**5. Conclusions**

As efficient logistics emerges as a prerequisite for agricultural enterprises competing in today's market, understanding the cost-effectiveness of transportation has been drawing interest. In terms of reducing logistics costs, increasing the performance of crop producers, and improving access for agricultural outputs, the development of transport networks and improvement of transportation activities along food supply chains are essential.

This paper relied on a case study of twelve large agricultural enterprises in Russia as a basis to derive an evaluation framework for assessing the effectiveness of their logistics operations in the conditions of fragmented grain storage infrastructure. The authors extended the standard EOQ approach to measuring the annual fuel consumption of a farm and presented a way to optimize fuel costs based on the differentiation between loaded and empty trips, distances of transportation, and modes of transport use. After these differences were considered, the approach involved revealing the deviation of the actual volume of fuel reserve maintained by a farm from the optimized one.

Depending on the location of an agricultural enterprise in relation to grain storage facilities, two possible tactics to optimize fuel costs were identified. First, if there is an elevator of sufficient capacity within the distance of 0–60 km from a farm, that farm should use their own fleet of vehicles. In the cases of Group 2 farms, higher deviations of *Vs* from optimized *Vt* were observed compared to Group 1. It recorded a lower efficiency of outsourcing in shorter distances. Second, if a farm dispatches grain to an elevator located out of the 0–60 km area, it is reasonable to outsource transportation operations to a specialized agent. In Group 4 enterprises, *Vs* values were very close to optimized levels of fuel costs, while Group 3 farms experienced higher fuel expenditures when using their own fleet of vehicles for longer distances.

In this paper, the authors attempted to demonstrate a comparison-based pathway using dynamic optimization tools that an agricultural enterprise can use to reduce logistics costs by optimizing fuel expenditures and utilizing the fleet of vehicles properly, depending on the location of a farm in relation to the existing storage and transport infrastructure. The study, however, has certain limitations since various cost factors were not considered in their interrelationship. Particularly, for those farms which employ own fleet of vehicles, a combination of fuel costs management with proper scheduling and routing optimization may make it posssible to reduce logistics costs more effectively. For the farms which tend to outsource transport operations, a more thorough study of the relationship between transport infrastructure and services development, from one side, and transport costs, from the other, is required. Ideally, transport services' development leads to an increase in transport volume and decrease in fares, but this occurs only when there is a competition among transport providers. In practice, in many countries, governments subsidize fuel expenditures at least partly, as well as practice various regulations in transportation services and thus distort the competitive environment. This, in turn, increases indirect expenses, keeps transport prices high, and does not stimulate the parties to optimize fuel consumption. Also, this study was site-specific, and its findings mainly reflect the situation of the transportation of crops by large agricultural enterprises in Russia. In other territories, the studies may require adjustments for some of the parameters, particularly *FCRl* and *FCRe*, as well as the average distance of transportation from a farm to the receival site. Data collection requires special arrangements since most of the data used in the transformed EOQ model are not readily available from secondary sources, but require on-site collection. Typically, large agroholdings are reluctant to share

their logistics data with outsiders, therefore, support from local administration or trade associations may be needed.

The key recommendation that can be drawn from the analysis and discussion presented in this paper is that in agriculture, the management of logistics costs should consider combining the operation of trucks by a farm with the outsourcing of transportation operations to address the fragmentation of transport and storage infrastructure. Dynamic optimization of fuel costs is crucial in fostering the performance of agricultural producers and helping them to overcome the infrastructural constraints they face.

**Author Contributions:** T.G. designed the research framework; A.A. performed the data collection and analyzed the data; V.E. assembled and wrote the paper.

**Funding:** This research and the APC were funded by the Fundamental Research Funds for the Central Universities, China, grant numbers HEUCFJ170901, HEUCFP201829, HEUCFW170905, 3072019CFG0901, 3072019CFP0902.

**Conflicts of Interest:** The authors declare no conflict of interest.
