J.M. McKinion

Crop simulation models in agronomic systems

Publisher Summary This chapter discusses some crop simulation models in agronomic systems. Many crop models or parts of crop models have been built to help the researcher and students understand the operation of some part of an agronomic cropping system, for example, soil water flow, stomata1 control, or fertilizer nutrient movement. In addition to understanding various parts of agronomic systems, the modelers want to see what can be expected to happen if some change is made in that system. Field tests are very expensive, especially as the numbers of variables and/or treatments increase, and years of results are needed. A proven model of the system helps to evaluate these treatments and indicates which ones could be expected to give the desired results. The cotton model, GOSSYM, has been widely validated. GOSSYM has been used in a user-friendly form on a PC microcomputer as a tool for on-farm management decisions pertaining to nitrogen fertilizer applications, irrigation scheduling, and timing of harvest-aid chemicals. By combining GOSSYM with an expert system program, COMAX, the on-farm management decisions have been run in several combinations to give the user an optimal plan for fertilizer and irrigation scheduling.

Application of the GOSSYM/COMAX system to cotton crop management

Abstract The GOSSYM/COMAX model-based-reasoning system, a decision aid for cotton crop management, has been extensively tested over the last five years on commercial and research farms across the Cotton Belt in the southern United States. This paper gives a brief description of the cotton crop model GOSSYM and the companion expert system COMAX, reports the results of these extensive field tests, and gives projections for future enhancements of the system.

Interactions of CO2 enrichment and temperature on cotton growth and leaf characteristics

Abstract Studies on the interactive effects of atmospheric CO 2 and temperature on growth and leaf morphology, particularly on stomatal index and density are limited. Upland cotton was grown in naturally-lit plant growth chambers at 30/22°C day/night temperatures from planting until squaring or the fifth or sixth leaf emerged. Five growth chambers were maintained at ambient (350 μ l l −1 ) CO 2 and another five at twice ambient (700 μ l l −1 ) CO 2 throughout the experiment. Day/night temperature treatments of 20/12, 25/17, 30/22, 35/27 and 40/32°C were imposed at each CO 2 treatment for 42 days after squaring. The plants were irrigated with half-strength Hoaglands nutrient solution three times per day. Growth of plant parts was determined at the end of the experiment. Stomatal characteristics, nonstructural carbohydrates and specific leaf weight were measured on the fully expanded tenth mainstem leaf. Stomatal density and index were not affected by elevated CO 2 . Stomata and epidermal cell numbers per leaf increased in high CO 2 and were positively correlated with final leaf sizes irrespective of CO 2 level. Our results suggest that plants do not acclimate to elevated CO 2 by changing stomatal density within a single generation. Leaves had greater area and accumulated more biomass when grown in high CO 2 . Growth stimulation expressed as dry weight at 700 μ l l −1 over dry weight at 350 μ l l −1 CO 2 was uniform across temperatures. Temperature optimum for vegetative and reproductive growth was 30/22°C and was not altered by CO 2 enrichment. Fruit retention was severely curtailed at the two higher temperatures compared to 30/22°C in both CO 2 environments. Increased carbohydrate storage in leaves may be an added advantage for initiation and growth of vegetative structures such as branches at all temperatures. However, it is unlikely that high temperature effects on flower abortion will be ameliorated by high CO 2 . Species/cultivars that retain fruits at high temperatures would be more productive both in the present-day cotton producing environments and are even more desirable in the future warmer world.

Expert systems for agriculture

Abstract Recent advances in computer technology have been made possible the development of Expert Systems. Expert Systems are special computer software applications that are capable of carrying out reasoning and analysis functions in narrowly defined subject areas at proficiency levels approaching that of a human expert. The prime targets for the development of expert systems applications in agriculture are the narrowly defined subject areas which have experts available for solving problems. All commercial crop production systems in existance today are potential candidates for Expert Systems. These Expert Systems would take the form of integrated crop management decision aids which would encompass irrigation, nutritional problems and fertilization, weed control-cultivation and herbicide application, and insect control and insecticide and/or nematicide application. Additional subject areas of potential are plant pathology, salinity management, crop breeding, animal pathology, and animal herd management. The advantage of Expert Systems is that once developed they can raise the performance of the average worker to the level of an expert.

Carbon dioxide and temperature effects on pima cotton growth

Abstract Temperature and CO2 are major environmental variables that affect plant growth and development. Limited information is available concerning how these factors affect plants, as well as specific interactions between the two. We conducted two experiments in controlled environmental chambers where temperature and CO2 were controlled and other environmental factors were not limiting. The purpose was to determine how cotton grew and responded to a range of temperatures and CO2 concentrations. During vegetative development, stem growth was quite sensitive to CO2 resulting in more effective early-season light capture. Plants did not develop more nodes when exposed to additional CO2, while node number increased more at higher temperatures. Individual leaf growth was about 18% greater at optimum temperature in 450 μl l−1 than in 350 μl l−1 CO2, but did not increase from 450 μl l−1 CO2 to 700 μl l−1 CO2. However, the time required for a leaf to reach mature size was not influenced by CO2. Leaf area, on the whole plant basis, was about 33% greater on plants grown at optimum temperature in high CO2 than in ambient CO2. The greater leaf area on a whole plant basis was achieved by a combination of larger leaves and additional leaves produced primarily on the branches. There was a 28% increase in number of bolls produced at 700 μl l−1 CO2 at optimum temperature compared with bolls produced at 350 μl l−1 CO2. There was not, however, an increase in boll size due to high CO2. At 35.5°C, little growth response to high CO2 environments occurred at 700 μl l−1 CO2 compared with 350 μl l−1 CO2, but approximately a 45% increase occurred in the plants grown at 18.9–26.9°C. Less total biomass was produced at 35.5°C than at 26.9°C and no bolls were produced in either CO2 environment at the higher temperature. The most important response to temperature and CO2 occurred at high temperatures where the effects of elevated CO2 on plant growth were masked by apparent high-temperature injury that limited growth of all plant organs, particularly, reproductive growth.

Implementing generic, object-oriented models in biology

Abstract This paper describes object-oriented programming (OOP) in terms relevant to the modeling of biological systems. Object-oriented programming is not a new technique but remains mostly unexploited in biosystems modeling. For biological scientists, the ideas of object-oriented design, based on the notions of taxonomy, discrete structures, behavior, and scale, make the approach intrinsically familiar and thus inherently compelling. We present a new OOP framework that may serve as a generic foundation for the production of plant models. This OOP framework represents a first step towards the development of generic model architectures and provides modularity, mechanistic richness, and advantages in process evaluation previously unavailable.

SPAR—A Soil-Plant-Atmosphere Research System

ABSTRACTCOMPREHENSIVE studies of plant dynamics re-quire simultaneous measurements of plant roots and tops in a controlled environment. The objectives of this research were to design, construct, and test a computer-controlled environmental system for studying whole-plant responses. Three independently controlled and monitored sunlit chambers, the Soil-Plant-Atmosphere Research (SPAR) system, were constructed attheUSDA-SEA, Coastal Plains Soil and Water Conservation Re-search Center, Florence, SC. Each SPAR unit is a base steel soil bin, (2 x 0.5 x 1 m) on top of which is an acrylic plastic aerial chamber (1.5 m high), secured and sealed to the base. The temperatures of the aerial chamber and the soil bin can be controlled independently by air-conditioners and heaters. Micrometereological, soil, and plant variables are measured automatically with a micro-processor-based digital data acquisition system. In each chamber, COz can be measured each minute to determine the amount of C02 absorbed by the plant, which must be replaced to maintain a constant C02 level. Apparent net photosynthesis is calculated from C02 measurements and corrected for chamber leakage. The SPAR system was evaluated using cotton to de-termine the potential amount of root dry matter accumu-lation and proliferation in the soil under constant soil matric potential and non-limiting photosynthate supply. Initial results indicated that the SPAR system provides a precisely controlled soil and aerial environment to accurately and rapidly measure automatically some plant stresses and growth rates. Dependence of these rates on incoming energy indicates the need to rapidly and continuously measure soil-plant-atmosphere pro-cesses, because integration of these measurements for long periods tends to mask these responses.

Calibration of GOSSYM: Theory and practice☆

Abstract GOSSYM-COMAX, the cotton simulation model and expert system, has been in use across the cotton belt for the last 6 years. Behind the capability of GOSSYM to simulate a wide range of cultivars is the VARIETY file. This variety file contains 50 parameters that modify the growth and development of twelve varieties from the Delta, Acala, Stripper, and PIMA cultivars. Whenever enhancements to GOSSYM are made and before a new version is released, the model is calibrated with standard data sets to verify the accuracy of the model for each variety class. These standard data sets have developmental and carbohydrate partitioning data such as: height; number of nodes, squares, and bolls; dry weights of stems, leaves and fruits; and lai . These data sets, representative of various cultivars, are used to modify variety-dependent model parameters to obtain an optimal simulation of field data. This paper presents the procedure undertaken in the calibration process and some of the recognized knowledge gaps.

Automated System for Measurement of Evapotranspiration from Closed Environmental Growth Chambers

ABSTRACT Asystem for the automatic measurement of evapotranspiration is described. The system was designed for use with closed environmental growth chambers in which crops are grown. Although measurements were taken every 15 min, longer measurement periods can be used. The system was very reliable and accurate over crop growing periods up to five months.

Automating the parameterization of mathematical models using genetic algorithms

Abstract Genetic algorithms were used to automate the calibration of complex components of models. The use of genetic algorithms for calibration has broad applicability to both functions and large systems involving several functions. Genetic algorithms are a powerful tool recently developed to “evolve” solutions to problems characterized by massive search spaces. Genetic algorithms use methods analogous to the process of natural evolution. Chromosome analogs were used to represent values to be optimized. The algorithm used selection procedures and genetic operators to produce new chromosome analogs. A “fitness” function was used to evaluate the populations of chromosomes. For our research, fitness was indicated by how well a model simulated observed data. Highly “fit” chromosomes were saved and used to produce new generations and through an iterative process of selection and genetic rearrangement, highly fit strings were evolved that represented near-optimal solutions to parameterization problems. The evaluation of model “goodness-of-fit” affects the behavior of the genetic algorithm. The nature of these effects were investigated by using different indices of model goodness of fit. The effects on search (e.g., how fast an appropriate solution was found) were compared for four measures of goodness of fit. This research represents an innovative approach to solving an important problem, that of the calibration of mathematical models. Its usefulness is demonstrated on specific applications which allowed the determination of calibration parameters for a poikilotherm and photosynthesis models. The implementation of this genetic algorithm is proposed as a stand-alone framework for the development of similar applications for complex, process-based biological models that require periodic parameterization.