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The aim of this paper is to assess the loss of grain production caused by agrometeorological disasters in the Inner Mongolia Autonomous Region (IMAR), which is an important grain production base of China. The disaster-yield assessment model was formed and verified using grain production, yield, planting area and agrometeorological disaster conditions data of the IMAR from 1981 to 2020, and the loss of grain yield caused by meteorological disasters was assessed by this model. The results showed that grain production, yield and planting area observed an escalating trend from 1981 to 2020, and the rate of increase was 78.85×104t·y-1, 100.97kg·ha-1·y-1 and 74.48×10³ha-1·y-1. The covered and affected area of agrometeorological disasters for the IMAR and National both increased and then decreased during the 1981-2020 period. Drought was the most important agrometeorological disaster in the IMAR, and the covered and affected area was 64.10% and 62.45%, respectively, accounting the total covered and affected area of all kinds of disasters in 1981-2020. The grey correlation analysis showed that drought had the highest correlation with grain yield at the level of disaster covered and affected rate, and hail had the highest correlation with grain yield at the level of disaster destory rate. The disaster-yield assessment model constructed by this study was of high simulation accuracy, and the linear regression coefficients (R²) and the slop between the simulated and measured grain yield was 0.99 and 0.98, respectively (P < 0.01). The average relative simulation yield was 0.20% and relative simulation error of the following year grain yield was 2.49%. Grain yield loss rate due to disaster in the IMAR decreased (R²=0.77，P<0.01) from 1981 to 2020 with a rate of 0.48 percentage points·y-1, and average grain yield loss rate was 14.79% and there were 68.42% of years which grain yield loss rate above 10%. The disaster-yield assessment model of the IMAR formulated in this study can simulate and predict grain yield, and assess the loss of grain yield due to disasters, which can meet the needs of agrometeorological services. [ABSTRACT FROM AUTHOR]

This study aimed to deal with the systematic classification of a genus of Crabronidae Belomicrus A.Costa.,from Inner Mongolia,China.【Method】Based on extensive collection of specimens and refer to the existed literature,using the methods of comparative morphology,the systematics of a genus of Belomicrus from Inner Mongolia,China,were described in this paper.【Result】This paper dealed with the systematics of two new record species,Belomicrus kaszabi Tsuneki.,Belomicrus shatalkini Antropov.,and a genus of Belomicrus,were described in this paper.A key of the species of the Belomicrus from China was provided.The type specimen of the new species was deposited in the Insect Collections of Yunnan Agricultural University.【Conclusion】The new record genus of Belomicrus are all distributed in Inner Mongoliaarea of our country,and it has not been found in other areas.It needs to be studied in further. [ABSTRACT FROM AUTHOR]

In China, ultra-small-scale agricultural production remains low agricultural income which cannot afford adequate livelihood and as well meet their basic needs of rural households. Smallholder production systems have seriously hindered the sustainable development of China’s agriculture. Family farms with a moderate land scale is a way to develop China's agriculture. Moderate land scale of China's agriculture is affected by multiple factors like average social income level, grain production income, etc. So moderate land scale is a dynamic value. This paper aimed to assess moderate land scales of grain family farms at the municipal level in China based on the method of income equalization. The research findings are as follows. The moderate land scale of grain family farms was 13-14 hm2 in 2016-2017, equivalent to the local household contracted area of 30 times. According to the policy formulated by China’s central government in 2014, the moderate scale of one family farm should be 10-15 times of average household contracted area. That is, the current standard of moderate scale is low. Mainland China can be divided into 9 districts and 38 sub districts according to the different types of land scale management. The moderate scale of the Northeast Region and the Inner Mongolia Plateau Region are 15-17 hm2, the North China Plain Region is about 10 hm2, the Lower Yangtze River Region and the Southeast Coastal Region are 7-9 hm2, the Southwest Mountainous Region is 6 hm2, the Loess Plateau Region is 18 hm2, and the Northwest Arid Region is 25 hm2 . The scale of the Qinghai-Tibet Plateau Region is the highest, 30 hm2 . The ratio of the moderate scale to the average cultivated land area per household in the Inner Mongolia Plateau Region is 8, and the ratio in the North China Plain Region and the Lower Yangtze River Region is about 35. It is difficult for China to achieve scale management on the whole, and the difficulty gradually decreases from the southeast coast to the northwest inland. The boundary line extends from Beijing to Nujiang Lisu Nationality Autonomous region, Yunnan. The moderate scales of grain family farms in the southeast region of the line are mostly 6-10 hm2, and it is difficult to achieve. In contrast, the moderate scales in the northwest region of the line are mostly above 15 hm2, and it is easy to achieve. China's scale management reform is at the initial stage. The promotion speed of scale management should not be fast to avoid social unrest. It is recommended to raise the standard of moderate management scale to the range of 15-20 times, and the standard can be adjusted according to the actual situation in different regions. The government should also promote institutional innovation to reduce high land transfer transaction costs. System reform can be carried out in three aspects. Firstly, the government should promote farmers who do not rely on agriculture to actively release land resources; Secondly, the local governments should actively play a coordinating role to promote land centralization and scale management; Finally, a unified and perfect land circulation market should be established [ABSTRACT FROM AUTHOR]

Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the presence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to investigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Correlation analysis found that the S4, S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral reflectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning machine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their determination coefficient (R²), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents before and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculated salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accurate, with its associated R² and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R² and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]