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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.

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.

We updated the routines used to estimate leaf maintenance respiration in the Energy Land Model (ELM) using a comprehensive global respiration data base. The updated algorithm includes a temperature acclimating base rate, an updated instantaneous temperature response, and new plant functional type (PFT) specific parameters. The updated maintenance respiration algorithm resulted in a very large increase in global maintenance respiration (MR) of 16.1 Pg (38%), but the signal was not geographically uniform. The increase was concentrated in the tropics and humid warm-temperate forests. The increase in maintenance respiration led to large but proportionally smaller decreases in global net primary production (NPP, 19%) and in average global leaf area index (LAI, 15%). The effect on global gross primary production (GPP) was a more modest 5.7 Pg (4%). A detailed site level analysis also demonstrated a wide range of effects the updated algorithm can have on the seasonal cycle of GPP. Output from the updated and old models did not differ markedly in how closely they matched a suite of benchmarks. Given the substantial impact on the land surface carbon cycle, a neutral influence on model benchmarks, and better alignment with empirical evidence, a maintenance respiration algorithm similar to the one presented here should be adopted into ELM.

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.

Research Highlights: We modeled climate-biome envelopes at high resolution in the Western Great Lakes Region for recent and future time-periods. The projected biome shifts, in conjunction with heterogeneous distribution of protected land, may create both great challenges for conservation of particular ecosystems and novel conservation opportunities. Background and Objectives: Climate change this century will affect the distribution and relative abundance of ecological communities against a mostly static background of protected land. We developed a climate-biome envelope model using a priori climate-vegetation relationships for the Western Great Lakes Region (Minnesota, Wisconsin and Michigan USA and adjacent Ontario, Canada) to predict potential biomes and ecotones&mdash, boreal forest, mixed forest, temperate forest, prairie&ndash, forest border, and prairie&mdash, for a recent climate normal period (1979&ndash, 2013) and future conditions (2061&ndash, 2080). Materials and Methods: We analyzed six scenarios, two representative concentration pathways (RCP)&mdash, 4.5 and 8.5, and three global climate models to represent cool, average, and warm scenarios to predict climate-biome envelopes for 2061&ndash, 2080. To assess implications of the changes for conservation, we analyzed the amount of land with climate suited for each of the biomes and ecotones both region-wide and within protected areas, under current and future conditions. Results: Recent biome boundaries were accurately represented by the climate-biome envelope model. The modeled future conditions show at least a 96% loss in areas suitable for the boreal and mixed forest from the region, but likely gains in areas suitable for temperate forest, prairie&ndash, forest border, and prairie. The analysis also showed that protected areas in the region will most likely lose most or all of the area, 18,692 km2, currently climatically suitable for boreal forest. This would represent an enormous conservation loss. However, conversely, the area climatically suitable for prairie and prairie&ndash, forest border within protected areas would increase up to 12.5 times the currently suitable 1775 km2. Conclusions: These results suggest that retaining boreal forest in potential refugia where it currently exists and facilitating transition of some forests to prairie, oak savanna, and temperate forest should both be conservation priorities in the northern part of the region.

Significance Over the course of the 20th century, US maize yields have improved by more than a factor of five. Whereas this trend is often attributed exclusively to technological improvements, here, we also identify contributions from improved temperatures during the growing season. More than one-quarter of the increase in crop yield since 1981 is estimated to result from trends toward overall warmer conditions, but with cooling of the hottest growing-season temperatures, and from adjustments in crop timing toward earlier planting and longer maturation varieties., 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 °C) and hot (>29 °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.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) shows promise for monitoring the productivity of global agricultural systems. SIF-based primary productivity metrics have demonstrated higher fidelity to large-scale patterns of crop productivity than reflectance-based vegetation indices when averaged across the growing season. In-season crop yield forecasting typically relies upon reflectance-based vegetation indices, raising the question of whether in-season monitoring could be improved by utilizing SIF. Here, we analyze patterns of US agricultural productivity from USDA surveys and their in-season relationships with coarse-resolution GOME-2 SIF, high-resolution downscaled SIF, SIF-based primary productivity metrics, MODIS NDVI, and MODIS GPP. We find that coarse-resolution SIF-based metrics and NDVI exhibit similar out-of-sample in-season (April–July and April–August) predictive ability, even when spatially filtering higher-resolution NDVI data to cropland areas. The downscaled SIF product performed more poorly than the coarse-resolution SIF, and MODIS GPP performed more poorly than MODIS NDVI. All forecasts are improved by incorporating county fixed effects to control for cross-sectional differences between counties. NDVI-based metrics allow for significantly better yield predictions during drought conditions than SIF-based metrics, suggesting limited added value of SIF for early warning of drought impacts. The benefits of SIF for crop monitoring should be continually evaluated as the frequency and quality of SIF measurements continue to improve.

Conversion of native ecosystems to cropland and the use of irrigation are considered dominant pathways through which agricultural land-use change alters regional climate. Recent research proposes that increases in cropland productivity, or intensification, also influences climate through increasing evapotranspiration. Increases in evapotranspiration are expected to have the greatest temperature influence on extremely hot summer days with high vapor pressure deficits. Here, the generalizability and importance of such relationships are assessed by examining historical land-use and climate trends in seven regions across the globe, each containing a major temperate or subtropical cropping area. Trends in summer high-temperature extremes are sequentially compared against trends in cropland area, area equipped for irrigation, precipitation, and summer cropping intensity. Trends in temperature extremes are estimated using quantile regression of weather station observations, and land-use data are from agricultural inventories and remote sensing. Intensification is the best predictor of trends in extreme temperatures among the factors that are considered and is generally associated with trends that are 0.2°–0.4°C decade−1 cooler than in adjacent regions. Neither cropland area nor precipitation trends are systematically associated with extreme temperature trends across regions, although high temperatures are suppressed over those portions of central North America and East Asia experiencing growth in irrigation. Both the temperature trends associated with intensification and increased irrigation can be understood as a consequence of increased latent cooling. These results underscore that the weather experienced by crops is not entirely external but also depends on agricultural practices.

More intense precipitation is an expected consequence of anthropogenic climate change. Now research quantifies the effect of more concentrated rainfall on American agriculture.

Aim: Plant trait databases often contain traits that are correlated, but for whom direct (undirected statistical dependency) and indirect (mediated by other traits) connections may be confounded. The confounding of correlation and connection hinders our understanding of plant strategies, and how these vary among growth forms and climate zones. We identified the direct and indirect connections across plant traits relevant to competition, resource acquisition and reproductive strategies using a global database and explored whether connections within and between traits from different tissue types vary across climates and growth forms. Location: Global. Major taxa studied: Plants. Time period: Present. Methods: We used probabilistic graphical models and a database of 10 plant traits (leaf area, specific leaf area, mass- and area-based leaf nitrogen and phosphorous content, leaf life span, plant height, stem specific density and seed mass) with 16,281 records to describe direct and indirect connections across woody and non-woody plants across tropical, temperate, arid, cold and polar regions. Results: Trait networks based on direct connections are sparser than those based on correlations. Land plants had high connectivity across traits within and between tissue types; leaf life span and stem specific density shared direct connections with all other traits. For both growth forms, two groups of traits form modules of more highly connected traits; one related to resource acquisition, the other to plant architecture and reproduction. Woody species had higher trait network modularity in polar compared to temperate and tropical climates, while non-woody species did not show significant differences in modularity across climate regions. Main conclusions: Plant traits are highly connected both within and across tissue types, yet traits segregate into persistent modules of traits. Variation in the modularity of trait networks suggests that trait connectivity is shaped by prevailing environmental conditions and demonstrates that plants of different growth forms use alternative strategies to cope with local conditions. © 2019 John Wiley and Sons Ltd

Forest light availability strongly regulates understory community composition, and low availability may confer resistance to invasion by exotic species, yet common buckthorn (Rhamnus cathartica L.) invades North American temperate forests with a broad range of light habitats. It is unclear to what extent buckthorn’s success is due to high mid-season shade tolerance versus shade avoidance permitted by early leaf out and late senescence. We used buckthorn seedlings planted into a forest diversity experiment in Cloquet, Minnesota, USA and a combined buckthorn physiology-canopy light model to test (1) how buckthorn germination, growth, and survival depend on canopy shading and (2) how canopy species richness and phenology affect light availability and buckthorn performance. Based on the mean of May, August, and October light measurements, we found that canopies that permitted ≤3% transmission of incoming light had almost complete mortality of buckthorn and that growth of surviving buckthorn was strongly tied to light availability, but not canopy richness. Compared to deciduous canopies or deciduous-evergreen mixtures, evergreen canopies restricted light availability the most and led to the smallest and least likely to survive buckthorn. Our canopy models further indicated a tight linkage between buckthorn performance and spring and autumn light availability, but not summer light availability. We conclude that spring and autumn light availability are key regulators of buckthorn performance and that buckthorn relies on shade avoidance via an extended phenology to succeed in temperate forests. Consequently, we suggest species with extended spring or autumn canopy cover offer the greatest resistance to invasion, and communities which are often leafless during such periods are most vulnerable to invasion by buckthorn and similar invasive shrubs.

Kovaleski and Baseggio (1) emphasize the importance of genetics and agronomic practices for determining trends in crop yield. Our recent analysis of US maize yields (2) explicitly evaluates how changes in certain agronomic practices and cultivar characteristics contribute to yield increases. We recognize the myriad factors that contribute to yield gains and welcome Kovaleski and Baseggio’s (1) additional analyses. We used US Department of Agriculture crop progress data (3) to calculate the yield gains attributable to earlier planting and longer-maturing cultivars. These changes in crop-development timing reflect the breeding and cultural practices that Kovaleski and Baseggio (1) reference but are also associated with changes in climate and … [↵][1]1To whom correspondence should be addressed. Email: eebutler{at}umn.edu. [1]: #xref-corresp-1-1

High temperature extremes during the growing season can reduce agricultural production. At the same time, agricultural practices can modify temperatures by altering the surface energy budget. Here we identify centennial trends towards more favourable growing conditions in the US Midwest, including cooler summer temperature extremes and increased precipitation, and investigate the origins of these shifts. Statistically significant correspondence is found between the cooling pattern and trends in cropland intensification, as well as with trends towards greater irrigated land over a small subset of the domain. Land conversion to cropland, often considered an important influence on historical temperatures, is not significantly associated with cooling. We suggest that agricultural intensification increases the potential for evapotranspiration, leading to cooler temperatures and contributing to increased precipitation. The tendency for greater evapotranspiration on hotter days is consistent with our finding that cooling trends are greatest for the highest temperature percentiles. Temperatures over rainfed croplands show no cooling trend during drought conditions, consistent with evapotranspiration requiring adequate soil moisture, and implying that modern drought events feature greater warming as baseline cooler temperatures revert to historically high extremes.

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 (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-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., This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the “Light of West China” Program of the Chinese Academy of Sciences.

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., New global datasets of upper canopy vegetation respiration have become available and their impact on global carbon cycle models is unclear. Here, the authors show the implications of these parameterisations with a global gridded land model and report significantly higher global plant respiration estimates.

Understanding how, why and by what mechanisms agricultural practices, technologies and products spread out of their zones of original development is a central theme of archaeology. To date, very few studies have combined agro-ecological modeling with detailed analyses of archaeobotanical remains to outline the kinds of challenges that ancient humans faced as they moved crops into environments different from their original homeland of domestication. This paper employs ecological niche modeling to outline the constraints faced by ancient humans as they moved rice, millets and eventually wheat and barley into the mountainous region of Southwest China. In particular, we propose that moving rice into this region presented considerable challenges for its cultivators and we infer that its spread into this area was facilitated by breeding cold adapted varieties of rice or by combining its cultivation with that of millet. High altitude areas did not take up full-scale agriculture until the introduction of cold adapted western Eurasian domesticates such as wheat and barley. The temperature niche models reinforce the adoption of these regionally varied agricultural strategies and support the significance of domesticates other than rice for the spread of agriculture into Southwest China.

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.

Predictions of climate warming raise concerns about food security. However, the extent to which adaptation can offset heat-related yield losses remains unclear. Now research that used spatial adaptation of US maize crops as a surrogate for future adaptation finds that the yield reduction resulting from warming of 2 °C above pre-industrial levels can be approximately halved using existing management practices.

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.

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.

Maize yield is sensitive to high temperatures, and most large scale analyses have used a single, fixed sensitivity to represent this vulnerability over the course of a growing season. Field scale studies, in contrast, highlight how temperature sensitivity varies over the course of development. Here we couple United States Department of Agriculture yield and development data from 1981–2012 with weather station data to resolve temperature sensitivity according to both region and growth interval. On average, temperature sensitivity peaks during silking and grain filling, but there are major regional variations. In Northern states grain filling phases are shorter when temperatures are higher, whereas Southern states show little yield sensitivity and have longer grain filling phases during hotter seasons. This pattern of grain filling sensitivity and duration accords with the whole-season temperature sensitivity in US maize identified in recent studies. Further exploration of grain filling duration and its response to high temperatures may be useful in determining the degree to which maize agriculture can be adapted to a hotter climate.

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