Abstract  Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75-0.99), overall accuracy of 94% (87.5-99%), and producers and users accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.

Abstract  This paper showcases the suitability of an environmentally extended input–output framework to provide macroeconomic analyses of an expanding bioeconomy to allow for adequate evaluation of its benefits and trade-offs. It also exemplifies the framework’s applicability to provide early design stage evaluations of emerging technologies expected to contribute to a future bioeconomy. Here, it is used to compare the current United States (U.S.) bioeconomy to a hypothetical future containing additional cellulosic ethanol produced from two near-commercial pathways. We find that the substitution of gasoline with cellulosic ethanol is expected to yield socioeconomic net benefits, including job growth and value added, and a net reduction in global warming potential and nonrenewable energy use. The substitution fares comparable to or worse than that for other environmental impact categories including human toxicity and eutrophication potentials. We recommend that further technology advancement and commercialization efforts focus on reducing these unintended consequences through improved system design and innovation. The framework is seen as complementary to process-based technoeconomic and life cycle assessments as it utilizes related data to describe specific supply chains while providing analyses of individual products and portfolios thereof at an industrial scale and in the context of the U.S. economy.

Abstract  Improved nationwide understanding of the spatiotemporal trends in irrigated croplands is urgently needed to improve assessment and monitoring of water resources and help navigate rising challenges related to regional and local water scarcity, increasing demands of agricultural production, and environmental sustainability. Despite the urgent need, substantial gaps in our understanding of the spatial and temporal distribution of irrigated agriculture and crop water use remain, hindering efforts to precisely estimate agricultural water use. We addressed this data gap by extending our recently developed methodology to generate annual 30-meter resolution datasets of irrigated agricultural extent across the conterminous U.S. for each year in 1997-2017. To map annual irrigation distribution, the entire period was divided into two eras: USDA NASS census years (i.e., 1997, 2002, 2007, 2012, and 2017) and other years. For the census years, we derived two sets of training data; the first (automatically generated based on vegetation greenness) was used in the western U.S., while the second dataset (manually collected via visual interpretation of very high-resolution imagery) emphasized coverage of the more humid eastern U.S. These census-year samples were then temporally extended to provide annual training data for the full study period. The input variables for irrigation vs. non-irrigation classification consisted of remote sensing features, climate variables, and soil texture. Our evaluation for the 2012 irrigation map had an overall accuracy of ~90% at the aquifer level, which provides a significant improvement over previous datasets. Initial results of mapping annual irrigation extent and its changes over time in the Ogallala Aquifer for the period of 2000-2017 also produced irrigation estimates consistent to USDA-NASS county-level census statistics, suggesting the approach holds promise for characterizing broad-scale trends in irrigation while also capturing critical fine-scale details in spatial and temporal dynamics. The preliminary results revealed overall increasing levels of irrigation over the Ogallala Aquifer for 2000-2017, primarily introduced for expanded production of corn and soybeans, although many more-localized trends and patterns exist throughout the region and nationwide.

Abstract  The Missouri River Basin (MORB) has experienced a resurgence of grassland conversion to crop production, which raised concerns on water quality. We applied the Soil and Water Assessment Tool (SWAT) to address how this conversion would impact water quality. We designed three crop production scenarios representing conversion of grassland to: (a) continuous corn; (b) corn/soybean rotation; and (c) corn/wheat rotation to assess the impact. The SWAT model results showed: (a) the lower MORB produced high total nitrogen (TN) and total phosphorus (TP) load before conversion (baseline) due mainly to high precipitation and high agricultural activity; (b) the greatest percentage increases of TN and TP occurred in the North and South Dakotas, coinciding with the highest amount of grassland conversion to cropland; and (c) grassland conversion to continuous corn resulted in the greatest increase in TN and TP loads, followed by conversion to corn/soybean and then conversion to corn/wheat. Although the greatest percentage increases of TN and TP occurred in the North and South Dakotas, these areas still contributed relatively low TN and TP to total basin loads after conversion. However, watersheds, predominantly in the lower MORB continued to be “hotspots” that contributed the greatest amounts of TN and TP to the total basin loads—driven by a combination of grassland conversion, high precipitation, and loading from pre-existing cropland. At the watershed outlet, the TN and TP loads were increased by 6.4% (13,800 t/yr) and 8.7% (3,400 t/yr), respectively, during the 2008–2016 period for the conversion to continuous corn scenario.

Abstract  Sustainability is a key principle in natural resource management, and it involves operational efficiency, minimisation of environmental impact and socio-economic considerations; all of which are interdependent. It has become increasingly obvious that continued reliance on fossil fuel energy resources is unsustainable, owing to both depleting world reserves and the green house gas emissions associated with their use. Therefore, there are vigorous research initiatives aimed at developing alternative renewable and potentially carbon neutral solid, liquid and gaseous biofuels as alternative energy resources. However, alternate energy resources akin to first generation biofuels derived from terrestrial crops such as sugarcane, sugar beet, maize and rapeseed place an enormous strain on world food markets, contribute to water shortages and precipitate the destruction of the world's forests. Second generation biofuels derived from lignocellulosic agriculture and forest residues and from non-food crop feedstocks address some of the above problems; however there is concern over competing land use or required land use changes. Therefore, based on current knowledge and technology projections, third generation biofuels specifically derived from microalgae are considered to be a technically viable alternative energy resource that is devoid of the major drawbacks associated with first and second generation biofuels. Microalgae are photosynthetic microorganisms with simple growing requirements (light, sugars, CO2, N, P, and K) that can produce lipids, proteins and carbohydrates in large amounts over short periods of time. These products can be processed into both biofuels and valuable co-products. This study reviewed the technologies underpinning microalgae-to-biofuels systems, focusing on the biomass production, harvesting, conversion technologies, and the extraction of useful co-products. It also reviewed the synergistic coupling of microalgae propagation with carbon sequestration and wastewater treatment potential for mitigation of environmental impacts associated with energy conversion and utilisation. It was found that, whereas there are outstanding issues related to photosynthetic efficiencies and biomass output, microalgae-derived biofuels could progressively substitute a significant proportion of the fossil fuels required to meet the growing energy demand.

Abstract  Microalgae are receiving increased global attention as a potential sustainable "energy crop" for biofuel production. An important step to realizing the potential of algae is quantifying the demands commercial-scale algal biofuel production will place on water and land resources. We present a high-resolution spatiotemporal assessment that brings to bear fundamental questions of where production can occur, how many land and water resources are required, and how much energy is produced. Our study suggests that under current technology, microalgae have the potential to generate 220 x 10(9) L yr(-1) of oil, equivalent to 48% of current U. S. petroleum imports for transportation. However, this level of production requires 5.5% of the land area in the conterminous United States and nearly three times the water currently used for irrigated agriculture, averaging 1421 L water per liter of oil. Optimizing the locations for microalgae production on the basis of water use efficiency can greatly reduce total water demand. For example, focusing on locations along the Gulf Coast, southeastern seaboard, and Great Lakes shows a 75% reduction in consumptive freshwater use to 350 L per liter of oil produced with a 67% reduction in land use. These optimized locations have the potential to generate an oil volume equivalent to 17% of imports for transportation fuels, equal to the Energy Independence and Security Act year 2022 "advanced biofuels" production target and utilizing some 25% of the current irrigation demand. With proper planning, adequate land and water are available to meet a significant portion of the U. S. renewable fuel goals.

Abstract  The Upper Mississippi River Basin and Ohio-Tennessee River Basin comprise the majority of the United States Corn Belt region, resulting in degraded Mississippi River and Gulf of Mexico water quality. To address the water quality implications of increased biofuel production, biofuel scenarios were tested with a Soil and Water Assessment Tool (SWAT) model revision featuring improved biofuel crop representation. Scenarios included corn stover removal and the inclusion of two perennial bioenergy crops, switchgrass and Miscanthus, grown on marginal lands (slopes >2% and erosion rates >2 t/ha) and nonmarginal lands. The SWAT model estimates show water quality is not very sensitive to stover removal. The perennial bioenergy crops reduce simulated sediment, nitrogen (N), and phosphorus (P) yields by up to 60%. Simulated sediment and P reductions in marginal lands were generally twice that occurring in the nonmarginal lands. The highest unit area reductions of N occurred in the less sloping tile-drained lands. Productivity showed corn grain yield was independent from stover removal, while yields of the two perennial bioenergy crops were similar in the marginal and nonmarginal lands. The results suggest planning for biofuel production in the Corn Belt could include the removal of stover in productive corn areas, and the planting of perennial bioenergy crops in marginal land and in low-sloped tile-drained areas characterized by high N pollution. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.

Abstract  This study examines fresh renewable water resources available for bioenergy feedstock production in the United States. The impacts of feedstock irrigation on surface and groundwater resources available to nonbioenergy sectors were quantified using a pair of water availability indexes: streamflow availability index and percolation flow availability index. The two metrics were applied to both historical (2008) and three possible future biomass production scenarios from the 2016 U.S. Billion-Ton Report at the county level. For both historical and future scenarios, we found that the consumptive irrigation requirements for bioenergy feedstock account for <0.01% of annual streamflow in all but three counties in Nebraska. Results suggest that the irrigation demand of future biomass production could be supplied by annual renewable groundwater flow in about 94% of feedstock-growing counties that use groundwater for irrigation, representing about 92% of production tonnage. Counties that require irrigation from nonrenewable groundwater resources are mostly located in the Northern Plains and Pacific regions. We also evaluated the sensitivity of crop water footprint estimation to soil moisture carryover by comparing blue water estimates from six different empirical and process-based methods. Our findings suggest that accounting for preseason soil moisture is critical for representative blue water estimation, so that the irrigation water consumption is not overestimated. This is especially true in the Corn Belt region, where blue water estimates with and without preseason soil moisture would be about 1.9 versus 45.5 billion m(3)/year under the historical scenario. This difference is smaller in semiarid regions like the High Plains, but the blue water estimate can still triple if soil moisture is not considered. From the perspective of renewable surface water and groundwater resources, scaling feedstock production up in the High Plains and California will require careful planning integrated with water management strategies to improve water resource conservation.

Abstract  Impact of climate change on the water resources of the United States exposes the vulnerability of feedstock-specific mandated fuel targets to extreme weather conditions that could become more frequent and intensify in the future. Consequently, a sustainable biofuel policy should consider: (a) how climate change would alter both water supply and demand; and (b) in turn, how related changes in water availability will impact the production of biofuel crops; and (c) the environmental implications of large scale biofuel productions. Understanding the role of biofuels in the water cycle is the key to understanding many of the environmental impacts of biofuels. Therefore, the focus of this study is to model the rarely explored interactions between land use, climate change, water resources and the environment in future biofuel production systems. Results from this study will help explore the impacts of the US biofuel policy and climate change on water and agricultural resources. We used the Soil and Water Assessment Tool (SWAT) to analyze the water quantity and quality consequences of land use and land management related changes in cropping conditions (e.g., more use of marginal lands, greater residue harvest, increased yields), plus management practices due to biofuel crops to meet the Renewable Fuel Standard target on water quality and quantity.

Abstract  To address issues of energy security and greenhouse gas (GHG) mitigation, substantial amounts of corn-derived ethanol are used in U.S. gasoline. Currently, ethanol comprises 10% of the U.S. gasoline pool (E10), but there is interest in increasing this - possibly doubling the amount currently used. Production of corn ethanol raises several concerns with respect to environmental and ecological impacts. This paper reviews the available literature regarding the impacts on water, soil, and air quality. A companion paper addresses issues of biodiversity, ecosystems, land use change, greenhouse gas (GHG) emissions, and sustainability. We emphasize recent information appearing since comprehensive reports on this topic were issued by the U.S. EPA and NRC/NAS in 2011. The principal environmental concerns arise from the intensive agricultural activities associated with corn cropping. Nutrient runoff contributes to eutrophication, algal growth, and hypoxia in downstream water bodies; in addition to elevated nitrate pollutant levels in drinking water sources. Water requirements of corn ethanol vary by over 2-orders of magnitude among corn-growing states, depending upon the amount of irrigation used. Significant increases in corn production would likely involve expansion into areas requiring more intensive irrigation. Expansion into Conservation Reserve Program (CRP) lands raises concerns about increased erosion, deterioration of soil quality, loss of biodiversity, and reduction of ecosystem services. Largely because of energy-intensive agricultural activities (including fertilizer production), upstream emissions of most air pollutants of concern are considerably higher for corn ethanol compared to gasoline. Current fuel ethanol levels do not provide any benefit with respect to ground level ozone, and this is unlikely to change with use of E20. However, externalities associated with life-cycle emissions (such as eutrophication, acidification, health effects, etc.) are greater - and more costly - for corn ethanol compared to gasoline. Such externalities are expected to worsen in moving from E10 to E20 fuels.

Abstract  ABSTRACT SIMILARITIES between the crop coefficient curve and a vegetation index showed potential for modeling a vegetation index into a crop coefficient. Therefore, the possibility of directly estimating the crop coefficient from measured reflectance properties of a crop/soil scene was investigated. Reflected canopy radiation in the 0.63 to 0.69 jwm and 0.76 to 0.90 jjim band widths was measured normal to and two meters above corn (Zea mays L.), and the normalized difference (ND) vegetation index was computed. The seasonal ND curve was curvilinear and resembled the basal crop coefficient (K^b) curve for corn. Leaf area index and canopy shading were 3.2 and 77.6%, respectively, when the ND reached its maximum valvue. A linear transformation of the ND was developed by equating the ND at effective cover and for dry, bare soil at the experimental site to the K^b at effective cover and for dry soil evaporation, respectively. This transformation produced a seasonal curve very similar to the basal crop coefficient curve and was named the basal spectral crop coefficient (K^J. Crop coefficients derived from spectral measurements are independent of the usual time base parameters, planting date and effective cover date, associated with traditional crop coefficients. Thus, the basal spectral crop coefficient is a real-time crop coefficient that permits the crop to express its response to weather, management practices, and stresses.

Abstract  This paper develops and proposes a simplified operational remote sensing approach to assist crop growth models in reproducing actual processes in the field by relating satellite based remote sensing data and key canopy biophysical parameters. While relationships between spectral vegetation indices (VI) and biomass production have been conducted in the past, we specifically pursue the relationship between crop transpiration and biomass production as described in the FAO-66 Aquacrop manual. The authors point to a possible general relationship between a transpiration coefficient (herein we propose the basal crop coefficient, Kcb, as a proxy) and biomass production. In parallel, many studies have demonstrated the well-established relationship between Kcb and remote sensing based VI. Thus, the relationship between both parameters has a strong basis but must be demonstrated. We analyze the relationship between biomass production and the reflectance based Kcb using field data obtained during 11 years in irrigated and rainfed soybeans and maize in eastern Nebraska. The analysis confirms that the relationship is strong and paves the way for the use of remote sensing data for a quantitative analysis of crop biomass production and yield.

Abstract  Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7 mm d−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

Abstract  Decreased groundwater levels of the Ogallala Aquifer have increased interest in simulating crop responses to deficit irrigation strategies to evaluate the sustainable irrigation management for profitable crop production. However, the ability of widely used simulation models to accurately represent crop responses to deficit irrigation is not thoroughly evaluated. Therefore, the objective of this research was to evaluate the efficacy of the plant stress algorithms in Soil and Water Assessment Tool (SWAT) to simulate corn (Zea mays L.) responses to deficit irrigation treatments. Results showed simulated corn leaf area index (LAI), biomass, and yield under full irrigation scenarios matched measured data reasonably well at two study sites. However, clear reductions in model performance statistics for corn LAI simulations were found under the deficit irrigation scenarios for both sites (Nash-Sutcliffe efficiency [NSE] <0.49; percent bias [PBIAS] >14%). Additionally, considerable overestimation of yield occurred in the deficit irrigation scenarios for both sites (PBIAS >30% in most years). The unsatisfactory results from simulations of both LAI and yield under the deficit irrigation scenarios suggested potential deficiencies of the plant stress algorithms in SWAT. Two apparent limitations of the plant stress algorithms were (i) the equation for computing actual plant growth factor using a singular stress factor, determined by the maximum value of four plant stress factors of water, temperature, nitrogen, and phosphorus, and (ii) the computed actual plant growth factor only adjusting potential daily accumulations of LAI rather than modifying the shape of the LAI development by adjusting related parameters.

Abstract  A correct evaluation of water losses as evapotranspiration (ET) by crops is important for allocating irrigation water and improving water use efficiency. Field experiments were conducted throughout 2009/2010 (second ratoon) and 2010/2011 (third ratoon) in a sugarcane field of a commercial distillery located on the coastal area of Paraiba state, Brazil. The main objective of this study was to determine crop coefficient, water requirements and water use efficiency (WUE) of sugarcane grown in a tropical climate. The experimental design was by randomized block design with four irrigation treatments and three replications using two center pivots. Crop evapotranspiration (ET) was determined by field soil water balance and reference evapotranspiration (ETo) was obtained based on Penman–Monteith method (FAO/56), using data of air temperature, relative humidity, wind speed and solar radiation from Data Collection Platform, located next to the experimental site. The experimental area was cultivated with irrigation applied weekly by a center pivot system in addition to rainfall. The irrigation scheduling was based on four irrigation levels (T1 = 25%, T2 = 50%, T3 = 75% and T4 = 100% of ETo). Results showed that ET and WUE are strongly influenced by soil water availability. When averaged across two years, productivity increased according to increases in water level. Sugarcane ET ranged from 2.7 (rain-fed condition) to 4.2 mm day−1 (100% ETo irrigation treatment)

Abstract  Sustainable management of agricultural water resources requires improved understanding of irrigation patterns in space and time. We produced annual, high-resolution (30 m) irrigation maps for 1999–2016 by combining all available Landsat satellite imagery with climate and soil covariables in Google Earth Engine. Random forest classification had accuracies from 92 to 100% and generally agreed with county statistics (r2 = 0.88–0.96). Two novel indices that integrate plant greenness and moisture information show promise for improving satellite classification of irrigation. We found considerable interannual variability in irrigation location and extent, including a near doubling between 2002 and 2016. Statistical modeling suggested that precipitation and commodity price influenced irrigated extent through time. High prices incentivized expansion to increase crop yield and profit, but dry years required greater irrigation intensity, thus reducing area in this supply-limited region. Data sets produced with this approach can improve water sustainability by providing consistent, spatially explicit tracking of irrigation dynamics over time.

Abstract  The Ogalalla Aquifer is used to supplement insufficient precipitation for agricultural production in the semiarid Texas High Plains. However, decades of pumping combined with minimal recharge has resulted in decreased well capacity in most areas. A calibrated Soil and Water Assessment Tool (SWAT) model was used to compare simulated yields, crop water use, and required irrigation for crop rotations of the region using measured long-term (90 years) historical weather data. Crop rotations included continuous corn and cotton, corn–cotton, sorghum–cotton, cotton–winter wheat, and corn–winter wheat. Results demonstrated that a calibrated SWAT model simulated crop water use and yields well for all listed crops except cotton. The plant growth algorithms in SWAT appear unable to simulate representative cotton yields typical of cotton management in the Texas High Plains. A work-around for a limitation of the auto-irrigate function in SWAT to be suspended during the dormancy period of winter wheat was also used. Summary statistics for crop yield, crop water use, and irrigation were presented for all rotations. Long-term water use of simulations and irrigation probability exceedance statistics are presented for all simulated crops. These data may serve as a decision support tool for producers considering crop rotation strategies.