Search

Abstract: Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA''s Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available. [Copyright &y& Elsevier]

Degradation of vegetation and soils in drylands, sometimes called desertification, is thought to be a serious threat to the sustainability of human habitation, but maps of the extent and severity of degradation at country and global scales do not exist. Degraded land, by definition, has suffered a change relative to its previous condition set by its climate, soil properties, topography and expectations of land managers. The local net production scaling (LNS) method, tested here in Zimbabwe, estimates potential production in homogeneous land capability classes and models the actual productivity using remotely-sensed observations. The difference between the potential and actual productivities provides a map of the location and severity of degradation. Six years of 250 m resolution MODIS data were used to estimate actual net production in Zimbabwe and calculate the LNS using three land capability classifications. The LNS maps agreed with known areas of degradation and with an independent degradation map. The principal source of error arose because of inhomogeneity of some land capability classes caused by, for example, the inclusion of local hot-spots of high production and differences in precipitation caused by local topography. Agriculture and other management can affect the degradation estimates and careful inspection of the LNS maps is essential to verify and identify the local causes of degradation. The Zimbabwe study found that approximately 16% of the country was at its potential production and the total loss in productivity due to degradation was estimated to be 17.6 Tg Cyr−1, that is 13% of the entire national potential. Since the locations of degraded land were unrelated to natural environmental factors such as rainfall and soils, it is clear that the degradation has been caused by human land use, concentrated in the heavily-utilized, communal areas. [Copyright &y& Elsevier]

Wheat is the most important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely production forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) developed an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel (area within the CMG pixel occupied by wheat), and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: the Unites States (US), Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest), while conserving an accuracy of 10% in the production forecast. [ABSTRACT FROM AUTHOR]

Highlights • We present a new method to monitor wheat yield using daily MODIS DVI. • We test the model over the USA and Ukraine from 2001 to 2017. • We can monitor the yield at the subnational level with a RMSE lower than 0.6 MT/ha. • The RMSE at the national scale is 0.1 MT/ha for the US and 0.2 MT/ha for Ukraine. • The model captures low winter wheat yields during years with extreme weather events. Abstract Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%). [ABSTRACT FROM AUTHOR]