1. Introduction

2. Materials and Methods

2.1. Literature Review and Conceptual Framework

Many government organizations and institutions in Indonesia have conducted numerous studies on using tangerine production inputs. As a result, this part will present the prior study’s findings and the factors influencing tangerine production.

2.1.1. Literature Review

1.

Educational Length and Farming Experience;

Mulyaningsih et al. [46] state that education is one of the elements that influence a farmer’s decision to accept or implement a training program. Furthermore, Marhawati [47] stated that the amount of education will impact orange farmers’ ability to absorb information and innovate in their farm development. In addition to education, farming experience can influence farm production [48]. This result aligns with previous research conducted by Nainggolan et al. [40], who discovered that the longer a farmer has been farming, the larger the produce they produce. These two aspects affect the production process. This viewpoint is consistent with studies by Chairunnisa and Juliannisa [49], illustrating how high education and a qualified workforce might impact output. Furthermore, Tania and Amar [50] emphasized that sufficient education and work experience will boost production. Therefore, education and farming experience have a substantial influence on farm production.

2.

Pesticide Use and Fertilization;

In Indonesia, the tropical environment is one factor that contributes to the growth of pests and plant diseases, both of which can potentially diminish the amount of citrus plants produced. Aphids, fruit flies, stem borers, leafminers, leafworms, and cycads are some pests frequently discovered in citrus plants [51]. In addition to pests that threaten citrus crop production, diseases also pose as big a threat as pests in reducing citrus production. Diseases often found in citrus plants include CVPD (citrus vein phloem degeneration), anthracnose, stem base rot, tatter leaf, exocortis, and psorosis [52]. Proper handling is necessary to control pests and diseases [6]. Pest and disease control can be done by applying pesticides, proper nutrients, pruning, and controlling weeds [53]. Using pesticides and handling other factors properly can increase citrus crop production [54]. Fertilization is one way to improve soil fertility, where soil fertility will determine the quality and quantity of agricultural crop production [55]. During the vegetative phase, plants need nutrients in metabolism and photosynthesis [56]. Based on empirical evidence, a great amount of research has been carried out to investigate the impact of fertilizer application on the production of citrus crops. Sari et al. [57] did research that explains this phenomenon. Based on the findings of this research, it was determined that the fulfillment of nutrient requirements in citrus trees is a crucial factor in developing these plants. Then, Ramadhan et al. [58] and Ramadhana et al. [59] said that citrus plants will produce a high fruit set when NPK fertilizer is administered to them. As a result, citrus producers will be able to increase their output. NPK fertilizer can be applied to boost output in citrus plants, as stated by Sakhidin et al. [60]. This is accomplished by increasing the amount of fruits that these plants produce. According to Mario et al. ‘s [61] and Saragih and Harmain’s research from [62], the application of fertilizer to grown plants has a sizable and beneficial impact on the amount of plant production.

3.

Land Size and Ownership Status;

Based on empirical evidence, numerous studies have been carried out over the past five years to investigate the positive and considerable impact that land area has on citrus farm productivity. The scope of this research encompasses several different regions in Indonesia. According to Kharismawati and Karjati [63], land is significant in farm management concerning its role as a production component. As Pradnyawati and Cipta [64] point out, the area of land affects the amount of land planted. The amount of land planted is directly connected to the production [65]. The more land area planted, the higher its production will be. A substantial causal association between land area characteristics and citrus crop output has been reported by Langit and Ayuningsasi [66] in the Bangli Regency, Wijayanti and Hascaryani [67] in the Malang Regency, Namah et al. [48] in North Mollo Regency, and Kristiandi et al. [68] in the Sambas Regency. These findings are consistent with the research carried out in Egypt by Kassem et al. [69], which the same researchers undertook. Similarly, the study by Otieno [53] in Africa concluded that the land area element plays a key role in a positive and considerable impact on the quantity of citrus output.

Additionally, the size of the land area is another aspect that can affect citrus production. Land ownership status is another factor that can have an impact. The term “land ownership status” refers to the situation in which a farmer is granted permission to engage in farming activities on agricultural land on which they have the right to do so. Land ownership status is correlated with production outcomes, and this correlation exists between the two. The findings of several studies, including those conducted by Ainurrahma et al. [70] and Manatar et al. [71], indicate a substantial positive association between the status of land ownership and the quantity of harvest that farmers produce. This link is significant and can be considered beneficial. The findings of a study that was carried out by Pasaribu and Istriningsih [72] also demonstrated that the status of land ownership, which includes ownership, rent, and profit sharing, has been demonstrated to have a considerable impact on the quantity of production, which in turn would have a direct impact on the income of farmers. The investigation results led to the discovery of this piece of information. Additionally, Rondhi and Adi [73] asserted that the status of land ownership has a considerable and favorable influence on the quantity of agricultural output produced by farmers. An impact of this kind is beneficial. Research conducted by Koirala et al. [74] reveals that the status of land ownership significantly influences the amount of production that farmers produce.

4.

Number of Family Members and Labor;

According to Kassem et al. [69], there is a very close connection between the number of dependents in farmer households and the requirements of each family member concerning their own needs. Both Nainggolan and Ulma’s [39] study and Nainggolan et al.’s [40] study have demonstrated via research that an increase in the number of family members leads to an increase in the productivity of farmers, which in turn leads to an increase in the money that farmers derive from their agriculture. A study conducted by Marhawati [47] concluded that an increase in the number of dependents that farmers have will increase both their performance and their drive to provide for their family members. All of the findings from the research are in agreement with this result. According to Jamil et al. [75], the increased family requirement brought about by many dependents will also affect a person’s ability to use all of their abilities, including hard and soft skills, because they anticipate a higher income. In addition, Khairunnisa et al. [76] stated that the number of family members who are financially dependent on the farmer’s income has a beneficial impact on the amount of food the farmer produces. According to Purba and Purwoko [77], the number of family members who are employed not only contributes to an increase in production but also serves as a driving force behind that overall increase. According to Simanjuntak and Amrizal [78], people who work for farmers are an essential part of making more food.

5.

Using Transportation and Tangerine Farming Distance to Farmer House;

According to Jamil et al. [75], one contributing factor that positively impacts labor productivity is the distance to the farmer’s home between the worker’s place of residence and the workplace area. The findings of a study that was carried out by Indraningsih [79] indicate that the distance that separates a farmer’s house and farmland is a factor that adds to the overall agricultural performance of the farmer. According to the findings of Dewantoro et al. [80], which are comparable to the findings of this study, there is a negative correlation between the distance between the land and the farmer’s residence and the productivity of farmers in managing their farms. However, when the distance to the farm is reduced, the productivity of the farm increases. This statement suggests that the distance between the farm and the farm positively influences the amount of farm food produced. Furthermore, the utilization of transportation instruments is intricately linked to the farm’s proximity, hence facilitating convenient access for farmers to their fields. Consequently, transportation will indirectly exert a substantial beneficial impact on farmer production [62]. Furthermore, using technology, specifically transportation equipment, can effectively streamline and minimize production expenses in the transportation sector. Consequently, transportation equipment is pivotal in augmenting output levels [81].

6.

Age, Gender and Farmer Groups.

The Farmers who play a big part in citrus production are distinguished by some qualities, one of which is its age. According to Saputra et al.’s [82] research, the economic value of citrus fruit will decrease with each extra year that farmers live. The findings Purba and Purwoko [77], which show that sweet orange growers have health difficulties with each extra age, seem to be slightly different from the findings of this study. However, health problems, such as fever, cold, cough, toothache, muscle discomfort, and so on, do not prevent people from going about their regular duties. The age range of 15 to 64 years is regarded as a person’s productive age, as stated by Mulyaningsih et al. [46]. In order to increase the amount of work produced, it is strongly advised that individuals within this age group work or participate in various farming activities. According to Marhawati [47], citrus farmers of a productive age are more likely to receive information, innovate, and make decisions more quickly when it comes to developing their farms. Furthermore, farmer production is significantly influenced by their age. Kassem et al. [69] found that this was the case. Due to the fact that the capabilities of each individual are tremendously different, gender is one of the aspects that play a part in the production of citrus growers. Since the process involves more powerful physical abilities, the male gender is more required to prepare the land and transport such crops. According to Khairunnisa et al. [76], the female gender is found to be more necessary during planting and maintenance. Then, Mulyaningsih et al. [46] found that male farmers are generally more active in seeking knowledge and inventing in their farming activities. According to Iyai et al.’s [83] research, gender variations play a role in determining the level of productivity that farmers achieve. Furthermore, male farmers are likelier to be more innovative in their farming activities than their female counterparts.t continues here.

2.1.2. Conseptual Framework

The objective of establishing research variables, including dependent and independent variables, is to provide limitations in a study. According to Andrade [84], a variable is an item of observation that possesses a value that can be used to measure it, which allows for the variable to be specified within the context of an investigation. Moreover, the causal relationship that exists between the independent variable and the dependent variable is that which is included in the conceptual framework. One of the relationships, referred to as the causal relationship, is presented here. In order to develop the conceptual framework that is depicted in Figure 1, the foundation that was laid was the literature review that came before. This framework was developed to address the aim of this current inquiry. Because of this, we found many elements that were independent of one another. More specifically, the production of tangerines is the dependent variable being explored in this study (Figure 1).

Figure 1. Conceptual framework.

Figure 1. Conceptual framework.

2.2. Research Site, Data Collection Method, and Research Sample

The Bontomatene District, which is situated in the Selayar Islands Regency of the South Sulawesi Province in Indonesia, served as the location for the research that was carried out. The data collection took place in three different villages within the Bontomatene District, which we selected as the locations for the study. These villages are Bontona Saluk, Batangmata Sapo, and Tamalanrea. Tangerine-producing communities were considered when selecting the three villages that were picked. The core quantitative data for this study came from a representative sample of tangerine producers from each of the three communities. The production of tangerine crops harvested in 2023 was the primary factor considered while selecting these growers. In October 2023, primary data was gathered by conducting structured interviews with a certain group of farmers. A preset questionnaire was utilized to conduct the interviews.

Figure 2. Research location.

Figure 2. Research location.

This study used primary data, which was information obtained from individuals who supplied specific information through interviews [85]. The data-gathering process involved structured interviews with tangerine farmers in the Bontomatene District of the Selayar Islands Regency. The questionnaire that was used for the interviews had been written in advance. All of the participants in this study were farmers of tangerines in the district, with a total population of 1,622. Following that, the probability random sampling method was utilized to carry out the sampling for this investigation. Yamane’s Formula, found in Equation (1) [86], was utilized to ascertain the quantity of samples

$$\begin{gathered} \mathrm{n}={\displaystyle \frac{\mathrm{N}}{1+\mathrm{N}{\mathrm{e}}^2}} \\ \mathrm{n}={\displaystyle \frac{1622}{1+1622{\left(8\mathrm{\%}\right)}^2}} \\ \mathrm{n}=156 \end{gathered}$$

(1)

Description: n = Number of samples; N = Total population; e = sample error rate (8%).

2.3. Binary Logistic Regression Analysis

To analyze and categorize binary and proportional response data sets, statisticians and researchers use a technique known as logistic regression (LR) [87]. This technique is applied to assess and classify the data. Numerous people consider it one of the most important ways because it is a statistical and data mining strategy. In addition, Brusco et al. [88] argues that the model is widely utilized and is widely considered one of the most essential statistical models in the field of predictive analytics. Moreover, he states that the model is prevalent. This investigation aims to ascertain whether there is a causal connection between a response variable and one or more predictor variables, as mentioned in the research conducted by Henning et al. [89]. According to the goals of this investigation, the dependent variable, which was the production of tangerines, was categorized as categorical-binary data, with high and low production levels assigned to it. This particular data attribute was advantageous to the model, according to Henning et al. [89]. The findings that they mentioned also indicate that this is the case. These statistical indicators are established to evaluate the amount of contribution the independent variable has to the evaluated variable [90]. The regression coefficient, the significance of the coefficient, and the odds ratio are some of the several indicators included in this set of indicators.

Table 1. Indicator test for binary logistic regression model

Table 1. Indicator test for binary logistic regression model

Indicators	Descriptions	Criteria	Interpretations
CR	Coefficient of Regression	CR > 0	The positive impact of the independent variable on tangerine production.
		CR < 0	The negative impact of the independent variable on tangerine production.
		CR = 0	There is no correlation between the independent variable and the amount of tangerines produced.
Sig.	A significance level represents the likelihood of making mistakes.	Sig. < 0.01	Tangerine production is significantly influenced by the independent variable, as evidenced by a confidence interval level of 99%.
		Sig. < 0.05	Tangerine production is significantly influenced by the independent variable, as evidenced by a confidence interval level of 95%.
		Sig. < 0.10	Tangerine production is significantly influenced by the independent variable, as evidenced by a confidence interval level of 90%.
Exp.(B)	An odds ratio (Exp. (B) describes the relationship between a variable and the likelihood of an event occurring.	Exp.(B) > 0	As the odds ratio (Exp (B)) increases, the probability of a farmer producing a significant amount of tangerines increases.
		Exp.(B) < 0	As the odds ratio (Exp (B)) increases, the probability of a farmer producing a significant amount of tangerines decreases.
		Exp.(B) = 0	The production of tangerines does not include any association with the independent variable.

Source: Adapted from Salam et al. [91].

2.8. Coefficient of Determination and Simultaneous Test

The overall significance testing method is employed to examine the impact of every βi in the model that has been derived as a composite. The assessment’s findings will indicate whether or not a predictor variable can be included in the model. According to Palkovič and Šoporová. [95], Salam et al. [91] and Yuniarsih et al. [93], the G value in the G Test is calculated and provided in Equation (8).

$$\mathrm{G}=-2\log \left({\displaystyle \frac{\mathrm{l}\mathrm{i}\mathrm{k}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{d}\left(\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{B}\right)}{\mathrm{l}\mathrm{i}\mathrm{k}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{d}\left(\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{A}\right)}}\right)$$

(8)

Table 2. Definition, measurement unit, expected hypothesis signs, and significance results of the independent variables.

Table 2. Definition, measurement unit, expected hypothesis signs, and significance results of the independent variables.

Dependent Variables (PJ), where 1 = high production, 0= otherwise
Independent Variables		Measure-ment Unit	Data types	Expected hypothesis signs/ Significance*	References
Variable names	Symbols
Farmer Age	AG	year	continuous	+/SIG	[69,85]
Gender	DG	1=male; 0=other	categorical	+/SIG	[46,76,83]
Educational Length	LED	year	continuous	+/SIG	[69,83,98]
Farming Experience	Pn	year	continuous	+/SIG	[69,76]
Farmer Group	DFG	1=yes; 0=no	categorical	+/SIG	[75,99]
Land Ownership Status	DLOS	1=own; 0=other	categorical	+/SIG	[70,72,74,100]
Using Transportation	TR	1=yes; 0=no	categorical	+/SIG	[10,62,100]
Number of Family Members	JAG	people	continuous	+/SIG	[47,69]
Tangerine Farming Distance to Farmer House	JU	km	continuous	-/SIG	[62,69,101]
Land Area	BL	Ares	continuous	+/SIG	[53,69,85,102]
Farm Labor	TEK	man-days	continuous	+/SIG	[63,66,67,91,100,102]
Insecticides	INS	ml	continuous	+/SIG	[39,53,100,103]
Herbicides	HRB	ml	continuous	+/SIG
Urea Fertilizer	PKU	kg	continuous	+/SIG	[53,91,100,103,104,105]
Manure	PKD	kg	continuous	+/SIG
NPK Fertilizer	PNPK	kg	continuous	+/SIG

Decision-making criteria:

• H₀ is rejected if G >ɤ 2; the model with independent variables is significant at a 5% significance level.
• H₀ is accepted if G < 2; the model with independent variables is insignificant at the 5% significance level.

3. Results

3.2. Cox & Snell R-Square and Nagelkerke R-Square Test

We use the Cox & Snell R-Square and the Nagelkerke R-Square, two statistical tools, to find out how much the independent variables—age, gender, farming experience, land ownership status, farmer groups, transportation, family size, farm distance, land area, labor, insecticides, herbicides, urea fertilizer, and NPK fertilizer—variate concerning the dependent variable—tangerine production. Table 3 shows that the overall value of the Nagelkerke R-Square is 0.876. It can be stated that the independent variables, which consist of factors such as age, gender, farming experience, land ownership status, farmer groups, using transportation, number of family members, farm distance, land area, labor, insecticides, herbicides, urea fertilizers, and NPK fertilizers, are able to explain the dependent variable, which is tangerine production, by 87.6%. Other independent variables unrelated to the observation account for the remaining 12.4% of the variance being explained.

Table 3. Cox & Snell R Square dan Nagelkerke R-Square test result.

Table 3. Cox & Snell R Square dan Nagelkerke R-Square test result.

Model Summary
	-2 Log likelihood	Cox & Snell R-Square	Nagelkerke R - Square
Step 1	47,810	0,649	0,876

The Chi-square value is more significant than the Chi-square table, given that the former is equal to 26.296 and the latter is 164.451. Table 4, part of the omnibus test table, displays the results of the simultaneous test used to draw this conclusion. Because the significance value in the table is 0.000, which is less than the threshold of 0.05, the null hypothesis (H₀) is rejected. Because of this, we can say that the null hypothesis is false. Because this rejection is carried out according to the decision rule that says H₀ is rejected at a significant level α if G is more than the Chi-Square (χ2 (α, v)) value and the significance value in the test statistic is less than α. This finding establishes a causal relationship between the dependent variable and one or more independent factors. Several factors that are considered independent variables are as follows: age, gender, farming experience, land ownership status, farmer groups, using transportation, number of family members, farm distance, land area, labor, pesticides, herbicides, insecticides, urea fertilizers, manure, and NPK fertilizers. Each of these aspects has a major and actual impact on the production of tangerines.

Table 4. T Simultaneous test (Omnibus test) result of the effect of input use on tangerine production.

Table 4. T Simultaneous test (Omnibus test) result of the effect of input use on tangerine production.

	Omnibus tests of coefficients model
		Chi-square	df	Sig.
Step 1	Step	164.451	16	0.000
	Block	164.451	16	0.000
	Model	164.451	16	0.000

3.3. Partial Test (Wald Test)

The Partial Test, often known as the Wald test (W), is a statistical procedure for identifying which model parameters are statistically significant. This test can be carried out by taking the square root of the parameter estimate’s quotient, the parameter estimate α, and the standard error. A significance level of α = 0.05 is used consistently throughout this test. In order to reject the null hypothesis (H₀) at the significance α, the value of W must be greater than or equal to χ2 (α, df). This information can be seen in Table 5.

Table 5. The result of the partial test (Wald test) the effect of input use on tangerine production.

Table 5. The result of the partial test (Wald test) the effect of input use on tangerine production.

Variable in the Equation
Independent Variables		B	S.E.	Wald	df	Sig.	Exp(B)
	Farmer Age (AG)	0.068	0.43	2.440	1	0.118	1.070
	Gender (DG)	0.28	1.535	0.000	1	0.986	0.972
	Educational Length (LED)	0.331	0.168	3.854	1	0.050**	1.392
	Farming Experience (Pn)	-0.039	0.050	0.607	1	0.436	0.961
	Farmer Group (DFG)	2.318	1.183	3.82	1	0.050**	10.157
	Land Ownership Status (DLOS)	0.465	0.936	0.247	1	0.619	1.592
	Using Transportation (TR)	0.185	0.990	0.035	1	0.852	1.203
	Number of Family Members (JAG)	0.790	0.435	3.290	1	0.070	2.203
	Tangerine Farming Distance to Farmer House (JU)	-1.875	0.791	5.626	1	0.018**	0.153
	Land Area (BL)	4.301	4.872	0.779	1	0.377	73.777
	Farm Labor (TEK)	0.050	0.025	4.078	1	0.043**	1.051
	Insecticides (INS)	-2.787	1.205	5.349	1	0.021**	0.062
	Herbicides (HRB)	3.908	1.536	6.475	1	0.010**	49.821
	Urea Fertilizer (PKU)	0.013	0.006	4.020	1	0.045**	1.013
	Manure (PKD)	0.004	0.001	11.132	1	0.001***	1.004
	NPK Fertilizer (PNPK)	0.020	0.010	4.553	1	0.033**	1.020
	Constant	-19.426	6.476	8.995	1	0.003	0.000
	Dependent Variable: tangerine production (PJ)

Notes: **significant at 95% confidence level, ***significant at 99%.

The Chi-square (χ2) test results showed that the table’s value was 3.841. This figure was ascertained using a 0.05 significance level and 1 degree of freedom. According to the data in Table 5, nine separate variables significantly affected the tangerine harvest. At this stage, several factors are taken into account, including the farmer’s degree of education, the farmer’s group, the distance from the farmer’s house to the tangerine farm, the farm labor, the pesticides, the herbicides, the urea fertilizers, the manure, and the NPK fertilizers. The Wald test result for these variables is greater than the 3.841 value in the Chi-square table, and the significance value is less than 0.05, so keep that in mind. The alternative hypothesis (H₀) is accepted, and the null hypothesis (H₀) is rejected. It is clear from this study that other independent variables, especially pesticides, do not have a significant impact on tangerine output. There is a rejection of the alternative hypothesis (H₁) and an acceptance of the null hypothesis (H₀). This result is because the variable in question has a Wald test value below the 3.841 value seen in the Chi-square table, and the significance value is higher than 0.05. As a result, we accept the null hypothesis.

It was discovered, based on the findings of a significance test that was carried out on the model, that the factors of age, gender, farming experience, land ownership status, farmer groups, using transportation, number of family members, farm distance, land area, labor, insecticides, herbicides, urea fertilizer, and NPK fertilizer all had a significant impact on the production of tangerines. An example of this would be the number of family members. The binary logistic regression model built was supplied in Equation (12), even though the pesticide variable does not considerably impact the production of tangerines.

$$g\left(PJ\right)=ln\left[{\displaystyle \frac{\pi \left(PJ\right)}{1-\pi \left(PJ\right)}}\right]$$

(12)

$$\begin{array}{ll}=-19.426& +0.331{LED}_{2022}+2.318{DFG}_{2022}–1.875{JU}_{2022}\\ & +0.050{TEK}_{2022}–2.787{INS}_{2022}+3.908{HRB}_{2022}\\ & +0.013{PKU}_{2022}+0.004{PKD}_{2022}\\ & +0.020{PNPK}_{2022}\end{array}$$

3.4. Model Fit Test

An evaluation of how well the model fits the data is carried out with the help of a model fit test. The purpose of this test is to assess whether the observed values are identical to or very comparable to those that the model predicts. The model used must have a Goodness of Fit (GoF) component to be considered valid. If there is a correlation between the data that is included in the model and the data that is seen, then the model is regarded as being in accordance with the GoF for compliance. When performing binary logistic regression, the Chi-Square value in the Hosmer and Lemeshow test can be used to evaluate the approach utilized to test the practicability of the model. This model can be used to determine whether or not the model is plausible.

Table 6. Model fit test result of the effect of input use on tangerine production.

Table 6. Model fit test result of the effect of input use on tangerine production.

Hosmer and Lemeshow Test
Step	Chi-square	df	Sig.
1	2,772	8	0,948

The number of degrees of freedom was set to 8, and the significance level was 0.05. The Chi-square table calculation yielded a result of 15.507. The computation yielded this. Here, we made use of both of these considerations. A 0.948 significant value and a 1.504 Chi-square value were computed and obtained, respectively. Table 6 displays the provided information. Suppose there is no discrepancy between the 22 observations and the predictions. In that case, we can conclude that the null hypothesis (H₀) is accepted because the computed Chi-square value (2,772) is less than the Chi-square table value (15.507), and the significance value (0.948) is greater than the α value (0.05). The significance value is lower than the Chi-square table value, which is why this is the case. Another way of putting it is that the model suits its intended purpose.

3.5. Interpretation of Odds Ratio

Each time there is an increase of one unit of the independent variable, the odds ratio value depicts the change in the tendency, which can be either an increase or a decrease. The findings of our investigation are presented in Table 5, which displays the odds ratio (Exp. (B) value) for each independent variable responsible for the production of tangerines. Furthermore, the ninth significance independent variables on tangerine production were summarized in Figure 3, and the interpretation of the odds ratio of each significant independent variable is stated as follows:

Figure 3. The graphical summary of the causes and effects of tangerine.

Figure 3. The graphical summary of the causes and effects of tangerine.

4. Discussions

5. Conclusions

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