Average Monthly Temperature Research Articles

Evaluation of environmental impact of red tide around Pearl River Estuary, Guangdong, China

This paper presents a case study of red tide hazards around the Pearl River Estuary (PRE). Red tide hazards, meteorological data, and seawater monitoring data were collected from 1996 to 2020 at different locations around the PRE to investigate the internal and external factors influencing the occurrence of red tides. The enhancement of the assessment of estuarine trophic status (ASSETS) method enables us to evaluate the effects of meteorological factors and seawater eutrophication status on the red tide risk level. Using ASSETS, we established a framework for red tide risk assessment of the Pearl River Estuary. We analysed the external and internal factors causing the red tide based on meteorological data and seawater monitoring data in the PRE. The results show that the temperature was higher than the annual monthly average temperature of 1.265 °C, and east and north winds at velocities of 3–4 m/s could result in the formation of red tides. However, precipitation inhibits the formation of the red tide in PRE.

Assessment of Thrips Diversity Associated with Two Olive Varieties (Chemlal & Sigoise), in Northeast Algeria

In this study, the diversity of thrips (Insecta: Thysanoptera) on two varieties of olive trees (Chemlal and Sigoise) in northeast Algeria (Province of Batna), was evaluated for 3 years (2019–2021). In addition, the fluctuations in the numbers of phytophagous thrips were estimated according to the varieties phenological stages. A total of 19 species are identified and the olive thrips (Liothrips oleae) have just been reported for the first time in Algeria. Only 5 females of this species were collected in May 2021 on the Sigoise variety at the fruit-setting stage. Haplothrips tritici(17.25%), Frankliniella occidentalis (16.29%) and Thrips tabaci (16.29%) are the most present. It is noticed that the thrips were present on the olive tree only in spring (April to May), when the average monthly temperatures are between 10–26 °C, but linear regression analyses were not confirmed that temperature explain the variation in thrips numbers, which may be due to other climatic factors such as the rainfall, while olive varieties and phenological stages are affecting the population of thrips, their number was higher on the Sigoise variety, especially at flowering stage in the case of H. tritici and F. occidentalis while T. tabaci was most noticeable at the fruit growth stage. The number of this species was relatively low, just until the inflorescence stage, where thrips start to appear in Sigoise before Chemlal.

Внутригодовая изменчивость температуры приземного воздуха и её моделирование для города Ставрополя

Introduction. It is known that the time course of temperature during the year is seasonal. However, the rates of temperature growth in transition periods are different. This complicates the prediction of such an important parameter, in particular for agriculture, as the air temperature of the surface layer of the atmosphere. Accordingly, the article analyzes the intra-annual variability of air temperature and compares it with a sinusoidal model of the annual temperature course. Materials and research methods. The time series of the air temperature of the surface layer of the atmosphere for the city of Stavropol are considered and the task is to compare the actual and theoretical values of annual amplitudes. In the elementary (sinusoidal) model, this theoretical value turns out to be directly proportional to the annual variance. The time series of temperature differences (inter-monthly changes), the so-called discrete derivatives, is also considered, and the comparison of the usual and discrete derivative of the sine function as an elementary model of the annual temperature course is carried out. A new value is introduced – the standard deviation of variability, which also characterizes the variance, yet not for a number of temperatures, but their differences. Research results and their discussion. It is noted that the theoretical (i.e., valid for the sinusoidal model) and the actual value of the amplitude of the annual temperature course are quite well matched. However, due to non-periodic deviations, the difference between these values can reach 2 °C, which is characterized by asymmetry on the intra-annual variability chart. In addition, the average annual temperature course is considered and its analysis is carried out using discrete derivatives. It is shown that the ratio of the annual temperature amplitude to the amplitude of the discrete derivative of this value is equal to the cyclic frequency of fluctuations of the annual temperature course. Conclusions. The results obtained confirm the possibility of using a sinusoidal model of the annual temperature course (although with some limitations related to the asymmetry and shift of intra-annual peak points). The analysis of the annual temperature course using discrete derivatives and its comparison with the sinusoidal model revealed the features of the average annual intra-annual variability of surface air temperature for the city of Stavropol for the period 1944 – 2022. Sometimes there is a change in the rate of heating or cooling; in rare cases, there is an inversion when considering the time series of average monthly temperatures).

The effect of the global climate change on the vitality of viticulture in Russia

We described in this article the features of the impact of global climate change on wine-growing enterprises of the Russian Federation. Statistical data on the areas under vineyards of all agricultural organizations in the country were analyzed and displayed by regions. The daily amounts of solar radiation arriving at the horizontal surface in the study area are shown and similar climatic conditions are found with some French regions. Information was collected on the dynamics of average monthly and annual temperatures in the southern cities of the RF for 1980 and 2021, which proved that in all regional centers, the main regions of industrial viticulture, over the past 40 years the average monthly and annual temperature has only increased. We provided tables and figures displaying unique information on agroclimatic indicators of the regions of interest. Based on the data presented, conclusions are drawn and measures are proposed for the rational cultivation of vineyards, taking into account the distinctive features of different climatic zones in a changing climate.

Correlation between air temperature and SO2 pollutant in the city of Skopje

The aim of this scientific research study is to analyze the temperature as a climatic factor in the city of Skopje in correlation with the concentration of SO2 air pollutant. In order to establish the correlation between the two variables, to that effect, the first analysis was made in order to show the average monthly and annual air temperatures, which was followed by an analysis of the average monthly values of SO2 air pollutant, by collecting and processing the data captured at the air quality monitoring stations located in the city of Skopje for a ten-year period (2012-2021). The analysis of temperature as a climate factor in correlation with SO2 pollutant was made in the of Skopje, due to the fact that the country's largest population resides in this city but also that air pollutants of all types of pollution sources are present there. The Pearson method was used to establish the correlation between the air temperature and the presence of SO2 pollutant, which confirmed the influence of air temperature on the concentrations of SO2 pollutant in the air. More specifically, the results show a negative correlation, which had its peak in the winter season.

Assessments of long-term precipitation and temperature variations during snow cover periods using high-elevation AMeDAS data in central Japan

Long-term hourly data records of the Automated Meteorological Data Acquisition System (AMeDAS) with snow depth measurements at three high elevation sites were analyzed in central Japan to assess the decadal scale winter climate variabilities with the effects of observation biases. Year-to-year variations of persistent snow cover periods (PSCPs) did not show a significant shortening trend with the delays of starting the PSCP associated with El Niño in December and forward to the ending of the PSCP with nationwide warming in March. Significant warming trends in the surface air temperature averaged in PSCPs were not identified, and length of the PSCP strongly contributed to the year-to-year variations of monthly averaged winter air temperatures. Long-term variabilities of precipitation records were primarily affected by changing the gauge type and data resolutions. After a simple correction of gauge undercatch by air temperature and wind speed, the amount of precipitation increased by around 20 % at the northern station but only by 3 % in the south due to frequent rain-on-snow events. In the two northern stations, snowfall amounts on the order of around 10 % were estimated to be missing in the precipitation records, where a long-term decreasing trend in occurrences of freezing precipitation was also found. The statistics calculated by AMeDAS data were verified using the data of the Vaisala Present Weather Detector in the Sugadaira Highland.

Effects of the temperature-humidity index on conception rates in Holstein heifers and cows receiving in vitro-produced Japanese Black cattle embryos.

We investigated the effect of the temperature-humidity index (THI) on the conception rate (CR) in Holstein heifers and cows receiving in vitro-produced (IVP) Japanese Black cattle fresh embryos. IVP embryos were transferred to Holstein heifers (n = 1,407) and cows (n = 3,189) on 245 commercial farms. The monthly average ambient temperature (AT) and THI ranged from 4.7 to 29°C and 41 to 81, respectively; both were the highest in August. The monthly CR ranged from 16.3% to 46.7% in cows and 23.8% to 74.1% in heifers. The CR of heifers was unaffected by THI, AT, or the month of embryo transfer. However, these parameters affected the CR of cows. The CR at THI values of 61-65 and 71-75 was greater than that at THI > 75, whereas other THI values had no effect. The CR at temperatures > 25°C was lower (P = 0.008) than that at temperatures of 15-20°C and 20-25°C. Moreover, the CR was lowest (P = 0.003) in July. THI and parity (P = 0.057 and P = 0.001, respectively) and AT and parity (P = 0.019 and P = 0.001, respectively) showed significant effects on CR; however, there was no interaction between these two factors. In conclusion, AT > 25°C and THI > 75 adversely affect the CR outcome in cows but not in heifers.

Research on the Norilsk Nickel under climate change-based onthe Fama-French model

Global warming is becoming more serious, and climate change around the world poses enormous challenges and opportunities for economic activity in all fields. The risk of climate change has become a common risk faced by many companies around the world. Then this paper selected three different companies in the mining industry. They are Anglo American plc, BHP Group Limited and Norilsk Nickel gave a basic introduction to the company and counted their financial situation. Then this article collects the five-year monthly maximum temperature, minimum temperature and average tem-perature in the Arctic to directly show the direct and significant impact of global warming on the earth. Among them, a company in Russia was selected for Fama-French model analysis, and the linear regression results were ob-tained. It can be seen that climate change has different degrees of impact on MKT, SMB and HML. Among them, MKT (market risk premium) has the greatest impact on it, indicating that the company's own business is relatively stable, and the price is not significantly affected by its own fluctuations, but is greatly affected by the market and industry.

Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models

In this paper, the average monthly temperature of the Karachi region, Pakistan, has been modelled. The time period of the procured dataset is from January 1989 to December 2018. The Autoregressive Integrated Moving Average (ARIMA) modelling technique in conjunction with the Box–Jenkins approach has been applied to forecast the average monthly temperature of the study area. A total of 83.33% of the trained dataset is used for construction of the model, and the remaining 16.67% of the dataset is used for the validation of the model. The best-fitted model is identified as ARIMA (2, 1, 4), generated on the basis of minimum values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) procedures. The accuracy parameters considered are Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Both parameters show that the model is 98.152% and 98.413% accurate, respectively. In addition, the Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier (ARCH-LM) test has been conducted to check the presence of heteroscedasticity in the residuals of the identified model. This test shows no heteroscedasticity present in the residual series. By means of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, the most appropriate orders of the ARIMA model are determined and evaluated. The model has been employed to investigate the time series variables’ precise impact on the scale of the regional warming scenario. Accordingly, the created model can help in determining future strategies related to weather conditions in the Karachi region. From the forecast result, it is found that the average temperature seems to show an increasing trend. Such an increasing trend can potentially upset the weather conditions and economic activities of the coastal area of Pakistan.

Analysis of Temperature Data Using the Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar, Pakistan

The trend analysis approach is used to estimate changing climate and its impact on the environment, agriculture and water resources. Innovative polygonal trend analyses are qualitative methods applied to detect changes in the environment. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for temperature trend detection in Soan River Basin (SRB), Potohar region, Pakistan. The average monthly temperature data (1995–2020) for 11 stations were used to create polygon graphics. Trend length and slope were calculated separately for arithmetic mean and standard deviation. The innovative methods produced useful scientific information, with the identification of monthly shifts and trend behaviors of temperature data at different stations. Some stations showed an increasing trend and others showed decreasing behavior. This increasing and decreasing variability is the result of climate change. The winter season temperature is increasing, and the months of December to February are getting warmer. Summer is expanding and pushing autumn towards winter, swallowing the early period of the cold season. The monthly polygonal trends with risk graphs depicted a clear picture of climate change in the Potohar region of Pakistan. The phenomena of observed average temperature changes, indicated by both qualitative methods, are interesting and have the potential to aid water managers’ understanding of the cropping system of the Potohar region.

Spatiotemporal evolution analysis and prediction of drought in Henan Province based on standardized precipitation evapotranspiration index

Abstract Drought is one of the major natural disasters in the world, and it is important to understand and grasp the spatiotemporal evolution characteristics of drought for drought monitoring and prevention. In this study, the standardized precipitation evapotranspiration index (SPEI) was calculated based on monthly precipitation and average temperature data from 15 meteorological stations in Henan Province, China during the period 1987–2020. The spatiotemporal evolution characteristics of droughts in Henan Province were analyzed using run theory and Moran's I, and drought prediction was carried out using grey disaster theory. The results show that: (1) During the period 1987–2020, the overall drought in Henan Province shows a slow intensification trend, and SPEI-12 has a certain adaptability in the drought characterization of Henan Province; (2) The frequency of droughts in the northern and eastern regions of Henan Province is high, the southern region is prone to continuous drought, the drought ephemeris and intensity show a strong positive correlation, and the spatial spillover effect of autumn drought is the most obvious; (3) The grey disaster prediction model has excellent accuracy and predicts that the next possible drought in Henan Province will be in the east of Henan Province in the spring of 2023.

THE INFLUENCE OF NITROGEN, PHOSPHORUS, AND POTASSIUM FERTILIZERS ON SPRING BARLEY PRODUCTION AND HARVEST INCREASE

The study was conducted in the experiments with long-term fertilizers in non-irrigation conditions in 2016 and 2021. The experiments were based on the two-factor subdivision method. Factor A doses of Nitrogen and Phosphorus N0P0, N60P40, N60P80 and N120P80 respectively factor B Potassium doses K0, K40, K80 and K120. Fertilizers were applied in the fall under the plow and in the spring. The average monthly temperature was higher in April, June and July for 2016 and June and July in 2021, compared to the 65-year average. The average values ​​of monthly precipitation were higher in 2016 during the vegetation period, and in 2021 they were higher in May and July, reduced in April and June compared to the average for 65 years. Following the obtained results, the production indicators were analyzed under the action of applying nitrogen, phosphorus and potassium fertilizers, respectively the obtained crop increase.

ESTIMATION OF THE DEGREE OF CLIMATE CONTINENTALITY IN SARATOV REGION AT THE BEGINNING OF THE XXI CENTURY

A quantitative assessment of climate change is given in the territory of the Saratov region at the beginning of the 21st century. As a result of the research, an increase in annual air temperature was noted everywhere - its average value for the region was 7,1°С, the largest increase in average monthly temperatures was observed in winter. An analysis of the annual amount of precipitation showed their increase in the west of the region and in its central part, a decrease in precipitation was observed in the north of the region and in the Trans-Volga region. The conditions of the degree of climate continentality are estimated in different natural and climatic zones: forest-steppe, arid steppe and semi-desert for the period from 2005 to 2020. The calculated indices of climate continentality using the methods of G. Zenker, I. Schrepfer, L.A. Gorchinsky, S.P. Khromov, N.N. Ivanov showed a synchronous trend towards a decrease in the degree of continentality, which is associated with regional warming, which is most pronounced in the cold period.

Epidemiological Study of Pathogenic Leptospira in Raccoons (Procyon lotor) in a Suburb of Tokyo, Japan.

Leptospirosis is a zoonosis that affects humans and animals worldwide. Raccoons (Procyon lotor), adopted in urban environments, may act as potential reservoirs of Leptospira. We investigated the prevalence of pathogenic Leptospira in the kidney and urine samples of raccoons living in Tokyo, as well as anti-leptospiral antibodies in their serum, and aimed to examine the factors that expose raccoons to Leptospira. Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) were used to detect leptospiral DNA and anti-leptospiral antibodies, respectively. Thirty-six of 156 raccoons (23.1%) were positive by PCR, and 16 of 165 raccoons (9.7%) were positive by ELISA. The prevalence and seroprevalence rates differed depending on the raccoon dispersal period. We used univariable logistic regression to estimate the environmental factors associated with pathogenic Leptospira and anti-leptospiral antibodies in raccoons. Significant differences were observed in the PCR results for the seasons (spring−summer) (p = 0.01), average monthly temperature (p < 0.01), and average monthly rainfall (p < 0.01). No significant difference was seen in the ELISA results, but raccoons in larger urban areas tended to have higher seroprevalence rates (p = 0.06). We identified a pattern of leptospiral spread in raccoon dispersal and environmental factors that expose raccoons to Leptospira.

Retrospective study on bovine clinical mastitis and associated milk loss during the month of its peak occurrence at the National Dairy Farm in the Emirate of Abu Dhabi, United Arab Emirates.

Commercial dairy establishments are relatively young in the United Arab Emirates (UAE), and as a result, there is lack of epidemiological data on mastitis in dairy farms. A retrospective data of seven years (2015-2021) were used to estimate the cumulative average monthly incidence rate of bovine clinical mastitis and evaluate associated milk loss at the National Dairy Farm. Data were extracted from the records of lactating dairy cows (n = 1300-1450) and analyzed using repeated measure and one-way ANOVA, non-parametric Spearman correlation, paired and unpaired t tests. The highest average cumulative monthly incidence rate was 49 cases per 1000 cows-year that was recorded in 2019 while the lowest was 19 cases per 1000 cows-year in 2021. The cumulative average monthly incidence rate of clinical mastitis significantly (p < 0.001) varied among the seven years. The cumulative average monthly incidence rate was associated with average monthly humidity (p < 0.01) and average monthly rainfall (p < 0.05); however, it was not associated with the average monthly temperature (p > 0.05). The average daily milk yield of cows with clinical mastitis (Mean ± SEM; 18.6 ± 0.54 kg) was significantly (p < 0.001) lower than the average daily milk yield of clinical mastitis free cows (40.5 ± 0.29 kg). The largest average monthly milk loss due to clinical mastitis was 5% of the average total monthly milk production in 2019 while the lowest was 2% of the average total monthly milk production in 2021. The result of the study indicated the direct influence of weather conditions such as increased rainfall and humidity, which caused an upsurge in the incidence rate of clinical mastitis, leading to an increased loss in milk and hence the economy of the dairy farm. Proactive preventive measures along with good dairy farm practices that help mitigate the impacts of harsh weather conditions are recommended.

Hydrometeorological Data Analysis and Drought Indices of Rawandoz Area, Iraqi-Kurdistan Region

Water balancing techniques are strategies for resolving key theoretic and applied hydrologic challenges. The primary goals of the study are to examine the climatic characteristics and conditions that will be utilized in the water balancing approach used in the Rawandoz located in northeastern Iraq. The meteorological data from Soran meteorological station’s 2000 to 2020 were applied to analyze the climate patterns of the research region. The annual rainfall average is 750.50 mm, while the annual evaporation is 1497.80 mm. The average monthly humidity, temperature, and sunshine remained 73.8 percent, 18.50 °C, and 8 hours/day, respectively. The Kharrufa technique has been calculated the potential evapotranspiration (PET), water deficit and surplus and the Soil Conservation Service method (SCS) has been used to calculate surface runoff (Rs). The annual potential evapotranspiration, water surplus, and deficit are calculated to be 1900.27 mm, 430.10 mm, and 1579.90 mm, respectively, with an annual runoff of 376 mm. The standardized precipitation index (SPI) was produced to assess climatic drought over a variety of duration; the mean value is 0.562, indicating that the area is moist. The results of many climate classification reveal that the study area in humid to moist climate classification.

The effect of photovoltaic industry on the two variables of temperature and wind and possible impact on dust storm in Tehran province

Introduction: Recently, the industry of launching solar power plants in arid/ semi-arid regions has developed. A considerable part of Iran is covered by dry/semi-arid areas, which are exposed to severe wind erosion. Considering the occurrence of dust in these areas, this study investigated the possible impact of the photovoltaic industry on the prevention of dust storms.&#x0D; Materials and methods: The effect of solar power plant construction on atmospheric variables and its possible effect on the occurrence of dust were investigated in Tehran province. The criterion is to compare the monthly average wind and temperature respectively at the height of 10 and 2 m inside and outside the power plant (obtained from the TerraClimate database for 2020 and 2021) on the western side and at the closest point to the power plant.&#x0D; Results: The monthly average of wind and temperature respectively at the height of 10 and 2 m in 2020-2021 decreased inside of the power plant with respect to the outside of that. Significant differences were observed on temperature and wind inside and outside of the power plant during the peak heat in the summer, which increase with increasing temperature. The maximum of differences of monthly average of wind and temperature variables inside and outside of the power plant for Pars Ray Energy Bahar, Tehran Wastewater and Sohail Power Plants respectively are (0.5 °C, 0.55 m/s), (0.6 °C, 0.3 m/s), and (0.7 °C, 0.3 m/s).&#x0D; Conclusion: The photovoltaic industry can change the effective solar radiation, and possibly decrease the occurrence of dust storms by weakening wind power.

Contemporary Climate Change and Its Hydrological Consequence in the Volga Federal District, European Russia

An analysis of spatiotemporal variability of air temperature and precipitation in the Volga Federal District (European Russia) between 1966 and 2021 was carried out. Based on data from 20 meteorological stations, relatively evenly located on the territory under consideration, the spatial distribution of average monthly and average annual air temperatures and monthly and annual precipitation was assessed; some indicators of the temporal variability of these variables in the period under consideration were calculated and analyzed. It was revealed that throughout the Volga Federal District, there was a tendency of climate warming in all months, and a slight increase in annual precipitation, except for the southeast of the district, where the precipitation trend was negative. It is noted that in the period 1955–1998, the number of negative air temperature anomalies was approximately equal to the number of positive ones; however, in the later period 1999–2021, the number of positive anomalies significantly exceeded the number of negative ones. Based on reanalysis data, climatic maps of vaporization and runoff in the Volga Federal District during 1966–2021 were created. The dependence of air temperature fluctuations on the nature of atmospheric circulation was revealed using the NAO, AO, and SCAND indices. On the example of the central part of the district (Republic of Tatarstan), some increase in summer aridity of the climate was revealed by using Budyko’s dryness index, Selyaninov’s hydrothermal coefficient, and Sapozhnikov’s humidification coefficient. The indicators of runoff and evaporation were also calculated using the methods of Schreiber and Ivanov. Against the background of the positive trend in vaporization rates, favorable conditions for a decrease in runoff were noted.

Estimation of dissolved organic carbon from inland waters at a large scale using satellite data and machine learning methods

Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the global carbon cycle and has significant public health effects. Machine learning (ML) together with remote sensing has emerged as a powerful and promising combination to quantify water quality parameters from space. However, inland water sample data for DOC is limited. Hence, little is known about the potential to quantify DOC content in inland waters, especially over large-scale areas. This study presents the first attempt to estimate DOC in inland waters over a large-scale area using satellite data and ML methods with the newly published open-source dataset AquaSat. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were trained using more than 16 thousand samples across the continental United States matched with satellite data from Landsat 5, 7 and 8 missions. Satellite data from the Landsat missions were further extended with environmental data from the ERA5-Land product and used as input to train the ML algorithms. Our results show that including environmental data as inputs considerably improved the prediction of DOC for all ML algorithms, with GPR showing the most promising performance results with moderate estimation errors (RMSE: 4.08 mg/L). Permutation feature importance analysis showed that the wavelength range in the visible Green band (from Landsat) and the monthly average air temperature (from ERA5-Land) were the most important variables for the ML approaches. The results demonstrate the predictive strength of GPR and its useful feature to derive per pixel standard deviations for detailed analysis. Our results further highlight the important role of considering environmental processes to explain DOC variations over large scales. The application and performance of the GPR in mapping spatiotemporal variations of DOC in an entire water body were discussed by taking Lake Okeechobee (the 8th largest freshwater lake in the U.S.) as an illustrative example. While performance evaluation showed that DOC concentrations can be retrieved with adequate accuracy, algorithm development was challenged by the heterogenous nature of large-scale open source in situ data, issues related to atmospheric correction, and the low spatial and temporal resolution of the environmental predictors. This research demonstrates how open source, large-scale datasets like AquaSat in combination with ML and satellite remote sensing can make research toward large-scale estimation of inland water DOC more realistic while highlighting its remaining limitations and challenges.

Decomposition of Different Components of Muli Bamboo in Sub-tropical Forests of Mizoram, Northeast India: Effect of Climate

In the present study decomposition of leaf, branch, culm and sheath litter of the bamboo Melocanna baccifera in relation to monthly average precipitation (MAP) and monthly average temperature (MAT) was carried out. It was observed that the remaining mass at 180 days was 11% in leaf, 62% in sheath, 72% in branch and 78% in culm litter. Significant positive correlation of k (month -1 ) was recorded with MAT in leaf litter; with MAP in branch litter; with MAP and MAT in culm litter, however an inverse correlation with MAP in sheath litter was recorded. The negative response of sheath litter to MAP can be attributed to resistance during decomposition in the initial stage which corresponds to more rainy months.