Variables Minimum Temperature Research Articles

July–August minimum temperature variability encoded in latewood blue intensity in southeastern China

AbstractTree‐ring blue intensity (BI) is well known as a relatively new proxy, which has been utilized for temperature reconstructions over some regions of the worldwide, and has great research potential. In this study, based on tree‐ring data from Pinus massoniana in northwestern Wuyi Mountains in southeastern China, we established a series of tree‐ring width (TRW), earlywood BI (EWBI), latewood BI (LWBI) and delta BI (ΔBI) chronologies. The correlation analysis results indicated that BI chronologies are better than TRW chronology in relation to climate factors; EWBI chronology is a positive correlation with three temperatures (Tmean, Tmax and Tmin), whereas ΔBI and LWBI chronologies almost are negative correlations with temperatures. The LWBI chronology exhibited the highest correlation in July–August Tmin during 1955–2021 (r = −0.61, p < 0.01) and for the reconstruction. The reconstruction model could explain 36.64% of the observed variance. The reconstructed series spanned from 1843 to 2021 and documented three warm periods (1843–1863, 1874–1888 and 1992–2021) and one cold period (1911–1991), and Tmin rapidly warming since 1950s, which is similar to variation in the surrounding temperature reconstruction and is also consistent with the rate of global warming published by the IPCC. The result shows that the summer Tmin anomaly in southeast China is related significantly to the quasi‐biennial oscillation in the western Pacific and Indian Ocean.

Weather variable selection for whitefly population prediction modeling by using backward elimination regression

The present investigation discusses the selection process of the most influencing weather variables for developing a prediction model for whitefly, Bemisia tabaci (Gennadius), based on the backward elimination method. This method aids in the selection of a model with fewer variables by eliminating those that are less pertinent, thereby enhancing precision and mitigating model complexity. In the pursuit of achieving a balance between simplicity and model fit, the conventional 5% level of significance (p-value ≤ 0.05) was utilized along with six weather variables viz., maximum temperature, minimum temperature, evaporation rate, sunshine hours, rainfall, and evening relative humidity. Through an iterative elimination process, it was determined that only three variables-minimum temperature, sunshine hours, and evening relative humidity-significantly contributed to the prediction model. Subsequently, these three variables were retained for predicting whitefly population counts, while the remaining less relevant variables were discarded. The model was found to be around 74 percent accurate in predicting the dynamics of whitefly.

Winter–spring minimum temperature variations inferred from tree-ring δ13C in southeastern China

Winter–spring minimum temperature variations inferred from tree-ring δ13C in southeastern China

An Analysis of Meteorological Data in Sumatra and Nearby using Agglomerative Clustering

Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatra is also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerative clustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11. The clusters are then analyzed to find their unique pattern. The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The results can be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities shows that there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling.

April–September minimum temperature reconstruction based on Sabina tibetica ring-width chronology in the central eastern Tibetan Plateau, China

Minimum temperatures have remarkable impacts on tree growth at high-elevation sites on the Tibetan Plateau, but the shortage of long-term and high-resolution paleoclimate records inhibits understanding of recent minimum temperature anomalies. In this study, a warm season (April–September) reconstruction is presented for the past 467 years (1550–2016) based on Sabina tibetica ring-width chronology on the Lianbaoyeze Mountain of the central eastern Tibetan Plateau. Eight warm periods and eight cold periods were identified. Long-term minimum temperature variations revealed a high degree of coherence with nearby reconstructions. Spatial correlations between our reconstruction and global sea surface temperatures suggest that warm season minimum temperature anomalies in the central eastern Tibetan Plateau were strongly influenced by large-scale ocean atmospheric circulations, such as the El Niño-Southern Oscillation and the Atlantic Multidecadal Oscillation.

Spatio-temporal variability of climatic variables and its impacts on rice yield in Bangladesh

Bangladesh is a significant contributor to the global food basket but is also one of the most vulnerable countries to climate change. Understanding regional climatic variability helps reduce climate risks and ensure food security. This research examined monthly, seasonal, and yearly temperature, rainfall, and relative humidity in Bangladesh using data from 35 meteorological stations from 1970–2020. The study utilized the Mann–Kendall method to evaluate trends and employed Sen’s slope to quantify their magnitude. Additionally, quantile regression was applied to analyze the impact of climatic variables on rice yield. The findings revealed that maximum (Tmax) and minimum (Tmin) temperatures were steadily increasing, with the southwest experiencing a more rapid rise compared to other regions in Bangladesh. Seasonal Tmax and Tmin rose in most parts of Bangladesh, particularly during the monsoon. In most areas, there was a significant (p < 0.05) increase in Tmax from June to September and Tmin in December. The variability of minimum temperature was considerably affected by warming throughout the country. The eastern area had the most significant (p < 0.05) annual rainfall increase rate, while the northern region had the lowest. There was evidence of inter-seasonal rainfall shipment, with post-monsoon rainfall rising compared to monsoon season. The quantile regression showed that the rice yield of Aus (summer) and Aman (wet) seasons were influenced by maximum temperature and relative humidity, whereas the rice yield of the Boro (dry) season was affected by maximum temperature, rainfall, and relative humidity. Across geographical segmentation, the regression analysis indicated a high level of variability in the northern climate. Overall, the intensity of mean temperature increased throughout the country. So, there is a need to develop heat or drought-resistant rice varieties and modernized irrigation facilities to mitigate these climate risks. However, farmers should employ automated weather-based advisory services for sustaining rice productivity and food security.

Estimation of the representative temperatures of a typical electrochemical battery cell when cooling under natural convection conditions

Commercial thermal management systems for electrochemical batteries use information from temperature sensors on the battery surface as the main process characteristic. The main problem of batteries lies in the intense heat energy dissipation in the closed internal volume due to chemical reactions at cyclic charge and discharge. In addition, increasing in the energy intensity of modern batteries, charge and discharge rates, as well as cases of fires and explosions of the battery energy storage systems (ESS) indicate the need to analyze the thermal state of internal electrode battery structures. A multi-layered battery structure and low thermal conductivity of some materials requires ensuring minimum temperature variations between individual battery layers and the battery housing surface, as well as between the battery cells. Currently known instrumental methods for measuring battery cell temperatures do not provide reliable estimates of cell electrolyte, anode and cathode temperatures. The article presents the results of a theoretical analysis of representative temperatures of a typical cell of a fairly common lead-acid battery in typical operating conditions. The results were obtained using the electrothermal model and the solution of the system of non-stationary equations of heat conductivity in the two-dimensional formulation with the finite difference method. Typical operating conditions (ambient temperature of 20 °C, convective heat transfer coefficient 5 of W/(m2K), charge time of 8 h) were considered. The maximum cell temperature was 36 °C, the average temperature was 31 °C, and the case temperature was 26 °C. It has been concluded that it is impossible to predict real thermal condition of the battery without analysis of temperature differences by its cross-sections. Moreover, the temperature of the external surface of the battery housing cannot be used as a key characteristic of battery life analysis.

Rice Blast Forecasting Using Interval Valued Data at Coimbatore, India

Aims: The persistence of rice blast, caused by the fungus Magnaporthe oryzae, continues to pose a significant threat to rice production worldwide, impacting both yields and food security. The primary goal of this study is to apply interval-valued independent weather data to accurately model the dependent variable of percentage disease incidence.
 Study Design: In this paper, we present a detailed study on forecasting rice blast outbreaks through the application of Average method, Center method and Min Max method using interval valued weather data and percentage disease incidence.
 Place and Duration of Study: The blast disease data include percent disease incidence (PDI) collected at the Paddy Breeding Station (PBS), Tamil Nadu Agricultural University, Coimbatore, from 2018 to 2021.And Weather variables includes the following: Maximum Temperature, Minimum Temperature, Relative humidity (morning), Relative humidity (evening) from 2018 to 2021.
 Methodology: The available interval weather parameter data and disease incidence data are utilized to fit a regression model, specifically employing simple linear regression and multiple linear regression, in the R version 4.3.0.
 Results: Upon analyzing various methods, it is evident that the variables of Minimum temperature exhibit a significant relationship with a high level of significance, indicating a significance level at
 P 0.001.
 Conclusion: Minimum temperature shows more contribution in disease incidence followed by relative humidity at evening.

Determination of Small-Region Temperature Variation With RDTS System

A novel measurement scheme is proposed, which is capable of detecting temperature variations in sensing fibers based on Raman scattering with centimeter-level spatial resolution. The problem of inaccuracy in temperature determination when the length of temperature variation region (TVR) is less than the minimum required length of the test fiber is overcome by a procedure including determining the TVR length firstly, calculating the area value from the measured temperature rise curve secondly, and obtaining practical temperature change at small-region TVR from the pre-calibrated relation between the temperature and the area value at different TVR lengths finally. Theoretical analysis and experimental measurement are presented, and the influence of the uncertainty of TVR length on the detectable minimum temperature variation is also discussed. As an example, the experimental result shows that temperature variation of 60 °C can be measured with an accuracy of ± 3 °C when the TVR length is as low as 16 cm. The proposed scheme can greatly increase the spatial resolution of the Raman distributed temperature sensing system and enhance its applications in various fields.

Temporal Changes in Minimum and Maximum Temperatures at Selected Locations of Southern Africa

Agriculture is threatened by ever increasing temperatures and this trend is predicted to continue for the near and distant future. The negative impact of rising temperatures on agri-food systems is also compounded by the erratic and highly variable rainfall in most parts of southern Africa. Minimum and maximum temperatures’ variability and trend analysis were undertaken using daily time series data derived from 23 meteorological stations spread across Malawi, Mozambique, South Africa and Zimbabwe. The modified Mann–Kendall and Theil–Sen slope models were used to assess temperature trends and their magnitudes. Temperature varied with location and minimum temperature was more variable than maximum temperature. Semi-arid regions had higher variation in minimum temperature compared to humid and coastal environments. The results showed an upward trend in minimum (0.01–0.83 °C over a 33–38 year period) and maximum (0.01–0.09 °C over a 38–57 year period) temperatures at 9 and15 locations, respectively. A downward trend in minimum temperature (0.03–0.20 °C over 38–41 years) occurred in South Africa at two locations and Dedza (Malawi), while a non-significant decline in maximum temperature (0.01 °C over 54 years) occurred at one location in coastal dry sub-humid Mozambique. The results confirm the increase in temperature over 33–79 years, and highlight the importance of including temperature when designing climate change adaption and mitigation strategies in southern Africa and similar environments.

Computational analysis and evaluation of thermal comfort environment of a space by reducing the inside air temperature

The tents are used as temporary shelters and evenly they sometimes become a home for a number of years when the returns to permanent shelter is very slow or unaffordable for any person so that the comfort of these shelters are very much essential like home. This paper is primarily concerned with identifying the objectives of reducing the inside air temperature of shelter using different retro-reflective materials. In the present work computational fluid dynamics analysis have been performed for a shelter under the summer climatic conditions to reduce the inside air temperature of shelter using different retro-reflective materials like polyvinyl chloride, wood-fibre, polystyrene, aerogel and phenolic foam. For CFD analysis free stream temperature of air is set 311 K taken with constant heat flux of 900 W/m2 solar radiations is used on roof, second order upwind discretization scheme is used to approximate the solution for momentum and pressure. Result show that that minimum temperature variation of 4.8 degreesK by using retro-reflective material as phenolic-foam and 18.74 % temperature percentage variation compared with other reflective materials.

The Sensitivity of Meteorological Dynamics to the Variability in Catchment Characteristics

Evaluating meteorological dynamics is a challenging task due to the variability in hydro-climatic settings. This study is designed to assess the sensitivity of precipitation and temperature dynamics to catchment variability. The effects of catchment size, land use/cover change, and elevation differences on precipitation and temperature variability were considered to achieve the study objective. The variability in meteorological parameters to the catchment characteristics was determined using the coefficient of variation on the climate data tool (CDT). A land use/cover change and terrain analysis was performed on Google Earth Engine (GEE) and ArcGIS. In addition, a correlation analysis was performed to identify the relative influence of each catchment characteristic on the meteorological dynamics. The results of this study showed that the precipitation dynamics were found to be dominantly influenced by the land use/cover change with a correlation of 0.65, followed by the elevation difference with a correlation of −0.47. The maximum and minimum temperature variations, on the other hand, were found to be most affected by the elevation difference, with Pearson correlation coefficients of −0.53 and −0.57, respectively. However, no significant relationship between catchment size and precipitation variability was observed. In general, it is of great importance to understand the relative and combined effects of catchment characteristics on local meteorological dynamics for sustainable water resource management.

CFD Analysis of Cone Shaped Coil Heat Exchanger by Using Copper Oxide Nanofluid with Ethylene Glycol and Water as Its Base Fluid in Aluminium Tube with Different Mass Flow Rate

Abstract: The recovery of waste heat has been a topic of concern for large-scale industrial companies for several decades. This recovery not only makes an operation more environment friendly, but it also helps to cut costs. In addition to this, it can reduce the amount of resources needed to power a facility. Many industries have implemented different methods of waste heat recovery. In this present paper the efforts are made to understand that how to compare the heat transfer rate in Cone shaped coiled tube heat exchanger using CuO Nano fluid with Water and Ethylene glycol as a base fluid in Cone shaped Heat Exchanger with the help of CFD on aluminium tube. Cone Shaped coil was fabricated by bending 500 mm length of aluminium tube having 10mm tube diameter, 50 mm pitch coil diameter, 25mm pitch and tapered angle 20 . The comparison of pressure drop and temperature variation between different mass flow rate with water and ethylene glycol as its base fluid in aluminium tube found in this analysis. The result indicates that the CuO nanofluid with ethylene glycol as a base fluid at 0.02m/s mass flow rate have maximum pressure drop and also have minimum temperature variation compare with other mass flow rate.

Sliding movement temperature analysis between composite conductor rail and a collector shoe

Electrical traction systems come in different forms, depending on the railway and terrain. The collector shoe is a critical component that impacts the train’s overall safety. The majority of collector shoes consist of several types of gray cast iron that are highly resistant to sliding wear. Wear is produced by friction and electrical current at temperatures over the melting point, which results in decreased sliding speeds. Applying an electric current to a sliding contact alters the process temperature of the wear without changing the sliding speed. The study is essential for industrial applications, especially in subway transit. This study aims to learn more about the wear characteristics of a composite aluminum–stainless steel conductor rail moving across a current collector. The authors employed a finite element analysis and a series of electric current testing. This research will produce some intriguing test findings. Based on a friction pair consisting of a composite conductor rail of aluminum and stainless steel and gray cast iron, a sliding friction contact model was built in the COMSOL finite element software. When the findings of the finite element analysis and the pin-on-plate test technique are compared, the maximum temperature variance is 10%, while the minimum temperature variation is 0.8%. Additionally, the results reveal that acclimation and rupture occur more rapidly when speed rises, and a steady temperature rise may be achieved more quickly.

Clinical comparison of diode laser- and LED-activated tooth bleaching: 9-month follow-up.

This study aims to evaluate the effect of diode laser- or LED-activated tooth bleaching on color change, tooth sensitivity(TS), temperature variation, and gingival irritation (GI) for 9months. Thirty-five subjects having anterior teeth with a color of A2 or darker were enrolled in the study. In a split-mouth design, one side of each arch was activated by a diode laser (Epic X, Biolase), and the other side was activated by an LED (Radii Plus, SDI) in conjunction with a bleaching agent (35%, Whiteness HP). The color change was evaluated by subjective (VitaClassic/Vita3D Master Bleachguide) and objective (spectrophotometer, Vita Easyshade) methods for up to 9months. TS and GI were assessed by visual analogue scale (VAS) and gingival index, respectively, at the same recall periods. During the bleaching, the temperature variation was also recorded using a thermocouple. Statistical analyses were performed (p < 0.05). In the color evaluation, no statistically significant difference was found between diode laser and LED (p > 0.05), except for the 6-month spectrophotometric assessment (ΔE00, ΔEab), where higher values were obtained with the laser (p < 0.05). The temperature difference and maximum temperature with diode were found to be significantly higher than LED (p < 0.05). Higher values were obtained with LED when the mean temperatures were compared (p < 0.05). There was no difference between the two activation methods in terms of TS and GI at any of the recalls (p > 0.05). The bleaching activated either with diode laser or LED performed similar clinical performance in terms of effective color change, tooth sensitivity, and gingival irritation with minimum temperature variations.

Recent floods in Kerala and its impact on rice yield in Kuttanad- A retrospective analysis

Unprecedented severe floods, which affected Kerala during 2018 and 2019 submerged Kuttanad almost completely and the effects of these severe floods to following rice crop were studied. In these severe floods, the following rice crop puncha reflected a marked increase in yield mainly due to increase in soil fertility and less incidence of pests and diseases. In view of total rice production in the kuttanad region, severe floods have even a positive effect on rice production as the ground water table rose to facilitate water to the rabi crop. It was observed that there has been a notable variation in minimum temperature and relative humidity. All these factors contributed significantly to increase in rice yield. Floods are not always disastrous and can sometimes be advantageous as indicated by the post flood rice yields in kuttanad

Winter Potato Water Footprint Response to Climate Change in Egypt

The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.

Environmental Variables and their Relation with the SARS-COV-2 Transmission: A Data Mining Approach

In the present paper are analyzed the correlations between the meteorological variables such as maximum temperature, minimum temperature, relative humidity, precipitation with the number of infections by SARS-CoV-2. The data set for the meteorological variables was obtained from web site of the National Service of Meteorology and Hydrology of Peru (SENAMHI), while the number of infections by SARS-CoV-2 was obtained from the denominated data positive by COVID 19 reported in web site by the Ministry of Health from Peru. After the preprocessing and the fusion of the data sets, it was obtained a data subset with 365 registers and 6 columns. To detect the correlations between the meteorological variables and the number of infections by SARS-CoV-2, the Spearman’s rank correlation coefficient was computed. The results show significant correlations between the variables minimum temperature and number of infections by SARS-CoV-2 for a rho=-0.45, p-valor=0.00&lt;0.05; relative humidity and number of infections by SARS-CoV-2 for a rho=-0.24, p-valor=0.00&lt;0.05; precipitation and number of infections by SARS-CoV-2 for a rho=-0.24, p-valor=0.00&lt;0.05. According to the results obtained, we concluded that minimum temperatures facilitate the SARS-CoV-2 transmissibility.

Increase in irrigated wheat yield in north-west Mexico from 1960 to 2019: Unravelling the negative relationship to minimum temperature

This in-depth analysis of the rise in farm yield (FY) of irrigated wheat in the Yaqui Valley of north-west Mexico, from around 2 to 7 t/ha between 1960 and 2019, begins by highlighting a weather component overlooked in modelling and other thinking about wheat and climate change, namely the dominant role of natural annual variation in minimum temperature (Tmin). Year to year fluctuations around the increasing yield trend were substantial (st.dev. 591 kg/ha) and were highly correlated negatively with annual fluctuations in the average Tmin January to March (Tmin J-M) which ranged from 5.0 to 11.5 oC in this period lasting from mid tillering to early-mid grain fill. Dividing the 60-year period into three consecutive 20-year periods and using multiple linear regression improved the accuracy of estimates of FY as a linear function of time in each period (responding inter alia to better technology including breeding and agronomy), with slopes of 4.5%, 0.6%, and 1.8% p.a. for 1960–79, 1980–99, and 2000–19, respectively. Tmin J-M coefficients were −218, −413, −406 kg/ha/oC, or −5.9, −8.1, −6.6%/oC, respectively, with R2 always close to 0.9. Combining results, the FY response to Tmin J-M was close to −7%/oC, such that over the 60-year period the Tmin J-M increase of 1.0 oC contributed a 7% reduction in FY at constant CO2. The improved estimate of FY slopes with respect to time revealed novel variation across the 60 year period to be dissected in a following paper. Annual fluctuations in Tmax J-M were not correlated with variation in Tmin J-M nor with variation in FY. Crop simulation modelling of potential yield (PY) corroborated the negative effect of increased Tmin J-M on yield and suggested that increased Tmin J-M primarily decreased days to anthesis, biomass and number of grains (/m2). Measurements retrieved from long term well-managed experiments in the Valley under constant agronomy and cultivars confirmed all the above predicted responses to Tmin J-M. Our results align with the few published studies increasing crop night temperature in the critical period of around 30 days up to the end of anthesis when grains/m2, and hence yield, was inversely related to rate of development. In the Yaqui Valley this was strongly associated with Tmin variation. This phenomenon, and the roles of Tmax and solar radiation (Rs) variation, are discussed in detail.

Analysing the impacts of climate variability on the yield of Kharif rice over Punjab, Pakistan

AbstractRainfall and temperature are essential climate parameters that determine crop yield variability at a regional level. The purpose of this study is to analyse the impacts of climate variability on the Kharif rice crop in the Punjab regions, Pakistan for the period of 1979–2018. Skewness and kurtosis, standardization, and Mann–Kendall methods were used to measure the effects of climate variability on Kharif rice yield over the Punjab regions, Pakistan. As compared to other regions, high variability in rice yield is found in the Sargodha region. Results revealed that high maximum and minimum temperatures were noticed in all investigated regions of Punjab. More variation in minimum temperature is detected than the maximum temperature in the Central region. The regions in Southern and Western Punjab were mostly influenced by maximum temperature. Mean temperature shows a significant negative relationship to rice yield during a vegetative phase in the Gujranwala and Rawalpindi regions. The maximum temperature has a negative significant effect on the plant establishment resulting in a reduction in the number of rice plants during the tillering and stem elongates stages in Bahawalpur and Sargodha regions. In the Gujranwala and Multan regions, high maximum temperature increases the floral initiation in the rice plant which reduces the rice grain weight in the heading and flowering stages. The minimum temperature declares a significant negative effect on the Kharif yield of rice in the Gujranwala, Faisalabad, and Multan zones during the re‐plantation stage. Rainfall has a significant positive impact on rice yield during the re‐plantation and tillering stages in the Lahore, Sargodha, and D.G. Khan regions, respectively. In other regions, rainfall indicates an insignificant effect in the re‐plantation stage. Overall, rainfall indicates significant effects in re‐plantation and tillering stages in the Lahore, Sargodha, and D.G. Khan zones. The outcomes of the research indicate how the regional variability in rainfall and temperature affects Kharif rice yield in Punjab, Pakistan.