To see the other types of publications on this topic, follow the link: Rice Data Simulator.
Journal articles on the topic 'Rice Data Simulator'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Rice Data Simulator.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
Balaji, S. Arun, and K. Baskaran. "Feed Forward Back Propagation Neural Network Coupled With Rice Data Simulator for Prediction of Rice Production in Tamilnadu." International Journal of Computer Science, Engineering and Information Technology 4, no. 5 (2014): 11–31. http://dx.doi.org/10.5121/ijcseit.2014.4502.
Full text
3
Wimalasiri, Eranga M., Ebrahim Jahanshiri, Tengku Adhwa Syaherah Tengku Mohd Suhairi, et al. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification." Sustainability 12, no. 18 (2020): 7781. http://dx.doi.org/10.3390/su12187781.
Full textAbstract:
Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.
4
Zhu, Meiqing, Yimeng Jiao, Chenchen Wu, et al. "Simulation and Optimization of Double-Season Rice Yield in Jiangxi Province Based on High-Accuracy Surface Modeling–Agricultural Production Systems sIMulator Model." Agriculture 15, no. 10 (2025): 1034. https://doi.org/10.3390/agriculture15101034.
Full textAbstract:
The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm2), MAE decreased by 31.43% (670.92→460.03 kg/hm2), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm2), MAE reduced by 26.37% (718.05→528.72 kg/hm2), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry.
5
Uchechukwu Moses, Oyibo, and Nosiri Onyebuchi Chikezie. "KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network." International Journal of Wireless & Mobile Networks 14, no. 03 (2022): 35–54. http://dx.doi.org/10.5121/ijwmn.2022.14303.
Full textAbstract:
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
6
Oyibo, Uchechukwu Moses and Nosiri Onyebuchi Chikezie. "KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network." International Journal of Wireless & Mobile Networks (IJWMN) 14, no. 3 (2022): 35–54. https://doi.org/10.5281/zenodo.6861603.
Full textAbstract:
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
7
Afshar, Mehdi H., Timothy Foster, Thomas P. Higginbottom, et al. "Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring." Remote Sensing 13, no. 5 (2021): 924. http://dx.doi.org/10.3390/rs13050924.
Full textAbstract:
Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.
8
Wajid, Aftab, Khalid Hussain, Ayesha Ilyas, Muhammad Habib-ur-Rahman, Qamar Shakil, and Gerrit Hoogenboom. "Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments." Agriculture 11, no. 11 (2021): 1166. http://dx.doi.org/10.3390/agriculture11111166.
Full textAbstract:
Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.
9
Shaikh, Asad, Alexander Churyumov, Andrey Pozdniakov, and Tatiana Churyumova. "Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel." Applied Sciences 10, no. 2 (2020): 530. http://dx.doi.org/10.3390/app10020530.
Full textAbstract:
This study describes deformation behavior and fracture during compression and tension at high temperatures of ferritic/martensitic 13CrMoNbV steel. Hot compression and tensile tests were carried out in the temperature range of 1100–1275 °C with a thermomechanical simulator Gleeble 3800. The true stress and ultimate tensile strength decrease with an increase in the deformation temperature. The modified Arrhenius-type constitutive model was built for 13CrMoNbV ferritic/martensitic steel using the experimental stress–strain compression data. The modified Rice and Tracy ductile fracture criteria were calculated using finite element simulation of the tensile test at different temperatures. The comparison between experimental and computed force vs. displacement curves shows high predictability of the deformation and fracture models for ferritic/martensitic 13CrMoNbV steel.
10
Zhao, Panpan, Yang Zhou, Fengfeng Li, et al. "The Adaptability of APSIM-Wheat Model in the Middle and Lower Reaches of the Yangtze River Plain of China: A Case Study of Winter Wheat in Hubei Province." Agronomy 10, no. 7 (2020): 981. http://dx.doi.org/10.3390/agronomy10070981.
Full textAbstract:
The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.
11
Kouadio, Louis, Kristina Fraser, Ali Ibrahim, Kazuki Saito, Fatondji Dougbedji, and Kalimuthu Senthilkumar. "Assessing yield stability of pearl millet and rice cropping systems across West Africa using long-term experiments and a modeling approach." PLOS One 20, no. 5 (2025): e0317170. https://doi.org/10.1371/journal.pone.0317170.
Full textAbstract:
Long-term field experiments (LTEs) provide invaluable insights into temporal yield patterns of agronomic interventions. However, the number of LTEs and agronomic management options tested withing these experiments remain limited compared to the diversity of farming systems in West Africa. Well-tested crop models may be used to identify crop management strategies with high temporal yield stability. This study examines the yield stability of pearl millet and rice under various management options in West Africa, utilizing both experimental and modeling approaches. The Agricultural Production Systems Simulator (APSIM) for pearl millet and rice were calibrated and tested for locally-recommended varieties using LTE data from Niger (pearl millet) and Senegal (rice). Yield stability was evaluated with multiple metrics, including the adjusted coefficient of variation, the sustainable yield index, and the Finlay-Wilkinson regression coefficient. Both APSIM models exhibited a strong performance for grain yield, with Willmott’s indices of agreement at 0.74 for pearl millet and 0.90 for rice, and absolute root mean square errors of 0.19 and 1.20 Mg ha-1, respectively. The models effectively reproduced yield stability patterns across a variety of management options including planting date, planting density, fertilizer treatments, and residue retention. Combining fertilizer applications with crop residue retention enhanced yield stability in pearl millet, while season-specific nitrogen management strategies reduced yield variability in rice. Our study underscores the potential of well-tested crop models to complement LTEs in investigating pearl millet and rice yield stability, offering actionable insights for agronomic intensification strategies to enhance productivity and sustainability.
12
Gunarathna, M. H. J. P., Kazuhito Sakai, M. K. N. Kumari, and Manjula Ranagalage. "A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models." Agronomy 10, no. 2 (2020): 285. http://dx.doi.org/10.3390/agronomy10020285.
Full textAbstract:
As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM–Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.
13
Sidhu, Ravneet Kaur, Ravinder Kumar, and Prashant Singh Rana. "Long short-term memory neural network-based multi-level model for smart irrigation." Modern Physics Letters B 34, no. 36 (2020): 2050418. http://dx.doi.org/10.1142/s0217984920504187.
Full textAbstract:
Rice is a staple food crop around the world, and its demand is likely to rise significantly with growth in population. Increasing rice productivity and production largely depends on the availability of irrigation water. Thus, the efficient application of irrigation water such that the crop doesn’t experience moisture stress is of utmost importance. In the present study, a long short-term memory (LSTM)-based neural network with logistic regression has been used to predict the daily irrigation schedule of drip-irrigated rice. The correlation threshold of 0.75 was used for the selection of features, which helped in limiting the number of input parameters. Also, a dataset based on the recommendation of a domain expert, and another used by the tool Agricultural Production Systems Simulator (APSIM) was used for comparison. Field data comprising of weather station data and past irrigation schedules has been used to train the model. Grid search algorithm has been used to optimize the hyperparameters of the model. Nested cross-validation has been used for validating the results. The results show that the correlation-based selected dataset is as effective as the domain expert-recommended dataset in predicting the water requirement using LSTM as the base model. The models were evaluated on different parameters and a multi-criteria decision evaluation (Technique for Order of Preference by Similarity to Ideal Solution [TOPSIS]) was used to find the best performing.
14
Zhao, Yanxi, Dengpan Xiao, Huizi Bai, et al. "Climate Change Impact on Yield and Water Use of Rice–Wheat Rotation System in the Huang-Huai-Hai Plain, China." Biology 11, no. 9 (2022): 1265. http://dx.doi.org/10.3390/biology11091265.
Full textAbstract:
Global climate change has had a significant impact on crop production and agricultural water use. Investigating different future climate scenarios and their possible impacts on crop production and water consumption is critical for proposing effective responses to climate change. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs) provided by Coupled Model Intercomparison Project Phase 6 (CMIP6), we applied the well-validated Agricultural Production Systems sIMulator (APSIM) to simulate crop phenology, yield, and water use of the rice–wheat rotation at four representative stations (including Hefei and Shouxian stations in Anhui province and Kunshan and Xuzhou stations in Jiangsu province) across the Huang-Huai-Hai Plain, China during the 2041–2070 period (2050s) under four Shared Socioeconomic Pathways (i.e., SSP126, SSP245, SSP370, and SSP585). The results showed a significant increase in annual mean temperature (Temp) and solar radiation (Rad), and annual total precipitation (Prec) at four investigated stations, except Rad under SSP370. Climate change mainly leads to a consistent advance in wheat phenology, but inconsistent trends in rice phenology across four stations. Moreover, the reproductive growth period (RGP) of wheat was prolonged while that of rice was shorted at three of four stations. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO2 concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, our study indicated that the change in Temp, Rad, Prec, and [CO2] have different impacts on different crops and at different stations. Therefore, in the impact assessment for climate change, it is necessary to explore and analyze different crops in different regions. Additionally, our study helps to improve understanding of the impacts of climate change on crop production and water consumption and provides data support for the sustainable development of agriculture.
15
Anser, Muhammad Khalid, Tayyaba Hina, Shahzad Hameed, Muhammad Hamid Nasir, Ishfaq Ahmad, and Muhammad Asad ur Rehman Naseer. "Modeling Adaptation Strategies against Climate Change Impacts in Integrated Rice-Wheat Agricultural Production System of Pakistan." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2522. http://dx.doi.org/10.3390/ijerph17072522.
Full textAbstract:
There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.
16
Lukman, Medriavin Silalahi, Jatikusumo Dimas, Budiyanto Setiyo, Artadima Silaban Freddy, Uli Vistalina Simanjuntak Imelda, and Dendi Rochendi Agus. "Internet of things implementation and analysis of fuzzy Tsukamoto in prototype irrigation of rice." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6022–33. https://doi.org/10.11591/ijece.v12i6.pp6022-6033.
Full textAbstract:
This research raises the topic of modern technology in the field of rice fields. The problem in this research is determining the fuzzy inference system algorithm for electronic engineering. The prototype was built by Raspberry Pi and python-based to the internet of things. The objective of this research is to design a new model for the rice field monitoring/control system and display every condition based on the internet of things. So that the hypothesis of this research can answer the phenomena that occur in rice fields, including drought problems, maintained plant conditions. The test results showed that irrigation control automatically runs optimally by scheduling, automatic irrigation control of water pH degree value detection analyzed by fuzzy Tsukamoto method at Z=3.5 defuzzification value for low and high irrigation control, and Z value=1.83 for normal irrigation control. Furthermore, the scheduling of spraying liquid fertilizer obtained the results of duration for 60 min in accordance with the needs of fertilizer dose. Lastly, for monitoring data on the website successfully accessed anywhere from the use of hosting servers and domains. Finally, it can be concluded that fuzzy Tsukamoto's algorithm is appropriate to be applied to the modern rice field system.
17
Khan, Muhammad Aamir, Alishba Tahir, Nabila Khurshid, Muhammad Iftikhar ul Husnain, Mukhtar Ahmed, and Houcine Boughanmi. "Economic Effects of Climate Change-Induced Loss of Agricultural Production by 2050: A Case Study of Pakistan." Sustainability 12, no. 3 (2020): 1216. http://dx.doi.org/10.3390/su12031216.
Full textAbstract:
This research combined global climate, crop and economic models to examine the economic impact of climate change-induced loss of agricultural productivity in Pakistan. Previous studies conducted systematic model inter-comparisons, but results varied widely due to differences in model approaches, research scenarios and input data. This paper extends that analysis in the case of Pakistan by taking yield decline output of the Decision Support System for Agrotechnology Transfer (DSSAT) for CERES-Wheat, CERES-Rice and Agricultural Production Systems Simulator (APSIM) crop models as an input in the global economic model to evaluate the economic effects of climate change-induced loss of crop production by 2050. Results showed that climate change-induced loss of wheat and rice crop production by 2050 is 19.5 billion dollars on Pakistan’s Real Gross Domestic Product coupled with an increase in commodity prices followed by a notable decrease in domestic private consumption. However, the decline in the crops’ production not only affects the economic agents involved in the agriculture sector of the country, but it also has a multiplier effect on industrial and business sectors. A huge rise in commodity prices will create a great challenge for the livelihood of the whole country, especially for urban households. It is recommended that the government should have a sound agricultural policy that can play a role in influencing its ability to adapt successfully to climate change as adaption is necessary for high production and net returns of the farm output.
18
Meng, L., R. Paudel, P. G. M. Hess, and N. M. Mahowald. "Seasonal and interannual variability in wetland methane emissions simulated by CLM4Me' and CAM-chem and comparisons to observations of concentrations." Biogeosciences 12, no. 13 (2015): 4029–49. http://dx.doi.org/10.5194/bg-12-4029-2015.
Full textAbstract:
Abstract. Understanding the temporal and spatial variation of wetland methane emissions is essential to the estimation of the global methane budget. Our goal for this study is three-fold: (i) to evaluate the wetland methane fluxes simulated in two versions of the Community Land Model, the Carbon-Nitrogen (CN; i.e., CLM4.0) and the Biogeochemistry (BGC; i.e., CLM4.5) versions using the methane emission model CLM4Me' so as to determine the sensitivity of the emissions to the underlying carbon model; (ii) to compare the simulated atmospheric methane concentrations to observations, including latitudinal gradients and interannual variability so as to determine the extent to which the atmospheric observations constrain the emissions; (iii) to understand the drivers of seasonal and interannual variability in atmospheric methane concentrations. Simulations of the transport and removal of methane use the Community Atmosphere Model with chemistry (CAM-chem) model in conjunction with CLM4Me' methane emissions from both CN and BGC simulations and other methane emission sources from literature. In each case we compare model-simulated atmospheric methane concentration with observations. In addition, we simulate the atmospheric concentrations based on the TransCom wetland and rice paddy emissions derived from a different terrestrial ecosystem model, Vegetation Integrative Simulator for Trace gases (VISIT). Our analysis indicates CN wetland methane emissions are higher in the tropics and lower at high latitudes than emissions from BGC. In CN, methane emissions decrease from 1993 to 2004 while this trend does not appear in the BGC version. In the CN version, methane emission variations follow satellite-derived inundation wetlands closely. However, they are dissimilar in BGC due to its different carbon cycle. CAM-chem simulations with CLM4Me' methane emissions suggest that both prescribed anthropogenic and predicted wetlands methane emissions contribute substantially to seasonal and interannual variability in atmospheric methane concentration. Simulated atmospheric CH4 concentrations in CAM-chem are highly correlated with observations at most of the 14 measurement stations evaluated with an average correlation between 0.71 and 0.80 depending on the simulation (for the period of 1993–2004 for most stations based on data availability). Our results suggest that different spatial patterns of wetland emissions can have significant impacts on Northern and Southern hemisphere (N–S) atmospheric CH4 concentration gradients and growth rates. This study suggests that both anthropogenic and wetland emissions have significant contributions to seasonal and interannual variations in atmospheric CH4 concentrations. However, our analysis also indicates the existence of large uncertainties in terms of spatial patterns and magnitude of global wetland methane budgets, and that substantial uncertainty comes from the carbon model underlying the methane flux modules.
19
Tiu, Jonathan, Annie C. Harmon, James D. Stowe, et al. "Feasibility and Validity of a Low-Cost Racing Simulator in Driving Assessment after Stroke." Geriatrics 5, no. 2 (2020): 35. http://dx.doi.org/10.3390/geriatrics5020035.
Full textAbstract:
There is a myriad of methodologies to assess driving performance after a stroke. These include psychometric tests, driving simulation, questionnaires, and/or road tests. Research-based driving simulators have emerged as a safe, convenient way to assess driving performance after a stroke. Such traditional research simulators are useful in recreating street traffic scenarios, but are often expensive, with limited physics models and graphics rendering. In contrast, racing simulators developed for motorsport professionals and enthusiasts offer high levels of realism, run on consumer-grade hardware, and can provide rich telemetric data. However, most offer limited simulation of traffic scenarios. This pilot study compares the feasibility of research simulation and racing simulation in a sample with minor stroke. We determine that the racing simulator is tolerated well in subjects with a minor stroke. There were correlations between research and racing simulator outcomes with psychometric tests associated with driving performance, such as the Trails Making Test Part A, Snellgrove Maze Task, and the Motricity Index. We found correlations between measures of driving speed on a complex research simulator scenario and racing simulator lap time and maximum tires off track. Finally, we present two models, using outcomes from either the research or racing simulator, predicting road test failure as linked to a previously published fitness-to-drive calculator that uses psychometric screening.
20
Son, N. T., C. F. Chen, C. R. Chen, L. Y. Chang, and S. H. Chiang. "RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 993–96. http://dx.doi.org/10.5194/isprs-archives-xli-b8-993-2016.
Full textAbstract:
Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.
21
Son, N. T., C. F. Chen, C. R. Chen, L. Y. Chang, and S. H. Chiang. "RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 993–96. http://dx.doi.org/10.5194/isprsarchives-xli-b8-993-2016.
Full textAbstract:
Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.
22
Larsson, Per Tomas, Jasna Stevanic-Srndovic, Stephan V. Roth, and Daniel Söderberg. "Interpreting SAXS data recorded on cellulose rich pulps." Cellulose 29, no. 1 (2021): 117–31. http://dx.doi.org/10.1007/s10570-021-04291-x.
Full textAbstract:
AbstractA simulation method was developed for modelling SAXS data recorded on cellulose rich pulps. The modelling method is independent of the establishment of separate form factors and structure factors and was used to model SAXS data recorded on dense samples. An advantage of the modelling method is that it made it possible to connect experimental SAXS data to apparent average sizes of particles and cavities at different sample solid contents. Experimental SAXS data could be modelled as a superposition of a limited number of simulated intensity components and gave results in qualitative agreement with CP/MAS 13C-NMR data recorded on the same samples. For the water swollen samples, results obtained by the SAXS modelling method and results obtained from CP/MAS 13C-NMR measurements, agreed on the ranking of particle sizes in the different samples. The SAXS modelling method is dependent on simulations of autocorrelation functions and the time needed for simulations could be reduced by rescaling of simulated correlation functions due to their independence of the choice of step size in real space. In this way an autocorrelation function simulated for a specific sample could be used to generate SAXS intensity profiles corresponding to all length scales for that sample and used for efficient modelling of the experimental data recorded on that sample. Graphical abstract
23
V., Harithalekshmi, G. K. Das, Surendra Kumar Chandniha, H. V. Puranik, and J. L. Chaudhary. "Evaluation of CERES –Rice Model for Simulating Rice Yield and Phenophases." International Journal of Plant & Soil Science 36, no. 7 (2024): 371–76. http://dx.doi.org/10.9734/ijpss/2024/v36i74742.
Full textAbstract:
In this study, the performance of the CERES-Rice model in simulating the growth and yield of the Rajeshwari variety in the Raipur district of Chhattisgarh, India, was evaluated. Utilizing observed data from 2021 and 2022, the model was calibrated and validated using key parameters such as days to anthesis, physiological maturity, and yield. Calibration involved adjusting genetic coefficients to improve simulation accuracy, with validation ensuring the model's reliability beyond the calibration period. The comparison between observed and simulated data for crop performance parameters showed that the model performed reasonably well. For days to Anthesis, the RMSE was 4.32 with a d-stat of 0.59, and an error of 5.4%. For Days to Panicle initiation, the RMSE was 1.83, the d-stat was 0.82, and the error was -4.7%. For days to PM, the RMSE was 6.7, the d-stat was 0.65, and the error was 3.0%. Yield showed an RMSE of 472.4, a d-stat of 0.81, and an error of 7.7%. F The mean simulated values closely matched the observed means, indicating overall good model accuracy. In this study, fine tuning the genetic coefficients of CERES rice model for the variety Rjeshwari was done and can be used for further studies and applications.
24
Guo, Xianyu, Junjun Yin, Kun Li, and Jian Yang. "Fine Classification of Rice Paddy Based on RHSI-DT Method Using Multi-Temporal Compact Polarimetric SAR Data." Remote Sensing 13, no. 24 (2021): 5060. http://dx.doi.org/10.3390/rs13245060.
Full textAbstract:
In recent years, the compact polarimetric (CP) synthetic aperture radar (SAR) has become a hotspot of SAR Earth observation. Meanwhile, CP SAR provides both relatively rich polarization information and large swath-width for rice mapping. Fine classification of rice paddy plays an important role in growth monitoring, pest prevention and yield estimation of rice. In this study, the multi-temporal CP SAR data were firstly simulated by fully polarimetric RADARSAT-2 data, and 22 CP parameters from each of the six temporal CP SAR data were extracted. Then we built a rice height-sensitive index (RHSI). Furthermore, a decision tree (DT) method was established by using the optimal CP parameters based on RHSI. Finally, the classification results of rice paddy based on DT and support vector machine (SVM) methods were compared. Results showed that the RHSI-DT method could obtain better results, with an overall accuracy of 97.94% and a kappa coefficient of 0.973, which was 2% higher and 0.03 larger than those of the SVM method. Besides, we found that the surface scattering of m-χ decomposition (m-χ_s (0627)) and ΔShannon entropy intensity Hi (Hi (1015)-Hi (0627)) were highly effective parameters to distinguish paddies of transplanting hybrid rice (T-H) and direct-sown japonica rice (D-J).
25
Shams, Shaila Islam, Sanitchon Jirawat, and Khairul Hasan Ahmed. "Rice phenology and growth simulation using DSSAT- CERES-Rice crop model under the different temperatures changing with climatic condition." International Journal of Agricultural Sciences and Technology 1, no. 2 (2021): 1–11. https://doi.org/10.51483/IJAGST.1.2.2021.1-11.
Full textAbstract:
This research paper aims to evaluate the performance of DSSAT CERES-Rice model in simulating the impact of different (28 °C, 30 °C and 32 °C) increased temperatures change with the relations of five upland rice genotypes (Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm) on grain yield for future crop management. Results showed that temperature significantly affected grain yields, harvest index, flowering and maturity date which indicate that medium temperature (30 °C) gave highest grain yield bearing genotype Dawk Kahm (6,700 kg/ ha) whereas at maximum temperature (32 °C), simulated grain yields varied from 3094 to 6460 kg/ ha. Root Mean Square Error (RMSE) values of simulated and observed data less than 10% indicated that grain weight, leaf area index, tillers number and harvest index had more consistency agreement with the yield. Thus, it was proved that the CERES-Rice crop simulation model was more useful as a tool for different phenological traits under changing temperature conditions. And the model approximated grain yields at different temperatures with reasonable accuracy.
26
Zhou, Gaoxiang, Xiangnan Liu, and Ming Liu. "Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation." Remote Sensing 11, no. 3 (2019): 268. http://dx.doi.org/10.3390/rs11030268.
Full textAbstract:
Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAImean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation.
27
Guo, Xianyu, Kun Li, Yun Shao, et al. "Inversion of Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data." Sensors 18, no. 7 (2018): 2271. http://dx.doi.org/10.3390/s18072271.
Full textAbstract:
Timely and accurate estimation of rice parameters plays a significant role in rice monitoring and yield forecasting for ensuring food security. Compact-polarimetric (CP) synthetic aperture radar (SAR), a good compromise between the dual- and quad-polarized SARs, is an important part of the new generation of Earth observation systems. In this paper, the ability of CP SAR data to retrieve rice biophysical parameters was explored using a modified water cloud model. The results showed that S1 was superior to other CP variables in rice height inversion with a coefficient of determination (R2) of 0.92 and a root-mean-square error (RMSE) of 5.81 cm. RL was the most suitable for inverting the volumetric water content of the rice canopy, with an R2 of 0.95 and a RMSE of 0.31 kg/m3. The m-χ decomposition produced the highest accuracies for the ear biomass: R2 was 0.89 and RMSE was 0.17 kg/m2. The highest accuracy of leaf area index (LAI) retrieval was obtained for RH (right circular transmit and horizontal linear receive) with an R2 of 0.79 and a RMSE of 0.33. This study illustrated the capability of CP SAR data with respect to retrieval of rice biophysical parameters, especially for height, volumetric water content of the rice canopy, and ear biomass, and this mode may offer the best option for rice-monitoring applications because of swath coverage.
28
Zhang, Jing, Yuxin Miao, William Batchelor, Junjun Lu, Hongye Wang, and Shujiang Kang. "Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model." Agronomy 8, no. 11 (2018): 263. http://dx.doi.org/10.3390/agronomy8110263.
Full textAbstract:
Efficient use of nitrogen (N) fertilizer is critically important for China’s food security and sustainable development. Crop models have been widely used to analyze yield variability, assist in N prescriptions, and determine optimum N rates. The objectives of this study were to use the CERES-Rice model to simulate the N response of different high-latitude, adapted flooded rice varieties to different types of weather seasons, and to explore different optimum rice N management strategies with the combinations of rice varieties and types of weather seasons. Field experiments conducted for five N rates and three varieties in Northeast China during 2011–2016 were used to calibrate and evaluate the CERES-Rice model. Historical weather data (1960–2014) were classified into three weather types (cool/normal/warm) based on cumulative growing degree days during the normal growing season for rice. After calibrating the CERES-Rice model for three varieties and five N rates, the model gave good simulations for evaluation seasons for top weight (R2 ≥ 0.96), leaf area index (R2 ≥ 0.64), yield (R2 ≥ 0.71), and plant N uptake (R2 ≥ 0.83). The simulated optimum N rates for the combinations of varieties and weather types ranged from 91 to 119 kg N ha−1 over 55 seasons of weather data and were in agreement with the reported values of the region. Five different N management strategies were evaluated based on farmer practice, regional optimum N rates, and optimum N rates simulated for different combinations of varieties and weather season types over 20 seasons of weather data. The simulated optimum N rate, marginal net return, and N partial factor productivity were sensitive to both variety and type of weather year. Based on the simulations, climate warming would favor the selection of the 12-leaf variety, Longjing 21, which would produce higher yield and marginal returns than the 11-leaf varieties under all the management strategies evaluated. The 12-leaf variety with a longer growing season and higher yield potential would require higher N rates than the 11-leaf varieties. In summary, under warm weather conditions, all the rice varieties would produce higher yield, and thus require higher rates of N fertilizers. Based on simulation results using the past 20 years of weather data, variety-specific N management was a practical strategy to improve N management and N partial factor productivity compared with farmer practice and regional optimum N management in the study region. The CERES-Rice crop growth model can be a useful tool to help farmers select suitable precision N management strategies to improve N-use efficiency and economic returns.
29
Rahman, M. Sayedur. "A rainfall simulation model for agricultural development in Bangladesh." Discrete Dynamics in Nature and Society 5, no. 1 (2000): 1–7. http://dx.doi.org/10.1155/s1026022600000339.
Full textAbstract:
A rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications.
30
Yadav, Manish, B. B. Vashisht, Gajender Yadav, Chiranjeev Kumawat, N. S. Paschapur, and Satender Kumar. "Simulation of Rice Performance under Alkaline Soil Pedon Using CERES-Rice Model." Journal of Soil Salinity and Water Quality 16, no. 3 (2024): 407–15. https://doi.org/10.56093/jsswq.v16i3.159510.
Full textAbstract:
A multi-location field experiment was conducted on crop establishment methods of rice under different soil texture. Field experiments in Indian Punjab were conducted on three soil textures including sandy loam, sandy clay loam, and clay loam soils. The experiment was conducted in randomized block design with nine treatment combinations of direct-seeded rice (DSR) and transplanted flooded rice (TFR), along with different irrigation strategies. The results of multi locations field study were used for CERES-Rice model calibration and validation. Simulation study was performed in a soil pedon near Ferozpur, Punjab to evaluate the performance of rice establishment methods under alkaline soil enlivenment. The CERES-Rice model showed satisfactory accuracy in simulating grain yield, biomass, and evapotranspiration (ET), with low RMSE values, indicating minimal residual variation. Other evaluation indices such as Nash-Sutcliffe Modelling Efficiency (ME), R² values demonstrated strong correlation between observed and simulated data. The index of agreement (d) values, ranging from 0.71 to 0.76, indicated good reliability of the model. These results confirmed the model's predictive capability and effectiveness under varying climatic conditions, supporting improved crop management. The simulated grain yield of TFR in neutral soil (Pedon 1) was 5.7 t ha⁻¹ and reduced to 5.0 t ha⁻¹ for direct seeded rice. In contrast, alkaline soil (Pedon 2) had lower yields (4.9 t ha⁻¹ for TFR) and crop failure for DSR, reflecting poor nutrient dynamics and water retention. The water use efficiency for TFR in alkaline soil was slightly reduced, highlighting challenges in such soils.
31
Sakthivel, R., Ch Vijayalakshmi, M. Vanitha, et al. "Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding." PLOS ONE 19, no. 9 (2024): e0308796. http://dx.doi.org/10.1371/journal.pone.0308796.
Full textAbstract:
Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.
32
Sun, Xiaolu, Xiaohui Yang, Jinjin Hou, Bisheng Wang, and Quanxiao Fang. "Modeling the Effects of Rice-Vegetable Cropping System Conversion and Fertilization on GHG Emissions Using the DNDC Model." Agronomy 13, no. 2 (2023): 379. http://dx.doi.org/10.3390/agronomy13020379.
Full textAbstract:
The cropping system conversion, from rice to vegetable, showed various influences on the greenhouse gases (GHG) emission with conversion time and fertilizer/irrigation management. In this study, we evaluated the DeNitrification-DeComposition (DNDC) model for predicting carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) emissions and crop yields as rice converted to vegetable cropping system under conventional or no fertilization from 2012 to 2014. Then, we quantified the long-term (40 years) impacts of rice-vegetable cropping system conversions and fertilization levels (0, 50, 100 and 150% conventional fertilization rate) on GHGs emissions and global warming potentials (GWP) using the calibrated model. The DNDC model-simulated daily GHG emission dynamics were generally consistent with the measured data and showed good predictions of the seasonal CH4 emissions (coefficient of determination (R2) = 0.96), CO2 emissions (R2 = 0.75), N2O emissions (R2 = 0.75) and crop yields (R2 = 0.89) in response to the different cropping systems and fertilization levels across the two years. The overall model performance was better for rice than for vegetable cropping systems. Both simulated and measured two-year data showed higher CH4 and CO2 emissions and lower N2O emissions for rice than for vegetable cropping systems and showed positive responses of the CO2 and N2O emissions to fertilizations. The lowest GWP for vegetable without fertilization and highest the GWP for rice with fertilization were obtained. These results were consistent with the long-term simulation results. In contrast to the two-year experimental data, the simulated long-term CH4 emissions increased with fertilization for the rice-dominant cropping systems. The reasonable cropping systems and fertilization levels were recommended for the region.
33
Yang, Le, Panpan Wu, Suyong Yang, and Peng Shao. "Research on the Construction and Visualization of a Three-Dimensional Model of Rice Root Growth." Applied Engineering in Agriculture 36, no. 6 (2020): 847–57. http://dx.doi.org/10.13031/aea.13543.
Full textAbstract:
HighlightsThis article proposes a three-dimensional rice root growth model based on the differential L-system.We tested the accuracy of the model output, and the measured values and the simulated values were compared.A three-dimensional visualization of the growth simulation system was implemented, and the dynamic growth process of rice roots was visually reproduced.Abstract. Three-dimensional visualization studies on the morphological characteristics of rice root systems are important for improving farmland management and for the selective breeding and genetic improvement of rice. To clarify the rules governing the structure and distribution of rice roots, the three-dimensional (3D) coordinates and morphological parameters of rice roots were measured in hydroponic experiments at different growing periods, and the rice root structure was measured with a high degree of accuracy. The initial position, growth direction, and rate were then determined via statistical analysis of the data. In this article, a 3D rice root growth model based on the differential L-system is proposed; in this system, the biological characteristics based on the topological structure and the actual growth laws of rice roots are quantified. We adopted the growing degree day (GDD) as the driving factor that describes the growth law of rice roots and tested the accuracy of the model output. In this model, a 3D visualization of the growth simulation system of rice roots is implemented via Visual C++ and the OpenGL standard library on the basis of algorithms for the constructed 3D rice root growth model. The model output realistically recreates the dynamic growth process of rice roots under different conditions. A large amount of experimental data and comparative analysis show that the average accuracies achieved by the proposed system concerning total root length, root surface area and root volume are 96.95%, 95.97%, and 93.98%, respectively. These results verify the high reliability of the constructed model and the effective simulation of the morphological characteristics and growth laws of rice roots at different growth periods, laying the foundation for future research on the laws of changes in morphological structure and the physiological and ecological factors of rice roots at different growth stages. Keywords: Differential L-system, Rice roots, Simulation, Three-dimensional growth model, Visualization.
34
Tian, Jinglian, Yongzhong Tian, Yan Cao, Wenhao Wan, and Kangning Liu. "Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data." Sensors 23, no. 13 (2023): 5876. http://dx.doi.org/10.3390/s23135876.
Full textAbstract:
To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the “water edge effect” in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.
35
Suntaro, Kiattisak, Khwanruedi Sangchum, Supawan Tirawanichakul, and Yutthana Tirawanichakul. "Artificial Neural Network Approach for Impingement Drying of Germinated Brown Rice Soaking with Turmeric Solution." Applied Mechanics and Materials 372 (August 2013): 463–66. http://dx.doi.org/10.4028/www.scientific.net/amm.372.463.
Full textAbstract:
The objectives of this research are to determine the evolution of moisture transfer for germinated Thai jasmine Khao Dawk Mali 105 (KDML105) brown rice variety using impingement drying by eight commonly empirical drying modeling and artificial neural network (ANN) method. The experiments were carried out with drying temperatures of 80-100°C, initial moisture content of KDML105 rice samples soaking with turmeric solution was of 54-55% dry-basis and the desired final moisture content for each drying conditions was fixed at 14-16% dry-basis. The air flow rate was fixed at 7.0 m/s. The measured data in each drying conditions were simulated for getting drying equation by non-linear regression analysis. The results showed that the rice soaking with herb turmeric solution had no effect to drying kinetics and the simulated data using empirical drying equation of Henderson model had the best fitting to all measured data (R2of 0.9978-0.9995 and RMSE of 0.0001441-0.000414). For applying ANN modeling approach, the drying temperature and drying time were considered as the input variables for the topology of neural network while the moisture ratio was the output layer. The simulation results concluded that the simulated values of the ANN model, which was not concerned with any complicated physical properties of grain rice kernels, could be used for prediction drying kinetics and was relatively high accuracy compared to those predicted results of empirical models. So the ANN method without any complicated properties related of rice samples can approach for good prediction their drying kinetics as well as the complicated drying simulations method.
36
Wang, Bo, Yu Liu, Qinghong Sheng, Jun Li, Jiahui Tao, and Zhijun Yan. "Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data." Sustainability 14, no. 13 (2022): 8009. http://dx.doi.org/10.3390/su14138009.
Full textAbstract:
The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.
37
Bhatt, Chetan Kumar, and Ajeet Singh Nain. "Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand." MAUSAM 75, no. 3 (2024): 649–58. http://dx.doi.org/10.54302/mausam.v75i3.5905.
Full textAbstract:
In this study, the utility of assimilation of multi-temporal and multi-polarized Sentinel-1A SAR (Synthetic Aperture Radar) data with rice crop model for mapping and predicting rice yield for district Udham Singh Nagar, Uttarakhand has been discussed. In this approach information regarding rice distribution over the district was achieved by mapping of rice fields from Sentinel-1A SAR images using support vector classification, and then the CERES RICE model which is embedded in DSSAT-4.7 was re-initialized by performing assimilation method in which the temporal single-polarized rice backscattering coefficients were grouped for each rice pixel for the district. The optimal input parameters with assimilation method in re-initializing the model allows a good temporal agreement between rice backscattering coefficients derived from Sentinel-1A SAR images and the rice backscattering coefficient derived from coupled model i.e. integration of CERES RICE (DSSAT-4.7) and semi-empirical rice backscatter model through Leaf Area Index (LAI). After re-initialization the yield of rice was calculated from each rice pixel and yield map of the area of study was developed. The results showed that the coupled model gave an estimate of rice yield of 3190 kg/ha which was quite near to the five years average district yield which was 3160 kg/ha with a difference of 30 kg/ha between coupled and five years average rice yield of the district y. On the basis of results obtained it can be concluded that Sentinel-1A SAR data has great potential for monitoring and mapping of rice with the ability to predict the yield of rice crop. The prediction of rice crop is an important step that could be used to assist farmers and policy makers by providing in-season estimates of the rice yield and production. This information could be used for better planning of the resources.
38
Chizeck, Howard Jay. "Modelling, simulation and control in a data-rich environment." Computer Methods and Programs in Biomedicine 25, no. 2 (1987): 135–40. http://dx.doi.org/10.1016/0169-2607(87)90049-6.
Full text
39
Lindquist, John L., and Martin J. Kropff. "Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)-EchinochloaCompetition." Weed Science 44, no. 1 (1996): 52–56. http://dx.doi.org/10.1017/s0043174500093541.
Full textAbstract:
A simulation model of rice-barnyardgrass competition for light was used for two management applications. First, simulations using 47 weather data sets from four locations in Asia were conducted to evaluate the influence of weather variation on single year economic threshold densities of barnyardgrass. Second, rapid leaf area expansion and leaf area index were evaluated as potential indicators of improved rice competitiveness and tolerance to barnyardgrass. Influence of weather variation on single year economic thresholds was small under the assumption that competition was for light only. Increasing early leaf area expansion rate reduced simulated barnyardgrass seed production and increased single year economic thresholds, suggesting that the use of competitive rice cultivars may reduce the need for chemical weed control. The model predicted that rice leaf area index 70 to 75 d after planting was a good indicator of early leaf area expansion rate.
40
Chang, Lena, Yi-Ting Chen, Jung-Hua Wang, and Yang-Lang Chang. "Rice-Field Mapping with Sentinel-1A SAR Time-Series Data." Remote Sensing 13, no. 1 (2020): 103. http://dx.doi.org/10.3390/rs13010103.
Full textAbstract:
This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.
41
Li, Zhonghe, Chesheng Zhan, Shi Hu, Like Ning, Lanfang Wu, and Hai Guo. "Evaluation of global gridded crop models (GGCMs) for the simulation of major grain crop yields in China." Hydrology Research 53, no. 3 (2022): 353–69. http://dx.doi.org/10.2166/nh.2022.087.
Full textAbstract:
Abstract Multimodel ensembles are powerful tools for evaluating agricultural production. Multimodel simulation results provided by the Global Gridded Crop Model Intercomparison (GGCMI) facilitate the evaluation of the grain production situation in China. With census crop yield data, the performance of nine global gridded crop models (GGCMs) in China was evaluated, and the yield gaps of four crops (maize, rice, soybean, and wheat) were estimated. The results showed that GGCMs better simulated maize yields than those of other crops in the northeast, north, northwest, east, and center. GEPIC (CLM-CROP) performed best in simulating maize (wheat) yield in the north, east, and northwest (southwest and south), due to reasonable parameter (cultivar and phenology parameters) settings. Because the rice phenology parameters were calibrated against phenological observation networks and a simple nitrogen limitation index was introduced, ORCHIDEE-CROP performed well in rice yield simulation and soybean yield simulation (center and southwest). Among four crops, wheat has the largest yield gap (7.3–14.1%), in which the poor soil of northwest (14.1%) exposes wheat to relatively high nutritional stress. Thus, in northwest China, optimizing nitrogen management in wheat production can effectively mitigate the negative impact of climate change on crop production.
42
Umar, Haruna Suleiman, Amin Mahir Abdullah, Mad Nasir Shamsudin, and Zainal Abidin Mohamed. "Welfare Implication of Paddy Price Support Withdrawal from Malaysian Rice Sector: Partial Equilibrium Method Approach." Agricultura Tropica et Subtropica 48, no. 3-4 (2015): 45–52. http://dx.doi.org/10.1515/ats-2015-0007.
Full textAbstract:
Abstract The study was designed to analyze societal welfare implication of paddy price support withdrawal, as an alternative policy, from rice sector in Malaysia. Time series data (1980-2012) were collected and analyzed through different stages of analyses. The first stage of analysis involved time series econometric model namely, Auto Regressive Distributed Lag (ARDL), which was used in coefficients estimation. Estimated coefficients were subjected to, and passed the relevant diagnostic tests. The estimated elasticities were then used for the second stage of analysis- scenario simulation. Finally, the generated simulation results were further used in estimating the societal welfare changed through appropriate estimation technique. Results show producer welfare loss of about RM189 million, and RM198 million was saved as revenue. The net gain or societal welfare improvement was about RM9 million. Simulated results show up to 10% reduction in paddy producer price or farm income; this could serve as disincentive to rice producers. Since the country is concerned about achieving rice self-sufficiency and rice food security, necessary precautionary measures have to be instituted to prevent farmers exit from paddy farming, by putting a concerted effort towards channeling the trickle-down benefit of societal welfare improvement, resulting from policy option, to rice producers particularly the dominant smallholder group.
43
Barajas, John Raymond, and Arpon Lucero. "Gasification of rice hulls into methane by rumen fluid: A simulation study." MATEC Web of Conferences 268 (2019): 07002. http://dx.doi.org/10.1051/matecconf/201926807002.
Full textAbstract:
Methane gas, the third widely-used source of energy, has been identified as a potential alternative to climate-change causing fuels. Due to increased climate change awareness, recent trends in researches have shifted their focus on optimizing production rates of methane gas. This study contributes to research by investigating the degree of conversion of rice hulls into methane using rumen fluid. We first identify the kinetic parameters defining therate of hydrolyzing rice hulls into glucose by implementing a semi-factorial experimental design. We then simulated methane production in four different reactor configurations. Historical data were extracted from different literatures and these were subsequently used in the simulation study. Simulation results showed that the continuous stirred tank reactor (CSTR) gave the shortest reaction time and highest methane yield which ranged from 7.1 9.5 mol/L and 3 4 days, respectively. In conclusion, this study provides an alternative approach to the conduct of understanding the optimal conditions necessary to achieve maximal methane gas production.
44
Castro, João Rodrigo de, Santiago Vianna Cuadra, Luciana Barros Pinto, João Marcelo Hoffmann de Souza, Marcos Paulo dos Santos, and Alexandre Bryan Heinemann. "Parametrization of Models and Use of Estimated Global Solar Radiation Data in the Irrigated Rice Yield Simulation." Revista Brasileira de Meteorologia 33, no. 2 (2018): 238–46. http://dx.doi.org/10.1590/0102-7786332003.
Full textAbstract:
Abstract The objective of this study was to evaluate the use of estimated global solar radiation data in the simulations of potential yield of irrigated rice. Global solar radiation was estimated by four empirical models, based on air temperature, and a meteorological satellite derivated. The empirical models were calibrated and validated for 10 sites, representative of the six rice regions of the State of Rio Grande do Sul - Brazil. To evaluate the impact of the radiation estimates on irrigated rice yield simulations, the CERES-Rice model, calibrated for four cultivars, was used. The estimates of global solar radiation of the empirical models based on the air temperature showed deviations, from the observed values, of 20 to 30% and the estimated by satellite deviations of more than 30%. The global solar radiation data estimated by the Hargreaves and Samani, Donatelli and Campbell and derived satellite (PowerNasa) type air temperature-based empirical models can be used as input data in simulation models of crop growth, development and productivity of irrigated rice.
45
Mucharam, Iim, Ernan Rustiadi, Akhmad Fauzi, and Harianto. "Assessment of Rice Farming Sustainability: Evidence from Indonesia Provincial Data." International Journal of Sustainable Development and Planning 15, no. 8 (2020): 1323–32. http://dx.doi.org/10.18280/ijsdp.150819.
Full textAbstract:
Indonesia is rated the highest rice consumer and the third-largest producer in the world, consequently, farming is one of the most strategic production systems in the country. Therefore, this study aims to assess the sustainability of rice farming at the provincial level in Indonesia. Furthermore, 32 sustainability indicators, which are categorized into five dimensions, namely economic, ecological, social, technological, and institutional were used. The rapid appraisal approach (Rapsusagri), consisting of multi-dimensional scaling (MDS) analysis was adopted to assess the sustainability of rice farming. Monte Carlo simulation was used to define the validity and sensitivity analysis to assess the dominant attributes which affect sustainability. The result showed that the economic and social dimensions are at a better level, meanwhile the ecological, technological, and institutional still have various weaknesses and needs improvement. Furthermore, irrigated paddy areas, agricultural infrastructure, rice productivity, use of chemical and organic fertilizers, cropping index, land suitability, village accessibility, officers, and agricultural extension institution were pointed out as the leveraging indicators for sustaining the rice farming system. Also, provinces in Java Island were found to have higher sustainability levels than others. However, it is predicted that this condition will last for a short period due to rapid land conversion, therefore Indonesia needs to consider the development of rice production areas outside Java islands.
46
Rizky, Dwi Prawoto*1 Isti Fadah2 &. Sumani3. "INVESTMENT SIMULATION OF REPLACING PRODUCTION MACHINERY AT A RICE FACTORY SUKORENO MAKMUR." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 6, no. 7 (2019): 45–50. https://doi.org/10.5281/zenodo.3342941.
Full textAbstract:
This study aims to assess the feasibility of investing in production machinery replacement in the Sukoreno Makmur rice mill business. In this study, Monte Carlo simulation was used to project various conditions that might occur in the future. This study uses 3 methods to assess the feasibility of investing in production machinery replacement, namely NPV, IRR, and DPP methods. The results of the calculation show that the investment plan for replacing the old production machine with a new production machine is feasible with the result of a positive NPV value, the IRR value is greater than the WACC value, and the payback time is equal to the expected payback time of the company owner. In this study data data to be simulated are production costs, sales revenue, and bank loan interest rates.
47
RAMAN, R. K., S. D. WAHI, and A. K. PAUL. "Linear discriminant function under multivariate non-normal rice (Oryza sativa) and maize (Zea mays) data." Indian Journal of Agricultural Sciences 82, no. 5 (2012): 426–9. http://dx.doi.org/10.56093/ijas.v82i5.17803.
Full textAbstract:
The performance of linear discriminant function was studied under multivariate non-normal situations. The different multivariate non-normal populations were simulated by using the mean vectors and dispersion matrices of rice (Oryza sativa L.) and maize (Zea mays L.) data sets. Further 50 different independent samples were simulated for different dimensions and sample sizes for maize and rice data to obtain empirical probabilities of misclassification. On fitting linear discriminant function to non-normal data the empirical probabilities of misclassification were higher as compared to misclassifying probabilities obtained by using normal approximation. In large sample sizes and in higher dimensions the differences between empirical and normal approximation of probabilities of misclassification were found almost negligible.
48
SANCHO SALAS, ANDREA, DANIEL BUITRAGO CARAZO, ANDRES CHACON REDONDO, et al. "ARCHITECTURE ADAPTATION TO CLIMATE CHANGE: DATA PROJECTION AND ENERGY SIMULATION OF TWO SCENARIOS FOR PUBLIC BUILDINGS IN COSTA RICA." DYNA 96, no. 4 (2021): 347–50. http://dx.doi.org/10.6036/9581.
Full textAbstract:
Climate change represents the biggest challenge in a global scale. Architecture should be able to adapt to this new conditions and ensure comfort and energy efficiency. The main goal of this research is to adapt existent public buildings and reach hygrothermal comfort for two climate change scenarios in Costa Rica. To determine the study zone three areas were analyzed: ecological, demographic and regional economic. Subsequently case studies are chosen through criteria to evaluate the impact of the intervention. A scaled bioclimatic analysis is done for each case and through a comparative analysis two cases are selected to be simulated. Hourly Weather Data files are created for the year 2080 by using observed data from “Instituto Meteorológico Nacional” (IMN) and projected data from “Centro de Investigaciones Geofísicas” (CIGEFI). Lastly, two adaptation proposals are done for each case, a mild one and a complete redesign; it’s behavior is evaluated using the software DesignBuilder® and design recommendations are proposed. Keywords: bioclimatic architecture, climate change, hygrothermal comfort, adaptation, simulation.
49
Gilardelli, Carlo, Tommaso Stella, Roberto Confalonieri, et al. "Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data." European Journal of Agronomy 103 (February 2019): 108–16. http://dx.doi.org/10.1016/j.eja.2018.12.003.
Full text
50
Jansen, D. M. "Potential rice yields in future weather conditions in different parts of Asia." Netherlands Journal of Agricultural Science 38, no. 4 (1990): 661–80. http://dx.doi.org/10.18174/njas.v38i4.16556.
Full textAbstract:
The effects of future climate change on potential yields of rice cv. IR36 grown at present and in 2020 and 2100 in China, India, Indonesia, Thailand and South Korea were estimated using the crop growth simulation model MACROS which combines the effects of temp., radiation, wind speed, air humidity and crop status on physiological processes. Historic weather data of these sites were adapted to possible changes in temp. and CO2 level, to mimic climate change. Simulated yields rose in low and middle temp. change scenarios, but decreased in the high temp. scenario. Water use efficiency decreased in the high temp. scenario irrespective of CO2 scenario, and increased otherwise. (Abstract retrieved from CAB Abstracts by CABI’s permission)