Time Lag Effect Research Articles

Effect of Altitude and Topography on Vegetation Phenological Changes in the Niubeiliang Nature Reserve of Qinling Mountains, China

Due to the fragility of the habitats in mountain nature reserves, the vegetation is extremely sensitive to climate change, and its phenological changes are more specific. Therefore, it is of great significance to study the effects of topography and climate on the vegetation phenology in mountain nature reserves. Based on the vegetation phenology data retrieved from MODIS EVI2 during 2000 to 2017, combined with temperature data, spatial trend analysis and correlation analysis methods were used to study the effects of topographic and climatic factors on vegetation phenology in the Niubeiliang Nature Reserve of the Qinling Mountains. The results showed that the GSS (growing season start) was advanced with a rate of 4.24 days/10a, and the rates in the northern and southern slopes were almost the same; the GSE (growing season end) was delayed with a rate of 3.29 days/10a, and the rate in the northern slope was faster; and the GSL (growing season length) was prolonged. There were seasonal differences and north–south differences in the effects of topography on the phenophase. The phenophase changed regularly with the increase in altitude. The higher the altitude, the more significant the effect. The steeper the slope, the later the GSS, the earlier the GSE, and the more significant its effect on the GSE. The aspect had little effect on GSS but a more significant effect on GSE, which was the latest on the sunny slope and the earliest on the zero slope. Temperature affected both the GSS advance and the GSE delay, and both had a time-lag effect of approximately 2–3 months. Its effect was more significant in the GSE, in the southern slopes, and in the high-altitude areas.

Baseflow variation and its response to precipitation in Jiuqushui watershed, Southern Jiangxi Province, China

Baseflow is an important part of water resources. Exploring the characteristics of baseflow and its response to precipitation is of great significance to optimize the partition of water resources. Based on the data of daily precipitation and runoff from 1982 to 2019 in Jiuqushui watershed, we analyzed the characteristics of baseflow change, investigated the time lag effect of baseflow on precipitation, and calculated the contribution rate of precipitation to baseflow change by using digital filtering method, cross wavelet transform method, and slope change ratio of accumulative quantity method. The results showed that during the study period, the variation trend of annual baseflow depth and annual baseflow index was not significant, with annual average values of 384.21 mm and 0.44, respectively. The depth of baseflow in spring and summer was greater than that in autumn and winter, while the baseflow index showed an opposite pattern. Annual precipitation affected the dynamic change of annual baseflow depth, with the effects in spring and summer being stronger than that in autumn and winter. The lag time of baseflow in spring, summer, autumn and winter were 3.5-10.3, 1.5-8.5, 2-10, 2-13 and 5-20.5 days, while the average annual lag time was 6.4, 4.9, 5.3, 6.8 and 10.8 days, respectively. The annual baseflow depth changed abruptly in 1992. The contribution rate of precipitation to baseflow change was 68.2%, and that of other factors was 31.8%. The results could provide scientific basis for evaluating the hydrological effects of forests and ensuring water ecological security of rivers in the red soil region of southern China.

How does the leniency of personal bankruptcy law affect entrepreneurship in EU countries?

Several studies examined how some characteristics of personal bankruptcy laws influenced entrepreneurial developments during the last two decades. Our main objective is to analyze the association between self-employment and the leniency of the personal bankruptcy systems in 24 EU countries. Unlike previous studies, we measure differences and changes in the leniency of the regulations with a composite index that incorporates 35 variables. Based on a cross-country database of self-employment ratios and various control variables spanning the years 2000 to 2019, we apply a panel regression model. We find that the implementation of new regulations and reforms in personal bankruptcy legislation in more lenient directions positively correlates with entrepreneurial developments measured by self-employment rates. This is more significant in the group of countries where the eligibility criteria for entrepreneurs are not constrained. We find a one-year negative time-lag effect and conclude that strong anticipation of the law for a more lenient system can immediately change the risk-reward profile, and thereby influence entrepreneurship before implementing the actual reform. An important policy implication is that a major reform in regulation or the first implementation of conservative legislation has the same order of magnitude of effect on promoting entrepreneurship as other public policy reforms of similar purpose.

Optimal growth temperature of Arctic soil bacterial communities increases under experimental warming.

Future climate warming in the Arctic will likely increase the vulnerability of soil carbon stocks to microbial decomposition. However, it remains uncertain to what extent decomposition rates will change in a warmer Arctic, because extended soil warming could induce temperature adaptation of bacterial communities. Here we show that experimental warming induces shifts in the temperature–growth relationships of bacterial communities, which is driven by community turnover and is common across a diverse set of 8 (sub) Arctic soils. The optimal growth temperature (T opt) of the soil bacterial communities increased 0.27 ± 0.039 (SE) and 0.07 ± 0.028°C per °C of warming over a 0–30°C gradient, depending on the sampling moment. We identify a potential role for substrate depletion and time‐lag effects as drivers of temperature adaption in soil bacterial communities, which possibly explain discrepancies between earlier incubation and field studies. The changes in T opt were accompanied by species‐level shifts in bacterial community composition, which were mostly soil specific. Despite the clear physiological responses to warming, there was no evidence for a common set of temperature‐responsive bacterial amplicon sequence variants. This implies that community composition data without accompanying physiological measurements may have limited utility for the identification of (potential) temperature adaption of soil bacterial communities in the Arctic. Since bacterial communities in Arctic soils are likely to adapt to increasing soil temperature under future climate change, this adaptation to higher temperature should be implemented in soil organic carbon modeling for accurate predictions of the dynamics of Arctic soil carbon stocks.

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.

Global trends in total fertility rate and its relation to national wealth, life expectancy and female education

ObjectivesAlong with the development of the times and progress of the society, the total fertility rate (TFR) markedly changed in each country. Therefore, it is critical to describe the trend of TFR and explore its influencing factors. However, previous studies did not consider the time lag and cumulative effect in the associations between the influencing factors and TFR. Thus, our study aimed to analyze the associations from a new dimension.MethodsThe study was employed using national-level data from the World Bank and United Nations Development Programme. Distributed lag non-linear models with 5-year lag were used to examine the independent associations between the relevant factors and TFR.ResultsThe cumulative exposure-TFR curves were inverted U-shaped for log gross domestic product (GDP) per capita and life expectancy at birth, while the cumulative exposure-response curves were approximately linear for female expected years of schooling and human development index (HDI). However, it is worth noting that in the developed regions, TFR increased slightly with the high level of GDP per capita, female expected years of schooling and HDI.ConclusionsNowadays, with the growth of GDP per capita, life expectancy at birth, female expected years of schooling and HDI, TFR are on a drastic downward trend in most regions. Besides, with the development of society, when levels of the factors continued to increase, TFR also showed a slight rebound. Therefore, governments, especially those in developing countries, should take measures to stimulate fertility and deal with a series of problems caused by declining TFR.

Growth peak of vegetation and its response to drought on the Mongolian Plateau

Global change in recent decades has caused severe degradation of grassland ecosystems in arid and semi-arid regions in the world. In the context of global change, the maximum gross primary production (GPPmax) and its response to drought on the Mongolian Plateau (MP) remain unclear. Here, we used long time-series datasets (temperature, precipitation, GPP) and calculated GPPmax, timing of GPPmax (TGM), and Standardized Precipitation Evapotranspiration Index (SPEI) to explore the changes in peak growth of vegetation and its response to drought on the MP from 1982 to 2018. Our results show that GPPmax and TGM presented high spatial heterogeneity. The mean GPPmax was 336 g C·m−2 over the past three decades, with a decreasing trend at a rate of 0.32 g C·m−2·year−1; the mean TGM was on DOY (day of year) 197, with little year-to-year change, TGM received the time-lag effect (mostly 1, 2, 10 months in time scale) of drought was found in 35.3% of the MP, while the cumulative effect of drought on TGM occurred only 16.3% of the MP. These results reveal changes in vegetation growth peaks on the MP and their response to drought over three decades and can contribute to our understanding of the response and feedbacks of MP vegetation to global change.

Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information

Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting.

Attribution analysis of land degradation in Hainan Island based on geographical detector

Rapid and accurate surveys of land are essential for protecting ecosystems. This study aimed to survey the spatial–temporal land degradation patterns and explore its driving mechanism based on a geographical detector in Hainan Island from 2000 to 2020. First, the areas of forest, cultivated land, and construction land were analysed, and their mutation year was determined through the Mann-Kendall abrupt test. Then, change vector analysis was used to create a double-evaluation system by combining the normalized difference vegetation index and net primary productivity. Finally, the geographical detector was used to identify the driving mechanism of different land-use types. The results show that 2013 was the mutation year of the three land-use types. From 2000 to 2013 (Stage 1), the land degradation areas of forest, cultivated land, construction land, grassland, and unutilised land reached 1977.13, 613.25, 165.81, 261.06, and 4.25 km2, respectively. From 2013 to 2020 (Stage 2), the degraded land area accounted for only 0.91 % of the island's total area. From the single factor perspective, the elevation (DEM), least distance to residential area (LDP), and variety of mean annual temperature (VTem) were found to be the dominant factors of land degradation of cultivated land and grassland, forest, construction land in Stage 1. On the other hand, in Stage 2, the main factor of land degradation was the variety of the density of GDP (VGDP) in cultivated land, grassland, and construction land, while it was the variety of mean annual precipitation (VPre) in the forest. Moreover, all pair-factors provided a higher determinant power for degraded land areas than a single factor. From the interaction effect perspective in Stage 1, VPOP ∩ LDR (i.e. the variety of the density of population density and least distance to roads or railways), LDP ∩ LDR, and VTem ∩ LDR were the most significant pair-factors of the interaction effect on land degradation of cultivated land, forest and grassland, and construction land. In Stage 2, the most significant pair-factors of the interaction effect in different land-use types were DEM ∩ VPOP and VPre ∩ POP in degraded cultivated land, VPre ∩ DEM and VPre ∩ LDR in degraded forest land, DEM ∩ VTem in degraded grassland, and VGDP ∩ LDR in degraded construction land. Given the time-lag effects of factors and the attribution analysis of land degradation, we suggest limiting the construction of roads and residential areas, increasing biodiversity, and sheltering forests to protect terrestrial ecosystems in the future.

A perception of the nexus “resistance, recovery, resilience” of vegetations responded to extreme precipitation pulses in arid and semi-arid regions: A case study of the Qilian Mountains Nature Reserve, China

Unprecedented pulses of extreme precipitation due to climate change are causing significant stresses and impacts on regional and even global ecosystems. However, the relationship of vegetation response to this disturbance is unclear, such as phase characteristics, extent, timing, and degree. We summarize the nexus between vegetation resistance, recovery, and resilience under three stages of extreme precipitation pulses: duration, lagging, and post-disturbance, and then construct a pragmatic scheme to quantify and validate this complex relationship based on precipitation and Normalized Difference Vegetation Index (NDVI) data for the Qilian Mountains Nature Reserve (QMNR) from 2000 to 2020. The results show that the four extreme precipitation pulses were spring 2010 (118.98 mm), summer 2007 (312.25 mm), autumn 2010 (109.74 mm), and winter 2018 (6.84 mm). Extreme precipitations had a significant effect on vegetation in at least 98.5 % of the area, and there was also a two-month time lag effect. Specifically, the percentage of negative vegetation resistance in the face of four seasons of extreme precipitation pulses was 18.3 %, 2.0 %, 15.4 %, and 21.7 %, respectively, compared to negative recovery rates of 4.8 %, 11.9 %, 17.8 % and 10.2 % respectively, resilience was even more severe, with 20.1 %, 10.9 %, 16.1 % and 16.3 % of vegetation failing to rebound to normal levels within two months. The negative resistance, negative recovery, and weak resilience of vegetation under short-term extreme precipitation pulses are approximately 4.8, 3.7, and 5.3 times more fierce than long-term vegetation degradation. A total of 62 % of the four seasonal areas of severe negative resistance, severe negative recovery, and weak resilience were located in areas of moderate and significant steepness, which confirms that extreme precipitation pulses cause serious degradation of vegetation. Response of vegetation under extreme precipitation pulses is perceived, quantified, and validated in this study, which is essential for addressing climate change.

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth’s ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones.

Environmental risks and sphingolipid signatures in adult asthma and its phenotypic clusters: a multicentre study

BackgroundAdult asthma is phenotypically heterogeneous with unclear aetiology. We aimed to evaluate the potential contribution of environmental exposure and its ensuing response to asthma and its heterogeneity.MethodsEnvironmental risk was evaluated...

Improved soil–structure interaction model considering time-lag effect

An important aspect of soil– structure interaction analysis is establishing a high-precision conversion relationship between the dynamic impedance in the frequency domain and impulse response function in the time domain. Accordingly, an improved Nakamura model was developed in this study to convert dynamic impedance to an impulse response function. The causes and distribution rules of repeated phase angles were investigated based on the traveling time-lag effect, rendering the improved method more robust. In addition, the impulse response function and interaction force can achieve high fitting accuracy by stripping the singular term from the conventional term. In this study, the time-domain impulse response function and interaction force were derived based on the force–displacement relationship. Further, the accuracy and stability of the improved method were verified using a semi-infinite bar on an elastic foundation. Then, the effects of frequency interval, maximum frequency, and shape of the coefficient matrix on the impulse response function and interaction force were studied by considering the analytical solution of a spherical cavity embedded in full space as an example. Finally, two interaction systems were used to verify the applicability of the improved method in analyzing the soil–structure dynamic interaction.

The influence of investment lag on company value in a non-financing constrained region

ABSTRACT We focus on the impact of the investment time lag on a company’s value. We set up the original model in the start-up phase and the mature phase, which reflects the effect of investment time lag on the company’s value without financing constraints. Furthermore, based on the model, we obtain the explicit expressions of the company’s value in the start-up phase and the mature phase. In the empirical analysis, considering financing costs, investment opportunities and cash flow, we use PVAR to investigate how investment time lag affects the company’s value.

Model-scale tests to examine water pressures acting on potentially buoyant underground structures in clay strata

Throughout the service life, underground structures are subjected to transient and sustained hydrostatic pressures. The reservoir impoundment results in an increase in water level, as well as hydraulic gradient, which can endanger the uplift performance of infrastructure. In uplift design, a reduction factor is often suggested for buoyant force acting on underground structures in clays due to the time lag effect. However, the mechanism of pore pressure generation in clays is not fully understood. This investigation presents a novel U-shaped test chamber to assess the pore pressure generation with time in the horizontal branch subjected to an increase in reservoir level in the left vertical branch. A mathematical model is developed to explain the time lag effect of pore pressure generation. The test program also involves the evaluation of uplift pressure acting on foundation model in the right vertical branch due to adjacent reservoir impoundment. It is found that the time lag effect of pore pressure generation in clays can be observed irrespective of hydraulic gradient, but a higher hydraulic gradient can lead to a faster response in pore pressure sensors. A reduction factor of 0.84–0.87 should be considered to reduce the conservatism of uplift design.

Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales.

Temporal effects of climatic factors on vegetation phenology on the Loess Plateau, China

AbstractStudying the start (SOS) and end (EOS) of the vegetation growing season can improve vegetation prediction under climate change. Climatic factors have temporal effects on vegetation growth, including the no time effect (no), time-lag effect (lag), time-accumulation effect (acc), and both time-lag and -accumulation effects (lagacc). The linear regression equations between SOS/EOS and climatic factors were constructed for the Loess Plateau (LP). Subsequently, we analyzed the effects of single and multiple climatic factors on vegetation phenology under four temporal effect scenarios and investigated the response of vegetation phenology to the time-lag and time-accumulation effects of climatic factors, under the lagacc scenario. Among the four temporal effects, lagacc explained the effects of climate on vegetation phenology to the greatest degree, and it is the optimal temporal effect for simulating the relationship between vegetation phenology and climate on the LP. Moreover, the explanation degrees of multiple climatic factors were higher than those of single climatic factors across different temporal effects and vegetation types. Simultaneously considering multiple climatic factors improved predictability of their impact on vegetation phenology. Under lagacc, responses of SOS to temperature (TMP) and precipitation (PRE) exhibited 1.44 ± 0.43/3.49 ± 0.85 and 1.38 ± 0.30/3.38 ± 0.71 months lag/accumulation, respectively, and responses of EOS to TMP and PRE exhibited 1.35 ± 0.17/2.37 ± 0.34 and 1.59 ± 0.19/4.16 ± 0.50 months lag/accumulation, respectively, across the entire LP. The results show that both lagacc and multiple climatic factors require consideration when establishing relationships between climatic factors and vegetation phenology.

Evaluation of the Groundwater and Irrigation Quality in the Zhuoshui River Alluvial Fan between Wet and Dry Seasons

The Zhuoshui River alluvial fan is one of the most important groundwater and agricultural areas in Taiwan. Abundant groundwater resources are the main source of domestic water supply and irrigation water. However, groundwater recharge and groundwater quality have been greatly affected under extreme climate and hydrological conditions. Hence, the quality of groundwater has been a topic of concern to the public. In this study, groundwater level and groundwater quality data of the Zhuoshui River alluvial fan from 2008 to 2020 were used to divide the wet and dry season groups according to the sampling dates. An independent samples t-test was used to evaluate the differences in the mean groundwater level and the mean concentration between the wet and dry seasons. The test results show that there was no statistically significant difference in the mean groundwater level between the wet and dry seasons. This may result from the time lag effects of groundwater recharge. Except for groundwater temperature, bicarbonate, and total organic carbon (TOC), there were no significant differences among the mean concentrations of other groundwater quality parameters in Aquifer 1 and Aquifer 2 between the wet and dry seasons. In terms of the alluvial fan location, although the soil texture, land utilization, cropping systems, and hydrogeology of the proximal, mid-, and distal fan may affect groundwater quality variations, it seems that only Aquifer 1 is affected by surface water infiltration, resulting in significant differences in mean groundwater temperature, mean concentrations of major ions, and nitrate between the wet and dry seasons, whereas Aquifer 2 is less affected. At the same time, owing to the geological conditions and intensive cultivation in the Zhuoshui River alluvial fan, nitrate and arsenic could represent a high risk to the public’s health if groundwater is used as a source for domestic water supply or irrigation water in the distal fan area, whether in the wet season or dry season. Meanwhile, due to global climate change and uneven droughts and floods, the hydrological conditions of the so-called “wet season” and “dry season” are obviously different from those in the past. Compared with precipitation, groundwater level may be a better indicator for understanding variations in groundwater quality.

Performance evaluation of two-stage production systems with time-lag effects: An application in the horticulture industry

In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach.

Effect of time-lag on two mutually competing plant populations under allelochemicals

This paper examines a bionomic model of competing organisms in the presence of allelochemicals with differing time-dependent densities. A competition mathematical model is proposed in this paper. It is shown that when one plant produces an allelochemical, it gives stimulatory effect to itself and inhibitory effect to the other plant. Equilibrium points are calculated and stability analysis is performed about non-zero equilibrium point. Hopf-bifuraction is observed with the help of delay parameter introducing in term of allelochemicals. Model is verified with already existing data for the effect of allelochemical on plant growth by Zahid et.al (2016). The numerical results are substantiated using MATLAB.