Although weather data are widely acknowledged to contain measurement errors, the implications of these errors for models that relate weather to yields have not been adequately examined. From statistical theory and applications in many other fields, it is clear that measurement error in a single predictor variable can lead to bias in estimating the effects of that variable, as well as any other correlated predictors. Of particular concern for statistical crop models is that errors in measuring precipitation can lead to bias in inferences about yield responses to both temperature and precipitation. In this study, simulation extrapolation (SIMEX) is used to gauge the importance of measurement error for two recent studies that employed statistical crop models. In both cases, estimates of yield responses to temperature were only slightly changed when considering measurement errors. However, yield responses to precipitation were significantly larger when assuming that precipitation is measured with 30% error, compared to the common assumption of error-free measurements. Thus, results indicate that studies that ignore measurement errors are unlikely to be biased for estimating T sensitivity of yields, but can easily underestimate P sensitivity by a factor of two or more. More work is needed to test effects of measurement errors in other cases, as well as to better quantify the magnitudes of errors in weather measurements for cropped regions. As a rough substitute for detailed measurement error analysis, sensitivity tests that double the yield response to precipitation are advised when applying statistical crop models to projections from climate ensembles. Depending on the magnitude of precipitation projections, which in turn depend on the spatial and temporal scale of analysis, the conclusions of a study may or may not be altered by considering the effects of measurement errors.

Statistical studies of rainfed maize yields in the United States and elsewhere have indicated two clear features: a strong negative yield response to accumulation of temperatures above 30 °C (or extreme degree days (EDD)), and a relatively weak response to seasonal rainfall. Here we show that the process-based Agricultural Production Systems Simulator (APSIM) is able to reproduce both of these relationships in the Midwestern United States and provide insight into underlying mechanisms. The predominant effects of EDD in APSIM are associated with increased vapour pressure deficit, which contributes to water stress in two ways: by increasing demand for soil water to sustain a given rate of carbon assimilation, and by reducing future supply of soil water by raising transpiration rates. APSIM computes daily water stress as the ratio of water supply to demand, and during the critical month of July this ratio is three times more responsive to 2 °C warming than to a 20% precipitation reduction. The results suggest a relatively minor role for direct heat stress on reproductive organs at present temperatures in this region. Effects of elevated CO₂ on transpiration efficiency should reduce yield sensitivity to EDD in the coming decades, but at most by 25%.

Sugarcane area is currently expanding in Brazil, largely in response to domestic and international demand for sugar-based ethanol. To investigate the potential hydroclimatic impacts of future expansion, a regional climate model is used to simulate 5 years of a scenario in which cerrado and cropland areas (~1.1E6 km²) within south-central Brazil are converted to sugarcane. Results indicate a cooling of up to ~1.0°C during the peak of the growing season, mainly as a result of increased albedo of sugarcane relative to the previous landscape. After harvest, warming of similar magnitude occurs from a significant decline in evapotranspiration and a repartitioning toward greater sensible heating. Overall, annual temperature changes from large-scale conversion are expected to be small because of offsetting reductions in net radiation absorption and evapotranspiration. The decline in net water flux from land to the atmosphere implies a reduction in regional precipitation, which is consistent with progressively decreasing simulated average rainfall for the study period, upon conversion to sugarcane. However, rainfall changes were not robust across three ensemble members. The results suggest that sugarcane expansion will not drastically alter the regional energy or water balance, but could result in important local and seasonal effects.

Field experiments and simulation models are useful tools for understanding crop yield gaps, but scaling up these approaches to understand entire regions over time has remained a considerable challenge. Satellite data have repeatedly been shown to provide information that, by themselves or in combination with other data and models, can accurately measure crop yields in farmers’ fields. The resulting yield maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and thus improve understanding of crop yield gaps. This review discusses the use of remote sensing to measure the magnitude and causes of yield gaps. Examples from previous work demonstrate the utility of remote sensing, but many areas of possible application remain unexplored. Two simple yet useful approaches are presented that measure the persistence of yield differences between fields, which in combination with maps of average yields can be used to direct further study of specific factors. Whereas the use of remote sensing may have historically been restricted by the cost and availability of fine resolution data, this impediment is rapidly receding.

Highlights:

• Satellite data can help overcome spatial and temporal scaling issues that challenge simulation and experiment based analyses of yield gaps.
• Yield gap profiles, based on multiple years of satellite data, provide a useful measure of how persistent yield-controlling factors are through time.
• Satellite data capable of discriminating crop yields on individual fields are more available and affordable than ever.

The article is part of a special issue on crop yield gap analysis.