## 5 Discussion  
Increasing food prices at the global level is also in line with rice prices in the Indonesian market, which have shown an increasing trend in the last 10 years and a tendency to decline with difficulty. The same threat could be faced in other countries in the world [1], because it is relatively tricky to control price reduction. The world population continues to increase, as noted in [3], which tends to be the same condition in Indonesia, and its impact means an increasing need for access to food. The fact that the population continues to increase will undoubtedly lead to an increase in food needs, and coupled with the number of poor people, price shocks can be a threat to environmental sustainability. Extreme volatility  
Figure 5: The relationship between seasonal factors and the level of price volatility at provincial level. Figure 6: Time series of rice price volatility with climate indices. of rice prices has been seen in several periods in the Indonesian market. It also causes difficulties in accessing food by the public, as described in [5]. Although the results obtained show that global market conditions do not influence Indonesia's rice market, this is different from research that tends to show better food security [10]. It is feared that global climate change will increase the threat to food availability [22]. The carelessly designed climate mitigation policies can increase the number of people at risk of hunger [2]; a volatility model that performs better with climate and macroeconomic variables is expected to reduce this gap with more relevant information. In line with [38], the result supports the global goal of food production efforts to be carried out simultaneously with climate change mitigation. The effects of the five climate indices used in this paper show that climate has a strong influence on rice market conditions. These conditions are directly caused by decreased production and many other things that need to be studied in depth in the future. It is successfully confirmed that rice price volatility shows a similar pattern or a time difference, but with a strong correlation. Threats arising from changes in the global environment are in line with findings showing that the readiness of the market system, especially for people with low income, is not the object with the most loss [27]. A government that can control price volatility needs to prepare the best scenario to reduce social impacts. A model used to measure the volatility of rice prices can optimally present outputs at the provincial level. The unit root test [52] with ADF is still a practical test to measure the magnitude of the stationary variables. The choice of a time series model with ARIMA is known to be dominant in the research area. As obtained from previous research [51], rice at the provincial level in Indonesia has also been measured well. Measuring the ARCH effect with ARCH-LM can measure the heteroscedasticity effect in order to determine the six provinces for which the GARCH model must be used. The best model besides GARCH is EGARCH, which can respond to lagged errors in asymmetry so that the residue for rice price volatility can be measured as a whole. Further integration of the different models is needed to better explain multi-level and cross-scale feedback in food systems [64]. Cumulatively, the picture of national rice price volatility can be seen by combining all data at the provincial level. The climate data approach used to identify the value of price volatility shows relatively strong interactions. These price shocks appear in line with the time series of climate anomalies. A strong El Nino effect, as measured by the ONI and Nino 3.4, indicates strong price shocks in the Indonesian region from late 2009 to the early La Nina conditions that followed. Volatility returned to a slope but then La Nina strengthened in late 2010 to early 2011, causing the volatility to bounce back.