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

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Food Safety, Security, Sustainability and Nutrition as Priority Objectives of the Food Sector

The future food systems will have to provide food and nutrition security while facing unprecedented sustainability challenges: this underlines the need for a transition to more sustainable food systems. Taking into account these premises and considering the complexity of food systems, this book aims to present original research articles, reviews, and commentaries concerning the following:Advancements in food and beverage;Dietary supplements, nutraceuticals, and functional food;Food allergy and public health;Food and nutritional toxicology;Food biotechnology and food processing;Food microbiology and food safety;Food packaging;Food safety and food inspection;Food security and environmental impacts;Food waste management;Nutrition and metabolism;Sustainable food systems and agro-ecological food production

Assessing Normalized Difference Vegetation Index (NDVI) data to estimate winter wheat yields and analyze winter wheat by homogeneous subregions at field scale in Kansas.

Doctor of PhilosophyDepartment of GeographyMarcellus M CaldasWheat (Triticum aestivum L.) is the 4th largest staple crop produced worldwide. While global demand has increased over the last 15 years, the rate of increase of global cereal production has slowed or stagnated. Accurate information about crop production is key for local-scale research, farmers, and decision-making evaluation due to the typically high spatial variability in agricultural production, especially in environmentally heterogeneous high-producing regions. The main goal of this dissertation was to investigate the potential of satellite imagery in predicting winter wheat yields and analyze winter wheat yields by homogeneous subregions at field scale in Kansas, the largest producer of winter wheat in the U.S. The first chapter examined the performance of different satellite sensors (from coarse to moderate resolution - MODIS, Landsat, and Sentinel) in predicting winter wheat yields. The following chapters analyze the winter wheat yield prediction using environmentally distinct subregions regarding weather and management practices and multisource data (NDVI, weather, and climate). Linear Regression and a robust machine learning model, (i.e., Random Forest) were applied to predict winter wheat yields. The results, using NDVI predictor variables, were not enough to explain field-scale winter wheat yield variability across much of Kansas, where Landsat USGS achieved the lowest prediction error among all sensors (RMSE = 0.95 Mg ha-1). The results proved to be more accurate when using Landsat NDVI variables to predict winter wheat yields in more homogeneous subregions (NC, SC, and West), with the best prediction in NC (RMSE = 0.76 Mg ha-1). NC, SC, and West Kansas achieved the best results when including weather and management variables along with NDVI (RMSE of 0.59 Mg ha-1 , 0.66 Mg ha-1, and 0.69 Mg ha-1in NC, SC, and West), and outperformed the prediction when using all fields-yields across Kansas ( RMSE=0.78 Mg ha-1). The prediction model showed that it is possible to predict yield in early crop developmental stages; however, after adding weather and management variables, NDVI predictor variables in the late stages of the growing season were the most important for winter wheat yield prediction. NDVI was more significant in predicting winter wheat yields in NC and West than in SC Kansas. NC showed management of fertilizers ( N, P, Cl) as good yield predictors and could be used along with NDVI to estimate yields. SC and West predictor variables relied more on variables related to environmental conditions or management practices related to environmental conditions, such as fungicide application, soil water storage, and sowing date. Overall, this research demonstrates that the applicability of empirical winter wheat yield modeling using NDVI predictor variables in Kansas is environmentally dependent. Lastly, winter wheat yield prediction using satellite imagery at the field scale could be benefited using this subregional scheme in Kansas