Crop Yield Forecasting

method is applied to choose three to five sample plots within each sample PSU, with the
help arable of area) high-resolution method is imagery. used to Considering select the PSUs. the crop In the planting second, area a of simple different random provinces, sampling the
sample method plot is applied area is to set choose to two three hectares to five (30 “mu”) sample in plots Jiangsu within and each Hubei, sample and to PSU, five hectares with the
(75 help “mu”) of high-resolution for Liaoning, imagery. Henan and Considering Jinlin. the crop planting area of different provinces,
the sample plot area is set to two hectares (30 “mu”) in Jiangsu and Hubei, and to five
hectares (75 “mu”) for Liaoning, Henan and Jinlin.
Step 4: Conduction of field survey. The field survey is conducted at two times. The sample
plot registration is first carried out using GPS and PDAs to locate and make records of the
Step 4: Conduction of field survey. The field survey is conducted at two times. The sample
sample plot registration plots. Then, is first for the carried purposes out using of area GPS estimation, and PDAs during to locate the crop and make planting records season, of the
crop sample planting plots. area Then, is measured for the purposes with PDAs of area and estimation, other tools, during such the the ground crop planting truth data. season,
the crop planting area is measured with PDAs and other tools, such the ground truth data.
Step 5: Area estimation. Based on the field area survey of sample plots within each sample
PSU,
Step 5:
the
Area
area
estimation.
of certain
Based
crops at
on
county
the field
level
area
can
survey
be computed
of sample
by
plots
means
within
of
each
a small
sample
area
PSU, the area of certain crops at county level can be computed by means of a small area
statistical model, or similarly to the provincial area estimation, using Equation 3.1 below
statistical model, or similarly to the provincial area estimation, using Equation 3.1 below
(Wang (Wang and and Wei Wei 2014): 2014):
!
! !
!!!
AA = !!! aa !" ww !" ww ! , Equation 3.1
where A stands for the province’s estimated sown area; m denotes the total number of
sample PSUs within the province, and n denotes the total number of sample plots within
each sample PSU; xx x !" indicates the survey sown area of sample plot within PSU ij indicates the survey sown area of sample plot j within sample PSU i;
www !" indicates the weighting factor from sample plot to sample and ww ! ij indicates the weighting factor from sample plot j to sample PSU i; and w i indicates the
weighting
weighting
factor
factor
from
from
sample
sample
PSU
PSU
i to
to
the
the
province.
province.
Compared to traditional area sampling surveys, the CAERSS brings improvements in terms
Compared to traditional area sampling surveys, the CAERSS brings improvements in terms of
of objectiveness, timeliness and cost efficiency. However, the CAERSS has not been
objectiveness, extended for area timeliness estimation and cost at national efficiency. level However, due to problems the CAERSS in the has quality not been of extended the basic
for statistical area estimation information, national the acquisition level due capacity to problems of RS in data, the quality the standardization of the basic statistical of data
information, acquisition and the acquisition processing, capacity RS crop of RS identification data, the standardization measurement of data assessment acquisition and
processing, validation. Currently, RS crop identification the NBS is working and measurement on these issues assessment and aims and to establish validation. a Currently, crop area
and RS system in the near future.
the NBS is working on these issues and aims to establish a crop area and RS system in the
near future.
3.1.2. Area estimation in the MoA
The CHARMS, developed by the RSAC, is the MoA’s current operational system for crop
monitoring and crop forecasting. In CHARMS, the crop planting area estimation is achieved
3.1.2. mainly by Area means estimation of two in methods: the MoA stratified sampling using RS and ground random
The sampling CHARMS, using developed GPS. The by former the RSAC, is the is method the MoA’s of major current application, operational and system the latter for crop is a
supplement. The brief introduction of the two methods set out below essentially follows
monitoring and crop forecasting. In CHARMS, the crop planting area estimation is achieved
that given by Wu and Sun (2008).
mainly by means of two methods: stratified sampling using RS and ground random sampling
using
I. The
GPS.
Stratified
The former
Sampling
is the
Method
method of major application, and the latter is a supplement.
The Crop brief area introduction estimation of using the the two Stratified methods Sampling set out below Method essentially (SSM) follows can be that divided given into by four Wu
and steps: Sun (2008).
Step 1: Identification of the cropping zone. Although many crops are planted in China, the
I.
RSAC
The Stratified
focuses its
Sampling
attention
Method
on the main crops, including wheat, maize, soybean, rice and
cotton. These are mainly concentrated in about 15 provinces. Therefore, the cropping zone
Crop area estimation using the Stratified Sampling Method (SSM) can be divided into four
of each main crop is determined, to enable focused study and further analysis.
steps:
Step 1: 2: Identification Selection of of the the cropping sample unit. zone. The Although sampling many unit crops is are based planted on in the China, county the
RSAC administrative focuses boundaries its attention within on the the main main crops, producing including provinces wheat, maize, and a relief soybean, map rice with and a
scale of 1:50,000 or 1:25,000. According to cropping zones, the stratification is first applied
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Crop Yield Forecasting: Methodological and Institutional Aspects
72