yield Management." Journal of Marketing 63, no. 1 (January 1999): 44. http://dx.doi.org/10.2307/1252000.

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45

Vila, Natalia, and Manuel Córcoles. "Yield Management and Airline Strategic Groups." Tourism Economics 17, no. 2 (April 2011): 261–78. http://dx.doi.org/10.5367/te.2011.0037.

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46

Vinod, Ben. "Evolution of yield management in travel." Journal of Revenue and Pricing Management 15, no. 3-4 (April 8, 2016): 203–11. http://dx.doi.org/10.1057/rpm.2016.15.

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47

Desiraju, Ramarao, and Steven M. Shugan. "Strategic Service Pricing and Yield Management." Journal of Marketing 63, no. 1 (January 1999): 44–56. http://dx.doi.org/10.1177/002224299906300104.

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48

Ferri, Cesare, Sherief Reda, and R. Iris Bahar. "Parametric yield management for 3D ICs." ACM Journal on Emerging Technologies in Computing Systems 4, no. 4 (October 2008): 1–22. http://dx.doi.org/10.1145/1412587.1412592.

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49

Gupta, Diwakar, and William L. Cooper. "Stochastic Comparisons in Production Yield Management." Operations Research 53, no. 2 (April 2005): 377–84. http://dx.doi.org/10.1287/opre.1040.0174.

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50

Kimes, Sheryl E., and Richard B. Chase. "The Strategic Levers of Yield Management." Journal of Service Research 1, no. 2 (November 1998): 156–66. http://dx.doi.org/10.1177/109467059800100205.

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