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The US Midwest encompasses one of the largest intensive maize (Zea mays L.) production environments in the world. Managing these lands in a more sustainable way is essential to reducing environmental stresses. This study explores the potential of Adapt-N, a dynamic biogeochemical model, to more precisely manage N inputs compared to a static N management approach, the Maximum Return to N (MRTN). Data from 16 multiple N rate trials conducted over two years (2013–2014) in three Midwest states were used to reconstruct two yield response functions: quadratic (QD) and linear-plateau (LP), allowing estimation of the Economic Optimal N Rate (EONR), and yields resulting from Adapt-N and MRTN recommendations. Model-based N rates were better correlated with the EONR based on the LP function, and were similar based on the QD function. Applying a dynamic approach to N recommendations allowed a significant reduction in applied N (averaging 28 kg ha−1; 13%) without compromising yield, thereby maintaining farmer’s profits while reducing simulated environmental N losses. Longer-term simulations showed that the largest reductions in N rates by Adapt-N compared to the MRTN occurred in dry seasons when early season N losses were small. This study shows that model-based N recommendations can have both economic and environmental benefits compared to a static N management approach.

Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that nitrogen balance—the difference between nitrogen inputs and nitrogen outputs in an agricultural production system—is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them.

N itrogen (N) is an essential nutrient for crop production, and the manufacture of synthetic N fertilizer has facilitated impressive advances in food production that have assisted in the avoidance of widespread hunger despite a rapidly growing twentieth century global population. However, this nutrient also causes major environmental concerns, including nitrate (NO3−) contamination of groundwater in agricultural regions and hypoxia and dead zones in estuaries. Also, gaseous forms of N contribute to air quality issues. Nitrous oxide (N2O), a potent greenhouse gas, contributes to climate change, and ammonia (NH3) emissions contribute to fine particulate air pollution, a cause of respiratory diseases (Pinder et al. 2007). Anthropogenic N pollution, therefore, has large social costs, with estimated potential ecosystem and health damages of US$157 billion annually (Sobota et al. 2015). Despite many mitigation efforts, the N loss from agricultural systems continues to be a major concern, and in 2017 the hypoxic zone in the Gulf of Mexico reached the greatest extent ever recorded (NOAA 2017). There is a pressing need to improve the management of agricultural N, and the 4R concept has been proposed as a conceptual path toward more efficient N management (Snyder et al. 2009; TFI 2017). This approach promotes field…

Here we study the soilscape (soil-landscape) evolution of a field site in the semiarid zone of Israel. This region, like similar regions around the world, was subject to intensive loess accumulation during the Pleistocene and early Holocene. Today, hillslopes in this region are dominated by exposed bedrock with deep loess depositions in the valleys and floodplains. The drivers and mechanism that led to this soilscape are unclear. Within this context, we use a soilscape evolution model (mARM5D) to study the potential mechanisms that led to this soilscape. We focus on advancing our conceptual understanding of the processes at the core of this soilscape evolution by studying the effects of fluvial and diffusive sediment transport mechanisms, and the potential effects of climatic and anthropogenic drivers. Our results show that, in our field site, dominated by aeolian soil development, hillslope fluvial sediment transport (e.g., surface wash and gullies) led to downslope thinning in soil, while diffusive transport (e.g., soil creep) led to deeper and more localized soil features at the lower sections of the hillslopes. The results suggest that, in this semiarid, aeolian-dominated and soil-depleted landscape, the top section of the hillslopes is dominated by diffusive transport and the bottom by fluvial transport. Temporal variability in environmental drivers had a considerable effect on soilscape evolution. Short but intensive changes during the late Holocene, imitating anthropogenic land use alterations, rapidly changed the site's soil distribution. This leads us to assume that this region's soil-depleted hillslopes are, at least in part, the result of anthropogenic drivers.

Environmental nitrogen (N) losses (e.g., nitrate leaching, denitrification, and ammonia volatilization) frequently occur in maize ( L.) agroecosystems. Decision support systems, designed to optimize the application of N fertilizer in these systems, have been developed using physically based models such as the Precision Nitrogen Management (PNM) model of soil and crop processes, which is an integral component of Adapt-N, a decision support tool providing N fertilizer recommendations for maize production. Such models can also be used to estimate N losses associated with particular management practices and over a range of current climates and future climate projections. The objectives of this study were to update the PNM model to include an option for simulating soil-water processes in artificially drained soils, and to calibrate the revised PNM model and test it against multiyear field studies in New York and Minnesota with different soils and management practices. Minimal calibration was required for the model. Denitrification rate constants were calibrated by minimizing the error between simulated and observed nitrate leaching for each study site. The normalized root mean squared error of cumulative daily drainage for the validation sets ranged from 10 to 23%. For cumulative daily nitrate leaching, the normalized root mean squared error ranged from 11 to 28% for the validation sets. The minimal calibration required and relatively simple data inputs make the PNM model a broadly applicable tool for simulating water and N flows in maize systems.

Scale dependence of Hortonian rainfall-runoff processes has received much attention in the literature but has not been fully resolved. To further explore this issue, a recently developed model was applied to simulate rainfall-infiltration-runoff processes at multiple spatial scales. The model consists of the coupling between a two-dimensional runoff routing module and a two-layer infiltration module, thus accounting for spatial variability in soil properties, soil surface sealing, topography, and partial vegetation cover. A 76 m2 semiarid experimental plot with sparse cover of vegetation patches and a sealed soil surface in inter-patch bare areas was used as a representative elementary area (REA). A series of four larger artificial plots of different areas was created based on this REA to examine the scale dependence of rainfall-runoff relationships in the case of stationary heterogeneity. Results show that runoff depth (or runoff coefficient) decreases with increasing scale. This trend is more prominent at scales less than 10 times the REA length. Power law relationships can quantitatively describe the scaling law. The major mechanism of the scale effect is run-on infiltration. However, rainfall intensity and soil properties can both affect the scaling trend through their interaction with run-on. Higher intensity and less temporal variability of rainfall can both reduce the scale effect. Temporally intermittent rainfall may produce spatially oscillating infiltration rates at large scales. Vegetation patterns are another factor that may affect the scaling. Random-vegetation patterns, compared with regular patterns with similar statistical properties, change the spatial distributions, but do not significantly change either the total amount and statistical properties of infiltration and runoff or the scale dependence of the rainfall-runoff process.

Vegetation cover in dry regions is a key variable in determining desertification. Soils exposed to rainfall by desertification can form physical crusts that reduce infiltration, exacerbating water stress on the remaining vegetation. Paradoxically, field studies show that crust removal is associated with plant mortality in desert systems, while artificial biological crusts can improve plant regeneration. Here it is shown how physical crusts can act as either drivers of or buffers against desertification depending on their environmental context. The behavior of crusts is first explored using a simplified theory for water movement on a uniform, partly vegetated slope subject to stationary hydrologic conditions. Numerical model runs supplemented with field data from a semiarid Long-Term Ecological Research site are then applied to represent more realistic environmental conditions. When vegetation cover is significant, crusts can drive desertification, but this process is potentially self-limiting. For low vegetation cover, crusts mitigate against desertification by providing water subsidy to plant communities through a runoff-runon mechanism.

Soil surface sealing is a widespread natural process occurring frequently in bare soil areas between vegetation patches. The low hydraulic conductivity that characterizes the seal layer reduces both infiltration and evaporation fluxes from the soil, and thus has the potential to affect local vegetation water uptake (VWU). This effect is investigated here using experimental data, 2-D physically based modeling, and a long-term climatic data set from three dry sites presenting a climatic gradient in the Negev Desert, Israel. The Feddes VWU parameters for the dominant shrub at the study site (Sarcopoterium spinosum) were acquired using lysimeter experiments. The results indicate that during the season surface sealing could either increase or decrease VWU depending on initial soil water content, rainfall intensity, and the duration of the subsequent drying intervals. These factors have a marked effect on interannual variability of the seal layer effect on VWU, which on average was found to be 26% higher under sealed conditions than in the case of unsealed soil surfaces. The seal layer was found to reduce the period where the vegetation was under water stress by 31% compared with unsealed conditions. This effect was more pronounced for seasons with total rainfall depth higher than 10 cm/yr, and was affected by interseasonal climatic variability. These results shed light on the importance of surface sealing in dry environments and its contribution to the resilience of woody vegetation.

Aeolian-derived soils are found throughout the world. Soil evolution processes in aeolian-dominated landscapes differ from processes in bedrock-weathering landscapes by a number of key aspects including the lack of (1) soil production depth dependency, (2) surface armoring, and (3) grain size self-organization in the soil profile. We use here a soil evolution model (mARM5D) to study the differences between aeolian and bedrock-weathering-dominated landscapes by analyzing soil evolution on a hillslope under various aeolian and bedrock-soil supply settings subject to fluvial and diffusive sediment transport. The model simulates spatial and temporal variation in soil particle size distribution (PSD) and profile depth for each grid cell on the landscape, as a function of physical weathering, aeolian deposition, and diffusive and fluvial sediment transport. Our results indicate that surface armoring plays a major role in soil evolution. Under bedrock-weathering-dominated conditions, armoring reduces soil erosion and in conjunction with depth-dependent soil production, leads to steady state soil grading and depth and a relatively uniform soil distribution. In contrast, aeolian-dominated landscapes tend to have considerable spatial variability in soil depth and PSD. Our results also indicate that in contrast with diffusive transport, which is assumed to be PSD independent, fluvial sediment transport is strongly influenced by the soil production mechanism (aeolian or bedrock weathering). Based on the results presented here, we propose that aeolian-dominated landscapes are more responsive to environmental changes (e.g., climatic and anthropogenic) compared with bedrock-weathering landscapes. We further propose that this sensitivity may help explain the patchy soil distribution that is often observed in aeolian-dominated regions.

Soil water content (SWC) is a key factor affecting spatio-temporal dynamics of vegetation structure and resilience. In semi-arid environments, seal formation at the surface of bare soils is a widespread phenomenon that significantly affects flow processes and consequently, the SWC regime. However, very little knowledge currently exists regarding the way surface sealing modulates SWC temporal variability during different rainfall regimes and time scales. This study uses a physically based and spatially explicit model at the hillslope scale to quantify the effect of soil surface sealing on temporal variability of SWC for a variety of rainfall events and different time scales. Specifically, we focus on the relation between the mean SWC at its temporal variability at different hillslope locations, and the factors affecting it. Surface sealing was found to significantly affect SWC temporal variability at all studied time scales. The relation between SWC mean value and its temporal variability was found to be negative at the rainfall event timescale, and to shift to positive at the seasonal and decadal timescales. At the rainfall event scale, in both sealed and unsealed systems, maximal temporal variability could be found at the upper interfluvial areas of the hillslope. On longer time scales, hillslope locations with maximal temporal variability depend on the characterization of the soil surface, i.e., sealed or unsealed. Considering water to be a limiting factor in dry environments, persistent higher SWC values and lower temporal variability in the presence of a seal layer might favor local vegetation establishment.

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses. Static Stanford-type N recommendation tools are typically promoted in the United States, but new dynamic model-based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt-N, a dynamic simulation tool that combines soil, crop, and management information with real-time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N-rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower-estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site-specific conditions, the Adapt-N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha⁻¹). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability.

Many mathematical models have been proposed to explain the emergence of vegetation patterns in arid and semiarid environments, but only a few of them take into account the heterogeneity in the system properties. Here we present a rigorous study of the effects of heterogeneous soil-water diffusivity on vegetation patterns, using two mathematical models. The two models differ in the pattern-forming feedback that they capture; one model captures the infiltration contrast between vegetated and bare-soil domains, whereas the other model captures the increased growth rate of denser vegetation due to an enhanced ability to extract water from the soil. In both models, the most significant effect of the heterogeneity on the soil-water diffusivity is the increased durability of patterned vegetation to a reduced precipitation rate. An additional effect is that the heterogeneity makes the desertification process, namely, the transition from a spotted vegetation pattern to a bare-soil state, more gradual than in the homogeneous system. Our findings suggest that the heterogeneity cannot be neglected in the study of critical transitions in heterogeneous ecosystems and, particularly, in the study of the desertification process due to climate changes or anthropogenic disturbances.

Gains in nitrogen use efficiency in the production of corn (Zea mays L.) remain low due to management constraints and difficulties in accurately predicting the optimum fertilizer application rate. Retailers and consumers are looking for robust sustainability indicators to help drive the industry towards more sustainable food production, including the simple input-output based ‘N balance’ metric. Seven-year simulations for 25 locations across five US Corn Belt States (NE, IA, MN, IL, IN) were conducted using the biogeochemical Adapt-N® model to determine (i) realistically achievable N balance values when N rates are optimized, (ii) the effects of climate and soil type on achievable N balance values, and (iii) the relative importance of N application timing (fall, spring, split in-season) and formulation (+/− nitrapyrin) in reducing N balance. Split in-season applications reduced N rates by 39% and 22% over fall and spring applications and N balance by 36% and 22%, respectively. Adding nitrapyrin to fall or spring preplant applications modestly reduced N inputs by 9% and 4% and N balance by 18% and 12%. Split N management reduced N losses by 52% and 31% of total area-scaled N losses compared to fall and spring N applications and adding nitrapyrin by 13% and 10%, respectively. Benefits from improved timing and formulation were greater in the more humid eastern part of the region. Split in-season N management allows farmers to reach sustainable N balance levels in 88% of cases, with the remainder mostly affected by mid-season droughts. Economic assessment found partial profit to be enhanced with lower N balance, suggesting that N balance reductions may be achieved through voluntary approaches. The model simulations offered ranges of realistic N balance values that can be used to inform policy discussions. It appears that N balance is best applied when averaged over multiple seasons and threshold levels should be guided by characteristics of the production environment, including soil type and climate.

[1] The hydrological response of semiarid watersheds to short but intense rainfall events is complex due to feedbacks among water fluxes, vegetation patches, topography, and soil properties. This paper seeks to quantify the combined impact of soil-surface sealing, microtopography, and vegetation patches on surface hydrologic processes on a semiarid hillslope. A modeling approach using a two-layer conceptual infiltration model and two-dimensional surface runoff model was developed to study rainfall-runoff relationships. This combined model is applicable to heterogeneous areas with spatially varying soil properties, landscape, and land-cover characteristics. Data from a semiarid site in southern Israel was used to evaluate the model and analyze fundamental hydrologic mechanisms. Our results indicate that seal layer, microtopography, and vegetation play important roles in dry land runoff processes: seal layer controls runoff generation; vegetation patches affect overland flow by enhancing local infiltration rates; microtopography has a small impact on the total amount of runoff, but shapes the spatial pattern of overland flow. The presence of vegetation patches amplifies the effect of microtopography by increasing spatial variability of infiltration and runoff. Results also show that water resource allocation favors vegetation patches through surface runoff, with maximum local cumulative infiltration one order of magnitude higher than the amount of water available from rainfall. This suggests a sophisticated system of water resource distribution in semiarid ecosystems through complex interactions among environmental factors.

The US Midwest is the largest and most intensive corn (Zea mays, L.) production region in the world. However, N losses from corn systems cause serious environmental impacts including dead zones in coastal waters, groundwater pollution, particulate air pollution, and global warming. New approaches to reducing N losses are urgently needed. N surplus is gaining attention as such an approach for multiple cropping systems. We combined experimental data from 127 on-farm field trials conducted in seven US states during the 2011–2016 growing seasons with biochemical simulations using the PNM model to quantify the benefits of a dynamic location-adapted management approach to reduce N surplus. We found that this approach allowed large reductions in N rate (32%) and N surplus (36%) compared to existing static approaches, without reducing yield and substantially reducing yield-scaled N losses (11%). Across all sites, yield-scaled N losses increased linearly with N surplus values above ~48 kg ha−1. Using the dynamic model-based N management approach enabled growers to get much closer to this target than using existing static methods, while maintaining yield. Therefore, this approach can substantially reduce N surplus and N pollution potential compared to static N management.

[1] Spatial and temporal variability of water content in the upper soil layer, close to the surface, affects the intensity of hydrological processes. The impact of accounting for soil surface sealing on the spatial and temporal variability of the water content at the hillslope scale was studied in a semiarid environment. Relevant physical properties of the experimental site were derived by means of extensive field surveys, resulting in a detailed database used to characterize the 8240 cells used to represent the hillslope domain. The simulated spatial and temporal water content variability was achieved by aggregating a numerical 1-D simulation at each of these cells. Accounting for surface sealing improved water content predictions, more efficiently during the drying regime. Furthermore, extensive synthetic simulations show the sealed layer to be a highly efficient mechanism reducing temporal water content variability, compared to an unsealed system. It was also found that reduced evaporation in the sealed domain compensates for the loss in infiltrated water due to runoff enhancement. Depending on rainfall intensity and soil depth, a transition could occur from a positive feedback mechanism where the seal layer suppresses evaporation and conserves water stored in the profile to a negative one where the seal layer mainly reduces infiltration and increases water losses through runoff. Thus, the sealing process was found to substantially affect water budgets at all observed scales in the experimental site.