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1 – 10 of over 5000Xiaojie Xu and Yun Zhang
Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present…
Abstract
Purpose
Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.
Design/methodology/approach
The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.
Findings
The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.
Originality/value
Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.
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Fan Li, Dangui Li, Maarten Voors, Shuyi Feng, Weifeng Zhang and Nico Heerink
Soil nutrient management and fertilizer use by farmers are important for sustainable grain production. The authors examined the effect of an experimental agricultural extension…
Abstract
Purpose
Soil nutrient management and fertilizer use by farmers are important for sustainable grain production. The authors examined the effect of an experimental agricultural extension program, the science and technology backyard, in promoting sustainable soil nutrient management in the North China Plain (NCP). The science and technology backyard integrates farmer field schools, field demonstrations, and case-to-case counselling to promote sustainable farming practices among rural smallholders.
Design/methodology/approach
The authors conducted a large-scale household survey of more than 2,000 rural smallholders. The authors used a multivariate regression analysis as the benchmark to assess the effect of the science-and-technology backyard on smallholder soil nutrient management. Furthermore, the authors used coarse exact matching (CEM) methods to control for potential bias due to self-selection and the (endogenous) switching regression approach as the main empirical analysis.
Findings
The results show that the science-and-technology backyard program increased smallholders' wheat yield by approximately 0.23 standard deviation; however, no significant increase in maize yield was observed. Regarding soil nutrient use efficiency, the authors found a significant improvement in smallholders' phosphorus and potassium use efficiencies for both wheat and maize production, and a significant improvement in nitrogen use efficiency for wheat production, but no significant improvement of nitrogen use efficiency for maize production.
Originality/value
This study evaluated a novel participatory agricultural extension model to improve soil nutrient management practices among smallholders. The integration of agronomists' scientific knowledge and smallholders' local contextual experiences could be an effective way to improve farmers' soil nutrient management. This study provides the first quantitative estimates based on rigorous impact assessment methods of this novel extension approach in rural China.
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This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies…
Abstract
This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.
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