Search

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

Accurate monitoring of crop spatial distribution in topographically complex areas is of great significance for guiding agricultural management production and reasonable allocation of resources. However, scattered crop distribution and high spatial heterogeneity pose challenges for precise classification. The objective of this study was to explore a method for simultaneous precise identification of multiple crops with multi-temporal high-resolution satellite data under complex terrain conditions, thus further providing a theoretical and practical basis for crop identification in Chongqing city area and the complex low hills area in southwest China. The western Chongqing was selected as the study area. First, a multi-scale segmentation algorithm was adopted to construct homogeneous units from fields of the same feature type. This approach helped to achieve high accuracy classification by avoiding overly fragmented classification results based on pixel classification. Secondly, the multi-temporal Sentinel-2/MSI remote sensing images were used to explore the weathering patterns and characteristic parameters during the fertility period of major food crops. The spectral indices, such as NDVI, RVI and NDWI, were constructed to compare the differences in remote sensing spectral information of different crop types at various fertility periods based on ground sample points. Such information was then combined with the topographic features to determine the optimal combination for crop identification. Finally, an object-oriented decision tree logical classification rule set was established to extract major grain crop growing areas. The results showed that, (1) the multi-scale segmentation method was an effective approach that can make crop identification on a field basis. In addition, the vegetation edge segmentation was optimal at a segmentation scale of 30 and a compactness and shape factor of 0.5. (2) The target feature discrimination threshold was established by combining the spectral feature indices of NDWI in April, RVI in June, NDVI in August and NDVI in August with the topographic features such as altitude and slope. The overall accuracy of the classification reached 90.8%, being 85.7%, 83.3% and 80.7%, for paddy rice, maize, and sweet potato, respectively. This paper showed that the crop planting area identification method based on multiple spectral indices combined with topographic features can achieve high recognition accuracy and has practical significance. [ABSTRACT FROM AUTHOR]

The aims of this paper is to illustrate the importance of grain production in China's food security strategy based on the three Northeastern provinces (NEP) in the new period, which quantitative assessment of climate change under the background of the loss caused by meteorological disasters of regional food production is clarify. In this paper, using grain planting area, yield and agricultural disaster statistics from 1981 to 2020, comparison analysis grain production and disaster characteristics between NEP and the whole country respectively. The disaster-yield assessment model was used to estimate the disaster-yield loss and final yield of the NEP by inputting disaster data in recent 10 years, and the sensitivity and stability of the disaster-yield assessment model were re-tested. The results showed that :(1) from 1981 to 2020, the grain planting area and total output in NEP increased significantly, and the proportion of the total output of NEP increased steadily, and the proportion of the total output of the three northeast provinces reached 1/5 of the total output of the whole country by 2020. (2) The disaster situation in NEP showed a significant trend of first increasing and then decreasing. The incrasing of average grain yield during the past 40 years was 65.96kg⋅ha-1 and 252.5kg⋅ha-1 per year for NEP and the whole country, while the incrasing of average grain yield during the past 10 years was 52.6kg⋅ha-1 per year for NEP significantly. (3) From 2011 to 2020, the average affected area and affected area in China were 23704.5x10³ha and 11204.7x10³ha, respectively, and 3899.1x10³disasters of the whole country and the three northeastern provinces were significantly lower than those of the previous three years, making it the decade with the least severe disasters in 40 years. (4) The simulation accuracy of the disaster-yield assessment model was high. The linear regression coefficients (R²) of simulated grain yield and measured grain yield in Heilongjiang, Jilin and Liaoning provinces were 0.98, 0.90 and 0.88, respectively. The slopes were 1.05, 1.02 and 0.98 (P < 0.01), respectively. The loss rate of grain yield due to agro-disaster was 10.4%, 17.9% and 18.0% respectively in the three province, while which was more than 8.0%, 17.0% and 16.0% for 20a.years. (5) The model slightly overestimated the grain yield of Jilin and Liaoning in recent 10 years due to the overall light disaster situation. Based on the data from 1981 to 2010, the regional grain disaster loss assessment model was proved to be able to evaluate the loss of grain yield caused by meteorological disasters well, and had the performance of predicting grain yield, and has the feasibility of operational application. The impact of meteorological disasters on the grain output in northeast China is higher than the average level of disasters in the whole country. Considering that the grain output in Northeast China accounts for a high proportion of the national grain output, preventing the risk of agro-meteorological disasters in Northeast China in the new period is of great importance to guarantee the national food security. [ABSTRACT FROM AUTHOR]

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]

The effects of irrigation on winter wheat yield and water use efficiency (WUE) were mostly explored based on site-based experiments in North China Plain (NCP) region. However, due to the influences of contrasting environmental factors (e.g. climate and soil properties), these results cannot be accurately cross-compared among different studies, resulting in the difficulty to get the generalizable pattern at regional scale. Here meta-analysis was conducted to comprehensively evaluate the effects of irrigation on winter wheat yield and WUE in NCP region, with the observation data of 1876 pairs from 186 field experimental papers totally. The differences of the irrigation effects in different regions, precipitation year types, soil texture, and nitrogen (N) application levels were explored, and the corresponding optimal irrigation amounts for reaching high yield and WUE were ascertained. Results showed that compared with non-irrigation during the winter wheat growing period, irrigation increased the yield of winter wheat in NCP by 32.0%-38.3%, and reduced the WUE by 27.1%-30.1%. The yield increment due to irrigation in the northwest of NCP (39.6%-53.5%) with total precipitation during the winter wheat growing period below 150mm was higher than that in the southeast of NCP (24.3%-27.1%) with total precipitation during the winter wheat growing period higher than 150mm, while the decrease of WUE due to irrigation in northwest of NCP (32.4%-37.5%) was higher than that in the southeast of NCP (22.0%-24.3%). The optimum irrigation amount for high yield and WUE of winter wheat was 150-180mm in the northwest and 120-150mm in the southeast. Specifically, for different precipitation year types, the optimum irrigation amounts were 120-150mm with 2 times during jointing and flowering stages for dry year, during jointing and heading stages for normal year, and during jointing and booting stages for wet year. For different soil texture, irrigation under loamy soil had the highest increases in yield, while clay soil had the lowest decreases in WUE; the optimum irrigation amounts of winter wheat with four different soil texture of sandy soil, loam, clay loam and clay were 60-90mm, 120-150mm, 180-210mm, and 150-180mm, respectively. For different N application levels, N application of 120-240kg.ha-1 led to the highest yield and WUE under the irrigation amounts of 80-140mm, of which the higher yield was obtained under irrigation amounts of 110-140mm, and the higher WUE was obtained under the irrigation amounts of 80-110mm. Collectively, the NCP region can achieve the goals of high yield and WUE when irrigation amount was 120-150mm with 2 times but at different stages during different precipitation year types (i.e. dry year, normal year and wet year). Meanwhile, the combination of loam conditions with the N application of 120-240kg.ha-1 can synergistically improve the yield and water use efficiency of winter wheat. [ABSTRACT FROM AUTHOR]

Ammonia is the only alkaline gas in the atmosphere. Reducing ammonia emissions plays an important role in controlling atmospheric pollution. Livestock and poultry breeding industry is an important source of ammonia emissions. Scientific assessment of ammonia emissions and reduction potential in different links is of great significance. In this paper, the ammonia emission factors of different livestock and poultry breeding houses in China and abroad, the main emission reduction technologies and efficiency were summarized. Combined with the number of mechanical ventilation farms and the animal population of major livestock and poultry obtained from the second national pollution source census, the ammonia emission reduction potential of exhaust air from mechanical ventilation livestock farms in China was analyzed. The results show that the reduction potential of ammonia emission from mechanically ventilated livestock houses in China is 265.6 thousand tons; pigs, laying hens and broilers were the three main sources of livestock and poultry, and their emission reduction potential were 101.8 thousand tons, 77.3 thousand tons and 67.5 thousand tons, respectively. The emission reduction potential of the three livestock and poultry types accounted for 92.8 %. Regionally, the main implementing regions are East China, South Central and North China, with ammonia emission reduction potential accounting for 35.1%, 26.9% and 14.9%, respectively. It is proposed to carry out the construction of ammonia emission reduction facilities in mechanical ventilation barn of pig, laying hens and broiler, which can provide guarantee for the national ammonia emission reduction and the improvement of atmospheric environmental quality. [ABSTRACT FROM AUTHOR]

The cultivation of apples plays an important role in China's fruit production. Climate change has brought about more frequent and severe drought and waterlogging disasters, which have negatively impacted apple yield quantity and quality. Through the summary of previous research studies done on the impact of drought and waterlogging disasters and its effect on apple production, methods for disaster prevention and mitigation can be provided to reduce the impact of these disasters to the industry. In this paper, the methods of classification and systematic analysis combined with studying actual disaster cases were used to review the latest research into the effects of droughts and waterlogging disasters on apple production in China and identify the next steps for future research and development into this subject. The intensity and impacts of droughts and waterlogging disasters on apple production are determined by weather, regional climate, topography, soil composition, and several other factors. Adopting the morphological indicators to study these disasters, most existing research into this area only focuses on meteorological factors and rarely consider agrometeorological factors such as soil composition, current crop conditions, and more. The distribution of apple drought disasters in China decreases from north to south. The prevention measures of apple drought and waterlogging included irrigation, strengthening orchard soil management, implementing the integration of soil and fertilizer, etc. More of the current research done into this subject takes place in the Northern parts of China and often lack the necessary information to be put into use in disaster monitoring and early warning applications. In the future, research into apple droughts and waterlogging disasters should focus on increasing in orchard observation data collection, constructing a disaster risk monitoring and early warning model, and implementing the model into apple growing. [ABSTRACT FROM AUTHOR]