Modelling Winter Rapeseed (Brassica napus L.) Growth and Yield under Different Sowing Dates and Densities Using AquaCrop Model

Xie, Ziang; Kong, Jiying; Tang, Min; Luo, Zhenhai; Li, Duo; Li, Rui; Feng, Shi-Ming; Zhang, Chao

doi:10.3390/agronomy13020367

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…This phenomenon could be observed more frequently due to climate change. This difference in the phenological cycle can also be observed between two different sites [41], [42]. The difference between sites and years could cause both the temporal shift and the difference in the duration of the growing cycle of rapeseed.…”

Section: ) the Effect Of The Number Of Images And The Shift In The Ph...mentioning

confidence: 95%

Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

Maleki,

Baghdadi,

Dantas

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate crop type information is of paramount importance for decision makers. This paper focuses on refining rapeseed field detection. This goal is achieved by creating high accuracy rapeseed maps using Sentinel-1 (S1) time series and secondly, by developing different solutions for mapping the rapeseed fields when there are constraints in ground samples collection. Proposed solutions include transferring a model developed over one year to other years with no re-training, and developing models with limited training samples. The research evaluates the performance of Random Forest (RF) and three deep learning (DL) algorithms: Long Short-Term Memory Fully Convolutional Network (LSTM-FCN), InceptionTime, and Multilayer Perceptron (MLP). All four algorithms were used to classify the S1 time series with a large number of ground samples from the same years for training and testing. Smaller sample sizes were then tested for the training phase (100, 300, 500 and 1000 samples in a study site of 800 km2). Model transferability is tested across years. The impact of S1 image count on transfer accuracy is examined. Additionally, the effect of the phenological shift in the rapeseed growth cycle of 15 and 30 days between the training and test years was also investigated. The findings demonstrate strong model performance when training and testing occur in the same year (F1score up to 95%). Within sample sizes of 300 to 1000, RF and InceptionTime stand out with high accuracy (F1-score>90%). When employing different years for training and testing with ample sample sizes, all four algorithms correctly classified rapeseed (F1-score between 85.5% and 92.7%). In cases of a reduced number of images, the performance of InceptionTime and LSTM-FCN decreased (16% decrease in the F1-score), while RF and MLP maintain their performance. Notably, RF outperforms DL algorithms with an F1 score of 89.1%. In the context of a phenological shift, only InceptionTime and LSTM-FCN demonstrated strong performance (F1-score between 87.7% and 92.6%).

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Section: ) the Effect Of The Number Of Images And The Shift In The Ph...mentioning

confidence: 95%

Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

Maleki,

Baghdadi,

Dantas

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Investigation of the quality of rapeseed oil derived from different varieties and growth periods based on GC-IMS technique

Rao,

Li,

Ouyang

et al. 2024

Microchemical Journal

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Accumulative and adaptive responses of maize transpiration, biomass, and yield under continuous drought stress

Cui,

Tang,

Zhou

et al. 2024

Front. Sustain. Food Syst.

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IntroductionContinuous drought stress aggravates agricultural losses and threats food security. However, the responses of crops to continuous drought stress remain uncertain.MethodsTo make up the limitations of field experiment and achieve the setting of multiple continuous drought stress scenarios, AquaCrop model is calibrated and validated using field experiment data of summer maize in 2017 and 2018 seasons. Then, the whole growth processes under different continuous drought stress scenarios at two growth stages of maize are simulated. The quantitative responses of transpiration (Tr), biomass accumulation, and yield formation to continuous drought stress are analyzed.Results and discussionThe results show that when the maize encounters serious drought at the seedling stage, the reduction rates of Tr at the jointing stage, the tasseling stage, and the milking stage are 57.45%, 43.61%, and 5.24%, respectively. Drought stress at a growth stage of maize not only have negative impacts on transpiration and biomass accumulation at this stage, but also have after-effects on these elements at the subsequent stages. In addition, continuous serious drought at the seedling and jointing stages reduces yield by 100%, which is higher than the sum of the loss rates at these two stages [>33.30% + 24.16%)], while the loss rate due to continuous light drought is lower than the sum [20.66% < (18.80% + 12.45%)]. The impact of continuous drought stress at two growth stages generally exceeds the sum of the impacts of the two single stages. Nevertheless, drought at the seedling stage promotes the adaptability of maize to drought, alleviating the negative impacts of light drought at the jointing stage, while the adaptability disappears when drought at the jointing stage is serious. Therefore, in the actual production of maize, serious drought at the seedling stage should be avoided to ensure seed survival. Meanwhile, continuous drought at the seedling and jointing stages should be prevented to reduce the severe accumulative effects, which guides drought disaster reduction and sustainable agricultural production.

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Modelling Winter Rapeseed (Brassica napus L.) Growth and Yield under Different Sowing Dates and Densities Using AquaCrop Model

Cited by 5 publications

References 38 publications

Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

Investigation of the quality of rapeseed oil derived from different varieties and growth periods based on GC-IMS technique

Accumulative and adaptive responses of maize transpiration, biomass, and yield under continuous drought stress

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