Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data

被引:77
|
作者
Argento, F. [1 ,2 ]
Anken, T. [2 ]
Abt, F. [3 ]
Vogelsanger, E. [1 ]
Walter, A. [1 ]
Liebisch, F. [1 ,4 ]
机构
[1] Swiss Fed Inst Technol, Inst Agr Sci, Univ Str 2, CH-8092 Zurich, Switzerland
[2] Agroscope, Digital Prod, Tanikon 1, CH-8356 Ettenhausen, Switzerland
[3] Educ & Counselling Ctr Arenenberg, Swiss Future Farm, CH-8268 Salenstein, Switzerland
[4] Agroscope, Water Protect & Subst Flows, Reckenholzstr 191, CH-8046 Zurich, Switzerland
关键词
Nitrogen management; Winter wheat; UAV; Variable rate application; VARIABLE-RATE FERTILIZATION; VEGETATION INDEXES; NUTRITION INDEX; SMALLHOLDER FARMERS; PRECISION; MAIZE; ALGORITHMS; AIRCRAFT; SENSORS; SYSTEMS;
D O I
10.1007/s11119-020-09733-3
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.
引用
收藏
页码:364 / 386
页数:23
相关论文
共 50 条
  • [21] Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data
    Georgi, Claudia
    Spengler, Daniel
    Itzerott, Sibylle
    Kleinschmit, Birgit
    [J]. PRECISION AGRICULTURE, 2018, 19 (04) : 684 - 707
  • [22] Three Methods of Site-Specific Yield Mapping as a Data Source for the Delineation of Management Zones in Winter Wheat
    Stettmer, Matthias
    Mittermayer, Martin
    Maidl, Franz-Xaver
    Schwarzensteiner, Juergen
    Huelsbergen, Kurt-Juergen
    Bernhardt, Heinz
    [J]. AGRICULTURE-BASEL, 2022, 12 (08):
  • [23] Impact of site-specific weed management on herbicide savings and winter wheat yield
    Hamouz, P.
    Hamouzova, K.
    Holec, J.
    Tyser, L.
    [J]. PLANT SOIL AND ENVIRONMENT, 2013, 59 (03) : 101 - 107
  • [24] Soil moisture estimation for spring wheat in a semiarid area based on low-altitude remote-sensing data collected by small-sized unmanned aerial vehicles
    Wang, Wei
    Wang, Xiaoping
    Wang, Lijuan
    Lu, Yaling
    Li, Yaohui
    Sun, Xuying
    [J]. JOURNAL OF APPLIED REMOTE SENSING, 2018, 12 (02):
  • [25] Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
    Caruso, Giovanni
    Palai, Giacomo
    Gucci, Riccardo
    Priori, Simone
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (03):
  • [26] Corn yield prediction in site-specific management zones using proximal soil sensing, remote sensing, and machine learning approach
    Bantchina, Bere Benjamin
    Qaswar, Muhammad
    Arslan, Selcuk
    Ulusoy, Yahya
    Gundogdu, Kemal Sulhi
    Tekin, Yucel
    Mouazen, Abdul Mounem
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 225
  • [27] Remote sensing of mangrove wetlands: Relating canopy spectra to site-specific data
    Ramsey, EW
    Jensen, JR
    [J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1996, 62 (08): : 939 - 948
  • [28] Economic feasibility of site-specific optical sensing for managing nitrogen fertilizer for growing wheat
    Jon T. Biermacher
    Francis M. Epplin
    B. Wade Brorsen
    John B. Solie
    William R. Raun
    [J]. Precision Agriculture, 2009, 10 : 213 - 230
  • [29] Economic feasibility of site-specific optical sensing for managing nitrogen fertilizer for growing wheat
    Biermacher, Jon T.
    Epplin, Francis M.
    Brorsen, B. Wade
    Solie, John B.
    Raun, William R.
    [J]. PRECISION AGRICULTURE, 2009, 10 (03) : 213 - 230
  • [30] Construction Site Multi-Category Target Detection System Based on UAV Low-Altitude Remote Sensing
    Liang, Han
    Cho, Jongyoung
    Seo, Suyoung
    [J]. REMOTE SENSING, 2023, 15 (06)