Computational Methods for Agricultural Research: Advances and Applications

Computational Methods for Agricultural Research: Advances and Applications

Hércules Antonio do Prado (Brazilian Enterprise for Agricultural Research and Catholic University of Brasilia, Brazil), Alfredo Jose Barreto Luiz (Brazilian Enterprise for Agricultural Research, Brazil) and Homero Chaib Filho (Brazilian Enterprise for Agricultural Research, Brazil)
Release Date: October, 2010|Copyright: © 2011 |Pages: 524
ISBN13: 9781616928711|ISBN10: 1616928719|EISBN13: 9781616928735|DOI: 10.4018/978-1-61692-871-1


Despite currents migration trends towards population centers, agriculture remains one of the most important staples of human culture, as these centers are dependent on a constant food supply.

Computational Methods for Agricultural Research: Advances and Applications brings computing solutions to ancient practices and modern concerns, sowing the seeds for a sustainable, constant food supply. This book treats subjects as old modeling flood patterns and predicting potential climates to distinctly 21st century topics such as pesticide leaching models and the impact of agricultural policy. All of these studies utilize cutting-edge computational techniques of interest to both academics and practitioners in agriculture but also computational modeling researchers, creating a reference practical significance.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Agricultural Reuse of Treated Wastewater
  • Climate and soil prediction
  • Computational Modeling
  • Cultivation and harvest planning
  • Flood Management Strategies
  • Impact of agriculture policy
  • Impact of technology
  • Neural Networks
  • Pesticide leaching models
  • Seasonal precipitation forecasting

Reviews and Testimonials

This book emphasizes scientific computing and applications in agriculture. ... The richness of this book is precisely its diversity, which is capable of stimulating convergence among different areas of knowledge and may result in unexpected advances, both in the area of computational methods and in agricultural research.

– Hércules Antonio do Prado, Brazilian Enterprise for Agricultural Research and Catholic University of Brasilia, Brazil; Alfredo Jose Barreto Luiz, Brazilian Enterprise for Agricultural Research, Brazil; and Homero Chaib Filho, Brazilian Enterprise for Ag

Table of Contents and List of Contributors

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Hércules Antonio do Prado, Alfredo Jose Barreto Luiz, Homero Chaib Filho
Editorial Advisory Board
Table of Contents
Hércules Antonio do Prado, Alfredo Jose Barreto Luiz, Homero Chaib Filho
Chapter 1
Geraldo Silva e Souza, Eliseu Alves
Embrapa (Brazilian Agricultural Research Corporation), a governmental agricultural research institution from Brazil, is a case of successful... Sample PDF
Scientific Computing in the Context of a Successful Agricultural Research Enterprise
Chapter 2
Fernando Garagorry, Homero Filho
This chapter examines the measurement of motion in agriculture. Not only in Brazil, but in several Latin-American countries, important changes have... Sample PDF
Concentration and Dynamics of the Brazilian Agriculture
Chapter 3
Carlos Pérez-Hugalde, Patricia Delgado-Pérez, Raúl Romero-Calcerrada
The present chapter discusses an analysis of land cover change based on the enhanced transition matrix, and provides a detailed interpretation of... Sample PDF
Land Cover Change: Statistical Indexes Using the Enhanced Transition Matrix
Chapter 4
Luciana Romani, Elaine de Sousa, Marcela Ribeiro, Ana de Ávila, Jurandir Zullo, Caetano Traina, Agma Traina
This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and... Sample PDF
Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields
Chapter 5
Alfredo Luiz, Antonio Formaggio, José Epiphanio
Having the ability to estimate crop areas is a necessity ever more pressing for all the stakeholders of productive chains. For many scientists... Sample PDF
Objective Sampling Estimation of Crop Area Based on Remote Sensing Images
Chapter 6
Fernando Soares, José Alba, Elódio Sebem, Marcos Wrege
A potential climate study for sugarcane of a sector of Rio Grande do Sul State, Brazil is presented here. GIS technology was applied for evaluation... Sample PDF
Study of Potential Climate for Sugarcane Production in the State of Rio Grande Do Sul, Brazil, By Means of Geoprocessing
Chapter 7
Marcos Santos da Silva, Edmar Ramos de Siqueira, Olívio Teixeira, Maria Manos, Antônio Monteiro
This work assessed the capacity of the self-organizing map, an unsupervised artificial neural network, to aid the process of territorial design... Sample PDF
Using Self-Organizing Maps for Rural Territorial Typology
Chapter 8
José Alba, Marcos Wrege, Marilice Garrastazu
Zoning based on climate and soil characteristics does not represent a full ecological-economic zoning procedure, as the Brazilian law establishes... Sample PDF
Zoning Based on Climate and Soil for Planting Eucalyptus in Southern Region of Rio Grande do Sul State, Brazil
Chapter 9
Sanjay Jena, Marcus Poggi de Aragão
The planning of agricultural cultivation and harvesting is a complex task. However, this area of study is still relatively young. This chapter... Sample PDF
A Mixed Integer Programming Approach for Sugar Cane Cultivation and Harvest Planning
Chapter 10
Lucinio Asensio, Rosario Gómez de Barreda, Miguel Ruiz, José-Luis Miguel de Diego, Elvira Miqueleiz
In agricultural economics, one of the greatest weaknesses in mathematical programming models for the evaluation of agricultural processes is the... Sample PDF
An Application of a Positive Mathematical Programming Model to Analyse the Impact of Agricultural Policy Measures in the Spanish Agricultural Sector
Chapter 11
Maria Pessoa, Elizabeth Fernandes, Sonia Nascimento de Queiroz, Vera Ferracini, Marco Gomes, Manoel Dornelas de Souza
The present chapter provides a brief explanation on some aspects involved in the development of models and mathematical-modelling simulations, to... Sample PDF
Mathematical-Modelling Simulation Applied to Help in the Decision-Making Process on Environmental Impact Assessment of Agriculture
Chapter 12
Lana dos Santos, Marcos Arenales, Alysson Costa, Ricardo Santos
This chapter is concerned with a set of optimization problems associated to crop rotation scheduling in the context of vegetable crop production... Sample PDF
A Linear Optimization Approach for Increasing Sustainability in Vegetable Crop Production
Chapter 13
Rômulo Júnior, Renê Rigitano, Jos Boesten
The use of Pesticide Leaching Models (PLM) for risk assessment may be an efficient and attractive way of assessing solutions to some agricultural... Sample PDF
Pesticide Leaching Models in a Brazilian Agricultural Field Scenario
Chapter 14
Lisa Brouwers, Magnus Boman
A geographically explicit flood simulation model was designed and implemented as a tool for policy making support, illustrated here with two simple... Sample PDF
A Computational Agent Model of Flood Management Strategies
Chapter 15
Pedro Corrêa, Mariana Carvalhaes, Antonio Saraiva, Fabrício Rodrigues, Elisângela Rodrigues, Ricardo Luis de Azevedo da Rocha
Computational modeling techniques for species geographic distribution are critical to support the task of identifying areas with high risk of loss... Sample PDF
Computational Techniques for Biologic Species Distribution Modeling
Chapter 16
Adriano Rolim da Paz, Cíntia Uvo, Juan Bravo, Walter Collischonn, Humberto Ribeiro da Rocha
Agriculture is vulnerable to the interannual climate variability and to its unpredictability, in such a way that most agricultural decisions taken... Sample PDF
Seasonal Precipitation Forecast Based on Artificial Neural Networks
Chapter 17
Hella Ben Brahim, Lucien Duckstein
The present chapter reports on a practical case of decision making in agricultural TWW reuse. The studied site concerns the Cebala TWW irrigated... Sample PDF
Descriptive Methods and Compromise Programming for Promoting Agricultural Reuse of Treated Wastewater
Chapter 18
Marcos Visoli, Sandro Bimonte, Sônia Ternes, François Pinet, Jean-Pierre Chanet
Animal traceability is a very important question for several government and private institutions from many points of view: economical, sanitary... Sample PDF
Towards Spatial Decision Support System for Animals Traceability
Chapter 19
Susumu Uchida, Kiyotada Hayashi, Masaei Sato, Shingo Hokazono
Increased public focus on agri-environmental issues and recent policies on agricultural sustainability have necessitated the construction of a life... Sample PDF
Construction of Agri-Environmental Data Using Computational Methods: The Case of Life Cycle Inventories for Agricultural Production Systems
About the Contributors


This book emphasizes scientific computing and applications in agriculture. It is said in one of its chapters that Mathematics is the stairs given to man by God to reach the infinity. Currently, we are living in the information age and we can not climb the steps of this stairs without information technology and quantitative methods. The Mathematics part of the statement has generalized to scientific computing, even considering its more basic philosophical foundations.

The chapters cover a wide range of applications and techniques from different countries, groups and scientific institutions. Although some of them are still in an early stage and better suited for academic or research environment, others are almost ready to be used in real conditions of rural areas. The richness of this book is precisely its diversity, which is capable of stimulating convergence among different areas of knowledge and may result in unexpected advances, both in the area of computational methods and in agricultural research.

Chapter I has an introductory nature and aims at giving us an account about a successful agricultural research institution that owes an important part of its triumph to a strong investment in scientific computing. This chapter is a record from people who played important roles both in the creation of the organization and the adoption of scientific computing as a support for agricultural research.   

Next, Chapter II addresses particularly the study of the motion of agricultural activities. The methodology based on statistical descriptive methods associated with concepts from elementary physics and from basics geographical processing allows evaluating the spatial concentration and the dynamics of agricultural products as well as of center of mass of crops productions. This methodology can be used in order to give an overall summary of the spatial changes along the years. The procedure also permits studying the agricultural development and is particularly relevant to countries where substantial changes in the rural space use are ongoing, as it occurs in Latin America.

Working in a similar domain, Chapter III presents, in a didactic way, a method to assess changes in land cover using the approach of the transition matrix. The method allows the evaluation of gains and losses, swap and net change in different types of land cover and also permits the identification of systematic transitions in a region between two points in time. The method is a powerful tool for evaluating trends in agriculture within the land use policies that seek to environmental sustainability.

Chapter IV presents an approach to analyze, monitor and discover knowledge from remote sensing images associated to climate data. The techniques are based on the Fractal Theory, data streams and time series mining: the FDASE algorithm; a method that combines intrinsic dimension measurements with statistical analysis; and the CLIPSMiner algorithm applied to multiple continuous time series of climate data. The monitoring tool presented permits that, instead of spending hours analyzing graphs and charts, the specialists may count on methods that spot the regions and the periods where they should pay more attention during the decision making process.

Also handling digital images, Chapter V provides the theoretical and practical basis to estimate crop areas using statistical objective sampling and remote sensing data. Formulas are presented and can be applied to other land covers that occupy large portions of surface, such as forest, water bodies and urban areas. The estimate of crop areas is of great potential, since it allows predictions provided with quantification of the error. The method can be performed at different scales, from the smallest, as municipalities, to the larger, such as countries.

Chapter VI describes how to use the Geographic Information Systems - GIS to combine data of weather and soil in order to define areas of potential suitable for the cultivation of sugarcane. The system generates a map of the distribution of the risk of frost and, as a result, the crop management can be designed in a way that sugarcane achieves physiological maturity before the period of increased climate risk, decreasing the chances of low productivity. This application of GIS shows potential to become a powerful tool for management of rural environment and of public policy.

Likewise, dealing with spatial issues, Chapter VII assesses the capacity of the self-organizing map, an unsupervised artificial neural network, for aiding the process of territorial planning through methods of visualization and clustering applied to a multivariate set of geospatial and temporal data. Population growth and hence the demand for quality public services and projects for regional infrastructure, require from public managers greater flexibility in decision-making so that they are observing the requirements of fairness, efficiency and effectiveness. Therefore, territorial typology provided by this method offers an important feedback for the development of a collective solution to issues relating to rural development.

To define the best area for commercial cultivation of eucalyptus in a certain region, Chapter VIII uses satellite imagery, mapping and geo-referenced data on climate, the soil and watersheds, beyond the topography, with the aim of to establish forest policies considering socioeconomic issues. Specifically, this study allows evaluating the aptitude of a specific region, according to the environmental requirements for each species of eucalyptus, the soil capacity of water storage and the risk of frost.
There are decision support systems designed to evaluate the agro-ecological processes which face obstacles to obtaining reliable data, what compromises their accuracy and safety. To address this problem, Chapter IX presents a way to provide the greatest possible number of variables for the analysis of agro-environmental processes. To that end, it uses computational methods and descriptive statistics to compose and organize a database.

Speaking of databases, in most of them the information is represented in textual format. Chapter X deals with the problems of taxonomy generation of a set of texts. The goal is to provide means for experts to create taxonomies based on techniques of cluster analysis. The method consists of applying a cyclical process to obtain a satisfactory taxonomy, with the following steps: generating configurations of clusters; examining the configurations through interaction with experts; and redo the settings based on this expert analysis. A case study in the Pedology domain is presented.
Chapter XI addresses the complex task of planning cultivation and harvesting of sugar cane with focus on tactical and operational aspects which determine the best way to manage fields, maximizing profit. The purposed mixed integer programming model extends the classical Packing formulation, adding a network flow structure to represent the harvest scheduling. Experiments performed with real-world instances ensure a realistic presentation of the processes. The presented algorithms are promising to solve, in reasonable time, even larger problems as they appear in practice.

Although the reuse of water, especially rainwater, has historically had a wide use in various regions of the world, there is still resistance to the reuse of wastewater, especially those used for agricultural irrigation. This important topic is worked in Chapter XII that tries to demonstrate the economic feasibility of reuse of water resources in agriculture, which can be one more element in environmental protection and aid to regions with water scarcity. The use of descriptive methods, based on the Lp metric and multicriteria approach, is called by the authors as compromise programming. This methodology proves to be quite useful to define the reuse of wastewater or not, describing reuse as an option to be promoted and evaluated.
The mathematical modeling is applied in Chapter XIII to optimize crop rotation scheduling under some ecological restrictions. The work includes detailed description of three optimization problems that are solved in order to put together the ecological criteria with technical constraints specific of crops. To achieve its objectives, the authors use the technique of column generation to allow the construction of rotation plans in an iterative scheme. The results indicate that the methodology is appropriate and can incorporate new features as they become necessary or can discard those with less importance to a region or farming system specific.

In agricultural economics, one of the greatest drawbacks of mathematical programming models to evaluate agricultural processes is to calibrate the model in a base year. That is because it is difficult, if not impossible, to introduce in the models all the variables affecting farmer’s decisions to obtain reliable results. Chapter XIV presents a way to get the calibration of these models even using limited information. By using the dual form of the original model, this methodology allows to reproduce the situation existing in a baseline situation of the unit modeled (e.g. farm or region). This method, called the Positive Mathematical Programming, is currently being used in a great number of analyses of new agricultural policies in Europe.

Chapter XV discusses the important process of leaching of pesticides and their risk of groundwater contaminating. It presents the basic concepts of simulation models and shows the mathematical development to prepare the model to assess the risk of water contamination by pesticides. The chapter shows the efficiency of the Pesticide Leaching Models (PLM) in use in the European Community to define policies controlling the use of pesticides, and their possible use by the authorities responsible for managing the risk of groundwater contamination.

Confronting another challenge, the focus of Chapter XVI is in technological solutions to guarantee a reliable bovine traceability, what is very important for countries producers and consumers of beef cattle. It is presented a decision support system dedicated to animal geolocation and to the analysis of sanitary problems. The system architecture is designed in three layers: acquisition, data management and spatial decision support. It has potential use to governmental institutions and farmers in case of a recall, saving time and costs apart from collaborating to achieve the food security.
Chapter XVII presents a simulation model structured into five modules, in which the use of multi-agent approach has the main role. It is a remarkable example of the combination of several techniques for the simulation of agricultural areas subject to flooding that offers the possibility to evaluate different strategies to be adopted according to the severity of the occurrence of floods.

Another use of models is demonstrated in Chapter XVIII that presents a fairly didactic approach of modeling technique to evaluate the spatial distribution of species. An application is made on data from Babassu (Orbignya spp.), a palm tree native from Amazon region. Modeling techniques using neural networks and elements of descriptive statistics, together with the concept of maximum entropy, are used to evaluate the degree of loss of biodiversity. The use of the concept of ecological niche is very important in this context because it may allow assessment of the effects experienced by some species due to changes underway in the region.

The knowledge of precipitation conditions is so valuable information for agricultural management purposes that justify the continuous development and improvement of methods for forecasting. Chapter XIX presents the development of an Artificial Neural Network (ANN) model for seasonal precipitation forecast based on climate indices, focusing on the practical aspects of selecting the best predictors, defining ANN architecture, data handling and ANN training and validation. Climate predictions can lead decisions towards a strategic view, aiming at minimizing unwanted impacts and taking advantage of favorable conditions. This approach can be successfully used to provide site-specific precipitation forecasts, valuable for the agriculture-related decisions.

Author(s)/Editor(s) Biography

Hércules Antonio do Prado, Ph.D. is a researcher in Computer Science, focusing his work on data/text mining, neural networks, knowledge-based systems, and knowledge management. In 1984 he joined the Brazilian Agricultural Research Corporation and, in 1992, the Catholic University of Brasília as an assistant professor. He received his D.Sc. in Computer Science at the Federal University of Rio Grande do Sul, Brazil (2001) his M.Sc. in Systems Engineering from the Federal University of Rio de Janeiro (1989). In 1999 he stayed in the Information Sciences Department of University of Pittsburgh as a Visitor Scholar, developing research for his doctoral program. He undergraduated in Computer Systems at the Federal University of São Carlos, Brazil (1976).
Dr Alfredo José Barreto Luiz is a researcher in Applied Statistics at the Brazilian Agricultural Research Corporation (Embrapa) and a post-doctoral fellow at brazilian National Institute of Space Research (INPE). He received his D.Sc. in Remote Sensing at INPE and his Master degree in Statistics and Quantitative Methods from the University of Brasília (UnB). He did his undergraduate course in Agronomy at Federal University of Lavras (UFLa). His research interest includes the study of scenarios and the spatial and temporal dynamics of agriculture, beside data analysis in biometrics and environmetrics.
Homero Chaib Filho was born in 1954 in Belém-PA, Brazil. In 1970 was awarded the diploma of merit, by the National Council of nuclear energy of Brazil, for the monograph on the development of nuclear energy in Brazil. In 1977 has taken his bachelor’s degree in Mathematics at Universidade de Brasília, Brazil. In 1983, took his master in Production Engineering, with emphasis on Operation Research, at Universidade de Santa Catarina, Brazil. At Escuela superior de Ingenieros agrónomos of the Universidad de Madri, took his Doctoral degree in Applied Mathematics, in 1990. Since 1977, he was a researcher at Embrapa, where he worked in the areas of data analysis and computational methods. In 2009 came to retire at Embrapa to seek new horizons.


Editorial Board

  • Dr. Carlos Romero, Universidad Politécnica de Madrid, Spain
  • Dr. Cláudio Chauke Nehme, Catholic University of Brasília, Brazil
  • Dr. Eduardo Delgado Assad, Embrapa Agriculture Informatics, Brazil
  • Dr. Eliseu Roberto de Andrade Alves, Brazilian Agricultural Research Corporation, Brazil
  • Dr. Fernando Luis Garagorry, Embrapa Management and Strategy, Brazil
  • Dr. Lucinio Júdez Asencio, Universidad Politécnica de Madrid, Spain
  • Dr. Miguel de Castro Neto, Universidade Nova de Lisboa, Portugal
  • Dr. Pierre Bommel, Cirad Ur Green, France
  • Dr. Russell S. Yost, University of Hawaii, USA
  • Dr. Wilson Alberto Contreras Espinosa, Universidad de Pamplona, Colombia