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Quantitative estimates of the impacts of climate change on economic outcomes are important for public policy. We show that the vast majority of estimates fail to account for well-established uncertainty in future temperature and rainfall changes, leading to potentially misleading projections. We reexamine seven well-cited studies and show that accounting for climate uncertainty leads to a much larger range of projected climate impacts and a greater likelihood of worst-case outcomes, an important policy parameter. Incorporating climate uncertainty into future economic impact assessments will be critical for providing the best possible information on potential impacts.

Crop rotation has been widely used to enhance crop yields and mitigate adverse climate impacts. The existing research predominantly focuses on the impacts of crop rotation under growing season (GS) climates, neglecting the influences of non-GS (NGS) climates on agroecosystems. This oversight limits our understanding of the comprehensive climatic impacts on crop rotation and, consequently, our ability to devise effective adaptation strategies in response to climate warming. In this study, we examine the impacts of both GS and NGS climate conditions on the yield effect of the preceding crop in corn-soybean rotation systems from 1999 to 2018 in the US Midwest. Using causal forest analysis, we estimate that crop rotation increases corn and soybean yields by 0.96 and 0.22 t/ha on average, respectively. We then employ statistical models to indicate that increasing temperatures and rainfall in the NGS reduce corn rotation benefits, while warming GS enhances rotation benefits for soybeans. By 2051-2070, we project that warming climates will reduce corn rotation benefits by 6.74% under Shared Socioeconomic Pathway (SSP) 1-2.6 and 17.18% under SSP 5-8.5. For soybeans, warming climates are expected to increase rotation benefits by 8.36% under SSP 1-2.6 and 13.83% under SSP 5-8.5. Despite these diverse climate impacts on both crops, increasing crop rotation could still improve county-average yields, as neither corn nor soybean was fully rotated. If we project that all continuous corn and continuous soybeans are rotated by 2051-2070, county-average corn yields will increase by 0.265 t/ha under SSP 1-2.6 and 0.164 t/ha under SSP 5-8.5, while county-average soybean yields will gain 0.064 t/ha under SSP 1-2.6 and 0.076 t/ha under SSP 5-8.5. These findings highlight the effectiveness of crop rotation in the face of warming NGS and GS in the future and can help evaluate opportunities for adaptation., (© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Agriculture's global environmental impacts are widely expected to continue expanding, driven by population and economic growth and dietary changes. This Review highlights climate change as an additional amplifier of agriculture's environmental impacts, by reducing agricultural productivity, reducing the efficacy of agrochemicals, increasing soil erosion, accelerating the growth and expanding the range of crop diseases and pests, and increasing land clearing. We identify multiple pathways through which climate change intensifies agricultural greenhouse gas emissions, creating a potentially powerful climate change-reinforcing feedback loop. The challenges raised by climate change underscore the urgent need to transition to sustainable, climate-resilient agricultural systems. This requires investments that both accelerate adoption of proven solutions that provide multiple benefits, and that discover and scale new beneficial processes and food products.

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater-groundwater within or near the crop rooting zone-as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions., Competing Interests: Competing interests statement:The authors declare no competing interest.

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality-reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period-we estimate a potential of 41 to 50 Mt net additional annual CO 2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity., Competing Interests: Competing interests statement:The authors declare no competing interest.

Recycling nutrients from wastewater could simultaneously decrease the carbon intensity of traditional ammonia supply chains and increase the accessibility of local fertilizer. Despite the theoretical potential, techno-economic viability of wastewater nutrient recovery in sub-Saharan Africa has been poorly characterized at subnational scales. This work proposes a multicriteria suitability index to describe techno-economic viability of wastewater-derived fertilizer technologies with district-scale resolution. This index, with a range from 0 to 1 (highest suitability), incorporates key drivers, including population density, soil conditions, sanitation levels, and fertilizer prices. We found that suitability varies widely within and across countries in sub-Saharan Africa and that the primary limiting factor is the absence of sanitation infrastructure. Regions with a minimum of 10% cropland area and a suitability index of at least 0.9 were identified as highly suitable target regions for initial deployment. While they comprise only 1% of the analyzed area, these regions are home to 39 million people and contain up to 3.7 million hectares of cropland. Wastewater-derived fertilizer technologies could deliver an average of 25 kg of nitrogen per hectare of cropland, generating additional food equivalent to the annual consumption of 6 million people. Screening for high suitability can inform selection of effective lighthouse demonstration sites that derisk technology deployment and promote the transition to a more circular nutrient economy.

Climate change will likely increase crop water demand, and farmers may adapt by applying more irrigation. Understanding the extent to which this is occurring is of particular importance in India, a global groundwater depletion hotspot, where increased withdrawals may further jeopardize groundwater resources. Using historical data on groundwater levels, climate, and crop water stress, we find that farmers have adapted to warming temperatures by intensifying groundwater withdrawals, substantially accelerating groundwater depletion rates in India. When considering increased withdrawals due to warming, we project that the rates of net groundwater loss for 2041-2080 could be three times current depletion rates, even after considering projected increases in precipitation and possible decreases in irrigation use as groundwater tables fall. These results reveal a previously unquantified cost of adapting to warming temperatures that will likely further threaten India's food and water security over the coming decades.

Trace soil minerals are a critical determinant of both crop productivity and the mineral concentration of crops, therefore potentially impacting the nutritional status of human populations relying on those crops. We link health data from nearly 0.3 million children and one million adult women across India with over 27 million soil tests drawn from a nationwide soil health program. We find that soil zinc availability is positively associated with children's linear height growth, and soil iron availability is positively associated with hemoglobin levels. The link between soil zinc and childhood stunting is particularly robust-a one standard deviation increase in satisfactory soil zinc tests is associated with approximately 11 fewer children stunted per 1000. We also find that this zinc-stunting relationship is strongest in wealthier households. Our results suggest that soil mineral availability impacts human nutritional status and health in at least some areas of India, and that agronomic fortification may be a beneficial intervention., (© 2023. Springer Nature Limited.)

Agricultural irrigation induces greenhouse gas emissions directly from soils or indirectly through the use of energy or construction of dams and irrigation infrastructure, while climate change affects irrigation demand, water availability and the greenhouse gas intensity of irrigation energy. Here, we present a scoping review to elaborate on these irrigation-climate linkages by synthesizing knowledge across different fields, emphasizing the growing role climate change may have in driving future irrigation expansion and reinforcing some of the positive feedbacks. This Review underscores the urgent need to promote and adopt sustainable irrigation, especially in regions dominated by strong, positive feedbacks., (© 2023. Springer Nature Limited.)

Cover crops are gaining traction in many agricultural regions, partly driven by increased public subsidies and by private markets for ecosystem services. These payments are motivated by environmental benefits, including improved soil health, reduced erosion, and increased soil organic carbon. However, previous work based on experimental plots or crop modeling indicates cover crops may reduce crop yields. It remains unclear, though, how recent cover crop adoption has affected productivity in commercial agricultural systems. Here we perform the first large-scale, field-level analysis of observed yield impacts from cover cropping as implemented across the US Corn Belt. We use validated satellite data products at sub-field scales to analyze maize and soybean yield outcomes for over 90,000 fields in 2019-2020. Because we lack data on cover crop species or timing, we seek to quantify the yield impacts of cover cropping as currently practiced in aggregate. Using causal forests analysis, we estimate an average maize yield loss of 5.5% on fields where cover crops were used for 3 or more years, compared with fields that did not adopt cover cropping. Maize yield losses were larger on fields with better soil ratings, cooler mid-season temperatures, and lower spring rainfall. For soybeans, average yield losses were 3.5%, with larger impacts on fields with warmer June temperatures, lower spring and late-season rainfall, and, to a lesser extent, better soils. Estimated impacts are consistent with multiple mechanisms indicated by experimental and simulation-based studies, including the effects of cover crops on nitrogen dynamics, water consumption, and soil oxygen depletion. Our results suggest a need to improve cover crop management to reduce yield penalties, and a potential need to target subsidies based on likely yield impacts. Ultimately, avoiding substantial yield penalties is important for realizing widespread adoption and associated benefits for water quality, erosion, soil carbon, and greenhouse gas emissions., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Nitrogen oxides (NO x ) are among the most widely emitted pollutants in the world, yet their impacts on agriculture remain poorly known. NO x can directly damage crop cells and indirectly affect growth by promoting ozone (O 3 ) and aerosol formation. We use satellite measures of both crop greenness and NO x during 2018-2020 to evaluate crop impacts for five major agricultural regions. We find consistent negative associations between NO 2 and greenness across regions and seasons. These effects are strongest in conditions where O 3 formation is NO x limited but remain significant even in locations where this pathway is muted, suggesting a role for direct NO x damage. Using simple counterfactuals and leveraging published relationships between greenness and growth, we estimate that reducing NO x levels to the current fifth percentile in each region would raise yields by ~25% for winter crops in China, ~15% for summer crops in China, and up to 10% in other regions.

Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate-a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 μg/[Formula: see text] of [Formula: see text] (particulate matter of a diameter less than 2.5 μm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry., Competing Interests: Competing interest statement: M.B., D.B.L., and S.E. are cofounders of AtlasAI, a company that uses machine learning to measure infrastructure quality and economic outcomes in the developing world., (Copyright © 2021 the Author(s). Published by PNAS.)

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties. We show that a strategy that utilizes weather-driven temporal variations in drought exposure is inconclusive because of the infrequent occurrence of substantial drought. In contrast, a strategy that exploits within-county spatial variability in drought exposure, driven primarily by differences in soil water storage capacity, reveals robust trends over time. Yield sensitivity to soil water storage increased by 55% on average across the US Corn Belt since 1999, with larger increases in drier states. Although yields have been increasing under all conditions, the cost of drought relative to good weather has also risen. These results highlight the difficulty of simultaneously raising average yields and lowering drought sensitivity., (© 2020. The Author(s), under exclusive licence to Springer Nature Limited.)

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30 m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download online.

The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE. As a consequence, enhancing WUE by breeding or biotechnology has proven challenging but not impossible. This review aims to synthesize new knowledge of WUE arising from advances in phenotyping, modeling, physiology, genetics, and molecular biology in the context of classical theoretical principles. In addition, we discuss how rising atmospheric CO 2 concentration has created and will continue to create opportunities for enhancing WUE by modifying the trade-off between photosynthesis and transpiration.

We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency's 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare. Newly available evidence about a wide range of observed and projected impacts strengthens the association between the risk of some of these impacts and anthropogenic climate change, indicates that some impacts or combinations of impacts have the potential to be more severe than previously understood, and identifies substantial risk of additional impacts through processes and pathways not considered in the Endangerment Finding., (Copyright © 2019, American Association for the Advancement of Science.)

Background: Rising atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of crops. The associated disease burden and optimal mitigation strategies remain unknown. We sought to understand where and to what extent increasing carbon dioxide concentrations may increase the global burden of nutritional deficiencies through changes in crop nutrient concentrations, and the effects of potential mitigation strategies., Methods and Findings: For each of 137 countries, we incorporated estimates of climate change, crop nutrient concentrations, dietary patterns, and disease risk into a microsimulation model of zinc and iron deficiency. These estimates were obtained from the Intergovernmental Panel on Climate Change, US Department of Agriculture, Statistics Division of the Food and Agriculture Organization of the United Nations, and Global Burden of Disease Project, respectively. In the absence of increasing carbon dioxide concentrations, we estimated that zinc and iron deficiencies would induce 1,072.9 million disability-adjusted life years (DALYs) globally over the period 2015 to 2050 (95% credible interval [CrI]: 971.1-1,167.7). In the presence of increasing carbon dioxide concentrations, we estimated that decreasing zinc and iron concentrations of crops would induce an additional 125.8 million DALYs globally over the same period (95% CrI: 113.6-138.9). This carbon-dioxide-induced disease burden is projected to disproportionately affect nations in the World Health Organization's South-East Asia and African Regions (44.0 and 28.5 million DALYs, respectively), which already have high existing disease burdens from zinc and iron deficiencies (364.3 and 299.5 million DALYs, respectively), increasing global nutritional inequalities. A climate mitigation strategy such as the Paris Agreement (an international agreement to keep global temperatures within 2°C of pre-industrial levels) would be expected to avert 48.2% of this burden (95% CrI: 47.8%-48.5%), while traditional public health interventions including nutrient supplementation and disease control programs would be expected to avert 26.6% of the burden (95% CrI: 23.8%-29.6%). Of the traditional public health interventions, zinc supplementation would be expected to avert 5.5%, iron supplementation 15.7%, malaria mitigation 3.2%, pneumonia mitigation 1.6%, and diarrhea mitigation 0.5%. The primary limitations of the analysis include uncertainty regarding how food consumption patterns may change with climate, how disease mortality rates will change over time, and how crop zinc and iron concentrations will decline from those at present to those in 2050., Conclusions: Effects of increased carbon dioxide on crop nutrient concentrations are anticipated to exacerbate inequalities in zinc and iron deficiencies by 2050. Proposed Paris Agreement strategies are expected to be more effective than traditional public health measures to avert the increased inequality., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SB receives a stipend as a specialty consulting editor for PLOS Medicine and serves on the journal's editorial board.

Elevated atmospheric CO 2 concentrations ([CO 2 ]) are expected to increase C3 crop yield through the CO 2 fertilization effect (CFE) by stimulating photosynthesis and by reducing stomatal conductance and transpiration. The latter effect is widely believed to lead to greater benefits in dry rather than wet conditions, although some recent experimental evidence challenges this view. Here we used a process-based crop model, the Agricultural Production Systems sIMulator (APSIM), to quantify the contemporary and future CFE on soybean in one of its primary production area of the US Midwest. APSIM accurately reproduced experimental data from the Soybean Free-Air CO 2 Enrichment site showing that the CFE declined with increasing drought stress. This resulted from greater radiation use efficiency (RUE) and above-ground biomass production at elevated [CO 2 ] that outpaced gains in transpiration efficiency (TE). Using an ensemble of eight climate model projections, we found that drought frequency in the US Midwest is projected to increase from once every 5 years currently to once every other year by 2050. In addition to directly driving yield loss, greater drought also significantly limited the benefit from rising [CO 2 ]. This study provides a link between localized experiments and regional-scale modeling to highlight that increased drought frequency and severity pose a formidable challenge to maintaining soybean yield progress that is not offset by rising [CO 2 ] as previously anticipated. Evaluating the relative sensitivity of RUE and TE to elevated [CO 2 ] will be an important target for future modeling and experimental studies of climate change impacts and adaptation in C3 crops., (© 2017 John Wiley & Sons Ltd.)

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population., Competing Interests: The authors declare no conflict of interest.

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield-temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security 'hot spots'. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production., (© 2016 John Wiley & Sons Ltd.)

The emergence of satellite sensors that can routinely observe millions of individual smallholder farms raises possibilities for monitoring and understanding agricultural productivity in many regions of the world. Here we demonstrate the potential to track smallholder maize yield variation in western Kenya, using a combination of 1-m Terra Bella imagery and intensive field sampling on thousands of fields over 2 y. We find that agreement between satellite-based and traditional field survey-based yield estimates depends significantly on the quality of the field-based measures, with agreement highest ([Formula: see text] up to 0.4) when using precise field measures of plot area and when using larger fields for which rounding errors are smaller. We further show that satellite-based measures are able to detect positive yield responses to fertilizer and hybrid seed inputs and that the inferred responses are statistically indistinguishable from estimates based on survey-based yields. These results suggest that high-resolution satellite imagery can be used to make predictions of smallholder agricultural productivity that are roughly as accurate as the survey-based measures traditionally used in research and policy applications, and they indicate a substantial near-term potential to quickly generate useful datasets on productivity in smallholder systems, even with minimal or no field training data. Such datasets could rapidly accelerate learning about which interventions in smallholder systems have the most positive impact, thus enabling more rapid transformation of rural livelihoods.

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains., (Copyright © 2016, American Association for the Advancement of Science.)

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change., (© 2015 John Wiley & Sons Ltd.)

Climate change can reduce crop yields and thereby threaten food security. The current measures used to adapt to climate change involve avoiding crops yield decrease, however, the limitations of such measures due to water and other resources scarcity have not been well understood. Here, we quantify how the sensitivity of maize to water availability has increased because of the shift toward longer-maturing varieties during last three decades in the Chinese Maize Belt (CMB). We report that modern, longer-maturing varieties have extended the growing period by an average of 8 days and have significantly offset the negative impacts of climate change on yield. However, the sensitivity of maize production to water has increased: maize yield across the CMB was 5% lower with rainfed than with irrigated maize in the 1980s and was 10% lower (and even >20% lower in some areas) in the 2000s because of both warming and the increased requirement for water by the longer-maturing varieties. Of the maize area in China, 40% now fails to receive the precipitation required to attain the full yield potential. Opportunities for water saving in maize systems exist, but water scarcity in China remains a serious problem.

Characterization of drought environment types (ETs) has proven useful for breeding crops for drought-prone regions. Here, we consider how changes in climate and atmospheric carbon dioxide (CO2 ) concentrations will affect drought ET frequencies in sorghum and wheat systems of northeast Australia. We also modify APSIM (the Agricultural Production Systems Simulator) to incorporate extreme heat effects on grain number and weight, and then evaluate changes in the occurrence of heat-induced yield losses of more than 10%, as well as the co-occurrence of drought and heat. More than six million simulations spanning representative locations, soil types, management systems, and 33 climate projections led to three key findings. First, the projected frequency of drought decreased slightly for most climate projections for both sorghum and wheat, but for different reasons. In sorghum, warming exacerbated drought stresses by raising the atmospheric vapor pressure deficit and reducing transpiration efficiency (TE), but an increase in TE due to elevated CO2 more than offset these effects. In wheat, warming reduced drought stress during spring by hastening development through winter and reducing exposure to terminal drought. Elevated CO2 increased TE but also raised radiation-use efficiency and overall growth rates and water use, thereby offsetting much of the drought reduction from warming. Second, adding explicit effects of heat on grain number and grain size often switched projected yield impacts from positive to negative. Finally, although average yield losses associated with drought will remain generally higher than that for heat stress for the next half century, the relative importance of heat is steadily growing. This trend, as well as the likely high degree of genetic variability in heat tolerance, suggests that more emphasis on heat tolerance is warranted in breeding programs. At the same time, work on drought tolerance should continue with an emphasis on drought that co-occurs with extreme heat., (© 2015 John Wiley & Sons Ltd.)

Europe has experienced a stagnation of some crop yields since the early 1990s as well as statistically significant warming during the growing season. Although it has been argued that these two are causally connected, no previous studies have formally attributed long-term yield trends to a changing climate. Here, we present two statistical tests based on the distinctive spatial pattern of climate change impacts and adaptation, and explore their power under a range of parameter values. We show that statistical power for the identification of climate change impacts is high in many settings, but that power for identifying adaptation is almost always low. Applying these tests to European agriculture, we find evidence that long-term temperature and precipitation trends since 1989 have reduced continent-wide wheat and barley yields by 2.5% and 3.8%, respectively, and have slightly increased maize and sugar beet yields. These averages disguise large heterogeneity across the continent, with regions around the Mediterranean experiencing significant adverse impacts on most crops. This result means that climate trends can account for ∼ 10% of the stagnation in European wheat and barley yields, with likely explanations for the remainder including changes in agriculture and environmental policies.

A key question for climate change adaptation is whether existing cropping systems can become less sensitive to climate variations. We use a field-level data set on maize and soybean yields in the central United States for 1995 through 2012 to examine changes in drought sensitivity. Although yields have increased in absolute value under all levels of stress for both crops, the sensitivity of maize yields to drought stress associated with high vapor pressure deficits has increased. The greater sensitivity has occurred despite cultivar improvements and increased carbon dioxide and reflects the agronomic trend toward higher sowing densities. The results suggest that agronomic changes tend to translate improved drought tolerance of plants to higher average yields but not to decreasing drought sensitivity of yields at the field scale.

Solar energy installations in deserts are on the rise, fueled by technological advances and policy changes. Deserts, with a combination of high solar radiation and availability of large areas unusable for crop production are ideal locations for large solar installations. However, for efficient power generation, solar infrastructures use large amounts of water for construction and operation. We investigated the water use and greenhouse gas (GHG) emissions associated with solar installations in North American deserts in comparison to agave-based biofuel production, another widely promoted potential energy source from arid systems. We determined the uncertainty in our analysis by a Monte Carlo approach that varied the most important parameters, as determined by sensitivity analysis. We considered the uncertainty in our estimates as a result of variations in the number of solar modules ha(-1), module efficiency, number of agave plants ha(-1), and overall sugar conversion efficiency for agave. Further, we considered the uncertainty in revenue and returns as a result of variations in the wholesale price of electricity and installation cost of solar photovoltaic (PV), wholesale price of agave ethanol, and cost of agave cultivation and ethanol processing. The life-cycle analyses show that energy outputs and GHG offsets from solar PV systems, mean energy output of 2405 GJ ha(-1) year(-1) (5 and 95% quantile values of 1940-2920) and mean GHG offsets of 464 Mg of CO2 equiv ha(-1) year(-1) (375-562), are much larger than agave, mean energy output from 206 (171-243) to 61 (50-71) GJ ha(-1) year(-1) and mean GHG offsets from 18 (14-22) to 4.6 (3.7-5.5) Mg of CO2 equiv ha(-1) year(-1), depending upon the yield scenario of agave. Importantly though, water inputs for cleaning solar panels and dust suppression are similar to amounts required for annual agave growth, suggesting the possibility of integrating the two systems to maximize the efficiency of land and water use to produce both electricity and liquid fuel. A life-cycle analysis of a hypothetical colocation indicated higher returns per m(3) of water used than either system alone. Water requirements for energy production were 0.22 L MJ(-1) (0.28-0.19) and 0.42 L MJ(-1) (0.52-0.35) for solar PV-agave (baseline yield) and solar PV-agave (high yield), respectively. Even though colocation may not be practical in all locations, in some water-limited areas, colocated solar PV-agave systems may provide attractive economic incentives in addition to efficient land and water use.

Plants grown in elevated [CO(2) ] have lower protein and mineral concentrations compared with plants grown in ambient [CO(2) ]. Dilution by enhanced production of carbohydrates is a likely cause, but it cannot explain all of the reductions. Two proposed, but untested, hypotheses are that (1) reduced canopy transpiration reduces mass flow of nutrients to the roots thus reducing nutrient uptake and (2) changes in metabolite or enzyme concentrations caused by physiological changes alter requirements for minerals as protein cofactors or in other organic complexes, shifting allocation between tissues and possibly altering uptake. Here, we use the meta-analysis of previous studies in crops to test these hypotheses. Nutrients acquired mostly by mass flow were decreased significantly more by elevated [CO(2) ] than nutrients acquired by diffusion to the roots through the soil, supporting the first hypothesis. Similarly, Mg showed large concentration declines in leaves and wheat stems, but smaller decreases in other tissues. Because chlorophyll requires a large fraction of total plant Mg, and chlorophyll concentration is reduced by growth in elevated [CO(2) ], this supports the second hypothesis. Understanding these mechanisms may guide efforts to improve nutrient content, and allow modeling of nutrient changes and health impacts under future climate change scenarios., (© 2012 Blackwell Publishing Ltd.)

Genetic improvements in heat tolerance of wheat provide a potential adaptation response to long-term warming trends, and may also boost yields in wheat-growing areas already subject to heat stress. Yet there have been few assessments of recent progress in breeding wheat for hot environments. Here, data from 25 years of wheat trials in 76 countries from the International Maize and Wheat Improvement Center (CIMMYT) are used to empirically model the response of wheat to environmental variation and assess the genetic gains over time in different environments and for different breeding strategies. Wheat yields exhibited the most sensitivity to warming during the grain-filling stage, typically the hottest part of the season. Sites with high vapour pressure deficit (VPD) exhibited a less negative response to temperatures during this period, probably associated with increased transpirational cooling. Genetic improvements were assessed by using the empirical model to correct observed yield growth for changes in environmental conditions and management over time. These 'climate-corrected' yield trends showed that most of the genetic gains in the high-yield-potential Elite Spring Wheat Yield Trial (ESWYT) were made at cooler temperatures, close to the physiological optimum, with no evidence for genetic gains at the hottest temperatures. In contrast, the Semi-Arid Wheat Yield Trial (SAWYT), a lower-yielding nursery targeted at maintaining yields under stressed conditions, showed the strongest genetic gains at the hottest temperatures. These results imply that targeted breeding efforts help us to ensure progress in building heat tolerance, and that intensified (and possibly new) approaches are needed to improve the yield potential of wheat in hot environments in order to maintain global food security in a warmer climate.

Efforts to anticipate how climate change will affect future food availability can benefit from understanding the impacts of changes to date. We found that in the cropping regions and growing seasons of most countries, with the important exception of the United States, temperature trends from 1980 to 2008 exceeded one standard deviation of historic year-to-year variability. Models that link yields of the four largest commodity crops to weather indicate that global maize and wheat production declined by 3.8 and 5.5%, respectively, relative to a counterfactual without climate trends. For soybeans and rice, winners and losers largely balanced out. Climate trends were large enough in some countries to offset a significant portion of the increases in average yields that arose from technology, carbon dioxide fertilization, and other factors.

Biomass-derived energy offers the potential to increase energy security while mitigating anthropogenic climate change, but a successful path toward increased production requires a thorough accounting of costs and benefits. Until recently, the efficacy of biomass-derived energy has focused primarily on biogeochemical consequences. Here we show that the biogeophysical effects that result from hypothetical conversion of annual to perennial bioenergy crops across the central United States impart a significant local to regional cooling with considerable implications for the reservoir of stored soil water. This cooling effect is related mainly to local increases in transpiration, but also to higher albedo. The reduction in radiative forcing from albedo alone is equivalent to a carbon emissions reduction of , which is six times larger than the annual biogeochemical effects that arise from offsetting fossil fuel use. Thus, in the near-term, the biogeophysical effects are an important aspect of climate impacts of biofuels, even at the global scale. Locally, the simulated cooling is sufficiently large to partially offset projected warming due to increasing greenhouse gases over the next few decades. These results demonstrate that a thorough evaluation of costs and benefits of bioenergy-related land-use change must include potential impacts on the surface energy and water balance to comprehensively address important concerns for local, regional, and global climate change.

As efforts to mitigate climate change increase, there is a need to identify cost-effective ways to avoid emissions of greenhouse gases (GHGs). Agriculture is rightly recognized as a source of considerable emissions, with concomitant opportunities for mitigation. Although future agricultural productivity is critical, as it will shape emissions from conversion of native landscapes to food and biofuel crops, investment in agricultural research is rarely mentioned as a mitigation strategy. Here we estimate the net effect on GHG emissions of historical agricultural intensification between 1961 and 2005. We find that while emissions from factors such as fertilizer production and application have increased, the net effect of higher yields has avoided emissions of up to 161 gigatons of carbon (GtC) (590 GtCO(2)e) since 1961. We estimate that each dollar invested in agricultural yields has resulted in 68 fewer kgC (249 kgCO(2)e) emissions relative to 1961 technology ($14.74/tC, or approximately $4/tCO(2)e), avoiding 3.6 GtC (13.1 GtCO(2)e) per year. Our analysis indicates that investment in yield improvements compares favorably with other commonly proposed mitigation strategies. Further yield improvements should therefore be prominent among efforts to reduce future GHG emissions.

Improving nitrogen use efficiency (NUE) in the major cereals is critical for more sustainable nitrogen use in high-input agriculture, but our understanding of the potential for NUE improvement is limited by a paucity of reliable on-farm measurements. Limited on-farm data suggest that agronomic NUE (AE(N)) is lower and more variable than data from trials conducted at research stations, on which much of our understanding of AE(N) has been built. The purpose of this study was to determine the magnitude and causes of variability in AE(N) across an agricultural region, which we refer to as the achievement distribution of AE(N). The distribution of simulated AE(N) in 80 farmers' fields in an irrigated wheat system in the Yaqui Valley, Mexico, was compared with trials at a local research center (International Wheat and Maize Improvement Center; CIMMYT). An agroecosystem simulation model WNMM was used to understand factors controlling yield, AE(N), gaseous N emissions, and nitrate leaching in the region. Simulated AE(N) in the Yaqui Valley was highly variable, and mean on-farm AE(N) was 44% lower than trials with similar fertilization rates at CIMMYT. Variability in residual N supply was the most important factor determining simulated AE(N). Better split applications of N fertilizer led to almost a doubling of AE(N), increased profit, and reduced N pollution, and even larger improvements were possible with technologies that allow for direct measurement of soil N supply and plant N demand, such as site-specific nitrogen management.

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

In the 21st century, mapping and monitoring the occurrence of soil degradation will be an important component of successful land management. Remote sensing, with its unique ability to measure across space and time, will be an increasingly indispensible tool for assessing degradation. However, much of the recent experience and progress in using remote sensing and other geospatial technologies to map soil degradation is reported outside of the peer-reviewed literature. This motivated the organization of a special collection of papers focused on remote sensing of soil degradation, to highlight recent successes, common challenges, and promising new approaches. This introductory paper provides an overview of the papers, gaps in knowledge, and future research directions. Across several regions and types of degradation, many assessments to date have relied heavily on data from the Landsat satellite sensor. Many approaches have also relied at some point on subjective visual interpretation, either of the satellite imagery itself or to provide field data used to train models that use satellite data. While subjectivity is not necessarily bad, it precludes repeatability and makes it even more important to rigorously test model estimates with independent data. Overall, it remains quite challenging to find robust relationships between remote sensing measures and soil degradation, particularly for slight to moderate levels of degradation. There have nonetheless been some clear successes, and there remains great potential for progress. Promising directions outlined in the papers include using multi-year measures of vegetation condition, combining different sensor systems including optical and radar data, and using advanced statistical techniques such as Bayesian networks and decision trees.

Armed conflict within nations has had disastrous humanitarian consequences throughout much of the world. Here we undertake the first comprehensive examination of the potential impact of global climate change on armed conflict in sub-Saharan Africa. We find strong historical linkages between civil war and temperature in Africa, with warmer years leading to significant increases in the likelihood of war. When combined with climate model projections of future temperature trends, this historical response to temperature suggests a roughly 54% increase in armed conflict incidence by 2030, or an additional 393,000 battle deaths if future wars are as deadly as recent wars. Our results suggest an urgent need to reform African governments' and foreign aid donors' policies to deal with rising temperatures.

The quantity of land available to grow biofuel crops without affecting food prices or greenhouse gas (GHG) emissions from land conversion is limited. Therefore, bioenergy should maximize land-use efficiency when addressing transportation and climate change goals. Biomass could power either internal combustion or electric vehicles, but the relative land-use efficiency of these two energy pathways is not well quantified. Here, we show that bioelectricity outperforms ethanol across a range of feedstocks, conversion technologies, and vehicle classes. Bioelectricity produces an average of 81% more transportation kilometers and 108% more emissions offsets per unit area of cropland than does cellulosic ethanol. These results suggest that alternative bioenergy pathways have large differences in how efficiently they use the available land to achieve transportation and climate goals.

Converting forest lands into bioenergy agriculture could accelerate climate change by emitting carbon stored in forests, while converting food agriculture lands into bioenergy agriculture could threaten food security. Both problems are potentially avoided by using abandoned agriculture lands for bioenergy agriculture. Here we show the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand, based on historical land use data, satellite-derived land cover data, and global ecosystem modeling. The estimated global area of abandoned agriculture is 385-472 million hectares, or 66-110% of the areas reported in previous preliminary assessments. The area-weighted mean production of above-ground biomass is 4.3 tons ha(-1) y(-1), in contrast to estimates of up to 10 tons ha(-1) y(-1) in previous assessments. The energy content of potential biomass grown on 100% of abandoned agriculture lands is less than 10% of primary energy demand for most nations in North America, Europe, and Asia, but it represents many times the energy demand in some African nations where grasslands are relatively productive and current energy demand is low.

Investments aimed at improving agricultural adaptation to climate change inevitably favor some crops and regions over others. An analysis of climate risks for crops in 12 food-insecure regions was conducted to identify adaptation priorities, based on statistical crop models and climate projections for 2030 from 20 general circulation models. Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations. We also find that uncertainties vary widely by crop, and therefore priorities will depend on the risk attitudes of investment institutions.

Increased production of biomass for energy has the potential to offset substantial use of fossil fuels, but it also has the potential to threaten conservation areas, pollute water resources and decrease food security. The net effect of biomass energy agriculture on climate could be either cooling or warming, depending on the crop, the technology for converting biomass into useable energy, and the difference in carbon stocks and reflectance of solar radiation between the biomass crop and the pre-existing vegetation. The area with the greatest potential for yielding biomass energy that reduces net warming and avoids competition with food production is land that was previously used for agriculture or pasture but that has been abandoned and not converted to forest or urban areas. At the global scale, potential above-ground plant growth on these abandoned lands has an energy content representing approximately 5% of world primary energy consumption in 2006. The global potential for biomass energy production is large in absolute terms, but it is not enough to replace more than a few percent of current fossil fuel usage. Increasing biomass energy production beyond this level would probably reduce food security and exacerbate forcing of climate change.

The prevention of deforestation and promotion of afforestation have often been cited as strategies to slow global warming. Deforestation releases CO(2) to the atmosphere, which exerts a warming influence on Earth's climate. However, biophysical effects of deforestation, which include changes in land surface albedo, evapotranspiration, and cloud cover also affect climate. Here we present results from several large-scale deforestation experiments performed with a three-dimensional coupled global carbon-cycle and climate model. These simulations were performed by using a fully three-dimensional model representing physical and biogeochemical interactions among land, atmosphere, and ocean. We find that global-scale deforestation has a net cooling influence on Earth's climate, because the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration. Latitude-specific deforestation experiments indicate that afforestation projects in the tropics would be clearly beneficial in mitigating global-scale warming, but would be counterproductive if implemented at high latitudes and would offer only marginal benefits in temperate regions. Although these results question the efficacy of mid- and high-latitude afforestation projects for climate mitigation, forests remain environmentally valuable resources for many reasons unrelated to climate.