Quantitative information on the fate and efficiency of nitrogen （N） fertilizer applied to coarse textured calcareous soils in arid farming systems is scarce but, as systems intensify, is essential to support sustainable ag- ronomic management decisions. A mesh house study was undertaken to trace the fate of N fertilizer applied to cotton （Gossypium hirsutum L. cv., Huiyuan701） growing on a reconstructed profile （0-100 cm） of a calcareous （〉15% CaCQ） sandy loam soil. Two irrigation methods （drip irrigation, DI; and furrow irrigation, FI） and four N ap- plication rates （0, 240, 360 and 480 kg/hm2, abbreviated as No, N240, N360, and N480, respectively） were applied. 15N-labelled urea fertilizer was applied in a split application. DI enhanced the biomass of whole plant and all parts of the plant, except for root; more fertilizer N was taken up and mostly stored in vegetative parts; N utilization efficiency （NUE） was significantly greater than in FI. N utilization efficiency （NUE） decreased from 52.59% in N240 to 36.44% in N480. N residue in soil and plant N uptake increased with increased N dosage, but recovery rate decreased consis- tently both in DI and Fl. Plant N uptake and soil N residue were greater in DI than in FI. N residue mainly stayed within 0-40 cm depth in DI but within 40-80 cm depth in Ft. FI showed 17.89% of N leached out, but no N leaching occurred in DI. N recovery rate in the soil-plant system was 75.82% in DI, which was markedly greater than the 55.97% in FI. DI exhibited greater NUE, greater residual N in the soil profile and therefore greater N recovery rate than in FI; also, N distribution in soil profile shallowed in DI, resulting in a reduced risk of N leaching compared to FI; and enhanced shoot growth and reduced root growth in DI is beneficial for more economic yield formation. Com- pared to furrow irrigation, drip irrigation is an irrigation method where N movement favors the prevention of N from being lost in the plant-soil system and benefits a more eff
为了提高计算机视觉技术对棉花叶绿素含量的预测精度,该文应用计算机视觉识别方法,采用灰板校正图像亮度差异,对不同水分背景下棉花叶片叶绿素含量进行预测。结果表明,光谱特征参数DGCI(dark green color index)、R-B与叶绿素含量之间存在极显著线性关系,未使用灰板校正图像的DGCI、R-B与叶绿素含量的相关系数分别为0.8857和-0.8726,使用灰板校正归一化处理后的相关系数分别为0.9073和-0.9016,灰板校正后提高了参数与叶绿素含量的相关性。比较参数DGCI、R-B在校正前后对叶绿素含量的预测精度,结果显示校正后的DGCI、R-B建立的模型预测精度高于校正前,校正后参数DGCI的预测精度大于R-B。采用校正后参数DGCI建立的Chl.a+b预测方程,其预测值与叶绿素实测值间均方根误差和相对误差分别为0.1200和5.28%,决定系数为0.8812,预测精度较高。应用计算机视觉技术预测不同水分处理下棉花叶绿素含量具有可行性,使用灰板校正后参数DGCI可以作为快速无损预测棉花叶绿素含量的最佳参数。
