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1
Song, Lijuan, Shu Wang, and Wanjun Ye. "Establishment and Application of Critical Nitrogen Dilution Curve for Rice Based on Leaf Dry Matter." Agronomy 10, no. 3 (March 6, 2020): 367. http://dx.doi.org/10.3390/agronomy10030367.
Анотація:
In order to investigate the feasibility of using rice leaf critical nitrogen concentration as a nitrogen nutrition diagnosis index, a three-year positioning experiment with large-spike rice cultivar (Wuyoudao4) and multiple-spike rice cultivar (Songjing9) under five nitrogen levels (0, 60, 120, 180, and 240 kg·ha−1) was conducted. A critical nitrogen dilution curve and a nitrogen nutrition index (NNI) of rice leaf dry matter were constructed for Wuyoudao4 (Nc = 1.96LDM−0.56, R2 = 0.87, NNI was between 0.6–1.26, and Normalized Root Mean Square Error (n-RMSE) = 13.07%) and Songjing9 (Nc = 1.99LDM−0.44, R2 = 0.94, NNI was between 0.64–1.29, and n-RMSE = 15.89%). The relationship between dry matter and nitrogen concentration of rice leaves was a negative power function, and the model had good stability over the three years. The developed critical nitrogen concentration dilution curve, based on leaf dry matter, was able to diagnose nitrogen nutrition in rice efficiently. The model established in this study could be used to directly regulate and control the nitrogen nutrition of rice leaves.
2
de Souza, Romina, M. Teresa Peña-Fleitas, Rodney B. Thompson, Marisa Gallardo, and Francisco M. Padilla. "Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper." Remote Sensing 12, no. 5 (February 26, 2020): 763. http://dx.doi.org/10.3390/rs12050763.
Анотація:
Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each crop, five different N concentrations in the nutrient solution were frequently applied by drip irrigation. Proximal crop reflectance was measured with Crop Circle ACS470 and GreenSeeker handheld sensors, approximately every ten days, throughout the crops. The relative performance of VIs differed between phenological stages. Relationships of VIs with NNI were strongest in the early fruit growth and flowering stages, and less strong in the vegetative and harvest stages. The green band-based VIs, GNDVI, and GVI, provided the best results for estimating crop NNI in sweet pepper, for individual phenological stages. GNDVI had the best performance in the vegetative, flowering, and harvest stages, and GVI had the best performance in the early fruit growth stage. Some of the VIs evaluated are promising tools to estimate crop N status in sweet pepper and have the potential to contribute to improving crop N management of sweet pepper crops.
3
JIN, X. L., W. Y. DIAO, C. H. XIAO, F. Y. WANG, B. CHEN, K. R. WANG, and S. K. LI. "Estimation of wheat nitrogen status under drip irrigation with canopy spectral indices." Journal of Agricultural Science 153, no. 7 (October 2, 2014): 1281–91. http://dx.doi.org/10.1017/s0021859614001014.
Анотація:
SUMMARYCrop nitrogen (N) status is an important indicator of crop health and predictor of subsequent crop yield. The present study was conducted to analyse the relationships between nitrogen nutrition index (NNI), nitrogen biomass difference (ΔNB) and spectral indices in wheat, and then attempt to improve field N management. Spectral indices and concurrent sample N and biomass parameters were obtained from the Shihezi University experimental site in Xinjiang, China during 2009 and 2010. The results showed that all spectral indices were significantly correlated with NNI. Regression functions with the highest determination coefficient (R2) and the lowest root mean square error (RMSE) were used to improve prediction of NNI, and then the selected spectral index was used to estimate NNI and ΔNB. The strongest relationships were observed for the products of modified normalized difference 705 × biomass dry weight (BND705) and the enhanced vegetation index 2 (EVI2) for estimating NNI. There were also strong relationships between the NNI and the normalized NNI (ΔNNI) as well as between ΔNNI and ΔNB, with a linear relationship between ΔNB and the spectral index BND705 and a linear relationship between ΔNB and the spectral index EVI2. These results indicated that BND705 and EVI2 can be used to improve the accuracy of NNI estimation, and the correlations of ΔNB and NNI with BND705 and EVI2 can be used to further improve field N management in wheat.
4
Lindquist, John L., Sean P. Evans, Charles A. Shapiro, and Stevan Z. Knezevic. "Effect of Nitrogen Addition and Weed Interference on Soil Nitrogen and Corn Nitrogen Nutrition." Weed Technology 24, no. 1 (March 2010): 50–58. http://dx.doi.org/10.1614/wt-09-070.1.
Анотація:
Weeds cause crop loss indirectly by reducing the quantity of resources available for growth. Quantifying the effects of weed interference on nitrogen (N) supply, crop growth, and N nutrition may assist in making both N and weed management decisions. Experiments were conducted to quantify the effect of N addition and weed interference on soil nitrate-N (NO3-N) over time and the dependence of corn growth on NO3-N availability, determine the corn N nutrition index (NNI) at anthesis, and evaluate if relative chlorophyll content can be utilized as a reliable predictor of NNI. Urea was applied at 0, 60, and 120 kg N/ha to establish N treatments. Season-long weedy, weed-free, and five weed interference treatments were established by delaying weed control from time of crop planting to the V3, V6, V9, V15, or R1 stages of corn development. Soil NO3-N ranged from 20 kg N/ha without N addition to 98 kg N/ha with 120 kg N/ha added early in the season, but crop and weed growth reduced soil NO3-N to 10 kg N/ha by corn anthesis. Weed presence reduced soil NO3-N by up to 50%. Average available NO3-N explained 29 to 40% of the variation in corn shoot mass at maturity. Weed interference reduced corn biomass and NNI by 24 to 69%. Lack of N also reduced corn NNI by 13 to 46%, but reduced corn biomass by only 11 to 23%. Nondestructive measures of relative chlorophyll content predicted corn NNI with 65 to 85% accuracy. Although weed competition for factors other than N may be the major contributor to corn biomass reduction, the chlorophyll meter was a useful diagnostic tool for assessing the overall negative effects of weeds on corn productivity. Further research could develop management practices to guide supplemental N applications in response to weed competition.
5
Chen, Bo, Xianju Lu, Shuan Yu, Shenghao Gu, Guanmin Huang, Xinyu Guo, and Chunjiang Zhao. "The Application of Machine Learning Models Based on Leaf Spectral Reflectance for Estimating the Nitrogen Nutrient Index in Maize." Agriculture 12, no. 11 (November 2, 2022): 1839. http://dx.doi.org/10.3390/agriculture12111839.
Анотація:
Non-destructive acquisition and accurate real-time assessment of nitrogen (N) nutritional status are crucial for nitrogen management and yield prediction in maize production. The objective of this study was to develop a method for estimating the nitrogen nutrient index (NNI) of maize using in situ leaf spectroscopy. Field trials with six nitrogen fertilizer levels (0, 75, 150, 225, 300, and 375 kg N ha−1) were performed using eight summer maize cultivars. The leaf reflectance spectrum was acquired at different growth stages, with simultaneous measurements of leaf nitrogen content (LNC) and leaf dry matter (LDW). The competitive adaptive reweighted sampling (CARS) algorithm was used to screen the raw spectrum’s effective bands related to the NNI during the maize critical growth period (from the 12th fully expanded leaf stage to the milk ripening stage). Three machine learning methods—partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM)—were used to validate the NNI estimation model. These methods indicated that the NNI first increased and then decreased (from the 12th fully expanded leaf stage to the milk ripening stage) and was positively correlated with nitrogen application. The results showed that combining effective bands and PLS (CARS-PLS) achieved the best model for NNI estimation, which yielded the highest coefficient of determination (R2val), 0.925, and the lowest root mean square error (RMSEval), 0.068, followed by the CARS-SVM model (R2val, 0.895; RMSEval, 0.081), and the CARS-ANN model (R2val, 0.814; RMSEval, 0.108), which performed the worst. The CARS-PLS model was used to successfully predict the variation in the NNI among cultivars and different growth stages. The estimated R2 of eight cultivars by the NNI was between 0.86 and 0.97; the estimated R2 of the NNI at different growth stages was between 0.92 and 0.94. The overall results indicated that the CARS-PLS allows for rapid, accurate, and non-destructive estimation of the NNI during maize growth, providing an efficient tool for accurately monitoring nitrogen nutrition.
6
Mazurczyk, Władysław, and Barbara Lis. "The influence of nitrogen deficiency and excess in potato plants on biomass accumulation and distribution." Acta Agrobotanica 53, no. 1 (2013): 47–55. http://dx.doi.org/10.5586/aa.2000.006.
Анотація:
Nitrogen nutrition index (NNI), harvest index (HI), dry matter accumulated by different organs were estimated several times during the vegetation period of potato plants grown in the both field and pot experiments at the Experimental Station in Jadwisin, Poland. Nitrogen fertilizer as NH<sub>4</sub>NO<sub>3</sub> was applied at three doses (40, 120, and 200 kg·ha=<sup>-1<sup> N) in the field experiment and 2,7 g N in 1996 and 4g N in 1997 and 1998 per each plant in the pot experiment. Results showed that the values of NNI both in field and pot experiments increased from emergence till the about closing rows and then they gradually decreased. The NNI values were dependent on N rates. The higher were N doses, the higher were values of NNI. These differences were especially present during the first half of vegetation periods. The excess of N in potato plants (NNI values above 1,O) was recorded in the greater part of vegetation periods in plants grown at the dose 200 kg·ha<sup>-1</sup> N and for short length of time at the rate 120 kg·ha<sup>-1</sup> N. The excess of nitrogen was associated with the highest values of total biomass accumulation and with decreasing ofthe harvest index; (average NNI=0,53). Unsuffieient level of nitrogen nutrition (NNI values below 1,0) was found for the whole vegetation periods in all plants grown in pots and in the field at the dose 40 kg·ha<sup>-1</sup> N. These plants produced less total biomass but more of it was distributed to the tubers with average values of HI: 0,68 in field and 0,78 in pot experiments. Plants grown in the field under warmer weather conditions had better nitrogen nutrition status than those grown under cooler and wetter ones.
7
Ye, Chun, Ying Liu, Jizhong Liu, Yanda Li, Binfeng Sun, Shifu Shu, and Luofa Wu. "Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight." PLOS ONE 16, no. 11 (November 3, 2021): e0259204. http://dx.doi.org/10.1371/journal.pone.0259204.
Анотація:
In order to investigate the feasibility of using rice critical nitrogen concentration as a nitrogen nutrition diagnosis index, a two-year positioning field gradient experiment using four rice varieties and four nitrogen levels (0, 75, 150, 225 kg·ha–1 for early rice; 0, 90, 180, 270 kg·ha–1 for late rice) was conducted for early and late rice. The critical dilution curves (Nc%) of the double-cropped rice based on leaf dry matter (LDM) were constructed and verified using the field data. Two critical nitrogen dilution curves and nitrogen nutrition indexes (NNI) of rice LDM were constructed for early rice [Nc% = 2.66LDM−0.79, R2 = 0.88, NNI ranged between 0.29–1.74, and the average normalized root mean square error (n-RMSE = 19.35%)] and late rice [Nc% = 7.46LDM−1.42, R2 = 0.91, NNI was between 0.55–1.53, and the average (n-RMSE = 15.14%)]. The relationship between NNI and relative yield was a quadratic polynomial equation and suggested that the optimum nitrogen application rate for early rice was sightly smaller than 150 kg·ha–1, and that for late rice was about 180 kg·ha-1. The developed critical nitrogen concentration dilution curves, based on leaf dry matter, were able to diagnose nitrogen nutrition in the double-cropped rice region.
8
Lu, J., Y. Miao, W. Shi, J. Li, J. Wan, X. Gao, J. Zhang, and H. Zha. "Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 349–52. http://dx.doi.org/10.1017/s2040470017000115.
Анотація:
The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).
9
Gonzalez-Dugo, Victoria, Jean-Louis Durand, François Gastal, and Catherine Picon-Cochard. "Short-term response of the nitrogen nutrition status of tall fescue and Italian ryegrass swards under water deficit." Australian Journal of Agricultural Research 56, no. 11 (2005): 1269. http://dx.doi.org/10.1071/ar05064.
Анотація:
Grasslands are rarely irrigated, thus water deficits often induce a reduction of the nitrogen nutrition index (NNI) during summer. This is measured using the ratio between the actual N concentration and the minimum N concentration required to achieve the maximum growth rate. NNI is derived from the standing biomass by a simple relationship. This paper details the results of a field experiment, combining 2 levels of irrigation with 2 levels of nitrogen fertilisation during the summer, on 2 commonly cultivated grass species in pure swards (tall fescue, Festuca arundinacea L., and Italian ryegrass, Lolium multiflorum). Plant water status, NNI, root length density (RLD), soil volumetric water content (θv), and mineral nitrogen concentration [N] were followed under water deficit and recovery. In both species, RLD was high (>6 cm/cm3) in the 0–0.25 m soil layer. Whereas the NNI of tall fescue responded strongly to its water status, Italian ryegrass was most often above optimal nitrogen nutrition because of its slow growth in that particular season and its higher superficial RLD. However, its NNI generally followed the θv closely, whereas tall fescue exhibited a delay in response of NNI upon rewatering, suggesting lasting effects of water deficits on the absorption capacity of roots in that species.
10
Costa, Newton de Lucena, João Avelar Magalhães, Amaury Bularmaqui Bendahan, Antônio Neri Azevedo Rodrigues, Braz Henrique Nunes Rodrigues, and Francisco José Seixas Santos. "Response of Brachiaria brizantha cv. Piatã pastures to nitrogen fertilization." Research, Society and Development 9, no. 3 (February 19, 2020): e89932498. http://dx.doi.org/10.33448/rsd-v9i3.2498.
Анотація:
With the objective to evaluate the effects of nitrogen levels (0, 40, 80, 120 and 160 kg of N ha-1) on green dry matter (GDM) yield and morphogenetic and structural characteristics and nitrogen nutrition index (NNI) of Brachiaria brizantha cv. Piatã, was installed an experiment under field conditions in Roraima´s savannas. Nitrogen fertilization increased significantly (P<0.05) GDM yields, number of tillers, number of leaves tiller-1, average leaf size, leaf area index, leaf senescence rate, leaf appearance and elongation rates. Maximum GDM yields, leaf elongation rates, leaf length and number of leaves tiller-1 were obtain with the application of 145.9; 118.2; 108.9 and 133.6 kg of N ha-1, respectively. Nitrogen nutrition index alone with 120 or 160 kg N application was higher than the grass N internal critical level. The NNI, efficiency of utilization and apparent N recovery were inversely proportional to the increased N levels.
11
Song, Xiaoyu, Guijun Yang, Xingang Xu, Dongyan Zhang, Chenghai Yang, and Haikuan Feng. "Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors." Sensors 22, no. 2 (January 11, 2022): 549. http://dx.doi.org/10.3390/s22020549.
Анотація:
A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.
12
HUAT, JOEL, AMADOU TOURE, ATSUKO TANAKA, and GUILLAUME AMADJI. "CRITICAL NITROGEN DILUTION CURVE AND NITROGEN NUTRITION INDEX FOR JUTE MALLOW (CORCHORUS OLITORIUSL.) IN SOUTHERN BENIN." Experimental Agriculture 54, no. 4 (May 17, 2017): 549–62. http://dx.doi.org/10.1017/s0014479717000230.
Анотація:
SUMMARYIndigenous crops, such as jute mallow (Corchorus olitoriusL.) have high potential for improving nutrient efficiency and income source diversification of farmers in sub-Saharan Africa. A better understanding of plant responses to nitrogen (N) is essential in shedding light on the trend towards increasing fertilizer applications for commercially grown jute mallow. The aim of this study was to determine the critical N dilution curve in order to assess the N nutrition index (NNI) in jute mallow in southern Benin. Above-ground dry matter (DM) and N concentration were determined weekly during the 2010 and 2011 growing seasons and six N treatments of 0, 30, 60, 120, 180 or 240 kg N ha−1were tested under irrigated conditions. A critical N curve (Nc= 3.35 W−0.18), where W is the DM in Mg per ha, was plotted based on the N concentration in the whole plant. The critical N concentration (Nc) represents the minimal N concentration required to achieve maximum growth. According to significant differences in DM at each sampling date, data points were divided into two groups representing either N deficient or N excess conditions. All data points in the N deficient group were under the critical N curve and most data points in the N excess group were on or above the critical N curve, therefore confirming the validity of the critical N curve determined in southern Benin. The NNI calculated as the ratio between the measured N concentration and predictedNc, ranged from 0.55 to 1.30. The equation for the critical N curve and NNI determined in this study for jute mallow could potentially be used as a parameter for N application under non-deficient water conditions in southern Benin.
13
Dufrasne, I., S. Meura, J. F. Cabaraux, L. Istasse, and J. L. Hornick. "Nutrition index and soil nitrate residues in grazed pastures fertilised with mineral fertiliser, pig slurry or cattle compost." Proceedings of the British Society of Animal Science 2007 (April 2007): 49. http://dx.doi.org/10.1017/s1752756200019529.
Анотація:
A code of good practice was established by each European member state according to the nitrate directive. In Belgium, the nitrogen (N) inputs in pastures from slurry or compost are limited to 210 kg N/ha. Larger quantities can be applied when additional measurements, including soil nitrates analyses are carried on by the farmer. This trial aims to measure nitrogen balance, nitrogen nutrition index (NNI) and soil nitrates contents in pastures fertilised with mineral nitrogen fertiliser, pig slurry or cattle compost, the pastures being grazed by dairy cows and the fertilisation being brought at similar efficient N levels. NNI was calculated as the ratio of the actual N concentration to the sward N concentration it would have to be at a similar biomass in order to sustain a non limiting growth and a biomass accumulation (Lemaire and Gastal, 1997). Cattle compost was produced from cattle manure unloaded through the beaters of a spreader.
14
Aranguren, Marta, Ander Castellón, and Ana Aizpurua. "Crop Sensor Based Non-destructive Estimation of Nitrogen Nutritional Status, Yield, and Grain Protein Content in Wheat." Agriculture 10, no. 5 (May 1, 2020): 148. http://dx.doi.org/10.3390/agriculture10050148.
Анотація:
Minimum NNI (Nitrogen Nutrition Index) values have been developed for each key growing stage of wheat (Triticum aestivum) to achieve high grain yields and grain protein content (GPC). However, the determination of NNI is time-consuming. This study aimed to (i) determine if the NNI can be predicted using the proximal sensing tools RapidScan CS-45 (NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge)) and Yara N-TesterTM and if a single model for several growing stages could be used to predict the NNI (or if growing stage-specific models would be necessary); (ii) to determine if yield and GPC can be predicted using both tools; and (iii) to determine if the predictions are improved using normalized values rather than absolute values. Field trials were established for three consecutive growing seasons where different N fertilization doses were applied. The tools were applied during stem elongation, leaf-flag emergence, and mid-flowering. In the same stages, the plant biomass was sampled, N was analyzed, and the NNI was calculated. The NDVI was able to estimate the NNI with a single model for all growing stages (R2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values (R2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.
15
Liang, Jiaxing, Wei Ren, Xiaoyang Liu, Hainie Zha, Xian Wu, Chunkang He, Junli Sun, et al. "Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data." Agronomy 13, no. 8 (July 27, 2023): 1994. http://dx.doi.org/10.3390/agronomy13081994.
Анотація:
Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and six maize hybrids to determine the nitrogen nutrition index (NNI) and yield, and to diagnose the N status of the hybrids combined with multi-spectral data. The NNI threshold values varied with hybrids and years, ranging from 0.99 to 1.17 in 2018 and 0.60 to 0.71 in 2019. A proper agronomic optimal N rate (AONR) was constructed and confirmed based on the measured NNI and yield. The NNI (R2 = 0.64–0.79) and grain yield (R2 = 0.70–0.73) were predicted well across hybrids using a random forest model with spectral, structural, and textural data (UAV). The AONRs calculated using the predicted NNI and yield were significantly correlated with the measured NNI (R2 = 0.70 and 0.71 in 2018 and 2019, respectively) and yield (R2 = 0.68 and 0.54 in 2018 and 2019, respectively). It is concluded that data fusion can improve in-season N status diagnosis for different maize hybrids compared to using only spectral data.
16
Crema, Alberto, Mirco Boschetti, Francesco Nutini, Donato Cillis, and Raffaele Casa. "Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data." Remote Sensing 12, no. 14 (July 8, 2020): 2175. http://dx.doi.org/10.3390/rs12142175.
Анотація:
Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.
17
Huang, Shanyu, Yuxin Miao, Fei Yuan, Qiang Cao, Huichun Ye, Victoria I. S. Lenz-Wiedemann, and Georg Bareth. "In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages." Remote Sensing 11, no. 16 (August 8, 2019): 1847. http://dx.doi.org/10.3390/rs11161847.
Анотація:
Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R2) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_RNSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_RNSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.
18
Mou, Siwei, Dan Liu, Baoping Yang, Qingfang Han, and Xiaoxue Liu. "The Establishment and Validation of Critical Nitrogen Concentration Dilution Curve of Garlic Based on Leaf Area Index." Journal of Biobased Materials and Bioenergy 16, no. 2 (April 1, 2022): 312–21. http://dx.doi.org/10.1166/jbmb.2022.2181.
Анотація:
Irrational application of nitrogen fertilizer will increase the risk of environmental pollution, reduce crop yield and nitrogen use efficiency. Plant nitrogen diagnostic method can be used to improve nitrogen management in garlic cultivation. In this research, two representative garlic (Allium sativum L.) cultivars Cangshan and Gailiang in Guanzhong area were used as test materials for a two-year field experiment. The critical nitrogen concentration (Nc) dilution curve models was constructed and verified by analyzing the effects of six nitrogen application levels (0, 65, 130, 195, 260, 325 kg·ha−1) on leaf area index (LAI) and nitrogen concentration from regreening stage to late stage of garlic bolting. The results indicated that the relationship between Nc and LAI can be expressed by power function equation, Cangshan: Nc = 4.44LAI−0.51, Gailiang: Nc = 3.91LAI−0.4. Nitrogen nutrition index (NNI) increased with the rising of nitrogen level; meanwhile, the integrated NNI was closely related to relative yield. The maximum yield was obtained and the nitrogen nutrient index was close to 1 when the nitrogen level reached 195 kg·ha−1 in Cangshan and 260 kg·ha−1 in Gailiang, respectively. In general, Nc dilution curves depending on LAI can be used for precision nitrogen management of garlic in Guanzhong area.
19
Klem, Karel, Jan Křen, Ján Šimor, Daniel Kováč, Petr Holub, Petr Míša, Ilona Svobodová, et al. "Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks." Agronomy 11, no. 12 (December 20, 2021): 2592. http://dx.doi.org/10.3390/agronomy11122592.
Анотація:
Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.
20
Tremblay, Nicolas, Edith Fallon, and Noura Ziadi. "Sensing of Crop Nitrogen Status: Opportunities, Tools, Limitations, and Supporting Information Requirements." HortTechnology 21, no. 3 (June 2011): 274–81. http://dx.doi.org/10.21273/horttech.21.3.274.
Анотація:
Diagnosing nitrogen (N) sufficiency in crops is used to help insure more effective management of N fertilizer application, and several indicators have been proposed to this end. The N nutrition index (NNI) offers a reliable measurement, but it is relatively difficult to determine. This index is based on the relationship between plant tissue N concentration and the biomass of the plant's aerial parts. However, a good estimate of the NNI should be obtained by nondestructive methods that can be carried out quickly. Although dependent on sites, chlorophyll meter (CM) measurements have been correlated with the NNI in corn (Zea mays). Since chlorophyll can be estimated through remote sensing, the possibility of quickly obtaining measurements for large surface areas points to practical applications for precision agriculture. When combined with the mapping of soil properties such as apparent electrical conductivity (EC), elevation and slope, such chlorophyll measurements make it possible to derive N fertilization recommendations by taking into account natural variations in the soil. Recently, an instrument called the Dualex (FORCE-A, Orsay, France) is marketed, which uses measurement methods based on the fluorescent properties of plant tissues. It is similar to the CM in terms of its operating principle but it measures polyphenolics (Phen), compounds that accumulate in the epidermis of leaves under N stress. Epidermal transmittance to ultraviolet light is assessed by the fluorescence excitation ratio F(ultraviolet)/F(REF), where F(ultraviolet) is the fluorescence excitation detected following ultraviolet excitation, and F(REF) is the fluorescence detected on excitation at a reference wavelength, not absorbed by the epidermis. Although the Dualex generally did not identify more differences among treatments than the CM in our studies on wheat (Triticum aestivum), corn, and broccoli (Brassica oleracea ssp. italica), combining the two measurements in a chlorophyll/Phen ratio improved the relationships with crop N nutrition status appreciably. This ratio can also be estimated by remote sensing techniques. The NNI on its own does not constitute an economically optimal recommendation for N fertilizer [economically optimal N rate (EONR)]. The EONR is the N rate at which profit is greatest. Work is currently being done to use overfertilized reference plots for this purpose and to permit an improved correlation between the indicator (NNI or chlorophyll) and EONR.
21
Zha, Hainie, Yuxin Miao, Tiantian Wang, Yue Li, Jing Zhang, Weichao Sun, Zhengqi Feng, and Krzysztof Kusnierek. "Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning." Remote Sensing 12, no. 2 (January 8, 2020): 215. http://dx.doi.org/10.3390/rs12020215.
Анотація:
Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
22
Jia, Yan, Yu Zhao, Huimiao Ma, Weibin Gong, Detang Zou, Jin Wang, Aixin Liu, et al. "Analysis of the Effects of Population Structure and Environmental Factors on Rice Nitrogen Nutrition Index and Yield Based on Machine Learning." Agronomy 14, no. 5 (May 12, 2024): 1028. http://dx.doi.org/10.3390/agronomy14051028.
Анотація:
With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 104 plant/ha and 161.05 kg·ha−1 for the large-spike type variety of rice, 75.08 × 104 plant/ha and 159.52 kg·ha−1 for the intermediate type variety of rice, and 75.08 × 104 plant/ha and 133.47 kg·ha−1 for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.
23
Gée, Christelle, Emmanuel Denimal, Maël de Yparraguirre, Laurence Dujourdy, and Anne-Sophie Voisin. "Assessment of Nitrogen Nutrition Index of Winter Wheat Canopy from Visible Images for a Dynamic Monitoring of N Requirements." Remote Sensing 15, no. 10 (May 10, 2023): 2510. http://dx.doi.org/10.3390/rs15102510.
Анотація:
Hand-held chlorophyll meters or leaf-clip-type sensors indirectly and instantaneously measure leaf N content. They can provide an N nutrition index (NNI) value that is crucial information for adjusting the amount of N fertilizer to the actual N status of the plant. Although these measurements are non-invasive and non-destructive, they require numerous repetitions at the canopy scale. The objective of this work was to explore the potential of visible images to predict nitrogen status in winter wheat crops from estimating NNI and to compare these results with those deduced from classical methods. Based on a dark green colour index (DGCI), which combines hue, saturation and brightness, a normalized DGCI (nDGCI) was proposed as the ratio between the measurements of the study microplot and those of the over-fertilized microplot. The methodology was performed on winter wheat microplots with a nitrogen gradient. Half of the microplots were grown with a single cultivar (LG Absalon) and the other half with a mixture of four wheat cultivars. The impact of optical device (digital camera or smartphone), the white balance (Manual or Automatic), the crop growth stage (two-nodes or heading) and cultivars (single or mixed) on the relationship between (DGCI, nDGCI) and NNI was evaluated. The results showed a close correlation between the nDGCI values and the NNI_NTester values, especially on a single cultivar (LG Absalon; R2 = 0.73 up to 0.91 with smartphone). It suggested that the relationship is highly sensitive to the wheat cultivar. This approach with no specific calibration of images is promising for the estimation of N requirements in wheat field.
24
Silva, Luís, Luís Alcino Conceição, Fernando Cebola Lidon, and Benvindo Maçãs. "Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review." Agriculture 13, no. 4 (April 6, 2023): 835. http://dx.doi.org/10.3390/agriculture13040835.
Анотація:
Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.
25
DORDAS, Christos A., Anastasios S. LITHOURGIDIS, and Kalliopi GALANOPOULOU. "Intercropping of Faba Bean with Barley at Various Spatial Arrangements Affects Dry Matter and N Yield, Nitrogen Nutrition Index, and Interspecific Competition." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 47, no. 4 (November 20, 2019): 1116–27. http://dx.doi.org/10.15835/nbha47411520.
Анотація:
Intercropping is the cultivation of two or more crop species on the same area of land, and can improve yield, forage quality, and soil health. Despite the fact that intercropping is an old practice, it received significant attention the last years because of the environmental impact that it has. However, the effect of the various spatial arrangements of the different species that are used in an intercropping system was not determined. The objective of the present study was to study the yield, growth and nitrogen (N) uptake rate, N nutrition index (NNI) of barley, interspecific competition, quality and financial outcome of intercrops of faba bean (Vicia faba L. var. equina) and barley (Hordeum vulgare L.) with various spatial arrangements (1:1, 2:2, 2:1 alternate rows, and mixed in the same row). The land equivalent ratio (LER), relative crowding coefficient (K), actual yield loss (AYL) and system productivity index (SPI) values were greater for the FB:B intercrop of 2:1, indicating the advantage of intercropping in terms of dry matter and N yield. Sole cropping of barley showed a reduction in NNI by 7 %, whereas NNI for barley increased by an average of 14% in intercropping treatments. Based on biomass production and the competition indices for dry matter and N yield, and NNI the FB:B intercrop of 2:1 was more advantageous than faba bean and barley monocrops, as well as the other intercropping treatments that were tested.
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In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 4, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue.
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26
Djidonou, Desire, and Daniel I. Leskovar. "Seasonal Changes in Growth, Nitrogen Nutrition, and Yield of Hydroponic Lettuce." HortScience 54, no. 1 (January 2019): 76–85. http://dx.doi.org/10.21273/hortsci13567-18.
Анотація:
The effect of different nitrogen (N) concentrations on growth changes, leaf N concentration and accumulation patterns, N nutrition index (NNI), fresh weight yield, and N use efficiency (NUE) was determined for lettuce grown over three consecutive seasons (fall, winter, and spring) in a recirculating hydroponic system, under unheated and naturally lit hoop house in Uvalde, TX. The lettuce cultivars Buttercrunch, Dragoon, and Sparx were grown at six N concentrations, initially 100, 150, 200, 250, 300, and 400 mg·L−1 using a nutrient film technique (NFT). Leaf number, accumulated dry weight (DW) and N, and leaf area index (LAI) followed a logistic trend over time, characterized by a slow increase during early growth followed by a linear increase to a maximum. By contrast, plant total N concentrations were the highest at early stage and decreased slightly over time. Effect of season and cultivar on these growth traits was more pronounced than that of the N concentrations. Averaged across cultivar and N concentrations, DW in spring was 73% and 34% greater than that in fall and winter, respectively. At each sampling date, there were linear, quadratic, or cubic effects of N concentrations on each of these variables. The cultivar Sparx was the most productive, with 63% and 32% higher fresh weight yield in fall, 145% and 114% in spring, than ‘Buttercrunch’ and ‘Dragoon’, respectively. Increasing nutrient solution N concentrations from 100 to 400 mg·L−1 increased the yield from 5.9 to 6.7 kg·m−2 in fall, 8.1 to 10.7 kg·m−2 in winter, and 10.3 to 12.6 kg·m−2 in spring. The NUE was the highest at the lowest N concentration (100 mg·L−1) and decreased with increasing N concentrations. The NNI during mid- to late-growth stages was near or greater than one, even at the lowest N. These results demonstrated that N concentrations of 100–150 mg·L−1 maximized the growth and yield of hydroponically grown lettuce.
27
Lemaire, Gilles, and Ignacio Ciampitti. "Crop Mass and N Status as Prerequisite Covariables for Unraveling Nitrogen Use Efficiency across Genotype-by-Environment-by-Management Scenarios: A Review." Plants 9, no. 10 (October 2, 2020): 1309. http://dx.doi.org/10.3390/plants9101309.
Анотація:
Due to the asymptotic nature of the crop yield response curve to fertilizer N supply, the nitrogen use efficiency (NUE, yield per unit of fertilizer applied) of crops declines as the crop N nutrition becomes less limiting. Therefore, it is difficult to directly compare the NUE of crops according to genotype-by-environment-by-management interactions in the absence of any indication of crop N status. The determination of the nitrogen nutrition index (NNI) allows the estimation of crop N status independently of the N fertilizer application rate. Moreover, the theory of N dilution in crops indicates clearly that crop N uptake is coregulated by (i) soil N availability and (ii) plant growth rate capacity. Thus, according to genotype-by-environment-by-management interactions leading to variation in potential plant growth capacity, N demand for a given soil N supply condition would be different; consequently, the NUE of the crop would be dissimilar. We demonstrate that NUE depends on the crop potential growth rate and N status defined by the crop NNI. Thus, providing proper context to NUE changes needs to be achieved by considering comparisons with similar crop mass and NNI to avoid any misinterpretation. The latter needs to be considered not only when analyzing genotype-by-environment-by-management interactions for NUE but for other resource use efficiency inputs such as water use efficiency (colimitation N–water) under field conditions.
28
Paleari, Livia, Ermes Movedi, Fosco Vesely, William Thoelke, Sofia Tartarini, Marco Foi, Mirco Boschetti, Francesco Nutini, and Roberto Confalonieri. "Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice." Sensors 19, no. 4 (February 25, 2019): 981. http://dx.doi.org/10.3390/s19040981.
Анотація:
Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system’s capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties—needed to extend the system to new contexts—was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.
29
Renata, Duffková, and Brom Jakub. "Plant composition, herbage yield, and nitrogen objectives in Arrhenatherion grasslands affected by cattle slurry application." Plant, Soil and Environment 64, No. 6 (May 31, 2018): 268–75. http://dx.doi.org/10.17221/178/2018-pse.
Анотація:
Cattle slurry is commonly used to fertilize grasslands, so its impact on plant composition and herbage properties is important. Cattle slurry at annual rates of 60 (S1), 120 (S2), 180 (S3), and 240 kg nitrogen (N)/ha (S4) was applied to Arrhenatherion grasslands in moderately wet (WS), slopy (SS), and moderately dry (DS) sites cut three times a year over six years, to assess its effects on plant functional types, the Ellenberg N indicator value (Ellenberg N), herbage dry matter (DM) yield, herbage N content and offtake, N nutrition index (NNI), and N use efficiency (NUE). The site-specific changes in an increase in graminoid cover, Ellenberg N, herbage DM yield and N offtake, and NNI along with slurry application rates revealed, while cover of legumes, short forbs, and NUE decreased. In more productive sites (WS and SS), slurry application in the amount of 180 kg N/ha could be suggested as a slurry dose ensuring beneficial agronomic objectives. However, nature conservation requirements via maintaining plant biodiversity were not met. On the contrary, short-term slurry application up to 120 kg N/ha ensured on permeable DS not only sufficient agronomic objectives, but also plant biodiversity conservation requirements.
30
Shao, Hui, Yuxin Miao, Fabián G. Fernández, Newell R. Kitchen, Curtis J. Ransom, James J. Camberato, Paul R. Carter, et al. "Evaluating Critical Nitrogen Dilution Curves for Assessing Maize Nitrogen Status across the US Midwest." Agronomy 13, no. 7 (July 23, 2023): 1948. http://dx.doi.org/10.3390/agronomy13071948.
Анотація:
Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R2 = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest.
31
Guo, Bin-Bin, Xiao-Hui Zhao, Yu Meng, Meng-Ran Liu, Jian-Zhao Duan, Li He, Nian-Yuan Jiao, Wei Feng, and Yun-Ji Zhu. "Establishment of Critical Nitrogen Concentration Models in Winter Wheat under Different Irrigation Levels." Agronomy 10, no. 4 (April 12, 2020): 556. http://dx.doi.org/10.3390/agronomy10040556.
Анотація:
The aim of this study was to verify the applicability of the critical nitrogen concentration dilution curve (Nc) of wheat grown under different irrigation conditions in the field, and discuss the feasibility of using the N nutrition index (NNI) to optimize N fertilizer application. The high-yield, medium-protein wheat varieties Zhoumai 27 and Zhoumai 22 were used in field experiments in two different locations (Zhengzhou and Shangshui) in Huang-Huai, China. Plants were grown under rainfed and irrigation conditions, with five N application rates. Nc models of the leaves, stems, and whole plant were constructed, followed by establishment of an NNI model and accumulative N deficit model (Nand). As previous research reported, our results also showed that the critical N concentration and biomass formed a power function relationship (N = aDW−b). When the biomass was the same, the critical N concentration was higher under irrigation than rainfed treatment. Meanwhile, the fitting accuracy (R2) of the Nc model was also higher under irrigation than rainfed treatment in both sites, and was higher in the stems and whole plant. The NNI calculated using the Nc model increased with increasing N application, reflecting N deficiency. Moreover, there was a significant negative linear correlation between NNI and Nand, and both indices could be uniformly modeled between locations and water treatments. The accuracy of the Nand model was highest in the whole plant, followed by the leaves and stems. The models constructed in this paper provide a theoretical basis for accurate management of N fertilizer application in wheat production.
32
Zhang, Ke, Xiaojun Liu, Syed Tahir Ata-Ul-Karim, Jingshan Lu, Brian Krienke, Songyang Li, Qiang Cao, Yan Zhu, Weixing Cao, and Yongchao Tian. "Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches." Agronomy 9, no. 2 (February 22, 2019): 106. http://dx.doi.org/10.3390/agronomy9020106.
Анотація:
Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the normalized difference SPAD indexes, NDSIs) between the upper (the first and second leaves from the top), and lower (the third and fourth leaves from the top) leaves of Japonica rice. Four multi-location, multi-N rate (0–390 kg ha−1) field experiments were conducted using seven Japonica rice cultivars (9915, 27123, Wuxiangjing14, Wunyunjing19, Wunyunjing24, Liangyou9, and Yongyou8). Growth analyses were performed at different growth stages ranging from tillering (TI) to the ripening period (RP). We measured leaf N concentration (LNC), the N nutrition index (NNI), the NDSI, and rice grain yield at maturity. The relationships among the NDSI, LNC, and NNI at different growth stages showed that the NDSI values of the third and fourth fully expanded leaves more reliably reflected the N nutritional status than those of the first and second fully expanded leaves (LNC: NDSIL3,4, R2 > 0.81; NDSIothers, 0.77 > R2 > 0.06; NNI: NDSIL3,4, R2 > 0.83; NDSIothers, 0.76 > R2 > 0.07; all p < 0.01). Two new diagnostic models based on the NDSIL3,4 (from the tillering to the ripening period) can be used for effective diagnosis of the LNC and NNI, which exhibited reasonable distributions of residuals (LNC: relative root mean square error (RRMSE) = 0.0683; NNI: RRMSE = 0.0688; p < 0.01). The relationship between grain yield, predicted yield, and NDSIL3,4 were established during critical growth stages (from the stem elongation to the heading stages; R2 = 0.53, p < 0.01, RRMSE = 0.106). An NDSIL3,4 high-yield change curve was drawn to describe critical NDSIL3,4 values for a high-yield target (10.28 t ha−1). Furthermore, dynamic-critical curve models based on the NDSIL3,4 allowed a precise description of rice N status, facilitating the timing of fertilization decisions to optimize yields in the intensive rice cropping systems of eastern China.
33
Simic, Aleksandar, Violeta Mandic, Savo Vuckovic, Zorica Bijelic, Rade Stanisavljevic, Ratibor Strbanovic, and Dejan Sokolovic. "Assessment of yield, quality and nitrogen index of Agrostietum capillaris grassland as affected by fertilizations." Biotehnologija u stocarstvu 36, no. 1 (2020): 101–13. http://dx.doi.org/10.2298/bah2001101s.
Анотація:
Managing N, P and K inputs in semi-natural meadow production systems is important for achieving maximum yields in livestock farming. The objective of the present study was to estimate the effect of different NPK levels (N0P0K0, N50P50K50, N100P50K50, N100P100K100, N150P100K100 and N200P150K150 kg ha-1 yr-1) on the yield, quality and nitrogen nutrition index (NNI) in a grassland community of Agrostietum capillaris (semi-natural meadow) in western Serbia. The study was conducted during the seasons of 2005-2008. The values of the investigated parameters, except for the unit N uptake, were the highest in 2004/2005 due to favorable climate conditions. The levels of nitrogen significantly increased all of the studied parameters compared to the control treatment, except for unit N uptake. Mineral fertilizers at N200P150K150 provided the highest green forage yield (25.12 t ha-1), dry matter yield (8.12 t ha-1), crude protein yield (876.3 kg ha-1), nitrogen uptake (140.2 kg ha-1) and nitrogen nutrition index (70.2%), and the lowest unit N uptake (0.0022 kg N kg DMY-1). The use of mineral fertilizers increased green forage yield, dry matter yield and crude protein yield, increasing fertilizer from lowest to highest rate increased fresh and dry matter yield, as well as protein yield. Based on the results of the study, monitoring of nutrition indices would be necessary in order to increase productivity and economic benefits.
34
Ghiles, Kaci, Blavet Didier, Benlahrech Samia, Kouakoua Ernest, Couderc Petra, Deleporte Philippe, Desclaux Dominique, et al. "The effect of intercropping on the efficiency of faba bean – rhizobial symbiosis and durum wheat soil-nitrogen acquisition in a Mediterranean agroecosystem." Plant, Soil and Environment 64, No. 3 (March 21, 2018): 138–46. http://dx.doi.org/10.17221/9/2018-pse.
Анотація:
The aim of this study was to compare the rhizobial symbiosis and carbon (C) and nitrogen (N) accumulations in soil and plants in intercropping versus sole cropping in biennial rotation of a cereal – durum wheat (Triticum durum Desf.), and a N<sub>2</sub>-fixing legume – faba bean (Vicia faba L.) over a three-year period at the INRA (National Institue of Agronomic Research) experimental station in the Mauguio district, south-east of Montpellier, France. Plant growth, nodulation and efficiency in the use of rhizobial symbiosis (EURS) for the legume, nitrogen nutrition index (NNI) for the cereal, and N and C accumulation in the soil were evaluated. Shoot dry weight (SDW) and NNI were significantly higher for intercropped than for the sole cropped wheat whereas there was no significant difference on SDW between the intercropped and sole cropped faba beans. EURS was higher in intercropped than in sole cropped faba bean. Furthermore, by comparison with a weeded fallow, there was a significant increase in soil C and N content over the three-year period of intercropping and sole cropping within the biennial rotation. It is concluded that intercropping increases the N nutrition of wheat by increasing the availability of soil-N for wheat. This increase may be due to a lower interspecific competition between legume and wheat than intra-specific competition between wheat plants, thanks to the compensation that the legume can achieve by fixing the atmospheric nitrogen.
35
Jiang, Jie, Cuicun Wang, Hui Wang, Zhaopeng Fu, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. "Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat." Sensors 21, no. 16 (August 19, 2021): 5579. http://dx.doi.org/10.3390/s21165579.
Анотація:
The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.
36
Li, Dan, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, et al. "Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning." Remote Sensing 14, no. 2 (January 15, 2022): 394. http://dx.doi.org/10.3390/rs14020394.
Анотація:
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 < 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.
37
Gislum, René, Stamatios Thomopoulos, Jacob Glerup Gyldengren, Anders Krogh Mortensen, and Birte Boelt. "The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production." Nitrogen 2, no. 2 (May 9, 2021): 229–43. http://dx.doi.org/10.3390/nitrogen2020015.
Анотація:
Sufficient nitrogen (N) supply is decisive to achieve high grass seed yields while overfertilization will lead to negative environmental impact. From the literature, estimation of N rates taking into account the crop’s N status and its yield potential, seems promising for attaining high yields and averting adverse environmental impacts. This study aimed at an evaluation of remote sensing to predict final seed yield, N traits of the grass seed crop and the usability of nitrogen nutrition index (NNI) to measure additional N requirement. It included four years’ data and eight N application rates and strategies. Several reflectance measurements were made and used for the calculation of 18 vegetation indices. The predictions were made using partial least square regression and support vector machine. Three different yield responses to N fertilization were noted; one with linear response, one with optimum economic nitrogen (EON) at ~188 kg N ha−1, and one with EON at ~138 kg N ha−1. We conclude that although it is possible to make in-season predictions of NNI, it does not always portray the differences in yield potential; thus, it is challenging to utilize it to optimize N application.
38
Jiang, Jie, Zeyu Zhang, Qiang Cao, Yan Liang, Brian Krienke, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. "Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat." Remote Sensing 12, no. 22 (November 10, 2020): 3684. http://dx.doi.org/10.3390/rs12223684.
Анотація:
Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.
39
Sharma, Nirmal, Raquel Schneider-Canny, Konstantin Chekhovskiy, Soonil Kwon, and Malay C. Saha. "Opportunities for Increased Nitrogen Use Efficiency in Wheat for Forage Use." Plants 9, no. 12 (December 9, 2020): 1738. http://dx.doi.org/10.3390/plants9121738.
Анотація:
Wheat is a major cool-season forage crop in the southern United States. The objective of this study is to understand the effect of nitrogen (N) fertilization on wheat biomass yield, quality, nitrogen use efficiency (NUE), and nitrogen nutrition index (NNI). The experiments were conducted in a greenhouse and a hoop house in a split-plot design, with three replications. Twenty wheat cultivars/lines were evaluated at four N rates (0, 75, 150, and 300 mg N.kg−1 soil) in the greenhouse and (0, 50, 100, and 200 mg N.kg−1 soil) in the hoop house. In general, high-NUE lines had lower crude protein content than the low-NUE lines. None of the cultivars/lines reached a plateau for biomass production or crude protein at the highest N rate. The line × N rate interaction for NUE was not significant in the greenhouse (p = 0.854) but was highly significant in the hoop house (p < 0.001). NNI had a negative correlation with NUE and biomass. NUE had strong positive correlations with shoot biomass and total biomass but low to moderate correlations with root biomass. NUE also had a strong positive correlation with N uptake efficiency. Lines with high NUE can be used in breeding programs to enhance NUE in wheat for forage use.
40
Abu Bakar, Badril Hisham, Jusnaini Muslimin, Muhammad Naim Fadzli Abd. Rani, Mohammad Aufa Mhd Bookeri, Mohd Taufik Ahmad, Mohd Zamri Khairi Abdullah, and Ramlan Ismail. "On-The-Go Variable Rate Fertilizer Application Method for Rice Through Classification of Crop Nitrogen Nutrition Index (NNI)." ASM Science Journal 15 (May 17, 2021): 1–10. http://dx.doi.org/10.32802/asmscj.2021.608.
Анотація:
The standard practice among rice farmers in Malaysia is to apply fertilizer using a single application rate for the whole field. However, fertility conditions vary across the field. The excess use of fertilizer leads to increased input cost and can be damaging to the environment. The focus of this research was to develop a method to apply fertilizer on-the-go while sensing the crop nutrient status of rice plants. A machine learning approach was used to develop a crop nitrogen status prediction model. The model used spectral data from an active canopy reflectance sensor and several vegetation indices as inputs. The model was then incorporated into an on-the-go variable rate fertilizer application system. System performance was then evaluated in the field. The results from this work showed that the model had and accuracy of 83% in classifying the nitrogen status of the rice plants. The results also showed that our method was able to save up to 20% fertilizer use while maintaining yield. These findings are important for large estate farmers who are looking to increase productivity and efficiency.
41
RATJEN, A. M., and H. KAGE. "Nitrogen-limited light use efficiency in wheat crop simulators: comparing three model approaches." Journal of Agricultural Science 154, no. 6 (December 8, 2015): 1090–101. http://dx.doi.org/10.1017/s0021859615001082.
Анотація:
SUMMARYThree different explanatory indicators for reduced light use efficiency (LUE) under limited nitrogen (N) supply were evaluated. The indicators can be used to adapt dry matter production of crop simulators to N-limited growth conditions. The first indicator, nitrogen factor (NFAC), originates from the CERES-Wheat model and calculates the critical N concentration of the shoot as a function of phenological development. The second indicator, N nutrition index (NNI), calculates a critical N concentration as a function of shoot dry matter. The third indicator, specific leaf nitrogen (SLN) index (SLNI), has been newly developed. It compares the actual SLN with the maximum SLN (SLNmax). The latter is calculated as a function of the green area index (GAI). The comparison was based on growth curves and fitted to empirical data, and was carried out independently from a dynamic crop model. The data set included four growing seasons (2004–2006, 2012) in Northern Germany and seven modern bread wheat cultivars with varying N fertilization levels (0–320 kg N/ha). The influence of N shortage on LUE was evaluated from the beginning of stem elongation until flowering. With the exception of 2005, the highest productivity was observed for the highest N level. A moderate N shortage primarily reduced GAI and therefore light interception, while LUE remained stable under moderate N shortage. The relative LUE (rLUE) of a specific day was defined as the ratio of actual to maximal LUE. None of the indicators was proportional to rLUE, but the relationships were described well by quadratic plateau curves. The correlation between simulated and measured rLUE was significant for all explanatory indicators, but different in terms of mean absolute error and coefficient of determination (R2). The performance of SLNI and NNI was similar, but the goodness of prediction was much lower for NFAC. Compared with NNI and NFAC, SLNI corresponded to leaf N and was therefore sensitive to N translocation from leaves to growing grains during the reproductive stage. For this reason, SLNI may have the potential to improve simulation of dry matter production in wheat crop simulators.
42
Rodríguez, Alejandra, M. Teresa Peña-Fleitas, Francisco M. Padilla, Marisa Gallardo, and Rodney B. Thompson. "Soil Monitoring Methods to Assess Immediately Available Soil N for Fertigated Sweet Pepper." Agronomy 10, no. 12 (December 19, 2020): 2000. http://dx.doi.org/10.3390/agronomy10122000.
Анотація:
Excessive N application occurs in greenhouse vegetable production. Monitoring methods of immediately available soil N are required. [NO3−] in soil solution, sampled with ceramic cup samplers, and [NO3−] in the 1:2 soil to water (v/v) extract were evaluated. Five increasing [N], from very N deficient (N1) to very N excessive (N5) were applied throughout three fertigated pepper crops by combined fertigation/drip irrigation. The crops were grown in soil in a greenhouse. Soil solution [NO3−] was measured every 1–2 weeks, and extract [NO3−] every 4 weeks. Generally, for treatments N1 and N2, both soil solution and extract [NO3−] were continually close to zero, and increased with applied [N] for treatments N3–5. The relationships of both methods to the nitrogen nutrition index (NNI), an indicator of crop N status, were assessed. Segmented linear analysis gave R2 values of 0.68–0.70 for combined data from entire crops, for both methods. NNI was strongly related to increasing [NO3−] up to 3.1 and 0.9 mmol L−1 in soil solution and extracts, respectively. Thereafter, NNI was constant at 1.04–1.05, with increasing [NO3−]. Suggested sufficiency ranges were derived. Soil solution [NO3−] is effective to monitor immediately available soil N for sweet pepper crops in SE Spain. The extract method is promising.
43
Jia, Biao, Jiangpeng Fu, Huifang Liu, Zhengzhou Li, Yu Lan, Xue Wei, Yongquan Zhai, Bingyuan Yun, Jianzhen Ma, and Hao Zhang. "Estimation of Critical Nitrogen Concentration Based on Leaf Dry Matter in Drip Irrigation Spring Maize Production in Northern China." Sustainability 14, no. 16 (August 9, 2022): 9838. http://dx.doi.org/10.3390/su14169838.
Анотація:
The application of nitrogen (N) fertilizer not only increases crop yield but also improves the N utilization efficiency. The critical N concentration (Nc) can be used to diagnose crops’ N nutritional status. The Nc dilution curve model of maize was calibrated with leaf dry matter (LDM) as the indicator, and the performance of the model for diagnosing maize N nutritional status was further evaluated. Three field experiments were carried out in two sites between 2018 and 2020 in Ningxia Hui Autonomous Region with a series of N levels (application of N from 0 to 450 kg N ha−1). Two spring maize cultivars, i.e., Tianci19 (TC19) and Ningdan19 (ND19), were utilized in the field experiment. The results showed that a negative power function relationship existed between LDM and leaf N concentration (LNC) for spring maize under drip irrigation. The Nc dilution curve equation was divided into two parts: when the LDM < 1.11 t ha−1, the constant leaf Nc value was 3.25%; and when LDM > 1.11 t ha−1, the Nc curve was 3.33LDM−0.24. The LDM-based Nc curve can well distinguish data on the N-limiting and non-N-limiting N status of maize, which was independent of maize varieties, growing seasons, and stages. Additionally, the N nutrition index (NNI) had a significant linear correlation with the relative leaf dry matter (RLDM). This study revealed that the LDM-based Nc dilution curve could accurately identify spring maize N status under drip irrigation. NNI can thus, be used as a robust and reliable tool to diagnose the N nutritional status of maize.
44
Ziadi, Noura, Gilles Bélanger, and Annie Claessens. "Relationship between soil nitrate accumulation and in-season corn N nutrition indicators." Canadian Journal of Plant Science 92, no. 2 (March 2012): 331–39. http://dx.doi.org/10.4141/cjps2011-086.
Анотація:
Ziadi, N., Bélanger, G. and Claessens, A. 2012. Relationship between soil nitrate accumulation and in-season corn N nutrition indicators. Can. J. Plant Sci. 92: 331–339. Nitrogen management tools are required to optimize crop growth and yield while minimizing the likelihood of N losses to the environment. We previously determined that non-limiting N conditions for near maximum corn (Zea mays L.) grain yield are reached with the following threshold values for three in-season plant-based indicators of corn N nutrition determined at approximately the V12 stage of development: N nutrition index (NNI) = 0.88, leaf N (NL) concentration = 32.7 mg N g−1 leaf DM, and relative chlorophyll meter (RCM) values = 0.95. Our objective was to study the relationship between these plant-based indicators and soil NO3-N content in an effort to develop tools to reduce the likelihood of soil NO3-N accumulation without affecting grain yield. This study at 5 site-years in Québec consisted of six N fertilizer rates (20–250 kg N ha−1). The NNI, NL concentrations, RCM values, and soil (0–0.15 m) NO3-N content were measured weekly from July to early August, while soil NO3-N content to a 0.90-m depth was measured in late August and October. During the growing season from July to early August, the proportion of data points above the average soil NO3-N content was greater under non-limiting N conditions (NNI ≥ 0.88, NL concentrations ≥ 32.7 mg N g−1 leaf DM, or RCM values ≥ 0.95) than under limiting N conditions. Furthermore, the mean soil NO3-N content of the data points above the general average was much higher under non limiting than limiting N conditions in late August (167 vs. 78 kg NO3-N ha−1 for NNI and RCM; 166 vs. 112 kg NO3-N ha−1 for NL concentration) and October (68 vs. 49 kg NO3-N ha−1). High soil NO3-N accumulation during the season and at harvest occurs only when in-season plant-based N indicators are greater than their threshold values.
45
Xiong, Xin, Jingjin Zhang, Doudou Guo, Liying Chang, and Danfeng Huang. "Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L." Sensors 19, no. 11 (May 29, 2019): 2448. http://dx.doi.org/10.3390/s19112448.
Анотація:
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.
46
Chen, Zhichao, Yuxin Miao, Junjun Lu, Lan Zhou, Yue Li, Hongyan Zhang, Weidong Lou, Zheng Zhang, Krzysztof Kusnierek, and Changhua Liu. "In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing." Agronomy 9, no. 10 (October 9, 2019): 619. http://dx.doi.org/10.3390/agronomy9100619.
Анотація:
Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.
47
Xu, Haocong, Haibing He, Kun Yang, Haojie Ren, Tiezhong Zhu, Jian Ke, Cuicui You, Shuangshuang Guo, and Liquan Wu. "Application of the Nitrogen Nutrition Index to Estimate the Yield of Indica Hybrid Rice Grown from Machine-Transplanted Bowl Seedlings." Agronomy 12, no. 3 (March 20, 2022): 742. http://dx.doi.org/10.3390/agronomy12030742.
Анотація:
The purpose was to comprehensively compare the prediction accuracy of different nitrogen nutrition indexes (NNILAI and NNIDM) derived from critical nitrogen concentration (Nc) models established by the leaf area index (LAI) and dry matter (DM) in estimating the grain yield of indica hybrid rice grown from machine-transplanted bowl seedlings. Therefore, field experiments were conducted with two high-yielding indica hybrid rice varieties and five nitrogen application rates in 2018 and 2019. The results show that NNIDM peaked in the stem elongation stage, while NNILAI had its maximal value in the mid-tillering stage during the growth stages. The NNILAI had the highest correlation with the relative effective panicle number in the tillering stage when compared with the NNIDM, and the threshold points of the NNI were 0.971 (active tillering stage) and 1.106 (mid-tillering stage). Moreover, the NNILAI had the highest correlation with the relative seed setting rate in the stem elongation–panicle initiation stage compared with the NNIDM, and its threshold points were 1.116 (stem elongation stage) and 1.053 (panicle initiation stage). In contrast, the NNIDM had the highest correlation with the relative seed setting rate in the heading stage compared with the NNILAI, and its threshold point was 1.050 (heading stage). Therefore, the NNILAI in the tillering–panicle initiation stage and NNIDM in the heading stage should be merged to effectively improve the nitrogen nutrition status and its evaluation in addition to the prediction accuracy of the yield of indica hybrid rice grown from machine-transplanted bowl seedlings. This study provides a theoretical basis for improved understanding of the nitrogen status and yield prediction of indica hybrid rice grown from machine-transplanted bowl seedlings.
48
Huang, S., Y. Miao, F. Yuan, Q. Cao, H. Ye, V. Lenz-Wiedemann, R. Khosla, and G. Bareth. "Proximal fluorescence sensing for in-season diagnosis of rice nitrogen status." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 343–48. http://dx.doi.org/10.1017/s2040470017000280.
Анотація:
The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.
49
Lei, Hongjun, Yiming Fan, Zheyuan Xiao, Cuicui Jin, Yingying Chen, and Hongwei Pan. "Comprehensive Evaluation of Tomato Growth Status under Aerated Drip Irrigation Based on Critical Nitrogen Concentration and Nitrogen Nutrient Diagnosis." Plants 13, no. 2 (January 17, 2024): 270. http://dx.doi.org/10.3390/plants13020270.
Анотація:
In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety “FENOUYA” was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L−1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L−1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L−1), and three levels of nitrogen rates, namely, N1 (120 kg ha−1), N2 (180 kg ha−1) and N3 (240 kg ha−1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM−0.658), A2 (Nc = 101.116DM−0.455), A3 (Nc = 119.527DM−0.535), N1 (Nc = 33.819DM−0.153), N2 (Nc = 127.759DM−0.555) and N3 (Nc = 209.696DM−0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha−1 was the optimal nitrogen application rate, and 15 mg L−1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes.
50
Wan, Wenliang, Yanhui Zhao, Jing Xu, Kaige Liu, Sihui Guan, Yaqian Chai, Hongxing Cui, Pei Wu, and Ming Diao. "Reducing and Delaying Nitrogen Recommended by Leaf Critical SPAD Value Was More Suitable for Nitrogen Utilization of Spring Wheat under a New Type of Drip-Irrigated System." Agronomy 12, no. 10 (September 28, 2022): 2331. http://dx.doi.org/10.3390/agronomy12102331.
Анотація:
Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha−1), N2 (270 kg ha−1), N3 (240 kg ha−1) and N4 (0 kg ha−1)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r2) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha−1, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.