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Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1-3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity.

As climate change increases the frequency and intensity of extreme heat, cities and their urban heat island (UHI) effects are growing, as are the urban populations encountering them. These mutually reinforcing trends present a growing risk for urban populations. However, we have limited understanding of urban climates during extreme temperature episodes, when additional heat from the UHI may be most consequential. We observed a historically hot summer and historically cold winter using an array of up to 150 temperature and relative humidity sensors in and around Madison, Wisconsin, an urban area of population 402 000 surrounded by lakes and a rural landscape of agriculture, forests, wetlands, and grasslands. In the summer of 2012 (third hottest since 1869), Madison's urban areas experienced up to twice as many hours ⩾32.2 °C (90 °F), mean July TMAX up to 1.8 °C higher, and mean July TMIN up to 5.3 °C higher than rural areas. During a record setting heat wave, dense urban areas spent over four consecutive nights above the National Weather Service nighttime heat stress threshold of 26.7 °C (80 °F), while rural areas fell below 26.7 °C nearly every night. In the winter of 2013-14 (coldest in 35 years), Madison's most densely built urban areas experienced up to 40% fewer hours ⩽−17.8 °C (0 °F), mean January TMAX up to 1 °C higher, and mean January TMIN up to 3 °C higher than rural areas. Spatially, the UHI tended to be most intense in areas with higher population densities. Temporally, both daytime and nighttime UHIs tended to be slightly more intense during more-extreme heat days compared to average summer days. These results help us understand the climates for which cities must prepare in a warming, urbanizing world.

Spatial and temporal variation in the urban heat island (UHI) effect from March 2012 through October 2013 was characterized using continuous temperature measurements from an array of up to 151 fixed sensors in and around Madison, Wisconsin, an urban area of population 407 000 surrounded by lakes and a rural landscape of agriculture, forests, wetlands, and grasslands. Spatially, the density of the built environment was the primary driver of temperature patterns, with local modifying effects of lake proximity and topographic relief. Temporally, wind speed, cloud cover, relative humidity, soil moisture, and snow all influenced UHI intensity, although the magnitude and significance of their effects varied by season and time of day. Seasonally, UHI intensities tended to be higher during the warmer summer months and lower during the colder months. Seasonal trends in monthly average wind speed and cloud cover tracked annual trends in UHI intensity, with clearer, calmer conditions that are conducive to the stronger UHIs being more common during the summer. However, clear, calm summer nights still had higher UHI intensities than clear, calm winter nights, indicating that some background factor, such as vegetation, shifted baseline UHI intensities throughout the year. The authors propose that regional vegetation and snow-cover conditions set seasonal baselines for UHI intensity and that factors like wind and clouds modified daily UHI intensity around that baseline.

Corn cultivation in the United States is expected to increase to meet demand for ethanol. Nitrogen leaching from fertilized corn fields to the Mississippi-Atchafalaya River system is a primary cause of the bottom-water hypoxia that develops on the continental shelf of the northern Gulf of Mexico each summer. In this study, we combine agricultural land use scenarios with physically based models of terrestrial and aquatic nitrogen to examine the effect of present and future expansion of corn-based ethanol production on nitrogen export by the Mississippi and Atchafalaya Rivers to the Gulf of Mexico. The results show that the increase in corn cultivation required to meet the goal of 15-36 billion gallons of renewable fuels by the year 2022 suggested by a recent U.S. Senate energy policy would increase the annual average flux of dissolved inorganic nitrogen (DIN) export by the Mississippi and Atchafalaya Rivers by 10-34%. Generating 15 billion gallons of corn-based ethanol by the year 2022 will increase the odds that annual DIN export exceeds the target set for reducing hypoxia in the Gulf of Mexico to >95%. Examination of extreme mitigation options shows that expanding corn-based ethanol production would make the already difficult challenges of reducing nitrogen export to the Gulf of Mexico and the extent of hypoxia practically impossible without large shifts in food production and agricultural management.

As the demands for food, feed and fuel increase in coming decades, society will be pressed to increase agricultural production - whether by increasing yields on already cultivated lands or by cultivating currently natural areas - or to change current crop consumption patterns. In this analysis, we consider where yields might be increased on existing croplands, and how crop yields are constrained by biophysical (e.g. climate) versus management factors. This study was conducted at the global scale. Using spatial datasets, we compare yield patterns for the 18 most dominant crops within regions of similar climate. We use this comparison to evaluate the potential yield obtainable for each crop in different climates around the world. We then compare the actual yields currently being achieved for each crop with their 'climatic potential yield' to estimate the 'yield gap'. We present spatial datasets of both the climatic potential yields and yield gap patterns for 18 crops around the year 2000. These datasets depict the regions of the world that meet their climatic potential, and highlight places where yields might potentially be raised. Most often, low yield gaps are concentrated in developed countries or in regions with relatively high-input agriculture. While biophysical factors like climate are key drivers of global crop yield patterns, controlling for them demonstrates that there are still considerable ranges in yields attributable to other factors, like land management practices. With conventional practices, bringing crop yields up to their climatic potential would probably require more chemical, nutrient and water inputs. These intensive land management practices can adversely affect ecosystem goods and services, and in turn human welfare. Until society develops more sustainable high-yielding cropping practices, the trade-offs between increased crop productivity and social and ecological factors need to be made explicit when future food scenarios are formulated.

Early planting of maize (Zea mays L.) allows for longer‐season hybrids to be used in cool temperate regions. Given that a multidecadal trend toward earlier planting has been occurring across the Corn Belt, it was hypothesized that this shift has supported a portion of recent yield increases. The objectives were to quantify relationships among state level monthly climate variables, maize yields, and planting dates, and to investigate whether multidecadal trends of earlier planting contributed to rising yields during 1979 to 2005 in 12 central U.S. states. Year‐to‐year changes (i.e., first differences) of predictor variables (monthly mean temperature and precipitation and planting date) and yields were calculated, and multiple linear regression was used to estimate the effect of planting date trends on maize yield increases. In six of the 12 states, a significant relationship (P < 0.05) existed between first differences of planting dates and yields. Multiple linear regression suggested that the management change has potentially contributed between 19 and 53% of the state level yield increases in Nebraska, South Dakota, Minnesota, Iowa, Wisconsin, and Michigan. Yield increases between 0.06 and 0.14 Mg ha−1 were attributed to each additional day of earlier planting, which likely reflects a gradual adoption of longer‐season hybrids. Thus, if these earlier planting trends were to suddenly abate, a falloff in annual yield increases may follow in several Corn Belt states. Maize production in northern U.S. states appears to have benefited more significantly from earlier planting due to a shorter growing season in contrast to more southern locations.

Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012-2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ∼5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains >50% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.

Sustaining ecosystem services (ES), mitigating their tradeoffs and avoiding unfavorable future trajectories are pressing social-environmental challenges that require enhanced understanding of their relationships across scales. Current knowledge of ES relationships is often constrained to one spatial scale or one snapshot in time. In this research, we integrated biophysical modeling with future scenarios to examine changes in relationships among eight ES indicators from 2001-2070 across three spatial scales-grid cell, subwatershed, and watershed. We focused on the Yahara Watershed (Wisconsin) in the Midwestern United States-an exemplar for many urbanizing agricultural landscapes. Relationships among ES indicators changed over time; some relationships exhibited high interannual variations (e.g. drainage vs. food production, nitrate leaching vs. net ecosystem exchange) and even reversed signs over time (e.g. perennial grass production vs. phosphorus yield). Robust patterns were detected for relationships among some regulating services (e.g. soil retention vs. water quality) across three spatial scales, but other relationships lacked simple scaling rules. This was especially true for relationships of food production vs. water quality, and drainage vs. number of days with runoff >10 mm, which differed substantially across spatial scales. Our results also showed that local tradeoffs between food production and water quality do not necessarily scale up, so reducing local tradeoffs may be insufficient to mitigate such tradeoffs at the watershed scale. We further synthesized these cross-scale patterns into a typology of factors that could drive changes in ES relationships across scales: (1) effects of biophysical connections, (2) effects of dominant drivers, (3) combined effects of biophysical linkages and dominant drivers, and (4) artificial scale effects, and concluded with management implications. Our study highlights the importance of taking a dynamic perspective and accounting for spatial scales in monitoring and management to sustain future ES.

Increases in agricultural productivity are shown, using production statistics and a carbon accounting model, to explain as much as a quarter of the observed increase in the seasonal amplitude of the Northern Hemisphere atmospheric carbon dioxide cycle. Agricultural advance increases atmospheric CO 2 seasonality The atmospheric CO 2 record displays a seasonal cycle reflecting seasonal variations in CO 2 uptake by terrestrial vegetation. An increase in the amplitude of this seasonal cycle over the past five decades cannot be fully explained at present. Two groups now report that the intensification of agriculture may have been a key contributor to the increase in atmospheric CO 2 seasonal amplitude. Ning Zeng et al . used the VEGAS terrestrial biosphere model to show that enhanced mid-latitude agricultural productivity contributed 45% of the increasing amplitude of global net surface carbon fluxes for the period 1961 to 2010, compared to 29% from climate change and 26% from CO 2 fertilization. Josh Gray et al . used crop production statistics from the UN Food and Agriculture Organization and a carbon accounting model to demonstrate that as much as a quarter of the observed change in atmospheric CO 2 seasonality can be explained by elevated crop productivity, with maize, wheat, rice and soybean major contributors. These studies will contribute to a better understanding of the global carbon cycle, and highlight the extent to which human actions are changing large-scale biosphere–atmosphere interactions. Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO 2 ) concentrations has increased by as much as 50 per cent over the past 50 years 1 , 2 , 3 . This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation 4 , 5 , 6 . However, the precise causal mechanisms behind the observed changes in atmospheric CO 2 seasonality remain unclear 2 , 3 , 4 . Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO 2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.

Over-enrichment of phosphorus (P) in agroecosystems contributes to eutrophication of surface waters. In the Midwest US and elsewhere, climate change is increasing the frequency of high-intensity precipitation events, which can serve as a primary conduit of P transport within watersheds. Despite uncertainty in their estimates, process-based watershed models are important tools that help characterize watershed hydrology and biogeochemistry and scale up important mechanisms affecting water quality. Using one such model developed for an agricultural watershed in Wisconsin, we conducted a 2 × 2 factorial experiment to test the effects of (high/low) terrestrial P supply (PSUP) and (high/low) precipitation intensity (PREC) on surface water quality. Sixty-year simulations were conducted for each of the four runs, with annual results obtained for watershed average P yield and concentration at the field scale (220 × 220 m grid cells), P load and concentration at the stream scale, and summertime total P concentration (TP) in Lake Mendota. ANOVA results were generated for the 2 × 2 factorial design, with PSUP and PREC treated as categorical variables. The results showed a significant, positive interaction (p < 0.01) between the two drivers for dissolved P concentration at the field and stream scales, and total P concentration at the field, stream, and lake scales. The synergy in dissolved P was linked to nonlinear dependencies between P stored in manure and the daily runoff to rainfall ratio. The synergistic response of dissolved P loss may have important ecological consequences because dissolved P is highly bioavailable. Overall, the results suggest that high levels of terrestrial P supplied as manure can exacerbate water quality problems in the future as the frequency of high-intensity rainfall events increases with a changing climate. Conversely, lowering terrestrial manure P supply may help improve the resilience of surface water quality to extreme events.