Comprehensive signal indicators of financial distress prediction.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3047",leadTitle:null,fullTitle:"Advances in Data Mining Knowledge Discovery and Applications",title:"Advances in Data Mining Knowledge Discovery and Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. 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Early prediction methods are univariate analysis (UA), multiple discriminant analysis (MDA), logistic model, probit model, and so on [1, 2, 3, 4, 5]. With the development of computer technology, some new methods based on artificial intelligence technology with distributed computing capabilities that can deal with problems of nonlinear systems are widely introduced into the field of financial distress prediction. These methods include neural network (NN), genetic algorithm (GA), rough set theory (RST), case-based reasoning (CBR) and support vector machine (SVM), and so on [6, 7, 8, 9, 10, 11, 12, 13, 14]. Each model established for financial distress prediction, whether based on statistical methods or artificial intelligence methods, has advantages and disadvantages under different conditions. Let us take the most widely used multiple discriminant analysis (MDA) and back-propagation neural network (BPNN) for example. MDA has the advantage of simplicity and good interpretation, but the deficiency in its application is limited by strict assumptions that sometimes cannot be satisfied. Besides, MDA is a static discriminant model [2, 3, 6, 15, 20]. For the application of BPNN, it does not need any probability distribution assumption. BPNN is considered as an effective tool of pattern recognition for nonlinear systems. Therefore, many researchers have tried to apply triple BPNN in financial distress prediction, using the nonlinear pattern recognition capability of BPNN for classification of different financial state [7, 8, 15].
\nThe prediction often achieved through a cross-sectional analysis at different time points. That is, the sample data of period t−1, t−2, … before financial distress are studied by BPNN, respectively, and the features are extracted, based on what the judgment for the financial state of next new period is made [16, 17, 20]. This treatment is a relatively complete cross-sectional analysis. But the conclusions on discrimination among different time points are lack of logistic links. Therefore, this prediction is not completely dynamic. Furthermore, BPNN is a static neural network even when directly used in time-series prediction (Neural networks can be divided into static or dynamic neural networks based on whether they contain feedback loops or delay. BPNN is a backpropagation network without feedback and belongs to static neural networks.). There are some inherent problems such as overfitting, for example, the fitting error of training data has reduced, but the prediction error has increased at the same time. Even if the data are normalized, the effect is not satisfactory when the testing data are not sufficient [13, 18, 19, 20].
\nActually, corporate financial distress is a gradual and cumulative process, which is developed from a healthy state. The mutation is often the result at which the gradual change and cumulation have reached the critical condition. It is neither reasonable nor logical if only the cross-sectional data at the time point prior to the occurrence of financial distress are used to make a determination for the corporate future state. It should take into account two aspects at least when conducting the research on financial distress prediction: firstly, the alternative data for prediction should contain all the historical information; secondly, the prediction method is dynamic designed for financial distress characterized by cumulative variation [21, 22, 23]. However, the current discriminant models have some deficiencies in dynamic prediction. Also, there is a problem of massive data processing. This chapter attempts to make a dynamic improvement on prediction methods for financial distress based on Kalman filtering algorithm in order to solve the above problems.
\nThe main contribution of the paper is that it constructs a state-space model of corporate financial distress from the perspective of the cumulative effect of historical information on current state and improves Kalman filtering algorithm for dynamic prediction. A whole process of dynamic prediction for corporate financial distress is developed from a long period of time, and time-series data of high-frequency are collected for optimal estimation of financial state, which is seen as a stochastic process. The advantage of the model is proved by an empirical research, and the result shows that it can give relatively accurate warning before the occurrence of financial distress.
\nThe rest of this chapter is organized as follows. Dynamic prediction models consisting of a process model and a discriminant model based on Kalman filtering algorithm are described in Section 2. Then, a whole process of dynamic prediction for corporate financial distress is elaborated in Section 3. Section 4 presents empirical analysis for China’s manufacturing industry. Section 5 draws conclusions and discusses future research.
\nBased on the state-space method, we establish two models, being used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. We see the evolution of financial distress for a company as a stochastic process and establish a process model, which is used to describe the dynamic process of development of the financial state. We define the financial state as a set of vectors, which summarizes all the information necessary about the past behavior of the company except for the external effects of the inputs, so that it can uniquely describe the behavior of the company in the near future [24]. The financial state of a company often cannot be observed directly, but only some signal indicators associated with the financial state can be observed. Therefore, we establish a discriminant model, which is used to describe the correlation between the financial state and the signal indicators. The discriminant model can be a recursive form of any statistical model or artificial intelligence model, theoretically. At first, we take the linear models, which are simple and intuitive as an example and establish dynamic prediction models for financial distress, as
\nwhere Xt\n is the financial state of a company in period t; Zt\n is the signal indicators of the company in period t; W\n\nt-1 is the process noise of the financial state in period t−1; Vt\n is the observation noise of the signal indicators in period t; A\n\nt|t − 1 is used to describe the dynamic process of the financial state transferring from period t−1 to t; Ht\n is used to describe the mathematical relations between the financial state and the signal indicators in period t. Eq. (1) is a process model; and Eq. (2) is a discriminant model.
\nAssume that the process noise and the observation noise are Gaussian white noises, which are mutually independent and normally distributed, i.e.
\nwhere, Qt\n is a p × p-dimensional symmetric nonnegative definite covariance matrix of process noise Wt\n; Rt\n is a m × m-dimensional symmetric positive definite covariance matrix of observation noise Vt\n; δtj\n is Kronecker ‐ δ function.
\nThe above equations can be solved by Kalman filtering algorithm. The Kalman filter is named after Rudolph E. Kalman, who in 1960 published his famous paper describing a recursive solution to the discrete-data linear filtering problem. The Kalman filter is essentially a set of mathematical equations that implement a predictor–corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance, when some presumed conditions are met [25, 26]. Kalman filter is widely used for its relative simplicity and robust nature. Rarely do the conditions necessary for optimality actually exist, and yet, the filter apparently works well for many applications in spite of this situation. Application of Kalman filter in dynamic prediction for corporate financial state consists of five steps [27, 28]:
\nThe first step is to compute the one-step prediction of the financial state \n
The second step is to compute the error covariance matrix P\n\nt|t − 1 for one-step prediction
\nThe third step is to compute the Kalman gain Kt\n, which is a blending factor that is used to adjust the discrepancy between the predicted observation \n
The fourth step is to correct the one-step predicted financial state \n
The fifth step is to compute the error covariance matrix P\n\nt|t\n of the updated financial state estimation \n
These are the basic equations of Kalman filtering for a stochastic linear discrete financial system. The actual filtering process is an ongoing “predicting-correcting” process of a recursive nature. \nFigure 1\n below offers a complete picture of the operation of the Kalman filter in dynamic prediction for corporate financial state.
\nA complete picture of the operation of the Kalman filter in dynamic prediction for corporate financial state.
The Kalman filter does not require storing large amount of data in solving the problem. Once new data are observed, new filtering value can be calculated at any time. Therefore, this method facilitates real-time processing and is easy to implement on the computer.
\nAs previously described, corporate financial distress is a gradual and cumulative process, which is developed from a healthy state, and so the prediction should be long-term and continuous and the continuously updated time-series data should be collected for the dynamic prediction, which could be the fresh input into the Kalman filter in order to obtain the optimal estimation closer to the actual state. The whole process of dynamic prediction for corporate financial distress is described as follows.
\nFrom \nFigure 2\n, we can see that if we want to predict the corporate financial state at time t + 2, we just need to know the optimal estimation of the corporate financial state at time t and the signal indicators observed at time t + 1. The rest may be deduced by analogy; if we want to predict the corporate financial state at time t + n, we just need to know the optimal estimation of the corporate financial state at time t + n−2 and the signal indicators observed at time t + n−1. This continuous prediction does not require saving the observed data in the past. Every time the new signal indicators are observed, they are put into the Kalman filter as fresh. It helps solve the problems of storing, calling, and processing the massive data and thus greatly improving the speed of operation on the computer.
\nA whole process of dynamic prediction for corporate financial distress.
Further, if we want to predict the corporate financial state n-step ahead, we can obtain the n-step-ahead prediction algorithm derived from the basic Kalman filtering algorithm according to the dynamic prediction process described above.
\nThe general n-step-ahead prediction algorithm is derived as
\nThe n-step-ahead prediction error variance matrix is
\nAssume that the system parameters At\n and Qt\n have nothing to do with the time, then the above equations simplify to
\nBased on the Eqs. (9)–(12), we could use data at shorter time interval to predict n-step ahead, but the prerequisite of sufficiently long-term data to find out the trend of development of the financial state should be satisfied.
\nIn the dynamic prediction models for financial distress established in Section 2, we suppose that the financial state X cannot be observed. But in reality, whether the company is trapped in financial distress at time t-1 and before can be known at time t, that is part of X can be observed. We put this part of observed information into a likelihood equation in order to improve the accuracy of dynamic prediction. The probability of the company being trapped in financial distress is
\nwhere, Sc\n is the critical value; x\n\nt + 1 equals \n
If M is the last year that X can be observed, then the additional estimation equation is
\nThis additional estimation equation is used every year, no matter if 1 year is divided into n periods. If standing at managers’ position and suppose we know the corporate financial state at time t−1, when we stay at time t, and then the additional estimation equation can be embedded in the general n-step-ahead prediction algorithm every time to help improve the accuracy of dynamic prediction.
\nManufacturing industry is a major industry in China. “Made in China” has an important impact on the global economy. Therefore, prediction of corporate financial distress for China’s manufacturing industry is of great significance. Generally, the manufacturing companies have complete production processes, equilibrious production cycle, as well as a more stable trend of development of the financial state. The characteristics of these companies can be well described using the existing financial indicators, and the dynamic prediction method described above can be put into practice for these manufacturing companies.
\nIn this research, the data for our experiment are collected from the Shanghai Stock Exchange and Shenzhen Stock Exchange databases in China. ST (special treatment) companies because of financial problems are selected as distress samples; meanwhile, companies of similar asset size that have never been special treated are selected as healthy samples. The ST time is treated as period T. For a 6-month interval, the data 8 years or 16 periods before ST are selected as time-series sets for the distress samples. The time span of the paired samples is the same as the distress samples.
\nAccording to the above principles, the data of 152 listed companies are collected, and the time span is year 2002 to year 2009, year 2003 to year 2010, year 2004 to year 2011, respectively. A total of 60 ST companies and 60 paired companies of the first half of year 2010 and 2011 are treated as training set, which is used to derive the model. A total of 16 ST companies and 16 paired companies of the first half of year 2012 are treated as testing set, which is used to test the effect of the model.
\nFrom the holistic perspective, we select 29 financial indicators covering four aspects of profitability, solvency, management efficiency, and market reaction as alternative signal indicators. The effect of the corporate financial problems may be amplified or reduced in information transmission mechanism of the market, and the problems may be exposed to the open market in advance or with a delay. If the problems are exposed in advance, the indicators can be used as a pilot signal of financial distress prediction; if delayed exposure, it can also be served as comprehensive evaluation of financial distress or the signal for the trend of development in the future. These are indicators of market reaction. The 29 signal indicators are listed in \nTable 1\n.
\nType | \nCode | \nSignal indicators | \n
---|---|---|
Profitability | \n\nZ\n1\n | \nOperating profit margin | \n
\nZ\n2\n | \nNet profit margin | \n|
\nZ\n3\n | \nReturn on assets | \n|
\nZ\n4\n | \nReturn on equity | \n|
\nZ\n5\n | \nOperating profit margin growth | \n|
\nZ\n6\n | \nOperating revenue growth | \n|
\nZ\n7\n | \nTotal assets growth | \n|
Solvency | \n\nZ\n8\n | \nCurrent ratio | \n
\nZ\n9\n | \nQuick ratio | \n|
\nZ\n10\n | \nCash debt ratio | \n|
\nZ\n11\n | \nDebt coverage ratio | \n|
\nZ\n12\n | \nInterest coverage ratio | \n|
\nZ\n13\n | \nLiabilities to assets ratio | \n|
\nZ\n14\n | \nLiabilities to equity ratio | \n|
Management efficiency | \n\nZ\n15\n | \nTotal assets turnover | \n
\nZ\n16\n | \nFixed asset turnover | \n|
\nZ\n17\n | \nCurrent assets turnover | \n|
\nZ\n18\n | \nInventory turnover | \n|
\nZ\n19\n | \nAccounts receivable turnover | \n|
\nZ\n20\n | \nCash ratio of main business | \n|
\nZ\n21\n | \nCash return on assets | \n|
Market reaction | \n\nZ\n22\n | \nEarnings per share | \n
\nZ\n23\n | \nNet assets per share | \n|
\nZ\n24\n | \nOperating revenue per share | \n|
\nZ\n25\n | \nCapital reserve per share | \n|
\nZ\n26\n | \nRetained earnings per share | \n|
\nZ\n27\n | \nPrice to book ratio | \n|
\nZ\n28\n | \nEquity to invested capital ratio | \n|
\nZ\n29\n | \nNet cash flow per share | \n
Comprehensive signal indicators of financial distress prediction.
A three-dimensional database is established consisting of 16 periods’ time-series data of the above 152 sample enterprises, the financial state of which is represented by 27 signal indicators each (As operating profit margin growth (Z\n5) and interest coverage ratio (Z\n12) have much missing data, we ignore these two subsets of the data, leaving the rest 27 subsets.). The dynamic prediction method described above is based on the trend of the time-series data. The centralized tendency of signal indicators of profitability, solvency, management efficiency, and market reaction is shown in \nFigures 3\n–\n6\n (Some indicators of management efficiency and market reaction show cyclical fluctuations, so we amend these indicators by smoothing. \nFigures 5\n and \n6\n have been amended.).
\nThe centralized tendency of signal indicators of profitability for distress samples and healthy samples.
The centralized tendency of signal indicators of solvency for distress samples and healthy samples.
The centralized tendency of signal indicators of management efficiency for distress samples and healthy samples.
The centralized tendency of signal indicators of market reaction for distress samples and healthy samples.
From \nFigures 3\n–\n6\n, we can see most indicators show a certain trend, which is the foundation of dynamic prediction.
\nThen, we use nonparametric test of Mann-Whitney U to find out when the difference between distress samples and healthy samples occurs. The results show that the gap between the two is maximized 2 years before ST time and the significant difference occurs 4 years before ST time, that is, the earliest time to accurately predict the occurrence of financial distress should be 4 years before ST time.
\nWe use principal component analysis to eliminate the effect of multicollinearity on the original variables. We extract first 10 principal components, and the accumulative contribution rate is above 92% each for 152 companies. These principal components are linear combinations of the original signal indicators, which can be served as part of discriminant models for each company.
\nThe parameters of process model are estimated from the data of training set and also using the data of training set, the judgment for the threshold of financial distress is set as an interval, which has lower and upper confidence limit.
\nThe results show that the lower confidence limit is −0.796 and the upper confidence limit is 0.205. When the predictive value of a company’s financial state is lower than −0.796, the company may fall into severe financial distress; when the predictive value is higher than 0.205, the company is well operated; and when the predictive value is between −0.796 and 0.205, it is possible that the company is getting into financial distress.
\nThen, we test the effect of the dynamic prediction models using the data of testing set. Subject to space restrictions, we just list dynamic prediction figures for six companies, among which first three are ST companies, while the other three are non-ST companies. Names and stock codes of the companies are Sichuan Chemical Company Limited (000155), MCC Meili Paper Industry Co., Ltd. (000815), Guangzhou Guangri Stock Co., Ltd. (600894), Xinxiang Chemical Fiber Co., Ltd. (000949), Nantong Jiangshan Agrochemical & Chemicals Co., Ltd. (600389), Nanzhi Co., Ltd., and Fujian (600163), in turn. Dynamic prediction figures for these six companies are shown in \nFigure 7\n.
\nDynamic prediction figures for part of testing samples.
The testing results show that almost all the curves of predictive value fits the ones of real value for 32 testing samples.
\nOf 16 distress testing samples, 15 companies give mild alarm in period T−9 (4 years ahead), and 13 companies give severe alarm in period T−7 and period T−5 (3 years ahead and 2 years ahead). All the 16 companies give severe alarm in period T−3 (1 year ahead). This shows that the information the dynamic model absorbed and produced almost covers the characteristics of financial distress. An accurate warning can be made 4 years before financial distress, and the accuracy is 93.8% (We also take triple BPNN to make a comparison. The results show that the accuracy of prediction 1 year ahead to 4 years ahead is 100, 93.8, 62.5, and 43.8%, respectively. The accuracy sharply declines 3 years ahead. It shows that the triple BPNN has better effect for short-term prediction rather than long-term prediction.)
\nFor healthy testing samples, none is lower than the severe alarm limit. But sometimes, the predictive values appear slightly below the mild alarm limit, showing that there have been cases of temporary deviation from healthy state for healthy testing samples. The dynamic model conducts a track and thereafter modifies. This shows that the model can objectively track and effectively predict the overall financial state of a company from a long run.
\nIn this chapter, we focus on the dynamic nature of corporate financial distress and establish dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and a general n-step-ahead prediction algorithm based on Kalman filter is derived for prospective prediction. To validate the prediction performance of this method, we conduct an empirical study for China’s manufacturing industry. The empirical results have proved the accuracy and advance of predicting financial distress using this dynamic model. The accuracy of prediction 4 years before financial distress is 93.8%. In addition, this method also solves the problem of massive data processing as it does not require storing large amounts of historical data and thus can achieve real-time processing of data.
\nIn this research, we suppose the dynamic process of financial distress is linear. The Kalman filtering algorithm will be applied to a nonlinear dynamic model in the future research, and it will offer a wider range of applications.
\nThis research is supported by National Natural Science Foundation of China (Grant no. 71602188) and National Social Science Foundation of China (Grant no. 15ZDB167).
\nEnvironmental stresses, especially water stress, seriously limit plant growth as well as plant productivity [1, 2, 3]. Thus, we speak of water stress when the demand for water exceeds the available resources. This category includes countries where the availability of water per year and per inhabitant is less than 1700 m3, particularly in semi-arid regions. Arid zones are characterized by a negative hydric balance, mean annual rainfall of less than 800 mm, with a mean insolation of 2800 h/year and means annual temperatures of 23–27°C. These conditions lead to high evapotranspiration rates, carrying a water deficit in nearly the entire region [4]. Abiotic stresses and inefficient production techniques are the obvious factors limiting crop productivity, especially in the southern parts characterized by severe aridity [5, 6, 7].
Faba bean (Vicia faba L.) is a crucial grain crop through the word; ordering as the fourth crop legume after dry beans, dry peas and chickpeas. The bean is of great importance for nutrition, it enters in human consumption and also for animal feed [8]. North Africa appears to be one of the best faba bean output global regions and especially, Tunisia.
In Tunisia, faba bean covers more than 68% (70,000 ha) of the total area annually devoted to grain legumes crops and the national average yield is around 50,000 tons [9, 10]. Although the agro-economic importance of the bean is known, its cultivation is in decline, both in some countries of the world and in Tunisia [9]. The areas reserved for legumes in Tunisia in gradual fall [11]. This mainly affected the peas and broad beans. The national average bean yield is low and fluctuates widely from year to year. This is mainly due to climatic variations [6].
The seeds of the bean are very rich in proteins (25% of the dry matter of the seeds) and especially in lysine but poor in amino acid [12]. It is also a source of carbohydrates, mineral salts, fiber, vitamins and little lipids. The seeds are the most consumed parts and like all vegetables; the bean is self-sufficient for nitrogen nutrition. The bean is used as a green manure which serves as well for the improvement of the structure of the soil as for its enrichment in mineral elements. In addition to its nutritional and fertilizing (green manure) utility, the bean also has medicinal interests [13].
The success of agriculture in arid and semi-arid zones worldwide will be tremendously dependent on the ability of agricultural systems and farmers to adjust to climate change [14]. A vast majority of these areas, despite the huge production, suffer from limiting resource of water [15]. As that resource becomes more scarce and availability more difficult to predict, water managers and farmers will be forced to implement new creative solutions to water supply challenges. Expected exposure indicates improvement and better understanding of culture adaptation to water stress in such areas is high-priority to successful development in these regions under a changing climate. This work focuses on understanding the effect of water stress on morphological, physiological and biochemical aspects of Vicia faba L. plant grown in Tunisian Arid Region.
Water is a vital element for the growth and development of plants. It generally constitutes 85–90% of the total fresh weight of plants. Water is the natural vehicle for all the substances circulating in the plant, the elements absorbed in the soil or the organic substances produced in the leaves circulating in the saps of the xylem and phloem [16]. The need for water for the plant is proven by multiple observations such as wilting and death of plants left without water, hence the importance of irrigation and the relationship between vegetation and rainfall [17]. Plants can get the water they need through their roots. These extract water from the soil to pass it through the plant; part of this water is then released into the atmosphere (a phenomenon called transpiration) [18]. Omprakash et al. [19] defines water deficit or water stress as a period of insufficient water activity in the plant. According to this same author the notion of stress implies on the one hand a more or less abrupt deviation from the normal conditions of the plant and on the other hand a sensitive reaction of the plant in the different aspects of its physiology which changes appreciably with either adaptation to the new situation, ie to the limit degradation leading to a fatal outcome. On the contrary, according to Dodd and Ryan. [20] any water restriction resulting in a drop in the plant’s production potential, following a disturbance of its physiological activity caused by a deficit in water consumption is called water stress.
The experiment was carried out during 2019/2020, in a plastic greenhouse at the IRA (Institute of Arid Regions), located 22.5 km north-east of the city of Medenine. This region is located in the lower arid bioclimatic stage characterized by a low annual rainfall of around 150 mm/year; high temperature and frequent winds. Vicia faba seeds used in this test is the Vicia faba L. originating from Medenine and Gabès (ElHamma and Mareth). The latter ascensions tested were sown in pots filled with soil in February 2019 Three seeds were planted per pot; Three repetitions for each ascension were established. Water used for irrigation is tap water, titrating ±2 g/l and a pH of 7.6. Two water treatments were tested: T0 = 100% CC (capacity in the fields 100%) and T1 = 50% CC. The quantities of water (in ml/kg of soil) added each week (220 ml/kg for T0 and 110 ml/kg for T1). The dry weight of leaves and roots was carried out separately. The above-ground and underground dry matter is obtained after passing in an oven for 48 hours at 75°C. Relative water content of faba bean for each treatment were determined by the following method: Cut the leaf blade into small squares, weigh them to determine the fresh weights (FW) and then place them in a box of petri dish at 5°C. After 24 hours, the pieces of leaves are removed and then weighed at full turgor (TW). They are then placed in an oven at 75°C and then weighed again to determine their dry weight (DW). Chlorophyll contents determined by Gross method [21] and to quantify soluble sugars, the phenol method of Dubois et al. [22] was used.
Three accessions (El Hamma, Mareth and Medenine) of faba bean have been analyzed for dry matter accumulation. Significant effect of treatment (P < 0.005) was observed and no difference has been established between ascensions (Figure 1). Under moderate water stress treatment (T1), faba bean (i.e., El Hamma, Mareth and Medenine) accessions were significantly decreased their shoot and root DW as compared to control plants. Total leaf and root dry masses were all two to three times greater in the well-watered treatment (T0) than those in the water-limited one (T1). The watering treatment showed significant effects among localities on apparent shoot biomass and apparent root biomass (Figure 1). Risk-taking by minimal reduction of shoot DW under water stress was revealed as a physiological indicator for drought resilient at the flowering stage for faba bean [23]. The capacity of populations to maintain shoot biomass in water shortage was displayed to be an important trait for drought tolerance in soybean plants [24]. Under drought faba bean decreased their root DW (Figure 2). Root growth reduction was also reported for pea plant [25]. According to Chavoshi et al. [26] severe drought stress reduces biomass and seed yield (by 20–90%).
Changes in (a) Shoot DW, (b) Root DW for Vicia faba L. accensions in T0 (control) and T1 (moderate deficit irrigation (50%FC)). Means ± S.E. (n = 3). Means with different letters (a, b, c) are significantly different at P < 0.05 and values sharing a common letter are not significantly different at p < 0.05.
Changes in relative water content (RWC) for V. faba leaves in T0 (control) and T1 (moderate deficit irrigation (50% FC)).
As shown in Figure 2, RWC varied significantly among accessions and RWC was significantly higher as expected in non-stress conditions (T0) than in stressed plants (T1). This parameter is sufficiently influenced by water restriction (P < 0.001). Thus, leaves of the three ascensions studied reacted with a considerable drop in their relative water content (RWC). In El Hamma, the RWC is maintained at high values for irrigated plants and stressed plants, while in Mareth and Medenine, plants cultivated under T1, exhibited a significant decrease compared to the control of about 9.74% and 24.73% respectively. Repeated measures ANOVA, indicated significant interaction between accession and stress deficit (P < 0.01) for RWC. The analysis of the relative water content makes it possible to give a clue and describe in a global way the water status of beans in response to water stress. Decrease in RWC in plants under drought stress have been perceived in many plants and may depend on plant vigor. Likely, in this constraint, the ability to achieve good osmoregulation and to preserve cell turgor is reduced [27]. It seems that concentration of organic solutes to maintain membrane is not sufficient in this case [28, 29].
Chlorophyll ‘A’, ‘B’ and total chlorophyll contents, showed no significant difference among the accessions in the stress conditions, but significant difference under the non-stress conditions (Figure 3). The decrease in the chlorophyll contents in the three ascensions studied during the period of stress would be most likely due to the increased in catalytic activity of chlorophylls and degradation of photosynthetic pigments and this process is also the result of not providing the necessary factors for the synthesis of chlorophyll and the destruction of its structure under stress conditions [25]. Zhu [30] has been reported that a decrease in the chlorophyll content will be due to a decrease in the stomata aperture and aimed at limiting water losses by evaporation and by increased resistance to the entry of atmospheric CO2 necessary for photosynthesis [31]. These results are in agreement with Tairo et al. [32] who reported a significant increase in chlorophyll ‘A’, ‘B’ and total chlorophyll contents was seen in varieties 1(KAT B9), 4(F8 Drought line) and 5(JESCA) of common bean Phaseolus vulgaris (L.).
Changes in Chlorophyll (a), (b) and (a+b) for V. faba leaves in T0 (control) and T1 (moderate deficit irrigation (50% FC)).
Induced drought stress influenced (P < 0.001) soluble sugar contents (Figure 4). Untreated plants showed the lowest concentration (0.54 mg/g for Mareth accenssion). However, the highest soluble sugar content (1.31 mg/g) was noticed under water deficit condition for ElHamma cultivar. These metabolic adaptations that improve plant tolerance to osmotic or water stress involve an increased synthesis of compatible osmolytes as soluble sugars and proline. This increase in sugar content under stress might contribute for osmotic adjustment and plays an important role in scavenging free radicals and protect DNA from damaging effects of increased reactive oxygen species (ROS) levels due to water stress [33, 34]. Moreover, soluble sugar accumulation was considered as an indicator of plant tolerance to drought conditions. It improves water absorption and reduces water loss when stress leads to slower growth [35, 36].
Effect of water stress on soluble sugar contents of V. faba L. leaves.
The effect of sodium chloride (NaCl) concentrations (0.0, 60, 120, 240 mM) on growth and chlorophyll content of (Vicia faba L.) seedlings caused an increase for both fresh and dry weights of the shoot. Salinity significantly reduced chlorophyll ‘A’ content. It also significantly reduced chlorophyll ‘B’, total chl., and carotenoїds contents after ten days of treatment [37]. High-temperature stress gravely reduced the photosynthesis, stomatal conductanceand water use efficiency of V. faba [38, 39, 40]. The effects of magnesium (Mg) supplementation on the growth performance as well as, on photosynthetic pigment synthesis, were studied in Vicia faba L. plants exposed to heat stress and non-heat-stress conditions. Results revealed that growth attributes and total chlorophyll decreased in plants subjected to heat stress, whereas accumulation of organic solutes had seen. These results suggest that adequate supply of Mg is not only essential for plant growth and development, but also improves plant tolerance to heat stress by suppressing cellular damage induced by ROS through the enhancement of the accumulation of Proline and glycine betaine [41].
Crop production is influenced by multiple environmental abiotic factors including drought, soil salinity, waterlogging and temperature interferes with the whole metabolic activities in plants [42, 43]. Plant adopted different mechanisms and modifications to cope with stresses such as plant hormones that facilitating growth, nutrient allocation and development [44, 45]. Salicylic acid (SA) significantly increased the Mn+2, Co+3, Fe+2, Cu+3, K+ and Mg+2 while decreased the Na+, Ni+3, Pb+4, Zn+2, and Na+/K+ content of roots and soil content under salinity stress. SA (10-5 M) can be implicated to mitigate the adverse effects of salinity on maize plants [46, 47]. Additionally, exogenous application of gibberellic acid (GA3) reduced oxidative stress in C. capsularis seedlings. GA3 not only ameliorate photosynthesis, biomass and plant growth, foliar application of GA3 but also, increases metal (Cu) concentration in different organs of the plants when compared to 0 mg/L of GA3. GA3 plays a promising role in reducing ROS generation in the plant tissues [48, 49]. Furthermore, melatonin application (100 mM and 500 mM) maintaining plant growth and relative water content. More than that, improving the photosynthetic characteristics, total carbohydrate, and total phenolic content in leaf tissues of faba bean plants irrigated with saline water. Melatonin might directly eliminate ROS when produced under stressed conditions [50].
Mineral compounds play too a key role in the structural and functional integrity of the plant to better respond to abiotic stresses. Silicon application enhances drought tolerance of Kentucky bluegrass by improving plant water relations and morpho-physiological functions [51, 52]. Moreover, the applications of N fertilizer linearly increased kernel weight during both growing seasons [53, 54]. N deficiency reduces photosynthates production in plants while an optimum N fertilization may increase weight and kernel corn quantity resulting in higher crop growth rates [55]. In addition, a combined application of phosphorus (P) and biochar mitigates heat-induced adversities on physiological, agronomical and quality attributes of rice. The application of biochar+P recorded a grain yield of 7% (plant-1) of rice compared with control throughout different temperature treatments and cultivars. The highest grain production and better grain quality in biochar+P treatments might be due to enhanced water use efficiency, photosynthesis and grain size, which offsetting the adversities of high temperature stress [56, 57, 58]. So, fertilization enhances heat injury in rice (Oryza sativa L.) [59].
In conclusion, water stress seems to be the main limitation factor to growth process in V faba L. Under moderate stress (50%CC), the tested ascensions responded similarly on how to cope with water stress. But, El Hamma had the highest performance as compared with Medenine and Mareth accessions. V. faba maintains their relative water content at high values and accumulates a high concentration of soluble sugars in their tissues. Accordingly, the dehydration avoidance is considered to be an adaptive mechanism for minimizing water loss. The evaluated V. faba accessions responded to water stress also, by reducing shoot and root biomass. Besides, morpho-physiological attributes relative to shoots and roots play an important role in the response to water stress and contributed to drought tolerance of plants. Therefore, it is imperative to identify the specific physiological process of drought tolerance in faba bean, and to define as valuable selection criteria under stressful conditions. Thus, nutritional and economic values of V. faba L. can be enhanced in Tunisia.
The authors are grateful to Mr. Lachiheb Belgacem. lab technician from the Arid Land Institue for their collaboration in the laboratory analysis.
The authors declare no conflict of interest.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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