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Sensor based nitrogen management for cotton production in coastal plain soils.

机译:基于传感器的氮素管理,用于沿海平原土壤的棉花生产。

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摘要

The main objective of this four year study was to develop, refine, and employ sensor-based algorithims to determine the mid season nitrogen requirements for production of irrigated and dryland cotton (Gossypium hirsutum L.) in Coastal Plains soils. The secondary objective of the project was to develop and test equipment for variable rate application of nitrogen to commerical cotton fields utilizing the developed algorithim. Two different production fields at Clemson's Edisto Research and Education Center near Blackville, SC were used. One field, equiped with an overhead irrigation system, was used during the 2007 and 2010 production seasons to develop the algorithm for irrigated cotton. The second field was used during the 2008 and 2009 seasons for developing the algorithm for dryland cotton nitrogen management. Each field was divided into three separate zones based on soil electrical-conductivity (EC) data. The algorithim was developed using "Nitrogen Ramp Calibration Strips" (N-RCS) and varied prescription rate nitrogen plots. Three N-RCS were established in each production field, one per EC zone. The N-RCS was composed of 16 nitrogen rates (0 to 168.13 kg-N/ha) on 5.0 meter intervals. For the varied prescription rate plots, five different rates of nitrogen fertilizer (0, 33, 67, 100, and 134 0kg-N/ha) were replicated four times in plots of each zones using a Randomized Complete Block desgin arrangement.Optical sensor readings were collected from the test plots to determine cotton plant Normalized Difference Vegetation Index (NDVI) at different growth stages. The sensor readings were used to develop two different algorithims to be used in the estimation of mid-season nitrogen need of the cotton plants. Sensor readings collected between 40 and 60 days after planting were highly correlated (average R2> 0.80) with the final yield and nitrogen requirement. The Response Index (RI), the extent to which the crop will respond to additional N, was calculated by dividing the highest NDVI reading from N-RCS and N-rich strips (established in each zone) by NDVI measurements of the adjacent area in each zone. In Season Estimated Yield (INSEY) was used along with the actual field yield to produce a yield potenial (YP0) for each growing season one for irrigated cotton and one for dry land cotton. The algorithm is N rate= (YP0*RI-YP0)*%N/NUE. Where the %N is the percentage of nitrogen in cotton seeds after harvest and NUE is the nitrogen use efficiency, typically 50%.The algorithim developed from the 2008 growing season was used during the 2009 growing season to estimate the amount of mid-season side-dress nitrogen required for specific research plots in the production field. The algorithm reccommended a reduced rate of nitrogen (40% less) across the entire field compared to the normal grower practice (101 kg-N/ha) with no reduction in cotton yield. Similar results were obtained when using the Oklahoma State University Algorithm.Three different methods of nitrogen application were tested, one during each of the growing seasons of 2007-2009. During the 2007 production year a typical pull behind nitrogen side-dress applicator with a ground driven piston pump was used. This applicator was the most crude and innacurate method of fertilizer application used during the study. During the 2008 production year a custom built applicator was used. The applicator operated using a hydraulic pump in combination with an in-cab control system. The rates were adjusted using various orifices and solenoids. The final applicator, tested iv in 2009, was a typical three point hitch pull behind side-dress coulter rig controlled using a hydraullic Rawson controller for the piston pump. The three point hitch applicator has the potential to be the most accurate and versatile of any used during the project.Various equipment was tested throughout the study to determine the best and most accurate way to apply the mid-season N algorithm fertilizer recommendation. The parameters of specific equipment such as the GreenSeekersRTM for measuring NDVI were tested to determine the true accuracy based on height above crop canopy and time of day, which is related to the sun angle and solar radiation. The results of this test proved that the sensor is height sensitive with an optimal height range of .8128 to 0.9144 meters. It was determined from the test that the sensors are not sun angle sensitive and return a non statistical difference in readings throughout the day between the hours of 10 a.m. and 8 p.m. (EST). The sensors returned a lower number once the sun had set but the main reason for the lower number is due to the physiological response of the plant. It was found due to the response of the plant that it is not possible to obtain an accurate sensor reading at night. Sensor readings taken from two different travel directions were found to not be statistically different, thus the sensors were found not to be travel direction specific. The data remained constant independent of the orientation of the field. This study confirmed that there is a significant possibility to accurately predict in-season expected yield (INSEY) in cotton using mid-season NDVI sensor readings in conjunction with an accurate prediction of a reduced nitrogen requirement without a significant reduction in yield.Two different ultra-sonic height sensors were tested during the growing season of 2010 to determine the feasibility of determining plant height on-the-go. Both sensors gave promising results to accurately predict plant height with more testing and reprograming.
机译:这项为期四年的研究的主要目的是开发,改进和使用基于传感器的算法,以确定沿海平原土壤中灌溉和旱地棉花(陆地棉)的中期氮需求。该项目的第二个目标是利用开发的算法开发和测试用于氮肥在商品棉田中可变速率施用氮肥的设备。在南卡罗来纳州布莱克维尔附近的克莱姆森Edisto研究和教育中心使用了两个不同的生产现场。在2007年和2010年的生产季节中,使用一个配备了高架灌溉系统的田地开发了灌溉棉花的算法。第二个领域在2008年和2009年季节用于开发旱地棉花氮管理算法。根据土壤电导率(EC)数据,将每个字段划分为三个单独的区域。算法是使用“氮气坡道校准条”(N-RCS)和各种处方率氮气曲线开发的。每个生产领域建立了三个N-RCS,每个EC区域一个。 N-RCS由5.0间隔的16个氮素比率(0至168.13 kg-N / ha)组成。对于变化的处方率图,使用随机完整区块设计安排在每个区域的图中重复五次不同的氮肥用量(0、33、67、100和134 0kg-N / ha)四次。从试验区收集棉花以确定不同生长阶段的棉株标准化差异植被指数(NDVI)。传感器的读数被用于开发两种不同的算法,用于估算棉花中季氮需求量。种植后40至60天收集的传感器读数与最终产量和氮需求高度相关(平均R2> 0.80)。响应指数(RI)是农作物对附加氮的响应程度,其计算方法是将N-RCS和富含N的条带(在每个区域建立)的最高NDVI读数除以相邻区域的NDVI测量值。每个区域。在季节估计产量(INSEY)和实际田间产量一起使用时,每个生长季节产生一个产量潜力(YP0),一个用于灌溉棉花,一个用于旱地棉花。该算法为N rate =(YP0 * RI-YP0)*%N / NUE。其中%N是收获后棉籽中氮的百分比,NUE是氮的利用效率,通常为50%。从2008年生长期发展而来的算法在2009年生长期期间用于估算季节中期的数量在生产领域中为特定研究用地所需的氮肥。该算法建议在整个田间与正常的种植者实践(101 kg-N / ha)相比降低氮的利用率(减少40%),而棉花产量不会降低。使用俄克拉荷马州立大学算法获得了相似的结果。测试了三种不同的氮素施用方法,一种在2007-2009年的每个生长季节。在2007年的生产年度中,使用了典型的带地面驱动活塞泵的后推式氮气施肥器。该施肥器是研究过程中使用的最粗略和最不准确的施肥方法。在2008年的生产年度中,使用了定制的涂药器。涂药器使用液压泵结合驾驶室内控制系统进行操作。使用各种孔口和螺线管调节速率。最终的喷枪在2009年进行了IV级测试,是一个典型的三点悬挂拖拉式侧犁犁刀钻机,该犁刀使用液压罗森控制器控制活塞泵。三点悬挂施肥机是该项目中使用的最精确,用途最广泛的施肥机。在整个研究过程中,对各种设备进行了测试,以确定最佳和最准确的方式来应用季节中期N算法施肥建议。测试了用于测量NDVI的特定设备(例如GreenSeekersRTM)的参数,以根据作物冠层上方的高度和一天中的时间确定真实的精度,这与太阳角度和太阳辐射有关。测试结果证明该传感器对高度敏感,最佳高度范围为0.8128至0.9144米。从测试中确定,这些传感器对太阳角不敏感,并且在上午10点至晚上8点之间的全天读数返回非统计差异。 (美东时间)。一旦太阳下山了,传感器返回的数字就减少了,但主要原因是由于植物的生理反应。发现由于工厂的响应,夜间无法获得准确的传感器读数。发现从两个不同的行进方向获取的传感器读数在统计上没有差异,因此发现传感器不是特定于行进方向的。数据保持恒定,与磁场方向无关。这项研究证实,使用季中NDVI传感器读数并准确预测氮需求量的降低而不会显着降低产量,可以准确预测棉花的季节内预期产量(INSEY)。在2010年生长季节对声波高度传感器进行了测试,以确定在移动过程中确定植物高度的可行性。两种传感器都给出了可喜的结果,可以通过更多的测试和重新编程来准确地预测工厂的高度。

著录项

  • 作者

    Porter, Wesley M.;

  • 作者单位

    Clemson University.;

  • 授予单位 Clemson University.;
  • 学科 Agriculture Soil Science.Engineering Agricultural.
  • 学位 M.S.
  • 年度 2010
  • 页码 123 p.
  • 总页数 123
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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