Rainfall Levels Research Articles

Does the Deep Placement of Fertilizers Increase Potato Yields, Fertilization Efficiency and Reduce N2O Emissions from the Soil?

Despite the notable decline in potato cultivation areas across Poland and Europe, potatoes remain a crucial crop with diverse applications. Achieving the ambitious emission targets set by the EU for agricultural production may be easier with the practice of deep placement of slow-release fertilizers, which may increase yields and reduce greenhouse gas emissions. To examine the effect of deep placement of slow-release fertilizers on potato tuber yields, plant nutrient uptake, nutrient use efficiency, and soil N2O-N emissions, a two-year field experiment was conducted on loamy sand soil classified as Alblic Podzol (Ochric) soil, under temperate climate conditions prevailing in central Poland. The experiment involved a three-field rotation (potatoes, wheat, and peas), with potatoes being cultivated after peas in both years of the study. The experiment compared the effects of applying slow-release fertilizer at soil depths of 10 and 20 cm (DP10 and DP20) to fertilization with single-nutrient fertilizers applied to the soil surface (TD). The experiment utilized increasing doses of nitrogen and phosphorus, denoted as D0 (control), D1, D2, and D3, along with a standard dose of potassium across all tested fertilizer application methods. The results of this study confirmed that deep placement of slow-release fertilizers had limited effects on potato tuber yields. Deep placement of slow-release fertilizer increased plant nitrogen uptake by 2.8–13.5% compared to topdressing. Consequently, there was an improvement in nitrogen use efficiency from 29.8–75.0% on sites with fertilizer topdressing to 38.7–89.8% on sites with slow-release fertilizer deep placement. Phosphorus uptake by plants on sites with slow-release fertilizer deep placement was approximately 9.3–13.0% higher than on sites with fertilizer topdressing. This led to an enhancement in phosphorus use efficiency from about 15.1–19.5% on fertilizer topdressing sites to 19.4–25.4% on slow-release fertilizer deep placement sites. The impact of fertilizer deep placement was found to be less pronounced compared to the effects observed with increased nitrogen and phosphorus doses. The most important factors affecting tuber yield and nutrient use in potatoes were rainfall levels during the growing season. Deep fertilization did contribute to reduce soil N2O emissions by about 14%. However, further research involving different fertilization methods is needed to comprehensively assess the effectiveness of this practice in reducing greenhouse gas emissions.

Barley powdery mildew control in Western Australia and beyond

AbstractAustralia is one of the largest barley exporters in the world, with Western Australia accounting for some 40% of national production. The crop is predominantly grown in the south and south‐west of the state in winter and spring, where temperate conditions and higher rainfall levels are more suited to barley than northern and eastern regions. Between 2007 and 2013, prolonged outbreaks of barley powdery mildew (BPM) occurred. This was brought about by a combination of the extensive use of susceptible cultivars and an over‐reliance on a small number of single mode‐of‐action demethylation inhibitor fungicides, which select for mutations in the C14α‐demethylase (Cyp51A) gene. This review highlights the steps taken to reduce losses to BPM, breeding efforts to introduce resistance into cultivars and the success of pre‐breeding research to find new and durable resistance genes. We also draw comparisons with powdery mildew in Australian wheat, where similar factors are leading to substantial outbreaks.

Crop Recommendation System Using Machine Learning

Abstract: Agricultural Crop Recommendation System relies on a multitude of input factors. This proposes a hybrid approach for suggesting suitable crops in specific areas, taking into account various attributes like soil type, rainfall levels, temperature value, Nitrogen value, Phosphorus value, Potassium value, humidity value and PH value of the soil. Utilizing technology to recommend crops for agriculture empowers farmers to enhance crop yields by suggesting the most suitable crops based on geographic and climatic conditions with respect to soil. The fundamental concept of this paper revolves around implementing a crop selection method to address numerous agricultural and farmer-related challenges. Using a group of different machine learning models to make better predictions is like having a team of experts give you advice. Each expert has their own knowledge and experience, and by listening to all of them, you can make a better decision. Accuracy of different ML algorithms in crop recommendation system are such as that Support Vector Machine algorithm gives 99.54% accurate results, Neural networks algorithm gives 91% accurate results, Decision Tree algorithm gives us 87% of accuracy. While K- Nearest algorithm, Logistic Regression and Naive Bayes algorithm gives us 75%-85% accurate results. We’re gonna use machine learning algorithms to make the project more accurate and efficient enough to provide the real time results. In this crop recommendation system using machine learning Support Vector Classifier and Decision tree algorithms are being used as an expectation of most accurate outcomes of the model. Keywords: Support Vector Machine, Feature selection, ,,Crop suitability indices, Training dataset, Testing dataset, Model evaluation.

Application of Deep Belief Network in Weather Modeling: PM2.5 Concentration in Thailand

In Thailand, the number of particles matter with diameter of less than 2.5 microns or PM2.5 concentration exceed the standard in many areas, especially in Chiang Mai. This affects the image of the country in terms of economy, health, and environment. The objective of this research is to study the structure of model for PM2.5 concentration by using a Deep Belief Network (DBN) with the daily data set of PM2.5 concentration from the air quality monitoring station at Yupparaj Wittayalai School, Chiang Mai. The data was analyzed through an unsupervised path using the Minimizing Contrastive Divergence (MCD) algorithm, followed by a supervised path using Back-Propagation Neural Network (BPNN) algorithm to estimate the parameters of DBN. The result shows that the optimal DBN structure has 5 input nodes and 20 hidden neurons in the first hidden layer. This model has an 88.4 percent accuracy in forecasting PM2.5 concentration. In addition, this model can be applied for other weather forecasting such as rainfall or water level in a basin.

A Novel Approach to Enhance Landslide Displacement Prediction with Finer Monitoring Data: A Case Study of the Baijiabao Landslide

Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technology, which makes a more refined analysis possible. However, until now, few efforts have been made to predict landslide displacements using daily data, which is likely to substantially improve accuracy and is crucial for landslide early warning. A novel feature enhancement approach for extracting critical characteristics from daily rainfall and reservoir water level data for use in landslide displacement prediction is proposed in this study. Six models, including gated recurrent units (GRUs), long short-term memory (LSTM), and support vector regression (SVR) with an unenhanced dataset and GRU-E, LSTM-E, and SVR-E with an enhanced dataset, were employed for displacement predictions at four GPS monitoring stations on the Baijiabao landslide, a typical step-like reservoir landslide. The results show that the accuracy values of all the enhanced models were significantly improved, and the GRU-E model achieved the most significant improvement, with the RMSE decreasing by 24.39% and R2 increasing by 0.2693, followed by the LSTM-E and SVR-E models. Further, the GRU-E model consistently outperformed the other models, achieving the highest R2 of 0.6265 and the lowest RMSE of 16.5208 mm, significantly superior than the others. This study indicates the feasibility of improving the accuracy of landslide monthly displacement predictions with finer monitoring data and provides valuable insights for future research.

Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria

AbstractNigeria’s agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok’s (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.

Agricultural soil pollution due to climate change

The agricultural sector is very important for human life because of its role for economic purposes and meeting food needs. The use of land for agricultural activities cannot be avoided because humans need food every day. The agricultural sector has a negative impact on soil conditions due to excessive use of fertilizers and water. Objectives: The purpose of this study is to determine the impact of climate change (climate change) on soil pollution. Method and results: The method used for this paper is a qualitative approach with a descriptive explanation. Conclusion: Climate change has an effect on temperature, rainfall, and sea level. The earth's temperature will increase, and there will be extreme rain and extreme weather, while the impact on sea water will increase sea level. This condition can cause flooding, flooding will erode the soil surface resulting in a decrease in soil quality. Pollution due to waste will cause soil pollution and have an impact on rising temperatures.

Effect of Canopy Cover Level of Cacao and Shade Trees on Splash Erosion On Cacao Land

In smallholder cacao plantations, the protective crops used varied, so they have different effects on splash erosion. The management of land cover with a canopy of cacao and shade trees on cacao fields, aims to control of splash erosion. This study was conducted by directly measuring the magnitude of splash erosion under several levels of canopy cover. The magnitude of splash erosion was determined by measuring the depth of the eroded soil using the bottle cap method. The measurements were carried out every rain event (46 rain events with rainfall varied from 0.28 to 97.04 mm). The canopy cover level was determined by analyzing the images taken using a digital camera. The images were processed by Matlab software with closure approach. The data were analyzed by regression analysis to determine the relationship between canopy cover level and the depth of splash erosion that occurred. The results showed that the level of canopy cover influence the depth of splash erosion. In addition, this study indicates that the level of splash erosion not only influenced by the level of closure and rainfall, but also strongly influenced by the size of the leaf cover. Keywords: Cacao plantation, Canopy cover, Regression analysis, Splash erosion.

Investigating the changing pattern of groundwater levels and rainfall in the peninsular region of Bhagalpur and Khagaria, Bihar

Abstract Groundwater is the primary source of water for domestic and irrigation purposes in the peninsular region of the Bhagalpur and Khagaria districts of Bihar. Though this region is bounded by perennial rivers on three sides, the groundwater level is decreasing gradually because of over exploitation and misuse. In this study, spatial and temporal analyses of rainfall and groundwater levels for pre- and post-monsoon seasons from 1996 to 2020 have been carried out using GIS tools, graphical plots, and statistical methods of pattern recognition. The spatial analysis of rainfall shows less rainfall in the western region of the study area, whereas it shows heavy rainfall in the region near the Vikramshila Bridge. The temporal analysis of rainfall shows decreasing trend in the whole study area and the rate of decrease was 25.05 mm/year during 1996–2020. The results of the pre-monsoon groundwater levels analysis show decreasing trend in the majority of wells, and the rate of decrease varies from 0.005 to 0.102 m/year. By contrast, the post-monsoon groundwater levels showed an increasing trend varying from 0.005 to 0.083 m/year at wells located near the River Ganga, except at Maheshkhunt. Thus, there is a need for proper groundwater management for a sustainable future.

Phreatic Line Calculation of Reservoir Landslide under Complex Hydraulic Conditions—A Case Study

A seepage field, influenced by rainfall and reservoir water level fluctuation, is closely associated with the stability of the reservoir landslide. Understanding the phreatic line variation inside the landslide is of significant importance for the analysis and evaluation of slope stability. Currently, most of the boundaries of phreatic line analytical models and the hydrological conditions are simplified, resulting in discrepancies between the outcomes derived from these models and the actual situation. Given this, the newly proposed analytical model is refined by addressing the following two issues. Firstly, the consideration of variable-speed reservoir level fluctuations is incorporated, and secondly, the reservoir bank within the water-level fluctuation zone is treated as non-vertical. Under the combined effect of reservoir water level fluctuation and rainfall, the Boussinesq Differential Equation of unsteady seepage is established and applied to the Majiagou landslide in Three Gorges reservoir area. The results of the analytical solution are basically consistent with the measured groundwater level results, which has demonstrated the accuracy of the proposed model. Consequently, the proposed model can quickly and accurately calculate the groundwater level of landslides, which provides an effective means for the prediction and early warning of reservoir landslides.

Numerical modelling and quantification of coastal urban compound flooding

Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China.

Decadal Trends in Surface Elevation and Tree Growth in Coastal Wetlands of Moreton Bay, Queensland, Australia

Coastal wetlands surrounding urban environments provide many important ecosystem services including protection from coastal erosion, soil carbon sequestration and habitat for marine and terrestrial fauna. Their persistence with sea-level rise depends upon their capacity to increase their soil surface elevation at a rate comparable to the rate of sea-level rise. Both sediment and organic matter from plant growth contribute to gains in soil surface elevation, but the importance of these components varies among sites and with variation in climate over long time scales, for which monitoring is seldom available. Here, we analysed variation in surface elevation, surface accretion and mangrove tree growth over 15 years in Moreton Bay, Queensland, Australia, a period that spans variation in the El Niño/La Niña (ENSO) cycle, which strongly influences rainfall and sea level in the region. Piecewise structural equation models were used to assess the effects of biotic (tree growth, plant cover and bioturbation by invertebrates) and environmental factors on annual surface elevation increments throughout this period. Our model for mangroves identified that surface accretion and tree growth were both positively influenced by rainfall, but surface elevation was not, and thus, higher levels of compaction of the soil profile in high rainfall/high sea level years were inferred. In contrast, our saltmarsh model found that rainfall positively influenced surface accretion and elevation gains. Declines in surface elevation in the mangroves were influenced by the species composition of the mangrove, with higher levels of elevation loss occurring in mangrove forests dominated by Avicennia marina compared to those with a higher proportion of Rhizophora stylosa. Decadal-scale variation in ENSO affected mangrove tree growth, but surface elevation trends were more strongly influenced by variation in environmental conditions than by tree growth, although effects of biotic factors (mangrove species composition and bioturbation) on surface elevation trends were observed. Further research into tipping points with extreme ENSO events (either La Niña with high rainfall and high sea level or El Niño with low rainfall and low sea levels) will help clarify the future of mangrove and saltmarsh distribution within Moreton Bay.

Global Uncertainty, Climate Change and the Unemployment-Economic Growth Relationship in Nigeria

This study investigates global uncertainty, climate change and the unemployment-economic growth relationship in Nigeria. The study utilised the autoregressive distributed lag (ARDL) estimation technique using quarterly data, 1990–2020. Findings indicate that global uncertainty and unemployment impact negatively economic growth both in the short run and long run. Also, climatic change variables employed in this study such as temperature and level of rainfall have a negative impact on economic growth both in the long-run and short-run. The moderating effects of global uncertainty and climatic variables on the unemployment-economic growth relationship were positive, though insignificant. The policy implication underlying the finding is that tackling economic uncertainty and climate change is necessary for solving unemployment problem and the attainment of sustainable growth in Nigeria. JEL Codes: C22, D80, Q25, E24, O40

Effects of Microplastics on the Leaching of Nutrients and Cadmium from Soil

The environmental effects of microplastics, which are considered a type of emerging contaminants, have attracted increasing concern due to their small size, large specific surface area, strong adsorption capacity, and low degradability. Microplastics can change soil properties and affect the migration ability of nutrients and pollutants in soil, but their effects on the leaching of soil nutrients and heavy metals have not been sufficiently studied. A soil column leaching experiment was conducted to explore the effects of polystyrene (PS) and polylactic acid (PLA) microplastics at different mass fractions (0%, 0.2%, and 2%) on the leaching of nutrients and cadmium under simulated rainfall scenarios. The results showed that increasing rainfall intensity enhanced the leaching of nutrients and cadmium from soil. During downpour conditions, 2% PS significantly increased the leaching of total nitrogen and the content of available phosphorus in soil and reduced the leaching of inorganic phosphorus and the content of ammonium nitrogen in the soil, whereas it increased the content of available potassium during heavy rain. By comparison, 2% PLA reduced the leaching of nitrate nitrogen during heavy rain and intense rainfall and decreased the content of ammonium nitrogen in soil during intense rainfall and downpour conditions and the content of total nitrogen in soil during downpours. In addition, 0.2% PLA significantly increased cadmium leaching during downpours. To conclude, the effects of microplastics on the leaching of nutrients and cadmium were dependent on the type and concentration of microplastics, as well as the rainfall level. Our findings showed that the microplastics derived from both nondegradable PS and biodegradable PLA could affect the leaching of nutrients and heavy metals from soil.

The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022.

Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures. The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence. The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group. Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.

Application of the Fuzzy Mamdani Method for Predicting Rainfall in the City of Yogyakarta

Weather and climate play an important role in everyday life, influencing various aspects of society and the environment. The city of Yogyakarta as a cultural center cannot be separated from the impacts of climate change, including rainfall patterns which can have significant effectiveness on daily activities, agriculture and infrastructure. This research uses a fuzzy logic methodology approach with the Mamdani method to predict rainfall. Fuzzy logic has proven to be effective in cold situations and is able to overcome the challenges of missing or incomplete data. The data used in this research was obtained from Bappeda DIY. The implementation for predicting rainfall levels uses Matlab by utilizing the provided Fuzzy Logic Matlab Toolbox. Based on research conducted using a fuzzy logic test to calculate all research accuracy data, an average error value in RMSE was obtained of 39.88%. These results indicate that there is significant error in these predictions, indicating that the fuzzy logic control model is not completely accurate. Therefore, it is necessary to review the data used in this prediction to ensure its accuracy.

Estimated effects of climate variability on agricultural production in Colombia

The objectives of this document are to assess the impact of climate variability on agricultural production in Colombiaand identify the effectiveness of irrigation districts as an adaptation measure. For the analysis, a panel ofmultidimensional data with random effects was used to estimate the effect on crops reported in 1,101 Colombianmunicipalities, between 2007 and 2017. Two variables were taken as climatic shocks: the sum of the absolute differencesin the monthly precipitation compared to its historical mean and the average of the differences in the monthlytemperature with respect to its historical average. The interaction between irrigation districts and rainfall levels was usedas an adaptability variable. The results indicate that the three-dimensional data panel model presented in this papershowed great applicability despite its low use and the small number of documents that exist on climate effects onagricultural production. The main conclusions are that temperature increases lead to decreases in agricultural productionand that irrigation districts as a measure of adaptation to climate variability do not appear to have a significant impact tocounteract it. We found that practices such as the use of shades could influence the reduction of temperatures locally.Similarly, it is necessary to analyses the impact of measures that mitigate the effects associated with changes intemperature rather than those related to the precipitation regime.

The ensemble distance on model-based clustering for regions clustering based on rainfall: The case of rainfall in West Java Indonesia

Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data. According to the simulation results, the ensemble distance method could boost clustering accuracy by more than 11%. Based on the pattern of rainfall levels, we applied our methods to find clusters of locations in the Province of West Java (Indonesia). The findings indicate that the rainfall pattern in the same cluster is similar. The cluster model is effective and feasible for representing individual models in a cluster.

Drought Prediction with Feature Enhanced LSTM Model using Metaheuristic Optimization Algorithms

The impact of drought builds on all three fronts of economy, environment, and society is devastating. Predicting its arrival and duration is highly important to arrange any sort of mitigation plans. The association of detailed relationship between multiple variables makes drought prediction a highly complex task. Especially influence of global warming, polar sea extent variations and their influence on overall ocean temperature have altered the seasonal rainfall behaviors all over the world. In the midst of it, predictions centered on the history of rainfall levels become inaccurate. The proposed system is an optimized deep learning prediction model integrating indigenous knowledge (IK) is proposed to predict the drought. IK expressed in human language is translated using fuzzy function and fed to an improved Long Short Term Memory (LSTM) model. The LSTM model hyperparameters are optimized using a hybrid of Particle Swarm Optimization (PSO) with firefly to produce the meta-heuristics algorithm which will provide the best performance in presence of integration of IK features into modern meteorological features which solves the problem of local minima in LSTM hyperparameter optimization. The performance of the proposed results were tested compared with the meteorological information gathered by the Karnataka Natural Disaster Monitoring Centre (KNDMC) for the district named Chitradurga of the Karnataka state in India. The proposed system which is Indigenous Knowledge merged along the cross model attention network can produce at least 1.4% higher Nash–Sutcliffe model efficiency coefficient (NSE) and 30% lower Mean Absolute Error (MAE) in the prediction of Standard Precipitation Index (SPI) compared to Convolution Neural Networks (CNN) and LSTM based time series prediction models.

Portable Solar Powered Weather Station for Farmers

In this paper we have proposed a design which functions as a completely solar powered wireless weather station. The model is developed in such a way that it can be operated remotely and the output reading values are displayed on an easy to read LCD display with numeric values. The weather station is completely wireless as it incorporates a remote station with sensors to keep track of the weather powered by a solar panel and a base station to record and read weather data and in true terms to make it wireless. Base station can relay weather data as well as values to the hand held devices via internet. The remote station consists of sensors to measure rainfall, temperature and relative humidity level which are important weather parameters for our farmer friends with a design to optimize cost and power