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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]

Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images. To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model. For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R2 values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered. In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides opportunities to achieve better precision management and yield prediction by integrating historical crop yields and remote sensing techniques. [ABSTRACT FROM AUTHOR]

Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM. However, the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral-associated organic matter (MAOM) at the subfield scale within nine farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks. [ABSTRACT FROM AUTHOR]

Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers' fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ. [ABSTRACT FROM AUTHOR]

In pulmonary fibrosis, the proliferation of fibroblasts and their differentiation into myofibroblasts is often caused by tissue damage, such as oxidative damage caused by reactive oxygen species, which leads to progressive rupture and thus destruction of the alveolar architecture, resulting in cell proliferation and tissue remodeling. Bezafibrate (BZF) is an important member of the peroxisome proliferator-activated receptor (PPARs) family agonists, used in clinical practice as antihyperlipidemic. However, the antifibrotic effects of BZF are still poorly studied. The objective of this study was to evaluate the effects of BZF on pulmonary oxidative damage in lung fibroblast cells. MRC-5 cells were treated with hydrogen peroxide (H2O2) to induce oxidative stress activation and BZF treatment was administered at the same moment as H2O2 induction. The outcomes evaluated were cell proliferation and cell viability; oxidative stress markers such as reactive oxygen species (ROS), catalase (CAT) levels and thiobarbituric acid reactive substances (TBARS); col-1 and α-SMA mRNA expression and cellular elasticity through Young's modulus analysis evaluated by atomic force microscopy (AFM). The H2O2-induced oxidative damage decreased the cell viability and increased ROS levels and decreased CAT activity in MRC-5 cells. The expression of α-SMA and the cell stiffness increased in response to H2O2 treatment. Treatment with BZF decreased the MRC-5 cell proliferation, ROS levels, reestablished CAT levels, decreased the mRNA expression of type I collagen protein (col-1) and α-smooth muscle actin (α-SMA), and cellular elasticity even with H2O2 induction. Our results suggest that BZF has a potential protective effect on H2O2-induced oxidative stress. These results are based on an in vitro experiment, derived from a fetal lung cell line and may emerge as a possible new therapy for the treatment of pulmonary fibrosis. [ABSTRACT FROM AUTHOR]

Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM, however the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral associated organic matter (MAOM), at the sub-field scale within 9 farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones, and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas, and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks. [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]

Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and measuring stock change stipulates fixed depth sampling to at least 30 cm. The tendency of bulk density (BD) to decrease with decreasing disturbance and increasing SOC concentration and the assumption of constant SOC and BD within this depth profile promotes error in the estimates of SOC stock. A hypothetical but realistic change in BD from 1.5 to 1.1 g cm−3 from successive fixed depth sampling to 30 cm underestimates SOC stock change by 17%. Significant effort has been made to evaluate and reduce this fixed depth error by using the equivalent soil mass (ESM) approach, but with limited adoption. We evaluate the error in SOC stock assessment and change generated from fixed depth measurements over time relative to the ESM approach and propose a correction that can be readily adopted under current sampling and analytical methods. Our approach provides a more accurate estimate of SOC stock accumulation or loss that will help incentivize management practice changes that reduce the environmental impacts of agriculture and further legitimize the accounting practices used by the emerging carbon market and organizations that have pledged to reduce their supply chain greenhouse gas (GHG) footprints. [ABSTRACT FROM AUTHOR]

Soils comprise the largest pool of terrestrial carbon yet have lost significant stocks due to human activity. Changes to land management in cropland and grazing systems present opportunities to sequester carbon in soils at large scales. Uncertainty in the magnitude of this potential impact is largely driven by the difficulties and costs associated with measuring near-surface (0–30 cm) soil carbon concentrations; a key component of soil carbon stock assessments. Many techniques exist to optimize sampling, yet few studies have compared these techniques at varying sample intensities. In this study, we performed ex-ante, high-intensity sampling for soil carbon concentrations at four farms in the eastern United States. We used post hoc Monte-Carlo bootstrapping to investigate the most efficient sampling approaches for soil carbon inventory: K-means stratification, Conditioned Latin Hypercube Sampling (cLHS), simple random, and regular grid. No two study sites displayed similar patterns across all sampling techniques, although cLHS and grid emerged as the most efficient sampling schemes across all sites and strata sizes. The number of strata chosen when using K-means stratification can have a significant impact on sample efficiency, and we caution future inventories from using small strata n, while avoiding even allocation of sample between strata. Our findings reinforce the need for adaptive sampling methodologies where initial site inventory can inform primary, robust inventory with site-specific sampling techniques. [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]

Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and measuring stock change stipulates fixed depth sampling to at least 30 cm. The tendency of bulk density (BD) to decrease with decreasing disturbance and increasing SOC concentration and the assumption of constant SOC and BD within this depth profile promotes error in the estimates of SOC stock. A hypothetical but realistic change in BD from 1.5 to 1.1 g cm−3 from successive fixed depth sampling to 30 cm underestimates SOC stock change by 17%. Significant effort has been made to evaluate and reduce this fixed depth error by using the equivalent soil mass (ESM) approach, but with limited adoption. We evaluate the error in SOC stock assessment and change generated from fixed depth measurements over time relative to the ESM approach and propose a correction that can be readily adopted under current sampling and analytical methods. Our approach provides a more accurate estimate of SOC stock accumulation or loss that will help incentivize management practice changes that reduce the environmental impacts of agriculture and further legitimize the accounting practices used by the emerging carbon market and organizations that have pledged to reduce their supply chain greenhouse gas (GHG) footprints. [ABSTRACT FROM AUTHOR]

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) is a life-threatening acute inflammatory disease with high rates of morbidity and mortality worldwide. 4-Allyl-2,6-dimethoxyphenol (methoxyeugenol), a phenylpropanoid from a synthetic source, exhibits strong anti-inflammatory activity, but its effects on the inflammation of ALI have not yet been reported. In our study, the anti-inflammatory effects of methoxyeugenol were investigated on RAW 264.7 cells and a mice model of ALI. Our results showed that methoxyeugenol (7.5 and 30 µM) attenuated the proliferation and gene expression of interleukin (IL)-6 in LPS-stimulated RAW 264.7 cells. In a mice model of ALI induced with LPS, methoxyeugenol exhibited a significant protective effect, based on influx reduction of macrophages and neutrophils into the lungs; reduction in release of the cytokines IL-6, TNF-α, and IL-10; and in reactive oxygen species (ROS) formation. We show that the anti-inflammatory effects of methoxyeugenol are associated with the suppression of the NFκB signaling pathway. Moreover, we demonstrated for the first time that a phenolic compound, from a synthetic source, protects against lung tissue inflammation and promotes a reduction of NET formation. These findings provided evidence for the use of methoxyeugenol as a new strategy to control inflammation in ALI disease. [ABSTRACT FROM AUTHOR]

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps. [ABSTRACT FROM AUTHOR]

Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. [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]

Plants remove carbon dioxide from the atmosphere through photosynthesis. Because agriculture's productivity is based on this process, a combination of technologies to reduce emissions and enhance soil carbon storage can allow this sector to achieve net negative emissions while maintaining high productivity. Unfortunately, current row-crop agricultural practice generates about 5% of greenhouse gas emissions in the United States and European Union. To reduce these emissions, significant effort has been focused on changing farm management practices to maximize soil carbon. In contrast, the potential to reduce emissions has largely been neglected. Through a combination of innovations in digital agriculture, crop and microbial genetics, and electrification, we estimate that a 71% (1,744 kg CO2e/ha) reduction in greenhouse gas emissions from row crop agriculture is possible within the next 15 y. Importantly, emission reduction can lower the barrier to broad adoption by proceeding through multiple stages with meaningful improvements that gradually facilitate the transition to net negative practices. Emerging voluntary and regulatory ecosystems services markets will incentivize progress along this transition pathway and guide public and private investments toward technology development. In the difficult quest for net negative emissions, all tools, including emission reduction and soil carbon storage, must be developed to allow agriculture to maintain its critical societal function of provisioning society while, at the same time, generating environmental benefits. [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]

Warming generally leads to increased evaporative demand, altering the amount of water needed for growing crops. For the Midwest, some studies have suggested that reaching yield targets by 2050 will not be possible without additional precipitation or large expansion of irrigation. Here, we show that this claim is not supported by the historical summer climate trends, which indicate that the warming of daily average temperatures is largely driven by increases in minimum temperatures, while maximum temperatures have decreased. This has translated into a net decrease in vapor pressure deficit (VPD) and potential evapotranspiration (PET). With the increasing rainfall, this suggests that crop water deficits have likely become less frequent in the region despite the warming climate. By projecting these trends into 2050 and ancillary use of a crop model, we estimate minor changes in PET that would have minimal effects on corn yields (<6%) under persistence of these trends. Warming in the US Midwest is believed to increase the water needed to grow crops. This study finds that, on the contrary, due to rising rainfall and minimum temperature, and decreasing maximum temperature, potential crop water demand remains unchanged despite the warming climate. [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]

Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user's and producer's accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring. [ABSTRACT FROM AUTHOR]

Summary: Hepatocellular carcinoma (HCC) is the most prevalent type of tumor among primary liver tumors and is the second highest cause of cancer-related deaths worldwide. Current therapies are controversial, and more research is needed to identify effective treatments. A new synthetic compound, potassium 5-cyano-4-methyl-6-oxo-1,6-dihydropyridine-2-olate (CPBMF65), is a potent inhibitor of the human uridine phosphorylase-1 (hUP1) enzyme, which controls the cell concentration of uridine (Urd). Urd is a natural pyrimidine nucleoside involved in cellular processes, such as RNA synthesis. In addition, it is considered a promising biochemical modulator, as it may reduce the toxicity caused by chemotherapeutics without impairing its anti-tumor activity. Thus, the objective of this study is to evaluate the effects of CPBMF65 on the proliferation of the human hepatocellular carcinoma cell line (HepG2). Cell proliferation, cytotoxicity, apoptosis, senescence, autophagy, intracellular Urd levels, cell cycle arrest, and drug resistance were analyzed. Results demonstrate that, after incubation with CPBMF65, HepG2 cell proliferation decreased, mainly through cell cycle arrest and senescence, increasing the levels of intracellular Urd and maintaining cell proliferation reduced during chronic treatment. In conclusion, results show, for the first time, the ability of a hUP1 inhibitor (CPBMF65) to reduce HepG2 cell proliferation through cell cycle arrest and senescence. [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]

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]

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]

Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80's identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers' fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r2 values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14–77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2–5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large. [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]

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Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10–22% for maize and 5–13% for wheat) rather than in early season (5–17% for maize and 2–18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4–2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses. [ABSTRACT FROM AUTHOR]

Octyl gallate (OG) is an antioxidant commonly used in food, although there is no definition of its acceptable daily intake. There are reports in vitro and in vivo showing that food additives and drugs can alter lipid metabolism. Lipid droplet accumulation in hepatic cells is one of the main findings in the unregulated lipid metabolism and is strongly related to the development of nonalcoholic fatty liver disease (NAFLD). In this study, we investigated the effects of OG on lipid metabolism in the hepatocellular carcinoma cell line (HepG2). The results have shown, for the first time, that treatment with OG increased the overall amount of lipids, the triglyceride concentration, the lipid droplet area, and SREBP-1c and PPAR-γ gene expression. Taken together, the findings indicate that OG induces lipid droplet accumulation in HepG2 cells through the regulation of SREBP-1c and PPAR-γ gene expression without involving mTOR/S6K1 and may contribute to NAFLD when used as a food additive. [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]

Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV’s ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV’s multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value [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]

The therapeutic potential of Baccharis anomala DC. extracts was evaluated through its cytotoxic and antiproliferative effect and their phenotypic reversion property in activated hepatic stellate cells (HSCs). Baccharis anomala is distributed in Brazil (southeastern and south regions) and used for diuretic effect in folk medicine. Four fractions were obtained from the fractionation of the methanolic extract. Fractions III and IV decreased cell proliferation without increasing cell necrosis markers levels and induced cell cycle arrest in G1 phase. Fraction III induced phenotypic reversion through PPAR-γ activation pathway, while fraction IV did not alter PPAR-α/γ expression levels, suggesting that there is an independent PPAR-α/γ pathway involved. Hydroxybenzoic, chlorogenic and coumaric acids were identified. Fractions III and IV showed antiproliferative effect and ability to induce reversion of activated phenotype of HSCs. [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]

Distance between rows and plants are essential parameters that affect the final grain yield in row crops. This paper presents the results of research intended to develop a novel method to quantify the distance between maize plants at field scale using an Unmanned Aerial Vehicle (UAV). Using this method, we can recognize maize plants as objects and calculate the distance between plants. We initially developed our method by training an algorithm in an indoor facility with plastic corn plants. Then, the method was scaled up and tested in a farmer’s field with maize plant spacing that exhibited natural variation. The results of this study demonstrate that it is possible to precisely quantify the distance between maize plants. We found that accuracy of the measurement of the distance between maize plants depended on the height above ground level at which UAV imagery was taken. This study provides an innovative approach to quantify plant-to-plant variability and, thereby final crop yield estimates. [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]

Crop production in the Central High Plains is at an all-time high due to increased demand for biofuels, food, and animal products. Despite the need to produce more food by mid-century to meet expected population growth, under current management and genetics, crop production is likely to plateau or decline in the Central High Plains due to groundwater withdrawal at rates that greatly exceed recharge to the aquifer. The Central High Plains has experienced a consistent decline in groundwater storage due to groundwater withdrawal for irrigation greatly exceeding natural recharge. In this heavily irrigated region, water is essential to maintain yields and economic stability. Here, we evaluate how current trends in irrigation demand may impact groundwater depletion and quantify the impacts of these changes on crop yield and production through to 2099 using the well-established System Approach to Land Use Sustainability (SALUS) crop model. The results show that status quo groundwater management will likely reduce irrigated corn acreage by ~60% and wheat acreage by ~50%. This widespread forced shift to dryland farming, coupled with the likely effects of climate change, will contribute to overall changes in crop production. Taking into account both changes in yield and available irrigated acreage, corn production would decrease by approximately 60%, while production of wheat would remain fairly steady with a slight increase of about 2%. [ABSTRACT FROM AUTHOR]