|ALMANAC Simulation Model|
ALMANAC Simulation Model
Agricultural Land Management Alternative with Numerical Assessment Criteria
The 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 input real world field data into plant parameters used for the model, this enables our simulations to be more precise. The model has been used in nationwide assessments, ecosystem studies, biofuels, and for individual farmer fields.
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