Historical changes in global yields: major cereal and legume crops from 1982 to 2006

Abstract

Aim Recent changes in crop yields have implications for future global food security, which are likely to be affected by climate change. We developed a spatially explicit global dataset of historical yields for maize, soybean, rice and wheat to explore the historical changes in mean, year-to-year variation and annual rate of change in yields for the period 1982-2006. Location This study was conducted at the global scale. MethodsWe modelled historical and spatial patterns of yields at a grid size of 1.125°by combining global agricultural datasets related to the crop calendar and harvested area in 2000, country yield statistics and satellite-derived net primary production. Modelled yields were compared with other global datasets of yields in 2000 (M3-Crops and MapSPAM) and subnational yield statistics for 23 major crop-producing countries. Historical changes in modelled yields were then examined.Results Modelled yields explained 45-81% of the spatial variation of yields in 2000 from M3-Crops and MapSPAM, with root-mean-square errors of 0.5-1.8 t ha −1 . Most correlation coefficients between modelled yield time series and subnational yield statistics for the period 1982-2006 in major crop-producing regions were greater than 0.8. Our analysis corroborated the incidence of reported yield stagnations and collapses and showed that low and mid latitudes in the Southern Hemisphere (0-40°S) experienced significantly increased year-to-year variation in maize, rice and wheat yields in 1994-2006 compared with that in 1982-93. Main conclusions Our analyses revealed increased instability of yields across a broad region of the Southern Hemisphere, where many developing countries are located. Such changes are likely to be related to recent yield stagnation and collapses. Although our understanding of the impacts of recent climate change, particularly the incidence of climate extremes, on crop yields remains limited, our dataset offers opportunities to close parts of this knowledge gap.

Impacts of El Niño Southern Oscillation on the global yields of major crops

“…Grid-cell crop yields for the period 1982-2006 (the grid size is 1.125°in latitude and longitude) were obtained from the satellite statistics-aligned global data set of historical yields 33 , which is a combination of the Food and Agriculture Organization (FAO) of the United Nations country yield statistics and the grid-cell crop-specific net primary production derived from the NOAA/AVHRR (US National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer). These data are therefore estimates of yields rather than observed yields.…”

“…These data are therefore estimates of yields rather than observed yields. The reliability of yield estimates was evaluated using the subnational yield statistics in 23 major crop-producing countries 33 and the global data sets of crop yields in 2000 (refs 34,35). For maize, rice and wheat crops, yields from multiple cropping systems (major/secondary or winter/spring) are available, whereas yields from only a single major cropping system are available for soybean crops.…”

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

“…31 (described in 'Study region and input data' , above), whereas the third analysis was based on a data set derived from the DSSAT 38 model simulations of ref. 39.…”

Evaluating Global Land Degradation Using Ground-Based Measurements and Remote Sensing

“…Only the AVHRR satellite sensor provides a global, continuous time-series of NDVI data dating back to the 1980s. This sensor therefore provides the foundation of nearly all long-term, continuous estimates of land degradation including those developed by Bai and Dent (2009) for the GLADIS framework and the reconstruction of historical yields by Iizumi et al (2014). In particular, each of these analyses use the GIMMS NDVI dataset.…”

“…Indeed, researchers have already developed numerous remote sensing derived datasets that identify individual components of anthropogenic land degradation: declining vegetative health (Huete et al 2002), crop yields (Iizumi et al 2014), deforestation (Hansen et al 2010(Hansen et al , 2013, declining water supplies including groundwater depletion (Voss et al 2013), and even a partial proxy for soil salinity (Lobell et al 2009). The Food and Agriculture Organization (FAO) attempted to move beyond individual components and produce more inclusive estimates of land degradation at a global scale, based at least in part, on remote sensing products in its Global Assessment of Land Degradation and Improvement (GLADA) project.…”