s2fieldsofstudy:
Agricultural And Food Sciences
Physics

text:
Spatial Structure Characteristics of Slope Farmland Quality in Plateau Mountain Area: A Case Study of Yunnan Province, China

: As an important part of farmland, the slope farmland is widely distributed in the central and western plateau mountain region in China. It is necessary to scientiﬁcally evaluate the slope farmland quality (SFQ) and analyze the spatial structure characteristics of SFQ to ensure reasonable utilization and partition protection of slope farmland resources. This paper takes the typical plateau mountain region—Yunnan Province in China—as an example and systematically identiﬁes the leading factors of SFQ. The sloping integrated fertility index (SIFI) is adopted to reﬂect the SFQ. The evaluation system is built to quantitatively evaluate the SFQ and the spatial structure characteristics of SFQ were analyzed by a geostatistical model, autocorrelation analysis and spatial cold–hot spot analysis. The results show that the SFQ indexes in Yunnan Province are between 0.36 and 0.81, with a mean of 0.59. The SFQ grade is based on sixth-class, ﬁfth-class, seventh-class and fourth-class land. The SFQ indexes present a normal spatial distribution, and the Gaussian model ﬁts well with the semi-variance function of the spatial distribution of SFQ indexes. Furthermore, the spatial distribution of SFQ indexes is moderately autocorrelated. The structural factors play a major role in the spatial heterogeneity of SFQ indexes, but the inﬂuence of random factors should not be ignored. The spatial distribution of SFQ grades has a signiﬁcant spatial aggregation characteristic, and the types of local indicators of spatial association (LISA) are based on high–high (HH) aggregation and low–low (LL) aggregation. The cold spot and hot spot distributions of SFQ grades display the signiﬁcant spatial di ﬀ erence. The hot spot area is mainly distributed in Central Yunnan and the Southern Fringe, while the cold spot area mainly distributes in the Northeastern Yunnan, Northwestern Yunnan and Southwestern Yunnan. This study could provide a scientiﬁc basis for SFQ management and ecological environment protection in the plateau mountain region.

Introduction
Farmland is one of the most precious natural resources, as well as the basic resource to ensure food security and human sustainable development [1][2][3]. However, more and more farmland has been occupied in the world with the acceleration of urbanization in the past 60 years. Moreover, unreasonable cultivation results in the decrease in both the quantity and quality of farmland, thus seriously threatening food security and sustainable development of human beings [4][5][6][7]. Especially in some developing countries, the quantity and quality of farmland has decreased more significantly [8]. China is a typical developing country, and there are many problems such as farmland quality decrease, spatial fragmentation, ecological problems, etc. Hence, it has always been a topic of great concern potential classification, land bearing capacity evaluation, and so on. In 1976, Food and Agriculture Organization of the United Nations (FAO) proposed the first unified land evaluation system-A Framework for Land Evaluation-from a suitable perspective, moreover, its basic theory has become the foundation of many land productivity evaluation models [27]. Based on "A Framework for Land Evaluation", FAO further developed the agricultural ecological zone (AEZ) method to evaluate the potential of agricultural land use, and this model has been widely used in the world [28]. In the 1980s, the American Soil Conservation Service built the land evaluation and site assessment (LESA) system. This system includes two parts of land evaluation (LE) and site assessment (SA) [29,30].
At present, the frequently-used farmland quality evaluation model includes the weighted sum method, land productivity evaluation method, Pressure-state-response (P-S-R) frame model method, analytic hierarchy process, fuzzy comprehensive evaluation method, farmland potential evaluation method, suitability evaluation method, soil environment quality evaluation method, etc. Among them, the weighing-sum method, land productivity evaluation method, and P-S-R frame model method are the most widely used [31]. In addition, the introduction of GIS, remote sensing(RS), etc. has promoted the substantial development of research on the evaluation of farmland quality in regards to the updated data and evaluation accuracy [32][33][34]. In China, with the study of farmland quality evaluation and deep practice, the farmland quality evaluation standard system has gradually formed, represented by agriculture land classification, farmland productivity evaluation and farmland environment quality evaluation [35].
In recent decades, the farmland quality significantly degenerates, so the farmland quality evaluation has become a hot topic for research. The slope farmland is a special part of farmland, and the influencing factors of SFQ are different from those of other types of farmland. However, at present, there is little research on the SFQ evaluation. Hence, the research on the evaluation index system construction and evaluation model establishment of SFQ still need to be strengthened. Furthermore, the analysis of spatial structure characteristics of farmland quality is the foundation of farmland quality partition protection [36]. However, most of the existing research only describes the spatial and temporal distribution characteristics of farmland quality, and there is less research on the spatial variability and spatial clustering feature of farmland quality. As a typical plateau mountain region, the slope farmland is an important part of farmland resources in Yunnan. Therefore, it is of great significance to scientifically evaluate the SFQ for sustainable utilization and protection of regional slope farmland.
Here, this paper constructs the SFQ evaluation system based on the utilization characteristics and key influencing factors of the slope farmland in Yunnan Province. Thus, the SFQ in Yunnan is quantitatively evaluated and the spatial structure characteristics of SFQ are systematically analyzed, which can provide the scientific basis for the SFQ management and ecological environment protection of sloping farmland in the plateau mountain area.

Study Area
Yunnan is located in the Southwest border and Southwest Yunnan-Guizhou Plateau, with a land area of 0.39 million km 2 , in which the East Asian monsoon and South Asian monsoon converge. For the topography, it is high in the north and low in the south. Moreover, the mountainous area accounts for 84% of the total land area, and the hill accounts for only 10%. The topography gradually develops from mountain land to karst landform. Due to the common influence of ecological environment evolution and human activities, the region with moderate ecological vulnerability accounts for 32.02% of the total area, while the region with strong and extremely strong fragility accounts for 53.63% [37]. There is abundant rainfall and many rivers in Yunnan, but they are unevenly distributed in space and time. The average reference crop evaporation is between 786.3 and 1511.6 mm, with a mean of 1090.4 mm [38]. The main soil types include red earths, lateritic red earths, purplish soils, yellow earths and yellow-brown earths. Most of the farmland in the region has a slope of more than 3 • , so the agricultural production of slope farmland plays an important role in the agricultural activities in Yunnan. The research results show that the area of slope farmland in Yunnan is 4.7255 million hm 2 , accounting for 69.79% of the farmland area. The agricultural production of slope farmland plays an important role in the agricultural activities in Yunnan [15]. In order to make the SFQ evaluation consistent with the regional agricultural activities, Yunnan is divided into seven regions in this study, according to the comprehensive agricultural partition in Yunnan. The elevation and spatial distribution of slope farmland are shown in Figure 1, and the distribution characteristics of slope farmland in different partitions are listed in Table 1.
Sustainability 2020, 12, x FOR PEER REVIEW 4 of 21 agricultural production of slope farmland plays an important role in the agricultural activities in Yunnan. The research results show that the area of slope farmland in Yunnan is 4.7255 million hm 2 , accounting for 69.79% of the farmland area. The agricultural production of slope farmland plays an important role in the agricultural activities in Yunnan [15]. In order to make the SFQ evaluation consistent with the regional agricultural activities, Yunnan is divided into seven regions in this study, according to the comprehensive agricultural partition in Yunnan. The elevation and spatial distribution of slope farmland are shown in Figure 1, and the distribution characteristics of slope farmland in different partitions are listed in Table 1.

Data Sources
The digital elevation model (DEM) data of research region come from the Geospatial Data Cloud Platform of Chinese Academy of Sciences (http://www.gscloud.cn) with a spatial resolution of 30 m. This data set is obtained by processing the data of the first edition of Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), and it is a digital elevation data product with a spatial resolution of 30 m in the world. The land use data in Yunnan are from the Resource and Environment data Cloud Platform of Chinese Academy of Sciences (http://www.resdc.cn), with a spatial resolution of 30m. The basic data, such as thickness of the cultivated-layer, pH value of the soil, organic matter, total nitrogen, available phosphorus, rapidly available potassium, probability of irrigation, etc., are from the data set obtained from the formula fertilization by soil testing in Yunnan Province (2015). The basic data of effective soil thickness, soil bulk density and soil texture are from the data set of main properties of farmland quality in southern China published by the Ministry of Agriculture and Rural Affairs of China (2018).

Evaluation Unit
The evaluation unit is the basic unit of SFQ evaluation. In this study, the evaluation scale is a provincial region. According to the previous findings [39,40], the 30 m × 30 m high-precision grid (pixel) is taken as the minimum unit of the SFQ evaluation. Each grid (pixel) has the same attributes such as land use, soil type, elevation, and so on. This partition method of the evaluation unit can be conveniently used for the classifying and summarizing according to the standards, i.e., different patch units, agricultural partition, soil types, district ranges, and so on, with less loss of summary result information.

Construction of Evaluation Index System
The slope farmland system is a compound system formed by multiple factors such as climate, soil, geomorphology and human cultivation activities with the interaction. Moreover, its quality is affected by multiple factors [21]. As we select the SFQ evaluation index, the influence of each factor on the SFQ should be taken into account, so as to select the main factors as the evaluation indexes. In terms of the connotation, the slope farmland not only has the general attributes of farmland but also has its own special attributes, that is, larger soil erosion intensity, susceptibility to drought, obvious nutrient degradation, spatial distribution fragmentation, etc. [41,42]. Therefore, both the basic indexes of farmland and the characteristic indexes reflecting the slope farmland utilization should be considered for the selection of the SFQ evaluation index. Then, the primary index system is determined. Based on this, the evaluation indexes are discriminated and screened in order to eliminate the information overlap caused by the interaction among the indexes with a statistical analysis method, thus establishing the index system of minimum data set (MDS) [43,44]. Based on the above evaluation index selection principle and analytic hierarchy process, the index system of minimum data set for SFQ evaluation in Yunnan Province is established, as shown in Table 2. The criterion layer of the evaluation index system includes soil profile properties (B1), physicochemical properties (B2), soil nutrient (B3), site conditions (B4), spatial form (B5), moisture condition (B6) and soil erosion (B7), a total of seven dimensions. Furthermore, each criterion layer contains many specific indexes.

Quantification of Evaluation Index
In the evaluation indexes listed in Table 2, the indexes belong to the quantitative index in addition to the cultivated-layer texture of the conceptual index. According to the Chinese national standard Grade of Cultivated Land Quality (GB/T33469-2016), the membership of soil texture is assigned to be translated into the quantifiable evaluation index. For the quantitative indexes, the available soil thickness, cultivated-layer thickness, soil bulk density, pH value, organic matter, total nitrogen, available phosphorus, available potassium, elevation, accumulated temperature greater than or equal to 10 • C, rainfall, irrigation guarantee rate and farmland slope can be directly measured or obtained by investigation. However, the slope farmland quality indexes of regularity and continuity degree are obtained by analysis and calculation. The spatial morphology evaluation of slope farmland can be quantitatively measured from the geometrical shape of the farmland and the spatial continuity. Among them, the field regularity is described by the fractal dimension of landscape ecology (FRAC) [45] with a value range of 1.0~2.0. Here, the smaller the index is, the more regular the field shape is. Otherwise, the field shape is more complex.
Furthermore, the intensive large-scale utilization degree of slope farmland can be expressed by the continuity degree Q of slope farmland, and its value can be quantified according to the field size. Moreover, the larger the Q value is, the higher the continuity degree is. Otherwise, the continuity degree is lower [46]. The FRAC and Q are calculated by the following formulas.
where FRAC represents the regularity degree of slope farmland. Q is the continuity degree of slope farmland. P is the perimeter of slope farmland, m. a is the slope farmland area, m 2 . In addition, the range of FRAC value is between 1 and 2, and the range of Q value is between 20 and 100. The area threshold of slope farmland is obtained by the natural break point method. Moreover, the minimum threshold is 2.5 hm 2 , and the maximum threshold is 78 hm 2 .

Membership Function
In addition to the assignment for the soil texture membership of cultivated-layer according to the Chinese national standard Cultivated Land Quality Grade (GB/T33469-2016), other indexes select the membership function according to their positive and negative effects on the SFQ. According to the effects of the evaluation index and SFQ, the membership function can be divided into three types, namely, S-type membership function, anti-S-type membership function and parabolic-type membership function.
Correspondingly, some indexes, such as the available soil layer thickness, cultivated-layer thickness, organic matter, total nitrogen, available phosphorus, available potassium, accumulated temperature greater than or equal to 10 • C, rainfall and irrigation guarantee rate, etc., have a positive effect on the SFQ, and they select the S-type membership function. However, some indexes have a negative effect on the SFQ, such as bulk density of soil, elevation, farmland regularity, farmland slope, etc., so they select the anti-S-type membership function. The pH value of soil has a peak effect on the SFQ, thus selecting the parabolic-type membership function. According to the change range of SFQ evaluation index value of Yunnan Province, the parameter value of each evaluation index membership function is comprehensively determined by referring to relevant research results, as shown in Tables 3 and 4.
Field regularity Anti-S-type 1.00 1.34 C14 Slope Anti-S-type 3.00 25.00 Note: µ (x) is a membership function, x is the measured value of evaluation index, a and b are the lower limit and upper limit of the critical value, respectively, which are determined according to the measured results of the study area and the comprehensive comparison of relevant domestic research results.
5.5 6.5 7.5 8.5 Note: µ (x) is the membership function, x is the measured value of the evaluation index, a 1 and b 2 are the lower limit and upper limit of the critical value of the index, respectively, the minimum value and the maximum value of the measured value are taken in this study; b 1 and b 2 are the upper and lower boundary points of the most suitable value, and their values are determined according to the comprehensive comparison of the measured results in the study area.

Index Weight
In order to improve the accuracy of weight calculation, the evaluation index weight is determined by combining the principal component analysis (PCA), analytic hierarchy process (AHP) and entropy weight method (EWM). During the calculation process, the PCA, AHP and EWM are used to calculate the weight of each index. Simultaneously, the comprehensive weight is taken as the final weight of the evaluation, and the comprehensive weight is calculated as follows.
where C i is the comprehensive weight of the i-th evaluation index. C i1 is the weight obtained by the calculation of PCA. C i2 is the weight obtained by the calculation of AHP. And C i3 is the weight obtained by the calculation of EWM.

Evaluation Model and Quality Grading
Sloping integrated fertility index (SIFI) was calculated by the weighted sum method.
where SIFI is the SFQ index (dimensionless), and the numerical value range is between 0 and 1. K i is the membership value of the i-th evaluation index. C i is the weight of evaluation index of the ith evaluation index. n is the number of evaluation indexes. According to the Cultivated Land Quality Grade, the farmland quality is divided into ten grades with the equidistance method in sequence from small to large between the highest point and the lowest point of the comprehensive farmland quality index curve. It is found that the larger the farmland quality index is, the higher the farmland quality level is. Thus, the quality of the first-class farmland is the highest, and the quality of ten-class farmland is the lowest.

Geostatistical Analysis Method
As a spatial variable, the spatial change feature of SFQ is structural and random. The spatial variability is measured with the semi-variance function [47,48]. The semi-variance function is calculated as follows. where r(h) is the semi-variance variation function, and it can characterize the spatial variation pattern on the whole scale. h is the spatial segmentation distance between two sample points. Z (x i ) and Z(x i +h) are the values of spatial positions x i and x i +h, respectively. n(h) is the logarithm of sample with a spatial distance of h. The spatial variability of the research object is distinguished according to the parameters of the variability function. Among them, the size of nugget C 0 can reflect the influence degree of randomness factors on the regional SFQ. Furthermore, the Sill (C 0 +C) represents the maximum spatial variation degree of SFQ. The spatial distribution of SFQ indexes is moderately autocorrelated. Nugget coefficient C 0 /(C 0 +C) represents the proportion of random variation to the total variation of slope farmland. If the proportion is less than 25%, the spatial distribution of SFQ indexes belongs to the strong spatial autocorrelation. If the proportion is between 25% and 75%, the spatial distribution of SFQ indexes belongs to moderate spatial autocorrelation. If the proportion is greater than 75%, the spatial distribution of SFQ indexes belongs to a weak spatial correlation, indicating that the degree of spatial heterogeneity caused by the random part plays a major role [49,50].

Global Spatial Autocorrelation Analysis Method
Spatial autocorrelation indices were used to quantify landscape patterns. The global spatial autocorrelation of SFQ grades can be used to study the aggregation or dispersion degree of SFQ on the whole. This paper uses the global Moran's I index to calculate the spatial autocorrelation characteristics of SFQ grades [51,52], and the calculation formula is as follows.
where N is the number of research objects. X i is the observed value, and X is the mean of X i . W(i, j) is the spatial connection matrix between the research objects i and j. Moreover, the Moran's I value of global spatial autocorrelation is between −1 and 1. If Moran's I value is more than 0, the space is positively correlated, as well as the research object presents the spatial aggregation characteristic. However, if the Moran's I value is less than 0, the space is negatively correlated, as well as the research object presenting a random distribution. Furthermore, P value is used for the significance test.

Local Spatial Autocorrelation Analysis Method
The global Moran's I index can test the overall spatial distribution pattern of SFQ grade rather than reflect the spatial correlation and local significance level of elements or attributes between adjacent evaluation units [50,53]. This paper uses the local Moran's I index to analyze the spatial correlation characteristics of local SFQ grade. Then, the calculation formula of local Moran's I is as follows.
where N is the number of research objects. X i is the observed value, and X is the mean of X i . W(i, j) is the spatial connection matrix between the research objects i and j. Moreover, the Moran's I value of global spatial autocorrelation is between −1 and 1. If Moran's I value is more than 0, the space is positively correlated, as well as the research object presenting the spatial aggregation characteristic. However, if the Moran's I value is less than 0, the space is negatively correlated, as well as the research object presenting the spatially discrete characteristic. If Moran's I value is 0, the research object randomly distributes. Furthermore, p value is used for the significance test.

Spatial Cold and Hot Spots Analysis Method
The cold and hot spots analysis is one of the commonly-used methods to explore the local spatial clustering distribution characteristics. This method could collect the Getis-Orid G * i index of each element by calculating the elements, so as to determine whether there is high-value clustering or low-value clustering as well as to identify its location [50,54]. Furthermore, Getis-Orid G * i is a commonly-used index to describe the regional cold and hot spots, thus reflecting the correlation degree of SFQ grade between different research areas. The significance degree of Z (G * i ) is used to identify the spatial distribution of hot and cold spots in different regions.
In this paper, the natural discontinuity point method is used for Z(G * i ) classification. Simultaneously, the spatial cold and hot areas under different confidence levels are divided according to three confidence coefficients of 99%, 95% and 90%. The computational formulas of G * i and Z (G * i ) are as follows.

Data Calculation and Analysis
In this study, Excel 2013 software is used for basic data processing, likewise, SPSS19.0 software for data statistical analysis, ArcGIS 10.2 software for spatial data analysis and calculation. The spatial autocorrelation analysis was carried out by using Geoda software, and a Moran scatter plot and local indicators of spatial association (LISA) map were drawn to realize the visual expression of spatial autocorrelation features. In spatial autocorrelation analysis, the spatial weight should be determined first. There are two methods to determine the spatial weight matrix, which are based on adjacency relationship and distance relationship. Referring to the existing research results, this paper uses the K-nearest method to determine the spatial weight [55]. Figure 2a shows the spatial distribution of SFQ indexes. It can be seen that SIFI of Yunnan distributes between 0.36 and 0.81, with an average value of 0.59 ± 0.06. Moreover, the SIFI of most evaluation units is less than 0.6, and indeed the SIFI is generally lower. The difference in the SIFIs of different evaluation units is significant (p < 0.05), which can reflect the spatial difference of SFQ in different regions. From the spatial distribution, the SFQ indexes present a staggered coupling distribution characteristic of spatial aggregation and variability. Among them, the SFQ indexes are higher in the central Yunnan, the Southern Fringe, Western Yunnan, Southeastern Yunnan, while they are lower in Southwestern Yunnan and Northeastern Yunnan. In addition, there exists a certain difference in the means of SFQ indexes in different regions, but the difference is relatively smaller. The SFQ indexes in the Southern Fringe and Western Yunnan are relatively larger, while they are relatively smaller in the Northwestern Yunnan and Northeastern Yunnan. Figure 2b shows the spatial distribution of SFQ grades in Yunnan. Similarly, there exists a difference in the distribution area of SFQ grades in different regions, and the spatial distribution of SFQ grades displays obvious aggregation characteristics. The SFQ is based on sixth-class farmland, fifth-class farmland, seventh-class farmland and fourth-class farmland. And other grades of slope farmland distribute less, so the quality grade is generally lower. From the distribution of SFQ grades in different regions, the SFQ grade is relatively higher in the Southern Fringe, Central Yunnan, Western Yunnan and Southeastern Yunnan, mainly based on fourth-class farmland and fifth-class farmland.

Spatial Distribution Characteristics of SFQ
However, the SFQ grade is relatively lower in Northeastern Yunnan and Northwestern Yunnan, mainly based on sixth-class farmland and seventh-class farmland.
overall quality of farmland in Shandong Province is relatively high, but it has a slight downward trend [34]. Yang et al. also showed that the quality of farmland in Yunnan Province is generally at a medium-low level [59]. Although the farmland quality evaluation models used in the above study are different, and the results cannot be directly compared, but they can reflect the characteristics of farmland quality in different regions. As an important part of farmland, slope farmland has the characteristics of soil erosion, drought and water shortage compared with other farmland types. Our research shows that most of the SFQ grade in Yunnan is in the sixth, fifth and seventh grade, and the SFQ is generally low. This conclusion is consistent with the actual situation of slope farmland utilization in Yunnan Province.  Table 5 shows the areas and distribution proportions of different SFQ grades. It can be seen that the distribution areas of different SFQ grades present a pattern of a larger middle and both have smaller ends. Among the ten SFQ grades, the sixth-class farmland distributes most widely with an area of 1.2163 million hm 2 , accounting for 25.74% of the total area of slope farmland. The second one The quality of farmland is closely related to regional food security and ecological security, so special protection policies are generally implemented for farmland in the world. China implements the strictest cultivated land protection system in the world, but the pure administrative restraint mechanism cannot change the declining trend of farmland quantity and quality [56]. Studies have shown that the quality of farmland in China is generally low, and the medium and low-grade farmland accounts for more than two thirds of the total farmland area [57]. Due to the differences of nature and cultivation conditions in different regions of China, the quality of farmland varies greatly in different regions. Wei et al. show that the overall quality of farmland in Shaanxi Province is relatively low, and it shows a downward trend in the recent 10 years [58]. Wang et al. shows that the overall quality of farmland in Shandong Province is relatively high, but it has a slight downward trend [34]. Yang et al. also showed that the quality of farmland in Yunnan Province is generally at a medium-low level [59]. Although the farmland quality evaluation models used in the above study are different, and the results cannot be directly compared, but they can reflect the characteristics of farmland quality in different regions. As an important part of farmland, slope farmland has the characteristics of soil erosion, drought and water shortage compared with other farmland types. Our research shows that most of the SFQ grade in Yunnan is in the sixth, fifth and seventh grade, and the SFQ is generally low. This conclusion is consistent with the actual situation of slope farmland utilization in Yunnan Province. Table 5 shows the areas and distribution proportions of different SFQ grades. It can be seen that the distribution areas of different SFQ grades present a pattern of a larger middle and both have smaller ends. Among the ten SFQ grades, the sixth-class farmland distributes most widely with an area of 1.2163 million hm 2 , accounting for 25.74% of the total area of slope farmland. The second one is the fifth-class farmland with a distribution area of 1.1801 million hm 2 , accounting for 24.97% of the total area of slope farmland. Likewise, the distribution areas of fourth-class and seventh-class farmland are also larger, with 0.7975 million hm 2 and 0.8594 million hm 2 , respectively, accounting for 16.88% and 18.19% of the total area of slope farmland, respectively. However, the distribution areas of fourth-class and seventh-class farmland are smaller, with the distribution areas of 0.2477 million hm 2 and 0.3306 million hm 2 , respectively. Similarly, the distribution areas of first-class, second-class farmland, ninth-class farmland and tenth-farmland have the smaller distribution area, all accounting for less than 2%.

Spatial Variation Characteristics of SFQ
The geostatistical analysis model is a common method for the variability analysis of spatial variables. The geostatistical analysis is suitable for the sample data with normal distribution. Hence, the data distribution type should be firstly tested before the geostatistical analysis. Based on the exploratory analysis function for data, the ArcGIS10.2 software is used to plot a histogram of sample distribution, cumulative distribution curve, and normal Q-Q plot to analyze the spatial distribution type of SFQ indexes. Figure 3 shows the exploratory analysis results of spatial distribution of SFQ index. As can be seen from Figure 3a, the frequency histogram of SFQ indexes in Yunnan Province presents a typical normal distribution characteristic of frequency. It is found from the cumulative curve change in Figure 3b that the cumulative curve of sample frequency is S-shaped, which suggests that the sample distribution has typical normal distribution characteristic. From the normal Q-Q Plot in Figure 3c, the sample data concentratedly distribute in the line characterizing the normal distribution. The above analysis indicates that the spatial distribution of SFQ indexes in Yunnan presents a better normal distribution characteristic. This is consistent with the conclusion of research on distribution characteristics of farmland quality grade in Yunnan by Zeng et al. [60]. The selection of variable function is an important part of spatial variability analysis. In order to obtain the optimal variation function of the SFQ index distribution, four kinds of theoretical models, namely, the Circular model, Spherical model, Exponential model, and Gaussian model are used to fit the variation function. Then, the optimal model is selected by contrasting some parameters such as mean, mean square root, standardized mean, Root-Mean-Square Standardized, mean standard error. Among them, as the Root-Mean-Square Standardized is close to 1, the mean standard error is close to 0. Moreover, the smaller the other parameters are, the better the model fitting effect is [49]. Table 6 lists the fitting parameters of four theoretical models. Obviously, the four models can better fit the semi-variance function of SFQ indexes, and the difference among different fitting models is small on the whole. In addition, the Root-Mean-Square Standardized of Gaussian model is the closest to 1, and its mean standard error is the closest to 0. Therefore, it is suggested that the fitting effect of the Gaussian model is the best in the four models. In this paper, the Gaussian model is used to fit the semi-variance function of SFQ indexes. The selection of variable function is an important part of spatial variability analysis. In order to obtain the optimal variation function of the SFQ index distribution, four kinds of theoretical models, namely, the Circular model, Spherical model, Exponential model, and Gaussian model are used to fit the variation function. Then, the optimal model is selected by contrasting some parameters such as mean, mean square root, standardized mean, Root-Mean-Square Standardized, mean standard error. Among them, as the Root-Mean-Square Standardized is close to 1, the mean standard error is close to 0. Moreover, the smaller the other parameters are, the better the model fitting effect is [49]. Table 6 lists the fitting parameters of four theoretical models. Obviously, the four models can better fit the semi-variance function of SFQ indexes, and the difference among different fitting models is small on the whole. In addition, the Root-Mean-Square Standardized of Gaussian model is the closest to 1, and its mean standard error is the closest to 0. Therefore, it is suggested that the fitting effect of the Gaussian model is the best in the four models. In this paper, the Gaussian model is used to fit the semi-variance function of SFQ indexes. The geostatistical analysis method has been widely used in studying the spatial variation of soil quality elements and measuring land ecological space, but it is seldom used in the study of spatial pattern of farmland quality. Spatial heterogeneity of SFQ indexes is affected by both structural and random factors (also called non-structural factors) [50]. Among them, the random factors (external factors) include various obstacle factors and anthropogenic farming activities, while the structural factors (internal factors) include the factors dominating the SFQ, such as climatic condition, soil property, moisture condition, spatial morphological characteristic and so on.
According to the analysis results of spatial variability parameters of SFQ indexes, the Nugget C 0 is 0.5293, indicating that the random factors of spatial distribution of SFQ indexes are larger. The Partial Sill C is 0.4515, and the Sill (C 0 +C) is 0.9808. Thus, the Nugget coefficient C 0 /(C 0 +C) is 53.97%. This suggests that although the spatial variation caused by random factors is higher, the SFQ indexes are in the medium spatial autocorrelation on the whole. The structural factors, such as climatic condition, soil property, moisture condition, spatial morphological characteristic, etc., still play a major role. Furthermore, the influence of random factors on the spatial differentiation of SFQ indexes should not be ignored. This conforms to the great influence of unreasonable cultivation mode, soil erosion aggravation and frequent regional droughts of slope farmland in Yunnan in recent years. The range (A 0 ) refers to the distance as the variation function reaches the Sill as well as reflects the distance range of spatial autocorrelation of the research object. Here, the A 0 value of SFQ index in Yunnan is 17.96 km, which shows that the spatial autocorrelation range of SFQ in Yunnan is within 17.96km.

Spatial Autocorrelation Characteristics of SFQ
The autocorrelation characteristics among the spatial factors are mainly determined by global spatial autocorrelation and local spatial autocorrelation indexes. Here, the global spatial autocorrelation reflects the regional global spatial autocorrelation, while the local spatial autocorrelation further measures the correlation between the attribute eigenvalues of each geographic unit and those of its adjacent spatial units [51,52].

Global Spatial Autocorrelation Analysis
The SFQ grade is taken as the spatial attribute value, and the K-nearest method is used to determine the spatial weight. Then, the global Moran's I is calculated. According to the calculation results, Moran's I of the SFQ grade is 0.8489, Z is 1205.07, and p is 0.0000. As can be seen from the calculation results, Moran's I is more than 0, p is less than 0.01, and Moran's I is closer to 1. This indicates that the spatial distribution of the SFQ grade has a strongly positive spatial correlation, and the SFQ grade presents a significant spatial aggregation characteristic.
A Moran scatter plot can be used to qualitatively distinguish the relationship between some attribute values of a certain region and those of the surrounding region. The Moran scatter plot can be divided into four quadrants. That is, the first quadrant represents high-high (HH) aggregation, the second quadrant represents low-high (LH) aggregation, the third quadrant represents low-low aggregation (LL), and the fourth quadrant represents high-low (HL) aggregation. Figure 4 displays the Moran scatter plot of SFQ grades in Yunnan. Clearly, most of the SFQ grades focus on the first quadrant and the third quadrant, a few SFQ grades distribute in the second quadrant and the fourth quadrant. The results show that the spatial autocorrelation type of SFQ is based on HH aggregation and LL aggregation, and the spatial distribution of SFQ grades present a higher spatial aggregation characteristic on the whole.
distribution of farmland quality in Guangning County of China showed a certain aggregation rule, and the positive correlation types mostly appeared in the form of "group", while the negative correlation type had no obvious concentration area [61]. Li et al. showed that the farmland quality index in Zhejiang Province showed significant positive spatial correlation under different scales, and the global autocorrelation index of provincial spatial scale farmland quality index exceeded 0.8 [62]. The results of Yang et al. showed that the overall quality of cultivated land in Jiaozuo of Henan Province, was better, and showed a strong positive correlation in spatial distribution [55]. The research of Xiong et al. shows that there is a certain spatial positive autocorrelation in the distribution of village level cultivated land [63]. The results of this study are basically consistent with those of different regions mentioned above. The global spatial autocorrelation index of each partition is calculated to further identify the global spatial autocorrelation characteristics of different SFQ grades in different regions. Table 7 lists global autocorrelation analysis results of SFQ grades in different regions. It is found that Moran's I value of SFQ grade in different regions is larger, all more than 0.6, moreover, they pass the significance test. This result suggests that the spatial distribution of SFQ in seven regions shows strong spatial positive correlation, and the SFQ grade has significant spatial aggregation characteristics.

Local Spatial Autocorrelation Analysis
The local spatial autocorrelation analysis of SFQ grade can accurately determine the spatial location where the study objects aggregate or disperse, and the results can be represented by spatial Spatial autocorrelation analysis is a common method to explore the aggregation characteristics of spatial objects. In recent years, GIS based study on the spatial aggregation characteristics of farmland quality and protection zoning has been widely concerned. Wei et al. showed that the spatial distribution of farmland quality in Guangning County of China showed a certain aggregation rule, and the positive correlation types mostly appeared in the form of "group", while the negative correlation type had no obvious concentration area [61]. Li et al. showed that the farmland quality index in Zhejiang Province showed significant positive spatial correlation under different scales, and the global autocorrelation index of provincial spatial scale farmland quality index exceeded 0.8 [62]. The results of Yang et al. showed that the overall quality of cultivated land in Jiaozuo of Henan Province, was better, and showed a strong positive correlation in spatial distribution [55]. The research of Xiong et al. shows that there is a certain spatial positive autocorrelation in the distribution of village level cultivated land [63]. The results of this study are basically consistent with those of different regions mentioned above.
The global spatial autocorrelation index of each partition is calculated to further identify the global spatial autocorrelation characteristics of different SFQ grades in different regions. Table 7 lists global autocorrelation analysis results of SFQ grades in different regions. It is found that Moran's I value of SFQ grade in different regions is larger, all more than 0.6, moreover, they pass the significance test. This result suggests that the spatial distribution of SFQ in seven regions shows strong spatial positive correlation, and the SFQ grade has significant spatial aggregation characteristics.

Local Spatial Autocorrelation Analysis
The local spatial autocorrelation analysis of SFQ grade can accurately determine the spatial location where the study objects aggregate or disperse, and the results can be represented by spatial LISA distribution diagram. In LISA cluster map, HH aggregation (high-high type) and LL aggregation (low-low type) indicate that the SFQ grades have higher spatial aggregation characteristic (positive correlation type), while HL variation (high-low type) and LH variation (low-high type) indicate a negative spatial correlation. Hence, the region has a discrete characteristic. Figure 5 shows the LISA cluster map of local spatial autocorrelation of SFQ grades in Yunnan. It can be seen from Figure 5 that the LISA cluster types of SFQ grades in Yunnan include HH aggregation (high-high type), LL aggregation (low-low type), the HL variation (high-low type), LH (low-high type) and non-significance. Among them, the HH aggregation (high-high type) and LL aggregation (low-low type) are dominant, indicating that the SFQ grades have higher spatial aggregation characteristics. The SFQ grade characterized by HH aggregation (high-high type) widely distributes, mainly in the Southwest Yunnan, Northeast Yunnan and Northwest Yunnan, with only scattered distribution in other regions. The SFQ grades of evaluation units corresponding to the above regions are relatively lower. Likewise, the SFQ grade characterized by LL aggregation (low-low type) also has a wide distribution area, mainly distributing in central Yunnan, Southern Fringe and Southeastern Yunnan, and with only scattered distribution in other regions. The SFQ grades in these regions are relatively higher. The SFQ grades characterized by HL outlier (high-low type) and LH outlier (low-high type) have smaller distribution area, with only scattered distribution in each region. The non-significance type of SFQ grade mainly distributes in the Southern Fringe and is distributed in a scattered manner in other regions. LISA distribution diagram. In LISA cluster map, HH aggregation (high-high type) and LL aggregation (low-low type) indicate that the SFQ grades have higher spatial aggregation characteristic (positive correlation type), while HL variation (high-low type) and LH variation (lowhigh type) indicate a negative spatial correlation. Hence, the region has a discrete characteristic. Figure 5 shows the LISA cluster map of local spatial autocorrelation of SFQ grades in Yunnan. It can be seen from Figure 5 that the LISA cluster types of SFQ grades in Yunnan include HH aggregation (high-high type), LL aggregation (low-low type), the HL variation (high-low type), LH (low-high type) and non-significance. Among them, the HH aggregation (high-high type) and LL aggregation (low-low type) are dominant, indicating that the SFQ grades have higher spatial aggregation characteristics. The SFQ grade characterized by HH aggregation (high-high type) widely distributes, mainly in the Southwest Yunnan, Northeast Yunnan and Northwest Yunnan, with only scattered distribution in other regions. The SFQ grades of evaluation units corresponding to the above regions are relatively lower. Likewise, the SFQ grade characterized by LL aggregation (low-low type) also has a wide distribution area, mainly distributing in central Yunnan, Southern Fringe and Southeastern Yunnan, and with only scattered distribution in other regions. The SFQ grades in these regions are relatively higher. The SFQ grades characterized by HL outlier (high-low type) and LH outlier (low-high type) have smaller distribution area, with only scattered distribution in each region. The non-significance type of SFQ grade mainly distributes in the Southern Fringe and is distributed in a scattered manner in other regions.

Characteristics of Space Cold and Hot Spots in SFQ
The global spatial autocorrelation analysis could reveal the overall correlative structure characteristics of SFQ grades of different evaluation units, but it can't be used to measure the spatial correlation mode between elements or attributes of adjacent regions [50]. Hence, the spatial cold and hot spots analysis tools are needed to further explore the correlation degree of SFQ grades in local regions. In this study, the spatial distribution of cold and hot spots are obtained by analyzing the cold and hot spots for the SFQ grades in the county-level region in Yunnan, as shown in Figure 6. Obviously, the hot spot regions of SFQ grades (high-value region of SFQ grade) are mainly distributed in the central Yunnan and Southern Fringe. In these regions, the slope farmland has a small slope, high continuity degree, and light soil erosion degree. Moreover, the cultivation utilization level of slope farmland is higher, and the SFQ grade is higher. So these regions are the high-value regions of SFQ in Yunnan, as well as the key protection regions of SFQ.
The cultivation utilization level of slope farmland are maintained or improved by various land management measures. The cold spot region of SFQ mainly distributes in the Northeastern Yunnan,

Characteristics of Space Cold and Hot Spots in SFQ
The global spatial autocorrelation analysis could reveal the overall correlative structure characteristics of SFQ grades of different evaluation units, but it can't be used to measure the spatial correlation mode between elements or attributes of adjacent regions [50]. Hence, the spatial cold and hot spots analysis tools are needed to further explore the correlation degree of SFQ grades in local regions. In this study, the spatial distribution of cold and hot spots are obtained by analyzing the cold and hot spots for the SFQ grades in the county-level region in Yunnan, as shown in Figure 6. Obviously, the hot spot regions of SFQ grades (high-value region of SFQ grade) are mainly distributed in the central Yunnan and Southern Fringe. In these regions, the slope farmland has a small slope, high continuity degree, and light soil erosion degree. Moreover, the cultivation utilization level of slope farmland is higher, and the SFQ grade is higher. So these regions are the high-value regions of SFQ in Yunnan, as well as the key protection regions of SFQ.
Northwestern Yunnan, and Southwestern Yunnan. In these regions, the slope farmland has high fragmentation degree, large slope classification and serious soil erosion degree of slope surface. Furthermore, the unreasonable cultivation utilization mode results in the low SFQ grade and the difficulty in the effective performance of land productivity. The SFQ in these areas is preferentially adjusted and controlled. The SFQ adjustment measures would be implemented to further improve the SFQ level. The other regions belong to the non-significance type, and their SFQ grade is medium. Simultaneously, they are the key regions for the SFQ adjustment. Thus, the SFQ level would be further improved by the adjustment measures, so as to make the SFQ grade distribution type extend to the hot spot region.

Conclusions
With the increasingly prominent problem of farmland quality degradation, farmland quality evaluation has become a hot issue, but there are few studies on SFQ evaluation and its spatial pattern of different scales. In this paper, taking Yunnan Province, a typical plateau mountain area, as an example, through the establishment of the SFQ evaluation system, the quality of slope farmland in Yunnan Province was quantitatively evaluated, and the spatial structure characteristics of SFQ were analyzed. The main conclusions are as follows.
(1) The SFQ indexes in Yunnan Province distributes between 0.36 and 0.81, with a mean of 0.59 ± 0.06. The SIFIs of most evaluation units are less than 0.6. The spatial distributions of SFQ indexes are significantly different. Moreover, the SFQ grade is based on sixth-class, fifth-class, seventhclass and fourth-class land. The SFQ grade is relatively higher in the Southern Fringe, central Yunnan, Western Yunnan and Southeastern Yunnan, while the SFQ grade is relatively lower in Northeastern Yunnan and Northwestern Yunnan. (2) The SFQ indexes present a normal spatial distribution, and the Gaussian model can well fit the semi-variance function of SFQ indexes. The Nugget C0 of spatial distribution of SFQ indexes is 0.5293, and the Nugget coefficient C0/(C0+C) is 53.97%. Furthermore, the spatial distribution of SFQ indexes is moderately autocorrelated. The structural factors, such as climatic conditions, soil property, moisture conditions, spatial morphology, etc., play a major role in the spatial heterogeneity of SFQ indexes, but the influence of random factors should not be ignored. (3) The Moran's I value of global spatial autocorrelation of SFQ grades is 0.8489. The spatial distribution of SFQ grades has a significant spatial aggregation characteristic. The spatial autocorrelation types of SFQ grades are based on HH aggregation and LL aggregation, as well as the types of their LISA cluster are based on HH aggregation and LL aggregation. (4) The cold spot and hot spot distributions of SFQ grades display the significantly spatial difference. The hot spot area is mainly distributed in the Central Yunnan and the Southern The cultivation utilization level of slope farmland are maintained or improved by various land management measures. The cold spot region of SFQ mainly distributes in the Northeastern Yunnan, Northwestern Yunnan, and Southwestern Yunnan. In these regions, the slope farmland has high fragmentation degree, large slope classification and serious soil erosion degree of slope surface. Furthermore, the unreasonable cultivation utilization mode results in the low SFQ grade and the difficulty in the effective performance of land productivity. The SFQ in these areas is preferentially adjusted and controlled. The SFQ adjustment measures would be implemented to further improve the SFQ level. The other regions belong to the non-significance type, and their SFQ grade is medium. Simultaneously, they are the key regions for the SFQ adjustment. Thus, the SFQ level would be further improved by the adjustment measures, so as to make the SFQ grade distribution type extend to the hot spot region.

Conclusions
With the increasingly prominent problem of farmland quality degradation, farmland quality evaluation has become a hot issue, but there are few studies on SFQ evaluation and its spatial pattern of different scales. In this paper, taking Yunnan Province, a typical plateau mountain area, as an example, through the establishment of the SFQ evaluation system, the quality of slope farmland in Yunnan Province was quantitatively evaluated, and the spatial structure characteristics of SFQ were analyzed. The main conclusions are as follows.
(1) The SFQ indexes in Yunnan Province distributes between 0.36 and 0.81, with a mean of 0.59 ± 0.06. The SIFIs of most evaluation units are less than 0.6. The spatial distributions of SFQ indexes are significantly different. Moreover, the SFQ grade is based on sixth-class, fifth-class, seventh-class and fourth-class land. The SFQ grade is relatively higher in the Southern Fringe, central Yunnan, Western Yunnan and Southeastern Yunnan, while the SFQ grade is relatively lower in Northeastern Yunnan and Northwestern Yunnan. (2) The SFQ indexes present a normal spatial distribution, and the Gaussian model can well fit the semi-variance function of SFQ indexes. The Nugget C 0 of spatial distribution of SFQ indexes is 0.5293, and the Nugget coefficient C 0 /(C 0 +C) is 53.97%. Furthermore, the spatial distribution of SFQ indexes is moderately autocorrelated. The structural factors, such as climatic conditions, soil property, moisture conditions, spatial morphology, etc., play a major role in the spatial heterogeneity of SFQ indexes, but the influence of random factors should not be ignored. (3) The Moran's I value of global spatial autocorrelation of SFQ grades is 0.8489. The spatial distribution of SFQ grades has a significant spatial aggregation characteristic. The spatial autocorrelation types of SFQ grades are based on HH aggregation and LL aggregation, as well as the types of their LISA cluster are based on HH aggregation and LL aggregation. (4) The cold spot and hot spot distributions of SFQ grades display the significantly spatial difference.
The hot spot area is mainly distributed in the Central Yunnan and the Southern Fringe, while the cold spot area is mainly distributed in the Northeastern Yunnan, Northwestern Yunnan and Southwestern Yunnan.
The spatial distribution of SFQ grades in Yunnan Province has significant spatial aggregation characteristics, which provides a scientific basis for the management of slope farmland. The central Yunnan and Southern Fringe are high value areas of SFQ distribution in Yunnan, and their spatial autocorrelation type is mainly LL and is the core protection area of SFQ in Yunnan. It is necessary to implement various land management measures to maintain or improve the cultivation and utilization level of slope farmland. Northwest Yunnan, Northeast Yunnan and Southwest Yunnan are low value areas of SFQ distribution, and their spatial autocorrelation type is mainly H-H, which is the priority area of SFQ regulation. Therefore, it is necessary to further improve the quality level of slope farmland by implementing SFQ regulation measures. Western Yunnan and Southeast Yunnan are the middle value areas of SFQ distribution, and their spatial autocorrelation and cold hot spot distribution types are not significant, which are the key areas of SFQ regulation. It is necessary to further improve the quality level of slope farmland and extend the distribution type of SFQ grade to hot spot area through regulation measures.


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

text:
The Effect of Topical Anaesthesia on the Cortisol Responses of Calves Undergoing Dehorning

Simple Summary Dehorning in cattle involves the manual removal of horns which causes acute pain. Although the long-term solution to removing horns is to breed polled cattle, limitations include the complex inheritance for polled Brahman cattle, and negative interactions with productivity in dairy cattle. Best practice pain relief in the form of a local nerve block, a sedative and a non-steroidal anti-inflammatory drug prior to the procedure usually requires a veterinarian and may be impractical for some cattle production systems. Improved livestock welfare requires a practical and cost-effective pain relief strategy for dehorning that can be readily adopted commercially. We evaluated a modified topical local anaesthetic wound formulation that can be applied by farmers immediately following dehorning. While previous studies have demonstrated a reduction in wound sensitivity, there was no effect on cortisol concentrations. It is likely that the cortisol response reflects a combination of factors including the stress of handling, the initial pain of the dehorning procedure and haemorrhage. Abstract Dehorning causes pain and distress to cattle, and there is a need to provide effective and practical analgesia to improve animal welfare. We conducted an experiment to determine the effect of a modified post-operative topical wound management formulation containing two local anaesthetics (TA) on the plasma cortisol concentration (PCC) of scoop-dehorned calves. Two months old Holstein-Friesian heifer calves (n = 30) were randomly allocated to sham dehorning control (CON), scoop dehorning (D), or scoop dehorning with immediate post-operative application of the TA (DTA). Blood samples were obtained via jugular venepuncture prior to sham or actual dehorning, and 40 min, 1.5, 4 and 24 h later. PCC changed significantly over time (p < 0.01). There was a trend for lower PCC in DTA calves compared to D calves (p = 0.09), with the PCC area under the curve lowest in CON calves as compared to D and DTA calves (p = 0.02). Cortisol concentrations were similar between D and DTA at all time points. The TA did not reduce cortisol concentrations up to 24 h following treatment and the cortisol response likely reflects the pain induced by the procedure, the effect of handling and restraint, and haemorrhaging which limited adherence of the TA actives. A multimodal analgesic approach, as assessed through multiple pain indicators, should be the focus of future work.

Introduction
Dehorning is a routine husbandry procedure in many commercial dairy and beef production enterprises which involves the removal of the horn and horn-producing tissue in genetically horned breeds of cattle. Cattle are dehorned to enable safe handling, to reduce injury to other cattle, and to system has been extensively measured to gauge the distress of dehorning and the effectiveness of analgesics [9,10,25,26]. Plasma cortisol concentrations provide an indication of HPA activity and are useful indices as, within certain limits, secretion occurs in a graded way in response to the presumed noxiousness of different experiences [27,28]. Amputation dehorning causes a typical pattern in cortisol secretion which peaks 30 min post dehorning, decreases to a plateau, then returns to pre-treatment levels by 6-7 h post dehorning [9,10,13,25]. The measurement of cortisol is the most commonly used physiological indicator of distress in cattle and is often used as an indirect indicator of pain-induced stress [7,27,29], with changes to its typical secretory pattern having facilitated the evaluation of different analgesic regimes [9,25,26]. Previous research has demonstrated the effect of analgesia and anaesthesia for reducing the cortisol response to dehorning [13,25,26,30,31].
The aim of this experiment was to assess the plasma cortisol response of Holstein-Friesian calves to scoop dehorning following treatment of the wounds with the topical anaesthetic wound management formulation described.

Animals and Housing
The experimental protocol was approved by the Animal Ethics Committee of the University of Sydney (Approval No. 5832) and was conducted in accordance with the guidelines of the 'Australian code for the care and use of animals for scientific purposes' [32]. Thirty Holstein-Friesian heifer calves (8 weeks ± 1 week) were sourced from the commercial replacement herd at 'Corstophine Dairy' at The University of Sydney Camden campus farms in New South Wales, Australia. Calves were raised under commercial dairy operational conditions prior to the experiment. As per standard farm practice, the calves were separated from their mothers by 2 d of age and were thereafter group-housed in 1 ha paddocks before and during the experimental period. Calves were group-fed a milk ration at 10% of their body weight twice a day at 07:30 h and 15:30 h via an artificial teat, and were provided ad-libitum access to water and kikuyu-based pasture, and small amounts of calf grower pellets. Concurrent to the cortisol samples collected for this study, animals underwent quantitative sensory testing (QST) as part of a previously published experiment [22]. QST, using von Frey monofilaments (instruments which depending on length and diameter, apply a specific calibrated force to a skin surface [22]), has been used in previous studies to assess different analgesic interventions in humans and animals [21,33,34]. Two weights of von Frey monofilaments (10 and 300 g) were used to mechanically stimulate 6 sites within and adjacent to the dehorned site. Behavioural responses were observed and categorised as either absent, minor, moderate or severe [22].

Experimental Design and Treatments
The experiment was blocked across 3 days, with 10 calves treated each day (Table 1). This ensured all calves were dehorned in the morning within 2.5 h and allowed sufficient time for all sampling to be completed. Within each block, the same protocol was repeated, and all treatments were represented.  On the morning of each day of experimentation, calves were quietly moved from their paddocks to a holding yard and then individually into a spin-roll calf cradle (Arrow Farmquip, Tamworth, NSW, Australia) and placed into right lateral recumbency for treatment and sampling. Calves were randomly assigned to 1 of 3 treatments: sham scoop dehorning (control group, CON; n = 10); scoop dehorning (D; n = 10); and scoop dehorning with an application of a topical anaesthetic gel (Bayer Animal Health, Gordon, NSW, Australia) (DTA; n = 10). Sham dehorning was performed by manually manipulating and placing scoop dehorners over each horn bud for 3 s without incising the tissue. Dehorning was performed by placing small sharpened scoop dehorners (33 cm Supa-Scoop Dehorner, Farmhand, Shoof International Pty Ltd., Melbourne, Australia) over the horn bud region and pulling apart the handles of the device to excise the horn bud and immediate surrounding skin. Sham dehorning or dehorning took place between 08:30 and 11:00 h. DTA calves received an immediate application (within seconds from dehorning) of topical anaesthetic gel (TA) to the wound and surrounding cut tissue. Approximately 4 mL of TA was applied to each dehorning wound (total of 8 mL per calf) with a sterile, soft silicone brush. The TA was carried in a viscous solution, and a depth of approximately 2 to 3 mm was applied over the wound and adjacent tissue. The brush was cleaned between calves in a disinfectant solution (Hibitane ® , Coopers ® Animal Health, Intervet Pty Ltd., Bendigo East, VIC, Australia). Following treatment and data collection, calves were returned to their pens until subsequent data collection was required, where they were moved back to the holding yard and cradle.

Topical Anaesthetic Formulation
The TA contained two local anaesthetics, lignocaine hydrochloride (100.0 g/L) and bupivacaine hydrochloride (5.0 g/L), aluminium chlorohydrate (100.0 g/L) and cetrimide (5.0 g/L). This formulation was modified from a commercial product developed for mulesing wounds in lambs (Tri-Solfen ® , Bayer Animal Health, NSW, Australia) [21] and now registered in Australia for a range of husbandry procedures in sheep and cattle, including disbudding and dehorning [19]. The lignocaine concentration was doubled from the original formulation to produce a more concentrated wound formulation that was considered more suitable for the smaller surface area of the dehorning wound. Aluminium chlorohydrate was included with an intention of promoting haemeostasis in response to the moderate to excessive volumes of blood loss often induced by scoop dehorning. The viscosity of the gel was also increased with an intention to improve adherence of the TA to the wound.

Blood Collection and Analysis
Calves were concurrently subjected to QST prior to treatment and at 1 and 40 min, 1.5, 4 and 24 h post-treatment as previously reported [22]. Blood samples for the current study were obtained before QST at each time point. Calves were restrained for an average of 3.5 min (range 2-6 min) for blood and data collection.
Upon placement into lateral recumbency, a 5 mL blood sample was drawn from the jugular vein using an 18 G needle into a 9 mL EDTA vacutainer. The tube was inverted several times and placed on ice until centrifugation 7-9 h later. Further blood samples were collected at 40 min and 1.5, 4 and 24 h post treatment. Blood samples were centrifuged at 3000 RPM for 15 min and the separated plasma layer pipetted off into a labelled and sterile 5 mL serum vial. Samples were stored at −80 • C pending analysis.
Plasma cortisol concentrations were determined in duplicate using the commercially available radio-immunoassay kit (Coat-a-Count ® Cortisol RIA; Siemens Pty Ltd., Los Angeles, CA, USA). The lowest detectable concentration was 6.9 nmol/L and any samples below this threshold were denoted as 0 nmol/L. The intra-assay and inter-assay coefficients of variation were 4.4% and 5.8% respectively.

Statistical Analysis
Data were analysed to determine the effect of treatment on plasma cortisol concentration. The raw data had near normal distribution and constant variance and did not require transformation.
A residual maximum likelihood, linear mixed models (REML) analysis was conducted using the statistical software package, Genstat ® (18th edition, VSN International Ltd., Hemel Hempstead, UK). The fixed effects of the model were treatment (CON, D, DTA), time (pre-treatment, 40 min, 1.5 h, 4 h, 24 h post-treatment), and their interaction. The random effect of the model was calf ID.
The area under the curve with respect to the ground (AUC) [35] was examined to determine the magnitude and severity of the cortisol response to treatment over the 24 h period post-dehorning. AUC was calculated using the linear trapezoidal method [35] for the following time intervals: 0-24 h (AUC 0-24 h ), 0-40 min (AUC 0-40 min ), 40 min-1.5 h (AUC 40 min-1.5 h ), 1.5-4 h (AUC 1.5-4 h ), and 4-24 h (AUC 4-24 h ). Zero cortisol concentrations were modified to 3.4 nmol/L (midpoint between 0 and 6.9 nmol/L) to facilitate AUC calculations. Data were log e transformed to attain normal distribution and constant variance. In Genstat ® , a one-way analysis of variance (ANOVA) was used to determine treatment mean differences. For all statistical calculations, p ≤ 0.05 were considered statistically significant and pair-wise comparisons with differences greater than the corresponding least significant difference (LSD) were considered statistically significant. p > 0.05 ≤ 0.1 were considered as statistical tendencies.

Results
Two DTA calves were removed from the raw cortisol analysis due to missing data (CON, n = 10; D, n = 10; DTA, n = 8). There was a statistical tendency for a treatment x time interaction for cortisol concentration (p = 0.086; s.e.d. = 11.75) ( Figure 1). There was no difference between treatments pre-dehorning and the lowest cortisol response was observed in CON group (p = 0.086). DTA calves tended to have cortisol concentrations intermediate to CON and D calves.   There was a statistical tendency for a treatment effect on cortisol response (p = 0.079; s.e.d. = 12.88) ( Figure 3). Overall, the lowest cortisol concentrations were secreted by CON calves, the highest from D calves, and DTA calves were intermediate to both (mean 35, 64 and 57 nmol/L, respectively).   One DTA calf was removed from the AUC analysis as it had a large residual (CON, n = 10; D, n = 10; DTA, n = 7). AUC 0-24 h was significantly lower in CON calves than D and DTA calves (p = 0.02) ( Table 2). There was no significant difference in AUC 0-40 min between all treatment groups (p = 0.07). There was no significant difference in AUC between D and DTA calves at any time period (p > 0.05).

Discussion
The results of this experiment demonstrate the plasma cortisol response to surgical dehorning, with peak concentration occurring 40 min post procedural intervention. There was a clear effect of handling and procedural pain on cortisol response, with no difference in plasma cortisol concentrations (PCC) between the two cohorts of surgically dehorned calves.
We identified statistical trends for lower PCC and AUC in DTA compared to D calves. In addition, the PCC of dehorned calves exceeded that of sham dehorned calves. This suggests that timing of anaesthetic delivery may partly account for the observed cortisol response. Blockage of local nociception after the administration of a noxious stimuli is unlikely to greatly influence the HPA response, particularly as the elevated cortisol response presumably reflects both the stress of prolonged restraint plus haemorrhage from the wound site [36][37][38].
There was no significant difference in AUC 0-40 min between sham dehorned calves and dehorned calves regardless of treatment with TA, indicating the stress of handling and restraint on cortisol response [36]. The stress of sham dehorning has been well documented and causes a small yet significant rise in cortisol concentrations from base level [9,10,13,25,39,40]. This is due to the activation of the HPA system by a number of external stressors including restraint, handling and the presence of humans [36,41]. In the current study, there was no significant difference in peak cortisol concentration between sham dehorned and dehorned calves, which contrasts previous dehorning studies [9,10,13,25]. This difference may be attributed to the type and duration of restraint used in this study. In other experiments, calves were manually restrained for a relatively short period of time in a standing position (<15-45 s) [9,10,13,25,39,40], whereas in the present study, calves were restrained in a spin-roll calf cradle that placed them into right lateral recumbency for a longer period of time (mean = 3.5 min, range = 2-6 min). While other experiments tended to only restrain for a quick blood collection, the present study required additional restraint time for QST for a concurrent study [22], which involved mechanical stimulation of the wound with a von Frey filament. While blood-sampling occurred immediately upon restraint, multiple testing may have induced additional pain and stress, and prolonged the cortisol response up to 24 h where PCC did not return to baseline levels.
A similar study investigating the effect of the original TA formulation (Tri-Solfen ® Bayer HealthCare Animal Health Inc., Sydney, Australia) for castration of 3-mo Angus bull calves reported a similar effect of handling and procedural pain on cortisol response [42]. There was no difference in cortisol concentration at 0.5 or 1 h post-castration with or without post-operative TA application [42]. The effect of repeated handling on cortisol response is well documented [43][44][45]. The results of this study and the current study indicate the importance of habituation to handling prior to evaluating cortisol response. This is further evident when considering the results from two studies evaluating the effect of TA for surgical castration [46] and hot-iron tail-docking and surgical mulesing [47] in lambs. In both studies, TA significantly reduced the cortisol concentration 30 min post procedure as compared to untreated lambs [38]. The key difference is that lambs were intensively habituated to handling for 2 weeks prior to the commencement of the study [46,47]. This presents a major limitation to using cortisol as a measure of pain, particularly when working under commercial conditions, as per the current study where the opportunity to habituate calves was limited.
The effect of TA administration on cortisol secretion may also depend on the type of tissue damage induced by the surgical procedure. Scoop dehorning in younger calves typically creates bony wounds of small surface area, with haemorrhage, which may limit the quantity of TA absorption. Haemorrhage reduces the ability of the TA to adhere and remain on the wound and has been observed previously [23]. The lack of epithelial or mucosal tissue on small bony dehorning wounds means there is minimal surface area for effective TA absorption, with most nociceptors likely located in the wound edges. Though the more viscous formulation developed for this experiment improved wound adherence, it did not eliminate bleeding. While TA has been shown to significantly reduce peak cortisol concentration in surgically mulesed lambs, mulesing wounds are generally superficial and have a much larger surface area for absorption [47]. A similar reduction in peak cortisol concentration was observed in surgically castrated lambs treated with TA with efficacy likely attributable to the fast and effective anaesthesia of some of the scrotal nerves and mucosal tissue [46]. However, in neither study, did TA application reduce the elevated cortisol response to the levels of sham handled animals [46]. Further, the AUC of TA-treated mulesed lambs was similar or greater than non-treated lambs up to 24 h post-treatment with the authors recommending a combined analgesic strategy for an improved welfare outcome [47].
The use of multiple pain indicators is important for a multi-faceted evaluation of pain and analgesic efficacy. The concurrent study evaluated the QST response of calf dehorning wounds treated with TA as a measure of nociception [22]. We demonstrated a significant reduction in nociception at 40 min and 1.5 h post-dehorning in calves treated with TA as compared to untreated calves. However a correlated reduction in cortisol concentration in this time period was not indicated in the current study. As QST measures nociception through mechanical stimulation of the wound surface, it provides a direct measurement of anaesthesia [21,33,34]. Cortisol responses, when used independently of other measures, should be interpreted with caution as they are not a direct measurement of pain, but an indication of the overall noxiousness associated with an experience, including emotional and physical components [48]. Its limitations as an indicator of pain assessment include high individual variation, diurnal changes, and the wide variety of physical and emotional experiences both pleasant and unpleasant that activate the HPA system [24,27]. Further, cortisol plays an important role in maintaining blood volume and may rise even in the absence of pain, including during surgical procedures under general anaesthesia [37,38].
Calves in the current study were not provided any pre-operative anaesthesia or analgesia, and therefore were completely nociceptive during the dehorning procedure. This is significant given that the combination of restraint and activation of damaged nerves at and shortly after severance of the horn-bud is the likely major cause of the initial cortisol peak [13]. Thus the provision of pre-operative anaesthesia to eliminate procedural nociception should be evaluated in combination with TA.

Conclusions
The application of a modified topical local anaesthetic formulation immediately after scoop dehorning in young dairy calves did not reduce cortisol concentrations up to 24 h following treatment.


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Technology of semi-hard cheese using protein fat simulator

. The annual increase in the production of protein-sensitive dairy products leads to an increase in the available amount of their by-product of production – whey. A promising direction in the implementation of its biotechnological potential is the production of a protein imitator of the fat properties – whey protein microparticulate. The aim of this work was to study the possibility of its use in the technology of semi-hard cheeses. The introduction of a fat simulator in a normalized mixture changes its composition, including the ratio of protein fractions, the ratio of fat to protein, and calcium content. With a mass fraction of microparticulate more than 10 %, a flabby clot forms, which is extremely difficult to undergo further processing to obtain elastic cheese grain. High hydration of whey proteins increases the moisture content in the cheese mass after pressing and in the ripening process. Hydrolysis of paracasein and its fractions proceeds more intensively in the cheese with fat simulator. The developed product is characterized by a higher value of the moisture content, which, together with the preservation of the protein clot particles of the simulator by the mesh structure, increases the yield of cheese.

Introduction
The annual increase in the production of proteinsensitive dairy products leads to an increase in the available amount of their by-product -whey. Curd and cheese whey, the volume of which in the Russian Federation exceeds 7 million tons, is a challenging object for food technology [1][2][3]. Such indicators as shelf life, high microbiological contamination and titratable acidity, low concentration of solids, the presence of specific aromatic components hinder the widespread use of whey for food purposes. At the same time, this food object contains valuable nutrients, including functional ones [4].
Whey proteins are optimally balanced in their amino acid composition, can be a source of biologically active substances that perform protective, structural and other functions in the human body [5]. The development of alternative options for the modification of the whey protein cluster, which allows to efficiently and most fully realize its potential, is an urgent task of the global dairy industry.
A promising direction is to obtain a protein imitator of the fat properties -whey protein microparticulate. The technology for its production includes the purification of whey from casein dust and fat, ultrafiltration, as well as thermomechanical treatment [6][7]. This combination of exposure methods allows obtaining a food dispersion containing spherical particles similar in shape and size to fat globules [8].
The experience of using microparticulates is known both in the Russian Federation and abroad. Using a protein fat mimic allows achievement of numerous positive technological effects. Sour-milk drinks produced using microparticulate are characterized by high organoleptic properties such as thick, creamy consistency even with a low mass fraction of fat [9][10][11]. Great scientific and practical interest is attributed to the use of microparticulates in the production of protein products: cheeses and curd. Given its relevance, the aim of this work was to study the possibility of using a protein simulator of the fat properties in the technology of semi-hard cheeses.

Methods and Equipment
Cheese whey obtained in the conditions existing at the Kalacheevsky cheese factory (Kalach, Voronezh oblast, Russian Federation), a protein fat simulator developed on the basis of cheese whey using microparticulation technology, and semi-hard rennet cheese made using Rossiysky cheese technology were selected as objects of this study. Starter cultures and enzyme preparation manufactured by Chr. Hansen were used as technological auxiliary agents, as well as a solution of calcium chloride with a mass fraction of 40 %.
The process of microparticulation of cheese whey was carried out in the hardware workshop of PJSC Voronezhsky Dairy Plant (Voronezh, Russian Federation). The resulting microparticulate was used to obtain prototypes of normalized milk mixture for the production of cheese.
The indicators of the composition of the study objects, their physico-chemical properties were determined in accordance with the standards in force in the Russian Federation. The mass fraction of dry matter was estimated by weight loss in percent when the product sample was dried at a constant temperature. The mass fraction of protein was determined by the Kjeldahl method. To determine the mass share of lactose in the objects of study, the Bertrand method was used. The essence of this method is titration of iron (II) with a solution of KMnO4. Ferrous iron is obtained as a result of the redox reaction of ferrous ammonium sulfates with copper oxide (I), which is formed as a result of the reduction of divalent copper with reducing sugars. Determination of the mass fraction of fat was carried out by the acid method, which consists in isolating fat from the object of study and measuring its amount using a butyrometer.
In order to determine the total concentration of calcium in dairy raw materials, the complexometric method was used. It is based on the reverse titration method: an excess of Trilon B (disodium salt of ethylenediaminetetraacetic acid) is added to the objects of research, which is then bound with a solution of calcium chloride. Active acidity was determined by potentiometric method, and titratable acidity by titrimetric method.
The study of rennet coagulation was carried out according to the method described in GOST ISO 11815-2015 "Milk. Determination of the total milk-clotting activity of beef rennet." The determination of dynamic viscosity was carried out by tuning fork vibration on an SV-10 vibro-viscometer. The relative strength of the coagulum was determined as the ratio of the ultimate shear stress, estimated using a penetrometer, prototype to control. The mathematical processing of the experiment was carried out by the methods of mathematical statistics according to 5-10 experiments in a triple sequence.

Results
A distinct feature of the technology of Rossiysky cheese is its high level of lactic acid fermentation. The key stages of its production include the following sequence of technological operations: commissioning and preparation of raw materials, coagulation of milk, processing of a clot and cheese grain, molding and pressing, salting, ripening, preparation of cheese for sale.
For the production of Rossiysky cheese, pasteurized normalized milk is used. In the studies performed, part of the skimmed milk in the normalized milk mixture was replaced with the protein fat simulator ( Table 1).
The use of ultrafiltration in the preparation of a simulator leads to an increase in the proportion of solids, including proteins and calcium, in comparison with native cheese whey. The introduction of the microparticulate into a normalized mixture changes its composition, in particular, such important indicators for cheese making as the ratio of protein fractions, the ratio of fat to protein, and others (Table 2).
Normalized milk, including that with the microparticulate of whey proteins, was fermented, a milk-clotting enzyme preparation and a solution of calcium chloride were added. The composition of the starter culture for Rossiysky cheese includes mesophilic lactococci (Lac.lactis, Lac. cremoris) and lactic acid bacilli (Lb. plantarum, Lb. casei, Lb.bulgaricus). Coagulation of milk was carried out at a temperature of 32-34 °C. The use of the microparticulate led to an increase in the proportion of whey proteins, while the content of casein did not change. This led to the "dilution" effect, when the proportion of χ-casein interacting with chymosin in the total dry matter mass decreases, thereby having a negative effect on the rennet coagulation rate. So, when 10 % of the microparticulate was added to the normalized milk mixture, the duration of rennet coagulation increased by an average of 30 %, with the addition of 20 % of the microparticulate -by more than 100 %.
The change in the dynamic viscosity of the samples of the normalized mixture was determined from the moment the coagulant was added and until the onset of syneresis (Fig. 1).
At the first stage of rennet coagulation, enzymatic cleavage of χ-casein molecules occurs. The introduction of a protein fat simulator does not change the duration of the induction period of coagulation and does not interfere with the interaction of the enzyme and substrate, since the particles of the microparticulate whey proteins do not interact with chymosin. The onset of gelation is evidenced by an increase in the viscosity of the normalized milk mixture approximately 20 minutes after the addition of the milk-clotting enzyme. Paracasein particles form chains, which allows for visual marking of the beginning of the formation of rennet clot.
Particles of microparticulate, like fat globules, penetrate into the voids of the structure of rennet coagulus. Remaining in the cells, the trapped particles of the microparticulate have a negative effect on the rheological properties of the gel through softening it. The strength value of the formed clot decreased with the addition of 10 % of the microparticulate by 20 %, while using 15 % of the microparticulate caused the decrease of strength by 30 %. Thus, with a mass fraction of microparticles of more than 10 %, a flabby clot formed, which is extremely difficult to undergo further processing to obtain elastic cheese grain. By directed influence on the technological parameters of coagulation, clot hardening can be achieved by creating favorable conditions for producing cheese characterized by standard quality. The finished clot was cut into 8-10 mm cubes. The setting of the grain lasted for 15 to 20 minutes, then the grain was kneaded. The temperature of the second heating was 41-42 °C, the duration was 30-40 minutes. The resulting cheese grain during the entire technological process was characterized by a higher moisture content in comparison with the grain of the control sample (Fig. 2).

Fig. 2. Dynamics of cheese grain dehydration
The high water-binding ability of whey proteins in the composition of microparticulates hindered dehydration.
Salting of Rossiysky cheese, according to the standard technology, was carried out in grain, moldingin bulk, which contributed to the formation of a specific mesh pattern of cheese. Cheese got additional salt in brine for 2 to 3 days.
The ripening duration of Rossiysky cheese was 60 days.
High hydration of whey proteins increased the moisture content in the cheese mass after pressing and in the ripening process (Fig. 3). Thus created favorable conditions for the life of microflora of starter cultures intensified biochemical processes during maturation, which is of great importance in the formation of specific organoleptic characteristics of cheese. Under the influence of lactic acid bacteria, glucose formed during the hydrolysis of lactose is converted into lactic acid. In Rossiysky cheese, glucose is completely fermented already for 2-3 days of rupening. At the same time, the number of lactococci reaches its maximum value. Further, their number decreases sharply. Their death is accompanied by autolysis of cells and the release of intracellular enzymes, characterized by high proteolytic activity. The enzymes of the microflora of lactococcal starter cultures are the main factor in the ripening of cheeses with a low temperature of second heating, including Rossiysky. Their total number is proportional to the amount of microflora in the cheese.
During ripening, hydrolysis of paracasein occurs. If paracasein breaks down into peptones and polypeptides under the action of a milk-clotting enzyme introduced before coagulation, then endoenzymes of lactic acid microorganisms cause a deeper breakdown with the formation of amino acids. The amount of soluble nitrogenous compounds is continuously increasing. A decrease in the number of mesophilic lactic acid lactococci is accompanied by an increase in the content of lactic acid bacilli. Their reproduction is due to the ability to assimilate lactates as a carbon source.
Based on the level of soluble nitrogen (Fig. 4), it was found that the hydrolysis of paracasein and its fractions proceeds more intensively in cheese with microparticulate. This is due to the high moisture content in the cheese with microparticulate in comparison with a Control traditionally produced cheese. Moisture is a factor in increasing the number of lactic acid microorganisms that initiate biochemical changes in cheese during ripening. Besides, it helps to reduce the resistance to diffusion processes, thereby increasing the efficiency of the transport of enzymes to the substrate. In accordance with the regulatory documents of the Russian Federation, Rossiysky cheese refers to cheeses with a low temperature of the second heating and is characterized by the following key indicators: it has a firm crust without damage, moderately elastic, uniform consistency, pronounced cheesy, slightly sour taste and aroma. The section shows the color of cheese from white to light yellow; the pattern consists of irregular and angular caverns, evenly distributed throughout the cheese mass. The mass fraction of fat in the dry matter of cheese, in accordance with regulatory documentation, is (50 ± 1.6) %, moisture not higher than 46 %, table salt from 1.3 to 1.8 % inclusive. Active acidity of Rossiysky cheese is 5.15-5.35 рН.
The developed product is characterized by a higher value of the moisture content (Table 3), which, together with the retention of the clot of microparticulate particles by the mesh structure, increases the yield of cheese. The use of microparticulates led to an improvement in the consistency of cheese. The prototype was characterized as creamier and softed in comparison with the control. To assess the storage stbility of the obtained product in the sample laid for storage, we evaluated the change in titratable and active acidity, organoleptic properties, and controlled microbiological parameters. It was found that the use of microparticulate in the composition of rennet semi-hard Rossiysky cheese reduces its storage capacity by an average of 17 %. This is explained by a higher moisture content in the finished product compared to the one traditionally produced.

Discussion
The performed studies confirmed the possibility of replacing part of the normalized mixture for the production of semi-solid rennet Rossiysky cheese with a microparticulate whey protein. The presence of the protein fat simulat had a negative effect on the rennet coagulation process, since it reduced the proportion of casein contained in the total of solids, increasing the proportion of whey proteins. The decrease in rennet coagulation rate, as well as clot strength, confirms the advisability of using no more than 10 % of microparticulate in a normalized milk mixture. To obtain the standard duration of rennet coagulation, as well as condensation of the clot, a modification of technological parameters is proposed: temperature 35 °C, pH 6.4, the proportion of milk-clotting enzyme preparation 0.04 %, and the addition of calcium chloride at the rate of 40 g of anhydrous salt per 100 kg of normalized milk mixture. As a result of further processing, fresh cheese is formed, the mass fraction of moisture in which exceeds the corresponding value for the control sample. An increased proportion of moisture is retained in the cheese during ripening and in the mature product. This affects the duration of maturation, creating the possibility of its reduction. An increase in the mass fraction of moisture, as well as an increase in the proportion of whey proteins introduced into the cheese with the microparticulate, increase the yield of the product. A high moisture content in the cheese leads to a slight reduction in its storage stability.

Conclusion
The developed technology of rennet semi-hard Rossiysky cheese with the microparticulate whey protein allows one to replace some of the expensive raw materials with a whey modification product. This helps to reduce the cost of cheese, and allows returning some of the byproduct of the industry into the main production.