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IMPROVING THE SUSTAINABILITY OF LIVELIHOOD ASSETS AS A STRATEGY FOR PADDY SELF-SUFFICIENCY: A Case At Rural Hoeseholds In

Silvana Maulidah, Djoko Koestiono, Fitria Dina Riana Agribusiness Program Study, Departement of Social Economy, Agriculture Faculty, University of Brawijaya, , East , Indonesia

Corresponding Author: Silvana Maulidah ([email protected])

Silvana Maulidah, Djoko Koestiono, Fitria Dina Riana; Improving The Sustainability of Livelihood Assets As A Strategy for Paddy Selt-Sufficiency: A Case At Rural Hoeseholds In Indonesia

Keywords: livelihood assets, sustainable livelihood, paddy self-sufficiency; SEM-PLS, rural hoeseholds

ABSTRACT Understanding the importance of a concept of livelihood assets is a way to realize food sovereignty. Development policy formulation is directed towards improving the sustainability of rural communities' livelihoods. Indonesia as a country with a large portion of its population as rice consumers has established a special program for food security through mobilizing strategic sectors of the domestic agricultural economy. The purpose of this study is to determine the rice self-sufficiency strategy by increasing the sustainability of the livelihood assets of rural communities. The respondents of this study are rural households in the area of rice production centers in Indonesia. The selection of research locations was determined purposively based on the largest rice producing center in Province, that is Malang , Regency and Regency. The construction of the strategy model is obtained through a quantitative approach: SEM-PLS (Structural Equation Modeling - Partial Least Square) analysis. The results of the study indicate that in the context of creating rice self-sufficiency, it is necessary to increase the sustainability of community livelihood assets: Natural Assets (X2), Financial Assets (X3), and Physical Assets (X4) INTRODUCTION The Government of Indonesia through Presidential Regulation Number 2 of 2015 concerning the 2015-2019 National Medium-Term Development Plan (RPJMN) has established a vision of national development namely "Realization of a Sovereign,

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Independent and Personality Based on Mutual Cooperation". This vision is further elaborated into 7 (seven) Missions and 9 (nine) Priority Agenda known as Nawa Cita (Ministry of Agriculture of the Republic of Indonesia, 2016). This program was initiated to show the priority of the path to change towards a Indonesia's political sovereignty, as well as being independent in the economic field and having a personality in culture. One of the agenda is to realize economic independence by moving the strategic sectors of the domestic economy. Based on the details of Nawa Cita mentioned above, the priority agenda which is vital and dominates the livelihoods of the people of Indonesia is those related to the agricultural sector. This can be directly translated that the issue of increasing strategic food sovereignty is a necessity that must be achieved. In order to realize national food sovereignty, the Government of the Republic of Indonesia through the Ministry of Agriculture launched a Special Self-sufficiency Food Efforts program which includes 3 (three) strategic commodities: rice, corn and soybeans. This special effort is one of the policies in the context of accelerating the increase in food production, especially rice. East Java is a region that has potential in developing rice plants and is one of the national rice granary areas, where the contribution of production to the national is 15.8% (East Java Food Security Agency, 2016). As it is known that the main rice producing centers are rural households, therefore one of the efforts that can be used as an entry point in the study of rice self-sufficiency is to study their own livelihoods. Such a framework seeks to place the community as its focal point, that use the concept of people-centered or subject relations (UNDP, 2010; Chambers, 2004 in Maulidah 2019), which are poor or weak communities (considered / assumed / perceived poor or weak) are used as subjects in the study. Pragmatic knowledge and understanding of community power (assets or capital) are needed, analyzing how the conversion of people's assets into positive and productive livelihood assets (Dercon, 2001; Bebbington, 1999). Livelihood assets are fundamental conditions that affect and reflect the basic livelihoods of farmers, and serve as an approach that aims to end poverty alleviation and eradication (Bajwa, 2015). Livelihood assets consist of: (1) physical assets; (2) financial assets; (3) human assets; (4) social assets; and (5) natural assets. One way to improve understanding of livelihoods based on asset based farming communities is through the Sustainable Livelihoods Approach (SLA). This approach is not only used to determine factors influencing assets from livelihood sources, it is also used to assess the contribution of livelihood assets to production activities, and to plan future development activities strategies (Bajwa, 2015 ). This study aims to examine the self-sufficiency of rice (rice) using the asset-based sustainable livelihoods (SLA) approach in rural farm households. The livelihoods of farmers households are one of the global criteria and important factors affecting farming systems (Yang, et al, 2018). The purpose of this study is to determine the rice self-sufficiency strategy by increasing the sustainability of the livelihood assets of rural communities.

METHODS This research is a type of explanatory research, research that explains the symptoms or phenomena that occur in an object. In this study, researchers wanted to get an explanation of a phenomenon that occurred about the sustainable livelihood strategies of farmers in a special rice self-sufficiency program in East Java conducted by the Ministry of Agriculture. The selection of research locations was determined purposively based on the largest rice producing centers in East Java, , 9487

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Pasuruan Regency, and . The three districts are also regions that carry out special efforts to increase rice production. In this study the respondents were rice farmers in the study area. Determination of respondents using Probability Sampling with Stratified Random Sampling method based on the area of land owned by farmers. The number of respondents in this study were 180 respondents using the Slovin formula and consideration of the analytical tools used. The variables and indicators that measure in this research are stated in the following tables. Table 1. Indicators and desccription of livelihood assets Livelihood Indicators Indicators measurements Assets (perceiption) No Name No Name X1 Human X1.1 Level of education 1= elementary school; 2 = menengah; 3 = bachelor assets graduate X1.2 Medical history Actual number 1 = bad; 2 = average; 3 = well Including the health of the whole households of its members X1.3 Farming experience Actual number 1 = less than twenty years; 2 = twenty till thirty years; 3 = more than thirty years X1.4 Informal farming Actual number training 1 = never; 2 = seldom; 3 = often X2 Natural X2.1 Cultivated Land Actual number assets area 1= sewa; 2= less than one ha; 3= more than one ha X2.2 Quality of Actual number cultivated land 1 = bad; 2 = average; 3 = well X2.3 Irrigation 1 = bad; 2 = average; 3 = well X2.4 Agroecosystem 1 = bad; 2 = average; 3 = well condition X3 Financial X3.1 Households Income Actual number assets X3.2 Households Actual number Expenditure X3.3 Households Savings Actual number X3.4 Households Debts Actual number Including personal and bank loans X3.5 Farming Input Actual number subsidy 1 = less; 2 = enough; 3 = more X4 Physical X4.1 Faming Actual number assets infrastructure 1 = bad; 2 = average; 3= well X4.2 Farming machine Actual number 1 = bad; 2 = average; 3= well X4.3 supporting Actual number, such as coopertaion, bank, etc institution 1 = bad; 2 = average; 3= well X5 Social X5.1 Social organisation Actual number assets 1 = bad; 2 = average; 3= well X5.2 Participation Actual number 1 = bad; 2 = average; 3= well X5.3 Social networking Actual number 1 = bad; 2 = average; 3= well

Table 2. Indicators and desccription of Rice Self-Reliance Rice Self-Reliance Indicators Indicators measurements No Name No Name Y1 Production Y1.1 Level of production Actual number 1 = bad; 2 = average; 3= well 9488

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Y1.2 Level of productivity Actual number 1 = bad; 2 = average; 3= well Y2 Market Y2.1 Price Actual number 1 = bad; 2 = average; 3= well Y2.2 Market acces Actual number 1 = bad; 2 = average; 3= well

The data analysis used to determine the effect of livelihood assets on production and markets uses the SEM-PLS (Structural Equation Modeling - Partial Least Square) method. PLS is an alternative technique in SEM analysis where the data used are not normally distributed multivariate. According to Monecke and Leisch (2012) in the analysis using SEM-PLS consists of three components: the measurement model (outer model), the structural model (inner model) and the weighting scheme. SEM-PLS was adopted for the data analysis. The method is useful for causal-predictive analysis. It does not involve assumptions of homogeneity in variances and covariance of the dependent variable. It also can simultaneously test the structural and the measurement models, providing a complete analysis for the interrelationships. The study used PLS because it makes minimal demands on the data distributions, sample size, and measurement scales and as this study was exploratory (Hair et al., 2014). The collected data were processed and analysed by partial least-squares (PLS) path modelling with Smart-PLS 3.0.

RESULT AND DISCUSSION An analysis of the sustainability improvement of livelihood assets as a strategy for self-sufficiency in rice (cases in rural households in Indonesia) was carried out using the Structural Equation Modeling - Partial Least Square (SEM-PLS) analysis method. SEM-PLS analysis is carried out through 2 stages, the evaluation of the measurement model (outer model) and the evaluation of the structural model (iner model)

The Evaluation of The Measurement Model (Outer Model) The first stage in the analysis using SEM-PLS is to evaluate the measurement model (inner model). In this first stage consists of 2 stages of analysis, namely convergent validity, and discriminant validity consisting of cross loading, composite reliability and Average variance extracted (AVE) values. Bellow are the result of the outer model analysis. Convergent Validity Convergent validity analysis is used as an evaluation of the measurement model (outer model). In this analysis the test is carried out by measuring the correlation between the indicator score with the construct score. The results of this analysis can be measured from the loading factor (λ) value of each indicator. Indicators are said to be valid or have a correlation with the construct if it has a loading factor value (λ) ≥ 0.5 (Chin, 1998). Picture 1 bellow is the path diagram output image (path diagram) in the analysis that has been done.

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Picture 1 : Measurement Model PLS Algorithm

Picture 1 above shows that the loading factor of each indicator is ≥ 0.5. Thus the overall indicators in this analysis are declared valid or have a correlation with each construct. Furthermore, aside from looking at the loading factor values, to find out the significance of the correlation of indicators with their constructs can be seen from the t-statistics of each indicator. The results of the t-statistic indicator analysis are declared valid or significant if they have a t-statistic value ≥ t-table value (1.96). Table 1 bellow is the loading factor analysis results, and t-statistics.

Table 3. Loading Factor, and T-statistic Indicators Model Construct Indicators / Measurement Items Loading T-statistic Result Factor (≥1.96) (≥0.5) Human Assets (X1) Education (X1.1) 0.676 2.305 Valid Knowlegde (X1.2) 0.809 3.091 Valid Natural Assets (X2) Land Tenure (X2.1) 0.810 14.726 Valid Land Size (X2.2) 0.869 28.932 Valid Financial Assets (X3) Revenue (X3.1) 0.962 66.891 Valid Outcome (X3.2) 0.827 10.396 Valid Physical Assets (X4) Facilities and Infrastructure (X4.1) 0.699 6.964 Valid Production Equipment (X4.2) 0.875 28.062 Valid Accessibility (X4.3) 0.760 9.238 Valid Social Assets (X5) Organizational Participation (X5.1) 0.770 4.832 Valid Mutual Cooperation (X5.2) 0.807 5.949 Valid Production Aspects (Y1) Production (Y1.1) 0.922 64.041 Valid Productivity (Y1.2) 0.866 23.459 Valid Market Aspects (Y2) Price (Y2.1) 0.829 13.516 Valid Ease of Selling (Y2) 0.919 31.512 Valid

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Source : Primary Output Analysis (2019)

Based on the table above shows that each indicator each construct has a t-statistic value greater than t-table (1.96). Thus all indicators have a significant correlation with their extract. Discriminant Validity Discriminant Validity is the next step to measure the measurement model (outer model). The discriminant validity test can be seen from the cross loading value between the indicator and its construct. In this analysis it is said to be valid if the correlation between the construct and the indicator is greater than the contract correlation with the other indicators, in other words the construct can be said to predict the indicator better than the other construct. Table 2 bellow is the cross loading values in this analysis. Table 4. Discriminat Validity Test (Cross Loading) X1 X2 X3 X4 X5 Y1 Y2 X1.1 0.676 0.254 0.296 0.095 0.006 0.237 0.157 X1.2 0.809 -0.084 0.032 0.127 0.472 0.046 0.327 X2.1 0.200 0.810 0.426 0.492 0.142 0.506 0.326 X2.2 -0.032 0.869 0.193 0.317 -0.132 0.696 0.022 X3.1 0.216 0.423 0.962 0.043 0.065 0.519 0.175 X3.2 0.115 0.128 0.827 -0.060 -0.083 0.225 0.153 X4.1 0.125 0.280 0.179 0.699 0.169 0.254 0.231 X4.2 0.174 0.446 0.022 0.875 0.369 0.451 0.356 X4.3 0.026 0.354 -0.180 0.760 0.167 0.306 0.178 X5.1 0.283 0.045 -0.000 0.384 0.770 0.082 0.287 X5.2 0.275 -0.054 0.027 0.139 0.807 0.160 0.279 Y1.1 0.200 0.671 0.607 0.384 0.138 0.922 0.236 Y1.2 0.102 0.621 0.164 0.428 0.140 0.866 0.184 Y2.1 0.259 0.136 0.201 0.271 0.158 0.108 0.829 Y2.2 0.321 0.192 0.133 0.325 0.428 0.281 0.919 Source : Primary Output Analysis (2019) Based on the Table 2 above shows that the correlation value of each indicator with the construct has a greater value than the other constructs. Thus, in this analysis to test descriminant validity can be declared valid as a whole. Another method that can be used to assess discriminant validity is to look at the average variance extracted (AVE) and composite reliability values. Table 3 bellow is the results: Table 5. Composite Reability and Average Variance Extracted (AVE) Composite Average Variance Model Construct Result Reability Extracted (AVE) Human Assets (X1 0.713 0.556 Reliabel and Valid Natural Assets (X2) 0.827 0.706 Reliabel and Valid Financial Assets (X3) 0.891 0.805 Reliabel and Valid Physical Assets (X4) 0.823 0.611 Reliabel and Valid Social Assets (X5) 0.767 0.622 Reliabel and Valid Production Aspects (Y1) 0.889 0.800 Reliabel and Valid Market Aspects (Y2) 0.867 0.766 Reliabel and Valid Source : Primary Output Analysis 2019 According to Sarwono and Narimawati (2015) decriminant validity can be declared valid if it has a AVE value greater than 0.5. Based on the Table 3 above shows that the entire construct has a AVE value greater than 0.5. Thus it can be said that the entire construct in this study has reached the requirement of descriminant validity of the 9491

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AVE value. Furthermore, the composite reliability test in descriminant validity is used to assess or evaluate a measurement model (outer model). According to Sarwono and Narimawati (2015) constructs have good reliability if the composite reliability value is greater than 0.7. From the Table 3 above it can be seen that the entire construct has a composite reability value greater than 0.7 so that it can be said that the entire construct has been reliable. The Evaluation of The Structural Model (Iner Model) Evaluation of the structural model (inner model) is the last stage in the analysis using the SEM-PLS method. At this stage of analysis consists of several analyzes : the R- Squared test, and the T-Statistics test. Table 4 bellow is the result of inner model analysis: Table 6. Coefisient Parameter, T-Statistic, and R Square (R2) Coefisient T-Statistic Result R Square Model Construct (R2) Human Assets (X1) ➔ Production Aspects (Y1) 0.016 0.209 Non Sig Natural Assets (X2) ➔ Production Aspects (Y1) 0.564 8.429 Sig Financial Assets (X3) ➔ Production Aspects (Y1) 0.253 3.697 Sig 0.608 Physical Assets (X4) ➔ Production Aspects (Y1) 0.145 2.001 Sig Social Assets (X5) ➔ Production Aspects (Y1) 0.102 1.356 Non Sig Human Assets (X1) ➔ Market Aspects (Y2) 0.198 1.774 Non Sig Natural Assets (X2) ➔ Market Aspects (Y2) 0.018 0.147 Non Sig Financial Assets (X3) ➔ Market Aspects (Y2) 0.131 1.271 Non Sig 0.250 Physical Assets (X4) ➔ Market Aspects (Y2) 0.235 2.071 Sig Social Assets (X5) ➔ Market Aspects (Y2) 0.210 1.919 Non Sig Source : Primary Output Analysis (2019)

R-Squared (R2) R-Squared (R2) is done to see the level of goodness of fit of a structural model. The value of this assessment is used to see how much influence the independent latent variable has on the dependent latent variable. In this study to see how much influence the livelihood livelihood assets have on production and market aspects for rice self- sufficiency. Based on the Table 4 above shows that the results of the R-Squared (R2) for the influence of the suistainable livelihood assets on production amounted to 0.608. This shows that the sustainable livelihood aspect influences the production aspect by 60.8 percent, while the rest is influenced by other variables outside the research model. The results of the R-Square (R2) for the influence of the suistainable livelihood aspect of the market aspect are 0.250. This shows that the asset liable livelihood affects the market aspect by 25 percent and the rest is influenced by other variables outside the model.

T-Statistic T-statistic test or significance test is used to determine the effect of independent variables on the dependent variable. As for the SEM-PLS analysis, the independent variable in question is the exogenous latent variable and the dependent variable is the endogenous latent variable. Estimated values for path relationships in the structural model SEM-PLS analysis are used to determine the level of significance of the relationships between latent variables. The value of the T-Statistics can be obtained through the bootstrapping stage. The level of significance can be seen by comparing the value of T-Statistics with the T-Table. If the T-statistic value is greater than the T-

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table value (1.96), it can be concluded that the exogenous latent variable has a significant effect. Based on the Table 4 above shows that the suistainable livelihood assets that significantly positive influence the aspects of production are natural assets (X2) with a t-statistic value of 8,429, financial assets (X3) with a t-statistic value of 3,697 and physical assets (X4) with a t value -statistics of 2,001. This finding thats conforms with the findings of Lun Yang et., al, (2018) are the natural assets and financial assets. Besides the physical assets are contravenes with the findings of Lun Yang et., al, (2018). As for the suistainable livelihood assets that significantly positive influence the market aspect is physical assets (X4) with a t-statistic value of 2,071.

CONCLUSION AND SUGGESTION The empirical data of this study that has been analyzed using SEM-PLS revealed that the production aspect is influenced by natural assets, financial assets and physical assets. Beside the market aspect is influenced by only physical assets. So from the ten (10) hypotheses formulated, four (4) found to be correlated : three (3) correlated with the production aspects and one (1) correlated with the market aspects. While six (6) hypotheses were not supported depicting that they do not affect or have impact on production aspects two (2) hypotheses that are human assets and social assets, and do not affect or have impact on market aspects four (4) hypotheses that are human assets, natural assets, financial assets, and social assets. From the results of the study above recommendations for the government to improve the sustainability of livelihood aspects to achieve an increase in paddy self-sufficiency is to consider assets that have a significant positive impact. As for other assets that have insignificant impacts, they also need to be reviewed so that they can have a significant positive impact. For the next study, the recomendation to be able to increase the scope of the area and also the indicators used in the analysis. So that it can provide results that are able to broadly reflect the conditions.

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