Search

Baccharis anomala DC. (BA) is a plant species found in the tropical regions of South America and is widely used for its hepatoprotective effects, as well as for the treatment of gastrointestinal diseases. Studies have recently reported its antioxidant and anti-inflammatory potential. BA extract can reverse the activated phenotype of hepatic stellate cells (HSC), which plays a central role in extracellular matrix (ECM) deposition in the development of liver fibrosis. Thus, this study aimed to evaluate the effects of the treatment with BA extract on liver fibrosis in a CCl 4 -induced liver fibrosis model in BALB/c mice. Methanolic extract was obtained from BA leaves, a gas chromatography/mass spectrometry (GC/MS) to detect the compounds present was performed, and then administered by intraperitoneal injection in Balb/C mice at a concentration of 50 and 100 mg/kg together with the administration of CCl 4 for inducing liver fibrosis. After 10 weeks, blood analysis, histopathology, oxidative stress, as well as protein and gene expression in the hepatic tissue were performed. Treatment with BA extract was able to reduce profibrotic markers by reducing the expression of α-SMA and Col-1 proteins, as well as reducing the formation of free radicals and lipid peroxidation. (BA extract showed anti-inflammatory effects in the liver by suppressing NF-kB activation and reducing gene expression of signaling targets (IL-6 and iNOS). The data obtained showed that BA extract has antifibrotic and anti-inflammatory effects. [Display omitted] • B. anomala extract attenuates fibrosis development on liver. • B. anomala extract decreases ROS formation on liver of fibrotic mice. • B. anomala extract decreases the activation of NF-kβ on liver of fibrotic mice. [ABSTRACT FROM AUTHOR]

Over the last two decades, there has been significant advancements in the application of geospatial technologies in agriculture. Improved resolutions (spectral, spatial and temporal) of remotely sensed images, coupled with more precise on-the-ground systems (yield monitors, geophysical sensors) have allowed farmers to become more sensitive about the spatial and temporal variations of crop yields occurring in their fields. Previous research has extensively looked at spatial variability of crop yields at field scale, but studies designed to predict within-field spatial patterns of yield over a large number of fields as yet been reported. In this paper, we analyzed 571 fields with multiple years of yield maps at high spatial resolution to understand and predict within-field spatial patterns across eight states in the Midwest US and over corn, soybean, wheat and cotton fields. We examined the correlation between yield and 4 covariates, three derived from remote sensing imagery (red band spectral reflectance, NDVI and plant surface temperature) and the fourth from yield maps from previous years. The results showed that for spatial patterns that are stable over time the best predictor of the spatial variability is the historical yield map (previous years’ yield maps), while for zones within the field that are more sensitive to weather and thus fluctuate from one year to the next the best predictor of the spatial patterns are the within-season images. The results of this research help quantify the role of historical yield maps and within-season remote sensing images to predict spatial patterns. The knowledge of spatial patterns within a field is critical not only to farmers for potential variable rate applications, but also to select homogenous zones within the field to run crop models with site-specific input to better understand and predict the impact of weather, soil and landscape characteristics on spatial and temporal patterns of crop yields to enhance resource use efficiency at field level. [ABSTRACT FROM AUTHOR]

Leucine (Leu) is an essential branched-chain amino acid, present in dairy products, which has been investigated for its important role in cell signaling. The effects of Leu on several kinds of cells have been studied, altough little is known on its action upon bone cells and cell proliferation. Thus, the aim of this study is to investigate the effects of Leu supplementation on the proliferation of pre-osteoblasts from MC3T3-E1 lineage. MC3T3-E1 cells were kept in Alpha medium supplemented with 10% fetal bovine serum and 1% antibiotic-antimitotic. Cells were treated during 48 h by adding 50 μM of Leu, which corresponds to a 12.5% increase of the amino acid in the culture medium. The evaluation of viability and proliferation of cultured cells was performed using Trypan Blue dye. In order to identify the mechanisms related to the decreased cellular proliferation, assays were performed to assess cytotoxicity, apotosis, oxidative stress, inflammation, autophagy, senescence and DNA damage. Results showed that Leu supplementation decreased cell proliferation by 40% through mechanisms not related to cell necrosis, apoptosis, oxidative stress, autophagy or inhibition of the mTORC1 pathway. On the other hand, Leu supplementation caused DNA damage. In conclusion, Leu caused a negative impact on bone cell proliferation by inducing cell senescence through DNA damage. [ABSTRACT FROM AUTHOR]

The objective of this research was to quantify the effects of climate, soil, and management on spatial and temporal variation of maize yields across Malawi. We simulated four different nitrogen (N) management strategies to evaluate the impact of mineral and organic N amendments on maize yield across the agricultural lands of Malawi. Maize yield increased when the crop was grown with pigeonpea, or if mineral N fertilizer was added, but yield improvements under these management strategies varied spatially as result of different soil biophysical and chemical properties, weather, and their interactions with management. The increased yield from N addition showed that a significant increase in food production could be achieved in Malawi to reduce food insecurity. [ABSTRACT FROM AUTHOR]

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a “pre-competitive” space for model development to a “competitive space” for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools. [ABSTRACT FROM AUTHOR]

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha − 1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha − 1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching. [ABSTRACT FROM AUTHOR]

In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest. • A novel approach was developed to better predict in-season subfield corn yields. • Simulated plant water deficit was added in the model based on vegetation index. • Subfield predictions were conducted in 352 fields across US corn belt states with different environmental conditions. • In-season subfield maize yield prediction improved when crop water deficit was added remote sensing imagery-based model. [ABSTRACT FROM AUTHOR]

Highlights: [•] Spatial and temporal patterns of wheat grain yield were quantified. [•] Optimal amount of N fertilizer is not fixed but varies with the rainfall. [•] N fertilizer needs to be tactically applied and varies over space and years. [ABSTRACT FROM AUTHOR]

Abstract: Olive is of major eco-social importance for the Mediterranean Basin, a climate change and biodiversity hotspot of global relevance where remarkable climate change is expected over the next few decades with unknown ecosystem impacts. However, climate impact assessments on terrestrial ecosystems have long been constrained by a narrow methodological basis (ecological niche models, ENMs) that is correlative and hence largely omits key impact drivers such as trophic interactions and the effect of water availability, the latter being especially relevant to desertification- prone Mediterranean ecosystems. ENMs use correlative measures of water availability unsuitable for making projections about the future. To bridge this gap, mechanistic approaches such as physiologically-based weather- driven demographic models (PBDMs) may be used as they embed by design both the biology of trophic interactions and a mechanistic representation of soil water balance. Here we report progress towards assessing climate effects on olive culture across the Mediterranean region using mechanistic PBDMs that project regionally the multitrophic population dynamics of olive and olive fly as affected by daily weather and soil water balance. [Copyright &y& Elsevier]

Abstract: Wheat yield production in Mediterranean environment is highly affected by rainfall and amount of soil water stored into the soil before and during the growing season. Agricultural fields have been always considered as uniform entities and managed accordingly. However, uniform agronomic management in fields where spatial variability is present, is economically and environmentally inefficient. The objectives of this study were to: (i) identify spatially and temporally stable areas throughout the field, (ii) understand the influence of fallow and growing season rainfall on spatial and temporal variability of wheat yield. The study was carried out on a 12ha field located in Foggia, Southern Italy during five years wheat monoculture. One hundred geo-referenced points were sampled for deriving spatial maps of soil texture and organic carbon. Spatial maps of grain yield, normalized difference vegetation index (NDVI), soil electrical resistivity tomography (ERT) were collected non-destructively. Total growing season rainfall was correlated with grain yield after dividing it into long fallow (June–November), short fallow (September–November), growing season (December–May), vegetative (December–February), reproductive (March–May). The spatial maps were used to define spatial and temporal yield variability and to identify three stable zones within the field, “low yield stable” (LS), “average yield stable” (AS), “high yield stable” (HS). Long and short fallow rainfall was highly correlated with grain yield of HS zone with correlation coefficients ranging between 0.5 and 1. Growing season rainfall was mostly correlated with the AS zone. The crop response to rainfall was a result of dynamic interaction of spatial static properties such as soil texture, position in the landscape and dynamic properties (soil water content, infiltration and crop water use). [Copyright &y& Elsevier]

Abstract: Estimation of nitrate leaching from agricultural systems is critical to environment impact studies. Simulation models can help to assess and understand the dynamics of nitrates in the soil and in relation to fertilizer types and agronomical management. The main hypothesis of this study was that the long term annual application of mineral and organic N fertilizers, in irrigated sandy soil areas increases dramatically the quantity of NO3 − in the soil for potential nitrate leaching. The main objectives of this research were then to quantify inorganic N concentration in soil layers affected by mineral and organic N (manure and slurry) fertilizers; and to test the SALUS crop simulation model to identify the best N management strategy able to minimize NO3 − leaching without compromising crop growth and yields. A first set of rotational simulations were carried out for the 2007 and 2008 experiments, with the goal of comparing measured and simulated results. A second set of rotational simulations were carried out for a long term assessment (50years) of the two treatments in comparisons. Additional long term rotational simulation runs were carried out considering two different fertilization strategies based on the maize crop uptake in 2007 (276kgNha−1) and on the PUA (“Best Nitrogen Management with manures”) which defines the fertilization practices adopted in the nitrate vulnerable zones (no more than 170kgNha−1 year−1). Measured data showed that in the manure treatment, the highest nitrate-N concentration was 627.3mgkg−1 at 0.10m depth on August 2008, while the lowest was 2.50mgkg−1 at 1.40m depth on July 2007. In the slurry treatment, the maximum nitrate N concentration was 344.6mgkg−1 at 0.10m depth on August 2008 and the minimum was 8.9mgkg−1 at 0.60cm depth on September 2007. The model was able to closely reproduce the measured results. The simulation results of the inorganic soil N were 1228kgha−1 and 1370kgha−1 for the manure and slurry treatment respectively versus the measured soil inorganic N content at the beginning of the experiment of 1267kgha−1 for the manure treatment and 1323kgha−1 for the slurry treatment. The simulated cumulative amount of nitrate leaching was 9251kgha−1 for the manure treatment and 11,101kgha−1 for the slurry treatment for the 50years simulations. The simulations results also showed that when the inorganic N was used alone, the amount of leaching and soil N were significantly reduced. [Copyright &y& Elsevier]

Abstract: The Kyoto Protocol recognises trees as a sink of carbon and a valid means to offset greenhouse gas emissions and meet internationally agreed emissions targets. This study details biological carbon sequestration rates for common plantation species Araucaria cunninghamii (hoop pine), Eucalyptus cloeziana, Eucalyptus argophloia, Pinus elliottii and Pinus caribaea var hondurensis and individual land areas required in north-eastern Australia to offset greenhouse gas emissions of 1000tCO2e. The 3PG simulation model was used to predict above and below-ground estimates of biomass carbon for a range of soil productivity conditions for six representative locations in agricultural regions of north-eastern Australia. The total area required to offset 1000tCO2e ranges from 1ha of E. cloeziana under high productivity conditions in coastal North Queensland to 45ha of hoop pine in low productivity conditions of inland Central Queensland. These areas must remain planted for a minimum of 30years to meet the offset of 1000tCO2e. [Copyright &y& Elsevier]

Abstract: Wheat yield and protein content in a field are spatially variable due to inherent variability of soil properties and landscape. In Mediterranean environments yield variability in space and time is caused by irregular weather patterns, particularly rainfall, and by position in the landscape. A tested crop simulation model, SALUS, was used to select optimal nitrogen fertilizer rates using strategic and tactical approaches in a spatially variable field where three distinct management zones had been previously identified. The crop model was tested and then used to simulate seven N rates from 0 to 180kgNha−1 with a 30kgNha−1 increments for 56 years using historical weather data. The available soil water at the time of N sidedressing each year and each management zone was correlated with yield response to N to evaluate the possibility of using the stored soil water to tactically determine N rates. Assuming recent production costs and grain prices the simulations helped identify an optimal N rate for each of the zones based on agronomic, economic and environmental sustainability of N management. Results showed the high yielding zone had a maximum economic return and minimal environmental impact in terms of nitrate leaching by applying 90kgNha−1annually. On the other hand, the low yielding zone had little economic returns for application higher than 30kgNha−1. When simulated soil root-zone water was low at sidedressing, a lower fertilizer rate increased profit and decreased N leaching in the medium and high yielding zones. [Copyright &y& Elsevier]

Abstract: The integration of site-specific management principles and conservation tillage practices is a rather unexploited field of research despite their economical and environmental benefits. The objectives of this research were: (1) to investigate the farm economic net return of three conservation tillage practices (NIT – Non-Inversion Tillage, MT – Minimum Tillage and NT – No-Tillage) performed at variable intensity within predefined management zones; the HS – area with a consistently higher yield and LS – area with a consistently low yield, of a maize (Zea mays, L.) field in NE-Italy (2) to identify the most economically sound tillage practice for each management zone using long-term simulation results; (3) to assess the environmental impact of the three tillage systems with regards to soil organic carbon changes, CO2 losses and nitrate leaching using the SALUS model. Field trials were carried out on an 8-ha flat field, situated near Rovigo, NE Italy, on maize (Z. mays, L.). The farm gross margin was higher for NT in the year of study as well as the long-term simulated scenarios that resulted in higher yields over time. The NT tillage practices resulted in higher economic return in the both the HS and LS areas. Total soil carbon was higher in NT due to the crop residues retained on the surface. Nitrate leaching was higher in for the MT and for the LS area. [Copyright &y& Elsevier]

Abstract: Appropriate nitrogen management is one of the main challenges of agricultural production and for the environment. The objectives of this study were to evaluate the efficiency of crop N uptake in a long-term wheat crop in a Mediterranean environment of Southern Italy, and to identify optimal N rate for reasonable economic returns and minimum nitrate leaching using SALUS crop simulation model. The study was part of a long-term monoculture wheat system that started in 1991/1992 season, with two levels of nitrogen (0 and 90kgNha−1). Simulations of the treatment with no nitrogen (0N) and 90kgNha−1 (90N) were performed using the SALUS crop model for wheat. The model was tested against measurements of harvested grain yield, final N uptake, soil water content and total soil N. Long-term simulation over 56 years showed that grain yield median value was 3435kgha−1 for 0N and 3876kgha−1 for 90N. Simulation scenarios with different N rates (0, 30, 60, 90, 120, 180kgNha−1) showed that yield response was higher for 120N (3528kgha−1), with the 60 and 90N yields giving the same response, 3010 and 3054kgha−1, respectively. The most profitable treatments were 120N (302Euroha−1), followed by the 60N (220Euroha−1). The simulation results showed that nitrate leaching was higher for the N rate of 120 and 180 with a mean annual value of 49 and 81kgha−1, respectively. Results suggest that in such environment 60kgNha−1 can be the most appropriate as an N fertilization management due to the best trade-off between leaching and economic. Since N fertilization rates are linked to nitrous oxide (N2O) emissions and N leaching, a trade-off between N fertilization rates profit and grain yield should be thought as way to reduce environmental pollution while keeping productivity and profit. The adoption of simulation models to approximate the best N rate for durum wheat in rainfed Mediterranean environment proved to be a useful tool for supporting management decisions through quantifying the temporal variability related to weather uncertainty as it influences on the yield and nutrient dynamics. [Copyright &y& Elsevier]

There is an urgent need to transform unsustainable "linear" grain production systems in the United States (U.S.) and other countries like China, Brazil, Argentina, Canada, Russia, Australia and Europe, into more circular and sustainable systems to address the simultaneous challenges of resource depletion, environmental degradation, and the growing global demand for food under the threat of climate change. In this perspective, we survey the current state of circularity of U.S. grain production, and discuss how we can transform the systems into more circular systems. Specifically, we lay out a vision of circular grain production enabled by novel digital, mechanical, and biological technologies that allow closing loops of nutrient and energy flows within the farm, through the optimization of land-use choices and crop management. We also examine market- and policy-based mechanisms that could incentivize the widespread adoption of these key technologies. [Display omitted] • There is an urgent need to transform "linear" grain production systems into circular and sustainable systems • Digital agriculture technologies can increase circularity in the grain production systems • Regenerative practices, new genetics, robotics and electrification of fertilizer production will increase circularity • Widespread adoption of circular systems will depend critically on their profitability • Profitability will in turn depend on public policies needed to encourage farmers to adopt sustainable practices [ABSTRACT FROM AUTHOR]

Abstract: Increases in crop production per unit of water used is imperative for supplying adequate food, feed, and fiber in an environment where future water supplies are expected to decrease. Previous work on crop productivity per unit of water used (water use efficiency; WUE) has primarily dealt with crops grown under water limited conditions and have usually not considered crop management factors other than irrigation. Crop management can strongly influence yields when water is not limited. The aim of this paper is to demonstrate that transpiration per unit of productivity can vary greatly with agronomic management of crops when soil water supply is adequate or fixed. Moreover, when yield from crops with common development patterns are increased by better crop management and improved cultivars, WUE is also increased. In recent decades high yields of maize have been accomplished with increased fertilizer and cultivars that tolerate high plant populations and uniform spacing. Although transpiration and soil evaporation (ET) occur simultaneously in the field, they are difficult to measure as separate components. However, the crop simulation model CERES maize can reasonably estimate each component. The CERES Maize model was used to assess how plant population, genetic type and weather influence yields and WUE. Simulated yield response of an old and modern hybrid to a wide range of plant densities and uniformity patterns agreed reasonably well with observations suggesting that plant densities need to be near 10–11plantm−2 and uniformly spaced to obtain near maximum yield and WUE for Midwest USA climate. [Copyright &y& Elsevier]

Abstract: The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies. [Copyright &y& Elsevier]

Process-oriented crop growth models simulate plant growth over homogeneous areas. The advent of precision farming has resulted in the need to extend the use of point-based crop models to account for spatial processes. Spatial processes include surface and subsurface water flow and spatial and temporal interaction of plant growth with soil water, nutrient and pest stress and management practices. Our research has focused on developing methods to account for spatial interactions in the CROPGRO-Soybean and CERES-Maize models. These methods introduce new challenges for accurately and economically defining spatial inputs for the models. In spite of these challenges, both models have been used to evaluate causes of spatial yield variability with reasonable success. The purpose of this paper is to present several examples of strategies that we have found useful in using these models to assess spatial and temporal yield variability over different environmental conditions and to analyze economic return of prescriptions. Strategies to overcome spatial resolution in point-based crop models include calibration techniques to run point-based models at small scales within a field, using remote sensing to target measurements of models inputs to areas of similar plant response, and linking point-based models to three-dimensional water flow models to better represent water transport. Each strategy is demonstrated using case studies and comparison of simulated and measured data are presented. A method to estimate break-even costs associated with variable soybean cultivar placement in a field is outlined and presented as a case study as well. Crop models can provide useful estimates of potential economic return of prescriptions, as well as estimate the sensitivity of a prescription to weather. They can also estimate the value of weather information on management prescriptions. [Copyright &y& Elsevier]

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models. [ABSTRACT FROM AUTHOR]

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data. [ABSTRACT FROM AUTHOR]

• Integrated landscape and supply chain optimization leads to environmental benefit. • High-resolution stochastic model accounts for biomass yield uncertainty. • Framework for generating and integrating high-resolution yield and soil carbon data. This paper proposes an integrated stochastic mixed-integer linear programming model for biofuel supply chain and landscape design optimization that considers the interactions between uncertainty in biomass yield, spatially explicit feedstock availability, supply chain configuration, operational decisions, and the system's environmental impact. By modeling crop establishment and fertilization as strategic decisions made before the realization of uncertainty, model solutions identify integrated supply chain configurations better suited to mitigate uncertain biomass yields. Importantly, the paper presents an approach for gathering and processing the large amount of necessary real-world data, including an accurate accounting of soil carbon sequestration. A case study located in Michigan, USA, demonstrating the capabilities of the integrated model with realistic data, is presented. Results at a variety of harvesting site resolutions and number of uncertainty scenarios, show that large-scale instances, at fine spatial resolutions, identify attractive environmental solutions and that solving the stochastic problem leads to an economic benefit. [ABSTRACT FROM AUTHOR]

Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO 3 −, and NH 4 + from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha−1 yr−1) and volume-weighted mean concentrations (mg L−1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha−1 yr−1) and volume-weighted mean concentrations (mg L−1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO 3 − comprised the majority of total N leaching in all systems (59–92%). Average NO 3 − leaching (kg N ha−1 yr−1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH 4 + concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L−1). Perennial crops leached more NO 3 − in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14–38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters. Unlabelled Image • Organic carbon leaching was unaffected by nitrogen fertilization, but it was greater in poplar than in the herbaceous crops. • Nitrogen was leached mainly as nitrate, with the balance lost mostly as organic N and relatively little as ammonium. • Leaching of organic nitrogen increased with N fertilization. • Perennial cropping systems that were moderately fertilized leached less nitrogen than more heavily fertilized corn. [ABSTRACT FROM AUTHOR]

• Evapotranspiration from rainfed continuous maize and maize and soybean crops in rotation was similar during normal years. • Water use efficiencies were lower in the drought year, mainly due to lower crop yield. • Maize and soybean crops in this region use similar amounts of water over the growing season.\ • The relative proportion of maize and soybean crops in watersheds should not affect regional water balances. This study examined evapotranspiration (ET) from no-till, rainfed maize and soybean during three growing seasons (May-Sep) of normal rainfall years (2009, 2010, 2011) and a drought year (2012) in Michigan, USA, based on daily soil water uptake measured by time-domain reflectometry at multiple depths through the root zone. During normal rainfall years, growing-season ET was similar between continuous maize (mean ± standard deviation: 471 ± 47 mm) and maize in rotation (469 ± 51 mm). During the drought year, ET decreased by only 3% for continuous maize but by 20% for maize in rotation. During the normal rainfall years, ET for soybean (453 ± 34 mm) was statistically indistinguishable from ET for maize, and was lower during the drought year (333 mm). Water use efficiency (WUE), calculated from harvest yield (grain + corn stover) and ET, was 25.3 ± 4.2 kg ha−1 mm-1 for continuous maize and 27.3 ± 3.1 kg ha−1 mm−1 for maize in rotation during the normal rainfall years, whereas WUEs for both continuous maize and maize in rotation were much lower in the 2012 drought year (14.0 and 15.5 kg ha−1 mm−1, respectively), coincident with lower production. Soybean had a much lower WUE than maize during the three normal years (6.95 ± 0.96 kg ha−1 mm−1) and the drought year (4.57 kg ha−1 mm−1), also explained by lower yield. Both maize and soybean tended to use all available water in the soil profile; there was no consistent difference in ET between these crops, while yield varied markedly from year to year. [ABSTRACT FROM AUTHOR]

Accounting for nitrogen (N) release from organic amendments (OA) can reduce the use of synthetic N-fertiliser, sustain crop production, and potentially reduce soil borne greenhouse gases (GHG) emissions. However, it is difficult to assess the GHG mitigation potential for OA as a substitute of N-fertiliser over the long term due to only part of the organic N added to soil is being released in the first year after application. High-resolution nitrous oxide (N 2 O) and carbon dioxide (CO 2 ) emissions monitored from a horticultural crop rotation over 2.5 years from conventional urea application rates were compared to treatments receiving an annual application of raw and composted chicken manure combined with conventional and reduced N-fertiliser rates. The repeated application of composted manure did not increase annual N 2 O emissions while the application of raw manure resulted in N 2 O emissions up to 35.2 times higher than the zero N fertiliser treatment and up to 4.7 times higher than conventional N-fertiliser rate due to an increase in C and N availability following the repeated application of raw OA. The main factor driving N 2 O emissions was the incorporation of organic material accompanied by high soil moisture while the application of synthetic N-fertiliser induced only short-term N 2 O emission pulse. The average annual N 2 O emission factor calculated accounting for the total N applied including OA was equal to 0.27 ± 0.17%, 3.7 times lower than the IPCC default value. Accounting for the estimated N release from OA only enabled a more realistic N 2 O emission factor to be defined for organically amended field that was equal to 0.48 ± 0.3%. This study demonstrated that accounting for the N released from repeated application of composted rather than raw manure can be a viable pathway to reduce N 2 O emissions and maintain soil fertility. [ABSTRACT FROM AUTHOR]

Simulation models, informed and validated with datasets from long term experiments (LTEs), are considered useful tools to explore the effects of different management strategies on soil organic carbon (SOC) dynamics and evaluate suitable mitigative options for climate change. But, while there are several studies which assessed a better prediction of crop yields using an ensemble of models, no studies are currently available on the evaluation of a model ensemble on SOC stocks. In this study we assessed the advantages of using an ensemble of crop models (APSIM-NWheat, DSSAT, EPIC, SALUS), calibrated and validated with datasets from LTEs, to estimate SOC dynamics. Then we used the mean of the model ensemble to assess the impacts of climate change on SOC stocks under conventional (CT) and conservation tillage practices (NT: No Till; RT: Reduced Tillage). The assessment was completed for two long-term experiment sites (Agugliano – AN and Pisa – PI2 sites) in Italy under rainfed conditions. A durum wheat ( Triticum turgidum subsp. durum (Desf.) Husn.) – maize ( Zea mays L.) rotation system was evaluated under two different climate scenarios over the periods 1971–2000 (CP: Present Climate) and 2021–2050 (CF: Future Climate), generated by setting up a statistical model based on canonical correlation analysis. Our study showed a decrease of SOC stocks in both sites and tillage systems over CF when compared with CP. At the AN site, CT lost −7.3% and NT −7.9% of SOC stock (0–40 cm) under CF. At the PI2 site, CT lost −4.4% and RT −5.3% of SOC stocks (0–40 cm). Even if conservation tillage systems were more impacted under future scenarios, they were still able to store more SOC than CT, so that these practices can be considered viable options to mitigate climate change. Furthermore, at the AN site, under CF, NT demonstrated an annual increase of 0.4%, the target value suggested by the 4 per thousand initiative launched at the 21st meeting of the Conference of the Parties in Paris. However, RT at the PI2 needs to be coupled with other management strategies, as the introduction of cover crops, to achieve such target. [ABSTRACT FROM AUTHOR]

• Maize growth models differ widely in their simulations of daily evapotranspiration. • Most models fail to sufficiently reduce transpiration after crop maturation. • Most models fail to adequately reproduce effects of low humidity and high windspeed. • The median of models was often but not always the best performing. • Model inter-comparisons suggest avenues to improve simulation of maize ET. Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET. [Display omitted] [ABSTRACT FROM AUTHOR]

Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development. [Display omitted] • We investigate multi-model performance in simulating C and N fluxes in agriculture. • Correlated model residuals hinder reliable C–N flux estimates. • Residual correlation analysis is applied to ensemble crop and grassland models. • Partially calibrated models can be practical for implementing model ensembles. • Fully calibrated models are key to model development. [ABSTRACT FROM AUTHOR]

It has been reported that glucagon-like peptide-1 (GLP-1) agents have been associated with both the increased risk of cancer and inhibition of tumor growth and metastases. The aim of this study is to evaluate the effect of liraglutide on hepatocellular carcinoma cells – HepG2. Cytometry was used to evaluate mechanism related to decreased cell proliferation. Nuclear staining and morphometric analysis were also used to verify the process that was taking place after treatment with liraglutide, and in order to better understand the mechanism, TGF-β1 was performed. HepG2 cells decreased proliferation after liraglutide treatment without altering oxidative stress levels. Liraglutide was able to induce autophagy and senescence through the increase of TGF-β1 which possibly explains the growth decrease. We have demonstrated that liraglutide has an antiproliferative effect in HepG2 cells inducing autophagy and senescence by the increase of TGF-β1. [ABSTRACT FROM AUTHOR]

With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat ( Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor that affects the world population. Liver cancer inevitably causes great harms and its treatment is extremely difficult. Its development is related to the existence of chronic liver injury, such as in cirrhosis. Cancer is a disease related to the process of inflammation so, research with anti-inflammatory agents has been performed for the development of anti-tumor drugs. Fructose-1,6-bisphosphate (FBP), a metabolite of the glycolytic route, has shown anti-inflammatory actions. The purpose of this study is to investigate the effect of FBP on HepG2 cells growth and inflammatory parameters. Results showed that FBP decreased the proliferation of HepG2 cells through trypan blue assay, without causing necrosis, shown by the intracellular release of LDH. By flow cytometry, we observed a significant IL-8 decrease which is closely related to the tumoral progression and chemotherapeutic resistance, especially in HCC. Then, we found, by RT-PCR, a high expression level of pro-apoptotic protein, such as Bax and p53, and decreased the expression levels of anti-apoptotic proteins, like Bcl-2 suggesting apoptosis. Finally, our results showed that FBP can be a potential therapeutic agent to slow the progress of HCC. [ABSTRACT FROM AUTHOR]