Purpose: This study aimed to analyze the effect of Inflation on food prices in Palopo City, where the inflation rate is calculated based on index numbers collected from several commodities traded at each price level in the market, namely the Consumer Price Index (CPI). Theoretical framework: The Vector Autoregressive (VAR) method is simultaneous equation modeling that has several endogenous variables simultaneously, but each endogenous variable is explained by lags of its own value and other endogenous variables in the model. Forecasting using the VAR method is in some cases better than complex simultaneous equations. Design/methodology/approach: The stationarity test in this study uses the Augmented Dickey-Fuller (ADF) method, followed by determining lag optimal Akaike Information Criteria (AIC) and Schwarz information Criterion (SC) analysis. The stability test of the VAR model was carried out through the Johansen Cointegration Test at a 5% significance level which produced six equations with a trace statistically more significant than the critical value. Then an analysis of Impulse Response Functions (IRF) was carried out to see the dynamic response of the CPI variable to food commodity prices. The data used are time series for January 2018 to December 2020. Findings: The inflation response to food price fluctuations in Palopo City resulted in three conditions. The Palopo City CPI responded positively to the price fluctuations of garlic and red chili. In contrast, shallots and chicken meat price fluctuations responded negatively to the Palopo City CPI. The Palopo City CPI did not permanently affect the price fluctuations of cayenne pepper and chicken eggs. Research, Practical & Social implications: The practical implication of this research is that food commodity prices still have an influence on inflation movements. Local governments should cooperate with city governments in the production of food commodities by implementing cropping patterns on these commodities to overcome excess production, and it is necessary to carry out off-season production to achieve harvest deficits so that prices remain stable. Improvements to the trade system also need to be improved in addition to the production system. This is intended so that the distribution of food commodity products can be more efficient by implementing supply chain management. Originality/value: Collecting time series data of the Consumer Price Index (CPI) for 6 commodities, namely shallots, garlic, chili peppers, cayenne pepper, broiler meat, and broiler eggs, in a modern model, namely a multivariate time series model with a high level of predictive accuracy of the model among these variables to predict and analyze the effects among these series and estimate the interactions among these series with the latest data is the value of this research, and then provide recommendations for the government in making economic strategies, especially for those related to inflation and food prices.

This study aimed to examine the effects of the input factor allocation on tangerine production in Selayar Islands Regency, South Sulawesi Province. Binary logistic regression was the data analysis method utilized to achieve the research objective. Based on the findings, it was found that the factors of length of education, farmer group membership, farm distance, labor, pesticides, herbicides, urea fertilizer, manure, and NPK fertilizer all had a significant impact on the output of tangerines in farming. Factors such as education duration, participation in farmer groups, labor availability, and the use of herbicides, urea fertilizer, NPK fertilizer, and manure all positively and significantly impact tangerine farm production. However, the distance between farms and insecticide application imposes a substantial negative effect on tangerine yields. These results suggest that the tangerine farmers at the research site should increase their production by learning farming management, joining farmer groups, cultivating additional land, and using more urea, NPK, and manure. Pesticide application optimization can also boost tangerine production. In addition, farmers need a mix of conventional and organic fertilizers and limited pesticide use to stay productive.

Currently, cut rose centers in Batu City face many problems that threaten the sustainability of their businesses. Research that combines various dimensions to identify key variables affecting the sustainability of cut rose farming remains limited and is needed. Hence, this study aimed to map and identify the key variables influencing the sustainability of cut rose farming in Batu City, East Java, Indonesia. Primary data were collected through structured interviews with 155 cut rose farmers, selected using random sampling. The employed data analysis technique was the Matrix of Crossed Impact Multiplications Applied to a Classification (MICMAC). The results showed that the key variables influencing the sustainability of the cut flower rose farming business in Batu City are pest and plant disease attacks (PPDA), the use of chemical pesticides (UCP), market objectives (MO), the market chain (MC), and the source of capital (SC). In addition, this study identified key and representative variables from four dimensions based on the Social-Ecological-Technological & Innovation Systems (SETIS) framework, namely extension intensity (social), educational level (social), market objectives (economic), market chain (economic), capital sources (economic), pest and plant disease attack (ecological), use of chemical pesticides (ecological), and cultivation technology (technology and innovation). The study’s recommendations are: (1) increasing the extension intensity and educational level (social dimension); (2) expanding market objectives, shortening market chains, and managing capital appropriately (economic dimension); (3) implementing integrated pest and disease management and limiting the use of chemical pesticides (ecological dimension); and (4) increasing the use of cultivation technologies (technology and innovation dimension).