,A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions.,Over the last 30 years, the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) has conducted a wide range of research on cotton, including work to develop a series of "production models" designed to serve as decision aids to cotton producers. In 1996, ARS decided to develop a new "second generation" Cotton Production Model (CPM) that would retain the best features of the earlier versions in a new, more versatile, and more user friendly framework. The development process was completed to the stage of beta-testing, when the need to redirect limited resources to other priorities caused ARS to decide not to complete the validation process.,ARS believes that CPM, while only partially validated, has the potential to make useful contributions to American cotton producers when completed. For these reasons, ARS decided to make the model available for further development and commercialization.,The Cotton Production Model (CPM) was developed with a modular structure using an object-oriented programming language, C++. The model draws upon the latest scientific knowledge available, and is intended to be used with a wide variety of cotton types across the entire US Cotton Belt. CPM is written in C++ using a new modular structure that allows flexibility and adaptability. This object-oriented structure should allow modules to be incorporated into process-based models of other crop species (see Acock, B. and V. R. Reddy. 1977. Designing an object-oriented structure for crop models. Ecological Modeling 94: 33-44). In addition to being modular and generic, CPM has other advantages over earlier models. Compared to previous cotton models, CPM is more robust, more user-friendly, more easily maintained, and more easily updated with future advances in science. The algorithms that simulate crop growth are derived in part from the best of each of the previous models, and they incorporate new physiological information as well. A new feature of CPM is that it incorporates 2DSOIL, an excellent up-to-date soil and root process model (see Timlin, D. J., Y. Pachepsky, and B. Acock. 1996. A design for a modular, generic soil simulator to interface with plant models. Agronomy Journal 88:162-169 ). 2DSOIL tracks water movement through the soil-plant-atmosphere continuum with hourly time-steps. It also incorporates a new model of plant water relations that responds realistically to water stress. CPM has updated treatments of carbon and nitrogen stresses compared to previous models, and it is designed for easy addition of responses to phosphorus and potassium. Because the growth of each leaf, inter-node and fruit is simulated separately, CPM should be easily linked to pest or disease models.,CPM has the potential to be useful as a decision aid for cotton farmers and crop production consultants. If fully developed, it would be a valuable tool to optimize management inputs such as irrigation, fertilization, plant growth regulators, and defoliant application prior to harvest. In its current version, however, CPM has not yet been fully validated to be useful as a decision aid. The released version of CPM should be considered an advanced model suitable for research purposes. ARS does not endorse its use for any other purpose at this time. Of particular importance to a decision aid model is the user interface. The interface under which CPM has been developed and tested is one that was earlier developed for the soybean model, GLYCIM, and has been documented elsewhere (Acock, B., Pachepsky, Y. A., Mironenko, E. V., Whisler, F. D., and Reddy, V. R. 1999. GUICS: A Generic User Interface for On-Farm Crop Simulations. Agronomy Journal.

,SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.,Assessing the influence of nitrogen and water availability on development and growth of individual organs of winter wheat (Triticum aestivum L.) is critical in evaluating the response of wheat to environmental conditions. We constructed a simulation model (SHOOTGRO 2.0) of shoot vegetative development and growth from planting to early boot by adding nitrogen and water balances and response functions for seedling emergence, tiller and leaf appearance, leaf and internode growth, and leaf and tiller senescence to the existing wheat development and growth model, SHOOTGRO 1.0. Model inputs include daily maximum and minimum air temperature, rainfall, daily photosynthetically active radiation, soil characteristics necessary to compute soil N and water balances, and several factors describing the cultivar and soil conditions at planting. The model provides information on development and growth characteristics of up to six cohorts of plants within the canopy (cohort groupings are based on time of emergence). The cohort structure allows SHOOTGRO 2.0 to provide output on the frequency of occurrence of plants with specific features (tillers and leaves) within the canopy. The model was constructed so that only water availability limited seedling emergence. Resource availability (nitrogen and water) does not influence time of leaf appearance. Leaf and internode growth, and leaf and tiller senescence processes are limited by the interaction of N and water availability. Tiller appearance is influenced by the correspondence to: W.W. Wilhelm, USDA-ARS, Department of Agronomy, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0934, USA. 0304-3800/93/$06.00 0 1993 - Elsevier Science Publishers B.V. All rights reserved 184 W.W. WILHELM ET AL. interaction of N, radiation and water availability. Predicted and observed dates of emergence and appearance of the first tiller had correlation coefficients of 0.98 and 0.93, respectively. However, these events were, on average, predicted 3.2 and 5.2 days later than observed. SHOOTGRO 2.0 generally under-predicted the number of culms per unit land area, partially because the simulation is limited to a maximum of 16 culms/plant. Model output shows that the simulation is sensitive to N and water inputs. The model provides a tool for predicting vegetative development and growth of the winter wheat with individual culms identified and followed from emergence through boot. SHOOTGRO 2.0 can be used in evaluating alternative crop management strategies.,,

,PhenologyMMS is a simulation model that outlines and quantifies the developmental sequence of different crops under varying levels of water deficits, provides developmental information relevant to each crop, and is intended to be used either independently or inserted into existing crop growth models.,,

,The Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model simulates crop growth, competition, light interception by leaves, biomass accumulation, partitioning of biomass into grain, water use, nutrient uptake, and growth constraints such as water, temperature, and nutrient stress. Plant development is temperature driven, with duration of growth stages dependent on degree days. Each plant species has a defined base temperature and optimum temperature. The simulation of competition for light is based on Beer's law, allowing a different extinction coefficient (k) for each species. Light is partitioned between species based on k-values, leaf area index (LAI) and plant heights. LAI, light interception with Beer's law, and potential daily biomass increase with a species-specific value of radiation use efficiency (RUE). The model simulates competition for water and nutrients based on each species' current rooting zone and demand by each species. The daily increases in and biomass are reduced when plant-available water in the current rooting depth is insufficient to meet potential evapotranspiration. Total biomass is simulated with radiation use efficiency and grain yield with a harvest index approach, sensitive to water stress. Grain yield is simulated based on harvest index (HI), which is the grain yield as a fraction of the total aboveground dry matter at maturity.,Simulations using the BatchRun section of ALMANAC will create outputs for more than one scenario at a time. BatchRuns allow ALMANAC users to perform many runs at a time. For an example of BatchRun in use, see Dr. Behrman's 'Spatial forecasting of switchgrass productivity under current and future climate change scenarios' with simulations across the eastern half of the United States.,Soil, weather, tillage, and crop parameter are essential inputs for the model. Users typically access the extensive NRCS soils data, and readily available daily weather data, such as NOAA, for inputs. See HowtoSoils and NOAAfiles for downloading and ALMANAC formatting instructions. Weather inputs require values of daily maximum and minimum temperatures, rainfall, and solar radiation. ALMANAC contains a weather generator subroutine, based on concepts of the WGEN model. The generator is used when weather is not available, or the user does not wish to use or format existing data. Users can make runs with several years of weather in a few minutes, enabling them to efficiently simulate an extensive range of management, crop, and soil scenarios. Tillage requires users to select or create management data. ALMANAC offers a wide range of tillage operations including drainage, irrigation, fertilization, furrow diking, and liming. We recommend obtaining field data to gain parameters for new plants, or yield data of established plants to calibrate and validate simulations. Parameters for describing plant processes are easy to derive for a plant species or cultivar (see Sampling Protocol Standard with Photos). After parameters for describing plant processes are derived for a plant species or cultivar they are easily transfer among the models here in Temple (EPIC, APEX, SWAT).,ALMANAC is an important decision making tool with proven parameters and simulations that have been applied to many crop prediction and natural resource problems. ALMANAC can assist with future crop predictions such as how much biomass will be produced, what plant will be successful where, when is the optimal time to harvest, the effect of management on competing species, and management adjustment effects on land. ALMANAC is also used in natural resources regarding climate change, soil erosion, risk assessment, management decisions, plant competition, conservation effects and climate change on soil, water, competition. With this model users can determine how a plant will yield across time, how nutrients and water pass through the system, and how plants will be affected by management changes. We

,A hydrologic simulation model for studying the effects of management practices on movement of sediment and chemicals in response to rainfall or irrigation on small field areas. Includes models for plant growth and nutrient cycling, and operates on a continuous basis. Weather conditions and rainfall may be stochastically simulated.,,