Crop Yield Forecasting

Recommendations

Info

2.2.4. The use of GIS tools The main tools used to forecast crop yield are scientifically selected sample surveys from a very long list of farm operators (the list frame) and from the parcels of land of the entire country (the area frame). However, the NASS and crop specialists use GIS 10 tools and remotely sensed data to obtain a near real-time capability for a visual monitoring of crop growth and progress in the major production areas, on a weekly basis. Through the GIS capability, various layers of information (i.e. NDVI images, the crop progress of the specific stages of crop development, the crop conditions, frost isolines, survey data) are combined and visualized at state and county level (Wade and Hanuschak 1999). 2.2.5. Inputs from remote sensing In recent years, the USDA has developed two main geospatial web service-based platforms to integrate field survey data and remotely sensed imagery, for the purpose of visualizing agricultural data at county level. The USDA-NASS constructs a new area-based sampling frame for approximately two states each year. The remote-sensing acreage estimation project analyses satellite data for the major corn- and soybean-producing states, to produce independent crop acreage estimates at state and county levels and a crop-specific categorization called the Cropland Data Layer (CDL). To date, the CDL program has produced crop-specific land cover products in over 29 states, with an annual repeat coverage for 13 agriculturally intensive states. The USDA-NASS’ Remote Sensing Section (RSS) developed the CropScape 11 platform to produce NASS annual CDLs for the US; the platform is derived from imagery from the NASA-Landsat-8 12 and DMC/Deimos 13 satellites (see Figures B5.2 and B5.2a, Annex B5.1). The CDL allows: (i) to combine remote sensing imagery and NASS survey data to produce supplemental acreage estimates for the state’s major commodities; (ii) to produce a crop-specific digital land cover data layer for distribution in industry-standard “GIS” format; (iii) to produce a census by satellite with measurable error and unbiased estimators. The NASS’ CDL is officially released approximately two months after harvest (around January of each year). The NASS has also formed a partnership with the USDA’s Agricultural Research Service, to use satellite data as inputs for setting early season small-area yield estimates in several midwestern states. The new platform will use both the crop area CDL (30-m resolution) and MODIS-NDVI 14 data (250-m resolution), for an annual “crop yield” product that shows the relative yields for corn and soybeans within each field. These NASS remote sensing-derived products for crop area and crop yield are released after the harvest, while the monthly NASS crop yield forecasts made during the mid-season (from August to November) are essentially founded on ground-based surveys for each state (or for the top-producing states for the commodity under study). 10 GIS: Geographic Information System 11 USDA-NASS-RSS CropScape: http://nassgeodata.gmu.edu/CropScape/ 12 NASA-Landsat-8 satellite: http://landsat.gsfc.nasa.gov/?page_id=4071 13 DMC-Deimos satellite: http://www.geo-airbusds.com/en/84-deimos-1-optical-satellite-imagery 14 NASA-MODIS-Land: http://modis-land.gsfc.nasa.gov/vi.html Crop Yield Forecasting: Methodological and Institutional Aspects 161
During the growing season, the NASS also produces vegetation condition products on the basis of the NDVI, from the NOAA-AVHRR sensor, which provides the Department of Agriculture’s policymakers with an independent view of growing conditions across the country. To this end, the second platform is VegScape 15 (see Figure B5.4, Annex B5.1), and enables: (i) the improvement of the objectivity, robustness, quantification, and defensibility of the nationwide crop condition-monitoring programme; (ii) free online satellite-based assessment and monitoring of US crop conditions; (iii) the provision of tools for data exploration and visualization; (iv) the free dissemination of geospatial vegetation conditions at daily, weekly, and biweekly time intervals. 2.2.6. The main constraints The NASS program uses a statistical model to forecast crop yield and production 16 . State or regional estimates are obtained by first aggregating the input, and then using a statistical model. The advantages of using statistical models is that the calculations are simple to make, less time is required to run the model and the data requirements are limited. However, they are limited in terms of the information that they can provide beyond the range of values for which the model is parameterized. Also, while still being statistically correct, the output of these models may not have any agronomic significance. In addition, the models do not take into consideration the soil-plant-atmosphere continuum, which is important when dealing with regions having different soil types. For example, the response of a crop to a given amount of rainfall on sandy soil is different from that which the crop would display on a clay soil. The timing of the water stress occurring during the growing season is also important, and often ignored. For example, a heat stress that happens at flowering will reduce yield more than a heat stress occurring during the vegetative phase. This is important for the correct forecasting of yield, and for providing farmers with important agronomic advice (e.g. the timing and amount of fertilizer, time of sowing, irrigation, and so on). There are efforts to include more meaning into the statistical models, to avoid some of the problems described above. For example, the inclusion of crop evapotranspiration or the initial soil moisture content (obtained through microwave sensing) as parameters of the model, may improve predictability, but leaves agronomic questions unanswered (Basso et al. 2013) 2.3. Relevant practices for data sharing and analysis, harmonization, and integration The dissemination of reports follows a regular and precise schedule: • Monthly Crop Production reports (see Figure B5.5, Annex B5.1) are issued on or close to the tenth day of each month, and reflect conditions as of the first day of the month; • Weekly Crop Progress & Condition reports are issued on the first business day of each week, from April to November, and reflect the status and the conditions 15 USDA-NASS-RSS VegScape: http://nassgeodata.gmu.edu/VegScape/ 16 For details on this model, see USDA-NASS 2012 162 Crop Yield Forecasting: Methodological and Institutional Aspects
Page 1 and 2:
Crop Yield Forecasting: Methodologi
Page 3 and 4:
This publication was prepared with
Page 5 and 6:
Contents Acknowledgements 9 Preface
Page 7 and 8:
3. Linking up with crop production
Page 9 and 10:
ANNEXES............................
Page 11 and 12:
10 Crop Yield Forecasting: Methodol
Page 13 and 14:
org) monitors current year conditio
Page 15 and 16:
eference dataset, whereas actors of
Page 17 and 18:
Remaining challenges The main unkno
Page 19 and 20:
DAFF DBMS DEM DG-AGRI DMN DMP DPO D
Page 21 and 22:
RMSE RS RSAC RSS RVI SACOTA SAFEX S
Page 23 and 24:
FIGURE 4.10 FIGURE 4.11 FIGURE 4.12
Page 25 and 26:
24 Crop Yield Forecasting: Methodol
Page 27 and 28:
FIGURE 1.1 B-CGMS descriptive flowc
Page 29 and 30:
the CGMS. The CGMS uses daily meteo
Page 31 and 32:
estimate is computed by interviewin
Page 33 and 34:
FIGURE 1.2. Synthetic flowchart of
Page 35 and 36:
B1.8., Annex B1.2). The historical
Page 37 and 38:
• CIPF; • CRA-W; • Centre de
Page 39 and 40:
2.2.8. The Crop Growth Simulation M
Page 41 and 42:
take place, and the maximum daily t
Page 43 and 44:
sought to minimize the prediction e
Page 45 and 46:
Therefore, to optimize and automate
Page 47 and 48:
FIGURE 1.6. Architecture of the B-C
Page 49 and 50:
2.5. Human, financial and technical
Page 51 and 52:
in 2014 49 . Preliminary yield esti
Page 53 and 54:
national level) for 2014. GIS syste
Page 55 and 56:
1.2. Inventory of forecasts availab
Page 57 and 58:
and production can be predicted one
Page 59 and 60:
1.4. How do these different forecas
Page 61 and 62:
2. China national official sources:
Page 63 and 64:
The NSRCP’s key technology includ
Page 65 and 66:
2.1.2. Methodology and practices of
Page 67 and 68:
FIGURE 2.2 Flow chart of NDVI-based
Page 69 and 70:
2.1.3 Methodology and practices of
Page 71 and 72:
various periods. By integrating thr
Page 73 and 74:
indices have been developed, and th
Page 75 and 76:
FIGURE 2.3 Data sources and institu
Page 77 and 78:
2.4. Human, financial and technical
Page 79 and 80:
method is applied to choose three t
Page 81 and 82:
accuracy of the RS indicator and th
Page 83 and 84:
TABLE 2.4 Crop calendar and release
Page 85 and 86:
Institute for Research and Technolo
Page 87 and 88:
■■ Yield • Phenological Event
Page 89 and 90:
2. Morocco’s national official so
Page 91 and 92:
• Level 3: Forecast of crop yield
Page 93 and 94:
TABLE 3.5 Meteorological variables
Page 95 and 96:
two are pre-processing tasks (see F
Page 97 and 98:
FIGURE 3.5 Output data from Levels
Page 99 and 100:
• The agricultural mask, that wil
Page 101 and 102:
FIGURE 3.8 The coefficient of deter
Page 103 and 104:
involved in agriculture, and is sup
Page 105 and 106:
3.1.1.1. Area and Methodology used
Page 107 and 108:
variable and the total rainfall of
Page 109 and 110:
TABLE 3.8 Crop calendars and releas
Page 111 and 112: 110 Crop Yield Forecasting: Methodo
Page 113 and 114: In administrative terms, South Afri
Page 115 and 116: 1.2.1.1. The role of the Crop Estim
Page 117 and 118: to submit information. The main aim
Page 119 and 120: SAGIS releases the following inform
Page 121 and 122: FIGURE 4.2 Percentage of over/under
Page 123 and 124: the results from a subjective telep
Page 125 and 126: a point sample frame. Three types o
Page 127 and 128: Sample frame To set up the sample s
Page 129 and 130: North West are allocated proportion
Page 131 and 132: FIGURE 4.7 Methodology for determin
Page 133 and 134: 2.2.3. Provincial Department of Agr
Page 135 and 136: • Southern Annular Mode (SAM) fro
Page 137 and 138: To facilitate the running of such a
Page 139 and 140: 2.6. Innovation and integration wit
Page 141 and 142: Digitization has been completed for
Page 143 and 144: FIGURE 4.11 Random selection of a p
Page 145 and 146: georeferenced. In addition to the o
Page 147 and 148: of generating signature classes for
Page 149 and 150: and along with the infra-red bands
Page 153 and 154: the National Agricultural Statistic
Page 155 and 156: 1.2.3. Other regional and global so
Page 157 and 158: 1.4. How do these different forecas
Page 159 and 160: commodity of interest within the op
Page 161: Potato and winter wheat acres are c
Page 165 and 166: 3. Linking up with crop production
Page 169 and 170: drying have been taken into account
Page 173 and 174: Meteorological data • USA-NOAA ht
Page 175 and 176: FIGURE B1.2 Grid covering the area
Page 177 and 178: Annex B1.2 - The JRC-MARS Crop Yiel
Page 179 and 180: FIGURE B1.8 MCYFS: Synthetic schema
Page 181 and 182: FIGURE B1.10 The CGMS Statistical T
Page 183 and 184: IDSL indicates whether pre-anthesis
Page 185 and 186: Annex B1.5 - Websites Country Natio
Page 187 and 188: N_A5 By August 25 Seasonal report o
Page 189 and 190: IV. Official releases of crop produ
Page 191 and 192: M P7 M P8 _ By October 25 _ By Nove
Page 193 and 194: Annex B2.2 - Websites Country China
Page 195 and 196: FIGURE B3.2 Agrometeorological bull
Page 197 and 198: FIGURE B3.6 CGMS-MAROC’s web-mapp
Page 199 and 200: FIGURE B3.10 Crop production foreca
Page 201 and 202: from the various models are inserte
Page 203 and 204: FIGURE B3.14 Descriptive schema of
Page 205 and 206: Annex B3.3 - Websites Country Natio
Page 207 and 208: FIGURE B4.2 Gross income (South Afr
Page 209 and 210: Annex B4.3 - Example of SAGIS publi
Page 211 and 212: 4.2 Definition Oilseeds are defined
Page 213 and 214:
Other countries estimate production
Page 215 and 216:
Annex B4.5 - Advanced Sample Framew
Page 217 and 218:
216 Crop Yield Forecasting: Methodo
Page 219 and 220:
FIGURE B5.2 USDA-FAS-PECAD: Crop Ex
Page 221 and 222:
FIGURE B5.3A CropScape: Cropland Da
Page 223 and 224:
FIGURE B5.7 Global Agricultural Mon
Page 225 and 226:
224 Crop Yield Forecasting: Methodo
Page 227 and 228:
Navalgund, R.R., Parihar J.S., Ajai
Page 229 and 230:
Royer, A. & Genovese, G. (eds). 200
Page 231 and 232:
Huang, J., Wang, X., Li, X., Tian,
Page 233 and 234:
Wang, M. & Wei, G. 2014. Promoting
Page 235 and 236:
Zhao, J., Shi, K. & Wei, F. 2007. R
Page 237 and 238:
Hutson, J.L. 1984. Estimation of hy
Page 239 and 240:
Huddleston, H.F. 1978. Sampling tec
Page 241:
Layout: • Laura Monopoli Cover ph
show all

Crop Yield Forecasting

Create successful ePaper yourself

Delete template?

Save as template?