Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation.

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.

Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems.

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.

Stomatal behaviour moderates the water cost of CO2 acquisition for 21 boreal and temperate species under experimental climate change.

The linkage of stomatal behaviour with photosynthesis is critical to understanding water and carbon cycles under global change. The relationship of stomatal conductance (gs ) and CO2 assimilation (Anet ) across a range of environmental contexts, as represented in the model parameter (g1 ), has served as a proxy of the marginal water cost of carbon acquisition. We use g1 to assess species differences in stomatal behaviour to a decade of open-air experimental climate change manipulations, asking whether generalisable patterns exist across species and climate contexts. Anet -gs measurements (17 727) for 21 boreal and temperate tree species under ambient and +3.3°C warming, and ambient and ~40% summer rainfall reduction, provided >2700 estimates of g1 . Warming and/or reduced rainfall treatments both lowered g1 because those treatments resulted in lower soil moisture and because stomatal behaviour changed more in warming when soil moisture was low. Species tended to respond similarly, although, in species from warmer and drier habitats, g1 tended to be slightly higher and to be the least sensitive to the decrease in soil water. Overall, both warming and rainfall reduction consistently made stomatal behaviour more conservative in terms of water loss per unit carbon gain across 21 species and a decade of experimental observation.

Enhanced light interception and light use efficiency explain overyielding in young tree communities.

Diverse plant communities are often more productive than mono-specific ones. Several possible mechanisms underlie this phenomenon but their relative importance remains unknown. Here we investigated whether light interception alone or in combination with light use efficiency (LUE) of dominant and subordinate species explained greater productivity of mixtures relative to monocultures (i.e. overyielding) in 108 young experimental tree communities. We found mixed-species communities that intercepted more light than their corresponding monocultures had 84% probability of overyielding. Enhanced LUE, which arose via several pathways, also mattered: the probability of overyielding was 71% when, in a mixture, species with higher 'inherent' LUE (i.e. LUE in monoculture) intercepted more light than species with lower LUE; 94% when dominant species increased their LUE in mixture; and 79% when subordinate species increased their LUE. Our results suggest that greater light interception and greater LUE, generated by inter and intraspecific variation, together drive overyielding in mixed-species forests.

Peculiarly pleasant weather for US maize.

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 ([Formula: see text]C) and hot ([Formula: see text]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.

Mapping local and global variability in plant trait distributions.

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Implications of improved representations of plant respiration in a changing climate.

Land-atmosphere exchanges influence atmospheric CO2. Emphasis has been on describing photosynthetic CO2 uptake, but less on respiration losses. New global datasets describe upper canopy dark respiration (R d) and temperature dependencies. This allows characterisation of baseline R d, instantaneous temperature responses and longer-term thermal acclimation effects. Here we show the global implications of these parameterisations with a global gridded land model. This model aggregates R d to whole-plant respiration R p, driven with meteorological forcings spanning uncertainty across climate change models. For pre-industrial estimates, new baseline R d increases R p and especially in the tropics. Compared to new baseline, revised instantaneous response decreases R p for mid-latitudes, while acclimation lowers this for the tropics with increases elsewhere. Under global warming, new R d estimates amplify modelled respiration increases, although partially lowered by acclimation. Future measurements will refine how R d aggregates to whole-plant respiration. Our analysis suggests R p could be around 30% higher than existing estimates.

Random Forests for Global and Regional Crop Yield Predictions.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Asian archaeology. Comment on "Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 B.P. ".

Chen et al. (Reports, 16 January 2015, p. 248) argued that early Tibetan agriculturalists pushed the limits of farming up to 4000 meters above sea level. We contend that this argument is incompatible with the growing requirements of barley. It is necessary to clearly define past crop niches to create better models for the complex history of the occupation of the plateau.