A Discrete Element Model For Agricultural Decision Support

Revista Brasileira de Engenharia de Biossistemas, 2009

The change of data into information is the most important aspects when using precision agricultureconcept, in order to understand crop yield differences in the same area of production. The objectiveof this paper is to present a Management Crop Production System Methodology to help farmersmake decisions of a specific intervention on the low production and unprofitable areas. This workwas developed in Sete Lagoas, Minas Gerais state, Brazil, during three years (1999 through2002), in an area of 38 ha, irrigated by a center pivot, with corn crop under no-tillage system andthe results applied for developing a methodology and then applied to a production area of 115 ha,Recanto Farm, Sidrolândia, MS, Brazil. The yield maps were obtained using a MF-34 combine,with an yield monitor and DGPS system. The proposed methodology is being used to identify andto analyze the intervention areas by two maps: trend and profitability. This methodology providesthe farmers with a management system to analy...

Agrekon, 2008

This article presents a deterministic farm-level model developed to link to an existing partial equilibrium sector-level model of the grain and livestock sectors of South Africa. The objective is to create a linked system of models consisting of a sector-and farm-level model with the capability to analyse the likely effects of changes in policies and markets at both the sector and representative farm level in South Africa. A representative farm in the Free State Province is used to validate the farm-level model. The farm-level model is used to simulate a baseline as well as two scenarios of the representative farm for the period 2003 to 2010. Results indicate that the farm-level model simulates the representative farm rather accurately compared to historical data. The baseline and scenario results indicate that the linked system of models can be useful for policy and business decision-makers to analyse the impact of change in policies and markets at both the sector-and farm level.

rimisp.org

To bring about improvement in the complex agricultural systems found in the Southwest of Rio Grande do Sul, technological advice is necessary. Considering their characteristics, computer models are an important tool in supporting technological advice and in improving agricultural system performance. Models as tools for decision support need to be dynamic in concept to simulate the real farm environment. The information base is classified as "natural" and "simulated". The "natural" results from past experience. The "simulated" is based on quantitative formal scientific information. This work presents a framework that deals with adaptive behaviour as a response to natural and simulated information. As the farmer's decisions are sequential and dynamic, the model simulates the bio-socio-economic environment in which farm decisions occur at established timesteps, and asks the farmer about each decision. Case study simulations were made, and the results of the proposed methodology are presented to demonstrate the potential use of this approach.

International Journal of Business Innovation and Research, 2012

In this paper, the agroPLAN decision support system (DSS) is presented; agroPLAN is a DSS for the planning of agricultural production in agricultural holdings or in agricultural areas. It is one of the main farm management applications of the agroGOV.gr portal. The utilisation of the agroPLAN DSS supports the farmer-manager in the difficult process of farm management and decision-making. In particular, agroPLAN simulates different scenarios and policies and proposes alternative production plans. It is based on a multicriteria mathematical programming model and can achieve the optimum agricultural production plan in a farm or in an agricultural area combining different criteria to a utility function under a set of constraints concerning different categories of land, labour, available capital, etc. The objective of this application is to help the farmer-user to gain control of the external environment with easy access to knowledge and information.

In global agricultural, decision making at all levels are increasing rapidly due to unreliable and uneven distribution of combined experi-ence of complex processes associated with the food production. Besides flaws in human initiative judgement have far-reaching consequences for the sustainable agriculture development. Therefore, aiding the reliability and deficiencies of human judgement and quality decision-making has been a major focus of research throughout the history particularly with the development of Decision Support Systems (DSSs). DSS is interactive computer-based system that help decision makers solve unstructured problems under complex and uncertain conditions by providing access to data through procedures and analytical reasoning. These systems have been designed to address complex tasks involving multiple disciplines of agriculture, enabling us to mimic the necessary requirements of crop with respect to the land characteristics so that the optimum objectives speci-fied by the user is obtained. Experimental use of these systems has resulted in improved integrated crop production and management with restrict-ed use of the natural resources together with improved farm income and reduced negative environmental impact. With the advent of powerful computation, efficient database management system, on-line analytical processing (OLAP), improved knowledge representation, efficient searching techniques, data warehousing and mining, Geographical Information System (GIS) and Remote Sensing (RS); DSS are being extensively used world-wide using state of art technologies, integrating multiple models for sustainable agriculture development from farm to fork, despite “Problem of Implementation”. Once the barrier is crossed, it will give a leading role to DSS in global sustainable agriculture.

Decision support system (DSS) is a computer based tool which provides prediction of crop yield under specific scenario on the basis of crop production data (obtained by field experiments), climatic and environmental historical by using basic inputs from the decision maker that can help the user to make a fair decision. Many components, such as simulation models, databases, and mathematical equations, must be combined to formulate a completely developed decision support system (DSS). The purpose of these decision support system (DSS) is to provide improved soil and crop management practices for the betterment of agriculture and farmer's livelihood. These systems enable a person or a farmer to make right and fruitful decisions according to available resources. Decision support systems (DSS) are developed with the advancements in the field of information technology, which analyze the complex natural phenomenon of crop production by integrating different scientific fields and provide reduced data set which is more comprehensive and easy to understand for making a suitable decision. Decision support system (DSS) play a significant role in precision agriculture for crop production. DSS provide a site specific crop management practices for obtaining high yield under a specific climatic conditions. Decision support system can be used to assist agricultural policy makers for proper assessment of future policies and objectives. Decision support systems (DSS) can be used at farm level for providing information that used to improve plan and decisions. They also provide best plan of action that is useful at farm, regional, national and global level. DSS provides information about farming and agro-ecological systems and their risk attitudes. DSS can be used to analyze the benefits or risk involved in adopting a new practice at farm. These also give overview of economics involved in a farming practices, gives the cost benefit ratio for producing a crop under certain management practices. A decision support systems used in water resource planning and operational management. These help in planning a suitable irrigation scheduling and resource management. This planning reduces the water loss and enhance irrigation efficiency. DSS also used by the researchers to make their research more convenient and helpful. Accurate forecasting of climate allow the farmers and other agricultural decision maker to make right decision to minimize the impact of drastic climate. Better forecasting of climatic events allow these farmers and other decision makers to make a good production strategy or to modify decisions to avoid the unwanted impacts. Decision support systems (DSS) assist us to make right decisions ahead. Decision support systems also helpful in giving recommendations for a crop under a suitable climatic and soil scenario. Recommendations are made on the basis of previous researches analysis and their results manipulation.

2010

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Decision support systems in agriculture, food and the environment : trends, applications and advances / Basil Manos ... [et al.], editors. p. cm. Includes bibliographical references and index.

Frontiers in Plant Science, 2015

Reduced water availability and environmental pollution caused by nitrogen (N) losses have increased the need for rational management of irrigation and N fertilization in horticultural systems. Decision support systems (DSS) could be powerful tools to assist farmers to improve irrigation and N fertilization efficiency. Currently, fertilization by drip irrigation system (fertigation) is used for many vegetable crops around the world. The paper illustrates the theoretical basis, the methodological approach and the structure of a DSS called GesCoN for fertigation management in open field vegetable crops. The DSS is based on daily water and N balance, considering the water lost by evapotranspiration (ET) and the N content in the aerial part of the crop (N uptake) as subtraction and the availability of water and N in the wet soil volume most effected by roots as the positive part. For the water balance, reference ET can be estimated using the Penman-Monteith (PM) or the Priestley-Taylor and Hargreaves models, specifically calibrated under local conditions. Both single or dual Kc approach can be used to calculate crop ET. Rain runoff and deep percolation are considered to calculate the effective rainfall. The soil volume most affected by the roots, the wet soil under emitters and their interactions are modeled. Crop growth is modeled by a non-linear logistic function on the basis of thermal time, but the model takes into account thermal and water stresses and allows an in-season calibration through a dynamic adaptation of the growth rate to the specific genetic and environmental conditions. N crop demand is related to DM accumulation by the N critical curve. N mineralization from soil organic matter is daily estimated. The DSS helps users to evaluate the daily amount of water and N fertilizer that has to be applied in order to fulfill the water and N-crop requirements to achieve the maximum potential yield, while reducing the risk of nitrate outflows.

2006 Second International Symposium on Plant Growth Modeling and Applications, 2006

Growth model-based decision support system for crop management (GMDSSCM) was developed including process based models of 4 different crops, i.e. wheat, rice, rapeseed and cotton. This system aims in facilitating simulation and application of crop models for different purposes. Individual models each include six submodels for simulating phasic development, organ formation, biomass production, yield and quality formation, soil-crop water relations, and nutrient (N, P, K) balance. The implemented system can be used for evaluating individual and comprehensive management strategies based on the results of crop growth simulation under various environments and different genotypes. A Stand-alone version (GMDSSCMA) was established under the platforms VC++ and VB by adopting the characteristics of object-oriented and component-based software and with the effective integration and coupling of the growth-prediction and decision-making functions. A web-based system (GMDSSCMW) was further developed on a .net platform using C# language. These GMDSSCM systems have been used to predict dynamically crop growth and to make decisions regarding to management systems. This tool should be helpful for construction and application of informational and digital farming systems.