Spatio-Temporal Pattern Change of Winter Wheat Production and Its Implications in the North China Plain

Zhang, Zemin; Lu, Chunxia

doi:10.3390/su11113028

Cited by 11 publications

(14 citation statements)

References 39 publications

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“…The trend and change amplitude of FVC GP and FVC MS for each pixel during 2000–2019 were calculated using the Mann-Kendall (M-K) test [ 54 , 55 ] and the Sen’s slope [ 56 , 57 ] non-parametric methods, which are widely used for the trend analysis in vegetation, climate, hydrology, agriculture, and other fields [ 58 , 59 , 60 , 61 , 62 ], based on the “trend” package in the R project software [ 53 ]. The M-K formulae are shown in Equations (2)–(5), and the Sen’s slope in Equations (6) and (7).…”

Section: Methodsmentioning

confidence: 99%

“…Taking the change trend of annual FVC GP as the indicator of grassland dynamics during 2000–2019, we classified the grasslands into 3 categories, named as increased (IN), not changed (NC) and decreased (DE), based on the Z and FS% values ( Table 1 ). According to the M-K test, when the |Z| value exceeds 1.96 (the confidence is above 95%), the trend is significant, when it is between 0.675 and 1.96 (confidence is 50–95%), the trend is insignificant, and when it is below 0.675 (confidence is lower than 50%), no trend exists [ 59 , 60 , 62 ]. Based on these rules, the grasslands with the FVC GP in significantly increasing/decreasing trends were deemed as IN/DE category, and those without showing change trends in the FVC GP were classified as NC.…”

Section: Methodsmentioning

confidence: 99%

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Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019

Liu

2021

IJERPH

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Grassland covers 54% of the Tibetan Plateau (TP) and suffered overgrazing and degradation problems during past decades. To alleviate these problems, a series of policy measures have been implemented during recent two decades and inevitably caused changes of the grassland. To this end, this study quantitatively analyzed the grassland changes and the effects of reduced grazing intensity, and identified the hot plots of grassland degradation in the TP during 2000–2019. The grassland status was indicated by the Fractional Vegetation Cover in the green grass period (GP), i.e., FVCGP, and its changes and spatial variations were detected by analyzing the FVCGP trends and their distribution, using the Mann–Kendal, Sen’s Slope, and ArcGIS buffering methods, and data of the MOD13Q1 Collection 6 products and other sources. The results showed that 62.12% of the grasslands were significantly increased in the FVCGP, and 28.34% had no apparent changes. The remaining 9.54% of the grassland significantly decreased in the FVCGP, mainly occurring in the areas nearby roads, rivers, and lakes, and distributed mostly in a point pattern. Of the total FVCGP decreased grassland area, 27.03% was clustered and identified as the hot plots of grassland degradation in six main regions. Decreased grazing intensity and increased precipitation contributed to the increase of grassland FVC in the TP, while local overgrazing could be the main cause of the FVC decrease. To strength the grassland restoration in the TP, the government supports and supervision should be enhanced to further mitigate the grassland pressure of animal grazing, particularly in the hot plot areas of degradation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019

Liu

2021

IJERPH

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“…The NCP is located between 31.9-39.8 • N, 112.0-122.5 • E, and involves 327 counties, extending over parts of the provinces (municipalities) of Beijing, Tianjin, Hebei, Henan, Shandong, Anhui and Jiangsu (Figure 1) [32]. It covers an area of approximately 39 million ha, of which 81.8% was farmland, 8.7% built-up land, 6.9% forestland and grassland and 2.6% water bodies and unused land, based on the 300 m spatial resolution data interpreted from European Space Agency in 2015.…”

Section: Study Areamentioning

confidence: 99%

“…Factor decomposition method was used to calculate the contribution rates to the change of total production by yield and sown area, based on the time series data [32]. The equations for each county are shown as below:…”

Section: Factor Decompositionmentioning

confidence: 99%

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Clustering Analysis of Soybean Production to Understand its Spatiotemporal Dynamics in the North China Plain

Zhang

2020

Sustainability

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The production gap of soybean (Glycine max L. Merr.) has been expanding in China recently, due to the increasing demand and decreasing production. Identifying soybean production dynamics is contributable to appropriate adjustment of crop rotation system and efficient use of agricultural resources—and thus to ensure food security. Taking the North China plain (NCP) as a case area, this study first analyzed the spatiotemporal dynamics of soybean production during 1998–2015 based on the spatial autocorrelation method, and then calculated contributions to the total production by yield and sown area using the factor decomposition method. The results indicated that total soybean production in the NCP decreased dramatically from 1998 to 2015 and showed a decreasing trend in 80.4% (263) of the counties, mainly (83.9%) contributed by the shrinkage of sown area, largely caused by decreasing benefit. Two regions were found with significantly spatial clustering degree of soybean production. In the south part of NCP, soybean production was highly clustered in Anhui province, and in north it was mainly clustered in western Hebei plain. It was found that soybean production in the NCP was rather sensitive to the return gaps of soybean from maize (Zea mays L.). These imply that the reduced area of soybean production can be restored if the return is improved by adopting appropriate policies such as appropriate subsidies. These findings could be helpful for the policymakers to make soybean production planning in the NCP, contributing to the national revitalization strategy of soybean production.

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Assessing changes in potential yields and yield gaps of summer maize in the North China Plain

Zhang

2023

Food and Energy Security

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In recent years, China's crop production growth rate has decreased. This has provoked questions on whether the grain yields have reached their potential. Taking the North China Plain (NCP) as the study area, this study first simulated the potential yield of an irrigated maize crop with the WOFOST model, using daily weather data for 1960–2017. The yield gap was quantified for 1998–2017, and finally, the total potential maize production was estimated. The results indicate that during 1960–2017, the average potential yield for the region varied annually from 9.5 to 13.5 t ha−1, showing an obvious downward trend at a rate of 37.6 kg ha−1 per year. The long‐term mean potential yield at all 327 counties ranged from 9.9 to 13.7 t ha−1, increasing from south to north. The annual maize yield gap was between an average of 2.7–6.0 t ha−1 (29.0%–51.3%) of the potential yield for the NCP during 1998–2017. At county‐level, the yield gap varied between 1.9 and 7.8 t ha−1 and exceeded 5.0 t ha−1 for 99 counties (30.3% of all counties). Attainable yields can be increased by 0.1–5.2 t ha−1 at county‐level, and by 2.2 t ha−1 for the whole NCP, implying that the maize production has the potential to increase with 20.8 Mt. To increase maize production, the focus areas should be the southcentral NCP, which have a larger yield gap during 2012–2017.

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Spatio-Temporal Pattern Change of Winter Wheat Production and Its Implications in the North China Plain

Cited by 11 publications

References 39 publications

Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019

Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019

Clustering Analysis of Soybean Production to Understand its Spatiotemporal Dynamics in the North China Plain

Assessing changes in potential yields and yield gaps of summer maize in the North China Plain

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