A simulation model of kenaf for assisting fibre industry planning in northern Australia. IV. Analysis of climatic risk

1993 ◽  
Vol 44 (4) ◽  
pp. 713 ◽  
Author(s):  
PS Carberry ◽  
PS Carberry ◽  
RC Muchow ◽  
RC Muchow ◽  
RL McCown ◽  
...  
Keyword(s):  
Simulation Model ◽  
Coefficient Of Variation ◽  
Yield Potential ◽  
Weather Data ◽  
Climatic Data ◽  
Northern Australia ◽  
Essential Information ◽  
High Rainfall ◽  
Sowing Dates

The establishment of a pulp and paper industry based on kenaf (Hibiscus cannabinus L.) in semi-arid northern Australia requires clear demonstration of the long-term production potential of kenaf in this region. Owing to the high rainfall variability both within and among seasons, it would be difficult to assess the potential of a new dryland industry from traditional experimentation. Accordingly, this study was undertaken to assess the climatic risks to dryland kenaf production in the Northern Territory (NT) using the kenaf simulation model NTKENAF, which has been developed and validated for this climatic zone. The kenaf model was run, using long-term historical weather data, to determine optimal sowing strategies and expected yields at four representative sites in the NT. In the NT, a codict existed between sowing early, with resulting long duration and high yield potential, but high probability of plant mortality, and sowing later, with more reliable plant population, but shorter duration and lower yields. A general recommendation over all sites was for a sowing window extending from the start of November to mid-December each year; lower yields were simulated for earlier sowing dates due to problems with crop establishment, and for later sowing dates due to crop growth extending past the end of the wet season in most years. However, in circumstances of high rainfall prior to November, there was a yield advantage at several sites from sowing early. Over the 100 years of climatic data for Katherine (14� 28'S.) and sowing when 30 mm rainfall occurred in a 5-day period after 1 November, simulated stem yields for kenaf ranged from 800 to 17200 kg ha-1, with a mean stem yield of 8673 kg ha-1 and coefficient of variation of 42%. At the higher rainfall site of Douglas Daly (13� 48'S.), over 21 seasons and using the same sowing criterion, stem yields ranged from 4490 to 19 200 kg ha-1, with a mean stem yield of 12 509 kg ha-1 and coefficient of variation of 27%. Simulated stem yields were higher at the wettest site of Adelaide River (13� 06'S.) and lowest at the driest site of Larrimah (15� 36'S.). In the planning of a potential kenaf industry in the northern Australia, this research study has provided the essential information of yield probability distributions for kenaf crops grown at selected sites in the NT.

scholarly journals Model Simulasi dan Visualisasi Prediksi Potensi Hasil dan Produksi Kedelai di Jawa Timur

2016 ◽  
Vol 34 (3) ◽  
pp. 195
Author(s):  
Bambang S. Koentjoro ◽  
Imas S. Sitanggang ◽  
Abdul Karim Makarim
Keyword(s):  
Simulation Model ◽  
Systems Approach ◽  
Yield Potential ◽  
Soybean Plant ◽  
Climatic Data ◽  
Observation Data ◽  
Potential Yield ◽  
Soybean Yield ◽  
Soybean Production ◽  
Growth Simulator

<p>The prediction of national soybean yield and production could be improved its accuracy by integrating a simulation model and Geographic Information Systems (GIS). The objective of this research was to integrate a simulation model with a GIS, to predict the potential yield and production of soybean in the soybean production centers of East Java. This study was conducted from December 2013 till May 2014. The approach used in this study was a systems approach using a simulation model as solution to the problem. The model is SUCROS.SIM (Simple Universal Crops Growth Simulator), which was written using Powersim software and Spreadsheet in order to be fully integrated with GIS. The initial phase of the integration process between SUCROS.SIM and GIS are as follows (a) model validation, using input data of soybean plant assimilate partitioning, (b) climatic data (solar radiation, maximum and minimum temperatures) collected from the climatological station (BMKG) Karangploso Malang and (c) observation data of soybean yields of two varieties (Wilis and Argomulyo) at Muneng Experiment Station. It was found that the coefficients of determination of simulation model of soybean yield potential (R2) range from 0.945-0.992 and RMSE (Root Mean Square Error) values range from 0.11 to 0.25 t/ha. The average of soybean yield potential and production in 2012 at soybean production centers of East Java were 1.94 t/ha and 293,459 ton, respectively. The conclusion is SUCROS.SIM valid to be integrated with GIS.</p>

SGS Water Theme: influence of soil, pasture type and management on water use in grazing systems across the high rainfall zone of southern Australia

2003 ◽  
Vol 43 (8) ◽  
pp. 907 ◽  
Author(s):  
R. E. White ◽  
B. P. Christy ◽  
A. M. Ridley ◽  
A. E. Okom ◽  
S. R. Murphy ◽  
...  
Keyword(s):  
Water Use ◽  
Western Australia ◽  
Weather Data ◽  
Rooting Depth ◽  
Marginal Effect ◽  
Deep Drainage ◽  
High Rainfall ◽  
Daily Weather Data ◽  
Grazing Systems

Eleven experimental sites in the Sustainable Grazing Systems (SGS) national experiment were established in the high rainfall zone (HRZ, >600 mm/year) of Western Australia, Victoria and New South Wales to measure components of the water balance, and pathways of water movement, for a range of pastures from 1997 to 2001. The effect of widely spaced river red gums (Eucalyptus camaldulensis) in pasture, and of belts of plantation blue gums (E. globulus), was studied at 2 of the sites. The soil types tested ranged from Kurosols, Chromosols and Sodosols, with different subsoil permeabilities, to Hydrosols and Tenosols. The pasture types tested were kikuyu (Pennisetum clandestinum), phalaris (Phalaris aquatica), redgrass (Bothriochloa macra) and annual ryegrass (Lolium rigidum), with subterranean clover (Trifolium subterraneum) included. Management variables were set stocking v. rotational grazing, adjustable stocking rates, and level of fertiliser input. Soil, pasture and animal measurements were used to set parameters for the biophysical SGS pasture model, which simulated the long-term effects of soil, pasture type, grazing method and management on water use and movement, using as inputs daily weather data for 31 years from selected sites representing a range of climates. Measurements of mean maximum soil water deficit Sm were used to estimate the probability of surplus water occurring in winter, and the average amount of this surplus, which was highest (97–201 mm/year) for pastures in the cooler, winter-rainfall dominant regions of north-east and western Victoria and lowest (3–11 mm/year) in the warmer, lower rainfall regions of the eastern Riverina and Esperance, Western Australia. Kikuyu in Western Australia achieved the largest increase in Sm compared with annual pasture (55–71 mm), while increases due to phalaris were 18–45 mm, and those of native perennials were small and variable. Long-term model simulations suggested rooting depth was crucial in decreasing deep drainage, to about 50 mm/year for kikuyu rooting to 2.5 m, compared with 70–200 mm/year for annuals rooting to only 0.8 m. Plantation blue gums dried the soil profile to 5.25 m by an average of 400 mm more than kikuyu pasture, reducing the probability of winter surplus water to zero, and eliminating drainage below the root zone. Widely spaced river red gums had a much smaller effect on water use, and would need to number at least 14 trees per hectare to achieve extra soil drying of about 50 mm over a catchment. Soil type affected water use primarily through controlling the rooting depth of the vegetation, but it also changed the partitioning of surplus water between runoff and deep drainage. Strongly duplex soils such as Sodosols shed 50% or more surplus water as runoff, which is important for flushing streams, provided the water is of good quality. Grazing method and pasture management had only a marginal effect in increasing water use, but could have a positive effect on farm profitability through increased livestock production per hectare and improved persistence of perennial species.

A simulation model of kenaf for assisting fibre industry planning in northern Australia. V. Impact of different crop traits

1993 ◽  
Vol 44 (4) ◽  
pp. 731 ◽  
Author(s):  
RC Muchow ◽  
PS Carberry
Keyword(s):  
Simulation Model ◽  
Plant Breeding ◽  
Plant Traits ◽  
Rainfall Variability ◽  
Crop Improvement ◽  
Yield Potential ◽  
Yield Response ◽  
Climatic Data ◽  
Transpiration Efficiency ◽  
Growth Simulation

The production potential of rainfed kenaf in the Northern Territory (NT) (latitude 12-15�S.) has been assessed using a growth simulation model for the cultivar Guatemala 4. However, this raises the important question of how well-suited is this cultivar, and what are the likely yield gains which might be obtained by breeding or selecting a different cultivar. Answering these questions with conventional experimentation would be expensive, given the variable yield response among seasons associated with rainfall variability in the NT. Accordingly, the kenaf growth simulation model NTKENAF was used in conjunction with long-term climatic data for two sites in the NT to assess the value of different plant traits relative to Guatemala 4, that are potential selection criteria in plant breeding. Extending the duration from sowing to flowering resulted in relatively small gains in stem yield over Guatemala 4, but substantial yield losses were predicted by using an earlier flowering cultivar. Increasing the efficiency of water use (higher transpiration efficiency) greatly increased yield, and was the most risk-efficient crop improvement strategy. Unfortunately, the prospects for improving transpiration efficiency of kenaf by plant breeding remain uncertain. Increasing the amount of water available for crop growth by greater extent of soil water extraction had little effect on yield in this water-limited environment. Changing the yield potential of kenaf by altering the photosynthetic capacity (higher radiation use efficiency) was risk-efficient in some situations, but the mean yield change was relatively small. It is concluded from the simulation analysis, that the standard cultivar Guatemala 4 is well-suited to the NT environment.

scholarly journals Historical and Future Typical Meteorological Years for 33 locations in Greece: a handy tool for various applications

2021 ◽  
Author(s):  
Basil Psiloglou ◽  
Harry D. Kambezidis ◽  
Konstantinos V. Varotsos ◽  
Dimitris G. Kaskaoutis ◽  
Dimiitris Karagiannis ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Design Tool ◽  
Weather Data ◽  
Climate Simulation ◽  
Total Solar Radiation ◽  
Climatic Data ◽  
Data Set ◽  
Specific Location ◽  
Daily Data

&lt;p&gt;It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today.&amp;#160;A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most &amp;#8220;typical&amp;#8220; among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location.&amp;#160;TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models.&amp;#160;&lt;/p&gt;&lt;p&gt;TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).&lt;/p&gt;&lt;p&gt;Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station&amp;#8217;s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11&lt;sup&gt;o&lt;/sup&gt; (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios.&amp;#160;&lt;/p&gt;&lt;p&gt;This work was carried out in the framework of the &amp;#8220;Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures&amp;#8221; (KRIPIS-THESPIA-II) Greek national funded project.&lt;/p&gt;

scholarly journals Modelling Heat and Drought Adaptation in Crops

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 190
Author(s):  
Erik van Oosterom ◽  
Greg McLean ◽  
Kurt Deifel ◽  
Vijaya Singh ◽  
David Jordan ◽  
...  
Keyword(s):  
Heat Stress ◽  
Seed Set ◽  
Cropping Systems ◽  
Yield Potential ◽  
Drought Adaptation ◽  
Sowing Dates ◽  
Threshold Temperatures ◽  
Maximum Temperatures ◽  
Carry Over

Drought and heat stress are increasingly important abiotic limitations to productivity of sorghum. Here, we use long-term simulations to quantify the importance of transpiration rates to drought adaptation and the importance of threshold temperatures and tolerance above the threshold for adaptation of seed set to heat stress. Simulations were parameterised using results of detailed physiological studies. The importance of transpiration rates to drought adaption was studied by comparing productivity of maize and 3dwarf sorghum. These crops have similar transpiration efficiency but contrasting transpiration rates per unit green leaf area (TGLA), which was captured through differences in RUE. Results showed that the greater TGLA of maize reduced productivity under drought stress, but increased productivity in well-watered conditions, indicating a trade-off between yield potential and drought adaptation. The increased water use of maize associated with higher yield potential could negatively affect carry-over of soil water in a cropping systems context. Simulations for sorghum productivity under heat stress, using long-term weather records for six locations across the Australian sorghum belt, showed that the most common incidence of heat stress around anthesis was the occurrence of individual days with maximum temperatures of 36–38 °C. Because these temperatures were near the threshold that limits seed set, increased temperature thresholds generally minimised yield reductions. However, predicted temperature increases in coming decades justify additional selection for increased tolerance above the threshold. As manipulating sowing dates did not reduce risks of heat stress around anthesis, genetic improvement provides the best prospect to mitigate adverse effects on grain yield.

Accuracy of Weather Data in Long-Term Pavement Performance Program Database

2000 ◽  
Vol 1699 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Chung-Lung Wu ◽  
Gonzalo R. Rada ◽  
Aramis Lopez ◽  
Yingwu Fang
Keyword(s):  
Weather Conditions ◽  
Test Site ◽  
Climatic Conditions ◽  
Weighting Scheme ◽  
Pavement Performance ◽  
Weather Data ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Weather Stations

To provide accurate climatic data for pavements under the Long-Term Pavement Performance (LTPP) Program, a climatic database was developed in 1992 and subsequently revised and expanded in 1998. In the development of this database, up to five nearby weather stations were selected for each test site. Pertinent weather data for the selected weather stations were obtained from the U.S. National Climatic Data Center and the Canadian Climatic Center. With a 1/ R2 weighting scheme, site-specific climatic data were derived from the nearby weather station data. The derived data were referred to as “virtual”weather data. To evaluate the effect of environmental factors on pavement performance and design, automated weather stations (AWS) were installed at LTPP Specific Pavement Study Projects 1, 2, and 8 to collect on-site weather data. Since the virtual weather data were developed for all LTPP test sites and will be used for future pavement performance studies, it is essential that the derived virtual data be accurate and representative of the actual onsite climatic conditions. The availability of the AWS weather data has provided an opportunity to evaluate whether virtual weather data can be used to represent on-site weather conditions. Daily temperature data and monthly temperature and precipitation data were used in this experiment. On the basis of the comparisons made between the virtual and onsite measured (AWS) data, it appears that climatic data derived from nearby weather stations using the 1/R2 weighting scheme estimate the actual weather data reasonably well and thus can be used to represent on-site weather conditions in pavement research and design.

Trends in grain production and yield gaps in the high-rainfall zone of southern Australia

2016 ◽  
Vol 67 (9) ◽  
pp. 921 ◽  
Author(s):  
Michael Robertson ◽  
John Kirkegaard ◽  
Allan Peake ◽  
Zoe Creelman ◽  
Lindsay Bell ◽  
...  
Keyword(s):  
Western Australia ◽  
Crop Production ◽  
Crop Yields ◽  
Growing Season ◽  
Yield Potential ◽  
Annual Rainfall ◽  
Potential Yield ◽  
High Rainfall ◽  
Eastern Australia

The high-rainfall zone (HRZ) of southern Australia is the arable areas where annual rainfall is between 450 and 800 mm in Western Australia and between 500 and 900 mm in south-eastern Australia, resulting in a growing-season length of 7–10 months. In the last decade, there has been a growing recognition of the potential to increase crop production in the HRZ. We combined (1) a survey of 15 agricultural consultants, each of whom have ~40–50 farmer clients across the HRZ, (2) 28 farm records of crop yields and area for 2000–2010, (3) 86 wheat and 54 canola yield observations from well managed experiments, and (4) long-term simulated crop yields at 13 HRZ locations, to investigate recent trends in crop production, quantify the gap between potential and actual crop yields, and consider the factors thought to limit on-farm crop yields in the HRZ. We found in the past 10 years a trend towards more cropping, particularly in WA, an increased use of canola, and advances in the adaptation of germplasm to HRZ environments using winter and longer-season spring types. Consultants and the farm survey data confirmed that the rate of future expansion of cropping in the HRZ will slow, especially when compared with the rapid changes seen in the 1990s. In Victoria, New South Wales and South Australia the long-term water-limited potential yield in HRZ areas, as measured by experimental yields, consultant estimates and simulations for slow developing spring cultivars of wheat and canola was 5–6 and 2–3 t/ha for a decile 5 season. For Western Australia it was 4–5 and 2–3 t/ha, where yields were less responsive to good seasons than in the other states. The top performing farmers were achieving close to the water-limited potential yield. There are yield advantages of ~2 t/ha for ‘winter’ over ‘spring’ types of both wheat and canola, and there is scope for better adapted germplasm to further raise potential yield in the HRZ. Consultants stated that there is scope for large gains in yield and productivity by encouraging the below-average cropping farmers to adopt the practices and behaviours of the above-average farmers. The scope for improvement between the below- and above-average farmers was 1–3 t/ha for wheat and 0.5–1.5 t/ha for canola in a decile 5 season. They also stated that a lack of up-to-date infrastructure (e.g. farm grain storage) and services is constraining the industry’s ability to adopt new technology. Priorities for future research, development and extension among consultants included: overcoming yield constraints where growing-season rainfall exceeds 350 mm; adaptation of winter and long-season spring types of cereals and canola and management of inputs required to express their superior yield potential; and overcoming barriers to improved planning and timeliness for crop operations and adoption of technology.

Development and use of a barley crop simulation model to evaluate production management strategies in north-eastern Australia

1996 ◽  
Vol 47 (7) ◽  
pp. 997 ◽  
Author(s):  
PJ Goyne ◽  
H Meinke ◽  
SP Milroy ◽  
GL Hammer ◽  
JM Hare
Keyword(s):  
Simulation Model ◽  
Production Management ◽  
Field Experiments ◽  
Management Strategies ◽  
Crop Growth ◽  
Growth And Yield ◽  
Yield Potential ◽  
Weather Data ◽  
Eastern Australia ◽  
North Eastern

A study was undertaken to identify improved management strategies for barley (Hordeum vulgare L.), particularly in relation to time of planting, location, and frost risk in the variable climate of north-eastern Australia. To achieve this objective, a crop growth simulation model (QBAR) was constructed to integrate the understanding, gained from field experiments, of the dynamics of crop growth as influenced by soil moisture and environmental variables. QBAR simulates the growth and yield potential of barley grown under optimal nutrient supply, in the absence of pests, diseases, and weeds. Genotypic variables have been determined for 4 cultivars commonly grown in the northern cereal production areas. Simulations were conducted using long-term weather data to generate the probabilistic yield outcome of cv. Grimmet for a range of times of planting at 10 locations in the north-eastern Australian grain belt. The study indicated that the common planting times used by growers could be too late under certain circumstances to gain full yield potential. Further applications of QBAR to generating information suitable for crop management decision support packages and crop yield forecasting are discussed.

Evaluation of the CERES-Rice Model for Precision Nitrogen Management for Rice in Northeast China

2017 ◽  
Vol 8 (2) ◽  
pp. 328-332
Author(s):  
J. Zhang ◽  
Y. Miao ◽  
W.D. Batchelor
Keyword(s):  
Northeast China ◽  
Weather Data ◽  
Cool Season ◽  
Use Efficiency ◽  
Optimum N Rate ◽  
Management Recommendations ◽  
N Management ◽  
N Rates ◽  
N Use

Over-application of nitrogen (N) in rice (Oryza sativaL.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R2=0.89) and evaluation years (R2=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–1, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–1. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

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