Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China

Li, Zhengpeng; MingDan, Song; Feng, Hao; Zhao, Ying

doi:10.1002/jsfa.7467

Cited by 15 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time series of rain attenuation of a Ku-band signal during rain events at a tropical location was predicted by predicting the fade slope from a modified Van de Kamp (VDK) model [19]. Li et al assessed the yield forecast performance with increasing nitrogen input to determine when the acceptable predicted yield could be achieved using the CERES-Wheat model [20]. The performance of dynamical seasonal forecast systems was evaluated for the prediction of short-term rainfall anomalies over equatorial East Africa, based on observational datasets and the Asia-Pacific Climate Center (APCC) Ocean-Atmosphere coupled multi-model ensemble (MME) retrospective forecasts (hindcasts) [21].…”

Section: Related Workmentioning

confidence: 99%

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Chen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Short-term and local rainfall commonly occurs in southern China, and can result in intensive precipitation in the local region over a very short time. Considerable precipitation could cause an intense flood within a city when the runoff exceeds the capacity of a city drainage system. Predicting rainfall probability accurately is among the important scientific problems in meteorology and remains a challenge. In this study, a dislocation machine learning method based on a support vector machine was used to predict short-term and local rainfall probability using Feng Yun II geostationary meteorological satellite imagery and ground in situ observational data. The results were validated by a traditional threat score, which is commonly used in evaluation of weather predictions. The results indicate that the proposed algorithm can achieve good performance for a 1-h to 6-h forecast. The result implies that prediction accuracy could be improved by using a machine

show abstract

Section: Related Workmentioning

confidence: 99%

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Chen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Another limitation arises for seasonal forecasting in relation to the forcing meteorological data during the period between the forecast date and harvest time [17]. Seasonal weather forecasts either based on historical weather observation [18][19][20], on weather generators [21] or on climate model outputs [22] remain very uncertain. Given these limitations, the majority of the national agriculture departments use empirical regression-based models to forecast yield over large areas.…”

Section: Introductionmentioning

confidence: 99%

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

et al. 2021

View full text Add to dashboard Cite

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

show abstract

“…However, the following data was not taken into consideration in the construction of the neural models. The evolution of the prediction quality was done by using the available methodology in the literature [10,12,[32][33][34][35].…”

Section: Methodology For Validating the Neural Modelsmentioning

confidence: 99%

Multicriteria Prediction and Simulation of Winter Wheat Yield Using Extended Qualitative and Quantitative Data Based on Artificial Neural Networks

et al. 2019

View full text Add to dashboard Cite

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.

show abstract

Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China

Abstract: Yield prediction was highly influenced by the distribution of meteorological elements during the growing season and may show great improvement if future weather can be reliably forecast early. © 2015 Society of Chemical Industry.

Cited by 15 publications

References 33 publications

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Multicriteria Prediction and Simulation of Winter Wheat Yield Using Extended Qualitative and Quantitative Data Based on Artificial Neural Networks

Contact Info

Product

Resources

About