Search

Climate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean (Zea mays L. and Glycine max Merr.) rotation and an alternative perennial miscanthus (Miscanthus × giganteus Greef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (

HighlightsModel equifinality incurs user-driven decisions when modeling with AnnAGNPS.Prairie pothole models should be calibrated using a limited parameter search space.LiDAR-derived surface areas best represent prairie pothole hydrology in AnnAGNPS.Modeling field-scale prairie pothole spill-and-fill dynamics is currently limited.Abstract. The Annualized Agricultural Non-Point Source (AnnAGNPS) model was used to predict water volume in individual prairie potholes within the Des Moines Lobe of Iowa. AnnAGNPS can represent prairie potholes as static wetland areas located on preferential flow paths at field scale, whereas standard practice of watershed modeling is to fill depressions while preparing input data, and whereas models have lumped parameters at scales too large for individual pothole representation, or are considered too complex for widespread implementation. Here, we assess how key user inputs affect hydrologic calibration of 6 AnnAGNPS prairie pothole models, suggest methods for calibrating and modeling the impact of some potholes at field scale, and explain challenges in modeling prairie potholes with AnnAGNPS. Results indicate that user-defined pothole surface area does not significantly affect statistical calibration of the model. Nash-Sutcliffe Efficiency 0.26 to 0.79, R2 ranged from 0.37 to 0.80 and RSR ranged from 0.46 to 0.86. The area that best simulates pothole hydrology and expected physical parameters is derived from LiDAR and is considered the maximum extent of the pothole. Potholes were calibrated using a stepwise, iterative, limited search space method using multiple representations of their inundation extent. We graphically assessed statistical parameters to identify calibrated models and chose input parameters within reasonable ranges of published literature. This helped formulate our recommendation for the modeled pothole area. Finally, AnnAGNPS is limited in its ability to simulate field-scale pothole networks due to the placement of potholes within the model’s subbasin network. Investigating high-resolution subbasin delineation or more specific wetland placement may alleviate this issue. Keywords: AnnAGNPS, Farmed wetlands, Hydrology, Prairie potholes, Prairie pothole region.

Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (“medial axis”) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution.

ESTIMAÇÃO DO TAMANHO AMOSTRAL NA GEOESTATÍSTICA USANDO UM MODELO DE VARIOGRAMA GAUSSIANO NA PRESENÇA DE OUTLIERS ANDRÉ MENDES1, GERSON RODRIGUES DOS SANTOS2, PAULO CÉSAR EMILIANO3, AMY LEIGH KALEITA4, MATHEUS DE PAULA FERREIRA5 1 Departamento de Estatística, Universidade Federal de Viçosa-UFV, Av. Peter Henry Rolfs, s/n, Campus Universitário, cep: 36570-900, Viçosa-MG, Brasil, e-mail: amendesmat@yahoo.com.br 2 Departamento de Estatística, Universidade Federal de Viçosa-UFV, Av. Peter Henry Rolfs, s/n, Campus Universitário, cep: 36570-900, Viçosa-MG, Brasil, e-mail: gerson.santos@ufv.br 3 Departamento de Estatística, Universidade Federal de Viçosa-UFV, Av. Peter Henry Rolfs, s/n, Campus Universitário, cep: 36570-900, Viçosa-MG, Brasil, e-mail: paulo.emiliano@ufv.br 4 Department of Agricultural and Biosystems Engineering, Iowa State University, 605 Bissel Road, Ames, IA 50011-3270, Estados Unidos, e-mail: kaleita@iastate.edu 5 Departamento de Estatística, Universidade Federal de Viçosa-UFV, Av. Peter Henry Rolfs, s/n, Campus Universitário, cep: 36570-900, Viçosa-MG, Brasil, e-mail: matheus.paula@ufv.br RESUMO: A determinação de um tamanho de amostragem que seja adequado para a reconstrução da população, na análise de dados espaciais, é algo que tem sido estudado em vários trabalhos. Independentemente da área de estudo, qualquer variável pode conter outlier. Conforme sugerido por alguns pesquisadores, no intuito de eliminar tais dados discrepantes, metodologias vêm sendo criadas para atender às demandas das diversas áreas do conhecimento científico. O objetivo deste trabalho é utilizar o teorema da taxa de Nyquist para determinar um tamanho ideal para amostras georreferenciadas contendo outliers, oriundas de uma grade quadrática regular, no qual o modelo de dependência espacial é o Gaussiano. O que se pretende atingir é uma densidade de amostragem necessária para a reconstrução de mapas populacionais de variáveis nas quais as condições de regularidade necessárias em geoestatística foram verificadas. Como resultado pode-se concluir que o tamanho ideal de amostragem obtido na ausência de outliers, 115 pontos, foi bem inferior aos 228 pontos obtidos na presença dos outliers. Palavras-chave: geoestatística, amostragem, taxa Nyquist, outliers. ESTIMATION OF THE SAMPLING SIZE OF GEOSTATISTICS CONSIDERING GAUSSIAN VARIOGRAM MODEL IN THE PRESENCE OF OUTLIERS ABSTRACT: The determination of suitable sample size for population reconstruction in the analysis of spatial data is something that has been studied in several scientific papers. Regardless study area, any variable may contain outlier. As suggested by some researchers, in order to eliminate such discrepant data, methodologies have been created to meet the demands of various scientific areas knowledge. The purpose of this work is use the Nyquist Rate Theorem to determine an ideal size for georeferenced samples containing outliers from a regular quadratic grid in which the spatial dependence model is Gaussian. What we intend to achieve is a sampling density necessary for the population maps reconstruction of variables in which the necessary regularity conditions in Geostatistics were verified. As a result, it can be concluded that, the ideal sampling size obtained in the outliers absence, 115 points, was well below the 228 points obtained in the outliers presence. Keywords: geostatistics, sampling, Nyquist rate, outliers.

This study aims to compare inundation in two potholes using Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) with three Digital Elevation Models (DEMs): a 1 m DEM prepared from t...

The Prairie Pothole Region (PPR) of North America contains millions of shallow wetlands, called potholes, in a landscape that was originally midgrass and tallgrass prairie. Since the 1900s, the prairie pothole landscape has been altered through the installation of subsurface drainage to make the land suitable for agricultural production. Currently, many of these potholes are farmed but they are often areas of poor crop yields because of early growing season flooding. The objective of this study was to investigate the influence of different land use practices on depth, duration, and aerial extent of ponding in two potholes (termed Walnut and Bunny) in central Iowa. Three management scenarios were simulated and inundation levels were compared using the Annualized Agriculture Non-Point Source model (AnnAGNPS) — current: conventionally tilled farmed conditions in corn/soybean rotation with surface inlets in the potholes connecting to a subsurface drainage system; retired: pothole is converted to a mixture of grass, weeds, and low-growing brush, with surface inlets removed and the drainage system underneath the potholes disconnected; and conserved: conservation tillage throughout the field with surface inlets and drainage maintained in potholes. The average annual water depth for the conserved and retired scenarios was 7–8% lower than the average annual water depth for the current scenario. However, for the retired scenario, an increase in inundation was observed in Bunny because disconnecting the surface inlets reduced infiltration in the model. The potholes tend to flood more frequently in the early stages of plant development, which can lead to delayed management operations and potentially reduced yields. Over the 17 year simulation period, Bunny pothole exceeded its maximum volume storage capacity and overflowed five times. This information is important to prioritize areas for and to understand the hydrologic impacts of pothole restoration efforts in intensive agricultural landscapes.

The application of best management practices is a long-term conservation effort in Midwest U.S. croplands, and many farmers have adopted structural conservation practices (SCPs) to reduce soil erosion and surface water runoff, such as terraces and grassed waterways. Despite that, the geographic distribution of these practices is barely known in the region, and mapping initiatives are required to develop timely and spatially explicit inventories of SCP areas. This study presents the first mapping of SCPs in the agricultural areas over 12 Midwest U.S. states. Semantic segmentation model (adapted U-Net) and National Agriculture Imagery Program 2018–2019 data were used to map the SCP areas at 2-m spatial resolution (490.2 billion pixels). In general, mapping results achieved 78.2% overall accuracy across 20 counties. Our results indicate that 52% of SCP areas are distributed over Iowa (26%), Illinois (15%), and Nebraska (11%). In contrast, the states with the lowest SCP areas are Michigan and North Dakota, with less than 4% of SCP areas. Since the SCP extent is also dependent on the number of cropland areas per state, the percentage of SCP per cropland area was calculated. Specifically, the average percentage of SCP area per cropland is ~1.19%, ranging from 0.8 (e.g., North Dakota and south Minnesota) to 5.5% (e.g., northeast Kansas and southwest Iowa). Interestingly, results also illustrate that regions with high soil erosion rates present the largest percentage of SCP areas in croplands, indicating conservation efforts by farmers. While this preliminary analysis shows some limitations in the mapping quality (mislabel, non-accurate location or discontinuity of SCP areas), the framework has a potential for operational conservation monitoring. The development of such mapping has positive implications for conservation programs, and this geospatial inventory is easily accessible information for the large-area evaluation of conservation practices across Midwest U.S. croplands.

HighlightsNitrogen, total phosphorus, and total suspended solids concentrations were higher in early season inundation.Nitrate concentrations in farmed potholes decreased with multiday ponding.Farmed potholes act as hotspots, contributing P to drainage through surface intakes.Abstract. The prairie pothole region ranges from central Iowa to the northwest into Montana and south-central Canada, totaling around 700,000 km2. This area contains millions of potholes, or enclosed topographical depressions, which often inundate with rainfall. Many are located in areas that have been converted to agricultural land through installation of artificial drainage. However, even with drainage, potholes pond or remain saturated during and after significant rain events. In this two-year study, surface water depth was collected hourly (typically from after planting through harvest) from eight farmed potholes (drained and under corn-soybean rotation) on the Des Moines Lobe in central Iowa. Nutrient data were collected daily and tested for nitrogen (N) and phosphorus (P) when inundation depth exceeded 10 cm. The data were analyzed in two ways. First, seasonal differences were investigated using samples from the first day of each inundation event. Surface water concentrations were higher in the early growing season than late season for total N (TN), NO3-N, NH3-N, total P (TP), and total suspended solids (TSS). Secondly, average event concentration changes were determined. Nitrate reductions occurred in 85% of multiday events, but these reductions were offset by increases in P. Total P and dissolved reactive P (DRP) had significant increases that averaged 0.51 and 0.46 mg L-1 per event, respectively, with event lengths of 2 to 19 days. This study demonstrates that inundated farmed potholes reduce NO3-N but serve as in-field hotspots, contributing elevated TP and DRP to drainage waters. When a surface intake directly connects inundated farmed potholes to drainage, new strategies, such as field management or engineered technologies, are needed to mitigate P export. This study is useful in informing policy regarding field management and conservation of farmed potholes. Keywords: Farmed wetland, Nitrogen, Nutrients, Phosphorus, Prairie pothole.

Sobradinho reservoir has been suffering a severe water loss caused by multi-year drought in the Northeast Brazil. This reservoir contributes to the socio-economic development of the semi-arid region, and the monitoring of water shortage is crucial for people living in this climate-vulnerable region. In this study, we evaluate the surface water change and turbidity variability of Sobradinho reservoir during recent drought years (2013–2017). A time-series dataset was created using 109 Landsat-8 OLI images for mapping the water extent in the reservoir. A non-linear regression between measured turbidity and surface reflectance (red band) was developed and applied for turbidity retrievals. Additionally, we performed a long-term precipitation analysis (17-year) to assess the rainfall deficit over the catchment area. Our results show that the annual precipitation regimes are below the long-term average during 2012–2017 period, except 2013. We also found that negative anomalies occur during 26 out of 36 months between 2014 and 2016, mostly in the rainy season. Since the rainfall regimes and river discharges are the major drivers for water recharge, these drought years have a critical impact on the reservoir level. According to our results, the water surface receded about 2073 km2 (out of total 3303 km2) during September 2017; this represents a reduction of 62.8% in the total water extent. The surface water change is spatially distinct across the reservoir. For instance, the upper section of the reservoir was almost totally dried during September 2017, and the water coverage was ~8% (91.25 km2 out of 1128 km2). Although other sections had a relatively low water change (reduction of ~40%), the losses are significant in terms of area (~1035.5 km2). The receding of water extent affects the people living near to the reservoir, and local communities are more distant from water (up to 13 km). We also observed that the turbidity is seasonally dependent, and water clarity presents a strong variability between rainy and dry seasons. In general, the turbidity levels vary from clear water (0–20 NTU) during the dry season to turbid condition (>50 NTU) during the rainy season. A lack of access to clean and safe drinking water in some periods might be harmful to humans, livestock and domestic animals. Finally, this research contributes to the assessment of drought-related impacts in the Sobradinho, the largest reservoir in the Northeast Brazil. The water shortage is a recurring concern in the semi-arid region, and the remote sensing techniques provide spatially explicit information to enhance the livelihood resilience during drought years.

HighlightsFarmed pothole depressions in the Des Moines Lobe were observed to fill due to runoff and shallow subsurface flow.Six of the eight observed potholes flooded for five or more days some time during the two years of observation.Subsurface drainage and surface inlets reduced but did not prevent yield-limiting flooding in the observed potholes. Abstract. The prairie pothole region (PPR) ranges from central Iowa to the northwest into Montana and south central Canada, totaling around 700,000 km2. This area contains millions of potholes, or enclosed topographical depressions, which often inundate with rainfall. Many are located in areas that have been converted to arable agricultural land through installation of artificial drainage. However, even with drainage, potholes will pond or have saturated soil conditions during and after significant rain events. The portion of the PPR that extends into Iowa is known as the Des Moines Lobe. In this two-year study, surface water depth data were collected hourly from eight prairie potholes in the Des Moines Lobe in central Iowa to determine the surface water hydrology. These potholes included surface and subsurface drained row crops and undrained retired land, allowing for drainage comparisons. Inundation lasted five or more days at least once at six of the eight potholes, including four potholes with surface inlets and subsurface drainage, which resulted in four of fourteen growing seasons not producing a yield in part of the pothole. Water balances of four different drainage intensities showed increased infiltration due to subsurface drainage and up to 78% of outflow due to surface inlet drainage. Overall, drainage decreased the number of average inundation days, but heavy precipitation events still caused lengthy inundation periods that resulted in crop loss. Keywords: Farmed wetlands, Prairie pothole, Tile drainage, Water balance.

Closed surface depressions, also known as “potholes” play an important role in the hydrologic cycle and provide multiple environmental services including flood mitigation, water quality improvements and wildlife habitat. In the Prairie Pothole Region, which covers approximately 715,000 km2, including parts of three Canadian provinces (Saskatchewan, Manitoba, and Alberta) and five states in the U.S. (Minnesota, Iowa, North and South Dakota, and Montana), these potholes are typically farmed and are a dominant feature in the landscape. In this study, we evaluate the Annualized Agriculture Non-Point Source (AnnAGNPS) model for simulating the inundation behavior of two farmed potholes, termed Bunny and Walnut, in Prairie Pothole Region (PPR) of Iowa. Performance analysis considered the entire growing season (GS), corresponding to the span in which there was observed data, and only days in which water storage (WS) was observed. Results show that AnnAGNPS predicted pothole water depth acceptably but not pothole water volume because of the model’s inability to accurately represent the depth-volume relationship of a pothole. When calibrated to depth, Nash-Sutcliffe efficiency (NSE) values were 0.77 and 0.24 in the Walnut pothole and 0.56 and 0.30 in the Bunny pothole, for the GS calibration and validation periods, respectively. Our results demonstrate that the AnnAGNPS model can be used to predict the inundation depth of drained and farmed potholes, which is useful for assessing landscape impacts of these features. Appropriate applications of this model could include impact of inundation on crop yield or simulations of alternative farm management strategies to compare water delivery to the potholes.

Farmed prairie potholes are small, isolated depressions frequently classified as semi-permanent wetlands that make up a significant portion of land area in the Des Moines Lobe (DML) of the larger Prairie Pothole Region (PPR). Historically, these depressions have been subjected to significant drainage to improve their agricultural capacity. However, many assessments of the economic return of continuing to farm these depressions suggest that continued attempts to produce conventional row crops is not profitable and has other ecological consequences beyond crop drownout. This study expands the existing discussion of land use and drainage alternatives in a watershed modeling context. This study utilized the Annualized Agricultural Non-Point Source (AnnAGNPS) model to individually simulate the long-term hydrology of 6 prairie potholes using a matrix of land use and drainage modifications. Results suggest the presence of artificial drainage is the dominant factor in prairie pothole hydrology, while retirement and no-till practices can provide moderate reductions in flood inundation. Conservation tillage induces minimal change on flood metrics. Results show that average annual maximum inundated surface area is reduced by at most 50% across all simulations and the median annual days flooded could be reduced by 25 days, though this is less consistent when isolating high-precipitation years. Regardless of drainage status, in all scenarios there are, on average, more than two inundations events per year lasting 2–4 days. Longer events occur approximately once per year on average. Area inundation frequency curves suggest up to a 20% reduction in maximum pothole area inundated annually can be achieved at the 2-year return frequency. The availability of this data helps characterize the hydrology of farmed potholes more generally over a wide range of conditions, providing a reference for the prioritization of potholes for conservation or alternative management.

New spectral absorption photometry methods are introduced to estimate chlorophyll (Chl) content of corn leaves by smart phones. The first method acquires light passing through a leaf by smartphone camera, compensating for differences in illumination conditions. In order to improve performance of the method, spectral absorption photometry (SAP) with background illumination has been considered as well. Data were acquired by smartphone camera in Iowa State University maize fields. Various indices were extracted and their correlation with Chl content were examined by Minolta SPAD-502. Hue index in SAP reached R 2 value of 0.59. However, with light-aided SAP (LASAP), R 2 of 0.97 was obtained. Among traits, the vegetation index gave the most accurate indication. We can conclude that the high performance of LASAP method for estimating Chl content, leads to new opportunities offered by smart phones at much lower cost. This is a highly accurate alternative to SPAD meters for estimating Chl content nondestructively.

Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study. Provavelmente todo pesquisador já se deparou com observações que se "afastam" das demais, sugerindo a existência de inconsistências. O objetivo desse trabalho é propor um novo método de detecção de dados inconsistentes para dados geoespaciais contínuos baseando-se na Geoestatística, independentemente da causa geradora (erros de medição, execução e variabilidade inerente aos dados). A escolha pela Geoestatística está baseada em suas características ideais, como evitar erros sistemáticos, por exemplo. A importância da proposta de um método de detecção de dados inconsistentes está no fato de que alguns métodos existentes utilizados em dados geoespaciais consideram pressuposições teóricas dificilmente atendidas. De igual forma, a escolha do conjunto de dados está relacionada com a importância da tecnologia LiDAR (Light Detection and Ranging) na produção de Modelos Digitais de Elevação (MDE). Assim, com a nova metodologia foi possível detectar e mapear dados discrepantes. Comparando-a com um método muito utilizado de detecção, BoxPlot, verificou-se a importância e funcionalidade do novo método, já que o BoxPlot não detectou nenhum dado como discrepante. O método proposto apontou, em média, 1,2 % dos dados de possíveis outliers inferiores regionalizados e, em média, 1,4 % de possíveis outliers superiores regionalizados em relação ao conjunto de dados estudados.

Best management practices (BMPs) are effective in reducing nutrient and sediment export, but further understanding of the benefits of the stacked BMPs is needed. This catchment-scale study was established to evaluate the impact of hydrology and BMPs on phosphorus (P) and sediment losses. Two adjacent catchments, one with a lower level of BMP adoption (Low-BMP #11) and one with a higher level (High-BMP #12), were compared for total P (TP) and total suspended solids (TSS) export. The BMPs include nutrient management plans, reduced tillage, grassed waterways, terraces, and perennial vegetation. The TP event-flow-weighted (EFW) concentration was significantly higher at Low-BMP #11 (0.293 mg L-1) than at High-BMP #12 (0.069 mg L-1). There was no significant difference in TP base-flow-weighted (BFW) concentrations between Low-BMP #11 (0.035 mg L-1) and High-BMP #12 (0.037 mg L-1). The TSS-EFW (148.0 vs. 18.6 mg L-1) and TSS-BFW (13.3 vs. 6.9 mg L-1) concentrations were also higher at Low-BMP #11 than at High-BMP #12. High-BMP #12 had lower TP (0.36 vs. 0.59 kg ha-1 yr-1) and TSS (253 vs. 1,961 kg ha-1 yr-1) loading than Low-BMP #11. The lower TP export at High-BMP #12 was likely attributed to the effectiveness of stacked erosion control BMPs and nutrient management plans. Overall, lower P and sediment loading was observed when a greater areal extent of stacked practices was implemented at the catchment level. This finding provides vital information to encourage wider BMP adoption at the watershed scale.

The state of Iowa has long been recognized as a significant contributor of nitrogen loads to the Mississippi river basin. The nitrogen loads are mainly in the form of nitrates arising from high yield agriculture and animal agriculture. With excessive water flowing through the water system of Iowa, the surplus nitrogen in the soil gets carried into the Mississippi river basin and ultimately to the Gulf of Mexico, resulting in the generation of a hypoxic zone having a detrimental impact on the environment. Iowa is a leading producer of corn, soybean, animal products, and ethanol; hence, agriculture and animal agriculture are well rooted in its economy. With increasing ethanol demands, high yield agricultural practices, growing animal agriculture, and a connected economy, there is a need to understand the interdependencies of the Iowa food-energy-water (IFEW) nexus. In this work, a model of the IFEW system interdependencies is proposed and used as the basis for a computational system model, which can be used to guide decision-makers for improved policy formation to mitigate adverse impacts of the nitrogen export on the environment and economy. Global sensitivity analysis of the proposed IFEW system model reveals that the commercial nitrogen application rate for corn and corn yield are the critical parameters affecting nitrogen surplus in soil.

Assessing the effectiveness of management strategies to reduce agricultural nutrient efflux is hampered by the lack of affordable, continuous monitoring systems. Generalized water quality monitoring is possible using electrical conductivity. However environmental conditions can influence the ionic ratios, resulting in misinterpretations of established electrical conductivity and ionic composition relationships. Here we characterize specific electrical conductivity (k25) of agricultural drainage waters to define these environmental conditions and dissolved constituents that contribute to k25. A field investigation revealed that the magnitude of measured k25 varied from 370 to 760 µS cm-1. Statistical analysis indicated that variability in k25 was not correlated with drainage water pH, temperature, nor flow rate. While k25 was not significantly different among drainage waters from growing and post-growing season, significant results were observed for different cropping systems. Soybean plots in rotation with corn had significantly lower conductivities than those of corn plots in rotation with soybeans, continuous corn plots, and prairie plots. In addition to evaluating k25 variability, regression analysis was used to estimate the concentration of major ions in solution from measured k25. Regression results indicated that HCO3-, Ca2+, NO3-, Mg2+, Cl-, Na2+, SO42- were the major drainage constituents contributing to the bulk electrical conductivity. Calculated ionic molal conductivities of these analytes suggests that HCO3-, Ca2+, NO3-, and Mg2+ account for approximately 97% of the bulk electrical conductivity. Keywords: Electrical conductivity, Salinity, Subsurface drainage, Total dissolved solids.

Efflux of dissolved solutes in agricultural subsurface drainage systems adversely affects the ecosystems of receiving waters, degrades soil fertility, and represents an economic loss to farmers. These solutes are frequently studied without regard to their associated ions, which play a fundamental role in their transport characteristics. In this study, we conducted a literature review to identify major dissolved constituents in agricultural drainage waters characteristic of central Iowa and pinpointed causes of variability in the leaching rate of these constituents. This literature review is complemented by a thorough field investigation that analyzes major solute concentrations with respect to seasonal conditions, common cropping systems, and relationships among ions. Results from this investigation reveal that primary dissolved constituents consist of bicarbonate, calcium, nitrate, magnesium, chloride, sodium, and sulfate (in order of decreasing ppm concentration). Analysis of seasonal drainage samples showed that bicarbonate, calcium, and magnesium were present at greater concentrations during the post-growing season, while nitrate and chloride concentrations were greatest during the growing season. Seasonal variability of sulfate and sodium was negligible. Continuous corn and corn in annual rotation with soybeans had greater magnesium and chloride concentrations than soybeans in annual rotation with corn. Conversely, calcium concentrations were greater for soybean cropping systems compared to corn cropping systems. Bicarbonate and nitrate were not significantly different among any of the cropping systems. A strong correlation between bicarbonate and calcium suggests that agricultural lime dissolution was caused by mineral weathering, rather than by acidification due to N fertilizer applications or nitrification. An analysis of observed drainage flows, pH, and temperatures suggested that these parameters were not good indicators of differences in ionic composition. Keywords: Bicarbonate, Dissolved ions, Nitrate, pH, Subsurface drainage.

The site-specific nature of P fate and transport in drained areas exemplifies the need for additional data to guide implementation of conservation practices at the catchment scale. Total P (TP), dissolved reactive P (DRP), and total suspended solids (TSS) were monitored at five sites-two streams, two tile outlets, and a grassed waterway-in three agricultural subwatersheds (221.2-822.5 ha) draining to Black Hawk Lake in western Iowa. Median TP concentrations ranged from 0.034 to 1.490 and 0.008 to 0.055 mg P L-1 for event and baseflow samples, respectively. The majority of P and TSS export occurred during precipitation events and high-flow conditions with greater than 75% of DRP, 66% of TP, and 59% of TSS export occurring during the top 25% of flows from all sites. In one subwatershed, a single event (annual recurrence interval < 1 yr) was responsible for 46.6, 84.0, and 81.0% of the annual export of TP, DRP, and TSS, respectively, indicating that frequent, small storms have the potential to result in extreme losses. Isolated monitoring of surface and drainage transport pathways indicated significant P and TSS losses occurring through drainage; over the 2-yr study period, the drainage pathway was responsible for 69.8, 59.2, and 82.6% of the cumulative TP, DRP, and TSS export, respectively. Finally, the results provided evidence that particulate P losses in drainage were greater than dissolved P losses. Understanding relationships between flow, precipitation, transport pathway, and P fraction at the catchment scale is needed for effective conservation practice implementation. Iowa Department of Natural Resources [14ESDWQBCIKEN-0001]; USEPA; Iowa Water Center Funding for this project was provided by Iowa Department of Natural Resources contract 14ESDWQBCIKEN-0001, USEPA Section 319, and the Iowa Water Center. The authors would like to thank T.J. Lynn, the watershed coordinator for the Black Hawk Lake Watershed Project; Carl Pederson for assistance with field instrumentation; and Katherine van der Woude for assistance with sample collection and analysis.

Grassed waterways (GWWs) are an effective conservation practice for preventing ephemeral gully erosion resulting from channelized surface runoff in agricultural fields. However, field reconnaissance to identify areas of channelized erosion within a watershed can be time-consuming and labor-intensive. Recent advances in precision conservation and light detection and ranging (LiDAR) technologies can provide valuable information on environmentally sensitive areas that cause soil degradation. The objective of this study was to demonstrate that a compound topographic index (CTI) model supplemented with LiDAR data can be used to identify potential GWW locations and inform design recommendations. A LiDAR digital elevation model with a spatial resolution of 3 m was used to derive terrain attributes (slope, drainage area, and plan curvature). The GWW identification and design process was automated in the ArcGIS Python environment. The plan curvature identified erosion channels, but discontinuity in the model output was observed. The CTI model was calibrated to a field with GWWs installed under USDA-NRCS guidelines, which yielded a CTI threshold of 30. The calibrated model (CTI ≥ 30) was able to identify all 14 existing GWWs in the watershed. Field surveys were conducted in the watershed, and areas exhibiting evidence of channelized erosion were identified by the model for GWW placement. Furthermore, the CTI model overestimated (PBIAS = -23.34%) the lengths of predicted GWWs, suggesting a need to further extend the existing GWWs. The total surface area of the predicted GWWs was 29.3 ha in the study watershed, with depth of GWWs reaching 0.3 m. The design process provides an estimate of land to be set aside for conservation practices. The terrain analysis was effective in targeting conservation practice placement and improves the accuracy of field assessments.

In this work, a systematic study of the dielectric properties of environmentally-relevant electrolyte liquids is presented. Excessive amount of unwanted chemicals and ions in water sources can often cause environmental and health concerns. The lack of affordable and real-time sensors for these contaminants limits effective conservation and management strategies. To tackle these problems, we propose a method to exploit indicators extracted from dielectric spectra up to 20 GHz and thereby establish a basis for developing a low-cost sensing system. Results for nitrate, sulfate, and chloride ions show that the method can be judiciously used to uniquely estimate the concentration and type of ions.

As part of a recent workshop entitled "Imagining Tomorrow's University”, we were asked to visualize the future of universities as research becomes increasingly data- and computation-driven, and identify a set of principles characterizing pertinent opportunities and obstacles presented by this shift. In order to establish a holistic view, we take a multilevel approach and examine the impact of open science on individual scholars as well as on the university as a whole. At the university level, open science presents a double-edged sword: when well executed, open science can accelerate the rate of scientific inquiry across the institution and beyond; however, haphazard or half-hearted efforts are likely to squander valuable resources, diminish university productivity and prestige, and potentially do more harm than good. We present our perspective on the role of open science at the university.

Identifying and understanding the impact of field-scale soil moisture patterns is currently limited by the time and resources required to do sufficient monitoring. This study uses K-means clustering to find critical sampling points to estimate field-scale near-surface soil moisture. Points within the field are clustered based upon topographic and soils data and the points representing the center of those clusters are identified as the critical sampling points. Soil moisture observations at 42 sites across the growing seasons of 4 years were collected several times per week. Using soil moisture observations at the critical sampling points and the number of points within each cluster, a weighted average is found and used as the estimated mean field-scale soil moisture. Field-scale soil moisture estimations from this method are compared to the rank stability approach (RSA) to find optimal sampling locations based upon temporal soil moisture data. The clustering approach on soil and topography data resulted in field-scale average moisture estimates that were as good or better than RSA, but without the need for exhaustive presampling of soil moisture. Using an electromagnetic inductance map as a proxy for soils data significantly improved the estimates over those obtained based on topography alone.

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using six landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Overall, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.

This paper compares stakeholder perceptions of uncertainty of the sediment load predictions from each of two modeling methods. A stochastic approach to sediment load prediction using the Water Erosion Prediction Project (WEPP) model provides statistical data sufficient to develop probability distributions. The other method, a traditional deterministic approach called the Modified Universal Soil Loss Equation (MUSLE), computes a sediment load caused by a selected rainfall frequency and date of event. We explain the sediment load prediction methodologies and present results of each approach in the context of the study of stakeholder perceptions of model uncertainty. Three focus groups of stakeholders (science and technical professionals, environmentalists, and producers) agreed that more information provided by the stochastic method was helpful to them as stakeholders for decision making. With advances in computing technology, stochastic models can now be used beyond the research setting and applied to nonpoint source control planning by state agencies. Connecting the natural resource sciences and the social sciences methodologies provided valuable insights into how to better engage stakeholders in understanding and making decisions about soil erosion control.

The objective of this study was to assess the extent to which a common thread exists among all of the ag-based biological systems engineering programs across the U.S. through a course-by-course analysis of individual program curricula. Publically available curricula were used to determine the coursework requirements for 88 unique curricula in the U.S. Due to the lack of standardization of course titles in the discipline, disciplinary courses were grouped into themes, and summary tables showing the distribution of courses by theme in the different curricula were made. In addition, a self-organizing map was made using the categorized data to provide visual mapping of curricular similarity among programs. Results indicate that although all programs require similar basic math, science, and engineering fundamentals, there is wide variety in the discipline-specific requirements. For example, the two most common discipline-specific themes are required by only 61% and 75% of programs (basic engineering applied to agricultural and biological systems, and instrumentation and controls, respectively). Furthermore, results show that the name of the program and/or option generally conveys limited information about the content of the curriculum, although some differentiation between agricultural engineering programs and biological engineering programs is evident.

The slope length and steepness (LS) factor is one of the factors in the Revised Universal Soil Loss Equation (RUSLE) needed to estimate average annual erosion rate. The LS factor is often derived from digital elevation models (DEM). DEM errors and uncertainty could affect LS factor estimation and consequently erosion rate estimation. However, DEM uncertainties are not always accounted for, and the effects are not always evaluated in erosion rate estimation. This study compared the erosion rate estimation of a 62.81 ha agricultural crop area using a 7.5 min USGS DEM and DEMs developed using real-time kinematic differential GPS (RTK-DGPS) and dual-frequency DGPS (DF-DGPS) field surveys. Spatial estimation and uncertainty analysis was carried out using sequential Gaussian simulation (SGS). A total of 50 equiprobable DEM realizations were produced using SGS to assess DEM uncertainty and quantify its effect on erosion rate estimation. DEM uncertainty substantially affected the resulting erosion rate estimation. The uncertainty of the average annual erosion rate estimates across the study field was represented using 95% confidence intervals (CI). For the DF-DGPS and USGS DEMs, the percentages of the field area that have erosion rate CIs greater than 11.21 Mg ha-1 year-1 (5 tons acre-1 year-1) were 81% and 85%, respectively, which were substantially larger than that of the RTK DEM (0.41%). The average annual erosion rate map produced using a USGS DEM contained artifacts and underestimated the erosion rate estimation in many areas of the field. The results suggested that higher-accuracy DEMs generated using RTK-DGPS measurements are more appropriate for erosion rate estimation in an agricultural field. Knowledge of DEM uncertainty and its effect on the erosion rate estimation was useful to better judge the reliability of erosion rate estimates.

Modification of land cover systems is being studied in subsurface drained Iowa croplands due to their potential benefits in increasing soil water and nitrogen depletion thus reducing drainage and NO3–N loss in the spring period. The objective of this study was to evaluate the impacts of modified land covers on soil water dynamics. In each individual year, modified land covers including winter rye–corn (rC), winter rye–soybean (rS), kura clover as a living mulch for corn (kC), and perennial forage (PF), as well as conventional corn (C) and soybean (S), were grown in subsurface drained plots in north-central Iowa. Results showed that subsurface drainage was not reduced under modified land covers in comparison to conventional corn and soybean. Soil water storage (SWS) was significantly reduced by PF treatments during the whole growing seasons and by kC during May through July when compared to the cropping system with corn or soybean only (p

Capacitance type sensors have been widely used to monitor soil water content and salinity, but little is known about their response to specific ions in soil solution. The goal of this laboratory study was to investigate the sensitivity of two capacitance probes, ECH 2 O EC-5 and EC-10, to NO 3 concentration in soil solutions, as well as to understand the differences in sensor response to the presence of other ions in the solutions. Forty-five uniformly packed soil samples were prepared using a homogenized loam soil wetted to five volumetric water contents (VWC) with nine solutions containing different concentrations of NO 3 - , Cl - , or both. A two-stage analysis of the normalized data revealed that ion type and concentration had a significant effect (P ~ 0.01) only for the response of the EC-10 probes operating at a frequency of 5 MHz. The response of the EC-5 probes, operating at 70 MHz, was mainly explained by changes in VWC. Multiple linear regression models fitted to the EC-10 response to individual solutions showed that concentration had a statistically significant predictive value only for samples wetted with NO 3 solutions when temperature was incorporated in the model. The study confirmed that capacitance probes operating at a relatively low frequency of 5 MHz are more sensitive to changes in NO 3 concentration than in Cl. We attributed this effect to the difference in ion mass that manifests through an increase in conductivity at ion-specific frequency. Additional experimental work is needed, however, to accurately define optimal frequencies for measuring ion-induced effects on dielectric measurement and to quantify interrelations between them.

Corn stover is promoted as a readily available feedstock for cellulosic ethanol fermentation, potentially adding value to Iowa9s corn harvest. However, soil productivity and water quality could be adversely affected by poor residue management practices. This paper presents the results of computer simulations of soil erosion by water under various corn stover harvesting and management scenarios applied universally across the state of Iowa. The WEPP (Water Erosion Prediction Project) computer model was used to simulate soil loss in Iowa at 17,848 agricultural point locations of the 1997 National Resources Inventory. Location information at the township level of 9.66 by 9.66 km (6 by 6 mi) for the National Resources Inventory points allows for presentation of results in the form of gridded color-scale maps. The maps indicate corn stover removal risk at the following levels: extreme, high, medium, or low. Risk categories are based on the soil loss tolerance (T) and 1/2 T as constraints. This paper presents simulated impacts of corn stover removal on soil erosion only. Important considerations for maintaining soil organic carbon are reviewed and discussed. The simulation results suggest that the amount of harvestable corn stover is not uniform in Iowa when water erosion control guides management. No-till is necessary to maintain soil loss below permissible levels of T and 1/2 T in regions with steeper slopes. Maps and corresponding analyses in this paper help guide policy pertaining to the harvest of corn stover in Iowa.

The study presents hydrologic simulations assessing the potential impact of climate change on subsurface drainage and its pattern in Iowa’s subsurface drained landscapes. The contemporary (represen...

Knowledge of residue cover is crucial for targeting conservation efforts to reduce soil erosion, runoff, and associated environmental impacts ; however, a rapid, accurate, inexpensive methodology is not currently available. Previous studies have shown mixed results detecting crop residue using Landsat residue indices, but conditions generally included poor soil color contrast, emergent vegetation, or categorized residue cover. Our objectives were to evaluate a new normalized difference residue index (NDRI), along with other indices, over multiple image dates in 2005 and 2006 on dark soils in north-central Iowa. An automated method for field boundary delineation was used. The NDRI, using Landsat Bands 3 and 7, performed best overall, explaining 81% of the residue cover differences overall, 78% before emergence, and 9% after emergence. The normalized difference tillage index (NDTI), using Landsat Bands 5 and 7, also performed well explaining 68% of the variation overall, 86% before emergence, and 6% after emergence. Introduction of an empirical correction of the influence of green vegetation improved index performance. The NDTI outperformed the NDRI after green vegetation correction, explaining 67% of the variation versus 63%. The NDTI also returned the best RMSE (0.11) under preemergence conditions, and 0.15 after green vegetation correction. Generally, indices utilizing Landsat Band 7, which contain lignin and cellulose absorption bands absent in soil, returned the best residue detection results. Indices utilizing Landsat Band 4, where the reflectance of green vegetation is high, had difficulty detecting residue cover, especially after plant emergence.

A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming datasets. Because the DSSAT models are written to simulate crop growth and development within a homogenous unit of land, the Apollo DSS has specialized functions to manage running the DSSAT models to simulate and analyze spatially variable land and management. The DSS has modules that allow the user to build model input files for spatial simulations across predefined management zones, calibrate the models to simulate historic spatial yield variability, validate the models for seasons not used for calibration, and estimate the crop response and environmental impacts of nitrogen, plant population, cultivar, and irrigation prescriptions. This paper details the functionality of Apollo, and presents the results of an example application.