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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]

Achieving sustainable global food security is one of humanity's contemporary challenges. Here we present an analysis identifying key "global leverage points" that offer the best opportunities to improve both global food security and environmental sustainability. We find that a relatively small set of places and actions could provide enough new calories to meet the basic needs for more than 3 billion people, address many environmental impacts with global consequences, and focus food waste reduction on the commodities with the greatest impact on food security. These leverage points in the global food system can help guide how nongovernmental organizations, foundations, governments, citizens' groups, and businesses prioritize actions. [ABSTRACT FROM AUTHOR]

Expanding croplands to meet the needs of a growing population. changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha-1∼63 tons·ha-1) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha-1y-1 vs. 3.84 tons·ha-1y-1). There fore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions. [ABSTRACT FROM AUTHOR]

Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands. [ABSTRACT FROM AUTHOR]

The impacts of changing land cover on the soil-vegetation-atmosphere system are numerous. With the fraction of land used for farming and grazing expected to increase, extensive alterations to land cover such as replacing forests with cropland will continue. Therefore, quantifying the impact of global land-cover scenarios on the biosphere is critical. The Predicting Ecosystem Goods and Services Using Scenarios boundary layer (PegBL) model is a new global soil-vegetation-boundary layer model designed to quantify these impacts and act as a complementary tool to computationally expensive general circulation models and large-eddy simulations. PegBL provides high spatial resolution and inexpensive first-order estimates of land-cover change on the surface energy balance and atmospheric boundary layer with limited input requirements. The model uses a climatological-data-driven land surface model that contains only the physics necessary to accurately reproduce observed seasonal cycles of fluxes and state variables for natural and agricultural ecosystems. A bulk boundary layer model was coupled to the land model to estimate the impacts of changing land cover on the lower atmosphere. The model most realistically simulated surface-atmosphere dynamics and impacts of land-cover change at tropical rain forest and northern boreal forest sites. Further, simple indices to measure the potential impact of land-cover change on boundary layer climate were defined and shown to be dependent on boundary layer dynamics and geographically similar to results from previous studies, which highlighted the impacts of land-cover change on the atmosphere in the tropics and boreal forest. [ABSTRACT FROM AUTHOR]

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4) and changes in soil carbon stock are defined here as " agricultural carbon outcomes ". Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the " System-of-Systems" solution. Such a "System-of-Systems" solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices ( M ), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes ; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability , i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the "System-of-Systems" solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes. [ABSTRACT FROM AUTHOR]

Abstract Climate-smart agriculture (CSA) is widely promoted as an approach for reorienting agricultural development under the realities of climate change. Prioritising research-for-development activities is crucial, given the need to utilise scarce resources as effectively as possible. However, no framework exists for assessing and comparing different CSA research investments. Several aspects make it challenging to prioritise CSA research, including its multi-dimensional nature (productivity, adaptation and mitigation), the uncertainty surrounding many climate impacts, and the scale and temporal dependencies that may affect the benefits and costs of CSA adoption. Here we propose a framework for prioritising agricultural research investments across scales and review different approaches to setting priorities among agricultural research projects. Many priority-setting case studies address the short- to medium-term and at relatively local scales. We suggest that a mix of actions that span spatial and temporal time scales is needed to be adaptive to a changing climate, address immediate problems and create enabling conditions for enduring change. Highlights • Prioritising CSA research activities is challenging, primarily because it is multi-dimensional. • We propose and illustrate a simple six-element framework as a "map" to guide prioritisation. • Most priority setting activities need a mix of different quantitative and qualitative methods. • This mix needs to address actions that span spatial and temporal time scales to ensure CSA research effectiveness. [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]

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]

Half of the seafood consumed globally now comes from aquaculture, or farmed seafood. Aquaculture therefore plays an increasingly important role in the global food system, the environment, and human health. Traditionally, aquaculture feed has contained high levels of wild fish, which is unsustainable for ocean ecosystems as demand grows. The aquaculture industry is shifting to crop-based feed ingredients, such as soy, to replace wild fish as a feed source and allow for continued industry growth. This shift fundamentally links seafood production to terrestrial agriculture, and multidisciplinary research is needed to understand the ecological and environmental health implications. We provide basic estimates of the agricultural resource use associated with producing the top five crops used in commercial aquaculture feed. Aquaculture's environmental footprint may now include nutrient and pesticide runoff from industrial crop production, and depending on where and how feed crops are produced, could be indirectly linked to associated negative health outcomes. We summarize key environmental health research on health effects associated with exposure to air, water, and soil contaminated by industrial crop production. Our review also finds that changes in the nutritional content of farmed seafood products due to altered feed composition could impact human nutrition. Based on our literature reviews and estimates of resource use, we present a conceptual framework describing the potential links between increasing use of crop-based ingredients in aquaculture and human health. Additional data and geographic sourcing information for crop-based ingredients are needed to fully assess the environmental health implications of this trend. This is especially critical in the context of a food system that is using both aquatic and terrestrial resources at unsustainable rates. [ABSTRACT FROM AUTHOR]

Soils are subject to varying degrees of direct or indirect human disturbance, constituting a major global change driver. Factoring out natural from direct and indirect human influence is not always straightforward, but some human activities have clear impacts. These include land-use change, land management and land degradation (erosion, compaction, sealing and salinization). The intensity of land use also exerts a great impact on soils, and soils are also subject to indirect impacts arising from human activity, such as acid deposition (sulphur and nitrogen) and heavy metal pollution. In this critical review, we report the state-of-the-art understanding of these global change pressures on soils, identify knowledge gaps and research challenges and highlight actions and policies to minimize adverse environmental impacts arising from these global change drivers. Soils are central to considerations of what constitutes sustainable intensification. Therefore, ensuring that vulnerable and high environmental value soils are considered when protecting important habitats and ecosystems, will help to reduce the pressure on land from global change drivers. To ensure that soils are protected as part of wider environmental efforts, a global soil resilience programme should be considered, to monitor, recover or sustain soil fertility and function, and to enhance the ecosystem services provided by soils. Soils cannot, and should not, be considered in isolation of the ecosystems that they underpin and vice versa. The role of soils in supporting ecosystems and natural capital needs greater recognition. The lasting legacy of the International Year of Soils in 2015 should be to put soils at the centre of policy supporting environmental protection and sustainable development. [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]

Agricultural trade plays an important role in global food security and resource sustainability. Global food commodities trade is worth more than US$520 billion per year, could feed approximately two billion people, uses about 13% of worldwide cropland and pasture, and has geographically concentrated irrigation water demands. However, researchers rarely compare these monetary, nutritional, and resource metrics, which limits our ability to holistically evaluate the drivers and implications of trade. We found that each metric suggests distinct conclusions about the geography of globalized agriculture. For example, traded animal products have a disproportionate influence according to value-based and embodied pasture metrics. Traded wheat, soybean, and maize contain the most calories, use the most cropland, and strongly influence irrigation water consumption. We typify engagement in trade by assessing how countries allocate cropland to domestic versus foreign demand. Simultaneous consideration of multiple metrics could enhance decisionmaking surrounding trade by capturing the complex biophysical and economic context of agricultural globalization. [ABSTRACT FROM PUBLISHER]

Enhancing the resilience of ecosystem services (ES) that underpin human well-being is critical for meeting current and future societal needs, and requires specific governance and management policies. Using the literature, we identify seven generic policy-relevant principles for enhancing the resilience of desired ES in the face of disturbance and ongoing change in social-ecological systems (SES). These principles are (P1) maintain diversity and redundancy, (P2) manage connectivity, (P3) manage slow variables and feedbacks, (P4) foster an understanding of SES as complex adaptive systems (CAS), (P5) encourage learning and experimentation, (P6) broaden participation, and (P7) promote polycentric governance systems. We briefly define each principle, review how and when it enhances the resilience of ES, and conclude with major research gaps. In practice, the principles often co-occur and are highly interdependent. Key future needs are to better understand these interdependencies and to operationalize and apply the principles in different policy and management contexts. [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. [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]