Sub-seasonal variability of Luzon Strait Transport in a high resolution global model

The Luzon Strait is the main impact pathway of the Kuroshio on the circulation in South China Sea （SCS）. Based on the analysis of the 1997–2007 altimeter data and 2005–2006 output data from a high resolution global HYCOM model, the total Luzon Strait Transport （LST） has remarkable subseasonal oscillations with a typical period of 90 to 120 days, and an average value of 1.9 Sv into SCS. Further spectrum analysis shows that the temporal variability of the LST at different depth is remarkable different. In the upper layer （0–300 m）, westward inflow has significant seasonal and subseasonal variability. In the bottom layer （below 1 200 m）, eastward outflow exhibits remarkable seasonal variability, while subseasonal variability is also clear. In the intermediate layer, the westward inflow is slightly bigger than the eastward outflow, and both of them have obvious seasonal and subseasonal variability. Because the seasonal variation of westward inflow and eastward outflow is opposite, the total transport of intermediate layer exhibits significant 50–150 days variation, without obvious seasonal signals. The westward Rossby waves with a period of 90 to 120 days in the Western Pacific have very clear correlationship with the Luzon Strait Transport, this indicates that the interaction between these westward Rossby waves and Kuroshio might be the possible mechanism of the subseasonal variation of the LST.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

The influence of resistance exercise training prescription variables on skeletal muscle mass,strength,and physical function in healthy adults:An umbrella review

Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.

Influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel

The influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel is investigated.The DarcyBrinkman model is used to characterize the fluid flow dynamics in porous materials.The analytical solutions are obtained for the unidirectional and completely developed flow.Based on a normal mode analysis,the generalized eigenvalue problem under a perturbed state is solved.The eigenvalue problem is then solved by the spectral method.Finally,the critical Rayleigh number with the corresponding wavenumber is evaluated at the assigned values of the other flow-governing parameters.The results show that increasing the Darcy number,the Lewis number,the Dufour parameter,or the Soret parameter increases the stability of the system,whereas increasing the inclination angle of the channel destabilizes the flow.Besides,the flow is the most unstable when the channel is vertically oriented.

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

A Stroke-Limitation AMD Control System with Variable Gain and Limited Area for High-Rise Buildings

Collisions between a moving mass and an anti-collision device increase structural responses and threaten structural safety.An active mass damper(AMD)with stroke limitations is often used to avoid collisions.However,a strokelimited AMD control system with a fixed limited area shortens the available AMD stroke and leads to significant control power.To solve this problem,the design approach with variable gain and limited area(VGLA)is proposed in this study.First,the boundary of variable-limited areas is calculated based on the real-time status of the moving mass.The variable gain(VG)expression at the variable limited area is deduced by considering the saturation of AMD stroke.Then,numerical simulations of a stroke-limited AMD control system with VGLA are conducted on a high-rise building structure.These numerical simulations show that the proposed approach has superior strokelimitation performance compared with a stroke-limited AMD control system with a fixed limited area.Finally,the proposed approach is validated through experiments on a four-story steel frame.

Mid-infrared Period-Luminosity Relations of Gaia DR3 Long Period Variables

Long period variable(LPV)stars are very promising distance indicators in the infrared bands.We selected asymptotic giant branch(AGB)stars in the Large and Small Magellanic Cloud(LMC and SMC)from the Gaia Data Release 3 LPV catalog,and classified them into oxygen-rich(O-rich)and carbon-rich(C-rich)AGB stars.Using the Wide-field Infrared Survey Explorer database,we determined the W1-and W2-band period-luminosity relations(PLRs)for each pulsation-mode sequence of AGB stars.The dispersion of the PLRs of O-rich AGB stars in sequences C'and C is relatively small,around 0.14 mag.The PLRs of LMC and SMC are consistent in each sequence.In the W2 band,the PLR of large-amplitude C-rich AGB stars is steeper than that of small-amplitude C-rich AGB stars,due to their more circumstellar dust.By two methods,we find that some PLR sequences of O-rich AGB stars in the LMC are dependent on metallicity.The coefficients of the metallicity effect areβ=-0.533±0.213 mag dex~1andβ=-0.767±0.158 mag dex~1for sequence C in W1 and W2 bands,respectively.The significance of the metallicity effect in W1 band for the four sequences is 2.2-3.5σ.Both of these imply that distance measurements using O-rich Mira may need to take the metallicity effect into account.

A novel variable-order fractional chaotic map and its dynamics

In recent years,fractional-order chaotic maps have been paid more attention in publications because of the memory effect.This paper presents a novel variable-order fractional sine map(VFSM)based on the discrete fractional calculus.Specially,the order is defined as an iterative function that incorporates the current state of the system.By analyzing phase diagrams,time sequences,bifurcations,Lyapunov exponents and fuzzy entropy complexity,the dynamics of the proposed map are investigated comparing with the constant-order fractional sine map.The results reveal that the variable order has a good effect on improving the chaotic performance,and it enlarges the range of available parameter values as well as reduces non-chaotic windows.Multiple coexisting attractors also enrich the dynamics of VFSM and prove its sensitivity to initial values.Moreover,the sequence generated by the proposed map passes the statistical test for pseudorandom number and shows strong robustness to parameter estimation,which proves the potential applications in the field of information security.

Extent and Structure of Phenotypic Variability in Solenostemon rotundifolius Germplasm from Ghana and Burkina Faso

Solenostemon rotundifolius is a species belonging to the Lamiaceae family. It is currently one of the minor plants of high socio-economic interest. One of the limitations to promoting this species in Burkina Faso is the lack of varieties that meet consumers’ demands. Implementing a breeding program is an important step toward achieving this goal. Such a program is based on the variability of agronomical traits of interest within evaluated germplasm. This study aimed to assess the level of two germplasms variability of S. rotundifolius from Ghana and Burkina Faso for traits related to vegetative development, cycle, and yield. Agromorphological characterization of 174 accessions, including 116 from Ghana and 58 from Burkina Faso was carried out in Randomised Complete Block Design with three replications. The characterization was made on the basis of fifteen (15) quantitative traits related to the canopy and leaf size, the cycle, and the yield. Analysis of variance revealed significant differences between accessions within each germplasm for all the evaluated traits. The analysis of the structuration of this agromorphological variability allowed organizing the accessions into different groups. These results could lead to the identification of accessions within each germplasm for breeding purposes or future research on genotype-environment interactions.

An approximate analytical model for unconventional reservoir considering variable matrix blocks and simultaneous matrix depletion

In regard to unconventional oil reservoirs,the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network.It has been proven to cause inaccurate production evaluations because of the absence of matrix-macrofracture communication.In addition,most of the existing models are solved analytically based on Laplace transform and numerical inversion.Hence,an approximate analytical solution is derived directly in real-time space considering variable matrix blocks and simultaneous matrix depletion.To simplify the derivation,the simultaneous matrix depletion is divided into two parts:one part feeding the macrofractures and the other part feeding the microfractures.Then,a series of partial differential equations(PDEs)describing the transient flow and boundary conditions are constructed and solved analytically by integration.Finally,a relationship between oil rate and production time in real-time space is obtained.The new model is verified against classical analytical models.When the microfracture system and matrix-macrofracture communication is neglected,the result of the new model agrees with those obtained with the dual-porosity and triple-porosity model,respectively.Certainly,the new model also has an excellent agreement with the numerical model.The model is then applied to two actual tight oil wells completed in western Canada sedimentary basin.After identifying the flow regime,the solution suitably matches the field production data,and the model parameters are determined.Through these output parameters,we can accurately forecast the production and even estimate the petrophysical properties.

Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.

Intraseasonal variability of the Kuroshio observed via mooring at 18°N

Insufficient observations near the origin of the Kuroshio have led to incomplete understanding of the intraseasonal variability(ISV)of the Kuroshio.Direct measurements of the Kuroshio velocity were performed with an array of three profiler moorings(122.7°E,123°E,and 123.3°E)along 18°N from January 2018 to February 2020.The ISV of the Kuroshio at 18°N was investigated based on a combination of mooring observations and global high-resolution HYbrid Coordinate Ocean Model reanalysis data.The estimated time-averaged transport in the upper 350 m across the observation transect was 6.5±2.6 Sv(1.0 Sv=10^(6)m^(3)/s).Two significant ISV peaks at 50-60 and~100 d were recognized in the power spectra of the meridional velocity and transport.Further analysis indicated that the ISV at 50-60 d was caused by westward-propagating eddies at average propagation speed of~13 cm/s and wavelength of~635 km.Another ISV peak at~100 d was mainly caused by northward-propagating eddies generated in the North Equatorial Current region.Further investigation indicated that the ISV of the Kuroshio at 18°N is dominated by meridional transport,rather than by the zonal migration of the Kuroshio main axis.Our findings provide a better understanding of the ISV of the Kuroshio east of Luzon Island.

Improvement and security analysis of multi-ring discrete modulation continuous variable quantum secret sharing scheme

In order to avoid the complexity of Gaussian modulation and the problem that the traditional point-to-point communication DM-CVQKD protocol cannot meet the demand for multi-user key sharing at the same time, we propose a multi-ring discrete modulation continuous variable quantum key sharing scheme(MR-DM-CVQSS). In this paper, we primarily compare single-ring and multi-ring M-symbol amplitude and phase-shift keying modulations. We analyze their asymptotic key rates against collective attacks and consider the security key rates under finite-size effects. Leveraging the characteristics of discrete modulation, we improve the quantum secret sharing scheme. Non-dealer participants only require simple phase shifters to complete quantum secret sharing. We also provide the general design of the MR-DM-CVQSS protocol.We conduct a comprehensive analysis of the improved protocol's performance, confirming that the enhancement through multi-ring M-PSK allows for longer-distance quantum key distribution. Additionally, it reduces the deployment complexity of the system, thereby increasing the practical value.

Genetic variability and trait association analysis in linseed(Linum usitatissimum L.)for yield and related traits

Diversity information mining about a crop for different attributes is an essential step for effective breeding programs.The present investigation evaluates the quantum of genetic variability and determines the relationship among the important agro-economic traits based on two years of phenotypic data of 210 accessions of linseed.The traits,capsule weight per plant,capsule per plant,husk weight per plant,and seed weight per plant exhibited comparatively higher genetic coefficient of variation(GCV)and phenotypic coefficient of variation(PCV).In contrast,oil content and seed per capsule exhibited a lower value.The high magnitude of broad sense heritability was observed for all traits except seeds per capsule and husk weight per plant.The trait,capsules per plant,plant height,and days to 50%flowering showed high genetic advance coupled with high heritability.Hierarchical cluster analysis grouped 210 accessions into six distinct clusters.Out of 210,144(68.57%)accessions were grouped into three clusters(I,II,and III),in which cluster-III was the largest,containing 64 accessions followed by cluster II and cluster-I.The highest inter-cluster distance was observed between clusters-I and V(127.85),while the lowest was between clusters-II and IV(27.09).The positive correlation of capsule weight per plant with the seed weight per plant and a negative correlation with the days to 50%flowering indicates that high yielding linseed varieties with early flowering/maturity could be developed through direct and indirect selection.Further,seed yield and oil content could be enhanced together as indicated by ghe positive association among these two important traits.In this study,high yielding accessions with moderate to high oil content such as GP36,GP31,GP14,GP54,GP26,GP24,GP34,GP21,GP37 and GP27 and early flowering(less than 70 days)accessions such as GP2,GP26,GP27,CG33,CG44,CG42,CG132,and CG31 identified as potential genetic materials that could be exploited for developing early maturing varieties with high yield.In addition,information’s on various genetic parameters will help breeders to devise suitable breeding methodology for linseed genetic improvement for targeted traits.

Fuzzy Control Optimization of Loading Paths for Hydroforming of Variable Diameter Tubes

The design of the loading path is one of the important research contents of the tube hydroforming process.Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization.In this paper,the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object.Fuzzy control was used to optimize the loading path,and the fuzzy rule base was established based on FEM.The minimum wall thickness and wall thickness reduction rate were determined as input membership functions,and the axial feeds variable value of the next step was used as output membership functions.The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path.The round corner lamination rate of the tube is 91.2%under the fuzzy control optimized loading path,which was increased by 47.1%and 22.6%compared with linear loading Path 1 and Path 2,respectively.Based on the optimized loading path in the experiment,the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%.The experimental results were consistent with the simulation results,which verified the accuracy of fuzzy control.The research results provide a reference for improving the forming quality of thin-walled tubes and plates.

Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

Fermions: Spin, Hidden Variables, Violation of Bell’s Inequality and Quantum Entanglement

Using real fields instead of complex ones, it was recently claimed, that all fermions are made of pairs of coupled fields (strings) with an internal tension related to mutual attraction forces, related to Planck’s constant. Quantum mechanics is described with real fields and real operators. Schrodinger and Dirac equations then are solved. The solution to Dirac equation gives four, real, 2-vectors solutions ψ1=(U1D1)ψ2=(U2D2)ψ3=(U3D3)ψ4=(U4D4)where (ψ1,ψ4) are coupled via linear combinations to yield spin-up and spin-down fermions. Likewise, (ψ2,ψ3) are coupled via linear combinations to represent spin-up and spin-down anti-fermions. For an incoming entangled pair of fermions, the combined solution is Ψin=c1ψ1+c4ψ4where c1and c4are some hidden variables. By applying a magnetic field in +Z and +x the theoretical results of a triple Stern-Gerlach experiment are predicted correctly. Then, by repeating Bell’s and Mermin Gedanken experiment with three magnetic filters σθ, at three different inclination angles θ, the violation of Bell’s inequality is proven. It is shown that all fermions are in a mixed state of spins and the ratio between spin-up to spin-down depends on the hidden variables.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.