A computer system supporting agricultural machinery and farm tractor purchase decisions

Abstract

Operational research, i.e. searching for optimal solutions in a situation of uncertainty and risk, can also be used to support decisions to purchase expensive agricultural machinery. Although Polish farmers receive subsidies from the EU, it does not mean they do not need to make well-thought-out purchases, because wrong purchase decisions will have long-term consequences while using machinery. The article presents the results of the IFOP – the system which has been available on the Internet for several years. It collects data on farming machinery and vehicles based on users' voluntary but subjective opinions. The authors of this article developed an original multi-criteria method of evaluating the quality of these specific products, which enabled them to make relevant rankings of brands. It is an algorithmic-heuristic method, which uses pairwise comparison tools to determine the significance ratios of the criteria. This article presents the results of the 1 st and 2 nd IFOP edition (Race Ranking), which included several dozen brands of tractors registered in Poland. More than fifty qualitative ( Q ) and non-qualitative ( C ) traits of farm tractors were taken into account. According to Polish farmers, Valtra – a Finnish brand of farm tractors, part of the AGCO concern, was the most versatile ( Q = 4.39 and Q & C = 4.23). These tractors received the best opinions for their functionality, durability, ergonomics and safety.

Do Farm Subsidies Effectively Increase Grain Production? Evidence from Major Grain-Producing Regions of China

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Noise Emission in the Cabs of Modern Farm Tractors

“…need to meet increasingly high quality requirements [5]. This also applies to agricultural machinery and vehicles, which should be highly reliable and durable [6][7][8][9]. Although farm tractors are classified as non-road mobile machinery (NRMM), they must also meet increasingly high standards set by environmentalists.…”

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

“…As the proportion of operation tracks in a plot increases, the benefit of the agricultural machinery operation will also increase. Researchers focus on agricultural machinery scheduling and multi-machine collaborative operations [11][12][13][14][15] in order to maximize the optimization of agricultural machinery task allocation, bolster mutual cooperation and improve the utilization of agricultural machinery. The task of intelligent agricultural machinery operation path optimization has effectively improved the benefits generated by farmland operations and reduced the costs of agricultural machinery operations, both of which are particularly important in the case of large-scale cooperatives, which have many plots and wide service scopes.…”