WOFOST - WOrld FOod STudies

WOFOST - WOrld FOod STudies

WOFOST (WOrld FOod STudies) is a simulation model for the quantitative analysis of the growth and production of annual field crops.

It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. 

With WOFOST, you can calculate attainable crop production, biomass, water use, nitrogen uptake, etc. for a location given knowledge about soil, crop, weather and crop management (e.g. sowing date, irrigation, fertilization). WOFOST has been used by many researchers over the world and has been applied for many crops over a large range of climatic and management conditions (see documentation). WOFOST is one of the key components of the European MARS crop yield forecasting system. In the Global Yield Gap Atlas (GYGA) WOFOST is used to estimate the untapped crop production potential on existing farmland based on current climate and available soil and water resources.

WOFOST originated in the framework of interdisciplinary studies on world food security and on the potential world food production by the Center for World Food Studies (CWFS) in cooperation with the Wageningen University & Research, Department of Theoretical Production Ecology (WAU-TPE) and the DLO-Center for Agrobiological Research and Soil Fertility (AB-DLO), Wageningen, the Netherlands. After cessation of CWFS in 1988, the DLO Winand Staring Centre (SC-DLO) has continued development of the model in co-operation with AB-DLO and WAU-TPE. 

Currently, the WOFOST model is maintained and further developed by Wageningen Environmental Research in co-operation with the Plant Production Systems Group of Wageningen University & Research and the Food Security unit of the Joint Research Centre in Italy.

This website serves as a reference point for information about WOFOST and also provides binaries and source code (if possible) for several implementations of WOFOST.

Principles of WOFOST

To be able to deal with the ecological diversity of agriculture, three hierarchical levels of crop growth can be distinguished: potential growth, limited growth and reduced growth. Each of these growth levels corresponds to a level of crop production: potential, limited and reduced production.

Reality rarely corresponds exactly to one of these growth/ production levels, but it is useful to reduce specific cases to one of them, because this enables you to focus on the principal environmental constraints to crop production, such as light, temperature, water and the macro nutrients nitrogen (N), phosphorus (P) and potassium (K). Other factors can often be neglected because they do not influence the crop's growth rate.

More about the principles of WOFOST

Crop production levels

To be able to deal with the ecological diversity of agriculture, three hierarchical levels of crop growth can be distinguished: potential growth, limited growth and reduced growth. Each of these growth levels corresponds to a level of crop production: potential, limited and reduced production.  Reality rarely corresponds exactly to one of these growth/production levels, but it is useful to reduce specific cases to one of them, because this enables you to focus on the principal environmental constraints to crop production, such as light, temperature, water and the macro nutrients nitrogen, phosphorus and potassium.

Potential production: Crop growth is determined by CO2 concentration, irradiation, temperature, plant characteristics and planting date only. Potential production represents the absolute production ceiling for a given crop when grown in a given area under specific weather conditions. It is determined by the crop’s response to the temperature and solar radiation regimes during the growing season. Atmospheric CO2-concentration is assumed to be constant. All other factors are assumed to be in ample supply.

Attainable (Limited) production: In addition to irradiation, temperature and plant characteristics, the effect of the availability of water and plant nutrients is considered. If the supply of water or nutrients is sub-optimal during (parts of) the growing season, this leads to water- and/or nutrient-limited production, which is lower than potential production in terms of total plant biomass. In special cases the water-limited yield (harvestable product) may be higher than potential yield because of more favourable harvest index.

Actual (Reduced) production: At this level, the possible reduction in crop yield by biotic factors like weeds, pests and diseases is taken into account or by abiotic factors like frost kill, pollutants or salts in soils.

WOFOST distinguishes three levels of crop production:

  • Potential production: determined by crop variety, planting date, CO2 concentration, radiation and temperature;
  • Water-limited production, where water availability limits the
    potential production; For this purpose several waterbalance modules are
    available with different degrees of complexity and input data
    requirements.
  • Nutrient-limited production where nutrient availability limits the water-limited production.

Nutrient-limited production in WOFOST can be obtained through the use of the QUEFTS model which is basically a post-processing step of the water-limited production results. QUEFTS can take into account the impact of limitations in macro nutrients (N/P/K) on growth and productivity but it is not dynamic simulation model. For example, the impact of differences in fertilization during the crop cycle cannot be simulated by QUEFTS. The most recent version of WOFOST (8.1) does allow to do dynamic simulation of nutrient limitations on growth but is currently limited to nitrogen only. Moreover, WOFOST 8.1 has a sophisticated approach for simulation of organic matter and can therefore take into account the nitrogen availability that becomes available through decomposition of organize amendments.

Further reducing factors (weeds, pests, frost and diseases) are not taken into account by WOFOST.

Temporal and spatial scale

From a spatial perspective WOFOST is a one-dimensional simulation model, i.e. without reference to a geographic scale. However, the size of a region to which WOFOST can be applied is limited. This is due to aggregation effects caused by non-linear response of crop models to model inputs. The non-linear behaviour implies that aggregating input data and then running the model provides different results compared to running the model on the original data and then aggregating the model output.

In practice, this is resolved by splitting the model spatial domain into small spatial units where the model inputs (weather, crop, soil, management) can be assumed constant. Aggregation of simulation results is carried out by aggregating the simulation results for the individual spatial units to larger spatial units. In Europe, WOFOST is typically applied at spatial units of 10x10 km for which scaling errors are negligible.

From a temporal perspective, WOFOST typically simulates crop growth with a temporal resolution of one day.

Crop growth simulation

In WOFOST, crop growth is simulated on the basis of eco-physiological processes. The major processes are phenological development, CO2-assimilation, transpiration, respiration, partitioning of assimilates to the various organs, and dry matter formation. The following paragraphs provide a concise description of the main processes implemented in WOFOST.

Assimilation and respiration

The daily gross CO2-assimilation rate of a crop is calculated from the absorbed radiation, and the photosynthesis-light response curve of individual leaves. This response is dependent on temperature and leaf age. The absorbed radiation is calculated from the total incoming radiation and the leaf area. Daily gross CO2 assimilation is obtained by integrating the assimilation rates over the leaf layers and over the day.

Part of the formed assimilates is used for maintenance respiration of plant organs. The remaining carbohydrates are converted into structural plant material, such as cellulose and proteins (dry matter). There is some net loss of carbohydrates due to this conversion, called the growth respiration. Maintenance respiration is estimated on basis of the dry weight of the different organs and their chemical composition, modified by the ambient temperature.

Phenological development

The order and the rate of appearance of vegetative and reproductive organs characterize crop phenological development. The order of appearance is a crop characteristic, which is independent of external conditions. The rate of appearance can vary strongly, notably under the influence of temperature and photoperiod (day-length).

In WOFOST phenology is described by the dimensionless state variable development stage (DVS). For most annual crops, DVS is set to 0 at seedling emergence, 1 at flowering (for cereals) and 2 at maturity. The development rate is a crop/cultivar specific function of ambient temperature, possibly modified by photoperiod.

To account for the effect of temperature on development stage, the concept of thermal time is applied, sometimes called temperature sum or heat sum. Thermal time is the integral over time of the daily effective temperature (Te) after crop emergence. Te is the difference between the daily average temperature and a base temperature below which no development occurs. Above a certain maximum effective temperature, Te remains constant. DVS is calculated by dividing the thermal time by the thermal time required to pass to the next development stage.

The phenological development of some crops is also influenced by photoperiod and vernalization requirements. The impact of photoperiod is treated in WOFOST through a photoperiod reduction factor for the development rate until flowering, based on an optimum and a critical photoperiod. The impact of vernalization is simulated by assuming that a crop requires a number of (cultivar-specific) vernalization days in order to reach its vernalization requirement. One vernalization day is added to the vernalization state when the daily average temperatureis within the optimal temperature range for vernalization. A reduction factor for the development rate is calculated based on the daily vernalization state.

The development stage determines, among other things, the assimilate partitioning over the organs (leaves, stems, roots, storage organs), the specific leaf area and the maximum leaf CO2 assimilation rate.

Transpiration

Transpiration is the loss of water from a crop to the atmosphere. Water loss is caused by diffusion of water vapours from the open stomata to the atmosphere. The stomata need to be open to exchange gasses (CO2 and O2) with the atmosphere. To avoid desiccation, a crop must compensate for transpiration losses, by water uptake from the soil.

In WOFOST, an optimum soil moisture range for plant growth is determined as function of the evaporative demand of the atmosphere (reference potential transpiration of a fixed canopy), the crop group and total soil water retention capacity. Within the optimum range, the transpiration losses are fully compensated. Outside the optimum range, the soil can either be too dry or too wet. Both conditions lead to reduce water uptake by the roots, in a dry soil due to water shortage, in a wet soil due to oxygen shortage. A crop reacts to water stress with closure of the stomata. As a consequence, the exchange of CO2 and O2 between the crop and the atmosphere diminishes, and hence CO2-assimilation is reduced. WOFOST applies the ratio of actual over potential crop transpiration as a reduction factor to the gross assimilation rate.

Partitioning of dry matter

Partitioning is the subdivision of the net assimilates over the different plant organs. After germination, most assimilates are converted into leaf and root tissue and later into stem tissue. The partitioning to root tissue gradually diminishes and is zero if the development stage equals 1 (anthesis in cereals). From then on, the storage organs receive most of the available assimilates.

In WOFOST partitioning is implemented through so-called partitioning tables which describe the fraction of assimilates partitioned to the various organs as a function of the crop development stage. In the calculations, a fraction of the assimilates is assigned to the roots first, the remainder is divided over the above-ground organs (including below ground storage organs such as tubers).

Water balance

The moisture content in the root zone follows from the daily calculation of the water balance. In WOFOST four different soil water sub models are distinguished (depending on the implementation). The first and most simple soil water balance applies to the potential production situation. Assuming a continuously moist soil, the crop water requirements are quantified as the sum of crop transpiration and evaporation from the shaded soil under the canopy.

The second water balance in the water-limited production situation applies to a freely draining soil, where groundwater is so deep that it cannot have influence on the soil moisture content in the rooting zone. The soil profile is divided in two compartments, the rooted zone and the lower zone between actual rooting depth and maximum rooting depth. The subsoil below the maximum rooting depth is not defined. The second zone merges gradually with the first zone as the roots grow deeper towards the maximum rooting depth. This water balance applies to regional applications with limited information on soil properties.

The third water balance takes into account the impact of different soil layers and the influence of groundwater. This water balance uses a simple but elegant solution to estimate water flow through the soil taking into account both gravitational flow as well flow due to differences in matric suction. It is targeted at making good estimates of crop water availability under conditions when properties of the soil are well known, but without going into the complexity of Richards equation type of soil water models. This water balance is available in WOFOST 7.3 and WOFOST 8.1.

Finally, the WOFOST implementation connected to the SWAP model has a detailed water balance including solute transport which allows to make detailed simulations of the behaviour of water and solutes in the soil and its impact on plant growth.

Implementation of crop dynamics

WOFOST is a dynamic, explanatory model that simulates crop growth with time steps of one day, based on knowledge of processes at a lower level of integration. To ensure that the results of the simulation are correct, the different types of calculations (integration, driving variables and rate calculations) should be strictly separated. In other words, first all states should be updated, then all driving variables should be calculated, after which all rates of change should be calculated. If this rule is not applied rigorously, there is a risk that some rates will pertain to states at the current time whereas others will pertain to states from the previous time step.

In WOFOST, the calculations of rates and states are not mixed during a time step but are all executed separately. This is taken care of by grouping all the state calculations into one block as do all the rate calculations for the different components of the model.

Implementations of WOFOST

We provide several implementations of WOFOST in different programming languages (FORTRAN, python, java). Moreover, we provide the parameter sets required to run WOFOST for difference crops and a set of jupyter notebooks that demonstrate capabilities of PCSE/WOFOST.

Currently, four implementations of WOFOST are available from Wageningen University & Research:

  1. The original implementation in FORTRAN77. This implementation is still available, but is not actively maintained anymore.
  2. WOFOST implemented in the Python Crop Simulation Environment (PCSE);
  3. WOFOST implemented in the Wageningen Integrated Systems Simulator
    (WISS), a Java framework targeting the agro-ecological modelling domain;
  4. The Soil-Water-Atmosphere-Plant modelling system (SWAP).

All these implementations inherit their biophysical core from WOFOST 6.0, but differ in their abilities to deal with I/O (file, database), their user interface or general flexibility.

WOFOST 7.1

WOFOST 7.1 consists of the WOFOST crop model implemented in FORTRAN77, extended with a graphical user interface called the WOFOST Control Centre implemented in Borland Delphi (Only for Windows). This implementation of WOFOST is not maintained and it lacks some of the features of more recent versions. We do not recommend this implementation for most users.

PCSE/WOFOST

PCSE/WOFOST provides several versions of WOFOST implemented in pure python and is developed within a framework called Python Crop Simulation Environment (PCSE). The main development of WOFOST is now done with this implementation and it also serves as the reference implementation of WOFOST. It is well tested and recommended for most users.

The model is provided with full source code and documentation through public repositories under a permissive license (EUPL). The use of python ensures that PCSE/WOFOST integrates very well with tools in the scientific software stack. For example, PCSE/WOFOST has been used extensively in combination with Jupyter notebooks that allow researchers to publish code, results and explanations that are both readable and executable.

WISS/WOFOST

The WISS (Wageningen Integrated Systems Simulator) framework is under active development in order to fulfil the requirements of operational application of simulation models. WISS is written in the Java programming language and provides high numerical performance and robustness of the model implementations. The WOFOST implementation within the WISS framework has been tested against the existing implementations and is able to reproduce those results. Moreover, we are striving to compatibility between WISS and PCSE in terms of databases and parameter formats for soil types, crop parameters and agro-management.

SWAP

SWAP (Soil, Water, Atmosphere and Plant) simulates transport of water, solutes and heat in unsaturated/saturated soils and optionally uses the WOFOST7.2 model to simulate crop growth and crop demand for water and nutrients. The model is designed to simulate flow and transport processes at field scale level, during growing seasons and for long term time series. SWAP is implemented in Fortran90 and the WOFOST implementation in SWAP is actively maintained.

Support for WOFOST

We are limited in supporting others using WOFOST. Moreover, there is a considerable amount of documentation available through this website. So please read the documentation carefully before sending us trivial questions about WOFOST. However, in case of problems, please let us know and we will try to help you. Please contact the following people:

For support on WOFOST, you can contact:

Online WOFOST

Online WOFOST is a fast way to run WOFOST for regions in the world where wheat, maize and soy bean are dominantly cropped using local relevant data.

Hands-on experience of WOFOST is possible via WOFOST-online. It is a fast way to run WOFOST for regions in the world where wheat, maize and soy bean are dominantly cropped using (as much as possible) local relevant data.

Register

This application requires a username and password that can be requested by sending an e-mail to the following address: hendrik.boogaard@wur.nl.

Data sets

Input is taken from the following global data sets:

  • Weather: AgERA5 which use ECMWF-ERA5 data but aggregated to daily timesteps and adapted to finer topography, finer land use pattern and finer land-sea delineation of the ECMWF operational model at 0.1 degree grid;
  • Soil: WISE30SEC version 1.0 (Batjes, 2015);
  • Crop: start and phenological related TSUM parameters based on regional GAEZ calendars;
  • Crop delineation: based on the Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km;
  • Model: crop parametrisation as default WOFOST files.

    Important remarks

    • Results are given for two yield levels: potential (Yp) and water-limited (Yw). More information can be found on the Global yield gap atlas website.
    • Crop data has limited local relevance as it has been derived from
      regional calendars. Next release will include the possibilty to enter a
      location specific planting date and crop cycle length
    • Winter wheat simulation starts at the moment of “regrowth” (in spring)
    • The initialization of soil water, relevant for the water-limited
      simulation, starts halfway between field capacity and wilting point and
      runs 1 month before emergence.
    • The cumulated rainfall includes rainfall from the start of the initialization, thus 1 month before emergence.

    References

    Publications

    Barley

    Pohanková, E., P. Hlavinka, M. Orság, J. Takáč, K. C. Kersebaum, A. Gobin, and M. Trnka. 2018. “Estimating the Water Use Efficiency of Spring Barley Using Crop Models.” The Journal of Agricultural Science 156 (5): 628–44.

    Peltonen-Sainio, Pirjo, Lauri Jauhiainen, Taru Palosuo, Kaija Hakala, and Kimmo Ruosteenoja. 2016. “Rainfed Crop Production Challenges under European High-Latitude Conditions.” Regional Environmental Change 16 (5): 1521–33.

    Rötter, Reimund P., Taru Palosuo, Kurt Christian Kersebaum, Carlos Angulo, Marco Bindi, Frank Ewert, Roberto Ferrise, Petr Hlavinka, Marco Moriondo, and Claas Nendel. 2012. “Simulation of Spring Barley Yield in Different Climatic Zones of Northern and Central Europe: A Comparison of Nine Crop Models.” Field Crops Research 133: 23–36.

    Habekotté, B. 1994. “Evaluatie van Een Gewasgroeimodel Voor Opbrengstberekening van Verschillende Gewassen Uitgevoerd Ten Behoeve van Het Project" Introductie Geintegreerde Akkerbouw".” AB-DLO.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Cotton

    Khan, Muhammad Hamed, Ranvir Singh, Brent Clothier, and Tonny T. Vries. 2024. “Modelling the Long-Term Potential Effects of Modern Irrigation Systems on Soil-Water and Salt Balances, and Crop-Water Productivity in Semi-Arid Regions.” Research Square. https://doi.org/10.21203/rs.3.rs-5034740/v1.

    Wang, Desheng, Chengkun Wang, Lichao Xu, Tiecheng Bai, and Guozheng Yang. 2022. “Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang.” Agriculture 12 (7): 895. https://doi.org/10.3390/agriculture12070895.

    Falagas, A., and K. Karantzalos. 2019. “A Cotton Yield Estimation Model Based on Agrometeorological and High Resolution Remote Sensing Data.” In Precision Agriculture’19, 41–76. Wageningen Academic Publishers.

    Venugopalan, M. V., P. Tiwary, D. K. Mandal, and O. Challa. 2010. “Validation and Application of WOFOST Model for Yield Gap Analysis in Selected Soils of Maharashtra.” Agropedology 20 (1): 30–37.

    Bessembinder, J. J. E., A. S. Dhindwal, P. A. Leffelaar, T. C. Ponsioen, and Sher Singh. 2003. “Analysis of Crop Growth.” In Water Productivity of Irrigated Crops in Sirsa District, India, 59–83. Wageningen UR.

    Fieldbeans

    Habekotté, B. 1994. “Evaluatie van Een Gewasgroeimodel Voor Opbrengstberekening van Verschillende Gewassen Uitgevoerd Ten Behoeve van Het Project" Introductie Geintegreerde Akkerbouw".” AB-DLO.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Maize

    Eweys, Omar Ali, Abeer A. Elwan, and Taha I. Borham. 2017. “Integrating WOFOST and Noah LSM for Modeling Maize Production and Soil Moisture with Sensitivity Analysis, in the East of The Netherlands.” Field Crops Research 210: 147–61.

    Cheng, Zhiqiang, Jihua Meng, and Yiming Wang. 2016. “Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms.” Remote Sensing 8 (4): 303.

    Hassanli, Mohammad, Hamed Ebrahimian, Ehsan Mohammadi, Amirreza Rahimi, and Amirhossein Shokouhi. 2016. “Simulating Maize Yields When Irrigating with Saline Water, Using the AquaCrop, SALTMED, and SWAP Models.” Agricultural Water Management 176: 91–99.

    Bussay, Attila, Marijn van der Velde, Davide Fumagalli, and Lorenzo Seguini. 2015. “Improving Operational Maize Yield Forecasting in Hungary.” Agricultural Systems 141: 94–106.

    Kassie, B. T., M. K. Van Ittersum, H. Hengsdijk, S. Asseng, J. Wolf, and Reimund P. Rötter. 2014. “Climate-Induced Yield Variability and Yield Gaps of Maize (Zea Mays L.) in the Central Rift Valley of Ethiopia.” Field Crops Research 160: 41–53.

    Zhang, SuQing, JianTao Zhang, JiRui Li, YongZheng Cheng, and GuoQiang Li. 2014. “Calibration and Validation of WOFOST in Main Maize-Producing Regions in Henan.” Journal of Henan Agricultural Sciences 43 (8): 152–56.

    Djaby, Bakary, Kouadio Louis, Moussa El Jarroudi, De Wit Allard, and Bernard Tychon. 2013. “Spatial Distribution of Calibrated WOFOST Parameters and Their Influence on the Performances of a Regional Yield Forecasting System.” Sustainable Agriculture Research.

    Li, Y., W. Kinzelbach, J. Zhou, G. D. Cheng, and X. Li. 2012. “Modelling Irrigated Maize with a Combination of Coupled-Model Simulation and Uncertainty Analysis, in the Northwest of China.” Hydrology and Earth System Sciences 16 (5): 1465–80.

    Liu, Buchun, Xurong Mei, Guohua Lv, Youlu Yang, Meilan Bai, Yongfeng Wu, Jiqing Song, and Wenbo Bai. 2012. “The Maize Evapotranspiration in the Background of Climate Change: A Case Study in Arid Area.” Hydrological Processes 26 (5): 633–39.

    Wu, Dingrong, Qiang Yu, Enli Wang, and Huib Hengsdijk. 2008. “Impact of Spatial-Temporal Variations of Climatic Variables on Summer Maize Yield in North China Plain.” International Journal of Plant Production.

    Wokabi, S. M. 2003. “Effectiveness of the Wofost Simulation Model to Predict Maize Yield Gaps on the Eastern Slopes of Mt Kenya.” East African Agricultural and Forestry Journal 69 (2): 139–47.

    Danalatos, N. G., C. S. Kosmas, P. M. Driessen, and N. Yassoglou. 1994. “The Change in the Specific Leaf Area of Maize Grown under Mediterranean Conditions.” Agronomie 14 (7): 433–43.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Millet

    Klaij, M. C., and G. Vachaud. 1992. “Seasonal Water Balance of a Sandy Soil in Niger Cropped with Pearl Millet, Based on Profile Moisture Measurements.” Agricultural Water Management 21 (4): 313–30.

    Dutta, Dipanwita, N. R. Patel, and V. Venus. n.d. “Analyzing The Applicability Of PS-N Crop Growth Model For Prediction Of Millet Yield In Eastern Rajasthan Of India.”

    Potato

    Den, Tamara ten, Inge van de Wiel, Allard de Wit, Frits K. van Evert, Martin K. van Ittersum, and Pytrik Reidsma. 2022. “Modelling Potential Potato Yields: Accounting for Experimental Differences in Modern Cultivars.” European Journal of Agronomy 137 (July). https://doi.org/10.1016/j.eja.2022.126510.

    Dua, V. K., J. S. Minhas, Sanjay Rawal, S. P. Singh, S. K. Singh, Prince Kumar, Radhika Pathania, Tanvi Kapoor, Jagdev Sharma, and S. K. Sharma. 2018. “Calibration and Validation of WOFOST Model for Seven Potato (Solanum Tuberosum) Cultivars in India.” Indian Journal of Agronomy 63 (3): 357–65.

    Wang, N., P. Reidsma, A. A. Pronk, A. J. W. de Wit, and M. K. van Ittersum. 2018. “Can Potato Add to China’s Food Self-Sufficiency? The Scope for Increasing Potato Production in China.” European Journal of Agronomy 101: 20–29.

    Yan, Yulin, Pytrik Reidsma, and Joop Kroes. 2015. “Application of SWAP-WOFOST to Evaluate the Influence of Water and Oxygen Stress on Potato Yield in a Dutch Farm.” Unpublished M. Sc, Plant Production Systems. Thesis, Wageningen Agricultural University, Department of Plant Production Systems, Wageningen, The Netherlands, 61.

    Dua, V. K., P. M. Govindakrishnan, and B. P. Singh. 2014. “Calibration of WOFOST Model for Potato in India.” Potato Journal 41 (2).

    Dua, VK, PM Govindakrishnan, and BP Singh. 2014. “Calibration of WOFOST Model for Potato in India.”

    Mazurczyk, W., B. Lutomirska, and A. Wierzbicka. 2003. “Relation between Air Temperature and Length of Vegetation Period of Potato Crops.” Agricultural and Forest Meteorology 118 (3–4): 169–72.

    De Koning, G. H. J., C. A. Van Diepen, and G. J. Reinds. 1995. “Crop Growth Model WOFOST Applied to Potatoes.” In Modelling and Parameterization of the Soil-Plant-Atmosphere System: A Comparison of Potato Growth Models, 275–97.

    Habekotté, B. 1994. “Evaluatie van Een Gewasgroeimodel Voor Opbrengstberekening van Verschillende Gewassen Uitgevoerd Ten Behoeve van Het Project" Introductie Geintegreerde Akkerbouw".” AB-DLO.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Rapeseed

    Gilardelli, C., T. Stella, N. Frasso, G. Cappelli, S. Bregaglio, M. E. Chiodini, B. Scaglia, and R. Confalonieri. 2016. “WOFOST-GTC: A New Model for the Simulation of Winter Rapeseed Production and Oil Quality.” Field Crops Research 197: 125–32.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Rice

    Biswas, Ria, Saon Banerjee, and Banjul Bhattacharyya. 2018. “Impact of Temperature Increase on Performance of Kharif Rice at Kalyani, West Bengal Using WOFOST Model.” Journal of Agrometeorology 20 (1): 28–30.

    Ma, Shangjie, Zhiyuan Pei, and Yajuan He. 2016. “Study on Simulation of Rice Yield with WOFOST in Heilongjiang Province.” In International Conference on Computer and Computing Technologies in Agriculture, 40–51. Springer.

    Mukherjee, JOYDEEP, G. Singh, S. K. Bal, HARPREET Singh, and P. Kaur. 2011. “Comparative Evaluation of WOFOST and ORYZA2000 Models in Simulating Growth and Development of Rice (Oryza Sativa L.) in Punjab.” J Agrometeorol 13 (2): 86–91.

    Confalonieri, Roberto, Marco Acutis, Gianni Bellocchi, and Marcello Donatelli. 2009. “Multi-Metric Evaluation of the Models WARM, CropSyst, and WOFOST for Rice.” Ecological Modelling 220 (11): 1395–1410.

    Xie, Wenxia, Lijiao Yan, and Guanghuo Wang. 2006. “Simulation and Validation of Rice Potential Growth Process in Zhejiang by Utilizing WOFOST Model.” Zhongguo Shuidao Kexue 20 (3): 319–23.

    Bessembinder, J. J. E., A. S. Dhindwal, P. A. Leffelaar, T. C. Ponsioen, and Sher Singh. 2003. “Analysis of Crop Growth.” In Water Productivity of Irrigated Crops in Sirsa District, India, 59–83. Wageningen UR.

    Dobermann, Achim, David Dawe, Reimund P. Roetter, and Kenneth G. Cassman. 2000. “Reversal of Rice Yield Decline in a Long-Term Continuous Cropping Experiment.” Agronomy Journal 92 (4): 633–43.

    Cabrerea, JMCA, R. Roetter, and H. H. Van Laar. 1998. “Preliminary Results of Crop Model Development and Evaluation for Rice.”

    Sorghum

    Wolf, J., K. Ouattara, and I. Supit. 2015. “Sowing Rules for Estimating Rainfed Yield Potential of Sorghum and Maize in Burkina Faso.” Agricultural and Forest Meteorology 214: 208–18.

    Soybean

    Abadi, F. R., I. K. Tastra, and B. S. Koentjoro. 2018. “Preliminary Study of WOFOST Crop Simulation in Its Prospect for Soybean (Glycine Max L.) Optimum Harvest Time and Yield Gap Analysis in East Java.” AGRIVITA, Journal of Agricultural Science 40 (3).

    Kroes, J. G., P. Groenendijk, Diego de Abelleyra, Santiago R. Veron, Dmitry Plotnikov, Sergey Bartalev, Nana Yan, Bingfang Wu, Nataliia Kussul, and Steffen Fritz. 2017. “Environmental Impact Assessment of Agricultural Land Use Changes.” SIGMA.

    Wit, A. J. W. de, D. d’Abelleyra, Santiago Veron, J. G. Kroes, Iwan Supit, and H. L. Boogaard. 2017. “Technical Description of Crop Model (WOFOST) Calibration and Simulation Activities for Argentina, Pampas Region.” SIGMA.

    Setiyono, T. D., K. G. Cassman, J. E. Specht, A. Dobermann, A. Weiss, H. Yang, S. P. Conley, A. P. Robinson, P. Pedersen, and J. L. De Bruin. 2010. “Simulation of Soybean Growth and Yield in Near-Optimal Growth Conditions.” Field Crops Research 119 (1): 161–74.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Sugarbeet

    Haberle, J., and J. Klir. 2001. “Simulated and Observed Sugar Beet and Spring Wheat Yields and Yield Variability in a Long-Term Field Experiment.” Rostlinna Vyroba-UZPI (Czech Republic).

    Habekotté, B. 1994. “Evaluatie van Een Gewasgroeimodel Voor Opbrengstberekening van Verschillende Gewassen Uitgevoerd Ten Behoeve van Het Project" Introductie Geintegreerde Akkerbouw".” AB-DLO.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Sugarcane

    Hu, Shun, Liangsheng Shi, Kai Huang, Yuanyuan Zha, Xiaolong Hu, Hao Ye, and Qi Yang. 2019. “Improvement of Sugarcane Crop Simulation by SWAP-WOFOST Model via Data Assimilation.” Field Crops Research 232: 49–61.

    Shi, Liangsheng, Shun Hu, and Yuanyuan Zha. 2018. “Estimation of Sugarcane Yield by Assimilating UAV and Ground Measurements Via Ensemble Kalman Filter.” In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 8816–19. IEEE.

    Scarpare, Fábio Vale. 2011. “Simulação Do Crescimento Da Cana-de-Açúcar Pelo Modelo Agrohidrológico SWAP/WOFOST.” Universidade de Sao Paulo, Escola Superior de Agricultura ‘“Luiz de Queiroz”’, Piracicaba, Brazil.

    Sunflower

    Zhu, Jiangxu, Wenzhi Zeng, Tao Ma, Guoqing Lei, Yuanyuan Zha, Yuanhao Fang, Jingwei Wu, and Jiesheng Huang. 2018. “Testing and Improving the WOFOST Model for Sunflower Simulation on Saline Soils of Inner Mongolia, China.” Agronomy 8 (9): 172.

    Todorovic, Mladen, Rossella Albrizio, Ljubomir Zivotic, Marie-Therese Abi Saab, Claudio Stöckle, and Pasquale Steduto. 2009. “Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes.” Agronomy Journal 101 (3): 509–21.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].

    Wheat

    Ceglar, A., R. Van der Wijngaart, A. De Wit, R. Lecerf, H. Boogaard, L. Seguini, M. Van den Berg, A. Toreti, M. Zampieri, and D. Fumagalli. 2019. “Improving WOFOST Model to Simulate Winter Wheat Phenology in Europe: Evaluation and Effects on Yield.” Agricultural Systems 168: 168–80.

    Huang, Jianxi, Shiling Jia, Hongyuan Ma, Yingyu Hou, and Liang He. 2017. “Dynamic Simulation of Growth Process of Winter Wheat in Main Production Areas of China Based on WOFOST Model.” Transactions of the Chinese Society of Agricultural Engineering 33 (10): 222–28.

    Wu, Lu, Liping Feng, Yi Zhang, Jiachen Gao, and Jing Wang. 2017. “Comparison of Five Wheat Models Simulating Phenology under Different Sowing Dates and Varieties.” Agronomy Journal 109 (4): 1280–93.

    Bregaglio, Simone, Nicolò Frasso, Valentina Pagani, Tommaso Stella, Caterina Francone, Giovanni Cappelli, Marco Acutis, Riad Balaghi, Hassan Ouabbou, and Livia Paleari. 2015. “New Multi-Model Approach Gives Good Estimations of Wheat Yield under Semi-Arid Climate in Morocco.” Agronomy for Sustainable Development 35 (1): 157–67.

    Castañeda-Vera, Alba, Peter A. Leffelaar, Jorge Álvaro-Fuentes, Carlos Cantero-Martínez, and M. I. Mínguez. 2015. “Selecting Crop Models for Decision Making in Wheat Insurance.” European Journal of Agronomy 68: 97–116.

    Mishra, Sudhir Kumar, A. M. Shekh, V. Pandey, S. B. Yadav, and H. R. Patel. 2015. “Sensitivity Analysis of Four Wheat Cultivars to Varying Photoperiod and Temperature at Different Phenological Stages Using WOFOST Model.” Journal of Agrometeorology 17 (1): 74.

    Boogaard, Hendrik, Joost Wolf, Iwan Supit, Stefan Niemeyer, and Martin van Ittersum. 2013. “A Regional Implementation of WOFOST for Calculating Yield Gaps of Autumn-Sown Wheat across the European Union.” Field Crops Research 143: 130–42.

    Ma, Guannan, Jianxi Huang, Wenbin Wu, Jinlong Fan, Jinqiu Zou, and Sijie Wu. 2013. “Assimilation of MODIS-LAI into the WOFOST Model for Forecasting Regional Winter Wheat Yield.” Mathematical and Computer Modelling 58 (3–4): 634–43.

    Mishra, S. Kꎬ, A. M. Shekh, S. B. Yadav, Anil Kumar, G. G. Patel, V. Pandey, and H. R. Patel. 2013. “Simulation of Growth and Yield of Four Wheat Cultivars Using WOFOST Model under Middle Gujarat Region.” Journal of Agrometeorology 15 (1): 43.

    Wolf, Joost, Rudi Hessel, Hendrik Boogaard, Allard de Wit, Wies Akkermans, and Kees van Diepen. 2011. “Modeling Winter Wheat Production across Europe with WOFOST—The Effect of Two New Zonations and Two Newly Calibrated Model Parameter Sets.” Methods of Introducing System Models into Agricultural Research, no. methodsofintrod: 297–326.

    Shekhar, Chander, Diwan Singh, R. Singh, and V. Rao. 2008. “Prediction of Wheat Growth and Yield Using WOFOST Model.” Journal of Agrometeorology (Special Issue-Part 2) 400: 402.

    Song, Yanling, Deliang Chen, and Wenjie Dong. 2006. “Influence of Climate on Winter Wheat Productivity in Different Climate Regions of China, 1961–2000.” Climate Research 32 (3): 219–27.

    Bessembinder, J. J. E., A. S. Dhindwal, P. A. Leffelaar, T. C. Ponsioen, and Sher Singh. 2003. “Analysis of Crop Growth.” In Water Productivity of Irrigated Crops in Sirsa District, India, 59–83. Wageningen UR.

    Habekotté, B. 1994. “Evaluatie van Een Gewasgroeimodel Voor Opbrengstberekening van Verschillende Gewassen Uitgevoerd Ten Behoeve van Het Project" Introductie Geintegreerde Akkerbouw".” AB-DLO.

    Boons-Prins, E. R., G. H. J. De Koning, and C. A. Van Diepen. 1993. “Crop-Specific Simulation Parameters for Yield Forecasting across the European Community.” CABO-DLO [etc.].