Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints

In order to conduct research at required spatial resolution, we propose a model fusion involving interlinked calculations of regional projections by the global dynamic model GLOBIOM (Global Biosphere Management Model) and robust dynamic downscaling model, based on cross-entropy principle, for deriving spatially resolved projections. The proposed procedure allows incorporating data from satellite images, statistics, expert opinions, as well as data from global land use models. In numerous case studies in China and Ukraine, the approach allowed to estimate local land use and land use change projections corresponding to real trends and expectations. The disaggregated data and projections were used in national models for planning sustainable land use and agricultural development

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models

The potential impact of climate change on Australia's soil organic carbon resources

BACKGROUND: Soil organic carbon (SOC) represents a significant pool of carbon within the biosphere. Climatic shifts in temperature and precipitation have a major influence on the decomposition and amount of SOC stored within an ecosystem and that released into the atmosphere. We have linked net primary production (NPP) algorithms, which include the impact of enhanced atmospheric CO(2 )on plant growth, to the SOCRATES terrestrial carbon model to estimate changes in SOC for the Australia continent between the years 1990 and 2100 in response to climate changes generated by the CSIRO Mark 2 Global Circulation Model (GCM). RESULTS: We estimate organic carbon storage in the topsoil (0–10 cm) of the Australian continent in 1990 to be 8.1 Gt. This equates to 19 and 34 Gt in the top 30 and 100 cm of soil, respectively. By the year 2100, under a low emissions scenario, topsoil organic carbon stores of the continent will have increased by 0.6% (49 Mt C). Under a high emissions scenario, the Australian continent becomes a source of CO(2 )with a net reduction of 6.4% (518 Mt) in topsoil carbon, when compared to no climate change. This is partially offset by the predicted increase in NPP of 20.3% CONCLUSION: Climate change impacts must be studied holistically, requiring integration of climate, plant, ecosystem and soil sciences. The SOCRATES terrestrial carbon cycling model provides realistic estimates of changes in SOC storage in response to climate change over the next century, and confirms the need for greater consideration of soils in assessing the full impact of climate change and the development of quantifiable mitigation strategies

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

Climate change impact and adaptation for wheat protein

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production

U.S. Regional Agricultural Production in 2030 and 2095: Response to CO2 Fertilization and Hadley Climate Model (HadCM2) Projections of Greenhouse-Forced Climatic Change

Research activities underway to evaluate potential consequences of climate change and variability on the agriculture, water resources, and other U.S. sectors were mandated by the Global Change Research Act of 1990. These activities are being carried out in a public-private partnership under the guidance of the U.S. Global Change Research Program. Researchers at Pacific Northwest National Laboratory (PNNL) have been using integrated assessment methodologies to appraise the possible impacts of global warming and climatic variability on the behavior of managed and natural systems. This interim PNNL report contributes to the U.S. National Assessment process with an analysis of the modeled impacts of climatic changes projected by the Hadley/UKMO (HadCM2) general circulation model on agricultural productivity and selected environmental variables. The construction of climatic data for the simulation runs followed general guidelines established by the U.S. National Assessment Synthesis Team. The baseline climate data were obtained from national records for the period 1961 - 1990. The scenario runs for two future periods (2025 - 2030 and 2090 - 2099) were extracted from results of a HadCM2 run distributed at a half-degree spatial resolution. The Erosion Productivity Impact Calculator (EPIC) was used to simulate the behavior of 204 "representative farms" (i.e., soil-climate-management combinations) under baseline climate, the two future periods and their combinations with two levels of atmospheric C02 concentrations (365 and 560 ppm). Analysis of simulation results identified areas in Texas, New Mexico, Colorado, Utah, Arizona, and California that would experience large temperature increases by 2030. Slight cooling is expected by 2030 in parts of Alabama, Florida, Maine, Montana, Idaho, and Utah. Larger areas will experience increased warming by 2095. Uniform precipitation increases are expected by 2030 in the north eastern quarter of the country. These uniform precipitation increases are expected to expand to the eastern half of the country by 2095. Regional increases, decreases and no change in dryland corn yields were predicted under from future climate-change scenarios. Yield increases were predicted in the Lakes, Corn Belt and Northeast regions of the U.S. Increases in irrigated corn yields were predicted in almost all regions of the country. Soybean yields are anticipated to decrease in the Northern and Southern Plains, the Corn Belt Delta, Appalachian, and Southeast regions. Soybean yields in the Lakes and Northeast regions should increase. Simulated winter wheat exhibited consistent trends of yield increase under scenarios of climate change. Evapotranspiration in dryland corn is expected to increase under both scenarios of climate change. Water-use efficiency on dryland, however, would decrease by 2 - 4 kg ha-l mm-l under climate change. Surface runoff could decrease from baseline during the 2030 period and increase during the 2095 period. Dryland corn growing in 2030 would experience more days with water stress than it does under current climate. Corn production is likely to change in 2030 and 2095 in the three currently most important producing regions of the country. In 2030, corn production is expected to increase in the Corn Belt and Lakes regions and to decrease in the Northern Plains. The overall impact would be a decrease in national production. National production of dryland winter wheat is likely to increase in both future periods

Evaluation of three field-based methods for quantifying soil carbon

Citation: Izaurralde, Roberto C., Charles W. Rice, Lucian Wielopolski, Michael H. Ebinger, James B. Reeves Iii, Allison M. Thomson, Ronny Harris, et al. “Evaluation of Three Field-Based Methods for Quantifying Soil Carbon.” PLOS ONE 8, no. 1 (January 31, 2013): e55560. https://doi.org/10.1371/journal.pone.0055560.Three advanced technologies to measure soil carbon (C) density (g C mˉ²) are deployed in the field and the results compared against those obtained by the dry combustion (DC) method. The advanced methods are: a) Laser Induced Breakdown Spectroscopy (LIBS), b) Diffuse Reflectance Fourier Transform Infrared Spectroscopy (DRIFTS), and c) Inelastic Neutron Scattering (INS). The measurements and soil samples were acquired at Beltsville, MD, USA and at Centro International para el Mejoramiento del Maı´z y el Trigo (CIMMYT) at El Bata´n, Mexico. At Beltsville, soil samples were extracted at three depth intervals (0–5, 5–15, and 15–30 cm) and processed for analysis in the field with the LIBS and DRIFTS instruments. The INS instrument determined soil C density to a depth of 30 cm via scanning and stationary measurements. Subsequently, soil core samples were analyzed in the laboratory for soil bulk density (kg mˉ³), C concentration (g kgˉ¹) by DC, and results reported as soil C density (kg mˉ²). Results from each technique were derived independently and contributed to a blind test against results from the reference (DC) method. A similar procedure was employed at CIMMYT in Mexico employing but only with the LIBS and DRIFTS instruments. Following conversion to common units, we found that the LIBS, DRIFTS, and INS results can be compared directly with those obtained by the DC method. The first two methods and the standard DC require soil sampling and need soil bulk density information to convert soil C concentrations to soil C densities while the INS method does not require soil sampling. We conclude that, in comparison with the DC method, the three instruments (a) showed acceptable performances although further work is needed to improve calibration techniques and (b) demonstrated their portability and their capacity to perform under field conditions

Improved hydrological modeling with APEX and EPIC: Model description, testing, and assessment of bioenergy producing landscape scenarios

A Richards-based soil water model was implemented in the APEX and EPIC terrestrial ecosystem models to improve their hydrologic modeling capabilities. The Richards model together with two existing soil water models were calibrated and evaluated to assess their performance for simulating watershed-level hydrology under scenarios of landscape conversion to bioenergy crop production. The Richards model was shown to better reflect observed soil-water dynamics in grain (corn) and cellulosic (switchgrass) bioenergy agroecosystems, whereas all three models simulated historic streamflows comparably. Application of the models to understand the impacts of widespread landscape conversion from traditional agriculture to bioenergy producing landscapes indicated disparate conclusions, with the Richards-based simulations indicating a modest 1.0% reduction in streamflow whereas the existing models simulated sizable reductions of 10.6–16.1%. This study clearly demonstrates the impact of model methodology on system understanding and contextualizes the wide range of simulated streamflow impacts from bioenergy conversions reported in the literature.Full Tex