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Sangale, Bhagwan, U. M. Khodke H. W. Awari, and Vishal Ingle. "Crop Growth Simulation Modelling - A Review." International Journal of Current Microbiology and Applied Sciences 11, no. 1 (January 10, 2022): 78–84. http://dx.doi.org/10.20546/ijcmas.2022.1101.010.
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Agriculture plays a key role in overall economic and social wellbeing of the specially developing countries. Now it is the right option to increase the quality and quantity of food production through the technological and managerial interventions like crop growth and yield prediction models. Agricultural models are mathematical equations that represent the reactions that occur within the plant and the interactions between the plant and its environment. The model simulates or imitates the behaviour of real crop by predicting the growth of its components, such as leaves, roots, stems and grains. Thus, a crop growth model not only predicts the final state of total biomass or harvestable yield, but also contains quantitative information about major processes involved in the growth and development of a plant. Crop Growth Simulation models are a formal way to present quantitative knowledge about how a crop grows in interaction with its environment. Using weather data and other data about the crop environment, these models can simulate crop development, growth, yield, water, and nutrient uptake. Crop models are mathematical algorithms that capture the quantitative information of agronomy and physiology experiments in a way that can explain and predict crop growth and development. They can simulate many seasons, locations, treatments, and scenarios in a few minutes. Crop models contribute to agriculture in many ways. They help explore the dynamics between the atmosphere, the crop, and the soil, assist in crop agronomy, pest management, breeding, and natural resource management, and assess the impact of climate change.
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Bouman, B. A. M. "Crop modelling and remote sensing for yield prediction." Netherlands Journal of Agricultural Science 43, no. 2 (June 1, 1995): 143–61. http://dx.doi.org/10.18174/njas.v43i2.573.
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Methods for the application of crop growth models, remote sensing and their integrative use for yield forecasting and prediction are presented. First, the general principles of crop growth models are explained. When crop simulation models are used on regional scales, uncertainty and spatial variation in model parameters can result in broad bands of simulated yield. Remote sensing can be used to reduce some of this uncertainty. With optical remote sensing, standard relations between the Weighted Difference Vegetation Index and fraction ground cover and LAI were established for a number of crops. The radar backscatter of agricultural crops was found to be largely affected by canopy structure, and, for most crops, no consistent relationships with crop growth indicators were established. Two approaches are described to integrate remote sensing data with crop growth models. In the first one, measures of light interception (ground cover, LAI) estimated from optical remote sensing are used as forcing function in the models. In the second method, crop growth models are extended with remote sensing sub-models to simulate time-series of optical and radar remote sensing signals. These simulated signals are compared to measured signals, and the crop growth model is re-calibrated to match simulated with measured remote sensing data. The developed methods resulted in increased accuracy in the simulation of crop growth and yield of wheat and sugar beet in a number of case-studies.
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van Walsum, P. E. V., and I. Supit. "Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios." Hydrology and Earth System Sciences 16, no. 6 (June 1, 2012): 1577–93. http://dx.doi.org/10.5194/hess-16-1577-2012.
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Abstract. Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the "static" model, and one with endogenous crop growth simulation, the "dynamic" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.
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Kleemola, Jouko, and Tuomo Karvonen. "Modelling growth and nitrogen balance of barley under ambient and future conditions." Agricultural and Food Science 5, no. 3 (May 1, 1996): 299–310. http://dx.doi.org/10.23986/afsci.72748.
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According to current scenarios, atmospheric CO2 -concentration ([CO2]) and average air temperature will rise in the future. The predicted longer growing season in Finland would imply that more productive cultivars and even new crop species could be grown. Moreover, higher [CO2] is also likely to increase dry matter production of crops. This study analyzed the growth of spring barley (Hordeum vulgare L.) under ambient and suggested future conditions, and its response to N fertilization. Model simulations of soil temperature and of snow accumulation and melting were also studied. The calibration and validation results showed that the model performed well in simulating snow dynamics, soil temperature, the growth of barley, and the response of crop growth to N fertilization under present conditions. According to the simulation runs, if a cultivar was adapted to the length of the growing period, the increase in dry matter production was 23% in a low estimate scenario of climate change, and 56% in a high estimate scenario under a high level of nitrogen fertilization. The simulation study showed that the shoot dry weight increased by 43%, on average, under high N fertilization (150-200 kg N/ha), but by less (20%) under a low level of N (25-50 kg N/ha) when the conditions under a central scenario for the year 2050 were compared with the present ones.
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Racsko, P., and M. Semenov. "Analysis of mathematical principles in crop growth simulation models." Ecological Modelling 47, no. 3-4 (September 1989): 291–302. http://dx.doi.org/10.1016/0304-3800(89)90007-0.
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Chander, Subhash, Naveen Kalra, and P. K. Aggarwal. "Development and Application of Crop Growth Simulation Modelling in Pest Management." Outlook on Agriculture 36, no. 1 (March 2007): 63–70. http://dx.doi.org/10.5367/000000007780223704.
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Zhang, Yu, Changsheng Li, Xiuji Zhou, and Berrien Moore. "A simulation model linking crop growth and soil biogeochemistry for sustainable agriculture." Ecological Modelling 151, no. 1 (May 2002): 75–108. http://dx.doi.org/10.1016/s0304-3800(01)00527-0.
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Probert, M. E., P. S. Carberry, R. L. McCown, and J. E. Turpin. "Simulation of legume-cereal systems using APSIM." Australian Journal of Agricultural Research 49, no. 3 (1998): 317. http://dx.doi.org/10.1071/a97070.
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A major issue for the sustainability of cropping systems is the maintenance of soil fertility and especially the supply of nitrogen to cereal crops. Choice of appropriate management strategies, including the role of legumes, is problematic, especially where climatic variation is large. Simulation models provide the means of extrapolation from the site- and season-specific bounds of experimental data to permit scenario analyses that can explore alternative management options. This paper is a status report on the capabilities of the APSIM modelling framework to simulate legume-cereal systems. APSIM deals with water and nitrogen constraints to crop growth and is well suited to the task of modelling whole systems involving crop rotations. The components that are not yet fully developed are modules for growing the legume crops and coupling these with the module describing the dynamics of soil organic matter to obtain sensible predictions of nitrogen supply to subsequent crops. Evidence is provided that those parts of the system that can be represented by current APSIM modules are predicted satisfactorily. The closest approach to a whole system that has been simulated to date is grass or legume (Stylosanthes hamata cv. Verano) leys followed by crops of maize or sorghum grown in experiments at Katherine, NT. Predictions of the yields of the leys and the cereal crops, especially the benefit from the legume leys to a second crop, were sufficiently close to measured yields to suggest that there are good prospects for developing useful models of other systems involving legumes and cereals. A simulation scenario exploring a chickpea-wheat system demonstrates how models can be used to analyse both productivity and sustainability aspects of the system.
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Nguyen, Van Cuong, Seungtaek Jeong, Jonghan Ko, Chi Tim Ng, and Jongmin Yeom. "Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity." Remote Sensing 11, no. 18 (September 13, 2019): 2131. http://dx.doi.org/10.3390/rs11182131.
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Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions.
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Li, F. Y., P. D. Jamieson, P. R. Johnstone, and A. J. Pearson. "Mechanisms of nitrogen limitation affecting maize growth: a comparison of different modelling hypotheses." Crop and Pasture Science 60, no. 8 (2009): 738. http://dx.doi.org/10.1071/cp08412.
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Two hypothetical mechanisms exist for quantifying crop nitrogen (N) demand and N-deficit effects on crop growth. The Critical N mechanism uses a critical N concentration, while the Leaf N mechanism distinguishes active N in leaves from the N elsewhere in shoots. These two mechanisms were implemented in parallel in a maize model (Amaize) to evaluate their adequacy in predicting crop growth and development. In the Leaf N mechanism, two approaches for quantifying N-deficit effects, by reducing green leaf area (GAI) or diluting specific leaf nitrogen (SLN), were also examined. The model-predicted plant biomass, grain yield, and N uptake were compared with measurements from 47 maize crops grown on 16 sites receiving different N fertiliser treatments. The results showed that model-predicted plant biomass, grain yield and N uptake were insensitive to the approaches used for quantifying N-deficit effects in the Leaf N mechanism. The model-predicted plant biomass, grain yield and N uptake using either N approach were significantly related to measurements (P < 0.01) but had considerable deviations (r2 = 0.66–0.69 for biomass, 0.50–0.54 for grain yield: 0.17–0.33 for N uptake). The linear fits of the predicted against measured values showed no significant difference (P > 0.1) among the three N approaches, with the Leaf N mechanism predicting smaller deviation than the Critical N mechanism. However, the Critical N mechanism was better in simulating plant growth dynamics in early plant growth stages. The Leaf N mechanism distinguished functional from structural N pools in plants, having a sound physiological base. The simulation using the Leaf N mechanism with both SLN dilution and GAI reduction for quantifying N-deficit effects was the best in predicting crop growth and yield.
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KO, J., G. PICCINNI, W. GUO, and E. STEGLICH. "Parameterization of EPIC crop model for simulation of cotton growth in South Texas." Journal of Agricultural Science 147, no. 2 (January 15, 2009): 169–78. http://dx.doi.org/10.1017/s0021859608008356.
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SUMMARYParameterization in crop simulation modelling is a general procedure to calibrate a crop model to explore the best fit for a certain regional environment of interest. The parameters of radiation use efficiency (RUE) and light interception coefficient (k) of cotton (Gossypium hirsutum) for different cultivars were estimated under various irrigation conditions in South Texas in 2006 and 2007. A calibration procedure was then performed for determination of RUE using the environmental policy impact calculator (EPIC) crop model (Williams et al.1984). This was carried out using data sets obtained separately from the data for parameter estimation. The estimates of k and RUE were 0·63 and 2·5 g/MJ, respectively, which were determined based on the field experiment and variation of simulated lint yield. When the parameters were used with EPIC to simulate the variability in lint yields, a correlation coefficient of 0·86 and root mean square error (RMSE) of 0·22 t/ha were obtained, presenting no significant differences (paired t-test: P=0·282) between simulation and measurement. The results demonstrate that an appropriate estimate of the model parameters including RUE is essential in order to make crop models reproduce field conditions properly in simulating crop growth, yield and other variables.
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Bloomberg, W. J. "MODELLING APPLIED TO THE ANALYSIS OF CROP–PEST-MANAGEMENT INTERACTIONS." Memoirs of the Entomological Society of Canada 120, S143 (1988): 29–37. http://dx.doi.org/10.4039/entm120143029-1.
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AbstractFusarium attacking the roots of Douglas-firs in a forestry nursery was the focus of a computer simulation model that was tested against several years of nursery disease observations. Soil temperature, seedling growth, disease spread, and inoculum density and distribution were studied and their effects on measures to control Fusarium root rot were simulated. Interactions of these factors in space and time were examined from seedling germination to end of the growing season. The results from the model indicated that low soil temperature at the beginning of the growing season, rapid seedling growth, and reduction of disease inoculum levels in the upper soil horizons were the optimum prescription for disease reduction.
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van Walsum, P. E. V. "Influence of feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios." Hydrology and Earth System Sciences Discussions 8, no. 6 (November 17, 2011): 10151–93. http://dx.doi.org/10.5194/hessd-8-10151-2011.
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Abstract. Climate change impact modelling of hydrologic responses is hampered by climate-dependent model parameterizations. Reducing this dependency was one of the goals of extending the regional hydrologic modelling system SIMGRO with a two-way coupling to the crop growth simulation model WOFOST. The coupling includes feedbacks to the hydrologic model in terms of the root zone depth, soil cover, leaf area index, interception storage capacity, crop height and crop factor. For investigating whether such feedbacks lead to significantly different simulation results, two versions of the model coupling were set up for a test region: one with exogenous vegetation parameters, the "static" model, and one with endogenous simulation of the crop growth, the "dynamic" model WOFOST. The used parameterization methods of the static/dynamic vegetation models ensure that for the current climate the simulated long-term average of the actual evapotranspiration is the same for both models. Simulations were made for two climate scenarios. Owing to the higher temperatures in combination with a higher CO2-concentration of the atmosphere, a forward time shift of the crop development is simulated in the dynamic model; the used arable land crop, potatoes, also shows a shortening of the growing season. For this crop, a significant reduction of the potential transpiration is simulated compared to the static model, in the example by 15% in a warm, dry year. In consequence, the simulated crop water stress (the unit minus the relative transpiration) is lower when the dynamic model is used; also the simulated increase of crop water stress due to climate change is lower; in the example, the simulated increase is 15 percentage points less (of 55) than when a static model is used. The static/dynamic models also simulate different absolute values of the transpiration. The difference is most pronounced for potatoes at locations with ample moisture supply; this supply can either come from storage release of a good soil or from capillary rise. With good supply of moisture, the dynamic model simulates up to 10% less actual evapotranspiration than the static one in the example. This can lead to cases where the dynamic model predicts a slight increase of the recharge in a climate scenario, where the static model predicts a decrease. The use of a dynamic model also affects the simulated demand for surface water from external sources; especially the timing is affected. The proposed modelling approach uses postulated relationships that require validation with controlled field trials. In the Netherlands there is a lack of experimental facilities for performing such validations.
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Shawon, Ashifur Rahman, Jonghan Ko, Bokeun Ha, Seungtaek Jeong, Dong Kwan Kim, and Han-Yong Kim. "Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield." Remote Sensing 12, no. 3 (January 28, 2020): 410. http://dx.doi.org/10.3390/rs12030410.
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A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index (LAI), above-ground dry mass (AGDM), and yield, utilising the proximally sensed data integration into the modelling procedure. Field experiments were performed at two sites, one in 2017 and 2018 at Chonnam National University, Gwangju, and the other in 2017 at Jonnam Agricultural Research and Extension Services in Naju, Chonnam province, South Korea. The estimated parameters of radiation use efficiency, light extinction coefficient, and specific leaf area were 1.65 g MJ−1, 0.71, and 0.017 m2 g−1, respectively. Simulated LAI and AGDM values agreed with the measured values with significant model efficiencies in both calibration and validation, meaning that the proximal sensing data were effectively integrated into the crop model. The RSCM reproduced soybean yields in significant agreement with the measured yields in the model assessment. The study results demonstrate that the well-calibrated RSCM-soybean scheme can reproduce soybean growth and yield using simple input requirement and proximal sensing data. RSCM-soybean is easy to use and applicable to various soybean monitoring projects.
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Carberry, P. S., H. Meinke, P. L. Poulton, J. N. G. Hargreaves, A. J. Snell, and R. A. Sudmeyer. "Modelling crop growth and yield under the environmental changes induced by windbreaks. 2. Simulation of potential benefits at selected sites in Australia." Australian Journal of Experimental Agriculture 42, no. 6 (2002): 887. http://dx.doi.org/10.1071/ea02020.
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Recent reports in Australia and elsewhere have attributed enhanced crop yields to the presence of tree windbreaks on farms. One hypothesis for this observation is that, by reducing wind speed, windbreaks influence crop water and energy balances resulting in lower evaporative demand and increased yield. This paper is the second in a series aimed at developing and using crop and micrometeorological modelling capabilities to explore this hypothesis. Specifically, the objectives of this paper are to assist the interpretation of recent field experimentation on windbreak impacts and to quantify the potential benefits and the likelihood of windbreak effects on crop production through an economic analysis of crop yields predicted for the historical climate record at selected sites in Australia. The APSIM systems model was specified to simulate crop growth under the environmental changes induced by windbreaks and subsequently used to simulate the potential benefits on crop production at 2 actual windbreak sites and 17 hypothetical sites around Australia. With the actual windbreak sites, APSIM closely simulated measured crop growth and yield in open-field conditions. However, neither site demonstrated measurable windbreak impacts and APSIM simulations confirmed that such effects would have been either non-existent or masked by experimental variability in the years under study. For each year of the long-term climate record at 17 sites, APSIM simulated yields of relevant crops for transects behind hypothetical windbreaks that provided protection against all wind. When wind protection from all directions is assumed, average simulated yield increases at 5 H (height of windbreak) ranged from 0.2% for maize at Atherton to 24.6% for wheat grown at Dalby, resulting in gross margin changes of �$14.79/ha.crop and $24.13/ha.crop, respectively, for a 10 m high windbreak and 100 ha paddock and assuming a 20% yield loss due to tree competition in the 1.0�3.5 H section. Averaged across all sites and crops, the simulations predicted a yield advantage of 8.6% at 5 H for protection from wind in any direction, resulting in an average gross margin loss of �$0.60/ha.crop. At the 8 sites with available data for wind direction, and assuming protection only from wind originating within a 90� arc perpendicular to a hypothetical windbreak which was optimally orientated at each site, average simulated yield increases at 5 H ranged from 1.0% for wheat at Orange to 8.6% for wheat grown at Geraldton. For a 10 m high windbreak, 100 ha paddock and an assumed 20% yield loss in the 1.0�3.5 H section, the average result across all sites and crops was a 4.7% yield advantage at 5 H and an average gross margin loss of �$2.49/ha.crop. In conclusion, APSIM simulation and economic analyses indicated that yield benefits from microclimate changes can at least partly offset the opportunity costs of positioning tree windbreaks on farms.
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Pembleton, K. G., R. P. Rawnsley, J. L. Jacobs, F. J. Mickan, G. N. O'Brien, B. R. Cullen, and T. Ramilan. "Evaluating the accuracy of the Agricultural Production Systems Simulator (APSIM) simulating growth, development, and herbage nutritive characteristics of forage crops grown in the south-eastern dairy regions of Australia." Crop and Pasture Science 64, no. 2 (2013): 147. http://dx.doi.org/10.1071/cp12372.
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Pasture-based dairy farms are a complex system involving interactions between soils, pastures, forage crops, and livestock as well as the economic and social aspects of the business. Consequently, biophysical and farm systems models are becoming important tools to study pasture-based dairy systems. However, there is currently a paucity of modelling tools available for the simulation of one key component of the system—forage crops. This study evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) in simulating dry matter (DM) yield, phenology, and herbage nutritive characteristics of forage crops grown in the dairy regions of south-eastern Australia. Simulation results were compared with data for forage wheat (Triticum aestivum L.), oats (Avena sativa L.), forage rape (Brassica napus L.), forage sorghum (Sorghum bicolor (L.) Moench), and maize (Zea mays L.) collated from previous field research and demonstration activities undertaken across the dairy regions of south-eastern Australia. This study showed that APSIM adequately predicted the DM yield of forage crops, as evidenced by the range of values for the coefficient of determination (0.58–0.95), correlation coefficient (0.76–0.94), and bias correction factor (0.97–1.00). Crop phenology for maize, forage wheat, and oats was predicted with similar accuracy to forage crop DM yield, whereas the phenology of forage rape and forage sorghum was poorly predicted (R2 values 0.38 and 0.80, correlation coefficient 0.62 and –0.90, and bias correction factors 0.67 and 0.28, respectively). Herbage nutritive characteristics for all crop species were poorly predicted. While the selection of a model to explore an aspect of agricultural production will depend on the specific problem being addressed, the performance of APSIM in simulating forage crop DM yield and, in many cases, crop phenology, coupled with its ease of use, open access, and science-based mechanistic methods of simulating agricultural and crop processes, makes it an ideal model for exploring the influence of management and environment on forage crops grown on dairy farms in south-eastern Australia. Potential future model developments and improvements are discussed in the context of the results of this validation analysis.
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Wang, Long, Lingli Li, and Qingping Zhou. "Established Digital Model of Fruit Body Growth of Agrocybe cylindracea Based on Network Programming." Discrete Dynamics in Nature and Society 2021 (July 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/6643273.
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Agricultural information technology is an emerging technology based on the cross-fertilization of information technology and agricultural science, which has caused the rapid development of digital agriculture and smart agriculture. Crop growth models, as one of their core components, can dynamically simulate the crop growth and development process and its relationship with climate factors, soil properties, and management techniques, thus effectively overcoming the strong spatial and temporal limitations in traditional agricultural production management research and providing quantitative tools for early warning and effect assessment of crop productivity prediction under different conditions. This study focuses on the general technical approach, the latest research progress, and future development thinking developed by the authors in the construction and application of the tea mushroom growth model. Based on the fitting method of the data model curve of the Internet laboratory, this study is proposed to use network programming technology to digitally fit the growth curve of cap diameter and stalk diameter of the fruiting body in the process of Agrocybe cylindracea growth. The fitting system adopted the development mode of PHP + MYSQL and took the MYSQL database as the core, which made the curve fitting of the final experimental data fast, convenient, and more intelligent. This study laid a preliminary foundation for the effective establishment and improvement of laboratory Agrocybe cylindracea, fruiting body growth data statistics and analysis, and graphical output network sharing.
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GRANT, R. F., J. R. FREDERICK, J. D. HESKETH, and M. G. HUCK. "SIMULATION OF GROWTH AND MORPHOLOGICAL DEVELOPMENT OF MAIZE UNDER CONTRASTING WATER REGIMES." Canadian Journal of Plant Science 69, no. 2 (April 1, 1989): 401–18. http://dx.doi.org/10.4141/cjps89-052.
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The use of crop growth models for resource management decisions such as weed control will require the detailed simulation of plant structures and functions in order to determine crop response to resources. A crop growth model was constructed on the computing facility at the National Center for Supercomputing Applications which was intended to simulate the effect of changing water status on plant growth processes. The model was tested against field data collected during an experiment in which the morphology of a maize crop growing under an imposed water deficit over a shallow water table was compared to that of an irrigated control treatment. The effects of this deficit on soil and canopy water status, leaf tip appearance, and on the distribution of growth with node number were compared for the simulated and recorded data. The use of simple equations describing the partitioning of growth to successive nodes enabled reasonably accurate estimates to be made of the distribution of leaf, sheath and internode mass with node number during both deficit and irrigated treatments. Consequently, realistic estimates of the vertical distribution of leaf area could be made for use in subsequent studies of inter-specific competition for irradiance interception.Key words: Simulation modelling, water stress, leaf area, canopy, maize growth
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Robertson, M. J., G. J. Rebetzke, and R. M. Norton. "Assessing the place and role of crop simulation modelling in Australia." Crop and Pasture Science 66, no. 9 (2015): 877. http://dx.doi.org/10.1071/cp14361.
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Computer-based crop simulation models (CSMs) are well entrenched as tools for a wide variety of research, development and extension applications. Despite this, critics remain and there are perceptions that CSMs have not contributed to impacts on-farm or in the research community, particularly with plant breeding. This study reviewed the literature, interviewed 45 stakeholders (modellers, institutional representatives and clients of modelling), and analysed the industry-funded project portfolio to ascertain the current state of use of CSMs in the grains industry in Australia, including scientific progress, impacts and development needs. We found that CSMs in Australia are widely used, with ~100 active and independent users, ~15 model developers, and at any one time ~10 postgraduate students, chiefly across six public research institutions. The dominant platform used is APSIM (Agricultural Production Systems Simulator). It is widely used in the agronomic domain. Several cases were documented where CSM use had a demonstrable impact on farm and research practice. The updating of both plant and soil process routines in the models has slowed and even stalled in recent years, and scientific limitations to future use were identified: the soil–plant nitrogen cycle, root growth and function, soil surface water and residue dynamics, impact of temperature extremes on plant function, and up-to-date cultivar parameter sets. There was a widespread appreciation of and optimism for the potential of CSMs to assist with plant-breeding activities, such as environmental characterisation, trait assessment, and design of plant-breeding programs. However, we found little evidence of models or model output being used by plant breeders in Australia, despite significant impacts that have emerged recently in larger international breeding programs. Closer cooperation between geneticists, physiologists and breeders will allow gene-based approaches to characterise and parameterise cultivars in CSMs, demonstrated by recent progress with phenology in wheat. This will give models the ability to deal with a wider range of potential genotype × environment × management scenarios.
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Sands, Peter. "RESOURCE MODELLING: ITS NATURE AND USE." Memoirs of the Entomological Society of Canada 120, S143 (1988): 5–10. http://dx.doi.org/10.4039/entm120143005-1.
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AbstractTo improve management of resources, such as agricultural crops or forests, scientists attempt to analyse the resource systems and to predict the consequences or outcomes of interventions. They construct models of interactions of components of the systems, drawing on knowledge and experience. In agriculture, five types of models have become common — empirical, crop–weather, crop–growth, crop–system, and crop–process. The models aim mainly to predict crop yields when a series of actions are taken. They differ markedly in complexity, from a simple regression to a series of mechanistic relations aimed at simulating the crop system. The uses to which a model is to be put, and by whom it will be used, are major determinants of the nature of the model so modellers must work with the potential users. In fact, modelling is an exercise in human relations as much as in science. All things being equal, the simpler the model is that meets the objectives of the users, the better are the chances of its being used.
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Birch, Colin J., David Thornby, Steve Adkins, Bruno Andrieu, and Jim Hanan. "Architectural modelling of maize under water stress." Australian Journal of Experimental Agriculture 48, no. 3 (2008): 335. http://dx.doi.org/10.1071/ea06105.
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Two field experiments using maize (Pioneer 31H50) and three watering regimes [(i) irrigated for the whole crop cycle, until anthesis, (ii) not at all (experiment 1) and (iii) fully irrigated and rain grown for the whole crop cycle (experiment 2)] were conducted at Gatton, Australia, during the 2003–04 season. Data on crop ontogeny, leaf, sheath and internode lengths and leaf width, and senescence were collected at 1- to 3-day intervals. A glasshouse experiment during 2003 quantified the responses of leaf shape and leaf presentation to various levels of water stress. Data from experiment 1 were used to modify and parameterise an architectural model of maize (ADEL-Maize) to incorporate the impact of water stress on maize canopy characteristics. The modified model produced accurate fitted values for experiment 1 for final leaf area and plant height, but values during development for leaf area were lower than observed data. Crop duration was reasonably well fitted and differences between the fully irrigated and rain-grown crops were accurately predicted. Final representations of maize crop canopies were realistic. Possible explanations for low values of leaf area are provided. The model requires further development using data from the glasshouse study and before being validated using data from experiment 2 and other independent data. It will then be used to extend functionality in architectural models of maize. With further research and development, the model should be particularly useful in examining the response of maize production to water stress including improved prediction of total biomass and grain yield. This will facilitate improved simulation of plant growth and development processes allowing investigation of genotype by environment interactions under conditions of suboptimal water supply.
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Kropff, M. J., L. Bastiaans, and J. Goudriaan. "Implications of improvements in modeling canopy photosynthesis in SUCROS (a simple and universal crop growth simulator)." Netherlands Journal of Agricultural Science 35, no. 2 (May 1, 1987): 192–94. http://dx.doi.org/10.18174/njas.v35i2.16747.
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The behaviour of an improved version of the crop growth simulation program SUCROS was compared with the original version. The improved model had a more mechanistic way of modelling canopy photosynthesis and gave higher readings at low LAI on clear days and in autumn and winter when days were short and sun elevations were low. At low assimilation rates the older version underestimated crop production of sugarbeet by 5-25%. (Abstract retrieved from CAB Abstracts by CABI’s permission)
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Li, Y., W. Kinzelbach, J. Zhou, G. D. Cheng, and X. Li. "Modelling irrigated maize with a combination of coupled-model simulation and ensemble forecasting, in the west of China." Hydrology and Earth System Sciences Discussions 8, no. 2 (April 18, 2011): 3841–81. http://dx.doi.org/10.5194/hessd-8-3841-2011.
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Abstract. The hydrologic model HYDRUS-1D and the crop growth model WOFOST were coupled to efficiently manage water resources in agriculture and improve the prediction of crop production through the accurate estimation of actual transpiration with the root water uptake method and a soil moisture profile computed with the Richards equation during crop growth. The results of the coupled model are validated by experimental studies of irrigated-maize done in the middle reaches of northwest China's Heihe River, a semi-arid to arid region. Good agreement was achieved between the simulated evapotranspiration, soil moisture and crop production and their respective field measurements made under maize crop. However, for regions without detailed observation, the results of the numerical simulation could be unreliable for policy and decision making owing to the uncertainty of model boundary conditions and parameters. So, we developed the method of combining model simulation and ensemble forecasting to analyse and predict the probability of crop production. In our studies, the uncertainty analysis was used to reveal the risk of facing a loss of crop production as irrigation decreases. The global sensitivity analysis was used to test the coupled model and further quantitatively analyse the impact of the uncertainty of coupled model parameters and environmental scenarios on crop production. This method could be used for estimation in regions with no or reduced data availability.
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Heinemann, Alexandre B., Gerrit Hoogenboom, and Bogdan Chojnicki. "The impact of potential errors in rainfall observation on the simulation of crop growth, development and yield." Ecological Modelling 157, no. 1 (November 2002): 1–21. http://dx.doi.org/10.1016/s0304-3800(02)00209-0.
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Muchow, R. C., and B. A. Keating. "Assessing irrigation requirements in the Ord Sugar Industry using a simulation modelling approach." Australian Journal of Experimental Agriculture 38, no. 4 (1998): 345. http://dx.doi.org/10.1071/ea98023.
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Summary. Sustainable irrigation guidelines that maximise profitability and minimise water losses and accession to the watertable are required for the new Ord Sugar Industry. In addition, knowledge on crop water requirements is needed to guide water allocation and costing policies for the expanding Ord Irrigation Area where sugarcane is likely to be a dominant crop. Field data indicating water requirements for sugar in the Ord Irrigation Area are few and this paper deploys a modelling approach to extrapolate from knowledge of water requirements in other parts of the world. The approach links long-term climatic data with soil water characteristics of the main soil type, with a cropping systems model, to develop indicative estimates of irrigation water requirement and yield consequences for different management options for sugarcane production in the Ord. Analyses of the growth of 12-month old ratoon crops were conducted using the APSIM–Sugarcane model with historical climatic data from 1960 to 1985 and either a deep (188 mm available water to 160 cm depth) or shallow (144 mm of water to 120 cm depth) Cununurra clay soil. Under maximum attainable growth conditions where crops were irrigated after half the soil water supply was depleted, average sucrose yield ranged from 26.7 to 29.0 t/ha, and the irrigation requirement (assuming 100% application efficiency) ranged from 22.7 to 23.8 ML/ha depending on ratooning date. Soil water holding capacity had a major effect on the number of irrigations and the interval between irrigation for a given irrigation schedule but little effect on yield or irrigation requirement. Varying the irrigation schedule by changing the level of soil water depletion before irrigation and thus the irrigation frequency, showed the tradeoff between yield and irrigation requirement with the most profitable irrigation schedule depending on the price of sucrose and the cost of irrigation relative to other costs. Most of the year-to-year variation in irrigation water requirement could be explained by the highly variable effectiveness in soil storage of rainfall which ranged from 44 to 93%. This study has provided insight and indicative estimates of the yield and irrigation requirements for different irrigation management options for use in the establishment of an Ord River sugar industry. These estimates will be further refined as field data become available.
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Li, Y., W. Kinzelbach, J. Zhou, G. D. Cheng, and X. Li. "Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China." Hydrology and Earth System Sciences 16, no. 5 (May 22, 2012): 1465–80. http://dx.doi.org/10.5194/hess-16-1465-2012.
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Abstract. The hydrologic model HYDRUS-1-D and the crop growth model WOFOST are coupled to efficiently manage water resources in agriculture and improve the prediction of crop production. The results of the coupled model are validated by experimental studies of irrigated-maize done in the middle reaches of northwest China's Heihe River, a semi-arid to arid region. Good agreement is achieved between the simulated evapotranspiration, soil moisture and crop production and their respective field measurements made under current maize irrigation and fertilization. Based on the calibrated model, the scenario analysis reveals that the most optimal amount of irrigation is 500–600 mm in this region. However, for regions without detailed observation, the results of the numerical simulation can be unreliable for irrigation decision making owing to the shortage of calibrated model boundary conditions and parameters. So, we develop a method of combining model ensemble simulations and uncertainty/sensitivity analysis to speculate the probability of crop production. In our studies, the uncertainty analysis is used to reveal the risk of facing a loss of crop production as irrigation decreases. The global sensitivity analysis is used to test the coupled model and further quantitatively analyse the impact of the uncertainty of coupled model parameters and environmental scenarios on crop production. This method can be used for estimation in regions with no or reduced data availability.
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Xie, Ziang, Jiying Kong, Min Tang, Zhenhai Luo, Duo Li, Rui Liu, Shaoyuan Feng, and Chao Zhang. "Modelling Winter Rapeseed (Brassica napus L.) Growth and Yield under Different Sowing Dates and Densities Using AquaCrop Model." Agronomy 13, no. 2 (January 27, 2023): 367. http://dx.doi.org/10.3390/agronomy13020367.
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The sowing date and density are considered to be the main factors affecting crop yield. The determination of the sowing date and sowing density, however, is fraught with uncertainty due to the influence of climatic conditions, topography, variety and other factors. Therefore, it is necessary to find a comprehensive consideration of these factors to guide the production of winter rapeseed. A reliable crop model could be a crucial tool to investigate the response of rapeseed growth to changes in the sowing date and density. At present, few studies related to rapeseed model simulation have been reported, especially in the comprehensive evaluation of the effects of sowing date and density factors on rapeseed development and production. This study aimed to evaluate the performance of the AquaCrop model for winter rapeseed development and yield simulation under various sowing dates and densities, and to optimize the sowing date and density for agricultural high-efficient production in the Jianghuai Plain. Two years of experiments were carried out in the rapeseed growing season in 2020 and 2021. The model parameters were fully calibrated and the simulation performances in different treatments of sowing dates and densities were evaluated. The results indicated that the capability of the AquaCrop model to interpret crop development for different sowing dates was superior to that of sowing densities. For rapeseed canopy development, the RMSE for three sowing dates and densities scenarios were 7–22% and 16–23%, respectively. The simulated biomass and grain yield for different sowing dates treatments (RMSE: 0.8–2.1 t·ha−1, Pe: 0–35.3%) were generally better than those of different densities treatments (RMSE: 0.7–3.9 t·ha−1, Pe: 8.2–90%). Compared with other sowing densities, higher overestimation errors of the biomass and yield were observed for the low-density treatment. Adequate agreement for crop evapotranspiration simulation was achieved, with an R2 of 0.79 and RMSE of 26 mm. Combining the simulation results and field data, the optimal sowing scheme for achieving a steadily high yield in the Jianghuai Plain of east China was determined to be sowing in October and a sowing density of 25.0–37.5 plant·m−2. The study demonstrates the great potential of the AquaCrop model to optimize rapeseed sowing patterns and provides a technical means guidance for the formulation of local winter rapeseed production.
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Wang, Enli, and Thomas Engel. "Simulation of growth, water and nitrogen uptake of a wheat crop using the SPASS model." Environmental Modelling & Software 17, no. 4 (January 2002): 387–402. http://dx.doi.org/10.1016/s1364-8152(02)00006-3.
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Jongschaap, Raymond E. E. "Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration." Ecological Modelling 200, no. 1-2 (January 2007): 89–98. http://dx.doi.org/10.1016/j.ecolmodel.2006.07.015.
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Meinke, H., P. S. Carberry, H. A. Cleugh, P. L. Poulton, and J. N. G. Hargreaves. "Modelling crop growth and yield under the environmental changes induced by windbreaks 1. Model development and validation." Australian Journal of Experimental Agriculture 42, no. 6 (2002): 875. http://dx.doi.org/10.1071/ea02019.
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Yield advantages of crops grown behind windbreaks have often been reported, but underlying principles responsible for such changes and their long-term consequences on crop productivity and hence farm income have rarely been quantified. Physiologically and physically sound simulation models could help to achieve this quantification. Hence, the APSIM systems model, which is based on physiological principles such as transpiration efficiency and radiation use efficiency (termed here APSIMTE), and the Soil Canopy Atmosphere Model (SCAM), which is based on the Penman–Monteith equation but includes a full surface energy balance, were employed in developing an approach to quantify such windbreak effects. This resulted in a modified APSIM version (APSIMEO), containing the original Penman equation and a calibration factor to account for crop- and site-specific differences, which were tested against field data and simulations from both the standard APSIMTE and SCAM models. The APSIMEO approach was tested against field data for wheat and mungbean grown in artificial enclosures in south-east Queensland and in south-east Western Australia. For these sheltered conditions, daily transpiration demand estimates from APSIMEO compared closely to SCAM. As the APSIMEO approach needed to be calibrated for individual crops and environments, average transpiration demand for open field conditions predicted by APSIMEO for a given site was adjusted to equal that obtained using APSIMTE by modifying a calibration parameter β. For wheat, a β-value of 1.0 resulted in best fits for Queensland, while for Western Australia a value of 0.85 was necessary. For mungbean a value of 0.92 resulted in the best fit (Qld). Biomass and yields simulated by APSIMTE and the calibration APSIMEO for wheat and mungbean grown in artificial enclosures were generally distributed around the 1:1 line, with R2 values ranging from 0.92 to 0.97. Finally, APSIMEO was run at 2 sites using long-term climate data to assess the likely year-to-year variability of windbreak effects on crop yields. Assuming a 70% reduction in wind speed as representing the maximum potential windbreak effect, the average yield improvement for the Queensland site was 13% for wheat and 3% for mungbean. For wheat at the WA site the average yield improvement from reduced wind speed was 5%. In any year, however, effects varied from negative, neutral to positive, highlighting the highly variable nature of the expression of windbreak effects. This study has shown how physical and biological modelling approaches can be combined to aid our understanding of systems processes. Both the environmental physics perspective and the biological perspective have shortcomings when issues that sit at the interface of both approaches need to be addressed. While the physical approach has clear advantages when investigating changes in physical parameters such as wind speed, vapour pressure deficit (VPD), temperature or the energy balance of the soil–plant–atmosphere continuum, it cannot deal with complex, biological systems adequately. Conversely, the crop physiological approach can handle such biological interactions in a scientific and robust way while certain atmospheric processes are not considered. The challenge was not to try and capture all these effects in 1 model, but rather to structure a modelling approach in a way that allowed for inclusion of such processes where necessary.
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Wegehenkel, Martin, and Wilfried Mirschel. "Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the Opus model—A case study." Ecological Modelling 190, no. 1-2 (January 2006): 116–32. http://dx.doi.org/10.1016/j.ecolmodel.2005.02.020.
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Hill, J. O., M. J. Robertson, B. C. Pengelly, A. M. Whitbread, and C. A. Hall. "Simulation modelling of lablab (Lablab purpureus) pastures in northern Australia." Australian Journal of Agricultural Research 57, no. 4 (2006): 389. http://dx.doi.org/10.1071/ar05263.
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The capability to simulate lablab production across a range of environments in northern Australia provides a useful tool for exploring agronomic and management options and risk assessments for the crop. This paper reports on the development and testing of a model of lablab (annual cultivar cv. Highworth and perennial cultivar cv. Endurance) growth, designed for use in the cropping systems simulator, APSIM (Agricultural Production Systems Simulator). Parameters describing leaf area expansion, biomass accumulation, and partitioning were derived from field experiments, and other essential parameters were assumed from similar tropically adapted legumes. The model was tested against data from experiments including different locations, cultivars, sowing dates, soil types, and water availability. Observed biomass ranged from 63 to 13055 kg dry matter/ha and was predicted by the model in an independent test with a root mean square deviation of 770 kg dry matter/ha. Observed time courses of biomass production for both the annual and perennial cultivars were reproduced well, as was the partitioning of biomass into leaves and stems. The effect of variable rainfall and temperature in northern Australia was analysed using the model and historical climate data. Yield reductions were found in the more inland and southern-most parts of the region where summer rainfall and/or temperatures are lower.
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Condon, Anthony G. "Drying times: plant traits to improve crop water use efficiency and yield." Journal of Experimental Botany 71, no. 7 (January 8, 2020): 2239–52. http://dx.doi.org/10.1093/jxb/eraa002.
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Abstract Crop water use efficiency (WUE) has come into sharp focus as population growth and climate change place increasing strain on the water used in cropping. Rainfed crops are being challenged by an upward trend in evaporative demand as average temperatures rise and, in many regions, there is an increased irregularity and a downward trend in rainfall. In addition, irrigated cropping faces declining water availability and increased competition from other users. Crop WUE would be improved by, first, ensuring that as much water as possible is actually transpired by the crop rather than being wasted. Deeper roots and greater early crop vigour are two traits that should help achieve this. Crop WUE would also be improved by achieving greater biomass per unit water transpired. A host of traits has been proposed to address this outcome. Restricting crop transpiration through lower stomatal conductance is assessed as having limited utility compared with traits that improve carbon gain, such as enhancements to photosynthetic biochemistry and responsiveness, or greater mesophyll conductance. Ultimately, the most useful outcomes for improved crop WUE will probably be achieved by combining traits to achieve synergistic benefit. The potential utility of trait combinations is supported by the results of crop simulation modelling.
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McGrath, S. R., J. M. Virgona, and M. A. Friend. "Modelling the effect on stocking rate and lamb production of allowing ewes to graze a dual-purpose wheat crop in southern New South Wales." Animal Production Science 54, no. 10 (2014): 1625. http://dx.doi.org/10.1071/an14251.
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Slow pasture growth rates during winter limit the potential gross margins from autumn and early winter lambing in southern New South Wales (NSW) by limiting stocking rates and/or increasing supplementary feed requirements. Dual-purpose crops can reduce the winter feed gap in mixed-farming systems by increasing the available feed in winter. The simulation software AusFarm was used to model a mixed-farming system at Wagga Wagga with Merino ewes joined to terminal sires and grazing lucerne-subterranean clover pasture over a 41-year period. A paddock of dual-purpose wheat was then added to the system, and ewes were allowed to graze the wheat crop when feed on offer reached 850 kg DM/ha and before GS31. Weaned lambs were sold after late August if lamb growth rates fell below 20 g/head.day, mean lamb weight reached 45 kg or production feeding of lambs was required. Lambing in June resulted in the highest median gross margin whether or not ewes were able to graze the wheat crop during winter. Grazing of a dual-purpose wheat crop resulted in greater proportional increases in gross margins as stocking rate was increased, increased lamb production and reduced supplementary feeding costs, and reduced interannual variability in gross margin returns.
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SOLER, C. M. T., N. MAMAN, X. ZHANG, S. C. MASON, and G. HOOGENBOOM. "Determining optimum planting dates for pearl millet for two contrasting environments using a modelling approach." Journal of Agricultural Science 146, no. 4 (January 14, 2008): 445–59. http://dx.doi.org/10.1017/s0021859607007617.
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SUMMARYPearl millet [Pennisetum glaucum (L) R. Br.] is an important cereal crop in Niger, West Africa and a potential crop for the United States of America (USA). Only a few studies have been conducted in either country to identify the optimum planting dates for high and stable yields, in part because planting date experiments are resource-intensive. Crop simulation models can be an alternative research tool for determining optimum planting dates and other management practices. The objectives of the present study were to evaluate the performance of the Cropping System Simulation Model (CSM)–CERES-Millet model for two contrasting environments, including Mead, Nebraska, USA and Kollo, Niger, West Africa and to use the model for determining the optimum planting dates for these two environments. Field experiments were conducted in both environments to study the impact of nitrogen fertilizer on grain yield of three varieties in Kollo and three hybrids in Mead and their associated growth and development characteristics. The CSM–CERES-Millet model was able to accurately simulate growth, development and yield for millet grown in these two contrasting environments and under different management practices that included several genotypes and different nitrogen fertilizer application rates. For Kollo, the optimum planting date to obtain the maximum yield was between 13 and 23 May for variety Heini Kirei, while for the other varieties the planting dates were between 23 May and 2 June. For Mead, the planting date analysis showed that the highest simulated yield was obtained, on average, between 19 and 29 June for hybrid 59022A×89-083 and 1361M×6Rm. Further studies should focus on evaluation and application of the millet model for other agroclimatic regions where pearl millet is an important crop.
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Pedersen, Anders, Bjørn M. Petersen, Jørgen Eriksen, Søren Hansen, and Lars S. Jensen. "A model simulation analysis of soil nitrate concentrations—Does soil organic matter pool structure or catch crop growth parameters matter most?" Ecological Modelling 205, no. 1-2 (July 2007): 209–20. http://dx.doi.org/10.1016/j.ecolmodel.2007.02.016.
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Silvestro, Paolo Cosmo, Raffaele Casa, Jan Hanuš, Benjamin Koetz, Uwe Rascher, Dirk Schuettemeyer, Bastian Siegmann, Drazen Skokovic, José Sobrino, and Marin Tudoroiu. "Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations." Remote Sensing 13, no. 11 (May 29, 2021): 2138. http://dx.doi.org/10.3390/rs13112138.
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The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.
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Dimes, JP, RL McCown, and PG Saffigna. "Nitrogen supply to no-tillage crops, as influenced by mulch type, soil type and season, following pasture leys in the semi-arid tropics." Australian Journal of Experimental Agriculture 36, no. 8 (1996): 937. http://dx.doi.org/10.1071/ea9960937.
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Past cropping research in the semi-arid tropics of northern Australia has shown that in this climate and on the predominantly sesquioxidic soils, recovery of fertiliser nitrogen (N) by crops is often low. Conceptually, no-tillage, legume ley farming offers features for coping better with the constraints of climate, soil and high fertiliser transport costs to this remote region. This paper summarises the N cycle in a system in which pastures provide N for successive crops, and mulch at the time of crop establishment is provided by the killing of new pasture growth. The aim was further to provide a sound foundation for managing N supply in relation to demand in a climate that causes high variation and uncertainty for pasture N2 fixation and sequestering, the amount of early season re-growth (mulch), rate of mulch decomposition, nitrate leaching losses, and crop growth and N demand. The research approach combined field studies with simulation modelling. A series of field studies that included bare fallow and grass and legume pasture leys on clay loam and sandy loam soils, were conducted at Katherine over 4 wet seasons to study subsequent mineralisation of N. Experimental results were used to test the performance of a simulation model for predicting the observed variations consequent upon the various management options. Experimental results showed that the carbon (C) : N ratio of the residue and soil texture were important factors in determining N mineralisation, immobifisation, and nitrate leaching following chemical kill of pasture leys. However, the greatest variation was between seasons. A modified version of the CERES-Maize N model was able to simulate the accumulation of nitrate following a bare fallow and following pasture leys with high levels (above and below ground) of freshly killed residues with widely differing C:N ratio, the downward movement of nitrate-N in the soil and the interaction of these processes with seasonal rainfall. Despite a capability for simulation of the soil N dynamics in a cropping phase following pasture leys, ex~erimental results indicated how nitrate distribution following leys is influenced by pasture growth during the ley, and how this varied greatly with season and soil texture. The simulation capability reported here has been incorporated elsewhere into the development of a full system model, embracing both the ley phase and the crop phase.
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LAURILA, H. "Simulation of spring wheat responses to elevated CO2 and temperature by using CERES-wheat crop model." Agricultural and Food Science 10, no. 3 (January 3, 2001): 175–96. http://dx.doi.org/10.23986/afsci.5692.
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The CERES-wheat crop simulation model was used to estimate the changes in phenological development and yield production of spring wheat (Triticum aestivum L., cv. Polkka) under different temperature and CO2 growing conditions. The effects of elevated temperature (3-4°C) and CO2 concentration (700 ppm) as expected for Finland in 2100 were simulated. The model was calibrated for long-day growing conditions in Finland. The CERES-wheat genetic coefficients for cv. Polkka were calibrated by using the MTT Agrifood Research Finland (MTT) official variety trial data (1985-1990). Crop phenological development and yield measurements from open-top chamber experiments with ambient and elevated temperature and CO2 treatments were used to validate the model. Simulated mean grain yield under ambient temperature and CO2 conditions was 6.16 t ha-1 for potential growth (4.49 t ha-1 non-potential) and 5.47 t ha-1 for the observed average yield (1992-1994) in ambient open-top chamber conditions. The simulated potential grain yield increased under elevated CO2 (700 ppm) to 142% (167% non-potential) from the simulated reference yield (100%, ambient temperature and CO2 350 ppm). Simulations for current sowing date and elevated temperature (3°C) indicate accelerated anthesis and full maturity. According to the model estimations, potential yield decreased on average to 80.4% (76.8% non-potential) due to temperature increase from the simulated reference. When modelling the concurrent elevated temperature and CO2 interaction, the increase in grain yield due to elevated CO2 was reduced by the elevated temperature. The combined CO2 and temperature effect increased the grain yield to 106% for potential growth (122% non-potential) compared to the reference. Simulating the effects of earlier sowing, the potential grain yield increased under elevated temperature and CO2 conditions to 178% (15 days earlier sowing from 15 May, 700 ppm CO2, 3°C) from the reference. Simulation results suggest that earlier sowing will substantially increase grain yields under elevated CO2 growing conditions with genotypes currently cultivated in Finland, and will mitigate the decrease due to elevated temperature. A longer growing period due to climate change will potentially enable cultivation of new cultivars adapted to a longer growing period. Finally, adaptation strategies for the crop production under elevated temperature and CO2 growing conditions are presented.;
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Nehbandani, Alireza, Afshin Soltani, Faranak Nourbakhsh, and Amir Dadrasi. "Estimating crop model parameters for simulating soybean production in Iran conditions." OCL 27 (2020): 58. http://dx.doi.org/10.1051/ocl/2020057.
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Crop modelling has the potential to contribute to food security. In this study, to provide a simple model for estimating the soybean potential yield and phenological stages in Iran, a simulation model (SSM_iCrop2) was parameterized and tested. This model estimates the soybean phenological stages and potential yield based on the weather data (minimum and maximum temperature, solar radiation and rainfall) using the phenological models such as leaf area development, mass production and partitioning and soil water balance. Regarding the model parametrization, the two maturities groups of 3 and 5 with the temperature unit of 2000 and 2400 growth degrees day (GDD) were chosen. The model evaluation results indicated that the soybean yield ranged between 1.9 and 4.8 with the average of 3.5 t.ha−1, while the range of simulated yield changes between 1.8 and 4.7 with the average of 3.7 t.ha−1. Comparing the observed yield to the simulated yield, values of r, CV and RMSE were obtained 0.84, 13%, 0.5 t.ha−1 which indicates the high accuracy of the model. All of these results indicated that the estimated model parameters are high accuracy for use in the simulation of soybean yield at the country level.
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Perera, Ruchika S., Brendan R. Cullen, and Richard J. Eckard. "Using Leaf Temperature to Improve Simulation of Heat and Drought Stresses in a Biophysical Model." Plants 9, no. 1 (December 19, 2019): 8. http://dx.doi.org/10.3390/plants9010008.
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Despite evidence that leaf temperatures can differ by several degrees from the air, crop simulation models are generally parameterised with air temperatures. Leaf energy budget is a process-based approach that can be used to link climate and physiological processes of plants, but this approach has rarely been used in crop modelling studies. In this study, a controlled environment experiment was used to validate the use of the leaf energy budget approach to calculate leaf temperature for perennial pasture species, and a modelling approach was developed utilising leaf temperature instead of air temperature to achieve a better representation of heat stress impacts on pasture growth in a biophysical model. The controlled environment experiment assessed the impact of two combined seven-day heat (control = 25/15 °C, day/night, moderate = 30/20 °C, day/night, and severe = 35/25 °C, day/night) and drought stresses (with seven-day recovery period between stress periods) on perennial ryegrass (Lolium perenne L.), cocksfoot (Dactylis glomerata L.), tall fescue (Festuca arundinacea Schreb.) and chicory (Cichorium intybus L.). The leaf temperature of each species was modelled by using leaf energy budget equation and validated with measured data. All species showed limited homeothermy with the slope of 0.88 (P < 0.05) suggesting that pasture plants can buffer temperature variations in their growing environment. The DairyMod biophysical model was used to simulate photosynthesis during each treatment, using both air and leaf temperatures, and the patterns were compared with measured data using a response ratio (effect size compared to the well-watered control). The effect size of moderate heat and well-watered treatment was very similar to the measured values (~0.65) when simulated using T leaf, while T air overestimated the consecutive heat stress impacts (0.4 and 0). These results were used to test the heat stress recovery function (Tsum) of perennial ryegrass in DairyMod, finding that recovery after heat stress was well reproduced when parameterized with T sum = 20, while T sum = 50 simulated a long lag phase. Long term pasture growth rate simulations under irrigated conditions in south eastern Australia using leaf temperatures predicted 6–34% and 14–126% higher pasture growth rates, respectively at Ellinbank and Dookie, during late spring and summer months compared to the simulations using air temperatures. This study demonstrated that the simulation of consecutive heat and/or drought stress impacts on pasture production, using DairyMod, can be improved by using leaf temperatures instead of air temperature.
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ORLANDO, F., M. MANCINI, R. MOTHA, J. J. QU, S. ORLANDINI, and A. DALLA MARTA. "Modelling durum wheat (Triticum turgidum L. var. durum) grain protein concentration." Journal of Agricultural Science 155, no. 6 (December 14, 2016): 930–38. http://dx.doi.org/10.1017/s0021859616001003.
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SUMMARYThe goal of the present study was to improve the CERES-wheat model simulation of grain protein concentration (GPC) for winter durum wheat and to use the model as a basis for the development of a GPC Simplified Forecasting Index (SFIpro). The performances of CERES-wheat, which is one of the most widespread crop simulation models, with (i) its standard GPC routine and (ii) a novel equation developed to improve the model GPC simulation for durum wheat, were assessed through comparison with field data. Subsequently, CERES-wheat was run for a 56-year period in order to identify the most important status and forcing variables affecting GPC simulation. The number of dry days during the early growth stages and the leaf area index (LAI; green leaf area per unit ground surface area) at heading stage (LAI5) were identified as the main variables positively correlated with CERES-wheat predicted GPC, and so included in the SFIpro. At validation against observed data SFIpro was found to perform differently on the basis of observed plant LAI. In fact, SFIpro was able to forecast GPC variability for intermediate values of LAI5 ranging from 1 to 2, while it totally failed when LAI5 was outside this range (LAI5 < 1 or LAI5 > 2). The results suggest that the relationship between LAI and GPC is not linear and that the model assumptions for GPC simulation in CERES-wheat are only partially confirmed, being valid for an intermediate range of LAI.
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Makurira, H., H. H. G. Savenije, and S. Uhlenbrook. "Modelling field scale water partitioning using on-site observations in sub-Saharan rainfed agriculture." Hydrology and Earth System Sciences 14, no. 4 (April 6, 2010): 627–38. http://dx.doi.org/10.5194/hess-14-627-2010.
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Abstract. Smallholder rainfed farming systems generally realise sub-optimal crop yields which are largely attributed to dry spell occurrences during crop growth stages. However, through the introduction of appropriate farming practices, it is possible to substantially increase yield levels even with little and highly variable rainfall. The presented results follow research conducted in the Makanya catchment in northern Tanzania where gross rainfall amounts to less than 400 mm/season which is insufficient to support staple food crops (e.g. maize). The yields from farming system innovations (SIs), which are basically alternative cultivation techniques, are compared against traditional farming practices. The SIs tested in this research are runoff harvesting used in combination with in-field trenches and soil bunds (fanya juus). These SIs aim to reduce soil and nutrient loss from the field and, more importantly, promote in-field infiltration and water retention. Water balance components have been observed in order to study water partitioning processes for the "with" and "without" SI scenarios. Based on rainfall, soil evaporation, transpiration, runoff and soil moisture measurements, a water balance model has been developed to simulate soil moisture variations over the growing season. Simulation results show that, during the field trials, the average productive transpiration flow ranged between 1.1–1.4 mm d−1 in the trial plots compared to 0.7–1.0 mm d−1 under traditional tillage practice. Productive transpiration processes accounted for 23–29% while losses to deep percolation accounted for 33–48% of the available water. The field system has been successfully modelled using the spreadsheet-based water balance 1-D model. Conclusions from the research are that the SIs that were tested are effective in enhancing soil moisture retention at field scale and that diversions allow crop growth moisture conditions to be attained with early rains. From the partitioning analysis, it is also concluded that there is more scope for efficient utilisation of the diverted runoff water if storage structures could be installed to minimise runoff and deep percolation and, hence, regulate water flow to the root zone when required.
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Upreti, Deepak, Tim McCarthy, Macdara O’Neill, Kazeem Ishola, and Rowan Fealy. "Application and Evaluation of a Simple Crop Modelling Framework: A Case Study for Spring Barley, Winter Wheat and Winter Oilseed Rape over Ireland." Agronomy 12, no. 11 (November 20, 2022): 2900. http://dx.doi.org/10.3390/agronomy12112900.
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Globally, croplands represent a significant contributor to climate change, through both greenhouse gas emissions and land use changes associated with cropland expansion. They also represent locations with significant potential to contribute to mitigating climate change through alternative land use management practices that lead to increased soil carbon sequestration. In spite of their global importance, there is a relative paucity of tools available to support field- or farm-level crop land decision making that could inform more effective climate mitigation practices. In recognition of this shortcoming, the Simple Algorithm for Yield Estimate (SAFY) model was developed to estimate crop growth, biomass, and yield at a range of scales from field to region. While the original SAFY model was developed and evaluated for winter wheat in Morocco, a key advantage to utilizing SAFY is that it presents a modular architecture which can be readily adapted. This has led to numerous modifications and alterations of specific modules which enable the model to be refined for new crops and locations. Here, we adapted the SAFY model for use with spring barley, winter wheat and winter oilseed rape at selected sites in Ireland. These crops were chosen as they represent the dominant crop types grown in Ireland. We modified the soil–water balance and carbon modules in SAFY to simulate components of water and carbon budgets in addition to crop growth and production. Results from the modified model were evaluated against available in situ data collected from previous studies. Spring barley biomass was estimated with high accuracy (R2 = 0.97, RMSE = 95.8 g·m−2, RRMSE = 11.7%) in comparison to GAI (R2 = 0.73, RMSE = 0.44 m2·m−2, RRMSE = 10.6%), across the three years for which the in situ data was available (2011–2013). The winter wheat module was evaluated against measured biomass and yield data obtained for the period 2013–2015 and from three sites located across Ireland. While the model was found to be capable of simulating winter wheat biomass (R2 = 0.71, RMSE = 1.81 t·ha−1, RRMSE = 8.0%), the model was found to be less capable of reproducing the associated yields (R2 = 0.09, RMSE = 2.3 t·ha−1, RRMSE = 18.6%). In spite of the low R2 obtained for yield, the simulated crop growth stage 61 (GS61) closely matched those observed in field data. Finally, winter oilseed rape (WOSR) was evaluated against a single growing season for which in situ data was available. WOSR biomass was also simulated with high accuracy (R2 = 0.99 and RMSE = 0.52 t·ha−1) in comparison to GAI (R2 = 0.3 and RMSE = 0.98 m2·m−2). In terms of the carbon fluxes, the model was found to be capable of estimating heterotrophic respiration (R2 = 0.52 and RMSE = 0.28 g·C·m−2·day−1), but less so the ecosystem respiration (R2 = 0.18 and RMSE = 1.01 g·C·m−2·day−1). Overall, the results indicate that the modified model can simulate GAI and biomass, for the chosen crops for which data were available, and yield, for winter wheat. However, the simulations of the carbon budgets and water budgets need to be further evaluated—a key limitation here was the lack of available in situ data. Another challenge is how to address the issue of parameter specification; in spite of the fact that the model has only six variable crop-related parameters, these need to be calibrated prior to application (e.g., date of emergence, effective light use efficiency etc.). While existing published values can be readily employed in the model, the availability of regionally derived values would likely lead to model improvements. This limitation could be overcome through the integration of available remote sensing data using a data assimilation procedure within the model to update the initial parameter values and adjust model estimates during the simulation.
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MAHENDRAPPA, M. K., N. W. FOSTER, G. F. WEETMAN, and H. H. KRAUSE. "NUTRIENT CYCLING AND AVAILABILITY IN FOREST SOILS." Canadian Journal of Soil Science 66, no. 4 (November 1, 1986): 547–72. http://dx.doi.org/10.4141/cjss86-056.
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Nutrient availability in different forest soils must be known before increased wood production can be sustained either by adding supplemental nutrients or by judicious silvicultural operations to optimize the linkage between the variable nutrient requirements of forest crops. This is complicated by the variable availability of nutrients on forest sites during crop development. Forest crops unlike agricultural crops have long rotation periods which make it difficult to apply agricultural methods of estimating potentially available nutrients directly to forest soils. Presented in this review are (i) various approaches used in forestry to estimate the nutrient supplying potential of different sites, (ii) factors affecting nutrient availability, and (iii) evidence to suggest that nutrient cycling processes in forest ecosystems are important factors affecting tree growth. It is suggested that data from chemical analyses of soil samples collected at specific times and sites should be used with caution for both practical decision making and simulation modelling purposes. Key words: Nitrogen, phosphorus, litterfall, throughfall, stemflow, mineralization
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Carlini, Maurizio, and Sonia Castellucci. "Modelling and Simulation for Energy Production Parametric Dependence in Greenhouses." Mathematical Problems in Engineering 2010 (2010): 1–28. http://dx.doi.org/10.1155/2010/590943.
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Greenhouses crops in Italy are made by using prefabricated structures, leaving out the preliminary study of optical and thermal exchanges between the external environment and the greenhouse, dealing with heating and cooling and the effects of air conditioning needed for plant growth. This involves rather significant costs that directs the interest of designers, builders, and farmers in order to seek constructive solutions to optimize the system of such emissions. This work was done by building a model of gases using TRNSYS software, and these gases then have been checked for compliance. The model was constructed considering an example of a prefabricated greenhouse, located in central of Italy. Aspects of the structural components, and thermal and optical properties are analyzed in order to achieve a representation of reality.
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Wu, Alex, Al Doherty, Graham D. Farquhar, and Graeme L. Hammer. "Simulating daily field crop canopy photosynthesis: an integrated software package." Functional Plant Biology 45, no. 3 (2018): 362. http://dx.doi.org/10.1071/fp17225.
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Photosynthetic manipulation is seen as a promising avenue for advancing field crop productivity. However, progress is constrained by the lack of connection between leaf-level photosynthetic manipulation and crop performance. Here we report on the development of a model of diurnal canopy photosynthesis for well watered conditions by using biochemical models of C3 and C4 photosynthesis upscaled to the canopy level using the simple and robust sun–shade leaves representation of the canopy. The canopy model was integrated over the time course of the day for diurnal canopy photosynthesis simulation. Rationality analysis of the model showed that it simulated the expected responses in diurnal canopy photosynthesis and daily biomass accumulation to key environmental factors (i.e. radiation, temperature and CO2), canopy attributes (e.g. leaf area index and leaf angle) and canopy nitrogen status (i.e. specific leaf nitrogen and its profile through the canopy). This Diurnal Canopy Photosynthesis Simulator (DCaPS) was developed into a web-based application to enhance usability of the model. Applications of the DCaPS package for assessing likely canopy-level consequences of changes in photosynthetic properties and its implications for connecting photosynthesis with crop growth and development modelling are discussed.
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Wegehenkel, Martin, and Horst H. Gerke. "Comparison of real evapotranspiration measured by weighing lysimeters with simulations based on the Penman formula and a crop growth model." Journal of Hydrology and Hydromechanics 61, no. 2 (June 1, 2013): 161–72. http://dx.doi.org/10.2478/johh-2013-0021.
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Abstract Although the quantification of real evapotranspiration (ETr) is a prerequisite for an appropriate estimation of the water balance, precision and uncertainty of such a quantification are often unknown. In our study, we tested a combined growth and soil water balance model for analysing the temporal dynamics of ETr. Simulated ETr, soil water storage and drainage rates were compared with those measured by 8 grass-covered weighable lysimeters for a 3-year period (January 1, 1996 to December 31, 1998). For the simulations, a soil water balance model based on the Darcy-equation and a physiological-based growth model for grass cover for the calculation of root water uptake were used. Four lysimeters represented undisturbed sandy soil monoliths and the other four were undisturbed silty-clay soil monoliths. The simulated ETr-rates underestimated the higher ETr-rates observed in the summer periods. For some periods in early and late summer, the results were indicative for oasis effects with lysimeter-measured ETr-rates higher than corresponding calculated rates of potential grass reference evapotranspiration. Despite discrepancies between simulated and observed lysimeter drainage, the simulation quality for ETr and soil water storage was sufficient in terms of the Nash-Sutcliffe index, the modelling efficiency index, and the root mean squared error. The use of a physiological-based growth model improved the ETr estimations significantly.
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WEISS, A., and W. W. WILHELM. "The circuitous path to the comparison of simulated values from crop models with field observations." Journal of Agricultural Science 144, no. 6 (October 12, 2006): 475–88. http://dx.doi.org/10.1017/s0021859606006460.
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The Journal of Agricultural Science, Cambridge has been a fixture in dissemination of crop simulation models and the concepts and data upon which they are built since the inception of computers and computer modelling in the mid-20th century. To quantify the performance of a crop simulation model, model outputs are compared with observed values using statistical measures of bias, i.e. the difference between simulated and observed values. While applying these statistical measures is unambiguous for the experienced user, the same cannot always be said of determining the observed or simulated values. For example, differences in accessing crop development can be due to the subjectivity of an observer or to a definition that is difficult to apply in the field. Methods of determining kernel number, kernel mass, and yield can vary among researchers, which can add errors to comparisons between experimental observations and simulated results. If kernel moisture is not carefully determined and reported it can add error to values of grain yield and kernels per unit area regardless of the protocol used to collect these data. Inaccurate determination of kernel moisture will also influence computation of grain protein or oil content. Problems can also be associated with input data to the simulation models. Under-reporting of precipitation values from tipping bucket rain gauges, commonly found on automated weather stations, can introduce errors in results from crop simulation models. Using weather data collected too far from an experimental site may compound problems with input data. The importance of accurate soil and weather input data increases as the environment becomes more limiting for plant growth and development. Problems can also arise from algorithms that calculate important parameters in a model, such as daylength, which is used to determine a photoperiod response. Errors in the calculation of photoperiod can be related to the definition of sunrise and sunset and the inclusion or exclusion of civil twilight or to the improper calculation of the solar declination. Even the simple calculation of the daily mean air temperature can have an impact on the results from a non-linear algorithm. During a period when crop simulation modelling is moving in the difficult direction of incorporating genomic-based inputs, the critical importance of careful and accurate collection and reporting of field data and the need to develop robust algorithms that accommodate readily available or easily acquired input data should not be forgotten. As scientists we have an obligation to provide the best available knowledge and understanding as possible. Avoiding potential pitfalls will assist us as we develop new knowledge and understanding and incorporate these concepts into new or modified crop simulation models.
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Sarkar, Sukamal, Krishnendu Ray, Sourav Garai, Hirak Banerjee, Krisanu Haldar, and Jagamohan Nayak. "Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India." PeerJ 11 (February 15, 2023): e14903. http://dx.doi.org/10.7717/peerj.14903.
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Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha−1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha−1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.