WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality

Gilardelli, Carlo; Stella, Tommaso; Frasso, N.; Cappelli, G.; Bregaglio, Simone; Chiodini, Marcello Ermido; Scaglia, Barbara; Confalonieri, Roberto

doi:10.1016/j.fcr.2016.07.013

Cited by 26 publications

(13 citation statements)

References 24 publications

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“…In which, T max and T min are the daily maximum and minimum temperature ( • C), respectively. T base is the base temperature with 5 • C for winter oilseed rape [39,40].…”

Section: Model Description and Evaluationmentioning

confidence: 99%

Dynamics of Growth and Nitrogen Capture in Winter Oilseed Rape Hybrid and Line Cultivars under Contrasting N Supply

Zhang

Ren

et al. 2020

Agronomy

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Cultivation of winter oilseed rape hybrids has been introduced as a promising solution to improve the nitrogen use efficiency (NUE) and to reduce the large N balance surpluses in this crop. To achieve a better understanding of the underlying physiological mechanisms, field experiments were conducted over two years to investigate the dynamics of growth and N capture in an oilseed rape hybrid and its parental lines under both low (0 kg ha−1) and high (180 kg ha−1) N supply. The results showed that the dynamic trajectories of crop growth and N capture could be accurately characterized by logistic equation using growing degree days as the independent variable. At both N rates, the oilseed rape hybrid outperformed the parental lines in seed yield and aboveground biomass accumulation, which was more closely associated with the longer duration (td) of the rapid growth period (RGP), than with the higher maximum growth rate (vm). N uptake was the main factor driving genotypic variation in seed yield, with an increasing importance of N utilization efficiency at high N supply. The hybrid had significantly higher N uptake than the parental lines at both low and high N supply, because of larger vm for N accumulation during the RGP, which may present a scope for genetically improving NUE in oilseed rape. High N application enhanced crop biomass production and N accumulation, as a result of prolonged td and larger vm during the RGP. The initiation of RGP for N accumulation occurred after overwinter period, which could not be accelerated by high N supply, suggesting rational distribution of N fertilizer with reduced basal dose. However, larger amounts in spring would be beneficial for a better synchronization to crop N demand with lower environmental risks.

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“…In which, T max and T min are the daily maximum and minimum temperature ( • C), respectively. T base is the base temperature with 5 • C for winter oilseed rape [39,40].…”

Section: Model Description and Evaluationmentioning

confidence: 99%

Dynamics of Growth and Nitrogen Capture in Winter Oilseed Rape Hybrid and Line Cultivars under Contrasting N Supply

Zhang

Ren

et al. 2020

Agronomy

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“…In this context, competition in land use between food and energy crops is becoming a critical issue. As a result, there is a growing demand for decision-making support tools [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Niedbała

2019

Sustainability

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The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

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“…For example, Huang et al [35] found that more accurate county-level winter wheat estimation was obtained using the WOFOST-PROSAIL model. Furthermore, many researchers have developed crop growth models to estimate crop yields [36][37][38][39][40]. However, the crop growth models require more specific information, such as daily weather data, soil properties, and crop growth determining factors, which would make analytical costs excessive.…”

Section: Introductionmentioning

confidence: 99%

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Tuvdendorj

Zeng

et al. 2019

Remote Sensing

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In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

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WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality

Cited by 26 publications

References 24 publications

Dynamics of Growth and Nitrogen Capture in Winter Oilseed Rape Hybrid and Line Cultivars under Contrasting N Supply

Dynamics of Growth and Nitrogen Capture in Winter Oilseed Rape Hybrid and Line Cultivars under Contrasting N Supply

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

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