Changes in temperature, CO2, and precipitation under the scenarios of climate change for the next 30 yr present a challenge to crop production. This review focuses on the impact of temperature, CO2, ...and ozone on agronomic crops and the implications for crop production. Understanding these implications for agricultural crops is critical for developing cropping systems resilient to stresses induced by climate change. There is variation among crops in their response to CO2, temperature, and precipitation changes and, with the regional differences in predicted climate, a situation is created in which the responses will be further complicated. For example, the temperature effects on soybean Glycine max (L.) Merr. could potentially cause yield reductions of 2.4% in the South but an increase of 1.7% in the Midwest. The frequency of years when temperatures exceed thresholds for damage during critical growth stages is likely to increase for some crops and regions. The increase in CO2 contributes significantly to enhanced plant growth and improved water use efficiency (WUE); however, there may be a downscaling of these positive impacts due to higher temperatures plants will experience during their growth cycle. A challenge is to understand the interactions of the changing climatic parameters because of the interactions among temperature, CO2, and precipitation on plant growth and development and also on the biotic stresses of weeds, insects, and diseases. Agronomists will have to consider the variations in temperature and precipitation as part of the production system if they are to ensure the food security required by an ever increasing population.
Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf ...reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.
► Introduces the Agricultural Model Intercomparison and Improvement Project (AgMIP). ► Describes AgMIP Protocols for consistent research activities. ► Demonstrates AgMIP approaches using climate, ...crop, and economic model analyses. ► Wheat pilot results elucidate the relative uncertainties from crop and climate models. ► Outlines AgMIP crop-specific, regional, and global research activities.
The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones.
Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to CO2 with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with mid-century climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations’ resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.
Plant growth is influenced by above‐ and belowground environmental conditions and increasing atmospheric carbon dioxide (CO2) concentrations enhances growth and yield of most agricultural crops. This ...review covers current knowledge on the impact of increasing CO2 concentration on root dynamics of plants in terms of growth, root/shoot (R/S) ratio, root biomass, root length, root longevity, root mortality, root distribution, root branching, root quality, and the response of these root parameters to management practices including soil water and nutrients. The effects of CO2 concentration on R/S ratio are contradictory due to complexity in accurate underground biomass estimation under diverse crops and conditions. Roots become more numerous, longer, thicker, and faster growing in crops exposed to high CO2 with increased root length in many plant species. Branching and extension of roots under elevated CO2 may lead to altered root architecture and ability of roots to acquire water and nutrients from the soil profile with exploration of the soil volume. Root turnover is important to the global C budget as well as to nutrient cycling in ecosystems and individual plants. Agricultural management practices have a greater impact on root growth than rising atmospheric CO2 since management practices influence soil physical, chemical, and biological properties of soil, consequently affects root growth dynamics in the belowground. Less understood are the interactive effects of elevated CO2 and management practices including drought on root dynamics, fine‐root production, and water‐nutrient use efficiency, and the contribution of these processes to plant growth in water and nutrients limited environments.
Maize (
Zea mays
L.) and soybean (
Glycine max
(L.) Merr.) are the dominant grain crops across the Midwest and are grown on 75% of the arable land with small but economically important crops of wheat ...(
Triticum aestivum
L.) and oats (
Avena sativa
L.) but economically important crops. Historically, there have been variations in annual yields for maize and soybean related to the seasonal weather patterns. Key concerns are the impacts of future climate change on maize and soybean production and their vulnerability to future climate changes. To evaluate these, we analyzed the yield gaps as the difference between the attainable and actual yield at the county level and observed meteorological data to determine which seasonal meteorological variables were dominant in quantifying the actual/attainable yields. July maximum temperatures, August minimum temperatures, and July–August total precipitation were found to be the significant factors affecting the yield gap. These relationships were used to estimate the change in the yield gap through 2100 using both the RCP 4.5 and 8.5 climate scenarios for these variables for selected counties across the Midwest. Yield gaps increased with time for maize across the Midwest with the largest increases in the southern portion of the Corn Belt showing a large north-south gradient in the increase of the yield gap and minimal east-west gradient. Soybean was not as sensitive as maize because the projected temperatures do not exceed optimum temperature ranges for growth and reductions in production that are more sensitive to precipitation changes during the reproductive stages. Adaptation strategies for maize and soybean will require more innovation than simple agronomic management and require the linkage between geneticists, agronomists, and agricultural meteorologists to develop innovative strategies to preserve production in the Midwest.
Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. ...NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km
2 grid scale were comparable to county yields reported by the USDA–National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.
Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. ...However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
The antiplatelet drug clopidogrel is a new thienopyridine derivative whose mechanism of action and chemical structure are similar to those of ticlopidine. The estimated incidence of ...ticlopidine-associated thrombotic thrombocytopenic purpura is 1 per 1600 to 5000 patients treated, whereas no clopidogrel-associated cases were observed among 20,000 closely monitored patients treated in phase 3 clinical trials and cohort studies. Because of the association between ticlopidine use and thrombotic thrombocytopenic purpura and other adverse effects, clopidogrel has largely replaced ticlopidine in clinical practice. More than 3 million patients have received clopidogrel. We report the clinical and laboratory findings in 11 patients in whom thrombotic thrombocytopenic purpura developed during or soon after treatment with clopidogrel.
The 11 patients were identified by active surveillance by the medical directors of blood banks (3 patients), hematologists (6), and the manufacturer of clopidogrel (2).
Ten of the 11 patients received clopidogrel for 14 days or less before the onset of thrombotic thrombocytopenic purpura. Although 10 of the 11 patients had a response to plasma exchange, 2 required 20 or more exchanges before clinical improvement occurred, and 2 had relapses while not receiving clopidogrel. One patient died despite undergoing plasma exchange soon after diagnosis.
Thrombotic thrombocytopenic purpura can occur after the initiation of clopidogrel therapy, often within the first two weeks of treatment. Physicians should be aware of the possibility of this syndrome when initiating clopidogrel treatment.
•GPP and NEP was corn>prairie>soybean, excluding yield loss and burning.•The corn-soybean agroecosystem is a carbon source including carbon loss by yield.•High respiration in the off season and low ...CO2 assimilation affected soybean NEP.•GPP, IWUE* and LUE are significantly related to maximum air temperature.•Rain and VWC increased GPP of prairie, while it decreased GPP or IWUE* in cropland.
The Midwest is one of the most important production areas for corn and soybean worldwide, but also comprises remnants of natural tallgrass prairie vegetation. Future predictions suggest that corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) production in the Midwest may be limited by precipitation and temperature due to climate change. Cross-biome long-term studies in situ are needed to understand carbon assimilation and impact of climate change on the entire region. In this study, we investigated the differences of gross primary production (GPP) and net ecosystem production (NEP) among typical (agro-) ecosystems of corn, soybean and tallgrass prairie from eddy flux stations from 2006 to 2015 under contrasting weather conditions. Corn had the highest annual GPP and NEP with 1305 and 327gCm−2yr−1, while soybean had significantly lower GPP and NEP with 630 and −34gCm−2yr−1, excluding additional carbon loss by yield. Corn and soybean NEP was linear related (p<0.05) to leaf area index (LAI), height or phenological stage, confirming the strong link between plant growth and ecosystem carbon balance. Tallgrass prairie had average values of GPP and NEP of 916 and 61gCm−2yr−1, excluding loss of carbon by annual burning. Thus, prairie GPP and NEP were significantly lower than corn, but significantly higher than soybean. Probably the long fallow period on cropland, which enhanced heterotrophic respiration, and the low carbon assimilation of soybean reduced its overall carbon balance. In total, the corn-soybean agroecosystem acted as a carbon source due to carbon loss by yield removal. Values for GPP and NEP were reflected in inherent water use efficiency (IWUE*) and light use efficiency (LUE) among the agroecosystems. In addition, IWUE*, LUE or GPP of crops and tallgrass prairie were linearly related (p<0.05) to precipitation, volumetric soil water content (VWC) and maximum air temperature. Air temperature increased IWUE* in both, cropland and prairie vegetation. However, rainfall and VWC affected crops and prairie vegetation differently: while excessive rainfall and VWC reduced GPP or IWUE* in cropland, prairie vegetation GPP and LUE were adversely affected by reduced VWC or precipitation. Future measures of climate change adaption should consider the contrasting effects of precipitation and VWC among the different agro-ecosystems in the Midwestern USA.
Projections of temperature and precipitation patterns across the United States during the next 50 yr anticipate a 1.5 to 2°C warming and a slight increase in precipitation as a result of global ...climate change. There have been relatively few studies of climate change effects on pasture and rangeland (grazingland) species compared to those on crop species, despite the economic and ecological importance of the former. Here we review the literature on responses of pastureland and rangeland species to rising atmospheric CO2 and climate change (temperature and precipitation) and discuss plant and management factors likely to influence pastureland and rangeland responses to change (e.g., community composition, plant competition, perennial growth habit, seasonal productivity, and management methods). Overall, the response of pastureland and rangeland species to increased CO2 is consistent with the general responses of C3 and C4 vegetation, although exceptions exist. Both pastureland and rangeland species may experience accelerated metabolism and advanced development with rising temperature, often resulting in a longer growing season. However, soil resources will often constrain temperature effects. In general, it is expected that increases in CO2 and precipitation will enhance rangeland net primary production (NPP) whereas increased air temperatures will either increase or decrease NPP. Much of the uncertainty in predicting how pastureland and rangeland species will respond to climate change is due to uncertainty in future projections of precipitation, both globally and regionally. This review reveals the need for comprehensive studies of climate change impacts on pastureland and rangeland ecosystems that include an assessment of the mediating effects of grazing regimes and mutualistic relationships (e.g., plant roots-nematodes; N-fixing organisms) as well as changes in water, carbon, and nutrient cycling.
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