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

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Parsley production using organic fertilizers before planting and in top dressing

The objective of this research was to evaluate the effect of two organic fertilizers used before planting and in top dressing in the production of parsley. Seven treatments were evaluated, resulted from the factorial 2 x 3 + 1: two organic fertilizers (castor bean cake, and hoof and horn powder) x 3 modes of application (100% before planting; 100% in top dressing; 50% before planting and 50% in top dressing) + 1 control. The experimental design was in randomized blocks, with five replications and plots of 1 m2. Two harvests were done and in both the following characteristics were evaluated: the relative chlorophyll index ("Spad''), plant height, fresh (FW) and dry (DW) matter weight of shoot and macronutrients accumulation. There was no significant difference for the ‘’Spad” index in both harvests. For the other characteristics, the control was inferior to the other treatments. In the comparison among application modes, the treatment with castor bean cake, 100% before planting, was inferior to the other applications modes of this fertilizer for FW and DW. For hoof and horn powder, the 100% in top dressing application mode was superior to other applications. In the comparison between fertilizers, the hoof and horn powder was superior to castor bean cake in both harvests when application was done 100% before planting. The descending order of macronutrients accumulation was: K &gt; N &gt; Ca &gt; P &gt; Mg. Therefore, it is recommended to apply hoof and horn powder, 100% in top dressing application.The objective of this research was to evaluate the effect of two organic fertilizers used before planting and in top dressing in the production of parsley. Seven treatments were evaluated, resulted from the factorial 2 x 3 + 1: two organic fertilizers (castor bean cake, and hoof and horn powder) x 3 modes of application (100% before planting; 100% in top dressing; 50% before planting and 50% in top dressing) + 1 control. The experimental design was in randomized blocks, with five replications and plots of 1 m2. Two harvests were done and in both the following characteristics were evaluated: the relative chlorophyll index ("Spad''), plant height, fresh (FW) and dry (DW) matter weight of shoot and macronutrients accumulation. There was no significant difference for the ‘’Spad” index in both harvests. For the other characteristics, the control was inferior to the other treatments. In the comparison among application modes, the treatment with castor bean cake, 100% before planting, was inferior to the other applications modes of this fertilizer for FW and DW. For hoof and horn powder, the 100% in top dressing application mode was superior to other applications. In the comparison between fertilizers, the hoof and horn powder was superior to castor bean cake in both harvests when application was done 100% before planting. The descending order of macronutrients accumulation was: K &gt; N &gt; Ca &gt; P &gt; Mg. Therefore, it is recommended to apply hoof and horn powder, 100% in top dressing application

Nitrogen and chlorophyll status determination in durum wheat as influenced by fertilization and soil management: Preliminary results.

Handheld chlorophyll meters as Soil Plant Analysis Development (SPAD) have proven to be useful tools for rapid, no-destructive assessment of chlorophyll and nitrogen status in various crops. This method is used to diagnose the need of nitrogen fertilization to improve the efficiency of the agricultural system and to minimize nitrogen losses and deficiency. The objective of this study is to evaluate the effect of repeated conservative agriculture practices on the SPAD readings, leaves chlorophyll concentration and Nitrogen Nutrition Index (NNI) relationships in durum wheat under Mediterranean conditions. The experimental site is a part of a long-term-experiment established in 1994 and is still on-going where three tillage managements and three nitrogen fertilizer treatments were repeated in the same plots every year. We observed a linear relationship between the SPAD readings performed in the central and distal portion of the leaf (R2 = 0.96). In fertilized durum wheat, we found all positive exponential relationships between SPAD readings, chlorophyll leaves concentration (R2 = 0.85) and NNI (R2 = 0.89). In the unfertilized treatment, the SPAD has a good attitude to estimate leaves chlorophyll concentration (R2 = 0.74) and NNI (R2 = 0.77) only in crop grow a soil with relative high content of soil organic matter and nitrogen availability, as observed in the no tilled plots. The results show that the SPAD can be used for a correct assessment of chlorophyll and nitrogen status in durum wheat but also to evaluate indirectly the content of soil organic matter and nitrogen availability during different growth stages of the crop cycle

Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance

Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.publishedVersio

Future farming: Machine vision for nitrogen assessment in grain crops

- A pilot simulation and field study demonstrated potential for machine vision to estimate in-season soil and leaf nitrogen status using cameras and image analysis for real-time sensing and control - The modelled soil and leaf nitrogen were estimated with >80% accuracy using machine vision-detectable crop features estimated in combination with known underlying soil variability - Further trials will evaluate and refine the machine vision system at the Future Farm core site

Petiole extract chemical analysis to evaluate nutritional status in grapevine

The objective of this work was to evaluate the relationship between N, P and K concentration in petiole extract, blades and roots, to determine the nutritional status in grapevine. The experiment was done between December 2006 and February 2007. Three fertilization trials with N, P2O5 and K2O in one‑year‑old Vitis vinifera 'Red Globe' plants, grown in containers, were established. Nitrate, phosphate and potassium concentration were evaluated in the petiole extract, and nitrogen, phosphorus and potassium were determined in blades and roots. Petiole extract analysis was more sensitive than blade and root analysis to determine nitrate and nitrogen status. Meanwhile, blade analysis showed better plant response to fertilization with phosphorus and potassium. Petiole extract analysis can indicate the antagonism between nutrients under high doses of fertilization.Tradicionalmente se han analizado pecíolos y láminas para determinar el estado nutricional en la vid, pero ambos análisis suelen presentar inconsistencias entre el potencial productivo y los niveles óptimos dado por los estándares. Esta razón ha motivado la búsqueda de otros tejidos para el diagnóstico nutricional, presentándose el extracto peciolar como una alternativa. Con el objetivo de establecer la relación existente entre la concentración de N-P-K del extracto peciolar, la lámina y la raíz, se desarrolló y validó la metodología de extracto peciolar para vid, en plantas de un año de edad autoenraizadas de la variedad 'Red Globe', creciendo en macetas, a partir de tres ensayos de fertilización utilizando N, P2O5 y K2O. En el extracto peciolar se evaluó nitrato, fosfato y potasio y en láminas y raíces, nitrógeno, fósforo y potasio. Se encontró que el extracto peciolar es más sensible que la lámina y la raíz para determinar el estatus de nitrato y, además, representa mejor que la lámina y la raíz el estatus de nitrógeno. Mientras que la lámina representa mejor la respuesta de fertilización con fósforo (R2 = 0,80) y potasio (R2 = 0,71). El extracto peciolar detecta antagonismos entre elementos en altas dosis de fertilización.El objetivo de este trabajo fue evaluar la relación existente entre la concentración de N, P y K del extracto peciolar, la lámina y la raíz, para determinar el estado nutricional de la vid. El ensayo se desarrolló entre diciembre de 2006 y febrero de 2007. Se validó la metodología de extracto peciolar para Vitis vinifera 'Red Globe', en plantas de un año de edad autoenraizadas, creciendo en macetas, a partir de tres ensayos de fertilización utilizando N, P2O5 y K2O. En el extracto peciolar, se evaluó nitrato, fosfato y potasio, y en láminas y raíces, nitrógeno, fósforo y potasio. El análisis del extracto peciolar fue más sensible que el de la lámina y la raíz para determinar el estatus de nitrato y nitrógeno. Mientras que el análisis de la lámina representa mejor la respuesta de la planta a la fertilización con fósforo y potasio. El extracto peciolar puede detectar antagonismos entre elementos en altas dosis de fertilización

Arranjos de cultivo para taioba sob pomar de bananeira

As pesquisas associadas ao tema de manejo da biodiversidade em sistemas de produção englobam diversos aspectos, dentre eles a escolha dos melhores arranjos populacionais das plantas cultivadas. A utilização de hortaliças não convencionais, como a taioba Xanthosoma sagittifolium (L.) Schott, na diversificação dos sistemas produtivos é uma ótima alternativa do ponto de vista de segurança alimentar do produtor e também para a geração de renda. Contudo, na literatura ainda são escassas as informações sobre seu manejo e produção. Nesse sentido, objetivou-se avaliar diferentes densidades de plantio de taioba cultivada em consórcio, sob pomar de bananeiras, verificando seu desenvolvimento e produção. O experimento foi realizado na área experimental da Universidades Federal do Espírito Santo (UFES), localizado no município de Alegre ES. O delineamento foi em blocos casualizados, com seis repetições, no esquema de parcelas subdivididas. As parcelas foram compostas pelos sistemas de plantio em linha simples e duplas. As subparcelas foram compostas pelos espaçamentos entre plantas de 30, 40 e 50 cm. Foram avaliadas a área foliar, número de folhas, matéria fresca e matéria seca de folhas, índices de clorofila, flavonoides e balanço de nitrogênio. Adicionalmente estimou-se a Renda Bruta, através da estimativa da capacidade de produção de folhas, transformada em capacidade de produção de maços de folhas comerciais, que seriam produzidos em 1,0 hectare de taioba, a partir dos arranjos adotados no consórcio com bananeiras. As maiores produções de matérias fresca e seca da taioba foram observadas quando se adotou o maior espaçamento entre plantas (50 cm), tanto em linha simples quanto em linhas duplas. Os arranjos estudados não influenciaram no número de folhas emitidas pelas plantas, por isso, o maior rendimento bruto foi obtido com a maior densidade de plantas, alcançada com o plantio no espaçamento de 30 cm entre plantas, em linhas duplas. Sendo assim, os resultados demonstram que a diversificação da produção do pomar de bananeira, com plantio de taioba em entrelinhas alternadas, representou uma real alternativa de renda extra ao agricultor