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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.

    Data from: Generation and analysis of blueberry transcriptome sequences from leaves, developing fruit, and flower buds from cold acclimation through deacclimation

      There has been increased consumption of blueberries in recent years fueled in part because of their many recognized health benefits. Blueberry fruit is very high in anthocyanins, which have been linked to improved night vision, prevention of macular degeneration, anti-cancer activity, and reduced risk of heart disease. Very few genomic resources have been available for blueberry, however. Further development of genomic resources like expressed sequence tags (ESTs), molecular markers, and genetic linkage maps could lead to more rapid genetic improvement. Marker-assisted selection could be used to combine traits for climatic adaptation with fruit and nutritional quality traits.

      Genomes To Fields 2016

        Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018).

        Genomes To Fields 2015

          Phenotypic, genotypic, and environment data for the 2015 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Mar_2017).

          Genomes To Fields 2014

            Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3).

            Genomes To Fields (G2F) Inbred Ear Imaging Data 2017

              A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017).

              USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013

                The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (*Zea mays* L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information.

                Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) Simulation Model

                  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.