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Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of ‘steady growth’ in actual and attainable yields, ‘stalled floor’ where yield is stagnated and ‘ceiling pressure’ where yield gaps are closing. Over 60% of maize area is experiencing ‘steady growth’, in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing ‘ceiling pressure’. We show that ‘ceiling pressure’ correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

Continuation of historical trends in crop yield are critical to meeting the demands of a growing and more affluent world population. Climate change may compromise our ability to meet these demands, but estimates vary widely, highlighting the importance of understanding historical interactions between yield and climate trends. The relationship between temperature and yield is nuanced, involving differential yield outcomes to warm ($9-29\hspace{0.17em}°$C) and hot ($>29\hspace{0.17em}°$C) temperatures and differing sensitivity across growth phases. Here, we use a crop model that resolves temperature responses according to magnitude and growth phase to show that US maize has benefited from weather shifts since 1981. Improvements are related to lengthening of the growing season and cooling of the hottest temperatures. Furthermore, current farmer cropping schedules are more beneficial in the climate of the last decade than they would have been in earlier decades, indicating statistically significant adaptation to a changing climate of 13 kg·ha⁻¹· decade⁻¹. All together, the better weather experienced by US maize accounts for 28% of the yield trends since 1981. Sustaining positive trends in yield depends on whether improvements in agricultural climate continue and the degree to which farmers adapt to future climates.

Transitioning across biological scales is a central challenge in land surface models. Processes that operate at the scale of individual leaves must be scaled to canopies, and this is done using dedicated submodels. Here, we focus on a submodel that prescribes how light and nitrogen are distributed through plant canopies. We found a mathematical inconsistency in a submodel implemented in the Community and Energy Land Models (CLM and ELM), which incorporates twigs, branches, stems, and dead leaves in nitrogen scaling from leaf to canopy. The inconsistency leads to unrealistic (physically impossible) values of the nitrogen scaling coefficient. The mathematical inconsistency is a general mistake, that is, would occur in any model adopting this particular submodel. We resolve the inconsistency by allowing distinct profiles of stems and branches versus living leaves. We implemented the updated scheme in the ELM and find that the correction reduces global mean gross primary production (GPP) by 3.9 Pg C (3%). Further, when stems and branches are removed from the canopy in the updated model (akin to models that ignore shading from stems), global GPP increases by 4.1 Pg C (3.2%), because of reduced shading. Hence, models that entirely ignore stem shading also introduce errors in the global spatial distribution of GPP estimates, with a strong signal in the tropics, increasing GPP there by over 200 g C m⁻² yr⁻¹. Appropriately incorporating stems and other nonphotosynthesizing material into the light and nitrogen scaling routines of global land models, will improve their biological realism and accuracy.

Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non‐linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs.