Journal articles: 'WOFOST'

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Zhu, Jiangxu, Wenzhi Zeng, Tao Ma, et al. "Testing and Improving the WOFOST Model for Sunflower Simulation on Saline Soils of Inner Mongolia, China." Agronomy 8, no. 9 (2018): 172. http://dx.doi.org/10.3390/agronomy8090172.

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Monitoring and improving environmental stress in crops is vital for the sustainable development of agriculture and food security. Traditional experimental methods are costly and time-consuming, yet crop growth models focus mainly only on water and nutrient stresses. In this study, a new World Food Studies (WOFOST) model, WOFOST-ES, was developed by the addition of a general environmental stress factor (ES). To calibrate and validate WOFOST-ES, two-year micro-plot experiments and one-year field experiments with sunflower were conducted in the Hetao Irrigation District, China. The results of the micro-plot experiments indicated that the WOFOST model failed to simulate sunflower growth correctly but that the WOFOST-ES model was highly accurate in simulating both yield (R2 = 0.99, root mean square error (RMSE) = 56 kg/ha) and leaf area index (LAI) (R2 = 0.86, RMSE = 0.44). A statistical method for estimating ESs based on the dominant stress factor (salt at our study site) was also proposed as a supplemental tool for WOFOST-ES, and micro-plot and field experiments conducted in 2013 and 2017 both proved acceptable accuracy of the statistical method when using WOFOST-ES. Comparison between ESs and the water and salt stress factors of Feddes-type stress reduction functions indicated that ESs failed to reveal actual environmental stresses during the sunflower seeding stage but did reflect other environmental stresses in addition to water and salt during the bud, flowering, and maturity stages. Although the present WOFOST-ES model proved to be accurate, stable, and practical, future studies should be performed, focusing on the physical separation of ESs, their mechanistic quantification, and their evaluation at small time steps using more observations.

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Quintero, Diego, and Eliécer Díaz. "A comparison of two open-source crop simulation models for a potato crop." Agronomía Colombiana 38, no. 3 (2020): 382–87. http://dx.doi.org/10.15446/agron.colomb.v38n3.82525.

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An open-source model is a model that makes it possible to modify the source code. This tool can be a great advantage for the user since it allows changing or modifying some of the background theory of the model. World Food Studies (WOFOST) and AquaCropOS open-source crop models were compared using field recorded data. Both models are free open-source tools that allow evaluating the impacts of climate and water on agriculture. The objective of this research was to assess the model’s efficiency in simulating the yield and above-ground biomass formation of a potato crop on the Cundiboyacense plateau. WOFOST simulates biomass accumulation in the crop organs using partitioning of assimilates to establish the biomass fraction that turns into yield. AquaCropOS simulates total above-ground biomass accumulation using crop water productivity (WP) and considers the Harvest Index (HI) to calculate yield formation. Crop modules for both models were built using information recorded in previous studies by other authors; those works performed a physiological and phenological characterization of some potato varieties. It was found that the WOFOST model simulates yield formation better than AquaCropOS; despite that, AquaCropOS simulates total above-ground biomass better than WOFOST. However, AquaCropOS was as efficient as WOFOST in simulating yield formation.

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Zhuo, Wen, Jianxi Huang, Xinran Gao, et al. "Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model." Remote Sensing 12, no. 18 (2020): 2896. http://dx.doi.org/10.3390/rs12182896.

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Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.

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Hadiya, Nilesh, Neeraj Kumar, B. M. Mote, Chiragkumar Thumar, and D. Patil. "Comparative evaluation of WOFOST and CERES-rice models in simulating yield of rice cultivars at Navari." Oriental journal of computer science and technology 10, no. 1 (2017): 255–59. http://dx.doi.org/10.13005/ojcst/10.01.35.

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A field experiment was conducted during kharif season of 2015 to assess the prediction performance of CERES-Rice and WOFOST model for grain and straw yield of three rice cultivars viz., (V1:Jaya, V2: Gurjari and V3: GNR-2) sown under four different environments viz., (D1: 10/07/2015, D2: 25/07/2015, D3: 09/08/2015 and D4: 24/08/2015) with two nitrogen levels N1:75 and N2:100 kg NPK/ha-1.Results showed that the prediction of WOFOST model forgrain yield of rice cultivars under different treatments more close to the corresponding observed value with percent error PE between (18.66%)as camper to CERES-rice model with PE (28.56%), but for straw yield CERES-rice model give more close prediction than WOFOST model with PE (20.99%) and (27.33%) between predicted and observed value.

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Hensawang, Saruda, Sittisak Injan, Pariwate Varnakovida, and Usa Humphries. "Predicting Rice Production in Central Thailand Using the WOFOST Model with ENSO Impact." Mathematical and Computational Applications 26, no. 4 (2021): 72. http://dx.doi.org/10.3390/mca26040072.

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The World Food Studies Simulation Model (WOFOST) model is a daily crop growth and yield forecast model with interactions with the environment, including soil, agricultural management, and especially climate conditions. An El Niño–Southern Oscillation (ENSO) phenomenon directly affected climate change and indirectly affected the rice yield in Thailand. This study aims to simulate rice production in central Thailand using the WOFOST model and to find the relationship between rice yield and ENSO. The meteorological data and information on rice yields of Suphan Buri 1 variety from 2011 to 2018 in central Thailand were used to study the rice yields. The study of rice yield found that the WOFOST model was able to simulate rice yield with a Root Mean Square Error (RMSE) value of 752 kg ha−1, with approximately 16% discrepancy. The WOFOST model was able to simulate the growth of Suphan Buri 1 rice, with an average discrepancy of 16.205%, and Suphan Buri province had the least discrepancy at 6.99%. Most rice yield simulations in the central region were overestimated (except Suphan Buri) because the model did not cover crop damage factors such as rice disease or insect damage. The WOFOST model had good relative accuracy and could respond to estimates of rice yields. When an El Niño phenomenon occurs at Niño 3.4, it results in lower-than-normal yields of Suphan Buri 1 rice in the next 8 months. On the other hand, when a La Niña phenomenon occurs at Niño 3.4, Suphan Buri 1 rice yields are higher than normal in the next 8 months. An analysis of the rice yield data confirms the significant impact of ENSO on rice yields in Thailand. This study shows that climate change leads to impacts on rice production, especially during ENSO years.

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Radka Kodešová and Lukáš, Brodský. "Comparison of CGMS-WOFOST and HYDRUS-1D Simulation Results for One Cell of CGMS-GRID50." Soil and Water Research 1, No. 2 (2013): 39–48. http://dx.doi.org/10.17221/6504-swr.

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CGMS (Crop Growth Monitoring System) developed by JRC is an integrated system to monitor crop behaviour and quantitative crop yield forecast that operates on a European scale. To simulate water balance in the root zone the simulation model CGMS-WOFOST (SUPIT &amp; VAN DER GOOT 2003) is used that is based on water storage routing. This study was performed to assess a possible impact of simplifications of the water storage routing based model on simulated water regime in the soil profile. Results of CGMS-WOFOST are compared with results of a more precise Richards&rsquo; equation based model HYDRUS-1D (&Scaron;IMŮNEK et al. 2005). 16 scenarios are simulated using HYDRUS-1D. Each scenario represents a single soil profile presented in the selected cell of GRID50 in the Czech Republic. Geometry of the soil profiles, material (texture) definition, root distributions, measured daily rainfall, calculated daily evaporation from the bare soil surface and transpiration of crop canopy were defined similarly to CGMS-WOFOST inputs according to the data stored in the SGDBE40 database. The soil hydraulic properties corresponding to each soil layer were defined using the class transfer rules (W&Ouml;STEN et al. 1999). The bottom boundary conditions were defined either similarly to CGMS-WOFOST bottom boundary condition as a free drainage or as a constant water level 250 cm below the soil surface to demonstrate a ground water impact on the soil profile water balance. The relative soil moisture (RSM) in the root zone during the vegetation period was calculated to be compared with the similar output from CGMS. The RSM values obtained using HYDRUS-1D are higher than those obtained using CGMS-WOFOST mostly due to higher retention ability of HYDRUS-1D. The reasonably higher RSM values were obtained at the end of simulated period using the HYDRUS-1D for the constant water level 250 cm below the soil surface.

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JOYDEEP MUKHERJEE, LAKHWINDER SINGH, GURJOT SINGH, S.K. BAL, HARPREET SINGH, and PRABHJYOT KAUR. "Comparative evaluation of WOFOST and ORYZA2000 models in simulating growth and development of rice (Oryza sativa L.) in Punjab." Journal of Agrometeorology 13, no. 2 (2011): 86–91. http://dx.doi.org/10.54386/jam.v13i2.1347.

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Two simulation models, viz. WOFOST and ORYZA2000 were validated and compared to predict growth and productivity of 2 rice varieties (PR 116 and PR 118), under central plain regions of Punjab, India, during 2006-2008. The simulated values of dry weight of leaves, dry weight of stem, above ground biomass, leaf area index and grain yield did not differ significantly with observed values. Based on statistical evaluation of performance of crop simulation models, ORYZA2000 showed an advantage over WOFOST model in simulating crop growth parameters and grain yield of rice.

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Dewenam, Lucas Emmanuel Fesonae, Salah Er-Raki, Jamal Ezzahar, and Abdelghani Chehbouni. "Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation." Agronomy 11, no. 12 (2021): 2480. http://dx.doi.org/10.3390/agronomy11122480.

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The main goal of this investigation was to evaluate the potential of the WOFOST model for estimating leaf area index (LAI), actual evapotranspiration (ETa), soil moisture content (SM), above-ground biomass levels (TAGP) and grain yield (TWSO) of winter wheat in the semi-arid region of Tensift Al Haouz, Marrakech (central Morocco). An application for the estimation of the Yield Gap is also provided. The model was firstly calibrated based on three fields data during the 2002–2003 and 2003–2004 growing seasons, by using the WOFOST implementation in the Python Crop simulation Environment (PCSE) to optimize the different parameters that provide the minimum difference between the measured and simulated LAI, TAGP, TWSO, SM and ETa. Then, the model validation was performed based on the data from five other wheat fields. The results obtained showed a good performance of the WOFOST model for the estimation of LAI during both growing seasons on all validation fields. The average R2, RSME and NRMSE were 91.4%, 0.57 m2/m2, and 41.4%, respectively. The simulated ETa dynamics also showed a good agreement with the observations by eddy covariance systems. Values of 60% and 72% for R2, 0.8 mm and 0.7 mm for RMSE, 54% and 31% for NRMSE are found for the two validation fields, respectively. The model’s ability to predict soil moisture content was also found to be satisfactory; the two validation fields gave R2 values equal to 48% and 49%, RMSE values equal to 0.03 cm3/cm3 and 0.05 cm3/cm3, NRMSE values equal to 11% and 19%. The calibrated model had a medium performance with respect to the simulation of TWSO (R2 = 42%, RSME = 512 kg/ha, NRMSE = 19%) and TAGP (R2 = 34% and RSME = 936 kg/ha, NRMSE = 16%). After accurate calibration and validation of the WOFOST model, it was used for analyzing the gap yield since this model is able to estimate the potential yield. The WOFOST model allowed a good simulation of the potential yield (7.75 t/ha) which is close to the optimum value of 6.270 t/ha in the region. Yield gap analysis reveals a difference of 5.35 t/ha on average between the observed yields and the potential yields calculated by WOFOST. Such difference is ascribable to many factors such as the crop cycle management, agricultural practices such as water and fertilization supply levels, etc. The various simulations (irrigation scenarios) showed that early sowing is more adequate than late sowing in saving water and obtaining adequate grain yield. Based on various simulations, it has been shown that the early sowing (mid to late December) is more adequate than late sowing with a total amount of water supply of about 430 mm and 322 kg (140 kg of N, 80 kg of P and 102 kg of K) of fertilization to achieve the potential yield. Consequently, the WOFOST model can be considered as a suitable tool for quantitative monitoring of winter wheat growth in the arid and semi-arid regions.

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Yang, Tianle, Weijun Zhang, Tong Zhou, Wei Wu, Tao Liu, and Chengming Sun. "Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model." PLOS ONE 16, no. 10 (2021): e0246874. http://dx.doi.org/10.1371/journal.pone.0246874.

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The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), biomass, and final yield are experimentally measured. LAI data is retrieved from the UAV imagery and assimilated into a localized WOFOST model using least squares optimization. Sensitive parameters, i.e., specific leaf area (SLATB0, SLATB0.5, SLATB2) and maximum CO2 assimilation rate (AMAXTB1, AMAXTB1.3) are adjusted to minimize the discrepancy between the LAI obtained from the model simulation and inversion of the UAV data. The results show that the assimilated model provides a better estimation of the growth and development of winter wheat in the study area. The R2, RMSE, and NRMSE of winter wheat LAI simulated with the assimilated WOFOST model are 0.8812, 0.49, and 23.5% respectively. The R2, RMSE, and NRMSE of the simulated yield are 0.9489, 327.06 kg·hm−2, and 6.5%. The accuracy in model simulation of winter wheat growth is improved, which demonstrates the feasibility of integrating UAV data into crop models.

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Diepen, C. A., J. Wolf, H. Keulen, and C. Rappoldt. "WOFOST: a simulation model of crop production." Soil Use and Management 5, no. 1 (1989): 16–24. http://dx.doi.org/10.1111/j.1475-2743.1989.tb00755.x.

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Kulig, Bogdan, Barbara Skowera, Agnieszka Klimek-Kopyra, Stanisław Kołodziej, and Wiesław Grygierzec. "The Use of the WOFOST Model to Simulate Water-Limited Yield of Early Potato Cultivars." Agronomy 10, no. 1 (2020): 81. http://dx.doi.org/10.3390/agronomy10010081.

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In this work, an attempt was made to use the WOFOST (WOrld FOod Studies) model to simulate the potential and water-limited yield of early potato cultivars Lord and Denar. Data from cultivar experiments carried out at the Polish Research Centre for Cultivar Testing in 2004–2013 were used in the study. The Lord cultivar yielded 22.4–67.8 t fresh tuber weight per ha and 3.8–11.5 t ha−1 dry tuber weight during the study period. The highest tuber yields (over 10 t ha−1 dry weight) were obtained in 2009, 2011 and 2012, and the lowest in 2005 (3.8 t ha−1) and 2006 (2.65 t ha−1). The water-limited tuber yield simulated by WOFOST ranged from 3.6 to 10.9 t ha−1 dry weight and was about 0.45 t ha−1 higher on average than the actual yield. The planting period each year was between days 104 and 120 of the year, and harvesting took place between days 216 and 232. Water availability was a factor limiting the yield. The yield limited by water deficiency was 38.7% lower (irrespective of the cultivar) than the potential yield. The WOFOST model was sensitive to water deficiency, and the simulated (water-limited) yields were close to the actual yield or showed a clear downward trend indicating evident rainfall shortages in 2005 and 2006.

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S. K. MISHRA, A.M. SHEKH, S. B. YADAV, et al. "Simulation of growth and yield of four wheat cultivars using WOFOST model under middle Gujarat region." Journal of Agrometeorology 15, no. 1 (2013): 43–50. http://dx.doi.org/10.54386/jam.v15i1.1437.

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WOFOST is a mechanistic crop growth simulation model capturing the complex effect of climate, genotype and agronomic variable through several functions. Results showed that mean observed days to anthesis were 57.9 ± 2.5, 61.1 ± 2.1, and 59.5 ± 1.6 during 2009-10, 2010-11 and for pooled data, while simulated days to anthesis were 60.3 ± 3.9, 62.8 ± 2.0 and 61.6 ± 2.2, respectively. The values ofRMSE for simulated maximum LAI were 0.11, 0.08 and 0.08 for 2009-10, 2010-11 and for pooled data, respectively. The observed mean yields were 3406 ± 223, 3757 ± 684 and 3581 ± 430 kg ha-1 during 2009-10, 2010-11 and for pooled analysis while, respective simulated mean yields were 3496 ± 435, 4061 ± 684 and 3778 ± 494 kg ha-1. Likewise, measured above ground production were 8349 ± 752, 8495 ± 953 and 8422 ± 796 kg ha-1 during 2009-10, 2010-11 and for pooled data, while corresponding simulated biomass were 8787 ± 698, 8910 ± 733 and 8889 ± 653 kg ha-1, respectively. The very low value of correlation coefficient r=0.30 and standard deviation ± 4.26 proves the failure of WOFOST model for simulation of harvest index during 2009-10. However, during second year and for pooled analysis the model efficiently and accurately simulated the harvest index. The model performance was somewhere underestimated or overestimated but found within quite acceptable limits. The WOFOST model may be used for simulation and forecasting the yield of wheat.

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ALKAN, Çayan, and Fatih KONUKCU. "Determination of the Effect of Climate Change on Wheat Yield in the Porsuk Creek Watershed." ISPEC Journal of Agricultural Sciences 6, no. 2 (2022): 318–30. http://dx.doi.org/10.46291/ispecjasvol6iss2id296.

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The wheat is one of the main agricultural products that will be affected by climate change. The aim of the study is to determine the effect of climate change on wheat yield in Porsuk Creek Watershed. In this study, wheat yield analyzes in the Porsuk Creek watershed had been conducted using the past (2016-2017) and future (2020-2100) climate data produced according to the optimistic (RCP4.5) and pessimistic (RCP8.5) scenarios of the HadGEM2-ES global climate model, with the help of the WOFOST model. In the Porsuk Creek Watershed, a +23.8% difference for 2016 and a +1.2% difference for 2017 was determined between the observed and predicted by WOFOST model wheat biomass values (2017 values>2016 values and estimated>observed). According to the optimistic scenario results in the watershed, 0.73% wheat yield increase is expected in the near future (2020-2045). In all other remaining periods; it is estimated that there will be a decrease in wheat yields (between 0.43-1.5%). Compared to reference period (1970-2000), the climate change in the creek watershed will occur in the manner of temperature and precipitation increases in the future. As a result, although WOFOST tends to predict wheat yields greater than the observed values, it is thought that the model can be used with confidence to predict future wheat yields. As a result of this study, important data about the planning of wheat agriculture were produced by estimating the plant yield for the use of decision makers.

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de Wit, Allard, Hendrik Boogaard, Davide Fumagalli, et al. "25 years of the WOFOST cropping systems model." Agricultural Systems 168 (January 2019): 154–67. http://dx.doi.org/10.1016/j.agsy.2018.06.018.

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Kolotii, A., N. Kussul, A. Shelestov, et al. "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 39–44. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-39-2015.

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Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Hack-ten Broeke, Mirjam J. D., Joop G. Kroes, Ruud P. Bartholomeus, et al. "Quantification of the impact of hydrology on agricultural production as a result of too dry, too wet or too saline conditions." SOIL 2, no. 3 (2016): 391–402. http://dx.doi.org/10.5194/soil-2-391-2016.

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Abstract. For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, that can quantify the differences between years and also the effects of extreme weather events. Furthermore they would like a method that considers current farm management and that can distinguish three different causes of crop yield reduction: drought, saline conditions or too wet conditions causing oxygen shortage in the root zone. WaterVision Agriculture is based on the hydrological simulation model SWAP and the crop growth model WOFOST. SWAP simulates water transport in the unsaturated zone using meteorological data, boundary conditions (like groundwater level or drainage) and soil parameters. WOFOST simulates crop growth as a function of meteorological conditions and crop parameters. Using the combination of these process-based models we have derived a meta-model, i.e. a set of easily applicable simplified relations for assessing crop growth as a function of soil type and groundwater level. These relations are based on multiple model runs for at least 72 soil units and the possible groundwater regimes in the Netherlands. So far, we parameterized the model for the crops silage maize and grassland. For the assessment, the soil characteristics (soil water retention and hydraulic conductivity) are very important input parameters for all soil layers of these 72 soil units. These 72 soil units cover all soils in the Netherlands. This paper describes (i) the setup and examples of application of the process-based model SWAP-WOFOST, (ii) the development of the simplified relations based on this model and (iii) how WaterVision Agriculture can be used by farmers, regional government, water boards and others to assess crop yield reduction as a function of groundwater characteristics or as a function of the salt concentration in the root zone for the various soil types.

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Wang, Desheng, Chengkun Wang, Lichao Xu, Tiecheng Bai, and Guozheng Yang. "Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang." Agriculture 12, no. 7 (2022): 895. http://dx.doi.org/10.3390/agriculture12070895.

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Planting with non-film mulching is the fundamental means to eliminate the pollution of residual film in cotton fields. However, this planting approach should have regional adaptability. Therefore, the calibrated WOFOST model and an early mature cultivar CRI619 (Gossypium hirsutum Linn) were employed to simulate the cotton growth, and regions were then evaluated for planting in Xinjiang. A field experiment was conducted in 2019–2020 at the experimental irrigation station of Alar City, and the data were used to calibrate and validate the WOFOST model. The field validation results showed that the errors of the WOFOST simulation for emergence, flowering, and maturity were +1 day, +2 days, and +1 day, respectively, with good simulation accuracy of phenological development time. The simulated WLV, WST, WSO, and TAGP agreed well with measured values, with R2 = 0.96, 0.97, 0.99, and 0.99, respectively. The RMSE values of simulated versus measured WLV, WST, WSO, and TAGP were 175, 210, 199, and 251 kg ha−1, and showed high accuracy. The simulated soil moisture (SM) agreed with the measured value, with R2 = 0.87. The calibration model also showed high SM simulation accuracy, with RMSE = 0.022 (cm3 cm−3). Under all treatments, the simulated TAGP and yield agreed well with the measured results, with R2 of 0.76 and 0.70, respectively. RMSE of simulated TAGP and yield was 465 and 200 kg ha−1, and showed high accuracy. The percentage RMSE values (ratio of RMSE to the average measured value, NRMSE) of ETa and WUE were 9.8% and 11.7%, indicating extremely high precision (NRMSE < 10%) and high precision (10% < NRMSE ≤ 20%), respectively. The simulated results for phenology length at the regional scales showed that the effective accumulation temperature in counties such as Yingjisha and Luntai was not enough for the phenological maturity of the studied cotton cultivar. The southern area of Xinjiang had a generally higher yield than the northern area but required more irrigation. This research can provide a method for evaluating the adaptability of filmless cultivation techniques for cotton in different counties.

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Ding-Rong, WU, OUYANG Zhu, ZHAO Xiao-Min, YU Qiang, and LUO Yi. "The Applicability Research of WOFOST Model in North China Plain." Chinese Journal of Plant Ecology 27, no. 5 (2003): 594–602. http://dx.doi.org/10.17521/cjpe.2003.0086.

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Sun, Xiulu, Yizan Li, Marius Heinen, Henk Ritzema, Petra Hellegers, and Jos van Dam. "Fertigation Strategies to Improve Water and Nitrogen Use Efficiency in Surface Irrigation System in the North China Plain." Agriculture 13, no. 1 (2022): 17. http://dx.doi.org/10.3390/agriculture13010017.

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Irrigation and fertilisation are often over-applied, which exceeds crop requirements. Surface fertigation, a technique of applying pre-dissolved fertilisers together with irrigation water, seems to be a viable way to improve the on-farm performance in the North China Plain (NCP). Thus, we conducted a field experiment based on farmers’ practices from 2017 to 2019. Moreover, we calibrated and validated SWAP-WOFOST-N, a seasonal integrated agro-hydrology and crop growth model, to assess the effects of different practices on yield, water and nitrogen use efficiency (WUE and NUE) and resource loss. Lastly, we developed various scenarios using the model to determine improved strategies. The results showed that the SWAP-WOFOST and extended Soil-N model offered satisfactory accuracy when compared with field measured data for the tested domain of the hydrological and nitrogen cycle; farmers’ current irrigation and fertilisation practices resulted in low WUE and NUE, but the practice of split top-dressing nitrogen did not show significant improvement in the surface irrigation system; WUE, NUE and nitrogen loss were closely related to irrigation practices. We further concluded that an optimised irrigation practice combined with an optimal fertigation scenario is the feasible strategy to achieve sustainable crop yield, high WUE and NUE and reduced nitrogen loss.

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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.

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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.

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Bai, Tiecheng, Nannan Zhang, Benoit Mercatoris, and Youqi Chen. "Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model." Remote Sensing 11, no. 9 (2019): 1119. http://dx.doi.org/10.3390/rs11091119.

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Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha−1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m−2 for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R2 = 0.79) and prediction accuracy (RMSE = 0.17 m2 m−2). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R2 of 0.62 and RMSE of 0.74 t ha−1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha−1 for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops.

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Shi, Yinfang, Zhaoyang Wang, Cheng Hou, and Puhan Zhang. "Yield estimation of Lycium barbarum L. based on the WOFOST model." Ecological Modelling 473 (November 2022): 110146. http://dx.doi.org/10.1016/j.ecolmodel.2022.110146.

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Zhou, Jian, Guodong Cheng, Xin Li, Bill X. Hu, and Genxu Wang. "Numerical Modeling of Wheat Irrigation using Coupled HYDRUS and WOFOST Models." Soil Science Society of America Journal 76, no. 2 (2012): 648–62. http://dx.doi.org/10.2136/sssaj2010.0467.

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Liu, Jiandong, Jun Du, De-Li Liu, et al. "Spatial and Temporal Variations in the Potential Yields of Highland Barley in Relation to Climate Change in Three Rivers Region of the Tibetan Plateau from 1961 to 2020." Sustainability 14, no. 13 (2022): 7719. http://dx.doi.org/10.3390/su14137719.

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Spatial and temporal variations in the potential yields of highland barley is important for making policies on adaptation of agriculture to climate change in the Three Rivers Region (TRR), one of the main highland barley growing areas on the Tibetan Plateau. This research tries to explore a suitable strategy for simulating potential yields of highland barley by the WOFOST (WOrld FOod STudies) crop growth model, and further to identify variations in climate conditions and potential yields in TRR from 1961 to 2020 for making policies on adaptation of agricultural production to the climate change impacts on the Tibetan Plateau. Validation results indicated that WOFOST could accurately simulate the potential yields of highland barley with the global radiation estimated by the calibrated Angstrom model. The global radiation during the growth periods decreased at a rate of 0.047 MJ/m2a, while the temperature during the growth periods increased at rates ranging from 0.019 to 0.087 °C/a, which was greater than the average warming rate of the globe. The simulated potential yields ranged from 10,300 to 14,185 kg/ha in TRR, with an average decreasing rate of 28 kg/ha/a. The decrease in the potential yields was mainly attributed to the shortened critical period caused by warming effects, so cultivation of new varieties of highland barley with longer growth periods is suggested as an achievable strategy for the adaptation of highland barley to climate change in TRR.

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Aondongu, Achir Jerome, Iorshase Agaji, and Esiefarienrhe Bukohwo M. "A Hybrid WOFOST and Cropsyst Model for the Prediction of Crop Yield." International Journal of Computer Applications Technology and Research 8, no. 12 (2019): 004–12. http://dx.doi.org/10.7753/ijcatr0801.1002.

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Confalonieri, Roberto, Marco Acutis, Gianni Bellocchi, and Marcello Donatelli. "Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice." Ecological Modelling 220, no. 11 (2009): 1395–410. http://dx.doi.org/10.1016/j.ecolmodel.2009.02.017.

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Hu, Shun, Liangsheng Shi, Kai Huang, et al. "Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation." Field Crops Research 232 (February 2019): 49–61. http://dx.doi.org/10.1016/j.fcr.2018.12.009.

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Gilardelli, Carlo, Roberto Confalonieri, Giovanni Alessandro Cappelli, and Gianni Bellocchi. "Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change." Ecological Modelling 368 (January 2018): 1–14. http://dx.doi.org/10.1016/j.ecolmodel.2017.11.003.

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Wang, Chengkun, Nannan Zhang, Mingzhe Li, Li Li, and Tiecheng Bai. "Pear Tree Growth Simulation and Soil Moisture Assessment Considering Pruning." Agriculture 12, no. 10 (2022): 1653. http://dx.doi.org/10.3390/agriculture12101653.

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Few studies deal with the application of crop growth models to fruit trees. This research focuses on simulating the growth process, yield and soil moisture assessment of pear trees, considering pruning with a modified WOrld FOod Studies (WOFOST) model. Field trials (eight pruning treatments) were conducted in pear orchards in Alaer and Awat in Xinjiang, China and data were measured to calibrate and evaluate the modified model. In two pear orchards, the simulated total dry weight of storage organs (TWSO) and leaf area index (LAI) were in good agreement with the field measurements of each pruning intensity treatment, indicating that the R2 values of TWSO ranged from 0.899 to 0.976, and the R2 values of LAI ranged from 0.849 to 0.924. The modified model also showed high accuracy, with a normalized root mean square error (NRMSE) ranging from 12.19% to 26.11% for TWSO, and the NRMSE values for LAI were less than 10%. The modified model also had a good simulation performance for the soil moisture (SM) under all eight pruning intensity treatments, showing good agreement (0.703 ≤ R2 ≤ 0.878) and low error (NRMSE ≤ 7.47%). The measured and simulated results of different pruning intensities showed that the highest yield of pear trees was achieved when the pruning intensity was about 20%, and the yield increased and then decreased with the increase in pruning intensity. In conclusion, the modified WOFOST model can better describe the effects of summer pruning on pear tree growth, yield and soil moisture than the unmodified model, providing a promising quantitative analysis method for the numerical simulation and soil moisture assessment of fruit tree growth.

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Ren, Yiting, Qiangzi Li, Xin Du, et al. "Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning." Plants 12, no. 3 (2023): 446. http://dx.doi.org/10.3390/plants12030446.

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Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn’s vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.

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Pinto, Victor Meriguetti, Jos C. van Dam, Quirijn de Jong van Lier, and Klaus Reichardt. "Intercropping Simulation Using the SWAP Model: Development of a 2×1D Algorithm." Agriculture 9, no. 6 (2019): 126. http://dx.doi.org/10.3390/agriculture9060126.

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Intercropping is a common cultivation system in sustainable agriculture, allowing crop diversity and better soil surface exploitation. Simulation of intercropped plants with integrated soil–plant–atmosphere models is a challenging procedure due to the requirement of a second spatial dimension for calculating the soil water lateral flux. Evaluations of more straightforward approaches for intercrop modeling are, therefore, mandatory. An adaptation of the 1D model Soil, Water, Atmosphere and Plant coupled to the World Food Studies (SWAP/WOFOST) to simulate intercropping (SWAP 2×1D) based on solar radiation and water partitioning between plant strips was developed and the outcomes are presented. An application of SWAP 2×1D to maize–soybean (MS) strip intercropping was evaluated against the monocropping maize (M) and soybean (S) simulated with the 1D model SWAP/WOFOST, and a sensitivity analysis of SWAP 2×1D was carried out for the intercropping MS. SWAP 2×1D was able to simulate the radiation interception by both crops in the intercropping MS and also to determine the effect of the radiation attenuation by maize on soybean plants. Intercropped plants presented higher transpiration and resulted in lower soil evaporation when compared to their equivalent monocropping cultivation. A numerical issue involving model instability caused by the simulated lateral water flux in the soil from one strip to the other was solved. The most sensitive plant parameters were those related to the taller plant strips in the intercropping, and soil retention curve parameters were overall all significantly sensitive for the water balance simulation. This implementation of the SWAP model presents an opportunity to simulate strip intercropping with a limited number of parameters, including the partitioning of radiation by a well-validated radiation sharing model and of soil water by simulating the lateral soil water fluxes between strips in the 2×1D environment.

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Ma, Guannan, Jianxi Huang, Wenbin Wu, Jinlong Fan, Jinqiu Zou, and Sijie Wu. "Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield." Mathematical and Computer Modelling 58, no. 3-4 (2013): 634–43. http://dx.doi.org/10.1016/j.mcm.2011.10.038.

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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 (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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Pan, Haizhu, Zhongxin Chen, Allard de Wit, and Jianqiang Ren. "Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation." Sensors 19, no. 14 (2019): 3161. http://dx.doi.org/10.3390/s19143161.

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It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

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Zhao, Bingyu, Meiling Liu, Jianjun Wu, Xiangnan Liu, Mengxue Liu, and Ling Wu. "Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images." IEEE Access 8 (2020): 223675–85. http://dx.doi.org/10.1109/access.2020.3043003.

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Ceglar, A., R. van der Wijngaart, A. de Wit, et al. "Improving WOFOST model to simulate winter wheat phenology in Europe: Evaluation and effects on yield." Agricultural Systems 168 (January 2019): 168–80. http://dx.doi.org/10.1016/j.agsy.2018.05.002.

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Cheng, Zhiqiang, Jihua Meng, Jiali Shang, et al. "Improving Soil Available Nutrient Estimation by Integrating Modified WOFOST Model and Time-Series Earth Observations." IEEE Transactions on Geoscience and Remote Sensing 57, no. 5 (2019): 2896–908. http://dx.doi.org/10.1109/tgrs.2018.2878382.

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Gilardelli, C., T. Stella, N. Frasso, et al. "WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality." Field Crops Research 197 (October 2016): 125–32. http://dx.doi.org/10.1016/j.fcr.2016.07.013.

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Curnel, Yannick, Allard J. W. de Wit, Grégory Duveiller, and Pierre Defourny. "Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment." Agricultural and Forest Meteorology 151, no. 12 (2011): 1843–55. http://dx.doi.org/10.1016/j.agrformet.2011.08.002.

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ZHANG Jianping, 张建平, 赵艳霞 ZHAO Yanxia, 王春乙 WANG Chunyi, 杨晓光 YANG Xiaoguang, and 王靖 WANG Jing. "Evaluation technology on drought disaster to yields of winter wheat based on WOFOST crop growth model." Acta Ecologica Sinica 33, no. 6 (2013): 1762–69. http://dx.doi.org/10.5846/stxb201112071869.

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Dhillon, Maninder Singh, Thorsten Dahms, Carina Kuebert-Flock, Erik Borg, Christopher Conrad, and Tobias Ullmann. "Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany." Remote Sensing 12, no. 11 (2020): 1819. http://dx.doi.org/10.3390/rs12111819.

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This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).

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RAJI PUSHPALATHA, GOVINDAN KUTTY, and BYJU GANGADHARAN. "Sensitivity analysis of WOFOST for yield simulation of cassava over the major growing areas of India." Journal of Agrometeorology 23, no. 4 (2021): 375–80. http://dx.doi.org/10.54386/jam.v23i4.140.

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A study was conducted to assess the meteorological sensitivity of the WOFOST crop model in simulating the yield of cassava. The sensitivity was designed by changing the present meteorological data by ±1 to ±5 %. The results has shown the minimum temperature influencing the yield of cassava (variation: 4.94 to -7.65 %) followed by the maximum temperature (yield variation: 6.39 to -6.03 %) and solar radiation (yield variation: -2.41 to 2.07 %). The trends of these meteorological variables have been further analyzed over the major cassava growing regions in India to link its variations with cassava production. A significant trend has been detected during the monsoon season in northeast India, with a decadal change of 0.63ºC. At the same time, a significant trend was detected in the peninsular region during the winter season, with a value of 0.74ºC/decade. The rate of solar dimming in northeast India during the monsoon season was -0.53 hour/decade and during the autumn season, it was -0.25 hour/decade, respectively. The meteorological sensitivity of crop model on its yield and trends may assist the decision-makers in developing appropriate plans mitigations strategies to enhance crop production to ensure food security.

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RIA BISWAS, SAON BANERJEE, and BANJUL BHATTACHARYYA. "Impact of temperature increase on performance of kharif rice at Kalyani, West Bengal using WOFOST model." Journal of Agrometeorology 20, no. 1 (2018): 28–30. http://dx.doi.org/10.54386/jam.v20i1.498.

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WOFOST model (version 7.1.2) was used to study the impacts of elevated thermal environment on kharif rice at Kalyani situated in lower Gangetic region of West Bengal. The model was calibrated and validated with experimental data collected during kharif season of 2010 to 2013. The simulated yield data was well matched with actual data. The sensitivity analysis for effect of temperature change on crop maturity showed that if temperature was increased by 10C and 20C the maturity period was delayed by 3 and 7 days respectively. The range of simulated yield was 3150 kgha-1 to 5046 kg ha-1 whereas the actual yield in the experimental field ranged from 2907 kg ha-1 to 5495 kg ha-1. The model shows 96 per cent accuracy to predict rice yield with R2 value 0.82 and RMSE value 337.87. It was also observed that the sowing should be done before 15th July to obtain higher yield of kharif rice in the study region.

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Ebrahimipak, N. A., and A. Egdernezhad. "Assessment of AquaCrop, WOFOST and CropSyst models for Estimating Sugar Beet Yield under Water Deficit Conditions." Journal of Water and Soil Science 23, no. 1 (2019): 199–207. http://dx.doi.org/10.29252/jstnar.23.1.15.

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SUDHIR KUMAR MISHRA, A.M. SHEKH, V. PANDEY, S.B. YADAV, and H.R. PATEL. "Sensitivity analysis of four wheat cultivars to varying photoperiod and temperature at different phenological stages using WOFOST model." Journal of Agrometeorology 17, no. 1 (2015): 74–79. http://dx.doi.org/10.54386/jam.v17i1.978.

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The sensitivity analysis of three T. aesivum (GW 322, GW 496 and GW 366) and one T. durum (GW 1139) cultivar of wheat was performed to see the possible change in the grain yield of wheat due to changed sunshine hours (BSS), maximum and minimum temperatures using WOFOST model. The potential condition was assumed with congenial weather and adequate management practices. The results showed that the increase in sunshine hours was found to increase the yield in all cultivars and vice versa. The rise in maximum and minimum temperatures had adverse effect on wheat yield. The increase in the maximum temperature by 5°C may cause reduction in yield by 24 to 29%. The effect of the minimum temperature was also of the similar order, but the varietal differences were observed. Among the cultivars, GW 496 was found to be most sensitive to maximum temperature and less to bright sunshine hours. Among the different stages, flowering to dough stage was found to be most sensitive stage.

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Chervenkov, Hristo, Valentin Kazandjiev, and Veska Gorgieva. "Application of the crop model WOFOST in grid using meteorological input data from reanalysis and objective analysis." Időjárás 122, no. 3 (2018): 305–20. http://dx.doi.org/10.28974/idojaras.2018.3.5.

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Todorovic, Mladen, Rossella Albrizio, Ljubomir Zivotic, Marie-Therese Abi Saab, Claudio Stöckle, and Pasquale Steduto. "Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes." Agronomy Journal 101, no. 3 (2009): 509–21. http://dx.doi.org/10.2134/agronj2008.0166s.

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Wu, Shangrong, Peng Yang, Jianqiang Ren, Zhongxin Chen, and He Li. "Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm." Remote Sensing of Environment 255 (March 2021): 112276. http://dx.doi.org/10.1016/j.rse.2020.112276.

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Zhuo, Wen, Jianxi Huang, Xiangming Xiao, et al. "Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation." European Journal of Agronomy 139 (September 2022): 126556. http://dx.doi.org/10.1016/j.eja.2022.126556.

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Melintescu, A., D. Galeriu, and A. Marica. "Using WOFOST crop model for data base derivation of tritium and terrestrial food chain modules in RODOS." Radioprotection 37, no. C1 (2002): C1–1241—C1–1246. http://dx.doi.org/10.1051/radiopro/2002154.

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Journal articles on the topic 'WOFOST'

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