Production Systems Simulator Research Articles

Evaluation and Enhancement of Transfer line in Production Process by Simulation

Plant Simulation software enables the simulation and optimization of production systems and processes. Using Plant Simulation, you can optimize material flow, resource utilization and logistics for all levels of plant planning from global production facilities, through local plants, to specific lines. In times of increasing cost and time pressures in production, along with ongoing globalization, logistics has become a key factor in the success of a company. The need to deliver JIT (just-in-time)/JIS (just-in-sequence), introduce Kanban, plan and build new production lines and manage global production networks (to name a few) requires objective decision criteria to help management evaluate and compare alternative approaches. Plant Simulation helps create digital models of logistic systems (e.g., production) to explore the systems’ characteristics and to optimize their performance. The digital model enables users to run experiments and what-if scenarios without disturbing an existing production system or – when used in the planning process – long before the real system is installed. Extensive analysis tools, statistics and charts let users evaluate different manufacturing scenarios and make fast, reliable decisions in the early stages of production planning. Plant Simulation helps users to Detect and eliminate problems that otherwise would require cost-and time consuming correction measures during production ramp up Minimize the investment cost of production lines without jeopardizing required output, Optimize the performance of existing production systems by taking measures that have been verified in a simulation environment prior to implementation.

Long fallows can maintain whole-farm profit and reduce risk in semi-arid south-eastern Australia

The practice of leaving a paddock out of production for an entire growing season, known as long fallowing, is used to accumulate soil water and mineral nitrogen (N) and break disease cycles in low-rainfall environments where in-crop rainfall is unreliable. This study used whole-farm economics and rule-based crop simulation at a site in the grain-growing Mallee region (300–350mm annual rainfall) of south-eastern Australia to determine if long fallowing could be used to reduce production risk and maintain profit by reducing input costs. The Agricultural Production Systems sIMulator (APSIM) was used to model crop production on a hypothetical 4000-ha property over 20 years. Three fixed rotations (continuous wheat, wheat-chickpea and wheat-fallow) were managed as whole-farm systems, with unique soil water-based fertiliser rules for each rotation and a sowing window designed to reflect the improved timeliness of operations associated with reducing cropped area. Two flexible rule-based sequences were also created, where long fallow replaced a crop if plant available water and mineral N thresholds were not met at sowing. APSIM outputs were used for whole-farm economic analyses and to assess the returns and risks of each farming system. Converting 50% of a continuously-cropped wheat farm to fallow increased average annual net cash flow by 44% and decreased the variability of inter-annual profit by 49%. A long fallow-wheat rotation was more profitable than a chickpea-wheat rotation when chickpea prices were below $AUD800/t. Wheat production that incorporated long fallowing into the system when there were less than 100 water-nitrogen units at sowing was the most profitable system, increasing average net cash flow by 63% compared to continuous wheat production.

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies.

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Improving sorghum productivity under changing climatic conditions: A modelling approach

Climate variability and change will have far reaching consequences for smallholder farmers in sub-Saharan Africa, the majority of whom depend on agriculture for their livelihoods. Crop modelling can help inform the improvement of agricultural productivity under future climate. This study applies the Agricultural Production Systems sIMulator (APSIM) to assessing the impacts of projected climate change on two (early and medium maturing) sorghum varieties under different management practices. Results show high model accuracy with excellent agreement between simulated and observed values for crop phenology and leaf number per plant. The prediction of grain yield and total biomass of an early maturing variety was fair RMSEn (22.9 and 23.1%), while that of the medium maturing was highly accurate RMSEn (14.9 and 11.9%). Sensitivity analysis performed by changing the calibrated variables of key plant traits in the model, showed higher significant yield change by + or - 10 % changed in radiation use efficiency, (RUE), coefficient extinction (Coeff_ext) and Phyllocron (Phyllo) for early maturing variety while + or - 10 % changed in phyllochron and RUE showed a significant yield change for the medium maturing variety. Under climate change scenerios using RCP 8.5, the simulated yield for the early–maturing variety revealed high inter-annual variability and potential yield loss of 3.3% at Bamako and 1% at Kano in the near-future (2010–2039) compared to baseline (1980–2009). The mid-century (2040–2069) projected yield decline by 4.8% at Bamako and 6.2% at Kano compared to baseline (1980–2009). On the contrary, the medium maturing variety indicated significantly yield gain with high yielding potential in both climate regimes compared to the baseline period (1980–2009). The simulated grain yield increased by 7.2% at Bamako and 4.6% at Kano, in the near-future (2010–2039) while in the mid-century (2040–2069) projected yield increase of 12.3% and 2% at Bamako and Kano compared to baseline (1980–2009). Adaptation strategies under climate change for varying sowing dates in the near-future (2010–2039, indicated that June sowing had a higher positive yield gained over July and August sowing for early maturing variety; July sowing simulated positive gained by 5 -11% over June and August sowing for medium maturing variety in both locations. Similarly, under the mid-century (2040–2069), among the sowing dates and in both locations, June sowing indicates lowest negative yield change over July and August sowing for early maturing variety. However, for medium maturing variety, July sowing had the highest yield gain of 16% over June and August sowing at Bamako and June highest positive yield gained of 11.4% over July and August at Kano. Our study has, therefore, demonstrated the capacity of APSIM model as a tool for testing management, plant traits practices and adoption of improved variety for enhancing the adaptive capacity of smallholder farmers under climate change in the Sudanian zone of West Africa. This approach offers a promising option to design more resilient and productive farming systems for West Africa using the diverse sorghum germplasm available in the region.

Forage chicory model: Development and evaluation

Chicory has been promoted as an alternative forage crop for livestock farming. It can produce large biomass yields and is highly palatable to animals. However, managing forage chicory in pastoral farms can be challenging because of its growth pattern and low persistence. There is thus a need for modelling tools that can help to understand and manage forage chicory within the farm system. In this work we review the main botanical characteristics of the chicory plant and describe a model developed to simulate the phenology and biomass accumulation of chicory as forage. The model was developed using the Plant Model Framework and it is available for use within the Agricultural Production Systems Simulator. Four experiments with treatments including the use of irrigation, different N fertiliser rates, and defoliation regimes, were used to test the model. We show that the model was able to simulate the biomass accumulation pattern, with R2 values of 0.40-0.54 for standing above-ground biomass and 0.64-0.89 for cumulative harvested material. The performance was weaker when describing plant nitrogen, with R2 of 0.20-0.25 for N content in the above-ground biomass. This indicates that care must be taken when using the model to simulate N balance, and that model refinements are required. For this, more information is needed on the partitioning and mobilisation of N amongst the various organs and on how biomass allocation changes across seasons.

Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6–26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Assessment of the sustainability of different cropping systems under three irrigation strategies in the North China Plain under climate change

The annual double-cropping system of winter wheat and summer maize requires a large amount of irrigation which has led to the rapid depletion of groundwater resources in the North China Plain (NCP). Alternate cropping systems and limited irrigation strategies should be developed for the purposes of maintaining sustainable groundwater use now and in the future. In this study, the water use and crop production of seven cropping systems under three irrigation strategies were assessed using the Agricultural Production Systems SIMulator (APSIM) during 1987–2017 as a baseline and in 2040, 2060, and 2080 under climate change conditions at a typical site in the NCP. The APSIM was calibrated and validated using field experimental data collected during 2007–2016. The seven cropping systems included the current double annual cropping system (2C/1Y) and six other reduced cropping-intensity systems with either three crops every two years (3C/2Y) or one crop per year (1C/1Y). The three irrigation strategies were full irrigation (FI), minimum irrigation (MI, only one irrigation at sowing for seedling establishment) and critical stage irrigation (CI, adding one more irrigation at the critical stage based on MI). The results showed that under current growing conditions, sustainable groundwater use could be achieved with 2C/1Y under MI, 3C/2Y and 1C/1Y under FI. However, the annual yield production was reduced by 9–22% under 3C/2Y and 54–79% under 1C/1Y compared with that under 2C/1Y. The results indicated that 2C/1Y was a better choice for crop production under similar water use. The simulated yield for future scenarios was lower than that during the baseline period; and the reduction rate varied from 2 to 11% under FI; 6–9% under CI; and 10–21% under MI, suggesting that crop production would be more negatively affected under water-limited conditions than that under full water supply condition. The annual water use of the reduced cropping systems was projected to increase because of relative high soil evaporation during the fallow period (Ef). Water use efficiency (WUE) was reduced partly due to the increased Ef. The traditional 2C/1Y under MI had the potential to sustain the groundwater balance in the region and concurrently resulted in higher grain production and WUE than that of the systems with reduced cropping intensity, both now and under climate change, and therefore, this system should be prioritized in this region.

Farm systems analysis of two thistles of differing seasonal pasture growth impacts in North Island hill country

Models for infestations of Californian thistle (Cirsium arvense) and variegated thistle (Silybum marianum) were used to modify fortnightly pasture growth forecasts using Agricultural Production Systems simulator software using climate and soil data from a single farm (Tangihanga Station) over four aspects and three slope classes. Modelling using Farmax software was used to estimate profitability using the current farm enterprises with or without either the Californian or variegated thistles. Modelled pasture production, based on field observations of thistle infestation, was similar to estimates using animal intake (from Farmax). Californian thistle reduced pasture production in summer and autumn, while variegated thistle reduced autumn, winter and spring pasture growth. Californian thistle had a much greater overall presence (20%) than variegated thistle (9%). Both types of thistle reduced the potential to finish lambs in summer and reduced ewe wintering numbers, while the presence of Californian thistles also reduced over-wintering cattle numbers, by reducing summer-autumn pasture accumulation. Cost of control for Californian thistle ($233/ha) over two years was higher than for variegated thistle ($184/ha) over four years. Net profitability was reduced by 24% ($87/ha) by the presence of Californian thistle, and by 37% ($135/ha) by the presence of variegated thistle.

Identifikasi Bahaya Confined Space pada Proses Perawatan Tangki Kondensat T-0701 B pada Perusahaan Minyak dan Gas dengan Pendekatan Hazard Identification Risk Assessment and Risk Control (HIRARC) dan Job Safety Analysis (JSA)

Pada Central Processing Plant (CPP) G sedang dilakukan perawatan tangki kondensat. Perawatan dilakukan dengan pencucian dan pelapisan pada bagian dalam tangki. Untuk mengetahui bahaya dan bagaimana mengendalikan bahaya tersebut, maka identifikasi bahaya pada tangki kondensat perlu dilakukan. Identifikasi bahaya pekerjaan perawatan tangki menggunakan metode HIRARC dengan acuan guideline HIRARC Department of Occupational Safety and Health Ministry of Human Resource, Malaysia 2008. Selanjutnya untuk mempermudah pekerja mengetahui bahaya dan pengendalian risikonya maka dituliskan dalam bentuk JSA sesuai dengan OSHA 3071, 2002. Dasar pengambilan kontrol risiko adalah API 2217A Guidelines for Safe Work in Inert Confined Space in the Petroleum and Petrochemical Industries 2009. Berdasarkan hasil analisa dari seluruh proses perawatan tangki ini terdapat 110 potensi bahaya dan terdapat 36 potensi bahaya sedang dan 5 potensi bahaya berat. Tahapan kerja washing tank memiliki jumlah potensi bahaya yang paling banyak yaitu 18 potensi bahaya. Rekomendasi yang diberikan antara lain : melakukan safety briefing rutin sebelum dan sesudah pekerjaan, melakukan isolasi energi dengan aman, peletakkan blower hisap pada manhole bawah, memasang warning sign berupa larangan masuk kecuali pekerja dengan confined space entry permit, memastikan APD lengkap serta dalam kondisi baik dan terpasang dengan benar.

The development of a methodology of learning to use simulation in the analysis of production system performances

Considering the dynamic environment in which the present companies work, global market, fierce competition, frequent changes of techniques and technologies, more diverse demand from clients, changing norms and ethics, it is a pressure that rises. To cope with these complex factors, the modelling and simulation of production systems became a main approach in the middle of 20th century. To develop a model and to later use it in simulation is an “artistic” balancing of opposites that demand finesse and skills; on one side the model must be easy to use, that meaning it must involve a level of abstraction from reality, on the other side the model must be a faithful representation of the system it mimics. Therefore, in this article we will develop a learning platform for a methodology to use the modelling and simulation in the analysis of production systems performances. The platform and the methodology will be later experimented and validated, leading to a higher level of research quality.

Customization-Oriented Product Flexible Manufacturing Experience System Design Based on VR

It has gradually become the trend of the world to integrate the individualized and diversified needs of customers into products, which has brought new pressure to enterprises, and the diversification of users’ needs has led to the diversification of the market. Manufacturing industry must adjust the existing production mode to adapt to these changes. This paper puts forward the research and development of product flexible customization experience system based on VR considering users’ customization requirement. Through the research of data modularization modelling and dynamic product family model and configuration for product flexible customization, the functions and operation processes of typical industries are analysed. By using modularization idea, the general rules are found out from customer requirements, and the multi-layer module division and model from production system planning to equipment design are studied. Group coupling technology, explore customer modular customization methods and technologies for equipment manufacturing industry. Combining with a case of customization of logistics equipment system, this paper explores how to combine VR technology application with production system simulation technology, build a digital personalized flexible configuration experience system, provide an immersive visual environment of interactive design for customers, and improve the market competitiveness of enterprises.

Optimal genotype × environment × management as a strategy to increase grain maize productivity and water use efficiency in water-limited environments and rising temperature

Water limitation is a major concern for the present and future of agriculture, particularly in fragile areas such as arid and semi-arid regions under climate change conditions. In such ecosystems, sustainability of maize production can partially be achieved by investigating the interaction of genotype, environment and management, particularly as effected by climate change. The Agricultural Production Systems sIMulator (APSIM) was applied to assess the effect of future climate change on maize grain yield and water use efficiency in Khuzestan province for two cultivars (as genotype), two seasons (as environment) and nine irrigation regimes (as management). Projections of future climate of Khuzestan was conducted using the baseline period of 1980–2010 and Miroc5 general circulation model for 2040–2070 under two RCPs using The Agricultural Model Intercomparison and Improvement Project (AgMIP) methodology at 15 locations. The simulation results demonstrated that grain yield and water use efficiency will have decreased 6.04% and 5.4%, respectively across the province in 2050 under both RCP4.5 and RCP8.5. This decrease can be mitigated and sustained by adapting a suitable genotype × environment × management interaction. The optimal genotype × environment × management for winter and summer seasons were SC260 × winter sowing × 10-time irrigation per crop season and SC704 × summer sowing × 10-time irrigation per crop season, respectively. Shifting from winter maize cultivation to summer sowing in the future could result in a change in environmental conditions that include a reduced mean temperature (−2.6 °C) and an increase rainfall (81.3 mm) and length of maize growing season (7.9 days). This will reduce water consumption by maize by 185.37 mm, increase water use efficiency by 4.38 kg ha−1 mm−1 and grain yield by 88.32 kg ha−1. The results show that sustainability can be obtained for maize cropping systems through the choice of an optimal genotype × environment × management in the forthcoming period.

Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context.

Several circulation models are forecasting climate changes in the Mediterranean region. Accordingly, it is expected that water scarcity in the region will be higher with drastic shifts of hydrological and erosive watershed responses. In Tunisia, wheat yields have been variable over the years and are lower than the potential yields. In response, the adoption of conservation agriculture (CA), introduced into Tunisia in 1999 to help adaptation to climate change, has resulted in a substantial reduction in agricultural productivity. CA areas increased from 52 ha in 1999 to 14,000 ha in 2015. Using a modelling approach, the present paper evaluates the potential of CA to adapt wheat-based-systems to climate change in Tunisia. The Agricultural Production Systems Simulator (APSIM) model was used to predict the effect of tillage (conventional tillage [CT] vs. zero-tillage [ZT] and soil residue retention [ZT-RR]) on wheat productivity and soil fertility. Two contrasting locations in Tunisia were studied; one semi-arid (Kef) and one sub-humid (Bizerte). Results showed that the sustainable production of durum wheat under climate change conditions in Tunisia is possible through the adoption of CA practices (ZT and ZT-RR) in both sub-humid and semi-arid areas. In fact, mulching (residue retention) is more effective than CT (under semi-arid and sub-humid conditions) in enhancing wheat yield (15%), water use efficiency (18% and 13%) and soil organic carbon accumulation (0.13 t ha-1 year-1 and 0.18 t ha-1 year-1). It is also more effective for soil resilience - preventing water erosion (1.7 t ha-1 year-1 and 4.6 t ha-1 year-1 of soil loss). The present study allowed identification of 260,000 ha as priority areas for CA adoption; this represent one-third of the total cereal area in Tunisia. Appropriate evaluation of the benefits of CA on sustainable agricultural intensification would provide more arguments for effectively supporting CA adoption in Tunisia.

Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model

Process-based crop models are popular scientific tools to study the impacts of environmental conditions and management decisions on crop growth. Some cultivar parameters in crop models cannot be measured directly and need to be estimated. In this research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). Both theoretical and real-world evaluations were conducted to compare the performances of these two methods. The maize yields from 2003 to 2006 were used for model calibration, and the yields from 2007 to 2013 were used for model validation. Both GLUE and DREAM performed well in the theoretical and real-world evaluation. During the validation period (2007–2013), when the heteroscedastic model error assumption was adopted in DREAM, on average approximate 90% of observed yield values were captured in the 95% confidence band of DREAM (P-factor = 90.47%), which was larger than that using GLUE (P-factor = 80.93%). Meanwhile the uncertainty bands of DREAM (R-factor = 4.42) were wider than those of GLUE (R-factor = 2.32). If only one parameter set was allowed to be used in the simulation, the weighted mean parameter values according to the likelihood of each parameter set performed better than the parameter set with maximum likelihoods for GLUE while the opposite is true for DREAM. But considering future analysis in the real-world evaluation, the moderate performance of these two methods suggests that a single parameter set obtained by neither GLUE nor DREAM is satisfactory and ensemble simulation is needed. Overall, GLUE and DREAM had similar performance, but GLUE is more convenient and simpler to use than DREAM. So we think GLUE is a better choice than DREAM for estimating cultivar parameters of APSIM-maize.

Risk Attitudes Affect Livestock Biosecurity Decisions With Ramifications for Disease Control in a Simulated Production System.

Hog producers' operational decisions can be informed by an awareness of risks associated with emergent and endemic diseases. Outbreaks of porcine epidemic diarrhea virus (PEDv) have been re-occurring every year since the first onset in 2013 with substantial losses across the hog production supply chain. Interestingly, a decreasing trend in PEDv incidence is visible. We assert that changes in human behaviors may underlie this trend. Disease prevention using biosecurity practices is used to minimize risk of infection but its efficacy is conditional on human behavior and risk attitude. Standard epidemiological models bring important insights into disease dynamics but have limited predictive ability. Since research shows that human behavior plays a driving role in the disease spread process, the explicit inclusion of human behavior into models adds an important dimension to understanding disease spread. Here we analyze PEDv incidence emerging from an agent-based model (ABM) that uses both epidemiological dynamics and algorithms that incorporate heterogeneous human decisions. We investigate the effects of shifting fractions of hog producers between risk tolerant and risk averse positions. These shifts affect the dynamics describing willingness to increase biosecurity as a response to disease threats and, indirectly, change infection probabilities and the resultant intensity and impact of the disease outbreak. Our ABM generates empirically verifiable patterns of PEDv transmission. Scenario results show that relatively small shifts (10% of the producer agents) toward a risk averse position can lead to a significant decrease in total incidence. For significantly steeper decreases in disease incidence, the model's hog producer population needed at least 37.5% of risk averse. Our study provides insight into the link between risk attitude, decisions related to biosecurity, and consequent spread of disease within a livestock production system. We suggest that it is possible to create positive, lasting changes in animal health by nudging the population of livestock producers toward more risk averse behaviors. We make a case for integrating social and epidemiological aspects in disease spread models to test intervention strategies intended to improve biosecurity and animal health at the system scale.

Prediction of changing climatic effect and risk management by using simulation approaches for rice-wheat system in Bangladesh

A study was carried out on the impact of climate change in rice-wheat systems on farmers’ livelihood in Dinajpur region of Bangladesh to evaluate the usefulness of the implication of simulation approaches to predict climate change effect and to manage risk for this cropping system. Trade-off analysis for multidimensional impact assessment (TOA-MD) model was used in the study with a combination of simulated baseline production and future simulated yield using Decision Support Systems for Agro-technology Transfer (DSSAT) and Agricultural Production Systems SIMulator (APSIM) in rice and wheat production system. Five different climate scenarios of Global Circulation Models (GCMs) were considered. The projections showed to have a negative economic impact between 50 and 82% for the difference in the magnitude and in the impact of different GCMs which was not possible to overcome. The survey revealed that northwest region of Bangladesh is likely to be affected by climate change and has high levels of vulnerability due to limited access to alternative livelihood activities other than farming. Simulation results showed no additional economic gain from wheat cultivation under changed climatic conditions, but increased economic profit was obtained from rice cultivation due to increased productivity trend. Therefore, study suggests an adaptation package of 50 mm additional irrigation water for wheat cultivation that could be an appropriate strategy to mitigate climate change risk in wheat cultivation. This practice had a positive impact on projected per capita income gains of about 2.05%in the study area and reduced poverty rate by about 1.99%. The study also revealed that prediction of the APSIM model for adaptation impact of climate change on economic return and per capita income of farmers was superior to DSSAT model.
 Bangladesh J. Agril. Res. 44(2): 311-326, June 2019

제강공정의 배열 및 부생가스를 활용한 바이오수소 생산 시스템의 동적 시뮬레이션

본 논문에서는 제철소의 배열과 부생가스를 이용한 바이오수소 시스템을 제안하고 간헐적으로 발생하는 skirt의 냉각수를 활용하는 경우에 대하여 동적 시뮬레이션을 수행하고 시스템 구성요소의 시간에 따른 온도 변화를 실험과 비교하였다. 1톤급 반응기 시뮬레이터와 시뮬레이션을 비교한 결과 각 구성요소에서의 온도변화폭과 경향이 잘 예측되는 것을 확인하였다. 실험과 시뮬레이션은 설계조건 뿐 아니라 2가지 탈설계 조건에서도 잘 일치하였다. 신뢰성을 확인한 후 시뮬레이션을 이용하여 실제 현장에 설치될 10톤급 반응기의 설계를 수행하였다. 시뮬레이션을 수행한 결과 축열조의 열원 유효성 및 반응기의 최소 온도 편차를 보장하는 시스템의 최소 체적을 발견하였다.

Simulating deep drainage and nitrate leaching on a regional scale: implications for groundwater management in an intensively irrigated area

Groundwater management in irrigation areas requires a comprehensive understanding of the interactions between agricultural management practices and the underlying groundwater resource. Unsaturated zone modelling can be used to predict rates of water and solute movement through the soil profile. By incorporating expert knowledge and measured data, it is possible to investigate the relative influence of management vs environmental factors on water and solute movement. The Agricultural Production Systems sIMulator (APSIM) has been applied to simulate the impacts of both environmental factors and agricultural management factors on deep drainage and nitrate leaching in the Lower Burdekin, North Queensland, Australia. Based on the results of this modelling, predicted annual average deep drainage rates were found to be controlled primarily by soil properties, irrigation scheduling, and, to a lesser extent, annual rainfall. The key factors which were found to control nitrate leaching were: irrigation water quality, fertilizer application rates, soil type, crop management, and irrigation scheduling. However, increases in the concentrations of nitrate in irrigation water resulted in a 64-fold increase in predicted nitrate-leaching rates, compared with a 22-fold increase in predicted nitrate-leaching rates associated with increased fertilizer application rates. As significant quantities of nitrate are predicted to be leached below the root zone, the immediate priority should be to increase monitoring of groundwater quality with a focus on quantifying the transport of nutrients to sensitive receptors.

Modelling and simulation tool for off-grid PV-hydrogen energy system

ABSTRACTThis paper introduces a user-oriented software tool for simulation of a solar energy-based hydrogen production system. The developed tool goes beyond the realm of electric load and includes a hydrogen cooking load facility, as an efficient means of utilising the hydrogen produced. A model rural household in Nigeria has been used to evaluate the tool. It was found that a 2.42 kW solar photovoltaic module, 0.6 kW electrolyser and 3.7 kWh battery would be enough to provide steady 24-h power for a modest daily energy demand of 2.2 kWh. In the results, the excess energy realised was used in a H2-cooker to partly meet the estimated 1.9 kWh/day cooking demand of the household over a simulated year period.

Analysis of growth functions that can increase irrigated wheat yield under climate change

AbstractThis study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield.