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Journal articles on the topic 'Crop improvement'

To see the other types of publications on this topic, follow the link: Crop improvement.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Choudhary, Mukesh, Vishal Singh, Vignesh Muthusamy, and Shabir Hussain Wani. "Harnessing Crop Wild Relatives for Crop Improvement." LS: International Journal of Life Sciences 6, no. 2 (2017): 73. http://dx.doi.org/10.5958/2319-1198.2017.00009.4.

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2

Merchán, Kelly. "Crop Improvement ≠ Plant Breeding." CSA News 66, no. 5 (April 22, 2021): 28–31. http://dx.doi.org/10.1002/csan.20445.

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3

CLEGG, MICHAEL T. "Genetics of Crop Improvement." American Zoologist 26, no. 3 (August 1986): 821–34. http://dx.doi.org/10.1093/icb/26.3.821.

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4

Gosal, Satbir S., Shabir H. Wani, and Manjit S. Kang. "Biotechnology and Crop Improvement." Journal of Crop Improvement 24, no. 2 (April 29, 2010): 153–217. http://dx.doi.org/10.1080/15427520903584555.

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5

Evans, Adrian. "Innovations in crop improvement." Crop Protection 12, no. 3 (May 1993): 237. http://dx.doi.org/10.1016/0261-2194(93)90116-z.

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6

Smith, Steven M. "Crop improvement utilizing biotechnology." Agricultural Systems 36, no. 2 (January 1991): 246–47. http://dx.doi.org/10.1016/0308-521x(91)90032-6.

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7

Praveen Rao, V. "Breeding for Crop Improvement." Current Science 114, no. 02 (January 25, 2018): 256. http://dx.doi.org/10.18520/cs/v114/i02/256-257.

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8

Springer, Nathan M. "Epigenetics and crop improvement." Trends in Genetics 29, no. 4 (April 2013): 241–47. http://dx.doi.org/10.1016/j.tig.2012.10.009.

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9

Ramulu, K. S., V. K. Sharma, T. N. Naumova, P. Dijkhuis, and M. M. van Lookeren Campagne. "Apomixis for crop improvement." Protoplasma 208, no. 1-4 (March 1999): 196–205. http://dx.doi.org/10.1007/bf01279090.

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10

Sourdille, Pierre, and Pierre Devaux. "Crop Improvement: Now and Beyond." Biology 10, no. 5 (May 10, 2021): 421. http://dx.doi.org/10.3390/biology10050421.

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11

Singh, Arvinder, and Muskan Bokolia. "CRISPR/Cas for Crop Improvement." Resonance 26, no. 2 (February 2021): 227–40. http://dx.doi.org/10.1007/s12045-021-1121-4.

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12

Shigeoka, S. "Transgenic approaches to crop improvement." Japanese journal of crop science 71, Supplement2 (2002): 318–21. http://dx.doi.org/10.1626/jcs.71.supplement2_318.

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13

Lai, Kaitao, Michał T. Lorenc, and David Edwards. "Genomic Databases for Crop Improvement." Agronomy 2, no. 1 (March 20, 2012): 62–73. http://dx.doi.org/10.3390/agronomy2010062.

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14

Cody, Jon, Nathan Swyers, Morgan McCaw, Nathaniel Graham, Changzeng Zhao, and James Birchler. "Minichromosomes: Vectors for Crop Improvement." Agronomy 5, no. 3 (July 6, 2015): 309–21. http://dx.doi.org/10.3390/agronomy5030309.

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15

Shen, Lisha, and Hao Yu. "Epitranscriptome engineering in crop improvement." Molecular Plant 14, no. 9 (September 2021): 1418–20. http://dx.doi.org/10.1016/j.molp.2021.08.006.

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16

Kumari, Rima. "Allele Mining for Crop Improvement." International Journal of Pure & Applied Bioscience 6, no. 1 (February 28, 2018): 1456–65. http://dx.doi.org/10.18782/2320-7051.6073.

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17

Bevan, Michael W., Cristobal Uauy, Brande B. H. Wulff, Ji Zhou, Ksenia Krasileva, and Matthew D. Clark. "Genomic innovation for crop improvement." Nature 543, no. 7645 (March 2017): 346–54. http://dx.doi.org/10.1038/nature22011.

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18

Evans, L. T. "Is Crop Improvement Still Needed?" Journal of Crop Improvement 14, no. 1-2 (September 13, 2005): 1–7. http://dx.doi.org/10.1300/j411v14n01_01.

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19

Sneller, Clay H., Randall L. Nelson, T. E. Carter, and Zhanglin Cui. "Genetic Diversity in Crop Improvement." Journal of Crop Improvement 14, no. 1-2 (September 13, 2005): 103–44. http://dx.doi.org/10.1300/j411v14n01_06.

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20

Zhang, Jingyu, Xin-Min Li, Hong-Xuan Lin, and Kang Chong. "Crop Improvement Through Temperature Resilience." Annual Review of Plant Biology 70, no. 1 (April 29, 2019): 753–80. http://dx.doi.org/10.1146/annurev-arplant-050718-100016.

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Abnormal environmental temperature affects plant growth and threatens crop production. Understanding temperature signal sensing and the balance between defense and development in plants lays the foundation for improvement of temperature resilience. Here, we summarize the current understanding of cold signal perception/transduction as well as heat stress response. Dissection of plant responses to different levels of cold stresses (chilling and freezing) illustrates their common and distinct signaling pathways. Axillary bud differentiation in response to chilling is presented as an example of the trade-off between defense and development. Vernalization is a cold-dependent development adjustment mediated by O-GlcNAcylation and phosphorylation to sense long-term cold. Recent progress on major quantitative trait loci genes for heat tolerance has been summarized. Molecular mechanisms in utilizing temperature-sensitive sterility in super hybrid breeding in China are revealed. The way to improve crop temperature resilience using integrative knowledge of omics as well as systemic and synthetic biology, especially the molecular module program, is summarized.

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21

Brennan, Charles. "Concise Encyclopaedia of Crop Improvement." International Journal of Food Science & Technology 44, no. 10 (October 2009): 2085. http://dx.doi.org/10.1111/j.1365-2621.2008.01771.x.

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22

Parry, M. A. J., P. J. Madgwick, C. Bayon, K. Tearall, A. Hernandez-Lopez, M. Baudo, M. Rakszegi, et al. "Mutation discovery for crop improvement." Journal of Experimental Botany 60, no. 10 (June 10, 2009): 2817–25. http://dx.doi.org/10.1093/jxb/erp189.

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23

Burgess, Darren J. "Branching out for crop improvement." Nature Reviews Genetics 18, no. 7 (June 5, 2017): 393. http://dx.doi.org/10.1038/nrg.2017.48.

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24

Martin, Gregory B. "Gene discovery for crop improvement." Current Opinion in Biotechnology 9, no. 2 (April 1998): 220–26. http://dx.doi.org/10.1016/s0958-1669(98)80119-5.

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25

Dunwell, Jim M. "Transgenic approaches to crop improvement." Journal of Experimental Botany 51, suppl_1 (February 2000): 487–96. http://dx.doi.org/10.1093/jexbot/51.suppl_1.487.

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26

Verhage, Leonie. "The colour of crop improvement." Plant Journal 103, no. 6 (September 2020): 1965–66. http://dx.doi.org/10.1111/tpj.14971.

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27

Rafalski, Antoni. "Molecular techniques in crop improvement." Plant Science 163, no. 6 (December 2002): 1177. http://dx.doi.org/10.1016/s0168-9452(02)00330-8.

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28

Brown, D. C. W., and T. A. Thorpe. "Crop improvement through tissue culture." World Journal of Microbiology & Biotechnology 11, no. 4 (July 1995): 409–15. http://dx.doi.org/10.1007/bf00364616.

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29

Rafalski, J. Antoni. "Association genetics in crop improvement." Current Opinion in Plant Biology 13, no. 2 (April 2010): 174–80. http://dx.doi.org/10.1016/j.pbi.2009.12.004.

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30

Varshney, Rajeev K., Pallavi Sinha, Vikas K. Singh, Arvind Kumar, Qifa Zhang, and Jeffrey L. Bennetzen. "5Gs for crop genetic improvement." Current Opinion in Plant Biology 56 (August 2020): 190–96. http://dx.doi.org/10.1016/j.pbi.2019.12.004.

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31

Kerchev, Pavel, Barbara De Smet, Cezary Waszczak, Joris Messens, and Frank Van Breusegem. "Redox Strategies for Crop Improvement." Antioxidants & Redox Signaling 23, no. 14 (November 10, 2015): 1186–205. http://dx.doi.org/10.1089/ars.2014.6033.

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32

St. Martin, S. K. "Plant Adaption and Crop Improvement." Crop Science 38, no. 1 (January 1998): 274–75. http://dx.doi.org/10.2135/cropsci1998.0011183x003800010047x.

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33

Lin, Rongshuang. "Concise Encyclopedia of Crop Improvement." Journal of Environmental Quality 38, no. 3 (May 2009): 1329. http://dx.doi.org/10.2134/jeq2008.0023br.

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34

Hussain,, G., M. S. Wani,, M. A. Mir,, Z. A. Rather, and K. M. Bhat,. "Micrografting for fruit crop improvement." African Journal of Biotechnology 13, no. 25 (June 18, 2014): 2474–83. http://dx.doi.org/10.5897/ajb2013.13602.

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35

Heffner, Elliot L., Mark E. Sorrells, and Jean-Luc Jannink. "Genomic Selection for Crop Improvement." Crop Science 49, no. 1 (January 2009): 1–12. http://dx.doi.org/10.2135/cropsci2008.08.0512.

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36

GOODMAN, R. M., H. HAUPTLI, A. CROSSWAY, and V. C. KNAUF. "Gene Transfer in Crop Improvement." Science 236, no. 4797 (April 3, 1987): 48–54. http://dx.doi.org/10.1126/science.236.4797.48.

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37

Sane, P. V., and U. C. Lavania. "Innovative Approaches to Crop Improvement." Proceedings of the Indian National Science Academy 80, no. 1 (March 18, 2014): 17. http://dx.doi.org/10.16943/ptinsa/2014/v80i1/55082.

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38

McCouch, Susan. "Wild Alleles for Crop Improvement." Nature Biotechnology 17, S5 (December 1999): 32. http://dx.doi.org/10.1038/70392.

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39

Pauls, K. P. "Plant biotechnology for crop improvement." Biotechnology Advances 13, no. 4 (January 1995): 673–93. http://dx.doi.org/10.1016/0734-9750(95)02010-1.

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40

Cramer, Rainer, Laurence Bindschedler, and Ganesh Agrawal. "Plant Proteomics in Crop Improvement." PROTEOMICS 13, no. 12-13 (June 2013): 1771. http://dx.doi.org/10.1002/pmic.201370104.

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41

Cortés, Andrés J., María Ángeles Castillejo, and Roxana Yockteng. "‘Omics’ Approaches for Crop Improvement." Agronomy 13, no. 5 (May 19, 2023): 1401. http://dx.doi.org/10.3390/agronomy13051401.

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42

Murín, Gustáv, and Karol Mičieta. "Improvement of Crop Production by Means of a Storage Effect." International Journal of Environmental and Agriculture Research 3, no. 5 (May 31, 2017): 12–25. http://dx.doi.org/10.25125/agriculture-journal-ijoear-apr-2017-26.

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43

Goldman, I. L. "Principles of Crop Improvement. 2nd ed." HortTechnology 10, no. 3 (January 2000): 638b—640. http://dx.doi.org/10.21273/horttech.10.3.638b.

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44

Ward, Richard W. "Principles of Crop Improvement, 2nd Edition." Crop Science 40, no. 2 (March 2000): 562–63. http://dx.doi.org/10.2135/cropsci2000.0006br.

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45

Kim, Hyeran, Sang-Tae Kim, Sang-Gyu Kim, and Jin-Soo Kim. "Targeted Genome Editing for Crop Improvement." Plant Breeding and Biotechnology 3, no. 4 (November 30, 2015): 283–90. http://dx.doi.org/10.9787/pbb.2015.3.4.283.

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46

Messina, Carlos D., Fred van Eeuwijk, Tom Tang, Sandra K. Truong, Ryan F. McCormick, Frank Technow, Owen Powell, et al. "Crop Improvement for Circular Bioeconomy Systems." Journal of the ASABE 65, no. 3 (2022): 491–504. http://dx.doi.org/10.13031/ja.14912.

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HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.

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47

Messina, Carlos D., Fred van Eeuwijk, Tom Tang, Sandra K. Truong, Ryan F. McCormick, Frank Technow, Owen Powell, et al. "Crop Improvement for Circular Bioeconomy Systems." Journal of the ASABE 65, no. 3 (2022): 491–504. http://dx.doi.org/10.13031/ja.14912.

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HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.

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48

Temesgen, Begna. "Speed breeding to accelerate crop improvement." International Journal of Agricultural Science and Food Technology 8, no. 2 (June 2, 2022): 178–86. http://dx.doi.org/10.17352/2455-815x.000161.

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Global food security has become a major issue as the human population grows and the environment changes, with the current rate of improvement of several important crops inadequate to meet future demand. Crop plants have extended generation times, which contributes to the slow rate of progress. However, speed breeding has revolutionized the entire world by reducing generation time and speeding up breeding and research programs to improve crop varieties. In the absence of an integrated pre-breeding program, breeding new and high-performing cultivars with market-preferred traits can take more than ten years. After the first cross with parental genotypes, a large amount of time, space, and resources are committed to the selection and genetic advancement stages during the early stages of breeding. Speed breeding has the ability to shorten the time it takes to develop, market, and commercialize cultivars. Crop improvement in the face of a fast-changing environment and an ever-increasing human population is a major concern for scientists around the world. Current crop enhancement projects are progressing at a rate that is insufficient to meet food demand. Crop redesign is urgently needed for climate resilience, as well as long-term yield and nutrition. Crop progress is slowed significantly by the long generation time required by crop plants during the breeding process. Speed breeding is now being used on a large scale to shorten generation time and support multiple crop generations per year as a solution in this approach. Researchers are now using an integrated approach to improve breeding efficiency, combining speed breeding with current plant breeding and genetic engineering methods. Speed breeding is a promising approach for achieving nutritional security and sustainable agriculture by shortening breeding cycles for food and industrial crop enhancement. Speed breeding is a methodology that allows plant breeders to improve crop production by adjusting temperature, light duration, and intensity to boost plant development. It uses an artificial source of light, which is kept on continuously, to activate the photosynthetic process, which leads to growth and reproduction much earlier than normal. This will assist in meeting the demands of the future’s rising population. This can be accomplished using a variety of technologies, including genotyping, marker-assisted selection, high throughput phenotyping; gene editing, genomic selection, and re-domestication, all of which can be combined with speed breeding to allow plant breeders to keep up with a changing climate and growing human population.

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49

Soriano, Jose Miguel. "Molecular Marker Technology for Crop Improvement." Agronomy 10, no. 10 (September 24, 2020): 1462. http://dx.doi.org/10.3390/agronomy10101462.

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Since the 1980s, agriculture and plant breeding have changed with the development of molecular marker technology. In recent decades, different types of molecular markers have been used for different purposes: mapping, marker-assisted selection, characterization of genetic resources, etc. These have produced effective genotyping, but the results have been costly and time-consuming, due to the small number of markers that could be tested simultaneously. Recent advances in molecular marker technologies such as the development of high-throughput genotyping platforms, genotyping by sequencing, and the release of the genome sequences of major crop plants open new possibilities for advancing crop improvement. This Special Issue collects sixteen research studies, including the application of molecular markers in eleven crop species, from the generation of linkage maps and diversity studies to the application of marker-assisted selection and genomic prediction.

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50

Rajagopal, Velamoor, Ramaswamy Manimekalai, Krishnamurthy Devakumar, Rajesh, Anitha Karun, Vittal Niral, Murali Gopal, et al. "A database for coconut crop improvement." Bioinformation 1, no. 2 (August 11, 2005): 75–77. http://dx.doi.org/10.6026/97320630001075.

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