Navin Ramankutty

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

In the coming years, continued population growth, rising incomes, increasing meat and dairy consumption and expanding biofuel use will place unprecedented demands on the world's agriculture and natural resources. Can we meet society's growing food needs while reducing agriculture's environmental harm? Here, an international team of environmental and agricultural scientists uses new geospatial data and models to identify four strategies that could double food production while reducing environmental impacts. First, halt agricultural expansion. Second, close 'yield gaps' on underperforming lands. Third, increase cropping efficiency. And finally, we need to change our diets and shift crop production away from livestock feed, bioenergy crops and other non-food applications. 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.

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.

In recent years, several extreme weather disasters have partially or completely damaged regional crop production. While detailed regional accounts of the effects of extreme weather disasters exist, the global scale effects of droughts, floods and extreme temperature on crop production are yet to be quantified. Here we estimate for the first time, to our knowledge, national cereal production losses across the globe resulting from reported extreme weather disasters during 1964-2007. We show that droughts and extreme heat significantly reduced national cereal production by 9-10%, whereas our analysis could not identify an effect from floods and extreme cold in the national data. Analysing the underlying processes, we find that production losses due to droughts were associated with a reduction in both harvested area and yields, whereas extreme heat mainly decreased cereal yields. Furthermore, the results highlight ~7% greater production damage from more recent droughts and 8-11% more damage in developed countries than in developing ones. Our findings may help to guide agricultural priorities in international disaster risk reduction and adaptation efforts.

Summary The possible responses of ecosystem processes to rising atmospheric CO 2 concentration and climate change are illustrated using six dynamic global vegetation models that explicitly represent the interactions of ecosystem carbon and water exchanges with vegetation dynamics. The models are driven by the IPCC IS92a scenario of rising CO 2 ( Wigley et al . 1991 ), and by climate changes resulting from effective CO 2 concentrations corresponding to IS92a, simulated by the coupled ocean atmosphere model HadCM2‐SUL. Simulations with changing CO 2 alone show a widely distributed terrestrial carbon sink of 1.4–3.8 Pg C y −1 during the 1990s, rising to 3.7–8.6 Pg C y −1 a century later. Simulations including climate change show a reduced sink both today (0.6–3.0 Pg C y −1 ) and a century later (0.3–6.6 Pg C y −1 ) as a result of the impacts of climate change on NEP of tropical and southern hemisphere ecosystems. In all models, the rate of increase of NEP begins to level off around 2030 as a consequence of the ‘diminishing return’ of physiological CO 2 effects at high CO 2 concentrations. Four out of the six models show a further, climate‐induced decline in NEP resulting from increased heterotrophic respiration and declining tropical NPP after 2050. Changes in vegetation structure influence the magnitude and spatial pattern of the carbon sink and, in combination with changing climate, also freshwater availability (runoff). It is shown that these changes, once set in motion, would continue to evolve for at least a century even if atmospheric CO 2 concentration and climate could be instantaneously stabilized. The results should be considered illustrative in the sense that the choice of CO 2 concentration scenario was arbitrary and only one climate model scenario was used. However, the results serve to indicate a range of possible biospheric responses to CO 2 and climate change. They reveal major uncertainties about the response of NEP to climate change resulting, primarily, from differences in the way that modelled global NPP responds to a changing climate. The simulations illustrate, however, that the magnitude of possible biospheric influences on the carbon balance requires that this factor is taken into account for future scenarios of atmospheric CO 2 and climate change.

Human activities over the last three centuries have significantly transformed the Earth's environment, primarily through the conversion of natural ecosystems to agriculture. This study presents a simple approach to derive geographically explicit changes in global croplands from 1700 to 1992. By calibrating a remotely sensed land cover classification data set against cropland inventory data, we derived a global representation of permanent croplands in 1992, at 5 min spatial resolution [Ramankutty and Foley, 1998]. To reconstruct historical croplands, we first compile an extensive database of historical cropland inventory data, at the national and subnational level, from a variety of sources. Then we use our 1992 cropland data within a simple land cover change model, along with the historical inventory data, to reconstruct global 5 min resolution data on permanent cropland areas from 1992 back to 1700. The reconstructed changes in historical croplands are consistent with the history of human settlement and patterns of economic development. By overlaying our historical cropland data set over a newly derived potential vegetation data set, we analyze our results in terms of the extent to which different natural vegetation types have been converted for agriculture. We further examine the extent to which croplands have been abandoned in different parts of the world. Our data sets could be used within global climate models and global ecosystem models to understand the impacts of land cover change on climate and on the cycling of carbon and water. Such an analysis is a crucial aid to sharpen our thinking about a sustainable future.

Humans have fundamentally altered global patterns of biodiversity and ecosystem processes. Surprisingly, existing systems for representing these global patterns, including biome classifications, either ignore humans altogether or simplify human influence into, at most, four categories. Here, we present the first characterization of terrestrial biomes based on global patterns of sustained, direct human interaction with ecosystems. Eighteen “anthropogenic biomes” were identified through empirical analysis of global population, land use, and land cover. More than 75% of Earth's ice-free land showed evidence of alteration as a result of human residence and land use, with less than a quarter remaining as wildlands, supporting just 11% of terrestrial net primary production. Anthropogenic biomes offer a new way forward by acknowledging human influence on global ecosystems and moving us toward models and investigations of the terrestrial biosphere that integrate human and ecological systems.

Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite‐derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite‐derived products (Boston University's MODIS‐derived land cover product and the GLC2000 data set). Our data are presented at 5 min (∼10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2–17.1) million km 2 of cropland (12% of the Earth's ice‐free land surface) and 28.0 (90% confidence range of 23.6–30.0) million km 2 of pasture (22%) in the year 2000.

Global demand for agricultural products such as food, feed, and fuel is now a major driver of cropland and pasture expansion across much of the developing world. Whether these new agricultural lands replace forests, degraded forests, or grasslands greatly influences the environmental consequences of expansion. Although the general pattern is known, there still is no definitive quantification of these land-cover changes. Here we analyze the rich, pan-tropical database of classified Landsat scenes created by the Food and Agricultural Organization of the United Nations to examine pathways of agricultural expansion across the major tropical forest regions in the 1980s and 1990s and use this information to highlight the future land conversions that probably will be needed to meet mounting demand for agricultural products. Across the tropics, we find that between 1980 and 2000 more than 55% of new agricultural land came at the expense of intact forests, and another 28% came from disturbed forests. This study underscores the potential consequences of unabated agricultural expansion for forest conservation and carbon emissions.

Croplands cover ∼15 million km 2 of the planet and provide the bulk of the food and fiber essential to human well‐being. Most global land cover data sets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 min by 5 min (∼10 km by 10 km) latitude‐longitude grid. The resulting land use data sets depict circa the year 2000 the area (harvested) and yield of 175 distinct crops of the world. We aggregate these individual crop maps to produce novel maps of 11 major crop groups, crop net primary production, and four physiologically based crop types: annuals/perennials, herbaceous/shrubs/trees, C 3 /C 4 , and leguminous/nonleguminous.

Here we present a new terrestrial biosphere model (the Integrated Biosphere Simulator ‐ IBIS) which demonstrates how land surface biophysics, terrestrial carbon fluxes, and global vegetation dynamics can be represented in a single, physically consistent modeling framework. In order to integrate a wide range of biophysical, physiological, and ecological processes, the model is designed around a hierarchical, modular structure and uses a common state description throughout. First, a coupled simulation of the surface water, energy, and carbon fluxes is performed on hourly timesteps and is integrated over the year to estimate the annual water and carbon balance. Next, the annual carbon balance is used to predict changes in the leaf area index and biomass for each of nine plant functional types, which compete for light and water using different ecological strategies. The resulting patterns of annual evapotranspiration, runoff, and net primary productivity are in good agreement with observations. In addition, the model simulates patterns of vegetation dynamics that qualitatively agree with features of the natural process of secondary succession. Comparison of the model's inferred near‐equilibrium vegetation categories with a potential natural vegetation map shows a fair degree of agreement. This integrated modeling framework provides a means of simulating both rapid biophysical processes and long‐term ecosystem dynamics that can be directly incorporated within atmospheric models.

In the coming decades, continued population growth, rising meat and dairy consumption and expanding biofuel use will dramatically increase the pressure on global agriculture. Even as we face these future burdens, there have been scattered reports of yield stagnation in the world's major cereal crops, including maize, rice and wheat. Here we study data from ∼2.5 million census observations across the globe extending over the period 1961-2008. We examined the trends in crop yields for four key global crops: maize, rice, wheat and soybeans. Although yields continue to increase in many areas, we find that across 24-39% of maize-, rice-, wheat- and soybean-growing areas, yields either never improve, stagnate or collapse. This result underscores the challenge of meeting increasing global agricultural demands. New investments in underperforming regions, as well as strategies to continue increasing yields in the high-performing areas, are required.

Abstract. The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

The concurrent effects of increasing atmospheric CO 2 concentration, climate variability, and cropland establishment and abandonment on terrestrial carbon storage between 1920 and 1992 were assessed using a standard simulation protocol with four process‐based terrestrial biosphere models. Over the long‐term(1920–1992), the simulations yielded a time history of terrestrial uptake that is consistent (within the uncertainty) with a long‐term analysis based on ice core and atmospheric CO 2 data. Up to 1958, three of four analyses indicated a net release of carbon from terrestrial ecosystems to the atmosphere caused by cropland establishment. After 1958, all analyses indicate a net uptake of carbon by terrestrial ecosystems, primarily because of the physiological effects of rapidly rising atmospheric CO 2 . During the 1980s the simulations indicate that terrestrial ecosystems stored between 0.3 and 1.5 Pg C yr −1 , which is within the uncertainty of analysis based on CO 2 and O 2 budgets. Three of the four models indicated (in accordance with O 2 evidence) that the tropics were approximately neutral while a net sink existed in ecosystems north of the tropics. Although all of the models agree that the long‐term effect of climate on carbon storage has been small relative to the effects of increasing atmospheric CO 2 and land use, the models disagree as to whether climate variability and change in the twentieth century has promoted carbon storage or release. Simulated interannual variability from 1958 generally reproduced the El Niño/Southern Oscillation (ENSO)‐scale variability in the atmospheric CO 2 increase, but there were substantial differences in the magnitude of interannual variability simulated by the models. The analysis of the ability of the models to simulate the changing amplitude of the seasonal cycle of atmospheric CO 2 suggested that the observed trend may be a consequence of CO 2 effects, climate variability, land use changes, or a combination of these effects. The next steps for improving the process‐based simulation of historical terrestrial carbon include (1) the transfer of insight gained from stand‐level process studies to improve the sensitivity of simulated carbon storage responses to changes in CO 2 and climate, (2) improvements in the data sets used to drive the models so that they incorporate the timing, extent, and types of major disturbances, (3) the enhancement of the models so that they consider major crop types and management schemes, (4) development of data sets that identify the spatial extent of major crop types and management schemes through time, and (5) the consideration of the effects of anthropogenic nitrogen deposition. The evaluation of the performance of the models in the context of a more complete consideration of the factors influencing historical terrestrial carbon dynamics is important for reducing uncertainties in representing the role of terrestrial ecosystems in future projections of the Earth system.

ABSTRACT Aim To map and characterize anthropogenic transformation of the terrestrial biosphere before and during the Industrial Revolution, from 1700 to 2000. Location Global. Methods Anthropogenic biomes (anthromes) were mapped for 1700, 1800, 1900 and 2000 using a rule‐based anthrome classification model applied to gridded global data for human population density and land use. Anthropogenic transformation of terrestrial biomes was then characterized by map comparisons at century intervals. Results In 1700, nearly half of the terrestrial biosphere was wild, without human settlements or substantial land use. Most of the remainder was in a seminatural state (45%) having only minor use for agriculture and settlements. By 2000, the opposite was true, with the majority of the biosphere in agricultural and settled anthromes, less than 20% seminatural and only a quarter left wild. Anthropogenic transformation of the biosphere during the Industrial Revolution resulted about equally from land‐use expansion into wildlands and intensification of land use within seminatural anthromes. Transformation pathways differed strongly between biomes and regions, with some remaining mostly wild but with the majority almost completely transformed into rangelands, croplands and villages. In the process of transforming almost 39% of earth's total ice‐free surface into agricultural land and settlements, an additional 37% of global land without such use has become embedded within agricultural and settled anthromes. Main conclusions Between 1700 and 2000, the terrestrial biosphere made the critical transition from mostly wild to mostly anthropogenic, passing the 50% mark early in the 20th century. At present, and ever more in the future, the form and process of terrestrial ecosystems in most biomes will be predominantly anthropogenic, the product of land use and other direct human interactions with ecosystems. Ecological research and conservation efforts in all but a few biomes would benefit from a primary focus on the novel remnant, recovering and managed ecosystems embedded within used lands.

Increased phosphorus (P) fertilizer use and livestock production has fundamentally altered the global P cycle. We calculated spatially explicit P balances for cropland soils at 0.5° resolution based on the principal agronomic P inputs and outputs associated with production of 123 crops globally for the year 2000. Although agronomic inputs of P fertilizer (14.2 Tg of P·y −1 ) and manure (9.6 Tg of P·y −1 ) collectively exceeded P removal by harvested crops (12.3 Tg of P·y −1 ) at the global scale, P deficits covered almost 30% of the global cropland area. There was massive variation in the magnitudes of these P imbalances across most regions, particularly Europe and South America. High P fertilizer application relative to crop P use resulted in a greater proportion of the intense P surpluses (>13 kg of P·ha −1 ·y −1 ) globally than manure P application. High P fertilizer application was also typically associated with areas of relatively low P-use efficiency. Although manure was an important driver of P surpluses in some locations with high livestock densities, P deficits were common in areas producing forage crops used as livestock feed. Resolving agronomic P imbalances may be possible with more efficient use of P fertilizers and more effective recycling of manure P. Such reforms are needed to increase global agricultural productivity while maintaining or improving freshwater quality.

While a new class of Dynamic Global Ecosystem Models (DGEMs) has emerged in the past few years as an important tool for describing global biogeochemical cycles and atmosphere‐biosphere interactions, these models are still largely untested. Here we analyze the behavior of a new DGEM and compare the results to global‐scale observations of water balance, carbon balance, and vegetation structure. In this study, we use version 2 of the Integrated Biosphere Simulator (IBIS), which includes several major improvements and additions to the prototype model developed by Foley et al. [1996]. IBIS is designed to be a comprehensive model of the terrestrial biosphere; the model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO 2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). In order to test the performance of the model, we have assembled a wide range of continental and global‐scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO 2 flux. Using these field data and model results for the contemporary biosphere (1965–1994), our evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO 2 flux agree reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results.

Humans have transformed the surface of the planet through agricultural activities, and today, ∼12% of the land surface is used for cultivation and another 22% is used for pastures and rangelands. In this paper, we have synthesized satellite‐derived land cover data and agricultural census data to produce global data sets of the distribution of 18 major crops across the world. The resulting data are representative of the early 1990s, have a spatial resolution of 5 min. (∼10 km), and describe the fraction of a grid cell occupied by each of the 18 crops. The global crop data are consistent with our knowledge of agricultural geography, and compares favorably to another existing data set that partially overlaps with our product. We have also analyzed how different crops are grown in combination to form major crop belts throughout the world. Further, we analyzed the patterns of crop diversification across the world. While these data are not sufficiently accurate at local scales, they can be used to analyze crop geography in a regional‐to‐global context. They can also be used to understand the global patterns of farming systems, in analyses of food security, and within global ecosystem and climate models to understand the environmental consequences of cultivation.

We explore the role of agriculture in destabilizing the Earth system at the planetary scale, through examining nine planetary boundaries, or "safe limits": land-system change, freshwater use, biogeochemical flows, biosphere integrity, climate change, ocean acidification, stratospheric ozone depletion, atmospheric aerosol loading, and introduction of novel entities.Two planetary boundaries have been fully transgressed, i.e., are at high risk, biosphere integrity and biogeochemical flows, and agriculture has been the major driver of the transgression.Three are in a zone of uncertainty i.e., at increasing risk, with agriculture the major driver of two of those, landsystem change and freshwater use, and a significant contributor to the third, climate change.Agriculture is also a significant or major contributor to change for many of those planetary boundaries still in the safe zone.To reduce the role of agriculture in transgressing planetary boundaries, many interventions will be needed, including those in broader food systems.

The eighteenth-century Malthusian prediction of population growth outstripping food production has not yet come to bear. Unprecedented agricultural land expansions since 1700, and technological innovations that began in the 1930s, have enabled more calorie production per capita than was ever available before in history. This remarkable success, however, has come at a great cost. Agriculture is a major cause of global environmental degradation. Malnutrition persists among large sections of the population, and a new epidemic of obesity is on the rise. We review both the successes and failures of the global food system, addressing ongoing debates on pathways to environmental health and food security. To deal with these challenges, a new coordinated research program blending modern breeding with agro-ecological methods is needed. We call on plant biologists to lead this effort and help steer humanity toward a safe operating space for agriculture.

Abstract Aim This study makes quantitative global estimates of land suitability for cultivation based on climate and soil constraints. It evaluates further the sensitivity of croplands to any possible changes in climate and atmospheric CO 2 concentrations. Location The location is global, geographically explicit. Methods The methods used are spatial data synthesis and analysis and numerical modelling. Results There is a cropland ‘reserve’ of 120%, mainly in tropical South America and Africa. Our climate sensitivity analysis indicates that the southern provinces of Canada, north‐western and north‐central states of the United States, northern Europe, southern Former Soviet Union and the Manchurian plains of China are most sensitive to changes in temperature. The Great Plains region of the United States and north‐eastern China are most sensitive to changes in precipitation. The regions that are sensitive to precipitation change are also sensitive to changes in CO 2 , but the magnitude is small compared to the influence of direct climate change. We estimate that climate change, as simulated by global climate models, will expand cropland suitability by an additional 16%, mainly in the Northern Hemisphere high latitudes. However, the tropics (mainly Africa, northern South America, Mexico and Central America and Oceania) will experience a small decrease in suitability due to climate change. Main conclusions There is a large reserve of cultivable croplands, mainly in tropical South America and Africa. However, much of this land is under valuable forests or in protected areas. Furthermore, the tropical soils could potentially lose fertility very rapidly once the forest cover is removed. Regions that lie at the margins of temperature or precipitation limitation to cultivation are most sensitive to changes in climate and atmospheric CO 2 concentration. It is anticipated that climate change will result in an increase in cropland suitability in the Northern Hemisphere high latitudes (mainly in developed nations), while the tropics will lose suitability (mainly in developing nations).

Extreme heat stress during the crop reproductive period can be critical for crop productivity. Projected changes in the frequency and severity of extreme climatic events are expected to negatively impact crop yields and global food production. This study applies the global crop model PEGASUS to quantify, for the first time at the global scale, impacts of extreme heat stress on maize, spring wheat and soybean yields resulting from 72 climate change scenarios for the 21st century. Our results project maize to face progressively worse impacts under a range of RCPs but spring wheat and soybean to improve globally through to the 2080s due to CO2 fertilization effects, even though parts of the tropic and sub-tropic regions could face substantial yield declines. We find extreme heat stress at anthesis (HSA) by the 2080s (relative to the 1980s) under RCP 8.5, taking into account CO2 fertilization effects, could double global losses of maize yield (ΔY = −12.8 ± 6.7% versus − 7.0 ± 5.3% without HSA), reduce projected gains in spring wheat yield by half (ΔY = 34.3 ± 13.5% versus 72.0 ± 10.9% without HSA) and in soybean yield by a quarter (ΔY = 15.3 ± 26.5% versus 20.4 ± 22.1% without HSA). The range reflects uncertainty due to differences between climate model scenarios; soybean exhibits both positive and negative impacts, maize is generally negative and spring wheat generally positive. Furthermore, when assuming CO2 fertilization effects to be negligible, we observe drastic climate mitigation policy as in RCP 2.6 could avoid more than 80% of the global average yield losses otherwise expected by the 2080s under RCP 8.5. We show large disparities in climate impacts across regions and find extreme heat stress adversely affects major producing regions and lower income countries.

The Amazon Basin is one of the world's most important bioregions, harboring a rich array of plant and animal species and offering a wealth of goods and services to society. For years, ecological science has shown how large-scale forest clearings cause declines in biodiversity and the availability of forest products. Yet some important changes in the rainforests, and in the ecosystem services they provide, have been underappreciated until recently. Emerging research indicates that land use in the Amazon goes far beyond clearing large areas of forest; selective logging and other canopy damage is much more pervasive than once believed. Deforestation causes collateral damage to the surrounding forests – through enhanced drying of the forest floor, increased frequency of fires, and lowered productivity. The loss of healthy forests can degrade key ecosystem services, such as carbon storage in biomass and soils, the regulation of water balance and river flow, the modulation of regional climate patterns, and the amelioration of infectious diseases. We review these newly revealed changes in the Amazon rainforests and the ecosystem services that they provide.

The dynamics of deforestation in the Brazilian Amazon are complex. A growing debate considers the extent to which deforestation is a result of the expansion of the Brazilian soy industry. Most recent analyses suggest that deforestation is driven by the expansion of cattle ranching, rather than soy. Soy seems to be replacing previously deforested land and/or land previously under pasture. In this study, we use municipality-level statistics on agricultural and deforested areas across the Legal Amazon from 2000 to 2006 to examine the spatial patterns and statistical relationships between deforestation and changes in pasture and soybean areas. Our results support previous studies that showed that deforestation is predominantly a result of pasture expansion. However, we also find support for the hypothesis that an increase of soy in Mato Grosso has displaced pasture further north, leading to deforestation elsewhere. Although not conclusive, our findings suggest that the debate surrounding the drivers of Amazon deforestation is not over, and that indirect causal links between soy and deforestation may exist that need further exploration. Future research should examine more closely how interlinkages between land area, prices, and policies influence the relationship between soy and deforestation, in order to make a conclusive case for 'displacement deforestation'.

ABSTRACT Aim To assemble a data set of global crop planting and harvesting dates for 19 major crops, explore spatial relationships between planting date and climate for two of them, and compare our analysis with a review of the literature on factors that drive decisions on planting dates. Location Global. Methods We digitized and georeferenced existing data on crop planting and harvesting dates from six sources. We then examined relationships between planting dates and temperature, precipitation and potential evapotranspiration using 30‐year average climatologies from the Climatic Research Unit, University of East Anglia (CRU CL 2.0). Results We present global planting date patterns for maize, spring wheat and winter wheat (our full, publicly available data set contains planting and harvesting dates for 19 major crops). Maize planting in the northern mid‐latitudes generally occurs in April and May. Daily average air temperatures are usually c . 12–17 °C at the time of maize planting in these regions, although soil moisture often determines planting date more directly than does temperature. Maize planting dates vary more widely in tropical regions. Spring wheat is usually planted at cooler temperatures than maize, between c . 8 and 14 °C in temperate regions. Winter wheat is generally planted in September and October in the northern mid‐latitudes. Main conclusions In temperate regions, spatial patterns of maize and spring wheat planting dates can be predicted reasonably well by assuming a fixed temperature at planting. However, planting dates in lower latitudes and planting dates of winter wheat are more difficult to predict from climate alone. In part this is because planting dates may be chosen to ensure a favourable climate during a critical growth stage, such as flowering, rather than to ensure an optimal climate early in the crop's growth. The lack of predictability is also due to the pervasive influence of technological and socio‐economic factors on planting dates.

ABSTRACT Aim 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. Location This study was conducted at the global scale. Methods 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’. Results 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. Main conclusions 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.

C hanges in land cover and in the way people use the land have become recognized over the last 15 years as important global environmental changes in their own right (Turner 2002).They are also intertwined in many ways with other environmental issues, such as climate change and carbon cycle, loss of biodiversity, sustainability of agriculture, and provision of safe drinking water.The international scientific community has created new interdisciplinary research programs to understand the multiple causes and consequences of land-cover and land-use change (Lambin et al. 2003).There has been a concomitant rapid expansion in the availability of data and information.However, there has not yet been a systematic examination, using global and regional observations, of the status and trends in terrestrial and coastal land-cover or related important ecosystem processes.The information needs for such a synthesis are diverse.Remote sensing has an important contribution to make in documenting the actual change in land cover on regional and global spatial scales from the mid-1970s (Achard et al. 2002, DeFries et al. 2002, Lambin et al. 2003).It also has a role to play in evaluating indices of change in ecological processes, such as net primary production and rainfall use efficiency (Prince et al. 1998).Remote sensing information is found in a widely scattered literature, some of it refereed, some in the gray literature, and some unpublished as yet.There is also an obvious need for good inventory data and statistics about land cover and land-cover change at subnational, national, and in-ternational scales, augmented by a need for subnational and national indicators of condition, status, and trends of the global environment.Finally, there is a need to determine the interrelationships of remotely sensed and statistical inventory data, to integrate heterogenous data sources.The tremendous investment in scientific analysis of remote sensing data over the last decade, and the profusion of studies based on other data sources, provides a basis for a synthesis.Although information is not complete globally, several products are now available that depict the land cover of Earth globally in the 1990s and in 2000-2001.The same is true for snapshots of many important regions with substantial land-

Biofuels from land-rich tropical countries may help displace foreign petroleum imports for many industrialized nations, providing a possible solution to the twin challenges of energy security and climate change. But concern is mounting that crop-based biofuels will increase net greenhouse gas emissions if feedstocks are produced by expanding agricultural lands. Here we quantify the 'carbon payback time' for a range of biofuel crop expansion pathways in the tropics. We use a new, geographically detailed database of crop locations and yields, along with updated vegetation and soil biomass estimates, to provide carbon payback estimates that are more regionally specific than those in previous studies. Using this cropland database, we also estimate carbon payback times under different scenarios of future crop yields, biofuel technologies, and petroleum sources. Under current conditions, the expansion of biofuels into productive tropical ecosystems will always lead to net carbon emissions for decades to centuries, while expanding into degraded or already cultivated land will provide almost immediate carbon savings. Future crop yield improvements and technology advances, coupled with unconventional petroleum supplies, will increase biofuel carbon offsets, but clearing carbon-rich land still requires several decades or more for carbon payback. No foreseeable changes in agricultural or energy technology will be able to achieve meaningful carbon benefits if crop-based biofuels are produced at the expense of tropical forests.

Improved understanding of the influence of climate on agricultural production is needed to cope with expected changes in temperature and precipitation, and an increasing number of undernourished people in food insecure regions. Many studies have shown the importance of seasonal climatic means in explaining crop yields. However, climate variability is expected to increase in some regions and have significant consequences on food production beyond the impacts of changes in climatic means. Here, we examined the relationship between seasonal climate and crop yields in Tanzania, focusing on maize, sorghum and rice. The impacts of both seasonal means and variability on yields were measured at the subnational scale using various statistical methods and climate data. The results indicate that both intra- and interseasonal changes in temperature and precipitation influence cereal yields in Tanzania. Seasonal temperature increases have the most important impact on yields. This study shows that in Tanzania, by 2050, projected seasonal temperature increases by 2 °C reduce average maize, sorghum, and rice yields by 13%, 8.8%, and 7.6% respectively. Potential changes in seasonal total precipitation as well as intra-seasonal temperature and precipitation variability may also impact crop yields by 2050, albeit to a lesser extent. A 20% increase in intra-seasonal precipitation variability reduces agricultural yields by 4.2%, 7.2%, and 7.6% respectively for maize, sorghum, and rice. Using our preferred model, we show that we underestimate the climatic impacts by 2050 on crop yields in Tanzania by 3.6%, 8.9%, and 28.6% for maize, sorghum and rice respectively if we focus only on climatic means and ignore climate variability. This study highlights that, in addition to shifts in growing season means, changes in intra-seasonal variability of weather may be important for future yields in Tanzania. Additionally, we argue for a need to invest in improving the climate records in these regions to enhance our understanding of these relationships.

Human activities have shaped significantly the state of terrestrial ecosystems throughout the world. One of the most direct manifestations of human activity within the biosphere has been the conversion of natural ecosystems to croplands. In this study, we present an analysis of the geographic distribution and spatial extent of permanent croplands. This analysis represents the area in permanent croplands during the early 1990s for each grid cell on a global 5 min (∼10 km) resolution latitude‐longitude grid. To create this data set, we have combined a satellite‐derived land cover data set with a variety of national and subnational agricultural inventory data. A simple calibration algorithm was used so that the spatial land cover data were generally consistent with nonspatial agricultural inventory data. The spatial distribution of croplands represented in this analysis presents a quantitative depiction of global agricultural geography. The regions of the world known to have intense cultivation (e.g., the North American corn belt, the European wheat‐corn belt, the Ganges floodplain, and eastern China) are clearly portrayed in this analysis. It also captures the less intensely cultivated regions of the world, usually surrounding the regions mentioned above, and regions characterized by subsistence agriculture (e.g., Sahelian Africa). Data generated from this kind of analysis can be used within global climate models and global ecosystem models to assess the importance of permanent croplands on environmental processes. In particular, these data, combined with models, could help evaluate the role of changing land cover on regional climate and carbon cycling. Future efforts will need to concentrate on other land use systems, including pastures and regions of shifting cultivation. Furthermore, land use and land cover data must be extended to include an historical dimension so as to evaluate the changing state of the biosphere over time. This article contains supplementary material.

Global societies are becoming increasingly urban. This shift toward urban living is changing our relationship with food, including how we shop and what we buy, as well as ideas about sanitation and freshness. Achieving food security in an era of rapid urbanization will require considerably more understanding about how urban and food systems are intertwined. Here we discuss some potential understudied linkages that are ripe for further examination.

Abstract Agriculture has had a tremendous impact on soil nutrients around the world. In some regions, soil nutrients are depleted because of low initial soil fertility or excessive nutrient removals through intense land use relative to nutrient additions. In other regions, application of chemical fertilizers and manure has led to an accumulation of nutrients and subsequent water quality problems. Understanding the current level and spatial patterns of fertilizer and manure inputs would greatly improve the ability to identify areas that might be sensitive to aquatic eutrophication or to nutrient depletion. The authors calculated spatially explicit fertilizer inputs of nitrogen (N) and phosphorus (P) by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. They also calculated spatially explicit manure inputs of N and P by fusing global maps of animal density and international data on manure production and nutrient content. Significantly higher application rates were found for both fertilizers and manures in the Northern Hemisphere, with maxima centered on areas with intensive cropland and high densities of livestock. Furthermore, nutrient use is confined to a few major hot spots, with approximately 10% of the treated land receiving over 50% of the use of both fertilizers and manures. The authors’ new spatial disaggregation of the rich International Fertilizer Industry Association (IFA) fertilizer-use dataset will provide new and interesting avenues to explore the impact of anthropogenic activity on ecosystems at the global scale and may also have implications for policies designed to improve soil quality or reduce nutrient runoff.

Abstract An accurate estimate of carbon fluxes associated with tropical deforestation from the last two decades is needed to balance the global carbon budget. Several studies have already estimated carbon emissions from tropical deforestation, but the estimates vary greatly and are difficult to compare due to differences in data sources, assumptions, and methodologies. In this paper, we review the different estimates and datasets, and the various challenges associated with comparing them and with accurately estimating carbon emissions from deforestation. We performed a simulation study over legal Amazonia to illustrate some of these major issues. Our analysis demonstrates the importance of considering land‐cover dynamics following deforestation, including the fluxes from reclearing of secondary vegetation, the decay of product and slash pools, and the fluxes from regrowing forest. It also suggests that accurate carbon‐flux estimates will need to consider historical land‐cover changes for at least the previous 20 years. However, this result is highly sensitive to estimates of the partitioning of cleared carbon into instantaneous burning vs. long‐timescale slash pools. We also show that carbon flux estimates based on ‘committed flux’ calculations, as used by a few studies, are not comparable with the ‘annual balance’ calculation method used by other studies.

Changes in land cover affect climate through the surface energy and moisture budgets. Here we assess the importance of these biogeophysical effects for present-day climate, and quantify the radiative forcing of historical climate change by land use change for comparison with radiative forcings due to anthropogenic changes in greenhouse gases and aerosols. We also discuss the implications of biogeophysical effects for the use of forestry as a tool for mitigating climate change through carbon sequestration. Our model results suggest that since most historical deforestation has taken place in temperate regions where the main climatic effect is an increase in surface albedo, the dominant biogeophysical effect of past land cover change has been a cooling. The northern mid-latitude agricultural regions are simulated to be approximately 1–2 K cooler in winter and spring in comparison with their previously forested state. This conflicts with the suggestion that land use change is responsible for the warming observed over the 20th century. The increase in albedo by 1750 is simulated to exert a negative radiative forcing of approximately −2 W m−2 locally over Europe, China and India, suggesting a potential anthropogenic influence on climate before fossil fuel burning began. The present-day global mean radiative forcing by anthropogenic surface albedo change relative to the natural state is simulated to be −0.2 W m−2, which is comparable with the estimated forcings relative to pre-industrial times by stratospheric and tropospheric ozone, N2O, the halocarbons, and the direct effect of anthropogenic aerosols. In cold regions, afforestation or reforestation would decrease the surface albedo and induce a positive radiative forcing (warming) which could partly or completely offset the negative forcing (cooling) due to carbon sequestration. This suggests that carbon sink plantations could be less effective than expected at reducing warming, and could even cause further warming. However, we note that reforestation (or avoided deforestation) in tropical regions could exert a double cooling effect through carbon sequestration and increased evaporation and cloud cover.

Abstract. Irrigation intensifies land use by increasing crop yield but also impacts water resources. It affects water and energy balances and consequently the microclimate in irrigated regions. Therefore, knowledge of the extent of irrigated land is important for hydrological and crop modelling, global change research, and assessments of resource use and management. Information on the historical evolution of irrigated lands is limited. The new global historical irrigation data set (HID) provides estimates of the temporal development of the area equipped for irrigation (AEI) between 1900 and 2005 at 5 arcmin resolution. We collected sub-national irrigation statistics from various sources and found that the global extent of AEI increased from 63 million ha (Mha) in 1900 to 111 Mha in 1950 and 306 Mha in 2005. We developed eight gridded versions of time series of AEI by combining sub-national irrigation statistics with different data sets on the historical extent of cropland and pasture. Different rules were applied to maximize consistency of the gridded products to sub-national irrigation statistics or to historical cropland and pasture data sets. The HID reflects very well the spatial patterns of irrigated land as shown on historical maps for the western United States (around year 1900) and on a global map (around year 1960). Mean aridity on irrigated land increased and mean natural river discharge on irrigated land decreased from 1900 to 1950 whereas aridity decreased and river discharge remained approximately constant from 1950 to 2005. The data set and its documentation are made available in an open-data repository at https://mygeohub.org/publications/8 (doi:10.13019/M20599).

[1] Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7–18% of global losses.

Organic agriculture is often proposed as a more sustainable alternative to current conventional agriculture. We assess the current understanding of the costs and benefits of organic agriculture across multiple production, environmental, producer, and consumer dimensions. Organic agriculture shows many potential benefits (including higher biodiversity and improved soil and water quality per unit area, enhanced profitability, and higher nutritional value) as well as many potential costs including lower yields and higher consumer prices. However, numerous important dimensions have high uncertainty, particularly the environmental performance when controlling for lower organic yields, but also yield stability, soil erosion, water use, and labor conditions. We identify conditions that influence the relative performance of organic systems, highlighting areas for increased research and policy support.

Most studies of the influence of weather and climate on food production have examined the influence on crop yields. However, climate influences all components of crop production, includes cropping area (area planted or harvested) and cropping intensity (number of crops grown within a year). Although yield increases have predominantly contributed to increased crop production over the recent decades, increased cropping area as well as increases in cropping intensity, especially in the tropics, have played a substantial role. Therefore, we need to consider these important aspects of production to get a more complete understanding of the future impacts of climate change. This article reviews available evidence on how climate might influence these under-studied components of crop production. We also discuss how farmer decision making and technology might modulate the production response to climate. We conclude by discussing important knowledge gaps that need to be addressed in future research and potential ways for moving forward.

The widely reported claim that smallholders produce 70–80% of the world’s food has been a linchpin of agricultural development policy despite limited empirical evidence. Recent empirical attempts to reinvestigate this number have lacked raw data on how much food smallholders produce, and have relied on model assumptions with unknown biases and with limited spatial and commodity coverage. We examine variations in crop production by farm size using a newly-compiled global sample of subnational level microdata and agricultural censuses covering more countries (n=55) and crop types (n=154) than assessed to date. We estimate that farms under 2ha globally produce 28–31% of total crop production and 30–34% of food supply on 24% of gross agricultural area. Farms under 2ha devote a greater proportion of their production to food, and account for greater crop diversity, while farms over 1000ha have the greatest proportion of post-harvest loss.

Smallholder farming is the most prevalent form of agriculture in the world, supports many of the planet's most vulnerable populations, and coexists with some of its most diverse and threatened landscapes. However, there is little information about the location of small farms, making it difficult both to estimate their numbers and to implement effective agricultural, development, and land use policies. Here, we present a map of mean agricultural area, classified by the amount of land per farming household, at subnational resolutions across three key global regions using a novel integration of household microdata and agricultural landscape data. This approach provides a subnational estimate of the number, average size, and contribution of farms across much of the developing world. By our estimates, 918 subnational units in 83 countries in Latin America, sub-Saharan Africa, and South and East Asia average less than five hectares of agricultural land per farming household. These smallholder-dominated systems are home to more than 380 million farming households, make up roughly 30% of the agricultural land and produce more than 70% of the food calories produced in these regions, and are responsible for more than half of the food calories produced globally, as well as more than half of global production of several major food crops. Smallholder systems in these three regions direct a greater percentage of calories produced toward direct human consumption, with 70% of calories produced in these units consumed as food, compared to 55% globally. Our approach provides the ability to disaggregate farming populations from non-farming populations, providing a more accurate picture of farming households on the landscape than has previously been available. These data meet a critical need, as improved understanding of the prevalence and distribution of smallholder farming is essential for effective policy development for food security, poverty reduction, and conservation agendas.

Latin America has the planet's largest land reserves for agriculture and had the most rapid agricultural expansion during the twenty-first century. A large portion of the expansion replaced forests, as shown by many local and regional studies. However, expansion varied regionally and also replaced other land covers. Further, it is important to distinguish between changes in cropland and pastureland as they produce food at different levels of efficiency and intensity. We used thirteen years (2001–2013) of MODerate Resolution Imaging Spectroradiometer satellite imagery to characterize cropland and pastureland expansion at multiple scales across Latin America. From 2001 to 2013, 17% of new cropland and 57% of new pastureland replaced forests throughout Latin America. Cropland expansion from 2001 to 2013 was less (44.27 Mha) than pastureland (96.9 Mha), but 44% of the 2013 cropland total was new cropland, versus 27% of the 2013 pastureland total, revealing higher regional expansion rates of row crop agriculture. The majority of cropland expansion was into pastureland within core agricultural regions of Argentina, Brazil, Bolivia, Paraguay, and Uruguay. On the contrary, pastureland largely expanded at frontiers, such as central Brazil, western Paraguay, and northern Guatemala. As others have suggested, regional agriculture is strongly influenced by globalization. Indeed, we find an overall decrease in agricultural expansion after 2007, coinciding with the global economic slowdown. The results illustrate agricultural cropland and pastureland expansion across Latin America is largely segregated, and emphasize the importance of distinguishing between the two agricultural systems, as they vary in land use intensity and efficiency.

Urban agriculture (UA) is promoted because of its contribution to food security and poverty alleviation. A considerable literature highlights these benefits, but there are also criticisms that they are overstated. We review these divergent perspectives and assess the potential for UA to contribute to urban food security in different regions, based on a low threshold of urban land required to grow the daily vegetable intake for the urban poor. We find that UA is feasible in these terms in high-income countries, but its potential is low, except in the most optimistic scenario, in low-income countries, where it might be most useful. We conclude that UA can only make a limited contribution in achieving urban food security in low-income countries.

Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits-"win-wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.

While changes in temperature and precipitation extremes are evident, their influence on crop yield variability remains unclear. Here we present a global analysis detecting yield variability change and attributing it to recent climate change using spatially-explicit global data sets of historical yields and an agro-climatic index based on daily weather data. The agro-climatic index used here is the sum of effective global radiation intercepted by the crop canopy during the yield formation stage that includes thresholds for extreme temperatures and extreme soil moisture deficit. Results show that year-to-year variations in yields of maize, soybean, rice and wheat in 1981–2010 significantly decreased in 19%–33% of the global harvested area with varying extent of area by crop. However, in 9%–22% of harvested area, significant increase in yield variability was detected. Major crop-producing regions with increased yield variability include maize and soybean in Argentina and Northeast China, rice in Indonesia and Southern China, and wheat in Australia, France and Ukraine. Examples of relatively food-insecure regions with increased yield variability are maize in Kenya and Tanzania and rice in Bangladesh and Myanmar. On a global scale, over 21% of the yield variability change could be explained by the change in variability of the agro-climatic index. More specifically, the change in variability of temperatures exceeding the optimal range for yield formation was more important in explaining the yield variability change than other abiotic stresses, such as temperature below the optimal range for yield formation and soil water deficit. Our findings show that while a decrease in yield variability is the main trend worldwide across crops, yields in some regions of the world have become more unstable, suggesting the need for long-term global yield monitoring and a better understanding of the contributions of technology, management, policy and climate to ongoing yield variability change.

Organic farming is one of the fastest growing sectors of world agriculture. Although it represents only 1% of world agricultural area, organic is one of the most recognized food labels and most people in developed countries consume some amount of organic food today. There is a wide range of interpretations of what organic means by different actors in the sector. Here we examine eight different organic regulations from across the world to understand how they have codified the large diversity of ideas inherent in organic agriculture. Our analysis shows that organic practices and regulations do not differ substantially between countries – across the board organic regulations define organic mainly in terms of 'natural' vs. 'artificial' substances that are allowed (or not) as inputs. This interpretation of organic as “chemical-free” farming, largely void of broader environmental principles, does not fully incorporate the original ideas of organic theoreticians who conceived it as a holistic farming system aimed primarily at improving soil health, thereby leading to improved animal, human, and societal health. This narrow focus of organic regulations can be explained by the interest of organic consumers who predominantly buy organic because they believe it is healthier and more nutritious due to the absence of harmful substances. Organic regulations need to place more emphasis on environmental best practices in order to ensure that organic agriculture can contribute to sustainability objectives.

Global policies call for connecting protected areas (PAs) to conserve the flow of animals and genes across changing landscapes, yet whether global PA networks currently support animal movement-and where connectivity conservation is most critical-remain largely unknown. In this study, we map the functional connectivity of the world's terrestrial PAs and quantify national PA connectivity through the lens of moving mammals. We find that mitigating the human footprint may improve connectivity more than adding new PAs, although both strategies together maximize benefits. The most globally important areas of concentrated mammal movement remain unprotected, with 71% of these overlapping with global biodiversity priority areas and 6% occurring on land with moderate to high human modification. Conservation and restoration of critical connectivity areas could safeguard PA connectivity while supporting other global conservation priorities.

Six Earth system models of intermediate complexity that are able to simulate interaction between atmosphere, ocean, and land surface, were forced with a scenario of land cover changes during the last millennium. In response to historical deforestation of about 18 million sq km, the models simulate a decrease in global mean annual temperature in the range of 0.13–0.25°C. The rate of this cooling accelerated during the 19th century, reached a maximum in the first half of the 20th century, and declined at the end of the 20th century. This trend is explained by temporal and spatial dynamics of land cover changes, as the effect of deforestation on temperature is less pronounced for tropical than for temperate regions, and reforestation in the northern temperate areas during the second part of the 20th century partly offset the cooling trend. In most of the models, land cover changes lead to a decline in annual land evapotranspiration, while seasonal changes are rather equivocal because of spatial shifts in convergence zones. In the future, reforestation might be chosen as an option for the enhancement of terrestrial carbon sequestration. Our study indicates that biogeophysical mechanisms need to be accounted for in the assessment of land management options for climate change mitigation.

Abstract. There is a large uncertainty in the relative roles of human land use, climate change and carbon dioxide fertilization in changing desert dust source strength over the past 100 years, and the overall sign of human impacts on dust is not known. We used visibility data from meteorological stations in dusty regions to assess the anthropogenic impact on long term trends in desert dust emissions. We did this by looking at time series of visibility derived variables and their correlations with precipitation, drought, winds, land use and grazing. Visibility data are available at thousands of stations globally from 1900 to the present, but we focused on 357 stations with more than 30 years of data in regions where mineral aerosols play a dominant role in visibility observations. We evaluated the 1974 to 2003 time period because most of these stations have reliable records only during this time. We first evaluated the visibility data against AERONET aerosol optical depth data, and found that only in dusty regions are the two moderately correlated. Correlation coefficients between visibility-derived variables and AERONET optical depths indicate a moderate correlation (0.47), consistent with capturing about 20% of the variability in optical depths. Two visibility-derived variables appear to compare the best with AERONET observations: the fraction of observations with visibility less than 5 km (VIS5) and the surface extinction (EXT). Regional trends show that in many dusty places, VIS5 and EXT are statistically significantly correlated with the Palmer drought severity index (based on precipitation and temperature) or surface wind speeds, consistent with dust temporal variability being largely driven by meteorology. This is especially true for North African and Chinese dust sources, but less true in the Middle East, Australia or South America, where there are not consistent patterns in the correlations. Climate indices such as El Nino or the North Atlantic Oscillation are not correlated with visibility-derived variables in this analysis. There are few stations where visibility measures are correlated with cultivation or grazing estimates on a temporal basis, although this may be a function of the very coarse temporal resolution of the land use datasets. On the other hand, spatial analysis of the visibility data suggests that natural topographic lows are not correlated with VIS5 or EXT, but land use is correlated at a moderate level. This analysis is consistent with land use being important in some regions, but meteorology driving interannual variability during 1974–2003.

Numerical simulations of idealized deforestation and overgrazing are performed for the Niger and Lake Chad basins of West Africa with a terrestrial ecosystem model IBIS (integrated biosphere simulator) and an aquatic transport model THMB (terrestrial hydrology model with biogeochemistry). The study reveals how land use changes affect hydrological regimes at the watershed scale. The results show that tropical forests, due to being situated in the regions of highest rainfall and exerting strong influence on evapotranspiration, have a disproportionately large impact on the water balance of the entire basin. Total deforestation (clearcutting) increases the simulated runoff ratio from 0.15 to 0.44, and the annual streamflow by 35–65%, depending on location in the basin, although forests occupy only a small portion (<5%) of the total basin area. Complete removal of grassland and savanna, which occupy much greater areas of the basins, result in an increase in simulated annual streamflow by 33–91%. The numerical simulations indicate that the hydrological response to progressive land cover change is non-linear and exhibits a threshold effect. There is no significant impact on the water yield and river discharge when the deforestation (thinning) percentage is below 50% or the overgrazing percentage below 70% for savanna and 80% for grassland areas; however, the water yield is increased dramatically when land cover change exceeds these thresholds. This threshold effect is a combined result of the non-linearity of the separate response of transpiration and soil and canopy evaporation to the imposed land cover changes.

Summary 1. We present a simple algorithm for reconstructing spatially explicit historical changes in croplands. We initialize our simulation with a satellite‐derived characterization of present‐day croplands c. 1992. This data set of croplands is then used within a simple model, along with historical cropland inventory data at the national and subnational level, to reconstruct historical crop cover. We present an annual data set of cropland areas in North America between 1850 and 1992, at a spatial resolution of 5 min (≈10 km). 2. The reconstructed changes in North American crop cover are generally consistent with qualitative descriptions of change. Crop cover is initially concentrated in the eastern portions of the continent, and subsequently migrates westward into the Midwestern United States and the Prairie Provinces of Canada. We also see cropland abandonment in the eastern portions of the continent during the 20th century. The simulation, however, fails to characterize adequately the changes in crop cover in Mexico. 3. We also estimate the extent to which the different vegetation types of North America have been cleared for cultivation. We find that savannas/grasslands/steppes and forests/woodlands have undergone the most extensive conversion (1.68 and 1.40 million km 2 cleared, respectively, since 1850). We further discuss the wider implications of such large‐scale changes in land cover.

We explore in this paper how animals can be affected by variation in climate, topography, vegetation characteristics, and body size. We utilize new spatially explicit state-of-the-art models that incorporate principles from heat and mass transfer engineering, physiology, morphology, and behavior that have been modified to provide spatially explicit hypotheses using GIS. We demonstrate how temporal and spatial changes in microclimate resulting from differences in topography and vegetation cover alter animal energetics, and behavior. We explore the impacts of these energetic predictions on elk energetics in burned and unburned stands of conifer in winter in Yellowstone National Park, chuckwalla lizard distribution limits in North America, California Beechey Ground squirrel and Dusky Footed woodrat mass and energy requirements and activity patterns on the landscape, their predator prey interactions with a rattlesnake, Crotalus viridis, and shifts in that food web structure due to topographic and vegetative variation. We illustrate how different scales of data/observation provide different pieces of information that may collectively define the real distributions of a species. We then use sensitivity analyses of energetic models to evaluate hypotheses about the effects of changes in core temperature (fever) global climate (increased air temperature under a global warming scenario) and vegetation cover (deforestation) on winter survival of elk, the geographic distribution of chuckwallas and the activity overlap of predator and prey species within a subset of commonly observed species in a terrestrial food web. Variation in slope and aspect affect the spatial variance in solar radiation incident on the ground, hence ground surface temperature, at the same elevation, same hourly 2 m air temperatures, and wind speeds. We illustrate visually how spatial effects and landscape heterogeneity make statistical descriptions of animal responses problematic, since multiple distributions of their responses to climate, topography, and vegetation on the landscape can yield the same descriptive statistics, especially at high (30 m) resolution. This preliminary analysis suggests that the model has far-reaching implications for hypothesis testing in ecology at a variety of spatial and temporal scales.

Urban agriculture (UA) has been drawing a lot of attention recently for several reasons: the majority of the world population has shifted from living in rural to urban areas; the environmental impact of agriculture is a matter of rising concern; and food insecurity, especially the accessibility of food, remains a major challenge. UA has often been proposed as a solution to some of these issues, for example by producing food in places where population density is highest, reducing transportation costs, connecting people directly to food systems and using urban areas efficiently. However, to date no study has examined how much food could actually be produced in urban areas at the global scale. Here we use a simple approach, based on different global-scale datasets, to assess to what extent UA is constrained by the existing amount of urban space. Our results suggest that UA would require roughly one third of the total global urban area to meet the global vegetable consumption of urban dwellers. This estimate does not consider how much urban area may actually be suitable and available for UA, which likely varies substantially around the world and according to the type of UA performed. Further, this global average value masks variations of more than two orders of magnitude among individual countries. The variations in the space required across countries derive mostly from variations in urban population density, and much less from variations in yields or per capita consumption. Overall, the space required is regrettably the highest where UA is most needed, i.e., in more food insecure countries. We also show that smaller urban clusters (i.e., <100 km2 each) together represent about two thirds of the global urban extent; thus UA discourse and policies should not focus on large cities exclusively, but should also target smaller urban areas that offer the greatest potential in terms of physical space.

Climate volatility could change in the future, with important implications for agricultural productivity. For Tanzania, where food production and prices are sensitive to climate, changes in climate volatility could have severe implications for poverty. This study uses climate model projections, statistical crop models, and general equilibrium economic simulations to determine how the vulnerability of Tanzania's population to impoverishment by climate variability could change between the late 20th Century and the early 21st Century. Under current climate volatility, there is potential for a range of possible poverty outcomes, although in the most extreme of circumstances, poverty could increase by as many as 650,000 people due to an extreme interannual decline in grain yield. However, scenarios of future climate from multiple climate models indicate no consensus on future changes in temperature or rainfall volatility, so that either an increase or decrease is plausible. Scenarios with the largest increases in climate volatility are projected to render Tanzanians increasingly vulnerable to poverty through impacts on staple grains production in agriculture, with as many as 90,000 additional people entering poverty on average. Under the scenario where precipitation volatility decreases, poverty vulnerability decreases, highlighting the possibility of climate changes that oppose the ensemble mean, leading to poverty impacts of opposite sign. The results suggest that evaluating potential changes in volatility and not just the mean climate state may be important for analyzing the poverty implications of climate change.

Amid widespread concerns about biodiversity loss, a single clear conservation message is engaging leading conservationists: the proposal to give half the surface of the Earth back to nature. Depending on the landscape conservation strategy, we find that, globally, 15–31% of cropland, 10–45% of pasture land, 23–25% of non-food calories and 3–29% of food calories from crops could be lost if half of Earth’s terrestrial ecoregions were given back to nature. In response to continuing habitat and biodiversity loss, leading conservationists have proposed setting aside half the earth for nature. This study evaluates the trade-offs with food production and finds losses in croplands, pasture and calories that vary with the conservation strategy.

Significance Agriculture is a key driver of tropical deforestation, and there is heated debate about whether productivity-enhancing crop innovations can slow such environmental degradation. For fixed food demand, globally higher yields will reduce cropland and hence deforestation. However, regional innovations often boost agricultural profitability and lower prices, thereby leading to cropland expansion in the innovating region. This paper develops a framework for understanding the impact of regional innovations on global land use and the environment. Although the historical Green Revolution in Asia, Latin America, and the Middle East is shown to have been land sparing, a future Green Revolution in Africa could lead to global cropland expansion in the context of a more fully integrated global agricultural economy.

Lack of accurate maps on the extent of global cropland, and particularly the spatial distribution of major crop types, hampers policy and strategic investment and could potentially impede efforts to improve food security in an environment characterized by continued market volatility and a changing climate. Here we discuss the pressing need for the provision of spatially explicit cropland datasets at a global scale and review the strengths and weaknesses of the various approaches used to develop such data.

Increased global agricultural output since the 1990s has been largely driven by innovations that raised the efficiency of use of labour, land, capital and other inputs—referred to as total factor productivity (TFP) growth. Yet debates over the future of farming still weigh heavily on models of agricultural land use and socioecological trade-offs along traditional (partial factor productivity) growth paths of ‘intensification’ or ‘extensification’. Overlooking the role of TFP in the evolution of global agriculture not only obscures the changing drivers of productivity growth but also misses vital linkages with agricultural sustainability and farming system resilience. We describe two pathways for growth—technology-based and ecosystem-based—and link these in a heuristic framework that emphasizes sustainability and resilience outcomes in farming systems. Interdisciplinary research is urgently needed to empirically examine the dynamic interplay of TFP growth, farming system sustainability and resilience. Such insights will help to transform TFP growth as metric into actionable efforts on farms and beyond. Since the 1990s, global agricultural output has been driven largely by innovations that raised the efficiency of using labour, land and other inputs, together called total factor productivity (TFP). This Perspective discusses this reality and suggests two pathways for future growth: technology-based and ecosystem-based. Future research on farm-system sustainability and resilience should leverage these options.

A slowdown in global agricultural expansion, spurred by land limitations, improved technologies, and demand for specific crops has led to increased agricultural intensification. While agricultural expansion has been heavily scrutinized, less attention has been paid to changes within cropland systems. Here we present a method to detect individual cropland field parcels from temporal Landsat imagery to improve cropland estimates and better depict the scale of farming across South America. The methods consist of multi-spectral image edge extraction and multi-scale contrast limited adaptive histogram equalization (CLAHE) and adaptive thresholding using Landsat Surface Reflectance Climate Data Record (CDR) products. We tested our methods across a South American region with approximately 82% of the 2000/2001 total cropland area, using a Landsat time series composite with a January 1, 2000 to August 1, 2001 timeframe. A thematic accuracy assessment revealed an overall cropland f-score of 91%, while an object-based assessment of 5480 fields showed low geometric errors. The results illustrate that Landsat time series can be used to accurately estimate cropland in South America, and the low geometric errors of the per-parcel estimates highlight the applicability of the proposed methods over a large area. Our approach offers a new technique of analyzing agricultural changes across a broad geographic scale. By using multi-temporal Landsat imagery with a semi-automatic field extraction approach, we can monitor within-agricultural changes at a high degree of accuracy, and advance our understanding of regional agricultural expansion and intensification dynamics across South America.

Multiple breadbasket failure is a risk to global food security. However, there are no global analyses that have quantitatively assessed if global crop production has actually tended towards synchronized failure historically. We show that synchronization in production within major commodities such as maize and soybean has declined in recent decades, leading to increased global stability in production of these crops. In contrast, synchrony between crops has peaked, making global calorie production more unstable. Under the hypothetical event of complete synchronized failure we estimate simultaneous global production losses for rice, wheat, soybean and maize to lie between -17% and -34%. We find that offsetting these losses by reducing variation in production across all growing locations, and raising production ceilings in breadbaskets, are far more effective than strategies focused on reducing variability in breadbaskets alone or closing production gaps in low productive locations. Our findings suggest that maintaining asynchrony in the food system requires a central place in discussions of future food demand under mean climate change, population growth and consumption trends.

Big data and mobile technology are widely claimed to be global disruptive forces in agriculture that benefit small-scale farmers. Yet the access of small-scale farmers to this technology is poorly understood. We show that only 24–37% of farms of <1 ha in size are served by third generation (3G) or 4G services, compared to 74–80% of farms of >200 ha in size. Furthermore, croplands with severe yield gaps, climate-stressed locations and food-insecure populations have poor service coverage. Across many countries in Africa, less than ~40% of farming households have Internet access, and the cost of data remains prohibitive. We recommend a digital inclusion agenda whereby governments, the development community and the private sector focus their efforts to improve access so that data-driven agriculture is available to all farmers globally. Big data and mobile technology are considered a growing boon for small-scale farmers. This study finds that only 24–37% of farms of <1 ha are currently served by third generation (3G) or 4G services, compared to 74–80% of farms of >200 ha, and that regions with poor yields and low food security have poor service coverage.

The global demand for palm oil has grown rapidly over the past several decades. Much of the output expansion has occurred in carbon- and biodiversity-rich forest lands of Malaysia and Indonesia (M&I), contributing to record levels of terrestrial carbon emissions and biodiversity loss. This has led to a variety of voluntary and mandatory regulatory actions, as well as calls for limits on palm oil imports from M&I. This paper offers a comprehensive, global assessment of the economic and environmental consequences of alternative policies aimed at limiting deforestation from oil palm expansion in M&I. It highlights the challenges of limiting forest and biodiversity loss in the presence of market-mediated spillovers into related oilseed and agricultural commodity and factor markets, both in M&I and overseas. Indeed, limiting palm oil production or consumption is unlikely to halt deforestation in M&I in the absence of active forest conservation incentives. Policies aimed at restricting palm oil production in M&I also have broader consequences for the economy, including significant impacts on consumer prices, real wages, and welfare, that vary among different global regions. A crucial distinction is whether the initiative is undertaken domestically, in which case the M&I region could benefit, or by major palm oil importers, in which case M&I loses income. Nonetheless, all policies considered here pass the social welfare test of global carbon dioxide mitigation benefits exceeding their costs.

Ecosystem restoration is an important means to address global sustainability challenges. However, scientific and policy discourse often overlooks the social processes that influence the equity and effectiveness of restoration interventions. In the present article, we outline how social processes that are critical to restoration equity and effectiveness can be better incorporated in restoration science and policy. Drawing from existing case studies, we show how projects that align with local people's preferences and are implemented through inclusive governance are more likely to lead to improved social, ecological, and environmental outcomes. To underscore the importance of social considerations in restoration, we overlay existing global restoration priority maps, population, and the Human Development Index (HDI) to show that approximately 1.4 billion people, disproportionately belonging to groups with low HDI, live in areas identified by previous studies as being of high restoration priority. We conclude with five action points for science and policy to promote equity-centered restoration.

While the earth's climate can affect the structure and functioning of terrestrial ecosystems, the process also works in reverse. As a result, changes in terrestrial ecosystems may influence climate through both biophysical and biogeochemical processes. This two-way link between the physical climate system and the biosphere is under increasing scrutiny. We review recent developments in the analysis of this interaction, focusing in particular on how alterations in the structure and functioning of terrestrial ecosystems, through either human land-use practices or global climate change, may affect the future of the earth's climate.

The magnitude and location of terrestrial carbon sources and sinks remains subject to large uncertainties. Estimates of terrestrial CO2 fluxes from ground-based inventory measurements typically find less carbon uptake than inverse model calculations based on atmospheric CO2 measurements, while a wide range of results have been obtained using models of different types. However, when full account is taken of the processes, pools, time scales and geographic areas being measured, the different approaches can be understood as complementary rather than inconsistent, and can provide insight as to the contribution of various processes to the terrestrial carbon budget. For example, quantitative differences between atmospheric inversion model estimates and forest inventory estimates in northern extratropical regions suggest that carbon fluxes to soils (often not accounted for in inventories), and into non-forest vegetation, may account for about half of the terrestrial uptake. A consensus of inventory and inverse methods indicates that, in the 1980s, northern extratropical land regions were a large net sink of carbon, and the tropics were approximately neutral (albeit with high uncertainty around the central estimate of zero net flux). The terrestrial flux in southern extratropical regions was small. Book-keeping model studies of the impacts of land-use change indicated a large source in the tropics and almost zero net flux for most northern extratropical regions; similar land use change impacts were also recently obtained using process-based models. The difference between book-keeping land-use change model studies and inversions or inventories was previously interpreted as a “missing” terrestrial carbon uptake. Land-use change studies do not account for environmental or many management effects (which are implicitly included in inventory and inversion methods). Process-based model studies have quantified the impacts of CO2 fertilisation and climate change in addition to land use change, and found that these environmental effects are in the right order of magnitude to account for the “missing” terrestrial carbon uptake. Despite recent carbon losses due to fire and insect attack in Canada and Russia, the northern extratropical regions generally have been a net carbon sink, only partially due to land-use changes such as abandonment of agricultural land. In the tropics, inventory data and flux measurements in extant forests support the existence of an environmental or management sink that counterbalances the effect of deforestation. Woody encroachment in savannas may also be a significant (but as yet poorly quantified) cause of tropical carbon uptake.

Complex interactions in the climate system can give rise to strong positive feedback mechanisms that may lead to sudden climatic changes. The prolonged Sahel drought and the Dust Bowl are examples of 20th century abrupt climatic changes that had serious effects on ecosystems and societies. Here we analyze global historical rainfall observations to detect regions that have undergone large, sudden decreases in rainfall. Our results show that in the 20th century about 30 regions in the world have experienced such changes. These events are statistically significant at the 99% level, are persistent for at least ten years, and most have magnitudes of change that are 10% lower than the climatological normal (1901–2000 rainfall average). This analysis illustrates the extent and magnitude of abrupt climate changes across the globe during the 20th century and may be used for studying the dynamics of and the mechanisms behind these abrupt changes.

Abstract The United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt. Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha−1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields. This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha−1 yr−1), but have steadily declined to a relative rate of 0.78% yr−1 (49.2 kg ha−1 yr−1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha−1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution.

Abstract The classic story of historical land-cover change in the United States suggests that agricultural clearing in the 1800s was followed by agricultural abandonment at the turn of the twentieth century and subsequent forest regrowth—often referred to as a forest transition. Most descriptions present statistical data from historical censuses and surveys to make this case. Here we show that the historical data on cropland and forest area change for the United States need to be interpreted with care. Some earlier studies have exaggerated the extent of cropland abandonment and forest regrowth by failing to account for changes in definitions of croplands over time and changes in political boundaries in the case of forests. We reexamined the historical data to find that cropland and forest area for the United States as a whole have not undergone large-scale abandonment and regrowth but rather stabilized around the mid-twentieth century. Moreover, we find that, consistent with local and regional case studies, croplands were indeed abandoned in the eastern portions of the continent accompanied by forest regrowth, but there was compensating cropland expansion and forest clearing in the west. Our study suggests the need to exercise caution when using historical data to understand land-cover change and for developing theories such as forest transition. [Supplemental material is available for this article. Go to the publisher's online edition of Annals of the Association of American Geographers for the following free supplemental resource: (1) a table of cropland harvested area for the states of the United States from 1879 to 2002.] El clásico recuento de los cambios históricos en la cobertura del suelo en los Estados Unidos sugiere que al desbrozo agrÍcola ocurrido durante el siglo XIX le siguió a la vuelta del XX el abandono de las tierras de cultivo y el subsiguiente recrecimiento del bosque—proceso al que a menudo se le conoce como transición forestal. La mayoría de las descripciones presentan datos estadísticos de censos y levantamientos de campo históricos en soporte de sus puntos de vista. En nuestro estudio mostramos que los datos históricos sobre el cambio de área de tierras cultivadas y bosques para los Estados Unidos deben interpretarse con cuidado. Algunos de los estudios anteriores han exagerado la extensión de campos de cultivo abandonados y de los bosques que las reemplazaron, al no tener en cuenta cambios en la propia definición de tierra de cultivo y las modificaciones que ocurrieron en los límites políticos en lo que se refiere a bosques a través del tiempo. Examinamos los datos históricos para así establecer que el área de tierras cultivadas y bosques de los Estados Unidos, en conjunto, no ha experimentado abandono en gran escala y recrecimiento forestal, sino que por el contrario se estabilizó a mediados del siglo XX. Aun más, encontramos que, consistente con estudios de casos locales y regionales, tierras cultivadas de las regiones orientales del continente en verdad fueron abandonadas y acompañadas de recrecimiento del bosque, pero que, en compensación, ocurrió una expansión del área cultivada y reducción de los bosques en el oeste. Nuestro estudio sugiere obrar con cautela cuando se utilizan datos históricos para entender los cambios de la cubierta del suelo y para desarrollar teorías como la de la transición forestal. [Hay disponible material suplementario para este artículo. Acceder a la edición online del publicista de Annals of the Association of American Geographers para el siguiente recurso suplementario gratuito: (1) una tabla del área de las tierras de cultivos cosechados en los estados de EE.UU. de 1879 a 2002.]. Key Words: farmland abandonmentforest transitionhistorical dataland-cover changeUnited States关键词: 农田废弃森林过渡历史数据土地覆盖变化美国Palabras clave: abandono de tierras de cultivo transición forestaldatos históricoscambio de la cobertura de la tierraEstados Unidos Acknowledgments We would like to thank Lisa M. Curran, Jonathan A. Foley, and Billie L. Turner II for useful feedback on this article. Notes 1. Historical data on population were available consistently back to 1800. Agricultural data were not available until 1880, so Clawson used the relationship between population and agricultural data from 1880 to 1975 to estimate cropland area between 1800 and 1880. 2. This volume presented data going back to 1880; it is not clear why Clawson did not use the 1880 figure but rather estimated the value. 3. Note that often the census report was issued one year after the data were collected. For example, the 1880 census reported data on agriculture in 1879. HisStatUS (and Clawson) assigned the values to the year of the report, whereas we assigned the values to the year of data collection in this article. 4. Note that cropland harvested denotes the area of land devoted to harvested crops; if a piece of land is multiple cropped, it is not counted more than once. Therefore, our data until 1920 are not entirely consistent because we simply added the harvested area of individual crops to get cropland harvested. However, multiple cropping was likely nonexistent in the early 1900s; even today, only about 4 percent of cropland harvested is multiple cropped (U.S. Department of Agriculture 2008). 5. Cultivated summer fallow. 6. Williams cited his data sources as CitationClawson (1979) and U.S. Forest Service (1982). It is clear that the legacy of Clawson's first estimates have been reproduced since in other studies. 7. Footnotes to the cropland data in HisStatUS are as follows. Footnote 5: “Cropland harvested only” for the data in years 1880 to 1920; Footnote 4: “Includes Alaska and Hawaii” for data in years 1930, 1940, and 1950; and Footnote*: “Except as indicated by footnote 4, denotes first year for which figures include Alaska and Hawaii.” The exclusion of Alaska and Hawaii in some earlier reports, although noteworthy, is insignificant because they contain only 7 percent of the total cropland in the United States currently, and contained less than that in the past. 8. According to a table footnote from the 1945 census, “The 1940 figures are not strictly comparable with those for 1945. The 1945 figures include land used only for pasture, which has been plowed within 7 years. The 1940 figures include land pastured, which could have been plowed and used for crops without additional clearing, drainage, or irrigation. This land may not have been plowed within 7 years prior to 1940” (U.S. Census 1947, as cited in CitationWaisanen and Bliss 2002, 84–4). 9. Indeed, a USDA report suggests that there is an inverse relationship between cropland used for crops and cropland idled because of federal programs (USDA 2008).

Since time immemorial, humankind has changed landscapes in attempts to improve the amount, quality, and security of natural resources critical to its well being, such as food, freshwater, fiber, and medicinal products. Through the increased use of innovation, human populations have, slowly at first, and at increasingly rapid pace later on, increased its ability to derive resources from the environment, and expand its territory. Several authors have identified three different phases - the control of fire, domestication of biota, and fossil-fuel use - as being pivotal in enabling increased appropriation of natural resources (Goudsblom and De Vries 2004; Turner II and McCandless 2004).

STEM PhDs increasingly contribute to commercial science, such as patenting. We analyze faculty’s role in training STEM PhD students as new inventors on patents at leading research universities, emphasizing the drivers of gender differences. We ...STEM PhDs are a critical source of human capital in the economy, contributing to commercial as well as academic science. We examine whether STEM PhD students become new inventors (file their first patent) during their doctoral training at the top 25 U.S. ...

Food security is a key global concern. By 2050, the global population will exceed 9 billion, and a 50% increase in annual agricultural output will be required to keep up with demand. There are significant additional pressures on existing agricultural land through increased competition from the biofuel sector and the need to elevate feed production, which is being driven by higher levels of meat consumption in low‐ and middle‐income countries.

Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.

A key research frontier in global change research lies in understanding processes of land change to inform predictive models of future land states.We believe that significant advances in the field are hampered by limited attention being paid to critical points of change termed land-use regime shifts.We present an analytical framework for understanding land-use regime shifts.We survey historical events of land change and perform in-depth case studies of soy and shrimp development in Latin America to demonstrate the role of preconditions, triggers, and self-reinforcing processes in driving land-use regime shifts.Whereas the land-use literature demonstrates a good understanding of within-regime dynamics, our understanding of the drivers of land-use regime shifts is limited to ex post facto explications.Theoretical and empirical advances are needed to better understand the dynamics and implications of land-use regime shifts.We draw insights from the regime-shifts literature to propose a research agenda for studying land change.

Sustainable intensification of agricultural production is expected to be an important pathway for achieving future food security while protecting the environment. Recognizing that there is no single answer to how different dimensions of intensification can be achieved sustainably, we identify opportunities for research across spatial scales. We focus specifically on research questions around advances in technology and management and suggest that progress on these questions can be made by (i) improving understanding of trade-offs, especially across scales, (ii) recognition of the context-specificity of how agricultural intensification can become more sustainable, and (iii) development, access and wider use of global datasets for integrative research.

Abstract Several safe boundaries of critical Earth system processes have already been crossed due to human perturbations; not accounting for their interactions may further narrow the safe operating space for humanity. Using expert knowledge elicitation, we explored interactions among seven variables representing Earth system processes relevant to food production, identifying many interactions little explored in Earth system literature. We found that green water and land system change affect other Earth system processes strongly, while land, freshwater and ocean components of biosphere integrity are the most impacted by other Earth system processes, most notably blue water and biogeochemical flows. We also mapped a complex network of mechanisms mediating these interactions and created a future research prioritization scheme based on interaction strengths and existing knowledge gaps. Our study improves the understanding of Earth system interactions, with sustainability implications including improved Earth system modelling and more explicit biophysical limits for future food production.

Abstract Ordinary least squares linear regression (LR) has long been a popular choice among researchers interested in using historical data for estimating crop yield response to climate change. Today, the rapidly growing field of machine learning (ML) offers a wide range of advanced statistical tools that are increasingly being used for more accurate estimates of this relationship. This study compares LR to a popular ML technique called boosted regression trees (BRTs). We find that BRTs provide a significantly better prediction accuracy compared to various LR specifications, including those fitting quadratic and piece-wise linear functions. BRTs are also able to identify break points where the relationship between climate and yield undergoes significant shifts (for example, increasing yields with precipitation followed by a plateauing of the relationship beyond a certain point). Tests we performed with synthetically simulated climate and crop yield data showed that BRTs can automatically account for not only spatial variation in climate–yield relationships, but also interactions between different variables that affect crop yields. We then used both statistical techniques to estimate the influence of historical climate change on rice, wheat, and pearl millet in India. BRTs predicted a considerably smaller negative impact compared to LR. This may be an artifact of BRTs conflating time and climate variables, signaling a potential weakness of models with excessively flexible functional forms for inferring climate impacts on agriculture. Our findings thus suggest caution while interpreting the results from single-model analyses, especially in regions with highly varied climate and agricultural practices.

In preparation for the deployment of a new mechanism that could address as much as one fifth of global greenhouse gas emissions by reducing emissions from deforestation and forest degradation (REDD+), important work on methodological issues is still needed to secure the capacity to produce measurable, reportable, and verifiable emissions reductions from REDD+ in developing countries. To contribute to this effort, we have diagnosed the main sources of uncertainty in the quantification of emission from deforestation for Panama, one of the first countries to be supported by the Forest Carbon Partnership Facility of the World Bank and by UN-REDD. Performing sensitivity analyses using a land-cover change emissions model, we identified forest carbon stocks and the quality of land-cover maps as the key parameters influencing model uncertainty. The time interval between two land-cover assessments, carbon density in fallow and secondary forest, and the accuracy of land-cover classifications also affect our ability to produce accurate estimates. Further, we used the model to compare emission reductions from five different deforestation reduction scenarios drawn from governmental input. Only the scenario simulating a reduction in deforestation by half succeeds in crossing outside the confidence bounds surrounding the baseline emission obtained from the uncertainty analysis. These results suggest that with current data, real emission reductions in developing countries could be obscured by their associated uncertainties. Ways of addressing the key sources of error are proposed, for developing countries involved in REDD+, for improving the accuracy of their estimates in the future. These new considerations confirm the importance of current efforts to establish forest monitoring systems and enhance capabilities for REDD+ in developing countries.

A combination of high commodity crop prices, rising global food demand, and technological advances has transformed the scale of global crop production. Farming in South America is a prime example, where large-scale cash crops, such as soy, have transformed the land use dynamics at the forest frontier. We evaluate this transformation in sub-Andean South America by estimating crop and forest cover and detecting individual cropland field parcels using Landsat imagery in 5 year intervals over a 24 year period. From 1990 to 2014, cropland expansion onto deforested land was increasingly driven by large fields (>50 ha), whose contribution increased from 32% to 48% (+16% increase), while the contribution of smaller fields (<20 ha) declined from 36% to 26% (−10% decrease). This shift toward large-scale farming replacing cleared land across the region has important implications for food security and biodiversity conservation. Policy efforts will need to target different actors and transcend national borders in order to tackle the changing nature of South American deforestation.

Interactions between land systems and food systems are central in land system science. Here, we highlight three interrelated emerging directions for renewing this research agenda: 1) What is the role of input providers, and in particular external advisors, financial institutions, and investment modes in shaping farm management decisions? 2) Beyond the overall land footprint of distinct production modes and diets, what are the impacts of emerging, rapidly shifting, diet trends? and 3) How to grapple with the trade-offs between labor as a substitutable input versus labor as a source of livelihood, and in particular, what is the labor productivity and drudgery of alternative forms of agriculture such as organic farming and agroecology, and how does this balance with the rate of off-farm labor force absorption.

Groundwater irrigation using electric pumps plays a key role in India's agricultural water supply. Power utilities across different states use two common tariff modes to charge groundwater consumers: flat tariffs, where payments are fixed according to a pump's power rating, and metered tariffs based on units of power actually consumed. In this review, we use empirical evidence from past studies across multiple jurisdictions in India to compare the two tariff structures in terms of three key features: administrative burden on utilities; equity of groundwater access between high-income and low-income farmers; and influence on farmers' pumping behavior. Our analysis shows that flat tariffs have low administrative costs and more equitable distributional outcomes, but provide no incentive to farmers for water conservation. Conversely, metered tariffs have the potential to encourage judicious consumption, but are expensive to manage and disadvantageous to low-income farmers who often buy water from wealthier groundwater well owners. Flawed tariff policies, in conjunction with large subsidies for agricultural power, have caused rapid groundwater depletion in many regions as well as massive financial losses to power utilities and governments – both state and central. Since there is considerable heterogeneity in agricultural practices and groundwater availability across India, we propose location-specific strategies for rationalizing agricultural power tariffs in different regions. While the groundwater-abundant eastern regions can benefit from a hybrid flat-cum-metered tariff that encourages farmer-to-farmer water sales, western states facing unsustainable groundwater exploitation should develop tariff policies that ration power, prioritize its supply during the most critical seasons, and reward farmers who reduce their groundwater consumption. Not only will such tariff policies help conserve groundwater, but also augment government financial resources for social welfare programs such as education, health, energy access etc. Thus, improved power policies can provide substantial assistance in India's progress towards multiple UN Sustainable Development Goals.

Abstract Agricultural monitoring, seasonal crop forecasting and climate change adaptation planning all require identifying where, when, how and which crops are grown. Global gridded cropping system data products offer useful information for these applications. However, not only the main sources of information (satellites, censuses, surveys and models) but also the spatial and temporal resolutions of these data products are quite distant from each other because of different user requirements. This is a barrier to strengthening collaborations among the research communities working to increase the capacity of societies to manage climate risks for global food systems, from extreme weather disasters to climate change. A first step is to improve cropping system data products so they can be used more seamlessly across various applications than they are currently. Toward this goal, this article reviews global gridded data products of crop variables (area, yield, cropping intensity, etc) using systematic literature survey, identifies their current limitations, and suggests directions for future research. We found that cropland or crop type mapping and yield or production estimation/prediction together accounted for half of the research objectives of the reviewed studies. Satellite-based data products are dominant at the finer resolution in space and time (&lt;10 km and daily to annual), while model-based data products are found at the coarser resolutions (&gt;55 km and ⩾decadal). Census-based data products are seen at intermediate resolutions (10–55 km and annual to decadal). The suggested directions for future research include the hybridization of multiple sources of information, improvements to temporal coverage and resolution, the enrichment of management variables, the exploration of new sources of information, and comprehensiveness within a single data product.

An atmospheric transport model and observations of atmospheric CO 2 are used to evaluate the performance of four Terrestrial Carbon Models (TCMs) in simulating the seasonal dynamics and interannual variability of atmospheric CO 2 between 1980 and 1991. The TCMs were forced with time varying atmospheric CO 2 concentrations, climate, and land use to simulate the net exchange of carbon between the terrestrial biosphere and the atmosphere. The monthly surface CO 2 fluxes from the TCMs were used to drive the Model of Atmospheric Transport and Chemistry and the simulated seasonal cycles and concentration anomalies are compared with observations from several stations in the CMDL network. The TCMs underestimate the amplitude of the seasonal cycle and tend to simulate too early an uptake of CO 2 during the spring by approximately one to two months. The model fluxes show an increase in amplitude as a result of land‐use change, but that pattern is not so evident in the simulated atmospheric amplitudes, and the different models suggest different causes for the amplitude increase (i.e., CO 2 fertilization, climate variability or land use change). The comparison of the modeled concentration anomalies with the observed anomalies indicates that either the TCMs underestimate interannual variability in the exchange of CO 2 between the terrestrial biosphere and the atmosphere, or that either the variability in the ocean fluxes or the atmospheric transport may be key factors in the atmospheric interannual variability.

The Amazon basin contains some of the most productive ecosystems on the planet, yet we have little understanding of the long‐term behavior. By examining historical climate records over the Amazon, we identify several modes of climatic variability—including previously undocumented long‐term modes. Furthermore, using a process‐based ecosystem model, we show that these variations in climate generate variations in terrestrial carbon fluxes on short (3–4 year), intermediate (8–9 year), and long (24–28 year) time scales. The long‐term cycles in terrestrial carbon balance have not been previously suggested. Finally, we find that time‐lags between productivity and decomposition enhance the short‐term variations in net carbon balance, while slightly dampening the long‐term variations. Given the worldwide attention on terrestrial carbon cycling, and the potential for “carbon sinks”, we suggest that an improved understanding of long‐term climatic and ecosystem processes is crucial. Other regions should be examined for potential long‐term carbon cycle variations.

Abstract A water stress–compensating root-water-uptake module was developed based upon a newly proposed water stress reduction function and an asymptotic root distribution function. The water stress reduction function takes into account both soil water pressure head and soil resistance to water flow. It requires only physically based parameters that eliminate the need for empirical calibration. The root-water-uptake module, incorporated into a complete Soil–Vegetation–Atmosphere Transfer (SVAT) simulation model, was tested for a variety of soil, crop, and climatic conditions across Canada. Both the proposed water stress reduction and the asymptotic root distribution function performed similarly to existing ones, with the maximum difference in normalized root-mean-square error (NRMSE) between the new and existing water stress reduction function being 0.6%, and between the asymptotic and an exponential root distribution function being 1.2%. The entire root-water-uptake module worked as well as, or better than, published ones. Because the new module uses fewer empirical parameters, it becomes particularly useful in large-scale modeling applications of land surface, hydrology, and terrestrial ecosystems where such parameters are usually not readily available.

The 1973 “Miami Model” was the first global‐scale empirical model of terrestrial net primary productivity (NPP), and its simplicity and relative accuracy has led to its continued use. However, improved techniques to measure NPP in the field and the expanded spatial and temporal range of observations have prompted this study, which reexamines the relationship of climatic variables to NPP. We developed several statistical models with paired climatic variables in order to investigate their relationships to terrestrial NPP. A reference data set of 3023 NPP field observations was compiled for calibration and parameter optimization. In addition to annual mean temperature and precipitation, as in the Miami Model, we chose more ecologically relevant climatic variables including growing degree‐days, a soil moisture stress index, and photosynthetically active radiation (PAR). Calculated annual global NPP ranged from 36 to 74 Pg‐C yr −1 , comparable with previous studies. Comparisons of geographic patterns of NPP were made using biome and latitudinal averages.

Abstract. European ecosystems are thought to take up large amounts of carbon, but neither the rate nor the contributions of the underlying processes are well known. In the second half of the 20th century, carbon dioxide concentrations have risen by more that 100 ppm, atmospheric nitrogen deposition has more than doubled, and European mean temperatures were increasing by 0.02 °C yr−1. The extents of forest and grasslands have increased with the respective rates of 5800 km2 yr−1 and 1100 km2 yr−1 as agricultural land has been abandoned at a rate of 7000 km2 yr−1. In this study, we analyze the responses of European land ecosystems to the aforementioned environmental changes using results from four process-based ecosystem models: BIOME-BGC, JULES, ORCHIDEE, and O-CN. The models suggest that European ecosystems sequester carbon at a rate of 56 TgC yr−1 (mean of four models for 1951–2000) with strong interannual variability (±88 TgC yr−1, average across models) and substantial inter-model uncertainty (±39 TgC yr−1). Decadal budgets suggest that there has been a continuous increase in the mean net carbon storage of ecosystems from 85 TgC yr−1 in 1980s to 108 TgC yr−1 in 1990s, and to 114 TgC yr−1 in 2000–2007. The physiological effect of rising CO2 in combination with nitrogen deposition and forest re-growth have been identified as the important explanatory factors for this net carbon storage. Changes in the growth of woody vegetation are suggested as an important contributor to the European carbon sink. Simulated ecosystem responses were more consistent for the two models accounting for terrestrial carbon-nitrogen dynamics than for the two models which only accounted for carbon cycling and the effects of land cover change. Studies of the interactions of carbon-nitrogen dynamics with land use changes are needed to further improve the quantitative understanding of the driving forces of the European land carbon balance.

Greenhouse gases from the combination of land use change and agriculture are responsible for the largest share of global emissions, but are inadequately considered in the current set of international climate policies. Under the Kyoto protocol, emissions generated in the production of agricultural commodities are the responsibility of the producing country, introducing potential inequities if agricultural products are exported. This study quantifies the greenhouse gas emissions from the production of soybeans and beef in the Amazon basin of Brazil, a region where rates of both deforestation and agricultural exports are high. Integrating methods from land use science and life-cycle analysis, and accounting for producer–consumer responsibility, we allocate emissions between Brazil and importing countries with an emphasis on ultimately reducing the greenhouse gas impact of food production. The mechanisms used to distribute the carbon emissions over time allocate the bulk of emissions to the years directly after the land use change occurred, and gradually decrease the carbon allocation to the agricultural products. The carbon liability embodied in soybeans exported from the Amazon between 1990 and 2006 was 128 TgCO2e, while 120 TgCO2e were embodied in exported beef. An equivalent carbon liability was assigned to Brazil for that time period.