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Cambodia is witnessing a “Goldilocks moment” in demographic change concurrent with shifts in land use, hydrology, and climate. These trends interact and affect food production, food costs, and food security. Drivers of these trends are typically examined separately with interacting factors considered along disciplinary margins. While science models to explore these interacting effects have been proposed, there remains an applied research gap in integrating these pieces and assessing interdisciplinary opportunities for developing food security solutions. Developed following a request from USAID to elucidate food security conditions in Cambodia, here the authors present their geospatial synthesis of the biophysical and socioeconomic drivers of current food security risk, as well as explore future trends for those conditions. The overall structure shows several interlocking or mutually reinforcing trends in systems that point towards a significant intensification of food insecurity in the near future. They offer an assessment of future targets for food systems innovation.

Crop models are widely used to predict plant growth, water input requirements, and yield. However, existing models are very complex and require hundreds of variables to perform accurately. Due to these shortcomings, large-scale applications of crop models are limited. In order to address these limitations, reliable crop models were developed using a deep neural network (DNN) – a new approach for predicting crop yields. In addition, the number of required input variables was reduced using three common variable selection techniques: namely Bayesian variable selection, Spearman's rank correlation, and Principal Component Analysis Feature Extraction. The reduced-variable DNN models were capable of estimating future crop yields for 10,000,000 different weather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables. To establish clear superiority of the methodology, the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance (mRMR). The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals. Specifically, the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly (78.6% accuracy) to the original DNN crop model with 400 neurons in 10 layers, even though the size of the neural network was reduced by 80-fold. This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.

When rainwater harvesting is utilized as an alternative water resource in buildings, a combination of municipal water and rainwater is typically required to meet water demands. Altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. Still, it is unknown if similar reactions occur within building plumbing following a transition in source water quality. The goal of this study was to investigate changes in water chemistry and microbiology at a green building following a transition between municipal water and rainwater. We monitored water chemistry (metals, alkalinity, and disinfectant byproducts) and microbiology (total cell counts, plate counts, and opportunistic pathogen gene markers) throughout two source water transitions. Several constituents including alkalinity and disinfectant byproducts served as indicators of municipal water remaining in the system since the rainwater source does not contain these constituents. In the treated rainwater, microbial proliferation and Legionella spp. gene copy numbers were often three logs higher than those in municipal water. Because of differences in source water chemistry, rainwater and municipal water uniquely interacted with building plumbing and generated distinctively different drinking water chemical and microbial quality profiles.

HighlightsASABE and ISAE convened the Global Water Security Conference for Agriculture and Natural Resources in Hyderabad, India, in 2018.Recommendations represent collective contribution of attendees and presenters in seven key priorities.Continuation of a narrow focus on technical aspects will likely prevent the success of technical solutions.Scientists and engineers should work together across all disciplines and boundaries to ensure global water security. Keywords: Climate change, Crop water productivity, Food security, Irrigation efficiency, Natural resource policy, Wastewater reuse, Water resource infrastructure, Water scarcity.

HighlightsWe provide context and perspectives on 13 articles in the Global Water Security collection.Limited irrigation, precision irrigation, and sustainable water resources management were critical themes.The collection emphasizes the need for adopting location-specific technologies to achieve global water security.Advances in data acquisition, data analysis, and modeling should be utilized to aid managing water resources. Abstract. This article introduces the Global Water Security collection in this issue of Transactions of the ASABE and issue 36(1) of Applied Engineering in Agriculture. Researchers, educators, industry partners, agricultural producers, and policymakers from 19 countries met at Hyderabad, India, to discuss critical issues and advancements at the Global Water Security Conference for Agriculture and Natural Resources. The conference was organized jointly by ASABE and the Indian Society of Agricultural Engineers (ISAE). This special collection consists of 13 articles selected from the 245 meeting presentations as well as invited articles. A perspectives article in this collection summarizes seven key priorities identified for action at the conference: reduce food waste, increase wastewater reuse, increase agricultural resiliency and efficiency, optimize irrigation efficiency and increase crop water productivity, improve water supply management, improve water resource infrastructure, and enhance water resource decision-making and policy formulation. The remaining 12 articles address a wide range of water security topics grouped by four themes: sustainable management of water resources (3 articles), limited irrigation for water conservation (5 articles), precision irrigation management (2 articles), and water management in hilly regions (2 articles). While these articles are not inclusive of all water security challenges in the agriculture and natural resources sectors, they highlight selected important challenges and potential solutions. The research presented in this special collection emphasizes the importance of developing and using appropriate location-specific technologies that increase water application efficiency and water use efficiency while maintaining adequate water supplies for natural resource functions and ecosystem services to ensure global water security. Keywords: Climate change, Crop water productivity, Food security, Irrigation efficiency, Natural resource policy, Wastewater reuse, Water resource infrastructure, Water scarcity.

A rapidly expanding world population and extreme climate change have made food production a crucial challenge in the twenty-first century. Improving crop management could be an effective solution for this challenge. However, due to the associated cost and time to perform field works, researchers are widely rely on agricultural systems modeling to examine the impacts of different crop management scenarios. Meanwhile, due to the complexity of agricultural systems modeling, their applications in producing practical knowledge for producers are limited. Meanwhile, deep learning techniques have been recognized as the preferred method compared to other machine learning techniques, especially when dealing with large datasets. In addition, deep learning techniques are easily adopted by non-experts due to the feature of learning ability that can automatically discover the classifications from raw data. Meanwhile, one of the drawbacks of using deep learning techniques is the training time, which can last anywhere from a couple of weeks to even a few months. Therefore, the goal of this study is to examine the applicability of deep learning techniques to compute a numerical model of crop growth. In this study, an agricultural systems model known as the Decision Support System for Agrotechnology Transfer (DSSAT) is used to evaluate the impacts of irrigation amount and time of application on crop yield. A deep learning network is utilized and trained by incorporating the large amounts of DSSAT models inputs (i.e., precipitation date, precipitation amount, irrigation date irrigation amount) and output (i.e., maize yield at the end of the growing season). However, in order to simplify the process, we combined the amount of irrigation and rainfall together and presented them in the form of the amount of water per day. Experimental results have demonstrated the effectiveness of this proposed deep learning technique in crop yield prediction.

Increasing climate variability and extreme weather events impose significant challenges to the crop production systems throughout the world. Alternative agricultural treatment systems have been proposed to manage these challenges. However, these treatments have not been sufficiently studied for their ability to improve climate resilience, especially in terms of profitability and risk management, which are important metrics of resilience that determine farm-level adaptation. Hence, we evaluated the climate resilience of three alternative agricultural treatments for a long-term (27-years) rotation of corn-soybean-wheat, cast in the temperate humid climate of Southwest Michigan, United States. The three alternative treatments include no-till, reduced input, and US Department of Agriculture (USDA) certified organic. These are compared to the conventional treatment along with the same crop rotation. Means and volatility of expected gross margins and risk preferences were used as the evaluation metrics. Results demonstrate that the net revenues from the USDA certified organic are largely expected to exceed the net revenues of conventional treatment. Also, for all commodities, organic treatment may exert greater annual stability in revenues. The no-till treatment dominates conventional and reduced input practices in expected annual net revenues with a relatively lower risk to those revenues in light of climate extremes. Furthermore, the organic and no-till treatments showed appropriateness to cater to a range of farmers with different risk preferences. Therefore, the organic and the no-till treatments were deemed climate-resilient. The conventional and reduced input treatments did not show resilience thus will be vulnerable to the changing climate. Despite the economic support for adopting resilient practices, growers have been slow to adopt new approaches. We suggest future research needs for understanding grower motivations for adopting climate-resilient practices and consider policy implications.

Aquaculture in Bangladesh has grown dramatically in an unplanned manner in the past few decades, becoming a major contributor to the rural economy in many parts of the country. National systems for the collection of statistics have been unable to keep pace with these rapid changes, and more accurate, up to date information is needed to inform policymakers. Using Sentinel-2 top of atmosphere reflectance data within Google Earth Engine, we proposed six different strategies for improving fishpond detection as the existing techniques seem unreliable. These techniques include: (1) identification of the best time period for image collection, (2) testing the buffer size for threshold optimization, (3) determining the best combination of image reducer and water-identifying indices, (4) introduction of a convolution filter to enhance edge-detection, (5) evaluating the impact of ground truthing data on machine learning algorithm training, and (6) identifying the best machine learning classifier. Each enhancement builds on the previous one to develop a comprehensive improvement strategy called the enhanced method for fishpond detection. We compared the results of each improvement strategy to known ground truthing fishponds as the metric of success. For machine learning classifiers, we compared the precision, recall, and F1 score to determine the quality of results. Among four machine learning methods studied here, the classification and regression trees performed the best with a precision of 0.738, recall of 0.827, and F1 score of 0.780. Overall, the proposed strategies enhanced fishpond area detection in all districts within the study area.

Among livestock systems, grazing is likely to be most impacted by climate change because of its dependency to feed quality and availability. In order to reduce the impact of climate change on grazing livestock systems, adaptation measures should be implemented. The goal of this study is to identify the best pasture composition for a representative grazing dairy farm in Michigan in order to reduce the impacts of climate change on production. In order to achieve the goal of this study, three objectives were sought: (1) identify the best pasture composition, (2) assess economic and resource use impacts of pasture compositions under future climate scenarios, and (3) evaluate the resiliency of pasture compositions. A representative farm was developed based on a livestock practices survey and incorporated into the Integrated Farm System Model (IFSM). For the pasture compositions, four cool-season grass species and two legumes were evaluated under both current and future climate scenarios. The effectiveness of adaptation measures based on economic and resource use criteria was evaluated. Overall, the pasture composition with 50% perennial ryegrass (Lolium multiflorum) and 50% red clover (Trifolium pratense) was identified as the best. In addition, the increase in precipitation and temperature of the most intensive climate scenario could significantly improve farm net return per cow (Bos taurus) and whole farm profit while no significant impact was observed on resource use criteria. Finally, the overall sensitivity assessment showed that the most resilient pasture composition under future climate scenarios was ryegrass with red clover and the least resilient was orchardgrass (Dactylis glomerata) with white clover (Trifolium repens).

In this study, the Soil and Water Assessment Tool (SWAT) is coupled with the Bacteria Source Load Calculator (BSLC) to develop alternative scenarios of agricultural best management practice (BMP) implementation and septic system repair to achieve total maximum daily load (TMDL) targets for Escherichia coli (E. coli) in a small agricultural watershed located in Michigan, USA. Due to the uncertainty involved in bacterial source estimation from wildlife and failing septic systems in agricultural watersheds, we propose a method for estimating their respective bacteria loading during SWAT calibration, which was performed at eight sampling locations and validated at a ninth. BMP implementation scenarios were prioritized based on their ability to meet TMDL targets at the lowest cost using a spatially targeted subwatershed ranking identifying the greatest E. coli source contribution. BMP effectiveness is driven by E. coli source locations defined in the BSLC-SWAT linkage. The framework developed here, from linking BSLC to SWAT to ranking subwatersheds, was effective in meeting the TMDL targets and is readily transferable to other watersheds. Agricultural BMPs were unsuccessful in meeting TMDL targets, but the target can be met by repairing about 60 of 110 estimated failing septic tanks.

Global demand for livestock products is expected to double by 2050, mainly due to improvement in the worldwide standard of living. Meanwhile, climate change is a threat to livestock production because of the impact on quality of feed crop and forage, water availability, animal and milk production, livestock diseases, animal reproduction, and biodiversity. This study reviews the global impacts of climate change on livestock production, the contribution of livestock production to climate change, and specific climate change adaptation and mitigation strategies in the livestock sector. Livestock production will be limited by climate variability as animal water consumption is expected to increase by a factor of three, demand for agricultural lands increase due to need for 70% growth in production, and food security concern since about one-third of the global cereal harvest is used for livestock feed. Meanwhile, the livestock sector contributes 14.5% of global greenhouse gas (GHG) emissions, driving further climate change. Consequently, the livestock sector will be a key player in the mitigation of GHG emissions and improving global food security. Therefore, in the transition to sustainable livestock production, there is a need for: a) assessments related to the use of adaptation and mitigation measures tailored to the location and livestock production system in use, and b) policies that support and facilitate the implementation of climate change adaptation and mitigation measures.

Computational fluid dynamics (CFD) is defined as a branch of fluid mechanics that solves fluid flow problems using numerical algorithms and methods. Using CFD to model water and contaminants transport in rivers, lakes, and coastal areas can significantly improve our understanding to manage these hydrodynamic systems. In this paper, 111 studies have been reviewed on different aspects of CFD application in ecohydrology that include: flow simulation and pressure distribution, sediment transport, hyporheic zone, fish habitat and temperature distribution. In general, three-dimensional models are reported to be more capable than two-dimensional models to predict flow features and have been used more recently due to their ability to capture secondary flow. Two-dimensional models with secondary flow correction terms are acceptable and are being used as well. Discretization techniques and turbulence closures are also reported with comparisons. In general, majority of the studies were focused on flow and sediment transport; however, our knowledge about temperature distributions in near-bed regions and pool-riffle structures is limited and can be the subject of future studies.

“How much conservation is enough?” is one of the most important and difficult questions to answer. In this work, we demonstrate an approach to specifically answer this question for conservation strategies designed to address nonpoint source pollution in agriculturally-dominated watersheds. We developed empirical models relating conservation investments and actions to measures of stream water quality and fish community health. Our results are consistent with other studies that demonstrate a need for extensive implementation of conservation practices in agricultural landscapes to see measurable improvements in ecological conditions. Our results also demonstrate the influence spatial grain can have on answering “how much conservation is enough?” Our coarse-grained analyses suggest that water quality in at the outlets of four watersheds could be improved to the point that water quality was no longer limiting the fish community with only about 18% of the agricultural lands treated with conservation practices and incentive payments totaling $7.7M. Yet, finer-grained subbasin analyses predict fish communities would still be limited in many tributaries of these watersheds even with ~ 50% of lands treated and incentive payments totaling ~$44M. Consequently, coarsegrained analyses could significantly underestimate scope of the solution needed to address these impacts to stream ecosystems. Finding balanced solutions to address agricultural nonpoint source pollution throughout the Great Lakes will require unprecedented collaboration from local to regional scales. Herein, we provide examples of how this work is supporting collaborative efforts to establish realistic ecological goals and associated performance measures and strategic implementation of practices throughout the Saginaw Bay drainage.

In this study, ten bioenergy crop rotations (corn, corn-stover, sorghum, soybean, corn-soybean, corn-soybean-canola, corn-stover-soybean, miscanthus, switchgrass, and sorghum-soybean) were selected based on local stakeholder (economically motivated) and regulator (environmentally motivated) preferences. These crops were implemented on diverse landscapes (agricultural, marginal, and agricultural plus marginal lands) one at time for 17 years using a SWAT model of the Saginaw River Watershed in Michigan. The bioenergy crops were evaluated based on 100 percent, 50 percent, and zero percent weight assigned to both stakeholders’ and regulators’ preferences using analytic hierarchy process (AHP), an optimization and decision-making technique that aims to satisfy multiple conflicting objectives. The corn-soybean-canola rotation was selected in all landscapes based on economic benefits (stakeholders’ preferences). Meanwhile, perennial grasses (miscanthus and switchgrass) were selected based on environmental benefits (regulators’ preferences), because they maintain permanent cover, require fewer inputs than traditional row crops, and are less management intensive. When implementing bioenergy crops on marginal lands, pollution generation greatly increased at the field level, indicating that these lands are likely not viable for bioenergy crop production to meet potential future renewable energy demand.

Droughts affect many sectors, such as agriculture, economic, social, human health, and ecosystems. Many drought indices have been developed; yet, none of them quantifies the impacts of drought on stream health. The purpose of this study is to define a new drought index capable of assessing fish vulnerability. To accomplish this, a hydrological model, called the Soil and Water Assessment Tool (SWAT), and the Regional-scale Habitat Suitability model were integrated in order to understand the state of drought within 13,831 stream segments within the Saginaw Bay Watershed. The ReliefF algorithm was used as the variable selection method, and partial least squared regression was used to develop two sets of predictor models capable of determining current and future drought severities. Forty-seven different climate scenarios were used to investigate drought model predictability of future climate scenarios. The results indicated that the best drought model has a high capability for predicting future drought conditions with R2 values ranging from 0.86 to 0.89. In general, the majority of reaches (94%) will experience higher drought probability under future climate scenarios compared to current conditions. The procedure introduced in this study is transferable to other watersheds with regional standards for environmental flow to measure the impacts of drought on stream health.

Ecologically relevant hydrologic indices are being used for developing environmental flow baselines. EflowStats is an R-package developed by the US Geological Survey that calculates 171 ecologically relevant hydrologic indices. Although EflowStats provides a set of easy to use functions, once it is applied to a large area with thousands of streams, it requires a long processing time. This particularly limits its application in problems that are dealing with optimizations, requiring evaluation of thousands of scenarios. As a result, a new package called MATLAB Hydrological Index Tool (MHIT) was developed, incorporating different technologies, such as vectorization, memoization, and parallelism to considerably reduce the processing time. Both EflowStats and MHIT were tested against a long-term (20 years) daily streamflow data set from the Saginaw River Watershed in Michigan. In summary, MHIT reduces the processing time by 227 times for the study area. Therefore, MHIT is much more suitable for dealing with Big Data.

Effective watershed management requires the evaluation of agricultural best management practice (BMP) scenarios which carefully consider the relevant environmental, economic, and social criteria involved. In the Multiple Criteria Decision-Making (MCDM) process, scenarios are first evaluated and then ranked to determine the most desirable outcome for the particular watershed. The main challenge of this process is the accurate identification of the best solution for the watershed in question, despite the various risk attitudes presented by the associated decision-makers (DMs). This paper introduces a novel approach for implementation of the MCDM process based on a comparative neutral risk/risk-based decision analysis, which results in the selection of the most desirable scenario for use in the entire watershed. At the sub-basin level, each scenario includes multiple BMPs with scores that have been calculated using the criteria derived from two cases of neutral risk and risk-based decision-making. The simple additive weighting (SAW) operator is applied for use in neutral risk decision-making, while the ordered weighted averaging (OWA) and induced OWA (IOWA) operators are effective for risk-based decision-making. At the watershed level, the BMP scores of the sub-basins are aggregated to calculate each scenarios' combined goodness measurements; the most desirable scenario for the entire watershed is then selected based on the combined goodness measurements. Our final results illustrate the type of operator and risk attitudes needed to satisfy the relevant criteria within the number of sub-basins, and how they ultimately affect the final ranking of the given scenarios. The methodology proposed here has been successfully applied to the Honeyoey Creek-Pine Creek watershed in Michigan, USA to evaluate various BMP scenarios and determine the best solution for both the stakeholders and the overall stream health.

Bioenergy is becoming increasingly relevant as an alternative to fossil fuels. Various bioenergy feedstocks are suggested as environmentally friendly solutions due to their positive impact on stream health and ability to sequester carbon, but most evaluations for bioenergy feedstocks have not evaluated the implications of bioenergy crop production holistically to date. Through the application of multi-objective optimization on 10 bioenergy feedstock rotations in a Michigan watershed, a Pareto front is searched to identify optimal trade-off solutions for three objective functions representing stream health, environmental emissions/carbon footprint, and economic feasibility. Various multi-criteria decision-making techniques are then applied to the resulting Pareto front to select a set of most-preferred trade-off solutions, which are compared to optimal solutions from each individual objective function. The most-preferred trade-off solutions indicate that a diverse mix of rotations are necessary to optimize all three objectives, whereas the individually optimal solutions do not consider a diverse range of feedstocks, thereby making the proposed multi-objective treatment an important and pragmatic strategy.

Measuring and improving resiliency is the key goal of climate risk management in rainfed agriculture. Currently, available metrics are generally used to qualitatively measure the resilience of agricultural systems at broader scales. Moreover, these metrics showed non-linear responses to climate variability, thus often fail to capture the temporal dynamics of resilience at field scales. Our objective for this study is to combine a few soil moisture-based metrics to gauge the resiliency of three promising rainfed agricultural treatments, namely the no-till, organic, and reduced input treatments against the conventional treatment. These treatments have been established at the Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) experiment in a randomized complete block design with six blocks per treatment. All four of these treatments consisted of maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) in rotation. Long-term (1993–2018) soil moisture data from this experiment was collected to compute the soil moisture metrics while the total crop biomass, crop yield, and soil organic carbon data were statistically analyzed to evaluate the robustness of the metrics to gauge the resiliency of these systems. Results have shown that, among the soil moisture metrics, the mean relative difference, the Spearman’s rank correlation coefficient, and soil water deficit index were suitable, while the index of temporal stability was not suitable to gauge the resiliency of different rainfed agricultural systems. The no-till treatment was identified as the most resilient treatment in terms of soil moisture retention, effectiveness for drought mitigation, and crop yields. Meanwhile, the reduced input treatment was the least resilient in terms of soil water conservation and drought recovery. The results of this study can be extended to other Midwest regions of the United States and similar climatological areas around the world.

In recent years, inefficient water resources management has caused to serious crisis in supplying water for urban demand within the watersheds. Therefore, one of the most important challenges for urban decision-makers (DMs) is selecting the most desirable water supply scenario, satisfying the several sustainable development criteria. Accordingly, this paper has developed a risk-based multiple criteria decision-making (MCDM) model for selecting the best scenario based on the modified ordered weighted averaging (MOWA) operator. This operator considers the risk-taking degree of DMs, the criteria importance degree and the DMs’ power weight, simultaneously. By use of this operator, the scenario scores have been calculated in several risk cases of decision-making, depending on the satisfaction of the number of criteria. This approach has been developed for Mashhad watershed in north-eastern Iran, to select the best water supply scenario for urban demand in 2040. The evaluation of scenarios has been performed with respect to the sustainable development criteria based on the urban watershed modelling. Finally, depending on each case of risk-taking degree, the most preferable scenario has been determined for satisfying the important criteria within the watershed. Development of this model is recommended for any watershed management modelling to supply urban demand.

Sustainability and the health of freshwater ecosystems are vital to insure their safe and continued use. This study introduces a new approach to improve stream health to a desirable condition at the lowest cost by optimizing the best management practice (BMP) implementation plan. Several hydrological models including the Soil and Water Assessment Tool (SWAT) and Hydrologic Index Tool were integrated and the results were used to develop a stream health model. SWAT model was calibrated and validated against daily streamflow data from nine US geological gauging stations for a 10-year-period while the stream health model was calibrated and validated against 193 biological monitoring sites operated by the Michigan Department of Natural Resources. The stream health model was guided by a genetic algorithm to design the watershed-scale management strategies that included five BMPs. Out of 182 BMP implementation scenarios, eight unique scenarios resulted in an overall excellent stream health for the Honeyoey Creek-Pine Creek Watershed in Michigan. In addition, no tillage was the most selected BMP in three of the eight implementation scenarios. The BMP implementation costs for these eight scenarios ranged from 4.28 to 6.41 million dollars. Therefore, the integration of genetic algorithm techniques in stream health modeling resulted in a savings of over 2 million dollars. In addition, the implementation of the lowest costing scenario resulted in a 52% improvement and 36% reduction in stream health scores, with respect to stream length, compared to current conditions. The technique introduced here can be successfully adapted in different regions to identify the optimal solution from both environmental and economic points of view.

The focus of this review is to discuss the current uses and developments of macroinvertebrate and fish indicators in riverine ecosystems. Macroinvertebrates and fish are commonly used indicators of stream heath, due to their ability to represent degradation occurring at the local or regional scales, respectively. A total of 78 macroinvertebrate and fish indices were reviewed, and the frequently used macroinvertebrate and fish indices are discussed in detail in the context of aquatic ecosystem health evaluation. This review also discusses several types of common components, or metrics, used in the creation of indices. Following this, the review will focus on the different methods used for macroinvertebrate and fish collection, in both wadeable and non-wadeable aquatic ecosystems. With the basics of macroinvertebrate and fish indices discussed, emphasis will be placed on the application of indices and the different regions for which they are developed. The final section will provide a summary of the benefits and limitations of macroinvertebrate and fish indices. In general, the majority of studies have been performed in wadeable streams; therefore, our knowledge about these indices in non-wadeable streams is limited, which should be the subject of future research.

Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

Summary The dynamics and relationships between society and nature are complex and difficult to predict. Anthropogenic activities affect the ecological integrity of our natural resources, specifically our streams. Further, it is well-established that the costs of these activities are born unequally by different human communities. This study considered the utility of integrating stream health metrics, based on stream health indicators, with socio-economic measures of communities, to better characterize these effects. This study used a spatial multi-factor model and bivariate mapping to produce a novel assessment for watershed management, identification of vulnerable areas, and allocation of resources. The study area is the Saginaw River watershed located in Michigan. In-stream hydrological and water quality data were used to predict fish and macroinvertebrate measures of stream health. These measures include the Index of Biological Integrity (IBI), Hilsenhoff Biotic Index (HBI), Family IBI, and total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa. Stream health indicators were then compared to spatially coincident socio-economic data, obtained from the United States Census Bureau (2010), including race, income, education, housing, and population size. Statistical analysis including spatial regression and cluster analysis were used to examine the correlation between vulnerable human populations and environmental conditions. Overall, limited correlation was observed between the socio-economic data and ecological measures of stream health, with the highest being a negative correlation of 0.18 between HBI and the social parameter household size. Clustering was observed in the datasets with urban areas representing a second order clustering effect over the watershed. Regions with the worst stream health and most vulnerable social populations were most commonly located nearby or down-stream to highly populated areas and agricultural lands.

Erosion of agricultural cropland has been identified as a major source of sediment accumulating in water reservoirs. Emphasis is increasingly on finding the most cost-effective ways to control soil erosion to reduce reservoir sedimentation. In this study, biophysical and economic models for the Tuttle Creek Lake watershed in northeastern Kansas were integrated to determine if implementation of alternative cropland management strategies to reduce reservoir sediment are more cost-effective than dredging. In the Tuttle Creek Lake watershed, we found that if the marginal costs of agricultural best management practices (BMP) implementation become >$6.90/t of sediment reduction, then dredging becomes the economically preferred alternative. Meeting this cost requires that BMP in the form of filter strips and no-till cultivation are implemented in a targeted, cost-effective manner, not in a random pattern of voluntary adoption that may characterize BMP adoption in some watersheds. Although reservoir dred...

Uncertainty of best management practice (BMP) performance in future climates is an important consideration for water resources managers. The objective of this study was to quantify the level of uncertainty in performance of seven agricultural BMPs due to climate change in reducing sediment, total nitrogen, and total phosphorus loads. The Soil and Water Assessment Tool coupled with mid-21st century climate data from the Community Climate System Model were used to develop climate change scenarios for the Tuttle Creek Lake Watershed of Kansas and Nebraska. Uncertainty level of each BMP was determined using Latin Hypercube Sampling, a constrained Monte Carlo sampling technique. Samples were taken from distributions of several variables (monthly precipitation, temperature, CO2, and BMP implementation parameters). Cumulative distribution functions were constructed for each BMP, pollutant, and climate scenario combination. Results demonstrated that BMP performance uncertainty is amplified in the extreme climate scenario. Among BMPs, native grass replacement generally had higher uncertainty level but also had the greatest reductions. This study highlights the importance of incorporating uncertainty analysis into mitigation strategies aiming to reduce negative impacts of climate change on water resources. Copyright © 2013 John Wiley & Sons, Ltd.

Freshwater resources are vital for human and natural systems. However, anthropogenic activities, such as agricultural practices, have led to the degradation of the quality of these limited resources through pollutant loading. Agricultural Best Management Practices (BMPs), such as wetlands, are recommended as a valuable solution for pollutant removal. However, evaluation of their long-term impacts is difficult and requires modeling since performing in-situ monitoring is expensive and not feasible at the watershed scale. In this study, the impact of natural wetland implementation on total phosphorus reduction was evaluated both at the subwatershed and watershed levels. The study area is the Saginaw River Watershed, which is largest watershed in Michigan. The phosphorus reduction performances of four different wetland sizes (2, 4, 6, and 8 ha) were evaluated within this study area by implementing one wetland at a time in areas identified to have the highest potential for wetland restoration. The subwatershed level phosphorus loads were obtained from a calibrated Soil and Water Assessment Tool (SWAT) model. These loads were then incorporated into a wetland model (System for Urban Stormwater Treatment and Analysis IntegratioN-SUSTAIN) to evaluate phosphorus reduction at the subwatershed level and then the SWAT model was again used to route phosphorus transport to the watershed outlet. Statistical analyses were performed to evaluate the spatial impact of wetland size and placement on phosphorus reduction. Overall, the performance of 2 ha wetlands in total phosphorus reduction was significantly lower than the larger sizes at both the subwatershed and watershed levels. Regarding wetland implementation sites, wetlands located in headwaters and downstream had significantly higher phosphorus reduction than the ones located in the middle of the watershed. More specifically, wetlands implemented at distances ranging from 200 to 250 km and 50–100 km from the outlet had the highest impact on phosphorus reduction at the subwatershed and watershed levels, respectively. A multi criteria decision making (MCDM) method named VIKOR was successfully executed to identify the most suitable wetland size and location for each subwatershed considering the phosphorus reduction and economic cost associated with wetland implementation. The methods introduced in this study can be easily applied to other watersheds for selection and placement of wetlands while considering environmental benefits and economic costs.

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors.

In this study, ten best management practices (BMP) were implemented on agricultural areas in the Saginaw River Watershed using the Soil and Water Assessment Tool model based on four targeting methods (Load per Subbasin Area Index (LPSAI), Load per Unit Area Index (LPUAI), Concentration Impact Index (CII), and Load Impact Index (LII)). The effective BMPs both for targeting and non-targeting pollutants were contour farming (CF) (except total nitrogen reduction during total phosphorus targeting scenario), residue management 1000 kg/ha and 2000 kg/ha, strip cropping, recharge structures, terracing, and native grass (NG). In contrast, conservation tillage and no tillage did not reduce significant amount of pollutants for any combination of targeting methods and priority areas. In regard to spatial correlation between targeting methods, a strong relationship was found between the LPSAI and LPUAI methods both for the sediment and total nitrogen targeting scenarios. In addition, a similar result was found between the CII and LPSAI targeting methods. Regarding the spatiotemporal variability of BMP implementation plan, distinct change in priority area was observed in the case of NG implementation by the end of the second year; however, this impact was minimal for CF due to less pollutant reduction efficiency compared to NG. Copyright © 2012 John Wiley & Sons, Ltd.

Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R(2) and lowest mean squared error (MSE) for FamilyIBI (R(2)=0.50, MSE=29.80), HBI (R(2)=0.57, MSE=0.20), and EPTtaxa (R(2)=0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures.

Increasing concerns regarding water quality in the Great Lakes region are mainly due to changes in urban and agricultural landscapes. Both point and non-point sources contribute pollution to Great Lakes surface waters. Best management practices (BMPs) are a common tool used to reduce both point and non-point source pollution and improve water quality. Meanwhile, identification of critical source areas of pollution and placement of BMPs plays an important role in pollution reduction. The goal of this study is to evaluate the performance of different targeting methods in 1) identifying priority areas (high, medium, and low) based on various factors such as pollutant concentration, load, and yield, 2) comparing pollutant (sediment, total nitrogen-TN, and total phosphorus-TP) reduction in priority areas defined by all targeting methods, 3) determine the BMP relative sensitivity index among all targeting methods. Ten BMPs were implemented in the Saginaw River Watershed using the Soil and Water Assessment Tool (SWAT) model following identification of priority areas. Each targeting method selected distinct high priority areas based on the methodology of implementation. The concentration based targeting method was most effective at reduction of TN and TP, likely because it selected the greatest area of high priority for BMP implementation. The subbasin load targeting method was most effective at reducing sediment because it tended to select large, highly agricultural subbasins for BMP implementation. When implementing BMPs, native grass and terraces were generally the most effective, while conservation tillage and residue management had limited effectiveness. The BMP relative sensitivity index revealed that most combinations of targeting methods and priority areas resulted in a proportional decrease in pollutant load from the subbasin level and watershed outlet. However, the concentration and yield methods were more effective at subbasin reduction, while the stream load method was more effective at reducing pollutants at the watershed outlet. The results of this study indicate that emphasis should be placed on selection of the proper targeting method and BMP to meet the needs and goals of a BMP implementation project because different targeting methods produce varying results.

Woznicki, Sean A. and A. Pouyan Nejadhashemi, 2011. Sensitivity Analysis of Best Management Practices Under Climate Change Scenarios. Journal of the American Water Resources Association (JAWRA) 48(1): 90-112. DOI: 10.1111/j.1752-1688.2011.00598.x Abstract: Understanding the sensitivity of best management practices (BMPs) implementation as climate changes will be important for water resources management. The objective of this study was to determine how the sensitivity of BMPs performance vary due to changes in precipitation, temperature, and CO2 using the Soil and Water Assessment Tool. Sediment, total nitrogen, and total phosphorus loads on an annual and monthly basis were estimated before and after implementation of eight agricultural BMPs for different climate scenarios. Downscaled climate change data were obtained from the National Center for Atmospheric Research Community Climate System Model for the Tuttle Creek Lake watershed in Kansas and Nebraska. Using a relative sensitivity index, native grass, grazing management, and filter strips were determined to be the most sensitive for all climate change scenarios, whereas porous gully plugs, no-tillage, and conservation tillage were the least sensitive on an annual basis. The monthly sensitivity analysis revealed that BMP sensitivity varies largely on a seasonal basis for all climate change scenarios. The results of this research suggest that the majority of agricultural BMPs tested in this study are significantly sensitive to climate change. Therefore, caution should be exercised in the decision-making processes.

The challenges we face in transitioning to a global production of biomass as renewable feedstock sources in a way that is both economical feasible environmentally sustainable are ubiquitous. In this study, the Soil and Water Assessment Tool (SWAT) was used to predict the possible long-term environmental implications, specifically water quality, due to large-scale bioenergy cropping system expansion based on four landuse scenarios and 15 bioenergy crop rotations for four watersheds, totaling 244 model simulations. The study area consists of four watersheds totaling 53,358 km 2 located in Michigan. The results suggest that perennial grass species are the most suitable for large-scale implementation, whereas traditional intensive row crops should be implemented with caution on such a broad scale. Row crops also had the highest increases of high priority areas for sediment, nitrogen, and phosphorus. Based on the data from this study, it is not recommended that marginal land be converted to any bioenergy rotation in areas with preexisting high nitrogen levels. Statistical analyses demonstrate that perennial grass species significantly reduce sediment on all lands except marginal lands. With the exception of row crops cultivated on marginal lands and all agricultural land, the majority of bioenergy crops significantly reduce total phosphorus loads.

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest.

Retaining water at the root level of crops has been a major focus in precision irrigation system from technological, societal, and environmental points of view. Subsurface water retention technology (SWRT) through impermeable membranes placed at certain depths under has shown 1.4 to 3.4-fold increase in production in crops. However, the sizing, placement and surface water scheduling are important parameters for achieving an optimal yield. In this paper, for the first time, we consider a water flow simulation model through soil (HYDRUS-2D) which is integrated with an evolutionary multi-objective optimization (EMO) algorithm to find optimal membrane configurations and surface water supply under multiple conflicting objectives. The initial results presented in this paper clearly demonstrate the merit of such a collaboration and supports further studies. Not only the integrated approach finds optimal membrane configurations and water supply, but also reveals a number of useful insights and knowledge about optimal precision irrigation, a matter which has long term contributions to water conservation, ground-water preservation, and many natural resources directly affecting modern society.

In this study an analytical hierarchy process (AHP) was used for ranking best management practices (BMPs) in the Saginaw River Watershed based on environmental, economic and social factors. Three spatial targeting methods were used for placement of BMPs on critical source areas (CSAs). The environment factors include sediment, total nitrogen, and total phosphorus reductions at the subbasin level and the watershed outlet. Economic factors were based on total BMP cost, including installation, maintenance, and opportunity costs. Social factors were divided into three favorability rankings (most favorable, moderately favorable, and least favorable) based on area allocated to each BMP. Equal weights (1/3) were considered for the three main factors while calculating the BMP rank by AHP. In this study three scenarios were compared. A comprehensive approach in which environmental, economic, and social aspects are simultaneously considered (Scenario 1) versus more traditional approaches in which both environmental and economic aspects were considered (Scenario 2) or only environmental aspects (sediment, TN, and TP) were considered (Scenario 3). In Scenario 1, only stripcropping (moderately favorable) was selected on all CSAs at the subbasin level, whereas stripcropping (49-69% of CSAs) and residue management (most favorable, 31-51% of CSAs) were selected by AHP based on the watershed outlet and three spatial targeting methods. In Scenario 2, native grass was eliminated by moderately preferable BMPs (stripcropping) both at the subbasin and watershed outlet levels due the lower BMP implementations cost compared to native grass. Finally, in Scenario 3, at subbasin level, the least socially preferable BMP (native grass) was selected in 100% of CSAs due to greater pollution reduction capacity compared to other BMPs. At watershed level, nearly 50% the CSAs selected stripcropping, and the remaining 50% of CSAs selected native grass and residue management equally.

Hydrologic fluxes in the Great Lakes region have changed relative to pre-settlement conditions in response to major land use changes during the past 150 years. Land surface characteristics and processes including leaf area index, roughness, albedo, soil moisture, and rates of momentum, energy and water vapor exchange are strongly influenced by land use. Changes in land use including urbanization and de(/re)forestation continue to affect the nature and magnitude of groundwater surface water interactions and water availability influencing ecosystems and their services. One of the goals of the present work is to develop a baseline scenario relative to which the impacts of land use changes on hydrological and environmental processes can be evaluated. In addition, the study can help in quantifying the potential impacts of future projected changes in land use in order to mitigate the negative impacts of these changes on goods and services of value to society.

Droughts are the world’s costliest natural disaster which affects water supply, crop yield, habitats, ecosystems, and many other social and economic sectors (Wilhite, 2000b; Keyantash and Dracup, 2002; Mishra and Singh, 2010; Sheffield and Wood, 2011). Numerous indices exist to assess drought spatial and temporal extends (Palmer, 1965; McKee et al., 1993; Heim, 2002; Keyantash and Dracup, 2002; Dai et al., 2004; Hayes, 2006; Mishra and Singh, 2010). However, the majority of indices are sensitive to drought impacts on crop production and loss, neglecting other aspects of environmental sustainability. In the context of natural systems, droughts damage aquatic ecosystems by altering native biological communities such as fish and macroinvertebrate assemblages (Lake, 2011). Therefore, it is important to evaluate the impacts of drought on stream health. The goal of this study is to develop a new index that quantifies the impacts of drought on stream ecosystems.

Global warming has become one of the major challenges in maintaining global food security. This paper reviews the impacts of climate change on fourteen strategic crops for eight sub-Saharan Africa countries. Climate change is projected to increase median temperature by 1.4–5.5°C and median precipitation by −2% to 20% by the end of the 21st century. However, large levels of uncertainty exist with temporal and spatial variability of rainfall events. The impact of climate change on crop yields in the region is largely negative. Among the grain crops, wheat is reported as the most vulnerable crop, for which up to 72% of the current yield is projected to decline. For other grain crops, such as maize, rice and soybean, up to 45% yield reductions are expected by the end of this century. Two grain crops, millet and sorghum, are more resilient to climate change for which projected impacts on crop yields are