Coefficient of prediction of ET0 for Mellegue catchment.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"603",leadTitle:null,fullTitle:"Methodologies, Tools and New Developments for E-Learning",title:"Methodologies, Tools and New Developments for E-Learning",subtitle:null,reviewType:"peer-reviewed",abstract:"With the resources provided by communication technologies, E-learning has been employed in multiple universities, as well as in wide range of training centers and schools. This book presents a structured collection of chapters, dealing with the subject and stressing the importance of E-learning. It shows the evolution of E-learning, with discussion about tools, methodologies, improvements and new possibilities for long-distance learning.\nThe book is divided into three sections and their respective chapters refer to three macro areas. The first section of the book covers methodologies and tools applied for E-learning, considering collaborative methodologies and specific environments.\nThe second section is about E-learning assessment, highlighting studies about E-learning features and evaluations for different methodologies. The last section deals with the new developments in E-learning, emphasizing subjects like knowledge building in virtual environments, new proposals for architectures in tutoring systems, and case studies.",isbn:null,printIsbn:"978-953-51-0029-4",pdfIsbn:"978-953-51-5575-1",doi:"10.5772/1115",price:139,priceEur:155,priceUsd:179,slug:"methodologies-tools-and-new-developments-for-e-learning",numberOfPages:346,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"d264034a8e51ed0f264b4c935b115c81",bookSignature:"Elvis Pontes, Anderson Silva, Adilson Guelfi and Sérgio Takeo Kofuji",publishedDate:"February 3rd 2012",coverURL:"https://cdn.intechopen.com/books/images_new/603.jpg",numberOfDownloads:40835,numberOfWosCitations:28,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:21,numberOfDimensionsCitationsByBook:2,hasAltmetrics:0,numberOfTotalCitations:58,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 1st 2011",dateEndSecondStepPublish:"March 29th 2011",dateEndThirdStepPublish:"August 3rd 2011",dateEndFourthStepPublish:"September 2nd 2011",dateEndFifthStepPublish:"December 31st 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"51858",title:"MSc",name:"Elvis",middleName:null,surname:"Pontes",slug:"elvis-pontes",fullName:"Elvis Pontes",profilePictureURL:"https://mts.intechopen.com/storage/users/51858/images/system/51858.jpg",biography:"Elvis Pontes is a full Professor at the University Estácio de Sá, and Adjunct Professor at the Universidade Paulista and at SENAC. 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\r\n\r\n\tLaparoscopic surgery is now fully established in most countries worldwide and is a rapidly developing field. Advances in technology are enabling surgeons to undertake a diverse range of operations of increasing complexity. This book collects the experiences and thoughts of many experts in this field, exploring several concepts and controversial topics. The main focus of this book aims to be highlighting the potential technical and anatomical hazards during laparoscopic surgery and in particular the early diagnosis and treatment of complications after surgery. Technical aspects of many operations and advances in laparoscopic instrumentation are discussed at length.
\r\n\r\n\tThe book will be of interest and will serve as a useful and timely reference to all clinicians involved in laparoscopic surgery, including established surgeons, surgeons in training, nurses, anesthetists, and medical students.
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The wearable device market is currently having a worldwide profit of around $34 billion and is expected to reach above $50 billion by 2022 owing to wearables’ ease of use, flexibility, and convenience [4]. Real-time monitoring, operational efficiency, and fitness tracking are reported as main factors supporting the market growth of health wearable devices such as smart watches, smart glasses, and other wellness gadgets, with expected $12.1 billion world market by 2021 [5].
\nIn the past decade, the recent progress in developing wearable devices was more focused on monitoring physical parameters, such as motion, respiration rate, etc. [3, 6, 7]. Today, there is a great interest in evolving wearable sensors capable of detecting chemical markers relevant to the status of health. Different approaches have been applied by researchers to design and fabricate wearable biosensors for remote monitoring of metabolites and electrolytes in body fluids including tear, sweat, and saliva [3, 8, 9, 10]. A great example would be the development of small and reliable sensors that would allow continuous glucose monitoring in diabetic patients [11, 12]. Diabetes is a chronic disease that can significantly impact on quality of life and reduce life expectancy. However, diabetics can stay one step ahead of the disease by monitoring their blood glucose level to minimize the complication of the disease by proper administration of insulin. Currently, blood analysis is the gold standard method for measuring the level of glucose in patient’s blood. However, this technique cannot be applied without penetrating the skin, which can be painful and inconvenient, and requires user obedience. Therefore, current research focuses on the development of portable and wearable devices capable of continuous glucose sensing through noninvasive detection techniques.
\nA majority of the recent studies in this field have targeted the area of personalized medicine, endeavoring to develop miniaturized wearable devices featuring real-time glucose monitoring in diabetic patients [12, 13, 14, 15]. One great example is contact lens which is an ideal wearable device that can be worn for hours without any pain or discomfort [16]. Integration of glucose biosensors into contact lenses has recently been demonstrated by several research groups [9, 17, 18]. However, the level of glucose in tear fluid is very low (0.1–0.6 mM), requiring a high sensitivity of the sensor for picking up the signal from expected chemical reaction [3, 19]. Yao et al. [16] have fabricated a contact lens with integrated sensor for continuous tear glucose monitoring with wireless communication system over a distance of several centimeters. The sensor demonstrated a fast response of 20 s with a minimum detection of less than 0.01 mM glucose, which is 10–60 times lower than glucose level in human tear [16].
\nIn addition to glucose, lactate is an important metabolite in the human body, which gets converted into l-lactate under hypoxic condition [20]. l-Lactate levels in tear fluid is about 1–5 mmol L−1, which might increase significantly due to some heath conditions including ischemia, inadequate tissue oxygenation, stroke, and different types of cancer [21]. Thomas et al. [22] demonstrated an invasive detection of lactate in human tear by integrating an amperometric lactate sensor with Pt working (WE) and reference (RE) electrodes as well as a counter electrode (CE) as current drain, on a polymer-based contact lens, measuring lactate in situ in human tears without any need for physical sampling [22].
\nVery recently, Park et al. [17] reported a novel approach for fabricating fully transparent and stretchable smart contact lens capable of wirelessly monitoring the level of glucose in the tears of diabetic patients. Figure 1 shows the layout of fabricated devices made of glucose sensors, wireless circuit, and display pixel on soft and transparent contact lens substrate (Figure 1a and b). The circuit diagram of the device is illustrated in Figure 1a, with radio frequency antenna receiving signals from a transmitter and a rectifier converting the signals to DC (Figure 1a and c). A continuous network of ultralong Ag nanofibers was used as stretchable electrodes for the antenna and interconnects (Figure 1d). In the case of any change in the concentration of glucose in tear, the sensor resistance changes resulting in the light-emitting diode (LED) pixel turning on or off. The device was tested in vitro using a live rabbit, providing substantial finding for smart contact lenses as one of the promising wearable devices in healthcare system [17].
\n(a) (i) Schematic illustration and (ii) operation of the soft, smart contact lens and (iii) the circuit diagram of the smart contact lens system. The soft, smart contact lens is composed of (b) a hybrid substrate; (c) functional devices including rectifier, LED, and glucose sensor; and (d) a transparent, stretchable conductor for antenna and interconnects [
In addition to tear, sweat electrolyte concentrations and blood serum are related [2, 8]. As one of the most readily accessible human biofluids, a great deal of information about the human body and its physical performance could be obtained via monitoring sweat electrolyte concentrations [23, 24]. Several groups have reported the key biomarkers in human sweat (e.g., sodium level, pH change, lactate concentration) relevant to human health and well-being, for monitoring athletic performance during sporting activities [25]. Jia et al. fabricated a skin-worn tattoo-based sensor for real-time monitoring of lactate in human sweat, offering substantial benefits for biomedical as well as sport applications [25]. In another approach, Curto et al. [26] fabricated a wearable and flexible microfluidic platform capable of monitoring changes in the sweat pH in real time. Anastasova et al. [27] developed a flexible microfluidic device for real-time monitoring of metabolite such as lactate as well as electrolytes such as pH and sodium in human sweat. Recently, Gao et al. [28] developed a flexible and wearable device (Figure 2) made of arrays of sensors for real-time monitoring of heavy metals, such as Zn, Cu, and Hg in human sweat. The device fabrication method is presented in Figure 2a, showing the deposition and stripping steps on microelectrodes. The sensing mechanism was based on an electrochemical detection of targeted heavy metals through four microelectrodes, including Au and Bi working electrodes, Ag reference electrode, and an Au counter electrode (Figure 2b and c). The fabricated device demonstrated high stability and selectivity toward heavy metals, providing a great platform to advancing the field of wearable biosensors for healthcare application, via monitoring the level of some heavy metals in human sweat [28]. A balanced level of Zn is necessary in the human body as a low and high Zn concentration can lead to pneumonia and liver damages, respectively [29, 30]. High level of Cu in the human body can lead to several diseases including Wilson’s disease and heart, kidney, and liver failures as well as brain diseases [31, 32]. The fabricated device demonstrated high stability and selectivity toward heavy metals, providing a great platform to advancing the field of wearable biosensors for healthcare application [28].
\n(a) A schematic showing the concept of deposition and stripping on microelectrodes. (b) A schematic showing the composition of the microsensor array. (c) Optical image of a flexible sensor array interfacing with a flexible printed circuit connector [
Saliva, as a great diagnostic fluid, can be used in personal health devices for real-time monitoring of chemical markers including salivary lactate analysis [33]. Chai et al. developed a saliva nanosensor with a radio-frequency identification tag, integrated into dental implants for detecting cardiac biomarkers in saliva and predicting close heart attack in patients suffering from cardiovascular diseases [34]. In another approach, an instrumented mouthguard was designed and fabricated by Kim et al. [35] for measuring salivary uric acid levels which could be a biomarker for several diseases including hyperuricemia, gout, physical stress, and renal syndrome. The fabricated device showed high selectivity and sensitivity to low level of uric acid as well as great stability during a 4-h operation period [35]. Mannoor et al. [36] developed a hybrid biosensor made of graphene layers printed onto water-soluble silk, for noninvasive detection of bacteria through body fluids including sweat and saliva. This graphene/silk hybrid device illustrated an extremely high sensitivity to bacteria in body fluid with detection limits down to a single bacterium [36]. In addition, the fabricated device provided the potential users with battery-free operation and wireless communication system via radio frequency [36]. Arakawa et al. [37] designed and fabricated a salivary sensor equipped with a wireless measurement system, embedded onto a mouthguard support, featuring a high sensitivity toward detection of glucose over a range of 5–1000 μmol L−1. The device demonstrated a great stability during a 5-h real-time glucose monitoring period in an artificial saliva with a phantom jaw [37]. In a similar approach, de Castro et al. [38] developed a microfluidic paper-based device integrated into a mouthguard, for continues monitoring of glucose and nitrite in human saliva. The saliva samples were collected from periodontitis and/or diabetes patients as well as healthy individuals. The fabricated device featured a low detection limit of 27 and 7 μmol L−1 for glucose and nitrite, respectively [38].
\nIn summary, there is a great potential for micro- and nanosensors’ integration into healthcare monitoring devices, developing new technologies for noninvasive detection of diseases in the human body. Flexible wearable devices offer promising capabilities in real-time monitoring of body fluids including tear, sweat, and saliva. However, more research is required to expand the use of wearable platforms in continuous analysis of body fluids, providing reliable real-time detection of targeting ions and proteins, among other complex analytes.
\nWeather forecast is playing a vital role in society, environment, and sociable development. Their utilities have exceeded the simple mission of providing information for the users about the weather behavior during the upcoming period. Indeed, the whole concept was changing where it has been applied recently for politics and deciders to reduce socioeconomic losses that could be potentially generated by climate, which plays a substantial role in assuring life quality and economic prosperity. For example, the United States has cited 96 natural disasters that occurred between 1980 and 2006, with total losses that exceed 700 billion dollars. Up to now, around 629 fatalities per year are directly caused by weather disasters. Moreover, more than 60,000 premature deaths are recorded annually and are originally caused by poor air quality. Additionally, more than 1.5 million road crashes are originally caused by weather causing 7400 deaths, 700,000 injuries, and around 42 billion losses. Economically, more than 42 billion dollars are estimated to be lost due to weather traffic delays [1]. On the other hand, researchers are using weather forecasting to set new strategies that benefit the most from weather changes. Environmentalists are rather focusing on protecting the ecosystem from any possible climate change threats.
The services of weather are rising sharply. Gained profits from weather forecasts and warning disasters have reached over 31 billion dollars. Eventually, good forecasting will automatically lead to earlier warming and thus to good precautions, which will eventually contribute to reduce weather fatalities and economic loss. Technics of observing and studying weather behavior are continuously progressing, but understanding how the climate reacted in the future is still often too hard to simulate. Related to this point, the diversity of machine learning algorithms has led to predict various climate responses, providing more and more accurate forecasts. Since then, details that deal with the numerical and spatial resolution are being continuously developing, acquiring more challenging computing capabilities.
In the meanwhile, the climate is still too sensitive and chaotic. Even so, it is often too hard to obtain the perfect forecast, but ideally, every weather forecast model needs to get a certain measure of confidence, depending on many external parameters (type of data, coordinates, altitude, etc.). Generally, the best forecast model is the one that implements voluminous data and various methods/models. We must therefore limit the potential parameters that influence mostly the behavior of climate. As a consequence, machine learning algorithms can generate multiple forecast scenarios with slightly different initial inputs and/or changes in some stochastic parametrizations or model formulations. The forecast uncertainty is dependent mostly on the initial states of the atmosphere where it was observed, plus a certain random factor. Any changes in the format of weather data can modify the real observed status of the atmosphere at a certain point. Consequentially, a generated model needs much time between processing and delivering to the users.
Machine learning is a field of computer science based on artificial intelligence. The whole concept is based on provide learning capability to the users (computers or other devices) without being explicitly operated. It aims to conceive a suitable models and algorithms to learn and forecast based on input data [2, 3]. In addition, machine learning algorithms are efficiently used to describe the behavior of the dataset, dealing with noisy and nonstationary data. It uses a model input features by producing an expected output and forecasts suitable output features established on its historical records. Given the wide availability of weather data, fast and accurate decision-making is becoming a vital and more important than ever. Therefore, machine learning algorithms are one of the best alternatives of forecasting weather behavior. Besides, they can easily adapt themselves to changing trends inside datasets and can thus generate models based on input data instead of applying a conventional generalized model.
Many research studies have spoken about weather forecasting using several analysis methods. Preliminary studies about weather forecasting were based on persistence and statistical methods such as regression models [4]. These models are the most common approaches that use statistical technique for weather prediction. One of the most famous regression models is polynomial approach, which provides an effective way to describe complex and voluminous dataset in a nonlinear form. Additionally, polynomial regression models are based on the observed relationship between the dependent and independent variables to find out the most suitable polynomial equation order.
Therefore, the present chapter lays out the outcome of using the polynomial regression models for weather forecasting and to expose all the factors and/or parameters that can potentially affect the efficiency of the prediction. We will reveal the risk of dealing with big data volumes and how the format of the input data will affect the accuracy of the model. We will review the application of the Pearson correlation in retrieving or homogenizing the data and how it can affect the prediction climate behavior. We will discuss the best polynomial algorithm that fits mostly with the type of the data and its ability to generate a valid, concrete and unquestionable weather forecast model.
Meteorological data are derived originally from two sources. In the first case, daily climate data were collected and processed in AquaCrop to calculate a yearly average for every one of the following parameters: maximum and minimum temperature (
The main algorithm used in this study is polynomial regression. It has been widely applied, and its statistical tools are famous [5]. Generally speaking, it is a form of linear regression that is why we do call it sometimes “polynomial linear regression model.” It is a form of regression analysis that links the independent variable (
Hence, we can find in some polynomial equations a certain factor (θ) called residual error.
In another way, we can define polynomial regression as a form of linear regression between dependent and independent variables where we add some terms or factors (based on a curvilinear relationship) to convert it into a polynomial regression. Nonetheless, we can simply return to the linear regression model. In that case, the previous equation will be:
The general behavior of any climate data is set to be a nonlinear way. As a result, the linear regression model will be too difficult to visualize or predict entirely the data. For that reason, it will be too hard to draw the best line that fits mostly the weather data. The performance of the model will be too far from reality, and the projection of the weather forecast will be, consequently, too doubtful. In this case, we opt for the polynomial regression to fit the data graph with a low value of error (Figure 1).
Difference between linear and polynomial regression.
Python has several methods for finding the curvilinear relationship between data points. Based mainly on mathematic equations, the algorithm will succeed in drawing the best polynomial regression line that fits the most with the original input data. Later on, we can use the collected information to predict future values with a specific timescale. In Python, many packages were imported to operate this algorithm, but the main needed libraries are: “pandas,” “matplotlib,” “numpy,” and “sklearn.”
The process is quite easy with Python (Figure 2). The first step consists of importing major libraries especially “Pandas,” “numpy,” and “sklearn.” You need to import the CSV file, which must have at least two columns; the column A contains the date (you have the right to put the conventional format of date you want, but be aware that it must be understood by Python!), and the second column that contains the observed values (which corresponds to the climate stats in our cases). Next, you have to specify the corresponding column to every variable and order the value gradually, by using the imported library “numpy.” Going this far, all you have to do next is to apply the corresponded polynomial order that ideally fits with the observed data. You can assess the behavior of the polynomial regression graphically (using the libraries matplotlib) or statically (
Script of polynomial regression model in python.
Statically: based on
By definition,
Graphically: based on back-forecasting
When we say predict, it is often related to anticipate the future response based on past or actual values. Instead, we want to compare the forecast equation with real data. So, here we do invent the conception of back-forecasting. Generally, any time series model looks the same by going forward or backward and can, therefore, predict the future as well as the past. Meaning instead of going forward, we use the model to go backward in time and predict the past. The model uses two backwards passes. The first one is used to calculate early data by the estimating parameters while the second one is made to establish the forecasting equation to calculate the forward values (future). In Figure 3, we used the back-forecasting method to squeeze most information out of the 468 monthly data (1981 to 2019). The more back-forecasted graph is close to the real data plot, the more accurate the forecasting equation will be.
Figure assimilation between back-forecasting plot (based on ARIMA (1,1,1)) and real rainfall values of Zaghouan.
Also known as the bivariate correlation, it is used to measure the strength of two sets of data based on the linear relationship. Mathematically speaking, it refers to the ratio between the covariance and the standard deviation of two variables (Eq. (3)). As such formula, the resultant value will always set between −1 and 1 where values close to 1 are referring to good correlation as the non-correlated values are close to zero. Generally speaking, the absolute values of the given correlation will be automatically linked to the strength of the values (negative values that are close to −1 are indicating that the two variables are inversely correlated, meaning that the rise of the first variable will lead to a decrease of the other).
The general equation of
Where
Cov(
σ: Standard deviation.
We have to test the relationship between the data based on the Pearson coefficient. By proceeding this correlation, we have neglected all data from the stations that have a correlation less than 0.6 as it is recommended in some studies [6]. Lately, we have to rerun the polynomial regression for the validated data that has only a strong correlation. The program is too simple with Python, you can find here (Figure 4) the script of the operating Pearson correlation and how to export the results into a CSV file.
Script of the application of Pearson correlation in python.
The watershed of Mellegue is situated between 36° 25′ 50.43″, 35° 12′ 20.74″ north and 7° 11′ 30.98″, 8° 55′ 7.99″ east (Figure 5). The area covers 10,500 km2 where approximately 60% of the surface belongs to Algeria, whereas the remaining surface, as well as the outlet, is situated in Tunisia. The local climate is known as arid to semiarid with a slight change in sub-humid in the north. As a consequence, the area is covered with low vegetation where few forests are situated mainly in the northeast of the basin. The average yearly temperature is around 17°c with a slight change of 1–2°C. The winter is responsible for providing 50% of the annual rainfall. The so-called Oued Mellegue represents the main river of the catchment. It is 290 km long, cuts the watershed in a northeastern southwestern direction. Due to its large area, we assembled weather data from 23 stations scattered all around the catchment. Later on, the large amount of data was combined to compute a yearly average value for each weather variable. By homogenizing all the weather data together, it will be very helpful in understanding and visualizing the response of the watershed due to climate change and relieving voluminous data processing by the model. Hence, dealing with each station separately will lead to a lack in predicting the climate behavior of the area [7].
Geographic location of the watershed of Mellegue.
The city of Zaghouan is a Tunisian metropolitan located under the coordinates 36° 24′ north/10° 09′ east (Figure 6). The city is situated in the northeast of Tunisia, specifically on the hill of the Jbel Zaghouan (1295 m altitude). The region is known for the abundance of high reliefs and a large number of water sources because of the active seismicity of the zone, mainly manifested in the fault of Zaghouan. This mechanic structure extends for approximately 80 km along with the northeast-southwest Atlasic trend where total vertical displacement exceeds 5 km. The climate of the study area does belong to the semiarid where annual temperature goes on the average of 18°C, whereas total rainfalls are approximately 500 mm. The estimated population of the city is around 20,837. Local activities rely in a big part on agricultural activities; around 300,000 ha are dedicated to agriculture where about 1.4 million quintals are gained yearly [8, 9].
Geographic location of the city of Zaghouan.
The results of polynomial regression (Figure 7 and Table 1) are showing a general decrease in ET0 compared with the initial states, which recorded 1250 mm/year in 2019. The overall decrease is estimated at the average of 11 mm/year as it will reach, by the end of 2030, 1115 mm /year. The polynomial regression based on Pearson correlation has intensely decreasing the results, where forecasting ET0 is estimated to reach 1060 mm in 2030 with an average loss of 16.2 mm/year. Many causes have been mentioned earlier to link the decrease of reference evapotranspiration. But two of the most influenced factors are revealed to be the temperature and winds. In fact, many studies have predicted the increase in temperature, which may go up to 5°C (depending on the coordinates, local climate, etc.). Inversely, the rise of the surface temperature will lead to a general decrease in ET0. This phenomenon happened to be known as the “Evaporation Paradox” [10, 11]. On the other hand, the expansion of urbanized areas will also affect ET0 where it will undoubtedly generate more polluted air, which has a severe negative impact on the behavior of ET0 [12]. Additionally, the general decrease of water bodies could lead to an indirect decrease in the annual value of ET0 [13].
Quadratic polynomial regression forecast.
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.442 | 71982.188 | −67.603 | 0.016 | Valid |
2 | −135589.162 | 140.694 | −0.036 | Valid | |
3 | −898739.862 | 903.865 | −0.227 | Valid | |
4 | −1260271.181 | 1264.865 | −0.317 | Valid | |
5 | −1060164.556 | 1063.572 | −0.266 | Valid | |
6 | −182580.374 | 185.367 | −0.047 | Valid | |
7 | −355144.733 | 358.103 | −0.090 | Valid | |
8 | −333614.344 | 336.200 | −0.084 | Valid | |
9 | −306578.982 | 308.705 | −0.077 | Valid | |
10 | −856433.461 | 858.485 | −0.215 | Valid | |
11 | −1394177.144 | 1402.250 | −0.352 | Valid | |
12 | −1436810.518 | 1445.436 | −0.363 | Valid | |
13 | −1245118.628 | 1252.851 | −0.315 | Valid | |
14 | −878430.329 | 882.910 | −0.222 | Valid | |
15 | −520073.866 | 522.075 | −0.131 | Valid | |
16 | −2341253.349 | 2342.237 | −0.585 | Valid | |
17 | −2708961.470 | 2715.734 | −0.680 | Valid | |
18 | −856517.732 | 873.275 | −0.222 | Valid | |
19 | −36331.810 | 37.729 | −0.010 | Valid | |
20 | −1060164.556 | 1063.572 | −0.266 | Valid | |
21 | 2746547.110 | −2734.734 | 0.681 | Invalid | |
22 | 316197.814 | −327.556 | 0.085 | Invalid |
Coefficient of prediction of ET0 for Mellegue catchment.
Based on the following results (Figure 8 and Table 2), the forecast of rainfalls is generally stabilized with a slightly increasing tendency, where it predicts a value of precipitation that reaches 575 mm/year in 2030. Inversely, the application of Pearson polynomial regression has shown a decreasing in
Quadratic polynomial regression forecast of
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.45 | 1955249.056 | −1944.213 | 0.483 | Invalid |
2 | 613267.850 | −612.659 | 0.153 | Invalid | |
3 | 715018.701 | −716.287 | 0.179 | Invalid | |
4 | 2811409.072 | −2808.597 | 0.702 | Invalid | |
5 | −362350.042 | 383.213 | −0.101 | Valid | |
6 | 2694179.641 | −2670.272 | 0.662 | Valid | |
7 | 2680735.502 | −2657.038 | 0.658 | Valid | |
8 | 2108146.344 | −2084.830 | 0.515 | Valid | |
9 | 572407.085 | −558.133 | 0.136 | Valid | |
10 | 315977.725 | −312.109 | 0.077 | Valid | |
11 | 2445104.816 | −2433.165 | 0.605 | Invalid | |
12 | 3188164.611 | −3170.534 | 0.788 | Valid | |
13 | 2497308.704 | −2475.376 | 0.614 | Valid | |
14 | 1209763.715 | −1185.469 | 0.290 | Valid | |
15 | 1464559.714 | −1439.674 | 0.354 | Valid | |
16 | −60450.995 | 72.904 | −0.021 | Valid | |
17 | −1642843.790 | 1643.988 | −0.411 | Valid | |
18 | 6646561.474 | −6631.849 | 1.654 | Invalid | |
19 | 8793045.164 | −8760.092 | 2.182 | Invalid | |
20 | −1108225.764 | 1126.116 | −0.286 | Valid | |
21 | 44922.783 | −33.671 | 0.006 | Valid | |
22 | 4539127.186 | −4520.044 | 1.125 | Invalid |
Coefficient of prediction of P for Mellegue catchment.
According to the following result, polynomial forecasts have announced a slight decrease in maximum temperature (
Quadratic polynomial regression forecast of
Station | Poly ( | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.467 | −4980.173 | 5.042 | −0.001 | Invalid |
2 | −9726.882 | 9.776 | −0.002 | Invalid | |
3 | −14511.587 | 14.568 | −0.004 | Invalid | |
4 | −23027.041 | 23.057 | −0.006 | Invalid | |
5 | −2610.073 | 2.607 | −0.001 | Valid | |
6 | −1249.637 | 1.281 | 0.000 | Invalid | |
7 | 31420.577 | −31.295 | 0.008 | Valid | |
8 | 5659.772 | −5.656 | 0.001 | Valid | |
9 | 4127.832 | −4.116 | 0.001 | Valid | |
10 | −29400.171 | 29.353 | −0.007 | Invalid | |
11 | −20620.933 | 20.694 | −0.005 | Invalid | |
12 | −18063.359 | 18.138 | −0.005 | Invalid | |
13 | −12779.390 | 12.826 | −0.003 | Invalid | |
14 | 35242.347 | −35.089 | 0.009 | Valid | |
15 | −499.410 | 0.506 | 0.000 | Valid | |
16 | −15299.754 | 15.350 | −0.004 | Invalid | |
17 | −21567.206 | 21.631 | −0.005 | Invalid | |
18 | −17724.813 | 17.840 | −0.004 | Invalid | |
19 | −17130.249 | 17.181 | −0.004 | Invalid | |
20 | −2610.073 | 2.607 | −0.001 | Valid | |
21 | 63652.590 | −63.429 | 0.016 | Valid | |
22 | −20955.317 | 20.796 | −0.005 | Valid |
Coefficient of prediction of Tmax for Mellegue catchment.
Quadratic polynomial regression forecast of
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.648 | −15061.536 | 15.056 | −0.0038 | Valid |
2 | −15104.129 | 15.116 | −0.0038 | Valid | |
3 | −16539.754 | 16.569 | −0.0041 | Valid | |
4 | −19188.385 | 19.222 | −0.0048 | Valid | |
5 | −17771.726 | 17.806 | −0.0045 | Valid | |
6 | −1198.441 | 1.210 | −0.0003 | Invalid | |
7 | 1745.406 | −1.719 | 0.0004 | Invalid | |
8 | −15167.497 | 15.148 | −0.0038 | Valid | |
9 | −21793.646 | 21.777 | −0.0054 | Valid | |
10 | −11902.214 | 11.955 | −0.0030 | Valid | |
11 | −13972.995 | 13.983 | −0.0035 | Valid | |
12 | −11936.938 | 11.972 | −0.0030 | Valid | |
13 | −11066.352 | 11.111 | −0.0028 | Valid | |
14 | −22346.007 | 22.350 | −0.0056 | Valid | |
15 | −20194.330 | 20.203 | −0.0051 | Valid | |
16 | −31769.096 | 31.718 | −0.0079 | Valid | |
17 | −19493.627 | 19.517 | −0.0049 | Valid | |
18 | −17911.162 | 17.991 | −0.0045 | Valid | |
19 | −15827.268 | 15.904 | −0.0040 | Valid | |
20 | −17771.726 | 17.806 | −0.0045 | Valid | |
21 | 55076.342 | −54.831 | 0.0136 | Invalid | |
22 | 16150.051 | −15.964 | 0.0039 | Valid |
Coefficient of prediction of
According to the following results of Figure 11, it is found out that quartic polynomial regression (order (
Quartic polynomial regression forecast of P for the case of Zaghouan.
Considering the application of polynomial regression for the case of Mellegue watershed, it seems that the best polynomial equation is quadratic (
It seems that polynomial regression cannot deal with big data. The catchment is a very large basin. So, trying to understand/study a specific phenomenon needs to collect a large amount of data that has to be taken from different stations, in such a way to cover the entire area. Homogenizing the data for a resultant average response is very helpful for the machine learning algorithm to reduce error uncertainties from voluminous data, and so polynomial regression can fit the generated weather curvature [20]. Unfortunately, these data are not efficient at all for decision-makers and environmentalists because it can generate a misinterpretation as a response for treating/dealing with the potential causes for such a phenomenon.
As we see in the previous example, choosing the right order that fits mostly with the data type is often challenging to find. This is considered as one of the major handicaps in working with polynomial regression [19]. To deal with such drawbacks, choosing the right order must not be arbitrary and needs some visualization and expert of dealing with similar data (Plot,
Another important drawback that should be reported is the incapacity of the polynomial regression to deal with very sensitive data such as weather. Even high orders cannot keep up with original data curves. Therefore, many samplers (points) will not be taken into account by the polynomial model, which will affect the projection of forecasted weather results. Additionally, as seen in most of the previous examples, we can say that the prediction of the weather shows a linear trend that does not conform to the general feature of the weather as it behaves seasonally, and so it must have a certain sinusoidal tendency. In the two previous study areas, most of the polynomial orders are quadratic equations (
Referring to the case of Zaghouan, statistics of polynomial regression have revealed a pessimistic result. Regardless of the order (
Difference in the behavior of polynomial regression for monthly and yearly data (case of Zaghouan).
There are many advantages from using polynomial regression. Besides their simplicity in processing, they operate well in giving the best relationship between independent and dependent variables. Another good aspect regarding polynomial functions is their aptitude of fitting numerous curvatures, which depend mainly on the type and trend of the data. To conclude with, a polynomial function may not be so accurate, but it can generate an acceptable weather forecast. Let us say that it could be helpful to forecast the general behavior of climate at a certain specific range of time without the need of being too precise.
The Pearson correlation is revealed to be effective in evaluating the statistical relationship or the dependencies between two variables. Even though the method is based on the covariance and mathematical equation, it still not scientifically reliable to determine the strength and direction of the association based on Pearson correlation only. We see in the previous example of Mellegue catchment how the r coefficient has showed the strength of correlation between the stations. Every climate parameter has responded differently from the other. We can see, from Tables 1
Polynomial regression algorithm was outperformed by Python for weather forecasting. We want to see the performance of the model by taking into account the type of input data where it was operated on two concrete case studies. Eventually, we want to test the relationship between stations based on Pearson correlation as a way to retrieve the data and homogenizing it. The results have opened a wide debate to discuss; convenient polynomial order was revealed to be quadratic, which agreed with the general idea that most applied polynomial order is first and second degree [21]. Additionally, the model found a good capacity to fit various complex data [22]. On the other hand, the performance of polynomial regression based on Pearson correlation has altered significantly the weather prognosis accuracy. The behavior of the model has shown a drastic change by going from monthly to yearly data. Depending on the plot observation and coefficients variables, polynomial regression happened to fit more with yearly data. This is due to the simple fact that polynomial regression will operate more efficiently with moderate to low input data. The second fact is that this type of regression is too sensitive to mutable data such as seasonal climate. That circumstance was discovered in the case of Zaghouan (monthly data), where the model has not succeeded in finding the best polynomial order (
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',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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\n\nMetadata for all publications is also automatically deposited in IntechOpen's OAI repository, making them available through the Open Access Infrastructure for Research in Europe's (OpenAIRE) search interface further establishing our compliance.
\n\nIn other words, publishing with IntechOpen guarantees compliance.
\n\nRead more about Open Access in Horizon 2020 here.
\n\nWhich scientific publication to choose?
\n\nWhen choosing a publication, Horizon 2020 grant recipients are encouraged to provide open access to various types of scientific publications including monographs, edited books and conference proceedings.
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In addition, biostimulants applied to plants enhance nutrition efficiency, abiotic stress tolerance and/or plant quality traits, regardless of its nutrient contents. Several researches have been developed in order to evaluate the biostimulants in improving plant development subjected to stresses, saline environment, and development of seedlings, among others. Furthermore, various raw materials have been used in biostimulant compositions, such as humic acids, hormones, algae extracts, and plant growth-promoting bacteria. In this sense, this chapter aims to approach the use of biostimulants in plant growth according to the raw material used in their compositions as well as their effects on plants subjected to abiotic stresses.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Ana Carolina Feitosa de Vasconcelos and Lúcia Helena Garófalo Chaves",authors:null},{id:"32894",doi:"10.5772/27308",title:"Antimicrobial and Antioxidant Activities of Some Plant Extracts",slug:"antimicrobial-and-antioxidant-activities-of-some-plant-extracts",totalDownloads:7022,totalCrossrefCites:6,totalDimensionsCites:19,abstract:null,book:{id:"1958",slug:"phytochemicals-as-nutraceuticals-global-approaches-to-their-role-in-nutrition-and-health",title:"Phytochemicals as Nutraceuticals",fullTitle:"Phytochemicals as Nutraceuticals - Global Approaches to Their Role in Nutrition and Health"},signatures:"Elita Scio, Renata F. Mendes, Erick V.S. Motta, Paula M.Q. Bellozi, Danielle M.O. Aragão, Josiane Mello, Rodrigo L. Fabri, Jussara R. Moreira, Isabel V.L. de Assis and Maria Lúcia M. Bouzada",authors:[{id:"69660",title:"Dr.",name:"Elita",middleName:null,surname:"Scio",slug:"elita-scio",fullName:"Elita Scio"},{id:"76248",title:"MSc.",name:"Rodrigo",middleName:null,surname:"Fabri",slug:"rodrigo-fabri",fullName:"Rodrigo Fabri"},{id:"76251",title:"MSc.",name:"Danielle",middleName:null,surname:"Aragão",slug:"danielle-aragao",fullName:"Danielle Aragão"},{id:"76253",title:"Ms.",name:"Renata",middleName:null,surname:"Mendes",slug:"renata-mendes",fullName:"Renata Mendes"},{id:"76256",title:"Mr.",name:"Erick",middleName:null,surname:"Motta",slug:"erick-motta",fullName:"Erick Motta"},{id:"76259",title:"Ms.",name:"Isabel",middleName:null,surname:"Assis",slug:"isabel-assis",fullName:"Isabel Assis"},{id:"76261",title:"Ms.",name:"Jussara",middleName:null,surname:"Moreira",slug:"jussara-moreira",fullName:"Jussara Moreira"},{id:"76262",title:"MSc.",name:"Maria Lucia",middleName:null,surname:"Bouzada",slug:"maria-lucia-bouzada",fullName:"Maria Lucia Bouzada"},{id:"76264",title:"Ms.",name:"Paula",middleName:null,surname:"Bellozi",slug:"paula-bellozi",fullName:"Paula Bellozi"},{id:"76266",title:"MSc.",name:"Josiane",middleName:null,surname:"Mello",slug:"josiane-mello",fullName:"Josiane Mello"}]}],mostDownloadedChaptersLast30Days:[{id:"69956",title:"Biostimulants and Their Role in Improving Plant Growth under Abiotic Stresses",slug:"biostimulants-and-their-role-in-improving-plant-growth-under-abiotic-stresses",totalDownloads:2499,totalCrossrefCites:8,totalDimensionsCites:22,abstract:"Biostimulants are products that reduce the need for fertilizers and increase plant growth, resistance to water and abiotic stresses. In small concentrations, these substances are efficient, favoring the good performance of the plant’s vital processes, and allowing high yields and good quality products. In addition, biostimulants applied to plants enhance nutrition efficiency, abiotic stress tolerance and/or plant quality traits, regardless of its nutrient contents. Several researches have been developed in order to evaluate the biostimulants in improving plant development subjected to stresses, saline environment, and development of seedlings, among others. Furthermore, various raw materials have been used in biostimulant compositions, such as humic acids, hormones, algae extracts, and plant growth-promoting bacteria. In this sense, this chapter aims to approach the use of biostimulants in plant growth according to the raw material used in their compositions as well as their effects on plants subjected to abiotic stresses.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Ana Carolina Feitosa de Vasconcelos and Lúcia Helena Garófalo Chaves",authors:null},{id:"69577",title:"Role of Fungi in Agriculture",slug:"role-of-fungi-in-agriculture",totalDownloads:1544,totalCrossrefCites:3,totalDimensionsCites:5,abstract:"Fungi are a group of eukaryotic organisms and source of food, organic acids, alcohol, antibiotics, growth-promoting substances, enzymes, and amino acids. They include microorganisms like molds, yeasts, and mushrooms. They live on dead or living plants or animals’ tissue. Fungi are very different from other living organisms; they are the primary decomposers of substances in the ecological system. Fungi are tremendous decomposer of organic waste material and most readily attack cellulose, lignins, gums, and other organic complex substances. Fungi can act also under a wide range of soil reaction from acidic to alkaline soil reactions. Fungi conjointly play a basic role in different physiological processes as well as mineral and water uptake, chemical change, stomatal movement, and biosynthesis of compounds termed biostimulants, auxins, lignan, and ethylene to enhance the flexibility of plants to ascertain and cope environmental stresses like drought, salinity, heat, cold, and significant metals.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Muthuraman Yuvaraj and Murugaragavan Ramasamy",authors:[{id:"280193",title:"Dr.",name:"Muthuraman",middleName:null,surname:"Yuvaraj",slug:"muthuraman-yuvaraj",fullName:"Muthuraman Yuvaraj"},{id:"289410",title:"Dr.",name:"Murugaragavan",middleName:null,surname:"Ramasamy",slug:"murugaragavan-ramasamy",fullName:"Murugaragavan Ramasamy"}]},{id:"62653",title:"Phytochemical Composition: Antioxidant Potential and Biological Activities of Corn",slug:"phytochemical-composition-antioxidant-potential-and-biological-activities-of-corn",totalDownloads:2149,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Corn seeds are used as a nutritional source for humans, and the stem and leaves are utilized as fodder for cattle throughout the world. Corn silk and corn cob are usually discarded as waste. This chapter highlights the nutritional as well as medicinal importance of various parts of corn plant. All parts of corn plant are good source of a variety of bioactive phytochemical compounds which possess antioxidant potential. The principal phytochemicals present in corn seed and corn silk include polyphenols, phenolic acids, flavonoids, anthocyanins, glycosides, carotenoids, and polysaccharides of biological importance, reducing compounds and some water-soluble vitamins. The presence of these phytochemicals makes corn a medicinal plant which shows various biological activities particularly the antioxidant, antimicrobial, antidiabetic, anti-obesity, antiproliferative, hepatoprotective, cardioprotective, and renal-protective activities. On the account of its high antioxidant potential, all parts of corn plant can be used for the management of oxidative stress and the treatment of various diseases.",book:{id:"7206",slug:"corn-production-and-human-health-in-changing-climate",title:"Corn",fullTitle:"Corn - Production and Human Health in Changing Climate"},signatures:"Haq Nawaz, Saima Muzaffar, Momna Aslam and Shakeel Ahmad",authors:[{id:"230900",title:"Mr.",name:"Haq",middleName:null,surname:"Nawaz",slug:"haq-nawaz",fullName:"Haq Nawaz"},{id:"244066",title:"Prof.",name:"Saima",middleName:null,surname:"Muzaffar",slug:"saima-muzaffar",fullName:"Saima Muzaffar"},{id:"263041",title:"Ms.",name:"Momna",middleName:null,surname:"Aslam",slug:"momna-aslam",fullName:"Momna Aslam"},{id:"263042",title:"Dr.",name:"Shakeel",middleName:null,surname:"Ahmad",slug:"shakeel-ahmad",fullName:"Shakeel Ahmad"}]},{id:"70950",title:"Role of Biofertilizers in Plant Growth and Soil Health",slug:"role-of-biofertilizers-in-plant-growth-and-soil-health",totalDownloads:1371,totalCrossrefCites:4,totalDimensionsCites:11,abstract:"Biofertilizers nowadays have been realised for shifting fortunes in agriculture. It has been proven successful technology in many developed countries while in developing countries exploitation of bioinoculants is hampered by several factors. Scientific knowledge on bioinoculants and its usage will pave way for its effective usage. At the same time overlooking the significance of ensuring and maintaining a high quality standard of the product will have negative impact. Hence a proper knowledge of bioinoculants and its functioning will pave way to tape the resources in a better way. Thus the chapter provide overview knowledge about different bacterial, fungal and algal biofertilizers, its associations with plants and transformations of nutrients in soil. Adopting a rational approach to the use and management of microbial fertilizers in sustainable agriculture thrive vast potential for the future.",book:{id:"8004",slug:"nitrogen-fixation",title:"Nitrogen Fixation",fullTitle:"Nitrogen Fixation"},signatures:"Murugaragavan Ramasamy, T. Geetha and M. Yuvaraj",authors:[{id:"289410",title:"Dr.",name:"Murugaragavan",middleName:null,surname:"Ramasamy",slug:"murugaragavan-ramasamy",fullName:"Murugaragavan Ramasamy"}]},{id:"67454",title:"Nitrogen Fertilization I: Impact on Crop, Soil, and Environment",slug:"nitrogen-fertilization-i-impact-on-crop-soil-and-environment",totalDownloads:1472,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"Nitrogen (N) is a major limiting nutrient to sustain crop yields and quality. As a result, N fertilizer is usually applied in large quantity to increase crop production throughout the world. Application of N fertilizers has increased crop yields and resulted in achievement of self-sufficiency in food production in many developing countries. Excessive application of N fertilizers beyond crops’ demand, however, has resulted in undesirable consequences of degradation in soil, water, and air quality. These include soil acidification, N leaching in groundwater, and emissions of nitrous oxide (N2O), a potent greenhouse gas that contributes to global warming. Long-term application of ammonia-based N fertilizers, such as urea, has increased soil acidity which rendered to soil infertility where crops fail to respond with further application of N fertilizers. Another problem is the groundwater contamination of nitrate-N (NO3-N) which can be a health hazard to human and livestock if its concentration goes above 10 mg L−1 in drinking water. The third problem is emissions of N2O gas which is 300 times more powerful than carbon dioxide in terms of global warming potential. This chapter examines the effect of N fertilization on soil and environmental quality and crop yields.",book:{id:"8004",slug:"nitrogen-fixation",title:"Nitrogen Fixation",fullTitle:"Nitrogen Fixation"},signatures:"Upendra M. Sainju, Rajan Ghimire and Gautam P. 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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. 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From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). W inner of the Bimbo PanAmerican Nutrition, Food Science and Technology Award 2006 and 2012, South AmericaHuman Nutrition, Professional Category. 2006 award in pharmacology, Bernardo\r\nHoussay, in recognition of his meritorious works of research. Angel Catalá belongto the Editorial Board of Journal of lipids, International Review of Biophysical ChemistryFrontiers in Membrane Physiology and Biophysics, World Journal oExperimental Medicine and Biochemistry Research International, W orld Journal oBiological Chemistry, Oxidative Medicine and Cellular Longevity, Diabetes and thePancreas, International Journal of Chronic Diseases & Therapy, International Journal oNutrition, Co-Editor of The Open Biology Journal.",institutionString:null,institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}},editorTwo:null,editorThree:null},{id:"12",title:"Human Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",isOpenForSubmission:!0,annualVolume:11408,editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. 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Additionally, to understand how to maintain and improve physical function in older adults, to conduct studies about the mechanism of sarcopenia and determine when possible interventions are needed.",institutionString:null,institution:{name:"Ritsumeikan University",institutionURL:null,country:{name:"Japan"}}}},{id:"13",title:"Plant Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",isOpenForSubmission:!0,annualVolume:11409,editor:{id:"332229",title:"Prof.",name:"Jen-Tsung",middleName:null,surname:"Chen",slug:"jen-tsung-chen",fullName:"Jen-Tsung Chen",profilePictureURL:"https://mts.intechopen.com/storage/users/332229/images/system/332229.png",biography:"Dr. Jen-Tsung Chen is currently a professor at the National University of Kaohsiung, Taiwan. He teaches cell biology, genomics, proteomics, medicinal plant biotechnology, and plant tissue culture. 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