New approach in modeling spring wheat yielding based on dry periods

Szulczewski, Wiesław; Żyromski, Andrzej; Biniak-Pieróg, Małgorzata

doi:10.1016/j.agwat.2011.10.023

Cited by 7 publications

(8 citation statements)

References 28 publications

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“…Existing measures in assessment of forecasts are quite diverse due to the differences in perception of a 'good' forecast between the forecast users and the forecasters and even among individuals of any of the two groups (Murphy, 1993;Potgieter et al, 2003;Krause et al, 2005;Szulczewski et al, 2012). Since the assessment of forecast value involves considerable user inputs and is beyond the scope of this study, we focused on measures to assess the forecast quality and consistency of the ICCYF in forecasting the yields of spring wheat, barley and canola.…”

Section: Model Validationmentioning

confidence: 99%

“…Potgieter et al, 2003;Krause et al, 2005;Szulczewski et al, 2012), we calculated and compared those indices that are relevant to the questions aforementioned, that is, Bravais and Pearson coefficient of determination (R 2 ), Root mean squared error (RMSE), relative RMSE (RRMSE), coefficient of residual mass (CRM), mean absolute percentage error (MAPE) and model efficiency index (MEI). Initial results showed that some of these indices were highly correlated (e.g.…”

Section: Model Validationmentioning

confidence: 99%

“…Since the assessment of forecast value involves considerable user inputs and is beyond the scope of this study, we focused on measures to assess the forecast quality and consistency of the ICCYF in forecasting the yields of spring wheat, barley and canola. However, the quality and consistency are multifaceted in nature and numerous statistical indices are used to address their measures (Potgieter et al, 2003;Krause et al, 2005;Szulczewski et al, 2012). In addressing quality and consistence measures, we tried to answers the following questions: (1) how credible are the forecasts associated with different crops, lead times and crop regions?…”

Section: Model Validationmentioning

confidence: 99%

“…The MEI, or Nash-Sutcliffe efficiency index (Krause et al, 2005;Szulczewski et al, 2012) is defined as:…”

Section: Model Validationmentioning

confidence: 99%

“…Campbell et al, 1997;Supit, 1997;Potgieter et al, 2003;Qian et al, 2009b;Bornn and Zidek, 2012;Szulczewski et al, 2012), or remote sensing derived index, such as the normalized difference vegetation indices (NDVI) (e.g. Bullock, 1992;Boken and Shaykewich, 2002;Doraiswamy et al, 2004;Mkhabela et al, 2011;Johnson, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

Chipanshi

Zhang

Kouadio

et al. 2015

Agricultural and Forest Meteorology

127

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a b s t r a c tEarly warning information on crop yield and production are very crucial for both farmers and decisionmakers. In this study, we assess the skill and the reliability of the Integrated Canadian Crop Yield Forecaster (ICCYF), a regional crop yield forecasting tool, at different temporal (i.e. 1-3 months before harvest) and spatial (i.e. census agricultural region -CAR, provincial and national) scales across Canada. A distinct feature of the ICCYF is that it generates in-season yield forecasts well before the end of the growing season and provides a probability distribution of the forecasted yields. The ICCYF integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms. The model was evaluated against yield survey data of spring wheat, barley and canola during the 1987-2012 period. Our results showed that the ICCYF performance exhibited a strong spatial pattern at both CAR and provincial scales. Model performance was better from regions with a good coverage of climate stations and a high percentage of cropped area. On average, the model coefficient of determination at CAR level was 66%, 51% and 67%, for spring wheat, barley and canola, respectively. Skilful forecasts (i.e. model efficiency index > 0) were achieved in 70% of the CARs for spring wheat and canola, and 43% for barley (low values observed in CAR with small harvested area). At the provincial scale, the mean absolute percentage errors (MAPE) of the September forecasts ranged from 7% to 16%, 7% to14%, and 6% to 14% for spring wheat, barley and canola, respectively. For forecasts at the national scale, MAPE values (i.e. 8%, 5% and 9% for the three respective crops) were considerably smaller than the corresponding historical coefficients of variation (i.e. 17%, 10% and 17% for the three crops). Overall, the ICCYF performed better for spring wheat than for canola and barley at all the three spatial scales. Skilful forecasts were achieved by mid-August, giving a lead time of about 1 month before harvest and about 3-4 months before the final release of official survey results. As such, the ICCYF could be used as a complementary tool for the traditional survey method, especially in areas where it is not practical to conduct such surveys.Crown

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Section: Model Validationmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

“…The MEI, or Nash-Sutcliffe efficiency index (Krause et al, 2005;Szulczewski et al, 2012) is defined as:…”

Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

Chipanshi

Zhang

Kouadio

et al. 2015

Agricultural and Forest Meteorology

127

View full text Add to dashboard Cite

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New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco

Bregaglio

Frasso

Pagani

et al. 2014

Agron. Sustain. Dev.

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International audienceWheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011–2012 and 2012–2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R2 always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought

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Quantitative and qualitative assessment of agricultural water resources under variable climatic conditions of Silesian Lowlands (Southwest Poland)

Orzepowski

Paruch

Pulikowski

et al. 2014

Agricultural Water Management

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New approach in modeling spring wheat yielding based on dry periods

Cited by 7 publications

References 28 publications

Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape

New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco

Quantitative and qualitative assessment of agricultural water resources under variable climatic conditions of Silesian Lowlands (Southwest Poland)

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