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As wind energy expands to achieve the United States' net zero emission goals, compensatory mitigation will be required to offset negative effects on birds and bats. The golden eagle (Aquila chrysaetos) is particularly susceptible to collision with wind turbines, but only 1 option for offsetting mortalities has been approved by the United States Fish and Wildlife Service despite many sources of anthropogenic‐caused mortality. We update a previously developed vehicle‐collision model with data collected during 3 winters from 2016 to 2019 and integrate a resource equivalency analysis so that relocation of road‐killed game animals can be used as mitigation to offset incidental mortality. We parameterized golden eagle behaviors using motion‐sensitive cameras placed at roadside carcasses. We quantified the effects of different carcass‐relocation schemes based on vehicle and carcass characteristics observed for Wyoming, USA. Our model results indicate that while eagles saved per relocated carcass depends on relocation interval and vehicle traffic volume, carcass relocation is a viable mitigation strategy; up to 7 eagles could be saved each year in some counties. While some uncertainty remains about the precise credit received from each carcass relocated, delaying the inclusion of additional mitigation methods prevents opportunities for conservation action. An adaptive management program could be a way forward where management and monitoring are combined to further improve estimates of mitigation credit. [ABSTRACT FROM AUTHOR]

Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world's future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture's environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing 'yield gaps' on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture., Contemporary agriculture faces enormous challenges (1-3). Even with recent productivity gains, roughly one in seven people lack access to food or are chronically malnourished, stemming from continued poverty and mounting [...]

In the coming decades, a crucial challenge for humanity will be meeting future food demands without undermining further the integrity of the Earth's environmental systems (1-6). Agricultural systems are already [...]

Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world's future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture's environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing 'yield gaps' on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture., Contemporary agriculture faces enormous challenges (1-3). Even with recent productivity gains, roughly one in seven people lack access to food or are chronically malnourished, stemming from continued poverty and mounting [...]

Crop yields are projected to decrease under future climate conditions, and recent research suggests that yields have already been impacted. However, current impacts on a diversity of crops subnationally and implications for food security remains unclear. Here, we constructed linear regression relationships using weather and reported crop data to assess the potential impact of observed climate change on the yields of the top ten global crops–barley, cassava, maize, oil palm, rapeseed, rice, sorghum, soybean, sugarcane and wheat at ~20,000 political units. We find that the impact of global climate change on yields of different crops from climate trends ranged from -13.4% (oil palm) to 3.5% (soybean). Our results show that impacts are mostly negative in Europe, Southern Africa and Australia but generally positive in Latin America. Impacts in Asia and Northern and Central America are mixed. This has likely led to ~1% average reduction (-3.5 X 1013 kcal/year) in consumable food calories in these ten crops. In nearly half of food insecure countries, estimated caloric availability decreased. Our results suggest that climate change has already affected global food production. [ABSTRACT FROM AUTHOR]

Global agriculture is under pressure to meet increasing demand for food and agricultural products. There are several global assessments of crop yields, but we know little about the uncertainties of their key findings, as the assessments are driven by the single best yield dataset available when each assessment was conducted. Recently, two different spatially explicit, global, historical yield datasets, one based on agricultural census and the other largely based on satellite remote sensing, became available. Using these datasets, we compare the similarities and differences in global yield gaps, trend patterns, growth rates and changes in year-to-year variability. We analyzed maize, rice, wheat and soybean for the period of 1981 to 2008 at four resolutions (0.083°, 0.5°, 1.0° and 2.0°). Although estimates varied by dataset and resolution, the global mean annual growth rates of 1.7–1.8%, 1.5–1.7%, 1.1–1.3% and 1.4–1.6% for maize, rice, wheat and soybean, respectively, are not on track to double crop production by 2050. Potential production increases that can be attributed to closing yield gaps estimated from the satellite-based dataset are almost twice those estimated from the census-based dataset. Detected yield variability changes in rice and wheat are sensitive to the choice of dataset and resolution, but they are relatively robust for maize and soybean. Estimates of yield gaps and variability changes are more uncertain than those of yield trend patterns and growth rates. These tendencies are consistent across crops. Efforts to reduce uncertainties are required to gain a better understanding of historical change and crop production potential to better inform agricultural policies and investments. [ABSTRACT FROM AUTHOR]

Abstract: Phosphorus (P) is an essential element for plant growth. Low P availability in soils is likely to limit crop yields in many parts of the world, but this effect has never been quantified at the global scale by process‐based models. Here we attempt to estimate P limitation in three major cereals worldwide for the year 2000 by combining information on soil P distribution in croplands and a generic crop model, while accounting for the nature of soil‐plant P transport. As a global average, the diffusion‐limited soil P supply meets the crop's P demand corresponding to the climatic yield potential, due to the legacy soil P in highly fertilized areas. However, when focusing on the spatial distribution of P supply versus demand, we found strong limitation in regions like North and South America, Africa, and Eastern Europe. Averaged over grid cells where P supply is lower than demand, the global yield gap due to soil P is estimated at 22, 55, and 26% in winter wheat, maize, and rice. Assuming that a fraction (20%) of the annual P applied in fertilizers is directly available to the plant, the global P yield gap lowers by only 5–10%, underlying the importance of the existing soil P supply in sustaining crop yields. The study offers a base for exploring P limitation in crops worldwide but with certain limitations remaining. These could be better accounted for by describing the agricultural P cycle with a fully coupled and mechanistic soil‐crop model. [ABSTRACT FROM AUTHOR]

Efficiently allocating nitrogen (N) across space maximizes crop productivity for a given amount of N input and reduces N losses to the environment. Here we quantify changes in the global spatial efficiency of cropland N use by calculating historical trade-off frontiers relating N inputs to possible N yield assuming efficient allocation. Time series cropland N budgets from 1961 to 2009 characterize the evolution of N input-yield response functions across 12 regions and are the basis for constructing trade-off frontiers. Improvements in agronomic technology have substantially increased cropping system yield potentials and expanded N-driven crop production possibilities. However, we find that these gains are compromised by the declining spatial efficiency of N use across regions. Since the start of the Green Revolution, N inputs and yields have moved farther from the optimal frontier over time; in recent years (1994-2009), global N surplus has grown to a value that is 69% greater than what is possible with efficient N allocation between regions. To reflect regional pollution and agricultural development goals, we construct scenarios that restrict reallocation, finding that these changes only slightly decrease potential gains in nitrogen use efficiency. Our results are inherently conservative due to the regional unit of analysis, meaning a larger potential exists than is quantified here for cross-scale policies to promote spatially efficient N use. [ABSTRACT FROM AUTHOR]

Nature 490, 254-257 (2012); doi:10.1038/nature11420 In this Letter, a minor error in the mapping program used to generate Fig. 1 incorrectly displayed reduced yield gap estimates across a few areas; [...]

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2O emissions at the country scale by aggregating all crops, under the assumption that N2O emissions are linearly related to N application. However, field studies and meta-analyses indicate a nonlinear relationship, in which N2O emissions are relatively greater at higher N application rates. Here, we apply a super-linear emissions response model to crop-specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2O emissions from croplands. We estimate 0.66 Tg of N2O-N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2O emissions range from 20% to 40% lower throughout sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2O emissions estimates. [ABSTRACT FROM AUTHOR]

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data. [ABSTRACT FROM AUTHOR]

A growing and more affluent human population is expected to increase the demand for resources and to accelerate habitat modification, but by how much and where remains unknown. Here we project and aggregate global spatial patterns of expected urban and agricultural expansion, conventional and unconventional oil and gas, coal, solar, wind, biofuels and mining development. Cumulatively, these threats place at risk 20% of the remaining global natural lands (19.68 million km2) and could result in half of the world’s biomes becoming >50% converted while doubling and tripling the extent of land converted in South America and Africa, respectively. Regionally, substantial shifts in land conversion could occur in Southern and Western South America, Central and Eastern Africa, and the Central Rocky Mountains of North America. With only 5% of the Earth’s at-risk natural lands under strict legal protection, estimating and proactively mitigating multi-sector development risk is critical for curtailing the further substantial loss of nature. [ABSTRACT FROM AUTHOR]

The intensity coherence function of time for partially saturated acoustic propagation through internal waves is calculated with a method that is improved over previous treatments. Two specific improvements are introduced: the usual expansion in (1/Λ[uppercase_phi_synonym]2) is carried out to a higher order, and then the terms of that expansion are calculated with a new perturbative method. The method is applied to propagation without a sound channel, for both phase-screen and continuous-medium cases. The validity of the new perturbative method is estimated by calculating the next order error terms. Accuracies at the few-percent level are found. The new analytic formulas are also corroborated with numerical integration. Finally, the method is applied to a specific ocean-acoustic experiment [Azores Fixed Acoustic Range (AFAR)]. In order to achieve good agreement with experiment it will be necessary to add an accurate treatment of the sound channel to the present perturbation method. © 2001 Acoustical Society of America. [ABSTRACT FROM AUTHOR]

Earlier work by Miller et al. [J. Acoust. Soc. Am. 86, 326-345 (1989)] suggested that the surface gravity wave frequency-direction spectrum could be measured using acoustic tomography signals. An experiment was performed in Monterey Bay, California, from 12-16 December 1988 to validate this earlier work. During that period, an acoustic tomography source with a center frequency of 224 Hz and bandwidth of 16 Hz was deployed, continuously repeating its coded transmissions every 1.9375 s. The transmissions were received on a number of modified sonobuoys placed on the continental shelf at a range of approximately 55 km. Although the acoustic propagation was complicated by the presence of the Monterey Submarine Canyon, a hybrid ray and mode interpretation explains the observations. The main effect of the surface waves on the acoustic signals is determined to occur in the shallow continental shelf portion of the path. On the shelf, it is shown that the resolved acoustic normal modes exhibit the fluctuations in travel time described in our earlier work. The acoustic measurements of the surface wave spectrum compared well with measurements made by a pitch-roll wave buoy maintained in the area by the National Data Buoy Center (NDBC). The acoustic and buoy spectra agreed well on the location of the peak swell frequency; the root-mean-square (rms) wave amplitudes from the two measures agreed to within 7%. [ABSTRACT FROM AUTHOR]

Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user's accuracy and producer's accuracy of our generated map are around 85% and 80% in our study region. [ABSTRACT FROM AUTHOR]

In December 1988, an experiment was conducted in Monterey Bay, California in order to verify earlier work [J. H. Miller, J. F. Lynch, and C. S. Chiu, J. Acoust. Soc. Am. 86, 326-345 (1989)] which proposed using acoustic tomography to observe the surface wave frequency-direction spectrum. In that paper, the relationships between both ray and model travel time spectra and the surface wave frequency-direction spectrum were derived for an otherwise range-independent waveguide. A mode description of the propagation on the continental shelf portion of the acoustic path in Monterey Bay is shown to be appropriate. The previous formulation to include a range-dependent bathymetry was modified. Tomographically derived wave spectra using the modal formalism agree well with spectra obtained from a wave buoy [Work supported by the Naval Postgraduate School and ONR.] [ABSTRACT FROM AUTHOR]