*2.4. Procedure of the SEM Analysis*

The analysis was conducted using the following steps (Byrne 2010; Hair et al. 2017) in Table 2.


**Table 2.** Steps of the Measurement Model Analysis.

#### **3. Results**

*3.1. Formation of the Indicators Variable*

3.1.1. Agricultural Development

Agricultural development is influenced by the development of the rural agricultural infrastructure facilities (X1) and rural farmers' resources (X2) variables (Asian Development Bank (ADB) 2012; Monchuk 2014; United Nations 2011; Koutsampelas and Polycarpou 2013). Therefore, the construct formation of each variable is as follows:

Rural Agricultural Infrastructure Facilities Development

The development of rural agricultural infrastructure facilities showed four constructs with a high loading factor that improve farm business performance. Therefore, it impacts the poverty alleviation of rural farmers, as shown in Figure 2.

The results in Figure 2 show that constructing roads, dams, reservoirs, bridges, and piers and expanding agricultural land and the availability of fertilizers and medicines in rural areas increase agricultural production. This was indicated by the regression coefficients of 0.82, 0.81, 0.74, and 0.80. The first positive effect was enhanced agricultural production facilities and infrastructure, enabling farmers to increase their farming land productivity. The second impact was improved accessibility of agricultural field instructors (PPL) in providing counseling to improve farmers' farming skills and work ethics and increase their farming business production. The third effect was increased accessibility of laborers, enabling farmers to carry out farming activities easily, from land processing to post-harvest.

**Figure 2.** Final Indicators for the Development of Rural Infrastructure Facilities.

Rural Farmer Resource Development

The rural farmer resource development variable with the highest loading factor was used in the final measurement model. The loading factor value varied from 0.74 to 0.82. The rural farmer resources variable's development improved farm business performance, affecting poverty alleviation. Figure 3 shows the final measurement of the rural farmer resource development construct.

**Figure 3.** The Final Indicator of the Rural Farmer Resource Development.

The results in Figure 3 show that agricultural extension workers foster and train farmers and improve farm management. They contribute to improving farming skills and increasing agricultural production in the North Buton Regency, as shown by the positive regression coefficients of 0.74, 0.82, 0.76, and 0.79. The production also increases due to agricultural intensification and extensification programs supported by rural infrastructure development. This increases farmers' accessibility to capital and information resources, increasing production at reasonable prices at the farmer-level. The result is improved farm business performance and farmers' income.

#### 3.1.2. Improved Farm Business Performance

The modified analysis showed that increased farm business performance was included in the four indicators with the highest loading factors of Y1.3, Y1.4, Y1.6, and Y1.9. Therefore, the observable construct with the most significant loading factor was used in the last measurement. The analysis for rural agricultural infrastructure facilities and rural farmer resource development showed the path model for improving farm business performance, as presented in Figure 4.

**Figure 4.** Final Indicators of Improved Farm Business Performance.

Figure 5 shows that rural infrastructure and farmer resource development increase farm performance in the North Buton Regency. This was shown by the increased quality of farm production and the market share of agricultural production, with regression coefficients of 0.80 and 0.89, respectively. Furthermore, the effect was shown by increased farmer groups and farming skills, as well as the price of agricultural production, with regression coefficients of 0.86 and 0.90, respectively. Therefore, agricultural development through infrastructure and farmer resource development significantly improves farm business performance. This was indicated by increased production, farmers' income, and rural poverty alleviation.

#### 3.1.3. Poverty Alleviation

The construct fit test for all indicators showed that the path coefficient significantly exceeded the recommended regression weight of 0.50 (Hair et al. 2017). The four indicators represented the overall variation of the poverty alleviation variable. Therefore, the poverty alleviation construct could be valid (Hair et al. 2017). The variable analysis for each construct that affects rural poverty alleviation is seen in the path coefficients in Figure 5:

The results showed that improving farming performance alleviates rural poverty. The variable indicator showed that farmers provide three meals daily for all members with a regression coefficient of 0.90. They buy cooking utensils, chairs, cupboards, and televisions with a regression coefficient of 0.90 and 0.91. Every year, farmers buy one new pair of clothes for all family members, indicating that the income is also increasing, as shown by the regression coefficient of 0.82. Furthermore, infrastructural development reduces transportation costs for agricultural production and increases farmers' accessibility to capital resources and production inputs. The development also facilitates the accessibility of extension workers (PPL), increasing production. There is an additional investment, increasing market demand and farmers' income and alleviating poverty for the rural population of the North Buton Regency.

**Figure 5.** Final Indicators of Rural Poverty Alleviation.

#### *3.2. Structural Modelling*

3.2.1. The Influence of Agricultural Development on Improving Farm Business Performance

Agricultural development by improving rural agricultural infrastructure facilities and farmers' resources significantly improves farm business performance, with a path coefficient of 0.94. Figure 6 shows how rural farm business performance is affected by agricultural development through rural agricultural infrastructure facilities (X1) and rural farmer resource development (X2).

**Figure 6.** Structural Model for the Influence of Agricultural Development on Improving Farming Business Performance.

Previous studies found that agricultural infrastructure development and farmers' human resources improve farming performance (Clark 2005). This study also found that

the agricultural development constructs improve farm business performance with a path coefficient of 0.74. Statistical values relating to goodness-of-fit are given in Table 3.


**Table 3.** Goodness-of-Fit Agricultural Development on Farm Business Performance.

Source: Processed data.

Many studies pointed to the crucial role of human resources quality in alleviating poverty. They showed the influence of investment in rural farm infrastructure facilities on reducing rural poverty (Clark and Alkire 2008).
