<![CDATA[To assess the success of development in agriculture can be done by looking at the level of welfare of farmers, which can be measured from the Farmer Exchange Rate (NTP). NTP is the ratio between the price index value received by farmers and the price index value paid by farmers. NTP is suspected to have a relationship with various factors, where to determine the influence of these factors can be done through modeling both as response variables and predictor variables. NTP values in several sectors can be observed from an object of research in a certain period of time. Therefore, panel data regression can be used in modeling the relationship between NTP and the factors that influence it. The purpose of this research is to analyze the factors that are thought to influence the Food Crop Farmer Exchange Rate (NTTP) by using panel data regression. The factors referred to are land area, harvested area, production, productivity, GRDP of the agricultural sector, inflation, and the consumer price index.. The data used comes from the five largest rice-producing provinces in Indonesia according to data from the Ministry of Agriculture in 2020. This research data is sourced from the website of the Badan Pusat Statistik (BPS) and the Indonesian Ministry of Agriculture for the 2008-2017 time period. The independent variabels in the study were land area, harvested area, production, productivity, agricultural sector GDP, inflation, and the consumer price index, while the dependent variabel was NTTP. The results of the regression analysis, it can be concluded that the Common Effect Model is the best model of the NTTP panel regression in 5 provinces of Indonesia with an R-Squared value of 53.25% and an error value of 7.55% accuracy of the estimation results using MAPE. This shows that the factors that are thought to affect NTTP such as Productivity, Inflation, and CPI have a significant influence, while the variabels of Land Area, Harvest Area, Production, GRDP of the Agricultural Sector are not significant in the regression model and the rest is influenced by other factors. outside of this research. The value of the MAPE accuracy error rate shows a percentage below 10% which means the forecast value is very accurate.]]>
