Wheat trade tends to happen between countries with contrasting extreme weather stress and synchronous yield variation

Vishwakarma, Srishti; Xin, Zhang; Lyubchich, Vyacheslav

doi:10.1038/s43247-022-00591-7

Cited by 7 publications

(5 citation statements)

References 52 publications

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“…Winter wheat is a crucial grain crop that plays a pivotal role in global food security and agricultural sustainability. In recent years, the significance of winter wheat research has been underscored by the growing challenges posed by climate change, population growth, and the need for sustainable agricultural practices [1,2]. Crop biophysical and biochemical parameters provide important information about various aspects of crop conditions that have direct implications for productivity.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Zhang,

Yi,

Wang

et al. 2024

Remote Sensing

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Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Zhang,

Yi,

Wang

et al. 2024

Remote Sensing

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“…The complex network method is an important tool to study the characteristics and dynamics of international trade systems using topological features, which was widely applied in public transportation [ 5 , 6 ], air quality [ 7 ], disease transmission [ 8 , 9 ], and natural resource flow [ 10 , 11 , 12 ]. In terms of grain trade [ 13 , 14 ], Srishti et al [ 15 ] analyzed the impact of extreme weather on wheat trade by the complex network; Zhou et al [ 16 ] built the global rice network and studied its evolution characteristics. Lu et al [ 17 ] constructed different weighted and unweighted trade networks and analyzed the trade networks of soybean, soybean oil, and soybean meal using complex network analysis.…”

Section: Introductionmentioning

confidence: 99%

Structural Evolution of Global Soybean Trade Network and the Implications to China

Wang

Liu

Wang

et al. 2023

Foods

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China experiences a serious shortage of soybean supplies and relies heavily on international trade with high vulnerability and large uncertainty, which maybe sows food security risks. It is of great significance to analyze the structural evolution of the global soybean trade network and its implications to China for ensuring food security. This paper constructed a global soybean trade network (GSTN) and analyzed the structural evolutionary characteristics of GSTN from 2000 to 2020 using the complex network analysis method and simulated the impact of targeted destruction on China through scenario analysis. The results showed that GSTN was gradually complex exhibiting a small word and a scale-free network property. The global soybean exporter was dominated by some major soybean-producing countries in America. The US played an important role in maintaining GSTN’s robustness. China was the world’s largest soybean importer; unfortunately, its soybean imports relied heavily on a few countries, and the anti-interference ability of China’s soybean trade tended to decline. Therefore, China’s soybean trade was increasingly vulnerable to being tightly controlled by other countries when some uncertain factors occurred, such as trade frictions and changes in policy decisions from importing and exporting countries. The US and Brazil were key countries with significant soybean trade ties to China. To assess the impact of the two countries on China’s soybean trade, targeted destruction method was used through destroying them in the network. Targeted destruction scenario analysis indicated the two countries played important roles in the anti-interference ability of China’s soybean trade. Brazil played a positive role in China’s control of soybean trade flows, while the US did not. Some policies for China’s soybean production and international trade were proposed. A balance between the domestic production and import of soybean is needed. Optimizing the soybean trade import system and seeking more trade partners is crucial. Improving soybean self-sufficiency is the fundamental way to reduce the high-import dependence. The study provided some insights for coping with international market fluctuations and improving the sustainability of China’s soybean trade.

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“…Recognizing that the linear assumption is inadequate for complicated time series forecasting, researchers proposed an artificial neural network (ANN), which functions as a universal approximation function 13 . Other often used machine learning algorithms include random forest (RF) 14 , 15 and gradient boosting (GB) 16 , 17 . And when uncertainty is factored in, the predicting process may be quantified using probability forecasting, another form of regression 18 .…”

Section: Introductionmentioning

confidence: 99%

Streamflow classification by employing various machine learning models for peninsular Malaysia

AlDahoul,

Momo,

Chong

et al. 2023

Sci Rep

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Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting.

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Wheat trade tends to happen between countries with contrasting extreme weather stress and synchronous yield variation

Cited by 7 publications

References 52 publications

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Structural Evolution of Global Soybean Trade Network and the Implications to China

Streamflow classification by employing various machine learning models for peninsular Malaysia

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