Crop Yield Forecasting

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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 selected for further analysis, to consider the NDVI’s response to vegetation
greenness and biomass.
(2) Preliminary yield estimation. The NBS’s historical crop production statistics are used to
(2)
build
Preliminary
the relationship
yield estimation.
with the
The
spatial
NBS’s
accumulation
historical crop
of the
production
NDVI at
statistics
different growth
are used
stages,
to build
the relationship with the spatial accumulation of the NDVI at different growth stages, using
using Equation 2.2:
Equation (2) Preliminary 2.2: yield estimation. The NBS’s historical crop production statistics are used to build
the relationship with the spatial accumulation of the NDVI at different growth stages, using
Equation 2.2: YY = aa + bb × NNNNNNNN Equation 2.2
Here, Y Y is is the the estimated YY = aa crop + bb production × NNNNNNNN at at county level; ∑ NNNNNNNN Equation NDVI is is the 2.2
spatial accumulation of
crop of crop NDVI NDVI from from 0.2 0.2 to 0.8 to 0.8 in in a county a county in in a a given period; a is the constant and and b b is is the the
coefficient. Here, coefficient. Y is the Stepwise
Stepwise estimated regression
regression crop production is
is
used
used at to
to county determine
determine level; the
the NNNNNNNN optimal
optimal is the period
period spatial from
from accumulation among
among
all
all of
stages, crop using the criteria that the probability-of-F-to-enter was lesser than 0.10 and stages,
NDVI
using
from
the
0.2
criteria
to 0.8
that
in
the
a county
probability-of-F-to-enter
in a given period;
was
a
lesser
is the
than
constant
0.10 and
and
the
b
probability-of-F-to-remove
production and was county greater crop than planting 0.11. area. The average yield is derived from the NBS’s
is the
probability-of-F-to-remove coefficient. Stepwise regression was greater is used than to 0.11. determine The average the optimal yield is derived period from the among NBS’s all
predicted stages, using the criteria that the probability-of-F-to-enter was lesser than 0.10 and the
probability-of-F-to-remove predicted production and was county greater crop than planting 0.11. area. The average yield is derived from the NBS’s
(3) predicted Yield validation. production The and estimated county crop yield planting is then area. compared with the observed yield data from
ground (3) Yield samplings validation. at The county estimated level. If yield the accuracy is then compared is lesser with than the 95 percent, observed which yield data means from that
the (3) ground Yield growth validation. samplings conditions The at county are estimated different level. yield If from the is accuracy then those compared existing lesser during with than the 95 an observed percent, average yield which year, data then means from the
meteorological ground data, drought indicators or other crop condition indicators are added to refine
that the samplings growth conditions county level. are different If the accuracy from those is lesser existing than during 95 percent, an average which year, means then the that
the yield growth prediction conditions model, are using different Equation from 2.3: those existing during an average year, then the
meteorological data, drought indicators or other crop condition indicators are added to refine
meteorological data, drought indicators or other crop condition indicators are added to refine
the
the
yield
yield
prediction
prediction model, using Equation 2.3:
YY model, = aa + using bb × Equation NNNNNNNN + 2.3: CC Equation 2.3
in which C is the correction YY = aa + bb term × NNNNNNNN based + on CC data from the CMA’s Equation meteorological 2.3 stations or
other drought and crop growth indicators derived from various data sources, such as MODIS,
Landsat in TM, CBERS, SPOT and HJ-1. A new yield estimation will be generated and examined
in which which C C is is the the correction term based on data from the CMA’s meteorological stations stations or or
by other repeating drought the and second crop growth and third indicators steps. derived from various data sources, such as MODIS,
other drought and crop growth indicators derived from various data sources, such as MODIS,
Landsat TM, CBERS, SPOT and HJ-1. new yield estimation will be generated and examined
Landsat TM, CBERS, SPOT and HJ-1. A new yield estimation will be generated and examined
(4) by repeating Once the the yield second model and is third calibrated, steps. the NDVI data for the current season can be used to
predict by repeating the average the second yield of and specific third steps. crops for the current year, in each county. The yield at
county (4) Once level the can yield then model be aggregated is calibrated, to obtain the NDVI the yield data at for provincial the current level. season can be used to
predict (4) Once the the average yield model yield of is specific calibrated, crops the for NDVI the data current for the year, current in each season county. can be The used yield to at
county predict level the can average then be yield aggregated of specific to crops obtain for the the yield current at provincial year, level. each county. The yield at
county level can then be aggregated to obtain the yield at provincial level.
FIGURE 2.2
Flow chart of NDVI-based statistical model in CHARMS
FIGURE 2.2
Flow chart of NDVI-based statistical model in CHARMS
Crop Yield Forecasting: Methodological and Institutional Aspects 65