Determinants of Agricultural Income to Promote Economic Sustainability in Indonesia

Indonesia is an agricultural country where most of the population works as farmers. The share of the agricultural sector in Gross Domestic Product (GDP) ranks third, with an average informal sector employment of 88.34% in the last five years. This study aims to analyze the factors that influence the contribution of the agriculture sector to economic sustainability growth, proxied by national income, in the short and long run through the Error Correction Model (ECM) regression analysis tool with time series data of Indonesia for the 1991 – 2020 period through four independent variables; arable land, agricultural machinery, GDP growth per capita, and labor productivity of the agricultural sector. The results showed that Arable Land has a positive effect on Agricultural Income, significant in the long run but insignificant in the short run. Agricultural Machinery and Labor Productivity have a significant positive effect both in the short and long run. Then, the GDP per capita growth has a significant negative effect in the short and long run. From the results of the research, several policy recommendations have been compiled for the advancement of the agricultural sector in Indonesia, one of them is the intensification of machinery utilization.


Introduction
The agricultural sector is one of the leading sectors in Indonesia.Agriculture has a strategic role as an economic driver [1].It showed by the share of agricultural income in the Gross Domestic Product (GDP).Therefore, the government encourages the agricultural sector to strengthen the economy.In addition, the role of agriculture in creating jobs, national food security, social protection, and increasing the income of farming families continues to be encouraged by the government.Meanwhile, the share of the agricultural sector in increasing national income in Indonesia is very high when compared to others.Agriculture is in the third position after the manufacturing and trade sectors.Nonetheless, efforts to consistently maintain the resilience of the contribution of agriculture must continue to be improved, bearing in mind that we are currently entering an era of disruption that is more focused on industrialization, beside the area of agricultural land is also shrinking [2], [3].
In the last five years, the agricultural share of GDP tended to be stable.The existence of the Covid-19 pandemic made this sector even stronger than before.It is shown by the share of GDP that reached 12.85% in 2021, the highest throughout 2017 -2021.There are many influencing factors, for example, the occurrence of ruralization due to the termination of employment so that many people return to the village and ultimately increase the number of workers in the agricultural sector [4].The existence of social distancing also makes people participate in carrying out activities in agriculture, including urban farming.It proves that agriculture can withstand economic shocks and deserves to be continuously developed in line with technological advances, considering that agriculture is one of the sectors that can use as a medium for optimizing natural resources with the minimum negative externality impacts.
Next, one of the roles of the agricultural sector is as a provider of employment.As a developing country, Indonesia still depends on agriculture, especially in providing employment [5].Most people in Indonesia work as farmers, and the average percentage absorption of informal workers in agriculture reaches 88.34% each year.The agricultural sector is one of the objective sectors of the 2030 Sustainable Development Goals (SDGs).Agriculture is also mentioned in the formulation of strategies to achieve SDGs, especially in the first point regarding poverty alleviation, and the second point regarding hunger eradication.The desired of this strategy is expected to increase productivity in the agricultural sector, especially through sustainable production techniques.Apart from contributing to GDP in aggregate and playing a major role in absorbing informal workers, agriculture also has many other strategic roles, starting from supporting the food needs of the population, generating foreign exchange through the export of several commodities, providing raw materials for the processing industry, increasing business diversification opportunities, increasing regional income, poverty alleviation, and driving the movement of other economic sectors [6], [7].
Looking at the urgency of the agricultural sector as has been described, it is necessary to identify the factors that influence the agricultural share of GDP.The policy recommendations are formulated to improve existing policies and trigger new policies in agriculture.In compiling policy recommendations, consideration is needed on several aspects, starting from the availability and condition of the land, the use of technology, then labor productivity in the agricultural sector itself, referring to these matters, to be used as parameters for the contribution of the agricultural sector to GDP, the authors choose independent variables in the form of availability of arable land, the proportion of tractor use, growth in per capita income, and labor productivity in the agricultural sector.This research is expected to help complement the results of similar previous studies and serve as a reference for subsequent research on the same topic.In addition, practically, the findings from this study are expected to be useful as a reference for preparing action plans for stakeholders for the advancement of the agricultural sector in Indonesia.
Previous studies become the basis of reference for the author to determine the arrangement of variables along with the design of the research.If previous studies carried out the analysis of influence and contribution factors through a descriptive, regressive, and structural point of view, the novelty that the author will bring is to try to identify and formulate a strategy from the research results.This research also establishes a new variable, namely GDP per capita growth as a measure of welfare.This research does not only stop at the analysis of the influence coefficient of each variable but also on problemsolving as outlined in the form of discussions and policy recommendations in the agricultural sector.Retrieval of secondary data within a period of 30 years also allows the identification results obtained to be more accurate.The Error Correction Model (ECM) analysis tool also makes it possible to see the impact in the long and short run.With these novelties, the research results are expected to be useful not only theoretically but also practically for stakeholders involved in the agricultural sector in Indonesia.This paper will be continued with Part II regarding Methods, Section III Results, and Discussion, and closed with Section IV in the form of a Conclusion.

Methods
This research was conducted using a quantitative approach of secondary data from Indonesia for the 1991 -2020 period.The Eagle Granger Error Correction Model (ECM) is the analytical tool used.The selection of ECM is based on achieving the research objective, which is to see the short-run and long-run effects of each variable.This study uses two types of variables, namely independent and dependent variables.The dependent variable set is the Contribution of the Agricultural Sector to GDP, for the independent variables consist of 4 units, namely Arable Land (LS), Use of Agricultural Machinery (PMP), GDP per capita growth (PGK), and Labor Productivity of Agricultural Sector (PTKSP).
The data processing followed the rules of the Eagle-Granger ECM, starting with stationarity testing in 2 stages.The conditions that must be met are that the data cannot be stationary at the level (I(0)) but all must be stationary at the level of the first difference.The decision on stationary criteria is based on the value of the probability, which is less than α (0.05).In this study, the approach used to test stationarity is the Augmented Dickey-Fuller (ADF) unit root test.The empirical model used in this study is derived from the grand theory of the Cobb-Douglass production model which has gone through the process of changing the linear form into a logarithmic equation.The logarithmic process is carried out to equalize the units of each variable in the form of percent (%).The long-run model is: The short-run model: Because in this study several variables already have percent units, in the process of running the data in the E-Views 9 software not all of them use logarithmic notation.The direction of influence in the model is adjusted to the set hypothesis.Research using the ECM analysis tool is described in two models, namely the long-run and short-run models.

Results and Discussion
From the test results using the Augmented-Dickey Fuller (ADF) approach, the data used for all nonstationary variables at the Level and Stationary levels at the 1st Difference level, is indicated by a probability value of less than α (0.05%).Source: data processed, 2022.The cointegration test of the ECT variable on the unit root shows that the probability is less than α (0.05%), and all t statistics are negative.This shows that the error correction is valid (according to the Eagle Granger ECM procedure) and all the variables used are cointegrated or have a long-run relationship.

Long Term ECM Regression Estimation Results
After being proven to meet the requirements of the stationarity and cointegration tests, the next step is the long-run model regression.The regression results show that all variables have a significant probability of influence.

Short Term ECM Regression Estimation Results
The ECM regression analysis tool is not only used to see long-run influences.But also shortrun.From the short-run regression, it turns out that any variable that has insignificant influence probability, namely Arable Land.Meanwhile, other variables still have a significant effect.
(1) Arable Land Variable Arable Land in the long run has a significant positive effect on the percentage contribution of the agricultural sector to GDP.The coefficient value is 10.0693, meaning that when arable land increases by 1%, the contribution of the agricultural sector to GDP will increase by 10.0693%.Meanwhile, in the short run, this variable has an insignificant effect.This is because arable land requires a series of other processes so that it can contribute optimally to the productivity of the agricultural sector, including irrigation systems, measurements, rotation of crop varieties, and others.Although not significant, the coefficient of influence is still positive.The results of this study are supporting previous research from [8]- [11] where land has a positive effect on the total production of the agricultural sector.
(2) Agricultural Machinery Use Next, the Agricultural Machinery variable also has a significant positive effect.With a coefficient of 0.4857, it means that the contribution of the agricultural sector to GDP will increase by 0.4857% when the use of agricultural machinery increases by 1%.In the short run, Agricultural Machinery also has a significant positive effect, the coefficient is 0.4366.When the use of agricultural machinery increases by 1%, the contribution of the agricultural sector to GDP will also increase by 0.4366%.The results of this study are following previous studies from [12]- [15].

(3) GDP per Capita Growth
The third variable is GDP per capita growth, the effect on the percentage of the agricultural sector is significantly negative.When GDP per capita grows by 1%, then the contribution of the agricultural sector to GDP will decrease by 0.1276%.In the short run, this variable also has a significant negative effect.The coefficient of -0.0655 indicates that the contribution of the agricultural sector to GDP will decrease by 0.0655% when income per capita grows by 1%.

(4) Labor Productivity of the Agricultural Sector
The last independent variable is the Labor Productivity of the Agricultural Sector.This variable has a significant positive effect on the agricultural sector's contribution to GDP with a coefficient of 0.2617.This can be interpreted that, when the Agricultural Labor Productivity increases by 1%, then the contribution of the Agricultural Sector to GDP will also increase by 0.2617%.This variable in the short run proved to have a significant positive effect.When labor productivity increases by 1%, the contribution of the agricultural sector to GDP will increase by 0.4467%, this is indicated by the large statistical coefficient.These results are following previous research from [16]- [20].(5) Error Correction Term (ECT) Apart from being a cointegration sign, the ECT variable can also be used to see the duration of the influence condition of the dependent variable by the current period on the next period.The results of the study show that all variables this year have an effect on the condition of the dependent variable for 0.6603 years into the future.This is indicated by the existence of the ECT(-1) variable which is proven to be cointegrated with a significant probability at a coefficient of -0.6603.This ECT also can be interpreted that the speed of adjustment of the short-run model to reach long-run equilibrium by 66,03% per month.
To ensure that the model formed is BLUE (Best, Linear, Unbiased Estimator), a classical assumption test is performed.The results show that the residual data is proven to be normally distributed, free from multicollinearity problems, and there is no data autocorrelation problem between periods of study.From these results, it can be ascertained that the models formed, both long-run and short-run estimates, are free from problems with classical assumption testing.

Source: Data processed, 2022
Apart from functioning as a provider of food supplies, the agricultural sector also functions as a provider of employment.The issue of agriculture is also contained in point 2 of the Sustainable Development Goals (SDGs), namely no hunger.The aims to be realized are more precisely about increasing the availability and variety of food supply including increasing healthy food choices, as well as maintaining sustainable agriculture and agricultural practices primarily through increased productivity and sustainable production techniques.To achieve this goal, the government has initiated several policies.The Covid-19 pandemic period has proven that the agricultural sector is the sector that is most resistant to economic shocks.Exports from this sector continued to grow positively by 16.2% (year on year) as of September 2020 (Ministry of Finance, 2020).
Policies regarding the agricultural sector are not only issued by the Ministry of Agriculture but also involve other ministries, one of which is the Ministry of Economy.In 2021, the Coordinating Minister for the Economy announced several policies related to the agricultural sector, including: (

1) Policy Related to Export Orientation
Policies regarding agricultural sector exports are realized in several aspects, from simplifying procedures to increasing access to funding.Policies regarding agriculture that have been implemented include UU No. 29 of 2000 concerning Plant Variety Protection, UU No. 22 of 2019 concerning the Sustainable Agricultural Cultivation System, Law UU. 19 of 2013 concerning Protection and Empowerment of Farmers, as well as Law No. 13 of 2010 concerning Horticulture.However, since 2020, all these laws have been summarized in UU No. 11 of 2020 concerning Job Creation.This law contains 4 main things.First, related to simplification, acceleration, certainty in licensing, as well as export or import approval.Second, the digitalization of SMEs.Third, the synergy of State-Owned Enterprise (Badan Usaha Milik Negara/BUMN) for the distribution of agricultural products from production centers to consumer centers through the development of a logistics system.Then, the last one is related to strengthening inter-regional cooperation in fulfilling food.
The meaning from first point of the Job Creation Law is that it includes various facilities, ranging from ease of business permits for certain scale agricultural cultivation, administrative simplification for applications for plant variety protection rights, to easy access to agricultural information systems by the public and business actors.
With this policy, it is hoped that the agricultural sector can grow more rapidly, especially with access to international markets (exports).Ease and simplification of licensing, as well as information disclosure, will encourage stakeholders in this sector to be more efficient in carrying out the process towards exports.
(2) Policies Related to Food Security Orientation Food security policies are not only related to increasing the total production of the agricultural sector but also related to the welfare of farmers.Some of the policies related to welfare are the Agricultural Work-Intensive Program, Productive Presidential Assistance for MSME in the Agricultural Sector, Micro Interest Subsidies or People's Business Credit, and Cooperative Financing Support with a Revolving Fund Scheme.
Next, related to food security, the government has also prepared seven main schemes.First, the development of corporate-based food estates (both in Central Kalimantan and North Sumatra) within the framework of strengthening the national food system.Second, the development of rice business clusters uses a land management approach which was initially segmented into one area.Third, the development of export-oriented horticultural areas with the Creating Shared Value (CSV) partnership model between the Central Government, Regional Governments, the private sector, and farmers.Fourth, Closed Loop inclusive partnerships in horticultural commodities as a form of implementing synergy between academia, business, government, and community (ABGC).
Fifth, the development of 1,000 cow village programs to increase cattle population and productivity.Sixth, the development of the national seaweed industry to optimize domestic production.Then, the seventh is the development of farmer and fisherman corporations with a direction toward an upstream-downstream agribusiness system that prioritizes their empowerment.
(3) Agricultural Sector Performance The impact of the implemented policies can be seen in the performance of the agricultural sector.Indicators that can be seen to see the performance of the agricultural sector include the GDP of the agricultural sector and the development of exports in the agricultural sector.Furthermore, when viewed from export performance, both total exports and FOB values have always grown positively.From these results, this paper gives some recommendations: (1) Increasing Land Availability Agriculture in the current era is not only limited to arable land.However, because most farmers still use conventional methods, increasing the availability of arable land is the main step that must be taken to increase the total production of the agricultural sector.The National Statistics Bureau recorded a decrease in rice harvested area by 20,610 hectares or 0.19 percent, to 10.66 million hectares in 2020 from 10.68 million hectares in 2019.This reduction in land area has the potential to reduce total agricultural production and threaten food security.
The availability of agricultural land is not only related to arable land.Volcanic land, peat land, and dry land can also be used as agricultural land if it is processed using certain techniques, such as in developed countries.This will help increase the number of land availability so that the output of the agricultural sector also has the potential to increase.In addition to increasing, existing arable land also needs to be maintained. (

2) Agricultural Technology Intensification
The most common agricultural technologies used in Indonesia today are tractors and automatic rice harvesting machines or commonly known as Combi.Other technologies from developed countries that can be implemented in Indonesia are Smart Hydroponics, Smart Urban Farming, machines for making and selecting superior seeds, fruit harvesters, automatic planting machines, automatic fertilizing machines, large pest spray machines, and many more.
(3) Balancing the welfare of the population and the green economy Most developed countries do rely on industry and services, but directly, this harms environmental quality and increases the amount of land conversion.Therefore, an increase in per capita income or an increase in the welfare of the population must be balanced with the formulation of a green economy strategy, one of which is the modernization of the agricultural sector.(4) Increasing Labor Productivity in the Agricultural Sector Increasing labor productivity can be achieved by increasing skills and expertise.Technology intensification or the provision of sophisticated technology will be in vain if the workforce as the subject who runs agriculture is unable to use it optimally.

Conclusion
Agriculture is the only sector that continues to grow positively during the Covid-19 pandemic.This sector is included in the three largest sectors contributing to GDP.The crucial role of this sector is as a provider of food needs and employment in the informal sector.The results of research on time series data for Indonesia for 1991 -2020 using the Eagle-Granger ECM analysis tool show that, in the long and short run, the independent variables together have a significant effect on the dependent variable.Partially, arable land, in the long run, has a significant positive effect on the contribution of the agricultural sector to GDP but is insignificant in the short run.Next, agricultural machinery also has a significant positive effect, both in the long and short run.The GDP per capita growth has a significant negative effect in the long and short run.The last independent variable is Labor Productivity.Partially, this variable has a significant positive effect in the long and short run.
Several policies related to the agricultural sector have been implemented, including policies related to exports and food security as embodied in the Job Creation Law.From the research results, policy recommendations can be formulated, starting from increasing land availability, intensifying agricultural technology, balancing population welfare with the green economy, and increasing labor productivity in the agricultural sector.This study has several limitations, including the representation of the capital variable which is based only on land, the technology variable only refers to the use of tractors, and the labor variable which uses replacement data in the form of productivity.The next research is expected to be more innovative so that it can help perfect the results of this research based on the deficiencies that have been described.

Figure 3 .
Figure 3. GDP of Agriculture in Trillion Rupiah Source: National Statistics Bureau, 2022.The performance of the agricultural sector when viewed from the GDP of Business Fields throughout the period of the first quarter of 2021 to the first quarter of 2022 shows an upward trend, although it had fallen in the fourth quarter of 2021.The average agricultural sector GDP during this period reached IDR 266,447.08 billion.

Table 2 .
ADF Stationarity Test Results These results are following the rules or requirements so that they can proceed to the next process, namely the cointegration test.The cointegration test was performed after the Ordinary Least Square (OLS) regression.Residual regression results are formed in a new variable called Error Correction Term (ECT).

Table 6 .
Summary of Classical Assumption Test Results

Table 7 .
Agricultural Sector Export Development