Modeling the climate suitability of tea [Camellia sinensis(L.) O. Kuntze] in Sri Lanka in response to current and future climate change scenarios

Jayasinghe, Sadeeka Layomi; Kumar, Lalit

doi:10.1016/j.agrformet.2019.03.025

Cited by 104 publications

(60 citation statements)

References 87 publications

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“…A change of the ideal growing conditions, including rainfall and temperature, would severely affect the geographic distribution, yield, and quality of tea [2,22,47]. According to the results, increased rainfall increases the suitability for tea cultivation.…”

Section: Discussionmentioning

confidence: 98%

“…The mean annual precipitation in Sri Lanka fluctuates from under 900 mm in the driest parts of the southeast and northwest of the country to more than 5000 mm in the wettest parts of the central highlands. The study region is characterised by an annual mean temperature that varies from 27 • C in the coastal areas to 16 • C at Nuwara Eliya in the central highlands, which is 1900 m above sea level (m a.s.l) [22]. Sri Lanka is characterised by a central-southern mountain range that rises at its peak to 2524 m above sea level ( Figure 1).…”

Section: Study Areamentioning

confidence: 99%

“…These climatic variations are reflected in the range of quality characters which are Ceylon Tea's leading and most valued features. Tea is highly sensitive to the changes in climatic and topographic covariates [22]. The identification of the impact of the varied climate and topography on the manufacturing of tea has led to the refinement of a range of fine teas that are unique to each agro-climate district.…”

Section: Introductionmentioning

confidence: 99%

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Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

2020

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How the current distribution of tea cultivation is influenced by specific environmental conditions in Sri Lanka is yet to be explored. Therefore, this study aims to assess the differences between tea and non-tea growing areas with respect to climatic and topographic covariates, and to determine the major covariates that control tea distributions. Climatic data of temperature and rainfall were extracted from WorldClim-Global Climate Data; the elevation, slopes, and aspects were obtained from Global Multi-resolution Terrain Elevation Data; and the solar radiation data was computed using a clear-sky solar radiation model. Random points were created on rasterised environmental layers for tea-growing and non-tea growing areas, stratified into low, mid, and high regions, using ArcGIS version 10.4.1 (Environmental Systems Research Institute: ESRI Redlands, CA, USA).Correlations were derived between covariates and tea and non-tea growing areas. According to the logistic regression analysis, there was no significant influence of the south-west, west, and north-west aspect compared to the north aspect when all other covariates were held constant. The odds ratio indicated that an area with a one-unit higher solar radiation was 1.453 times more likely to be a tea growing area. Similarly, a per unit increase in slope increases the likelihood of an area being suitable for tea cultivation by 1.039 times. When the annual mean temperature increased, the suitability of tea cultivation decreased, but an increased rainfall had increased the suitability of an area for tea cultivation. Areas with a north facing slope had the highest suitability for tea cultivation. This research demonstrated that tea growing could be expanded into a variety of locations as long as these variables are either found or managed in order to obtain the critical levels. In addition, it is proposed that the results of this study could be utilised in the assessment of the climate or/and land suitability for tea.

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Section: Discussionmentioning

confidence: 98%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

2020

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“…The OA is calculated by dividing the sum of the entries that form the major diagonal (i.e., the number of correct classes) by the total number of samples for each crop. The kappa coefficient ( k ) [68] measures the accuracy of the model predictions by comparing it with the accuracy expected to occur by chance with k values ranging from -1 (poor) to 1 (good) [30]. The multiclass area under receiver operating characteristic curve (AUC) was used to validate model fit by comparing and averaging all pairwise class AUC.…”

Section: Methodsmentioning

confidence: 99%

“…Crop suitability is a measure of the climatic and other biophysical characteristics of an area to sustain a crop production cycle to meet current or expected targets [28, 29]. When combined with climate projections, suitability assessments are used to gauge shifts in crop potential under climate change [30, 31]. Despite massive developments in agricultural production technology, weather and climate still play a significant role in influencing agricultural production in Africa and elsewhere [32-34].…”

Section: Introductionmentioning

confidence: 99%

Impacts of climate change on agro-climatic suitability of major food crops and crop diversification potential in Ghana

Chemura

Schauberger

Gornott

2020

Preprint

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11Crop diversification is a promising climate change adaptation strategy for food production stability. 12However, without quantitative assessments of where, with which crop mixes and to what extent 13 diversification is possible now and under future climatic conditions, efforts to expand crop diversification 14 under Nationally Determined Contributions (NDCs) and National Action Plans (NAP) are unsystematic. 15In this study, we used extreme gradient boosting, a machine learning approach to model the current 16 climatic suitability for maize, sorghum, cassava and groundnut in Ghana using yield data and 17 agronomically important variables. We then used multi-model future climate projections for the 2050s and 18 two greenhouse gas emissions scenarios (RCP 2.6 and RCP 8.5) to predict changes in the suitability range 30 31 32 2 35 Agricultural intensification, crop diversification, irrigation, improved crop varieties and other agronomic 36 management strategies are needed to stabilize or enhance food production now and under projected 37 climatic conditions. Of these options, crop diversification requires special attention because it can stabilize 38 national and local food production [3-6], improve dietary choices [7-10], increase on-farm ecosystem 39 services [11-14], avert micro-nutrient deficiencies and improve household health outcomes [7, 15] and 40 increase incomes of smallholder farmers [16-18]. 41 42 Studies have shown that crop diversity contributes to resilience through a type of "insurance" where a 43 failure in one crop is mitigated by another crop/cultivar and also through more efficient resource 44 partitioning at various scales [5, 19, 20]. As such, crop diversification has been shown to be beneficial to 45 building climate resilience in many countries such as in China [21], Canada [22], Kenya [23], Ethiopia 46 [24], Malawi [9, 18], Zimbabwe [25] and also Ghana, [26, 27]. Crop diversification is achieved through 47 multiple cropping where farmers grow two or more crops in sequence (relay cropping) or together 48 (intercropping and mixed cropping) within the same year. The ability of farmers to diversify is based on 49 the concurrent (for intercropping) or sequential (for relay cropping) biophysical suitability of the crops in 50 their area. 51 52 Crop suitability is a measure of the climatic and other biophysical characteristics of an area to sustain a 53 crop production cycle to meet current or expected targets [28, 29]. When combined with climate 54 projections, suitability assessments are used to gauge shifts in crop potential under climate change [30, 55 31]. Despite massive developments in agricultural production technology, weather and climate still play a 56 significant role in influencing agricultural production in Africa and elsewhere [32-34]. In particular, under 57 rain-fed conditions the production potential of a crop depends on the climatic conditions of an area. 58 Therefore, each crop will thrive within a specific climatic envelope that can be enhanced by management 59 -yet climate cha...

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ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

Chen

Liu

et al. 2022

Journal of Chemometrics

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Yunnan is known for its rich biodiversity and is known as the Wild Mushroom Kingdom. Boletes are a world-renowned wild edible mushroom, with unique sensory characteristics, nutritional value and medicinal value extraordinary.However, the species and geographical origin of boletes influence their price and quality. In this study, a method was developed to identify species and geographical origin simultaneously. Therefore, Fourier transform near-infrared (FT-NIR) data sets of boletes were collected and converted to two-dimensional correlation spectroscopy (2D-COS). On this basis, the species and geographic origins of boletes were identified using Residual neural network (ResNet) image analysis model. The results showed that FT-NIR could identify boletes species and geographical origins, 7000-4000 cm À1 band was more suitable for species identification, 7000-5300 cm À1 band was more suitable for geographical origins identification. In addition, the environmental factors with high contribution to the distribution of boletes were screened based on the maximum entropy (MaxEnt) model. This allows characterization of the potential geographic distribution of boletes. The results showed that precipitation factors played a vital role in its distribution and might even be responsible for the difference in chemical composition.

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Modeling the climate suitability of tea [Camellia sinensis(L.) O. Kuntze] in Sri Lanka in response to current and future climate change scenarios

Cited by 104 publications

References 87 publications

Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

Impacts of climate change on agro-climatic suitability of major food crops and crop diversification potential in Ghana

ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

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