Spatial–temporal changes in risk of climate-related yield reduction of winter wheat during 1973–2014 in Anhui province, southeast China

Huang, Jin; Chen, Jinhua; Zhang, Fangmin; Hu, Zhenghua

doi:10.1007/s00704-022-03929-5

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Second, previous assessment indicators of disaster loss were overly single, such as using only the yield reduction rate, making it difficult to fully present the issues in agricultural production [40,41]. An overall picture of the comprehensive risk of maize production under long-term yield trends, conventional disasters, and catastrophes is presented here by considering characteristics such as trends, tendencies, fluctuations, dispersions, and extremes of disaster losses.…”

Section: Discussionmentioning

confidence: 99%

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

Fan¹,

Wang

Lin³

et al. 2023

Agriculture

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The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention.

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Section: Discussionmentioning

confidence: 99%

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

Fan¹,

Wang

Lin³

et al. 2023

Agriculture

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“…Because the dynamic variation of the barycenter can reflect the contrast and shift of regional element distribution (Huang et al, 2021), it was used to perform spatial analysis of barycenter changes in this study. The barycenter of MRSEI was calculated using the following equations:…”

Section: Barycenter Modelmentioning

confidence: 99%

Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index

Zhao,

Han,

et al. 2023

J. Arid Land

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Otindag Sandy Land in China is an important ecological barrier to Beijing; the changes in its ecological quality are major concerns for sustainable development and planning of this area. Based on principal component analysis and path analysis, we first generated a modified remote sensing ecological index (MRSEI) coupled with satellite and ground observational data during 2001-2020 that integrated four local indicators (greenness, wetness, and heatness that reflect vegetation status, water, and heat conditions, respectively, as well as soil erosion). Then, we assessed the ecological quality in Otindag Sandy Land during 2001-2020 based on the MRSEI at different time scales (i.e., the whole year, growing season, and non-growing season). MRSEI generally increased with an upward rate of 0.006/a during 2001-2020, with clear seasonal and spatial variations. Ecological quality was significantly improved in most regions of Otindag Sandy Land but degraded in the southern part. Regions with ecological degradation expanded to 18.64% of the total area in the non-growing season. The area with the worst grade of MRSEI shrunk by 15.83% of the total area from 2001 to 2020, while the area with the best grade of MRSEI increased by 9.77% of the total area. The temporal heterogeneity of ecological conditions indicated that the improvement process of ecological quality in the growing season may be interrupted or deteriorated in the following non-growing season. The implementation of ecological restoration measures in Otindag Sandy Land should not ignore the seasonal characteristics and spatial heterogeneity of local ecological quality. The results can explore the effectiveness of ecological restoration and provide scientific guides on sustainable development measures for drylands.

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“…Principal component analysis is a multivariate statistical method for data dimensionality reduction. This method transforms multiple variables into a few uncorrelated comprehensive variables through dimensionality reduction (Xu et al 2020;Huang et al 2021). Its mathematical method comprises the data matrix of K observation variables and N observation samples:…”

Section: Principal Component Analysismentioning

confidence: 99%

Comparison and applicability analysis of different evapotranspiration models for maize farmland in the lower Yellow River Plain based on the eddy covariance system

Ren

Zhang

et al. 2023

Preprint

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Evapotranspiration is an important part of the hydrological cycle and a key indicator to measure hydrological and energy transfer in the soil-plant-atmosphere continuum (SPAC). In this study, maize farmland in the lower Yellow River, an important grain production base in China, was selected as the research object. Based on the actual observation data of the eddy covariance system during the summer maize growth cycle, ten common evapotranspiration estimation models, including the FAO-56 Penman‒Monteith (P-M) model, Hargreaves–Samani (H-S) model, Priestley–Taylor (P-T) model, Makkink (Ma) model, Jensen–Haise (J-H) model, Irmark–Allen (I-A) model, Doorenbos–Pruitt (D-P) model, McCloud (Mc) model, Kimberly–Penman (K-P) model and Abtew (Ab) model, were evaluated in estimating the applicability of the actual evapotranspiration. The mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and index of agreement (D) were chosen as evaluation indices. The Pearson correlation test and principal component analysis methods were used to explore the main influencing factors of actual evapotranspiration. The comprehensive ranking of the applicability of each model in the study area was obtained by synthesizing each index: J-H > P-T > Mc > K-P > I-A > P-M > D-P > Ab > Ma > H-S. it could be concluded that the J-H model was the most suitable in the study area, followed by the P-T model, while the H-S model attained the worst simulation performance. The evapotranspiration of summer maize at the tasseling-milky maturity stage in this region was the highest, and the solar radiation, net radiation and photosynthetically active radiation exhibited a strong correlation with evapotranspiration and greatly impacted evapotranspiration. This study plays an important role in the development of efficient water-saving agriculture, irrigation forecasting and sustainable utilization of water resources in the core area of grain production in China.

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Spatial–temporal changes in risk of climate-related yield reduction of winter wheat during 1973–2014 in Anhui province, southeast China

Cited by 6 publications

References 30 publications

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index

Comparison and applicability analysis of different evapotranspiration models for maize farmland in the lower Yellow River Plain based on the eddy covariance system

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