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This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production. [ABSTRACT FROM AUTHOR]

The feedstock supply chain of a biorefinery is critical in determining the economic and environmental performance of biofuels. This study investigated various feedstock supply chains for a centralized biorefinery based on different objective functions and how the biorefinery responds to supply chain volatility. For the analysis, a hypothetical centralized biorefinery was supplied by switchgrass grown on marginal lands in Michigan. From an economic standpoint, the minimum biofuel selling price is an important metric of the objective function, and the greenhouse gas mitigation potential is a similarly important metric for global warming impact. A trade‐off therefore exists between the economic and environmental performance of biofuels under this system configuration. However, when a carbon tax credit is applied to soil organic carbon sequestration in switchgrass production, the metric of the objective function may not be the key factor in establishing a supply chain as long as it is associated with biorefinery capacity as well as economic or environmental values. Year‐to‐year fluctuations in cellulosic biomass yield (supply chain volatility) might also prevent the biorefinery from operating at full capacity. Cellulosic biomass pellets can serve as an auxiliary feedstock for the centralized cellulosic biorefinery system to minimize the impacts of supply chain volatility. [ABSTRACT FROM AUTHOR]

Energy crops for biofuel production, especially switchgrass (Panicum virgatum), are of interest from a climate change perspective. Here, we use outputs from a crop growth model and life cycle assessment (LCA) to examine the global warming intensity (GWI; g CO2 MJ−1) and greenhouse gas (GHG) mitigation potential (Mg CO2 year−1) of biofuel systems based on a spatially explicit analysis of switchgrass grown on marginal land (abandoned former cropland) in Michigan, USA. We find that marginal lands in Michigan can annually produce over 0.57 hm3 of liquid biofuel derived from nitrogen‐fertilized switchgrass, mitigating 1.2–1.5 Tg of CO2 year−1. About 96% of these biofuels can meet the Renewable Fuel Standard (60% reduction in lifecycle GHG emissions compared with conventional gasoline; GWI ≤37.2 g CO2 MJ−1). Furthermore, 73%–75% of these biofuels are carbon‐negative (GWI less than zero) due to enhanced soil organic carbon (SOC) sequestration. However, simulations indicate that SOC levels would fail to increase and even decrease on the 11% of lands where SOC stocks >>200 Mg C ha−1, leading to carbon intensities greater than gasoline. Results highlight the strong climate mitigation potential of switchgrass grown on marginal lands as well as the needs to avoid carbon rich soils such as histosols and wetlands and to ensure that productivity will be sufficient to provide net mitigation. [ABSTRACT FROM AUTHOR]

Marginal land has received wide attention for its potential to produce bioenergy feedstocks while minimizing diversion of productive agricultural land from food crop production. However, there has been no consensus in the literature on how to define or identify land that is marginal for food crops and beneficial for bioenergy crops. Studies have used different definitions to quantify the amount of such land available; these have largely been based on assumed biophysical thresholds for soil quality and productivity that are unchanging over space and time. We discuss the limitations of these definitions and the rationale for considering economic returns and environmental outcomes in classifying land as marginal. We then propose the concept of "socially" marginal which is defined as land that is earning close to zero returns after accounting for the monetized costs of environmental externalities generated. We discuss a broad set of criteria for classifying land as socially marginal for food crops and suitable for bioenergy crops; with these criteria, this classification depends on spatially varying and time‐varying factors, such as climate and market conditions and policy incentives. While there are challenges related to identifying this marginal land, satellite and other large‐scale datasets increasingly enable such analysis at a fine spatial resolution. We also discuss reasons why landowners might choose not to convert bioenergy‐suitable land to bioenergy crops, and thus the need for policy incentives to support conversion of land that is socially beneficial for bioenergy crop production. [ABSTRACT FROM AUTHOR]

We evaluated the optical and thermal reflectance of maize (Zea mays L.) interseeded with cover crops using remotely sensed canopy temperature and multispectral imagery. In 2017 and 2018 annual ryegrass (Lolium multiflorum Lam.), crimson clover (Trifolium incarnatum L.), oilseed radish (Raphanus sativus L.), and a mixture of annual ryegrass and crimson clover were interseeded in maize at V3 and V6 at three different cover crop seeding rates in small research plots at two experimental farm sites within the network of Michigan State University. The same cover crop species were interseeded in maize at V3 and V6 at a single seeding rate in on‐farm replicated strip trials and also a full‐scale field trial at five locations in Michigan. Canopy temperature and multispectral reflectance were remotely measured 10–12 times throughout each season at all sites using fixed wing aircraft at 1‐m spatial resolution. Optical and thermal reflectance were also measured remotely using an unmanned aerial vehicle (UAV) with 2‐cm spatial resolution three times during the growing season at the small plot sites. Normalized difference vegetation index (NDVI) and normalized difference red‐edge (NDRE) were calculated for each of the experimental sites. No significant differences were detected between the interseeded treatments and control with regards to the optical and thermal reflectance and maize grain yield. Variability at field scale was due to inherent differences and not caused by the interseeding treatments. Core Ideas: Optical and thermal reflectance did not show any differences in cover crops treatments.Interseeded cover crops did not affect maize growth and yield.At field scale, the inherent spatial variability had greater impact than interseeded cover crop treatments. [ABSTRACT FROM AUTHOR]

Nonrenewable groundwater contributes ∼20% of global irrigation water. As a result, key agricultural regions around the world are on unsustainable trajectories due to aquifer depletion, threatening food production and local economies. With increasing resource scarcity in the central High Plains Aquifer in the United States, an innovative stakeholder‐driven groundwater management framework emerged in Kansas referred to as the Local Enhanced Management Area (LEMA) program. This framework enables groups of irrigators to join together to implement measures to conserve groundwater. Here, we assessed the efficacy of the first LEMA to move the region toward sustainability with a process‐based crop model driven by well records and satellite‐derived annual land use. We found increased irrigation efficiency under the LEMA program reduced groundwater extraction by 25% (40 million m3). However, only 22% of pumping reductions benefitted the net water balance (9 million m3) due to decreased irrigation return flow resulting from increased irrigation efficiency. We then estimated economic impacts using simulated crop yields, commodity prices, and estimated energy saved from reduced groundwater pumping. Cost savings from reduced pumping were about 4.5 times greater than the income lost from minor yield penalties. This suggests that the program promotes both economic and water sustainability, but water targets may need to be more strict to stabilize groundwater levels. As aquifer depletion threatens crop production in many parts of the world, approaches that integrate dynamic process‐based models with in situ and satellite data can inform economically and hydrologically sustainable management strategies. Our work highlights the need to consider both economic factors and root zone processes when evaluating irrigation conservation programs. Key Points: We assessed management impacts with a satellite‐driven crop model, well data, commodity prices, and energy costsGroundwater reductions minimally decreased crop yields; improved irrigation efficiency limited the benefits to the aquifer water balanceEnergy cost savings exceeded yield penalties, increasing net profits while saving water [ABSTRACT FROM AUTHOR]

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management. [ABSTRACT FROM AUTHOR]

The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha−1 year−1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha−1 year−1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha−1 year−1 under projected climate. On average, this was 0.3 Mg C ha−1 year−1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR. [ABSTRACT FROM AUTHOR]

Broadcast interseeding cover crops in corn (Zea mays L.) from the V2–V7 corn growth stages provides farmers the opportunity to establish cover crops over large areas quickly compared with drill interseeders. The objectives of this research were to evaluate broadcast interseeded cover crop establishment and biomass production as well as cover crop effect on corn grain yield in farmer's fields in Michigan. In 2017 and 2018, annual ryegrass (Lolium multiflorum Lam.), crimson clover (Trifolium incarnatum L.), and oilseed radish (Raphanus sativus L.) were broadcast interseeded at the V3 and V6 corn growth stages in nine farm fields. Cover crop density was measured 30 d after interseeding; cover crop density and biomass were measured in October prior to grain corn harvest. Cover crop density varied across site‐years; annual ryegrass usually had the highest density. Fall biomass production of oilseed radish was usually equal to or greater than annual ryegrass and crimson clover biomass. Rainfall during the interseeding period improved cover crop emergence. Cover crop density and biomass were higher in sites that were tilled prior to corn planting compared with no‐till, likely due to better seed to soil contact. Grain yield did not differ in the cover crop vs. no cover crop control treatments. Successfully establishing cover crops by broadcast interseeding in corn is dependent on specific location conditions; conventional tillage and rainfall improved establishment and biomass production. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is an inflammatory process, and has high incidence and mortality. ALI and the acute respiratory distress syndrome are two common complications worldwide that result in acute lung failure, sepsis, and death. Pro‐inflammatory substances, such as cytokines and chemokines, are responsible for activating the body's defense mechanisms and usually mediate inflammatory processes. Therefore, the research of substances that decrease the uncontrolled response of organism is seen as potential for patients with ALI. Octyl gallate (OG) is a phenolic compound with therapeutic actions namely antimicrobial, antiviral, and antifungal. In this study, we evaluated its action on lipopolysaccharide (LPS)‐activated alveolar macrophages RAW 264.7 cells and ALI in male mice. Our results demonstrated protective effects of OG in alveolar macrophages activated with LPS and mice with ALI. The OG treatment significantly decreased the inflammatory markers in both studies in vitro and in vivo. The data suggested that OG can act as an anti‐inflammatory agent for ALI. [ABSTRACT FROM AUTHOR]

Field indicators of forage nutritive value could help farmers with rapid management decisions to optimize timing and intensity of grazing and meet objectives regarding animal nutrition. The objective of this research was to evaluate the likely relationships among leaf blade nutritive value, herbage mass and leaf stage of pasture regrowth under different growing seasons and residual sward heights. Experiments were performed on perennial ryegrass (Lolium perenne L.) and tall fescue (Festuca arundinacea Schreb.) pastures during spring and summer of 2016. In both pastures, three residual sward height treatments (3, 6 and 12 cm) were imposed on plots arranged in a split plot design, replicated in three blocks. Sward plots were harvested 5–6 times at intervals spaced 7–10 days apart to measure herbage mass, plant morphology, neutral detergent fibre (NDF), and the 24‐hr in vitro digestibility of NDF (NDFD) and dry matter (DMD) of leaf blades. Pastures showed strong (R2:.62 to.70), but variable, negative relationships between NDFD and herbage mass that varied with the rate at which pasture grew in each season of experimentation. Although there was a consistent NDFD decline as leaf stage of regrowth progressed (R2:.75 to.97), the NDFD also decreased as residual sward height increased, most notably in tall fescue. Additionally, findings indicate that the greater leaf length plasticity of tall fescue compared to residual sward heights may offer opportunities to manage both post‐ and pre‐grazing targets to achieve tall fescue forages with a similar high nutritive value as perennial ryegrass. However, the evaluation of this hypothesis at the farm level and its impacts on animal intake and performance warrants further careful investigations. [ABSTRACT FROM AUTHOR]

Leaching from annual corn (Zea mays L.) crops is a primary source of nitrate (NO3−) pollution of ground and surface waters. Here, we compare NO3− losses from no‐till corn with losses from various alternative perennial cropping systems (switchgrass [Panicum virgatum L.], miscanthus [Miscanthus ×giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz], a native grass mixture, and restored prairie), as well as hybrid poplar (Populus nigra L. × P. maximowiczii A. Henry 'NM6'), all grown on a well‐drained soil in Michigan. Soil water was sampled from below the root zone using suction cup samplers during nonfrozen periods (March–November) between 2009 and 2016. Leaching was estimated from NO3− concentrations in soil water and modeled drainage (percolation) rates. Drainage rates were not significantly different among crops, constituting ∼30% of total annual precipitation. Aboveground net primary production (Mg ha−1 yr−1) averaged across the 7 yr was highest in poplar (30.8 ± 1.9 [SE]) followed by miscanthus (23.9 ± 2.4) and corn (20.4 ± 0.9). Volume‐weighted mean NO3− concentrations (mg N L−1) and NO3− leaching (kg ha−1 yr−1) averaged across the 7 yr were 9.2 and 34.1, 2.3 and 5.9, and 3.0 and 7.2, respectively, for corn, perennial grasses and poplar. Approximately 10 to 32% of applied N was lost as NO3− from these crops, with the highest percent losses from poplar (32%) followed by corn (20%). Perennial cropping systems leached considerably more NO3− in first few years after planting, but over 7 yr they lost much less NO3− than corn. Perennial crops may therefore help ameliorate NO3− pollution in agricultural landscapes even if they receive modest N fertilization. Core Ideas: Over 7 yr, perennial grasses and poplar trees leached much less nitrate than cornNitrate leaching from grasses and poplar was comparable with corn for 1–2 yr after planting.Perennial crops can ameliorate nitrate pollution in agricultural landscapes. [ABSTRACT FROM AUTHOR]

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. The projected impact of 1.5 and 2.0°C warming above the pre‐industrial period on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impacts of warming of <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. [ABSTRACT FROM AUTHOR]

The leaf morphogenesis of plants is the most important determinant of the nutritive value dynamics in vegetative pasture regrowth. The aim of this study was to develop a simulation model of the pasture nutritive value dynamics based on a morphogenetic approach that takes into account the effects of leaf age and leaf length on forage digestibility in relation to defoliation management. The model was developed and evaluated with detailed data from two independent experiments (Exp. 1 and Exp. 2, respectively) on tall fescue [Lolium arundinaceum (Schreb.) Darbysh.], including descriptions of morphogenesis, neutral detergent fiber (NDF) and digestibility of NDF (NDFD) and dry matter (DMD) of leaf blades. The model precisely and accurately simulated the forage digestibility dynamics of pasture regrowth observed under different residual pasture heights in Exp. 2. The main calculated statistics for NDF, NDFD, and DMD were root mean square deviation < 4% points, R² = 0.92, concordance correlation coefficient = 0.86 and bias correlation factor = 0.89. The evaluated model was used to investigate the responses of forage nutritive value to several combinations of residual pasture heights (2-15 cm) and defoliation intervals (one to five leaves per tiller). This study highlights the inclusion of the NDFD trait associated to leaf morphogenesis as a mechanistic way to improve predictions of DMD dynamics in vegetative regrowth under different defoliation managements. [ABSTRACT FROM AUTHOR]

The objectives of this research were to (i) evaluate the effects of N fertilizer, irrigation, and residual pasture heights on pasture growth, (ii) validate the ability of the SALUS model to predict dynamics of pasture growth, and (iii) evaluate during long-term period the effects of using different N fertilizer levels and defoliation strategies on pasture growth, N fertilizer use, and water use efficiency (WUE). Eight single-season experiments were performed at plot scale (8 m²) in Buenos Aires (Argentina, ARG) and Michigan. In ARG different N fertilizer rates (from 0-500 kg N ha-1) were imposed on both rainfed and irrigated tall fescue [Lolium arundinaceum (Schreb.) Darbysh.] pasture during autumn, spring, and summer. In the United States, three residual pasture height treatments (30, 60, and 120 mm) were imposed on both tall fescue and ryegrass (Lolium perenne L.) pasture in the spring and summer. The SALUS was parameterized to simulate tall fescue and ryegrass growth using soil, weather, and different pasture treatments previously tested in ARG and the United States. Results showed that the SALUS accurately represented the response of herbage mass to irrigation and added N in the ARG site (RMSE < 650 kg DM ha-1) and to differences in residual pasture heights in the U.S. experiment (RMSE < 509 kg DM ha-1). Ten-year simulations (2000-2010) demonstrated that suitable combinations of N fertilizer and residual pasture heights can significantly improve the use of N fertilizer by ~300% and water by ~230% through increases in herbage production. [ABSTRACT FROM AUTHOR]

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1% points, representing a relative change of −8.6%. [ABSTRACT FROM AUTHOR]

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment. [ABSTRACT FROM AUTHOR]

Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Application rates of synthetic fertilizer to agricultural fields can be reduced through better understanding of N supplied by organic amendments (OA). Field and simulation experiments were performed to quantify the effect of N released from OA application on crop production and selected soil properties in an intensively managed vegetable crop rotation. The SALUS crop model was used to simulate yield, soil N, and soil organic carbon (SOC) dynamics under different combinations of composted or raw OA and synthetic N fertilizer application rates. SALUS accurately simulated aboveground crop biomass production (r² = 0.91, RMSE = 1.7 t ha-1) and crop N uptake (r² = 0.96, RMSE = 15 kg N ha-1) under different N management strategies as well as SOC level (r² = 0.51, RMSE = 1 t C ha-1) and soil mineral N (r² = 0.58, RMSE = 56 kg N ha-1). No difference in crop biomass production was found with N fertilizer reductions up to 27% of the conventional N fertilizer rate when combined with OA application. A 12-yr scenario analysis using SALUS indicated that conventional N fertilizer can be further reduced by up to 50% while sustaining crop biomass production, thereby potentially reducing N losses to the environment. Data gathered from the field study and simulation scenarios highlighted the positive effect of composted OA to maintain soil C levels. This contrasts with average annual SOC losses of 3.7% observed in long-term simulation scenarios in systems with only N fertilizer or raw OA applications. [ABSTRACT FROM AUTHOR]

Switchgrass ( Panicum virgatum) productivity on marginal and fertile lands has not been thoroughly evaluated in a systematic manner that includes soil-crop-weather-management interactions and to quantify the risk of failure or success in growing the crop. We used the Systems Approach to Land Use Sustainability ( SALUS) model to identify areas with low risk of failing to having more than 8000 kg ha−1 yr−1 switchgrass aboveground net primary productivity ( ANPP) under rainfed and unfertilized conditions. In addition, we diagnosed constraining factors for switchgrass growth, and tested the effect of nitrogen fertilizer application on plant productivity across Michigan for 30 years under three climate scenarios (baseline climate in 1981-2010, future climate with emissions using RCP 2.6 and RCP 6.0). We determined that <16% of land in Michigan may have at least 8 Mg ha−1 yr−1 ANPP under rainfed and unfertilized management with a low risk of failure. Of the productive low-risk land, about 25% was marginal land, with more than 80% of which was affected by limited water availability due to low soil water-holding capacity and shallow depth. About 80% of the marginal land was N limited under baseline conditions, but that percentage decreased to 58.5% and 42.1% under RCP 2.6 and RCP 6.0 climate scenarios, respectively, partly due to shorter growing season, smaller plants and less N demand. We also found that the majority of Michigan's land could have high switchgrass ANPP and low risk of failure with no more than 60 kgN ha−1 fertilizer input. We believe that the methodology used in this study works at different spatial scales, as well as for other biofuel crops. [ABSTRACT FROM AUTHOR]

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield-temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security 'hot spots'. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) yield and its response to nitrogen (N) are affected by the spatial variability of the interaction between weather, management, and soil properties. The objectives of this study were (i) to evaluate the response of spatial variability of maize yield by homogeneous zones (HZs) to different N fertilizer rates under rainfed conditions, (ii) to test the ability of the SALUS (System Approach to Land Use Sustainability) model to simulate the effects of N rates on maize yield under rainfed and irrigated conditions, and (iii) to estimate spatial and temporal N fertilizer response risk in maize through the use of long-term simulations. In two field experiments in Parana, Argentina (-31.8333°, -60.5167°) in 2011 (Field 1) and 2012 (Field 2), four fertilization treatments (0, 70, 140, and 210 kg ha-1) were evaluated in different HZs. The SALUS model was used to evaluate spatial variability in yield, N response, and net revenue over the long-term period (1971-2012). Results showed that yield was significantly affected by N rate (p < 0.01) in both fields and by HZ in Field 2 (p < 0.05), whereas N response was only affected by N rate. Simulated yield was significantly affected by N. The model accounted for the spatial variability, showing HZ effect (p < 0.001) and a significant HZ × N interaction (p < 0.0001). The optimal economic return N rate differed between HZs in both fields. Our procedure demonstrated the ability to improve N management by the selection of appropriate N rates across the field, thereby improving N use efficiency and growers' profits and reducing the potential for negative environmental impacts. [ABSTRACT FROM AUTHOR]

The response of wheat crops to elevated CO2 ( eCO2) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha−1 and 1600 to 3900 kg ha−1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol−1 CO2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm ( P = 0.10) by eCO2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY- CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency ( RUE) or transpiration efficiency ( TE) was not critical to define the overall response to eCO2. However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2, water and temperature is required to resolve these model discrepancies. [ABSTRACT FROM AUTHOR]

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield ( GY) and grain protein concentration ( GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. [ABSTRACT FROM AUTHOR]

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [ CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames ( USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [ CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [ CO2] among models. Model responses to temperature and [ CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. [ABSTRACT FROM AUTHOR]

(+)-Catechin is a type of catechin present in large amounts in açaí fruits and cocoa seeds. Besides its antioxidant and anti-inflammatory activities, little is known about its effects in the liver, especially during hepatic fibrosis. We report here the effects of (+)-catechin on hepatic stellate cells. (+)-Catechin induced quiescent phenotype in GRX cells, along with an increase in lipid droplets. Proliferator-activated receptor γ mRNA expression was upregulated, whereas type I collagen mRNA expression was downregulated. Pro-inflammatory cytokines were not influenced by (+)-catechin, whereas the levels of interleukin 10 were significantly increased. The data provide evidence that (+)-catechin can reduce hepatic stellate cell activation. [ABSTRACT FROM AUTHOR]

Perennial grain cropping systems could address a number of contemporary agroecological problems, including soil degradation, NO3 leaching, and soil C loss. Since it is likely that these systems will be rotated with other agronomic crops, a better understanding of how rapidly perennial grain systems improve local ecosystem services is needed. We quantified soil moisture, lysimeter NO3 leaching, soil labile C accrual, and grain yields in the first 2 yr of a perennial grain crop under development [kernza wheatgrass, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] relative to annual winter wheat (Triticum aestivum L.) under three management systems. Overall, differences between annual and perennial plants were much greater than differences observed due to management. In the second year, perennial kernza reduced soil moisture at lower depths and reduced total NO3 leaching (by 86% or more) relative to annual wheat, indicating that perennial roots actively used more available soil water and captured more applied fertilizer than annual roots. Carbon mineralization rates beneath kernza during the second year were increased 13% compared with annual wheat. First-year kernza grain yields were 4.5% of annual wheat, but second year yields increased to 33% of wheat with a harvest index of 0.10. Although current yields are modest, the realized ecosystem services associated with this developing crop are promising and are a compelling reason to continue breeding efforts for higher yields and for use as a multipurpose crop (e.g., grain, forage, and biofuel). [ABSTRACT FROM AUTHOR]

Process-based soil organic C (SOC) models are widely used for simulating, monitoring, and verifying soil C change. In such models, determining the initial distribution of SOC across multiple pools is often not well defined, yet pool initialization can strongly influence the subsequent SOC dynamics. We developed a model-based procedure to initialize SOC fractions that uses site-specific soil, climatic, and land use history information, along with measured initial total SOC, to estimate SOC distribution across pools. The procedure consists of creating sets of soc fractions for scenarios including different field histories, soil texture, and management practices for medium- and long-term simulations to provide a broad spectrum of conditions, using data from an experimental site in Georgia. Tables created using this procedure can help model users select the Soc fractions needed to properly initialize the model. The model is executed for the duration of the prior land use history time period, and the simulated final total SOC is compared with the soil C measurement at the beginning of the subsequent experimental time period. If these values are equal, the final SOC fractions from the land use history simulations are used with the measured soil C to start the simulation of the cropping system being studied. If these values are not equal, then the procedure is repeated iteratively until the measured value of soil C is adequately predicted. We demonstrated the improved prediction accuracy by using the proposed procedure in simulating soil C for a long-term field experiment in Michigan. [ABSTRACT FROM AUTHOR]

Proximal and remote soil sensing are particularly valuable for precision agriculture application, and there is great scope for their synergic use. Different hybrid methods were compared, allowing the joint exploitation of hyperspectral satellite data and geophysical data for estimating soil properties at the field scale. Clay, sand, and available water content were estimated with a sufficient degree of accuracy, especially when using the regression-kriging technique. There is great interest in the development of inexpensive and rapid soil mapping methods for precision agriculture applications. Proximal and remote soil sensing are particularly valuable for this purpose, and there is great scope for their synergic use. The objective of this study was to compare different methods allowing the joint exploitation of hyperspectral satellite data and geophysical data for estimating soil properties at the field scale. Soil samples were collected in an agricultural field in Central Italy for the determination of several soil properties. Satellite images were acquired by the CHRIS-PROBA sensor, both under bare soil conditions and when the field was covered by a wheat crop. Geophysical data were obtained by the automatic resistivity profiling method (ARP), providing apparent soil electrical resistivity of the 0 to 50 cm layer. Regression-kriging (RK), partial least square regression (PLSR), and a combination of PLSR with geostatistics through kriging of the PLSR residuals (PLSR-K) were applied to estimate soil properties by employing all combinations of the available covariates. A multiple jack-knifing procedure was used for a statistical comparison of the ratio of performance to deviation (RPD) statistics across 300 replicates. Clay, sand, and available soil water content were estimated with a sufficient degree of accuracy (RPD > 1.4), especially when using the RK technique. PLSR-K estimated these variables with intermediate ability by using only remote sensing covariates and obtained, in most cases, better results than PLSR. For other soil variables, the prediction ability was unsatisfactory (RPD < 1.4) due to smaller sample and range and weaker correlation with the covariates. [ABSTRACT FROM AUTHOR]

Crop yield is affected by many factors, including the availability of water and nutrients and planting distribution. Models developed to predict yield based only on water use can be misleading due to the dominance of crop management factors. The ability of crop models to predict the impact of management strategies on yield is illustrated in this paper. Studies on water use efficiency (WUE) have primarily dealt with crops grown under water limited conditions and have usually not considered crop management factors other than irrigation. Improvements in crop management strategies, such as supplemental irrigation, proper fertilizer use, plant population and row-spacing, and new cultivars, attempt to minimize a crop exposure to soil water deficits. Under these circumstances, factors other than water supply can become limiting and production related to evaporation may not be a useful approach. Crop simulation models like CERES and SALUS have proven capability to provide reasonable estimates of each evaporation component as affected by management. Using simulated transpiration to estimate growth through transpiration efficiency, or simulated growth to estimate transpiration has major limitations when management influences yield and water supply is fixed or non-limiting. The microenvironment in sparse canopies strongly influences transpiration and soil evaporation due to large possible variations in sensible heat arising from variations in wetness of the soil surface. Crop growth is related to photosynthesis and plant development rates and is influenced primarily by intercepted photosynthetically active radiation whereas evaporation is influenced by a wider spectrum of energy. Factors related to management such as plant population and nutrient supply influences biomass production but has less influence on evaporation rates. Our analysis of published data and simulation studies indicates the use of simulated transpiration to estimate growth, or growth simulations to estimate transpiration is an unreasonable approach when management influences yield and water supply is fixed or not-limiting. [ABSTRACT FROM AUTHOR]