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      Effect of seasonal variation on yield and leaf quality of tea clone (Camellia sinensis (L.) O. Kuntze) in South West Ethiopia

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          Abstract

          The tea plant is widely cultivated in southwest Ethiopia. But the impact of seasonal variation on monthly yield, leaf quality, and the long-term yield response potential of clones has not been studied. The objective of the study is to determine the impact of seasonal change and climate variables on the yield and leaf quality of tea plants in southwest Ethiopia. The experiment consisted of five clones and four seasons under a split-plot design and was replicated three times. The results indicated that the yield and leaf quality showed significant variation in the different seasons at P < 0.05. The highest peak yields of 12.68, 12.59, and 11.3 kg plot −1 were recorded in May, June, and April, respectively, and the yield suddenly dropped by 5.1% in July. Then the soft banjhi increased by 5–10% in July. The yield response potential of clones is highly affected by monthly climate variation at P < 0.05. Clone BB-35 recorded the highest (18.8 kg plot −1) yield in June, followed by clones 11/4 (18.3) in May, 11/56 (14.7) in November, 6/8 (11.7) in December, and 12/38 (5.78 kg plot −1) in June. The lowest mean green leaf and a longer shoot replacement cycle were created due to a decrease in rainfall to 760 mm/month and rising temperatures above 26.35 °C in winter. The leaf phenological response of tea clones is strongly governed by the monthly temperature and suitable precipitation pattern of a season. The highlands have two harvesting seasons, i.e., a dry and a wet harvesting season. The dry harvesting season, which exists between the middle of December and March, accounts for 18.3–24.3% of the total annual yield. The wet harvesting season is subdivided further into two peak harvesting seasons. The first harvest is characterized by a short plucking round, and the highest peak yield occurs in April, May, and June, accounting for 40.22–42.2% of the total annual yield. The second wet harvesting season begins in September and ends in the middle of December, contributing to 35.5–40% of the annual yield. Seasonal variation has a direct impact on leaf quality and clone yielding potential. Clones show higher yield and shorter plucking rounds at maximum temperatures above 23.03 °C and below 26.35 °C, but temperatures above 28.34 °C and below 10.38 °C have a negative effect on leaf quality and yield. Over the last two decades, rainfall, maximum, and mean temperatures all increased by 16.09 mm y-1, 0.127 °C, and 0.0566 °C y −1, respectively, and the tea plant showed a strong correlation with maximum temperature (76%), whereas mean temperature (44.6%) and annual rainfall (32.8%) correlated weakly. Green leaf production is well explained by around 85.4% of the observed climate variance, with an increase of 1287.18 tonnes y −1, and highland tea production will exhibit a positive net benefit from expected climate change in the future.

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          Usefulness of bioclimatic models for studying climate change and invasive species.

          Bioclimatic models (also known as envelope models or, more broadly, ecological niche models or species distribution models) are used to predict geographic ranges of organisms as a function of climate. They are widely used to forecast range shifts of organisms due to climate change, predict the eventual ranges of invasive species, infer paleoclimate from data on species occurrences, and so forth. Several statistical techniques (including general linear models, general additive models, climate envelope models, classification and regression trees, and genetic algorithms) have been used in bioclimatic modeling. Recently developed techniques tend to perform better than older techniques, although it is unlikely that any single statistical approach will be optimal for all applications and species. Proponents of bioclimatic models have stressed their apparent predictive power, whereas opponents have identified the following unreasonable model assumptions: biotic interactions are unimportant in determining geographic ranges or are constant over space and time; the genetic and phenotypic composition of species is constant over space and time; and species are unlimited in their dispersal. In spite of these problematic assumptions, bioclimatic models often successfully fit present-day ranges of species. Their ability to forecast the effects of climate change or the spread of invaders has rarely been tested adequately, however, and we urge researchers to tie the evaluation of bioclimatic models more closely to their intended uses.
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            Climate change and eastern Africa: a review of impact on major crops

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              The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities

              Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020–2050), representing assessment priorities for ex situ conservation; (2) identifies ‘core localities’ that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing plantations will be neagtively impacted by climate change.
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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                24 February 2023
                March 2023
                24 February 2023
                : 9
                : 3
                : e14051
                Affiliations
                [a ]Department of Horticulture College of Agriculture in Mizan Tepi University Southwest Ethiopia, Ethiopia
                [b ]Ethiopia Coffee and Tea Authority, Oromia, Ethiopia
                [c ]Department of Postharvest Management of Jimma University College of Agriculture, Oromia, Ethiopia
                [d ]Jimma University Laboratory of Drug Quality (JuLaDQ) and School of Pharmacy, Jimma University, Oromia, Ethiopia
                Author notes
                []Corresponding author. tesfayebentitiffo@ 123456gmail.com
                Article
                S2405-8440(23)01258-6 e14051
                10.1016/j.heliyon.2023.e14051
                10011197
                36925555
                9193c286-1369-46fe-a6c1-c5f11bd5cf64
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 September 2022
                : 20 February 2023
                : 21 February 2023
                Categories
                Research Article

                climate,rainfall,season,shoot replacement,temperature
                climate, rainfall, season, shoot replacement, temperature

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