Crop Yield Forecasting

2.1.2. Methodology and practices of the Ministry of Agriculture
The crop information and statistics released by the MoA can also be obtained through two
different approaches, the traditional one being the complete reporting system supplemented
by a sampling survey (Zhao and Zhou 2010), and the new one being CHARMS. Similar to that
of the NBS, the MoA’s complete reporting system is based on statistical work from agricultural
departments (bureaus) at different administrative levels. The statistical department within
each agricultural bureau is responsible for collecting crop information from the lower levels
and for reporting aggregated crop estimates to higher levels, through unified tables and a
2.1.2.
dedicated
Methodology
network. For
and
the
practices
county-level
of
crop
the Ministry
output, sampling
of Agriculture
surveys are only conducted
The crop information and statistics released by the MoA can also be obtained through two
different
in the major
approaches,
grain-producing
the traditional
provinces.
one being
A detailed
the complete
internal
reporting
report timeline
system
of
supplemented
MoA crop
by estimates a sampling (China survey 2013) (Zhao is available and Zhou in 2010), Table B2.2, and the Annex new B2.1. one being CHARMS. Similar to that
of the NBS, the MoA’s complete reporting system is based on statistical work from agricultural
departments The technological (bureaus) and at social different advancements administrative have levels. made The it possible statistical to department replace the traditional within each
agricultural
statistical
bureau
system
is
with
responsible
an RS monitoring
for collecting
system.
crop
Therefore,
information
the following
from the
sections
lower levels
will focus
and for
reporting aggregated crop estimates to higher levels, through unified tables and a dedicated
mainly on the methodology based on an RS system – CHARMS.
network. For the county-level crop output, sampling surveys are only conducted in the major
grain-producing provinces. A detailed internal report timeline of MoA crop estimates (China
2013) is available in Table B2.2, Annex B2.1.
2.1.2.1. The remote sensing monitoring system
The CHARMS technological has been and operated social advancements by the MoA’s RSAC have made since 1999 it possible (Chen to et replace al. 2011). the It provides traditional
statistical agricultural system information with an on RS crop monitoring condition, system. crop area Therefore, variation, the yield following and production sections estimation will focus
mainly on the methodology based on an RS system – CHARMS.
and agriculture disaster monitoring (including for disasters such as drought, floods, frost
damage, pest diseases) for five major crops including wheat, rice, maize, soybean and cotton.
2.1.2.1. The remote sensing monitoring system
CHARMS Other crops has are been being operated gradually by the added MoA’s to the RSAC system. since The 1999 crop (Chen yield et estimation al. 2011). module It provides is
agricultural one of CHARMS’ information main on components, crop condition, and crop integrates area variation, RS, GISs, yield ground and sampling production and estimation various
and yield agriculture forecasting disaster models monitoring estimate (including the various for crops’ disasters yield in such China’s as main drought, grain-producing floods, frost
damage, regions. pest Two diseases) yield models for five are major generally crops used including for yield wheat, forecasts: rice, the maize, NDVI-based soybean and statistical cotton.
Other crops are being gradually added to the system. The crop yield estimation module is one
model (Ren et al. 2008) and the Crop Growth Monitoring System (CGMS) (Huang et al. 2011).
of CHARMS’ main components, and integrates RS, GISs, ground sampling and various yield
forecasting models to estimate the various crops’ yield in China’s main grain-producing
regions. Two yield models are generally used for yield forecasts: the NDVI-based statistical
model I. The (Ren Normalized et al. 2008) Difference and the Vegetation Crop Growth Index-based Monitoring System statistical (CGMS) yield (Huang model et al. 2011).
The basic idea behind this statistical model, based on the research conducted by Ren et
I. The al. (2008), Normalized is to establish Difference the Vegetation relationship Index-based between the statistical crop production yield model and the spatial
The
accumulation
basic idea behind
of the Normalized
this statistical
Difference
model,
Vegetation
based on the
Index
research
(NDVI).
conducted
A stepwise
by
regression
Ren et al.
(2008), is to establish the relationship between the crop production and the spatial
is used to select the critical estimation period and optimize the parameters. Then, the yield
accumulation of the Normalized Difference Vegetation Index (NDVI). A stepwise regression is
used estimation to select is computed the critical from estimation the estimated period production and optimize by the dividing parameters. the crop Then, planting the area. yield
estimation Figure 2.2 is below computed illustrates from the the flow estimated chart for production this methodology. by dividing The procedure the crop planting consists area. of
Figure the four 2.2 steps below set illustrates out below the (Chen flow chart et al. for 2011): this methodology. The procedure consists of the
four steps set out below (Chen et al. 2011):
(1) Data pre-processing and NDVI preparation. The pre-processing of RS images includes
(1) Data pre-processing and NDVI preparation. The pre-processing of RS images includes
geometric correction, radiation calibration, reprojection, and atmospheric correction. Then,
geometric correction, radiation calibration, reprojection, and atmospheric correction. Then, the
NDVI
the NDVI
is computed
is computed
by calculating
by calculating
the normalized
the normalized
difference
difference
between
between
the red
the
(Rr)
red (Rr)
and
and
the
the
near-
near-infrared (Rnir) bands (Rnir) of bands RS data of RS with data Equation with Equation 2.1: 2.1:
NNNNNNNN = ! !"#!! !
! !"# !! !
Equation 2.1
The crop arable land and crop-specific mask from the MoA’s RSAC are used to extract the
NDVI on crop pixels in an RS image, after the Savitzky–Golay filter has been applied to smooth
any noise or disturbance that may affect the data. In addition, the NDVI ranges from 0.2 to 0.8
are
64
selected for further analysis, to consider the NDVI’s response to vegetation greenness and
Crop Yield Forecasting: Methodological and Institutional Aspects
biomass.