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미국
SHOOTGRO
,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.,,
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PhenologyMMS
공공데이터포털
,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.,,
SPUR2
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,Please note: This software is no longer being updated or maintained, and is out of date.,SPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives.,,
Estimated spring crop yields using Flex Cropping Tool
공공데이터포털
,Average estimated yields and associated CV values for current (2018) model runs. Based on work done by Harsimran Kaur et al in 2017. The following is from her thesis: Agro-ecological classes (AECs) of dryland cropping systems in the inland Pacific Northwest have been predicted to become more dynamic with greater use of annual fallow under projected climate change. At the same time, initiatives are being taken by growers either to intensify or diversify their cropping systems using oilseed and grain legume crops. The main objective of this study was to use a mechanistic model (CropSyst) to provide yield and soil water forecasts at regional scales which could compare fallow versus spring crop choices (flex/opportunity crop). Model simulations were based on historic weather data (1981-2010) as well as combined with actual year weather data for simulations at pre-planting dates starting in Dec. for representative years. Yield forecasts of spring pea, canola and wheat were compared to yield simulations using only weather of the representative year via linear regression analysis to assess pre-plant forecasts. Crop yield projections on pre-plant forecast date of Feb 1st had higher R2 with yield simulated using actual years weather data and lower CVs across the region as compared to forecasts based on historic weather data and other pre-season forecast dates (Dec. 1st and Jan. 1st). Therefore, Feb. 1st was considered the most reliable time to predict yield and other relevant outputs such as available water forecasts on a regional scale. Regional forecast maps of predicted spring crop yields and CVs showed ranges of 1 to 4367 kg/ha and 11 to 293% for spring canola, 72 to 2646 kg/ha and 11 to 143% for spring pea and 39 to 5330 kg/ha and 11 to 158% for spring wheat across study region for a representative year. These data combined with predicted available water after fallow and following spring crop yield as well as estimates of winter wheat yield reduction would collectively serve as information contributing to decisions related to crop intensification and diversification.,
GOSSYM
공공데이터포털
,GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.,
Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system
공공데이터포털
,We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings this multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6-4.3 ha management units collected over 4 years (2019-2022). The data includes high resolution topographic data collected via real-time kinematic GPS, densely sampled soil texture and chemical properties, and meteorological data from an on-site weather station.,
2DSOIL version 03
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,2D finite element water, solute, and heat mover model for plant models.,Most crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System.,
Dataset for plant production responses to climate across water-limited regions
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This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).
Dataset for plant production responses to climate across water-limited regions
공공데이터포털
This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).
Dataset for plant production responses to climate across water-limited regions
공공데이터포털
This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).
Data for Plant production responses to precipitation differ along an elevation gradient and are enhanced under extremes (Northern Arizona, 1991-2016)
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This dataset is from a precipitation manipulation experiment conducted at five grassland sites along an elevation gradient near Flagstaff, AZ. The data consist of pre- (1991 - 2015) and post-experimental (2016) treatment plant production and precipitation measurements. The plant production measurements were taken from satellite and hand-held spectroradiometer, in addition to plot-based harvests at the end of growing season.
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