uri,href,identifier,attrs.Abstract,attrs.Author,attrs.DOI,attrs.Date,attrs.Issue,attrs.Journal,attrs.Pages,attrs.Title,attrs.Volume,attrs.Year,attrs._record_number,attrs._uuid,attrs.reftype,child_publication
/reference/c5857041-2594-47cf-a6bc-3fab052fa903,https://data.globalchange.gov/reference/c5857041-2594-47cf-a6bc-3fab052fa903,c5857041-2594-47cf-a6bc-3fab052fa903,"The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981–2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.","Liang, Xin-Zhong; Wu, You; Chambers, Robert G.; Schmoldt, Daniel L.; Gao, Wei; Liu, Chaoshun; Liu, Yan-An; Sun, Chao; Kennedy, Jennifer A.",10.1073/pnas.1615922114,"March 21, 2017",12,"Proceedings of the National Academy of Sciences of the United States of America",E2285-E2292,"Determining climate effects on US total agricultural productivity",114,2017,21170,c5857041-2594-47cf-a6bc-3fab052fa903,"Journal Article",/article/10.1073/pnas.1615922114
/reference/c6b4dffc-de18-4d19-b6a8-a2bc29448f09,https://data.globalchange.gov/reference/c6b4dffc-de18-4d19-b6a8-a2bc29448f09,c6b4dffc-de18-4d19-b6a8-a2bc29448f09,,,,,,,,"Effects of Drought on Forests and Rangelands in the United States: A Comprehensive Science Synthesis",,2016,20154,c6b4dffc-de18-4d19-b6a8-a2bc29448f09,"Edited Book",/report/gtr_wo93b
/reference/c6d7ea28-baeb-44cc-bef0-4e7d8bebe087,https://data.globalchange.gov/reference/c6d7ea28-baeb-44cc-bef0-4e7d8bebe087,c6d7ea28-baeb-44cc-bef0-4e7d8bebe087,"Removal of corn (Zea mays L.) residues at high rates for biofuel and other off‐farm uses may negatively impact soil and the environment in the long term. Biomass removal from perennial warm‐season grasses (WSGs) grown in marginally productive lands could be an alternative to corn residue removal as biofuel feedstocks while controlling water and wind erosion, sequestering carbon (C), cycling water and nutrients, and enhancing other soil ecosystem services. We compared wind and water erosion potential, soil compaction, soil hydraulic properties, soil organic C (SOC), and soil fertility between biomass removal from WSGs and corn residue removal from rainfed no‐till continuous corn on a marginally productive site on a silty clay loam in eastern Nebraska after 2 and 3 years of management. The field‐scale treatments were as follows: (i) switchgrass (Panicum virgatum L.), (ii) big bluestem (Andropogon gerardii Vitman), and (iii) low‐diversity grass mixture [big bluestem, indiangrass (Sorghastrum nutans (L.) Nash), and sideoats grama (Bouteloua curtipendula (Michx.) Torr.)], and (iv) 50% corn residue removal with three replications. Across years, corn residue removal increased wind‐erodible fraction from 41% to 86% and reduced wet aggregate stability from 1.70 to 1.15 mm compared with WSGs in the upper 7.5 cm soil depth. Corn residue removal also reduced water retention by 15% between −33 and −300 kPa potentials and plant‐available water by 25% in the upper 7.5 cm soil depth. However, corn residue removal did not affect final water infiltration, SOC concentration, soil fertility, and other properties. Overall, corn residue removal increases erosion potential and reduces water retention shortly after removal, suggesting that biomass removal from perennial WSGs is a desirable alternative to corn residue removal for biofuel production and maintenance of soil ecosystem services.","Blanco‐Canqui, Humberto; Mitchell, Robert B.; Jin, Virginia L.; Schmer, Marty R.; Eskridge, Kent M.",10.1111/gcbb.12436,,9,"GCB Bioenergy",1510-1521,"Perennial warm‐season grasses for producing biofuel and enhancing soil properties: An alternative to corn residue removal",9,2017,25584,c6d7ea28-baeb-44cc-bef0-4e7d8bebe087,"Journal Article",/article/10.1111/gcbb.12436
/reference/c76d7935-9da3-4c4b-9186-86dc658bcc74,https://data.globalchange.gov/reference/c76d7935-9da3-4c4b-9186-86dc658bcc74,c76d7935-9da3-4c4b-9186-86dc658bcc74,,"Gamble, Janet L.; Balbus, John; Berger, Martha; Bouye, Karen; Campbell, Vince; Chief, Karletta; Conlon, Kathryn; Crimmins, Allison; Flanagan, Barry; Gonzalez-Maddux, Cristina; Hallisey, Elaine; Hutchins, Sonja; Jantarasami, Lesley; Khoury, Samar; Kiefer, Max; Kolling, Jessica; Lynn, Kathy; Manangan, Arie; McDonald, Marian; Morello-Frosch, Rachel; Redsteer, Margaret Hiza; Sheffield, Perry; Thigpen Tart, Kimberly; Watson, Joanna; Whyte, Kyle Powys; Wolkin, Amy Funk",10.7930/J0Q81B0T,,,,"247–286","Ch. 9: Populations of concern",,2016,19381,c76d7935-9da3-4c4b-9186-86dc658bcc74,"Book Section",/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern
/reference/c779538d-b066-4e38-8527-ff3f7552f26e,https://data.globalchange.gov/reference/c779538d-b066-4e38-8527-ff3f7552f26e,c779538d-b066-4e38-8527-ff3f7552f26e,"The Southwestern US is a five-state region that has supported animal agriculture since the late 16th Century when European settlers crossed the Rio Grande into present day west Texas and southern New Mexico with herds of cattle, sheep, goats and horses. For the past 400 years the rangeland livestock industry, in its many forms and manifestations, has developed management strategies and conservation practices that impart resilience to the climatic extremes, especially prolonged droughts, that are common and extensive across this region. Livestock production from rangelands in the southwest (SW) is adapted to low rainfall and high ambient temperatures, but will have to continue to adapt management strategies, such as reduced stocking rates, proper grazing management practices, employing animal genetics suited to arid environments with less herbaceous production, erosion control conservation practices, and alternative forage supplies, in an increasingly arid and variable climatic environment. Even though the aging demographics of western ranchers could be a deterrent to implementing various adaptations, there are examples of creative management coalitions to cope with climatic change that are emerging in the SW that can serve as instructive examples. More importantly, there are additional opportunities for incorporation of transformative practices and technologies that can sustain animal agriculture in the SW in a warmer environment. Animal agriculture in the SW is inherently resilient, and has the capacity to adapt and transform as needed to the climatic changes that are now occurring and will continue to occur across this region. However, producers and land managers will need to thoroughly understand the vulnerabilities and sensitivities that face them as well as the ecological characteristics of their specific landscapes in order to cope with the emerging climatic changes across the SW region.","Havstad, K. M.; Brown, J. R.; Estell, R.; Elias, E.; Rango, A.; Steele, C.",10.1007/s10584-016-1834-7,"November 08",,"Climatic Change",,"Vulnerabilities of southwestern U.S. rangeland-based animal agriculture to climate change",,2016,23531,c779538d-b066-4e38-8527-ff3f7552f26e,"Journal Article",/article/10.1007/s10584-016-1834-7
/reference/c8348455-9866-465b-8291-35119f3eb615,https://data.globalchange.gov/reference/c8348455-9866-465b-8291-35119f3eb615,c8348455-9866-465b-8291-35119f3eb615,"While most models project large increases in agricultural drought frequency and severity in the 21st century, significant uncertainties exist in these projections. Here, we compare the model-simulated changes with observation-based estimates since 1900 and examine model projections from both the Coupled Model Inter-comparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5). We use the self-calibrated Palmer Drought Severity Index with the Penman-Monteith potential evapotranspiration (PET) (sc_PDSI_pm) as a measure of agricultural drought. Results show that estimated long-term changes in global and hemispheric drought areas from 1900 to 2014 are consistent with the CMIP3 and CMIP5 model-simulated response to historical greenhouse gases and other external forcing, with the short-term variations within the model spread of internal variability, despite that regional changes are still dominated by internal variability. Both the CMIP3 and CMIP5 models project continued increases (by 50–200 % in a relative sense) in the 21st century in global agricultural drought frequency and area even under low-moderate emissions scenarios, resulting from a decrease in the mean and flattening of the probability distribution functions (PDFs) of the sc_PDSI_pm. This flattening is especially pronounced over the Northern Hemisphere land, leading to increased drought frequency even over areas with increasing sc_PDSI_pm. Large differences exist in the CMIP3 and CMIP5 model-projected precipitation and drought changes over the Sahel and northern Australia due to uncertainties in simulating the African Inter-tropical convergence zone (ITCZ) and the subsidence zone over northern Australia, while the wetting trend over East Africa reflects a robust response of the Indian Ocean ITCZ seen in both the CMIP3 and CMIP5 models. While warming-induced PET increases over all latitudes and precipitation decreases over subtropical land are responsible for mean sc_PDSI_pm decreases, the exact cause of its PDF flattening needs further investigation.","Zhao, Tianbao; Dai, Aiguo",10.1007/s10584-016-1742-x,"October 01",3,"Climatic Change",535-548,"Uncertainties in historical changes and future projections of drought. Part II: Model-simulated historical and future drought changes",144,2017,23595,c8348455-9866-465b-8291-35119f3eb615,"Journal Article",/article/10.1007/s10584-016-1742-x
/reference/c84eac2e-049f-4d86-8019-e72c98bd8fbf,https://data.globalchange.gov/reference/c84eac2e-049f-4d86-8019-e72c98bd8fbf,c84eac2e-049f-4d86-8019-e72c98bd8fbf,,"Hatfield, Jerry L.; Prueger, John H.",10.1016/j.wace.2015.08.001,2015/12/01/,"Part A","Weather and Climate Extremes",4-10,"Temperature extremes: Effect on plant growth and development",10,2015,23528,c84eac2e-049f-4d86-8019-e72c98bd8fbf,"Journal Article",/article/10.1016/j.wace.2015.08.001
/reference/c918cb9e-c955-497f-b242-e68359b56b77,https://data.globalchange.gov/reference/c918cb9e-c955-497f-b242-e68359b56b77,c918cb9e-c955-497f-b242-e68359b56b77,,"Asseng, S.; Ewert, F.; Martre, P.; Rötter, R. P.; Lobell, D. B.; Cammarano, D.; Kimball, B. A.; Ottman, M. J.; Wall, G. W.; White, J. W.; Reynolds, M. P.; Alderman, P. D.; Prasad, P. V. V.; Aggarwal, P. K.; Anothai, J.; Basso, B.; Biernath, C.; Challinor, A. J.; De Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Koehler, A. K.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J. E.; Palosuo, T.; Priesack, E.; Eyshi Rezaei, E.; Ruane, A. C.; Semenov, M. A.; Shcherbak, I.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P. J.; Waha, K.; Wang, E.; Wallach, D.; Wolf, J.; Zhao, Z.; Zhu, Y.",10.1038/nclimate2470,12/22/online,,"Nature Climate Change",143-147,"Rising temperatures reduce global wheat production",5,2015,23497,c918cb9e-c955-497f-b242-e68359b56b77,"Journal Article",/article/10.1038/nclimate2470
/reference/c97a2716-9162-4e1d-ad39-ca1589a8d760,https://data.globalchange.gov/reference/c97a2716-9162-4e1d-ad39-ca1589a8d760,c97a2716-9162-4e1d-ad39-ca1589a8d760,"Estimating the impact of heat waves on human mortality is key when it comes to the design of effective climate change adaptation measures. As the usual approach—relying on detailed health data in form of hospital records—is not feasible for many countries, a different methodology is needed. This work presents such an approach. Based on singular spectrum analysis and using monthly mortality rates—partly ranging back to 1960—it derives excess mortality estimates for 27 European countries. Excess mortality is then regressed against a heat wave measure in order to assess the health impacts of extreme heat. The analysis demonstrates that many European countries are severely affected by heat waves: On average, 0.61%—and up to 1.14% in case of Portugal—of all deaths are caused by extreme heat events. This finding confirms the understanding that climate change is a major environmental risk to public health: In the 27 examined European countries, over 28,000 people die every year due to exposure to extreme heat.","Merte, Steffen",10.1007/s10584-017-1937-9,"June 01",3,"Climatic Change",321-330,"Estimating heat wave-related mortality in Europe using singular spectrum analysis",142,2017,23562,c97a2716-9162-4e1d-ad39-ca1589a8d760,"Journal Article",/article/10.1007/s10584-017-1937-9
/reference/c9b7bbf7-7002-4a58-ad10-adb2f5d29b47,https://data.globalchange.gov/reference/c9b7bbf7-7002-4a58-ad10-adb2f5d29b47,c9b7bbf7-7002-4a58-ad10-adb2f5d29b47,"Long-term warming trends across the globe have shifted the distribution of temperature variability, such that what was once classified as extreme heat relative to local mean conditions has become more common. This is also true for agricultural regions, where exposure to extreme heat, particularly during key growth phases such as the reproductive period, can severely damage crop production in ways that are not captured by most crop models. Here, we analyze exposure of crops to physiologically critical temperatures in the reproductive stage ( T crit ), across the global harvested areas of maize, rice, soybean and wheat. Trends for the 1980–2011 period show a relatively weak correspondence ( r = 0.19) between mean growing season temperature and T crit exposure trends, emphasizing the importance of separate analyses for T crit . Increasing T crit exposure in the past few decades is apparent for wheat in Central and South Asia and South America, and for maize in many diverse locations across the globe. Maize had the highest percentage (15%) of global harvested area exposed to at least five reproductive days over T crit in the 2000s, although this value is somewhat sensitive to the exact temperature used for the threshold. While there was relatively little sustained exposure to reproductive days over T crit for the other crops in the past few decades, all show increases with future warming. Using projections from climate models we estimate that by the 2030s, 31, 16, and 11% respectively of maize, rice, and wheat global harvested area will be exposed to at least five reproductive days over T crit in a typical year, with soybean much less affected. Both maize and rice exhibit non-linear increases with time, with total area exposed for rice projected to grow from 8% in the 2000s to 27% by the 2050s, and maize from 15 to 44% over the same period. While faster development should lead to earlier flowering, which would reduce reproductive extreme heat exposure for wheat on a global basis, this would have little impact for the other crops. Therefore, regardless of the impact of other global change factors (such as increasing atmospheric CO 2 ), reproductive extreme heat exposure will pose risks for global crop production without adaptive measures such as changes in sowing dates, crop and variety switching, expansion of irrigation, and agricultural expansion into cooler areas.","Gourdji, Sharon M. ; Adam M. Sibley; David B. Lobell",10.1088/1748-9326/8/2/024041,,2,"Environmental Research Letters",024041,"Global crop exposure to critical high temperatures in the reproductive period: Historical trends and future projections",8,2013,23613,c9b7bbf7-7002-4a58-ad10-adb2f5d29b47,"Journal Article",/article/10.1088/1748-9326/8/2/024041
/reference/ca3887b4-e477-4450-b44c-69b1161977a0,https://data.globalchange.gov/reference/ca3887b4-e477-4450-b44c-69b1161977a0,ca3887b4-e477-4450-b44c-69b1161977a0,,"Prager, Daniel; Christopher Burns; Nigel Key",,,August,,,"Examining farm sector and farm household income ",,2017,23636,ca3887b4-e477-4450-b44c-69b1161977a0,"Electronic Article",/generic/fb22cc2a-e156-481b-9014-c91e0d6e848d
/reference/ca67dba0-56b7-4ac4-ae5e-0e712a590ddd,https://data.globalchange.gov/reference/ca67dba0-56b7-4ac4-ae5e-0e712a590ddd,ca67dba0-56b7-4ac4-ae5e-0e712a590ddd,"Using an ensemble of coupled physical‐biogeochemical models driven with regionalized data from global climate simulations we are able to quantify the influence of changing climate upon oxygen conditions in one of the numerous coastal seas (the Baltic Sea) that suffers worldwide from eutrophication and from expanding hypoxic zones. Applying various nutrient load scenarios we show that under the impact of warming climate hypoxic and anoxic areas will very likely increase or at best only slightly decrease (in case of optimistic nutrient load reductions) compared to present conditions, regardless of the used global model and climate scenario. The projected decreased oxygen concentrations are caused by (1) enlarged nutrient loads due to increased runoff, (2) reduced oxygen flux from the atmosphere to the ocean due to increased temperature, and (3) intensified internal nutrient cycling. In future climate a similar expansion of hypoxia as projected for the Baltic Sea can be expected also for other coastal oceans worldwide.","Meier, H. E. M.; Andersson, H. C.; Eilola, K.; Gustafsson, B. G.; Kuznetsov, I.; Müller‐Karulis, B.; Neumann, T.; Savchuk, O. P.",10.1029/2011GL049929,,24,"Geophysical Research Letters",,"Hypoxia in future climates: A model ensemble study for the Baltic Sea",38,2011,25545,ca67dba0-56b7-4ac4-ae5e-0e712a590ddd,"Journal Article",/article/10.1029/2011GL049929
/reference/cd6bd680-f138-498d-a9b6-0f08b968d6e8,https://data.globalchange.gov/reference/cd6bd680-f138-498d-a9b6-0f08b968d6e8,cd6bd680-f138-498d-a9b6-0f08b968d6e8,,"Challinor, A. J.; Watson, J.; Lobell, D. B.; Howden, S. M.; Smith, D. R.; Chhetri, N.",10.1038/nclimate2153,04//print,4,"Nature Climate Change",287-291,"A meta-analysis of crop yield under climate change and adaptation",4,2014,20341,cd6bd680-f138-498d-a9b6-0f08b968d6e8,"Journal Article",/article/10.1038/nclimate2153
/reference/ced8505a-f36f-4c7b-8a0d-ec7f08482297,https://data.globalchange.gov/reference/ced8505a-f36f-4c7b-8a0d-ec7f08482297,ced8505a-f36f-4c7b-8a0d-ec7f08482297,,"Houghton, Adele; Austin, Jessica; Beerman, Abby; Horton, Clayton",10.1155/2017/3407325,,,"Journal of Environmental and Public Health",16,"An approach to developing local climate change environmental public health indicators in a rural district",2017,2017,23534,ced8505a-f36f-4c7b-8a0d-ec7f08482297,"Journal Article",/article/10.1155/2017/3407325
/reference/d0b6d345-8a94-4b3a-a191-0f09505948a1,https://data.globalchange.gov/reference/d0b6d345-8a94-4b3a-a191-0f09505948a1,d0b6d345-8a94-4b3a-a191-0f09505948a1,,"Derner, Justin D.; Augustine, David J.",10.1016/j.rala.2016.05.002,2016/08/01/,4,Rangelands,211-215,"Adaptive management for drought on rangelands",38,2016,21586,d0b6d345-8a94-4b3a-a191-0f09505948a1,"Journal Article",/article/10.1016/j.rala.2016.05.002
/reference/d14eb52d-2a33-414b-bf2f-a868b2417600,https://data.globalchange.gov/reference/d14eb52d-2a33-414b-bf2f-a868b2417600,d14eb52d-2a33-414b-bf2f-a868b2417600,"Agriculture consumes more than two thirds of the total freshwater of the planet. This issue causes substantial conflict in freshwater allocation between agriculture and other economic sectors. Regulated deficit irrigation (RDI) is key technology because it helps to improve water use efficiency. Nonetheless, there is a lack of understanding of the mechanisms with which plants respond to RDI. In particular, little is known about how RDI might increase crop production while reducing the amount of irrigation water in real-world agriculture. In this review, we found that RDI is largely implemented through three approaches: (1) growth stage-based deficit irrigation, (2) partial root-zone irrigation, and (3) subsurface dripper irrigation. Among these, partial root-zone irrigation is the most popular and effective because many field crops and some woody crops can save irrigation water up to 20 to 30 % without or with a minimal impact on crop yield. Improved water use efficiency with RDI is mainly due to the following: (1) enhanced guard cell signal transduction network that decreases transpiration water loss, (2) optimized stomatal control that improves the photosynthesis to transpiration ratio, and (3) decreased evaporative surface areas with partial root-zone irrigation that reduces soil evaporation. The mechanisms involved in the plant response to RDI-induced water stress include the morphological traits, e.g., increased root to shoot ratio and improved nutrient uptake and recovery; physiological traits, e.g., stomatal closure, decreased leaf respiration, and maintained photosynthesis; and biochemical traits, e.g., increased signaling molecules and enhanced antioxidation enzymatic activity.","Chai, Qiang; Gan, Yantai; Zhao, Cai; Xu, Hui-Lian; Waskom, Reagan M.; Niu, Yining; Siddique, Kadambot H. M.",10.1007/s13593-015-0338-6,"December 18",1,"Agronomy for Sustainable Development",3,"Regulated deficit irrigation for crop production under drought stress. A review",36,2015,25592,d14eb52d-2a33-414b-bf2f-a868b2417600,"Journal Article",/article/10.1007/s13593-015-0338-6
/reference/d2af0d06-91aa-4e53-99e1-4dad2ac9195a,https://data.globalchange.gov/reference/d2af0d06-91aa-4e53-99e1-4dad2ac9195a,d2af0d06-91aa-4e53-99e1-4dad2ac9195a,,"Mallakpour, Iman; Villarini, Gabriele",10.1038/nclimate2516,03//print,3,"Nature Climate Change",250-254,"The changing nature of flooding across the central United States",5,2015,19562,d2af0d06-91aa-4e53-99e1-4dad2ac9195a,"Journal Article",/article/10.1038/nclimate2516
/reference/d3e0a9e1-9ff9-492c-ba13-9f24976fa65a,https://data.globalchange.gov/reference/d3e0a9e1-9ff9-492c-ba13-9f24976fa65a,d3e0a9e1-9ff9-492c-ba13-9f24976fa65a,,"CENR,",,,,,154,"Scientific Assessment of Hypoxia in U.S. Coastal Waters. Interagency Working Group on Harmful Algal Blooms, Hypoxia, and Human Health of the Joint Subcommittee on Ocean Science and Technology",,2010,1501,d3e0a9e1-9ff9-492c-ba13-9f24976fa65a,Report,/report/cenrs-hypoxia-2010
/reference/d51156cc-0034-4afc-b2b7-1ad99efde458,https://data.globalchange.gov/reference/d51156cc-0034-4afc-b2b7-1ad99efde458,d51156cc-0034-4afc-b2b7-1ad99efde458,,"Brown, M.E.; J.M. Antle; P. Backlund; E.R. Carr; W.E. Easterling; M.K. Walsh; C. Ammann; W. Attavanich; C.B. Barrett; M.F. Bellemare; V. Dancheck; C. Funk; K. Grace; J.S.I. Ingram; H. Jiang; H. Maletta; T. Mata; A. Murray; M. Ngugi; D. Ojima; B. O’Neill; C. Tebaldi",10.7930/J0862DC7,,,,146,"Climate Change, Global Food Security, and the U.S. Food System",,2015,23655,d51156cc-0034-4afc-b2b7-1ad99efde458,Report,/report/usda-climate-change-global-food-security-us-food-system-2015
/reference/d5ed58e8-a5b6-48a4-b648-7fe64fc6ecd5,https://data.globalchange.gov/reference/d5ed58e8-a5b6-48a4-b648-7fe64fc6ecd5,d5ed58e8-a5b6-48a4-b648-7fe64fc6ecd5,,"Havlík, Petr; Valin, Hugo; Mosnier, Aline; Obersteiner, Michael; Baker, Justin S.; Herrero, Mario; Rufino, Mariana C.; Schmid, Erwin",10.1093/ajae/aas085,,2,"American Journal of Agricultural Economics",442-448,"Crop productivity and the global livestock sector: Implications for land use change and greenhouse gas emissions",95,2013,23532,d5ed58e8-a5b6-48a4-b648-7fe64fc6ecd5,"Journal Article",/article/10.1093/ajae/aas085
/reference/d664baed-2396-4be8-8e03-54d74d733c44,https://data.globalchange.gov/reference/d664baed-2396-4be8-8e03-54d74d733c44,d664baed-2396-4be8-8e03-54d74d733c44,"Renewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas (GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands. Performance data for such systems is sparse, and non‐linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low‐impact commercial‐scale feedstock production. Process‐based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes. However, there are few examples of ecosystem model parameterization against field trials on both prime and marginal lands or of conducting landscape‐scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity. In this work we used a data‐diverse, multi‐criteria approach to parameterize and validate the DayCent biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high‐resolution case study analysis of a real‐world cellulosic biofuel landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint. Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land‐use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity. This in turn implies a large sustainable cellulosic biofuel landscape design space within which a system can be optimized to meet economic or environmental objectives.","Field, John L.; Marx, Ernie; Easter, Mark; Adler, Paul R.; Paustian, Keith",10.1111/gcbb.12316,,6,"GCB Bioenergy",1106-1123,"Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design",8,2016,25569,d664baed-2396-4be8-8e03-54d74d733c44,"Journal Article",/article/10.1111/gcbb.12316
/reference/d6e020ac-3c60-45fe-b76f-c4e9d4502838,https://data.globalchange.gov/reference/d6e020ac-3c60-45fe-b76f-c4e9d4502838,d6e020ac-3c60-45fe-b76f-c4e9d4502838,"The Accurate daily reference evapotranspiration (ET) values are needed to estimate crop water demand for irrigation management and hydrologic modeling purposes. The Bushland Reference ET Calculator (BET) was developed to implement a user-friendly interface for calculating hourly and daily grass and alfalfa reference ET using the Java Programming Language. The calculator uses the American Society of Civil Engineers (ASCE) Standardized Reference ET equation for calculating both grass and alfalfa reference ET at hourly and daily time steps from a single set or time series of weather data. Daily reference ET can be calculated either by calculating the hourly reference ET values and summing them up or by calculating a daily value using daily statistics of the climatic data (means, maxima, and minima). Graphing capabilities include line graph and scatter plot for quality assurance and quality control purposes. Descriptive statistics can be calculated for selected or all of the variables. Although the “Bushland Reference ET Calculator” was designed and developed for use mainly by producers and crop consultants to manage irrigation scheduling, it can also be used in educational training, research, and other practical applications. This article demonstrates the use of the Bushland Reference ET Calculator that is available from the USDA-ARS Conservation and Production Research Laboratory web site to interested users at no cost.","Gowda, Prasanna H.; Howell, Terry A.; Baumhardt, R. Louis; Porter, Dana O.; Marek, Thomas H.; Nangia, Vinay",10.13031/aea.32.11673,,3,"Applied Engineering in Agriculture",383,"A user-friendly interactive tool for estimating reference ET using ASCE standardized Penman-Monteith equation",32,2016,25568,d6e020ac-3c60-45fe-b76f-c4e9d4502838,"Journal Article",/article/10.13031/aea.32.11673
/reference/d721e218-0d4a-47ef-81a1-a148a38bca7c,https://data.globalchange.gov/reference/d721e218-0d4a-47ef-81a1-a148a38bca7c,d721e218-0d4a-47ef-81a1-a148a38bca7c,"Long-term declines in oxygen concentrations are evident throughout much of the ocean interior and are particularly acute in midwater oxygen minimum zones (OMZs). These regions are defined by extremely low oxygen concentrations (<20–45 μmol kg−1), cover wide expanses of the ocean, and are associated with productive oceanic and coastal regions. OMZs have expanded over the past 50 years, and this expansion is predicted to continue as the climate warms worldwide. Shoaling of the upper boundaries of the OMZs accompanies OMZ expansion, and decreased oxygen at shallower depths can affect all marine organisms through multiple direct and indirect mechanisms. Effects include altered microbial processes that produce and consume key nutrients and gases, changes in predator-prey dynamics, and shifts in the abundance and accessibility of commercially fished species. Although many species will be negatively affected by these effects, others may expand their range or exploit new niches. OMZ shoaling is thus likely to have major and far-reaching consequences.","Gilly, William F.; J. Michael Beman; Steven Y. Litvin; Bruce H. Robison",10.1146/annurev-marine-120710-100849,,1,"Annual Review of Marine Science",393-420,"Oceanographic and biological effects of shoaling of the oxygen minimum zone",5,2013,23768,d721e218-0d4a-47ef-81a1-a148a38bca7c,"Journal Article",/article/10.1146/annurev-marine-120710-100849
/reference/d8089822-678e-4834-a1ec-0dca1da35314,https://data.globalchange.gov/reference/d8089822-678e-4834-a1ec-0dca1da35314,d8089822-678e-4834-a1ec-0dca1da35314,,"Parris, A.
P. Bromirski
V. Burkett
D. Cayan
M. Culver
J. Hall
R. Horton
K. Knuuti
R. Moss
J. Obeysekera
A. Sallenger
J. Weiss",,,,,37,"Global Sea Level Rise Scenarios for the United States National Climate Assessment. NOAA Tech Memo OAR CPO-1",,2012,2432,d8089822-678e-4834-a1ec-0dca1da35314,Report,/report/noaa-techmemo-oar-cpo-1-2012
/reference/d812667f-d643-497f-b969-be0acd154c4d,https://data.globalchange.gov/reference/d812667f-d643-497f-b969-be0acd154c4d,d812667f-d643-497f-b969-be0acd154c4d,,"Eisler, Mark C.; Michael R. F. Lee; John F. Tarlton; Graeme B. Martin; John Beddington; Jennifer A. J. Dungait; Henry Greathead; Jianxin Liu; Stephen Mathew; Helen Miller; Tom Misselbrook; Phil Murray; Valil K. Vinod; Robert Van Saun; Michael Winter",10.1038/507032a,,,Nature,32-34,"Agriculture: Steps to sustainable livestock",507,2014,23517,d812667f-d643-497f-b969-be0acd154c4d,"Journal Article",/article/10.1038/507032a