\r\n\t \r\n\tThey are hypersensitive to chemical pollution, habitat degradation, a variation of river and groundwater quality, climate change and even the sun's ultraviolet radiation, amphibians are among the vertebrate groups most endangered by human activity, and their abundance in wetlands is always one of the best indicators of good environmental conservation. \r\n\t \r\n\tIn this book, we have considered all aspects of amphibians biology, diversity, conservation and potential use of amphibians as environmental indicators.
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\n
1. Introduction
\n
While the aging population in many advanced countries around the world is increasing, the increase is especially rapid, and the lack of nursing facilities has become a serious social issue in Japan. According to the Ministry of Health, Labour and Welfare (MHLW) [1, 2], though the admission capacity of special nursing homes is approximately 498,000 as of 2014, the number of people requesting admission was 524,000. This grave situation highlights the severity of the lack of certified care workers as well as nursing facilities. In order to increase the number of certified care workers, the MHLW released a “Basic guideline concerning measures to increase workers in social welfare services” and is making efforts to secure personnel. Though the number of people registering as a certified care worker is increasing, the lack of workers remains an issue.
\n
Meanwhile, though the number of nursing facilities and its capacity are increasing, the utilization rate of such facilities remains at the same level and the lack of facilities has not improved. Though subsidy is provided by the national and local governments for the construction of nursing facilities, the amount of subsidy cannot be greatly increased due to the need for more childcare and medical facilities as well, making it difficult to expect a great increase of nursing facilities in the future. Therefore, as a solution to the lack of facilities, the construction of new nursing facilities should be prioritized in areas with greater needs. In order to make this possible, first of all, it is necessary to accurately grasp the areas that lack nursing facilities.
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Additionally, there are various facility types, and certain characteristics can be seen in selecting the location according to the type. For example, commercial establishments such as convenience stores are located in busy areas that are highly populated in order to get more customers and increase profit. On the other hand, educational and public facilities such as schools tend to be located in areas where many people can fairly access them. Similar to the latter case, instead of being concentrated in certain areas, nursing facilities must be located where everyone in need can access it in a fair manner. Based on the background mentioned above, using geographic information systems (GIS) and public open data, the present study aims to quantitatively evaluate the current situation of nursing facility locations in urban areas within Japan as the target.
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\n
\n
2. Related work
\n
The present study is related to A. studies concerning the sufficiency degree of nursing facilities, B. studies concerning care services provided from nursing facilities, and C. studies concerning facility location problems. The following will introduce the major preceding studies in the above three study areas and discuss the originality of the present study in comparison with the others.
\n
In A. studies concerning the sufficiency degree of nursing facilities, as this topic attracts attention especially in Japan, there were a lot of preceding studies until now. As mentioned in Section 1, the reason for this is that the increase of aging population is especially rapid and the lack of nursing facilities has become a serious social issue in recent Japan. Yamada et al. (2008) [3] grasped the regional difference in demand for nursing facilities by means of interviews. Yamamoto et al. (2015) [4] considered elements such as the positioning of various equipment and nursing modality within the recovery rehabilitation ward as spatial structure and examined the reality of activities of residents in their leisure time. Acker et al. (2015) [5] assessed the nursing facility characteristics, quality ratings, and the views of facility administrators about the implications of an increasing number of foreign-born employees in Washington State in the USA. Takase et al. (2016) [6] grasped the reality of the operation of home care and organized the travel distance of caretakers as well as the service zone using the location and road information of users, caretakers, and offices in Japan. Fujita et al. (2017) [7] organized objections concerning the construction of nursing facilities and examined the changes in social awareness toward such conditions over time in Japan. Hunnicutt et al. (2018) [8] examined and quantified geographic variation in the initiation of commonly used opioids and prescribed dosage strength among older nursing home residents in the USA. Tahara et al. (2018) [9] clarified the attitude of social welfare facility staff regarding the acceptance of evacuees when natural disasters occur and elucidated factors that obstruct the acceptance of such evacuees.
\n
For B. studies concerning care services provided from nursing facilities, there were a lot of preceding studies especially in the USA. Yun et al. (2010) [10] developed and validated an algorithm to identify the use of nursing facility services and differentiate short- from long-term care using Medicare claim data. Walsh et al. (2012) [11] analyzed potentially avoidable hospitalizations (PAHs) for dually eligible beneficiaries receiving long-term or post-acute care services to inform the development of health policies and better care programs and outcomes for this population. Cherubini et al. (2012) [12] examined resident and facility characteristics associated with hospitalization in a cohort of the older nursing home residents in Italy. Onder et al. (2012) [13] assessed the nursing home residents in Europe, focusing on the services and health for the elderly in Long TERm care (SHELTER) study. King et al. (2013) [14] examined how skilled nursing facility (SNF) nurses the transitional care of individuals admitted from hospitals, the barriers they experience, and the outcomes associated with variation in the quality of transitions. Neuman et al. (2014) [15] measured the association between SNF performance measures and hospital readmissions among Medicare beneficiaries receiving post-acute care at SNFs. Fry et al. (2018) [16] used robot cats to reduce the total number of falls in the facility quality improvement methods (strategy for improvement, design, setting, participants, interventions, measurements, and evaluation). Yamaguchi (2018) [17] grasped the relationships between received quality of care and information sharing among workers in nursing facilities for the elderly.
\n
For C. studies concerning facility location problems, Segawa et al. (1996) [18] developed a system that can simulate factors related to childcare facility improvements, such as the extension of childcare hours and the location of new childcare facilities. Nagashima et al. (2014) [19] proposed an algorithm that derives the best location for electric vehicle (EV) power stations by means of the mean field approximation. Ozgen et al. (2014) [20] combined a two-phase possibilistic linear programming approach and a fuzzy analytical hierarchical process approach to optimize two objective functions (minimum cost and maximum qualitative factor benefit) in a four-stage (suppliers, plants, distribution centers, and customers) supply chain network in the presence of vagueness. Munemasa et al. (2015) [21] used the linear relaxation method to propose a method that derives the best solution for minimizing travel costs for the urban model made up of residential and business areas. Zhang et al. (2016) [22] investigated a facility location problem incorporating service competition and disruption risks, developing a new binary bilevel linear programming (BBLP) model. Ohdate et al. (2017) [23] considered relocation of facilities for the management of public facilities and categorized them based on building, function, and location to create an evaluation method for them. Nagai et al. (2017) [24] proposed an agent-based urban model in which the relationship between a central urban area and a suburban area was simply expressed. Usui et al. (2018) [25] theoretically investigated the relationship between the continuous walking distance distribution and the density of resting places.
\n
On the other hand, in recent Japan, there are distinctive preceding studies that adopted an economic method into C. studies concerning facility location problems. Tanaka et al. (2015) [26] applied the quintile share ratio (QSR), which is an indicator showing the degree of bias in income, to the facility locational analysis for linear cities. Additionally, with QSR as a reference, Tanaka et al. (2016) [27] used the median share ratio (MSR), which is an equity measure, to develop a facility location evaluation model in a linear city with one or two facilities, as well as a uniformly distributed population. Furuta et al. (2017) [28] used a method that generalized the QSR and proposed a solution to optimize multiple facility locations in cases where the demand and candidate facility locations are discrete.
\n
Regarding studies related to A, though the studies grasped the awareness of the local residents and awareness and behavior of users, as well as the actual condition of operation for nursing facilities, the location of the facilities was not considered. Regarding studies related to B, though the studies investigated the care services provided from nursing facilities, the location of the facilities was not considered. Regarding studies related to C, Nagashima et al. (2014) [19] and Munemasa et al. (2015) [21], respectively, considered EV power stations and business-and-residential distribution as their study subject and proposed a method to derive efficient locations. Though Segawa et al. (1996) [18] conducted simulations with the assumption that the facilities will be relocated, as there are currently many existing nursing facilities in cities of Japan, the above simulations cannot propose realistic solutions for such facilities. Additionally, though Tanaka et al. (2016) [27] and Furuta et al. (2017) [28] focused on the equity concerning the facility location evaluation method, it has only been applied to virtual cities and not to any actual cities. Therefore, with the results of the preceding studies mentioned above as a reference, the present study will demonstrate the originality by considering the lack of nursing facilities, which has become a serious social issue, and quantitatively evaluating current facility locations.
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\n
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3. Evaluation framework and methods
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3.1 Evaluation framework and process
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For the evaluation method of nursing facility locations, PostgreSQL Ver. 9.6.1 and ArcGIS Pro Ver. 2.0 of Environmental Systems Research Institute (ESRI) were used. The evaluation framework and process are as mentioned below:
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3.1.1 Creating the distribution maps of the aging population and nursing facilities as well as a road network map
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The distribution maps of the aging population and nursing facilities as well as a road network map are created in digital map form using GIS. These three types of digital maps are superimposed, and the closest road node from a representative point from each nursing facility and area (divided by town and street) is set on the road network map.
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3.1.2 Calculating the specialization coefficient of the population aging rate and adding it to the road network map
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The specialization coefficient of the population aging rate is calculated using the aging population in addition to the total population data from each area, and the results are added to the road network map.
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3.1.3 Calculating the shortest route using A* algorithm
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By applying the data obtained in section 3.1.2 to A* algorithm, which is explained in the following section, the shortest route between each nursing facility and each area is calculated.
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3.1.4 Evaluating nursing facility location using the specialization coefficient of the population aging rate
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Using the distribution maps of the aging population obtained in section 3.1.1 and the shortest route calculated in section 3.1.3, the evaluation points are calculated for each area.
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3.2 Evaluation method
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3.2.1 Creating the distribution maps of the aging population and nursing facilities as well as road network maps
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3.2.1.1 Distribution map of the aging population
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The age group being evaluated in the present study is those over the age of 75. There are many cases where those over 65 can use nursing facilities. However, according to Hashimoto (2015) [29], the life expectancy and health span in Japan have become higher in recent year, with the latter being 71.19 for men and 74.21 for women. Therefore, assuming most users of nursing facilities are over 75, the age group was set for those over 75. As for the evaluation target area, in order to calculate evaluation points according to each area, GIS is used to display the distribution of the aging population on the digital map.
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3.2.1.2 Distribution map of nursing facilities
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GIS is used to display the distribution of nursing facilities on the digital map. While nursing facilities include facility types such as admission type, commuter type, and other related facilities, the present study will only consider admission type of nursing facilities.
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3.2.1.3 Creating a road network map
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As for the distance between nursing facilities and each area in the present study, the road network distance is used instead of the linear distance. This is because the linear distance may be extremely short compared to the distance when traveling along the roads to the nursing facilities, and this may cause the estimate of the travel distance of users to be shorter than it actually is.
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First, GIS is used to display the road network map of the evaluation target area in digital map form. Next, the node closest to every nursing facility and the representative point in each area will be set up on the digital map. The representative point is the central point of the area, and the nodes are the intersections and endpoints of the roads. This is set up as the distance is calculated according to each node. In the present study, the node set as the representative point of the area is the start node, and the node set as the nursing facility is the end node.
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3.2.2 Calculating the specialization coefficient of the population aging rate and adding it to the road network map
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3.2.2.1 Introducing the specialization coefficient of the population aging rate
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In the present study, the distance is weighted so that the road distance of areas with a large demand is longer, while the road distance of areas with a small demand is shorter, as shown in the weighting pattern diagram in Figure 1. If a user from a certain area selects a nursing facility that requires him or her to go through a transit area with a large demand, there is a high chance that there will be competition among users, as those from the transit area will most likely select the same nursing facility. Therefore, the weighting is conducted as mentioned above with the assumption that psychological distance of the users will be longer.
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Figure 1.
Weighting pattern diagram.
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The specialization coefficient of the population aging rate is used as the weighting coefficient. The specialization coefficient of the population aging rate is the value indicating how high the population aging rate is in a certain area. Besides the specialization coefficient of the population aging rate, the population aging rate and the aging population can both be considered as a weighting coefficient. However, because the population aging rate is the ratio with the value always being 1 or less, the distance is always calculated to be shorter. Additionally, the difference in the aging population among the different areas is substantial enough to greatly affect the distance. On the other hand, the specialization coefficient of the population aging rate does not make the value too big or too small; hence, it is suitable as a weighting coefficient.
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3.2.2.2 Calculating the specialization coefficient of the population aging rate
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The specialization coefficient of the population aging rate in each area is calculated. In order to calculate the specialization coefficient, the population aging rate must be calculated first. The population aging rate and the specialization coefficient of the population aging rate are, respectively, calculated using Eqs. (1) and (2):
where \n\n\n\nA\ni\n\n is the population aging rate of area \ni\n (%),\n\n\np\n\n75\ni\n\n\n is the population of those over 75 in area \ni\n (people), and\n\n\np\ni\n\n is the total population of area \ni\n (people).
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\n\n\nSC\ni\n\n=\n\n\nA\ni\n\nA\n\n\nE2
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\nwhere\n\n\nSC\ni\n\n is the specialization coefficient of the population aging rate in area \ni\n,\n\nA\ni\n\n is the population aging rate of area \ni\n (%), \nA\n is the population aging rate of all areas (%).
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3.2.2.3 Adding the specialization coefficient of the population aging rate to the road network map
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The specialization coefficient of the population aging rate calculated in Eq. (2) is added to the road network map using PostgreSQL. This is done by multiplying the distance between nodes by the specialization coefficient of the population aging rate. As a rule for addition, the specialization coefficient of the population aging rate is added if the nodes are in the same area as in nodes (i) and (ii) of Figure 2. However, if the nodes are in different areas as in nodes (ii) and (iii), the average value of the specialization coefficient of the population aging rate is added.
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Figure 2.
Example of specialization coefficient of the population aging rate added to the road network map.
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3.2.3 Calculating the shortest route using A* algorithm
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The shortest route between each nursing facility and each area will be calculated using A* algorithm. A representative search method for finding the shortest route is the Dijkstra method (Dijkstra, 1959) [30]. However, in the present study, the latitude and longitude are used for the coordinates of the representative points in each area as well as the nursing facilities, and the estimate value of the shortest routes between the two points are available in advance. Therefore, A* algorithm, which is the improved version of the Dijkstra method that can effectively calculate the shortest routes, is used.
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In general, when considering the shortest route which starts at the start node, goes through node n, and ends at the end node, the route is expressed as shown in Eq. (3):
\nwhere\n\n\nf\n∗\n\n\nn\n\n is the shortest route distance (m),\n\n\ng\n∗\n\n\nn\n\n is the shortest distance from the start node to n (m), and \n\nh\n∗\n\n\nn\n\n is the shortest distance from n to the end node (m).
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Though \n\n\nf\n∗\n\n\nn\n\n\n can be easily obtained if the values of \n\n\ng\n∗\n\n\nn\n\n\n and \n\n\nh\n∗\n\n\nn\n\n\n are already known, in reality, the values of \n\n\ng\n∗\n\n\nn\n\n\n and \n\n\nh\n∗\n\n\nn\n\n\n\nare impossible to obtain beforehand. Therefore, the method of replacing \n\n\nf\n∗\n\n\nn\n\n\n with the estimated \n\nf\n\nn\n\n\n is called A* algorithm, and it is expressed as shown in Eq. (4):
\n
\n\nf\n\nn\n\n=\ng\n\nn\n\n+\nh\n\nn\n\n\n\nE4
\n
\nwhere \nf\n\nn\n\n is the estimate value of the shortest route, \ng\n\nn\n\n is the estimate value of the shortest route from the start node to n, and \nh\n\nn\n\n is the estimate value of the shortest route from n to the end node.
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An example of actually searching for the shortest route using A* algorithm on a computer is shown in Figure 3. In this example, the shortest route from coordinates (2,2) to (5,5) is obtained. The gray cells are set as impassable, while F, G, and H correspond to \n\nf\n\nn\n\n,\ng\n\nn\n\n,\n\n and \n\nh\n\n\nn\n\n\n of Eq. (4). However, G is the actual travel distance from (2,2) to the current cell, and the distance for moving one cell over in any direction is counted as 1. Additionally, for the estimate value of the shortest distance from the current cell to (5,5), H is set as the higher value of the difference between the x-coordinate and the y-coordinate of the two points.
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Figure 3.
Example of shortest route calculation using A* algorithm.
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The algorithm process is as follows:
Calculate the values of F, G, and H, surrounding the first cell.
Move to the cell with the value of F being the lowest.
Calculate the values of F, G, and H, surrounding the moved cell.
Among the cells newly calculated in the algorithm process 3, if the value of F is the same as or lower than that of the current cell, it is moved to that cell. If the value of F is higher than that of the current cell, the search on this route is discontinued. Then, among the value of cells already calculated, if the values of F, G, and H can be calculated and the value of F is the same or almost the same as that of the current cell, it is moved to this cell.
The algorithm processes 3 and 4 are repeated until the destination cell is reached.
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The cells leading to the destination cell at this time is the shortest route, and the value of F obtained at the end is the distance of the shortest route. The shortest route in this example is indicated by the gray dotted line arrow, and the distance of the shortest route is 5. Though 1 was set as the distance when it was moved one cell over in the example of Figure 3, the distance can be changed according to each moved cell. In the present study, the distance of G is set as the product of the actual travel distance and the specialization coefficient of the population aging rate mentioned in Section 3.2.2.
\n
\n
\n
3.2.4 Evaluation of nursing facility locations using the specialization coefficient of the population aging rate
\n
The p-median problem is a facility location problem in which the total distance from the users to their closest facility is minimized, and this problem can be modeled as shown in Eq. (5). This model obtains the optimal location to reduce the load of users in all areas to the extent possible by changing the value of Xij:
\nwhere \n\nX\nij\n\n∈\n\n0\n1\n\n.\n is the allocation for facility \nj\n in area\n\ni\n, \n\nw\ni\n\n is the demand in area\n\ni\n, and \n\nd\nij\n\n is the distance from area \ni\n to facility \nj\n\n.
\n
In the present study, the evaluation method is developed based on the p-median problem. Eq. (5) is a model that obtains the suitable location to reduce the travel load of users in all areas as much as possible and is different to the purpose of the present study. Therefore, in order to match the purpose of the present study, Eq. (5) is changed into Eq. (6):
\nwhere\n\n\nZ\ni\n\n is the evaluation point for area \ni\n, \n\nw\ni\n\n is the demand in area \ni\n, and\n\n\nd\nij\n\n is the distance from area \n\ni\n to facility \nj\n (m).
\n
Xij of Eq. (5) is removed in order for the facility location to stay the same, and by calculating the evaluation points according to each area, the facility location situation in each area can be quantitatively grasped. Additionally, as for the originality of the present study, distance dij is weighted by the specialization coefficient of the population aging rate as mentioned in Section 3.2.2. With Dij as the weighted distance, Eq. (6) can be expressed as Eq. (7):
\nwhere,\n\n\nZ\ni\n\n is the evaluation point for area \ni\n,\n\nw\ni\n\n is the demand in area \ni\n, and\n\n\nD\nij\n\nis the weighted distance of area \ni\n to facility \nj\n (m).
\n
The evaluation points of each area is calculated after applying the aging population and the distance of shortest routes to nursing facilities of each area obtained in Sections 3.2.1 and 3.2.2, and the results are displayed on the digital map using GIS.
\n
\n
\n
\n
\n
4. Selection of evaluation target area and data processing
\n
\n
4.1 Selection of evaluation target area
\n
For the evaluation target area in the present study, Chofu City, Tokyo, was selected. Chofu City is located in the suburban area of Tokyo as shown in Figure 4. Chofu City was selected as the evaluation target area, as the aging population (age 65 or over) has already exceeded the youth population (under age 15), and the aging population is expected to increase in the future. According to the survey on the aging population in Chofu City [31], 23,545 people, equivalent to approximately 10% of the total population, fit in the current age range of the present study subject which is 75 and over. In the present study, evaluation will be conducted in the unit of 105 areas within Chofu City.
\n
Figure 4.
Location of Chofu City in Tokyo.
\n
\n
\n
4.2 Data processing
\n
\n
4.2.1 Data overview
\n
The utilized data and the utilization method of the data in the present study are shown in Table 1.
\n
\n
\n
\n\n
\n
Utilized data
\n
Utilization method of data
\n
\n\n\n
\n
Population by age (National Census 2010 by the Statistics Bureau)
\n
Create the distribution map of the aging population Calculate the specialization coefficient of the population aging rate and add it to the road network map Calculate the shortest route using A* algorithm
\n
\n
\n
Local care resources (Regional figures—Chofu City, Tokyo, by the Japan Medical Association)
\n
Create the distribution map of nursing facilities
\n
\n
\n
OpenStreetMap
\n
Create the road network map Calculate the shortest route using A* algorithm
\n
\n\n
Table 1.
List of utilized data.
\n
\n
\n
4.2.2 Distribution map of aging population and nursing facilities
\n
The distribution of the aging population and nursing facilities are, respectively, shown in Figures 5 and 6. As shown in Figure 5, the southeastern part has a high aging population, and there are old housing complexes in this area. In Figure 6, the admission type of nursing facilities is only shown as mentioned in Section 3.2.1, indicating that they are distributed throughout the entire Chofu City excluding the central area.
\n
Figure 5.
Distribution of the aging population.
\n
Figure 6.
Distribution of nursing facilities.
\n
\n
\n
\n
\n
5. Evaluation
\n
\n
5.1 Evaluation for case with the specialization coefficient
\n
Figure 7 is the evaluation results when using the specialization coefficient of the population aging rate. Areas with particularly high evaluation points are outlined by bold black lines. As evident in Figure 7, in addition to the areas with a high aging population mentioned in Section 4.2.2, evaluation points were also high in the eastern areas. On the other hand, evaluation points in the northern part of Chofu City were generally low.
\n
Figure 7.
Evaluation result when using the specialization coefficient.
\n
\n
\n
5.2 Evaluation for case without the specialization coefficient
\n
Figure 8 is the evaluation result without the use of the specialization coefficient of the population aging rate. Particularly high evaluation points are outlined by bold black lines in the same manner as Figure 7, and among the areas outlined, four areas were the same as those outlined in Figure 7.
\n
Figure 8.
Evaluation results when the specialization coefficient was not used.
\n
Figure 9.
The difference between evaluation results with and without the specialization coefficient.
\n
\n
\n
5.3 Comparing the evaluation results with and without the specialization coefficient
\n
Figure 9 shows the difference between the evaluation results with and without the use of the specialization coefficient of the population aging rate. Areas with evaluation points higher in the former results are black, while areas with points higher in the latter are gray. Additionally, areas with particularly large or small differences are outlined with black bold lines. Regarding gray areas with high evaluation points when not using the specialization coefficient of the population aging rate, the evaluation points when using the specialization coefficient of the population aging rate were even higher, highlighting the lack of nursing facilities. With 32 areas marked gray and 25 marked black, it was made evident that the evaluation results when using the specialization coefficient of the population aging rate were lower overall.
\n
\n
\n
5.4 Extraction of areas lacking nursing facilities
\n
Based on the evaluation results of Section 5.1 (the evaluation results for cases with the specialization coefficient), the five areas with the highest evaluation points will be extracted as areas lacking facilities. The five areas outlined in light gray in Figure 7 have a particularly high population of those 75 and over, and it can be said that there is currently a lack of nursing facilities. Additionally, as the population of those aged 65–74 are also high in these areas, further shortages of nursing facilities can be expected.
\n
\n
\n
\n
6. Conclusion
\n
The conclusion of the present study can be summarized in the following three points:
In the present study, the model of the p-median problem used to obtain the optimal location of facilities was modified, and a method to evaluate the current situation concerning the shortage or overage of nursing facilities by area was proposed. By using the A* algorithm to calculate the distance between nursing facilities and areas, the shortest routes were efficiently calculated. Additionally, the specialization coefficient of the population aging rate used for the distance weighting can be used, because the users of nursing facilities are limited to the aging population, and the evaluation method which used this coefficient is a method specialized for nursing facilities.
Regarding the evaluation method in the present study, as evaluations are conducted using quantitative data such as the specialization coefficient of the population aging rate and the distance between nursing facilities and areas, the evaluation results are also quantitative, making it an effective indicator for evaluating the locations of nursing facilities. Additionally, as the current location situation of nursing facilities is evaluated by the area, the comparison of the shortage or overage of nursing facilities in each area as well as the extraction of areas lacking facilities can be easily conducted. Furthermore, as the evaluation results are clearly displayed on the digital map using GIS, the shortage or overage of facilities can be visually understood.
In the present study, the specialization coefficient of the population aging rate and the distance between nursing facilities and areas were calculated based on public open data such as the National Census and OpenStreetMap. As evaluations are conducted based on public information, by obtaining population data and geospatial data similar to the present study, evaluations can be conducted using data in other areas as well as for the past and future. Therefore, the evaluation method in the present study has a high temporal reproducibility as well as spatial reproducibility. For example, by using the “future population estimate by region in Japan” of the National Institute of Population and Social Security Research as future data, the shortage or overage of nursing facilities in the future can be evaluated.
\n
As research topics for the future, the improvement of the evaluation method for cases where there are multiple facilities within a certain range as well as the application of the evaluation method of the present study in the evaluation of other facility locations and other areas can be considered.
\n
\n
Conflict of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
\n',keywords:"nursing facility, aging population, p-median problem, facility location problem, geographic information systems (GIS), public open data",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63971.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63971.xml",downloadPdfUrl:"/chapter/pdf-download/63971",previewPdfUrl:"/chapter/pdf-preview/63971",totalDownloads:546,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"July 8th 2018",dateReviewed:"September 7th 2018",datePrePublished:"November 5th 2018",datePublished:"November 13th 2019",dateFinished:"October 8th 2018",readingETA:"0",abstract:"Recently, in Japan, the increase of aging population is especially rapid, the lack of nursing facilities has become a serious social issue, and the political measures against it are continuously enacted. Though the number of nursing facilities and its capacity are increasing, the utilization rate of such facilities remains at the same level, and the lack of facilities has not improved. Based on the background mentioned above, using geographic information systems (GIS) and public open data, the present study aims to quantitatively evaluate the current situation of nursing facility locations in urban areas within Japan. In the present study, the model of the p-median problem used to obtain the optimal location of facilities was modified, and a method to evaluate the current situation concerning the shortage or overage of nursing facilities by area was proposed. As evaluations are conducted using quantitative data, the evaluation results are also quantitative, making it an effective indicator for evaluating the locations of nursing facilities. Additionally, the specialization coefficient of the population aging rate and the distance between nursing facilities and areas were calculated based on public open data. Therefore, the evaluation method has a high temporal reproducibility as well as spatial reproducibility.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63971",risUrl:"/chapter/ris/63971",book:{slug:"geographic-information-systems-and-science"},signatures:"Koya Tsukahara and Kayoko Yamamoto",authors:[{id:"215057",title:"Dr.",name:"Kayoko",middleName:null,surname:"Yamamoto",fullName:"Kayoko Yamamoto",slug:"kayoko-yamamoto",email:"k-yamamoto@is.uec.ac.jp",position:null,institution:{name:"University of Electro-Communications",institutionURL:null,country:{name:"Japan"}}},{id:"271648",title:"MSc.",name:"Koya",middleName:null,surname:"Tsukahara",fullName:"Koya Tsukahara",slug:"koya-tsukahara",email:"kayoko.yamamoto@uec.ac.jp",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work",level:"1"},{id:"sec_3",title:"3. Evaluation framework and methods",level:"1"},{id:"sec_3_2",title:"3.1 Evaluation framework and process",level:"2"},{id:"sec_3_3",title:"3.1.1 Creating the distribution maps of the aging population and nursing facilities as well as a road network map",level:"3"},{id:"sec_4_3",title:"3.1.2 Calculating the specialization coefficient of the population aging rate and adding it to the road network map",level:"3"},{id:"sec_5_3",title:"3.1.3 Calculating the shortest route using A* algorithm",level:"3"},{id:"sec_6_3",title:"3.1.4 Evaluating nursing facility location using the specialization coefficient of the population aging rate",level:"3"},{id:"sec_8_2",title:"3.2 Evaluation method",level:"2"},{id:"sec_8_3",title:"3.2.1 Creating the distribution maps of the aging population and nursing facilities as well as road network maps",level:"3"},{id:"sec_8_4",title:"3.2.1.1 Distribution map of the aging population",level:"4"},{id:"sec_9_4",title:"3.2.1.2 Distribution map of nursing facilities",level:"4"},{id:"sec_10_4",title:"3.2.1.3 Creating a road network map",level:"4"},{id:"sec_12_3",title:"3.2.2 Calculating the specialization coefficient of the population aging rate and adding it to the road network map",level:"3"},{id:"sec_12_4",title:"3.2.2.1 Introducing the specialization coefficient of the population aging rate",level:"4"},{id:"sec_13_4",title:"3.2.2.2 Calculating the specialization coefficient of the population aging rate",level:"4"},{id:"sec_14_4",title:"3.2.2.3 Adding the specialization coefficient of the population aging rate to the road network map",level:"4"},{id:"sec_16_3",title:"3.2.3 Calculating the shortest route using A* algorithm",level:"3"},{id:"sec_17_3",title:"3.2.4 Evaluation of nursing facility locations using the specialization coefficient of the population aging rate",level:"3"},{id:"sec_20",title:"4. Selection of evaluation target area and data processing",level:"1"},{id:"sec_20_2",title:"4.1 Selection of evaluation target area",level:"2"},{id:"sec_21_2",title:"4.2 Data processing",level:"2"},{id:"sec_21_3",title:"Table 1.",level:"3"},{id:"sec_22_3",title:"4.2.2 Distribution map of aging population and nursing facilities",level:"3"},{id:"sec_25",title:"5. Evaluation",level:"1"},{id:"sec_25_2",title:"5.1 Evaluation for case with the specialization coefficient",level:"2"},{id:"sec_26_2",title:"5.2 Evaluation for case without the specialization coefficient",level:"2"},{id:"sec_27_2",title:"5.3 Comparing the evaluation results with and without the specialization coefficient",level:"2"},{id:"sec_28_2",title:"5.4 Extraction of areas lacking nursing facilities",level:"2"},{id:"sec_30",title:"6. Conclusion",level:"1"},{id:"sec_34",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Ministry of Health, Labour and Welfare: A Survey on Nursing Facilities and Offices.Tokyo: Ministry of Health, Labour and Welfare, 2016. 2p\n'},{id:"B2",body:'Ministry of Health, Labour and Welfare: Situation of the Applicants for Entrance to Nursing Facilities. Tokyo: Ministry of Health, Labour and Welfare, 2016. 3p\n'},{id:"B3",body:'Yamada A, Satoh E. Difference of management and nursing needs of small scale caring home for the elderly according to regionality. Journal of Architecture, Planning and Environmental Engineering. 2008;73(633):2355-2363\n'},{id:"B4",body:'Takase A, Yamada A, Nohara Y, Satoh E. Study on efficient placement and operation of home-visiting care in Provincial City. Journal of the City Planning Institute of Japan. 2016;51(3):901-908\n'},{id:"B5",body:'Yamamoto T, Yanase R. A study on behavior of inpatients and spatial characteristic in convalescence rehabilitation wards. Journal of Architecture and Planning. 2015;80(714):1745-1751\n'},{id:"B6",body:'Acker K, Pletz AM, Katz A, Hagopian A. Foreign-born care givers in Washington state nursing homes: Characteristics, associations with quality of care, and views of administrators. Journal of Aging and Health. 2015;27(4):650-669\n'},{id:"B7",body:'Fujita Y, Saio N. Transition and problems of social consciousness about welfare facilities for the aged and children. Journal of Architecture, Planning and Environmental Engineering. 2017;82(733):697-703\n'},{id:"B8",body:'Hunnicutt JN, Baek J, Alcusky M, Hume AL, Liu SH, Ulbricht CM, et al. Geographic variation in the initiation of commonly used opioids and dosage strength in United States Nursing Homes. Medical Care. 2018;56(10):847-857. DOI: 10.1097/MLR.0000000000000972\n'},{id:"B9",body:'Tahara M, Kitagawa K. Takayama: A study on the function of social welfare facilities as evacuation centers. Japanese Journal of Social Welfare. 2018;53(1):16-28\n'},{id:"B10",body:'Yun H, KilgoreJeffrey ML, Curtis JR, DelzellLisa E, Gary LC, Saag KG, et al. Identifying types of nursing facility stays using medicare claims data: An algorithm and validation. Health Services & Outcomes Research Methodology. 2010;10(1–2):100-110\n'},{id:"B11",body:'Walsh EG, Wiener JM, Haber S, Bragg A, Freiman M, Ouslander JG. Potentially avoidable hospitalizations of dually eligible medicare and medicaid beneficiaries from nursing facility and home- and community-based services waiver programs. Journal of the American Geriatrics Society. 2012;60(5):821-829\n'},{id:"B12",body:'Cherubini A, Eusebi P, Dell’Aquila G, Landi F, Gasperini B, Bacuccoli R, et al. Predictors of hospitalization in Italian Nursing Home residents: The U.L.I.S.S.E. project. Journal of the American Medical Directors Association. 2012;13(1):84.e5-84.e10\n'},{id:"B13",body:'Onder G, Carpenter I, Finne-Soveri H, Gindin J, Frijters D, Henrard JC, et al. Assessment of Nursing Home Residents in Europe: The Services and Health for Elderly in Long TERm care (SHELTER) study. BMC Health Services Research. 2012;12(5). DOI: 10.1186/1472-6963-12-5\n'},{id:"B14",body:'King BJ, Gilmore-Bykovskyi AL, Roiland RA, Polnaszek BE, Bowers BJ, Kind AJH. The consequences of poor communication during transitions from hospital to skilled nursing facility: A qualitative study. Journal of the American Geriatrics Society. 2013;61(7):1095-1102\n'},{id:"B15",body:'Neuman MD, Wirtalla C, Rachel M, Werner RM. Association between skilled nursing facility quality indicators and hospital readmissions. Journal of the American Medical Association. 2014;312(15):1542-1551\n'},{id:"B16",body:'Fry L, Walker M, Ledesma S, Gluch K, Deason K. Use of robotic cats to reduce falls in skilled nursing facility. Journal of Post-Acute and Long-Term Care Medicine. 2018;19(3):B22-B23. DOI: 10.1016/j.jamda.2017.12.070\n'},{id:"B17",body:'Yamaguchi I. Causal relationships between perceived quality of care and information sharing among workers in care facilities for the elderly. Japanese Journal of Communication Studies. 2018;46(2):131-149\n'},{id:"B18",body:'Segawa S, Sadahiro Y. A decision-making support system for child care facilities using GIS. Theory and applications of GIS. 1996;4(1):11-18\n'},{id:"B19",body:'Nagashima T, Shinzato T, Kaku I. Facility location problem of quick electric recharging system for electric vehicles. Journal of Japan Industrial Management Association. 2014;64(4):557-560\n'},{id:"B20",body:'Ozgen D, Gulsun B. Combining possibilistic linear programming and fuzzy AHP for solving the multi-objective capacitated multi-facility location problem. Information Sciences. 2014;268:185-201\n'},{id:"B21",body:'Munemasa Y, Honda Y, Imai K. Constrained optimum arrangement structure which minimizes travel cost of residence and jobs. Journal of Architecture and Planning. 2015;80(712):1373-1380\n'},{id:"B22",body:'Zhang Y, Snyderc LV, Ralphs TK, Xue Z. The competitive facility location problem under disruption risks. Transportation Research Part E: Logistics and Transportation Review. 2016;93:453-473\n'},{id:"B23",body:'Ohdate S, Tsunoda M, Yi S. A study on phased optimization of the gross area of public facilities via facility relocation: Verification of the evaluation process and case studies in Machida-City. Journal of Architecture and Planning. 2017;82(732):363-370\n'},{id:"B24",body:'Nagai H, Kurahashi S. Bustle changes the city: Facility for stopping off and modeling urban dynamics. Transactions of the Japanese Society for Artificial Intelligence. 2017;32(1):D-G26_1-D-G2610\n'},{id:"B25",body:'Usui H, Hino K. Continuous walking distance and criteria for density of resting places: Application to Tokyo Central Station and Otemachi Station. Journal of Architecture and Planning. 2018;83(748):1049-1056\n'},{id:"B26",body:'Tanaka K, Furuta T. Quintile share ratio in a linear city with one and two facilities. Journal of the City Planning Institute of Japan. 2015;50(3):628-635\n'},{id:"B27",body:'Tanaka K, Furuta T. Evaluating inequality of facility location in a linear city using median share ratio. Journal of the City Planning Institute of Japan. 2016;51(3):894-900\n'},{id:"B28",body:'Furuta T, Tanaka K. Minimizing quantiles share ratio in multiple facility location problem with total distance constraint. Transactions of the Operations Research Society of Japan. 2017;60:36-49\n'},{id:"B29",body:'Hashimoto S. A Study on the Index Value Calculation of Healthy Life Expectancy An Examination of the Healthy Life Expectancy Regarding Promotion of National Health Promotion Exercise (Health Japan 21) in the 21st Century; 2015. pp. 26-38\n'},{id:"B30",body:'Dijkstra EW. A note on two problems in connexion with graphs. Numerische Mathematik. 1959;1(1):269-271\n'},{id:"B31",body:'Chofu City: A Survey on the Aging Population in Chofu City [Internet]. 2018. Available from: http://www.city.chofu.tokyo.jp/www/contents/1515569448760/index.html [Accessed: August 23, 2018]\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Koya Tsukahara",address:"t1730063@edu.cc.uec.ac.jp",affiliation:'
University of Electro-Communications, Tokyo, Japan
University of Electro-Communications, Tokyo, Japan
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Williams and Fred Bidandi",authors:[{id:"215494",title:"Dr.",name:"Fred",middleName:null,surname:"Bidandi",fullName:"Fred Bidandi",slug:"fred-bidandi"},{id:"253224",title:"Prof.",name:"John James",middleName:null,surname:"Williams",fullName:"John James Williams",slug:"john-james-williams"}]},{id:"62758",title:"Risk Analysis and Land Use Planning",slug:"risk-analysis-and-land-use-planning",signatures:"Valentina Svalova",authors:[{id:"62677",title:"Dr.",name:"Valentina",middleName:null,surname:"Svalova",fullName:"Valentina Svalova",slug:"valentina-svalova"}]},{id:"64323",title:"Political Economy and the Work of Kenneth Arrow",slug:"political-economy-and-the-work-of-kenneth-arrow",signatures:"Norman Schofield",authors:[{id:"151701",title:"Prof.",name:"Norman",middleName:null,surname:"Schofield",fullName:"Norman Schofield",slug:"norman-schofield"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"75413",title:"Russian Wheat Aphid Distribution in Wheat Production Areas: Consequences of Management Practices",doi:"10.5772/intechopen.96375",slug:"russian-wheat-aphid-distribution-in-wheat-production-areas-consequences-of-management-practices",body:'\n
\n
1. Introduction
\n
Establishment success and rate of spread will determine the invasive ability of a specific organism [1]. The success of an invasive species will further be determined by both abiotic and biotic factors that will influence the adaptation and spread within the geographic range of establishment [2]. Liu et al [3] believe that Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov) possesses many of the features that define a ‘good invader’ and as a result became a global threat to wheat production. RWA has originally spread from central Asia [4] to other major wheat (Triticum aestivum L.) producing countries in the world. It is considered a primary pest of dryland winter wheat in North America [5] and South Africa [6]. RWA, like other exotic aphid species, is capable of surviving at low numbers for a relatively long period and can have sudden population outbreaks in new areas [7]. The most recent record of this aphid invading a new area was in 2016 in Southern Australia and RWA is consequently considered a major threat to cereal production in Australia as well [8]. In an updated distribution model for predicting potential spread of RWA, Avila et al. [9] suggested that RWA would be able to establish in all major wheat- and barley-growing regions in New Zealand. The first record of RWA outside its original area of distribution was in South Africa in 1978. Initially the distribution was confined to the Bethlehem area in the Eastern Free State, but by 1979, the RWA had spread to other wheat-producing areas in the country [6]. The first record of RWA in the United States was in 1986 [5]. RWA invaded all the Central European countries from the south-east [10] and was first detected in the Czech Republic in 1993 [11, 12]. It was found that RWA expanded from its Mediterranean distribution range to the northwest. It seems that the expansion route has covered Serbia, Hungary and the Czech Republic [11]. Puterka et al. [13] determined that the origin of populations distributed in South Africa, Central and North America was in Turkey with an indication of random establishment by commerce rather than through migration. Zhang et al. [14], however, found evidence of long-term existence and expansion of RWA in China and speculate that RWA are not frequently transported by human agricultural activities. With the expansion of wheat fields it is possible that aphid populations may spread to areas via natural pathways such as flight or wind currents. Once established in an area RWA is very adaptable to changes in the environment. Because of its wide distribution, considerable effort has gone into developing management strategies against this global wheat pest. Currently there are two management options: breeding for deployment of resistant wheat cultivars and chemical control.
\n
RWA-resistant cultivars were released and deployed in South Africa during 1992, and more than 70% of the wheat production area in South Africa was planted with Russian wheat aphid-resistant cultivars [15]. The durability of resistant cultivars was, however, challenged by the occurrence of RWA biotypes, first in Colorado in 2003 [16], and in South Africa in 2006 [17]. Russian wheat aphid biotypic variation was also found in Hungary [18] and Chile [19]. Since 2006, five distinct RWA biotypes have been recorded in the wheat production areas of the Eastern Free state (summer rainfall area), South Africa, RWASA2 in 2006; RWASA3 in 2009; RWASA4 in 2011 and most recently RWASA5 in 2018.
\n
The second management option, chemical control, is also practiced in South Africa, mainly in the Western Cape (winter rainfall area) and on irrigation wheat in central and western Free State and Northern Cape. Chemical control has long term, negative impacts on the environment, especially other insect groups such as predators, pollinators, and decomposers. Hill, et al. [20] demonstrated that broad spectrum pesticide application in grain crops can lead to secondary outbreaks of pests due to alteration of natural enemy communities. The active ingredients registered for RWA control on wheat in South Africa are limited and include acetamiprid, chlorpyrifos, chlorpyrifos + cypermethrin, demeton-S-methyl, dimethoate, imidacloprid, parathion, prothiofos and thiamethoxam. With widespread and continuous use of these active ingredients, there is the possibility that RWA can build up resistance against these specific active ingredients. About 20 species in the Aphididae have evolved resistance to insecticides [21] and can be associated with detectable changes in reproductive rates [22]. Brewer and Kaltenbach [23] demonstrated that there is detectable variation in RWA insecticide susceptibility and reproductive rates after exposure to chlorpyrifos. Chlorpyrifos selection seen in wheat production may result in large scale changes in susceptibility and control failures. Russian wheat aphid variation in virulence to small grains occurs [24, 25] as well as variation in fecundity [26, 27]. There is a possibility that RWA can also evolve virulence to active ingredients in chemicals. In their recommendations for managing RWA expansion into all major grain regions of Australia Ward et al. [28] include sustainable management practices, given the somewhat indiscriminate use of insecticides to control RWA to date. They also include regular testing of field populations for evolution of insecticide resistance in their recommendations. To determine how RWA populations change over time annual monitoring was done from 2010 to 2019 in the wheat production areas of South Africa. The most recent observations is discussed here.
\n
\n
\n
2. Material and methods
\n
\n
2.1 Survey and collection of RWA at landscape level
\n
RWA samples were collected annually during the wheat growing season in South Africa from 2010 to 2019. All main wheat production areas within the known distribution of the RWA were sampled. The same areas were sampled each year and where possible the same fields (Figures 1 and 2). There are two main dryland wheat production areas in South Africa where RWA commonly occur, the Western Cape (winter rainfall area) (Figure 1) and the Free State (a summer rainfall area) (Figure 2), with irrigated wheat production areas in the Central and Western Free State and Northern Cape (Figure 2). Sampling sites were selected off primary or secondary roads that transected major wheat or barley production areas. Sites were 10-20 km apart with distances depending on the continuity of wheat fields. In the Western Cape an average of 32 fields were sampled (Figure 1) and in the Free State an average of 61 fields were sampled (Figure 2). Samples were collected from cultivated wheat, barley and oats as well as volunteer wheat, wild oats, rescue grass and false barley in road reserves and around cultivated fields. Infested leaves were placed in Petri dishes containing moist filter paper and stored in an icebox for transportation to the glasshouse. The number of aphids per plant, percentage plants infested, growth stage of the plants and damage on the plants were recorded. The geographical co-ordinates and elevation where the samples were collected were also captured on a GPS and all the information of each sample collected was entered into a database (Windows Office –Excel).
\n
Figure 1.
Sampling sites for Russian wheat aphid (RWA) in the Western cape (winter rainfall area), South Africa from 2010 to 2019.
\n
Figure 2.
Sampling sites for Russian wheat aphid (RWA) in the Free State (summer rainfall area), South Africa from 2010 to 2019.
\n
\n
\n
2.2 Establishing clone colonies of collected RWA samples
\n
A single female aphid from each sample collected in the field was transferred to a wheat plant and caged (gauze size: 315micron) to produce a clone colony. RWA clone colonies are kept in glasshouse cubicles at night/day temperatures of 16 °C/22 °C and maintained on various wheat cultivars to avoid pre-adaptation to a specific cultivar until they multiplied sufficiently to be used for screening. Each clone colony is cultured for an average period of two to three months before screening.
\n
\n
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2.3 Screening of clone colonies of collected RWA samples for determination of potential biotypes
\n
The biotype of each RWA clone was determined by screening its feeding damage on 11 previously established plant resistant sources containing designated resistance genes Dn1 to Dn9 and Dnx and Dny (Table 1). Infestations of RWASA1 cause susceptible damage symptoms on wheat entries containing the Dn2 and Dn3 gene (Table 1). RWASA2 cause susceptible damage symptoms on wheat entries containing Dn1, Dn2, Dn3, Dn8 and Dn9 resistance genes (Table 1). RWASA3 is distinguished from RWASA2 by its added virulence to Dn4 and RWASA4 is distinguished from RWASA3 by its added virulence to Dn5 (Table 1). RWASA5 is distinguished from RWASA4 by its added virulence to Dn6 and Dnx (Table 1).
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Wheat genotype
\n
\nDn\n R gene gengene
\n
RWASA1
\n
RWASA2
\n
RWASA3
\n
RWASA4
\n
RWASA5
\n
\n\n\n
\n
CO03797
\n
\nDn1\n
\n
R
\n
S
\n
S
\n
S
\n
S
\n
\n
\n
CO03804
\n
\nDn2\n
\n
S
\n
S
\n
S
\n
S
\n
S
\n
\n
\n
CO03811
\n
\nDn3\n
\n
S
\n
S
\n
S
\n
S
\n
S
\n
\n
\n
Yumar
\n
\nDn4\n
\n
R
\n
R
\n
S
\n
S
\n
S
\n
\n
\n
CO9500043
\n
\nDn5\n
\n
R
\n
R
\n
R
\n
S
\n
S
\n
\n
\n
CO960223
\n
\nDn6\n
\n
R
\n
R
\n
R
\n
R
\n
S
\n
\n
\n
94 M370
\n
\nDn7\n
\n
R
\n
R
\n
R
\n
R
\n
R
\n
\n
\n
Karee-Dn8
\n
\nDn8\n
\n
R
\n
S
\n
S
\n
S
\n
S
\n
\n
\n
Betta-Dn9
\n
\nDn9\n
\n
R
\n
S
\n
S
\n
S
\n
S
\n
\n
\n
PI586955
\n
\nDnx\n
\n
R
\n
R
\n
R
\n
R
\n
S
\n
\n
\n
Stanton
\n
\nDny\n
\n
R
\n
R
\n
S
\n
S
\n
S
\n
\n\n
Table 1.
Comparison of plant reaction of the five Diuraphis noxia biotypes identified in South Africa.
\n
Ten seeds of each plant entry were planted in a seedling tray filled with sterilized sand in a randomized complete block design with four replications for each biotype determination. Plant entries were randomly assigned to rows and were separated by border rows planted with RWA susceptible Tugela. Plants were kept in glasshouse cubicles at night/day temperatures of 16 °C/22 °C, natural light. Immediately after planting, the seedling trays were placed in gauze (315micron) cages to avoid contamination by secondary aphids. Plants were infested at the two-leaf stage with collected RWA clone colonies. Plants were rated with a ten-point damage rating scale, which included leaf chlorosis and leaf rolling [29]. A score from 1–4 describes leaf chlorosis, 5–6 striping on the leaves and 7–10 rolling. Once the susceptible wheat Tugela showed susceptible damage symptoms, all plants were rated. RWA biotypes were classified by using damage ratings for each plant entry where the plant was considered resistant (R) if the damage rating was 1–6.5 and susceptible (S) if the damage rating was above 6.5–10. Each clone was given a biotype designation based on the differential virulence profile to the Dn1 to Dn9 and Dnx and Dny resistance genes (Table 1).
\n
Biotype (clones) groups across all plant differentials were analyzed using a two-way (clone, plant entry) analysis of variance (ANOVA). Mean damage rate entries with significant (P < 0.05) clone-by-plant interactions were separated by Fisher’s protected least significant difference (LSD) test at the 5% level (SAS Institute 2003).
\n
\n
\n
\n
3. Results and discussion
\n
Representative samples of five RWA biotypes were collected in the different wheat production areas in South Africa, with a range of different climatic conditions and different host plants from 2010 to 2019 (Figure 1 and Figure 2). The number of samples collected in a specific area varied depending on the area planted with wheat or barley or the availability of alternative hosts and the level of infestation. An average of 32 fields were sampled in the Western Cape (Figure 1) and 61 in the Free State (Figure 2). Environmental conditions, including temperature, humidity, rainfall, soil type and availability of host plants play an important role in the population increase and distribution of different RWA biotypes. Because these variables change from year to year and between different areas, the distribution of RWA biotypes will vary over years and between different geographical areas.
\n
Analysis of the main effects of damage rating for the five Russian wheat aphid biotype colonies indicated a significant clone (F = 117.48; df = 3; P < 0.0001), plant entry (F = 133.59; df = 11; P < 0.0001) and clone-by-plant entry interaction (F = 12.82; df = 33; P < 0.0001), suggesting that the plant entries responded differently to the different aphid clones. Biotypes are identified by the distinct feeding damage responses they produce on wheat carrying different RWA resistance genes from Dn1 to Dn9 [30]. Infestations of RWASA1 caused susceptible damage symptoms on the wheat entry containing the Dn2 and Dn3 gene (Table 1). RWASA2 caused susceptible damage symptoms on wheat entries containing Dn1, Dn2, Dn3, Dn8 and Dn9 resistance genes (Table 1). RWASA3 is distinguished from RWASA2 by its added virulence to Dn4 and RWASA4 is distinguished from RWASA3 by its added virulence to Dn5 (Table 1). RWASA5 was the most virulent biotype in South Africa with susceptible responses to ten plant differentials containing ten different Dn genes (Table 1). Randolph et al. [31] found the American RWA2 to be the most virulent strain tested with susceptible responses to 12 plant differentials.
\n
The concentration of RWA biotypes occurred mainly in the Eastern Free State with very few wheat fields infested with RWASA1 (original biotype, reported in 1978). RWASA1 occurred mainly in the Western Free State and Northern Cape. Since 2006, five distinct RWA biotypes have been recorded in the wheat production areas of the Eastern Free State, RWASA2 in 2006; RWASA3 in 2009; RWASA4 in 2011 and RWASA5 in 2018. The populations of RWA biotypes fluctuated over the years with RWASA2 being the dominant biotype from 2010 to 2011, RWASA3 dominating from 2012 to 2013 and RWASA4 from 2014 to 2016 (Figure 3). During the 2018 season RWASA5, was recorded for the first time on 8 wheat fields in the Lindley, Reitz and Danielsrus areas in the Eastern Free State. During 2019 this biotype had increased and spread to other areas of the Eastern Free State and was recorded on 12 wheat fields in the Eastern Free State. This biotype was dominant from 2018 to 2019 (Figure 3). Merrill et al. [32] found, in a general survey of aphid mixtures for virulence to resistant Yumar (with Dn4 gene) in Colorado from 2004 to 2008, that Dn4 virulence increased from 82% in 2005 to 98% in 2008. When a new RWA biotype appear, this new biotype seem to be able to outcompete the previous biotypes in the area and displace the other biotypes. Puterka et al. [33] found, in an area-wide study in the USA during 2005, that RWA2 almost completely displaced the original biotype. A survey from 2010 to 2013 revealed a change in biotypic diversity of RWA populations in the United States, with RWA 1,6 and 8 across regions showing high percentages during 2011 (64–80%) and 2013 (69–90%) [34]. In South Africa RWA biotype with added virulence to genes used in resistant wheat cultivars were recorded every 2 to 3 years in the Eastern Free State where RWA resistant wheat cultivars were commonly deployed. These newly recorded RWA biotypes became the dominant biotype in these areas until a more virulent RWA biotype was recorded (Figure 3). The most recently recorded biotype during 2018, RWASA5, is virulent against all known Dn genes used in wheat except Dn7 (94 M370) (Table 1). With the increase and spread of more virulent RWA biotypes the use of insecticides may again become the main management option in these areas. Merrill et al. [35] found that even though resistant wheat cultivars historically provided excellent management of RWA on wheat crops in Colorado, the increase of new RWA biotypes resulted in all commercially available winter wheat cultivars being susceptible to RWA feeding damage and associated yield losses. This led to insecticides once again becoming the main management tactic used on Colorado wheat [35]. In the Western Cape, where chemical control is the most common control measure for RWA, RWASA1 remained the only biotype and the biotype diversity seen in the Eastern Free State was not experienced in this area. There was however, an increase in RWASA1 incidence in the Western Cape from between 30 to 60% fields infested from 2010 to 2016 to between 70 to 100% fields infested with RWA from 2017 to 2019 on the fields that were annually surveyed (Figure 4). In a survey of farmers in the Western Cape during the 2017 wheat production season 75% of the respondents observed RWA on their crops [36]. All these farmers use chemical control, in the form of preventative spray, to control RWA, because it is cheap and effective [36]. The fact that RWASA1 became more widespread in the Western Cape and that in some cases live populations were collected in fields recently sprayed with insecticides may indicate insecticide resistance. The active ingredients registered for RWA control on wheat in South Africa are limited and include acetamiprid, chlorpyrifos, chlorpyrifos + cypermethrin, demeton-S-methyl, dimethoate, imidacloprid, parathion, prothiofos and thiamethoxam. The most common active ingredients used by producers in the Western Cape are chlorpyrifos, dimethoate, imidacloprid and thiametoxam (Mr K. Naicker, Cape RnD, Meridian Agritech). In the Western USA, chlorpyrifos was the predominantly used insecticide, with area-wide treatment of wheat acreage in specific localities [37]. Puterka et al. [13] detected genetic variation and potential for biotypic diversity in RWA among world-wide collections of RWA from countries in Eurasia, South Africa and the United States in 1990. This variation in other traits may be indictors of adaptations, which could confer RWA resistance to chlorpyrifos [23]. Brewer and Kaltenbach [23] demonstrated that variation in RWA susceptibility to chlorpyrifos and associated reproductive rates occur in the small grains growing region of the USA. Furthermore, approximately 20 species in the Aphididae have evolved resistance to insecticides [21] that can be associated with detectable changes in reproductive rates [22]. In South Africa RWA showed considerable biotypic adaptation and change in reproductive rate to resistant wheat [25, 27, 38], resulting in five RWA biotypes occurring in wheat production areas where RWA resistant wheat were deployed in the Eastern Free State. This may be an indication that RWA in South Africa have the adaptive ability to develop resistance to active ingredients of insecticides used to control them in the Western Cape. Large-scale changes in susceptibility were detected in other aphids in which consistent and severe selection pressure occurred [21]. Brewer and Kaltenbach [23] stated that even though control failure problems have not been reported, periodic assessment of RWA populations of field derivation is necessary. Ward et al. [28] also recommend regular testing of field populations to understand if insecticide resistance is likely to evolve in Australia. According to Brewer and Elliott [39] better understanding of the mediating effects of host plant and habitat manipulations may accelerate our ability to plan cereal production systems with improved ability to suppress cereal aphids, including future invading species.
\n
Figure 3.
Russian wheat aphid (RWA) SA biotype distribution in the Free State, South Africa (summer rainfall area) from 2010 to 2019 (average fields sampled: 61).
\n
Figure 4.
Percentage of wheat fields surveyed in the Western cape, (winter rainfall area), South Africa, infested with Russian wheat aphid (RWA) SA biotype1 (average fields sampled: 32).
\n
\n
\n
4. Conclusion
\n
Given the invasive ability, evolutionary adaptability to changing conditions, virulence, and fecundity of RWA, it remains a threat to global wheat production and wheat cultivation. RWA remain present in all the wheat production areas of South Africa and these populations are becoming more virulent as indicated by the spread of the recently recorded biotype, RWASA5, in the Eastern Free State. Management practices in different regions of South Africa may cause increased virulence in RWA populations. Based on these observations testing of field populations to understand if insecticide resistance is evolving in RWA populations in the Western Cape is warranted. It is important that future management practices focus on sustainability instead of the indiscriminate use of insecticides globally to control RWA to date. Increasing diversity in fields through undersowing, reduced tillage, intercropping and incorporation of cover crops will be an effective start to sustainable management practices. Vegetation strips have favorable microclimate for survival of generalist predators, and alternative prey and resources during winter, resulting in higher densities of generalist predators in cereal fields [40, 41]. This together with minimal use of insecticides, only when necessary, will increase the insects providing ecosystem services and predators, parasitoids and pathogens that will keep RWA populations and economical damage low. Management approaches against cereal aphid invasions differ depending on aphid ecology, specific system influences, and local management practices [42]. Any practice based on aphid population monitoring that facilitates threshold-based insecticide use will be effective across agroecosystems, with area-wide management systems being most appropriate to large-scale cereal production systems.
\n
\n
Acknowledgments
\n
The author wish to acknowledge the Agricultural Research Council (ARC)-Small Grain for facilities provided and the Winter Cereal Trust (WCT) for research funding.
\n
Conflict of interest
The author declare no conflict of interest.
\n',keywords:"Russian wheat aphid, Diuraphis noxia, wheat, Triticum aestivum, biotypes, insecticide resistance",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/75413.pdf",chapterXML:"https://mts.intechopen.com/source/xml/75413.xml",downloadPdfUrl:"/chapter/pdf-download/75413",previewPdfUrl:"/chapter/pdf-preview/75413",totalDownloads:38,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 9th 2020",dateReviewed:"February 3rd 2021",datePrePublished:"February 25th 2021",datePublished:null,dateFinished:"February 25th 2021",readingETA:"0",abstract:"Russian wheat aphid (RWA) is an international pest on wheat and occurs in most countries where large scale wheat cultivation is practiced. Consequently, considerable efforts have been made to manage RWA globally. The two management options used currently are chemical control and breeding for deployment of resistant wheat cultivars. There are however drawbacks to both of these management practices. Chemical control has a negative impact on the environment, especially other insect groups such as predators, pollinators and decomposers. With widespread and continuous use of the same active ingredients, there is the possibility that RWA can build up resistance against these specific active ingredients. The drawback with resistance breeding is that certain RWA populations can overcome the resistance in the wheat, resulting in new biotypes virulent to the resistant wheat cultivars.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/75413",risUrl:"/chapter/ris/75413",signatures:"Astrid Jankielsohn",book:{id:"9670",title:"Current Trends in Wheat Research",subtitle:null,fullTitle:"Current Trends in Wheat Research",slug:null,publishedDate:null,bookSignature:"Dr. Mahmood-Ur- Rahman Ansari",coverURL:"https://cdn.intechopen.com/books/images_new/9670.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83968-594-1",printIsbn:"978-1-83968-593-4",pdfIsbn:"978-1-83968-595-8",editors:[{id:"185476",title:"Dr.",name:"Mahmood-Ur-",middleName:null,surname:"Rahman Ansari",slug:"mahmood-ur-rahman-ansari",fullName:"Mahmood-Ur- Rahman Ansari"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Material and methods",level:"1"},{id:"sec_2_2",title:"2.1 Survey and collection of RWA at landscape level",level:"2"},{id:"sec_3_2",title:"2.2 Establishing clone colonies of collected RWA samples",level:"2"},{id:"sec_4_2",title:"2.3 Screening of clone colonies of collected RWA samples for determination of potential biotypes",level:"2"},{id:"sec_6",title:"3. Results and discussion",level:"1"},{id:"sec_7",title:"4. Conclusion",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"},{id:"sec_11",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nColautti, R.I. and MacIsaac H.J. 2004. A neutral terminology do define ‘invasive’ species. Diversity and Distributions. 10: 135-141.\n'},{id:"B2",body:'\nPrentis, P.J., JRU. Wilson, E.E. Dormontt, D.M. Richardson, and A.J. Lowe. 2008. Adaptive evolution in invasive species. Trends Plant Sci. 13: 288-294.\n'},{id:"B3",body:'\nLiu, X., J.L. Marshall, P. Stary, O. Edwards, G. Puterka, L. Dolatti, M. El Bouhssini, J. Malinga, J. Lage, and C.M Smith. 2010. Global phylogenetics of Diuraphis noxia (Hemiptera: Aphididae), an invasive aphid species: evidence for multiple invasions into North America. J. Econ. Entomol. 103(3): 958-965. DOI: 10.1603/EC09376\n'},{id:"B4",body:'\nDurr, H.J.R. 1983. Diuraphis noxia (Mordvilko) (Hemiptera: Aphididae), a recent addition to the aphid fauna of South Africa. Phytophylactica 15: 81-83.\n'},{id:"B5",body:'\nMorrison, W. P., and F. B. Peairs. 1998. Response model concept and economic impact, pp. 1-11. In S. S. Quisenberry and F. B. Peairs (eds.), A response model for and introduced pest – the Russian wheat aphid (Homoptera: Aphididae). Thomas Say Publications in Entomology, Entomological Society of America, Lanham, MD.\n'},{id:"B6",body:'\nWalters, M.C, F. Penn, F. Du Toit, T.C. Botha, K. Aalbersberg, P.H. Hewitt & S.W. Broodryk. 1980. The Russian wheat aphid. Fmg S. Afr. 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Barley Yellow Dwarf 4: 34.\n'},{id:"B11",body:'\nStary´, P. 1996. The expansive Russian wheat aphid Diuraphis noxia (Kurdj.) detected in the Czech Republic. J. Pest Sci. 69: 19-20.\n'},{id:"B12",body:'\nVosˇlajer, Z. 1999. Diuraphis noxia in the Czech Republic and relationship between the migration and the temperature conditions, pp. 589-596. In Proceedings, 5th International Conference on Pests in Agriculture, Part 2. Montpellier, France.\n'},{id:"B13",body:'\nPuterka, G.J., Black, W.C., Steiner, W.M., Burton, R.L. 1993. Genetic variation and phylogenetic relationships among worldwide collections of the Russian wheat aphid Diuraphis noxia (Mordvilko), inferred from allozyme and RAPD-PCR markers. Heredity. 70: 604-618.\n'},{id:"B14",body:'\nZhang, B., Edwards, O.R., Kang, L. and Fuller, S.J. 2012. Russian wheat aphids (Diuraphis noxia) in China: native range expansion or recent introduction? Molecular Ecology 21: 2130-2144.\n'},{id:"B15",body:'\nMarasas, C., P. Anandajayasekeram, V.L. Tolmay, P. Martella, J.L. Purchace, and G.J. Prinsloo. Socio-economic impact of Russian wheat aphid control research program. SACCAR/ARC Report, SACCAR, Gaborone, Botswana, (1999). 147pp. http://agris.fao.org/agris-search/search.do?recordID=QY2001000257\n\n'},{id:"B16",body:'\nHaley, S.D., F.B. Peairs, C.B. Walker, J.B. Rudolph, and T. L. Randolph. 2004. Occurrence of a new Russian wheat aphid biotype in Colorado. Crop Sci. 44: 1589-1592.\n'},{id:"B17",body:'\nTolmay, V.L., R.C. Lindeque & G.J. Prinsloo. 2007. Preliminary evidence of a resistance-breaking biotype of the Russian wheat aphid, Diuraphis noxia (Kurdjumov) (Homoptera: Aphididae), in South Africa. African Entomology 15(1): 228-230\n'},{id:"B18",body:'\nBasky, Z. 2003. Biotypic and pest status differences between Hungarian and South African populations of Russian wheat aphid, Diuraphis noxia (Kurdjumov) (Homoptera: Aphididae). Pest Manag. Sci. 59: 1152-1158.\n'},{id:"B19",body:'\nSmith, C.M., T. Belay, C. Stauffer, P. Stary, I. Kubeckova, and S. Starkey. 2004. Identification of Russian wheat aphid (Homoptera: Aphididae) populations virulent to the Dn4 resistance gene. J. Econ. Entomol. 97: 1112-1117.\n'},{id:"B20",body:'\nHill, M.P., Macfadyen, S., and Nash, M.A. 2017. Broad spectrum pesticide application alters natural enemy communities and may facilitate secondary pest outbreaks. PeerJ 5:e4179; DOI 10.7717/peerj.4179\n'},{id:"B21",body:'\nDevonshire, A.L. 1989. Occurrence of insecticide-resistant aphids. In A.K Minks and P. Harrewijn (eds.), Aphids, their Biology, Natural Enemies and Control, Vol. C, pp. 123-139. Elsevier, NY.\n'},{id:"B22",body:'\nO’Brien, P.J. and J.B. Graves. 1992. Insecticide resistance and reproductive potential of Aphis gossypii Glover. Southwestern Entomol. 17:115-121.\n'},{id:"B23",body:'\nBrewer, M.J. and Kaltenbach, J.E. 1995. Russian wheat aphid (Homoptera: Aphididae) population variation in response to chlorpyrifos exposure. Journal of the Kansas Entomological Society. 68(3): 346-354.\n'},{id:"B24",body:'\nPuterka, G.J., J.D. Burd, and R.L. Burton. 1992. Biotypic variation in a world-wide collection of Russian wheat aphid (Homoptera: Aphididae). J. Econ. Entomol. 85: 1497-1506.\n'},{id:"B25",body:'\nJankielsohn, A. 2011. Distribution and Diversity of Russian Wheat Aphid (Hemiptera: Aphididae) Biotypes in South Africa and Lesotho. J. Econ. Entomol. 104(5): 1736-1741.\n'},{id:"B26",body:'\nWebster, J.A., F. Du Toit, and T.W. Popham. 1993. Fecundity comparisons of the Russian wheat aphid (Homoptera: Aphididae) in Bethlehem, South Africa, and in Stillwater, Oklahoma. J. Econ. Entomol. 86: 544-548.\n'},{id:"B27",body:'\nJankielsohn, A. 2013. Host Associations of Diuraphis noxia (Homoptera: Aphididae) Biotypes in South Africa. Journal of Economic Entomology 06(6):2595-2601.\n'},{id:"B28",body:'\nWard, S., Van Helden, M., Heddle, T., Ridland, P.M., Pirtle, E. and Umina, P.A. 2020. Biology, ecology and management of Diuraphis noxia (Hemiptera: Aphididae) in Australia. Austral Entomology. 59(2): 238-252. https://doi.org/10.1111/aen.12453\n\n'},{id:"B29",body:'\nTolmay, V. L. 1995. The inheritance and mechanisms of Russian wheat aphid (Diuraphis noxia) resistance in two Triticum aestivum lines. M.Sc. thesis, University of the Orange Free State, Bloemfontein, South Africa.\n'},{id:"B30",body:'\nPuterka, G.J., Nicholson, S.J., Brown, M.J., Cooper, W.R., Peairs, F.B. and Randolph, T.L. 2014. Characterization of eight Russian wheat aphid (Hemiptera: Aphididae) biotypes using two-category resistant-susceptible plant responses. J. Econ. Entomol. 107(3): 1274-1283. DOI:http//dx.doi.org/10.1603/EC13408\n\n'},{id:"B31",body:'\nRandolph, T.L., F. Peairs, A. Weiland, J.B. Rudolph, and G. J. Puterka. 2009. Plant responses to seven Russian wheat aphid (Hemiptera: Aphididae) biotypes found in the United States. J. Econ. Entomol. 102 (5): 1954-1959.\n'},{id:"B32",body:'\nMerrill, S.C.,Walker, C.B., Peairs, F.B., Randolph, T.L., Hayley, S.D., and Hammon, R.W. 2009. Displacement of Russian wheat aphid, Diuraphis noxia (Kurdjumov), Biotype 1 in Colorado by Russian what aphid biotypes virulent to the wheat resistance gene Dn4. Colorado State University Experiment Station Technical Bulletin TB09-09.\n'},{id:"B33",body:'\nPuterka, G.J., Burd, J.D., Porter, D., Shufran, K., Baker, C., Bowling, B., and Patrick, C. 2007. Distribution and diversity of Russian wheat aphid (Hemiptera: Aphididae) in North America. J. Econ. Entomol. 100: 1679-1684. https://doi.org/10.1093/jee/100.5.1679.\n'},{id:"B34",body:'\nPuterka, G.J., Giles, K.L., Brown, M.J., Nicolson, J., Hammon, R.W., Peairs, F.B., Randolph, T.L., Michaels, G.J., Bynum, E.D., Springer, T.L., Armstrong, J.S. and Mornhinweg, D.W. 2015. Change in biotypic diversity of Russian wheat aphid (Hemiptera: Aphididae) populations in the United States. J. Econ. Entomol. 1-7. DOI:10.1093/jee/tov008.\n'},{id:"B35",body:'\nMerrill, S.C., Holtzer, T.O., Peairs, F.B. and Lester, P.J. 2009. Modeling sparial variation of Russian wheat aphid overwintering population densities in Colorado winter wheat. J. Econ. Entomol. 102(2): 533-541.\n'},{id:"B36",body:'\nDe Lange, W. 2017. Monetary valuation of the impact of aphids on selected commercial small grains in the Western Cape. CSIR Natural Resources and the Environment, July 2017.\n'},{id:"B37",body:'\nLegg, D.E., Ferrell, M., Taylor, D.T. and Kellogg, D.L. 1992. Pesticide use in Wyoming, 1990. RJ-211, Coop. Ext. Serv., Univ. of Wyoming, Laramie, Wyoming.\n'},{id:"B38",body:'\nJankielsohn, A. (2019). New Russian wheat aphid biotype found in the Free State. SA Grain, March 2019. P 70-71.\n'},{id:"B39",body:'\nBrewer, M.J. and Elliott, N.C. 2004. Biological control of cereal aphids in North America and mediating effects of host plant and habitat manipulation. Annu. Rev. Entomol. 49: 219-42. Doi:10.1146/annurev.ento.49.061802.123149.\n'},{id:"B40",body:'\nSunderland, K. and Samu, F. 2000. Effects of agricultural diversification on the abundance, distribution, and pest control potential of spiders: a review. Entomol. Exp. Appl. 95: 1-13.\n'},{id:"B41",body:'\nJmhasly, P. and Nentwig, W. 1995. Habitat management in winter wheat and evaluation of subsequent spider predation on insect pests. Acta Oecol. 16: 389-403.\n'},{id:"B42",body:'\nBrewer, M.J., Peairs, F.B., and Elliott, N.C. 2019. Invasive cereal aphids of North America: ecology and pest management. Annual Review of Entomology. 64:73-93. https://doi.org/10.1146/annurev-ento-011118-111838\n\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Astrid Jankielsohn",address:"jankielsohna@arc.agric.za",affiliation:'
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\n\t
Foundation for Science and Technology (FCT)
\n\t
Chinese Academy of Sciences
\n\t
Natural Science Foundation of China (NSFC)
\n\t
German Research Foundation (DFG)
\n\t
Max Planck Institute
\n\t
Austrian Science Fund (FWF)
\n\t
Australian Research Council (ARC)
\n
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