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Abstract Transforming the agri-food system from a “take-make-waste”, or linear production system, to a circular bioeconomy that reduces, recycles, recovers, reuses, and regenerates wastes and transitions from fossil to biobased fuels and products is being hailed as critical for meeting a growing population’s food and fuel needs in environmentally sustainable ways. While a transformation towards a circular bioeconomy is an appealing strategy to achieve multiple environmental goals, we argue that this strategy needs to go beyond a techno-centric focus and adopt an economic value-based lens to balance the desire for circularity with its costs, benefits, and distributional effects on society. This perspective analyzes the mechanisms that sustain the existing linear economy and proposes a novel social cost-benefit framework to determine the optimal level and path to circularity. We present five critical pathways to achieve a sustainable circular bioeconomy in a market economy consisting of decentralized decision-makers.

Phosphorus (P) pollution from nonpoint sources is a persistent agricultural externality. P fertilizer use in excess of crop removal leads to soil P accumulation and increases chance of P runoff from agricultural fields. For fertilizer-intensive crops such as corn, effective nutrient management can partly mitigate the externality associated with fertilizer use. In states such as Michigan with abundant freshwater resources that are vulnerable to P pollution and where corn is grown intensively, understanding yield response is instrumental for P management policies. We analyze corn yield response to P fertilizer and control for soil P. We use farmer survey data from 1650 corn farmers in Michigan and a crop-simulation model to obtain data in a metamodeling approach to estimate yield function. Using the estimated function in profit maximization set up, we calculate agronomically and economically optimal profit-maximizing P application rates for Michigan, across agricultural regions, and across different soil types. We find that corn yield responds to P only in low soil P (0–5 ppm or mg kg−1) fields and the economically optimal P rate for Michigan is [63, 68] kg ha−1 or [53, 61] lbs acre−1 for low soil P fields and would be zero for fields with adequate soil P. For existing soil P levels in Michigan, the self-reported P application of Michigan corn farmers exceeds the profit-maximizing P application rate. We compare our analysis results with self-reported survey data to demonstrate excess P applications. Since many fields in Michigan have high soil P, additional P application in excess of crop removal increases risks of eutrophication. We also find that 10 % increase in P prices faced by farmers would lead to 3.1 % reduction in the economically optimal P application rate. Based on these results, we discuss suitable policy options such as fertilizer “usage fee” and tailored fertilizer recommendations.

Abstract 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 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 Irrigation can increase crop yields and could be a key climate adaptation strategy. However, future water availability is uncertain. Here we explore the economic costs and benefits of existing and expanded irrigation of maize and soybean throughout the United States. We examine both middle and end of the 21st-century conditions under future climates that span the range of projections. By mid-century we find an expansion in the area where the benefits of irrigation outweigh groundwater pumping and equipment ownership costs. Increased crop water demands limit the region where maize could be sustainably irrigated, but sustainably irrigated soybean is likely feasible throughout regions of the midwestern and southeastern United States. Shifting incentives for installing and maintaining irrigation equipment could place additional challenges on resource availability. It will be important for decision makers to understand and account for local water demand and availability when developing policies guiding irrigation installation and use.

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

Ecological practices such as intercropping maize ( Zea mays ) with cowpea ( Vigna unguiculata L.) have been promoted to combat parasitic weeds like Striga ( Striga asiatica ). Intercropping has been promoted across Africa as a Striga control practice (SCP) and food security measure. Despite past efforts, millions of smallholder farmers (cultivating < 2 ha of maize) still struggle to implement SCPs. Social and ecological factors that prevent SCP implementation are well documented in the literature, but their underlying interactions have remained elusive. System dynamics modeling can uncover these interactions and assess their effect on intercropping rates as well as Striga emergence. This study presents a participatory mixed methods approach to build a system dynamics model based on two theories: diffusion of innovations and resource pool dynamics. The model estimates the population of fields where Striga emerged in response to intercropped fields when various interventions were implemented. According to model simulations, if new policies are not enacted to support intercropping, Striga is likely to spread to 2,625,000 maize fields, parasitizing almost 75% of smallholder farms across Central Malawi by 2036. The participatory approach allowed us to evaluate several policies, one of which sustained enough adopters to limit Striga emergence to < 500,000 fields, reducing the weed’s threat to food security. This policy considers how input costs and erratic rainfall can lead to disadoption, therefore, supporting the implementation of five to six consecutive years of intercropping by providing both fertilizer subsidies and demonstration plots. In this study, our participatory approach has shown to develop a model that can highlight interactions in social ecological systems, their leverage points, and how they can be exploited to develop effective food security policies.

Abstract 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.

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

Warming in the US Midwest is believed to increase the water needed to grow crops. This study finds that, on the contrary, due to rising rainfall and minimum temperature, and decreasing maximum temperature, potential crop water demand remains unchanged despite the warming climate.

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

Abstract 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.

The Experimental Zooprophylactic Institute of Southern Italy (Istitituto Zooprofilattico Sperimentale del Mezzogiorno, IZSM) is a public health institution operating within the Italian National Health Service. Over the past 5 years [IZSM] has promoted several research studies and interventions in an effort to tackle the ‘Land of Fires’ phenomenon, caused by the continued trafficking and uncontrolled incineration of waste that has affected some areas of Campania for decades. In this article, a mathematical model that generates a municipality index of environmental pressure is presented. The model was developed by a multidisciplinary team led by an environmental engineer and included researchers in the fields of veterinary and human medicine, biology and computer science. This model may serve as a geostratification tool useful for the design of human biomonitoring studies, although it may also be employed for strategic planning of remediation programs and public health interventions.

Abstract In addition to soil health and conservation benefits, cover crops (CCs) may offer weed control in the midwestern United States, but individual studies report varying effects. We conducted a meta‐analysis of studies measuring weed biomass (WBIO) or density (WDEN) in paired CC and no‐cover treatments in corn (Zea mays L.)–soybean [Glycine max (L.) Merr] rotations in the U.S. Midwest. Fifteen studies provided 123 paired comparisons of WBIO and 119 of WDEN. Only grass CCs significantly reduced WBIO, while no CC reduced WDEN. We found no evidence CC management factors (e.g., termination method) directly affected outcomes. Our dataset showed that a 75% reduction in WBIO requires at least 5 Mg ha−1 of CC. Simulations from a process‐based model (SALUS) indicated achieving 5 Mg ha−1 requires substantially earlier fall planting and later spring termination in most years, conflicting with typical cash‐crop planting and harvesting. We conclude CCs significantly reduce WBIO, but current CC management constraints render these reductions variable and uncertain.

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.

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

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

The potent greenhouse gas nitrous oxide (N _2 O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ∼60% of the agricultural flux. Empirical models of N _2 O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited geographic coverage; process-based biogeochemical models are rarely able to predict daily to monthly emissions with >20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured soil parameters. We used sub-daily N _2 O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper US Midwest (∼3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (∼1100 daily fluxes), to train an ML model that explained 65%–89% of daily flux variance with very few input variables—soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and nitrogen (N) fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. This represents a two to three times improvement over conventional process-based models and with substantially fewer input requirements. This coupled approach offers promise for better predictions of agricultural N _2 O emissions and thus more precise global models and more effective agricultural mitigation interventions.

This paper describes the results of an analysis demonstrating that high yields in maize can be obtained without additional water under current and projected vapor pressure deficits. The objective of the study was to quantify evapotranspiration (ET) in high-yielding maize under current and projected vapor pressure deficits using the energy balance contrasted with the transpiration efficiency (TE) approach. This study indicates a lack of accuracy and bias in the TE approach when future crop water requirements were estimated. High maize yields are achievable using on average 700 mm of water as demonstrated by the current record maize grain yield of 34 Mg ha, which is ∼23 Mg ha higher than the US average. These yields are achievable with approximately the same ET even under projected changes in vapor pressure deficit, through improved genetics and optimum agronomic management.

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.

Consumers and corporations are increasingly interested in understanding the sustainability of agricultural supply chains and reducing the environmental impacts of food, fiber, feed, and fuel production. This emerging need to quantify environmental impacts from agricultural production creates an opportunity for collaboration with the scientific community. Without such collaboration, sustainability efforts risk failure by adopting unrealistic goals or misguided approaches. This commentary explores the role of science in Field to Market, a nonprofit organization developing a sustainability program for US commodity crops, and highlights opportunities to address emerging science challenges. We evaluate changes over the past 35 years in key environmental impacts of crop production used to inform land managers as well as companies that are committed to improvements. Achieving improvements will only be possible if three key gaps are addressed regarding available simulation models and data, scale of implementation and uncertainty, and effectiveness of conservation practices. Filling these gaps presents an opportunity for dialogue between scientists, farmers, and private-sector stakeholders to advance scientific knowledge and promote the common objective of sustainable agriculture.

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g∙N∙m−2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g∙N∙m−2) (r2 = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas.

The Corn Belt of the United States, one of the most agriculturally productive regions in the world, experienced a globally anomalous decrease in annual temperatures and a concurrent increase in precipitation during the mid- to late-20th century. Here, we quantify the impact of this ‘warming hole’ on maize yields by developing alternative, no warming hole, climate scenarios that are used to drive both statistical and process-based crop models. We show that the warming hole increased maize yields by 5%–10% per year, with lower temperatures responsible for 62% of the simulated yield increase and greater precipitation responsible for the rest. The observed cooling and wetting associated with the warming hole produced increased yields through two complementary mechanisms: slower crop development which resulted in prolonged time to maturity, and lower drought stress. Our results underscore the relative lack of climate change impacts on central US maize production to date, and the potential compounded challenge that a collapse of the warming hole and climate change would create for farmers across the Corn Belt.

The increased spring rainfall intensity and amounts observed recently in the US Midwest poses additional risk of nitrate (NO _3 ) leaching from cropland, and contamination of surface and subsurface freshwater bodies. Several individual strategies can reduce NO _3 loading to freshwater ecosystems (i.e. optimize N fertilizer applications, planting cover crops, retention of active cycling N), but the potential for synergistic interactions among N management practices has not been fully examined. We applied portfolio effect (PE) theory, a concept originally developed for financial asset management, to test whether implementing multiple N management practices simultaneously produces more stable NO _3 leaching mitigation outcomes than what would be predicted from implementing each practice independently. We analyzed simulated data generated using a validated process-based cropping system model (APSIM) that covers a range of soils, weather conditions, and management practices. Results indicated that individual management practices alone explained little of the variation in drainage NO _3 loads but were more influential in the amount of residual soil NO _3 at crop harvest. Despite this, we observed a general stabilizing effect from adopting well-designed multi-strategy approaches for both NO _3 loads and soil NO _3 at harvest, which became more pronounced in years with high spring rainfall. We use the PE principle to design multi-strategy management to reduce and stabilize NO _3 leaching, which resulted in 9.6% greater yields, 15% less NO _3 load, and 61% less soil NO _3 at harvest than the baseline typical management. Our results make the case for applying the PE to adapt NO _3 leaching mitigation to increased climate variability and change, and guide policy action and on-the-ground implementation.

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.

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.

Water is essential for plant life but it is also the most limiting factor for their growth. The temporal variation in the supply of soil water from precipitation, the spatial variability of soil factors and landscape irregularities influencing soil water supply for crop plants affect food production and create a risky environment for growing crops. 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 not expected to increase substantially. Field studies indicate that when yield from crops with common development patterns are increased by better crop management, water use efficiency (WUE) is also increased. Increased yields from genetic improvement usually result in an increased WUE with the possible exception where season length is different. The objective of this review is to present the state of the art of research in the field of WUE and future perspective to optimize water resources in agriculture. Significant results will be presented considering the important issue of spatial and temporal scale.

Urban areas such as megacities (those with populations greater than 10 million) are hotspots of global water use and thus face intense water management challenges. Urban areas are influenced by local interactions between human and natural systems and interact with distant systems through flows of water, food, energy, people, information, and capital. However, analyses of water sustainability and the management of water flows in urban areas are often fragmented. There is a strong need to apply integrated frameworks to systematically analyze urban water dynamics and factors that influence these dynamics. We apply the framework of telecoupling (socioeconomic and environmental interactions over distances) to analyze urban water issues, using Beijing as a demonstration megacity. Beijing exemplifies the global water sustainability challenge for urban settings. Like many other cities, Beijing has experienced drastic reductions in quantity and quality of both surface water and groundwater over the past several decades; it relies on the import of real and virtual water from sending systems to meet its demand for clean water, and releases polluted water to other systems (spillover systems). The integrative framework we present demonstrates the importance of considering socioeconomic and environmental interactions across telecoupled human and natural systems, which include not only Beijing (the water-receiving system) but also water-sending systems and spillover systems. This framework helps integrate important components of local and distant human-nature interactions and incorporates a wide range of local couplings and telecouplings that affect water dynamics, which in turn generate significant socioeconomic and environmental consequences, including feedback effects. The application of the framework to Beijing reveals many research gaps and management needs. We also provide a foundation to apply the telecoupling framework to better understand and manage water sustainability in other cities around the world.

The objectives of this study are (i) to evaluate vegetation indices sensitivity to discriminate between two different cultivars; (ii) to determine the effects of site elevation and developmental stages on cultivar discrimination. The experiment was carried out for the growing season 2007/08 at “Agro di Pesche” (Central Italy, Molise region). Four experimental fields were located at different elevation ranging between 590 m to 922 m above the sea level (asl). For each field, two potato (Soluanum Tuberosum L.) cultivars were used. Leaf area was collected through non-destructive measurements, and a hand-held spectroradiometer was used to measure the reflected light from the canopy of the two cultivars. Results from the ANOVA show that the ratio between MCARI (Modified Chlorophyll Absorption Ratio Index) and OSAVI (Optimized Soil Adjusted Vegetation Index), NDRE (Normalized Difference Red Edge) and MCARI were able to discriminate among cultivars at different site elevations. NDVI (Normalized Difference Vegetation Index) was not able to discriminate the two cultivars because of the influence of soil reflectance and leaves distribution.

Soil, vegetation, climate and management are the main factors affecting environmental sensitivity to degradation, through their intrinsic characteristics or by their interaction with the landscape. Different levels of degradation risks may be observed in response to particular combinations of the aforementioned factors. For instance, the combination of inappropriate management practices and intrinsically weak soil conditions will result in a degradation of the environment of a severe level, while the combination of the same type of management with better soil conditions may lead to negligible degradation. The objective of this study was to identify the factors responsible for land degradation processes in Basilicata and to simulate through the adoption of the SALUS soil-plant-atmosphere system model potential measures to mitigate the processes. Environmental sensitive areas to desertification were first identified using the Environmental Sensitive Areas (ESAs) procedure. An analysis for identifying the weight that each contributing factor (climate, soil, vegetation, socio-economic management) had on the ESA was carried out and successively the SALUS model was executed to identify the best agronomic practices. The best agronomic management practice was found to be the one that minimized soil disturbance and increased soil organic carbon. Two alternative scenarios with improved soil quality and subsequently improving soil water holding capacity were used as mitigation measures. The new ESA were recalculated and the effects of the mitigation suggested by the model were assessed.

Yield rates vary spatially and maps produced by the yield monitor systems are evidence of the degree of withinfield variability. The magnitude of this variability is a good indication of the suitability of implementing a spatially variable management plan. Crop simulation models have the potential to integrate the effects of temporal and multiple stress interaction on crop growth under different environmental and management conditions. The strength of these models is their ability to account for stress by simulating the temporal interaction of stress on plant growth each day during the season. The objective of paper is to present a procedure that allows for the selection of optimal nitrogen fertilizer rates to be applied spatially on previously identified management zones through crop simulation modelling. The integration of yield maps, remote sensing imagery, ground truth measurements, electrical resistivity imaging allowed for the identifications of three distinct management zones based on their ability to produce yield and their stability over time (Basso et al., 2009). After validating the model, we simulated 7 N rates from 0 to 180 kg N/ha with a 30 kg N/ha increment. The model results illustrate the different N responses for each of the zone. The analysis allowed us to identify the optimal N rate for each of the zone based on agronomic, economic and environmental sustainability of N management.

A sustainable land management has been defined as the management system that allows for production, while minimizing risk, maintaining quality of soil and water. Tillage systems can significantly decrease soil carbon storage and influence the soil environment of a crop. Crop growth models can be useful tools in evaluating the impact of different tillage systems on soil biophysical properties and on the growth and final yield of the crops. The objectives of this paper were i) to illustrate the SALUS model and its tillage component; ii) to evaluate the effects of different tillage systems on water infiltration and time to ponding, iii) to simulate the effect of tillage systems on some soil biophysical properties. The SALUS (System Approach to Land Use Sustainability) model is designed to simulate continuous crop, soil, water and nutrient conditions under different tillage and crop residues management strategies for multiple years. Predictions of changes in surface residue, bulk density, runoff, drainage and evaporation were consistent with expected behaviours of these parameters as described in the literature. The experiment to estimate the time to ponding curve under different tillage system confirmed the theory and showed the beneficial effects of the residue on soil surface with respect to water infiltration. It also showed that the no-tillage system is a more appropriate system to adopt in areas characterized by high intensity rainfall.