Currently the CWB uses the Western Canada Wheat Yield (WCWY) model ..... select the range of NDVI data that can exactly represent the growing period (May ...
Boken,V. K. and C.F. Shaykewich, 2002. Improving an operational wheat yield model for the Canadian Prairies using phenological-stage-based normalized difference vegetation index, International Journal of Remote Sensing, 23 (20):4157-4170.
Improving an Operational Wheat Yield Model using Phenological Phasebased Normalized Difference Vegetation Index Vijendra K. Boken1* and Carl F. Shaykewich2 1
Department of Geography, University of North Dakota, Grand Forks, ND-58202, USA; 2Department of Soil Science, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2;
*Current address: Department of Geography and Earth Science, University of Nebraska at Kearney, Kearney, NE 68849, USA.
ABSTRACT Currently a model (referred to as a monthly model) employing monthly temperature and precipitation data is used by the Canadian Wheat Board to estimate spring wheat yields for the Canadian Prairies. The model uses a cumulative moisture index as an explanatory variable. In this paper, the performance of the monthly model was improved by first developing a daily model by employing daily, instead of monthly, data for the 1975-96 period and then by developing a hybrid model which incorporated into the daily model an additional variable derived from the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR)-based composited Normalized Difference Vegetation Index data for the 1987-96 period. Out of the seven variables derived, two variables — the average NDVI during the heading phenological-phase and the average NDVI during the entire growing season were found to be the best. The start and the end of the heading phase were estimated using a biometeorological time scale model. The performance of models was tested on five crop districts (1b, 3bn, 4b, 6a, and 9a) of Saskatchewan on the basis of coefficient of determination, R2. While using 1975-96 data, the values for R2 were 0.43, 0.82, 0.73, 0.71 and 0.00 in the case of daily model as opposed to 0.20, 0.71, 0.57, 0.58, and 0.00 in the case of monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. While using 1987-96 data, the values of R2 were 0.79, 0.96, 0.83, 0.95, and 0.39 in the case of the hybrid model as opposed to 0.13, 0.70, 0.75, 0.50, and 0.00 in the case of the monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. For district 9a, which experiences an adequate supply of soil moisture, the concept of cumulative soil moisture index was not found to hold well for yield estimation.
1.
Introduction
Spring wheat (Triticum aestivum L., hereafter referred to as wheat) is a major export crop of the Canadian Prairies; the Prairies extend northward from 49 0N (Canada-US border) to 54 0N latitudes, and east to west from eastern Manitoba, across Saskatchewan to western Alberta, approximately between 96 0W and 114 0W longitudes. Of the total wheat produced on the
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Prairies, approximately 75% is exported, through a grain marketing agency − the Canadian Wheat Board (CWB). Annual reports (e.g., Canadian Grain Commission, 1996) reveal that the exports have ranged from 10 to 20 million tonnes with an average of about 15 million tonnes during the past ten years. In order to develop an efficient marketing strategy for such a huge amount of export, the CWB requires an estimate of the pre-harvest wheat yield (production per unit area). The more accurate the yield estimates are, the wiser the export strategy and the higher the profits will be. Currently the CWB uses the Western Canada Wheat Yield (WCWY) model developed by Walker (1989) to obtain the pre-harvest yield estimates. The model is referred to in this paper as a monthly model, simply because it employs monthly data. A detailed description of the monthly model is provided in Section 2. The objective of this paper is to improve the performance of the monthly model, which was attempted in two stages. In the first stage, the daily weather data, instead of monthly, were employed and a daily model was developed. In the second stage, a Normalized Difference Vegetation Index (NDVI)-based variable was incorporated into the daily model and a hybrid model was developed; the NDVI data were derived from the National Oceanic and Atmospheric Administration (NOAA) / Advanced Very High Resolution Radiometer (AVHRR) data. Commencing with the description of the monthly model, the following sections present how the daily and the hybrid models were developed and tested for five crop districts in Saskatchewan, Canada.
2.The monthly Model The monthly model uses only weather data (monthly temperature and precipitation) because weather is the single most important factor influencing wheat yields in the Prairies. Weather-derived parameters have commonly been used for yield estimation for the Prairies (e.g., Raddatz et al., 1994). Other yield-influencing factors such as those pertaining to farm management practices, use of fertilizer and pesticides, and technology are stable over years (Walker 1989). Using monthly data recorded at a weather station, the monthly model determines a cumulative drought index (referred to as cumulative moisture index , CMI, in this paper) for the station as explained in the next section. The CMIs for all of the weather stations across the Prairies are averaged and the average CMI (i.e., ACMI) is regressed against average annual yield for the Prairies. The resulting regression model is what is referred here to as a monthly model. 2.1 Derivation of Cumulative Moisture Index (CMI) The CMI is the daily moisture index (DMI) cumulated over the growing period – from sowing to harvest. Despite the fact that, depending on weather conditions, there is year-to-year variation in the range for the growing period, the CMI is computed in this paper over the May 1 – Aug. 31 period. The DMI reflects the extent to which the daily soil-moisture requirement for wheat has been met. If the requirement has been met completely, the crop will attain an optimum growth and hence an optimum yield. The computation of DMI begins with an estimation of cumulative soil moisture (csm) on the first day of the growing season (i.e., May 1) and subsequently involves the crop-water
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requirement (cwr) and soil-water budgeting as explained in the following sections. For estimating csm, monthly precipitation is considered during the past 13 months from April in the previous year to April in the current year. The amount of the contribution from a month is first estimated by subtracting a threshold, b, from the precipitation for the month and multiplying the difference by a coefficient, a. The values of a and b for different months are provided in Table 1. Subsequently, csm was computed as a total of the contributions from all of 13 months. Table 1. Thresholds and coefficients used to estimate the cumulative soil moisture at the time of sowing Threshold/
Month*
coefficient 4
5
6
7
8
9
10
11
12
13
14
15
16
a
0.10 0.15
0.15
0.20
0.35
0.40
0.40
0.30
0.20
0.20
0.20
0.30
0.50
b (mm)
20
30
30
25
20
15
10
5
5
5
5
10
25
* The starting month (number 4) is April of the previous year. [Source: computer program to implement Walker (1989)’s model]
After obtaining csm, daily crop water requirement (cwr) is estimated by multiplying a conversion factor, vapour pressure deficit (vpd), and maximum growth (Gmax) the crop can achieve for a given cumulative growing degree days (cgd). The conversion factor is (18*0.5*14*3600*10) / (83100*Tavg) where Tavg is the average daily temperature in °K. The vpd is the difference between maximum and minimum vapour pressures. Replacing T with the maximum and minimum temperatures, one can estimate the maximum and minimum vapour pressures, respectively, in the following equation: vp = 10 e(52.576 – (6790.498/T) – 5.028 ln(T))
(1)
where vp is the vapour pressure at temperature T (°K). In the monthly model, the daily maximum and minimum temperatures were estimated by adding and subtracting, respectively, six degrees from the average temperature for the month; this was done because the average difference between maximum and minimum daily temperature for a month was found to be 12 °C during the growing period (Walker 1989). However the daily temperatures thus estimated remained constant throughout the month, which is an unrealistic temperature pattern. The next variable requiring estimation for computing cwr is Gmax, which is a function of the growing degree days (gd, the difference between the effective temperature, Teff, and the base temperature, Tbase. The values for Teff and Tbase are provided in table 2). With each passing day during the growing season, cgd varies, and so does the Gmax (fig. 1). Subsequent to estimation of csm and cwr, the soil-water budgeting was performed in order to compute the DMI.
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Table 2. Estimation of effective temperature and effective precipitation for each month during the growing season Temperature/Precipitation Month May
June
July
August
Base Temperature, Tbase(0C)
8
5
4
3
Effective Minimum Temperature, Teffmin(0C)
8
13
16 *
15**
Effective Maximum Temperature, Teffmax(0C)
11***
18
20
21
Effective Average Temperature, Teff(0C)a
T17
T18
T19
T20
Effective Precipitation, Peff (mm)b
P17 -15
P18 -10
P19 -10
P20 -15
Note: The cgd refers to the cumulative growing degree days; T16, T17, T18, T19, and T20 refer to the average temperature during April, May, June, July, and August, respectively; and P17, P18, P19, and P20 denote the total monthly precipitation for May, June, July, and August, respectively.
* If (cgd+31*(T19-Tbase))
