Grain yield components of pearl millet under optimum conditions can be used to identify germplasm with adaptation to and zones

There is evidence that high-tillering, small-panicled pearl millet landraces are better adapted to the severe, unpredictable drought stress of the and zones of NW India than are low-tillering, large-panicled modern varieties, which significantly outyield the landraces under favourable conditions. In this paper, we analyse the relationship of and zone adaptation with the expression, under optimum conditions, of yield components that determine either the potential sink size or the ability to realise this potential. The objective is to test whether selection under optimal conditions for yield components can identify germplasm with adaptation to and zones in NW India, as this could potentially improve the efficiency of pearl millet improvement programs targeting and zones. We use data from an evaluation of over 100 landraces from NW India, conducted for two seasons under both severely drought-stressed and favourable conditions in northwest and south India. Trial average grain yields ranged from 14 g m(-2) to 182 g m(-2). The landraces were grouped into clusters, based on their phenology and yield components as measured under well-watered conditions in south India. In environments without pre-flowering drought stress, tillering type had no effect on potential sink size, but low-tillering, large-panicled landraces yielded significantly more grain, as they were better able to realise their potential sink size. By contrast, in two low-yielding and zone environments which experienced pre-anthesis drought stress, low-fillering, large-panicled landraces yielded significantly less grain than high-tillering ones with comparable phenology, because of both a reduced potential sink size and a reduced ability to realise this potential. The results indicate that the high grain yield of low-tillering, large-panicled landraces under favourable conditions is due to improved partitioning, rather than resource capture. However, under severe stress with restricted assimilate supply, high-tillering, small-panicled landraces are better able to produce a reproductive sink than are large-panicled ones. Selection under optimum conditions for yield components representing a resource allocation pattern favouring high yield under severe drought stress, combined with a capability to increase grain yield if assimilates are available, was more effective than direct selection for grain yield in identifying germplasm adapted to and zones. Incorporating such selection in early generations of variety testing could reduce the reliance on random stress environments. This should improve the efficiency of millet breeding programs targeting and zones. (c) 2005 Elsevier B.V. All rights reserved.

Simulating pasture growth rates in Australian and New Zealand grazing systems

DairyMod, EcoMod, and the SGS Pasture Model are mechanistic biophysical models developed to explore scenarios in grazing systems. The aim of this manuscript was to test the ability of the models to simulate net herbage accumulation rates of ryegrass-based pastures across a range of environments and pasture management systems in Australia and New Zealand. Measured monthly net herbage accumulation rate and accumulated yield data were collated from ten grazing system experiments at eight sites ranging from cool temperate to subtropical environments. The local climate, soil, pasture species, and management (N fertiliser, irrigation, and grazing or cutting pattern) were described in the model for each site, and net herbage accumulation rates modelled. The model adequately simulated the monthly net herbage accumulation rates across the range of environments, based on the summary statistics and observed patterns of seasonal growth, particularly when the variability in measured herbage accumulation rates was taken into account. Agreement between modelled and observed growth rates was more accurate and precise in temperate than in subtropical environments, and in winter and summer than in autumn and spring. Similarly, agreement between predicted and observed accumulated yields was more accurate than monthly net herbage accumulation. Different temperature parameters were used to describe the growth of perennial ryegrass cultivars and annual ryegrass; these differences were in line with observed growth patterns and breeding objectives. Results are discussed in the context of the difficulties in measuring pasture growth rates and model limitations.

An individual-based model of the early life history of mackerel (Scomber scombrus) in the eastern North Atlantic, simulating transport, growth and mortality

The main purpose of this paper is to provide the core description of the modelling exercise within the Shelf Edge Advection Mortality And Recruitment (SEAMAR) programme. An individual-based model (IBM) was developed for the prediction of year-to-year survival of the early life-history stages of mackerel (Scomber scombrus) in the eastern North Atlantic. The IBM is one of two components of the model system. The first component is a circulation model to provide physical input data for the IBM. The circulation model is a geographical variant of the HAMburg Shelf Ocean Model (HAMSOM). The second component is the IBM, which is an i-space configuration model in which large numbers of individuals are followed as discrete entities to simulate the transport, growth and mortality of mackerel eggs, larvae and post-larvae. Larval and post-larval growth is modelled as a function of length, temperature and food distribution; mortality is modelled as a function of length and absolute growth rate. Each particle is considered as a super-individual representing 10 super(6) eggs at the outset of the simulation, and then declining according to the mortality function. Simulations were carried out for the years 1998-2000. Results showed concentrations of particles at Porcupine Bank and the adjacent Irish shelf, along the Celtic Sea shelf-edge, and in the southern Bay of Biscay. High survival was observed only at Porcupine and the adjacent shelf areas, and, more patchily, around the coastal margin of Biscay. The low survival along the shelf-edge of the Celtic Sea was due to the consistently low estimates of food availability in that area.

Simulating dynamic crop growth with an adapted land surface model – JULES-SUCROS: model development and validation

The increasing demand for ecosystem services, in conjunction with climate change, is expected to signif- icantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNET measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal vari- ability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land–atmosphere interactions.

Simulating potential growth and yield of oil palm (Elaeis guineensis) with PALMSIM: Model description, evaluation and application.

Reducing the gap between water-limited potential yield and actual yield in oil palm production systems through intensification is seen as an important option for sustainably increasing palm oil production. Simulation models can play an important role in quantifying water-limited potential yield, and therefore the scope for intensification, but no oil palm model exists that is both simple enough and at the same time incorporates sufficient plant physiological knowledge to be generally applicable across sites with different growing conditions. The objectives of this study therefore were to develop a model (PALMSIM) that simulates, on a monthly time step, the potential growth of oil palm as determined by solar radiation and to evaluate model performance against measured oil palm yields under optimal water and nutrient management for a range of sites across Indonesia and Malaysia. The maximum observed yield in the field matches the corresponding simulated yield for dry bunch weight with a RMSE of 1.7 Mg ha?1 year?1 against an observed yield of 18.8 Mg ha?1. Sensitivity analysis showed that PALMSIM is robust: simulated changes in yield caused by modifying the parameters by 10% are comparable to other tree crop model evaluations. While we acknowledge that, depending on the soils and climatic environment, yields may be often water limited, we suggest a relatively simple physiological approach to simulate potential yield, which can be usefully applied to high rainfall environments and is considered as a first step in developing an oil palm model that also simulates water-limited potential yield. To illustrate the application possibil- ities of the model, PALMSIM was used to create a potential yield map for Indonesia and Malaysia by sim- ulating the growth and yield at a resolution of 0.1?. This map of potential yield is considered as a first step towards a decision support tool that can identify potentially productive, but at the moment degraded sites in Indonesia and Malaysia. ?

Simulating Forest Shade to Study the Developmental Ecology of Tropical Plants: Juvenile Growth in Three Vines in India

Both light quantity and quality affect the development and autoecology of plants under shade conditions, as in the understorey of tropical forests. However, little research has been directed towards the relative contributions of lowered photosynthetic photon flux density (PPFD) versus altered spectral distributions (as indicated by quantum ratios of 660 to 730 nm, or R:FR) of radiation underneath vegetation canopies. A method for constructing shade enclosures to study the contribution of these two variables is described. Three tropical leguminous vine species (Abrus precatorius L., Caesalpinia bondicela Fleming and Mucuna pruriens (L.) DC.) were grown in two shade enclosures with 3-4% of solar PPFD with either the R:FR of sunlight (1.10) or foliage shade (0.33), and compared to plants grown in sunlight. Most species treated with low R:FR differed from those treated with high R:FR in (1) percent allocation to dry leaf weight, (2) internode length, (3) dry stem weight/length, (4) specific leaf weight, (5) leaf size, and (6) chlorophyll a/b ratios. However, these plants did not differ in chlorophyll content per leaf dry weight or area. In most cases the effects of low R:FR and PPFD were additional to those of high R:FR and low PPFD. Growth patterns varied among the three species, but both low PPFD and diminished R:FR were important cues in their developmental responses to light environments. This shadehouse system should be useful in studying the effects of light on the developmental ecology of other tropical forest plants.

Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh

Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30°C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures.

Simulating crop-parasitic weed interactions using APSIM: Model evaluation and application

The parasitic weed Orobanche crenata inflicts major damage on faba bean, lentil, pea and other crops in Mediterranean environments. The development of methods to control O. crenata is to a large extent hampered by the complexity of host-parasite systems. Using a model of host-parasite interactions can help to explain and understand this intricacy. This paper reports on the evaluation and application of a model simulating host-parasite competition as affected by environment and management that was implemented in the framework of the Agricultural Production Systems Simulator (APSIM). Model-predicted faba bean and O. crenata growth and development were evaluated against independent data. The APSIM-Fababean and -Parasite modules displayed a good capability to reproduce effects of pedoclimatic conditions, faba bean sowing date and O. crenata infestation on host-parasite competition. The r(2) values throughout exceeded 0.84 (RMSD: 5.36 days) for phenological, 0.85 (RMSD: 223.00 g m(-2)) for host growth and 0.78 (RMSD: 99.82 g m(-2)) for parasite growth parameters. Inaccuracies of simulated faba bean root growth that caused some bias of predicted parasite number and host yield loss may be dealt with by more flexibly simulating vertical root distribution. The model was applied in simulation experiments to determine optimum sowing windows for infected and non-infected faba bean in Mediterranean environments. Simulation results proved realistic and testified to the capability of APSIM to contribute to the development of tactical approaches in parasitic weed control.

Simulating the evolution of glyphosate resistance in grains farming in northern Australia.

Background and Aims: The evolution of resistance to herbicides is a substantial problem in contemporary agriculture. Solutions to this problem generally consist of the use of practices to control the resistant population once it evolves, and/or to institute preventative measures before populations become resistant. Herbicide resistance evolves in populations over years or decades, so predicting the effectiveness of preventative strategies in particular relies on computational modelling approaches. While models of herbicide resistance already exist, none deals with the complex regional variability in the northern Australian sub-tropical grains farming region. For this reason, a new computer model was developed. Methods: The model consists of an age- and stage-structured population model of weeds, with an existing crop model used to simulate plant growth and competition, and extensions to the crop model added to simulate seed bank ecology and population genetics factors. Using awnless barnyard grass (Echinochloa colona) as a test case, the model was used to investigate the likely rate of evolution under conditions expected to produce high selection pressure. Key Results: Simulating continuous summer fallows with glyphosate used as the only means of weed control resulted in predicted resistant weed populations after approx. 15 years. Validation of the model against the paddock history for the first real-world glyphosate-resistant awnless barnyard grass population shows that the model predicted resistance evolution to within a few years of the real situation. Conclusions: This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies.

Simulating and Forecasting the Demand for New Consumer Durables

A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.

Modeling of growth and motion of equiaxed dendrites in a convecting melt

A numerical micro-scale model is developed to study the behavior of dendrite growth in presence of melt convection. In this method, an explicit, coupled enthalpy model is used to simulate the growth of an equiaxed dendrite, while a Volume of Fluid (VOF) method is used to track the movement of the dendrite in the convecting melt in a two-dimensional Eulerian framework. Numerical results demonstrate the effectiveness of the enthalpy model in simulating the dendritic growth involving complex shape, and the accuracy of VOF method in conserving mass and preserving the complex dendritic shape during motion. Simulations are performed in presence of uniform melt flow for both fixed and moving dendrites, and the difference in dendrite morphology is shown.

A coupled VOF-IBM-enthalpy approach for modeling motion and growth of equiaxed dendrites in a solidifying melt

A coupled methodology for simulating the simultaneous growth and motion of equiaxed dendrites in solidifying melts is presented. The model uses the volume-averaging principles and combines the features of the enthalpy method for modeling growth, immersed boundary method for handling the rigid solid-liquid interfaces, and the volume of fluid method for tracking the advection of the dendrite. The algorithm also performs explicit-implicit coupling between the techniques used. A two-dimensional framework with incompressible and Newtonian fluid is considered. Validation with available literature is performed and dendrite growth in the presence of rotational and buoyancy driven flow fields is studied. It is seen that the flow fields significantly alter the position and morphology of the dendrites. (C) 2012 Elsevier Inc. All rights reserved.

Economic Effects of Global Warming under Stock Growth Uncertainty: The European Sardine Fishery

Global warming of the oceans is expected to alter the environmental conditions that determine the growth of a fishery resource. Most climate change studies are based on models and scenarios that focus on economic growth, or they concentrate on simulating the potential losses or cost to fisheries due to climate change. However, analysis that addresses model optimization problems to better understand of the complex dynamics of climate change and marine ecosystems is still lacking. In this paper a simple algorithm to compute transitional dynamics in order to quantify the effect of climate change on the European sardine fishery is presented. The model results indicate that global warming will not necessarily lead to a monotonic decrease in the expected biomass levels. Our results show that if the resource is exploited optimally then in the short run, increases in the surface temperature of the fishery ground are compatible with higher expected biomass and economic profit.