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1 chapter number decision-support systems in agriculture some successes and a bright future russell yost tasnee attanandana carol j pierce colfer and stephen itoga name of university country 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 1 introduction this chapter is dedicated to the capture preservation reuse and learning of agricultural knowledge we illustrate the potential of information technology with a simple example of a writing pen before the age of information a person with a pen or pencil could write beautiful poetry if she gave or loaned the pen to someone else she could no longer write poetry but the other person would have gained a tool that helped write poetry in this case the first person s loss is the second person s gain which in economic terms is a zero-sum game of importance is the fact that the relationship between the first and second person has changed and in order to continue writing poetry the first must obtain permission from the second to continue writing thus a certain measure of power has been passed with the possession of the pen and a dependency has changed between the first person and the second rare is the individual that does not see this as a clear disadvantage for the first person also rare is the relationship between two people that would not be strained by such a reversal imagine however if the first person were to make a copy of the pen and give it to the second person while retaining the use of the pen and thus suffer no loss in ability to write poetry the relationship between the first person and the second changes from one of dependency to one of collaboration and mutual empowerment rare is the relationship between two persons that would not be strengthened rather than strained by the sharing of our information age pen in this case no longer is it a zero-sum transaction no longer is there the capable and the incapable no longer is there gain of one at the loss of the other rather it has become a win-win situation in which all gain under these conditions the first person is more likely and could be stimulated to make copies and distribute pens to everyone in the world since it no longer results in their losing the tools to write poetry this is the potential of information technology information technology can enable and empower us to share tools without the loss of use of the tool ourselves it seems we have yet to fully exploit the potential of this technology 2 scope of this chapter we will concentrate this chapter on agricultural knowledge particularly that pertinent and relevant to tropical agroecosystems largely because the bulk of our experience with decision-aids has been concerned with such production systems our thesis is that successful decision-aids need to recognize the inherent complexity of such systems it is the thesis of this chapter that decision-aids can be tools to assist in the management of these

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2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 efficient decision support systems practice and challenges ­ from current to future book 1 complex yet critical elements of human food security partially through the capture of relevant knowledge and also through facilitating the accelerated learning/acquisition of the knowledge by others and also through improvement in that knowledge as a result of the organization and representation effort this chapter will describe some of the authors experience with decision-aids and their characteristics that have been useful in agriculture the initial motivation to develop a decision-aid derived from the confluence of four conditions occurring at the onset of a newly formed foreign technical assistance project in indonesia tropsoils 1981 1 the goal of the project was to provide improved soil and crop management for transmigrants farmers and producers from the over-populated rural areas of java indonesia in their new environment on sumatra with relatively large amounts of land but little land with the water needed for paddy rice cultivation the system anticipated by government planners and desired by farmers the new homesteads in sumatra provided little land suitable for paddy rice production the more extensive land differed drastically from that on java by being exceedingly acid with ph values of 4.0 to 4.5 and high levels of plant toxic aluminum aluminum saturation values frequently exceeded 50 indicating probable toxicity to food crop plants such as maize zea mays l peanut arachis hypogea l and especially mung bean vigna radiata other soil constraints to food crop productivity included low levels of essential soil nutrients phosphorus potassium which also were constraints not encountered in the rich javanese soils thus the need was great to provide new ways for the transmigrants to produce food and secure a livelihood in this strange new environment 2 two us universities were tapped to provide the technical assistance north carolina state university and the university of hawai`i at manoa these universities had extensive experience dealing with soils taxonomically identical paleudults1 to those at the project site sitiung west sumatra the immediate challenge was how could the experience of producers and growers in the southeast us central and south america which was largely experiential but also recently scientific be efficiently introduced and shared with the transmigrants who were in immediate need of food production technology on their new but unfamiliar land 3 a new perspective had just appeared in international agricultural development research circles that of farming systems research and development shaner et al 1982 the approach pointed out that farmers should be respected and very much involved in attempts to introduce new technology and practice this approach also seemed to coalesce with agroecosystems analysis as advocated by south east asian scientists in the suan network rambo and sajise 1984 4 recent developments in information technology specifically the new capabilities of software development efforts associated with artificial intelligence rich 1983 were purported to permit medical diagnosis hayes-roth et al 1983 it was hypothesized at the time that the detection and possibly the prescription of soil and crop management solutions to the weathered acid soils would be analogous to the diagnosis and prescription of appropriate medication in similarly complex human health situations with this motivation the initial decision-aids were developed with the perhaps pompous title of expert systems yost et al 1988 1 paleudults are soils of the ultisol order which are particularly old and highly weathered associated with high rainfall usually year-long see buol s.w f.d hole and r.j mccracken 1989 soil genesis and classification 3rd ed iowa state university ames.

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decision-support systems in agriculture some successes and a bright future 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 because decision-aids are often developed to improve the capture transfer in a learning sense and use of agricultural knowledge the search for and development of successful decision-aids needs to begin with a thorough knowledge of agriculture it might be yet more appropriate to search for the agricultural knowledge that is critical for providing food security and well-being one of the hypotheses of the decision-aids effort described herein was that it was possible to capture transfer in a learning sense and use this knowledge more directly in contrast to simply writing chapters books and articles on the knowledge which must then be read and assimilated before the knowledge could be used 3 agricultural knowledge agricultural knowledge that is the combined experience of how to grow and produce food fiber and bioproducts while securing a livelihood from the land is extremely complex comprised of multiple disciplines multiple persons with multiple levels of abstraction producer decisions range from considering the details of how a plant needs protection from pests and diseases to planning commodity trading and marketing all sometimes in a matter of minutes other producers worries range from which variety of food crop to plant to which field to plant first to issues of food availability and alternative sources of income should food production fail with such complexity uncertainty and variation over time it is not surprising that agriculture as an enterprise is considered highly risky white personal communication cornell university 2011 clusters the modern agricultural risks into 5 groups 1 production risk 2 marketing/price risks 3 financial risk 4 legal and environmental risk 5 human resource risks of those risks the primary one to be considered in this chapter is production risk production risk or productivity has been identified as an agroecosystem property by conway 1986 he groups the major properties of agroecosystems thusly 1 productivity 2 stability and 3 resilience rambo and sajise 1984 have expanded the number of properties to include those related to the human community associated with the agro-ecosystem table 1 property productivity stability sustainability now resilience equitability autonomy solidarity diversity 1989 rambo description the agroecosystem s output of goods and services the degree to which productivity remains constant the ability of a system to maintain its productivity when subjected to stress and shock a measure of how evenly the products of the agroecosystem are distributed among its human beneficiaries a measure of the extent to which a system s survival is dependent on other systems outside its control the ability of a social system i.e community to make and implement decisions about collective behavior measure of the number of different kinds/types of components usually providing a greater range of options for change when necessary the ability of the system to respond to change in its environment to ensure continuing survival adaptability rambo 1989 28 29 an example analysis of several agricultural systems from this perspective is given in yost et al 1997 table 1 agroecosystem properties conway 1987 marten and rambo 1988

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4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 efficient decision support systems practice and challenges ­ from current to future book 1 4 experiential knowledge while the totality of agricultural knowledge is as indicated above exceeding complex and diverse we will consider a small subset of that knowledge in this chapter we will focus on knowledge related to the growth and production of agricultural food crops and the role of nutrients either in deficit or excess in that relationship agricultural knowledge is extremely descriptive with many adjectives and nouns but few of the axioms postulates and theorems enjoyed by sciences such as physics and mathematics also as suggested above agricultural knowledge tends to be encyclopedic with relatively few universal nearly inviolable rules in addition to exercising relatively few universal rules it is also clearly interdisciplinary requiring close interaction among disciplines to adequately capture the experience acknowledging the interdisciplinarity is important because the methods and norms of the various disciplines differ and should be respected in order to obtain the best knowledge from each of the disciplines a personal experience illustrates differences among social and biological scientists for example among biological scientists data almost always refers exclusively to numerical knowledge weights of maize metric tons of root crops dollars per kilogram kilograms of fertilizers or amendments duration of crop cycles while social science data can be notes taken during an intensive interview during a focus group discussion or as a result of a recollection it is important in working with such diverse interdisciplinary knowledge that disciplines are respected for their methods techniques approaches and culture 4.1 collecting and recording agricultural knowledge accurate collection and recording of agricultural knowledge not surprisingly must reflect the complexity of the knowledge itself such collection is difficult and success not surprisingly seems to require methods appropriate for the knowledge probably some of the best methods from the point of view of completeness are those used by anthropologists their holistic perspective requires unusually complete thorough knowledge collection and recording using the most current methods available one good example is the ph.d dissertation of dr cynthia t fowler fowler 1999 describing an agricultural community kodi west sumba indonesia the dissertation required approximately 550 pages to record the relevant knowledge a small portion of the dissertation was later synthesized into an explanation of an apparent oddity ­ that an introduced plant from another continent came to be a local `sacred plant fowler 2005 another example of the capture of detailed agricultural knowledge is provided by the dissertation of dr m robotham robotham 1998 again some 550 pages were needed to describe the agricultural system in this case robotham attempted to generalize the knowledge and capture the decision-making logic from each of three villages located in the philippines ibid 1998 within each of the 3 sites selected to represent variation in philippine agriculture multiple households were interviewed using social science techniques with a total of some 17 households interviewed in all models of the apparent decision-making process were synthesized into decision-trees graphs that represent the flow of decision-making before appendix 2 to help compare and contrast the knowledge that had been developed for each of the villages influences of socio-economic forces on agroforestry adoption in the dominican republic were modeled using a rule-based system robotham 1996

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decision-support systems in agriculture some successes and a bright future 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 another effort conducted by members of the tropsoil team of our study in sumatra indonesia was the attempt to capture the similarities and differences among the local people in contrast with the transmigrants colfer et al 1989 the results suggested that the rule-based knowledge representation structure was not ideal to capture and structure the information it may have been that the knowledge was descriptive while the software was designed to capture decisions built on goals and rule-based logic 5 contributions of artificial intelligence to decision-aid development rich 1983 defines artificial intelligence ai as the study of how to make computers do things at which at the moment people do better she goes on to list various topics of interest problems as of the time of her book that scientists in the field were working on knowledge representation search strategies reasoning methods game playing theorem proving general problem solving perception visual speech natural language understanding expert problem solving symbolic mathematics medical diagnosis chemical analysis engineering design of particular interest to the authors of this chapter was the type of expert problem solving of medical diagnosis this application of a.i illustrates three contributions of a.i to agricultural knowledge knowledge representation search strategies and reasoning methods 5.1 characteristics of experts glaser and chi 1988 suggest that experts often display the following characteristics excel mainly in their own domains perceive large meaningful patterns in their domain they are fast they are faster than novices in performing the skills of their domain have superior short term and long term memory see and represent a problem in their domain at a deeper more principled level than novices spend a great deal of time analyzing a problem qualitatively have strong self-monitoring skills 5.2 knowledge representation one of the first systems to carry out medical diagnosis was the software mycin hayes-roth et al 1983 which used a rule-based system to record and exercise expert knowledge rulebased systems were constructed from a sequence of if then statements illustrated as follows 1 if blood temperature is warm and method of reproduction is live then animal mammal 2 if blood temperature is warm and method of reproduction is eggs then animal bird

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6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 efficient decision support systems practice and challenges ­ from current to future book 1 the analogy seems obvious between diagnosing and solving a medical condition and that of diagnosing and solving a condition that is constraining or limiting a plant or food crop this analogy was first recognized by several plant pathologists and resulted in the development of a system to detect soybean diseases michalski et al 1981 this structure was used in the first `expert systems developed by the authors rules used to capture the knowledge included for example rule 1 if the plant observed in the field is leucaena leucocephala l and the plant is growing well then it is very unlikely that soil acidity would limit most food crop yields 80/100 rule 2 if the soil of the field is red and in a tropical environment then it is likely that soil acidity will limit food crop yields 60/100 rules 1 and 2 illustrate ways that observational information i.e the presence of a particular plant can be recorded and can contribute to a conclusion that soil acidity may or may not be limiting rule-based systems were used to develop a wide range of diagnostic systems in addition these two rules illustrate a method to not only capture the logic in the if-then sequence but also record some expression of uncertainty in the declaration of the logical relationship in advanced rule-based systems combinations or rules with less than 100 confidence level would be combined to represent that uncertainty in the resulting conclusion some scientists developed methods of checking the consistency of combinations of various rules by examining the veracity of the resulting conclusion other methods of knowledge representation have been developed such as frames semantic nets but these are beyond the scope of this chapter given the complexity of agricultural knowledge improvements in structures supporting knowledge representation continue to be needed specifically challenging are ways to combine qualitative and quantitative knowledge in ways that conserve both unfortunately many combinations of these types of knowledge are possible only when the quantitative information is simplified to match the form of the qualitative and when the qualitative is expressed only in quantitative terms 5.3 search strategies as indicated in rich 1983 and other references strategies for efficient search through huge networks decision-trees and databases are needed ai has provided some clear examples of search strategies such as a depth-first bbreadth-first and 3best-first figure 1 a depthfirst strategy probes a knowledge-tree or a decision-tree by asking the detailed questions first in a limb of the tree top downward as the first path through the tree a breadth-first strategy in contrast searches all nodes at the same depth and then proceeds to the next lower level of nodes or questions the best-first however is a combination of the best features of the depth-first and the breadth-first strategy the best features are those in which a heuristic2 or specific knowledge guides the search to choose at each node either a depth-first or a breadth-first strategy depending on the knowledge it s interesting to note that novices often choose a depth-first strategy in probing a decision-tree and sometimes ask far too-detailed questions deep into the decision-tree too quickly resulting in a failed search in fact this occurs so often that when someone exercises a depth-first search and it 2 on a personal note my brazilian wife has shown me a very practical `heuristic she taught me how to cook rice by using the rule 1 add rice to the pan and 2 add only enough water to cover the rice by the depth of the distance between the tip of one s index finger and the first joint interestingly some have speculated that this distance may coincide with the inch in english measurements less controversial is that this as an extremely convenient meter stick!

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decision-support systems in agriculture some successes and a bright future 7 1 2 3 4 fails to find the correct answer we tend to conclude that person is a novice experts frequently use the best-first where they may switch between search strategies based on their experience and awareness of the decision-tree content depth 1 depth 2 depth 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 1 2 3 1,2,3 1 3 2 fig 1 decision-tree illustrating depth-first searches pathway example 1 breadth-first pathway example 2 and best-first pathway example 3 recently there has been renewed interest in search strategies that can exploit the rapidly expanding information base on the internet watson-jeopardy 2011 these strategies may make qualitative information much more accessible to computer based reasoning systems 5.4 reasoning methods a third contribution of ai to agricultural decision-aids the first being knowledge representation the second is search strategies is the choice between forward-chaining and backward chaining in terms of flow of the reasoning or inference through the decision-tree or set of rules briefly the forward-chaining method of reasoning begins with the observed facts and makes all possible inferences on the first pass through the decision-tree the second and subsequent passes collect all facts originally observed plus all conclusions resulting from the first pass through the decision-tree when the entire decision-tree is evaluated and all possible inferences are made the process is complete the backwardchaining method begins with the same decision-tree but first evaluates the goals or final conclusions or inferences of the decision-tree of which there typically only a few each of these goals is evaluated one at a time by determining what facts are needed for each of the goals to be concluded succeed in being inferred if any facts are missing that are needed for a specific goal then that goal is discarded and the next unevaluated goal is similarly evaluated many of the initial expert system software programs chose backward-chaining as a reasoning strategy the backward-chaining method of reasoning or progress through the decision-tree is often much more rapid than forward-chaining because major portions of the decision-tree are truncated if any rule does not have all of the necessary information and thus is evaluated as false readers interested in further details of these reasoning strategies are encouraged to consult recent texts or summaries on ai as this chapter is being written new techniques of reasoning are illustrating that machines such as ibm s watson can account for uncertainty in information and situations by rank ordering multiple solutions to a given problem the result is better performance than the best human players of the game jeopardy watson-jeopardy 2011 this event is sure to be a

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8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 efficient decision support systems practice and challenges ­ from current to future book 1 milestone in terms of managing complex and uncertain information far exceeding the previous success of ibm s deep blue that excelled with the much more algorithmic game of chess the success by watson included the access of 15 terabytes of information accessed by 10 racks of ibm power 750 servers which generated 80 teraflops of processing power henshen 2011 reports that the 80 teraflops of processing power together with improved information access methods reduced information access time from 2 hours to 3 seconds 6 example decision-aids some of the authors used an expert system development tool expert system shell to implement a rule-based system that used backward-chaining to diagnose acid soil conditions and prepare predictions of the amount of agricultural limestone needed to remove the soil acidity limitation to selected crops this software acid4 was described in yost et al 1986 and subsequent decision-aids we now present a list of various decisionaids developed and illustrate the range of uses methods of implementation purposes as well as unexpected benefits 6.1 acid4 rule-based system 6.1.1 goal facilitate the transfer of the acid soil management knowledgebase developed in southeast us central and south america to farmers and producers of the transmigration area of indonesia in general and sumatra in particular 6.1.2 objectives implement a set of rules that together represent both the scientific knowledge and farmer experience in managing acid soils for food crop production the primary source for the knowledge was a review paper by kamprath 1984 practical experience reported by gonzalez-erico et al 1979 and firsthand experience by the authors 6.1.3 implementing language exsys expert system shell hunington 1985 6.1.4 successes the acid4 decision-aid illustrated that soil management knowledge could indeed be captured and represented in a computer-based decision-aid the system permitted nonexperts with only inputs of measured soil acidity kcl-extractable acidity calcium and magnesium and a selected crop to receive predictions of lime requirements in tons of limestone per hectare eq 1 33 34 35 36 37 38 lime requirement tons hectare 1.4 exchangeable acidity ­ cas ecec 100 1 where lime requirement is the amount of limestone of 100 caco3 quality exchangeable acidity is the kcl-extractable toxic aluminum and hydrogen cas is the critical aluminum saturation which is the maximum amount of toxic aluminum and hydrogen the specific crop can tolerate while achieving maximum yields.

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decision-support systems in agriculture some successes and a bright future 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 ecec is the soil effective cation exchange capacity which is the sum of the cations al ca mg and k as measured by a neutral salt the predictions of amount of limestone thus included current soil status of soil acidity crop needs quality of the limestone and ancillary needs of calcium and magnesium in the soil an extensive comparison of adss which was a slightly improved version of acid4 indicated that the system made accurate predictions of lime requirements for maize zea mays l and soybean glycine max l but predictions for rice oryza sativa l and cassava manihot esculenta l needed improvement dierolf et al 1999 results from an exploratory rule-based system farmsys colfer et al 1989 illustrated that it was possible to merge multiple disciplines in a rule-based decision-aid ethnographic knowledge could be combined with knowledge of soil chemistry and management when diagnosing and prescribing management when acid soil conditions were encountered local minangkabau farmers preferred to grow rubber on their acid soils which required no limestone applications and no tilling of the soil transmigrant javanese and sundanese farmers on the other hand would not hesitate to ameliorate their acid soils by applying the recommended limestone and tilling the soil for annual food crop production yost et al 1992b through repeated use of the decision-aid users became familiar with typical requirements for particular crops given usual levels of measured soil acidity differences among soils and various crops in fact the users gained familiarity with the methodology and learned certain aspects of the knowledge of managing acid soils it is likely that some measure of the `expert knowledge was transferred to novice users through extensive use of the system perhaps the meta-level information was transferred to the decision-aid users as a result of using the system it is clear also that the detailed scientific knowledge was not transferred thus the mere use of the decision-aid does not replace the learning of the detailed algorithmic knowledge 6.1.5 observations further consideration of the factors affecting lime decisions indicated selection of lime materials could become impossibly complex a linear programming model was developed that evaluated limestone cost quality fineness neutralization capacity as well as calcium and magnesium content quantity available and distance from the location for up to 5 limestone materials these parameters were evaluated to provide a minimal cost choice of one or more the limestone materials that met the specified soil ph and ca and mg targets in a spreadsheet decision-aid li et al 1995 while the main benefit of the decision-aid acid4 was the use of the knowledge it contained the process of organizing and recording the knowledge led to greater scrutiny of the knowledge and the identification of gaps and imprecision which in turn led to improved subsequent research this is illustrated in the evaluation of adss a slight improvement over acid4 dierolf et al 1999 thus ironically the preparing of the knowledge for dissemination rather than detracting from the research process actually improved and accelerated it this meta-level analysis of the knowledge resulting from the crafting of the knowledge and representing it in the knowledge-base later proved to be extremely beneficial this in fact may be a replication of the patterns and larger framework that experts seem to develop over time glaser and chi 1988 6.1.6 disadvantages the acid4 system provided a hands-on introduction to capture important knowledge and for the transmigrants of west sumatra critical knowledge about how to produce food on -

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10 efficient decision support systems practice and challenges ­ from current to future book 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 these highly acid soils that differed so greatly from those of their experience the system had several disadvantages including the following the goal-driven rule-based system proved rather unsuited to capture some of the information in particular social science information did not necessarily fit well in the rule-based knowledge representation system colfer et al 1989 many on-farm production limitations were due to multiple constraints occurring together acid soils in particular are characterized by multiple constraints in addition to high acidity with toxic levels of aluminum and manganese levels of ph itself calcium magnesium and phosphorus are to be expected to be insufficient and possibly yield limiting as well fox et al 1985 a subsequent decision-aid was developed that attempted to address this problem see section 6.4 numass nutrient management decision support system later in this chapter the system required a computer this could be overcome by technicians and scientists running the software for the specific site or farm and communicating the results to the producer grower we later explore and propose a type of decision-aid that is completely graphic modification and updating of the software required rather expensive proprietary software one copy of the software could develop many systems le istiqlal 1986 a small free copy of the essential software was provided such that copies of the decision-aid could be copied and distributed inexpensively run-time version for subsequent decision-aids we used a procedure languages such as pascal or declarative languages such as prolog and hired programmers although the rules were given a numeric score of uncertainty this uncertainty was combined in an inflexible way that often did not represented neither good practice nor the scientifically verifiable behavior this effort led to subsequent improved representations of multiple sources of evidence bayesian cumulative probability yost et al 1999 an implementation of evidence accumulation described in pearl 1988 subsequent decision-aids included the cumulative probability to generate approximate confidence limits of numeric predictions of fertilizer needs using first order uncertainty analysis chen et al 1997 this remains an area requiring more accurate representation of evidence accumulation as well as the appropriate handling of contradictory evidence what are the most successful ways to carry out such calculations and accumulate evidence it is likely that some of the methods recently used by ibm s watson watson-jeopardy 201 would lead to better approaches than those described here it also is not yet clear how successful experts make such estimates if they do 6.2 propa papaya expert system that agricultural knowledge is highly interdisciplinary presents a challenge to the classical concept of an expert in a single discipline when a grower or producer contacts the university with an issue they sometimes are referred to several experts before determining which expert is the right one for the specific problem confusion and failure to succeed in the diagnostic effort may occur the goal of the propa decision-aid was to explore this dynamic by attempting to construct a decision-aid that would identify and solve typical problems possibly requiring multiple disciplines itoga et al 1990

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decision-support systems in agriculture some successes and a bright future 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 6.2.1 goal develop an expert system comprised of multiple experts dealing with complex agricultural problems 6.2.2 objectives capture the knowledge of various scientists working with the papaya carica papaya tropical fruit 6.2.3 implementing language prolog declarative language arity prolog® 6.2.4 successes the propa decision-aid illustrated that it was possible for a group of experts from various disciplines to assess a case of a papaya problem and sort out which expert would be the primary expert to solve the problem this was achieved through the use of a monitor and blackboard system that evaluated the interaction between the experts and the person with the papaya problem information each expert was assigned a dynamic relevancy factor which represented the success of their interaction with the papaya problem information the disciplines brought together for the problem-solving session included experts in 1 insect pests 2 nutrient management 3 disease identification and 4 general management and good practice itoga et al 1990 propa was able to show the user images of the various insects to assist and confirm their identification which greatly assisted the insect expert s diagnosis and recommendation process 6.2.5 disadvantages test runs of the final system with users indicated that they were often overwhelmed with the number of technical questions that were asked of them by the group of experts many users were not prepared to answer dozens of questions about the detailed appearance of the plant and thus could not respond to the experts when users could not respond to the expert s questions the experts were no longer able to proceed with a diagnosis 6.3 pdss phosphorus decision support system the pdss system development began in 1990 yost et al 1992a 6.3.1 goal capture the knowledge including both successful practice and the supporting scientific knowledge associated with the diagnosis prediction economic analysis and recommendations associated with managing nutrient phosphorus p in tropical food production systems 6.3.2 objectives capture a p management structure in a computer software decision-aid that would improve the management of the nutrient p.

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12 efficient decision support systems practice and challenges ­ from current to future book 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 6.3.3 implementing language delphi® rapid application development software pascal language 6.3.4 successes pdss builds on the results of the structuring of the knowledge for the soil acidity decisionaid acid4 as a result of the meta-analysis of the soil acidity decision-making process we identified four components in the general process of nutrient management 1diagnosis 2 prediction 3 economic analysis and 4 recommendation these components served the basis for constructing pdss and will now be discussed in succession 6.3.5 diagnosis a diagnosis of a particular condition in this case of a deficiency in soil and plant content of the nutrient phosphorus p is critical to bringing appropriate attention to the condition and consequently to its solution a diagnosis in this sense can be observed when an expert is confronted by a problem and asks a few quick questions and rapidly determines the importance of further questioning or not in this sense the expert is exercising the bestfirst search strategy discussed above such rapid assessments were observed when experienced scientists did field-visits discussing with farmers the conditions of their crops often during such visits and discussions a suggestion resulted that led to corrective action a diagnosis in this sense is our attempt to capture and implement an expert s best-first strategy of quickly assessing the seriousness of a situation and determining the best subsequent course of action in another sense a diagnosis is a call to action it is a decision about whether to act or not this definition and use is important in terms of problem-solving and may be somewhat different than the classic diagnosis used in disease identification the diagnosis we describe in this section is most effective if carried out by the person actually working with and intimately involved with managing the complex system a cropsoil production system in our case a frequent heuristic or rule of thumb is that if a disease or condition is caught early then it is more likely to be successfully cured or remedied likewise in complex systems of soil and crop management a condition can often best be solved if it is detected early before subsequent secondary complications or in some cases irreversible damage occurs the analogy with human medicine is clear for these reasons it seems prudent for the grower producer or farmer to be informed and empowered with sufficient knowledge to detect the need for action we also upon further analysis learned that there are other aspects of a good diagnosis that are important yost et al 1999 table 1 diagnostic knowledge can be useful even if it is qualitative highly observational and even if a substantial amount of uncertainty is present highly uncertain information when combined with other information with a similarly large amount of uncertainty can when taken together begin to show a pattern that is typical of the disease the condition or the state being detected a good diagnosis could result from multiple pieces of information none of which stands alone on its own but when combined together suggests a singular conclusion i.e all tending to indicate deficiency of a particular nutrient we implemented this characteristic of being able to combine qualitative quantitative as well as uncertain information by using a bayesian cumulative probability framework as indicated above in a chapter on diagnosis yost et al 1999 an example spreadsheet illustrating the calculations is shown in appendix 2 the combining of multiple pieces of information thus often led to a

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decision-support systems in agriculture some successes and a bright future 13 1 2 3 4 5 6 7 8 diagnosis when no individual piece of information was sufficient to provide a call for action it was possible to include a consistency check if mutually contradictory facts were observed for example if the probability of a nutrient deficiency for fact a was 0.9 that a p deficiency was likely where 0 means certainty of no deficiency 0.5 means complete ambivalence and 1.0 means total certainty and fact b had a probability of 0.2 then we have a situation of conflicting evidence a rule was written to send a message to list in the output that a serious contradiction is occurring table 1 considerations in developing diagnostic questions we suggest that the best diagnostic information tools questions are those that build on the common knowledge that on-site managers e.g farmers have readily available together with simple measures both qualitative and quantitative of fundamental characteristics of the production system -the tool/question should be simple to use by lay persons -results of the tool should be quick such as the simple observation of a symptom or property in the field -cost of the tool/question should be low or of no cost -the tool/question should be reliable as it should reliably indicate what action is to be taken observations -sometimes the result of the tool/question is that more expertise is required -incomplete or imperfect data should not completely invalidate the diagnosis -the tool/question should take full advantage of the farmer producer or field observer s observation and knowledge -the tool/question may lead to improved better diagnostic tools questions developed in a tpss 650 soil plant nutrient interactions by students n osorio x shuai w widmore r shirey university of hawai`i at manoa 9 10 11 12 13 14 15 16 17 18 19 20 21 22 we encountered two disadvantages of using the bayesian accumulation of probability framework 1 much of our evidence and multiple observations or measurements were highly correlated or multicollinear the multicollinearity contrasts with the assumed condition of independence in classic bayesian evidence accumulation and thus the calculated cumulative conditional probabilities were in slight error depending on the degree of multicollinearity 2 one could have strong evidence both for and against a condition as well as weak evidence for and against the condition or even a complete lack of information all of which would combine to a value of 0.5 as a result strong but conflicting evidence is wholly discounted one of our inadequate solutions to this situation was to monitor evidence and when evidence for and against a particular outcome differed substantially a message was attached to the conclusion warning of the information conflict.

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14 efficient decision support systems practice and challenges ­ from current to future book 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 6.3.6 predictions the prediction in pdss is usually a numerical amount of a specified amendment needed to resolve the nutrient deficient condition identified in the diagnostic section there may be additional inferences based on the additional data usually required to complete the numerical prediction there is a possibility that upon analysis of the additional information a prediction of no requirement may occur the prediction was developed using a combination of both scientific and local experiential knowledge the preferred knowledge is that occurring when the best scientific methodology is gleaned from the literature and tested in the unique local soil crop weather economic and social conditions to obtain and ensure such knowledge clearly requires intense work by the local scientists as well as the knowledge engineer the person who organizes the knowledge and structures it into the knowledge representation format of the decision-aid software in our case scientists have included both international experts as well as local agricultural scientists who were in the process of or had completed field studies of the prediction methodology the choice of which knowledge and how much detail needed to be recorded and represented in order to minimize excessive detail and yet retain the essential knowledge was and seems to be a challenging one this aspect has been lucidly discussed in stirzaker et al 2010 as stirzaker et al 2010 indicate the typically detailed information resulting from research needs to be smoothed and simplified to be most effectively used in a working system our experience has been identical and this aspect of building decision-aids seems to us to be one that requires close interaction and discussion with the intended users to best ascertain the appropriate level of detail thus it is clear that the direct transfer of algorithms and conclusions from a research effort is seldom possible without the requisite simplification described by stirzaker et al 2010 the intense and repeated contact between the developer and the client or user group has been essential in our experience this type of intense interaction has come to be termed extreme programming beck 1998 wells 2001 this programming style is based on frequent viewing and discussing of the software being developed with representative intended users one of the requirements of the prediction step that is necessary for the integration with the subsequent components is that there be a numeric prediction this numerical value forms the basis of the benefit/cost analyses carried out in the subsequent economic analysis section the prediction equation of the pdss decision-aid is thus an equation that began with the rather simple description given in yost et al 1992a shown in equation 2 34 35 36 37 38 39 40 41 where p requirement soil p required ­ soil p present /reactivity of the soil to added p 2 where p requirement is the kg/ha of fertilizer p needed to increase the soil p level soil p present to match the soil p required and thus meet the specific crop s requirement for nutrient p while equation 2 gives the basic structure of the p requirement prediction equation there were updates to the equation which gradually increased in detail and complexity eq 3 p requirement pcl ­ po pbc 0.8 pbc puptake 0.8 1 2 placement factor application depth 10 3

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decision-support systems in agriculture some successes and a bright future 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 pcl p critical level of the crop using a specific extractants soil p required of eq 3 po initial measured soil level of p using an specific extractant soil p present of eq 3 pbc phosphorus buffer coefficient using a specific extractant reactivity of the soil to added p of eq 3 puptake yield of crop component removedp content of the removed tissue not present in eq 3 application depth depth to which the fertilizer is incorporated not present in eq 3 placement factor a factor that represents the relative efficiency of localized placement in reducing the p fertilizer requirement not present in eq 3 the predictions developed in pdss as in acid4 also included an expression of the associated uncertainty in the acid4 and farmsys modules the uncertainties were personal estimates of the reliability of the rules being exercised in pdss a different approach was used that of error propagation burges and lettenmaier 1975 the error propagation calculation resulted in a very useful assessment of the equation s prediction this was later expressed as the confidence limits of the prediction an example of a prediction of p requirement was carried out on an experiment done at the centro internacional de agricultura tropical ciat in cali colombia and is illustrated in figure 2 an interesting result of this prediction was that the actual precision of the fertilizer prediction was approximately 50 of the requirement in most cases this large error pointed out the typically large uncertainty in fertilizer predictions one advantage of the first order uncertainty prediction was the ranking of sources of variability in the prediction equation this enabled prioritizing research effort to better understand and make predictions chen et al 1997 25 26 27 28 29 30 31 fig 2 comparison of pdss prediction with field estimates of the amount of fertilizer phosphorus needed to achieve maximum yield ciat 1993 6.3.7 economic analysis economic analysis was the third component this component clearly differed from the other portions of the decision making process requiring an economic calculation of profitability resulting from resolving the diagnosed problem using the prediction methodology as

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