Christopher J. Kucharik

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3–12 times each) spanning a range of impervious and tree canopy cover (0–100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1–5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60–90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2–3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.

Abrupt ecological changes occur rapidly relative to typical rates of ecosystem change and are increasingly observed in ecosystems worldwide, thereby challenging adaptive capacities. Abrupt ecological changes can arise from many processes, only some of which are transitions between alternative states. Focusing solely on the mean values for drivers and states is insufficient for diagnosing abrupt changes, because abrupt changes can be produced by changes in the variability of drivers and disturbance regimes. Diagnosing the likely causes of abrupt state changes in real-world systems remains difficult. Long-term data and experimental manipulations of drivers remains essential. Multiple changing drivers can interact to increase the likelihood of abrupt changes. Identifying interventions that decrease the risk of undesirable abrupt changes is an urgent priority. Abrupt ecological changes are, by definition, those that occur over short periods of time relative to typical rates of change for a given ecosystem. The potential for such changes is growing due to anthropogenic pressures, which challenges the resilience of societies and ecosystems. Abrupt ecological changes are difficult to diagnose because they can arise from a variety of circumstances, including rapid changes in external drivers (e.g., climate, or resource extraction), nonlinear responses to gradual changes in drivers, and interactions among multiple drivers and disturbances. We synthesize strategies for identifying causes of abrupt ecological change and highlight instances where abrupt changes are likely. Diagnosing abrupt changes and inferring causation are increasingly important as society seek to adapt to rapid, multifaceted environmental changes. Abrupt ecological changes are, by definition, those that occur over short periods of time relative to typical rates of change for a given ecosystem. The potential for such changes is growing due to anthropogenic pressures, which challenges the resilience of societies and ecosystems. Abrupt ecological changes are difficult to diagnose because they can arise from a variety of circumstances, including rapid changes in external drivers (e.g., climate, or resource extraction), nonlinear responses to gradual changes in drivers, and interactions among multiple drivers and disturbances. We synthesize strategies for identifying causes of abrupt ecological change and highlight instances where abrupt changes are likely. Diagnosing abrupt changes and inferring causation are increasingly important as society seek to adapt to rapid, multifaceted environmental changes. substantial changes in the mean or variability of a system that occur in a short period of time relative to typical rates of change. two or more states at which an ecosystem can persist, within the same range of driver variables. The alternative states of stochastic processes are characterized by different means, variances, and other statistical moments. Alternative stable state is the term often used in reference to deterministic processes, whereas work focused on stochastic processes often favors terms such as alternative attractor. Often used synonymously with multiple stable states, multiple equilibria, and basins of attraction. transitions that occur when a threshold is passed, causing the disappearance or appearance of alternative states. Often used synonymously with bifurcations. a process that has no random component and is, therefore, theoretically predictable. For instance, at a given combination of a drivers and state variables, a deterministic process consistently repeats the same behaviors. relatively discrete event in time that alters the biotic and/or abiotic components of an ecosystem. the spatial and temporal patterns of disturbances over a long period of time. A disturbance regime is characterized by multiple factors including frequency, return interval, size, intensity, and severity. we use ‘driver’ to refer to external factors that influence the dynamics of a system without themselves being affected by the system. Often synonymous with external forcing or extrinsic processes. ecosystems characterized by two or more alternative states, critical transitions between states, and sensitivity to initial conditions. In hysteretic ecosystems, the conditions required to change state in one direction differ from the conditions required to change back to the original state. an observed large change in an ecosystem. Regime shifts can be gradual or fast, can be produced by many processes, and can involve multiple external drivers and/or internal feedbacks. All abrupt ecological changes are regime shifts, but not all regime shifts are abrupt. A regime shift can or can not be a transition to an alternative state. Often synonymous with state transitions and phase shifts. (i) degree to which an ecosystem can tolerate changing driver variables and/or disturbance without shifting to a qualitatively different state. (ii) The rate of return of the system to its stable state or stationary distribution. Despite having two different definitions since the 1970s, the term resilience is often used imprecisely. All uses of resilience in this paper relate to the first definition. the combinations of states, driver variable values, and disturbance characteristics for which a system is not likely to undergo a regime shift. The boundary of a safe operating space is not a threshold; it is placed a safe distance inside any known or potential thresholds. Safe operating spaces are intended to maintain the resilience of the ecosystem, with resilience given by the first definition above. the characteristics used to describe the status of an ecosystem at a particular domain in space and time. For deterministic systems, the state is the values of variables used to describe the system, and for stochastic systems it could refer to as either the probability distribution of these state variables or realized values of the state variables. In practice, definitions of system state include both the mean and variability of systems. a process that through time generates a random variable characterized by a mean, variance, and higher statistical moments of the state variables of a system (e.g., skewness). Used to contrast deterministic processes that do not involve random variables.

Early planting of maize ( Zea mays L.) allows for longer‐season hybrids to be used in cool temperate regions. Given that a multidecadal trend toward earlier planting has been occurring across the Corn Belt, it was hypothesized that this shift has supported a portion of recent yield increases. The objectives were to quantify relationships among state level monthly climate variables, maize yields, and planting dates, and to investigate whether multidecadal trends of earlier planting contributed to rising yields during 1979 to 2005 in 12 central U.S. states. Year‐to‐year changes (i.e., first differences) of predictor variables (monthly mean temperature and precipitation and planting date) and yields were calculated, and multiple linear regression was used to estimate the effect of planting date trends on maize yield increases. In six of the 12 states, a significant relationship ( P < 0.05) existed between first differences of planting dates and yields. Multiple linear regression suggested that the management change has potentially contributed between 19 and 53% of the state level yield increases in Nebraska, South Dakota, Minnesota, Iowa, Wisconsin, and Michigan. Yield increases between 0.06 and 0.14 Mg ha −1 were attributed to each additional day of earlier planting, which likely reflects a gradual adoption of longer‐season hybrids. Thus, if these earlier planting trends were to suddenly abate, a falloff in annual yield increases may follow in several Corn Belt states. Maize production in northern U.S. states appears to have benefited more significantly from earlier planting due to a shorter growing season in contrast to more southern locations.

Rice is consumed by more people than any other grain. Globally, Vietnam is one of the largest exporters of rice, with the majority of production occurring in the tropical, low-lying Mekong River Delta. Agriculture in the Mekong River Delta is susceptible to yield losses from rising temperatures, sea level rise, and land use change as urban expansion replaces productive farmland. Most studies that assess climate change impacts to rice paddy yields are conducted at global- or continental-scales, and use general information on management practices to simulate production. Here, we use management information from farmers and published information on soils collected in Can Tho, a centrally-located province in the Mekong Delta. These data, along with projected mid-century (2040–2069) climate data for the RCP4.5 and RCP8.5 greenhouse gas emissions scenarios, are used to drive the Decision Support System for Agrotechnology Transfer (DSSAT) platform to project future rice paddy yields using the CERES-Rice model. The results indicate that yields decline for all three rice-growing seasons in Can Tho city for both emissions scenarios when CO2 fertilization is not considered (5.5–8.5% annually on average depending on the emissions scenario). Increasing irrigation and fertilizer did not offset these losses, but simulated CO2 fertilization did compensate for yield declines caused by increasing temperatures (yields were modeled to be up 23% higher when CO2 fertilization is considered). However, we caution that estimated yield gains from CO2 fertilization are optimistic, and these modeled values do not consider rises in ozone, which can diminish yields. Continued and future dam construction could negatively affect agriculture in the region, and current government policies prohibit rice paddy farmers from diversifying their livelihoods to adapt to these changes. Monitoring rice agroecosystems at a fine-scale, as this study does, is necessary to best capture the impact that varying management practices can have on local yields. When these differences are captured, future impacts of climate change can be modeled more effectively so that local policymakers can make informed decisions about how to offset yield losses and use farmland more efficiently.

The effects of climate variations on crop yield have been widely studied. However, the effects of soil on crop-climate responses are often ignored in crop yield prediction. We investigated the effects of soil texture and soil organic carbon concentration (SOC) on the yield responses of seven major crops (corn, winter wheat, soybean, cotton, barley, oats, rice) to growing season precipitation and temperature between 1958 and 2019 across the conterminous US. We also evaluated the effects of irrigation and groundwater depth on crop-climate responses. Crop yields were most sensitive to precipitation and temperature variability in coarse-textured soils and less responsive to these weather parameters in medium- and fine- textured soils. Increasing SOC concentration (> 2%) contributed to crop yields being less sensitive to precipitation – due to increased water retention, and less responsive to temperature – presumably due to increased buffering capacity against increased water lost through evapotranspiration. Irrigation and an intermediate depth to groundwater increase the resilience of crops to precipitation and temperature changes and these effects were also dependent on soil texture and SOC. To enhance food security for a rapidly growing global population under a changing climate, best management practices should be adopted that improve soil structure and carbon stocks that can increase soil available water storage (“Green Water”) and nutrient retention and promote energy conservation. The spatial-temporal variations of soil texture, SOC, and depth to groundwater should be considered in agricultural and ecosystem modeling to more accurately capture crop yield response to climate variations.

Sustainably accommodating future population growth and meeting global food requirements requires understanding feedbacks between ecosystems and belowground hydrological processes. Here, we introduce MODFLOW-AgroIBIS (MAGI), a new dynamic ecosystem model including groundwater flow, and use MAGI to explore the indirect impacts of land use change (urbanization) on landscape-scale agroecosystem productivity (corn yield). We quantify the degree to which urbanization can indirectly impact yield in surrounding areas by changing the amount of groundwater recharge locally and the water table dynamics at landscape scales. We find that urbanization can cause increases or decreases in yield elsewhere, with changes up to approximately +/− 40% under the conditions simulated due entirely to altered groundwater-land surface interactions. Our results indicate that land use change in upland areas has the largest impact on water table depth over the landscape. However, there is a spatial mismatch between areas with the largest water table response to urbanization elsewhere (upland areas) and locations with the strongest yield response to urbanization elsewhere (midslope areas). This mismatch arises from differences in baseline water table depth prior to urbanization. Yield response to urbanization in lowland areas is relatively localized despite large changes to the vertical water balance due to stabilizing ecohydrological feedbacks between root water uptake and lateral groundwater flow. These results demonstrate that hydrological impacts of land use change can propagate through subsurface flow to indirectly impact surrounding ecosystems, and these subsurface connections should be considered when planning land use at a landscape scale to avoid negative outcomes associated with land use change.

Over-enrichment of phosphorus (P) in agroecosystems contributes to eutrophication of surface waters. In the Midwest US and elsewhere, climate change is increasing the frequency of high-intensity precipitation events, which can serve as a primary conduit of P transport within watersheds. Despite uncertainty in their estimates, process-based watershed models are important tools that help characterize watershed hydrology and biogeochemistry and scale up important mechanisms affecting water quality. Using one such model developed for an agricultural watershed in Wisconsin, we conducted a 2 × 2 factorial experiment to test the effects of (high/low) terrestrial P supply (PSUP) and (high/low) precipitation intensity (PREC) on surface water quality. Sixty-year simulations were conducted for each of the four runs, with annual results obtained for watershed average P yield and concentration at the field scale (220 × 220 m grid cells), P load and concentration at the stream scale, and summertime total P concentration (TP) in Lake Mendota. ANOVA results were generated for the 2 × 2 factorial design, with PSUP and PREC treated as categorical variables. The results showed a significant, positive interaction (p < 0.01) between the two drivers for dissolved P concentration at the field and stream scales, and total P concentration at the field, stream, and lake scales. The synergy in dissolved P was linked to nonlinear dependencies between P stored in manure and the daily runoff to rainfall ratio. The synergistic response of dissolved P loss may have important ecological consequences because dissolved P is highly bioavailable. Overall, the results suggest that high levels of terrestrial P supplied as manure can exacerbate water quality problems in the future as the frequency of high-intensity rainfall events increases with a changing climate. Conversely, lowering terrestrial manure P supply may help improve the resilience of surface water quality to extreme events.

Sustaining food production, water quality, soil retention, flood, and climate regulation in agricultural landscapes is a pressing global challenge given accelerating environmental changes. Scenarios are stories about plausible futures, and scenarios can be integrated with biophysical simulation models to explore quantitatively how the future might unfold. However, few studies have incorporated a wide range of drivers (e.g., climate, land-use, management, population, human diet) in spatially explicit, process-based models to investigate spatial-temporal dynamics and relationships of a portfolio of ecosystem services. Here, we simulated nine ecosystem services (three provisioning and six regulating services) at 220 × 220 m from 2010 to 2070 under four contrasting scenarios in the 1,345-km2 Yahara Watershed (Wisconsin, USA) using Agro-IBIS, a dynamic model of terrestrial ecosystem processes, biogeochemistry, water, and energy balance. We asked (1) How does ecosystem service supply vary among alternative future scenarios? (2) Where on the landscape is the provision of ecosystem services most susceptible to future social-ecological changes? (3) Among alternative future scenarios, are relationships (i.e., trade-offs, synergies) among food production, water, and biogeochemical services consistent over time? Our results showed that food production varied substantially with future land-use choices and management, and its trade-offs with water quality and soil retention persisted under most scenarios. However, pathways to mitigate or even reverse such trade-offs through technological advances and sustainable agricultural practices were apparent. Consistent relationships among regulating services were identified across scenarios (e.g., trade-offs of freshwater supply vs. flood and climate regulation, and synergies among water quality, soil retention, and climate regulation), suggesting opportunities and challenges to sustaining these services. In particular, proactive land-use changes and management may buffer water quality against undesirable future climate changes, but changing climate may overwhelm management efforts to sustain freshwater supply and flood regulation. Spatially, changes in ecosystem services were heterogeneous across the landscape, underscoring the power of local actions and fine-scale management. Our research highlights the value of embracing spatial and temporal perspectives in managing ecosystem services and their complex interactions, and provides a system-level understanding for achieving sustainability of the food-water-climate nexus in agricultural landscapes.

Understanding the combined and separate effects of climate and land use change on the water cycle is necessary to mitigate negative impacts. However, existing methodologies typically divide data into discrete (before and after) periods, implicitly representing climate and land use as step changes when in reality these changes are often gradual. Here, we introduce a new regression-based methodological framework designed to separate climate and land use effects on any hydrological flux of interest continuously through time, and estimate uncertainty in the contribution of these two drivers. We present two applications in the Yahara River Watershed (Wisconsin, USA) demonstrating how our approach can be used to understand synergistic or antagonistic relationships between land use and climate in either the past or the future: (1) historical streamflow, baseflow, and quickflow in an urbanizing subwatershed; and (2) simulated future evapotranspiration, drainage, and direct runoff from a suite of contrasting climate and land use scenarios for the entire watershed. In the historical analysis, we show that ∼60% of recent streamflow changes can be attributed to climate, with approximately equal contributions from quickflow and baseflow. However, our continuous method reveals that baseflow is significantly increasing through time, primarily due to land use change and potentially influenced by long-term increases in groundwater storage. In the simulation of future changes, we show that all components of the future water balance will respond more strongly to changes in climate than land use, with the largest potential land use effects on drainage. These results indicate that diverse land use change trajectories may counteract each other while the effects of climate are more homogeneous at watershed scales. Therefore, management opportunities to counteract climate change effects will likely be more effective at smaller spatial scales, where land use trajectories are unidirectional.

Many soil health tests quantify some portion of soil organic matter, through both extractions and incubations. Four such tests have been identified as potentially useful measurements for farmers to track soil health on their farm, alongside typical routine soil testing: permanganate oxidizable carbon (POXC), mineralizable carbon (Min C), potentially mineralizable nitrogen (PMN), and autoclaved citrate extractable protein (ACE-N). These indicators have been related to crop yield, but not for soybean productivity. Our study relied on soil samples (0–15 cm) and field history data from 323 producer-managed soybean fields throughout Wisconsin between 2019 and 2021. Our first objective was to explore the impact of management history and inherent soil factors like texture and drainage class on soil health indicator values. Both conditional inference trees and random forest analysis were used to explore these relationships. Inherent factors like texture class and drainage class were important for determining capacity of a soil to achieve high soil health values. Some management factors were important as well, such as crop rotation (for POXC) and manure use (for ACE-N and PMN). Our second objective was to explore the relationship of these four soil health indicators and soybean seed yield using the same analyses. The conditional inference tree explaining soybean yield only showed soil order as a consistent predictor of yield. Random forest analysis showed latitude was the most important factor for soybean yield, followed by POXC. Further analysis showed a positive relationship between POXC and soybean yield (p < 0.001; Adj. R2 = 0.16). This exploratory study underlines the need for regional benchmarks beyond texture class for these soil health indicators, including factors like drainage class, soil order, and location. The positive relationship between soybean yield and POXC provides a quantifiable goal, which may support soybean farmers in prioritizing soil health.

Nitrogen (N) removal via benthic denitrification in large river systems can be a significant sink of terrestrial N and a source of nitrous oxide (N 2 O) to the atmosphere. Recent studies have demonstrated the fraction of in‐stream N removed from a river reach is related to the water residence time. We used the HYDRA aquatic transport model to examine the sensitivity of in‐stream N removal and the associated N 2 O emissions in the Mississippi River system to the interannual variability in climate. The results suggested an almost two‐fold range in the percent of N removed in the Mississippi River system and a three‐fold range in the associated N 2 O emissions, with the lowest percent removed (10–33%) and the highest N 2 O emissions (15.5–26.0 10 6 kg N) occurring in the wettest years. The results demonstrate the importance of considering climate variability and change in the management of nutrient export by large rivers.

Soil organic carbon (SOC) storage is a critical component of the overall sustainability of bioenergy cropping systems. Predicting the influence of cropping systems on SOC under diverse scenarios requires a mechanistic understanding of the underlying processes driving SOC accumulation and loss. We used a density fractionation technique to isolate three SOC fractions that are conceptualized to vary in SOC protection from decomposition. The free light fraction (FLF) is particulate SOC that is present in the inter-aggregate soil matrix, the occluded light fraction (OLF) is contained within aggregates, and the heavy fraction (HF) is associated with minerals. We evaluated surface (0 to 10 cm depth) SOC fraction changes from baseline conditions 5 years after biofuel cropping system establishment at two temperate sites with contrasting soil textures. The biofuel cropping systems included no-till maize, switchgrass, prairie, and hybrid poplar. The FLF concentration (g fraction C g bulk soil−1) did not change significantly from baseline levels under any of the cropping systems at either site after 5 years. Except for poplar, OLF concentrations were reduced in all systems at the site with coarse-textured soils and maintained at the site with fine-textured soils. In poplar systems, OLF concentrations were maintained on coarse-textured soils and increased on fine-textured soils. The HF concentrations also increased under poplar on the coarse-textured soil. A structural equation model indicated that OLF concentrations increased with lower litter C:N, and HF concentrations increased with greater litter quantity and lower litter C:N mass ratios. C:N increased over time within all SOC fractions, suggesting that all pools are sensitive to land-use change on sub-decadal timescales. In agreement with modern SOC theory, our empirical results indicate that increasing litter input quantity and promoting plant species with low C:N litter may improve SOC storage in aggregate and mineral-associated soil fractions.

Eutrophication of freshwaters occurs in watersheds with excessive pollution of phosphorus (P). Factors that affect P cycling and transport, including climate and land use, are changing rapidly and can have legacy effects, making future freshwater quality uncertain. Focusing on the Yahara Watershed (YW) of southern Wisconsin, USA, an intensive agricultural landscape, we explored the relative influence of land use and climate on three indicators of water quality over a span of 57 years (2014-2070). The indicators included watershed-averaged P yield from the land surface, direct drainage P loads to a lake, and average summertime lake P concentration. Using biophysical model simulations of future watershed scenarios, we found that climate exerted a stronger influence than land use on all three indicators, yet land use had an important role in influencing long term outcomes for each. Variations in P yield due to land use exceeded those due to climate in 36 of 57 years, whereas variations in load and lake total P concentration due to climate exceeded those due to land use in 54 of 57 years, and 52 of 57 years, respectively. The effect of land use was thus strongest for P yield off the landscape and attenuated in the stream and lake aquatic systems where the influence of weather variability was greater. Overall these findings underscore the dominant role of climate in driving inter-annual nutrient fluxes within the hydrologic network and suggest a challenge for land use to influence water quality within streams and lakes over timescales less than a decade. Over longer timescales, reducing applications of P throughout the watershed was an effective management strategy under all four climates investigated, even during decades with wetter conditions and more frequent extreme precipitation events.

This paper describes development of a nitrate decision support tool for groundwater wells (GW-NDST) that combines nitrate leaching and groundwater lag-times to compute well concentrations. The GW-NDST uses output from support models that simulate leached nitrate, groundwater age distributions, and nitrate reduction rates. The support models are linked through convolution to simulate nitrate transport to wells. Spatially distributed parameters were adjusted through calibration to 34,255 nitrate sample targets. Prediction uncertainty is illustrated via Monte Carlo realizations informed during calibration. Over 78% of target concentrations were within the simulated range of results from 450 realizations. An example forecasting scenario illustrates that a range of feasible outcomes exist and should be considered when interpreting forecasts for decision making. Uncertainty in forecasting is unavoidable; the intent of characterizing uncertainty in the GW-NDST is to facilitate decision making by increasing insight into the response of nitrate contamination to physical and chemical processes.

Precision agriculture informed by electromagnetic induction surveys could reduce groundwater withdrawals and nitrogen leaching from coarse soils. However, coarse, nonsaline soils often have extremely narrow ranges of mapped apparent electrical conductivity (ECa) and the efficacy of ECa for predicting soil physical properties is uncertain in this context. For this reason, it is also uncertain as to whether electromagnetic induction surveys are valuable for guiding precision agriculture on coarse, nonsaline soils. Additionally, the need to ground-truth electromagnetic induction surveys for individual agricultural fields with soil sampling and statistical model development hampers adoption of precision agriculture at the regional scale. Our research objectives were to quantify the variation in mapped ECa and develop statistical relationships between ECa and soil physical properties both within and across several agricultural fields in the Wisconsin Central Sands, a distinct hydropedological region with coarse, glaciolacustrine soils. We used nonparametric correlation analyses to identify associations and quantile regression, a statistical approach with no assumptions of normality or homoscedasticity, to identify predictive relationships between ECa and soil physical properties. We found strong, significant (p < 0.05) correlative and predictive relationships between ECa and topsoil (0–0.3 m) particle size fraction, organic matter content, and field capacity within and across several fields. Yet, we did not observe many significant relationships between ECa and subsoil (0.5–0.6 m) physical properties, which we attribute to heterogeneous soil layering and the low depth resolution of our soil sampling approach. Our findings demonstrate that proximal sensing of ECa can identify intrafield variability in soil properties under extremely narrow observed ECa ranges (0–11 mS m−1). Moreover, we found that interfield quantile regression models predicted soil physical properties across several agroecosystems. Heteroscedasticity was present in interfield ECa relationships with physical properties, which resulted in the need for different quantile regression models across the conditional distribution. The flexibility for accommodating heteroscedasticity in soils and simplicity of modeled functions make quantile regression a promising approach for developing interfield or regional models of ECa to predict soil physical properties in distinct, hydropedological regions with coarse soils.

The complex ecological and socioeconomic interactions that exist in urban areas create novel abiotic environments and novel ecosystems. Microclimates in urban areas have been studied during the summer months; however, climate change is also increasing the frequency of extreme cold outbreaks during winter. Quantifying microclimatic variation in soil surface temperatures, the level at which most plants and animals persist, can help predict the effects of climate on biota in temperate and high-latitude cities. Using fine-scale temporal and spatial data, we investigated how air temperature and snow characteristics interact with land cover and land use to drive variation in soil surface temperatures in greenspaces throughout a mid-sized city in the United States. We found that snow reduced variability in soil surface temperatures, which remained around 0 °C when snow depth was greater than or equal to 20 cm, with air temperature and snow depth interacting strongly to drive this effect. Tree cover promoted warmer soil surface temperatures regardless of snow cover, with relatively warmer soil surface temperatures in natural areas and residential yards compared to those in public landscaped areas (e.g., managed parks). In fact, under extremely low air temperatures (∼−30 °C), the warming effect of urban forests reached 4 °C, which provided substantial buffering from extreme cold. Our results highlight the potential for urban greenspaces with trees and snow to serve as refugia for organisms in the winter, and subsequently preserve essential ecosystem services provided by urban biodiversity. Consequently, urban planners should aim to maximize tree cover where possible and supplement greenspaces with vegetation (i.e. shrubs and leaf litter) that promotes snow retention during the winter.

Abstract Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance‐based risk maps using multi‐year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid ( SBA ), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models ( GAM s). Our models showed good to excellent performance in predicting SBA occurrence and abundance ( TSS = 0.70, AUC = 0.92; R 2 = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm (“Zonation”) to produce an abundance‐based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance‐based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.

Core Ideas Groundwater, root distribution, and compensation impact transpiration and NPP. Maize is less reliant on the compensated root water uptake mechanism as roots extend deeper. The impact of drought on corn lessens as roots become deeper. Maize increase its carbon fixation during dry years due to the compensated root water uptake. Shallow‐rooted corn benefits more from groundwater subsidy than deeper rooted corn. We investigated the potential impacts of shallow groundwater, root length density (RLD) distribution, and root water compensation on transpiration and net primary productivity (NPP). An agroecosystem model (AgroIBIS‐VSF) that is capable of simulating variably saturated water flow was driven with hourly weather observations in southern Wisconsin over 27 yr for various RLD distributions across a continuum of groundwater depth. The results indicated that the strength of the relationship between groundwater depth and water use in the critical water table depth zone is controlled by the root structure and root water uptake (RWU) strategy. In this zone, transpiration is progressively more sensitive to the groundwater level as roots become shallower. The impact of drought on corn ( Zea mays L.) lessens and corn becomes less reliant on compensated RWU capabilities as roots extend deeper. Simulations indicated that the use of the compensated RWU approach results in NPP increases of 38.1 (3.81%), 30.8 (2.74%), and 6.4 (0.55%) g C m −2 yr −1 during the driest years (i.e., when growing season precipitation is below the 30th percentile of the long‐term observations) for shallow, intermediate, and deep RLDs, respectively. Moreover, shallow groundwater supported RWU, and corn with a shallow RLD benefited the most from shallow groundwater, with an increase in annual transpiration of 230 mm. Our findings underscore the importance of incorporating compensatory RWU and selecting an appropriate and representative RLD for contrasting vegetation types in ecosystem models to simulate a more realistic plant response to variable climate and groundwater depth conditions.

The impact of land use change and agricultural management on the cycling of soil organic carbon (SOC) is not well understood, limiting our ability to manage for, and accurately model, soil carbon changes at both local and regional scales. To address this issue, we combined long-term soil incubations with acid-hydrolysis and dry combustion to parse total SOC (Ct) into three operationally defined SOC pools (active, slow, and recalcitrant) from 9 long-term sites with varying land uses on current and former tallgrass prairie soil. Land uses represented a gradient of soil disturbance histories including remnant prairie, restored prairie, grazed pasture, annual crop rotations, and continuous maize. Dry combustion was used to estimate total carbon (Ct, physical), while acid hydrolysis of both the active (Ca) and slow (Cs) pools was used to estimate a recalcitrant carbon pool (Cr, chemical). Non-linear modeling of CO2 efflux data from the long-term incubations was then used to estimate Ca, and the decomposition rates of both Ca and Cs (ka and kr, biological). The size of the slow pools Cs was then defined mathematically as Ct-(Ca + Cr). Remnant prairie had the highest Ct, while cool-season pasture and a 35-y-old restored prairie had higher Ct than the other agricultural systems. All agricultural systems, including pasture, had the highest fraction of Ct as Cr (∼50%), whose mean residence time (MRT) in these soils is ≥500 years (Paul et al., 2001a) demonstrating that this fraction persists, while the more labile fractions were lost over the course of a few months (Ca) to a few decades (Cs) as a result of tillage-intensive agriculture. The two- to four-decade MRT time of Cs indicated a pool likely to be more responsive to the 20 to 40 years of land-use practices used at some of the sites. The Cs pool was largest in the remnant- and 35-y-old prairies indicating significant C accrual and stabilization compared to the agricultural ecosystems. Interestingly, the remnant prairie maintained the highest Ca pool as well, demonstrating the strong connection between the quantity of fresh C inputs and the potential for long-term C stabilization and accrual. The accumulation of C in active (≈labile) pools as a first step toward long-term stabilization highlights the tenuous nature of early carbon gains, which can be quickly lost in response to climate change or poor management.

Reducing the size of the hypoxic zone in the Gulf of Mexico has proven to be a challenging task. A variety of mitigation options have been proposed, each likely to produce markedly different patterns of mitigation with widely varying consequences for the economy. The general consensus is that no single measure alone is sufficient to achieve the EPA Task Force goal for reducing the Gulf hypoxic zone and it appears that a combination of management practices must be employed. However, absent a highly resolved, multi-scale framework for assessing these policy combinations, it has been unclear what pattern of mitigation is likely to emerge from different policies and what the consequences would be for local, regional and national land use, food prices and farm returns. We address this research gap by utilizing a novel multi-scale framework for evaluating alternative N loss management policies in the Mississippi River basin. This combines fine-scale agro-ecosystem responses with an economic model capturing domestic and international market and price linkages. We find that wetland restoration combined with improved N use efficiency, along with a leaching tax could reduce the Mississippi River N load by 30-53\% while only modestly increasing corn prices. This study underscores the value of fine-resolution analysis and the potential of combined economic and ecological instruments in tackling nonpoint source nitrate pollution.

Core Ideas Groundwater in humid regions should be managed for services, not depletion. Climate variability can be a greater driver of potential recharge than crop type. Soil texture can be a significant driver of point‐based potential recharge estimates. Crop phenology may not be predictive of point‐based potential recharge estimates. The expansion of irrigated agriculture on landscapes underlain by coarse‐grained, glacial aquifers in Wisconsin, Minnesota, and Michigan changes the timing and magnitude of groundwater recharge. Water managers require improved estimates of groundwater recharge to manage pumping impacts on groundwater‐fed streams, lakes, and wetlands. We implemented a network of 25 passive capillary lysimeters to infer potential groundwater recharge and evapotranspiration (ET) from irrigated potato ( Solanum tuberosum L.), sweet corn and field corn ( Zea mays L.), and pea ( Pisum sativum L.)–pearl millet [ Pennisetum glaucum (L.) R. Br.] rotations in the Wisconsin Central Sands (WCS) from June through November of 2013 to 2016. We found that interannual climate variability, subtle differences in soil texture, and cropping system type drove potential recharge to varying degrees during the summer and fall seasons. Relatively finer soil texture was positively correlated to point estimates of potential recharge. This correlation was the strongest following large precipitation events. June to November cumulative potential recharge for 2013 to 2016 averaged 71 ± 235 mm across all lysimeters. Our findings suggest that aquifer depletion will be an episodic process that leaves surface waters most vulnerable to pumping and recharge impacts during and following drier years in the WCS. Differences among cropping systems were most pronounced under average precipitation conditions, which facilitated potential groundwater losses under field corn and pea–pearl millet rotations and potential groundwater gains under potato rotations. We conclude that regional water management strategies could be effective in buffering against the interannual climate variability of recharge, while localized management strategies could increase irrigation efficiency by targeting crop and soil textural drivers.

Food‐energy‐water (FEW) systems are increasingly vulnerable to shocks. Repeated floods, worsening droughts, sudden tariffs, and disease outbreaks all underscore the importance of strengthening production systems during a time of rapid global change. However, the laws, regulations, and incentive programs that govern these sectors were often developed in isolation, creating fragmented and lagged responses to previous crises, ineffective governance of FEW security, and unintended effects even when achieving policy goals. Here, we examine the Mississippi River Basin in the Midwest US to illustrate how policies designed to address one challenge had other unanticipated consequences. We argue for a long view of the future that honors the interconnectedness of FEW sectors with ecosystems (FEWE); values non‐provisioning ecosystem services; and prioritizes incentives that improve FEW production, farm profitability, and ecosystem health. Now is the time for reassessment of how well FEWE provide security to all humans and the environment, and to support integrated policies that avoid unintended future consequences.

Abstract Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014–2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation.

As scientists seek to better understand the linkages between energy, water, and land systems, they confront a critical question of scale for their analysis. Many studies exploring this nexus restrict themselves to a small area in order to capture fine-scale processes, whereas other studies focus on interactions between energy, water, and land over broader domains but apply coarse resolution methods. Detailed studies of a narrow domain can be misleading if the policy intervention considered is broad-based and has impacts on energy, land, and agricultural markets. Regional studies with aggregate low-resolution representations may miss critical feedbacks driven by the dynamic interactions between subsystems. This study applies a novel, gridded energy-land-water modeling system to analyze the local environmental impacts of biomass cofiring of coal power plants across the upper MISO region. We use this framework to examine the impacts of a hypothetical biomass cofiring technology mandate of coal-fired power plants using corn residues. We find that this scenario has a significant impact on land allocation, fertilizer applications, and nitrogen leaching. The effects also impact regions not involved in cofiring through agricultural markets. Further, some MISO coal-fired plants would cease generation because the competition for biomass increases the cost of this feedstock and because the higher operating costs of cofiring renders them uncompetitive with other generation sources. These factors are not captured by analyses undertaken at the level of an individual power plant. We also show that a region-wide analysis of this cofiring mandate would have registered only a modest increase in nitrate leaching (just +5% across the upper MISO region). Such aggregate analyses would have obscured the extremely large increases in leaching at particular locations, as much as +60%. Many of these locations are already pollution hotspots. Fine-scale analysis, nested within a broader framework, is necessary to capture these critical environmental interactions within the energy, land, and water nexus.

Abstract Variations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

Lake level regulation is commonly used to manage water resources and mitigate flood risk in watersheds with linked river–lake systems. In this study, we first assess exposure, in terms of both population and land area, to flooding impacts in the Yahara Watershed’s chain of four lakes in southern Wisconsin as affected by minimum lake level management. A flooding exposure assessment shows that the areas surrounding the upstream lakes, Mendota and Monona, have dense urban areas with high populations that are exposed to flooding; Waubesa has low elevations along its lakeshore, resulting in a large potential flooding area; and the most downstream lake, Kegonsa, has a large area of surrounding cropland that is exposed to flooding but impacts a limited population. We then use a linked modeling framework of a land surface model (Agro-IBIS) and a hydrologic-routing model (THMB) to simulate daily lake level over a study period of 1994–2013 in the Yahara Watershed with different minimum lake level management strategies. Modeling results show that the peak lake levels and corresponding exposed land area and population to flooding will decrease under a lower target minimum lake level. However, at the same time, the number of days that the lake level is below winter minimum will increase, which may adversely affect ecosystem health. In addition, our sensitivity analysis indicates that reducing target minimum lake levels will help mitigate flood risk in terms of both flood magnitude and frequency. Nevertheless, this must be balanced against the need to maintain adequately high lake levels for ecosystem services and recreational functions of the lakes.

Scientists and agricultural trade associations may further conservation outcomes by engaging with one another to uncover opportunities and engage in social learning via knowledge co-production. We observed, documented, and critically reviewed knowledge exchanges among scientists and agricultural stakeholders working on a multidecadal water conflict in Wisconsin. Differences in knowledge exchange and production were related to meeting spaces, organization, time management, and formality of interactions. We found that repetitive, semiformal meetings organized and led by growers facilitated knowledge exchange, co-production, and social learning. However, scientists often appeared uncomfortable in grower-controlled spaces. We suggest that this discomfort results from the widespread adoption of the deficit model of scientific literacy and objectivity as default paradigms, despite decades of research suggesting that scientists cannot view themselves as objective disseminators of knowledge. For example, we found that both scientists and growers produced knowledge for political advocacy but observed less transparency from scientists, who often claimed objectivity in politicized settings. We offer practical methods and recommendations for designing social learning processes as well as highlight the need to better prepare environmental and extension scientists for engaging in agribusiness spaces.

Dominant forms of agricultural production in the U.S. Upper Midwest are undermining human health and well being. Restoring critical ecosystem functions to agriculture is key to stabilizing climate, reducing flooding, cleaning water, and enhancing biodiversity. We used simulation models to compare ecosystem functions (food-energy production, nutrient retention, and water infiltration) provided by vegetation associated with continuous corn, corn-soybean rotation, and perennial grassland producing feed for dairy livestock. Compared to continuous corn, most ecosystem functions dramatically improved in the perennial grassland system (nitrate leaching reduced ~90%, phosphorus loss reduced ~88%, drainage increased ~25%, evapotranspiration reduced ~29%), which will translate to improved ecosystem services. Our results emphasize the need to incentivize multiple ecosystem services when managing agricultural landscapes.

There is an emerging need to estimate and verify soil carbon credits attributed to conservation tillage and prairie restoration in the Midwestern U.S. for the developing global carbon market. However, current soil sampling strategies may need to be augmented by empirical modeling to minimize costs while covering larger regions. Models were constructed relating soil bulk density (BD) to soil organic carbon (SOC) and total nitrogen (TN) concentrations using 146 sites in southern Wisconsin under varied land use to determine whether empirical models could reliably predict BD in an effort to support estimates of SOC sequestration for future carbon crediting programs. As expected, a significant exponential relationship resulted between %SOC and BD (R 2 = 0.90; P < 0.0001) across all sites. Exponential models were then constructed after categorizing data into undisturbed ecosystems, prairie restorations, and croplands, and showed that the correlation between observed and predicted BD values, along with model parameters, were quite different. Predicted values were most correlated to observed values for undisturbed sites (R 2 = 0.90), less correlated with prairie restorations (R 2 = 0.49), and the least correlated with croplands (R 2 = 0.25). This suggests that highly intensified crop management practices influence BD in a way that might make using %SOC or %TN as single predictor variables unreliable. It is suggested that models relating BD and soil chemical properties should consider the varied effects of land-use management over many different soil textures, particularly for the determination of carbon credits on agricultural land in temperate climate regions.

Structures from the Stone Age can provide unique insights into Late Glacial and Mesolithic cultures around the Baltic Sea. Such structures, however, usually did not survive within the densely populated Central European subcontinent. Here, we ...The Baltic Sea basins, some of which only submerged in the mid-Holocene, preserve Stone Age structures that did not survive on land. Yet, the discovery of these features is challenging and requires cross-disciplinary approaches between archeology and ...

Abstract Urban heat islands (UHIs) may increase the likelihood that frost sensitive plants will escape freezing nighttime temperatures in late spring and early fall. Using data from 151 temperature sensors in the Madison, Wisconsin, region during March 2012–October 2016, we found that during time periods when the National Weather Service (NWS) issued freeze warnings (threshold of 0.0°C) or frost advisories (threshold of 2.22°C) were valid, temperatures in Madison’s most densely populated, built-up areas often did not fall below the respective temperature thresholds. Urban locations had a mean minimum temperature of 0.72° and 1.39°C for spring and fall freeze warnings, respectively, compared to −0.52° and −0.53°C for rural locations. On average, 31% of the region’s land area experienced minimum temperatures above the respective temperature thresholds during freeze warnings and frost advisories, and the likelihood of temperatures falling below critical temperature thresholds increased as the distance away from core urban centers increased. The urban–rural temperature differences were greatest in fall compared to spring, and when sensor temperatures did drop below thresholds, the maximum time spent at or below thresholds was highest for rural locations during fall freeze warnings (6.2 h) compared to urban locations (4.8 h). These findings potentially have widely varying implications for the general public and industry. UHIs create localized, positive perturbations to nighttime temperatures that are difficult to account for in forecasts; therefore, freeze warnings and frost advisories may have varying degrees of verification in medium-sized cities like Madison, Wisconsin, that are surrounded by cropland and natural vegetation. Significance Statement The purpose of this study was to understand whether the urban heat island effect in Madison, Wisconsin, creates localized temperature patterns where county-scale frost advisories and freeze warnings may not verify. Approximately one-third of Madison’s urban core area and most densely populated region experienced temperatures that were consistently above critical low temperature thresholds. This is important because gardening and crop management decisions are responsive to the perceived risk of cold temperatures in spring and fall that can damage or kill plants. These results suggest that urban warming presents forecast challenges to the issuance of frost advisories and freeze warnings, supporting the need for improved numerical weather prediction at higher spatial resolution to account for complex urban meteorology.

The Wisconsin Central Sands is home to large scale vegetable production on sandy soils and managed with frequent irrigation, fertigation, and widespread nitrogen fertilizer application, all of which make the region highly susceptible to nitrate loss to groundwater. While the groundwater is used as the primary source of drinking water for many communities and rural residences across the region, it is also used for irrigation. Considering the high levels of nitrate found in the groundwater, it has been proposed that growers more accurately account for the nitrate in their irrigation water as part of nitrogen management plans. Our objectives were to 1) determine the magnitude of nitrate in irrigation water, 2) quantify the spatiotemporal variability of nitrate, and 3) determine key predictors of nitrate concentration in the region. We sampled irrigation water from 38 fields across six farms from 2018 to 2020. Across the 3 years of our study, nitrate concentration varied more across space than time. On average, our samples were tested at 19.0 mg L-1 nitrate-nitrogen, or nearly two times the U.S. Environmental Protection Agency (EPA) threshold for safe drinking water, equivalent to 48.1 kg ha-1 of applied nitrate-nitrogen with 25.4 cm (or 10 in.) of irrigation. To better understand the spatiotemporal variability in nitrate levels, week of sampling, year, well depth, well casing, and nitrogen application rate were analyzed for their role as predictor variables. Based on our linear mixed effects model, nitrogen application rate was the greatest predictor of the nitrate concentration of irrigation water (p < 0.05).

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and there has been a growth in the number of studies measuring intra-urban temperature variability. Recognizing that the physiological effects of heat depend on humidity as well as temperature, some of these measurement campaigns have included measurements of relative humidity alongside temperature. Reported analyses, however, have not reported whether spatial structure in humidity, independent from temperature, contributes significantly to intraurban heat variability. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the USA to examine this issue. It is shown that although there are spatial variations in relative humidity there are only very weak spatial variations in the absolute humidity within these cities. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidex, and the spatial variability of the heat metrics is dominated by temperature variability. A practical consequence of this is that a network of sensors that measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Crop production would decline in the Midwestern U.S. from climate change following a regional nuclear conflict between India and Pakistan. Using Agro-IBIS, a dynamic crop growth model, we simulated the response of maize and soybeans to cooler, drier, darker conditions from war-related smoke. We combined observed climate conditions for Iowa, Illinois, Indiana and Missouri with output from a general circulation simulation that injected 5 Tg of elemental carbon into the upper troposphere. Both maize and soybeans show notable reductions for a decade after the event. Maize yields would decline 10-40 percent while soybeans would drop 2-20 percent. Temporal variation in magnitude of yield for both crops generally follows the variation in climatic anomalies; greatest decline is in the five years following the 5 Tg event, then gradually levels out. Yield decline for both crops appears linked to changes in growing period duration and, less markedly, to reduced precipitation and altered maximum daily temperature during growth. Seasonal average of daily maximum temperature difference between the control and nuclear scenarios has a quadratic relationship to yield differences; extreme changes create increased yield loss, but mean changes are neutral.

The Early Anthropogenic Hypothesis posits that carbon emissions from ancient farming caused global warming by raising greenhouse gas concentrations (GHG) during the late-Holocene, in contrast to declining GHG during prior interglacials. Here, we explore whether this hypothesized pre-industrial anthropogenic climate change also fostered agriculture by creating more favorable growing conditions. We investigate this question using transient GCM experiments and the Cultivation Suitability Index, CSI, which quantifies farming potential based on climatic and soil factors. The Community Earth System Model (CESM) simulated the climate of the last 6000 years under two alternative forcing scenarios: (1) ACTUAL: orbital variations, historical land cover change, and observed GHG increase; and (2) NATURAL: orbital variations, fixed (natural) land cover, and expected natural GHG decline. The CSI was computed using CESM model output and observed soil properties. Ancient land clearance affected the simulated climate both biogeochemically (via carbon emissions) and biogeophysically (altered surface albedo and land-atmosphere energy fluxes). Biogeochemical effects generally dominated, as evidenced by a warmer (and slightly wetter) global climate in ACTUAL versus NATURAL by year 1850. But a few regions were cooler in ACTUAL, especially interior Eurasia during winter-spring, due to a higher surface albedo from cropland. The expansion of agriculture generally mitigated the orbitally induced decline in cultivation potential in boreal extratropics but worsened it in low latitudes. Our results suggest that ancient farming may have thus promoted a “push-pull” migration during the late-Holocene by inducing climate changes that encouraged a northward spread of agriculture.

Scully et al [1] in their recent contribution review and revise past life cycle assessments (LCAs) of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (Figure 1). This LUC estimate represents the midpoint of (i) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (ii) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review [2] that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of affecting perverse policy outcomes.

Projected changes of nine ecosystem services at 220-m×220-m from 2010 to 2070 under four contrasting scenarios in the 1345-km²Yahara Watershed (Wisconsin, USA) using Agro-IBIS, a dynamic model of terrestrial ecosystem processes, biogeochemistry, water and energy balance. Please refer to the meta-data page of this data for additional details.

Nitrate leached from agricultural fertilizer has created a host of environmental problems (Tilman et al. 2002). Improving nitrogen management can decrease its harmful effects on the environment (Socolow 1999). However, behavioral change rarely takes place automatically. Interventions are necessary to induce or require polluters to internalize the cost of pollution (Shortle and Horan 2017). Various instruments have been considered such as taxes on chemical fertilizer, subsidies for conservation practices, and regulatory restrictions to reduce the over-use of nitrogen. Some of these interventions can be expensive and have been increasingly criticized as inefficient or ineffective due to the one-size-fits-all approach to achieve the specified goals (Ribaudo 2011). With the assistance of spatially explicit data that identify the locations with the greatest potential for reducing nitrate leaching at least cost, targeted policy measures may substantially improve the cost-effectiveness of abatement efforts (Konrad et al. 2014). This paper aims to assess the impacts of a variety of such policy measures on agriculture in the U.S.

Mitigating the Hypoxia problem in the Gulf of Mexico has proven to be a challenging task. A variety of abatement options have been proposed, each likely to produce markedly different patterns of abatement with widely varying consequences for the economy. The general consensus is that no single measure alone is sufficient to achieve EPA Task Force goal for reducing the Gulf hypoxic zone and it appears that a combination of management practices must be combined. However, absent a highly resolved, multi-scale framework for assessing these policy combinations, it has been unclear what pattern of mitigation is likely to emerge from different policies and what the consequences would be for land use, food prices and farm returns. We address this research gap by utilizing a novel, multi-scale framework for evaluating alternative Nitrogen loss management policies in the Mississippi River Basin. This combines fine-scale agro-ecosystem responses with domestic and international market and price linkages. We find that wetland restoration combined with improved nitorgen use efficiency, along with a leaching charge could reduce the Mississippi River nitrogen load by 32\% while only modestly increasing the corn prices. This study underscores the value of fine-resolution analysis and the potential of combined economic and ecological instruments in tackling non point source nitrate pollution.

Scully et al [1] in their recent contribution review and revise past life cycle assessments (LCAs) of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (Figure 1). Their LUC estimate represents the midpoint of (i) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (ii) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review [2] that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of affecting perverse policy outcomes.