Wheat production forecasting for Pakistan from satellite data

Dempewolf, Jan; Adusei, Bernard; Becker‐Reshef, Inbal; Barker, B.; Potapov, Peter; Hansen, Matthew C.; Justice, Chris

doi:10.1109/igarss.2013.6723517

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…the forecast of yield for the majority of cases was within 10% of final reported values in Pakistan (Dempewolf et al, 2013). Ren et al used 10-day MODIS NDVI composites to forecast the yield of wheat for a sub-region of Shandong Province in China, and the results were within 5% of official statistics.…”

Section: Discussionmentioning

confidence: 75%

Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics

Nagy

Fehér

Tamás

2018

Computers and Electronics in Agriculture

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Stakeholders, policy makers, government planners and agricultural market participants in Central Eastern Europe require accurate and timely information about wheat and maize yield and production. The study site, the lowlands (altitude below 200m) of the Tisza river catchment is by far the most important wheat and corn producing region in the Carpathian basin, and even in Central Eastern Europe. The conventional sampling of on-field data and data processing for crop forecasting requires significant amounts of time before official reports can be released.Several studies have shown that wheat and maize yield can be effectively forecast using satellite remote sensing. In this study, a freely available MODIS NDVI satellite data based wheat and maize yield forecasting methodology was developed and evaluated for estimating yield losses effected by drought.Wheat and maize yield was derived by regressing reported yield values against time series of 15 different peak-season MODIS-derived NDVI. The lowest RMSE values at the river basin level for both wheat and maize yield forecast versus reported yield were found when using at least six or more years of training data. Wheat forecast for the 2000 to 2015 growing seasons were within 0.819 % and 19.08% of final reported yield values. Maize forecast at county level for the 2000 to growing seasons were within 0.299 % and 17.14% of final reported yield values. The Nash-Sutcliffe efficiency index (E 1 ) is positive with E 1 = 0.322 in the case of wheat forecast, and with E 1 =0.401 in the case of maize forecast, which means the developed and evaluated forecasting method performs acceptable forecast efficiency. Nevertheless the occurrence of extreme drought or extreme precipitation can alter the forecasting efficiency resulting over or underestimation. Overall statement, which based on MODIS NDVI, possible yield losses can easily be forecasted 6-8 weeks before harvesting and applying simple threshold levels, yield losses can be mapped simply.

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Section: Discussionmentioning

confidence: 75%

Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics

Nagy

Fehér

Tamás

2018

Computers and Electronics in Agriculture

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“…Previous studies have shown the validity of using satellite-derived vegetation indices for wheat yield forecasting. Dempewolf et al [32] achieved a higher agreement between forecast and reported yield for the 2011/2012 Rabi season using a similar methodology as employed in this study; however, it was based on NDVI only, used fewer training years, and when tested for other Rabi seasons, it performed significantly worse.…”

Section: Discussionmentioning

confidence: 99%

“…Mkhabela et al [31] forecasted the yield of major crops in the Canadian Prairies, including spring wheat, using 10-day MODIS NDVI composites. Dempewolf et al [32] achieved good agreement between wheat yield derived from MODIS-derived NDVI and reported yield for the 2011/2012 growing season for Punjab Province, Pakistan. The forecast of yield for the majority of cases was within 10% of final reported values.…”

Section: Vegetation Indicesmentioning

confidence: 93%

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

et al. 2014

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Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. 9654 reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three-or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

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“…Prediction of wheat yield based on satellite-derived NDVI was improved if auxiliary data such as grain yield from previous seasons were included in models [36]. Dempewolf et al (2013) [12] obtained the highest R 2 values of 0.964 for NDVI and the yield of wheat grown in Pakistan six weeks before harvest. In our studies, the highest R 2 value was observed at the end of April, i.e., about 12 weeks before harvest in the case of the field located in southeastern Poland.…”

Section: Determination Of Dates and Plant Growth Stages When Relationmentioning

confidence: 99%

Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

et al. 2020

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The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

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Wheat production forecasting for Pakistan from satellite data

Cited by 9 publications

References 14 publications

Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics

Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

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