Red Brome Flowering and Senescence Forecasts, Contiguous United States, Current Year and Prior Year

The USA-NPN red brome forecast predicts flowering and senescence in real-time. The red brome forecast is based on Prevéy et al (in prep.), which predicts phenology of the species based on temperature (growing degree days, GDD) and daylength. Daily GDD accumulations are calculated using the simple averaging method, and adjusted based on daylength, where longer days are assumed to promote plant development. The photoperiod adjustment is calculated as [daylength hours]/[24]. Each day's growing degree day accumulation is multiplied by the photoperiod adjustment. The flowering model uses a Dec 1 start date and a 23F base temperature. "Flowering soon" is predicted at 1283 GDDs (F), after which another week of average temperatures at 70F with 11 hours of daylight would be needed to trigger flowering. "Onset of flowering" is predicted at 1441 GDDs (F). The senescence model uses a Jan 1 start date and a 32F base temperature. "Starting to dry out and senesce" is predicted at 886 GDDs (F), after which another week of average temperatures of 80F and 14 hours of daylight would be needed to trigger senescence. "Senescence" is predicted at 1081 GDDs (F). The forecasts can support ranchers in timing grazing after grasses have grown enough to provide good forage, but prior to unpalatable flowering and seeding stages. This practice reduces the development of new seeds and subsequent spread of the species. The forecasts also support the interpretation of satellite imagery of invaded grassland green up and dry down. Forecasts are available for the current and prior year in the USA-NPN Visualization Tool (https://www.usanpn.org/data/visualizations) and the USA-NPN Geoserver (user access facilitated by the Geoserver Request Builder).<br><br><i>For all inquiries regarding this dataset, please contact the USA-NPN. This data is subject to the USA-NPN's</i> <i>Data Use Policy.</i>

USA-NPN红雀麦预测模型可实时预测开花与衰老过程。该红雀麦预测模型基于Prevéy等人（待发表）的研究，其依据温度（生长度日，GDD）和日照时长预测该物种的物候。每日GDD累积量采用简单平均法计算，并根据日照时长进行调整——模型假设更长的日照时长可促进植物发育。光周期调整系数的计算公式为[日照时长（小时）]/[24]，每日生长度日累积量需乘以该光周期调整系数。开花模型的起始日期为12月1日，基础温度为23华氏度。当累积GDD达到1283（华氏度基准）时，模型预测‘即将开花’；此后若连续一周平均温度为70华氏度且日照时长为11小时，则会触发开花。‘开花始期’的预测阈值为1441 GDD（华氏度基准）。衰老模型的起始日期为1月1日，基础温度为32华氏度。当累积GDD达到886（华氏度基准）时，模型预测‘开始干燥并衰老’；此后若连续一周平均温度为80华氏度且日照时长为14小时，则会触发衰老。‘衰老期’的预测阈值为1081 GDD（华氏度基准）。该预测可帮助牧场主确定放牧时机——在牧草生长至足以提供优质饲料后，但在其进入适口性差的开花和结籽阶段前进行放牧。这种做法可减少新种子的产生及该物种后续的扩散。该预测还可辅助解读入侵草地返青和枯落过程的卫星图像。当前及上一年度的预测数据可通过USA-NPN可视化工具（https://www.usanpn.org/data/visualizations）和USA-NPN地理服务器获取（用户可通过Geoserver请求生成器便捷访问）。<br><br><i>有关本数据集的所有咨询，请联系USA-NPN。本数据受USA-NPN</i><i>数据使用政策约束。</i>