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Journal articles on the topic 'Yield mapping'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Konopatzki, M. R. S., E. G. Souza, L. H. P. Nóbrega, M. A. Uribe-Opazo, G. Suszek, S. Rodrigues, and E. F. de Oliveira. "PEAR TREE YIELD MAPPING." Acta Horticulturae, no. 824 (April 2009): 303–12. http://dx.doi.org/10.17660/actahortic.2009.824.36.

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2

Hollist, Ray R., Ronald H. Campbell, and Robert Campbell. "Yield Mapping of Vegetable Crops." HortScience 32, no. 3 (June 1997): 503D—503. http://dx.doi.org/10.21273/hortsci.32.3.503d.

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Over the past few years, grain yield monitors have gained a significant hold in the market place. While the largest share of production agriculture acres are devoted to producing grain crops, high-value crops such as potatoes, tomatoes, sugarbeets, onions, and many others will benefit considerably by application of site-specific technology. Yield mapping is one of the tools that utilizes GPS technology and allows us to visualize our farms as an array of tiny parcels instead of one uniform aggregate. Yield mapping is simple, accurate measurement of yield at precise positions, the data from which is used to give us a visual report card of each parcel in that field. While yield mapping will not provide the entire basis of site-specific agriculture management, it begins to give a picture of how understanding spatial variation will revolutionize management of high-value crop production acres. The tools necessary to make yield measurements are now available. When combined with Differential GPS, the yield map becomes a powerful tool to identify atypical areas in the field. Without DGPS the process of identifying and treating areas within a field individually would be a nearly impossible task, and certainly cost-prohibitive. Identification of the spatial distribution of yield will contribute significantly to a grower's ability to make informed management decisions.

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3

Dennis, S. J., A. L. Taylor, K. O'Neill, W. Clarke-Hill, R. A. Dynes, N. Cox, C. Van Koten, and T. W. D. Jowett. "Pasture yield mapping: why & how." Journal of New Zealand Grasslands 77 (January 1, 2015): 41–46. http://dx.doi.org/10.33584/jnzg.2015.77.481.

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Spatial variation in pasture yield within a single paddock can be high. Measuring this variation has many potential benefits. For instance, cost-effective targeted treatments could be applied to low yielding areas resulting in increased pasture yield at a paddock scale with minimal cost. Understanding pasture yield variation requires tools that can measure it, and practical methodologies to guide how and when to use these tools to obtain useful data. The study reported here aimed to develop measurement protocols for using the C-Dax pasture meter to map yields of rotationally grazed pastures. The general principles should be applicable to other measurement tools. The pattern of pasture yield varies throughout the year. Because growing conditions change with the seasons, areas of a paddock that perform well in summer may perform poorly in winter, and vice-versa. Time of year is therefore an important consideration for measurement purposes. The recommended protocol developed from this project to estimate the spatial variation in annual yield on a paddock is to: • map 1 month following peak pasture growth rates; • drive at up to 50 m run spacings, but close enough to cover all features of interest; and • map as close to the grazing event as possible within the final third of the regrowth period. Keywords: Yield mapping, pasture, precision agriculture, pasture height, dairy, spatial management

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4

Kumar, N., P. L. Kulwal, H. S. Balyan, and P. K. Gupta. "QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat." Molecular Breeding 19, no. 2 (October 25, 2006): 163–77. http://dx.doi.org/10.1007/s11032-006-9056-8.

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5

Zhang, Tianyi, and Xiaoguang Yang. "Mapping Chinese Rice Suitability to Climate Change." Journal of Agricultural Science 8, no. 6 (May 10, 2016): 33. http://dx.doi.org/10.5539/jas.v8n6p33.

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<p>Climate change has the potential to affect Chinese rice production; however, the rice crop could become more suitable to new climatic conditions because of benefits derived from new agricultural technologies. In this paper, a county-level dataset and crop model were used to analyze actual rice yield suitability by measuring the yield gap and yield stability from 1980 to 2011 in 1561 counties of China. The results showed that the national yield gap between the actual and potential yields was approximately 23.0%, which is close to the threshold for profitable planting. However, a number of counties in the northeastern and southwestern regions showed a 30 to 50% yield gap, which indicates a relatively lower suitability of the rice. The rice yield stability results indicated that the actual stability has exceeded the potential stability in most of the counties of China, thus indicating a high level of suitability. Temporally, a decreasing trend was observed for both the yield gap and stability, suggesting that the suitability of rice in China has improved, which might be associated with the development of agricultural technology. The only noteworthy locations presenting a high yield gap and yield instability were several counties in the northeastern region. Since the northeastern region accounts for a significant proportion of China's rice production, further investigations should be conducted to identify the underlying causes of the yield gaps and determine methods of increasing the yield stability. The implementation of more suitable agricultural technology in the area is also suggested to improve the rice suitability in the region.</p>

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6

Abramov, N. V. "Yield mapping using satellite navigation systems." IOP Conference Series: Materials Science and Engineering 537 (June 18, 2019): 062022. http://dx.doi.org/10.1088/1757-899x/537/6/062022.

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7

Auernhammer, H., M. Demmel, T. Muhr, J. Rottmeier, and K. Wild. "GPS for yield mapping on combines." Computers and Electronics in Agriculture 11, no. 1 (October 1994): 53–68. http://dx.doi.org/10.1016/0168-1699(94)90052-3.

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8

Maltsev, K. A., O. P. Yermolaev, and V. V. Mozzherin. "Suspended sediment yield mapping of Northern Eurasia." Proceedings of the International Association of Hydrological Sciences 367 (March 3, 2015): 326–32. http://dx.doi.org/10.5194/piahs-367-326-2015.

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Abstract. The mapping of river sediment yields at continental or global scale involves a number of technical difficulties that have largely been ignored. The maps need to show the large zonal peculiarities of river sediment yields, as well as the level (smoothed) local anomalies. This study was carried out to create a map of river sediment yields for Northern Eurasia (within the boundaries of the former Soviet Union, 22 × 106 km2) at a scale of 1:1 500 000. The data for preparing the map were taken from the long-term observations recorded at more than 1000 hydrological stations. The data have mostly been collected during the 20th century by applying a single method. The creation of this map from the study of river sediment yield is a major step towards enhancing international research on understanding the mechanical denudation of land due mainly to erosion.

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9

Kroulík, M., M. Mimra, F. Kumhála, and V. Prošek. "Mapping spatial variability of soil properties and yield by using geostatic method." Research in Agricultural Engineering 52, No. 1 (February 7, 2012): 17–24. http://dx.doi.org/10.17221/4875-rae.

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The  Czech University of Agriculture in Prague (CUA) Farm at Lány started with precision farming technology several years ago. In the first step the yield and nutrients content were monitored. For precision application development, detailed description of soil conditions and interrelationship will be necessary. Pulling force and soil electric conductivity measurement as indirect measuring methods were used for mapping spatial soil variability. These methods demonstrate other ways for description of complex soil media.

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10

Panneton, Bernard. "Yield Mapping and GIS for Root Crops." HortScience 33, no. 3 (June 1998): 552b—552. http://dx.doi.org/10.21273/hortsci.33.3.552b.

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I will show how yield mapping data, aerial photography data, and other agronomic data (fertility, soil parameters) can be integrated into a Geographical Information System (GIS) and give a “feel” of the value of these tools to look at crop production as a whole. The capability of GIS in handling and displaying several layers of georeferenced data leads naturally to a decisionmaking process quite similar to the one used in traditional photo interpretation of aerial imagery. This approach can be very valuable for farm managers and consultants in crop production.

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11

Leclerc, Maxime, Viacheslav Adamchuk, Jaesung Park, and Xavier Lachapelle-T. "Development of Willow Tree Yield-Mapping Technology." Sensors 20, no. 9 (May 6, 2020): 2650. http://dx.doi.org/10.3390/s20092650.

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With today’s environmental challenges, developing sustainable energy sources is crucial. From this perspective, woody biomass has been, and continues to be, a significant research interest. The goal of this research was to develop new technology for mapping willow tree yield grown in a short-rotation forestry (SRF) system. The system gathered the physical characteristics of willow trees on-the-go, while the trees were being harvested. Features assessed include the number of trees harvested and their diameter. To complete this task, a machine-vision system featuring an RGB-D stereovision camera was built. The system tagged these data with the corresponding geographical coordinates using a Global Navigation Satellite System (GNSS) receiver. The proposed yield-mapping system showed promising detection results considering the complex background and variable light conditions encountered in the outdoors. Of the 40 randomly selected and manually observed trees in a row, 36 were successfully detected, yielding a 90% detection rate. The correctly detected tree rate of all trees within the scenes was actually 71.8% since the system tended to be sensitive to branches, thus, falsely detecting them as trees. Manual validation of the diameter estimation function showed a poor coefficient of determination and a root mean square error (RMSE) of 10.7 mm.

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12

Qu, Jianan Y., Zhijian Huang, and Jianwen Hua. "Mapping the fluorescence yield on turbid media." Applied Physics Letters 76, no. 8 (February 21, 2000): 970–72. http://dx.doi.org/10.1063/1.125908.

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13

Portis, Ezio, Rosario Paolo Mauro, Lorenzo Barchi, Alberto Acquadro, Giovanni Mauromicale, and Sergio Lanteri. "Mapping yield-associated QTL in globe artichoke." Molecular Breeding 34, no. 2 (March 16, 2014): 615–30. http://dx.doi.org/10.1007/s11032-014-0061-z.

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14

Momin, Md Abdul, Tony E. Grift, Domingos S. Valente, and Alan C. Hansen. "Sugarcane yield mapping based on vehicle tracking." Precision Agriculture 20, no. 5 (December 4, 2018): 896–910. http://dx.doi.org/10.1007/s11119-018-9621-2.

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15

Colaço, A. F., R. G. Trevisan, F. H. S. Karp, and J. P. Molin. "Yield mapping methods for manually harvested crops." Computers and Electronics in Agriculture 177 (October 2020): 105693. http://dx.doi.org/10.1016/j.compag.2020.105693.

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16

Yang, Chenghai, James H. Everitt, Joe M. Bradford, and Dale Murden. "Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability." Precision Agriculture 5, no. 5 (October 2004): 445–61. http://dx.doi.org/10.1007/s11119-004-5319-8.

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17

Sirikun, Chaiyan, Grianggai Samseemoung, Peeyush Soni, Jaturong Langkapin, and Jakkree Srinonchat. "A Grain Yield Sensor for Yield Mapping with Local Rice Combine Harvester." Agriculture 11, no. 9 (September 18, 2021): 897. http://dx.doi.org/10.3390/agriculture11090897.

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Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as well as pertinent information related to field, position and navigation. The developed system comprised a yield meter, GNSS receiver and a computer installed with customized software, which, when assembled on a local rice combine, mapped real-time rice yield along with grain moisture content. The performance of the developed system was evaluated at three neighboring (identically managed) rice fields. ArcGIS® software was used to create grain yield map with geographical information of the fields. The average grain yield values recorded were 3.63, 3.84 and 3.60 t ha−1, and grain moisture contents (w.b.) were 22.42%, 23.50% and 24.71% from the three fields, respectively. Overall average grain yield was 3.84 t ha−1 (CV = 63.68%) with 578.10 and 7761.58 kg ha−1 as the minimum and maximum values, respectively. The coefficients of variation in grain yield of the three fields were 57.44%, 63.68% and 60.41%, respectively. The system performance was evaluated at four different cutter bar heights (0.18, 0.25, 0.35 and 0.40 m) during the test. As expected, the tallest cutter bar height (0.40 m) offered the least error of 12.50% in yield estimation. The results confirmed that the developed grain yield sensor could be successfully used with the local rice combine harvester; hence, offers and ‘up-gradation’ potential in Thai agricultural mechanization.

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18

Li, Wei, Philippe Ciais, Elke Stehfest, Detlef van Vuuren, Alexander Popp, Almut Arneth, Fulvio Di Fulvio, et al. "Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale." Earth System Science Data 12, no. 2 (April 2, 2020): 789–804. http://dx.doi.org/10.5194/essd-12-789-2020.

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Abstract. Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS – carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5∘×0.5∘ spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0.5∘×0.5∘ global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).

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19

Qiao, J., A. Sasao, S. Shibusawa, N. Kondo, and E. Morimoto. "Mobile fruit grading robot : Mapping yield and quality of sweet pepper in real-time." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 205. http://dx.doi.org/10.1299/jsmermd.2004.205_3.

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20

Hunt, Merryn L., George Alan Blackburn, Luis Carrasco, John W. Redhead, and Clare S. Rowland. "High resolution wheat yield mapping using Sentinel-2." Remote Sensing of Environment 233 (November 2019): 111410. http://dx.doi.org/10.1016/j.rse.2019.111410.

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21

S. W. Searcy, J. K. Schueller, Y. H. Bae, S. C. Borgelt, and B. A. Stout. "Mapping of Spatially Variable Yield During Grain Combining." Transactions of the ASAE 32, no. 3 (1989): 0826–29. http://dx.doi.org/10.13031/2013.31077.

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22

Yang, Chenghai. "Airborne Hyperspectral Imagery for Mapping Crop Yield Variability." Geography Compass 3, no. 5 (August 31, 2009): 1717–31. http://dx.doi.org/10.1111/j.1749-8198.2009.00281.x.

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23

Roy, Pravakar, Abhijeet Kislay, Patrick A. Plonski, James Luby, and Volkan Isler. "Vision-based preharvest yield mapping for apple orchards." Computers and Electronics in Agriculture 164 (September 2019): 104897. http://dx.doi.org/10.1016/j.compag.2019.104897.

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24

Kraakman, Arnold T. W., Rients E. Niks, Petra M. M. M. Van den Berg, Piet Stam, and Fred A. Van Eeuwijk. "Linkage Disequilibrium Mapping of Yield and Yield Stability in Modern Spring Barley Cultivars." Genetics 168, no. 1 (September 2004): 435–46. http://dx.doi.org/10.1534/genetics.104.026831.

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25

Zagórda, Mirosław, and Maria Walczykova. "The application of various software programs for mapping yields in precision agriculture." BIO Web of Conferences 10 (2018): 01018. http://dx.doi.org/10.1051/bioconf/20181001018.

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Yields mapping can be done in GIS programs as well as in specialized software for agriculture. These can be programs designed for preparing informative and application maps and also software designed for integrated management of agricultural production that have a built-in GIS module. In the presented study, representatives of those three groups were taken into consideration – QGIS, Agro-Map, and FarmTMWorks Office (now Trimble Ag Software). The aim of the study was to evaluate the compatibility of the obtained yield maps in terms of basic statistical and geostatistical parameters, such as the average yield, the coefficient of variability, placement on the field zones with different yield ranges, and their percentage share in the area of the field surface. Calculations and visualization of the results were done for winter wheat, winter rape, and corn, cultivated in three-year rotation on one field. All three presented programs allow preparation of raw yield monitoring data so that yield maps produced with identical assumptions of interpolation parameters differ slightly. The difference in the use of these applications is mainly due to the further use of the resulting maps, the time required to learn the program, the task execution time, and the cost of purchase.

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26

Timmerman-Vaughan, Gail M., Annamaria Mills, Clare Whitfield, Tonya Frew, Ruth Butler, Sarah Murray, Michael Lakeman, John McCallum, Adrian Russell, and Derek Wilson. "Linkage Mapping of QTL for Seed Yield, Yield Components, and Developmental Traits in Pea." Crop Science 45, no. 4 (July 2005): 1336–44. http://dx.doi.org/10.2135/cropsci2004.0436.

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27

Spezia, Graciele R., Eduardo G. de Souza, Lúcia H. P. Nóbrega, Miguel A. Uribe-Opazo, Marcos Milan, and Claudio L. Bazzi. "Model to estimate the sampling density for establishment of yield mapping." Revista Brasileira de Engenharia Agrícola e Ambiental 16, no. 4 (April 2012): 449–57. http://dx.doi.org/10.1590/s1415-43662012000400016.

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Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD) and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.

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28

Schueller, J. K., J. D. Whitney, T. A. Wheaton, W. M. Miller, and A. E. Turner. "Low-cost automatic yield mapping in hand-harvested citrus." Computers and Electronics in Agriculture 23, no. 2 (August 1999): 145–53. http://dx.doi.org/10.1016/s0168-1699(99)00028-9.

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29

Q. U. Zaman, K.C. Swain, A. W. Schumann, and D. C. Percival. "Automated, Low-Cost Yield Mapping of Wild Blueberry Fruit." Applied Engineering in Agriculture 26, no. 2 (2010): 225–32. http://dx.doi.org/10.13031/2013.29540.

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30

Penney, D. C., S. C. Nolan, R. C. McKenzie, T. W. Goddard, and L. Kryzanowski. "Yield and nutrient mapping for site specific fertilizer management." Communications in Soil Science and Plant Analysis 27, no. 5-8 (March 1996): 1265–79. http://dx.doi.org/10.1080/00103629609369631.

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31

Gusmao, L., J. T. Mexia, and M. L. Gomes. "Mapping of Equipotential Zones for Cultivar Yield Pattern Evaluation." Plant Breeding 103, no. 4 (December 1989): 293–98. http://dx.doi.org/10.1111/j.1439-0523.1989.tb00388.x.

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32

Bandler, John W., José E. Rayas-Sánchez, and Qi-Jun Zhang. "Yield-driven electromagnetic optimization via space mapping-based neuromodels." International Journal of RF and Microwave Computer-Aided Engineering 12, no. 1 (December 6, 2001): 79–89. http://dx.doi.org/10.1002/mmce.10015.

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33

Niemietz, Kathrin, Kay Dornich, Torsten Hahn, A. Helbig, Stefan Hellwig, Karl Heinz Stegemann, and J. R. Niklas. "Mapping of Device Yield Relevant Electrical Si-Wafer Parameters." Solid State Phenomena 131-133 (October 2007): 511–16. http://dx.doi.org/10.4028/www.scientific.net/ssp.131-133.511.

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With an innovative measurement technique termed “microwave detected photoconductivity” (MDP) it is possible to investigate defects of silicon wafers contact less and topographically by evaluating photoconductivity transients detected via microwave absorption. Thus it is possible to obtain the electrical key parameters (e.g. diffusion length and lifetime) in a contact less, non destructive and topographic way. The method is ideal for the investigation of process induced defects as a function of different processing steps. The inhomogeneities of the diffusion length map of the pure wafer correlate very well with the key parameter map of photosensor devices. Even more details of process induced failures of devices can be detected in detail with MDP. In general, the measurement conditions together with their evaluation can be tuned to nearly ‘predict’ properties and production yield of final devices for a given chain of processing steps.

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Swain, Kishore C., Qamar U. Zaman, Arnold W. Schumann, David C. Percival, and Dionysis D. Bochtis. "Computer vision system for wild blueberry fruit yield mapping." Biosystems Engineering 106, no. 4 (August 2010): 389–94. http://dx.doi.org/10.1016/j.biosystemseng.2010.05.001.

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35

Stafford, J. V., B. Ambler, R. M. Lark, and J. Catt. "Mapping and interpreting the yield variation in cereal crops." Computers and Electronics in Agriculture 14, no. 2-3 (February 1996): 101–19. http://dx.doi.org/10.1016/0168-1699(95)00042-9.

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36

Birrell, Stuart J., Kenneth A. Sudduth, and Steven C. Borgelt. "Comparison of sensors and techniques for crop yield mapping." Computers and Electronics in Agriculture 14, no. 2-3 (February 1996): 215–33. http://dx.doi.org/10.1016/0168-1699(95)00049-6.

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37

Vansichen, R., and J. DeBaerdemaeker. "A Measurement Technique for Yield Mapping of Corn Silage." Journal of Agricultural Engineering Research 55, no. 1 (May 1993): 1–10. http://dx.doi.org/10.1006/jaer.1993.1028.

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38

Abtahi, Mozhgan, Mohammad Mahdi Majidi, Aghafakhr Mirlohi, and Fatemeh Saeidnia. "Association analysis for seed yield, forage yield and traits related to drought tolerance in orchardgrass (Dactylis glomerata)." Crop and Pasture Science 69, no. 11 (2018): 1150. http://dx.doi.org/10.1071/cp18178.

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Polycross designs bridge the two usual mapping approaches (bi-parental mapping and association analysis) and increase mapping power by incorporating greater genetic diversity. In this study, we used diverse genotypes selected from polycrossed progenies to identify marker loci associated with a set of seed- and forage-related traits as well as drought tolerance in orchardgrass (Dactylis glomerata L.). Associations were estimated between phenotypic traits and 923 DNA markers (including 446 inter-simple sequence repeats and 477 sequence-related amplified polymorphism markers). Positive relationship was found between forage yield and seed yield under normal and water-stress conditions, indicating that simultaneous improvement of seed and forage yield could be achieved in orchardgrass. The results of population structure analysis identified five main subpopulations possessing significant genetic differences. Under normal and water-stress conditions, respectively, 341 and 359 markers were significantly associated with the studied traits. Most of these markers were associated with more than one trait. Water-environment specificity of trait-associated markers indicates that genotype × environment interactions influence association analysis. However, 75 stable associations were identified across two moisture conditions for traits such as seed and forage yield. Marker–trait association revealed that markers M1/E1-5, M2/E6-5, M3/E4-6, P14-7 and P845-7 were consistently linked with drought-tolerance index. The identified marker alleles associated with multiple traits across environments may be considered for further analysis for their chromosome locations, the corresponding sequences and their potential functions.

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39

Genty, B., and S. Meyer. "Quantitative Mapping of Leaf Photosynthesis Using Chlorophyll Fluorescence Imaging." Functional Plant Biology 22, no. 2 (1995): 277. http://dx.doi.org/10.1071/pp9950277.

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A method has been developed for routine, non-invasive monitoring of the topography of leaf photochemistry. The method uses video images of leaf chlorophyll fluorescence, taken during steady-state photosynthesis and during a transitory saturation of photochemistry, to construct, pixel by pixel, an image of the photochemical yield of photosystem II (PSII). The photochemical yield of PSII was estimated according to Genty et al. (1989) (Biochimica et Biophysica Acta 990, 87-92). The effectiveness of the method was shown by mapping the heterogeneous distribution of photosynthetic activity after treatment with either a herbicide (DCMU), abscisic acid, or during the course of the induction of photosynthesis. Leaf CO2 assimilation was simultaneously monitored under non- photorespiratory conditions to estimate the average quantum yield of linear electron transport. A unique proportional relationship was found between the mean photochemical yield of PSII calculated from images of the photochemical yield of PSII, and the average quantum yield of linear electron transport in three plant species exposed to a wide range of treatments or conditions. This new ability to quantitatively visualise leaf photochemistry provides a powerful tool to probe the spatial distribution of leaf photosynthesis. Possible errors in estimating the photochemical yield of PSII from mean fluorescence measurements are discussed.

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40

Liu, Jia, Bihua Wu, Ravi P. Singh, and Govindan Velu. "QTL mapping for micronutrients concentration and yield component traits in a hexaploid wheat mapping population." Journal of Cereal Science 88 (July 2019): 57–64. http://dx.doi.org/10.1016/j.jcs.2019.05.008.

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41

Jingade, Pavankumar, and R. L. Ravikumar. "QTL Mapping and Identification of QTLs Linked to Yield and Yield Attributing Traits in Chickpea." Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 89, no. 3 (May 2, 2018): 815–21. http://dx.doi.org/10.1007/s40011-018-0991-z.

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Wang, Baohua, Wangzhen Guo, Xiefei Zhu, Yaoting Wu, Naitai Huang, and Tianzhen Zhang. "QTL Mapping of Yield and Yield Components for Elite Hybrid Derived-RILs in Upland Cotton." Journal of Genetics and Genomics 34, no. 1 (January 2007): 35–45. http://dx.doi.org/10.1016/s1673-8527(07)60005-8.

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Dong, Chengguang, Juan Wang, Quanjia Chen, Yu Yu, and Baocheng Li. "Detection of favorable alleles for yield and yield components by association mapping in upland cotton." Genes & Genomics 40, no. 7 (March 23, 2018): 725–34. http://dx.doi.org/10.1007/s13258-018-0678-0.

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Zang, Yunze, Xuehong Chen, Jin Chen, Yugang Tian, Yusheng Shi, Xin Cao, and Xihong Cui. "Remote Sensing Index for Mapping Canola Flowers Using MODIS Data." Remote Sensing 12, no. 23 (November 28, 2020): 3912. http://dx.doi.org/10.3390/rs12233912.

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Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.

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Reid, T. A., A. Navabi, J. C. Cahill, D. Salmon, and D. Spaner. "A genetic analysis of weed competitive ability in spring wheat." Canadian Journal of Plant Science 89, no. 4 (July 1, 2009): 591–99. http://dx.doi.org/10.4141/cjps08105.

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Competition with weeds decreases crop yields globally. Breeding for competitive ability against elevated weed pressure can be difficult because the selection for specific traits which contribute to competitive ability may result in yield losses. The widely studied International Triticeae Mapping Initiative (ITMI) population was used to study the genetics of traits associated with competitive ability in a high latitude (52-53ºN) wheat-growing environment in central Alberta, Canada. Grain yield without weed competition and under experimentally sown cultivated oat competition exhibited similar heritability. Grain yield was positively correlated with early season vigour, and negatively correlated with days to maturity in the competitive treatment only. In this study, similar heritability estimates between competition treatments suggest that selection in a weed free environment can lead to improvements in a weedy environment, but some high-yielding lines under competition would be eliminated during selection.Key words: Wheat, weed competition, competitive ability, International Triticeae Mapping Initiative, genetic correlation

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Jin, Zhenong, George Azzari, Marshall Burke, Stephen Aston, and David Lobell. "Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa." Remote Sensing 9, no. 9 (September 8, 2017): 931. http://dx.doi.org/10.3390/rs9090931.

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Maertens, K., M. Reyniers, J. De Baerdemaeker, H. Ramon, and R. De Keyser. "Grain Yield Mapping on Combine Harvesters: A Model-Based Approach." IFAC Proceedings Volumes 33, no. 29 (November 2000): 301–6. http://dx.doi.org/10.1016/s1474-6670(17)36795-2.

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Dong, Hongxu, Siyao Liu, Lindsay V. Clark, Shailendra Sharma, Justin M. Gifford, John A. Juvik, Alexander E. Lipka, and Erik J. Sacks. "Genetic mapping of biomass yield in three interconnected Miscanthus populations." GCB Bioenergy 10, no. 3 (August 16, 2017): 165–85. http://dx.doi.org/10.1111/gcbb.12472.

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Huang, Jinsong, and D. V. Griffiths. "Observations on Return Mapping Algorithms for Piecewise Linear Yield Criteria." International Journal of Geomechanics 8, no. 4 (July 2008): 253–65. http://dx.doi.org/10.1061/(asce)1532-3641(2008)8:4(253).

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Wang, Yi-Hong, Aniruddha Acharya, A. Millie Burrell, Robert R. Klein, Patricia E. Klein, and Karl H. Hasenstein. "Mapping and candidate genes associated with saccharification yield in sorghum." Genome 56, no. 11 (November 2013): 659–65. http://dx.doi.org/10.1139/gen-2013-0134.

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Sorghum (Sorghum bicolor (L.) Moench) is a high-yielding, stress tolerant energy crop for lignocellulosic-based biofuel production. Saccharification is a process by which hydrolytic enzymes break down lignocellulosic materials to fermentable sugars for biofuel production, and mapping and identifying genes underlying saccharification yield is an important first step to genetically improve the plant for higher biofuel productivity. In this study, we used the ICRISAT sorghum mini core germplasm collection and 14 739 single nucleotide polymorphism markers to map saccharification yield. Seven marker loci were associated with saccharification yield and five of these loci were syntenic with regions in the maize genome that contain quantitative trait loci underlying saccharification yield and cell wall component traits. Candidate genes from the seven loci were identified but must be validated, with the most promising candidates being β-tubulin, which determines the orientation of cellulose microfibrils in plant secondary cell walls, and NST1, a master transcription factor controlling secondary cell wall biosynthesis in fibers. Other candidate genes underlying the different saccharification loci included genes that play a role in vascular development and suberin deposition in plants. The identified loci and candidate genes provide information into the factors controlling saccharification yield and may facilitate increasing biofuel production in sorghum.

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