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Ag Data Commons Maintenance - Thursday 04/15/2021

The Ag Data Commons technical team needs to apply security updates to the system. This maintenance will take place on Thursday, April 15, 2021, between 7:00 am ET and 10:00 am ET. The website and application may experience brief, intermittent outages during this period. Avoid adding or editing datasets during this period to prevent losing information. This notice will be removed once maintenance is complete.

AgBase

    [AgBase](https://agbase.arizona.edu/index.html) Version 2.0 is a curated, open-source, Web-accessible resource for functional analysis of agricultural plant and animal gene products including gene ontology annotations. Its long-term goal is to serve the needs of the agricultural research communities by facilitating post-genome biology for agriculture researchers and for those researchers primarily using agricultural species as biomedical models. AgBase uses controlled vocabularies developed by the Gene Ontology (GO) Consortium to describe molecular function, biological process, and cellular component for genes and gene products in agricultural species.

    GOSSYM

      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.

      2DLEAF

        A 2D mechanistic model of CO2 and water vapor movement in a leaf and photosynthesis.

        GUICS Version 2.41

          GUICS is built on the usability paradigm developed by software designers and has a user-centered design based on results of human–computer interaction interface development lies in human–computer interaction studies.

          STARFM

            The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.