An Analysis of Agricultural Risk and Intelligent Monitoring Technology Fantao Kong 1, a, Shiwei Xu 2,b, Shengwei Wang 3,c and Haipeng Yu 4,d

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1 Advanced Materials Research Vol. 628 (2013) pp Online available since 2012/Dec/27 at (2013) Trans Tech Publications, Switzerland doi: / An Analysis of Agricultural Risk and Intelligent Monitoring Technology Fantao Kong 1, a, Shiwei Xu 2,b, Shengwei Wang 3,c and Haipeng Yu 4,d 1,2,3,4 Agricultural Information Institute of Chinese Academy of Agricultural Sciences / Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing, China, a kft606@163.com, b xushiwei@mail.caas.net.cn, c wangshengwei@caas.net.cn, d yuhaipeng1985@sina.com Keywords: Agricultural risk; Intelligent information technology; Monitoring technology; Prospects Abstract.This paper analyzed the risk issue in the process of agriculture development, presented the logic relationship among agricultural risk, monitoring, information, entropy and early-warning, and proposed the future development of agricultural intelligent monitoring technology. Intorduction High-level agricultural information is an important symbol of modern agriculture, the development of which relies heavily on the support and guidance of information technology. According to the fundamental goal of "high quality, efficiency, ecology and safety, to address the issues of agricultural information and risk prevention, monitoring and early warning could guarantee the healthy development of agriculture. Agricultural Security Risks Human beings were, are and will be inevitable to face all kinds of risks [1]. The ever-present risks are associated with human beings since the beginning of humanity. The history of human society is just one of continuously struggling with and managing a variety of risks [2]. Risk denotes the uncertainty of future result. Opinions vary on the risk concept: risk refers to (1) the chance of loss or the possibility of loss; (2) unpredictable results; (3) contingent disasters; (4) the uncertainty of loss; (5) the deviation or gap between actual results and expected outcomes; (6) the uncertainty of future earnings; (7) the syntheses of subjective risk and objective risk. Risk possesses characteristics of uncertainty, damage, objectivity, emergency, non-additivity, and controllability, etc. It is widely recognized that risk is contributed with risk factors, risk accidents and loss. Agricultural risk refers to the uncertainty of risk that people may suffer while engaging in agricultural production and operation, which is extremely difficult to forecast, or even if it s predictable, but irresistible [3]. The general features of the agricultural risk are as follows: First, duality. Since agricultural reproduction is the organic unity of the natural and economic reproduction, agricultural risk can be accordingly summed up as natural and economic risk, characterized by diversity, decentrality, and seasonality [4].Agricultural calamities in China presents the following features: various kinds, high frequency, great intensity, wide distribution, and heavy loss. Economic risk can either be divided into market risk and policy risk, or categorized by market risk, social risk and technical risk. Second, space-time. All sorts of risk manifest certain spatial and temporal property, among which agricultural risk is more prominent. Agricultural risk is labeled with specific spatial and temporal features, such as obvious regionality, broad coverage, and strong area combination. Agricultural production and operation shows a strong seasonal and concentration trait, while the consumption and demand of agricultural products keeps in balance all the year around. Therefore, the mismatch between production and consumption as well as supply and demand attributes to the spatial and temporal property of agricultural risk. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, (ID: /02/13,04:40:17)

2 266 Manufacturing Engineering and Technology for Manufacturing Growth Third, sociality. In case agricultural risk converts to actual losses, there will be direct or indirect impact on producers, operators, consumers and regulators, and will have a strong social effects. At present, agricultural risk has been the major hidden danger in rural economy and agricultural development. Losses caused by agricultural risk will transmit via price mechanism, and trigger the volatility of agricultural product prices, which will hinder the development of modern agriculture and the construction of new rural construction. Logical Relationships Among Social Information, Risk, Entropy, Monitoring and Early Warning Risk information, entropy, monitoring and early warning are a set of banded concepts, reflecting the basic situation of the agriculture safety from different perspectives and multiple levels. By means of intelligent monitoring and analysis technology, it is possible to accurately handle the risk trend, and make timely forecast and early warning to be fully prepared in advance. Risk and early warning. System science believes that everything is a complicated system, and the whole world is a more complex entity. Uncertainty, randomness, complexity, together with the additive and non-additive effect of each element, constitutes the risk which is taken place in the course of change and development. When facing various risks, people are always dwelling on the past, predicting the future, and actively responding to them. Extrapolating the future from past, we will generate a forecast. Once projection results deviate from the normal "threshold value", it will raise the alarm, i.e. early-warning. By virtue of observing, monitoring and analyzing various risks, it is possible to make prediction and decision with respect to the developing state of damage. When departuring from the normal trajectory and exceeding a particular threshold, it s time to sound the warning so as to take appropriate countermeasures to mitigate or address risks, to minimize damage and to safeguard relative safety, which is essentially an early-warning process. Thus, to some extent, risk management and early-warning analysis are two halves of a whole. The existence of risk is the prerequisite of early-warning, while early-warning is a significant method to reduce losses. Monitoring and Early-Warning. Monitoring is a timely manner to track and attach close attention to events or phenomena. Agricultural monitoring is to supervise, track and measure agricultural operation process and situation, for the purpose of determining whether agriculture is maintained within the safety scope, and formulating agricultural macroeconomic regulation and control policy. Agricultural safety monitoring covers a broad range, including agricultural condition monitoring, agricultural resources monitoring, agricultural market price monitoring, agricultural products quality monitoring, veterinary drug monitoring, feed monitoring, etc. In terms of monitoring, indicator is a necessity. The establishment of monitoring indicators should be scientific, standardized and forward-looking. Scientific and logical indicator system is the foundation and prerequisite for good early warning research. Agricultural monitoring is bound up with early-warning. Monitoring is the foundation of early-warning, and early-warning is an extension of monitoring. If there was no monitoring, early-warning would be like water without a source. On the contrary, early-warning feeds back to monitoring, and revises the monitoring method in line with early-warning results in a timely manner. Monitoring and early-warning have their own emphasis: agricultural monitoring, which is a description of the current status, is synchronization behavior of agricultural process and situation, and is a significant approach to achieve agricultural safety information; agricultural early-warning, which is a statement of future state, underlines the analysis of agricultural safety development and prospect in view of current agricultural situation, with future-oriented and forward-looking characteristic. Along with the progress and development of science and technology, agricultural intelligent Information analyses are gradually applied in agriculture early-warning [5]. By means of modern information technology, agricultural information intelligent analysis mainly concerns about agriculture production, consumption, agricultural price fluctuation analysis, risk identification, early forecast, and impact evaluation, etc, so as to accomplish rapid information acquisition, intelligent

3 Advanced Materials Research Vol judgment, simulation warning, and intelligent Information service [6]. In the course of developing modern agriculture, intelligent monitoring and early-warning technology has displayed strong vitality. Information, Risk and Entropy. Agriculture monitoring and early warning is not only an information collecting and processing procedure, but also a system engineering. Therefore, it is necessary to concisely probe into the logical relationship among information, risk and entropy [7]. C.E. Shannon indicated that information is used to eliminate uncertainty. To go deep into Shannon s definition of information, "uncertainty" can be perceived as risk to a certain extent, taking into account that the fundamental meaning of risk lies in the uncertainty of future results. Information could reduce or eliminate risk. Shannon developed information entropy as a measure for the uncertainty in a message while essentially inventing the field of information theory. Entropy is generally considered as the degree of disorder or chaos of system, and may lead on to specific definitions in different disciplines. Entropy can be described as a measure of energy dispersal level. The more uniform energy distribution is, the greater is the entropy. The principle of entropy increase means that the entropy of a system that is not isolated may remain constant, while in a system isolated from its environment, the entropy of that system will tend to increase monotonically. It indicates that along with isolated system converted from equilibrium to non-equilibrium state, the entropy increases monotonically, but when the system achieves balance, the entropy will reach its maximum. A broad definition of entropy refers to the physically quantitative measure of the degree of uncertainty. Negative entropy, served as an measurement of the amount of information, is regarded as the antithesis of entropy, that is to say negative entropy value is equivalent to the amount of information [8]. Information theory and cybernetics enlarge the definition of thermodynamic entropy. In terms of information theory, information entropy is used to measure the amount of information. Information entropy and thermodynamic entropy are a couple of relevant concepts. The more orderly the system is, the lower is the information entropy, and vice versa. The realization of entropy increase principle is also the process of uncertainty increasing, that is, the raise of risk. In terms of systematic entropy increase, the increasing risk is a kind of macroscopic manifestation of increasing entropy, and meanwhile increasing entropy is the underlying cause of increasing risk [9]. According to the theory of dissipative structure, it is necessary to increase negative entropy in order to make the system convert from disorder to order, from lower orderly level to a higher one, and eventually to reduce total entropy of the system. Effective risk management is actually a method to increase negative entropy. The thought of risk management coincide with entropy management [9], just two aspects of an issue, which is the basic path to access to information and to achieve negative entropy. Iinformation is negative entropy, which can reduce risk. Negative entropy is produced from the negative changes in the process of interactions between the system and its environment. Information can reduce the level of uncertainty of the state of motion of the system, that is, to reduce or eliminate the information entropy of the system, equivalent to the increase of the negative entropy of the system. With the increase of information, the risk gradually reduces; information and risk is inversely related, the more information, the smaller the risk, and vice versa. The relationship is shown in Figure 1. Fig. 1 Logic relation of information and risk

4 f(i) 268 Manufacturing Engineering and Technology for Manufacturing Growth I f Which is expressed in the mathematical model below: R= Where, R for risk; for information; for reverse function. For a given system, information, risk and entropy are closely linked through the "uncertainty". Information can reduce the risk; and to a certain extent, the entropy can be used to measure the risk; Information is negative entropy. The important task of the agricultural information science is the study of "information", while the risk and entropy are from another perspective to analyze and process information. Therefore, there must be certain logic relation between risk, entropy, information and monitoring, evaluation, projection and early warning. Assuming that the agricultural system is an isolated system, on impact of various risk factors, the entropy increase principle will lead to agriculture in the state of uncertainty; agricultural risk warning through information analysis, increases negative entropy process, and reduces system entropy. The certainty of agricultural system gradually increase with decreasing entropy; steps to reduce entropy of agricultural system are in tandem with those for early warning; the agricultural early warning is a risk management process. In this logic relation, monitoring is a linkage, and an important tool and methodology for securing the certainty for agricultural system. Fig. 2 Logic analysis on information, risk and early-warning Development Direction of Agricultural Intelligent Monitoring Technology As per the basic features of natural risk, technical risk, ecological risk, and policy risk during the agricultural production process, it is critical to build up risk assessment and early warning indicator system to achieve digital monitoring, and to explore risk perception and recognition methods based on fuzzy control technology. In the light of agricultural production emergencies, it is important to develop agricultural early warning system on production risk to fulfill long-range and early warning, by means of simulation models, expert systematic technology, and visualization technology, and to find rapid reaction responses to agricultural risk. According to the necessities of the agricultural risk monitoring and early warning, it is of great importance to (1) carry out theoretical research on the standardization collection, intelligent process, and accurate service of agricultural information; (2) to formulate standard and develop agriculture information monitoring and early warning norm and standard setting and standard system; (3) to build real-time acquisition and agricultural information sharing database, as well as an

5 Advanced Materials Research Vol automatically collecting platform for agricultural early warning information; (4) to conduct research on the key technology of agricultural intelligent processing; (5) to establish China s food security early warning information system; (6) to set up monitoring and early warning system of agricultural market prices; and (7) to accelerate the construction of agricultural information discipline and make breakthroughs in agricultural information technology, agricultural information management, and agricultural information analysis, in order to provide disciplinary support for the progress of agricultural intelligent monitoring technology. References [1] Xiaoqun Wang, Risk Management, Shanghai, Central University of Finance and Economics Publishing House, 2004, 5 [2] Jun Liu, Introduction to Risk Management, Beijing, China Finance Publishing House, 2005, 9, pp , [3] Information on [4] Zhaoji Liang, Zien Feng, Zhujun Ye, Introduction to Agricultural and Forestry Economy Management, Guangzhou, South China Agricultural University, 1998, pp. 309 [5] Shiwei Xu, Application and Prospect of Agriculture Intelligent Information Analysis, Journal of library and information sciences in agriculture, 2008, 20(12), pp [6] Shiwei Xu, Exploration on the Construction of Agricultural Information Analysis Discipline, Scientia Agricultura Sinica, 2007, 40 (supplementary 2), pp. 1-8 [7] Information on [8] Information on [9] Zengke Jia, Wanhua Qiu, Risk, Information and Entropy, Scientific Research, 27(8), pp

6 Manufacturing Engineering and Technology for Manufacturing Growth / An Analysis of Agricultural Risk and Intelligent Monitoring Technology /