text:
Role of the Biological Processes in Soil forming Process and Soil Fertility (Azerbaijan, Shamakhy)

(Figure 1) An importance of the biological processes, mainly microbiological process is great in a period of the soil science and biology science development, especially in biotechnology science formation. The significance for the microbiological process in the xx century is explained that an intensity of the processes in our environment, biosphere is closely connected with microorganisms activity. Synthesis and resynthesis of organic substance, humus mineralization and humification processes in soils are performed by microorganisms. Microbiological process is great in decomposition of the nutrient total forms, in provision of soils with the nutrient. It is already determined that the substances which are synthesized by microorganisms regulate the complex process occurring in the soil [1,2]. Turning of the plant residues and coarse organic substances into humus mainly occurs under an influence of the complex ecological factors and forms a basis of the soil fertility. Figure 1: Importance of the biological processes *Corresponding author: RF Bagirova, Institute of Soil Science and Agro chemistry, NAS, Azerbaijan Received Date: July 15, 2019 Published Date: July 19, 2019 ISSN: 2687-8100 DOI: 10.33552/ABEB.2019.02.000537

Introduction
( Figure 1) An importance of the biological processes, mainly microbiological process is great in a period of the soil science and biology science development, especially in biotechnology science formation. The significance for the microbiological process in the xx century is explained that an intensity of the processes in our environment, biosphere is closely connected with microorganisms activity. Synthesis and resynthesis of organic substance, humus mineralization and humification processes in soils are performed by microorganisms. Microbiological process is great in decomposition of the nutrient total forms, in provision of soils with the nutrient. It is already determined that the substances which are synthesized by microorganisms regulate the complex process occurring in the soil [1,2]. Turning of the plant residues and coarse organic substances into humus mainly occurs under an influence of the complex ecological factors and forms a basis of the soil fertility. The Course of the Study The microorganisms decompose organic substances in the soil, turn the nutrient in their composition to a form which can be appropriated for the plant, create a favorable condition for their nourishment. The microorganisms also perform sanitary work, prevent from contamination of the environment, especially, soil.
As it is not noted that there is a great role of the microorganisms in soil fertility formation. They carry on the organic substance decomposition till the whole mineralization. The plants synthesize the organic matter, the animals fulfil its mechanical and biochemical decomposition and prepare for humus formation in future. But the microorganisms finish the substance decomposition, synthesize the alters. In this connection an importance of the soil microflora is great in assimilation of the the soils, exposed to the anthropogenic pressure and degraded. This is also connected with intensive use of soils. Management of the soil fertility requires an investigation and regulation of their biological activity. In this connection some groups of the researchers gave the detailed information about the soil roofauna, biochemical processes, physico-chemical characters, anthropogenic soils genesis held on the international congresses in the Turkey republic in 1998 and " Anthropogenic soil forming problem" held in Moscow in 1997 [4][5][6][7].
We should note that only physico-chemical methods aren`t enough in investigation and analysis of soils. The soil scientists already consider that the research of the biological processes is a great problem in assimilation of the soils genetic characters, clarity of their fertility pecularities and definition of the processes direction.
Such an opinion is already formed that a closely relation available between the plant-soil and microorganisms was investigated for the purpose of soil fertility research.
Because of more humus in the mountain-black soils an activity of microorganisms is quick. Since before the xx century a role of microorganisms was exactly investigated in creation of the complex processes, including bio-chemical processes, especially humus formation, turning of the atmospheric nitrogen to the biological nitrogen. In connection with this problem Z.H.Aliyev., M.P. Babayev, P.A.Kostichev, Vinogradsky, Shakuri B.Q. and others expressed opinions [3,4,7,9]. The authors show that the microorganisms` role is fixing in decomposition of the plant residnes, dead organic world and coarse molecular organic substances and initial humus formation.
As is known the all groups in soil microorganisms are available and the separate groups possess a special function. The enough information was given about a participation of the same groups in humus forming process. A role of microorganisms in soil forming process and humus synthesis was studied by the Azerbaijan scientists. It was determined that the microbiological activity rises while a height of the zone increases till a definite level. The number and activity of microorganisms aren't at the same Level, they depend on hydrothermal regime, fertility, structure, plant cover, climate characters, humus supply, nutrient quantity and so on.

Discussion & Materials
As a result of the joint impact of the anthropogenic and natural factors the soils expose to the erosion process, their upper rotten.
Layer are washed out and Lose their fertility. The humus quantity in such soils gets reduced for the first time, its fraction structure detetime, its fraction structure deteriorates, the mobile part of humin acids which is agronomically valuable decreases. So, erosion negatively affects the main parameters of the humus forming process in soil. Erosion deteriorates decomposition of CO, its respiration in soil [9,10].
The erosion process weakens the microbiological process which is one of the main factors in soil fertility. It should be noted that the soil microflora is a decisive factor of the biochemical processes and regulates the humus synthesis and mineralization. Generally, the erosion process deteriorates physicocharacters, its water regime and food balance. The erosion process reduces water supply of soil, creates arid conditions and this is dangerous for dry-farming land.
An evaluation of all the factors (plant, soil, relief, climate and so on) is reguired for a comparative character of the soil productivity and fertility and this forms a complex valuation method of the Page 3 of 4 soils agro ecological condition. The microbiological processes in eroded and average eroded kinds of the mountain-black and steppe mountain-brown soils were investigated according to the seasons in the Shamakhy region which is situated in the south-eastern part of the Great Caucasus. An activity of bacteria, radiant fungus and microscopic fungus from microorganisms was studied [8].
Study of the microbiological activity in the un eroded mountain-black soils indicated that (Table 1) (Table 1).   (Table 2).

1.
Importance of the microbiological process which was paid attention in the xx century is that an intensity of the processes occurring in our environment, biosphere is connected with the microorganisms activity. The organic substance synthesis, rot mineralization and humification processes are performed by microorganisms. A role of the microbiological processes is great in decomposition of the total forms in the nutrient, provision of soils with the nutrient.

2.
The erosion process weakened the food potential in the mountain-black and steppe soils, deteriorating the soil fertility parameters limited its fruitfulness ability. The fertility parameters were higher in the mountain-black soils. The mountain-black soils were provided with the nutrient well.

3.
Because of the favorable structure in the mountainblack soils, the soil composition is rich in organic matter. The agrotechnical measures consist of performing the tillage and all the cultivation works against the winds direction and slopes diametrically and preserving in moist and so on.


extfieldsofstudy: Physics Mathematics

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
The Factors Influencing Market Price Of Sago Starch In Meranti Islands By Linear Regression

One of the primary food from Indonesia is sago, and one of the sago producers is the Meranti islands. In addition to processed sago products into foodstuffs, sago products are also sold as sago starch. This study discusses the three factors that influence the market price of sago starch are the number of sago farmers, the area of sago land and annual production. Machine learning approach used is the linear regression. After training data, it can be observed that there are two factors that have a significant influence on the market price of sago starch. The results can be reference material for the industrial and trading offices of Meranti islands in observing the annual sago price so that the farmers and sago entrepreneurs can get the appropriate price of sago starch.

Introduction
Indonesia is a country rich in natural resources, especially for primary food, one of the primaries is sago. Sago is a plant producing carbohydrate derived from its stem. Sago also has been consumed as a primary food in Eastern Indonesia. Until now, sago is the highest source of carbohydrates in the world because it can produce 200-400 kg of dried starch per stem [1]. One of the largest sago producers in Indonesia comes from districts of meranti islands. Meranti Islands is an expansion of Bengkalis territory established on December 19, 2008. This districts also included in the triangle of economic growth Indonesia -Malaysia -Singapore (IMS-GT) [2].
The sago harvested every period is used for residents in Meranti Islands is only 10% of the total production, but 90% of the sago produced is sold elsewhere such as Java Island in the form of sago starch. The fixed market price of sago starch in kilogram from the data of last seven years did not change much, but the number of farmers, the amount of sago farm and the amount of production per year are significantly increase [3]. This research will observe and analyze whether the number of sago farmers, the area of the farm and the amount of production remains to influence the price of sago. Researchers used a linear regression approach to analyze the impact of factors affecting the market price determined by the local government. Linear regression is an approach used to assess the effect of one or more exogenous to one endogenous variable [4]. The aim of this research can be a consideration of the local government in determining the market price so that sago farmers get a right price, sustainable sago plantation sector, increasing the regional economy and the welfare of sago farmers can be assured.

State of The Art
Districts of Meranti islands has nine subdistricts; the total area is 3,707.84 km 2 with 216,329 population. This area consists of four large island clusters and one of the islands districts in Riau Province. Geographically, this districts is in the international shipping and trade line of Malacca Strait and two neighboring countries, Singapore and Malaysia [5]. Sago plants grown in Meranti islands has become the primary commodity in driving the regional economy. Sago (Metroxylonsago, Rottb.) is a vital source of carbohydrate as a foodstuff besides rice, corn, and tubers. In Indonesia, sago has been processed into both food and non-food products [6]. The type of sago growing in the area of Meranti is sago buni / thorn, sago sangka and sago bemban [7] as shown in Figure 2:

Figure 2. Sago In Meranti Islands
The data generated from the production of agricultural products such as sago in meranti islands and its supporting factors can be processed to deliver modeling, visualization, prediction, and classification. This process is known as machine learning. In data analysis, machine learning is a method used to design complex models and algorithms that fit the predictions. This analytical model allows researchers, data scientists, engineers, and analysts to "produce reliable and recurring decisions and results" and find "hidden insights" through learning from historical relationships and trends in data [8], [9], [10]. One of the methods is the linear regression. It is a linear approach to modeling the relationship between an endogenous and one or more exogenous variables. Mathematically, the basic formula of linear regression can be written as follows [11]: Linear regression can be described as regression analysis where there is a linear relationship between the exogenous (x) and endogenous (y) variable. Visually, the relation between the x and y variables can be described as follows: One of previous work related to this research is Deforestation driven by urban population growth and agricultural trade in the twenty-first century. This work discusses the population growth and trade in farm products that affect deforestation. They said, reducing atmospheric carbon emissions from tropical deforestation is considered a cost-effective choice for mitigating climate change. They were using satellite-based estimates of forest loss for 2000 to 2005 to assess economic, agricultural and demographic correlates across 41 countries in the humid tropics. The methods used is linear regression and regression tree, show that forest loss is positively correlated with urban population growth and exports of agricultural products for this period. Rural population growth is not associated with forest loss, indicating the importance of urban-based and international demands for farm products as drivers of deforestation [12].

The Proposed Method
The required data comes from the industrial and trading offices of Meranti. The data collected are sago farmer's data, sago land area, annual production and market price per year. The data obtained from 2011 to 2018 is because the relevant office established independently since separated from Bengkalis district and become the new district in 2011. The number of sago farmers, the area of sago land and the annual production was the exogenous (x), while the market price per year was the endogenous (y). The relationship between variables seen in Figure 4   As a visualization, the relationship between variables will generate results in the form of linear regression graphs to visualize the distribution of the data, also regression tree as a rule generator. Later, it can be used as consideration for the decision maker. The red line in the linear regression graph is referred to as the best fit straight line as seen in figure 3 below [13]:

Result
Based on obtained data, as the first is to analyze the relationship between variables. The aim was to determine the influence of exogenous to endogenous. The results can be seen in the following table:    have the more significant influence on market price than the number of farmers. After obtained the amount of influence between variables, we created a visualization of linear regression; it can be seen in the following figure: Figure 6. Linear Regression Graph Based on Figure 6 it can be seen that the distribution of dots on the graph follows the exogenous variables. However, if we analyzed the market price as a class variable, it can be seen that the distribution of dots at 4800 and 5800 is relatively the same. This indicates that the shift of sago price follows the factors such as the number of sago farmers, annual production and area of sago land. In other words, If there was an escalation in these factors, then the price of sago will be adjusted as well. The results seen from the graph in figure 6 are also supported by the rule tree as follows:  Besides, the tree can deliver the average market price, so it is not fixed on the highest price or the lowest price only.

Conclusion
In this study based on field observations and data from the relevant source, the factors that influence the price of sago starch are the number of sago farmers, annual production and area of sago farm. In addition, to analyze the effect of exogenous to endogenous variables, using linear regression can also be delivered graphical visualization to see the distribution of data on which factors have a significant influence on the market price of sago starch. Besides, the resulting rule tree can be a reference price to avoid selling at the highest price or the lowest price only. The results of this study can indeed be a reference to the industry and trade offices meranti district in viewing the development of sago starch prices so that farmers and sago entrepreneurs gain an appropriate rate in selling their sago starch.
After the research was completed by mid-year of 2018, there was no other study to examines the factors that influence the market price of sago starch by machine learning. The method, linear regression, is a simple machine learning method. There should be a review using other ways to get more comparison. Also, several other factors that could be considered material for further research. There are the number of sago refineries and the distance between sago farms to sago refineries and available containers, which may affect the market price of sago starch. By using the additional factors is expected to enrich the research related to sago starch.


extfieldsofstudy: Biology Medicine

text:
Antioxidant and phytochemical activities of Amaranthus caudatus L. harvested from different soils at various growth stages

This study aimed at profiling the biological activities of Amaranthus caudatus cultivated on different soils in a glasshouse experiment. Five soil types namely; sandy clay loam, silty clay loam, clayey loam, loam and control (unfractionated soil) were experimentally formulated from primary particles of clay, sand and silt following the United State Department of Agriculture’s (USDA) soil triangle technique. After harvesting at pre-flowering (61 days after planting), flowering (71 days after planting) and post-flowering (91 days after planting) stages, crude extracts were obtained with water and ethanol. Total flavonoids, phenolic and proanthocyanidin contents of the extracts, as well as their biological activities, were determined using 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), 2,2 diphenyl-1-picrylhydrazyl ethanol (DPPH), nitric oxide and phosphomolybdate assays. It was observed that biological activity of A. caudatus varied with soil types, stages of maturity and solvents of extraction. The highest phytochemical yield was recorded in ethanolic extracts of clayey loam harvested prior to flowering and the same trend was replicated in the antioxidant properties of the plant. For optimal biological activity, it is recommended that clayey loam soil should be used for cultivation of A. caudatus and harvest should be made near flowering to capture high phytochemical yield from the species.

Natural compounds such as storage lipids, fragrances, essential oils, flavonoids, polyphenols, and pharmaceutics extracted from plants, have been extensively investigated for their food value and are used as precursors by cosmetics and pharmaceutical industries 1,2 . Over a hundred thousand of these secondary metabolites are either biosynthesised via acetate pathway or derived from alkaloids, phenylpropanoids and isoprenoids 3,4 . The biological activity of these natural products is mostly driven by the evolutionary process as related plant families usually make use of analogous chemical structures in building up resistance against diseases to improve their defence mechanism 5,6 .
A combination of soil and other environmental factors are capable of altering plant structure and its phytochemical activity, thereby affecting the interaction between the plant and the environment and ultimately, the depot of bioactive compounds in the leaves 7,8 . In addition, the variation observed in the germination response of A. caudatus to soil types is an indication that soil type, texture, water relation and mineral composition of the soil affect growth performance of the plant and most likely, the number of secondary metabolites produced by the plant 8 . Likewise, accumulation of vitamins and phenolic compounds has been reported to have a direct relationship with soil mineral content and it relates in inverse order with the growth rate of plants 9 .
As part of normal cellular activity, production of free radicals results from both endogenous and exogenous reactions taking place in a biological system. Most of these free radicals are short-lived derivatives of oxygen and they possess unpaired electrons capable of existing independent of other molecules [10][11][12] . A. caudatus is an ancient crop with protein content greater than most cereals 13 . It has been widely reported in the literature as an important source of bioactive compounds such as lectin, phenolics and flavonoids capable of trapping free electrons [14][15][16] .
Readings and statistical analysis. All absorbance was read at specified wavelengths using the USA made Diagnostic Automation, Inc microplate reader (SN: 259557). IC 50 (mg/mL), the concentration of water and ethanol extracts at which 50% inhibition or scavenging occurred was estimated at 95% confidence interval using MINITAB 17 statistical package. The variance of various mean phytochemical values of ethanol and aqueous extracts from different soils were also computed with MINITAB 17 and means were separated with Fischer's Least Significant Difference (LSD) test at α = 0.05.
Phytochemical profiling. Total flavonoids. The total flavonoid content was measured following the aluminium chloride spectrophotometric assay described by 20 with slight modification. The assay is based on the quantification of the yellow-orange colour of flavonoid-AlCl 3 complex arising from the reaction between flavonoids and AlCl 3. A stock of the plant extracts and standard (quercetin) was prepared in 1 mg/mL of the solvent of dissolution. Graded concentrations of quercetin (0.2-1.0 mg/mL) were prepared and 0.5 mL of each concentration (quercetin) and the extract was pipetted into separate test tubes. Afterwards, 2 mL of distilled water was added to the test tubes followed by 0.15 mL of 5% NaNO 2 . The mixture was vortexed and allowed to stand for ]. C is the concentration extrapolated from the standard linear graph, V is the volume of extract in mL and M is the mass of the extract used expressed in gram (g).
Total phenolic content. The total phenolic content of the extract was determined using Folin-Ciocalteu's procedure adopted by 21 with some modifications. The standard used was gallic acid, prepared as 1 mg/mL in methanol. One mg of the crude extract was dissolved in 1 mL of methanol and the standard's concentration was graded in a series of 0.2-1.0 mg/mL. To 0.5 mL of extract and standards in separate test tubes, 2.5 mL of Folin-Ciocalteu reagent was added. Thereafter, 2 mL of anhydrous Na 2 CO 3 (7.5% w/v) was added. The entire mixture was vortexed and incubated at 40 °C for 30 min. Immediately after incubation, 300 µL each of the extract solutions and graded concentrations of gallic acid was pipetted in triplicates into a 96-welled microplate and absorbance was measured at 750 nm with the aid of a microplate reader made in the USA by Diagnostic Automation, Inc (SN: 259557). The total phenolic content was evaluated as mg of gallic acid equivalent (GAE) per gram of crude extract deduced from the standard linear graph y = 0.2281x − 0.0264, R 2 = 0.964 using the equation CV M given above.
Proanthocyanidin content (condensed tannin). The total proanthocyanidin content was evaluated as described in 2 with minor modifications. 0.5 mL each of the stock (extract and standard) was reacted with 3 mL of vanillin (4% w/v), then with 1.5 mL of HCl. The resulting solution was vortexed and incubated at room temperature for 15 min. 300 µL each of the extract and graded concentrations of catechin was dispensed in microplate in three replicates and absorbance was read at 500 nm with the aid of a microplate reader. The total proanthocyanidin content was estimated as mg of catechin equivalent (CE)/g of the crude extract obtained from the calibration curve y = 4.7541x − 0.4801, R 2 = 0.9437 from the formula CV M used above.
Evaluation of antioxidant activity. The antioxidant activities of A. caudatus were estimated using nitric oxide (NO) inhibitory activity, DPPH free radical scavenging, phosphomolybdate -total antioxidant capacity (TAC) and ABTS free radical scavenging assays. Estimations were made against standard antioxidant compounds such as rutin and BHT.
Inhibition of nitric oxide (NO) production. The percentage inhibition of plant extracts against NO radicals was determined from the protocol described by Unuofin et al. 22 . A volume of 10 mM sodium nitroprusside prepared in 0.5 mM phosphate buffer saline (pH 7.4) was added to crude extracts of the plant and standard compounds (rutin and BHT) at different concentrations (0.025-0.400 mg/mL). The mixture was incubated at 27 °C for 2.5 hrs. After incubation, Griess reagent containing 0.33% sulphanilamide dissolved in 20% glacial acetic acid and mixed with 0.1% w/v of 1-naphthylethylenediamine in ratio 1:1 was added to the solution and allowed to react at room temperature for 30 min. A 300 µL volume each of graded concentrations of extracts and standard was dispensed in 96-welled microplate in three replicates and absorbance was read at 540 nm. The equation below was used to estimate the percentage NO inhibition of the extract and standard. The half-inhibitory concentration (IC 50 ) of the extracts was computed from the graph of mean percentage DPPH inhibitory activity (taken in triplicates) against the equivalent of tested samples concentrations in linear regression.
Phosphomolybdenum inhibition or total antioxidant capacity (TAC) assay. The total antioxidant capacity of the plant fractions was assayed following the method described in 26 . A volume of 0.3 mL of different solvent grades of crude extracts was added to 3 mL of reagent solution (4 mM ammonium molybdate, 0.6 M sulphuric acid, and 28 mM sodium phosphate) in glass test tubes. The stocked tubes were incubated in a water bath set at 95 °C for 90 min. After incubation, the mixture was allowed to cool off to room temperature and absorbance of the 300 µL of the mixture dispensed in a microplate was measured on a microplate reader at 695 nm. Gallic acid and rutin were used as standard and high percentage inhibition indicates high antioxidant activity and vice versa. The percentage inhibition was thus calculated as; Abs sample Abs control Abs sample 100 (3) where; Abs sample is the absorbance of sample + reagent solution. Abs control is the absorbance of reagent solution and methanol.
ABTS free radical scavenging capacity. This assay was carried out following the protocol adopted in 23 with modifications. In this method, the ABTS radical was generated by reacting equal volume of 2.45 mM of K 2 S 2 O 8 and 7 mM ABTS solution prepared in methanol. The resulting solution was kept in the dark for 12-18hrs at room temperature and further diluted with methanol in a ratio 1:50 until an absorbance of 0.700 ± 0.003 was attained at 734 nm. Equal volumes of plant extracts and standard drugs of different concentrations were reacted with ABTS + (1:1 v/v), left in the dark for 6 min and absorbance was taken at 734 nm. The percentage ABTS + scavenging capacity of the crude extracts and standard (BHT and rutin) was calculated using the formula; Abs sample Abs sample blank Abs control 100 (4) Statistical analysis. All readings were taken in triplicates and analysed with MINITAB 17 statistical software.
The mean values of tested samples were compared using a one-way analysis of variance (ANOVA). At p < 0.05, means were considered significantly different and ranked using Fisher's Least Significant Difference (LSD) paired wise comparison assuming equal means. Data were expressed as Means ± Standard Deviation.

Results
Effects of soil types on phytochemical content. Total phenolic content. The total phenolic content was expressed as mg/g of the test samples in gallic acid equivalent (GAE) as presented in Fig. 1a below. In all extracts, phenolic contents were higher in ethanolic than aqueous samples. At the pre-flowering stage, ethanolic extracts of harvest from clayey loam (SF 4 ) had the highest phenolic content. Also, phenolic content was highest in the aqueous samples of control soil. From the result obtained, there is no significant difference (p < 0.05) in the phenolic content of ethanolic extracts at the flowering stage but an aqueous extract of the clayey loam produced the highest phenol while silty clayey loam yielded the lowest at flowering stage. At post-flowering, there were no significant differences in phenolic compositions of aqueous extracts derived from all soils. While control and silty clay loam yielded highest of phenols in ethanolic extracts, least amount was obtained in the sandy clay loam. This assay shows that the most active sample was ethanol extract from clayey loam at the pre-flowering stage.
Total proanthocyanidin (condensed tannin). This was estimated as mg/g of crude extracts in catechin equivalent (CE) represented in Fig. 1b below. The results also revealed that ethanolic extracts yielded more condensed tannin than aqueous extracts in all soil types and at all stages of maturity under study. At the pre-flowering stage, clayey loam yielded the highest proanthocyanidin than other soils while the least content was recorded in sandy clayey loam. At p < 0.05, aqueous extracts showed no significant difference in proanthocyanidin content at flowering stage whereas ethanolic extracts from silty clayey loam had the highest yield of proanthocyanidin, while lowest yield was obtained from sandy clayey loam. At post flowering, the trends obtained in proanthocyanidin content of ethanolic samples were replicated although, at this stage, the phytochemical was highest significantly in aqueous samples of clayey loam. This assay also indicates that the most active sample was ethanol extract from clayey loam at the pre-flowering stage.
Total flavonoids. This was expressed as mg QE/g of tested samples as shown in Fig. 1c below. As obtained in phenol and condensed tannins, ethanolic extracts yielded more flavonoids than aqueous samples. At the pre-flowering stage, ethanolic samples of clayey loam soil yielded the highest flavonoids and the lowest value was recorded in sandy clayey loam soil. At the flowering stage, the ethanolic extract had the highest flavonoid on silty clayey loam and the least amount was recorded in sandy clayey loam. In the aqueous samples, flavonoid values ranged from 9.26 ± 0.31 to 10.25 ± 0.13 (mg QE/g) in the control and sandy clayey loam soils respectively. The lowest amount of flavonoid was quantified at the post-flowering stage, however, ethanolic extract from clayey loam produced the highest flavonoids whereas, in aqueous extracts, there were no significant differences in control, clayey loam and loamy soils samples.

Antioxidant activities.
Inhibition of nitric oxide production. At all growth stages studied, the highest percentage of NO inhibitory capacity of A. caudatus extracts was observed in ethanolic extracts of clayey loam samples for all concentrations. Standard drugs used were BHT and rutin and higher inhibitions were found in BHT except at 0.05 mg/mL concentration. Results obtained further show that the inhibitory activities of the plant extracts were solvent and concentration-dependent. For the pre-flowering stage (Fig. 2), the highest inhibition www.nature.com/scientificreports www.nature.com/scientificreports/ was recorded in ethanolic samples of clayey loam soil. It was also observed that clayey loam soil extract inhibits NO more than both standard drugs (BHT and rutin) used. On the other hand, the aqueous extracts showed the highest inhibition in control soil which is comparable to the activity of rutin.    (Fig. 3), results showed that ethanolic extracts inhibited NO radicals more than the standards (BHT and rutin) and aqueous samples although at highest concentrations used, there was no significance in the degree of inhibition between samples from different soils (P < 0.05). As observed in other growth stages, the inhibitory activities were more potent in the ethanolic extracts at post-flowering stage compared to standard drugs and water samples at various concentrations used (Fig. 4).
The IC 50 values for ethanolic extracts ranged as follow; SF 4 > SF 5 > SF 3 > SF 1 > SF 2 at pre-flowering stage, SF 4 > SF 1 > SF 5 > SF 3 > SF 2 at flowering stage and SF 5 > SF 3 > SF 4 = SF 1 > SF 2 at post flowering growth stages while for aqueous extracts (Figs 2-4), IC 50 values were in the order SF 1 > SF 2 > SF 3 > SF 5 > SF 4 , SF 3 > SF 2 > SF 5 > SF 5 > SF 4 and SF 5 > SF 1 > SF 4 > SF 3 > SF 2 respectively at pre-flowering, flowering and post-flowering stages ( Table 2). This assay indicates that the most active extract was from clayey loam (SF 4 ) at the pre-flowering stage. In addition, results showed that the extract was more potent than standard drugs (BHT and rutin). www.nature.com/scientificreports www.nature.com/scientificreports/ Percentage DPPH scavenging activity. The DPPH radical scavenging activity of tested samples revealed variations in scavenging capacity of extracts from various formulated soils and solvents of extraction. The standard antioxidant drug used was rutin with concentrations ranging from 0.005-0.08 mg/mL. Results obtained indicate that the scavenging activity was neither concentration nor solvent dependent. At the pre-flowering stage, the percentage scavenging capacity was highest and lowest respectively in ethanol extracts of clayey loam and control soils (Fig. 5). In addition, all ethanol extracts were more active than the rutin standard (at highest concentration) although the highest percentage scavenging was recorded in aqueous extracts of the control soil. Similarly, ethanolic extract from clayey loam soil exhibited the highest scavenging activity at flowering stage whereas; the standard was more active than the aqueous extracts (Fig. 6). At the post-flowering stage, ethanol and aqueous extracts of sandy clayey loam were more active than samples from other soil types, however, the rutin standard was more active than the aqueous samples (Fig. 7).
Results of graded series of sample concentrations were used to estimate the concentrations at which fifty per cent of DPPH radicals (IC 50 ) had been scavenged. In the ethanolic extract of pre-flowering stage harvest, IC 50 values ranged from 0.009 mg/mL in sandy clayey loam to 0.075 mg/mL in clayey loam and in water extract, values ranged from 0.0078 mg/mL in control soil to the highest value found in loam (Table 3). At flowering stage, IC 50 values ranged between 0.01 mg/mL in clayey loam and 0.076 mg/mL in sandy clayey loam of water samples and in ethanolic samples, value ranged from 0.008 mg/mL in loam to 0.035 mg/mL in control soil. The lowest IC 50 value of 0.009 mg/mL was recorded in water sample of loam in the post-flowering harvest as against the highest values in SF 4 and SF 1 wherein both cases; IC 50 was greater than 0.08 mg/mL. Also in ethanolic extracts, IC 50 ranged from 0.0097 to 0.022 mg/mL at post-flowering stage. This assay also indicates that the most active extract was from clayey loam (SF 4 ) at the pre-flowering and flowering stages. It further confirmed that the extract was more active than rutin.      www.nature.com/scientificreports www.nature.com/scientificreports/ Total antioxidant capacity (phosphomolybdate assay). The total antioxidant capacities of plant samples and standard drugs (gallic acid, rutin and BHT) were also evaluated using phosphomolybdate assay. Figures (8-10) indicate antioxidant activities of ethanol and aqueous extracts at pre-flowering, flowering and post-flowering stages. Pre-flowering samples show that the total antioxidant activity was neither solvent nor concentration-dependent. At the flowering stage, ethanolic extracts had the highest activity in sandy clayey loam and were more active than water extracts and standards. At the highest concentration, ethanol extract of clayey loam was more active than other post-flowering samples evaluated (8)(9)(10). Furthermore, the lowest IC 50 was observed in an aqueous extract from control soil and an ethanolic sample of clayey loam at pre-flowering stage (Table 4). At flowering and flowering stages, the IC 50 were lowest in aqueous extracts of control soil and ethanolic samples of sandy clayey loam. This assay, therefore, indicates that the most active extract was from clayey loam (SF 4 ) at pre-flowering and flowering stages. It further confirmed that antioxidant activities of plant extracts were comparable to rutin and gallic acid but higher than BHT.  (11)(12)(13). The highest percentages of scavenging activities were observed in ethanolic and aqueous extracts from clayey loam and control soils respectively at the pre-flowering stage. At the flowering stage, aqueous samples of sandy clayey loam showed the highest scavenging activity and at post-flowering, ethanolic extract of sandy clay loam had the highest activity. Table 5 below presents IC 50 data of ABTS scavenging activities of plant samples. Lowest IC 50 values were observed in ethanolic extract of clayey loam at the pre-flowering stage, aqueous extracts of loam at flowering stage and an aqueous sample of control soil at post-flowering. This assay, therefore, indicates that the most active extract was from clayey loam (SF 4 ) at the pre-flowering stage. It further confirmed that antioxidant activities of plant extracts were comparable to rutin and BHT at the pre-flowering stage.

Discussion
Therapeutic activity of plants is a reflection of their phytochemical richness 27 . The type of phytochemicals present in a plant affects its antioxidant activity. These phytochemicals include flavonoids, phenolic acids and alkaloids among others which play overlapping roles in plant defence mechanisms, pollinators attraction, singlet oxygen scavengers, high energy radiation absorbers, reducing agents, allelopathy and sometimes as transition metal chelators [28][29][30][31][32] .
The effect of soil on quantity and relative activity of these phytochemicals cannot be underestimated. A combination of mineral resources provided externally by the soil and internal nutrient trade-offs dictate plant's carbon-nutrient balance that influences synthesis and retention of defensive chemicals in plants 33 . This justified the need to assess phytochemical variations in Amaranthus caudatus cultivated on different soils to document the soil with the highest phytochemical yield considering various reports that stages of growth affect phytochemical and antioxidant properties of plants 34,35 .
The following factors namely; soil texture, bulk density and soil carbon (organic matter) vary considerably in undisturbed soil and they influence soil porosity 7 . Soils used for planting in this experiment had a varying  www.nature.com/scientificreports www.nature.com/scientificreports/ proportion of sand, silt and clay in their formulation. Low bulk density and high organic matter have been attributed to high clay content as well as high phenolic acids 36 . This further justifies the overall high phenolic content obtained in a plant grown on clayey loam soil. Also, the clayey loam soil has the highest water retention property than other soils investigated due to its highest clay content (Table 1). This may have restricted the carbon/nutrient trade-offs due to the dilution effect, hence, concentrating secondary metabolites in the plant.
Comparing the potency of extracts derived from the two extractants used, ethanolic extracts showed higher activity than water. This may be due to varying degrees of polarity and eluent strength of water and ethanol. Ethanol has been reported to be appropriate for extraction of compounds with wide range polarity while water is suitable for compounds with strong polarity 37 . Thus, a higher quantity of phenolic compounds, proanthocyanidin and flavonoids were observed in ethanol samples than water hence, better activity. This agrees with other reports that ethanol is more suitable for the extraction of phenolic compounds in plants 37,38 .
Also, total flavonoid and phenolic compositions of the investigated species varied with growth stages and soil types. Riipi et al. (2002) hypothesised that at flowering, differentiation dominates over the synthesis of secondary metabolites. Hence, the highest phytochemical yield recorded in samples harvested from clayey loam at the pre-flowering stage is in agreement with this hypothesis 33 . This agrees further with prior reports that protein-bound phenols, tannic polyphenols, and digestibility of organic matter decrease as plants approach reproductive stages 39,40 . Comparing this study to previous reports on A. caudatus, the total phenolic contents recorded were higher than previous data. For instance, Li et al. 15 , Pieretti et al. 18 , Jiménez-Aguilar and Grusak 16 and Enujiugha et al. 41 respectively reported 14.94 ± 0.32, 4.35 ± 0.19, 4.1 ± 1.1 and 0.007 ± 0.003 mg (GAE/g) of phenolic acids in different samples of A. caudatus as against the highest (104.87 ± 5.93 mg GAE/g) and the lowest (17.94 ± 1.04 mg GAE/g)) phenolic content recorded in this experiment. In contrast, Azeez et al. 42 reported a high phenolic acid of 101.63 ± 0.58 mg GAE/g in his A. caudatus sample which matches the phenolic acids content evaluated in pre-flowering samples from all soil types. Similarly, the flavonoid (54.75 ± 0.35 mg QE/g) reported by Azeez et al. 42 compares with pre-flowering flavonoids recorded in this study, probably due to the fact that the plant was harvested after four weeks (before flowering). Moreover, the proanthocyanidins in samples investigated   www.nature.com/scientificreports www.nature.com/scientificreports/ in this study were higher than the earlier record for the same plant. Previously, Jo et al. 43 and Enujiugha et al. 41 respectively had reported lower values of proanthocyanidin (0.516 ± 0.02 and 0.045 ± 0.000 mg TAE/g) in A. caudatus using the tannic acid standard as opposed catechin standard used in this study.
Furthermore, the results of antioxidant activities of the plant extracts evaluated through DPPH, NO, ABTS and phosphomolybdate assays elucidate the direct relationship between phytochemicals and antioxidant activities. This is in agreement with postulations in [44][45][46][47] that phytochemicals are precursors to biological activities in plants.
Moreover, similar trends observed in the percentage scavenging and IC 50 values of pre-flowering extracts when tested with DPPH + and ABTS + radicals may have to do with a high degree of solubility of the radicals in water and ethanol. Hence, the same mechanism of action of the two radicals in transferring labile hydrogen atoms 48 . The low IC 50 values of DPPH + and ABTS + radicals in this study (Tables 3, 5) confirms that samples possess potent antioxidant effect, giving rise to the extremely high percentage of scavenging (Figs 5-7, 11-13) recorded when extracts were tested with the two radicals. Lower percentages of scavenging in DPPH + (77.5% and 66.3%) and ABTS + (48.75 ± 1.1 and 87.1%) were respectively reported earlier by Ashok Kumar et al. 19 and Jo et al. 43 .   Figure 12. Effects of soil types on % ABTS scavenging capacity of A. caudatus at flowering stage. www.nature.com/scientificreports www.nature.com/scientificreports/ Nitric oxide (NO) is a reactive oxygen species that has been thought to resemble a mediator of responses produced by vascular endothelial cells. When reacted with superoxide (O 2 − ) in the epithelium, it may lead to blood pressure and oxidative damage of DNA due to the formation of ONOO − that has the tendency to mutilate supercoiled DNA structure 13,49 . Extracts of A. caudatus showed a high propensity to inhibit NO especially in the ethanolic samples where at times, activity was about three times higher than aqueous samples. For phosphomolybdate assay, percentage scavenging activity was neither solvent nor concentration-dependent. The same lowest IC 50 value of 0.093 was obtained in an aqueous sample of control soil and ethanolic extract of clayey loam at pre-flowering stage (Table 4). In addition, results for inhibition of NO in this study present a higher inhibitory effect compared with what was reported in 19 for the same plant. Therefore, variations obtained in antioxidant activities of crude extracts of harvests from different soils support some reports that soil environment affects the concentration of phytochemicals and antioxidant gradient 47,50,51 . conclusion Results from this study suggest that soil types and harvest stage affect phytochemicals and antioxidant properties of Amaranthus caudatus. The biological activity of the plant extract also depends on the solvent of extraction, stage of maturity, soil type and partly, the choice of the assay. All phytochemicals assessed were highest in the clayey loam at the pre-flowering stage. The same trend was replicated in the antioxidant activities evaluated, thus; flavonoids and phenolic acid contents contribute significantly to bioactive makeup of a plant. Based on this, it is recommended that for optimal biological potency, A. caudatus should be cultivated in clayey loam soil and harvested prior to flowering. Also, for pharmacological purposes, ethanol is preferred as a solvent of extraction. Thus, the vegetable should be consumed regularly for health improvement since it exhibited higher antioxidant activities more than known antioxidant drugs such as BHT, rutin and gallic acid.

Data Availability
The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.


text:
Studies on Genetic Variability, Heritability and Genetic Advances for Quantitative Characters in Finger millet (Eleusine coracana (L.) Gaertn.)

Finger millet is highly nutritious as its grain contains high-quality protein (7-10%). It is the richest source of calcium (344mg/100g), iron (3.9mg/100g) and other minerals. It is also rich in phosphorus (283mg/100g) and potassium (408mg/100g). The cereal has lowfat content (1.3%) and contains mainly unsaturated fat 100 g of finger millet has International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 09 (2019) Journal homepage: http://www.ijcmas.com

Introduction
Finger millet is an important staple food crop widely grown in Africa and South Asia. Among the millets, finger millet has a high amount of calcium, methionine, tryptophan, fiber, and sulfur-containing amino acids.
In addition, it has C 4 photosynthetic carbon assimilation mechanism, which helps to utilize water and nitrogen efficiently under hot and arid conditions without severely affecting yield Hittalmani (2017).
Finger millet is highly nutritious as its grain contains high-quality protein (7-10%). It is the richest source of calcium (344mg/100g), iron (3.9mg/100g) and other minerals. It is also rich in phosphorus (283mg/100g) and potassium (408mg/100g). The cereal has lowfat content (1.3%) and contains mainly unsaturated fat 100 g of finger millet has The present investigation was carried out to study the genetic variability, heritability and genetic advance among 137 finger millet genotypes for fifteen characters during Kharif 2018. Analysis of Variance showed significant differences for all the characters under study except for leaf width, number of panicle per plant and test weight indicating the presence of a substantial amount of genetic variability thus revealed that these genotypes have been developed from the different genetic background. On the basis of per se performance for different quantitative traits, genotype IE4734 was found to be the best genotype in Allahabad agro-climatic conditions. High estimates of GCV and PCV were observed for harvest index. High heritability coupled with high genetic advance was recorded for leaf width followed by test weight and grain yield per plant indicating the predominance of additive gene effects and the possibilities of effective selection for the improvement of these characters. roughly on an average of 336 Kcal of energy. The higher fiber content of finger millet helps in many ways as it prevents constipation, high cholesterol formation, and intestinal cancer. Hence, people suffering from diabetes are advised to eat finger millet and other small millets instead of rice Hadimani and Malleshi, (1993).

K e y w o r d s
Assessment of genetic variability is a basic step in the crop improvement program. Yield is being a complex character it is influenced by a number of yield contributing characters controlled by polygenes and also influenced by the environment. Genotypic and phenotypic association reveals the degree of association between different characters and thus, aids in selection to improve the yield and yield attributing characters. Heritability measures the relative amount of the heritable portion of variation while the genetic advance helps to measure the amount of progress that could be expected with selection in a character.

Materials and Methods
The experimental material consisted of 137 finger millet genotypes collected from ICRISAT, Hyderabad and NBPGR, New Delhi (Table 1). The experiment was conducted in randomized block design with three replications during Kharif-2018 at Field Experimentation Centre of the Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (Allahabad) U.P. All the recommended agronomic and cultural practices were followed for raising a healthy crop. Data were recorded on five randomly taken plants per replication of each genotype for fifteen characters viz., days to 50% flowering, days to maturity, plant height (cm), leaf length (cm),leaf width (cm),leaf area index, number of panicles per plant, number of fingers per panicle, finger length (cm),finger width (cm),stem girth (cm), biological yield/plant (g), grain yield/plant (g), harvest index, seed index. The analysis of variance was done as suggested by Punse and Sukhatme (1985). The genotypic and phenotypic coefficient of variation was calculated by the formulae as suggested by Burton (1952), heritability as per formulae suggested by Burton and Devane (1953) and genetic advance (Johnson et al., 1955).

Results and Discussion
The analysis of variance showed a wide range of variation and significant differences for all the characters under study except for leaf width, number of panicles per plant and test weight. This indicates that there was ample scope for selection of promising lines from the present gene pool for yield and its components in finger millet( Table 2).
Estimation of genotypic variance (σ 2 g) and phenotypic variance (σ 2 p) was obtained for different characters and wide range of variance were observed for all the characters. The highest genotypic variance (σ 2 g) and phenotypic variance (σ 2 p) were recorded for plant height (124.74 and 176.25) followed by days to 50% flowering (90.06 and 94.13), days to maturity (90.06 and 94.13), leaf area index (54.63 and 63.38), leaf length (33.29 and 51.02), biological yield per plant (23.81 and 24.87). While moderate genotypic variance (σ 2 g) and phenotypic variance (σ 2 p) were recorded for harvest index (16.20 and 17.15).
Whereas, finger length (1.94 and 2.04), number of fingers per panicle (0.85 and 0.89), grain yield per plant (0.52 and 0.53), finger width (0.03 and 0.04), stem girth (0.02 and 0.03), number of panicle per plant (0.00 and 0.01) showed genotypic variance (σ 2 g) and phenotypic variance (σ 2 p). The phenotypic variance was higher than the genotypic variance for all the yield and yield attributing characters indicates that the influence of environmental factors on these traits. Less difference in the estimates of genotypic and phenotypic variance for all the characters suggested that the variability present among the genotypes were mainly due to genetic reason with minimum influence of environment and hence heritable. The genotypic estimates of variability (Vg) being the most important, helps in the measurement of a particular character and gives a clue to compare the genetic variability for different characters. Similar results have been reported by John (2006), Ganapathy et al., (2011)and Karad and Patil (2013).
Phenotypic coefficient of variation ranged from 8.70 (days to maturity) to 41.45 (harvest index). Highest PCV was recorded for harvest index (41.45), whereas the lowest was recorded for days to maturity (8.70). Genotypic coefficient of variation ranged from 4.80 (number of panicles per plant) to 40.30 (harvest index). Highest GCV was recorded for Harvest index (40.30), whereas the lowest was recorded for a number of panicles per plant (4.80).
The coefficient of variation at phenotypic and genotypic levels was high for harvest index, grain yield per plant, biological yield per plant, leaf area index, test weight, finger width and finger length. Similar results were also obtained by Kumari and Singh (2015) for Harvest index and leaf area index, Patil(2013) for Grain yield per plant, finger length and test weight. Moderate for the traits like leaf width, number of fingers per panicle, plant height, stem girth, leaf length. Similar results were also obtained by Ulaganathan and Nirmalakumari (2011) for leaf length, leaf width and number of fingers per panicle, Ganapathy et al.,(2011) for plant height. Low PCV and GCV were observed for days to maturity. Similar results were obtained by Ganapathy et al.,(2011)for days to maturity. The magnitude of high GCV and PCV suggests that enough genetic variability is present among the finger millet genotypes for traits where PCV and GCV are moderate to low, the scope of selection for suitable characters is limited.
In present study, high heritability was recorded for leaf width, test weight, grain yield per plant, biological yield per plant, number of panicles per plant, days to flowering, days to maturity, finger length, harvest index, finger width, leaf area index, plant height, stem girth and leaf length. The maximum value was recorded for leaf width (99%) and the minimum was recorded for number of panicles per plant (21%). High heritability coupled with high genetic advance as percent mean in the present set of genotypes were recorded for leaf width (99% and 37.59%) followed by test weight (97% and 47.34%),grain yield per plant (97% and 77.41%), days to 50% flowering (96% and 22.11%), number of fingers per panicle (96% and 32.87), biological yield per plant (96% and 50.34%), finger length (95% and 44.22%), finger width (94% and 46.12%), harvest index (94% and 80.67%), leaf area index (86% and 47.15%), plant height (71% and 25.24%), stem girth (69% and 22.64%) and leaf length (65% and 21.38%) indicating a predominance of additive gene effects and the possibilities of effective selection for the improvement of these characters. Similar results were also obtained by John 2006 for Test weight and harvest index, Ganapathy et al.,(2011)for grain yield per plant, finger length and plant height, Kumari and Singh (2015) for leaf area index and days to 50% flowering, Ulaganathan and Nirmalakumari (2011) for leaf length. High heritability coupled with moderate genetic advance was recorded for days to maturity (96% and 17.5%), suggesting the greater role of both additive and nonadditive gene action in their inheritance.    Ulaganathan andNirmalakumari (2011) andKarad andPatil (2013).Low heritability coupled with low genetic advance was recorded for number of panicles per plant (21% and 4.50%). It is indicative of nonadditive gene action. The low heritability is being exhibited due to the favorable influence of environment rather than genotype and selection for such traits may not be rewarding (Table 3).
In the present study, the characters, leaf width followed by test weight and grain yield per plant had high heritability coupled with high genetic advance as percent means indicating the predominance of additive gene effects and the possibilities of effective selection for the improvement of these characters.


text:
Four Novel SNPs of MYO1A Gene Associated with Heat-Tolerance in Chinese Cattle

Simple Summary Chinese indigenous cattle were clustered into two groups of southern and northern breeds. The populations distributed in the hot south are mainly dominated by Bos indicus, and the populations distributed in the cold north are mainly dominated by Bos taurus. Myosin-1a (MYO1A) gene is a candidate gene related to pigmentation, which may be closely related to the heat tolerance traits of cattle. Therefore, our study aimed to explore the relationship between MYO1A gene and heat tolerance in Chinese indigenous cattle. Abstract Based on the previous GWAS research related to bovine heat tolerance trait, this study aimed to explore the effect of myosin-1a (MYO1A) gene on bovine heat tolerance trait, and find the molecular markers related to the heat tolerance of Chinese cattle. In our study, four novel candidate SNPs highly conserved in B. indicus breeds but barely existed in B. taurus were identified in MYO1A gene according to Bovine Genome Variation Database and Selective Signatures (BGVD). PCR and DNA sequencing were used to genotype 1072 individuals including 34 Chinese indigenous cattle breeds as well as Angus and Indian zebu. Two synonymous mutations (rs208210464 and rs110123931), one missense mutation (rs209999142; Phe172Ser), and one intron mutation (rs135771836) were detected. The frequencies of mutant alleles of the four SNPs gradually increased from northern groups to southern groups of Chinese cattle, which was consistent with the distribution of various climatic conditions of China. Additionally, four SNPs were significantly associated with four climatic conditions including annual mean temperature (T), relative humidity (H), temperature-humidity index (THI), and average annual sunshine hours (100-cloudiness) (SR). Among these, rs209999142 and Hap 1/1 had better performance than others. Our results suggested that rs209999142 was associated with heat-tolerance trait and rs208210464, rs110123931, and rs135771836 showed high phenotypic effect on heat-tolerance trait because of the strong linkage with rs209999142. These SNPs could be used as candidates for marker-assisted selection (MAS) in cattle breeding.

Introduction
Metabolic disorders, loss of appetite, slow growth, and impaired immune function may occur under heat stress, which would cause serious economic issue [1]. As the frequency and duration of heat-stress conditions increasing, projected future increases in temperature could have significant negative implications on the productivity and welfare of ruminants that play a significant role in terms of world food production [2]. Although advances in environmental cooling systems ameliorate production losses during summer months, heat stress continues to cost a lot annually. Increase in solar radiation can cause the surrounding air to warm up, which could create a heat-stress problem. Heat exchange by radiation depended upon the reflective properties of the hair coat. Light-colored hair coats reflected a greater proportion of incident solar radiation than hair coats that were dark in color [3]. Hence, cattle exposed chronically to high temperature became progressively lighter in color and there was some evidence that this change occurred more rapidly for Brahman than Brown Swiss or Jersey [4].
Family members of the myosin superfamily are widely expressed and tightly integrated in cellular networks of interlinked biochemical pathways where they function as integrators between signaling and the dynamics of cytoskeletal mechanics [5,6]. Class-1 myosins are single-headed and dynamically link the actin cytoskeleton to membranes, spend only a small fraction of time attached to actin [7]. This feature does not allow them to transport cargoes but instead to function as strain sensors at the cytoskeleton-plasma membrane interface, a function essential to the regulation of membrane tension and cell migration [7]. It suggests myosin-1 isoforms may be involved in symmetry breaking events. Processivity which is facilitated by a high duty ratio is a prerequisite to transport cargoes such as melanosomes or endosomes along the actin cytoskeleton over long distances to its cellular destination [8]. MYO1A gene is one member of Class-1 myosins. In line with the previous genomic SNP array data, MYO1A was considered to be a candidate gene related to pigmentation in cattle [9]. Then we identified a fragment with four novel SNPs in MYO1A gene in cattle by BGVD (http://animal.nwsuaf.edu.cn/code/index.php/BosVar) [10], which were highly conserved in B. indicus breeds but barely existed in B. taurus, may be candidate SNPs associated with adaptation under heat-stress condition. BGVD is a platform based on a large number of resequencing data of cattle, through which we can observe the expected distribution of SNPs in groups of dozens of different cattle breeds.
China exhibits five climatic zones, with 53 Chinese cattle breeds and rich genetic resources in this specific environment, and the thermal conditions are gradually increasing from north to south [11]. Chinese indigenous cattle originated from Bos taurus and Bos indicus, were clustered into two groups of southern and northern breeds [12]. Under a long-term introgression and selection between southern breeds and northern breeds, a distinct transition occurred from northern to southern, which has been demonstrated by genetic studies on Y chromosome polymorphisms, mitochondrial DNA markers, and whole-genome scan data [12][13][14][15].
Consequently, Chinese indigenous cattle breeds are suitable resources to detect SNPs in the bovine MYO1A gene and for testing the gene possible association with the heat tolerance trait. It is expected that the results of this study can be used for heat tolerance breeding.

Materials and Methods
All experiments performed in this study were approved by the International Animal Care and Use Committee of the Northwest A&F University (IACUC-NWAFU). Furthermore, the care and use of animals were fully compliant with local animal welfare laws, guidelines, and policies.

DNA Samples and Data Collection
Genomic DNA of 1072 individuals from 36 breeds including 34 Chinese indigenous cattle breeds as well as Angus (B. taurus) and Indian zebu (B. indicus) (Supplementary Materials Table S1) were isolated from ear tissues by the standard phenol-chloroform method [16]. DNA samples were diluted to a standard concentration (50 ng µL −1 ) and stored at −80 • C.
Geographical data over the last 30 years for the sampling sites of the 34 indigenous cattle breeds were collected from the Chinese Central Meteorological Office (http://data.cma.cn) (Table S1) and were used to estimate heat tolerance traits.

SNP Detecting and Genotyping
The polymerase chain reaction (PCR) primer was designed based on the bovine MYO1A sequence (GenBank accession no. NC_037332.1) by the Primer Premier 5.0 (Premier Biosoft International, Palo Alto, CA, USA). The forward primer was 5 -AAGACCATTCGCAATAACA-3 and the reverse primer was 5 -TTCCTCCTTCCTACAGCA-3 . PCR product size was 318 bp. PCR amplification system was performed in a 20 µL volume system: 50 ng genomic, 10 µL 2× PCR mix, 0.5 µM of each primers and 8 µL ddH 2 O. The cycling protocol was as follows: denaturation for 5 min at 95 • C; 35 cycles of 94 • C for 30 s, annealing at 51 • C for 30 s, primer extension at 72 • C for 30 s; with a final extension performed at 72 • C for 8 min. The PCR products were detected by electrophoresis on a 1% agarose gel stained with ethidium bromide and sequenced directly at Shanghai Sangon Biotech Company, Shanghai, China. Sequencing results were analyzed with SEQMAN TMIIv 6.1 (DNASTAR, Inc., Madison, WI, USA).
McDowell et al. [19] suggested that temperature-humidity index (THI) can be used as an indicator to ascertain heat load intensity of thermal climatic conditions by measuring the combined effects of annual mean temperature (T) and relative humidity (H). Solar radiation was affected by solar altitude angle, local climate types, and solar radiation time, which had the largest influence (r 2 = 0.92), and solar radiation time basically reflected the solar radiation amount [20]. In consequence, T, H, THI, and average annual sunshine hours (100-cloudiness) (SR) were used for association analysis with genotypes or haplotypes by SPSS 18.0 software (SPSS, Inc., Armonk, NY, US). THI was calculated based on the formula used by the National Oceanic and Atmospheric Administration [21]: , where T was temperature in degrees Celsius and H was relative humidity as a percentage. The statistical linear model was used: where Y i was the value of T, H, THI, and SR between 1951 and 1980; µ was the mean value; G i was the fixed effect of the genotypes or haplotypes; B i was the fixed effect of breeds; e i was the random residual effect.

SNPs Screening and Genotyping
In our study, four novel SNPs were detected in bovine MYO1A gene: rs208210464 (g.56383560G > A), rs209999142 (g.56383565T > C), rs110123931 (g.56383578T > C), and rs135771836 (g.56383635A > G). Among the four mutations, rs208210464 and rs110123931 were synonymous mutations. Besides, rs209999142 was a missense mutation which causes phenylalanine to serine amino acid substitution, and rs135771836 located in the intron region ( Figure 1). A distinct transition can be observed from northern breeds to southern breeds ( Figure 2, Supplementary Materials Table S2). Furthermore, frequencies of all mutant genotypes in Zebu population were more than 0.68, whereas frequencies of all wild genotypes in Angus population were more than 0.84 (Table S2).

Haplotype Analysis and Linkage Disequilibrium
To reveal the linkage relationships among rs208210464, rs209999142, rs110123931, and rs135771836, the linkage disequilibrium (LD) between the four mutations were estimated (Figure 3).

Association Analysis of SNPs and Haplotype Combination
The results of the association analysis between the four SNPs and the four environmental parameters (T, H, THI, and SR) for 34 breeds in 1006 Chinese indigenous cattle were shown in Table  3. For rs135771836, there was no significant difference between individuals with genotype GG and AG with SR. For rs208210464, individuals with genotype GG and AA were significantly different with SR (p < 0.05). For rs110123931, individuals with genotype CT and TT were significantly different

Association Analysis of SNPs and Haplotype Combination
The results of the association analysis between the four SNPs and the four environmental parameters (T, H, THI, and SR) for 34 breeds in 1006 Chinese indigenous cattle were shown in Table 3. For rs135771836, there was no significant difference between individuals with genotype GG and AG with SR. For rs208210464, individuals with genotype GG and AA were significantly different with SR (p < 0.05). For rs110123931, individuals with genotype CT and TT were significantly different with H (p < 0.05). In addition to the above, there were significant differences among different genotypes for all SNPs with all environmental parameters (T, H, THI, and SR) (p < 0.01). Table 3. Associations of four SNPs genotypes of MYO1A gene with annual mean temperature (T), relative humidity (H), temperature-humidity index (THI), average annual sunshine hours (SR).

Loci
Genotype ( The results of the association analysis between the ten combined haplotypes and the four environmental parameters (T, H, THI, and SR) for 34 breeds in 956 Chinese indigenous cattle were shown in Table 4. According to Table 4, Hap 1/1 was the most potentially related to heat tolerance traits. Tests of effects of the four environmental parameters on the MYO1A genotypes indicated that annual average temperature had the strongest correlation with the genotypes (Table S3).

Mutation Analysis
Among the four novel SNPs, rs208210464 and rs110123931 were synonymous mutations, rs209999142 was a missense mutation. rs209999142 was predicted the protein three-dimensional structure by the homologous modelling method with SWISS-MODEL software (Figure 4). There was a significant difference in the value of solvation between wild type and mutant type (Table 5), which meant wild type might had a stronger residue embedding ability than mutant type. Additionally, the conversion of phenylalanine at the 172 site to serine led to the rise of the value of qualitative model Animals 2019, 9, 964 7 of 10 energy analysis (QMEAN) from 0.73 to 0.79. Genetic codons before and after mutation was shown in Figure 5, and there were some differences before and after two synonymous mutations for codon bias, which may cause functional differences.
Among the four novel SNPs, rs208210464 and rs110123931 were synonymous mutations, rs209999142 was a missense mutation. rs209999142 was predicted the protein three-dimensional structure by the homologous modelling method with SWISS-MODEL software (Figure 4). There was a significant difference in the value of solvation between wild type and mutant type (Table 5), which meant wild type might had a stronger residue embedding ability than mutant type. Additionally, the conversion of phenylalanine at the 172 site to serine led to the rise of the value of qualitative model energy analysis (QMEAN) from 0.73 to 0.79. Genetic codons before and after mutation was shown in Figure 5, and there were some differences before and after two synonymous mutations for codon bias, which may cause functional differences.

Discussion
With the advent of escalating environmental stress due to climate change, the challenging tropical stress could have determine more favourable genotypes for bovine [23]. Nevertheless, as the environments and management intervention strategies implementation, environmental regulation removed environmental stressors, this would make natural selection be adversely affected. In

Discussion
With the advent of escalating environmental stress due to climate change, the challenging tropical stress could have determine more favourable genotypes for bovine [23]. Nevertheless, as the environments and management intervention strategies implementation, environmental regulation removed environmental stressors, this would make natural selection be adversely affected. In consequence, screening of candidate genes related to heat tolerance and breeding of heat-resistant cattle is of great significance to the production of cattle.
MYO1A, also known as "brush border myosin-I", has been identified as a cause of deafness and had tumor suppressor activity in the intestine [24,25]. Nonetheless, MYO7A and MYO5A, homologous genes of MYO1A, have been previously found to play an important role in melanin transport [26,27]. MYO5A is a major actin-based vesicle transport motor that binds to one of its cargos, the melanosome, by means of a RAB27A/MLPH receptor [27]. It is essential for the capture and local transport of melanosomes in dendritic tips, leading to a dilution of coat color after mutated [28]. MYO7A, which transports melanosome along the apical processes, may play a similar transport role in the pigmented retina [29]. The genes from same family have obvious similarity in structure and function, and encodes similar protein products. Through 3D modeling of MYO1A gene, we found that it has high sequence identity with MYO7A (39.14%) and MYO5A (40.27%), respectively. Therefore, we suggested that MYO1A has similar functions as MYO7A and MYO5A gene, melanosomes transport. Moreover, earlier reports found that MYO1A gene in the region of bovine chromosome 5 was suggested to have an effect on pigmentation in an F 2 -Backcross Charolais × Holstein population by a linkage mapping study [30]. Edea et al. [9] revealed that MYO1A gene related to coat color may have contributed to adaptation under tropical conditions by 80K Indicine BeadChip scan. Thus, it is the first study on the relationship between MYO1A gene and heat-tolerance.
The vast varieties in longitude, latitude, and altitude form the extremely complex climate of China. The northern part of the coastal zone has a temperate monsoon climate, whereas the south has a subtropical monsoon climate. The Qinghai-Tibet Plateau is a plateau climate, and other inland areas are basically temperate continental climates. In Figure 2, the mutant allele's frequencies of the four SNPs we detected had an extremely regular increasing trend from the south to the north. This trend was consistent with the distribution of different climatic conditions in China (Table S1). It also shows an obvious distribution trend from south to north. This confirmed with the fact that southern and northern breeds were affected by the introgression of heat-resistant Bos indicus and heat-sensitive Bos taurus, respectively [31].
According to Table 3, rs209999142, the missense mutation, showed stronger association with four climatic conditions than other mutations, which may be due to weakening of residue embedding ability and improvement of QMEAN. The synonymous mutations (rs208210464 and rs110123931) led the changes of codon bias, these changes may affect the efficiency of protein synthesis in the process of translation and thus the normal expression of function. As for rs135771836 (intron mutation), the association between this mutation and thermal adaptation may be due to the high strength linkage with mutation rs208210464 in exon. Combining the results of LD and association analysis, we suggested that rs209999142 was a causative variant and the others showed association with heat-tolerance trait due to the strong linkage with rs209999142. Hap1 had the highest haplotype frequency (0.39), and high-frequency haplotypes had been probably present in the populations for a long time, which may be directly or indirectly influenced by different rearing environments [32]. Combined with the results of combined haplotypes association analysis (Table 4), Hap 1/1 was the most possibly associated with heat-tolerance trait.

Conclusions
In summary, the identification of four different loci suggested that this gene may be a significant contributor to bovine heat-tolerance trait. Additional studies were required to definitively prove the real contribution of MYO1A to heat-tolerance in tissue and cell level. Furthermore, rs209999142 and


s2fieldsofstudy:
Agricultural And Food Sciences
Geology

text:
Size distribution and size of loess slides in response to slope height and slope gradient based on field survey data

Abstract This paper quantitatively examines the effects of slope height and slope gradient on landslide size distributions. We developed a loess slide inventory by using field survey data. Statistical analysis shows that most landslides are concentrated in areas with slope heights less than 60 m, and approximately 30% of the loess slides occurred on slopes with a gradient between 30° and 40°. However, high and steep slopes are rare in nature. We calculated the relative density of landslides, and the results showed that the relative density of landslides is greater on higher slopes with steeper slope gradients. Moreover, landslide size is correlated with slope height and slope gradient. The results demonstrate that landslide size increases as slope height increases and decreases as slope gradient increases. Furthermore, we determined the probability density of landslide area using kernel density estimation. The results showed that the landslide size distribution exhibits power law scaling above a certain size threshold, and the size threshold differs for different slope heights and slope gradients. The exponential scalings are influenced by slope height and slope gradient. Our results indicate that the exponential scaling decreases with increasing slope height and increases with increasing slope gradient. Large landslides are more frequent with a higher slope height and gentler slope gradient.

Introduction
Loess is an aeolian sediment widely distributed in arid and semiarid areas (Liu 1985). Loess has well-developed vertical joints, loose structures, large pores and collapsibility (Derbyshire 2001;Xu et al. 2012). These characteristics of loess make loess terrain highly landslide-prone Qiu et al. 2018). In particular, the unique geological, geomorphological and climate variations on the Loess Plateau make this area highly vulnerable to exogenous slope processes (Derbyshire and Mellors 1988;Shi et al. 2016;Peng et al. 2019). Loess slides represent one of most widespread natural hazards in the loess terrain and have significant impacts on local environments and society (Qiu et al. 2017;Zhang et al. 2017).
The size distribution of landslides plays an important role in determining landslide hazards, understanding the fundamental processes of landslide dynamics, and estimating erosion and sediment yield (Stark and Hovius 2001;Korup 2005;Guthrie et al. 2008;Guzzetti et al. 2008). Many authors have proposed that the landslide size-frequency distribution follows a negative power law relationship for larger landslides within a given area and shows a deflection from the power law for small landslide size, i.e., a rollover effect (Hovius et al. 1997;Turcotte et al. 2002;Malamud et al. 2004;Van Den Eeckhaut et al. 2007;Hungr et al. 2008). The scaling exponents of the power law vary with material properties, geological conditions, topography, physiographic variables of the landscape, and failure event types (Pelletier et al. 1997;Stark and Hovius 2001;Katz and Aharonov 2006;Guzzetti et al. 2008;Stark and Guzzetti 2009;Frattini and Crosta 2013;Gallen et al. 2015). Some authors interpreted the change of the power law exponent as resulting from trigger properties (Marc et al.  Valagussa et al. 2019). Although landslide size distribution has been researched by many authors, how the slope height and slope gradient influence the landslide size and size distribution, the rollover effect of landslide size distribution and its scaling exponents of the power law remain poorly understood. In particular, there exists controversy in the previous literature regarding the relationship between landslide size and slope gradient (Frattini and Crosta 2013;Katz et al. 2014;Chen et al. 2016). Therefore, it is necessary to quantitatively examine the effects of topography on the size distribution and size of landslides.
The scope of this work was to determine how topographic features, such as slope height and slope gradient, influence the size and size distribution of loess slides based on field survey data. To this end, we developed a landslide inventory based on a field survey, quantified the relationship between landslide volume and area, analyzed the number and relative density of landslides according to slope height and slope gradient, investigated the effects of slope height and slope gradient on landslide size, determined the probability density of landslides by using kernel density estimation, and compared the landslide size distribution within different slope heights and slope gradients.

Study area
The study area is located in the Xi'an region of Shaanxi Province on the humid southern edge of the Loess Plateau ( Figure 1). Steep topography and frequent extreme weather events make this area prone to loess slides. Figure 1 shows the distribution of landslides in this area. This area is characterized by a typical temperate continental monsoon climate with a mean annual rainfall of 720 mm and a mean air temperature of 13.1 C. Precipitation is mainly governed by a monsoon climate. Most of the precipitation is concentrated in the rainy season (July to September), which accounts for 55% of the total annual precipitation. Many landslides and severe water erosion events occur during this period. The altitude in the area ranges from 388 to 1310 m above sea level. The Bahe River is the major river system in the study area, while the Chanhe River is the major tributary in the study area ( Figure 1). This area has been uplifting since the early Quaternary and is covered by a thick loess-paleosol sequence. Stratigraphically, the loess in this study area can be divided into 4 formations from bottom to top: Late Pliocene Red Clay Formation, Wucheng Loess, Lishi Loess and Malan Loess. Loess is a clastic, homogeneous and predominantly silt-sized sediment formed by the accumulation of wind-blown silt, and it covers approximately 10% of the Earth's surface (Liu 1985). Loess has a loose and porous structure with a large difference between its wet and dry strength (Xu et al. 2012). Loess is a collapsible soil and instantaneously collapses, softens and distorts in the presence of water (Liu 1985;Derbyshire 2001). In contrast to other rock and soil types, vertical jointing in loess is particularly well developed, which influences the hydrological process in loess soil (Liu 1985;Derbyshire 2001). These distinctive properties make the loess area highly susceptible to erosion and landslides (Derbyshire et al. 1991;Zhang et al. 2014).

Landslide data
A detailed landslide inventory map, as the fundamental component of landslide studies, can provide basic information about the landslides that have occurred in the past (Guzzetti et al. 1999). A landslide inventory can be prepared by using different techniques depending on the purpose of the investigation, the scale of the maps, the resolution of the available imagery and the experience of the investigators (van Westen et al. 2006;Guzzetti et al. 2012). Despite the great advances in and convenience of remote sensing technology (Cheng et al. 2018;Wang et al. 2018;Cui et al. 2019), its application is limited by the presence of vegetation canopies, erosion and plowing (Brardinoni and Church 2004). Moreover, some important parameters, such as the slope gradient and slope height, cannot be measured accurately due to many errors. Therefore, in this work, we conducted a series of intensive field surveys from 2015 to 2017 to obtain and measure the loess slide information in the study area. We collected previously published and unpublished landslide reports from the study area and located additional landslides by interpretation of remote sensing images and Google Earth images before the field surveys. These surveys were aided by recent advances in mobile technologies and unmanned aerial vehicles (UAVs) ( Figure  2). We acquired high-resolution image and terrain data of landslides from a UAV named DJI Mavic Pro. Subsequent data processing was performed using a practical commercial photogrammetry software named Agisoft PhotoScan Professional Edition. Figure 3 shows 3D models, digital elevation models (DEMs) and digital orthophoto maps (DOMs) of typical loess slides in the study area. Table 1 shows the characteristic parameters of Fenpo landslide, Mawan landslide and Huijiazui landslide. In total, 91 loess slides were mapped and stored in the landslide inventory. Most of the landslide information was mapped and transformed into a GIS database. The information in this inventory includes the landslide locations, size and related topographic parameters. Although the field survey is an efficient tool with which to obtain certain important parameters, our field trip was concentrated on the areas along major and minor accessible roads.

Relative density of landslides
The relative density of landslides (RD) can be expressed as follows: where, RD is relative density, S is the total area of the study area, N is the total number of landslides, S i is the area in each class of slope gradient, and N i is the number of landslides in each class of slope gradient. An RD is equal to 1 indicates an average  landslide relative density. An RD is less than 1 implies that the relative density is below average density, while an RD of greater than 1 suggests that the relative density is higher than average density.

Probability density distribution of landslides
There is accumulating evidence showing that the probability density of landslides obeys a power law function for landslide size greater than a given minimum area . In this work, we used the power law relation to fit the probability density of landslides for loess slides that exceed a minimum area: where A is the landslide area; c is a constant; and a, a useful parameter to determine the abundance of small and large landslides, is the scaling exponent representing the slope of probability density curve in a log-log plot. Lower scaling exponents of a are associated with a higher frequency of large landslides.

Size distribution of loess slides
The empirical relationship between volume and area of loess slides is shown in Figure 4 based on data of 91 landslides in the field investigation. Figure 4 shows that there exists an obvious linear relationship between volumes and areas of loess slides for multiple orders of magnitude in the log-log coordinates. This result in the where V L is the volume of loess slides and A L is the area of loess slides.
To determine the uncertainty of this empirical equation, we adopted two empirical relationships to ascertain the bounds of landslide volume. Figure 4 shows the upper and lower 95% prediction intervals.
The volumes and areas of individual loess slides cover 5 and 4 orders of magnitude respectively. The corresponding coefficient of variation is 5.18. The mean volume of all loess slides in the study area is approximately 790,000 m 3 . Furthermore, we calculated the density of points through bivariate kernel density estimation and found most loess slides had volumes ranging from 10 4 to 10 5 m 3 . Approximately 80% of loess slides with thickness less than 10 m were shallow landslides.

Relationships between slope height and size distribution of loess slides
We derived the slope height using SAGA GIS (Module library: terrain analysis). Figure 5 shows the distribution of slope height in the study area. The number of loess slides decreased highly with increasing slope height ( Figure 6). The mean slope height of loess slides is approximately 39 m. Approximately 80% of all loess slides are concentrated in slope heights less than 60 m. However, higher slopes are rare in nature. As shown in Figure 6 (white bar), the percentage of domain decreased with increasing of slope height. Moreover, we used Equation (1) to compute the relative density of landslides with respect to slope height. The graph shows that the relative density of landslides is higher with higher slope height. The highest relative density of landslides is on slopes with heights exceeding 120 m ( Figure 6).
Furthermore, as illustrated in Figure 7, the landslide size increased with increasing slope height. Although there is still little theoretical basis for choosing a particular functional form, the significant increase in loess slide area with slope height is well described by a simple power-law regression (Figure 7): where A L is the loess slide area, and H is the slope height.  Furthermore, we calculated the probability densities of loess slide areas using kernel density estimation to analyze the size distribution of landslides for slope heights of <25 m, 25-50 m and >50 m ( Figure 8) and found that there were obvious rollover effects in the noncumulative distributions of loess slide areas in log-log coordinates. The frequency-size distribution of loess slides departs from the linear fractal relationship when the loess slide size is smaller than a certain area. The probability density distribution of landslides consistently satisfies negative power-law statistics for larger loess slide areas, with scaling exponent a of 1.23, 1.05 and 0.98 for slope heights of <25 m, 25-50 m and >50 m, respectively ( Figure 8). The scaling exponents of the power-law functions were clearly influenced by slope height. The scaling exponents decreased with increasing slope height. This pattern suggests that there are more frequent small landslides at lower slope heights.

Relationships between the slope gradient and size distribution of loess slides
We obtained the slope gradient using ArcGIS software 10.0. Figure 9 shows the distribution of slope gradient in the study area. To obtain greater insight into the relationship between landslide size distribution and slope gradient, the slope gradient was divided into seven classes: <10 , 10 -20 , 20 -30 , 30 -40 , 40 -50 , 50 -60 , >60 . With the increase of slope gradient, the number of landslides increases at first and then decreases (grey bars, Figure 10). The mean slope gradient of the loess slides was 37 . Approximately 30% of the loess slides occurred within a slope gradient between 30 and 40 . Moreover, the steeper slopes are rare in nature. As shown in Figure 10 (white bar), the percentage of domain decreased with increasing of slope gradient. Furthermore, we used Equation (1) to compute the relative density of landslides with respect to slope gradient. The graph shows that the relative density of landslides increases significantly with the increase in slope gradient, with the highest values occurring at a slope gradient >60 (solid line, Figure 10).
To quantify the size distribution and size of loess slides with different slope gradients, we further grouped slope gradient into three classes: <25 , 25 -50 and >50 . As illustrated in Figure 11, the slope gradient shows a clear influence on the landslide size. The mean loess slide areas for slope gradients <25 , 25 -50 and >50 were  1.10 Â 10 5 m 2 , 4.09 Â 10 4 m 2 and 8.13 Â 10 3 m 2 , respectively. The mean loess slide area for a slope gradient <25 is approximately 14 times larger than that for a slope gradient >50 . It is difficult for steep slopes to support large loess slides.
In addition, we calculated the landslide area probability density using a kernel density estimation for slope gradients of <25 , 25 -50 and >50 and found that the probability density distributions of loess slides are influenced by the slope gradient ( Figure 12). The probability density of landslides also satisfies power-law statistics at larger loess slide areas, with scaling exponent a of 0.60, 0.71 and 0.98 for loess slides  with slope gradients of <25 , 25 -50 and >50 , respectively ( Figure 12). The scaling exponents increase with increasing slope gradient. This suggests that there are more frequent small landslides with larger slope gradients.

The role of slope height on landslide size and size distribution
We analyzed the coupling between slope height and landslide number distribution and found that the majority of loess slides occur in areas with relatively small slope heights because of the very large proportion of the study area with small slope heights. The number of loess slides significantly decreases with increasing slope height. Our results here differ from previous findings observed in the loess ridge and hilly region from Qiu et al. (2016), who found that the loess slide number distribution versus slope height is unimodal. This result may be because the study area is a typical loess tableland region, and most of the study area is covered by slopes with low slope heights. These low slopes are always eroded by rivers and are susceptible to human activities (Zhang and Liu 2009).
In addition, landslide size controls the power of landslides (Lari et al. 2014;Valagussa et al. 2019). Previous studies have shown that the topography modulates landslide size and land that slide size increases with increasing slope height (Korup et al. 2007;Frattini and Crosta 2013;Gallen et al. 2015;Qiu et al. 2016). Most giant landslides occur in areas with high local relief and deeply incised valleys (Montgomery and Brandon 2002). This is because slope height limits landslide size, and large landslides rarely occur on slopes with low slope heights (Korup et al. 2007;Qiu et al. 2016). In this work, we found that landslide size increases with slope height. Our results are similar to findings from previous studies (Montgomery and Brandon 2002;Korup et al. 2007;Qiu et al. 2016).

The role of slope gradient on landslide size and size distribution
Slope gradients play an important role in landslides, and the relationship between landslide frequency and slope gradient has been researched by many authors (Dai and Lee 2002;Qiu et al. 2019). Based on field survey data, we found that most landslides occurred within a slope gradient between 30 and 40 .
There are some arguments about the role of slope gradient on landslide size. Frattini and Crosta (2013) found that small landslides are usually associated with high slope angles. Moreover, by using the limit equilibrium simulation method, Chen et al. (2016) reported that landslide size decreases with increasing slope gradient. However, by using the numerical discrete element method, Katz et al. (2014) showed that landslide size increases with increasing slope gradient. In this work, we found a relationship between the landslide size and slope gradient, with larger landslides associated with gentler slope gradients. In the case of the relationship between landslide size and slope gradient, our finding is similar to the result of Chen et al. (2016), who found that the landslide size decreases as the slope gradient increases, but differs from the result of Katz et al. (2014), who found that the landslide size increases as the slope gradient increases.

Rollover effect of landslide size distribution and its influencing factors
It is well known that large landslides are often very hazardous, and small landslides occur the most frequently (Hurst et al. 2013). Thus, analysis of landslide size distribution is essential for landslide hazard assessment (Valagussa et al. 2019). Many studies have observed that the landslide size distribution generally exhibits power-law scaling over a certain scale range and often departs from the power-law relationship below a certain landslide size (Hungr et al. 1999;Stark and Hovius 2001;Martin et al. 2002). The rollover effect of landslide size distributions is well known. Some authors attribute the reason for this rollover effect and scaling exponents to undersampling, soil moisture, topography, slope geometry and physiographic variables of landscapes (Pelletier et al. 1997;Stark and Hovius 2001;Turcotte et al. 2002;Malamud et al. 2004;Hungr et al. 2008). However, determining how to provide a quantitative relationship between the influencing factors and scaling exponents of landslide size distribution has been severely neglected. Here, we found that the landslide size distribution curve can be well described by a negative power law for landslides larger than a particular size. We fitted the landslide size distribution curves with a power-law function and obtained the scaling exponents of the curves. Moreover, the scaling exponents of the power law are influenced by slope height and slope gradient. The scaling exponents decreased with increasing slope height and increased with increasing slope gradient. Furthermore, Van Den Eeckhaut et al. (2007) found that the smaller the scaling exponent, the greater the contribution of large landslides to the total landslide inventory is, and vice versa. The above discussion indicates that larger landslides are commonly distributed in larger slope heights and smaller slope gradients. Therefore, our results here are similar to findings from Van Den Eeckhaut et al. (2007).

Conclusions
This study developed a loess slide inventory based on field investigations. The analysis of this landslide inventory elucidates the controls on landslide size and size distribution in loess terrain. Analysis of the landslide size distribution with respect to slope height demonstrated that loess slides are more frequent on slope heights less than 60 m, while the statistical analysis of landslide number and slope gradient suggests that loess slides are abundant on slope gradients between 30 and 40 . However, high and steep slopes are rare in nature. The relative density of landslides is obviously high on slopes with high slope height and steep slope gradient. Moreover, the results show that both slope height and slope gradient control landslide size; landslide size increases with increasing slope height and decreases with increasing slope gradient. Furthermore, the frequency-size distribution exhibits a power-law scaling for large landslides. The power-law exponent depends on slope height and slope gradient. This exponent decreases as the slope height increases and increases as the slope gradient increases. Small landslides are more frequent with lower slope height and larger slope gradient. This finding may have profound implications for landslide hazard assessment and long-term erosion dynamics.

Disclosure statement
No potential conflict of interest was reported by the authors.