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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders--from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders. [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]

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]

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]

Syncronizing plant available water with soil nitrogen (N) remains a critical aspect of agronomic management to enhance crop yield, grain quality, farmers' profit, and environmental sustainability. Their interaction is essential expecially in landscapes characterized by a highly spatial and temporal range of pedoclimatic conditions. To support farmers in making more informed decisions, validate dynamic process based crop simulation models have been successfully used to optimize N fertlization. In this study, we aimed to develop a tactical N fertilizer management strategy to increase profitability, improve grain quality and reduce N losses. The SALUS model was tested against measured durum wheat grain yield and grain quality data collected across independent farmers' fields in Tuscany (Italy). The model was then used to optimize N fertilization under different potential plant extractable soil water (PESW) conditions at the second topdressing N fertilization timing. The model was tested against measurements of grain yield and protein concentration at harvest, as well as phenological stage, biomass, and plant N content during the growing season. Simulations were carried out for 30 years of available weather, using different N rates. The simulations allowed the identification of optimal N rates for each PESW condition and soil type concerning economic and environmental sustainability. Results showed higher yield and higher leaching for the silty clay soil (Quercia; QUE) than for the loamy soil (Arbia; ARB). No major differences were predicted for protein content across soils. Profitability and emissions increased as N rate increased. The N fertilization strategy locally adopted by farmers was also analyzed across different PESW conditions at 2nd topdressing fertilization in comparison to other adopted N management strategies (timing of application and fertilization rates). The model showed that the conventional fertilization strategy does not maximize socio-ecological benefits, but was also the lowest among all the strategies tested. The maximum economic benefit for farmers was reached by applying 90 kg N at 1st and 60 kg N at 2nd topdressing fertilization, both in dry (due to sufficient soil water storage in the soil) and wet years. These results provide valuable insights for developing strategies that balance sustainability, resilience, quality, and profitability in wheat crop production in central Italy. • Use of SALUS model to optimize nitrogen (N) fertilization. • Simulation of different potential plant extractable soil water (PESW) conditions. • Simulations were carried out for 30 years, using different N rates. • Identification of N fertilization strategy that maximizes economic returns. [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]

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

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]

Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro and RapidEye™, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, ). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the NO emissions, nitrate leaching, and farmer's profit. [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]

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [ABSTRACT FROM AUTHOR]

The Crop Environment Resource Synthesis (CERES) models have been developed and utilized for the last 30 years to simulate crop growth in response to climate, soil, genotypes and management across locations throughout the world. We reviewed 215 papers found in the literature that contained field observed data where the CERES models were tested. Over 30 simulated variables of the CERES models have been tested in 43 different countries under various experimental treatments. Across all testing conditions, the CERES models simulated grain yield with a root mean square error (RMSE) of less than 1400 kg/ha (~10% relative error, RE), 1200 kg/ha (~20% RE) and 800 kg/ha (~10% RE) for maize, wheat, and rice, respectively. Phenological development was simulated with less than 7 days difference from the observations in most studies. The CERES models simulated aboveground biomass, harvest index, evapotranspiration, and soil water reasonably well too. The simulations of grain number (up to 4340 root mean square error, RMSE), grain weight (up to 22% error), intercepted photosynthetically active radiation (IPAR, up to 0.41 MJ/plant), leaf area index (LAI, 31.9% error), soil temperature (over 10°C difference), and nitrogen (N) dynamics (up to 80% error) were less accurate. In fact the average error of CERES model simulations tends to be higher under marginal crop growing conditions such as extreme heat or cold, water and nutrient deficit conditions. [ABSTRACT FROM AUTHOR]

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha) of the distribution. [ABSTRACT FROM AUTHOR]

Reactive nitrogen loss from agricultural fertilizer use remains a crucial environmental problem in the United States, contributing to ecosystem degradation and global climate change. This intractable problem requires a coupled human and natural systems approach that combines biophysical, sociological, and economic knowledge into an integrative analysis. Much is known about the biogeochemistry of nitrogen and agricultural nitrogen loss; however, much is not known about how soil variability and climate change will affect farmer decisionmaking. Although it is widely understood that personal values and beliefs, social norms, economics, and policies influence farmer decisionmaking, very little is known about decisionmaking specific to fertilizer management. In addition, little is known about the socioeconomic influences on decisionmaking across scales and how ecological change is perceived and responded to. Combining sociological, economic, and biophysical knowledge can provide key insights regarding how these factors interact and can support more effective strategies to address this persistent problem. [ABSTRACT FROM AUTHOR]

Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability. [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]

Grain protein has recently received attention in several countries because of a premium associated to increased protein concentration (PC) in grains. Nitrogen (N) fertilizer management with its timing of application and rates plays a crucial role in increasing PC. The objectives of the present study were to: (1) quantify the effects of different N fertilizer rates and timing of application on grain yield and grain protein of six durum wheat cultivars, and (2) identify the best cultivar in terms of net return, yield, and protein content in typical pedo-climatic condition of southern Italy. The field experiments were conducted in Foggia during three growing seasons (2002–2003, 2003–2004, and 2004–2005). Results from this study showed that N application at sowing did not influence the cultivars performance. N applied at stem elongation increased yield and protein content provided there is adequate soil water for N uptake. In absolute terms, the best combination was the cultivar Svevo managed using 104 kg N ha−1split between tillering and stem-elongation stage. The excessive N splitting reduces yield and does not provide any improvement in terms of protein content. [ABSTRACT FROM PUBLISHER]

Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250kgNha−1, with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650€ha−1 for the 200 N and 1485 to 2875€ha−1 for the 250 N. N leaching varied between 16.4 and 19.3kgNha−1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N2O emissions varied between 0.28kgN2Oha−1 for the 50kgNha−1 rate to a maximum of 1.41kgN2Oha−1 for the 250kgNha−1 rate. [ABSTRACT FROM AUTHOR]

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]

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

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]

Studies have shown interest in nutraceuticals for the prevention of liver diseases. Methoxyeugenol, is a molecule found in foods, such as nutmeg (Myristica fragrans Houtt.) and Brazilian red propolis. These two sources of methoxyeugenol, propolis and nutmeg, are used in folk medicine for the treatment of hepatic and gastrointestinal disorders, although little is known about their effects on the prevention of liver fibrosis. Natural PPAR (Peroxisome proliferator-activated receptor) agonists would represent unique molecules for therapy, considering the lack of therapeutics to treat liver fibrosis in chronic liver disease. Thus, investigation on new alternatives are necessary, including the search for natural compounds from renewable and sustainable sources. Liver fibrosis is a pathological process characterized by an exacerbated cicatricial response in the hepatic tissue, which compromises liver function. Therefore, inhibition of HSC (hepatic stellate cell) activation and hepatocyte damage are considered major strategies for the development of new anti-fibrotic treatments. This study aimed to investigate the effects of methoxyeugenol treatment on HSC phenotype modulation in human and murine cells, hepatocyte damage prevention, and protective effects in vivo , in order to evaluate its therapeutic potential for liver fibrosis prevention. We investigated the effects of methoxyeugenol in (i) in vitro models using human and murine HSC and hepatocytes, and (ii) in vivo models of CCl 4 (carbon tetrachloride) -induced liver fibrosis in mice. We herein report that methoxyeugenol decreases HSC activation through the activation of PPAR-ɣ, ultimately inducing a quiescent phenotype highlighted by an increase in lipid droplets, loss of contraction ability, and a decrease in the proliferative rate and mRNA expression of fibroblast markers. In addition, methoxyeugenol prevented hepatocytes from oxidative stress damage. Moreover, in mice submitted to chronic liver disease through CCl 4 administration, methoxyeugenol decreased the inflammatory profile, liver fibrosis, mRNA expression of fibrotic genes, and the inflammatory pathway signaled by NF-kB (Nuclear factor kappa B). We propose methoxyeugenol as a novel and potential therapeutic approach to treat chronic liver disease and fibrosis. [Display omitted] [ABSTRACT FROM AUTHOR]

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]

Traditional methods for studying tree roots are destructive and labor intensive, but available nondestructive techniques are applicable only to small scale studies or are strongly limited by soil conditions and root size. Soil electrical resistivity measured by geoelectrical methods has the potential to detect belowground plant structures, but quantitative relationships of these measurements with root traits have not been assessed. We tested the ability of two-dimensional (2-D) DC resistivity tomography to detect the spatial variability of roots and to quantify their biomass in a tree stand. A high-resolution resistivity tomogram was generated along a 11.75 m transect under an Alnus glutinosa (L.) Gaertn. stand based on an alpha- Wenner configuration with 48 electrodes spaced 0.25 m apart. Data were processed by a 2-D finite-element inversion algorithm, and corrected for soil temperature. Data acquisition, inversion and imaging were completed in the field within 60 min. Root dry mass per unit soil volume (root mass density, RMD)was measured destructively on soil samples collected to a depth of 1.05 m. Soil sand, silt, clay and organic matter contents, electrical conductivity, water content and pH were measured on a subset of samples. The spatial pattern of soil resistivity closely matched the spatial distribution of RMD. Multiple linear regression showed that only RMD and soil water content were related to soil resistivity along the transect. Regression analysis of RMD against soil resistivity revealed a highly significant logistic relationship (n = 97), which was confirmed on a separate dataset (n = 67), showing that soil resistivity was quantitatively related to belowground tree root biomass. This relationship provides a basis for developing quick nondestructive methods for detecting root distribution and quantifying root biomass, as well as for optimizing sampling strategies for studying root-driven phenomena. [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]

Abstract: An accurate estimate of nitrate (NO3 ng from agricultural land is critical to environment impact studies. Although NO3 ost always present in groundwater, their continued increase in managed agricultural land can lead to nitrate concentrations in groundwater above acceptable human health standards. The amount of NO3 during the growing season may be minimal compared to leaching losses that occur between the harvest of one crop and the planting of the next. In this study we compared the effect of inorganic N and raw and composted animal manure on crop productivity and N leaching under field conditions in a maize–alfalfa system using undisturbed drainage lysimeters in Michigan. The cropping system rotation consisted of 3 years of continuous maize (Zea mays L.) and 3 years of continuous alfalfa (Medicago sativa L.). One cropping system consisted of a maize crop grown in the 1994–1996 seasons and alfalfa in the 1997–1999 seasons. The other cropping system was alfalfa (1994–1996) then maize (1997–1999). Four N treatments were imposed on the cropping systems. Treatment 1 was a check, no N fertilizer; Treatment 2 was manure; Treatment 3 was compost; Treatment 4 was inorganic fertilizer. No significant differences in yields of maize and alfalfa were found between N treatments in the 6-year rotation, although the no N treatment in maize had consistently lower yields. The highest amount of NO3 g was measured in the manure treatment with a mean annual value of 55kg NO3 sp="0.25" />ha−1 in maize–alfalfa rotation and 59kg NO3 sp="0.25" />ha−1 in alfalfa–maize, followed by compost (35kg NO3 sp="0.25" />ha−1 in alfalfa–maize and 30kg NO3 sp="0.25" />ha−1 in maize–alfalfa), inorganic N (33kg NO3 sp="0.25" />ha−1 in alfalfa–maize and 25kg NO3 sp="0.25" />ha−1 in maize–alfalfa) and no N (27kg NO3 sp="0.25" />ha−1 in alfalfa–maize and 25kg NO3 sp="0.25" />ha−1 in maize–alfalfa). The highest rates of NO3 were also observed in the manure treatment with a mean value for the 6-year rotation of 0.14kg NO3 sp="0.25" />mm−1 in alfalfa–maize and 0.35kg NO3 sp="0.25" />mm−1 in maize–alfalfa. [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]

• The premium for malting barley has been highly variable over the past 14 years. • The optimal N rate varied from 120 to 140 kg /ha for malting barley quality. • The marginal net return ranged between 500 and 2000 GBP/ha. • Crop modelling showed large trade-off differences for N leaching. Barley is one of the most important Scottish crops with major economic implications for the Scottish economy because of its use in whisky and beer production. Managing nitrogen (N) fertilizer at field scale for barley is difficult because of the complexity to simultaneously achieve profitability, malting quality and reducing N losses to groundwater. The aim of this study was to model spatial and temporal optimal N fertilizer rates to reduce nitrate leaching while improving grain yield and quality in malting barley. A calibrated and validated crop growth model (DSSAT) was used to identify the optimal amount of N fertilization rates to be applied in the field. The optimal amount of N fertilizer varied between 120 and 140 kg N ha−1 (as average over the 34 years) to maximize the economic return, grain N% and minimize environmental impact. The malting barley premium paid to farmers varies with N content thus affecting marginal net return, with variation ranging from 500 to 2000 GBP ha−1. The use of long-term weather data for the simulations allowed the calculation of outcomes from 34 different growing seasons. This information combined with the different premium levels paid to farmers can be helpful in identifying the possible combinations between grain quality premium-N fertilizer-weather conditions. Modelling the long-term data showed that the simulated N leaching (kg N ha−1) was highly variable between years and the greatest trade-off was for the amount of N leached. The overall the applied N fertilizer rate had a strong effect on the risk of a trade-off developing over the 34 different growing seasons. [ABSTRACT FROM AUTHOR]

• GAM and ML models can be used as a farm-based decision support tool. • Decision support tools can be used in combination with remotely sensed imagery. • High spatio-temporal variability of pasture biomass was observed in pasture fields. Accurate daily estimates of pasture biomass can improve the profitability of pasture-based dairy system by optimising input of feed supplements and pasture utilisation. However, obtaining accurate pasture mass estimates is a laborious and time-consuming task. The aim of this study was to test the performance of an integrated method combining remote sensing imagery acquired with a multispectral camera mounted on an unmanned aerial vehicle (UAV), statistical models (generalised additive model, GAM) and machine learning algorithms (random forest, RF) implemented with publicly available data to predict future pasture biomass loads. This study showed that using observations of pasture growth along with environmental and pasture management variables enabled both models, GAM and RF to predict the pre-grazing pasture biomass production at field scale with an average error below 20%. If predictive variables (i.e. post-grazing pasture biomass) were excluded, model performance was reduced, generating errors up to 40%. The post-grazing biomass information at high spatial resolution (<1 m) acquired with the UAV-multispectral camera system was used as predictive variable for future pasture biomass. With the inclusion of the spatially explicit post-grazing biomass variable both models accurately predicted the pre-grazing pasture biomass with an error of 27.7% and 22.9% for RF and GAM, respectively. However, the GAM model performed better than RF in reproducing the spatial variability of pre-grazing pasture biomass. This study demonstrates the capability of statistical and machine learning models implemented with UAV or manually obtained pasture information along with publicly available data to accurately predict future pasture biomass at field and farm scale. [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]

Loss of reactive nitrogen (N) from agricultural fields in the U.S. Midwest is a principal cause of the persistent hypoxic zone in the Gulf of Mexico. We used eight years of high resolution satellite imagery, field boundaries, crop data layers, and yield stability classes to estimate the proportion of N fertilizer removed in harvest (NUE) versus left as surplus N in 8 million corn (Zea mays) fields at subfield resolutions of 30 × 30 m (0.09 ha) across 30 million ha of 10 Midwest states. On average, 26% of subfields in the region could be classified as stable low yield, 28% as unstable (low yield some years, high others), and 46% as stable high yield. NUE varied from 48% in stable low yield areas to 88% in stable high yield areas. We estimate regional average N losses of 1.12 (0.64–1.67) Tg N y−1 from stable and unstable low yield areas, corresponding to USD 485 (267–702) million dollars of fertilizer value, 79 (45–113) TJ of energy, and greenhouse gas emissions of 6.8 (3.4–10.1) MMT CO2 equivalents. Matching N fertilizer rates to crop yield stability classes could reduce regional reactive N losses substantially with no impact on crop yields, thereby enhancing the sustainability of corn-based cropping systems. [ABSTRACT FROM AUTHOR]