| Literature DB >> 32269583 |
Chalo Richard Muoki1, Tony Kipkoech Maritim1, Wyclife Agumba Oluoch2, Samson Machohi Kamunya1, John Kipkoech Bore2.
Abstract
Climate change triggered by global warming poses a major threat to agricultural systems globally. This phenomenon is characterized by emergence of pests and diseases, extreme weather events, such as prolonged drought, high intensity rains, hailstones and frosts, which are becoming more frequent ultimately impacting negatively to agricultural production including rain-fed tea cultivation. Kenya is predominantly an agricultural based economy, with the tea sector generating about 26% of the total export earnings and about 4% gross domestic product (GDP). In the recent years, however, the country has witnessed unstable trends in tea production associated with climate driven stresses. Toward mitigation and adaptation of climate change, multiple approaches for impact assessment, intensity prediction and adaptation have been advanced in the Kenyan tea sub-sector. Further, pressure on tea breeders to release improved climate-compatible cultivars for the rapidly deteriorating environment has resulted in the adoption of a multi-targeted approach seeking to understand the complex molecular regulatory networks associated with biotic and abiotic stresses adaptation and tolerance in tea. Genetic modeling, a powerful tool that assists in breeding process, has also been adopted for selection of tea cultivars for optimal performance under varying climatic conditions. A range of physiological and biochemical responses known to counteract the effects of environmental stresses in most plants that include lowering the rates of cellular growth and net photosynthesis, stomatal closure, and the accumulation of organic solutes such as sugar alcohols, or osmolytes have been used to support breeding programs through screening of new tea cultivars suitable for changing environment. This review describes simulation models combined with high resolution climate change scenarios required to quantify the relative importance of climate change on tea production. In addition, both biodiversity and ecosystem based approaches are described as a part of an overall adaptation strategy to mitigate adverse effects of climate change on tea in Kenya and gaps highlighted for urgent investigations.Entities:
Keywords: Camellia; breeding; modeling; molecular; physiology
Year: 2020 PMID: 32269583 PMCID: PMC7109314 DOI: 10.3389/fpls.2020.00339
Source DB: PubMed Journal: Front Plant Sci ISSN: 1664-462X Impact factor: 5.753
FIGURE 1(A) Effect of frost in tea plantation. (B) Severe hailstone damage resulting in suspension of tea plucking for several months. (C) Effect of planting eucalyptus trees near tea fields. (D) Wireless sensor network (WSN) that assist in timely weather data deployment.
FIGURE 2Projected suitable areas for tea growing in Kenya. (A) 2025, (B) 2050, and (C) 2075. Source: Adapted from Bore and Nyabundi (2016).
An overview of various breeding strategies employed to improve tolerance of tea to adverse environmental factors in Kenya.
| Techniques/approaches | Target trait | References |
| Relative water content | Drought tolerance | |
| Shoot water potential | Drought tolerance | |
| Gas exchange measurement | Drought tolerance | |
| Genotype (G) × Environment (E) interaction | Yield | |
| Total Polyphenols | Drought tolerance | |
| Amino acids (Proline) | Drought tolerance | |
| Amines (Glycinebetaine) | Drought tolerance | |
| Epicatechin and Epigallocatechin | Drought tolerance | |
| Short-time Withering Assessment of Probability for Drought Tolerance | Drought tolerance | |
| Combining ability | Drought tolerance, Quality and Yield | |
| Linkage analysis and QTL mapping (RAPD, AFLP and SSR) | Yield | |
| Linkage analysis and QTL mapping (DArT) | Drought tolerance and quality | |
| Bulked segregant analysis (BSA) | Yield | |
| Genomics | Drought tolerance and black tea quality | |
| Suppression subtractive hybridization | Drought tolerance | |
| Transcriptomics | Drought tolerance |
FIGURE 3Schematic diagram on tea improvement strategies and techniques that have evolved over a period of time. Arrows in between boxes 1, 2, 3, and 4 show the evolutionary time scale of the development of the strategies. CATs, clonal adaptability trials; CFTs, clonal field trials; MAS, marker assisted selection; PTs, progeny trials; QTLs, quantitative trait loci.
Breeding stocks and their expected genetic contribution in breeding program.
| High yield potential | High quality potential | Pest tolerance/resistance | Drought tolerance | High soil pH tolerance | Cold tolerance | Genetic study |
| TRFK 31/8 | TRFK 6/89 | TRFK 7/93 | TRFCA SFS150 | EPK TN14-3 | EPK TN14-3 | TRFK 12/21 |
| TRFK 303/5778 | GW Ejulu-L | TRFK 57/153 | TRFK 303/5778 | NDT Tai | TRFCA SFS150 | TRFK K-Purple |
| TRFK 301/4 | EPK TN 15-23 | AHP SC31/373 | EPK C12 | TRFK 31/302 | ||
| TRFK 301/5 | AHP S15/103 | NRIT Yabukita6 | TRFK 311/2872 | |||
| EPK C12 | EPK TN14-35 | NRIT Yutakamidori6 | TRFK 382/17 | |||
| BBLK 35 | TRFK 303/11993 | TRFK 382/27 | ||||
| AHP S15/109 | TRFK 54/404 | TRFK 386/27 | ||||
| AHP SC12/289 | TRFCA SFS1503 | TRFK 371/17 | ||||
| AHP SC31/37 | AHP CG28U8644 | TRFK 30610 | ||||
| AHP CG28V9299 | TRFK 301/14 | Wild | ||||
| AHP CG28U864 | TRFK L/164 |