Nitrogen Nutrition Index Research Articles

Tracing the nitrogen nutrient status of crop based on solar-induced chlorophyll fluorescence

Accurate and non-destructive monitoring of wheat nitrogen nutrition is of great significance for field fertilizer management to ensure crop yield and quality, reduce environmental pollution, and improve economic benefits. Compared with spectral vegetation indices (which are sensitive to greenness and structural parameters), or active fluorescence (which is limited to small-scale studies), solar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics. However, there has been few studies comparing agronomic parameters, photosynthetic parameters, vegetation indices and SIF as an indicator of nitrogen status. In this paper, we therefore explore these measures as tools for monitoring nitrogen nutrition. During the 2016–2017 growing season, we conducted a field experiment in Rugao, Jiangsu Province, China, using winter wheat (Triticum aestivum L.) and different nitrogen treatments. The sensitivity of SIF indices, vegetation indices, photosynthetic parameters and agronomic parameters to crop nitrogen status were compared. Our results demonstrated that, compared with vegetation indices and agronomic parameters, the ratio of SIF emission peaks (FY687/FY761) responded to nitrogen status most rapidly at both the leaf and canopy scales, as soon as the fourth day after treatment (DAT4). A wheat nitrogen nutrition index (NNI), based on FY687/FY761, was used to construct a leaf dry matter (LDM-based NNI) diagnostic model, which will be beneficial for monitoring and diagnosing the nitrogen nutrition status of wheat leaves. Our results also illuminate the physiological mechanism that enables SIF to be used as a tool to monitor nitrogen nutrient status, primarily through changes in the proportion of light energy distribution. These findings provide theoretical and technical support for monitoring and diagnosing wheat nitrogen nutrition status based on SIF technology.

Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data

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.

Determination of a critical nitrogen dilution curve and nitrogen nutrition index for maize in Western Nile Delta of Egypt

Critical nitrogen (N) dilution curves are valuable tools for diagnosing N deficiency and managing its levels in plants. The objectives of this study were to establish a critical N dilution curve for maize in Egypt’s West Delta region, assess the potential of using it to compute the N nutrition index (NNI) and compare this curve with existing curves for maize in other regions. The experiment was carried out across four site years (2020–2021), with seven N fertilization rates (0–360 kg N ha−1) used to induce variability in plant growth. Aboveground shoot biomass (W; Mg DM [dry matter] ha−1) and N concentration (Nc ; g kg−1 DM) were determined on five sampling dates during the growing season. A critical N dilution curve ( was developed, which differed moderately from those reported in France, Germany, and China. The NNI computed from this curve recognized both limited and non-limited N nutrition conditions. The findings of progressing NNI suggest that the period between V9 (9th leaf collar fully unfolded) and V12 is critical and that a sufficient supply of N fertilizer is essential. Furthermore, the NNI derived from an independent dataset, utilizing the Nc established in this study, exhibited a strong relationship with the relative grain yield. The relationship was characterized by a linear plateau function, which accounted for 74% of the observed variance. The results of this study will offer dependable guidance for detecting N levels in maize and customizing the application of precision N fertilizers to enhance yield and use efficiency.

Assessment of Nitrogen Nutrition Index of Winter Wheat Canopy from Visible Images for a Dynamic Monitoring of N Requirements

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.

Estimating the rice nitrogen nutrition index based on hyperspectral transform technology.

The rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance. This study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables. 1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1). The logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.

Nitrogen nutrition diagnosis and estimation of seed cotton yield based on critical nitrogen concentration for drip irrigation cotton in Southern Xinjiang, China.

The stability and adaptability of the critical nitrogen concentration in drip-irrigated cotton fields in Southern Xinjiang were investigated by measuring the nitrogen uptake (Nuptake), nitrogen nutrition index (NNI), and accumulated nitrogen deficit (Nand). A two-year field study was conducted using five nitrogen levels (0, 75, 150, 300, and 450 kg·hm-2), with cotton cultivars 'Xinluzhong55' and 'Xinluzhong78' as the test materials. We analyzed the effects of nitrogen addition on plant dry matter (PDM) and critical nitrogen concentration dilution curves (PNCc). The Nuptake, NNI, and Nand models as well as a model of the relationships between NNI, Nand, and the relative seed cotton yield (RY) were established based on the PNCc for both cultivars. The results showed that the PNCc, maximum nitrogen concentration (PNCmax), and minimum nitrogen concentration (PNCmin) dilution models in drip-irrigated cotton showed power function relationships with plant dry matter (PDM), with determination coefficients (R2) of 0.905 and 0.960, 0.919 and 0.950, 0.934 and 0.969 for Xinluzhong55 and Xinluzhong78, respectively. Model validation with independent experimental data showed that the critical nitrogen concentrations in cotton grown under drip-irrigated conditions were simulated satisfactorily. The Xinluzhong55 and Xinluzhong78 1:1 line R2, root mean square error (RMSE) and relative estimation error (RE) of the simulated and observed critical nitrogen concentrations were 0.987, 0.051 g·kg-1, 1.95% and 0.984, 0.044 g·kg-1, 1.91%, respectively, showing good stability and adaptability between the two years. According to the Nuptake, NNI, and Nand model, we concluded that 300 kg·hm-2 was the optimum level of nitrogen application in drip-irrigated cotton in Southern Xinjiang. Based on the fitting results of nitrogen level and seed cotton yield, the theoretical appropriate nitrogen applications for Xinluzhong55 and Xinluzhong78 were 322.7 and 336.4 kg·hm-2, respectively. The R2 of NNI, Nand and RY in different growth stages were greater than 0.900, the NNI, Nand and RY estimation model of seed cotton yield based on 1:1 lines of R2, RMSE, and RE were 0.899-0.989, 0.05-0.13, and 4.1%-9.9% and 0.902-0.981, 0.04-0.12, and 5.1%-9.5%, all of which were highly significant. These results could provide new directions for the evaluation and diagnosis of optimum nitrogen application levels and nitrogen status, as well as the estimation of seed cotton yield in drip-irrigated cotton in Southern Xinjiang.

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R2=0.89, RMSE=0.68 g m–2, RE=14.62% for calibration and R2=0.83, RMSE=1.08 g m–2, RE=19.71% for validation) was much better than that at the all-leaf level (R2=0.73, RMSE=2.20 g m–2, RE=26.70% for calibration and R2=0.70, RMSE=2.48 g m–2, RE=31.49% for validation) and at the plant level (R2=0.66, RMSE=4.46 g m–2, RE=30.96% for calibration and R2=0.63, RMSE=3.69 g m–2, RE=24.81% for validation). Similarly, the XGB model (R2=0.65, RMSE=0.09, RE=8.59% for calibration and R2=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R2=0.62, RMSE=0.10, RE=7.92% for calibration and R2=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R2=0.64, RMSE=0.09, RE=9.24% for calibration and R2=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

Winter wheat genotype ability to recover nitrogen supply by precedent crops under combined nitrogen and water scenarios

Legumes offer the opportunity to reduce the nitrogen (N) fertilizer requirements of the crop rotation but the capacity of the subsequent crop to recover the N provided by the legume determines the fertilizer saving. Therefore, breeding for genotypes capable of take up N under different limiting conditions is essential for understanding crop performance and optimizing fertilization. The objective of this work was to test the ability of four wheat ( Triticum aestivum L.) genotypes with different drought-tolerance (Cellule, Apache, Allez-y, and Nogal) to take up N from the precedent legume under different water scenarios. Two field experiments with pea–wheat and barley–wheat crop rotations, two irrigations levels, and three N treatments were conducted in central Spain over 3 years. The biomass, N uptake, and nitrogen nutrition index (NNI) were determined at flowering, and the grain yield (GY), grain N concentration (GNC), and N output at harvest. The precedent legume crops enabled a reduction in N fertilizer, but its effect depended on the genotype and climate conditions. The drought-tolerant genotypes (Apache and Cellule) had the highest NNI, biomass, GY, and N output under low water and N inputs, whereas Allez-y and Nogal obtained better performance under non-limiting conditions. Therefore, this study confirms differences between wheat genotypes, with different root system, in N uptake from the precedent legume in a crop rotation under different water levels and highlights the potential of NNI to assess wheat N status. • Barley-wheat and pea-wheat rotations under various water × nitrogen levels. • Study of four bread wheat genotypes with different drought-tolerance. • Drought-tolerant genotypes present higher capacity to take up N. • Nitrogen nutrition index (NNI) identified differences in N take up capability. • Identification of genotype nitrogen translocation performance should be improved.

Differential responses of yield and shoot traits of five tropical grasses to nitrogen and distance to trees in silvopastoral systems

Context Light intensity and nitrogen availability are important factors influencing the growth of C4 forage species. Trade-offs may occur in the adaptive responses of species to shading and nitrogen inputs, and functional shoot traits can help to explain the consequences of these responses for species performance. Aims Our objective was to gain understanding of the mechanisms involving shoot traits of grasses that determine above-ground dry matter yield (DMY) when resources, light and nitrogen all vary. Methods Five C4 perennial forage grasses were grown in six shading conditions (full sunlight vs five positions between Eucalyptus dunnii rows) with two nitrogen levels (0 vs 300 kg N ha−1 year−1) and clipped when the canopy reached 95% light interception. Path analysis was used to explore the relationship between DMY, shading levels, nitrogen nutrition index and shoot traits. Key results Yield increased between 126 and 569 g dry matter m−2 with nitrogen fertilisation. Plant nitrogen concentration was the most important predictor of DMY. Increased shading reduced DMY by 6.94–12.5 g dry matter m−2 for each 1% increase in shading. DMY was also modulated by shoot traits such as specific leaf area, sheath length and leaf area index (via leaf area and tiller density), but with different responses according to species. Conclusions The five species compared adopted different mechanisms involving shoot traits, revealing different strategies to cope with changes in light and nitrogen availability. Implications Agroforestry practitioners may want to choose forages that are more likely to maintain biomass yield as trees grow.

Índice de nutrição nitrogenada e morfogênese em papuã na Depressão Central do Rio Grande do Sul

ABSTRACT: This study assessed the nitrogen nutrition index, morphogenic characteristics and tiller structure of Alexandergrass (Urochloaplantaginea (LINK) Hitch) pasture submitted to different nitrogen (N) levels (zero, 150 or 300 kg ha-1 of N). The experimental design was entirely randomized with repeated measures arrangement. The experimental animals were Angus heifers under rotational stocking grazing method. The number of animals was variable to keep 30±5 cm post-grazing sward height. Nitrogen nutrition index increased linearly according N levels (Ŷ = 59.8 + 0.1216N; P &lt; 0.0001; r²=0.53). The leaf appearance rate adjusted to a positive linear regression model according the thermal sum (TS) with zero of N (Ŷ0N = 0.0077 + 0.0000087TS; P = 0.0308; r² = 0.72) and 150 kg ha-1 of N (Ŷ150N = 0.0020 + 0.000021 TS; P = 0.0022; r² = 0.92). The use of 300 kg ha-1 of N did not alter the leaf appearance rate (0.0124 leaf degree-days-1). The use of up to 300 kg ha-1 of N increases the Alexandergrass nitrogen content. The leaf appearance rate in Alexandergrass is modified using N while the morphogenic characteristics leaf expansion, stem expansion, phyllochron, leaf lifespan, leaf elongation duration and tiller structure are not altered by N utilization.

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat.

The organ-specific critical nitrogen (Nc) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The Nc dilution curve can be described by a power function (Nc = A1·W-A2), while parameters A1 and A2 control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A1 and A2 of the organ-specific Nc dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A1 and A2 are highly correlated. Although the variation of parameter A1 was less than that of A2, the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific Nc is in general consistent with NNI estimated with overall shoot Nc, indicating that a simple organ-specific Nc dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific Nc dilution curves across G × E × M conditions imply potential errors in Nc and crop N demand estimated using a general Nc dilution curve in crop models, highlighting a clear need for improvement in Nc calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen-biomass relations and N management practices under G × E × M.

Quantify the effect of manure fertilizer addition and optimal nitrogen input on rainfed wheat yield and nitrogen requirement using nitrogen nutrition index

Combined manure fertilizer addition with optimal nitrogen (N) input reduces nitrogen demands for wheat while maintaining or increasing its yield and nitrogen productivity. However, the effect of manure addition has not been well explored for dryland wheat production on the Loess Plateau, especially in year with less precipitation. Therefore, a 4-year field experiment was performed: manure fertilizer (with and without) and N fertilization levels (0, 60, 120, 180 and 240 kg ha–1) under two precipitation years (normal and dry year). The target was to quantify the effect of manure addition on: i) wheat yield and nitrogen use efficiency (NUE), ii) crop growth and soil properties, and iii) wheat N status [N accumulation, N nutrition index (NNI)]. Compared with chemical N alone, manure addition increased topsoil organic matter, total nitrogen and the stability of macro-aggregate (R0.25). Manure addition increased grain yield by 12.5% in normal year and by 38.2% in dry year. The improved topsoil quality, root system, biomass (nitrogen) accumulation all occurred in NM120 or NM180 with manure addition, which brought an improvement of wheat yield and NUE with a suitable nitrogen nutrition status. NM180 (i.e., 150 kg N ha–1 +2000 kg ha–1 manure) and NM120 (i.e., 90 kg N ha–1 +2000 kg ha–1 manure) achieved the highest grain yield in normal and dry years, respectively. In summary, the optimal fertilization decision strategy based on precipitation and nitrogen nutrition index can be applied for dryland wheat production.

Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework

Accurate and transferable wheat nitrogen status estimation is very important to plant phenotyping and smart agricultural management. The goal of this study was to establish a wheat nitrogen status estimation model across all growth stages by combining proximal sensing and meteorological data. From 2010–2020, nine multi-nitrogen rates field trials were conducted at five sites involving different wheat varieties. Proximal sensing data were acquired from a Crop Circle sensor at key growth stages and meteorological data were aggregated from planting to the corresponding sensing date. Deep neural network (DNN) and long short-term memory (LSTM) were adopted to estimate above-ground biomass, plant nitrogen uptake, plant nitrogen concentration, and the nitrogen nutrition index. Random forest (RF) was used as a benchmark regression model. Multi-task learning (MTL) based on DNN was conducted to estimate the four nitrogen indicators simultaneously. A genetic algorithm (GA) was tested to optimize the hyperparameters, connection weights, and loss function weights (for MTL) of neural networks separately. The results revealed that DNN (R2 =0.83–0.96) and MTL (R2 =0.81–0.96) achieved an overall comparable high accuracy with RF (R2 =0.83–0.97), whereas LSTM (R2 =0.76–0.93) did not improve the nitrogen status estimation in our dataset. This study presented a concise and efficient framework dedicated to exploring the transferability of phenotypic predictions and provided insights into understanding crop growth and nitrogen dynamics in response to environmental conditions.

Precision nitrogen management regimes to obtain high yield and good eating quality of medium indica hybrid rice in mechanical transplanting with bowl-type nursery tray (MTB) based on the critical nitrogen concentration

Mechanical transplanting with bowl-type nursery tray (MTB) is a new mechanical cultivation approach to obtain high-yield and superior-quality rice. However, the precision of nitrogen (N) management regimes for obtaining high-yield and good-quality medium indica hybrid rice in MTB is unclear. Therefore, field experiments were conducted with five N application amounts for two medium indica hybrid cultivars in MTB in three studied years. The results indicated that the highest grain yield reached 10.40–12.27 t ha−1 when the N application amount was increased to approximately 245 kg ha−1 across cultivars. In addition, increasing the N application rate significantly decreased the eating quality of both cultivars in the three years in MTB (P < 0.05). The intersections of “high yield” and “good quality” were found at the site with 90% of the maximum yield and optimal eating quality. The corresponding N application amount was 130–140 kg ha−1 across cultivars and years. Critical nitrogen concentration (Nc) models based on total dry matter (TDM), stem and sheath dry matter (SDM), and leaf area index (LAI) had higher fitting precision and predictive abilities for the N content of rice plants (R2 >0.9) than the Nc model based on leaf dry matter (LDM) in both cultivars. However, the stability was poor among the different study years for the nitrogen nutrition index (NNI) calculated using NcTDM compared with those calculated using NcSDM and NcLAI. The reliable NcSDM and NcLAI models were expressed as Nc= 1.97SDM−0.42 (R2 =0.91;RMSE=0.11;n-RMSE=8.94%) and Nc= 3.72LAI−0.34 (R2 =0.93;RMSE=0.37;n-RMSE=15.54%) for medium indica hybrid rice in MTB. The yield increased linearly with NNI from 0 to 0.99 before heading and from 0 to 0.88 at heading in both cultivars. The yield did not increase further with increasing NNI when NNI reached the upper threshold value. However, increasing NNI significantly decreased eating quality (P < 0.05). Finally, the lower threshold value of NNIs obtained 90% of the maximum yield and optimal eating quality were recommended as 0.94, 0.96, 0.90, and 0.80 at the late stage of tillering, initial booting, middle stage of booting, and heading, respectively. This study provides important theoretical references for precise N management of medium indica hybrid cultivars in MTB.

Spectral assessments of N-related maize traits: Evaluating and defining agronomic relevant detection limits

The spectral sensing of nitrogen (N)-related traits and the grain yield of maize ( Zea mays L.) is widely used in agricultural practice because it is rapid, non-destructive, and cost-efficient. However, there exists a lack of agronomically supported spectral detection limits. The agronomic aspects have not yet been fully considered based on commonly used statistical measures such as the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE) and should therefore be extended. In the present study, we evaluated regression models of spectral indices derived from an unmanned aerial vehicle (UAV) by capturing N-related traits such as above-ground and grain N uptake, the nitrogen nutrition index (NNI), and grain yield covering data sets of two years, sites, and four developmental stages of maize. The results suggest that an agronomic evaluation exclusively adopting widely used statistical measures is not fully adequate. The R² is essentially influenced by differentiation of the trait, which in turn depends on year effects and growth stages. Further statistics such as RMSE and MAE average the error and lead to an under- and overestimation for most observations. In this investigation, we defined an appropriate agronomical error interval for above-ground and grain N uptake, NNI, and grain yield of ± 40 and ± 25 kg N ha −1 , ± 0.2, and ± 1.4 t ha −1 , with a probability of at least 80%. These interval limits are consistent across years and growth stages. The consistency occurs because most spectral indices are dominated by biomass. Across all of them, the best-performing spectral indices combine GREEN, REDEDGE, and NIR bands. For spectral indices using the RED band, the range of the agronomic error interval performed equally with a slightly worse probability of data points inside the error limits. Agronomically based error limits should be included in addition to common statistical measures in the spectral assessment of N-related traits of maize and grain yield to optimize the ex-ante and/or ex-post analysis of N-fertilization.

Estimation of nitrogen nutrition index in chrysanthemum using chlorophyll meter readings

The nitrogen nutrition index (NNI) plays a critical role in improving crop nitrogen (N) diagnosis and predicting crop yield. Proximal sensing chlorophyll meters (CMs) have been widely used in precision agriculture, but few studies have focused on its utilization for estimating the NNI of chrysanthemum ( Dendranthema morifolium Ramat.). This study aimed to develop a model for NNI estimation in chrysanthemums using CM readings. Field experiments were conducted with five N fertilization treatments during two growing seasons (in 2019 and 2020). Chrysanthemum canopies were sampled across multiple days after planting (DAP) to determine plant N concentrations (PNC) and plant dry matter (PDM). CM measurements were recorded at defined DAP at different leaf layer positions (L1, L2, and L3 represent leaves that were 1/3, 1/2, and 2/3 apart from the top of the plant, respectively), and the normalized CM index (NCMI 1 , NCMI 2 , and NCMI 3 ) was calculated. All PNC and PDM data from the two seasons were then applied to fit the critical N (N c ) dilution curve using a Bayesian statistical model to estimate the NNI. Regression models were established to predict chrysanthemum NNI using CM readings and the NCMI as predictor variables. This study determined the N c of chrysanthemum to be N c = 2.318 ×PDM −0.204 . L1 was found to be the optimal leaf layer position for NNI estimation. Additionally, the NCMI-based prediction of NNI was found to be more reliable than that from the CM readings. NCMI 1 was the best predictor of chrysanthemum NNI across multiple DAP (NNI = 0.0741 ×e 2.6285NCMI1 , coefficient of determination = 0.59 *** , root mean square error = 0.110 NNI). The findings of this study can be applied to accurately predict chrysanthemum NNI from CM readings to provide technical support for N nutrition diagnosis in chrysanthemums. • This is the first study establishing the critical N dilution curve in chrysanthemum. • The upper leaf layer is optimal for chrysanthemum N nutrition index (NNI) estimations. • The normalized chlorophyll meter index showed to be the best predictor of chrysanthemum NNI .

A moderate reduction in irrigation and nitrogen improves water-nitrogen use efficiency, productivity, and profit under new type of drip irrigated spring wheat system.

Rational irrigation and nitrogen management strategies are crucial for wheat growth. However, the optimal amount of water and nitrogen for the newly developed drip irrigated spring wheat system (TR6S, one drip tube service for six rows of wheat, with a row spacing of 10 cm and an inter-block space of 25 cm, saves drip tubes and obtains higher profits) in dry and semi-arid areas remains unclear. Therefore, a field experiment was conducted with four nitrogen levels (300, 270, 240, and 0 kg ha−1 referred N300, N270, N240, and N0) and four irrigation levels (4500, 4200, 3900, and 3600 m3 ha−1 referred I4500, I4200, I3900, and I3600) during the 2021–2022 and 2022–2023 spring wheat seasons to analyze the effects of irrigation (I) and nitrogen (N) levels on grain yield, water-nitrogen use efficiency, profit, biomass accumulation, and nitrogen nutrient absorption status under TR6S. Compared with the traditional irrigation and nitrogen management strategy (N300–I4500, as control), lesser irrigation and nitrogen supply (I<3979 m3 ha−1 and N<273 kg ha−1) saved cost but led to lower grain yield, water use efficiency (WUE), agronomic efficiency of nitrogen fertilizer (AEN), and profit. However, a moderate reduction in irrigation and nitrogen supply (4500 m3 ha−1>I>3979 m3 ha−1 and 300 kg ha−1 >N>273 kg ha−1) improved grain yield, WUE, AEN, and profit. The increase in grain yield was mainly related to the rise in 1000-grain weight and kernels per spike. Although the moderate reduction in irrigation lowered soil moisture status, the dry matter pre-stored in the vegetative organs before anthesis that gets redistributed into grains during grain filling was improved. Moreover, the moderate reduction in nitrogen supply resulted in a more reasonable nitrogen nutrition index (NNI) of wheat plant, which improved flag leaf area and chlorophyll relative content (SPAD) at the anthesis stage. This also played a positive role in biomass accumulation and redistributed, yield structure optimization. Considering comprehensively yield, WUE, AEN and profit, combination of 285 kg ha−1 N and 4170 m3 ha−1 I was optimal irrigation and nitrogen application pattern for TR6S. This strategy can be applied to other arid and semi-arid regions.

The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content

Controlled tile drainage (CTD) practices are a promising tool for improving water balance, water quality and increasing crop yield by raising shallow groundwater level and capillary rise due to drainage flow retardation. We tested the effect of CTD on growth and grain yield of spring barley, at a study site in central Bohemia using vegetation indices from unmanned aerial vehicle (UAV) imagery and Sentinel-2 satellite imagery. Tile drainage flow was slowed by fixed water level control structures that increased soil moisture in the surrounding area according to the terrain slope. Vegetation indices based on red-edge spectral bands in combination with near-infrared and red bands were selected, of which the Normalized Red Edge-Red Index (NRERI) showed the closest relationships with shoot biomass parameters (dry biomass, nitrogen concentration and uptake, nitrogen nutrition index) from point sampling at the tillering stage. The CTD sites showed significantly more biomass using NRERI compared to free tile drainage (FTD) sites. In contrast, in the period prior to the implementation of CTD practices, Sentinel-2 satellite imagery did not demonstrate higher biomass based on NRERI at CTD sites compared to FTD sites. The grain yields of spring barley as determined from the yield map also increased due to CTD (by 0.3 t/ha, i.e., by 4%). The positive impact of CTD on biomass development and grain yield of spring barley was confirmed by the increase in soil moisture at depths of 20, 40 and 60 cm compared to FTD. The largest increase in soil water content of 3.5 vol% due to CTD occurred at the depth of 40 cm, which also had a higher degree of saturation of available water capacity and the occurrence of crop water stress was delayed by 14 days compared to FTD.

High responsiveness of maize grain yield to nitrogen supply is explained by high ear growth rate and efficient ear nitrogen allocation

Increasing the response of maize yield to nitrogen (N) application is essential for food security and economic benefits of the farmers. However, the physiological mechanism underlining N responsiveness is unclear. Three maize cultivars with different N-responsiveness, BMY (the low), ZD2 (the medium) and ZD958 (the high) were grown in the field under a wide range of N supply (from 0 to 240 kg N per ha) in two years. Plant growth rate, plant N uptake rate, ear growth rate and ear N allocation rate, and their relationship to grain yield formation were investigated during the early critical period (14 days before silking). The direct path coefficient of grain number to grain yield was 0.82, which explained 96% of the variation in grain yield (R 2 = 0.91). There was not a trade-off between grain number and other yield components. ZD958 got more grains per ear than the other cultivars in response to N supplies. The variation in grain number was mostly explained by the number of floret primordia (57% for BMY, 93% for ZD2 and ZD958). When N supply increased from 0 to 240 kg N ha -1 , ZD2 and ZD958 did not differ in nitrogen nutrition index, and were 7.2–32.8% higher than BMY. The number of grains and floret primordia was independent of plant growth rate and plant N uptake rate, although these rates increased with N supply. Ear growth rate and ear N allocation rate of ZD958 were higher compared with the other cultivars, especially under high N supply. Except BMY, the number of floret primordium and grain per ear depended on ear growth rate and ear N allocation rate. There was a positive correlation between ear growth rate and ear N allocation rate in ZD2 and ZD958. However, ear N allocation rate of BMY did not increase when its ear growth rate was greater than 19.2 mg o C -1 . It is concluded that, with increasing N supply, N-responsive maize cultivars had high ear growth rate and allocated more N into the ear during the early critical period, which enhanced the differentiation of floret primordia, hence increasing grain number and final grain yield. • High N-responsiveness of grain yield depends on grain number per ear. • Genotypic variation in grains number is explained by floret primordium development. • Floret primordium number in response to N depends on ear growth and ear N allocation.

Establishing a critical nitrogen dilution curve for estimating nitrogen nutrition index of potato crop in tropical environments

The critical nitrogen (N) dilution curve (CNDC) ( Nc = A 1 W -A2 ) and the N nutrition index (NNI) have shown to be a robust diagnostic tool to assess crop N status. Several CNDCs were established for potatoes ( Solanum tuberosum L.) in various conditions. However, it is difficult to know if these different CNDCs reflect a true variability across genotype × environment × management (G×E × M) scenarios, or are rather linked to estimation errors. Therefore, the aims of this study were to (i) estimate and compare CNDCs via a Bayesian statistical approach for different G×E × M scenarios in tropical environments and (ii) assess the uncertainty in the relationship between tuber yield and NNI. Field experiments were conducted in three site-years (SYs) in the southeastern region of Brazil. Treatments consisted of two potato cultivars (Agata and Electra) and five N fertilization managements [differing in the N rates (0 −320 kg N ha -1 ) and/or application timings]. Based on 95% credibility intervals, we found no significant differences between CNDCs across G×E × M scenarios assessed and thus derived a single generic CNDC for both cultivars and all SYs defined by Nc = 3.86 W - 0.34 . This global CNDC was used to compute NNI and discriminate tuber yields obtained under N-limiting and non-N-limiting conditions. An uncertainty analysis revealed that critical NNI threshold values for the tuber yield response to N management were not statistically different among G×E × M scenarios. Therefore, these results support the use of a unique CNDC for estimating the N status (NNI) of potato crop in tropical environments. We found no evidence of the need for SY- or cultivar-specific CNDCs. Future studies should evaluate N dilution curves and NNI for potato across different production environments worldwide. • A new critical N curve was derived for potato in tropical environments. • The critical N curve ( Nc = 3.86 W -0.34 ) was obtained using a Bayesian analysis. • No evidence of variability in the critical N nutrition index threshold for yield. • No evidence of the need for cultivar- or site-specific parameters.