In order to achieve maize self-sufficiency in Indonesia, efficiency ought to be increased. Therefore, this study is aimed to assess technical efficiency of wide range of farmers. It is done in order to formulate a policy to increase the productivity of maize. The study was conducted in regions that produce maize. Sample farmers were determined to be nonproportionate stratified random sampling by land class strata, namely wetland and dry land. Analysis methods utilized in this research are Data Envelopment Analysis (DEA) and Analysis Frontier Stochastic (SFA). As for assessment of factors affecting technical efficiency analysis, Tobit regression models is utilized. The results of this research exhibits that the technical efficiency (TE) utilizing SFA approach gained an average of 0.78. On the other hand, the DEA approach obtained an average of 0.9. SFA approach exhibits lower TE value and evenly distributed among farmers compared to DEA approach, where no value is less than 0.6. Factors affecting TE are education, land ownership, extension frequency, and demonstration plot. Based on DEA approach also obtained the fact that most farmers are on the conditions of increasing returns to scale.

By 2045, Indonesia's population is expected to reach 321.4 million, the fifth largest in the world after China, India, Nigeria, and the United States. It is an excellent challenge for Indonesia to provide food in the future as it keeps pace with the rapid population growth. This study aims to analyze forecasting the basic conditions of Indonesia’s rice economy 2019-2045. The research data use time-series data from 1961-2018, including data from the Central Bureau of Statistics (BPS), Ministry of Agriculture/Pusdatin, Food and Agriculture Organization (FAO), International Rice Research (IRR), Department of Commerce, United States Department of Agriculture (USDA), and ASEAN Food Safety Information System (AFSIS). Data analysis using the simultaneous equations model approach. The results show that in 2019-2045 the projection of rice productivity in 2025 is 64,465 quintals per hectare; in 2035, it is 68,797 quintals per hectare, and in 2045 it is 77,462 quintals per hectare. In 2045, the projected land area is 27.64 million hectares. Although Indonesia is forecast to experience a rice surplus of 37.80 million tonnes in 2045, the projected rice production and domestic rice consumption level indicate the potential for rice imports of 15 million tonnes.

In running farming system, farmers not only have a role important as owners who land they farm. But also they are as farm managers to make any decisions to face their farming problems under risk and uncertainty. These problems are categorized as internal and external factors related to price fluctuations of agricultural commodities. For that reason, farmers need to consider some strategies to overcome their farming problem for instance by choosing the best commodities that would give them an optimal profit. The maximax, maximin, savage, and laplace criterions were used to analyze decision making of horticulture farmer in determining which best horticultural commodities to plant according to their behaviour and attitude toward farming risk. Thus, horticulture farmer will be able to make a choice whether or not it is potato, cabbage, or, scallion that will be cultivated in the next planting period. Potato farmers are categorized as the optimistic farmer who loves farming risks and they are cautious. While scallion farmers are pessimistic farmers and they are risk averse. In addition, cabbage farmers are the ones who have the least regret.

The area of study is Tanantovea; a region located in Donggala Regency, Indonesia, towards 97 farmers participated in Good Agriculture Program (GAP) for local shallot agriculture that has met the Standard Operating Procedure (SOP) and 62 farmers who do not participate in the program. The purpose of the study is analyze the implementation of GAP towards production input between the farmers who participate in GAP and those who do not, and analyze partial productivity and Total Factor of Productivity (TFP) between the farmers who participate in GAP and those who do not. The t-test analyzes the production input shows that it is significant when α 1% two-tail test with probability rate of 0.000< 0.01; these indicate that there is different implementation of GAP towards production input between the farmers who participate in GAP and those who do not. The implementation of production input by GAP participants is higher than those the non-participants in that the participants are able to produce is 81.78% of seeds, 66.67% of organic fertilizer, 79.46% of inorganic fertilizer and 77.76% of pesticide. The amount of labor force by the GAP participants is 89.70 HOK or lower than the amount of labor force by non-participants that is 107.56 HOK. The t-test analyzing partial productivity shows that it is significant when α 10% in two-tailed test with probability rate of 0.052 < 0.10. These indicate there is different partial productivity between the farmers who participate in GAP and those who do not. The partial productivity of GAP participants is 6,289.25 kilograms per hectare and that of non-participants is 6,019.35 kilograms per hectare. For TFP, probability rate is 0.089 < 0.10 and the level of significance is 90% in two-tailed testing. These show significant difference in terms of TFP between the farmers who participate in GAP and those who do not. The average TFP of the GAP participants is 1.0076 kilogram per hectare and that of non-participant is 0.9622 kilograms per hectare.

This study aims to analyze the factors that influence sugarcane production in Malang Regency. This reaserach uses stochastic frontier production function. The model is specified which sugarcane production is influenced by amount of seed, the labor used, the amount of fertilizer, land used in sugar cane production, the frequency of ratooncane. The results showed that factors of sugarcane farming production in Malang Regency are influenced significantly by the number of seed, amount of fertilizer used, and ratooncane frequency. The seed and amount of fertilizer used in sugarcane production have positive effect in production; however, ratooncane frequency has negative effect in production of sugarcane significantly. On the other hand, land area and labor are not statistically significant at alpha 1%.