Abstract
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. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final 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.
Highlights
Pakistan is anticipating massive population growth over the coming decades
We developed a methodology for forecasting wheat yield for the Punjab Province using time series of satellite imagery and historic crop statistics
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; it was based on normalized difference vegetation index (NDVI) only, used fewer training years, and when tested for other Rabi seasons, it performed significantly worse
Summary
Pakistan is anticipating massive population growth over the coming decades. Population size is projected to increase from 185 million in 2014 to 271 million by 2050, which is nearly as high as the current U.S population, while only occupying an area equivalent to eight percent of the U.S land area [1]. Food production has not increased at the same rate as human population, resulting in concerns for food security. Accurate and timely information provided through large-scale monitoring of crop production and yield forecasting is an essential tool to help address these concerns. The most important food crop in Pakistan is wheat, which is cultivated during the Rabi season (winter season) on the majority of agricultural land in the Punjab province [3]. Wheat contributes 12.5% to the value added to GDP in agriculture and accounts for 2.6% of total
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