Patrizia Busato

System Approach to Biomass Harvest Operations: Simulation Modeling and Linear Programming for Logistic Design

The biomass logistics consists of multiple work processes intensively interlinked. The entire network operates in space and time coordinates. In this contest the feasibility is a function particularly of the logistic costs. Estimation of these costs is a complex task because the process involves the operation of a system of machines. The overall goal of this study was to present the combined use of both the simulation and linear programming models to optimize the flow of biomass from field to a power plant. The simulation predicted the overall system performance. The results from the simulation model were then used as input in the linear programming model, which chosen the best combination of equipment for each field distance and yield, in order to minimize the logistic costs, while satisfying some constraint like the number of hours available for harvest and the area to be harvested. The presented case study refers to corn silage harvest. The optimized logistic costs to harvest and delivery 7200 t of corn silo within a 5 km biomass collection radius is 22.2

Route planning for orchard operations

.tDM-1 while the cost raises to 34.5

Functional modeling for green biomass supply chains

.tDM-1 when collecting it in a range of 20 km.

Energy embodiment in Brazilian agriculture: an overview of 23 crops.

Mission and route planning for an agricultural robot.Orchards is a well-suited operational environment for the application of deterministic behaviour robotic systems.Modelling of inter- and intra-row orchards operations.Reduction in the non-working time ranged between 10.7% and 32.4%. Orchard operations are considered a promising area for the implementation of robotic systems because of the inherent structured operational environment that arises from time-independent spatial tree configurations. In this paper, a route planning approach is developed and tested using a deterministic behaviour robot (named AMS - autonomous mechanisation system). The core of the planning method is the generation of routing plans for intra- and inter-row orchard operations, based on the adaptation of an optimal area coverage method developed for arable farming operations (B-patterns). Experiments have verified that operational efficiencies can be improved significantly compared with the conventional, non-optimised method of executing orchard operations. Specifically, the experimental results showed that the non-working time reduction ranged between 10.7% and 32.4% and that the reduction in the non-working distance ranged between 17.5% and 40.2% resulting to savings in the total travelled distance ranged between 2.2% and 6.4%.

Logistics and Postharvest Handling of Locally Grown Produce

An engineering approach on describing biomass supply chains.Development of models that simulate in detail the operational in-field and transport activities.Identification of bottlenecks in three biomass supply chain systems. The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim? simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance.

Machine Learning in Agriculture: A Review

The amount of energy required to produce a commodity or to supply a service varies from one production system to another and consequently giving rise to differing levels of environmental efficiency. Moreover, since energy prices have been continuously increasing over time, this energy amount may be a factor that has economic worth. Biomass production has a variety of end-products such as food, energy, and fiber; thus, taking into account the similarity in end-product of different crops (e.g.: sunflower, peanuts, or soybean for oil) it is possible to evaluate which crops require less energy per functional unit, such as starch, oil, and protein. This information can be used in decision-making about policies for food safety or bioenergy. In this study, 23 crops were evaluated allowing for a comparison in terms of energy embodied per functional unit. Crops were grouped as follows: starch, oil, horticultural, perennial and fiber, to provide for a deeper analysis of alternatives for the groups, and subsidize further studies comparing conventional and alternative production systems such as organic or genetically modified organisms, in terms of energy. The best energy balance observed was whole sugarcane (juice, bagasse and straw) with a surplus of 268 GJ ha−1 yr−1; palm shows the highest energy return on investment with a ratio of approximately 30:1. For carbohydrates and protein production, cassava and soybean, respectively, emerged as the crops offering the greatest energy savings in the production of these functional foods.

Logistics and Economics of Rice Harvest and Post-Harvest Operations with the Use of Low Temperature In-Bin Drying System

Publisher Summary The aim of the postharvest handling of locally grown produce is to deliver quality produce to the consumer. Quality cannot be improved after harvesting; therefore it is important to harvest fruits and vegetables at the proper stage, size, and at optimal quality. Postharvest life, however, can be maintained and extended by optimized handling and reducing damage through the application of correct postharvesting techniques. Quality is the result of the interaction among many factors including produce, consumer perceptions, distribution, and postharvest handling systems. The legislation and regulations applied to the quality and safety assurance of FFVs (fresh fruits and vegetables) also have an impact on produce quality at sales point. The main issues for consumers are what product the consumer wants, and how much the consumer is willing to pay for it. Consumer expectation should be met through the implementation of adequate logistic and postharvest handling techniques. Although it is tempting to meet the needs of all consumers in a market, an effective strategy identifies those characteristics important to a distinct segment. This also applies to locally-grown FFVs. However, the logistic question remains: Whether it is possible to provide the volume requested at the right time, in the right place, in the right quality, and at the right price. The adequate postharvest handling of FFVs must account for the cultural, economic, technological, environmental, administrative, and legal context of the target market.

FruitGame: Simulation Model to Study the Supply Chain Logistics for Fresh Produce

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

A web mobile application for agricultural machinery cost analysis

As grain production becomes more vital and the constraints of climate change grip tighter on the farming systems worldwide, the improvement of efficiencies, the reduction of costs and the optimum allocation of resources is becoming even more important to the farmer.

Logistics and Economics of Grain Harvest and Transport Systems with the Use of Silo-Bag

Customers are asking better quality for the fresh produce, and that imply better management of the supply-chain. In the distribution of the fruit and fresh vegetable plays an important role the logistics, discipline that studies and optimises the management of the flow of products along the supply-chain. The high quality of logistic services could be achieved if the partners are aware of the supply-chain system and its behaviour in order to have an efficient response to the customer needs. A simulation game developed specifically for managers of businesses that handle fresh fruits and vegetables could help reduce losses and improve the quality of produce available to consumers (Prussia et al, 2001). The “beer game”, developed by researchers at MIT in early 60’s, shows the value of integrated supply chain management. In order to improve the supply-chain, the components of the fresh produce supply-chain were asked to play with the “beer game”. After the game, they asked for one tool specifically designed on the delivery of fresh produce. The authors designed and developed the FruitGame with an object oriented simulation language, Extend®, which implement the supply-chain typical for fresh produce, as a part of one Ph.D. research project. The model-game provides three more partners in the supply-chain vs. the “beer game”: the farmer, the final store and another wholesaler. It provides also more options to the players. They can, in the case of shortage of products, either to place bigger orders or to buy from other sources (by spending more money), or encounter loss in sales with an associated probability to loose the customer. The FruitGame track the supply-chain performance by simulating the production and distribution of each single box of fruits and vegetable. Each box has his attributes and this unique feature allow for tracking in detail the shelf-life of the fresh produce along the supply-chain. This model framework now is able to simulate networks of different products, forecasting the quality loss from packer to consumer, and allowing the testing of diverse logistic and transport solutions. New resources will be spent to make new scenarios in order to increase the shelf-life and reducing the costs.