Satellite and Fluorescence Remote Sensing for Rice Nitrogen Status Diagnosis in Northeast China

Nitrogen (N), as the most important element of crop growth and development, plays a decisive role in ensuring yield. However, the problems of over-application of N fertilizers have been repeatedly reported in China, which resulted in low N use efficiency and high risk of environmental pollution. The requirements of developing technologies for real-time and site-specific diagnosis of crop N status are the foundation to realize the precision N management, and also benefit to the improvement of the N use efficiency. Remote sensing technology provides a promising non-intrusive solution to monitor rice N status and to realize the precision N management over large areas. This research focuses on proposing N nutrition diagnosis methods and developing N fertilizer management strategies for paddy rice of cold regions in Northeast China. The main contents and results are presented as follows: (1)This study developed a new critical N (Nc) dilution curve for paddy rice of cold regions in Northeast China. The curve could be described by the equation Nc=27.7W^(-0.34) if W≥1 t/ha for dry matter (DM) or Nc=27.7g/kg DM if W<1 t/ha, where W is the aboveground biomass. Results indicated that the new Nc dilution curve was suitable for diagnosing short-season Japonica rice N status in Northeast China. The validation result indicated that it worked well to diagnose plant N status of the 11-leaf variety rice. (2)This study investigated the potential of the satellite remote sensing data for diagnosing rice N status and guiding the topdressing N application at the stem elongation stage in Northeast China. 50 vegetation indices (VIs) were computed based on the FORMOSAT-2 satellite data, and they were correlated with the field-based agronomic variables, i.e., aboveground biomass (AGB), leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), chlorophyll meter readings, and N nutrition index (NNI, defined as the ratio of actual PNC and critical PNC according to the new Nc dilution curves). The results presented that 45% of variation in the NNI was obtained by using a direct estimation method based on the best VI according to the FORMOSAT-2 satellite data, while 52% of the variation in the NNI was yielded by an indirect estimation method, which firstly used the VIs to estimate AGB and PNU, respectively, then estimated NNI according to these two variables. Moreover, based on the critical N uptake curve, a N recommendation algorithm was proposed. The algorithm was based on the difference between the estimated PNU and the critical PNU to adjust the topdressing N application rate. The results demonstrated that FORMOSAT-2 images have the potential to estimate rice N status and guide panicle N fertilizer applications in Northeast China. (3)This study also evaluated the potential improvements of the newest satellite sensors with the red edge band for diagnosing rice N status in Northeast China. The canopy-scale hyperspectral data were upscaled to simulate the wavebands of RapidEye, WorldView-2, and FORMOSAT-2, respectively. The VI analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to evaluate the N status indicators. The results indicated that the VIs based on the RE band from RapidEye and WorldView-2 data could explain more variability for N indicators than the VIs from FORMOSAT-2 data having no RE band. Moreover, the SMLR and PLSR results revealed that both the near-infrared and red edge band were important for N status estimation. (4)The proximal fluorescence sensor Multiplex_3 was used to evaluate the potential of fluorescence spectrum for estimating the N status of the cold regional paddy rice at different growth stages. The Multiplex indices and their normalized N sufficient indices (NSI) were used to estimate the five N status indicators, i.e., AGB, leaf N concentration (LNC), PNC, PNU, and NNI. The results indicated that there were strong relationships between the fluorescence indices (i.e., BRR_FRF, FLAV, NBI_G, and NBI_R) and (i.e., LNC, PNC, NNI), with the coefficient of determination between 0.40 and 0.78. In particular, NNI was well estimated by these fluorescence indices. Moreover, the NSI data improved the accuracy of the N diagnosis. These results of this study were useful for N nutrition diagnosis and variable fertilization of the cold regional paddy rice, which were significant for the ecological environment protection and the national food security

Large System Multi-objective Model of Optimal Allocation for Water Resources in Jiansanjiang Branch Bureau

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceAccording to the imbalance development and utilization of water resources, water shortages and other issues in Sanjiang Plain, taking Jiansanjiang branch bureau as an example, the multi-objective optimal allocation model of water resources is established with goal of maximum economic and social benefits. Only surface water, groundwater and transit water are considered overall and different water demands in industry, life and agriculture are satisfied can we realize the rational allocation of regional water resources. The large system decomposition-coordination theory and multi-objective genetic algorithm are applied to solve the model. The optimization results showed that, the water shortage situation in Jiansanjiang branch bureau is improved in planning years and surface water supply capacity can be increased gradually and groundwater resources can be effectively protected. The optimal allocation model and solution method are effective and feasible, and the optimal allocation results are reasonable. The research can provide scientific basis for rational development and utilization of water resources in Jiansanjiang branch bureau and Sanjiang Plain

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Shared Destiny: China Story Yearbook 2014

Humanity as never before shares a common destiny, whether it be in terms of the resources of the planet, the global environment, economic integration, or the movement of peoples, ideas, cultures. For better or worse humankind is a Community of Shared Destiny 命运共同体. The People’s Republic of China under the leadership of the Chinese Communist Party and its ‘Chairman of Everything’, Xi Jinping, has declared that it shares in the destiny of the countries of the Asia and Pacific region, as well as of nations that are part of an intertwined national self-interest. The Party, according to Marxist-Leninist-Maoist theory, is the vanguard of progressive social forces; it cleaves to the concept of shared destiny and its historical role in shaping that destiny. Since its early days nearly a century ago it has emphasised the collective over the individual, the end rather than the means. It addresses majority opinion while guiding and moulding the agenda both for today, and for the future

Shared Destiny

In the China Story Yearbook 2014: Shared Destiny, we take as our theme a concept emphasised by Xi Jinping, the leader of China's partystate, in October 2013 when he spoke of the People’s Republic being part of a Community of Shared Destiny, officially translated as a Community of Common Destiny. The expression featured in Chinese pronouncements from as early as 2007 when it was declared that the Mainland and Taiwan formed a Community of Shared Destiny. Addressing the issue of China’s relations with the countries that surround it at the inaugural Periphery Diplomacy Work Forum held in Beijing on 24 October 2013, Xi Jinping further developed the idea when he summed up the engagement between the People’s Republic and its neighbours by using a series of ‘Confucian-style’ one-word expressions: positive bilateral and multilateral relationships were to be based on amity, sincerity, mutual benefit and inclusiveness