Simulation of soybean growth and yield under northeastern Amazon climatic conditions

The objective of this work was to parameterize, calibrate, and validate a new version of the soybean growth and yield model developed by Sinclair, under natural field conditions in northeastern Amazon. The meteorological data and the values of soybean growth and leaf area were obtained from an agrometeorological experiment carried out in Paragominas, PA, Brazil, from 2006 to 2009. The climatic conditions during the experiment were very distinct, with a slight reduction in rainfall in 2007, due to the El Niño phenomenon. There was a reduction in the leaf area index (LAI) and in biomass production during this year, which was reproduced by the model. The simulation of the LAI had root mean square error (RMSE) of 0.55 to 0.82 m² m-2, from 2006 to 2009. The simulation of soybean yield for independent data showed a RMSE of 198 kg ha-1, i.e., an overestimation of 3%. The model was calibrated and validated for Amazonian climatic conditions, and can contribute positively to the improvement of the simulations of the impacts of land use change in the Amazon region. The modified version of the Sinclair model is able to adequately simulate leaf area formation, total biomass, and soybean yield, under northeastern Amazon climatic conditions.

Crop yield reduction in the tropics under climate change: Processes and uncertainties

Many modelling studies examine the impacts of climate change on crop yield, but few explore either the underlying bio-physical processes, or the uncertainty inherent in the parameterisation of crop growth and development. We used a perturbed-parameter crop modelling method together with a regional climate model (PRECIS) driven by the 2071-2100 SRES A2 emissions scenario in order to examine processes and uncertainties in yield simulation. Crop simulations used the groundnut (i.e. peanut; Arachis hypogaea L.) version of the General Large-Area Model for annual crops (GLAM). Two sets of GLAM simulations were carried out: control simulations and fixed-duration simulations, where the impact of mean temperature on crop development rate was removed. Model results were compared to sensitivity tests using two other crop models of differing levels of complexity: CROPGRO, and the groundnut model of Hammer et al. [Hammer, G.L., Sinclair, T.R., Boote, K.J., Wright, G.C., Meinke, H., and Bell, M.J., 1995, A peanut simulation model: I. Model development and testing. Agron. J. 87, 1085-1093]. GLAM simulations were particularly sensitive to two processes. First, elevated vapour pressure deficit (VPD) consistently reduced yield. The same result was seen in some simulations using both other crop models. Second, GLAM crop duration was longer, and yield greater, when the optimal temperature for the rate of development was exceeded. Yield increases were also seen in one other crop model. Overall, the models differed in their response to super-optimal temperatures, and that difference increased with mean temperature; percentage changes in yield between current and future climates were as diverse as -50% and over +30% for the same input data. The first process has been observed in many crop experiments, whilst the second has not. Thus, we conclude that there is a need for: (i) more process-based modelling studies of the impact of VPD on assimilation, and (ii) more experimental studies at super-optimal temperatures. Using the GLAM results, central values and uncertainty ranges were projected for mean 2071-2100 crop yields in India. In the fixed-duration simulations, ensemble mean yields mostly rose by 10-30%. The full ensemble range was greater than this mean change (20-60% over most of India). In the control simulations, yield stimulation by elevated CO2 was more than offset by other processes-principally accelerated crop development rates at elevated, but sub-optimal, mean temperatures. Hence, the quantification of uncertainty can facilitate relatively robust indications of the likely sign of crop yield changes in future climates. (C) 2007 Elsevier B.V. All rights reserved.

CSM-IXIM: A New Maize Simulation Model for DSSAT Version 4.5

Th e CERES-Maize model is the most widely used maize (Zea mays L.) model and is a recognized reference for comparing new developments in maize growth, development, and yield simulation. Th e objective of this study was to present and evaluate CSMIXIM, a new maize simulation model for DSSAT version 4.5. Code from CSM-CERES-Maize, the modular version of the model, was modifi ed to include a number of model improvements. Model enhancements included the simulation of leaf area, C assimilation and partitioning, ear growth, kernel number, grain yield, and plant N acquisition and distribution. Th e addition of two genetic coeffi cients to simulate per-leaf foliar surface produced 32% smaller root mean square error (RMSE) values estimating leaf area index than did CSM-CERES. Grain yield and total shoot biomass were correctly simulated by both models. Carbon partitioning, however, showed diff erences. Th e CSM-IXIM model simulated leaf mass more accurately, reducing the CSM-CERES error by 44%, but overestimated stem mass, especially aft er stress, resulting in similar average RMSE values as CSM-CERES. Excessive N uptake aft er fertilization events as simulated by CSM-CERES was also corrected, reducing the error by 16%. Th e accuracy of N distribution to stems was improved by 68%. Th ese improvements in CSM-IXIM provided a stable basis for more precise simulation of maize canopy growth and yield and a framework for continuing future model developments

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (lambda, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of lambda near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Simulation of within field variability of corn yield with Ceres-Maize model

The CERES-Maize model was used to estimate the spatial variability in corn (Zea mays L.) yield for 1995 and 1996 using data measured on soil profiles located on a 30.5 m grid within a 3.9 ha field in Michigan. The model was calibrated for one grid profile for the 1995 and then used to simulate corn yield for all grid points for the 2 yrs. For the calibration for 1995, the model predicted corn yield within 2%. For 1995, the model predicted yield variability very well (r(2) = 0.85), producing similar yield maps with differences generally within +/- 300 kg ha(-1). For 1996, the model predicted low grain yields (1167 kg ha(-1)) compared with measured (8928 kg ha(-1)) because the model does not account for horizontal water movement within the landscape or water contributions from a water table. Under nonlimiting water conditions, the model performed well (average of 8717 vs. 8948 kg ha(-1)) but under-estimated the measured yield variability.

Simulation of soybean growth and yield under northeastern Amazon climatic conditions

O objetivo deste trabalho foi parametrizar, calibrar e validar uma nova versão do modelo de crescimento e de produtividade da soja desenvolvido por Sinclair, em condições naturais de campo no nordeste da Amazônia. Os dados meteorológicos e os valores de crescimento e de área foliar da soja foram obtidos em um experimento agrometeorológico realizado em Paragominas, PA, de 2006 a 2009. As condições climáticas durante o experimento foram muito distintas, com uma ligeira redução na precipitação em 2007, em virtude do fenômeno El Niño. Houve redução no índice de área foliar (IAF) e na produção de biomassa neste ano, a qual foi reproduzida pelo modelo. A simulação do IAF apresentou raiz do erro quadrado médio (REQM) de 0,55 a 0,82 m2 m‑2, de 2006 a 2009. A simulação da produtividade da soja para os dados independentes apresentou um REQM de 198 kg ha‑1, ou seja, uma superestimativa de 3%. O modelo encontra-se calibrado e validado para as condições climáticas da Amazônia e pode contribuir positivamente para a melhoria das simulações dos impactos da mudança de uso da terra na região amazônica. A versão modificada do modelo de Sinclair simula adequadamente a formação de área foliar, a biomassa total e a produtividade da soja, nas condições climáticas do nordeste da Amazônia.

Cyclotron production of 11^C: experimental assessment of saturation yield and validation with Monte Carlo simulation

Nell'ambito della Fisica Medica, le simulazioni Monte Carlo sono uno strumento sempre più diffuso grazie alla potenza di calcolo dei moderni calcolatori, sia nell'ambito diagnostico sia in terapia. Attualmente sono disponibili numerosi pacchetti di simulazione Monte Carlo di carattere "general purpose", tra cui Geant4. Questo lavoro di tesi, svolto presso il Servizio di Fisica Sanitaria del Policlinico "S.Orsola-Malpighi", è basato sulla realizzazione, utilizzando Geant4, di un modello Monte Carlo del target del ciclotrone GE-PETtrace per la produzione di C-11. Nel modello sono stati simulati i principali elementi caratterizzanti il target ed il fascio di protoni accelerato dal ciclotrone. Per la validazione del modello sono stati valutati diversi parametri fisici, tra i quali il range medio dei protoni nell'azoto ad alta pressione e la posizione del picco di Bragg, confrontando i risultati con quelli forniti da SRIM. La resa a saturazione relativa alla produzione di C-11 è stata confrontata sia con i valori forniti dal database della IAEA sia con i dati sperimentali a nostra disposizione. Il modello è stato anche utilizzato per la stima di alcuni parametri di interesse, legati, in particolare, al deterioramento dell'efficienza del target nel corso del tempo. L'inclinazione del target, rispetto alla direzione del fascio di protoni accelerati, è influenzata dal peso del corpo del target stesso e dalla posizione in cui questo é fissato al ciclotrone. Per questo sono stati misurati sia il calo della resa della produzione di C-11, sia la percentuale di energia depositata dal fascio sulla superficie interna del target durante l'irraggiamento, al variare dell'angolo di inclinazione del target. Il modello che abbiamo sviluppato rappresenta, dunque, un importante strumento per la valutazione dei processi che avvengono durante l'irraggiamento, per la stima delle performance del target nel corso del tempo e per lo sviluppo di nuovi modelli di target.

Hydrologic simulation in water-yield analysis /

Mode of access: Internet.

The impact of El Nino Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil

In recent years, maize has become one of the main alternative crops for the autumn winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Nino Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of Sao Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Nino (warm sea surface temperature anomalies in the Pacific), La Nina (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Nino, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making. Copyright (C) 2009 Royal Meteorological Society

Simulation of irrigated Thanzania grass growth based on photothermal units, nitrogen fertilization and water availability

Simulation of irrigated Thanzania grass growth based on photothermal units, nitrogen fertilization and water availability. The mathematical model to predict the forage yield using photothennal units was utilized with success in Elephant grass, Thanzania and Brachiaria niziziensis in the absence of water stress and nitrogen stress. The aim of this study was to propose models to estimate the forage yield of Thanzania grass under different irrigation (25, 50,75, 100 e 125% of ETc) and nitrogen level in various regions of Brazil. As such, models were developed to estimate the dry matter production of Panicum maximum Jacq. frass cv Thanzania in different irrigation and nitrogen levels, using photothermal units. The models were adjusted to doses of 0, 30, 60, 110 and 270 kg of N ha(-1), doses were divided in applications after each evaluation, with a rest cycle of 35 days. The adjusted model presented good performance in predicting dry matter production of Thanzania grass, with r(2) = 0.9999. The results made it possible to verify that the proposed model can be used to predict forage production in different regions of Brazil. It can be estimated, with good precision. The production of Thanzania grass dry matter can be accurately estimated in specific places (in function of latitude and time of year), with the maximum and minimum temperature values.

HFE gene mutations in patients with primary iron overload: Is there a significant improvement in molecular diagnosis yield with HFE sequencing?

Rare HFE variants have been shown to be associated with hereditary hemochromatosis (HH), an iron overload disease. The low frequency of the HFE p.C282Y mutation in HH-affected Brazilian patients may suggest that other HFE-related mutations may also be implicated in the pathogenesis of HH in this population. The main aim was to screen for new HFE mutations in Brazilian individuals with primary iron overload and to investigate their relationship with HH. Fifty Brazilian patients with primary iron overload (transferrin saturation >50% in females and 60% in males) were selected. Subsequent bidirectional sequencing for each HFE exon was performed. The effect of HFE mutations on protein structure were analyzed by molecular dynamics simulation and free binding energy calculations. p.C282Y in homozygosis or in heterozygosis with p.H63D were the most frequent genotypic combinations associated with HH in our sample population (present in 17 individuals, 34%). Thirty-six (72.0%) out of the 50 individuals presented at least one HFE mutation. The most frequent genotype associated with HH was the homozygous p.C282Y mutation (n = 11, 22.0%). One novel mutation (p.V256I) was indentified in heterozygosis with the p.H63D mutation. In silico modeling analysis of protein behavior indicated that the p.V256I mutation does not reduce the binding affinity between HFE and beta 2-microglobulin ((beta 2M) in the same way the p.C282Y mutation does compared with the native HFE protein. In conclusion, screening of HFE through direct sequencing, as compared to p.C282Y/p.H63D genotyping, was not able to increase the molecular diagnosis yield of HH. The novel p.V256I mutation could not be implicated in the molecular basis of the HH phenotype, although its role cannot be completely excluded in HH-phenotype development. Our molecular modeling analysis can help in the analysis of novel, previously undescribed, HFE mutations. (C) 2010 Elsevier Inc. All rights reserved.

Improving wheat simulation capabilities in Australia from a cropping systems perspective - II. Testing simulation capabilities of wheat growth

To simulate cropping systems, crop models must not only give reliable predictions of yield across a wide range of environmental conditions, they must also quantify water and nutrient use well, so that the status of the soil at maturity is a good representation of the starting conditions for the next cropping sequence. To assess the suitability for this task a range of crop models, currently used in Australia, were tested. The models differed in their design objectives, complexity and structure and were (i) tested on diverse, independent data sets from a wide range of environments and (ii) model components were further evaluated with one detailed data set from a semi-arid environment. All models were coded into the cropping systems shell APSIM, which provides a common soil water and nitrogen balance. Crop development was input, thus differences between simulations were caused entirely by difference in simulating crop growth. Under nitrogen non-limiting conditions between 73 and 85% of the observed kernel yield variation across environments was explained by the models. This ranged from 51 to 77% under varying nitrogen supply. Water and nitrogen effects on leaf area index were predicted poorly by all models resulting in erroneous predictions of dry matter accumulation and water use. When measured light interception was used as input, most models improved in their prediction of dry matter and yield. This test highlighted a range of compensating errors in all modelling approaches. Time course and final amount of water extraction was simulated well by two models, while others left up to 25% of potentially available soil water in the profile. Kernel nitrogen percentage was predicted poorly by all models due to its sensitivity to small dry matter changes. Yield and dry matter could be estimated adequately for a range of environmental conditions using the general concepts of radiation use efficiency and transpiration efficiency. However, leaf area and kernel nitrogen dynamics need to be improved to achieve better estimates of water and nitrogen use if such models are to be use to evaluate cropping systems. (C) 1998 Elsevier Science B.V.