GOSSYM: The Story Behind the Model

GOSSYM: The Story Behind the Model Baker, D.N., M. Boone, L. Briones, H.F. Hodges, E. Jallas, J.A. Landivar, A. Marani, J.M. McKinion, K.R. Reddy, V.R. Reddy, S. Turner, F.D. Whisler, J. Willers 1 Abstract Here we narrate the scientific work forming the basis for the cotton simulation model GOSSYM. This included innovation in experimental research methods and in the summary and the application of data in the form of process rate functions and logic to mimic the behavior of field grown cotton. We also outline the various analytical applications of GOSSYM, including that of crop management decision support. We reference the time course of crop management user adoption and the independent evaluations of the system. All of this is presented in the context of the administrative environment supporting the research. Our purpose is to provide insight into the research and administrative processes involved in hopes that future managers of such programs will have realistic expectations and some awareness of what does and does not work. Keywords: crop management, decision support, crop simulation, cotton, research environment, technology transfer, Introduction This is the story of the decisions, the issues that were faced, the arguments that arose, the strategies and tactics that were adopted and the successes and failures that caused us grief and kept us motivated. Background The setting for this story was in the minds of a group of scientists and engineers with better than average backgrounds in mathematics and thermodynamics in a time of raging and rampaging scientific reductionism. Those proposing crop simulation models were quickly 1

D.N. Baker, Baker Consulting, 1230 Morningside Drive, Starkville, MS, M.L. Boone, Dept.of Plant and Soil Sci., MSU, A.L. Briones, Dept. of Soils, U. of Philippines Los Banos, H.F. Hodges, Dept.of Plant and Soil Sci., MSU, E. Jallas, CIRAD, Montpellier, FR., J.A. Landivar, D&PL, Scott, MS, A. Marani, U. of Jerusalem, Rehovot, Israel, J.M. McKinion, GAPA, USDAARS, Mississippi State, MS, K.R. Reddy, Dept.of Plant and Soil Sci., MSU, V.R. Reddy, Systems Lab., USDA-ARS, Beltsville, MD, S. Turner,GAPA, USDA-ARS, Mississippi State, MS, F.D. Whisler, Dept.of Plant and Soil Sci., MSU, Crop Sci., USDA-ARS, Mississippi State, MS, J.L. Willers, GAPA, USDA-ARS, Mississippi State, MS

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told by their Apeers@ that biological systems are infinitely complex, and that such systems were created by God and that only He could understand them. Anyone attempting to simulate a biological system simply lacked the training and credentials in the biological sciences to recognize this level of complexity, and the peers often helpfully pointed out to the wayward scientist=s supervisors the embarrassing foolishness of such an endeavor. It was a time when lip service was paid to team research, but when scientist grade levels were determined by their rate of production of senior and sole authored papers. It was a time when government research agencies like USDA-ARS (United States Department of Agriculture - Agricultural Research Service) were reorganized from a strong manager system to a peer panel system for making personnel and program management decisions. It took place in a time of transition from research programs which required vision and competent top down management to research projects which could be initiated and managed from the bottom up. It was therefore a period of unprecedented freedom and opportunity for well-led research laboratories and for chaos and confusion in those that happened not to be well led. In 1965, the two volumes of the proceedings of the Cotton Production Research Conferences occupied 5/8" of shelf space and most attendees at least scanned through all of the papers. In 1996, the same proceedings occupied 4 1/2" of shelf space, not including the poster papers, and practically no one even read the titles of the papers. It was, indeed, a time for drowning in information while starving for knowledge. It began in a time when four full season simulations of a cotton crop might be made in a 24-hour period on a mainframe computer and it arrived at a point where a full season simulation could be made in three seconds on a notebook PC and the Aruns@ were being selected, implemented and evaluated by computerized expert systems. The advances in computer technology were anticipated by our research team (McKinion and Baker, 1982) and they, along with the database we were capable of developing, were instrumental in choosing the level of aggregation of our models. The transition from research programs to research projects occurred at a time of intense and sometimes bitter competition at all levels for research funding. AZero based budgeting@ for research was proposed. Much of this time period experienced double digit inflation and a typical research team had to acquire new funding equivalent to a senior scientist position every year just to continue operating. Crop simulation began just as statistics was acquiring the status of a scientific religion and statistical methods were becoming rigorously enforced religious ritual in the publication of scientific papers. Biological system modelers, especially those building dynamic simulation models took the heretical position that the statistical significance of differences among means was much less interesting than the reasons for those differences and the older of those heretics, who could afford and survive the criticism leveled by their Apeers@, sometimes suggested that simulation could be viewed as an intellectual alternative to statistics in biological research, (because it required contextual validation of hypotheses about the nature of systems) and that statistics was too 2

often used as a substitute for common sense and rational thought. Nowhere would statistical zealots wreak more havoc on simulation modeling than in their Apeer@ review of articles describing the controlled environment experiments characterizing physiological process rates, but these zealots are now also impeding the development of precision agriculture methodology. Not long prior to the first attempts at crop simulation a statistical method called regression analysis was being refined and computers capable of inverting large matrices were becoming available on most college campuses. The fitting of multivariate data sets describing individual physiological process rates would become the most common mathematical method in crop simulation modeling. Predicting Crop Performance Now we take up a brief history of strategies to predict crop growth. In the late 1950's a 4H=er in Mississippi was reported by the popular press to have produced a 300 bu/acre corn crop. This astonishing report provoked lengthy animated discussions among agronomists on the faculty of Cornell University. The question was asked Aif a corn crop has yielded 300 bushels, what is its maximum potential yield? The penultimate field plot experiment was initiated by Professors Richard Bradfield and Robert Musgrave to answer this at two locations in upstate New York. In addition to corn and several small grains, a couple of forage systems were included. The design included lime, fertilizer and irrigation treatments at several levels including some considered to be far beyond the requirements of any of the crops. After three or four years, the maximum corn treatments had yielded only the same or slightly less than commercial farms on land adjoining the experiment stations. This conundrum resulted in a decision to Atake the laboratory to the field.@ They decided to do laboratory and phytotron-type research to explore physiological processes, but they would do it on plants in a cropping situation in the field. They and their students used the growth analysis concepts and terms of Watson and others (1947) e.g. leaf area index, relative growth rate, etc., where useful, but they were intent on characterizing the environmental factors that determine the rates of individual physiological processes. They would do this in controlled environments in field plantings. They were resolved to overcome the problems of artificially lit controlled environments. They began with the newly available infrared gas analyzers and wet test gas meters to record the amounts of pure CO2 required to maintain a set concentration in refrigerated gas-tight chambers in the field. They referred to this as net assimilation (after Watson) and then quickly recognized that this was inappropriate because the measurements were made at 15-minute intervals at set temperatures, etc., rather than over periods of days, so the term net photosynthesis was substituted, and this has been replaced by several other terms by other authors since. In order to separate the process of photosynthesis, light and dark respiration measurements 3

were made and characterized as functions of temperature. It was quickly realized that by sealing off the soil (as needed to avoid soil respiration effects) the condensate flowing from the cooling coils of the air conditioner was a good short-term representation of transpiration by the crop (if corrections were made for the water vapor content in the system). There was concern about an observer effect or chamber effect on these data so collaboration with E.R. Lemon and his students who were making open air, turbulent transfer measurements of gas exchange rates in the same crops began. We would note here, in passing, an item that would come into sharper focus a decade later. These researchers published data showing that corn leaves and crop canopies are not photosynthetically light saturated even at full sun intensities. This came at a time when the textbooks claimed light saturation at 1500 ft. candles. This work by Musgrave and Lemon and their students resulted in a long series of papers entitled Photosynthesis Under Field Conditions:… This was purely exploratory research seeking to discover the physiological factor or factors limiting crop yields. No thought of the dynamic simulation of field crops, much less decision support systems as we define them today was given. These ideas would come later. In the early 1960's, two former students of Musgrave began experiments that would describe physiological process rates in cotton. Baker (1965) continued the refinement of equipment and techniques for measuring light interception in row crops and, photosynthesis, respiration and transpiration over 15-minute time intervals in closed systems in the field. We developed regression models for cotton crop canopy light interception based on solar altitude, azimuth and leaf area index, and we found that these models could be used under unstressed conditions to adjust other regression equations describing closed canopy photosynthesis over very short time intervals. Moreover, we found that we could estimate respiratory losses precisely from temperature and plant biomass. We were close to building a materials balance. During the mid to late >60s, our budget became too restricted for the experimental research to continue. Therefore we began to work with the data we already had and that was the beginning of our development of a cotton simulation model. Continuing with the question of maximum yield, using typical solar radiation and temperature data we used programmable electronic calculators to calculate increments of dry matter production and to integrate them over the season from July 15 to harvest. This showed that a maximum potential lint yield would be 5 bales and 7 bales per acre in the Mississippi Delta and in the Arizona desert, respectively. This was taken, by research planners and by some of our competitors as evidence that photosynthesis was not a yield-limiting factor and that photosynthesis research in cotton was unnecessary. We felt the necessity, with limited funding, to refine these estimates of photosynthate production and to find out where all this photosynthate was going in crops that rarely yielded over 2 bales/acre. 4

At this time (the late >60s) Hesketh, (another of Musgrave=s students) was in the process of publishing data, (Hesketh, and Low, 1968; Moraghan et al., 1968) collected in the Canberra Phytotron, describing the effects of temperature on all of the developmental processes (time to first square and to bloom, and the plastochron intervals). We recognized that this was exactly the kind of information needed to begin the calculation of dry matter partitioning in cotton. At this time, Hesketh joined the USDA-ARS at Mississippi State and a collaborative effort commenced. Experiments were continued to refine the crop canopy photosynthesis and respiration rate functions, to characterize rates of leaf senescence (based on declining leaf photosynthetic efficiency), and to characterize the effect of temperature on dry matter accretion rates and the associated nitrogen allocation rates in cotton leaves, fruit, stems and roots. Also at this time, Stapleton et al. (1968), an agricultural engineer, began the development of a cotton crop management Decision Support System (DSS) on an analog computer. He organized two seminars involving, among others, C.T. deWit, (Wageningen), R.S. Loomis (U.C. Davis) and W.G. Duncan (U. of Kentucky). All of these agronomists had earlier experience in the development of computerized static leaf element models of crop canopy light interception. Now they began construction of dynamic simulation models not to serve as components of DSS but with the objective of establishing the physiological structure of several crop species. Within weeks after these two seminars, Stapleton organized another meeting of a dozen or so (mainly agricultural engineers) to discuss crop simulation. Several of these people later had sabbaticals with deWit, in Holland, and like deWit they were committed to the writing of their models in CSMP2. One of these people, Duncan, however, rejected CSMP in favor of Fortran. The second Stapleton meeting was so successful that the group (the Biological Systems Simulation Group), initially small and exclusive, met every year thereafter to discuss everything about crop simulation from administrative policy issues, publications, verification, validation, funding, DSS, and the consequences of inclusion or exclusion of specific physiological processes. Lacking computer skills, we in ARS at Mississippi State, immediately prevailed on W.G. Duncan to receive our regression equations describing light interception and the physiological process rates and to begin assembling a cotton simulation model. He called this model SIMCOT. At the end of a year the model was running, but it could not simulate plant development or fruiting. Worse it did not address the issue of natural (fruit) shed. Duncan really wanted to build a corn model (SIMAIZ), so he shipped the SIMCOT card deck to us in Mississippi and we began to learn Fortran and to employ engineering students who could write it. Duncan had laid this model out in modules (of which he supplied us with long hand diagrams) along physiological process lines. The drawings were crude, but the 2

Continuous Simulation Modeling Program is a Fortran based IBM7 product. 5

thinking was elegant. It stood the test of time and our cotton models still have that design. However, we discovered that Duncan had included in the plant stem a huge carbohydrate reserve capacity a la corn and it didn=t take us long to determine that this was the reason we could not simulate fruit shed. In 1973, a special session was organized at the Beltwide Cotton Production Research Conferences to discuss limitations to cotton yield and research opportunities therein. We presented an invited paper entitled AAnalysis of the Relation Between Photosynthetic Efficiency and Yield in Cotton@ (Baker et al., 1973). We intended to use an improved version of the SIMCOT model for this analysis. The model would be validated using a rainout shelter data set published earlier by Bruce and Römkens (1965). Then, we would vary our equations describing photosynthesis in various ways in order to observe effects on plant growth and yield. Hesketh=s equations describing plastochron intervals (phytotron data) as temperature functions and organ growth rates from field experiments were incorporated. His plastochrons and times to first fruit had been measured in spaced plants (pots) not heavily loaded with fruit. However, the model diverged wildly from the observed (Bruce and Römkens) fruiting and mainstem node numbers. It would simulate the early season mainstem and fruiting branch numbers correctly and it would give reasonable predictions of plant height during this exponential growth phase. But no form that we could come up with gave the sigmoid stem growth and development patterns and the model generated many times the actual fruit numbers. It should be noted here that we had a strict rule against expressing anything in the model as a function of time. This is about where we were in the development of the model at the beginning of the 1972 Christmas break at the University. The Cotton Conferences would be the first full week in January. The Fortran programming was being done by a very bright 3rd year electrical engineering student (McKinion) and for lack of funding the runs were being made on his student account at the University computing center. With classes in session we were only able to make about 4 Aruns@ per day. During the break, however, we had the computer nearly to ourselves. An accommodating system manager even assigned one of four terminals to us - a major improvement over IBM cards. In the ensuing days we made dozens of runs testing various formulations attempting to relate photosynthesis to plant growth and development. The results following first bloom were wildly out of line. Late one evening between Christmas and New Years we gave up. Retreating to a black board back in the laboratory we began to reinventory what we knew for sure and to write more equations. We knew that beginning with the work of Mason (1922) a Anutritional theory@ of boll shedding was developed. It suggested that the cotton plant would seek to balance boll demands for carbohydrates and other nutrients against the supply by abscising fruit. Wadleigh (1944) and many others (Eaton, 1955; Johnson, and Addicott,

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1967) had found evidence supporting this theory, but it had never been absolute and clear cut. In fact, Eaton and Ergle (1953) found no correlation between boll shedding and carbohydrate or nitrogen levels in various tissues. Further, they had noted that an early planting and a late planting had similar concentrations of carbohydrate and nitrogen in midAugust and that the early planting was shedding fruit rapidly, but the later planting was not. This had essentially killed the nutritional theory and it stimulated a great interest in hormone research to understand and control the fruit loss. However, we knew that experiments with gibberellic acid, which could greatly reduce shed, had not only failed to increase cotton yields, they had resulted in marginal, if any, increases in boll numbers. Moreover, hormone research had failed to yield any connection between photosynthesis or nutrient uptake rates and fruiting and yield. In hind-sight, it is obvious that no serious effort had been made to establish such a mechanism. In our inventory of the situation that night we knew the following: The Bruce and Römkens plant growth and development and fruiting patterns were similar to all field-grown cotton. The Bruce and Römkens data we used were from their well-irrigated treatment. Those plots were irrigated every time the soil water potential fell below -0.3 bar and they had been fertilized with 300 lb/acre N in four applications. Since insect control was meticulous, we recognized that our failure to simulate the plant development and fruit shed patterns had to do with carbohydrate supply and demand. We were completely confidant of our calculation of canopy light interception, photosynthesis and respiration. Our estimates of potential dry matter accumulation rates in leaves and bolls were based on field measurements in 1969. After some adjustment of the leaf data for water stress in the 1969 season, we felt those estimates were reasonable and not responsible for the wild behavior of the model. By this time, we had redefined maximum carbohydrate reserve capacity to be equal to 30 percent of the leaf dry weight. The problem appeared to be in our description of potential growth of stems and roots. We next proposed to write potential stem growth in the form dw/dt = kw, where w is plant dry weight and k is a constant. However, this form, lacking any cutoff point or limits generated an infinite sink strength as the plant got older. No amount of Acalibration@ would get rid of this behavior. It did, however, extend the period of reasonable behavior much further into the season. Finally, we recognized that this woody perennial was retiring biomass from the active stem and root sink as the season progressed. So we modified the potential stem growth equation as follows: )W = 0.2 + 0.06(STMWT - STMWT(IDAY) ) where )W is potential daily stem growth increment, STMWT is the stem weight on this day and STMWT(IDAY) is the weight increment added on today=s day number minus Iday. The coefficients 0.2 and .06 were obtained by fitting the first 42 day=s stem weights from the 1969 field experiments. A short series of runs were made and suddenly the model began to mimic the pattern of fruit shed and the lengthening of plastochrons as in the Bruce and 7

Römkens data. The subtraction of the woody component began 41 days after emergence and the amount subtracted turned out to be that added 24 days earlier. Further runs would quickly yield the age brackets of fruit to be shed and the additions to be made to unstressed plastochrons. Finally near perfect simulations of the Bruce and Römkens data were obtained as shown in Figures 1 and 2. The procedure described here to find the value of a plant parameter which is not directly measurable would be referred to later by Acock (Whisler et al., 1986) as one of the major applications of dynamic simulation models. He called it a Aphysiological probe.@ Having gotten a good simulation of the well-irrigated treatment of Bruce and Römkens, it was a simple matter to vary the plant population and the photosynthetic efficiency of the crop and plot the results as shown in Figure 3. When these results were presented the following week in Phoenix, a major howl of protest went up from the hormone physiologists present. They marched en masse to the podium and accused us of having simulated fruit shed in cotton with no specific reference to hormone systems. Our response in a letter to one of the leaders of this group was that we had not denied the action of hormones in cotton, rather we had merely presented evidence that these actions are predictable and are driven by both the carbohydrate supply and the total sink capacity in the system. We asked them to pursue this in the laboratory to map out the chemical linkages between metabolite supply and movement and hormonal action in the plant. They never responded and indeed hormone research in cotton virtually ended a short time later. Our work continued (as discussed below) finally leading us to suggest that fruit shed may occur if the velocity of metabolites flowing through the petiole is too low to provide a purging (of hormones responsible for the loss of mucilage in the abscission layer) effect, the background concentration of ethylene being driven as documented in the literature, by injury from water stress, insect damage, etc. This hypothesis has yet to be tested either experimentally or analytically. The Phoenix presentation of a dynamic simulation model that could, among other things, simulate vegetative regrowth on the relaxation of carbohydrate stress was the first of a series of major events that would occur in 1973. The success of the SIMCOT simulation of the well-watered treatment of Bruce and Römkens positioned us for the next step - the water stressed treatments. If we could simulate them, we would be ready to consider DSS. This was of interest because by then we were well into the Integrated Pest Management (IPM) Project (a follow-on to the International Biological Program) where we served as the ARS representative on the (its) steering committee. We had already succeeded in deflecting that project by suggesting that these pest ecosystems could best be modeled from the subprojects in the 19 universities rather than attempting all modeling in the management component at Berkley as originally planned. So, now there were teams funded under IPM

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(ours included) at many locations building models in support of Integrated Pest Management research. To reach the capability to simulate cotton growth and yield under water stress we decided we would have to scrap SIMCOT and make a fresh start in a more durable framework. A leave of absence was taken to work with J. R. Lambert at Clemson University on the new model. At that time the argument over whether models should be Apractical@ or scientific was raging. That this argument was so heated and emotional may seem odd. In hindsight, judging from the personnel lined up on the two sides, it seems that a major part of the motivation of the Apractical@ modelers was that they simply lacked the knowledge, training, and data to build Ascientific@ models. But their use of the term Apractical@ had an extremely potent effect on administrators who did not want to be identified with anything impractical. They made the claim that Ascientific@ models would require too much computer resources to be practical in DSS. We made (and published) formal ongoing projections of computer technology which told us that by the time we could get Ascientific@ models written and validated, suitable desktop PCs would be running in every farm office. At Clemson we named the new model GOSSYM to suggest that it would be as explicitly scientific as possible and it would contain a rhizosphere simulator (RHIZOS) which would provide a two dimensional simulation of the movement of roots, water and nutrients in the soil profile. RHIZOS ended up being about 80 percent of GOSSYM in terms of lines of code, mass storage requirements and execution time. In June, 1973, at the urging of the new MSU Agronomy Department Head, Dr. R.G. Creech, we organized a two-day symposium on the application of systems methods to crop production. It was attended by about 120 scientists from across the country. Many of them were members of the Biological Systems Simulation Group (the group started by Stapleton) and by scientists and modelers in the IPM project. Three sessions, Plant Systems, chaired by Creech, Insect Systems, chaired by the Head of the Entomology Department (Maxwell), a key player in the IPM Project, and Economic Implications, chaired by D.N. Baker. In the latter session we prevailed on the head of the Agricultural. Economics Department (Hurt) to deliver a paper Implications of Crop Production Simulations in the Field of Agricultural Economics. Unlike other Agricultural Economics departments which have always dealt with global economics, the Mississippi State University (MSU) economists were widely recognized for their work at the farm and enterprise level. We specifically asked him to outline what enterprise level economists would do differently if they had valid crop simulation models. We had not yet attempted DSS as it is conceived today and he missed the mark with his paper, but he would remain a strong supporter of Crop Simulation at MSU from then on and later as the Mississippi Agriculture and Forestry Experiment Station Director. In the plant systems session an important discussion occurred about whether or not hormone systems, translocation, vein loading with starch, osmotic adjustment, 12

photoperiodism, etc. would have to be modeled explicitly. In general, whether or not to include these plant characteristics was decided on a species by species basis. We already had a strong indication all of them could be ignored for cotton. Commercially grown cotton is not photoperiodic. Hesketh, in particular felt that with 24-hour time steps translocation would not be limiting. Indications to that effect and to the lack of significant feed back effects on photosynthesis from starch loading came from our high CO2 controlled environment experiments. The same choice was made by the corn modelers. However, some of the soybean modelers would choose to treat translocation explicitly, and photoperiodism would have to be dealt with in several species. The soybean modelers would have to calculate environmental effects on nitrogen fixation rates and all the models of annual plants would have to calculate senescence and death of the crop. Late in 1973, we invented a controlled environment system for the specific purpose of characterizing the physical and physiological process rates in crop ecosystems. Several design criteria were specified. First, it would be orders of magnitude less expensive to build and operate than the phytotrons of the day. Secondly, it would be vastly more powerful than phytotrons in the number and quality of environmental features that could be controlled. Control would be via computer. Third, like the gas tight chambers we had been using in crop canopies, it would provide continuous measurements of photosynthesis, respiration and transpiration. Beyond that, it would permit systematic and independent control of rates of photosynthesis (source) and growth (sink) to characterize the effects of metabolic stress on plastochrons and fruit shed. Fourth, like the phytotron and the field chambers, it would be naturally lit, but plants would be in rows in soil with light attenuation by adjacent plants outside the chamber simulated by shades that varied in density with depth. Finally, completing the design of this physical model of a field crop, ideas from the Auburn Rhizotron were borrowed (Taylor, 1969). The plexiglass chambers were placed on top of soil bins 1-meter deep, with wire reinforced glass faces in the front for root measurement. Each bin was enclosed in a metal surround with doors and other openings. The surround provides the capability of soil temperature control. The bin could be drip irrigated with nutrient solution. The units were named SPAR for Soil Plant Atmosphere Research. Initially three units were built at the USDA-ARS Coastal Plains Research Lab. in Florence, SC. They were operated in collaboration with Phene (1978). After three years they were moved to Mississippi State, MS and three more units were built and operated on the Clemson University campus. Eventually the Mississippi State installation was expanded to ten units and, in 1982 in order to make them sufficiently convenient to the scientists involved, the Crop Simulation Research Unit was moved en masse into offices and laboratories immediately adjacent to the SPAR pad. Except for salaries for permanent personnel and small amounts for occasional equipment purchases by USDA-ARS and the Mississippi Agriculture and Forestry Experiment Station, virtually all of the cost of this 13

installation was covered by research grants and contracts. Although it occasionally stood idle during periods when no soft money was available, the installation produced such an abundance of data of the specialized character required in process oriented models, that the development of cotton, soybean and wheat simulation models continued without interruption. About 12 Masters and PhD theses and dissertations were generated from these experiments. The administrative environment is important to the progress of any research. This is especially true of a new area of research. The attitudes not only of upline managers, but also peer groups who evaluate research proposals and personnel actions can be major factors affecting the progress of any new research. By 1972, ARS managers had become aware of the fact that Amodeling@ was going on at some locations in the Agency and some of them felt that their scientists were being paid to do research and not to Afool around with computers.@ There was sufficient concern about this at agency headquarters that a temporary committee (including some scientists) was appointed to assess the seriousness of the situation. Sure enough the committee reported that modeling was going on in several disciplinary areas including watershed and erosion management, the plant sciences and even among some entomologists. Moreover, it was going on at a number of locations and it was not just statistical models. There were also static models of particular processes and another kind of modeling called Asimulation@ had reared its head. In 1973, this committee was disbanded and a new committee3 was appointed. It was charged with making a recommendation on Awhat to do about modeling@ (in ARS). We mention this here because modeling, like any other research activity, takes place in an administrative environment. Funding and administrative support are crucial determinants for the success of any effort to apply research to the development of predictive models and decision support systems for agriculture. This committee met several times per year for four years arguing over policy options for managing ARS modeling. The argument was between staff scientists and administrators who wanted a modeling czar at headquarters to control and to do most modeling, and research scientists from the field, who wanted independence to do modeling as they saw fit. Finally, in December 1977, the committee issued a report on the Status of Modeling in ARS 3

Edgar R. Lemon, Northeastern Region, Chairperson - Soil physicist- built static models of gas exchange in crop canopies. Donald N. Baker, Southern Region - Plant Scientist - built crop simulation models. Bruce A. Crane, Data Systems Applications Division (HQ) Provided services agency wide. Kenneth G. Renard, Western Region - Hydrologic Engineer - built watershed models. E. Fred Shultz, Jr., Statistician - former head of Biometrical Services Division providing statistical services agency-wide. Glen E. Vanden Berg, Agricultural Engineer - Director ARS Northeast Region. John A. Witz, Southern Region - Agricultural Engineer - built trap simulation models. 14

(1977). It described the types of models and the locations and disciplines involved in modeling. Then the committee drafted and the agency published an Administrative Memorandum (AM130-20) creating and charging a permanent National Modeling Coordinating Committee. This committee would recommend disapproval, revision, or approval of modeling proposals which involved new funding above a certain amount or which involved more than one location. They could also recommend termination of existing modeling projects or their movement into a Athird stage@ for application. This AM also created a position of ASenior Modeler@ to be assigned at agency headquarters. At this time, the committee drafted and the agency also published three other AMs. AM520 stated modeling Policy and Responsibilities at all administrative levels. AM520-1 created an inventory system (File of Agricultural Research Models). And, AM520-2 set forth standards and responsibilities for Approval of Proposed (modeling) Projects. Finally, in January 1978, the agency Administrator=s Office published a Technical Manual (TM520) Concepts for Using Modeling as a Research Tool. The Senior Modeler was never hired and the new permanent Modeling Coordinating Committee was never appointed. Perhaps due to a lack of any first hand experience in modeling on the part of administrators and staff scientists and because of the need for freedom of action by researchers in the field, no action with reference to these administrative memoranda was ever taken in the years ahead. However, it did appear that as a result of the work of the modeling coordinating committee, opposition to modeling as a matter of management policy ended. In hindsight, it is clear that the concepts alluded to above should have been more directly aimed toward modeling as a basis for the development of decision support systems and that more deliberate advantage should have been taken of the opportunity to use models to identify knowledge gaps and research opportunities, and to set research priorities targeting the research on design and management of agricultural systems. Undoubtedly the most profoundly important event to occur in this program in 1973 was the arrival of Dr. F.D. Whisler at Mississippi State. He joined the University as Professor of Soil Physics. He arrived with a world-class record of publications and other achievements in soil physics and a keen interest in modeling. He immediately assumed responsibility for the continuing development of the RHIZOS model, including its incorporation into the soybean and wheat models. By mid-1973, GOSSYM was running on the MSU and Clemson mainframe computers. In late 1973, it was installed on a USDA mainframe in Beltsville, MD. Development of the model continued on that machine via a dedicated land line and remote terminal in the Boll Weevil Research Lab. at Mississippi State. This was so expensive that (agency) permission and funding were granted in 1974 to install ModComp minicomputers for model development and control of the SPAR installation at Mississippi State.

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No diary of activities to extend, validate or apply the GOSSYM model was kept, however, most of the subsequent developments are identifiable from pseudo-code in the source listing. In some cases, we must rely on related theses, or journal and book chapter publications for fixes on authors and dates. From 1973 on, the ecological range of GOSSYM would be extended in graduate thesis and postdoctoral projects. Most of these included a field validation experiment to evaluate the degree of improvement provided by the added logic and new code. In each case, the model was required to simulate earlier validation data sets as well. In this way, over a period of years, several excellent field validation data sets were acquired. The evapotranspiration (ET) subroutine was taken almost entirely from Ritchie (1972). The plant size mechanism there was deleted in favor of the GOSSYM light interception function. RHIZOS contained an UPTAKE subroutine which removed the volume of water calculated in ET from the soil profile. It removed varying amounts from each cell in the (2dimensional) RHIZOS matrix in proportion to the product of root weight capable of uptake and the hydraulic conductivity of the cell. Root biomass in each cell was quantified in three age categories with different weighting factors in each, i.e. young roots were more active in water uptake. The sum of the uptake from the rooted cells equaled the transpiration from ET. In addition evaporation from the surface layer of cells was calculated. Nitrate in the soil solution was also taken up (passively) by the roots and actively via Michaelis-Menten equations with different constants representing the different age classes of roots. One of the first changes made by Whisler in 1973 was to improve the stability of the capillary water redistribution subroutine (CAPFLO) by substituting the diffusivity function of Gardner and Mayhugh (1958) which operates on volumetric water content gradients rather than soil water potential gradients. Later, he would add a user input water table. In 1977, Dr. James Siefker inserted code into the CAPFLO subroutine which prevented more than 25 percent of the capacity of the cell to come from any one direction. This prevented instabilities that arise when too much water is moved on one iteration. In 1974, Whisler added a runoff capability to the GRAFLO subroutine based on methodology of the Soil Conservation Service. This would be rewritten in 1991 by Dr. Dana Porter to include the effect of slope. Also in 1974, the plant nitrogen budget (NITRO subroutine) was rewritten to utilize maximum and minimum N concentrations in various organs as reported by Jones et al. (1974). Their experiments, although done in field plantings, were designed expressly for the purpose of materials balance cotton simulation models. In 1975, Whisler added the root impedance (RIMPED) and CULVAT subroutines. RIMPED calculates root impedance from soil bulk density and water content and is based on articles by Campbell, Reicosky and Doty (1974) and Taylor and Gardner (1963). 16

In 1976, a major effort to modify GOSSYM for the simulation of Acala cotton crops was initiated in Israel in collaboration with Dr. Avi Marani. A number of relatively minor changes were made in the model parameters describing physiological processes. Moreover, the depth of the cells in the RHIZOS matrix was changed from 5 to 10 cm, giving the model a 200 cm soil depth. When pan evaporation data were available, evapotranspiration was calculated as 0.75* PANVAP. For this version of GOSSYM the effect of plant canopy on soil evaporation was changed to a function of LAI (from percent light interception), and the reduction in transpiration at very low soil water potentials was recalibrated. Validation data were collected by extension workers and others at 19 locations in Israel (5 distinctly different climate zones) in 1976, 1977 and 1978. Numbers of squares, bolls and mainstem nodes as well as weights of stems, bolls and leaves were recorded from 30 to 50 days after emergence till the end of the season at 10-day intervals. Graphs with circles representing the field observations and lines representing the simulations for each variable at each of the 57 crop years are included in a final project report to the U.S. - Israel Binational Science Foundation (1978). No statistical analysis was done, but visual inspection of these 348 graphs indicates that only 9 percent showed significant deviation between observation and simulation. Over-prediction of peak square numbers was the most common problem. It was apparently due to less than perfect early season insect control. We also found that some of these errors were associated with extreme water stress or very high planting densities. Analytical Applications The very good results of this extensive model validation effort had several effects on the overall crop simulation research effort. We had proven convincingly that generally valid crop simulation models can be built from controlled environment data characterizing physiological process rates. This added impetus to the process level materials balance oriented soybean and wheat modeling efforts we had under way. The theses of Landivar (1979) and Kharche (1984) would contain further model validation results. The thesis of Reddy (1981) contained validation results from 5 locations across the Cotton Belt. However, with the results in Israel, we knew we had, in hand, a powerful tool for a number of analytical applications. These would begin with breeding feasibility studies (Jenkins had earlier in 1973 used SIMCOT II to investigate square abscission in frego bract cotton concluding that lygus was responsible) including enhancement of photosynthetic efficiency, changing leaf area (okra leaf), and drought tolerance through enhanced rooting and changing stress sensitivity in stomatal action (Landivar, 1979). Landivar etal. (1983) found that a 54 percent increase in yield could be obtained from a 30 percent increase in photosynthetic efficiency, provided adequate mineral nutrients were available. This showed, however, that if the method of increasing photosynthetic efficiency was by an increase in leaf thickness, 17

the added photosynthate would be invested in leaf tissue rather than lint. This seemed to invalidate the use of CO2 exchange rate in a search for higher yields. On the other hand, the analysis showed that delaying leaf senescence 5 - 10 days would provide significant increases in yield. GOSSYM was used to simulate fruiting behavior in okra cottons simply by changing the potential dry matter accumulation in leaves (Landivar et al., 1983). The analysis showed that a larger fruit load occurred in the okra cotton than in the simulated normal leaf varieties because the reduced leaf growth relaxed carbohydrate stress induced delays in fruiting, but that the okra plant would be more determinant and would fail to produce enough photosynthate to support the added fruit and larger abscission rates would occur. Comparing manipulations of the root system showed that increased rooting density would be of little benefit whereas increased rooting depth could result in a 10 percent yield increase (Landivar, 1979). Increasing stomatal resistance by reducing the maximum stomatal aperture resulted in lower simulated lint yields when the crop was grown under moist conditions. However, when the total water applied was reduced to 186 mm, doubling the stomatal resistance allowed the crop to better budget its available water supply and to reduce the effect of water stress on photosynthate production, but it was associated with a 9 percent delay in maturity date (Landivar, 1979). One of the more intricate, counterintuitive, yet ultimately most reasonable analytical problems addressed with GOSSYM was that of tillage by Whisler (Baker et al.,1979). It demonstrated the utility of his RIMPED subroutine. It dealt with a hard pan at 10 to 20 cm depth found in the Bruce and Römkens experiment. A crop simulated with no pan in their water stressed treatment yielded 18 percent more than the crop with the pan. There was no difference in simulated yields in the well-watered treatment. Interestingly, the high yielding crop (no pan) had 20 percent fewer bolls than the lower yielding crop. Obviously the bolls were larger. Inspection of the values for total photosynthate produced during the first 160 days of growth showed 315 g/plant and 280 g/plant for the high and low yielding crops respectively. As noted earlier, in GOSSYM photosynthesis is proportional to light interception, a function of plant height. There were no differences in the simulated plant heights, in part because the crop with the pan was able to penetrate and resume vertical root growth with the occurrence of a rainfall event on day 100 (which softened the pan). Model output (Figs. 4, 5 and 6) showed little difference in soil water or nitrogen distribution patterns, but the crop with the hard pan had very restricted rooting in the region of the pan and it was the area where the soil nitrogen was located. Not able to invest in roots in that region the model crop with the pan invested significantly more in roots deep in the soil profile. This resulted in the crop with the pan having less stress during a drought than the crop with no pan. The explanation for the differences in photosynthate production and yield 18

Fig. 4 VOLUMETRIC WATER CONTENT OF SOIL AT THE END OF MAIN WITHOUT PAN cm**3/cm**3 SOIL 11111111112 12345678901234567890 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

44555555555555555544 44444555555555544444 44444445555554444444 43444444444444444434 55555555555555555555 66666666666666666666 66666666666666666666 66666666666666666666 66666666666666666666 66666666666666666666 66666666666666666666 33456666666666665433 55556666666666665555 55555777777777755555 55556677777777665555 55666677777777666655 66666777777777766666 66667777777777776666 66677777777777777666

20 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 TOTAL = 308.9165 mm WATER

DAY 100 LEGEND 0.00 < 0