Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

Influence of meteorological parameters on performance of rainfed cropping systems

Rainfall, its distribution along with distribution of temperature. relative humidity (RH), bright sunshine hours (SSH) suggest the possible growing season and crop performance in a given area. Field experiments on five economically feasible, sustainable, rainfed crop sequences viz. fallow (i.e. no crop) – rice-lentil, jute-rice-lentil, direct seeded rice-rice-lentil, mungbean-rice-lentil and sesame-rice-lentil were conducted at Kalyani Farm, W.B., during 1989-91. Mean monthly meteorological parameters viz. rainfall, potential evapotranspiration (PET), SSH, temperature (max. and min.) and RH (at 0700 and 1400 hrs LMT) were obtained from selected agrometeorological observatories (viz. Chinsurah, Haringhata and Barrackpore), adjacent to the Kalyani Farm located in Gangetic alluvial region. The relative yield performance of crops and sequences as influenced by meteorological parameters were studied. In Gangetic alluvial region early rain, moderate to high temperature with high RH during April/May resulted in good pre-kharif crop establishment. Heavy, well distributed precipitation during monsoon months along with moderate temperature and very high RH showed scope for rainfed transplanted kharif rice as the pivot of crop rotation. Kharif rice yields were high especially after jute or mungbean. Profile stored residual moisture along with low rainfall, low temperature and high RH during rabi season resulted in good performance of lentil. Among the five sequences studied, performance of' jute-rice-lentil and mungbean-rice-lentil were the best with sustainable production and net return.

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

Spatio-Temporal Modeling of Ozone Distribution in Tehran, Iran Based on Neural Network and Geographical Information System

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.

Assessment of COVID-19 Lockdown Impact on the Air Quality in Eastern Spain: PM and BTX in Urban, Suburban and Rural Sites Exposed to Different Emissions

In early 2020, the COVID-19 pandemic spread globally, and severe measures to control it were implemented. This study investigates the impact of the lockdown on the air quality of three provinces in the Valencia region, eastern Spain, in the years 2015–2020, focusing on particulate matter (PM). A thorough statistical analysis using different approaches is conducted. Hourly patterns are also assessed. In addition, the role of meteorological parameters on PM is explored. The results indicate an overall PM10 reduction of 16.5% when comparing the lockdown in 2020 and the 2015–2019 period, while PM2.5 increased by 3.1%. As expected, urban zones experienced higher reductions than suburban zones, which experienced a PM concentration increase. The impact of the drastic drops of benzene, toluene and xylene (77.4%, 58.0% and 61.8%, respectively) on the PM values observed in urban sites is discussed. Our study provides insights on the effect of activity changes over a wide region covering a variety of air quality stations, urban, suburban and rural, and different emission types. The results of this work are a valuable reference and suggest the need for considering different factors when establishing scientific air pollution control strategies.

Effect of meteorological parameters on Karnal bunt incidence in wheat under different agroclimatic zones of Punjab

To quantify the effect of meteorological parameters on incidence of Karnal bunt in wheat crop, an investigation was done using 9 to 12 season’s data of Bathinda and Ludhiana stations of Punjab. Maximum temperature during March in range of 25-31oC, minimum temperature of February (8.5-11.0oC), morning and evening relative humidity of March in range of 85-95 and 40-60 per cent respectively, rainfall more than 25 mm with sunshine hours 5.5-9.0 hrs/day during mid February to mid March favour Karnal bunt in wheat crop. Maximum temperature of March showed significant negative correlation with incidence of Karnal bunt whereas minimum temperature of February showed significant positive correlation with disease incidence at both locations. Morning and evening relative humidity showed significant positive correlation with disease incidence. Rain amount and rainy days during mid February to mid March significantly influenced disease incidence. Sunshine hours had negative correlation with disease incidence. Backward multiple linear regression (BMLR) analysis indicated maximum temperature, rainfall and sunshine hours play significant role in Karnal bunt incidence at Ludhiana. However, at Bathinda, maximum temperature, evening time relative humidity, rain amount and rainy days played significant role.