Agriculture Reference
In-Depth Information
reflected by the canopy or on their own specific light sources in the visible (650 or
590 nm) and NIR (770 or 880 nm) range, respectively. Spectral data collected by
these devices allow the calculation of the so-called normalised differences vegeta-
tion index (NDVI) according to the formula:
NDVI
¼
ð
NIR
Vis
Þ=
ð
NIR
þ
Vis
Þ
where
NIR
and
Vis
stand for the spectral reflectance measurements acquired in the
NIR
or visible (red) regions, respectively. The
NDVI
value is about 0.5 when the
vegetation chlorophyll content and thus, in the absence of any other stress factors,
plant N status is optimal; conversely in sub-optimal conditions the value of
NDVI
is
much lower.
Several examples of the use of these portable proximal sensors (which can also be
mounted on tractors) in the fine-tuning of variable-rate technology for site-specific
N fertilisation exist (Solari et al.
2008
; Diacono et al.
2013
). The possibilities to
easily and efficiently translate the information on N crop status, obtained by the
hand-held or proxy sensor approaches described as above, in site- and time-specific
recommendations for FBMPs can be invalidated by a plethora of biotic and/or biotic
stressors, including a non-optimal availability of nutrients other than N, which
influence the chlorophyll content of the leaves. Therefore, the parameters and the
vegetation index obtained are usually validated by setting up standardisation pro-
cedures providing for plots of the same cultivar in the same environment at different
N availability. In this way genetic, environmental and agronomical factors can be
eliminated as potential sources of error and making the data obtained by the sensor-
based approaches more reliable (Samborski et al.
2009
; Diacono et al.
2013
).
Hyperspectral radiometers providing contemporaneous reflectance measure-
ments over a relatively narrow wavebands (
10 nm), should make it possible to
identify specific regions of the spectrum which could be used to develop new
indices, highly sensitive to plant N status and unaffected by other exogenous factors
(Hansen and Schjoerring
2003
). Indeed, an increasing number of studies suggest
that field as well as airborne or spaceborne hyperspectral canopy radiometric data
can be useful for estimating plant nitrogen concentration in cultivated or natural
environments (Ollinger et al.
2008
, Stroppiana et al.
2009
), although recently some
criticisms about the remote sensing of leaf tissue constituents by hyperspectral data
have been raised (Knyazikhin et al.
2013
).
Leaf chlorophyll concentration is also an indirect diagnostic symptom for N status
of the crop. However, it is important to take into account that reduced chlorophyll
biosynthesis is a relatively late response to N starvation which only becomes evident
after the plant has initiated other molecular and physiological responses for
maintaining N homeostasis (Schatchtman and Shin
2007
; Gojon et al.
2009
).
Unfortunately, non-destructive reliable monitoring approaches comparable with
those above described for N have not been developed for the other mineral nutrients
whose availability affects crop yield (in particular P, K and S). Thus, for these
nutrients the chance to adopt FBMPs is limited to the classic chemical evaluation of
plant tissues and soils.
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