Farm Machinery Selection Research Articles

Evaluation of Current Farm Machinery Selection Practices of Wonji Shoa Sugar Factory

The Government of Ethiopia is trying to improve sugar industry sector by establishing new factories, expansion projects and rehabilitation of existing ones. The main input in this process is agricultural mechanization; however it has been resulted in a huge number of farm machinery of various types and sizes, without consideration of proper selection and matching between tractors and their attachments (implements). This approach had led to high cost of agricultural operation, resulted in an unbalanced distribution of machinery and the agricultural operations throughout the years. Wonji Shoa Sugar factory is one of the victims of the aforementioned problem. The present study was conducted to evaluate the current farm machinery selection practices of Wonji Shoa Sugar Factory. Three soil types namely; heavy clay, vertisol and sandy loam were selected with four field activities (subsoiling, ploughing, harrowing and furrowing). For subsoiling, ploughing and harrowing, 9410-R John Deer tractor with implements of 5 shank and 3.2 m wide mounted subsoiler, 2.8 m wide double gang offset disc plough, and 3.5 m wide semi mounted four gangs double offset were used respectively. For furrowing YTO-1804 tractor and a four bottom fully mounted 4.5 m wide rider was used. For each experiment, the draft force requirement of the implements, the drawbar power, fuel consumption, overall energy efficiency and power utilization ratio were determined at different tractor travel speeds. Subsoiling, ploughing and harrowing were activities were evaluated at the travel speed of w 3, 4, 5 and 6 km/hr. And a travel speed of 4, 5, 6 and 7 km/hr were used for furrowing operation. Linear regression tests were run to examine the interactions between the factors and determine their significant impact. The results of this study noted for furrowing operation as lowest draft force, drawbar power, fuel consumption and energy efficiency of 14 kN, 12.65kW, 13 lit/hr and 8.7 % respectively in sandy loam soil type. The maximum Draft force of 79.8 kW and drawbar power of 131 kW was noted for subsoiling operation in Heavy clay soil. Moreover the results of all operation in all soil type, as indicated by both overall energy efficiency and power utilization factor shows the mismatch between the tractor and implements, as the tractors were overpowered. That means a less amount of the available power from the tractor is being used to operate the implement. If the tractor had been matched with a bigger and compatible implement a better efficiency and higher productivity of the tractor implement combination could have been achieved.

Cost Minimizing Farm Machinery Selection for Rice Farming

In this study, we determine cost minimizing machinery selections for rice farming by solving mixed integer programs with various acreage constraints. Results indicate that small farms can lower costs by hiring customized machinery operations including plowing, rotary tillage, transplanting, harvesting. Farms equipped with large size farm machines can earn extra income and save fixed costs by providing customized services for neighboring small farms. The optimal size of a single paddy farm is found to be 45 to 50 hectares. Policy support is needed for improving machine performance that can lead to increased optimal acreage as well as decreased cost.

Definition of Reference Models for Power, Mass, Working Width, and Price for Tillage Implements

Farm machinery selection, operation and management directly impact crop cultivation processes and outputs. A priori quantification of technical and financial needs allows definition of proportionate distribution and management of available resources and simplification of selection process. Appropriate planning, association and adjustment of the power unit and implement are required for soil cultivation. Consideration of functional parameters of the implement, their proper estimation and operation directly impact the soil structure, productivity and return on investment. Thus, a modelling approach was implemented for the definition of possible parameter-price relations for tillage equipment. The performed analysis allowed us to investigate the main relevant parameters, quantify their impact, and elaborate forecasting models for price, power, mass and working width. The significant relevance of the technical parameters and adjustment issues were outlined for each tillage implement group. For harrows and cultivators, the dependencies between studied parameters expressed better predictive qualities, especially for price-mass relation (R² > 0.8). While for ploughs power and mass relation had a primary output (R² = 0.7). The prediction features of the models provided reliable results for the estimation of the indicative values of the price and parameters of the implements.

Integrating Sustainability Into the Machinery Selection Decisions in the Agriculture Sector

Mechanization in the agriculture sector requires well-informed technology selection decisions and a balance of financial and nonfinancial considerations. This article integrates sustainability and technical considerations into farm machinery selection decisions. Using an industry case study-where technology selection for the mechanization process is imperative-a multimethod decision-making framework is developed to select the best rice harvest machinery. For this purpose, the Delphi method is used to systematically identify the most relevant decision criteria; the analytic hierarchy process determines the criteria's importance, and the fuzzy technique for order of preference by similarity to ideal solution is applied to rank the alternatives. Given the wide variety of available harvesting technologies in the competitive market, it is shown that the sustainability criteria are receiving recent recognition among agriculture practitioners. The findings provide insights for the producers to help improve production, and exportation of the local brands in accordance with the international market needs.

An application of AHP for parameter importance evaluation for farm machinery selection

The farm machinery selection is a complex decision making task. It involves a variety of variables associated with farming which include social and economic aspects of the decision maker. The aim of this work is to develop decision support architecture for tractor selection process. Different parameters for selecting tractor were identified; their importance was studied by literature survey and evaluated with AHP. The study concludes that the parameter operation type receives highest importance in tractor selection process. Other parameters like, service, type of business, brand and cost are also important in tractor selection process.

Agricultural Equipment in Greece: Farm Machinery Management in the Era of Economic Crisis

Abstract The mechanization of the Greek agriculture began in the early 60s as a way to overcome the lack of labor force, due to the rural exodus. The approval from the farmers was encouraged by the financial support provided via national and European programs. Nowadays, thanks to this support, the index of mechanization of the Greek agriculture exceeds country's needs. However, there is inefficient use of farm machinery, due to the lack of the necessary complementary parts or overwhelming power of the tractors. The aim of this paper is to examine the management of farm machinery in Greece, during the economic crisis, a period of volatility in the markets and need for reduced production costs. It is shown that farmers got used to a mentality of fast-track farm management and farm machinery selection which can no longer be continued.

Farm Machinery Selection to Minimize the Cost of Agricultural Operations

Farm Machinery Selection to Minimize the Cost of Agricultural Operations

Farm Machinery Selection Using Simulation and Genetic Algorithms

Computer simulation and genetic algorithms were used to optimize peanut farm machinery selection. The objective of optimization was to maximize net returns above machinery costs. A computer simulation model was used to determine net returns above machinery costs. The simulation model determined net returns above machinery costs for a given machinery set, but did not find an optimum machinery set. The optimum machinery set was determined using two search schemesan exhaustive search and an artificially intelligent search. The exhaustive search scheme involved running the simulation model with all possible machinery sets, and then selecting the machinery set that produced the highest returns. Alternatively, genetic algorithms were used as an intelligent search scheme to generate machinery sets for the simulation model. A genetic algorithm found a near-optimal solution in 10% of the total time required by the exhaustive search. Modifications in the genetic algorithm not only reduced the search time by half, but also improved the quality of the solutions.

A Farm Machinery Selection and Management Program

A farm machinery selection and management program was developed and written in the C programming language to run on an IBM compatible personal computer. The program input information consists of a list of desired field operations with start date, acres, and hours per day for each operation. Other input includes crop yields, penalty dates for planting and harvesting, availability and cost of labor, and certain economic data. Stored data files contain machine list prices and productivity values, work-day probabilities, and equation constants for computing machine costs. For different machinery sets or a specified set of machinery, the program schedules the field operations and computes the total machinery-related costs including costs for the machines, labor, and timeliness. Using an optimization process, the lowest cost machinery set is determined and the eight lowest cost sets found during the process are presented. For any of the eight lowest cost sets, or for a specified set of machinery, the output includes a list of the machinery with prices and annual use, the work schedule, the cost for each operation, and the total machinery-related costs.

POSTOPTIMAL LINEAR PROGRAMMING ANALYSIS OF FARM MACHINERY

ABSTRACT Linear programming (LP) is a widely used technique for farm machinery selection. Shadow prices from LP models are typically used to identify farm machinery bottlenecks. Shadow prices represent the value of an additional unit of a scarce resource, such as planter hours during a given time period. This shadow price approach can be misleading since a machinery change may affect the usages of other scarce resources, such as tractors and labor. A postoptimality algorithm is presented which improves on the shadow price approach. This algorithm gives detailed information on the effects of a machinery change on all relevant tractor and labor resources. This information is useful for investigating complex machinery interactions. The postoptimality algorithm is illustrated with a case study in Texas.

FINDS: Farm-Level Intelligent Decision Support System

A Farm-level INtelligent Decision Support system, FINDS, for farm machinery selection and management is presented. FINDS combines the analytical strengths of (linear programming) LP and simulation with a knowledge-based expert system. The knowledge domain of FINDS contains facts and rules about LP modeling, simulation modeling, and interpretation of relevant outputs in terms of machinery management. In FINDS, machinery management involves the optimal selection of farm machinery and scheduling of machinery operation activities. The knowledge base of FINDS has full access to a farm-level LP analysis system and a companion farm simulation model. By integrating symbolic knowledge with the quantitative power of LP and simulation, FINDS can automate LP model development, generate simulation code and specify the appropriate input data. FINDS automatically utilizes the coupled LP and simulation models where appropriate and interprets the relevant output to recommend the optimal machinery management practices. Optimal machinery recommendations are based on the farm returns that are obtained and the level of risk that is acceptable to user. FINDS can also provide detailed explanations of the reasoning behind its machinery management recommendations. By automating difficult LP and simulation modeling procedures, FINDS allows more efficient and widespread usage of such complex farm machinery management tools.

Machinery selection using expert systems and linear programming

Information on a Farm-level INtelligent Decision Support system, FINDS, is presented. This system is desgined to aid in farm machinery selection through its use and interpretation of a farm management linear program (LP) which has been used in extension workshops to assist farm producers with farm-level decisions. Considerable expertise is required to interpret this farm management LP and it has not been used to its potential because there is a shortage of experts to work with individual producers. FINDS incorporates the expertise needed to use the LP for selecting farm machinery. The goal of FINDS is to help the user find machinery sets that can increase farm profit. The knowledge base of FINDS was structured using IF-THEN rules and LISP functions. With the rules and functions, FINDS automatically runs the LP model, extracts and interprets the relevant output, and recommends farm machinery changes that can increase farm profitability.

MARKOV CHAIN MODEL FOR SEQUENCES OF FIELD WORKDAYS

A computer program was written to estimate from sequential data the parameters for a Markov chain probability model. The program was applied to the analysis of field workday probabilities at 10 selected locations across Canada. The suitability of the first-order Markov chain model was tested and the locations across Canada were compared as to the probability and conditional probabilities of workdays. These probabilities estimated by the program also provide data which should be of interest in farm machinery selection.

Activity Network Techniques Applied to a Farm Machinery Selection Problem

Activity Network Techniques Applied to a Farm Machinery Selection Problem